From 806782dc0129b2d9cc1ded77450dc6e630967f37 Mon Sep 17 00:00:00 2001
From: brianbui0 <buibrian96@gmail.com>
Date: Mon, 19 Aug 2024 12:24:43 -0700
Subject: [PATCH 1/9] Create .DS_Store

---
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 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 .DS_Store

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From a54f997081a14f487f355a6b9a393988dc425bf5 Mon Sep 17 00:00:00 2001
From: brianbui0 <buibrian96@gmail.com>
Date: Thu, 22 Aug 2024 17:54:52 -0700
Subject: [PATCH 2/9] august 22

---
 .DS_Store           | Bin 6148 -> 6148 bytes
 Notebooks/.DS_Store | Bin 0 -> 6148 bytes
 2 files changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 Notebooks/.DS_Store

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From cf2700ce0e7e91fdc938a8ee5e9f5af430d4b90a Mon Sep 17 00:00:00 2001
From: brianbui0 <buibrian96@gmail.com>
Date: Fri, 23 Aug 2024 16:05:16 -0700
Subject: [PATCH 3/9] Create Brians

---
 CapstoneSteps/Brians | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 CapstoneSteps/Brians

diff --git a/CapstoneSteps/Brians b/CapstoneSteps/Brians
new file mode 100644
index 000000000..8b1378917
--- /dev/null
+++ b/CapstoneSteps/Brians
@@ -0,0 +1 @@
+

From 70f75b6acca58f9c7c050f456a667fd80401cad7 Mon Sep 17 00:00:00 2001
From: brianbui0 <buibrian96@gmail.com>
Date: Fri, 23 Aug 2024 16:05:49 -0700
Subject: [PATCH 4/9] Add files via upload

---
 CapstoneSteps/02_data_wrangling (2).ipynb | 4791 +++++++++++++++++++++
 1 file changed, 4791 insertions(+)
 create mode 100644 CapstoneSteps/02_data_wrangling (2).ipynb

diff --git a/CapstoneSteps/02_data_wrangling (2).ipynb b/CapstoneSteps/02_data_wrangling (2).ipynb
new file mode 100644
index 000000000..d3530d03a
--- /dev/null
+++ b/CapstoneSteps/02_data_wrangling (2).ipynb	
@@ -0,0 +1,4791 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 2 Data wrangling<a id='2_Data_wrangling'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.1 Contents<a id='2.1_Contents'></a>\n",
+    "* [2 Data wrangling](#2_Data_wrangling)\n",
+    "  * [2.1 Contents](#2.1_Contents)\n",
+    "  * [2.2 Introduction](#2.2_Introduction)\n",
+    "    * [2.2.1 Recap Of Data Science Problem](#2.2.1_Recap_Of_Data_Science_Problem)\n",
+    "    * [2.2.2 Introduction To Notebook](#2.2.2_Introduction_To_Notebook)\n",
+    "  * [2.3 Imports](#2.3_Imports)\n",
+    "  * [2.4 Objectives](#2.4_Objectives)\n",
+    "  * [2.5 Load The Ski Resort Data](#2.5_Load_The_Ski_Resort_Data)\n",
+    "  * [2.6 Explore The Data](#2.6_Explore_The_Data)\n",
+    "    * [2.6.1 Find Your Resort Of Interest](#2.6.1_Find_Your_Resort_Of_Interest)\n",
+    "    * [2.6.2 Number Of Missing Values By Column](#2.6.2_Number_Of_Missing_Values_By_Column)\n",
+    "    * [2.6.3 Categorical Features](#2.6.3_Categorical_Features)\n",
+    "      * [2.6.3.1 Unique Resort Names](#2.6.3.1_Unique_Resort_Names)\n",
+    "      * [2.6.3.2 Region And State](#2.6.3.2_Region_And_State)\n",
+    "      * [2.6.3.3 Number of distinct regions and states](#2.6.3.3_Number_of_distinct_regions_and_states)\n",
+    "      * [2.6.3.4 Distribution Of Resorts By Region And State](#2.6.3.4_Distribution_Of_Resorts_By_Region_And_State)\n",
+    "      * [2.6.3.5 Distribution Of Ticket Price By State](#2.6.3.5_Distribution_Of_Ticket_Price_By_State)\n",
+    "        * [2.6.3.5.1 Average weekend and weekday price by state](#2.6.3.5.1_Average_weekend_and_weekday_price_by_state)\n",
+    "        * [2.6.3.5.2 Distribution of weekday and weekend price by state](#2.6.3.5.2_Distribution_of_weekday_and_weekend_price_by_state)\n",
+    "    * [2.6.4 Numeric Features](#2.6.4_Numeric_Features)\n",
+    "      * [2.6.4.1 Numeric data summary](#2.6.4.1_Numeric_data_summary)\n",
+    "      * [2.6.4.2 Distributions Of Feature Values](#2.6.4.2_Distributions_Of_Feature_Values)\n",
+    "        * [2.6.4.2.1 SkiableTerrain_ac](#2.6.4.2.1_SkiableTerrain_ac)\n",
+    "        * [2.6.4.2.2 Snow Making_ac](#2.6.4.2.2_Snow_Making_ac)\n",
+    "        * [2.6.4.2.3 fastEight](#2.6.4.2.3_fastEight)\n",
+    "        * [2.6.4.2.4 fastSixes and Trams](#2.6.4.2.4_fastSixes_and_Trams)\n",
+    "  * [2.7 Derive State-wide Summary Statistics For Our Market Segment](#2.7_Derive_State-wide_Summary_Statistics_For_Our_Market_Segment)\n",
+    "  * [2.8 Drop Rows With No Price Data](#2.8_Drop_Rows_With_No_Price_Data)\n",
+    "  * [2.9 Review distributions](#2.9_Review_distributions)\n",
+    "  * [2.10 Population data](#2.10_Population_data)\n",
+    "  * [2.11 Target Feature](#2.11_Target_Feature)\n",
+    "    * [2.11.1 Number Of Missing Values By Row - Resort](#2.11.1_Number_Of_Missing_Values_By_Row_-_Resort)\n",
+    "  * [2.12 Save data](#2.12_Save_data)\n",
+    "  * [2.13 Summary](#2.13_Summary)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.2 Introduction<a id='2.2_Introduction'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This step focuses on collecting your data, organizing it, and making sure it's well defined. Paying attention to these tasks will pay off greatly later on. Some data cleaning can be done at this stage, but it's important not to be overzealous in your cleaning before you've explored the data to better understand it."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.2.1 Recap Of Data Science Problem<a id='2.2.1_Recap_Of_Data_Science_Problem'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The purpose of this data science project is to come up with a pricing model for ski resort tickets in our market segment. Big Mountain suspects it may not be maximizing its returns, relative to its position in the market. It also does not have a strong sense of what facilities matter most to visitors, particularly which ones they're most likely to pay more for. This project aims to build a predictive model for ticket price based on a number of facilities, or properties, boasted by resorts (*at the resorts).* \n",
+    "This model will be used to provide guidance for Big Mountain's pricing and future facility investment plans."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.2.2 Introduction To Notebook<a id='2.2.2_Introduction_To_Notebook'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notebooks grow organically as we explore our data. If you used paper notebooks, you could discover a mistake and cross out or revise some earlier work. Later work may give you a reason to revisit earlier work and explore it further. The great thing about Jupyter notebooks is that you can edit, add, and move cells around without needing to cross out figures or scrawl in the margin. However, this means you can lose track of your changes easily. If you worked in a regulated environment, the company may have a a policy of always dating entries and clearly crossing out any mistakes, with your initials and the date.\n",
+    "\n",
+    "**Best practice here is to commit your changes using a version control system such as Git.** Try to get into the habit of adding and committing your files to the Git repository you're working in after you save them. You're are working in a Git repository, right? If you make a significant change, save the notebook and commit it to Git. In fact, if you're about to make a significant change, it's a good idea to commit before as well. Then if the change is a mess, you've got the previous version to go back to.\n",
+    "\n",
+    "**Another best practice with notebooks is to try to keep them organized with helpful headings and comments.** Not only can a good structure, but associated headings help you keep track of what you've done and your current focus. Anyone reading your notebook will have a much easier time following the flow of work. Remember, that 'anyone' will most likely be you. Be kind to future you!\n",
+    "\n",
+    "In this notebook, note how we try to use well structured, helpful headings that frequently are self-explanatory, and we make a brief note after any results to highlight key takeaways. This is an immense help to anyone reading your notebook and it will greatly help you when you come to summarise your findings. **Top tip: jot down key findings in a final summary at the end of the notebook as they arise. You can tidy this up later.** This is a great way to ensure important results don't get lost in the middle of your notebooks."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this, and subsequent notebooks, there are coding tasks marked with `#Code task n#` with code to complete. The `___` will guide you to where you need to insert code."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.3 Imports<a id='2.3_Imports'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Placing your imports all together at the start of your notebook means you only need to consult one place to check your notebook's dependencies. By all means import something 'in situ' later on when you're experimenting, but if the imported dependency ends up being kept, you should subsequently move the import statement here with the rest."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Code task 1#\n",
+    "#Import pandas, matplotlib.pyplot, and seaborn in the correct lines below\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import os\n",
+    "\n",
+    "from library.sb_utils import save_file\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.4 Objectives<a id='2.4_Objectives'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are some fundamental questions to resolve in this notebook before you move on.\n",
+    "\n",
+    "* Do you think you may have the data you need to tackle the desired question?\n",
+    "    * Have you identified the required target value?\n",
+    "    * Do you have potentially useful features?\n",
+    "* Do you have any fundamental issues with the data?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.5 Load The Ski Resort Data<a id='2.5_Load_The_Ski_Resort_Data'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# the supplied CSV data file is the raw_data directory\n",
+    "ski_data = pd.read_csv('../raw_data/ski_resort_data.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Good first steps in auditing the data are the info method and displaying the first few records with head."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 330 entries, 0 to 329\n",
+      "Data columns (total 27 columns):\n",
+      " #   Column             Non-Null Count  Dtype  \n",
+      "---  ------             --------------  -----  \n",
+      " 0   Name               330 non-null    object \n",
+      " 1   Region             330 non-null    object \n",
+      " 2   state              330 non-null    object \n",
+      " 3   summit_elev        330 non-null    int64  \n",
+      " 4   vertical_drop      330 non-null    int64  \n",
+      " 5   base_elev          330 non-null    int64  \n",
+      " 6   trams              330 non-null    int64  \n",
+      " 7   fastEight          164 non-null    float64\n",
+      " 8   fastSixes          330 non-null    int64  \n",
+      " 9   fastQuads          330 non-null    int64  \n",
+      " 10  quad               330 non-null    int64  \n",
+      " 11  triple             330 non-null    int64  \n",
+      " 12  double             330 non-null    int64  \n",
+      " 13  surface            330 non-null    int64  \n",
+      " 14  total_chairs       330 non-null    int64  \n",
+      " 15  Runs               326 non-null    float64\n",
+      " 16  TerrainParks       279 non-null    float64\n",
+      " 17  LongestRun_mi      325 non-null    float64\n",
+      " 18  SkiableTerrain_ac  327 non-null    float64\n",
+      " 19  Snow Making_ac     284 non-null    float64\n",
+      " 20  daysOpenLastYear   279 non-null    float64\n",
+      " 21  yearsOpen          329 non-null    float64\n",
+      " 22  averageSnowfall    316 non-null    float64\n",
+      " 23  AdultWeekday       276 non-null    float64\n",
+      " 24  AdultWeekend       279 non-null    float64\n",
+      " 25  projectedDaysOpen  283 non-null    float64\n",
+      " 26  NightSkiing_ac     187 non-null    float64\n",
+      "dtypes: float64(13), int64(11), object(3)\n",
+      "memory usage: 69.7+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Code task 2#\n",
+    "#Call the info method on ski_data to see a summary of the data\n",
+    "ski_data.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`AdultWeekday` is the price of an adult weekday ticket. `AdultWeekend` is the price of an adult weekend ticket. The other columns are potential features."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This immediately raises the question of what quantity will you want to model? You know you want to model the ticket price, but you realise there are two kinds of ticket price!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Region</th>\n",
+       "      <th>state</th>\n",
+       "      <th>summit_elev</th>\n",
+       "      <th>vertical_drop</th>\n",
+       "      <th>base_elev</th>\n",
+       "      <th>trams</th>\n",
+       "      <th>fastEight</th>\n",
+       "      <th>fastSixes</th>\n",
+       "      <th>fastQuads</th>\n",
+       "      <th>...</th>\n",
+       "      <th>LongestRun_mi</th>\n",
+       "      <th>SkiableTerrain_ac</th>\n",
+       "      <th>Snow Making_ac</th>\n",
+       "      <th>daysOpenLastYear</th>\n",
+       "      <th>yearsOpen</th>\n",
+       "      <th>averageSnowfall</th>\n",
+       "      <th>AdultWeekday</th>\n",
+       "      <th>AdultWeekend</th>\n",
+       "      <th>projectedDaysOpen</th>\n",
+       "      <th>NightSkiing_ac</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alyeska Resort</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>3939</td>\n",
+       "      <td>2500</td>\n",
+       "      <td>250</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1610.0</td>\n",
+       "      <td>113.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>669.0</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>550.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Eaglecrest Ski Area</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>2600</td>\n",
+       "      <td>1540</td>\n",
+       "      <td>1200</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>640.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>53.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Hilltop Ski Area</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>2090</td>\n",
+       "      <td>294</td>\n",
+       "      <td>1796</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>152.0</td>\n",
+       "      <td>30.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Arizona Snowbowl</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>11500</td>\n",
+       "      <td>2300</td>\n",
+       "      <td>9200</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>777.0</td>\n",
+       "      <td>104.0</td>\n",
+       "      <td>122.0</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>260.0</td>\n",
+       "      <td>89.0</td>\n",
+       "      <td>89.0</td>\n",
+       "      <td>122.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Sunrise Park Resort</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>11100</td>\n",
+       "      <td>1800</td>\n",
+       "      <td>9200</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.2</td>\n",
+       "      <td>800.0</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>115.0</td>\n",
+       "      <td>49.0</td>\n",
+       "      <td>250.0</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>78.0</td>\n",
+       "      <td>104.0</td>\n",
+       "      <td>80.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 27 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  Name   Region    state  summit_elev  vertical_drop  \\\n",
+       "0       Alyeska Resort   Alaska   Alaska         3939           2500   \n",
+       "1  Eaglecrest Ski Area   Alaska   Alaska         2600           1540   \n",
+       "2     Hilltop Ski Area   Alaska   Alaska         2090            294   \n",
+       "3     Arizona Snowbowl  Arizona  Arizona        11500           2300   \n",
+       "4  Sunrise Park Resort  Arizona  Arizona        11100           1800   \n",
+       "\n",
+       "   base_elev  trams  fastEight  fastSixes  fastQuads  ...  LongestRun_mi  \\\n",
+       "0        250      1        0.0          0          2  ...            1.0   \n",
+       "1       1200      0        0.0          0          0  ...            2.0   \n",
+       "2       1796      0        0.0          0          0  ...            1.0   \n",
+       "3       9200      0        0.0          1          0  ...            2.0   \n",
+       "4       9200      0        NaN          0          1  ...            1.2   \n",
+       "\n",
+       "   SkiableTerrain_ac  Snow Making_ac  daysOpenLastYear  yearsOpen  \\\n",
+       "0             1610.0           113.0             150.0       60.0   \n",
+       "1              640.0            60.0              45.0       44.0   \n",
+       "2               30.0            30.0             150.0       36.0   \n",
+       "3              777.0           104.0             122.0       81.0   \n",
+       "4              800.0            80.0             115.0       49.0   \n",
+       "\n",
+       "   averageSnowfall  AdultWeekday  AdultWeekend  projectedDaysOpen  \\\n",
+       "0            669.0          65.0          85.0              150.0   \n",
+       "1            350.0          47.0          53.0               90.0   \n",
+       "2             69.0          30.0          34.0              152.0   \n",
+       "3            260.0          89.0          89.0              122.0   \n",
+       "4            250.0          74.0          78.0              104.0   \n",
+       "\n",
+       "   NightSkiing_ac  \n",
+       "0           550.0  \n",
+       "1             NaN  \n",
+       "2            30.0  \n",
+       "3             NaN  \n",
+       "4            80.0  \n",
+       "\n",
+       "[5 rows x 27 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 3#\n",
+    "#Call the head method on ski_data to print the first several rows of the data\n",
+    "ski_data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The output above suggests you've made a good start getting the ski resort data organized. You have plausible column headings. You can already see you have a missing value in the `fastEight` column"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.6 Explore The Data<a id='2.6_Explore_The_Data'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.6.1 Find Your Resort Of Interest<a id='2.6.1_Find_Your_Resort_Of_Interest'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Your resort of interest is called Big Mountain Resort. Check it's in the data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>151</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Name</th>\n",
+       "      <td>Big Mountain Resort</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Region</th>\n",
+       "      <td>Montana</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state</th>\n",
+       "      <td>Montana</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>summit_elev</th>\n",
+       "      <td>6817</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vertical_drop</th>\n",
+       "      <td>2353</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>base_elev</th>\n",
+       "      <td>4464</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>trams</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastEight</th>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastSixes</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastQuads</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>quad</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>triple</th>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>double</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>surface</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total_chairs</th>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Runs</th>\n",
+       "      <td>105.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TerrainParks</th>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LongestRun_mi</th>\n",
+       "      <td>3.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SkiableTerrain_ac</th>\n",
+       "      <td>3000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Snow Making_ac</th>\n",
+       "      <td>600.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>daysOpenLastYear</th>\n",
+       "      <td>123.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>yearsOpen</th>\n",
+       "      <td>72.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>averageSnowfall</th>\n",
+       "      <td>333.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AdultWeekday</th>\n",
+       "      <td>81.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AdultWeekend</th>\n",
+       "      <td>81.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>projectedDaysOpen</th>\n",
+       "      <td>123.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NightSkiing_ac</th>\n",
+       "      <td>600.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                   151\n",
+       "Name               Big Mountain Resort\n",
+       "Region                         Montana\n",
+       "state                          Montana\n",
+       "summit_elev                       6817\n",
+       "vertical_drop                     2353\n",
+       "base_elev                         4464\n",
+       "trams                                0\n",
+       "fastEight                          0.0\n",
+       "fastSixes                            0\n",
+       "fastQuads                            3\n",
+       "quad                                 2\n",
+       "triple                               6\n",
+       "double                               0\n",
+       "surface                              3\n",
+       "total_chairs                        14\n",
+       "Runs                             105.0\n",
+       "TerrainParks                       4.0\n",
+       "LongestRun_mi                      3.3\n",
+       "SkiableTerrain_ac               3000.0\n",
+       "Snow Making_ac                   600.0\n",
+       "daysOpenLastYear                 123.0\n",
+       "yearsOpen                         72.0\n",
+       "averageSnowfall                  333.0\n",
+       "AdultWeekday                      81.0\n",
+       "AdultWeekend                      81.0\n",
+       "projectedDaysOpen                123.0\n",
+       "NightSkiing_ac                   600.0"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 4#\n",
+    "#Filter the ski_data dataframe to display just the row for our resort with the name 'Big Mountain Resort'\n",
+    "#Hint: you will find that the transpose of the row will give a nicer output. DataFrame's do have a\n",
+    "#transpose method, but you can access this conveniently with the `T` property.\n",
+    "ski_data[ski_data.Name == 'Big Mountain Resort'].T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It's good that your resort doesn't appear to have any missing values."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.6.2 Number Of Missing Values By Column<a id='2.6.2_Number_Of_Missing_Values_By_Column'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Count the number of missing values in each column and sort them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>%</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>fastEight</th>\n",
+       "      <td>166</td>\n",
+       "      <td>50.303030</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NightSkiing_ac</th>\n",
+       "      <td>143</td>\n",
+       "      <td>43.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AdultWeekday</th>\n",
+       "      <td>54</td>\n",
+       "      <td>16.363636</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AdultWeekend</th>\n",
+       "      <td>51</td>\n",
+       "      <td>15.454545</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>daysOpenLastYear</th>\n",
+       "      <td>51</td>\n",
+       "      <td>15.454545</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TerrainParks</th>\n",
+       "      <td>51</td>\n",
+       "      <td>15.454545</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>projectedDaysOpen</th>\n",
+       "      <td>47</td>\n",
+       "      <td>14.242424</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Snow Making_ac</th>\n",
+       "      <td>46</td>\n",
+       "      <td>13.939394</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>averageSnowfall</th>\n",
+       "      <td>14</td>\n",
+       "      <td>4.242424</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LongestRun_mi</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1.515152</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Runs</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1.212121</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SkiableTerrain_ac</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0.909091</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>yearsOpen</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.303030</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total_chairs</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Name</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Region</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>double</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>triple</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>quad</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastQuads</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastSixes</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>trams</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>base_elev</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vertical_drop</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>summit_elev</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>surface</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   count          %\n",
+       "fastEight            166  50.303030\n",
+       "NightSkiing_ac       143  43.333333\n",
+       "AdultWeekday          54  16.363636\n",
+       "AdultWeekend          51  15.454545\n",
+       "daysOpenLastYear      51  15.454545\n",
+       "TerrainParks          51  15.454545\n",
+       "projectedDaysOpen     47  14.242424\n",
+       "Snow Making_ac        46  13.939394\n",
+       "averageSnowfall       14   4.242424\n",
+       "LongestRun_mi          5   1.515152\n",
+       "Runs                   4   1.212121\n",
+       "SkiableTerrain_ac      3   0.909091\n",
+       "yearsOpen              1   0.303030\n",
+       "total_chairs           0   0.000000\n",
+       "Name                   0   0.000000\n",
+       "Region                 0   0.000000\n",
+       "double                 0   0.000000\n",
+       "triple                 0   0.000000\n",
+       "quad                   0   0.000000\n",
+       "fastQuads              0   0.000000\n",
+       "fastSixes              0   0.000000\n",
+       "trams                  0   0.000000\n",
+       "base_elev              0   0.000000\n",
+       "vertical_drop          0   0.000000\n",
+       "summit_elev            0   0.000000\n",
+       "state                  0   0.000000\n",
+       "surface                0   0.000000"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 5#\n",
+    "#Count (using `.sum()`) the number of missing values (`.isnull()`) in each column of \n",
+    "#ski_data as well as the percentages (using `.mean()` instead of `.sum()`).\n",
+    "#Order them (increasing or decreasing) using sort_values\n",
+    "#Call `pd.concat` to present these in a single table (DataFrame) with the helpful column names 'count' and '%'\n",
+    "missing = pd.concat([ski_data.isnull().sum(), 100 * ski_data.isnull().mean()], axis=1)\n",
+    "missing.columns=['count', '%']\n",
+    "missing.sort_values(by='count', ascending = False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`fastEight` has the most missing values, at just over 50%. Unfortunately, you see you're also missing quite a few of your desired target quantity, the ticket price, which is missing 15-16% of values. `AdultWeekday` is missing in a few more records than `AdultWeekend`. What overlap is there in these missing values? This is a question you'll want to investigate. You should also point out that `isnull()` is not the only indicator of missing data. Sometimes 'missingness' can be encoded, perhaps by a -1 or 999. Such values are typically chosen because they are \"obviously\" not genuine values. If you were capturing data on people's heights and weights but missing someone's height, you could certainly encode that as a 0 because no one has a height of zero (in any units). Yet such entries would not be revealed by `isnull()`. Here, you need a data dictionary and/or to spot such values as part of looking for outliers. Someone with a height of zero should definitely show up as an outlier!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.6.3 Categorical Features<a id='2.6.3_Categorical_Features'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So far you've examined only the numeric features. Now you inspect categorical ones such as resort name and state. These are discrete entities. 'Alaska' is a name. Although names can be sorted alphabetically, it makes no sense to take the average of 'Alaska' and 'Arizona'. Similarly, 'Alaska' is before 'Arizona' only lexicographically; it is neither 'less than' nor 'greater than' 'Arizona'. As such, they tend to require different handling than strictly numeric quantities. Note, a feature _can_ be numeric but also categorical. For example, instead of giving the number of `fastEight` lifts, a feature might be `has_fastEights` and have the value 0 or 1 to denote absence or presence of such a lift. In such a case it would not make sense to take an average of this or perform other mathematical calculations on it. Although you digress a little to make a point, month numbers are also, strictly speaking, categorical features. Yes, when a month is represented by its number (1 for January, 2 for Februrary etc.) it provides a convenient way to graph trends over a year. And, arguably, there is some logical interpretation of the average of 1 and 3 (January and March) being 2 (February). However, clearly December of one years precedes January of the next and yet 12 as a number is not less than 1. The numeric quantities in the section above are truly numeric; they are the number of feet in the drop, or acres or years open or the amount of snowfall etc."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Region</th>\n",
+       "      <th>state</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alyeska Resort</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Eaglecrest Ski Area</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Hilltop Ski Area</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Arizona Snowbowl</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>Arizona</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Sunrise Park Resort</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>Arizona</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>325</th>\n",
+       "      <td>Meadowlark Ski Lodge</td>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Wyoming</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>326</th>\n",
+       "      <td>Sleeping Giant Ski Resort</td>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Wyoming</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>327</th>\n",
+       "      <td>Snow King Resort</td>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Wyoming</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>328</th>\n",
+       "      <td>Snowy Range Ski &amp; Recreation Area</td>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Wyoming</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>329</th>\n",
+       "      <td>White Pine Ski Area</td>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Wyoming</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>330 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                  Name   Region    state\n",
+       "0                       Alyeska Resort   Alaska   Alaska\n",
+       "1                  Eaglecrest Ski Area   Alaska   Alaska\n",
+       "2                     Hilltop Ski Area   Alaska   Alaska\n",
+       "3                     Arizona Snowbowl  Arizona  Arizona\n",
+       "4                  Sunrise Park Resort  Arizona  Arizona\n",
+       "..                                 ...      ...      ...\n",
+       "325               Meadowlark Ski Lodge  Wyoming  Wyoming\n",
+       "326          Sleeping Giant Ski Resort  Wyoming  Wyoming\n",
+       "327                   Snow King Resort  Wyoming  Wyoming\n",
+       "328  Snowy Range Ski & Recreation Area  Wyoming  Wyoming\n",
+       "329                White Pine Ski Area  Wyoming  Wyoming\n",
+       "\n",
+       "[330 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 6#\n",
+    "#Use ski_data's `select_dtypes` method to select columns of dtype 'object'\n",
+    "ski_data.select_dtypes('object')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You saw earlier on that these three columns had no missing values. But are there any other issues with these columns? Sensible questions to ask here include:\n",
+    "\n",
+    "* Is `Name` (or at least a combination of Name/Region/State) unique?\n",
+    "* Is `Region` always the same as `state`?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.6.3.1 Unique Resort Names<a id='2.6.3.1_Unique_Resort_Names'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Crystal Mountain    2\n",
+       "Alyeska Resort      1\n",
+       "Brandywine          1\n",
+       "Boston Mills        1\n",
+       "Alpine Valley       1\n",
+       "Name: Name, dtype: int64"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 7#\n",
+    "#Use pandas' Series method `value_counts` to find any duplicated resort names\n",
+    "ski_data['Name'].value_counts().head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You have a duplicated resort name: Crystal Mountain."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q: 1** Is this resort duplicated if you take into account Region and/or state as well?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Alyeska Resort, Alaska    1\n",
+       "Snow Trails, Ohio         1\n",
+       "Brandywine, Ohio          1\n",
+       "Boston Mills, Ohio        1\n",
+       "Alpine Valley, Ohio       1\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 8#\n",
+    "#Concatenate the string columns 'Name' and 'Region' and count the values again (as above)\n",
+    "(ski_data['Name'] + ', ' + ski_data['Region']).value_counts().head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Alyeska Resort, Alaska    1\n",
+       "Snow Trails, Ohio         1\n",
+       "Brandywine, Ohio          1\n",
+       "Boston Mills, Ohio        1\n",
+       "Alpine Valley, Ohio       1\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 9#\n",
+    "#Concatenate 'Name' and 'state' and count the values again (as above)\n",
+    "(ski_data['Name'] + ', ' + ski_data['state']).value_counts().head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "**NB** because you know `value_counts()` sorts descending, you can use the `head()` method and know the rest of the counts must be 1."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**A: 1** Your answer here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Region</th>\n",
+       "      <th>state</th>\n",
+       "      <th>summit_elev</th>\n",
+       "      <th>vertical_drop</th>\n",
+       "      <th>base_elev</th>\n",
+       "      <th>trams</th>\n",
+       "      <th>fastEight</th>\n",
+       "      <th>fastSixes</th>\n",
+       "      <th>fastQuads</th>\n",
+       "      <th>...</th>\n",
+       "      <th>LongestRun_mi</th>\n",
+       "      <th>SkiableTerrain_ac</th>\n",
+       "      <th>Snow Making_ac</th>\n",
+       "      <th>daysOpenLastYear</th>\n",
+       "      <th>yearsOpen</th>\n",
+       "      <th>averageSnowfall</th>\n",
+       "      <th>AdultWeekday</th>\n",
+       "      <th>AdultWeekend</th>\n",
+       "      <th>projectedDaysOpen</th>\n",
+       "      <th>NightSkiing_ac</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>104</th>\n",
+       "      <td>Crystal Mountain</td>\n",
+       "      <td>Michigan</td>\n",
+       "      <td>Michigan</td>\n",
+       "      <td>1132</td>\n",
+       "      <td>375</td>\n",
+       "      <td>757</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>102.0</td>\n",
+       "      <td>96.0</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>63.0</td>\n",
+       "      <td>132.0</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>64.0</td>\n",
+       "      <td>135.0</td>\n",
+       "      <td>56.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>295</th>\n",
+       "      <td>Crystal Mountain</td>\n",
+       "      <td>Washington</td>\n",
+       "      <td>Washington</td>\n",
+       "      <td>7012</td>\n",
+       "      <td>3100</td>\n",
+       "      <td>4400</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>2600.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>57.0</td>\n",
+       "      <td>486.0</td>\n",
+       "      <td>99.0</td>\n",
+       "      <td>99.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 27 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 Name      Region       state  summit_elev  vertical_drop  \\\n",
+       "104  Crystal Mountain    Michigan    Michigan         1132            375   \n",
+       "295  Crystal Mountain  Washington  Washington         7012           3100   \n",
+       "\n",
+       "     base_elev  trams  fastEight  fastSixes  fastQuads  ...  LongestRun_mi  \\\n",
+       "104        757      0        0.0          0          1  ...            0.3   \n",
+       "295       4400      1        NaN          2          2  ...            2.5   \n",
+       "\n",
+       "     SkiableTerrain_ac  Snow Making_ac  daysOpenLastYear  yearsOpen  \\\n",
+       "104              102.0            96.0             120.0       63.0   \n",
+       "295             2600.0            10.0               NaN       57.0   \n",
+       "\n",
+       "     averageSnowfall  AdultWeekday  AdultWeekend  projectedDaysOpen  \\\n",
+       "104            132.0          54.0          64.0              135.0   \n",
+       "295            486.0          99.0          99.0                NaN   \n",
+       "\n",
+       "     NightSkiing_ac  \n",
+       "104            56.0  \n",
+       "295             NaN  \n",
+       "\n",
+       "[2 rows x 27 columns]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ski_data[ski_data['Name'] == 'Crystal Mountain']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So there are two Crystal Mountain resorts, but they are clearly two different resorts in two different states. This is a powerful signal that you have unique records on each row."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.6.3.2 Region And State<a id='2.6.3.2_Region_And_State'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What's the relationship between region and state?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You know they are the same in many cases (e.g. both the Region and the state are given as 'Michigan'). In how many cases do they differ?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "33"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 10#\n",
+    "#Calculate the number of times Region does not equal state\n",
+    "(ski_data.Region != ski_data.state).sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You know what a state is. What is a region? You can tabulate the distinct values along with their respective frequencies using `value_counts()`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "New York               33\n",
+       "Michigan               29\n",
+       "Sierra Nevada          22\n",
+       "Colorado               22\n",
+       "Pennsylvania           19\n",
+       "Wisconsin              16\n",
+       "New Hampshire          16\n",
+       "Vermont                15\n",
+       "Minnesota              14\n",
+       "Idaho                  12\n",
+       "Montana                12\n",
+       "Massachusetts          11\n",
+       "Washington             10\n",
+       "New Mexico              9\n",
+       "Maine                   9\n",
+       "Wyoming                 8\n",
+       "Utah                    7\n",
+       "Salt Lake City          6\n",
+       "North Carolina          6\n",
+       "Oregon                  6\n",
+       "Connecticut             5\n",
+       "Ohio                    5\n",
+       "Virginia                4\n",
+       "West Virginia           4\n",
+       "Illinois                4\n",
+       "Mt. Hood                4\n",
+       "Alaska                  3\n",
+       "Iowa                    3\n",
+       "South Dakota            2\n",
+       "Arizona                 2\n",
+       "Nevada                  2\n",
+       "Missouri                2\n",
+       "Indiana                 2\n",
+       "New Jersey              2\n",
+       "Rhode Island            1\n",
+       "Tennessee               1\n",
+       "Maryland                1\n",
+       "Northern California     1\n",
+       "Name: Region, dtype: int64"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ski_data['Region'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A casual inspection by eye reveals some non-state names such as Sierra Nevada, Salt Lake City, and Northern California. Tabulate the differences between Region and state. On a note regarding scaling to larger data sets, you might wonder how you could spot such cases when presented with millions of rows. This is an interesting point. Imagine you have access to a database with a Region and state column in a table and there are millions of rows. You wouldn't eyeball all the rows looking for differences! Bear in mind that our first interest lies in establishing the answer to the question \"Are they always the same?\" One approach might be to ask the database to return records where they differ, but limit the output to 10 rows. If there were differences, you'd only get up to 10 results, and so you wouldn't know whether you'd located all differences, but you'd know that there were 'a nonzero number' of differences. If you got an empty result set back, then you would know that the two columns always had the same value. At the risk of digressing, some values in one column only might be NULL (missing) and different databases treat NULL differently, so be aware that on many an occasion a seamingly 'simple' question gets very interesting to answer very quickly!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "state       Region             \n",
+       "California  Sierra Nevada          20\n",
+       "            Northern California     1\n",
+       "Nevada      Sierra Nevada           2\n",
+       "Oregon      Mt. Hood                4\n",
+       "Utah        Salt Lake City          6\n",
+       "Name: Region, dtype: int64"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 11#\n",
+    "#Filter the ski_data dataframe for rows where 'Region' and 'state' are different,\n",
+    "#group that by 'state' and perform `value_counts` on the 'Region'\n",
+    "(ski_data[ski_data.Region != ski_data.state]\n",
+    " .groupby('state')['Region']\n",
+    " .value_counts())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The vast majority of the differences are in California, with most Regions being called Sierra Nevada and just one referred to as Northern California."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.6.3.3 Number of distinct regions and states<a id='2.6.3.3_Number_of_distinct_regions_and_states'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Region    38\n",
+       "state     35\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 12#\n",
+    "#Select the 'Region' and 'state' columns from ski_data and use the `nunique` method to calculate\n",
+    "#the number of unique values in each\n",
+    "ski_data[['Region', 'state']].nunique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Because a few states are split across multiple named regions, there are slightly more unique regions than states."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.6.3.4 Distribution Of Resorts By Region And State<a id='2.6.3.4_Distribution_Of_Resorts_By_Region_And_State'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If this is your first time using [matplotlib](https://matplotlib.org/3.2.2/index.html)'s [subplots](https://matplotlib.org/3.2.2/api/_as_gen/matplotlib.pyplot.subplots.html), you may find the online documentation useful."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1200x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Code task 13#\n",
+    "#Create two subplots on 1 row and 2 columns with a figsize of (12, 8)\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(12,8))\n",
+    "#Specify a horizontal barplot ('barh') as kind of plot (kind=)\n",
+    "ski_data.Region.value_counts().plot(kind='barh', ax=ax[0])\n",
+    "#Give the plot a helpful title of 'Region'\n",
+    "ax[0].set_title('Region')\n",
+    "#Label the xaxis 'Count'\n",
+    "ax[0].set_xlabel('Count')\n",
+    "#Specify a horizontal barplot ('barh') as kind of plot (kind=)\n",
+    "ski_data.state.value_counts().plot(kind='barh', ax=ax[1])\n",
+    "#Give the plot a helpful title of 'state'\n",
+    "ax[1].set_title('state')\n",
+    "#Label the xaxis 'Count'\n",
+    "ax[1].set_xlabel('Count')\n",
+    "#Give the subplots a little \"breathing room\" with a wspace of 0.5\n",
+    "plt.subplots_adjust(wspace=0.5);\n",
+    "#You're encouraged to explore a few different figure sizes, orientations, and spacing here\n",
+    "# as the importance of easy-to-read and informative figures is frequently understated\n",
+    "# and you will find the ability to tweak figures invaluable later on"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "How's your geography? Looking at the distribution of States, you see New York accounting for the majority of resorts. Our target resort is in Montana, which comes in at 13th place. You should think carefully about how, or whether, you use this information. Does New York command a premium because of its proximity to population? Even if a resort's State were a useful predictor of ticket price, your main interest lies in Montana. Would you want a model that is skewed for accuracy by New York? Should you just filter for Montana and create a Montana-specific model? This would slash your available data volume. Your problem task includes the contextual insight that the data are for resorts all belonging to the same market share. This suggests one might expect prices to be similar amongst them. You can look into this. A boxplot grouped by State is an ideal way to quickly compare prices. Another side note worth bringing up here is that, in reality, the best approach here definitely would include consulting with the client or other domain expert. They might know of good reasons for treating states equivalently or differently. The data scientist is rarely the final arbiter of such a decision. But here, you'll see if we can find any supporting evidence for treating states the same or differently."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.6.3.5 Distribution Of Ticket Price By State<a id='2.6.3.5_Distribution_Of_Ticket_Price_By_State'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Our primary focus is our Big Mountain resort, in Montana. Does the state give you any clues to help decide what your primary target response feature should be (weekend or weekday ticket prices)?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 2.6.3.5.1 Average weekend and weekday price by state<a id='2.6.3.5.1_Average_weekend_and_weekday_price_by_state'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>AdultWeekend</th>\n",
+       "      <th>AdultWeekday</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Alaska</th>\n",
+       "      <td>57.333333</td>\n",
+       "      <td>47.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Arizona</th>\n",
+       "      <td>83.500000</td>\n",
+       "      <td>81.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>California</th>\n",
+       "      <td>81.416667</td>\n",
+       "      <td>78.214286</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Colorado</th>\n",
+       "      <td>90.714286</td>\n",
+       "      <td>90.714286</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Connecticut</th>\n",
+       "      <td>56.800000</td>\n",
+       "      <td>47.800000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             AdultWeekend  AdultWeekday\n",
+       "state                                  \n",
+       "Alaska          57.333333     47.333333\n",
+       "Arizona         83.500000     81.500000\n",
+       "California      81.416667     78.214286\n",
+       "Colorado        90.714286     90.714286\n",
+       "Connecticut     56.800000     47.800000"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 14#\n",
+    "# Calculate average weekday and weekend price by state and sort by the average of the two\n",
+    "# Hint: use the pattern dataframe.groupby(<grouping variable>)[<list of columns>].mean()\n",
+    "state_price_means = ski_data.groupby('state')[['AdultWeekend', 'AdultWeekday']].mean()\n",
+    "state_price_means.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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CgwcPxsOHD9GrVy+Ympri/v37OH/+PO7fv4/vvvvupWOlp6eje/fuGD58ODIyMjBnzhxoa2tjxowZJdp++eWX6NOnDwRBwOjRoyt0L15eXrC0tESXLl1Qr149KBQKJCYmIjQ0FHp6evjyyy+ltmX9Njp37oyYmBiMHj0avXr1wu3btzFv3jyYm5tL724/30dp/36v+7slIqo0lW69REREHyVfX19RS0ur3N1j+/btK2poaIh3794VRfHZ7qlWVlYiAHHmzJmlXpOVlSV+/fXXoqOjo6ilpSXK5XLRyclJnDBhgtSPKD7blXfMmDGl9rF+/XrR0dFRlMlkor29vRgcHCyGh4eLAMRbt25J7XJzc8WJEyeKpqamora2tvif//xHPH78uCiXy8UJEyYo9Xn//n0xICBAtLOzEzU1NcXq1auLzZs3F2fOnClmZWWV+6xSUlLETz/9VNTX15c+nyKKpe/KK4qieOXKFfHzzz8XjY2NRS0tLdHa2lr08/MTc3NzRVEs+bkYUXz2iZOePXuK2tra4s6dOyv1LBMTE0UXFxdRV1dXBFDuLrHFMS9cuFAMCgoSLS0tRS0tLbFp06binj17lNoW77xb/Amb0upe9OOPP4qffPKJqK2tLerp6YlNmzYt8XwOHTok+vj4iNWrVxc1NTVFCwsL0cfHR/zll1/KjFsU/29X3g0bNogBAQFijRo1RJlMJrZr1048ffp0qdfk5eWJMplM9Pb2Lrfv523evFns37+/6ODgIOrp6YmampqitbW1OHDgQPHKlStKbcv6bYiiKIaEhIi2traiTCYT69evL65bt67U51bev9/r/G6JiCpLEMUXvhhOREREryQ+Ph4uLi7YuHEj+vfvr+pw3jkpKSmws7PDt99+i8mTJ6s6nDfujz/+QNeuXbFz50506tRJ1eEQEb3TuJSXiIjoFezbtw/Hjx9H8+bNoaOjg/PnzyMkJAQODg7o0aOHqsMjFbpy5Qr++usvTJo0Cc7OztInVoiIqGxMTImIiF6BgYEB9u7di2XLluHJkycwMTHBZ599huDgYKUdfenjM3r0aBw7dgzNmjVDVFRUhXYWJiL62HEpLxEREREREamU2subEBEREREREb05TEyJiIiIiIhIpZiYEhERERERkUpx8yOqUgqFAnfu3IG+vj43eyAiIiIi+oiJoognT56gVq1aUFMrf06UiSlVqTt37sDKykrVYRARERER0Tvi9u3bsLS0LLcNE1OqUvr6+gCe/fgMDAxUHA0REREREalKZmYmrKyspByhPExMqUoVL981MDBgYkpERERERBV6xY+bHxEREREREZFKMTElIiIiIiIilWJiSkRERERERCr10b1jKggCtm3bBl9f3yrt193dHc7Ozli2bFmV9lsWW1tbjB8/HuPHj3+j4/j5+eHx48fYvn37Gx2HiIiIiN4OURRRWFiIoqIiVYdC7zl1dXVoaGhUyWciP5jE1M/PD1FRUQCePaBatWrBx8cH33zzDYyMjFQcXcW8rWSTiIiIiD5O+fn5SEtLQ05OjqpDoQ+Erq4uzM3NoaWl9Vr9fDCJKQB4e3sjIiIChYWFuHLlCvz9/fH48WNs2rRJ1aEREREREamUQqHArVu3pEkcLS2tKpnpoo+TKIrIz8/H/fv3cevWLTg4OEBN7dXfFP2g3jGVyWQwMzODpaUlPv30U/Tp0wd79+4t0e7ff/9F9+7doaurCwcHB/z+++9K9YcOHULLli0hk8lgbm6O6dOno7CwUKrPzs7GoEGDoKenB3Nzc4SGhpYYIz8/H1OnToWFhQWqVauGVq1aIS4urlL3ExgYCGtra8hkMtSqVQsBAQFltl2yZAmcnJxQrVo1WFlZYfTo0cjKypLqIyMjYWhoiD179qB+/frQ09ODt7c30tLSpDZFRUWYOHEiDA0NYWxsjKlTp0IUxUrFTERERETvpvz8fCgUCtSqVQtyuRw6OjrQ1tbmweOVDh0dHcjlctSqVQsKhQL5+fmv9fv8oBLT5/3555/YvXs3NDU1S9QFBQWhd+/euHDhAjp16oQBAwbg4cOHAIB//vkHnTp1wieffILz58/ju+++Q3h4OObPny9dP2XKFMTGxmLbtm3Yu3cv4uLicObMGaUxhgwZgmPHjiE6OhoXLlzA559/Dm9vb9y4caNC8W/duhVLly7F2rVrcePGDWzfvh1OTk5ltldTU0NYWBguXbqEqKgoHDx4EFOnTlVqk5OTg8WLF2PDhg04fPgwUlNTMXnyZKk+NDQU69evR3h4OI4ePYqHDx9i27Zt5caZl5eHzMxMpYOIiIiI3l2vM6tF9KIq+z2JH4jBgweL6urqYrVq1URtbW0RgAhAXLJkiVI7AOLXX38tnWdlZYmCIIj/+9//RFEUxa+++kp0dHQUFQqF1GbVqlWinp6eWFRUJD558kTU0tISo6OjpfoHDx6IOjo64pdffimKoijevHlTFARB/Oeff5TG7tChgzhjxowy78HGxkZcunSpKIqiGBoaKtatW1fMz89/advSbNmyRTQ2NpbOIyIiRADizZs3le6rZs2a0rm5ubkYEhIinRcUFIiWlpZit27dyhxnzpw50rN+/sjIyCjzGiIiIiJ6+54+fSpeuXJFfPr0qapDoQ9Ieb+rjIyMCucGH9R/LvHw8EBiYiJOnjyJcePGwcvLC+PGjSvRrnHjxtLf1apVg76+PtLT0wEASUlJaN26tdJ6excXF2RlZeHvv/9GcnIy8vPz0bp1a6m+evXqcHR0lM7Pnj0LURRRt25d6OnpScehQ4eQnJxcoXv5/PPP8fTpU9jb22P48OHYtm2b0nLiF8XGxqJjx46wsLCAvr4+Bg0ahAcPHiA7O1tqo6uri9q1a0vn5ubm0n1nZGQgLS1N6b40NDTQokWLcuOcMWMGMjIypOP27dsVuj8iIiIiIqJiH9TmR9WqVUOdOnUAAGFhYfDw8EBQUBDmzZun1O7F5b2CIEChUAB49hLviy+Bi///PUtBECr0zqVCoYC6ujrOnDkDdXV1pTo9Pb0K3YuVlRWuXbuGffv2Yf/+/Rg9ejS+/fZbHDp0qET8f/31Fzp16oSRI0di3rx5qF69Oo4ePYqhQ4eioKCg3PuuyP2URyaTQSaTvVYfRERERKRattN3vrWxUkJ83so4gYGB2L59OxITEyt8zbv8lQxVfcbxbX0W84OaMX3RnDlzsHjxYty5c6fC1zRo0ADx8fFKCVt8fDz09fVhYWGBOnXqQFNTEydOnJDqHz16hOvXr0vnTZs2RVFREdLT01GnTh2lw8zMrMKx6OjooGvXrggLC0NcXByOHz+Oixcvlmh3+vRpFBYWIjQ0FP/5z39Qt27dSt0zAMjlcpibmyvdV2FhYYl3Z4mIiIiIVCU+Ph7q6urw9vZ+K+MJgqCUCF69ehWCIODkyZNK7Vq1agWZTKb0GZ78/Hzo6uri+++/fyuxvu8+6MTU3d0dDRs2xDfffFPha0aPHo3bt29j3LhxuHr1Kn777TfMmTMHEydOhJqaGvT09DB06FBMmTIFBw4cwKVLl+Dn56f00m/dunUxYMAADBo0CDExMbh16xYSEhKwcOFC7Nq1q0JxREZGIjw8HJcuXcKff/6JDRs2QEdHBzY2NiXa1q5dG4WFhVixYoXUds2aNRW+52JffvklQkJCsG3bNly9ehWjR4/G48ePK90PEREREdGbsH79eowbNw5Hjx5FamrqWx+/Xr16MDc3R2xsrFSWlZWFc+fOwdTUFPHx8VL5yZMn8fTpU3h4eLz1ON9HH3RiCgATJ07EunXrKvzuo4WFBXbt2oVTp06hSZMmGDlyJIYOHYqvv/5aavPtt9/C1dUVXbt2haenJ9q2bYvmzZsr9RMREYFBgwZh0qRJcHR0RNeuXXHy5ElYWVlVKA5DQ0OsW7cOLi4uaNy4MQ4cOIA//vgDxsbGJdo6OztjyZIlWLhwIRo1aoSNGzciODi4QuM8b9KkSRg0aBD8/PzQunVr6Ovro3v37pXuh4iIiIioqmVnZ2PLli0YNWoUOnfujMjISKX6kJAQ1KxZE/r6+hg6dChyc3OV6t3d3Uss0fX19YWfn1+p49na2gIAunfvDkEQpHN3d3elz0AeOXIEdevWRdeuXZXK4+LiYGFhAQcHBwDP8oP69etDW1sb9erVw+rVq5XG++eff9CnTx8YGRnB2NgY3bp1Q0pKSpnP48yZMzA1NcWCBQsAPNszZsSIETA1NYWBgQHat2+P8+fPS+0DAwPh7OyMDRs2wNbWFnK5HH379sWTJ0+kNhX5LOab8sG8Y/riD7NY//790b9/f+m8tHcqX5wVdHNzw6lTp8ocS09PDxs2bMCGDRuksilTpii10dTURFBQEIKCgioQ/TPP//B8fX3h6+tbobYAMGHCBEyYMEGpbODAgdLffn5+Jf5H5+vrq/Q8NDQ0sGzZsje+fpyIiIiIqLI2b94MR0dHODo64osvvsC4ceMwa9YsCIKALVu2YM6cOVi1ahXatWuHDRs2ICwsDPb29q88XkJCAkxNTREREQFvb29p7xgPDw9MmDABhYWF0NDQQGxsLNzd3eHq6orly5dL18fGxkqzpevWrcOcOXOwcuVKNG3aFOfOncPw4cNRrVo1DB48GDk5OfDw8EC7du1w+PBhaGhoYP78+fD29saFCxegpaWlFFtcXBx8fX0RHByMUaNGQRRF+Pj4oHr16ti1axfkcjnWrl2LDh064Pr166hevToAIDk5Gdu3b8eOHTvw6NEj9O7dGyEhIVJy+/xnMc3MzPDVV1/hzJkzcHZ2fuXnWFEf/IwpERERERG9/8LDw/HFF18AALy9vZGVlYUDBw4AAJYtWwZ/f38MGzYMjo6OmD9/Pho0aPBa49WoUQPAs5WMZmZm0rm7uzuys7ORkJAA4FmS6ObmBjc3N5w+fRo5OTnIz8/HiRMnpMR03rx5CA0NRY8ePWBnZ4cePXpgwoQJWLt2LQAgOjoaampq+OGHH+Dk5IT69esjIiICqampSrOwAPDbb7+ha9eu+O677zBq1CgAz5Lgixcv4pdffkGLFi3g4OCAxYsXw9DQEFu3bpWuVSgUiIyMRKNGjdCuXTsMHDhQeoZZWVkIDw/H4sWL0bFjRzg5OSEqKgpFRUWv9Rwr6oOZMSUiIiIiog/TtWvXcOrUKcTExAB4ttKvT58+WL9+PTw9PZGUlISRI0cqXdO6dWuld0GrioODAywtLREXF4eGDRvi3LlzcHNzg6mpKezs7HDs2DHIZDI8ffoU7du3x/3793H79m0MHToUw4cPl/opLCyEXC4H8GxZ7s2bN6Gvr680Vm5urtLnJk+ePIkdO3bgl19+UXrl7syZM8jKyirx2t/Tp0+Vrre1tVUa4/nPR1bks5hvEhNTIiIiIiJ6p4WHh6OwsBAWFhZSmSiK0NTUxKNHjyrUh5qaWonX+p7/tGJluLu7IzY2Fo0bN4aDgwNMTU0BPHslMDY2FjKZDDY2NrC1tcW9e/cAPFvO26pVK6V+ipcHKxQKNG/eHBs3biwxVvFMLfBs01NjY2OsX78ePj4+0hJfhUIBc3PzErOrwLMZ32Iv+2ymKnEpLxERERERvbMKCwvx448/IjQ0FImJidJx/vx52NjYYOPGjahfv77SZw8BlDivUaMG0tLSpPOioiJcunSp3LE1NTVLXcrq4eGB+Ph47Nu3D+7u7lK5m5sb4uLiEBcXh/bt2wMAatasCQsLC/z5558lPiVpZ2cHAGjWrBlu3LgBU1PTEm2KZ1UBwMTEBAcPHkRycjL69OkjJdbNmjXD3bt3oaGhUeJ6ExOTCjxlVOizmG8SE1MiIiIiInpnFW/UM3ToUDRq1Ejp6NWrF8LDw/Hll19i/fr1WL9+Pa5fv445c+bg8uXLSv20b98eO3fuxM6dOyv8aURbW1scOHAAd+/eVZqZ9fDwQHZ2NtavXw83NzepvPg90+ffLwWe7YgbHByM5cuX4/r167h48SIiIiKwZMkSAMCAAQNgYmKCbt264ciRI7h16xYOHTqEL7/8En///bdSTKampjh48CCuXr2Kfv36obCwEJ6enmjdujV8fX2xZ88epKSkID4+Hl9//TVOnz5doedckc9ivklcyktERERE9JFLCfFRdQhlCg8Ph6enp9LMYbGePXvim2++gYODA2bPno1p06YhNzcXPXv2xKhRo7Bnzx6prb+/P86fP49BgwZBQ0MDEyZMeOk3RkNDQ6XPT1pYWEhfxrCzs4ONjQ3++usvpcTUwsIC1tbWSE5OVup72LBh0NXVxbfffoupU6eiWrVqcHJykj5fo6uri8OHD2PatGno0aMHnjx5AgsLC3To0AEGBgYl4jIzM8PBgwfh7u6OAQMG4Oeff8auXbswc+ZM+Pv74/79+zAzM4Orqytq1qxZ4Wf97bffIisrC127doW+vj4mTZqEjIyMCl//OgRR1YuJ6YOSmZkJuVyOjIyMUv9HRERERESqkZubi1u3bsHOzg7a2tqqDoc+EOX9riqTG3ApLxEREREREakUE1MiIiIiIiJSKb5jSm9GsCUgE1QdBRF9yALfzjsvRERE9OZxxpSIiIiIiIhUionpe8bPzw++vr6qDoOIiIiIiKjKMDElIiIiIiIilWJi+h7Ly8tDQEAATE1Noa2tjbZt2yIhIUGqb968OUJDQ6VzX19faGhoIDMzEwBw9+5dCIKAa9euAQB++ukntGjRAvr6+jAzM0P//v2Rnp7+dm+KiIiIiIg+OkxM32NTp07Fr7/+iqioKJw9exZ16tSBl5cXHj58CABwd3dHXFwcAEAURRw5cgRGRkY4evQoACA2NhZmZmZwdHQEAOTn52PevHk4f/48tm/fjlu3bsHPz6/cGPLy8pCZmal0EBERERERVQZ35X1PZWdn47vvvkNkZCQ+++wzAMC6deuwb98+hIeHY8qUKXB3d0d4eDgUCgUuXrwIdXV1fPHFF4iLi0OnTp0QFxcHNzc3qU9/f3/pb3t7e4SFhaFly5bIysqCnp5eqXEEBwcjKCioRHmj3HCoibpVfNdERM+ZvlPVEbyWlBAfVYdARET0zmBi+p5KTk5GQUEBXFxcpDJNTU20bNkSSUlJAABXV1c8efIE586dw7Fjx+Dm5gYPDw/Mnz8fABAXF4fx48dL1587dw6BgYFITEzEw4cPoVAoAACpqalo0KBBqXHMmDEDEydOlM4zMzNhZWVV1bdLRERERG9SoPwtjvV2PvcVGBiI7du3IzExscLX2NraYvz48Ur/H/ld4efnh8ePH2P79u0VvuZVnoGqcCnve0oURQCAIAglyovL5HI5nJ2dERcXh0OHDsHd3R3t2rVDYmIibty4gevXr8Pd3R3AsxnYTz/9FHp6evjpp5+QkJCAbdu2AXi2xLcsMpkMBgYGSgcRERER0ZsQHx8PdXV1eHt7v5XxBEFQSgSvXr0KQRBw8uRJpXatWrWCTCZDTk6OVJafnw9dXV18//33byXW9x0T0/dUnTp1oKWlJb0vCgAFBQU4ffo06tevL5W5u7sjNjYWhw8fhru7OwwNDdGgQQPMnz8fpqamUturV6/i33//RUhICNq1a4d69epx4yMiIiIieqesX78e48aNw9GjR5GamvrWx69Xrx7Mzc0RGxsrlWVlZeHcuXMwNTVFfHy8VH7y5Ek8ffoUHh4ebz3O9xET0/dUtWrVMGrUKEyZMgW7d+/GlStXMHz4cOTk5GDo0KFSO3d3d+zevRuCIEjLcd3d3bFx40al90utra2hpaWFFStW4M8//8Tvv/+OefPmvfX7IiIiIiIqTXZ2NrZs2YJRo0ahc+fOiIyMVKoPCQlBzZo1oa+vj6FDhyI3N1ep3t3dvcQSXV9f3zI3+7S1tQUAdO/eHYIgSOfPbzAKAEeOHEHdunXRtWtXpfK4uDhYWFjAwcEBABAREYH69etDW1sb9erVw+rVq5XG++eff9CnTx8YGRnB2NgY3bp1Q0pKSpnP48yZMzA1NcWCBQsq/AwSEhLQsWNHmJiYQC6Xw83NDWfPnpXq/f390blzZ6VrCgsLYWZmhvXr15cZS1XgO6bvsZCQECgUCgwcOBBPnjxBixYtsGfPHhgZGUltXF1dAQBubm7SEl83NzcsW7ZMKTGtUaMGIiMj8dVXXyEsLAzNmjXD4sWL0bVr11eK7VKQF5f1EhEREVGV2bx5MxwdHeHo6IgvvvgC48aNw6xZsyAIArZs2YI5c+Zg1apVaNeuHTZs2ICwsDDY29u/8ngJCQkwNTVFREQEvL29oa6uDgDw8PDAhAkTUFhYCA0NDcTGxsLd3R2urq5Yvny5dH1sbKw0W7pu3TrMmTMHK1euRNOmTXHu3DkMHz4c1apVw+DBg5GTkwMPDw+0a9cOhw8fhoaGBubPnw9vb29cuHABWlpaSrHFxcXB19cXwcHBGDVqFABU6Bk8efIEgwcPRlhYGAAgNDQUnTp1wo0bN6Cvr49hw4bB1dUVaWlpMDc3BwDs2rULWVlZ6N279ys/y4pgYvqeef6/DGlrayMsLEz6YZVGLpejsLBQqczX11d6R/V5/fr1Q79+/ZTKSmtHRERERPS2hYeH44svvgAAeHt7IysrCwcOHICnpyeWLVsGf39/DBs2DAAwf/587N+/v8SMYWXUqFEDAGBoaAgzMzOp3N3dHdnZ2UhISEDr1q0RFxeHKVOmwNXVFQMHDkROTg40NDRw4sQJrFy5EgAwb948hIaGokePHgAAOzs7XLlyBWvXrsXgwYMRHR0NNTU1/PDDD9JkUkREBAwNDREXF4dPP/1UGv+3337DwIEDsXbtWqX/716RZ9C+fXule1y7di2MjIxw6NAhdO7cGW3atIGjoyM2bNiAqVOnSnF8/vnnZX6lo6pwKS8REREREb3Trl27hlOnTqFv374AAA0NDfTp00daXpqUlITWrVsrXfPieVVxcHCApaUl4uLikJmZiXPnzsHNzQ01a9aEnZ0djh07hhMnTuDp06do37497t+/j9u3b2Po0KHQ09OTjvnz5yM5ORnAs2W5N2/ehL6+vlRfvXp15ObmSm2AZ++t9uzZE1FRUSUmlCryDNLT0zFy5EjUrVsXcrkccrkcWVlZSu/rDhs2DBEREVL7nTt3Kn1W8k3hjCkREREREb3TwsPDUVhYCAsLC6lMFEVoamri0aNHFepDTU2txGrAgoKCV4qneIPRxo0bw8HBAaampgCevTIXGxsLmUwGGxsb2Nra4t69ewCeLedt1aqVUj/Fy4MVCgWaN2+OjRs3lhireOYWAGrXrg1jY2OsX78ePj4+JZb4voyfnx/u37+PZcuWwcbGBjKZDK1bt1b6CsegQYMwffp0HD9+HMePH4etrS3atWtXqXFeBWdMiYiIiIjonVVYWIgff/wRoaGhSExMlI7z58/DxsYGGzduRP369XHixAml6148r1GjBtLS0qTzoqIiXLp0qdyxNTU1UVRUVKLcw8MD8fHx2Ldvn/T5ReBZYhoXF4e4uDhp2WzNmjVhYWGBP//8E3Xq1FE67OzsAADNmjXDjRs3YGpqWqKNXP5/35g1MTHBwYMHkZycjD59+igl1hV5BkeOHEFAQAA6deqEhg0bQiaT4d9//1VqY2xsDF9fX0RERCAiIgJDhgwp9xlVFSamRERERET0ztqxYwcePXqEoUOHolGjRkpHr169EB4eji+//BLr16/H+vXrcf36dcyZMweXL19W6qd9+/bYuXMndu7ciatXr2L06NF4/PhxuWPb2triwIEDuHv3rtLMrIeHB7Kzs7F+/XqlDUXd3Nxw+vRpnDhxQukzMYGBgQgODsby5ctx/fp1XLx4EREREViyZAkAYMCAATAxMUG3bt1w5MgR3Lp1C4cOHcKXX36Jv//+WykmU1NTHDx4EFevXkW/fv2k/WQq8gzq1KmDDRs2ICkpCSdPnsSAAQOgo6NT4r6HDRuGqKgoJCUlYfDgweU+o6rCpbxERERERB+7wAxVR1Cm8PBweHp6Ks0cFuvZsye++eYbODg4YPbs2Zg2bRpyc3PRs2dPjBo1Cnv27JHa+vv74/z58xg0aBA0NDQwYcKEl35jNDQ0FBMnTsS6detgYWEhfb7Fzs4ONjY2+Ouvv5QSUwsLC1hbWyM5OVmp72HDhkFXVxfffvstpk6dimrVqsHJyUn6fI2uri4OHz6MadOmoUePHnjy5AksLCzQoUOHUr90YWZmhoMHD8Ld3R0DBgzAzz//jD59+iA5ObncZ7B+/XqMGDECTZs2hbW1Nb755htMnjy5RP+enp4wNzdHw4YNUatWrXKfUVURRG67SlUoMzMTcrkcGRkZ/FwMERER0TskNzcXt27dgp2dHbS1tVUdDr3DcnJyUKtWLaxfv17aSbgs5f2uKpMbcMaUiIiIiIiIoFAocPfuXYSGhkIul6Nr165vbWwmpkRERERERITU1FTY2dnB0tISkZGR0NB4e+kiE1MiIiIiIiKCra1tiU/qvC1MTOnNCLYEZIKqoyCiD8U7vCkHERERvT5+LuYd4+7uLu3OBTz7rxbLli2TzgVBwPbt26t0zBfHICIiIqIPF/c+papUVb8nzpi+A/z8/PD48eMKJZxpaWkwMjKq0vETEhJQrVq1Ku2TiIiIiN4tmpqaAJ7tuFratyuJXkVOTg6A//t9vSompu8ZMzOzKu+zRo0aVd4nEREREb1b1NXVYWhoiPT0dADPvp0pCHz1il6NKIrIyclBeno6DA0Noa6u/lr9MTF9zwiCgG3btsHX1xcpKSmws7PDr7/+ihUrVuDkyZNwcHDAmjVr0Lp1a+maX3/9FbNnz8bNmzdhbm6OcePGYdKkSVK9ra0txo8fLy0hDgwMxPr163Hv3j0YGxujV69eCAsLe9u3SkRERERVrHiSozg5JXpdhoaGVTJ5xsT0AzBz5kwsXrwYDg4OmDlzJvr164ebN29CQ0MDZ86cQe/evREYGIg+ffogPj4eo0ePhrGxMfz8/Er0tXXrVixduhTR0dFo2LAh7t69i/Pnz5c5dl5eHvLy8qTzzMzMN3GLRERERFQFBEGAubk5TE1NUVBQoOpw6D2nqan52jOlxZiYfgAmT54MHx8fAEBQUBAaNmyImzdvol69eliyZAk6dOiAWbNmAQDq1q2LK1eu4Ntvvy01MU1NTYWZmRk8PT2hqakJa2trtGzZssyxg4ODERQUVKK8UW441ETdqrlBIqLpO1UdwTshJcRH1SEQ0QdCXV29yhIKoqrAXXk/AI0bN5b+Njc3B/B/yzOSkpLg4uKi1N7FxQU3btxAUVFRib4+//xzPH36FPb29hg+fDi2bduGwsLCMseeMWMGMjIypOP27dtVcUtERERERPQRYWL6AXh+B6ziF9gVCgWAZy8lv/hSe3lbOltZWeHatWtYtWoVdHR0MHr0aLi6upa51EMmk8HAwEDpICIiIiIiqgwmph+4Bg0a4OjRo0pl8fHxqFu3bpnLN3R0dNC1a1eEhYUhLi4Ox48fx8WLF99GuERERERE9BHiO6YfuEmTJuGTTz7BvHnz0KdPHxw/fhwrV67E6tWrS20fGRmJoqIitGrVCrq6utiwYQN0dHRgY2PzliMnIiIiIqKPBRPTD1yzZs2wZcsWzJ49G/PmzYO5uTnmzp1b6sZHwLPtnkNCQjBx4kQUFRXByckJf/zxB4yNjSs17qUgLy7rJSIiIiKiChHE8l44JKqkzMxMyOVyZGRkMDElIiIiIvqIVSY34DumREREREREpFJMTImIiIiIiEilmJgSERERERGRSjExJSIiIiIiIpViYkpEREREREQqxcSUiIiIiIiIVIqJKREREREREakUE1MiIiIiIiJSKSamREREREREpFIaqg6APlDBloBMUHUURETvj8AMVUdARESkMpwx/YDZ2tpi2bJlZdanpKRAEAQkJia+tZiIiIiIiIhexMT0PXX79m0MHToUtWrVgpaWFmxsbPDll1/iwYMHFe7DysoKaWlpaNSo0RuMlIiIiIiIqHxMTN9Df/75J1q0aIHr169j06ZNuHnzJtasWYMDBw6gdevWePjwYYX6UVdXh5mZGTQ0uKKbiIiIiIhUh4npe2jMmDHQ0tLC3r174ebmBmtra3z22WfYv38//vnnH8ycOVNqm5OTA39/f+jr68Pa2hrff/+9VFfaUt5Dhw6hZcuWkMlkMDc3x/Tp01FYWPg2b4+IiIiIiD4yTEzfMw8fPsSePXswevRo6OjoKNWZmZlhwIAB2Lx5M0RRBACEhoaiRYsWOHfuHEaPHo1Ro0bh6tWrpfb9zz//oFOnTvjkk09w/vx5fPfddwgPD8f8+fPLjCcvLw+ZmZlKBxERERERUWVwDed75saNGxBFEfXr1y+1vn79+nj06BHu378PAOjUqRNGjx4NAJg2bRqWLl2KuLg41KtXr8S1q1evhpWVFVauXAlBEFCvXj3cuXMH06ZNw+zZs6GmVvK/YwQHByMoKKhEeaPccKiJuq9zq0REH5fpO9/4ECkhPm98DCIiolfBGdMPTPFMqSA8+1RL48aNpTpBEGBmZob09PRSr01KSkLr1q2lawHAxcUFWVlZ+Pvvv0u9ZsaMGcjIyJCO27dvV9WtEBERERHRR4KJ6XumTp06EAQBV65cKbX+6tWrMDIygomJCQBAU1NTqV4QBCgUilKvFUVRKSktLiu+rjQymQwGBgZKBxERERERUWUwMX3PGBsbo2PHjli9ejWePn2qVHf37l1s3LgRffr0KTORLE+DBg0QHx8vJaMAEB8fD319fVhYWLx27ERERERERKVhYvoeWrlyJfLy8uDl5YXDhw/j9u3b2L17Nzp27AgLCwssWLDglfodPXo0bt++jXHjxuHq1av47bffMGfOHEycOLHU90uJiIiIiIiqAjc/eg85ODjg9OnTCAwMRJ8+ffDgwQOYmZnB19cXc+bMQfXq1V+pXwsLC+zatQtTpkxBkyZNUL16dQwdOhRff/11pfu6FOTFZb1ERERERFQhgvj8uk2i15SZmQm5XI6MjAwmpkREREREH7HK5AZcn0lEREREREQqxcSUiIiIiIiIVIqJKREREREREakUE1MiIiIiIiJSKSamREREREREpFJMTImIiIiIiEilmJgSERERERGRSjExJSIiIiIiIpViYkpEREREREQqpaHqAOgDFWwJyARVR0FE9P4LzFB1BERERG8cZ0xVyN3dHePHj1d1GOXy8/ODr6+vqsMgIiIiIqIPGBPTKubn5wdBEDBy5MgSdaNHj4YgCPDz8wMAxMTEYN68eW85wspZvnw5IiMjVR0GERERERF9wJiYvgFWVlaIjo7G06dPpbLc3Fxs2rQJ1tbWUln16tWhr6+vihBfqqioCAqFAnK5HIaGhqoOh4iIiIiIPmBMTN+AZs2awdraGjExMVJZTEwMrKys0LRpU6nsxaW8q1evhoODA7S1tVGzZk306tVLqtu6dSucnJygo6MDY2NjeHp6Ijs7GwCgUCgwd+5cWFpaQiaTwdnZGbt375aujYuLgyAIePz4sVSWmJgIQRCQkpICAIiMjIShoSF27NiBBg0aQCaT4a+//uJSXiIiIiIieuOYmL4hQ4YMQUREhHS+fv16+Pv7l9n+9OnTCAgIwNy5c3Ht2jXs3r0brq6uAIC0tDT069cP/v7+SEpKQlxcHHr06AFRFAE8W24bGhqKxYsX48KFC/Dy8kLXrl1x48aNSsWck5OD4OBg/PDDD7h8+TJMTU1fek1eXh4yMzOVDiIiIiIiosrgrrxvyMCBAzFjxgykpKRAEAQcO3YM0dHRiIuLK7V9amoqqlWrhs6dO0NfXx82NjbS7GpaWhoKCwvRo0cP2NjYAACcnJykaxcvXoxp06ahb9++AICFCxciNjYWy5Ytw6pVqyocc0FBAVavXo0mTZpU+Jrg4GAEBQWVKG+UGw41UbfC/RARURmm71R1BOVKCfFRdQhERPQB4IzpG2JiYgIfHx9ERUUhIiICPj4+MDExKbN9x44dYWNjA3t7ewwcOBAbN25ETk4OAKBJkybo0KEDnJyc8Pnnn2PdunV49OgRACAzMxN37tyBi4uLUn8uLi5ISkqqVMxaWlpo3Lhxpa6ZMWMGMjIypOP27duVup6IiIiIiIiJ6Rvk7++PyMhIREVFlbuMFwD09fVx9uxZbNq0Cebm5pg9ezaaNGmCx48fQ11dHfv27cP//vc/NGjQACtWrICjoyNu3bolXS8Iyt8MFUVRKlNTU5PKihUUFJSIQUdHp0Q/LyOTyWBgYKB0EBERERERVQYT0zfI29sb+fn5yM/Ph5eX10vba2howNPTE4sWLcKFCxeQkpKCgwcPAniWeLq4uCAoKAjnzp2DlpYWtm3bBgMDA9SqVQtHjx5V6is+Ph7169cHANSoUQPAsyXBxRITE6voLomIiIiIiF4P3zF9g9TV1aXltOrq6uW23bFjB/7880+4urrCyMgIu3btgkKhgKOjI06ePIkDBw7g008/hampKU6ePIn79+9LieeUKVMwZ84c1K5dG87OzoiIiEBiYiI2btwIAKhTpw6srKwQGBiI+fPn48aNGwgNDX2zN09ERERERFRBTEzfsIoubTU0NERMTAwCAwORm5sLBwcHbNq0CQ0bNkRSUhIOHz6MZcuWITMzEzY2NggNDcVnn30GAAgICEBmZiYmTZqE9PR0NGjQAL///jscHBwAAJqamti0aRNGjRqFJk2a4JNPPsH8+fPx+eefv7H7vhTkxWW9RERERERUIYL4/IuHRK8pMzMTcrkcGRkZTEyJiIiIiD5ilckN+I4pERERERERqRQTUyIiIiIiIlIpJqZERERERESkUkxMiYiIiIiISKWYmBIREREREZFKMTElIiIiIiIilWJiSkRERERERCrFxJSIiIiIiIhUiokpERERERERqZSGqgOgD1SwJSATVB0FEREFZqg6AiIiopfijOl7QhAEbN++HQCQkpICQRCQmJio0piIiIiIiIiqAmdM3wI/Pz88fvxYSixfl5WVFdLS0mBiYlIl/REREREREakSE9P3kLq6OszMzFQdBhERERERUZXgUt63zN3dHQEBAZg6dSqqV68OMzMzBAYGKrW5ceMGXF1doa2tjQYNGmDfvn1K9S8u5S0qKsLQoUNhZ2cHHR0dODo6Yvny5UrX+Pn5wdfXF4sXL4a5uTmMjY0xZswYFBQUSG1++ukntGjRAvr6+jAzM0P//v2Rnp7+Rp4DERERERFRMc6YqkBUVBQmTpyIkydP4vjx4/Dz84OLiws6duwIhUKBHj16wMTEBCdOnEBmZibGjx9fbn8KhQKWlpbYsmULTExMEB8fjxEjRsDc3By9e/eW2sXGxsLc3ByxsbG4efMm+vTpA2dnZwwfPhwAkJ+fj3nz5sHR0RHp6emYMGEC/Pz8sGvXrjLHzsvLQ15ennSemZn5eg+HiIiIiIg+OkxMVaBx48aYM2cOAMDBwQErV67EgQMH0LFjR+zfvx9JSUlISUmBpaUlAOCbb77BZ599VmZ/mpqaCAoKks7t7OwQHx+PLVu2KCWmRkZGWLlyJdTV1VGvXj34+PjgwIEDUmLq7+8vtbW3t0dYWBhatmyJrKws6OnplTp2cHCw0tjFGuWGQ03UrcRTISKiN2L6zrc6XEqIz1sdj4iIPgxcyqsCjRs3Vjo3NzeXlswmJSXB2tpaSkoBoHXr1i/tc82aNWjRogVq1KgBPT09rFu3DqmpqUptGjZsCHV19VLHBYBz586hW7dusLGxgb6+Ptzd3QGgRD/PmzFjBjIyMqTj9u3bL42ViIiIiIjoeUxMVUBTU1PpXBAEKBQKAIAoiiXaC0L53wPdsmULJkyYAH9/f+zduxeJiYkYMmQI8vPzKzxudnY2Pv30U+jp6eGnn35CQkICtm3bBgAl+nmeTCaDgYGB0kFERERERFQZXMr7jmnQoAFSU1Nx584d1KpVCwBw/Pjxcq85cuQI2rRpg9GjR0tlycnJlRr36tWr+PfffxESEgIrKysAwOnTpysZPRERERERUeVxxvQd4+npCUdHRwwaNAjnz5/HkSNHMHPmzHKvqVOnDk6fPo09e/bg+vXrmDVrFhISEio1rrW1NbS0tLBixQr8+eef+P333zFv3rzXuRUiIiIiIqIK4YzpO0ZNTQ3btm3D0KFD0bJlS9ja2iIsLAze3t5lXjNy5EgkJiaiT58+EAQB/fr1w+jRo/G///2vwuPWqFEDkZGR+OqrrxAWFoZmzZph8eLF6Nq16yvdx6UgLy7rJSIiIiKiChHE0l5qJHpFmZmZkMvlyMjIYGJKRERERPQRq0xuwKW8REREREREpFJMTImIiIiIiEilmJgSERERERGRSjExJSIiIiIiIpViYkpEREREREQqxcSUiIiIiIiIVIqJKREREREREakUE1MiIiIiIiJSKSamREREREREpFIaqg6APlDBloBMUHUUREQfp8AMVUdARERUKZwxfU3u7u4YP368qsMgIiIiIiJ6bzExLYWfnx8EQcDIkSNL1I0ePRqCIMDPzw8AEBMTg3nz5r3lCF9PXFwcBEHA48ePVR0KERERERERE9OyWFlZITo6Gk+fPpXKcnNzsWnTJlhbW0tl1atXh76+vipCJCIiIiIi+iAwMS1Ds2bNYG1tjZiYGKksJiYGVlZWaNq0qVT24lJeW1tbfPPNN/D394e+vj6sra3x/fffS/UpKSkQBAExMTHw8PCArq4umjRpguPHjyuNHx8fD1dXV+jo6MDKygoBAQHIzs6W6levXg0HBwdoa2ujZs2a6NWrl1SXl5eHgIAAmJqaQltbG23btkVCQoI0voeHBwDAyMhIafZ39+7daNu2LQwNDWFsbIzOnTsjOTn59R8mERERERFROZiYlmPIkCGIiIiQztevXw9/f/+XXhcaGooWLVrg3LlzGD16NEaNGoWrV68qtZk5cyYmT56MxMRE1K1bF/369UNhYSEA4OLFi/Dy8kKPHj1w4cIFbN68GUePHsXYsWMBAKdPn0ZAQADmzp2La9euYffu3XB1dZX6njp1Kn799VdERUXh7NmzqFOnDry8vPDw4UNYWVnh119/BQBcu3YNaWlpWL58OQAgOzsbEydOREJCAg4cOAA1NTV0794dCoWizHvNy8tDZmam0kFERERERFQZgiiKoqqDeNf4+fnh8ePH+OGHH2BpaYmrV69CEATUq1cPt2/fxrBhw2BoaIjIyEi4u7vD2dkZy5YtA/BsxrRdu3bYsGEDAEAURZiZmSEoKAgjR45ESkoK7Ozs8MMPP2Do0KEAgCtXrqBhw4ZISkpCvXr1MGjQIOjo6GDt2rVSTEePHoWbmxuys7Oxa9cuDBkyBH///XeJZcTZ2dkwMjJCZGQk+vfvDwAoKCiAra0txo8fjylTpiAuLg4eHh549OgRDA0Ny3wO9+/fh6mpKS5evIhGjRqV2iYwMBBBQUElyq3Gb4GaTLfCz5yIiD4eKSE+qg6BiIjegszMTMjlcmRkZMDAwKDctpwxLYeJiQl8fHwQFRWFiIgI+Pj4wMTE5KXXNW7cWPpbEASYmZkhPT29zDbm5uYAILU5c+YMIiMjoaenJx1eXl5QKBS4desWOnbsCBsbG9jb22PgwIHYuHEjcnJyAADJyckoKCiAi4uL1L+mpiZatmyJpKSkcuNOTk5G//79YW9vDwMDA9jZ2QEAUlNTy7xmxowZyMjIkI7bt2+/9PkQERERERE9j98xfQl/f39pCe2qVasqdI2mpqbSuSAIJZbDPt9GEJ5977O4jUKhwH//+18EBASU6Nva2hpaWlo4e/Ys4uLisHfvXsyePRuBgYFISEhA8QR4cZ/FRFEsUfaiLl26wMrKCuvWrUOtWrWgUCjQqFEj5Ofnl3mNTCaDTCYrt18iIiIiIqLycMb0Jby9vZGfn4/8/Hx4eXm9lTGbNWuGy5cvo06dOiUOLS0tAICGhgY8PT2xaNEiXLhwASkpKTh48KDU5ujRo1J/BQUFOH36NOrXrw8AUh9FRUVSmwcPHiApKQlff/01OnTogPr16+PRo0dv5X6JiIiIiOjjxhnTl1BXV5eWwKqrq7+VMadNm4b//Oc/GDNmDIYPH45q1aohKSkJ+/btw4oVK7Bjxw78+eefcHV1hZGREXbt2gWFQgFHR0dUq1YNo0aNwpQpU1C9enVYW1tj0aJFyMnJkd5ptbGxgSAI2LFjBzp16gQdHR0YGRnB2NgY33//PczNzZGamorp06e/lfslIiIiIqKPGxPTCnjZi7pVrXHjxjh06BBmzpyJdu3aQRRF1K5dG3369AEAGBoaIiYmBoGBgcjNzYWDgwM2bdqEhg0bAgBCQkKgUCgwcOBAPHnyBC1atMCePXtgZGQEALCwsEBQUBCmT5+OIUOGYNCgQYiMjER0dDQCAgLQqFEjODo6IiwsDO7u7q90D5eCvN76cyMiIiIiovcTd+WlKlWZnbeIiIiIiOjDxV15iYiIiIiI6L3BxJSIiIiIiIhUiokpERERERERqRQTUyIiIiIiIlIpJqZERERERESkUkxMiYiIiIiISKWYmBIREREREZFKMTElIiIiIiIilWJiSkRERERERCqloeoA6AMVbAnIBFVHQURELwrMUHUEREREJXDG9C1wd3fH+PHjK9Q2Li4OgiDg8ePHZbYJDAyEs7NzlcRGRERERESkakxMX5Gfnx8EQcDIkSNL1I0ePRqCIMDPzw8AEBMTg3nz5lXZ2JMnT8aBAweqrD8iIiIiIiJVYmL6GqysrBAdHY2nT59KZbm5udi0aROsra2lsurVq0NfX7/KxtXT04OxsXGV9UdERERERKRKTExfQ7NmzWBtbY2YmBipLCYmBlZWVmjatKlU9uJS3ry8PEydOhVWVlaQyWRwcHBAeHi4Ut9nzpxBixYtoKurizZt2uDatWtS3YtLeQsLCxEQEABDQ0MYGxtj2rRpGDx4MHx9faU2u3fvRtu2baU2nTt3RnJyslSfkpICQRAQExMDDw8P6OrqokmTJjh+/HgVPCkiIiIiIqKyMTF9TUOGDEFERIR0vn79evj7+5d7zaBBgxAdHY2wsDAkJSVhzZo10NPTU2ozc+ZMhIaG4vTp09DQ0Ci3z4ULF2Ljxo2IiIjAsWPHkJmZie3btyu1yc7OxsSJE5GQkIADBw5ATU0N3bt3h0KhKDHu5MmTkZiYiLp166Jfv34oLCwsc+y8vDxkZmYqHURERERERJXBXXlf08CBAzFjxgxpxvHYsWOIjo5GXFxcqe2vX7+OLVu2YN++ffD09AQA2Nvbl2i3YMECuLm5AQCmT58OHx8f5ObmQltbu0TbFStWYMaMGejevTsAYOXKldi1a5dSm549eyqdh4eHw9TUFFeuXEGjRo2k8smTJ8PHxwcAEBQUhIYNG+LmzZuoV69eqfcTHByMoKCgEuWNcsOhJuqWeg0REanQ9J2qjuCNSgnxUXUIRET0Cjhj+ppMTEzg4+ODqKgoREREwMfHByYmJmW2T0xMhLq6upR0lqVx48bS3+bm5gCA9PT0Eu0yMjJw7949tGzZUipTV1dH8+bNldolJyejf//+sLe3h4GBAezs7AAAqamprzRusRkzZiAjI0M6bt++Xe59ERERERERvYgzplXA398fY8eOBQCsWrWq3LY6OjoV6lNTU1P6WxCefQ/0xWW3zytuU0wURaXzLl26wMrKCuvWrUOtWrWgUCjQqFEj5Ofnv9a4MpkMMpnsJXdDRERERERUNs6YVgFvb2/k5+cjPz8fXl5e5bZ1cnKCQqHAoUOHqmRsuVyOmjVr4tSpU1JZUVERzp07J50/ePAASUlJ+Prrr9GhQwfUr18fjx49qpLxiYiIiIiIXhdnTKuAuro6kpKSpL/LY2tri8GDB8Pf3x9hYWFo0qQJ/vrrL6Snp6N3796vNP64ceMQHByMOnXqoF69elixYgUePXokzXgaGRnB2NgY33//PczNzZGamorp06e/0lhERERERERVjYlpFTEwMKhw2++++w5fffUVRo8ejQcPHsDa2hpfffXVK489bdo03L17F4MGDYK6ujpGjBgBLy8vKUlWU1NDdHQ0AgIC0KhRIzg6OiIsLAzu7u6vPObLXAryqtQzISIiIiKij5cgvvgyIr33FAoF6tevj969e2PevHlvdezMzEzI5XJkZGQwMSUiIiIi+ohVJjfgjOkH4K+//sLevXvh5uaGvLw8rFy5Erdu3UL//v1VHRoREREREdFLcfOjD4CamhoiIyPxySefwMXFBRcvXsT+/ftRv359VYdGRERERET0Upwx/QBYWVnh2LFjqg6DiIiIiIjolXDGlIiIiIiIiFSKiSkRERERERGpFBNTIiIiIiIiUikmpkRERERERKRSTEyJiIiIiIhIpZiYEhERERERkUrxczH0ZgRbAjJB1VEQEaleYIaqIyAiInrnccb0DfPz84Ovr6+qwyAiIiIiInpnvbOJqZ+fHwRBwMiRI0vUjR49GoIgwM/P7+0H9h4KDAyEs7NziXJBELB9+/a3Hg8REREREdHz3tnEFACsrKwQHR2Np0+fSmW5ubnYtGkTrK2tVRgZERERERERVZV3OjFt1qwZrK2tERMTI5XFxMTAysoKTZs2lcp2796Ntm3bwtDQEMbGxujcuTOSk5Ol+vz8fIwdOxbm5ubQ1taGra0tgoODpfrAwEBYW1tDJpOhVq1aCAgIkOp++ukntGjRAvr6+jAzM0P//v2Rnp6uFOfly5fh4+MDAwMD6Ovro127dkrjA8DixYthbm4OY2NjjBkzBgUFBVJdaTOXhoaGiIyMrFD8GRkZGDFiBExNTWFgYID27dvj/PnzAIDIyEgEBQXh/PnzEAQBgiAgMjIStra2AIDu3btDEATp/Pz58/Dw8IC+vj4MDAzQvHlznD59+mX/VERERERERK/snd/8aMiQIYiIiMCAAQMAAOvXr4e/vz/i4uKkNtnZ2Zg4cSKcnJyQnZ2N2bNno3v37khMTISamhrCwsLw+++/Y8uWLbC2tsbt27dx+/ZtAMDWrVuxdOlSREdHo2HDhrh7966U1AHPksJ58+bB0dER6enpmDBhAvz8/LBr1y4AwD///ANXV1e4u7vj4MGDMDAwwLFjx1BYWCj1ERsbC3Nzc8TGxuLmzZvo06cPnJ2dMXz48Ao9g/LiF0URPj4+qF69Onbt2gW5XI61a9eiQ4cOuH79Ovr06YNLly5h9+7d2L9/PwBALpfDx8cHpqamiIiIgLe3N9TV1QEAAwYMQNOmTfHdd99BXV0diYmJ0NTULDO2vLw85OXlSeeZmZkVuiciIiIiIqJi73xiOnDgQMyYMQMpKSkQBAHHjh1DdHS0UmLas2dPpWvCw8NhamqKK1euoFGjRkhNTYWDgwPatm0LQRBgY2MjtU1NTYWZmRk8PT2hqakJa2trtGzZUqr39/eX/ra3t0dYWBhatmyJrKws6OnpYdWqVZDL5YiOjpYSuLp16yrFY2RkhJUrV0JdXR316tWDj48PDhw4UOHEtLz4Y2NjcfHiRaSnp0MmkwF4Nju7fft2bN26FSNGjICenh40NDRgZmYmXaejowPg2czs8+WpqamYMmUK6tWrBwBwcHAoN7bg4GAEBQWVKG+UGw41UbdC90dE9EGbvlPVEZCKpYT4qDoEIqJ33ju9lBcATExM4OPjg6ioKERERMDHxwcmJiZKbZKTk9G/f3/Y29vDwMAAdnZ2AJ4lWcCzjZQSExPh6OiIgIAA7N27V7r2888/x9OnT2Fvb4/hw4dj27ZtSrOd586dQ7du3WBjYwN9fX24u7sr9Z2YmIh27dqVO6vYsGFDaUYSAMzNzUssBy5PefGfOXMGWVlZMDY2hp6ennTcunWrxHLiipg4cSKGDRsGT09PhISEvLSPGTNmICMjQzqKZ3KJiIiIiIgq6p1PTIFns5aRkZGIiopSmsEs1qVLFzx48ADr1q3DyZMncfLkSQDPluECz95VvXXrFubNm4enT5+id+/e6NWrF4BnGyxdu3YNq1atgo6ODkaPHg1XV1cUFBQgOzsbn376KfT09PDTTz8hISEB27ZtU+q7eOaxPC8mrYIgQKFQKJ2LoqjU5vl3UMuLX6FQwNzcHImJiUrHtWvXMGXKlJfG9qLAwEDpndmDBw+iQYMG0j2XRiaTwcDAQOkgIiIiIiKqjHd+KS8AeHt7S4mgl5eXUt2DBw+QlJSEtWvXol27dgCAo0ePlujDwMAAffr0QZ8+fdCrVy94e3vj4cOHqF69OnR0dNC1a1d07doVY8aMQb169XDx4kWIooh///0XISEhsLKyAoASGwE1btwYUVFRKCgoKHfWtDw1atRAWlqadH7jxg3k5ORUKP5mzZrh7t270NDQkDYwepGWlhaKiopKlGtqapZaXrduXdStWxcTJkxAv379EBERge7du7/SvREREREREb3Me5GYqqurIykpSfr7eUZGRjA2Nsb3338Pc3NzpKamYvr06Uptli5dCnNzczg7O0NNTQ2//PILzMzMpJ1vi4qK0KpVK+jq6mLDhg3Q0dGBjY0NFAoFtLS0sGLFCowcORKXLl3CvHnzlPoeO3YsVqxYgb59+2LGjBmQy+U4ceIEWrZsCUdHxwrdX/v27bFy5Ur85z//gUKhwLRp05SS3PLi9/T0ROvWreHr64uFCxfC0dERd+7cwa5du+Dr64sWLVrA1tYWt27dQmJiIiwtLaGvrw+ZTAZbW1scOHAALi4ukMlk0NbWxpQpU9CrVy/Y2dnh77//RkJCQol3eImIiIiIiKrSe5GYAihziaiamhqio6MREBCARo0awdHREWFhYdK7oACgp6eHhQsX4saNG1BXV8cnn3yCXbt2QU1NDYaGhggJCcHEiRNRVFQEJycn/PHHHzA2Ngbw7HMrX331FcLCwtCsWTMsXrwYXbt2lfo2NjbGwYMHMWXKFLi5uUFdXR3Ozs5wcXGp8L2FhoZiyJAhcHV1Ra1atbB8+XKcOXOmQvEDwK5duzBz5kz4+/vj/v37MDMzg6urK2rWrAng2eZQMTEx8PDwwOPHjxEREQE/Pz+EhoZi4sSJWLduHSwsLHD9+nU8ePAAgwYNwr1792BiYoIePXqUurnRy1wK8uKyXiIiIiIiqhBBfPHlRqLXkJmZCblcjoyMDCamREREREQfscrkBu/F5kdERERERET04WJiSkRERERERCrFxJSIiIiIiIhUiokpERERERERqRQTUyIiIiIiIlIpJqZERERERESkUkxMiYiIiIiISKWYmBIREREREZFKMTElIiIiIiIildJQdQD0gQq2BGSCqqMgIqLKCsxQdQRERPQR4oxpFYuLi4MgCHj8+LGqQ3mp9ylWIiIiIiL6cDExLceaNWugr6+PwsJCqSwrKwuamppo166dUtsjR45AEATUqlULaWlpkMvlbzvcSmvTps17EysREREREX24mJiWw8PDA1lZWTh9+rRUduTIEZiZmSEhIQE5OTlSeVxcHGrVqoW6devCzMwMgvDuL2PV0tJ6b2IlIiIiIqIPFxPTcjg6OqJWrVqIi4uTyuLi4tCtWzfUrl0b8fHxSuUeHh4llsf+9ddf6NKlC4yMjFCtWjU0bNgQu3btkq67fPkyfHx8YGBgAH19fbRr1w7JyckAAIVCgblz58LS0hIymQzOzs7YvXu3dG1KSgoEQUBMTAw8PDygq6uLJk2a4Pjx41Kb8sZ/MdbIyEgYGhpiz549qF+/PvT09ODt7Y20tLSqfrREREREREQSJqYv4e7ujtjYWOk8NjYW7u7ucHNzk8rz8/Nx/PhxeHh4lLh+zJgxyMvLw+HDh3Hx4kUsXLgQenp6AIB//vkHrq6u0NbWxsGDB3HmzBn4+/tLS4eXL1+O0NBQLF68GBcuXICXlxe6du2KGzduKI0xc+ZMTJ48GYmJiahbty769esn9VHe+KXJycnB4sWLsWHDBhw+fBipqamYPHlyme3z8vKQmZmpdBAREREREVUGd+V9CXd3d0yYMAGFhYV4+vQpzp07B1dXVxQVFSEsLAwAcOLECTx9+hQeHh5ITU1Vuj41NRU9e/aEk5MTAMDe3l6qW7VqFeRyOaKjo6GpqQkAqFu3rlS/ePFiTJs2DX379gUALFy4ELGxsVi2bBlWrVoltZs8eTJ8fHwAAEFBQWjYsCFu3ryJevXqlTt+aQoKCrBmzRrUrl0bADB27FjMnTu3zPbBwcEICgoqUd4oNxxqom65YxER0Tto+k5VR6ASKSE+qg6BiOijxhnTl/Dw8EB2djYSEhJw5MgR1K1bF6ampnBzc0NCQgKys7MRFxcHa2vrUpO+gIAAzJ8/Hy4uLpgzZw4uXLgg1SUmJqJdu3ZSUvq8zMxM3LlzBy4uLkrlLi4uSEpKUipr3Lix9Le5uTkAID09/aXjl0ZXV1dKSov7K+6rNDNmzEBGRoZ03L59u9z+iYiIiIiIXsTE9CXq1KkDS0tLxMbGIjY2Fm5ubgAAMzMz2NnZ4dixY4iNjUX79u1LvX7YsGH4888/MXDgQFy8eBEtWrTAihUrAAA6OjovHf/FjYlEUSxR9nxiW1ynUCheOn5pXkySBUGAKIpltpfJZDAwMFA6iIiIiIiIKoOJaQUUb2oUFxcHd3d3qdzNzQ179uzBiRMnSn2/tJiVlRVGjhyJmJgYTJo0CevWrQPwbKbzyJEjKCgoKHGNgYEBatWqhaNHjyqVx8fHo379+pWKv6zxiYiIiIiI3gVMTCvAw8MDR48eRWJiojRjCjxLTNetW4fc3NwyE9Px48djz549uHXrFs6ePYuDBw9KieXYsWORmZmJvn374vTp07hx4wY2bNiAa9euAQCmTJmChQsXYvPmzbh27RqmT5+OxMREfPnllxWOvbzxiYiIiIiI3gXc/KgCPDw88PTpU9SrVw81a9aUyt3c3PDkyRPUrl0bVlZWpV5bVFSEMWPG4O+//4aBgQG8vb2xdOlSAICxsTEOHjyIKVOmwM3NDerq6nB2dpbeKw0ICEBmZiYmTZqE9PR0NGjQAL///jscHBwqHHt5479Jl4K8uKyXiIiIiIgqRBDLe4GQqJIyMzMhl8uRkZHBxJSIiIiI6CNWmdyAS3mJiIiIiIhIpZiYEhERERERkUoxMSUiIiIiIiKVYmJKREREREREKsXElIiIiIiIiFSKiSkRERERERGpFBNTIiIiIiIiUikmpkRERERERKRSTEyJiIiIiIhIpTRUHQB9oIItAZmg6iiI6GMRmKHqCIiIiOg1cMaUiIiIiIiIVIqJ6XMEQSj38PPzU3WIREREREREHxwu5X1OWlqa9PfmzZsxe/ZsXLt2TSrT0dFRRVhEREREREQfNM6YPsfMzEw65HI5BEFQKjt8+DCaN28ObW1t2NvbIygoCIWFhdL1giDghx9+QPfu3aGrqwsHBwf8/vvvUn1cXBwEQcCBAwfQokUL6Orqok2bNkrJLwD88ccf5Y4TGBgIa2tryGQy1KpVCwEBAVLd6tWr4eDgAG1tbdSsWRO9evWS6kRRxKJFi2Bvbw8dHR00adIEW7duVRr7ypUr6NSpE/T09FCzZk0MHDgQ//77b5U9YyIiIiIiohcxMa2gPXv24IsvvkBAQACuXLmCtWvXIjIyEgsWLFBqFxQUhN69e+PChQvo1KkTBgwYgIcPHyq1mTlzJkJDQ3H69GloaGjA39+/wuNs3boVS5cuxdq1a3Hjxg1s374dTk5OAIDTp08jICAAc+fOxbVr17B79264urpKfX/99deIiIjAd999h8uXL2PChAn44osvcOjQIQDPZozd3Nzg7OyM06dPY/fu3bh37x569+5d5nPJy8tDZmam0kFERERERFQZgiiKoqqDeBdFRkZi/PjxePz4MQDA1dUVn332GWbMmCG1+emnnzB16lTcuXMHwLMZ06+//hrz5s0DAGRnZ0NfXx+7du2Ct7c34uLi4OHhgf3796NDhw4AgF27dsHHxwdPnz6Ftrb2S8dZsmQJ1q5di0uXLkFTU1Mp5piYGAwZMgR///039PX1leqys7NhYmKCgwcPonXr1lL5sGHDkJOTg59//hmzZ8/GyZMnsWfPHqn+77//hpWVFa5du4a6deuWeE6BgYEICgoqUW41fgvUZLoVetZERET04UkJ8VF1CESkYpmZmZDL5cjIyICBgUG5bfmOaQWdOXMGCQkJSjOkRUVFyM3NRU5ODnR1nyVhjRs3luqrVasGfX19pKenK/X1fBtzc3MAQHp6OqytrV86zueff45ly5bB3t4e3t7e6NSpE7p06QINDQ107NgRNjY2Up23t7e0rPjKlSvIzc1Fx44dlWLJz89H06ZNpXuMjY2Fnp5eiftPTk4uNTGdMWMGJk6cKJ1nZmbCysrq5Q+UiIiIiIjo/2NiWkEKhQJBQUHo0aNHiTptbW3p7xdnMQVBgEKhUCp7vo0gCFL/FRmnePZy37592L9/P0aPHo1vv/0Whw4dgr6+Ps6ePYu4uDjs3bsXs2fPRmBgIBISEqT+d+7cCQsLC6V+ZTKZNHaXLl2wcOHCEmMXJ9Avkslk0vVERERERESvgolpBTVr1gzXrl1DnTp1VD6Ojo4Ounbtiq5du2LMmDGoV68eLl68iGbNmkFDQwOenp7w9PTEnDlzYGhoiIMHD6Jjx46QyWRITU2Fm5tbmWP/+uuvsLW1hYYGfxpERERERPR2MPuooNmzZ6Nz586wsrLC559/DjU1NVy4cAEXL17E/Pnz39o4kZGRKCoqQqtWraCrq4sNGzZAR0cHNjY22LFjB/7880+4urrCyMgIu3btgkKhgKOjI/T19TF58mRMmDABCoUCbdu2RWZmJuLj46Gnp4fBgwdjzJgxWLduHfr164cpU6bAxMQEN2/eRHR0NNatWwd1dfUqu08iIiIiIqJiTEwryMvLCzt27MDcuXOxaNEiaGpqol69ehg2bNhbHcfQ0BAhISGYOHEiioqK4OTkhD/++APGxsYwNDRETEwMAgMDkZubCwcHB2zatAkNGzYEAMybNw+mpqYIDg7Gn3/+CUNDQzRr1gxfffUVAKBWrVo4duwYpk2bBi8vL+Tl5cHGxgbe3t5QU6vcBs6Xgrxe+oIzERERERERwF15qYpVZuctIiIiIiL6cFUmN+B3TImIiIiIiEilmJgSERERERGRSjExJSIiIiIiIpViYkpEREREREQqxcSUiIiIiIiIVIqJKREREREREakUE1MiIiIiIiJSKSamREREREREpFJMTImIiIiIiEilNFQdAH2ggi0BmaDqKIiI6EMTmKHqCIiI6A3gjOkriIuLgyAIePz4scpicHd3x/jx41U2PhERERERUVV5bxPT9PR0/Pe//4W1tTVkMhnMzMzg5eWF48ePV+k4VZkACoIgHdWqVYODgwP8/Pxw5syZKum/Mvz8/ODr6/vWxyUiIiIiInrRe5uY9uzZE+fPn0dUVBSuX7+O33//He7u7nj48KGqQytXREQE0tLScPnyZaxatQpZWVlo1aoVfvzxR1WHRkREREREpBLvZWL6+PFjHD16FAsXLoSHhwdsbGzQsmVLzJgxAz4+PlK71NRUdOvWDXp6ejAwMEDv3r1x7949qb60WcPx48fD3d1dqj906BCWL18uzXSmpKRIbc+cOYMWLVpAV1cXbdq0wbVr114au6GhIczMzGBra4tPP/0UW7duxYABAzB27Fg8evQIAPDgwQP069cPlpaW0NXVhZOTEzZt2lRuv7t374ZcLpcS3IsXL6J9+/bQ0dGBsbExRowYgaysLABAYGAgoqKi8Ntvv0n3FRcXBwCYNm0a6tatC11dXdjb22PWrFkoKCh46X0RERERERG9qvcyMdXT04Oenh62b9+OvLy8UtuIoghfX188fPgQhw4dwr59+5CcnIw+ffpUeJzly5ejdevWGD58ONLS0pCWlgYrKyupfubMmQgNDcXp06ehoaEBf3//V7qfCRMm4MmTJ9i3bx8AIDc3F82bN8eOHTtw6dIljBgxAgMHDsTJkydLvT46Ohq9e/fGjz/+iEGDBiEnJwfe3t4wMjJCQkICfvnlF+zfvx9jx44FAEyePBm9e/eGt7e3dF9t2rQBAOjr6yMyMhJXrlzB8uXLsW7dOixdurTM2PPy8pCZmal0EBERERERVcZ7uSuvhoYGIiMjMXz4cKxZswbNmjWDm5sb+vbti8aNGwMA9u/fjwsXLuDWrVtSMrlhwwY0bNgQCQkJ+OSTT146jlwuh5aWFnR1dWFmZlaifsGCBXBzcwMATJ8+HT4+PsjNzYW2tnal7qdevXoAIM3GWlhYYPLkyVL9uHHjsHv3bvzyyy9o1aqV0rWrV6/GV199hd9++w0eHh4AgI0bN+Lp06f48ccfUa1aNQDAypUr0aVLFyxcuBA1a9aEjo4O8vLyStzX119/Lf1ta2uLSZMmYfPmzZg6dWqpsQcHByMoKKhEeaPccKiJupV6DkRERC+TouoAiIjojXgvZ0yBZ++Y3rlzB7///ju8vLwQFxeHZs2aITIyEgCQlJQEKysrpRnOBg0awNDQEElJSVUSQ3ESDADm5uYAnm3KVFmiKAJ4tjkSABQVFWHBggVo3LgxjI2Noaenh7179yI1NVXpul9//RXjx4/H3r17paQUeHbvTZo0kZJSAHBxcYFCoXjpcuOtW7eibdu2MDMzg56eHmbNmlVi3OfNmDEDGRkZ0nH79u1K3z8REREREX3c3tvEFAC0tbXRsWNHzJ49G/Hx8fDz88OcOXMAPEv2ihO95z1frqamJiWFxSrzPqWmpqb0d3GfCoWi0vdRnCjb2dkBAEJDQ7F06VJMnToVBw8eRGJiIry8vJCfn690nbOzM2rUqIGIiAil+yjr3p+PszQnTpxA37598dlnn2HHjh04d+4cZs6cWWLc58lkMhgYGCgdRERERERElfFeJ6YvatCgAbKzs6W/U1NTlWbwrly5goyMDNSvXx8AUKNGDaSlpSn1kZiYqHSupaWFoqKiNxr3smXLYGBgAE9PTwDAkSNH0K1bN3zxxRdo0qQJ7O3tcePGjRLX1a5dG7Gxsfjtt98wbtw4qbxBgwZITEyUngUAHDt2DGpqaqhbt26Z93Xs2DHY2Nhg5syZaNGiBRwcHPDXX3+9iVsmIiIiIiKSvJeJ6YMHD9C+fXv89NNP0nukv/zyCxYtWoRu3boBADw9PdG4cWMMGDAAZ8+exalTpzBo0CC4ubmhRYsWAID27dvj9OnT+PHHH3Hjxg3MmTMHly5dUhrL1tYWJ0+eREpKCv79999XmhF93uPHj3H37l389ddf2LdvH3r16oWff/4Z3333HQwNDQEAderUwb59+xAfH4+kpCT897//xd27d0vtr27duoiNjZWW9QLAgAEDoK2tjcGDB+PSpUuIjY3FuHHjMHDgQNSsWVO6rwsXLuDatWv4999/UVBQgDp16iA1NRXR0dFITk5GWFgYtm3b9lr3S0RERERE9DLv5eZHenp6aNWqFZYuXYrk5GQUFBTAysoKw4cPx1dffQXg2ZLV7du3Y9y4cXB1dYWamhq8vb2xYsUKqR8vLy/MmjULU6dORW5uLvz9/TFo0CBcvHhRajN58mQMHjwYDRo0wNOnT3Hr1q3Xin3IkCEAni1DtrCwQNu2bXHq1Ck0a9ZMajNr1izcunULXl5e0NXVxYgRI+Dr64uMjIxS+3R0dMTBgwfh7u4OdXV1hIaGYs+ePfjyyy/xySefQFdXFz179sSSJUuka4YPH464uDi0aNECWVlZiI2NRbdu3TBhwgSMHTsWeXl58PHxwaxZsxAYGFjp+7wU5MVlvUREREREVCGC+OJLlkSvITMzE3K5HBkZGUxMiYiIiIg+YpXJDd7LpbxERERERET04WBiSkRERERERCrFxJSIiIiIiIhUiokpERERERERqRQTUyIiIiIiIlIpJqZERERERESkUkxMiYiIiIiISKWYmBIREREREZFKMTElIiIiIiIildJQdQD0gQq2BGSCqqMgIqIPWWCGqiMgIqIqwhlTIiIiIiIiUikmpq/Jz88PgiBg5MiRJepGjx4NQRDg5+dXZeMFBgbC2dm5yvojIiIiIiJSNSamVcDKygrR0dF4+vSpVJabm4tNmzbB2tpahZERERERERG9+5iYVoFmzZrB2toaMTExUllMTAysrKzQtGlTqSwvLw8BAQEwNTWFtrY22rZti4SEBKk+Li4OgiDgwIEDaNGiBXR1ddGmTRtcu3YNABAZGYmgoCCcP38egiBAEARERkYCAJYsWQInJydUq1YNVlZWGD16NLKysqS+IyMjYWhoiD179qB+/frQ09ODt7c30tLSpDYJCQno2LEjTExMIJfL4ebmhrNnz76px0ZERERERASAiWmVGTJkCCIiIqTz9evXw9/fX6nN1KlT8euvvyIqKgpnz55FnTp14OXlhYcPHyq1mzlzJkJDQ3H69GloaGhI/fTp0weTJk1Cw4YNkZaWhrS0NPTp0wcAoKamhrCwMFy6dAlRUVE4ePAgpk6dqtRvTk4OFi9ejA0bNuDw4cNITU3F5MmTpfonT55g8ODBOHLkCE6cOAEHBwd06tQJT548KfO+8/LykJmZqXQQERERERFVBnflrSIDBw7EjBkzkJKSAkEQcOzYMURHRyMuLg4AkJ2dje+++w6RkZH47LPPAADr1q3Dvn37EB4ejilTpkh9LViwAG5ubgCA6dOnw8fHB7m5udDR0YGenh40NDRgZmamNP748eOlv+3s7DBv3jyMGjUKq1evlsoLCgqwZs0a1K5dGwAwduxYzJ07V6pv3769Up9r166FkZERDh06hM6dO5d638HBwQgKCipR3ig3HGqi7sseGxER0StLUXUARERUZThjWkVMTEzg4+ODqKgoREREwMfHByYmJlJ9cnIyCgoK4OLiIpVpamqiZcuWSEpKUuqrcePG0t/m5uYAgPT09HLHj42NRceOHWFhYQF9fX0MGjQIDx48QHZ2ttRGV1dXSkqL+36+3/T0dIwcORJ169aFXC6HXC5HVlYWUlNTyxx3xowZyMjIkI7bt2+XGycREREREdGLOGNahfz9/TF27FgAwKpVq5TqRFEEAAiCUKL8xTJNTU3p7+I6hUJR5rh//fUXOnXqhJEjR2LevHmoXr06jh49iqFDh6KgoKDUfov7Lo4LeLbD8P3797Fs2TLY2NhAJpOhdevWyM/PL3NsmUwGmUxWZj0REREREdHLcMa0Cnl7eyM/Px/5+fnw8vJSqqtTpw60tLRw9OhRqaygoACnT59G/fr1KzyGlpYWioqKlMpOnz6NwsJChIaG4j//+Q/q1q2LO3fuVDr+I0eOICAgAJ06dULDhg0hk8nw77//VrofIiIiIiKiyuCMaRVSV1eXluWqq6sr1VWrVg2jRo3ClClTUL16dVhbW2PRokXIycnB0KFDKzyGra0tbt26hcTERFhaWkJfXx+1a9dGYWEhVqxYgS5duuDYsWNYs2ZNpeOvU6cONmzYgBYtWiAzMxNTpkyBjo5OpfshIiIiIiKqDCamVczAwKDMupCQECgUCgwcOBBPnjxBixYtsGfPHhgZGVW4/549eyImJgYeHh54/PgxIiIi4OfnhyVLlmDhwoWYMWMGXF1dERwcjEGDBlUq9vXr12PEiBFo2rQprK2t8c033yjt2lsZl4K8yn0WRERERERExQTx+ZcMiV5TZmYm5HI5MjIymJgSEREREX3EKpMb8B1TIiIiIiIiUikmpkRERERERKRSTEyJiIiIiIhIpZiYEhERERERkUoxMSUiIiIiIiKVYmJKREREREREKsXElIiIiIiIiFSKiSkRERERERGpFBNTIiIiIiIiUikNVQdAH6hgS0AmqDoKIiJ6UwIzVB0BERF9QDhj+oGIjIyEoaGhqsMgIiIiIiKqtI8qMb179y7GjRsHe3t7yGQyWFlZoUuXLjhw4ICqQ6sUW1tbLFu2TKmsT58+uH79epWNkZKSAkEQkJiYWGV9EhERERERleajWcqbkpICFxcXGBoaYtGiRWjcuDEKCgqwZ88ejBkzBlevXlV1iK9FR0cHOjo6qg6DiIiIiIio0j6aGdPRo0dDEAScOnUKvXr1Qt26ddGwYUNMnDgRJ06cAACkpqaiW7du0NPTg4GBAXr37o179+5JfQQGBsLZ2RkbNmyAra0t5HI5+vbtiydPnkht3N3dERAQgKlTp6J69eowMzNDYGCgUiwZGRkYMWIETE1NYWBggPbt2+P8+fNKbX7//Xe0aNEC2traMDExQY8ePaT+//rrL0yYMAGCIEAQnr3HWdpS3rL6AABBELB9+3al9oaGhoiMjAQA2NnZAQCaNm0KQRDg7u5eqedNRERERERUUR9FYvrw4UPs3r0bY8aMQbVq1UrUGxoaQhRF+Pr64uHDhzh06BD27duH5ORk9OnTR6ltcnIytm/fjh07dmDHjh04dOgQQkJClNpERUWhWrVqOHnyJBYtWoS5c+di3759AABRFOHj44O7d+9i165dOHPmDJo1a4YOHTrg4cOHAICdO3eiR48e8PHxwblz53DgwAG0aNECABATEwNLS0vMnTsXaWlpSEtLK/Wey+ujIk6dOgUA2L9/P9LS0hATE1Nqu7y8PGRmZiodRERERERElfFRLOW9efMmRFFEvXr1ymyzf/9+XLhwAbdu3YKVlRUAYMOGDWjYsCESEhLwySefAAAUCgUiIyOhr68PABg4cCAOHDiABQsWSH01btwYc+bMAQA4ODhg5cqVOHDgADp27IjY2FhcvHgR6enpkMlkAIDFixdj+/bt2Lp1K0aMGIEFCxagb9++CAoKkvps0qQJAKB69epQV1eHvr4+zMzMyryf8vqoiBo1agAAjI2Nyx0nODhYaYxijXLDoSbqVng8IiJ6z0zfqeoI3mkpIT6qDoGI6L3yUcyYiqIIANKy19IkJSXByspKSkoBoEGDBjA0NERSUpJUZmtrKyWlAGBubo709HSlvho3bqx0/nybM2fOICsrC8bGxtDT05OOW7duITk5GQCQmJiIDh06vOLdosr6qIgZM2YgIyNDOm7fvv3GxyQiIiIiog/LRzFj6uDgAEEQkJSUBF9f31LbiKJYauL6YrmmpqZSvSAIUCgUSmXltVEoFDA3N0dcXFyJsYrfEa2KTYxe1ocgCFLCXqygoKDS48hkMmnml4iIiIiI6FV8FDOm1atXh5eXF1atWoXs7OwS9Y8fP0aDBg2QmpqqNON35coVZGRkoH79+lUWS7NmzXD37l1oaGigTp06SoeJiQmAZzOu5X3CRktLC0VFReWO87I+atSoofR+6o0bN5CTk6M0BoCXjkNERERERPS6PorEFABWr16NoqIitGzZEr/++itu3LiBpKQkhIWFoXXr1vD09ETjxo0xYMAAnD17FqdOncKgQYPg5uZWqU2DXsbT0xOtW7eGr68v9uzZg5SUFMTHx+Prr7/G6dOnAQBz5szBpk2bMGfOHCQlJeHixYtYtGiR1IetrS0OHz6Mf/75B//++2+p47ysj/bt22PlypU4e/YsTp8+jZEjRyrN9JqamkJHRwe7d+/GvXv3kJGRUWXPgIiIiIiI6HkfxVJe4NnnT86ePYsFCxZg0qRJSEtLQ40aNdC8eXN899130udTxo0bB1dXV6ipqcHb2xsrVqyo0jgEQcCuXbswc+ZM+Pv74/79+zAzM4Orqytq1qwJ4NknYX755RfMmzcPISEhMDAwgKurq9TH3Llz8d///he1a9dGXl5eiSW5FekjNDQUQ4YMgaurK2rVqoXly5fjzJkzUr2GhgbCwsIwd+5czJ49G+3atSt1+XFZLgV5wcDA4BWeEBERERERfWwEsbSshugVZWZmQi6XIyMjg4kpEREREdFHrDK5wUezlJeIiIiIiIjeTUxMiYiIiIiISKWYmBIREREREZFKMTElIiIiIiIilWJiSkRERERERCrFxJSIiIiIiIhUiokpERERERERqRQTUyIiIiIiIlIpJqZERERERESkUhqqDoA+UMGWgExQdRRERFSVAjNUHQEREX2gOGP6DouLi4MgCHj8+HGV9Ofn5wdfX98q6YuIiIiIiKiqMDF9B8THx0NdXR3e3t6qDoWIiIiIiOitY2L6Dli/fj3GjRuHo0ePIjU1VdXhEBERERERvVVMTFUsOzsbW7ZswahRo9C5c2dERkaW2fbBgwfo168fLC0toaurCycnJ2zatEmpzdatW+Hk5AQdHR0YGxvD09MT2dnZpfZ35swZmJqaYsGCBQCA3bt3o23btjA0NISxsTE6d+6M5OTkKrtXIiIiIiKi0jAxVbHNmzfD0dERjo6O+OKLLxAREQFRFEttm5ubi+bNm2PHjh24dOkSRowYgYEDB+LkyZMAgLS0NPTr1w/+/v5ISkpCXFwcevToUWp/cXFx6NChA4KCgjBz5kwAz5LkiRMnIiEhAQcOHICamhq6d+8OhUJRZvx5eXnIzMxUOoiIiIiIiCpDEMvKguitcHFxQe/evfHll1+isLAQ5ubm2LRpEzw9PREXFwcPDw88evQIhoaGpV7v4+OD+vXrY/HixTh79iyaN2+OlJQU2NjYlGjr5+eHx48fY8iQIRg4cCDWrl2Lfv36lRnb/fv3YWpqiosXL6JRo0altgkMDERQUFCJcqvxW6Am063YQyAiIvrApYT4qDoEIqK3LjMzE3K5HBkZGTAwMCi3LWdMVejatWs4deoU+vbtCwDQ0NBAnz59sH79+lLbFxUVYcGCBWjcuDGMjY2hp6eHvXv3Su+lNmnSBB06dICTkxM+//xzrFu3Do8ePVLq4+TJk+jZsyeioqJKJKXJycno378/7O3tYWBgADs7OwAo973XGTNmICMjQzpu3779ys+DiIiIiIg+TvyOqQqFh4ejsLAQFhYWUpkoitDU1CyRUAJAaGgoli5dimXLlsHJyQnVqlXD+PHjkZ+fDwBQV1fHvn37EB8fj71792LFihWYOXMmTp48KSWZtWvXhrGxMdavXw8fHx9oaWlJ/Xfp0gVWVlZYt24datWqBYVCgUaNGkn9l0Ymk0Emk1XVIyEiIiIioo8QZ0xVpLCwED/++CNCQ0ORmJgoHefPn4eNjQ02btxY4pojR46gW7du+OKLL9CkSRPY29vjxo0bSm0EQYCLiwuCgoJw7tw5aGlpYdu2bVK9iYkJDh48iOTkZPTp0wcFBQUAnm2slJSUhK+//hodOnRA/fr1S02OiYiIiIiIqhpnTFVkx44dePToEYYOHQq5XK5U16tXL4SHh2Pp0qVK5XXq1MGvv/6K+Ph4GBkZYcmSJbh79y7q168P4Nky3QMHDuDTTz+FqakpTp48ifv370v1xUxNTXHw4EF4eHigX79+iI6OhpGREYyNjfH999/D3NwcqampmD59+pt9CERERERERGBiqjLh4eHw9PQskZQCQM+ePfHNN9/g7NmzSuWzZs3CrVu34OXlBV1dXYwYMQK+vr7IyMgAABgYGODw4cNYtmwZMjMzYWNjg9DQUHz22WclxjAzM8PBgwfh7u6OAQMG4Oeff0Z0dDQCAgLQqFEjODo6IiwsDO7u7q90f5eCvF76gjMRERERERHAXXmpilVm5y0iIiIiIvpwcVdeIiIiIiIiem8wMSUiIiIiIiKVYmJKREREREREKsXElIiIiIiIiFSKiSkRERERERGpFBNTIiIiIiIiUikmpkRERERERKRSTEyJiIiIiIhIpZiYEhERERERkUppqDoA+kAFWwIyQdVREBG92wIzVB0BERHRO4EzpmXw8/ODr6+vqsOolJSUFAiCgMTERABAXFwcBEHA48ePVRoXERERERFRed75xNTPzw+CICAkJESpfPv27RCE15+RezGZe135+flYtGgRmjRpAl1dXZiYmMDFxQUREREoKCiokjEqqk2bNkhLS4NcLn+r4xIREREREVXGO5+YAoC2tjYWLlyIR48eVWm/+fn5Vd6fl5cXQkJCMGLECMTHx+PUqVMYM2YMVqxYgcuXL79y36+S1GppacHMzKxKEngiIiIiIqI35b1ITD09PWFmZobg4OBy2/36669o2LAhZDIZbG1tERoaqlRva2uL+fPnw8/PD3K5HMOHD4ednR0AoGnTphAEAe7u7krXLF68GObm5jA2NsaYMWPKTRCXLVuGw4cP48CBAxgzZgycnZ1hb2+P/v374+TJk3BwcAAA7N69G23btoWhoSGMjY3RuXNnJCcnS/0Uz+Ju2bIF7u7u0NbWxk8//QSFQoG5c+fC0tISMpkMzs7O2L17d5nxvLiUNzIyEoaGhtizZw/q168PPT09eHt7Iy0tTbomISEBHTt2hImJCeRyOdzc3HD27NlynzsREREREdHreC8SU3V1dXzzzTdYsWIF/v7771LbnDlzBr1790bfvn1x8eJFBAYGYtasWYiMjFRq9+2336JRo0Y4c+YMZs2ahVOnTgEA9u/fj7S0NMTExEhtY2NjkZycjNjYWERFRSEyMrJEf8/buHEjPD090bRp0xJ1mpqaqFatGgAgOzsbEydOREJCAg4cOAA1NTV0794dCoVC6Zpp06YhICAASUlJ8PLywvLlyxEaGorFixfjwoUL8PLyQteuXXHjxo2KPEYAQE5ODhYvXowNGzbg8OHDSE1NxeTJk6X6J0+eYPDgwThy5AhOnDgBBwcHdOrUCU+ePCm1v7y8PGRmZiodRERERERElfHe7MrbvXt3ODs7Y86cOQgPDy9Rv2TJEnTo0AGzZs0CANStWxdXrlzBt99+Cz8/P6ld+/btlRKxlJQUAICxsTHMzMyU+jQyMsLKlSuhrq6OevXqwcfHBwcOHMDw4cNLjfHGjRslZlxL07NnT6Xz8PBwmJqa4sqVK2jUqJFUPn78ePTo0UM6X7x4MaZNm4a+ffsCABYuXIjY2FgsW7YMq1ateum4wLMlwWvWrEHt2rUBAGPHjsXcuXOl+vbt2yu1X7t2LYyMjHDo0CF07ty5RH/BwcEICgoqUd4oNxxqom6FYiIi+mhN36nqCIheS0qIj6pDIKIPxHsxY1ps4cKFiIqKwpUrV0rUJSUlwcXFRanMxcUFN27cQFFRkVTWokWLCo/XsGFDqKurS+fm5uZIT08vs70oihV6nzM5ORn9+/eHvb09DAwMpOXEqampSu2ejzUzMxN37twp9R6TkpIqdD8AoKurKyWlQMl7Sk9Px8iRI1G3bl3I5XLI5XJkZWWViK3YjBkzkJGRIR23b9+ucCxERERERETAezRjCgCurq7w8vLCV199pTQLCpSeFIqiWKKP4uW0FaGpqal0LghCieW2z6tbt26FksQuXbrAysoK69atQ61ataBQKNCoUaMSmzGVFmtp91iZzY1Ku6fnn5Ofnx/u37+PZcuWwcbGBjKZDK1bty5zoyiZTAaZTFbh8YmIiIiIiF70Xs2YAkBISAj++OMPxMfHK5U3aNAAR48eVSqL/3/s3Xt8z/X///Hbe8N7540JwxjmfD6kkB1Ck0MOFUppOZTIyHlEG9Uop0iJD9tIoWIfHxU5bDPmMIflNNLCVkiJ95jj7P37w8/729uGjfF2uF8vl9flsvfz+Hi9vC9dLo+ez9fznZhIlSpVrFY9r1ekSBEAq1XV2/Xyyy+zZs0adu7cmaMuKyuLzMxMTp48SUpKCu+++y4tWrSgevXqeTpt2M3NjdKlS+d6j9WrV7/j2K9JSEggJCSENm3aWA6S+vvvvwtsfBERERERkes9cIlp7dq16d69OzNmzLAqHzJkCGvXrmX8+PH88ssvREdH8+mnn1q9T5qbEiVK4OjoyMqVK/nzzz8xmUy3HdugQYNo1qwZLVq0YObMmfz888/89ttvLFmyhCeeeIKDBw9StGhRPD09mT17Nr/++ivr1q1j8ODBeRp/2LBhTJw4kcWLF3PgwAFGjhxJcnIyAwcOvO2Yr+fr68uCBQtISUlhy5YtdO/eHUdHxwIbX0RERERE5HoP1Fbea8aPH8+SJUusyho0aMCSJUsYO3Ys48ePx8vLi3HjxuXY8nu9QoUKMX36dMaNG8fYsWNp3rw5cXFxtxWX0Whk9erVTJ06lS+++IKhQ4fi5ORE9erVCQkJoVatWtjZ2bFo0SLL56pVqzJ9+vQ8HZoUEhJCRkYGQ4YM4cSJE9SoUYPly5dbfoamIMybN4833niD+vXrU65cOT788MNbJve52RMehJubW4HFJSIiIiIiDy+DObcXMUVuU0ZGBu7u7phMJiWmIiIiIiKPsPzkBg/cVl4RERERERF5uCgxFREREREREZtSYioiIiIiIiI2pcRUREREREREbEqJqYiIiIiIiNiUElMRERERERGxKSWmIiIiIiIiYlNKTEVERERERMSmlJiKiIiIiIiITRWydQDykIooC0aDraMQEZH7XZjJ1hGIiMh9QCumYiUqKgoPDw9bhyEiIiIiIo8QJaa3ITg4GIPBwIQJE6zKY2JiMBjuzSrhL7/8gpOTE1999ZVVeXZ2Nk2bNqVTp073JA4REREREZE7pcT0Njk4ODBx4kROnTplk/mrVKnChAkTGDBgAMeOHbOUT548mV9//ZUvvvgi32Nevny5IEMUERERERHJEyWmt6lly5aUKlWKiIiIm7ZLTEzEz88PR0dHvL29CQkJITMzE4AZM2ZQu3ZtS9trK64zZ860lAUFBREaGprr2AMGDKBevXr06dMHgP379zN27Fhmz55N8eLFGTduHGXLlsVoNFKvXj1Wrlxp6Xv48GEMBgNLliwhICAABwcHvvzyyxxznDx5ksaNG/Pcc89x4cKFvD8gERERERGRPFJiepvs7e358MMPmTFjBr///nuubXbv3k1QUBCdO3dm165dLF68mA0bNvD2228DEBAQwN69e/n7778BiI+Pp3jx4sTHxwOQlZVFYmIi/v7+uY5vMBiIjIwkISGBOXPmEBwcTNeuXenYsSOffPIJkydPZtKkSezatYugoCCee+45Dh48aDXGiBEjCAkJISUlhaCgIKu633//nebNm1OtWjWWLl2Kg4NDjhguXrxIRkaG1SUiIiIiIpIfBrPZbLZ1EA+a4OBgTp8+TUxMDE2aNKFGjRrMnTuXmJgYOnXqxLVH2qNHDxwdHa221W7YsAF/f38yMzMxGo2UKFGCWbNm8fzzz1O/fn26du3K1KlT+fPPP9m0aRN+fn6cOnUKFxeXG8YTGRlJ7969KVOmDLt378bd3Z0yZcrQv39/Ro0aZWnXuHFjHn/8cWbOnMnhw4epUKEC06ZNY+DAgZY2UVFRDBo0iK1bt9KqVSs6dOjAJ598csN3Z8PCwggPD89R7j1oCXZGp3w/WxEREbnq8IS2tg5BROSOZGRk4O7ujslkws3N7aZttWJ6hyZOnEh0dDT79u3LUbd9+3aioqJwcXGxXEFBQWRnZ3Po0CEMBgN+fn7ExcVx+vRp9u7dS9++fbly5QopKSnExcXRoEGDmyalAK+//jpeXl6EhITg7u5ORkYGR48epVmzZlbtmjVrRkpKilVZo0aNcox3/vx5nnrqKTp27Mj06dNveqBTaGgoJpPJcqWnp980VhERERERkespMb1Dfn5+BAUFWa1MXpOdnc2bb75JcnKy5fr55585ePAglSpVAq5u542LiyMhIYG6devi4eGBn58f8fHxxMXFERAQkKc4ChUqRKFC1j9Le31CaTabc5Q5OzvnGMtoNNKyZUu+//77G25T/ndbNzc3q0tERERERCQ/lJgWgAkTJvC///2PxMREq/IGDRqwd+9efH19c1xFihQB/u8902+//daShPr7+7NmzZqbvl96M25ubpQuXZoNGzZYlScmJlK9evVb9rezs2PBggU0bNiQp59+mqNHj+Y7BhERERERkbxSYloAateuTffu3ZkxY4ZV+YgRI9i0aRP9+/cnOTmZgwcPsnz5cgYMGGBpU6tWLTw9PVm4cKElMQ0ICCAmJsaypfZ2DBs2jIkTJ7J48WIOHDjAyJEjSU5Otnqf9Gbs7e1ZuHAhdevW5emnn+b48eO3FYeIiIiIiMitFLp1E8mL8ePHs2TJEquyOnXqEB8fz+jRo2nevDlms5lKlSrRtWtXSxuDwYC/vz8xMTE0b97c0s/d3Z2KFSve9tbYkJAQMjIyGDJkCCdOnKBGjRosX76cypUr53mMQoUK8fXXX9O1a1eefvpp4uLiKFGiRJ767gkP0rZeERERERHJE53KKwUqPydviYiIiIjIw+uenMp7+vRp/vOf/xAaGso///wDwI4dO/jjjz9ud0gRERERERF5BN3WVt5du3bRsmVL3N3dOXz4MH369KFYsWIsW7aMI0eOMH/+/IKOU0RERERERB5St7ViOnjwYIKDgzl48CAODg6W8meffZb169cXWHAiIiIiIiLy8LutxDQpKYk333wzR3mZMmV0equIiIiIiIjky20lpg4ODmRkZOQoP3DgAI899tgdByUiIiIiIiKPjttKTDt06MC4ceO4fPkycPUnT9LS0hg5ciTPP/98gQYoIiIiIiIiD7fbSkwnTZrEX3/9RYkSJTh//jz+/v74+vri6urKBx98UNAxioiIiIiIyEPstk7ldXNzY8OGDaxbt44dO3aQnZ1NgwYNaNmyZUHHJyIiIiIiIg85g9lsNue30/z58+natStGo9Gq/NKlSyxatIgePXoUWIDyYMnPj+iKiIiIiMjDKz+5wW0lpvb29hw7dowSJUpYlZ88eZISJUpw5cqV/A4pDwnLl2+kK25Gg63DERERubfCTLaOQETkvpGfxPS23jE1m80YDDmTjt9//x13d/fbGVLyKSAggEGDBuW5fVxcHAaDgdOnT9+1mERERERERG5Hvt4xrV+/PgaDAYPBQIsWLShU6P+6X7lyhUOHDtG6desCD/JRFBwczOnTp4mJibF1KCIiIiIiIndVvhLTjh07ApCcnExQUBAuLi6WuiJFiuDj46OfixEREREREZF8yVdi+t577wHg4+ND165dcXBwuCtBibXMzEzeeustli5diqurK0OHDs3R5ssvv2TatGkcOHAAZ2dnnn76aaZNm5bjPeDt27czYsQI9u3bR7169YiMjKRq1aqW+s8//5xJkyaRnp5OhQoVePfdd3n11Vfv+j2KiIiIiMij67beMX3ttdeUlN5Dw4YNIzY2lmXLlvHTTz8RFxfH9u3brdpcunSJ8ePH8/PPPxMTE8OhQ4cIDg7OMdbo0aOZPHky27Zto1ChQvTs2dNSt2zZMgYOHMiQIUPYs2cPb775Jq+//jqxsbE3jO3ixYtkZGRYXSIiIiIiIvlxW6fyXrlyhalTp7JkyRLS0tK4dOmSVf0///xTYAE+qq69Y/rll1/i6elp+YkeuPp8y5YtyxtvvMG0adNy7Z+UlETjxo05c+YMLi4uxMXFERgYyJo1a2jRogUAP/zwA23btuX8+fM4ODjQrFkzatasyezZsy3jdOnShczMTL7//vtc5wkLCyM8PDxHufegJdgZne7wKYiIiNy/Dk9oa+sQRETua3f9VN7w8HCmTJlCly5dMJlMDB48mM6dO2NnZ0dYWNjtDCk3kJqayqVLl2jSpImlrFixYlbbbwF27txJhw4dKF++PK6urgQEBACQlpZm1a5OnTqWv728vAA4ceIEACkpKTRr1syqfbNmzUhJSblhfKGhoZhMJsuVnp6e/5sUEREREZFH2m0lpgsXLmTOnDkMHTqUQoUK8dJLL/Gf//yHsWPHsnnz5oKO8ZGWlwXtzMxMnnnmGVxcXPjyyy9JSkpi2bJlADlWswsXLmz5+9pP/mRnZ+co+/f8uf000DVGoxE3NzerS0REREREJD9uKzE9fvw4tWvXBsDFxQWT6eqPSbdr1+6GWz7l9vj6+lK4cGGrhP/UqVP88ssvls/79+/n77//ZsKECTRv3pxq1apZVkHzo3r16mzYsMGqLDExkerVq9/+DYiIiIiIiNxCvk7lvaZs2bIcO3aMcuXK4evry08//USDBg1ISkrCaDQWdIyPNBcXF3r16sWwYcPw9PSkZMmSjB49Gju7//t/CuXKlaNIkSLMmDGDvn37smfPHsaPH5/vuYYNG0aXLl1o0KABLVq04H//+x9Lly5lzZo1BXlLIiIiIiIiVm4rMe3UqRNr167liSeeYODAgbz00kvMnTuXtLQ03nnnnYKO8ZH38ccfc/bsWZ577jlcXV0ZMmSIZZUa4LHHHiMqKopRo0Yxffp0GjRowKRJk3juuefyNU/Hjh355JNP+PjjjwkJCaFChQpERkZa3lfNjz3hQdrWKyIiIiIieXJbp/Jeb8uWLWzcuBFfX998J0PycMnPyVsiIiIiIvLwyk9ucFsrpuvXr6dp06YUKnS1+xNPPMETTzxBVlYW69evx8/P73aGFRERERERkUfQbR1+FBgYmOtvlZpMJgIDA+84KBEREREREXl03FZieqOfEDl58iTOzs53HJSIiIiIiIg8OvK1lbdz587A1d+6DA4OtjqB98qVK+zatYumTZsWbIQiIiIiIiLyUMtXYuru7g5cXTF1dXXF0dHRUlekSBGefPJJ+vTpU7ARioiIiIiIyEMtX4lpZGQkcPXnScLCwnBycgLg8OHDxMTEUL16dYoXL17wUYqIiIiIiMhD67beMd25cyfz588H4PTp0zz55JNMnjyZjh078vnnnxdogCIiIiIiIvJwu+3EtHnz5gB8++23lCxZkiNHjjB//nymT59eoAGKiIiIiIjIw+22EtNz587h6uoKwE8//UTnzp2xs7PjySef5MiRIwUaoIiIiIiIiDzc8vWO6TW+vr7ExMTQqVMnVq1axTvvvAPAiRMncHNzK9AA5QEVURaMOX9SSERE5I6EmWwdgYiI3AW3tWI6duxYhg4dio+PD0888QRNmjQBrq6e1q9fv0ADlNtz+PBhDAYDycnJtg5FRERERETkpm4rMX3hhRdIS0tj27ZtrFy50lLeokULpk6dWmDBPWqCg4MxGAz07ds3R12/fv0svx+bF97e3hw7doxatWoVcJQiIiIiIiIF67YSU4BSpUpRv3597Oz+b4jGjRtTrVq1AgnsUeXt7c2iRYs4f/68pezChQt8/fXXlCtXLs/j2NvbU6pUKQoVuq3d2iIiIiIiIvfMbSemcnc0aNCAcuXKsXTpUkvZ0qVL8fb2ttomvXLlSp566ik8PDzw9PSkXbt2pKamWuqv38obFxeHwWBg7dq1NGrUCCcnJ5o2bcqBAwes5v/f//5Hw4YNcXBwoGLFioSHh5OVlXV3b1pERERERB5pSkzvQ6+//jqRkZGWz/PmzaNnz55WbTIzMxk8eDBJSUmsXbsWOzs7OnXqRHZ29k3HHj16NJMnT2bbtm0UKlTIatxVq1bxyiuvEBISwr59+/jiiy+Iiorigw8+uOF4Fy9eJCMjw+oSERERERHJD4PZbDbbOgi5Kjg4mNOnT/Of//yHsmXLsn//fgwGA9WqVSM9PZ3evXvj4eFBVFRUjr5//fUXJUqUYPfu3dSqVYvDhw9ToUIFdu7cSb169YiLiyMwMJA1a9bQokULAH744Qfatm3L+fPncXBwwM/Pj2effZbQ0FDLuF9++SXDhw/n6NGjucYcFhZGeHh4jnLvQUuwMzoVzIMRERGRfDs8oa2tQxCRR1xGRgbu7u6YTKZb/nqLVkzvQ8WLF6dt27ZER0cTGRlJ27ZtKV68uFWb1NRUXn75ZSpWrIibmxsVKlQAIC0t7aZj16lTx/K3l5cXcPVnfgC2b9/OuHHjcHFxsVx9+vTh2LFjnDt3LtfxQkNDMZlMlis9Pf2271tERERERB5NOhnnPtWzZ0/efvttAGbOnJmjvn379nh7ezNnzhxKly5NdnY2tWrV4tKlSzcdt3Dhwpa/DYarvzN6bftvdnY24eHhdO7cOUc/BweHXMczGo0Yjca83ZSIiIiIiEgulJjep1q3bm1JMoOCgqzqTp48SUpKCl988QXNmzcHYMOGDXc8Z4MGDThw4AC+vr53PJaIiIiIiEheKTG9T9nb25OSkmL5+9+KFi2Kp6cns2fPxsvLi7S0NEaOHHnHc44dO5Z27drh7e3Niy++iJ2dHbt27WL37t28//77dzy+iIiIiIhIbpSY3sdu9IKwnZ0dixYtIiQkhFq1alG1alWmT59OQEDAHc0XFBTEihUrGDduHB999BGFCxemWrVq9O7dO99j7QkPuuULziIiIiIiIqBTeaWA5efkLREREREReXjpVF4RERERERF5YCgxFREREREREZtSYioiIiIiIiI2pcRUREREREREbEqJqYiIiIiIiNiUElMRERERERGxKSWmIiIiIiIiYlNKTEVERERERMSmlJiKiIiIiIiITRWydQDykIooC0aDraMQERG5v4SZbB2BiMh9SSumDzGDwUBMTIytwxAREREREbkpJab5MGvWLFxdXcnKyrKUnT17lsKFC9O8eXOrtgkJCRgMBn755Zd7HabFsWPHePbZZ202v4iIiIiISF4oMc2HwMBAzp49y7Zt2yxlCQkJlCpViqSkJM6dO2cpj4uLo3Tp0lSpUsUWoQJQqlQpjEajzeYXERERERHJCyWm+VC1alVKly5NXFycpSwuLo4OHTpQqVIlEhMTrcoDAgLw9fVl0qRJVuPs2bMHOzs7UlNTAUhLS6NDhw64uLjg5uZGly5d+PPPPy3tw8LCqFevHvPmzaNcuXK4uLjw1ltvceXKFT766CNKlSpFiRIl+OCDD6zm+fdW3sOHD2MwGFi6dCmBgYE4OTlRt25dNm3aZNVnzpw5eHt74+TkRKdOnZgyZQoeHh4F8PRERERERERyp8Q0nwICAoiNjbV8jo2NJSAgAH9/f0v5pUuX2LRpE08//TQ9e/YkMjLSaox58+bRvHlzKlWqhNlspmPHjvzzzz/Ex8ezevVqUlNT6dq1q1Wf1NRUfvzxR1auXMnXX3/NvHnzaNu2Lb///jvx8fFMnDiRd999l82bN980/tGjRzN06FCSk5OpUqUKL730kmVr8saNG+nbty8DBw4kOTmZVq1a5Uh2r3fx4kUyMjKsLhERERERkfzQqbz5FBAQwDvvvENWVhbnz59n586d+Pn5ceXKFaZPnw7A5s2bOX/+PIGBgTg6OjJ27Fi2bt1K48aNuXz5Ml9++SUff/wxAGvWrGHXrl0cOnQIb29vABYsWEDNmjVJSkri8ccfByA7O5t58+bh6upKjRo1CAwM5MCBA/zwww/Y2dlRtWpVJk6cSFxcHE8++eQN4x86dCht27YFIDw8nJo1a/Lrr79SrVo1ZsyYwbPPPsvQoUMBqFKlComJiaxYseKG40VERBAeHp6jvNaFudiZnW7jCYuIiDxYDk9oa+sQREQeeFoxzafAwEAyMzNJSkoiISGBKlWqUKJECfz9/UlKSiIzM5O4uDjKlStHxYoV8fLyom3btsybNw+AFStWcOHCBV588UUAUlJS8Pb2tiSlADVq1MDDw4OUlBRLmY+PD66urpbPJUuWpEaNGtjZ2VmVnThx4qbx16lTx/K3l5cXgKXPgQMHaNy4sVX76z9fLzQ0FJPJZLnS09Nv2l5EREREROR6SkzzydfXl7JlyxIbG0tsbCz+/v7A1YOGKlSowMaNG4mNjeXpp5+29OnduzeLFi3i/PnzREZG0rVrV5ycrq4mms1mDIacv/d5fXnhwoWt6g0GQ65l2dnZN43/332ujX+tT26xmM3mm45nNBpxc3OzukRERERERPJDieltCAwMJC4uznLA0TX+/v6sWrWKzZs3ExgYaClv06YNzs7OfP755/z444/07NnTUlejRg3S0tKsVhr37duHyWSievXq9+R+rqlWrRpbt261Kvv3CcQiIiIiIiJ3gxLT2xAYGMiGDRtITk62rJjC1cR0zpw5XLhwwSoxtbe3Jzg4mNDQUHx9fWnSpImlrmXLltSpU4fu3buzY8cOtm7dSo8ePfD396dRo0b39L4GDBjADz/8wJQpUzh48CBffPEFP/74Y64ruiIiIiIiIgVFhx/dhsDAQM6fP0+1atUoWbKkpdzf358zZ85QqVIlq3dGAXr16sWHH35otVoK//eTLgMGDMDPzw87Oztat27NjBkz7sm9/FuzZs2YNWsW4eHhvPvuuwQFBfHOO+/w6aef5nusPeFB2tYrIiIiIiJ5YjDf6iVCKRAbN24kICCA33//3SqZvd/16dOH/fv3k5CQkKf2GRkZuLu7YzKZlJiKiIiIiDzC8pMbaMX0Lrt48SLp6emMGTOGLl263PdJ6aRJk2jVqhXOzs78+OOPREdH89lnn9k6LBEREREReYjpHdO77Ouvv6Zq1aqYTCY++ugjW4dzS1u3bqVVq1bUrl2bWbNmMX36dHr37m3rsERERERE5CGmrbxSoLSVV0REREREIH+5gVZMRURERERExKaUmIqIiIiIiIhNKTEVERERERERm1JiKiIiIiIiIjalxFRERERERERsSompiIiIiIiI2FQhWwcgD6mIsmA02DoKERF5FIWZbB2BiIjkk1ZM7wNRUVF4eHjc83l9fHyYNm3aPZ9XRERERETk3x6KxDQ4OBiDwYDBYKBw4cJUrFiRoUOHkpmZaevQ7mtJSUm88cYbtg5DREREREQecQ/NVt7WrVsTGRnJ5cuXSUhIoHfv3mRmZvL555/bOrT71mOPPWbrEERERERERB6OFVMAo9FIqVKl8Pb25uWXX6Z79+7ExMRgNpv56KOPqFixIo6OjtStW5dvv/3W0i8uLg6DwcDatWtp1KgRTk5ONG3alAMHDljahIWFUa9ePRYsWICPjw/u7u5069aNM2fOWNp8++231K5dG0dHRzw9PWnZsiWZmZmsX7+ewoULc/z4cat4hwwZgp+fX477OHDgAAaDgf3791uVT5kyBR8fH8xmM1euXKFXr15UqFABR0dHqlatyieffGLVPjg4mI4dOzJp0iS8vLzw9PSkf//+XL582dLm+q28U6ZMoXbt2jg7O+Pt7U2/fv04e/Zs/v4hRERERERE8umhSUyv5+joyOXLl3n33XeJjIzk888/Z+/evbzzzju88sorxMfHW7UfPXo0kydPZtu2bRQqVIiePXta1aemphITE8OKFStYsWIF8fHxTJgwAYBjx47x0ksv0bNnT1JSUoiLi6Nz586YzWb8/PyoWLEiCxYssIyVlZXFl19+yeuvv54j7qpVq9KwYUMWLlxoVf7VV1/x8ssvYzAYyM7OpmzZsixZsoR9+/YxduxYRo0axZIlS6z6xMbGkpqaSmxsLNHR0URFRREVFXXDZ2ZnZ8f06dPZs2cP0dHRrFu3juHDh9/0OV+8eJGMjAyrS0REREREJD8emq28/7Z161a++uorAgMDmTJlCuvWraNJkyYAVKxYkQ0bNvDFF1/g7+9v6fPBBx9YPo8cOZK2bdty4cIFHBwcAMjOziYqKgpXV1cAXn31VdauXcsHH3zAsWPHyMrKonPnzpQvXx6A2rVrW8bu1asXkZGRDBs2DIDvv/+ec+fO0aVLl1zj7969O59++injx48H4JdffmH79u3Mnz8fgMKFCxMeHm5pX6FCBRITE1myZInVmEWLFuXTTz/F3t6eatWq0bZtW9auXUufPn1ynXfQoEFWY44fP5633nqLzz777IbPOiIiwiqWa2pdmIud2emG/URERO6akd/bOoKH1uEJbW0dgog8pB6aFdMVK1bg4uKCg4MDTZo0wc/Pj6FDh3LhwgVatWqFi4uL5Zo/fz6pqalW/evUqWP528vLC4ATJ05Yynx8fCxJ6bU21+rr1q1LixYtqF27Ni+++CJz5szh1KlTlrbBwcH8+uuvbN68GYB58+bRpUsXnJ2dc72Xbt26ceTIEUv7hQsXUq9ePWrUqGFpM2vWLBo1asRjjz2Gi4sLc+bMIS0tzWqcmjVrYm9vn2vMuYmNjaVVq1aUKVMGV1dXevTowcmTJ296iFRoaCgmk8lypaen37CtiIiIiIhIbh6axDQwMJDk5GQOHDjAhQsXWLp0qaXu+++/Jzk52XLt27fP6j1TuLoKeY3BcPX3N7Ozs3Otv9bmWr29vT2rV6/mxx9/pEaNGsyYMYOqVaty6NAhAEqUKEH79u2JjIzkxIkT/PDDDzm2Cv+bl5cXgYGBfPXVVwB8/fXXvPLKK5b6JUuW8M4779CzZ09++uknkpOTef3117l06dIN7+n6mK935MgR2rRpQ61atfjuu+/Yvn07M2fOBLB6L/V6RqMRNzc3q0tERERERCQ/HpqtvM7Ozvj6+lqV1ahRA6PRSFpamtW23bvBYDDQrFkzmjVrxtixYylfvjzLli1j8ODBAPTu3Ztu3bpRtmxZKlWqRLNmzW46Xvfu3RkxYgQvvfQSqampdOvWzVKXkJBA06ZN6devn6Xs+hXg/Nq2bRtZWVlMnjwZO7ur/7/i+ndWRURERERE7oaHJjHNjaurK0OHDuWdd94hOzubp556ioyMDBITE3FxceG1114rkHm2bNnC2rVreeaZZyhRogRbtmzhr7/+onr16pY2QUFBuLu78/777zNu3Lhbjtm5c2feeust3nrrLQIDAylTpoylztfXl/nz57Nq1SoqVKjAggULSEpKokKFCrd9D5UqVSIrK4sZM2bQvn17Nm7cyKxZs257PBERERERkbx6qBNTgPHjx1OiRAkiIiL47bff8PDwoEGDBowaNarA5nBzc2P9+vVMmzaNjIwMypcvz+TJk3n22Wctbezs7AgODubDDz+kR48eeRqzffv2fPPNN8ybN8+qrm/fviQnJ9O1a1cMBgMvvfQS/fr148cff7zte6hXrx5Tpkxh4sSJhIaG4ufnR0RERJ5izc2e8CBt6xURERERkTwxmM1ms62DeFT06dOHP//8k+XLl9s6lLsmIyMDd3d3TCaTElMRERERkUdYfnKDh37F9H5gMplISkpi4cKF/Pe//7V1OCIiIiIiIvcVJab3QIcOHdi6dStvvvkmrVq1snU4IiIiIiIi9xUlpvdAXFycrUMQERERERG5bz00v2MqIiIiIiIiDyYlpiIiIiIiImJTSkxFRERERETEppSYioiIiIiIiE0pMRURERERERGbUmIqIiIiIiIiNqWfi5G7I6IsGA22jkJEROTBFGaydQQiIveUVkxFRERERETEppSY3kPp6en06tWL0qVLU6RIEcqXL8/AgQM5efKkrUMTERERERGxGSWm98hvv/1Go0aN+OWXX/j666/59ddfmTVrFmvXrqVJkyb8888/ufa7dOnSPY5URERERETk3lJieo/079+fIkWK8NNPP+Hv70+5cuV49tlnWbNmDX/88QejR48GwMfHh/fff5/g4GDc3d3p06cPAImJifj5+eHo6Ii3tzchISFkZmZaxj927Bht27bF0dGRChUq8NVXX+Hj48O0adMsbdLS0ujQoQMuLi64ubnRpUsX/vzzT0t9WFgY9erVY8GCBfj4+ODu7k63bt04c+bMvXlIIiIiIiLySFJieg/8888/rFq1in79+uHo6GhVV6pUKbp3787ixYsxm80AfPzxx9SqVYvt27czZswYdu/eTVBQEJ07d2bXrl0sXryYDRs28Pbbb1vG6dGjB0ePHiUuLo7vvvuO2bNnc+LECUu92WymY8eO/PPPP8THx7N69WpSU1Pp2rWrVTypqanExMSwYsUKVqxYQXx8PBMmTLjhvV28eJGMjAyrS0REREREJD90Ku89cPDgQcxmM9WrV8+1vnr16pw6dYq//voLgKeffpqhQ4da6nv06MHLL7/MoEGDAKhcuTLTp0/H39+fzz//nMOHD7NmzRqSkpJo1KgRAP/5z3+oXLmyZYw1a9awa9cuDh06hLe3NwALFiygZs2aJCUl8fjjjwOQnZ1NVFQUrq6uALz66qusXbuWDz74INfYIyIiCA8Pz1Fe68Jc7MxO+XlMIiIics3I729afXhC23sUiIjIvaEV0/vAtZVSg+Hqz6tcSy6v2b59O1FRUbi4uFiuoKAgsrOzOXToEAcOHKBQoUI0aNDA0sfX15eiRYtaPqekpODt7W1JSgFq1KiBh4cHKSkpljIfHx9LUgrg5eVltfJ6vdDQUEwmk+VKT0+/zacgIiIiIiKPKq2Y3gO+vr4YDAb27dtHx44dc9Tv37+fokWLUrx4cQCcnZ2t6rOzs3nzzTcJCQnJ0bdcuXIcOHAg13mvJbzX/r6W+F7f5t/lhQsXtqo3GAxkZ2ff8N6MRiNGo/GG9SIiIiIiIreiFdN7wNPTk1atWvHZZ59x/vx5q7rjx4+zcOFCunbtmmviCNCgQQP27t2Lr69vjqtIkSJUq1aNrKwsdu7caenz66+/cvr0acvnGjVqkJaWZrWiuW/fPkwm0w23GIuIiIiIiNwLSkzvkU8//ZSLFy8SFBTE+vXrSU9PZ+XKlbRq1YoyZcrc8B1OgBEjRrBp0yb69+9PcnIyBw8eZPny5QwYMACAatWq0bJlS9544w22bt3Kzp07eeONN3B0dLQkuy1btqROnTp0796dHTt2sHXrVnr06IG/v3+OrcMiIiIiIiL3krby3iOVK1dm27ZthIWF0bVrV06ePEmpUqXo2LEj7733HsWKFbth3zp16hAfH8/o0aNp3rw5ZrOZSpUqWZ2oO3/+fHr16oWfnx+lSpUiIiKCvXv34uDgAFzdkhsTE8OAAQPw8/PDzs6O1q1bM2PGjLtyv3vCg3Bzc7srY4uIiIiIyMPFYP73i4jy0Pj999/x9vZmzZo1tGjR4p7Nm5GRgbu7OyaTSYmpiIiIiMgjLD+5gVZMHxLr1q3j7Nmz1K5dm2PHjjF8+HB8fHzw8/OzdWgiIiIiIiI3pcT0IXH58mVGjRrFb7/9hqurK02bNmXhwoU5TtkVERERERG532grrxQobeUVERERERHIX26gU3lFRERERETEppSYioiIiIiIiE0pMRURERERERGbUmIqIiIiIiIiNqXEVERERERERGxKiamIiIiIiIjYlH7HVO6OiLJgNNg6ChERedCFmWwdgYiI3ANaMbWxsLAw6tWrd8fjxMXFYTAYOH36dJ77BAcH07FjxzueW0RERERE5E4oMb2L2rdvT8uWLXOt27RpEwaDgaeffpq1a9fe8VxNmzbl2LFjuLu757nPJ598QlRU1B3PLSIiIiIicieUmN5FvXr1Yt26dRw5ciRH3bx586hXrx5+fn54enrecIxLly7laa4iRYpQqlQpDIa8b591d3fHw8Mjz+1FRERERETuBiWmd1G7du0oUaJEjlXJc+fOsXjxYnr16pVjK++17bURERGULl2aKlWqAJCYmEi9evVwcHCgUaNGxMTEYDAYSE5OBnJu5Y2KisLDw4NVq1ZRvXp1XFxcaN26NceOHcsx1zUrV67kqaeewsPDA09PT9q1a0dqaurdeDQiIiIiIiIWSkzvokKFCtGjRw+ioqIwm82W8m+++YZLly7RvXv3XPutXbuWlJQUVq9ezYoVKzhz5gzt27endu3a7Nixg/HjxzNixIhbzn/u3DkmTZrEggULWL9+PWlpaQwdOvSG7TMzMxk8eDBJSUmsXbsWOzs7OnXqRHZ29g37XLx4kYyMDKtLREREREQkP3Qq713Ws2dPPv74Y+Li4ggMDASubuPt3LkzRYsWzbWPs7Mz//nPfyhSpAgAs2bNwmAwMGfOHBwcHKhRowZ//PEHffr0uencly9fZtasWVSqVAmAt99+m3Hjxt2w/fPPP2/1ee7cuZQoUYJ9+/ZRq1atXPtEREQQHh6eo7zWhbnYmZ1uGp+IiMgtjfze1hHIferwhLa2DkFECpBWTO+yatWq0bRpU+bNmwdAamoqCQkJ9OzZ84Z9ateubUlKAQ4cOECdOnVwcHCwlDVu3PiWczs5OVmSUgAvLy9OnDhxw/apqam8/PLLVKxYETc3NypUqABAWlraDfuEhoZiMpksV3p6+i3jEhERERER+TclpvdAr169+O6778jIyCAyMpLy5cvTokWLG7Z3dna2+mw2m3McavTvrcE3UrhwYavPBoPhpv3at2/PyZMnmTNnDlu2bGHLli3AzQ9gMhqNuLm5WV0iIiIiIiL5ocT0HujSpQv29vZ89dVXREdH8/rrr+fr9Nxq1aqxa9cuLl68aCnbtm1bgcZ48uRJUlJSePfdd2nRogXVq1fn1KlTBTqHiIiIiIhIbpSY3gMuLi507dqVUaNGcfToUYKDg/PV/+WXXyY7O5s33niDlJQUVq1axaRJkwDyleDeTNGiRfH09GT27Nn8+uuvrFu3jsGDBxfI2CIiIiIiIjejw4/ukV69ejF37lyeeeYZypUrl6++bm5u/O9//+Ott96iXr161K5dm7Fjx/Lyyy9bvXd6J+zs7Fi0aBEhISHUqlWLqlWrMn36dAICAm5rvD3hQdrWKyIiIiIieWIw5+VlRbnvLFy4kNdffx2TyYSjo6Otw7HIyMjA3d0dk8mkxFRERERE5BGWn9xAK6YPiPnz51OxYkXKlCnDzz//zIgRI+jSpct9lZSKiIiIiIjcDiWmD4jjx48zduxYjh8/jpeXFy+++CIffPCBrcMSERERERG5Y9rKKwVKW3lFRERERATylxvoVF4RERERERGxKSWmIiIiIiIiYlNKTEVERERERMSmlJiKiIiIiIiITSkxFREREREREZtSYioiIiIiIiI2pd8xlbsjoiwYDbaOQkRERO4nYSZbRyAi9ymtmD7kgoOD6dixo63DEBERERERuaFHIjENDg7GYDAwYcIEq/KYmBgMhnu7qmcwGDAYDGzevNmq/OLFi3h6emIwGIiLiyuw+T755BOioqIKbDwREREREZGC9kgkpgAODg5MnDiRU6dO2ToUvL29iYyMtCpbtmwZLi4uBT6Xu7s7Hh4eBT6uiIiIiIhIQXlkEtOWLVtSqlQpIiIibtouMTERPz8/HB0d8fb2JiQkhMzMTABmzJhB7dq1LW2vrbjOnDnTUhYUFERoaOhN53jttddYtGgR58+ft5TNmzeP1157LUfbP/74g65du1K0aFE8PT3p0KEDhw8fBmD//v04OTnx1VdfWdovXboUBwcHdu/eDeTcypudnc3EiRPx9fXFaDRSrlw5PvjgA0v97t27efrpp3F0dMTT05M33niDs2fP3vR+RERERERE7sQjk5ja29vz4YcfMmPGDH7//fdc2+zevZugoCA6d+7Mrl27WLx4MRs2bODtt98GICAggL179/L3338DEB8fT/HixYmPjwcgKyuLxMRE/P39bxpLw4YNqVChAt999x0A6enprF+/nldffdWq3blz5wgMDMTFxYX169ezYcMGXFxcaN26NZcuXaJatWpMmjSJfv36ceTIEY4ePUqfPn2YMGGCVQL9b6GhoUycOJExY8awb98+vvrqK0qWLGmZr3Xr1hQtWpSkpCS++eYb1qxZY7n/3Fy8eJGMjAyrS0REREREJD8MZrPZbOsg7rbg4GBOnz5NTEwMTZo0oUaNGsydO5eYmBg6derEtUfQo0cPHB0d+eKLLyx9N2zYgL+/P5mZmRiNRkqUKMGsWbN4/vnnqV+/Pl27dmXq1Kn8+eefbNq0CT8/P06dOnXDbbkGg4Fly5Zx5MgR/vvf/7Ju3TrGjRtHcnIy8+bNo2jRosTGxhIQEMC8efP46KOPSElJsbwLe+nSJTw8PIiJieGZZ54BoF27dmRkZFCkSBHs7OxYtWqVpf2/7/3MmTM89thjfPrpp/Tu3TtHbHPmzGHEiBGkp6fj7OwMwA8//ED79u05evSoJYH9t7CwMMLDw3OUew9agp3RKT//TCIiIvKQOzyhra1DEJF7KCMjA3d3d0wmE25ubjdt+8ismF4zceJEoqOj2bdvX4667du3ExUVhYuLi+UKCgoiOzubQ4cOYTAY8PPzIy4ujtOnT7N371769u3LlStXSElJIS4ujgYNGuTpXdFXXnmFTZs28dtvvxEVFUXPnj1zjefXX3/F1dXVEk+xYsW4cOECqamplnbz5s1j165d7Nixg6ioqBse6JSSksLFixdp0aLFDevr1q1rSUoBmjVrRnZ2NgcOHMi1T2hoKCaTyXKlp6ff8t5FRERERET+7ZH7HVM/Pz+CgoIYNWoUwcHBVnXZ2dm8+eabhISE5OhXrlw54Op23tmzZ5OQkEDdunXx8PDAz8+P+Ph44uLiCAgIyFMcnp6etGvXjl69enHhwgWeffZZzpw5kyOehg0bsnDhwhz9H3vsMcvfP//8M5mZmdjZ2XH8+HFKly6d65yOjo43jclsNt8wqb1RudFoxGg03nRcERERERGRm3nkElOACRMmUK9ePapUqWJV3qBBA/bu3Yuvr+8N+wYEBDBw4EC+/fZbSxLq7+/PmjVrSExMZODAgXmOo2fPnrRp04YRI0Zgb2+fo75BgwYsXryYEiVK3HDp+59//iE4OJjRo0dz/Phxunfvzo4dO3JNQitXroyjoyNr167NdStvjRo1iI6OJjMz07JqunHjRuzs7HI8KxERERERkYLyyG3lBahduzbdu3dnxowZVuUjRoxg06ZN9O/fn+TkZA4ePMjy5csZMGCApU2tWrXw9PRk4cKFlsQ0ICCAmJgYzp8/z1NPPZXnOFq3bs1ff/3FuHHjcq3v3r07xYsXp0OHDiQkJHDo0CHi4+MZOHCg5QCnvn374u3tzbvvvsuUKVMwm80MHTo01/EcHBwYMWIEw4cPZ/78+aSmprJ582bmzp1rmc/BwYHXXnuNPXv2EBsby4ABA3j11Vdzfb9URERERESkIDySK6YA48ePZ8mSJVZlderUIT4+ntGjR9O8eXPMZjOVKlWia9euljYGgwF/f39iYmJo3ry5pZ+7uzsVK1a85Uu9/2YwGChevPgN652cnFi/fj0jRoygc+fOnDlzhjJlytCiRQvc3NyYP38+P/zwAzt37qRQoUIUKlSIhQsX0rRpU9q2bUubNm1yjDlmzBgKFSrE2LFjOXr0KF5eXvTt29cy36pVqxg4cCCPP/44Tk5OPP/880yZMiXP93TNnvCgfD0LERERERF5dD0Sp/LKvZOfk7dEREREROThpVN5RURERERE5IGhxFRERERERERsSompiIiIiIiI2JQSUxEREREREbEpJaYiIiIiIiJiU0pMRURERERExKaUmIqIiIiIiIhNKTEVERERERERm1JiKiIiIiIiIjZVyNYByEMqoiwYDbaOQkRERGwpzGTrCETkAaEV07vk8OHDGAwGkpOTb9gmKioKDw+PexaTiIiIiIjI/eiRSUxnzZqFq6srWVlZlrKzZ89SuHBhmjdvbtU2ISEBg8HAL7/8cldj6tq1612Zw8fHh2nTphX4uCIiIiIiInfDI5OYBgYGcvbsWbZt22YpS0hIoFSpUiQlJXHu3DlLeVxcHKVLl6ZKlSp3NSZHR0dKlChxV+cQERERERG53z0yiWnVqlUpXbo0cXFxlrK4uDg6dOhApUqVSExMtCoPDAzkyy+/pFGjRri6ulKqVClefvllTpw4YWl36tQpunfvzmOPPYajoyOVK1cmMjLSat7ffvuNwMBAnJycqFu3Lps2bbLUXb+VNywsjHr16rFgwQJ8fHxwd3enW7dunDlzxtLmzJkzdO/eHWdnZ7y8vJg6dSoBAQEMGjQIgICAAI4cOcI777yDwWDAYPi/9zy/++47atasidFoxMfHh8mTJ1vF6uPjw4cffkjPnj1xdXWlXLlyzJ49+7aet4iIiIiISF49MokpXE3aYmNjLZ9jY2MJCAjA39/fUn7p0iU2bdpEYGAgly5dYvz48fz888/ExMRw6NAhgoODLf3HjBnDvn37+PHHH0lJSeHzzz+nePHiVnOOHj2aoUOHkpycTJUqVXjppZesthNfLzU1lZiYGFasWMGKFSuIj49nwoQJlvrBgwezceNGli9fzurVq0lISGDHjh2W+qVLl1K2bFnGjRvHsWPHOHbsGADbt2+nS5cudOvWjd27dxMWFsaYMWOIioqymn/y5Mk0atSInTt30q9fP9566y32799/w3gvXrxIRkaG1SUiIiIiIpIfj9SpvAEBAbzzzjtkZWVx/vx5du7ciZ+fH1euXGH69OkAbN68mfPnzxMYGEjFihUtfStWrMj06dNp3LgxZ8+excXFhbS0NOrXr0+jRo2AqyuO1xs6dCht27YFIDw8nJo1a/Lrr79SrVq1XGPMzs4mKioKV1dXAF599VXWrl3LBx98wJkzZ4iOjuarr76iRYsWAERGRlK6dGlL/2LFimFvb29Z5b1mypQptGjRgjFjxgBQpUoV9u3bx8cff2yVbLdp04Z+/foBMGLECKZOnUpcXNwN442IiCA8PDxHea0Lc7EzO+XaR0RERB4RI7+3dQQ3dXhCW1uHICL/3yO1YhoYGEhmZiZJSUkkJCRQpUoVSpQogb+/P0lJSWRmZhIXF0e5cuWoWLEiO3fupEOHDpQvXx5XV1cCAgIASEtLA+Ctt95i0aJF1KtXj+HDh1ttB76mTp06lr+9vLwArLYDX8/Hx8eSlF7rc639b7/9xuXLl2ncuLGl3t3dnapVq97y3lNSUmjWrJlVWbNmzTh48CBXrlzJNV6DwUCpUqVuGm9oaCgmk8lypaen3zIWERERERGRf3ukElNfX1/Kli1LbGwssbGx+Pv7A1CqVCkqVKjAxo0biY2N5emnnyYzM5NnnnkGFxcXvvzyS5KSkli2bBlwdbsvwLPPPsuRI0cYNGgQR48epUWLFgwdOtRqzsKFC1v+vva+Z3Z29g1j/Hf7a32utTebzVbjXHOt/GbMZnOe+t1s/twYjUbc3NysLhERERERkfx4pBJTuLpqGhcXR1xcnGUFFMDf359Vq1axefNmAgMD2b9/P3///TcTJkygefPmVKtWLdeVw8cee4zg4GC+/PJLpk2bdlcPC6pUqRKFCxdm69atlrKMjAwOHjxo1a5IkSJWq6AANWrUYMOGDVZliYmJVKlSBXt7+7sWs4iIiIiIyK08Uu+YwtXEtH///ly+fNmyYgpXE9O33nqLCxcuEBgYiIODA0WKFGHGjBn07duXPXv2MH78eKuxxo4dS8OGDalZsyYXL15kxYoVVK9e/a7F7urqymuvvcawYcMoVqwYJUqU4L333sPOzs5qNdTHx4f169fTrVs3jEYjxYsXZ8iQITz++OOMHz+erl27smnTJj799FM+++yzuxaviIiIiIhIXjySien58+epVq0aJUuWtJT7+/tz5swZKlWqhLe3N3D151xGjRrF9OnTadCgAZMmTeK5556z9ClSpAihoaEcPnwYR0dHmjdvzqJFi+5q/FOmTKFv3760a9cONzc3hg8fTnp6Og4ODpY248aN480336RSpUpcvHgRs9lMgwYNWLJkCWPHjmX8+PF4eXkxbtw4q4OPCtKe8CBt6xURERERkTwxmPPygqLctzIzMylTpgyTJ0+mV69etg6HjIwM3N3dMZlMSkxFRERERB5h+ckNHrkV0wfdzp072b9/P40bN8ZkMjFu3DgAOnToYOPIREREREREbo8S0wfQpEmTOHDgAEWKFKFhw4YkJCRQvHhxW4clIiIiIiJyW5SYPmDq16/P9u3bbR2GiIiIiIhIgXnkfi5GRERERERE7i9KTEVERERERMSmlJiKiIiIiIiITSkxFREREREREZtSYioiIiIiIiI2pcRUREREREREbEo/FyN3R0RZMBpsHYWIiMiDK8xk6whERO4ZrZg+gA4fPozBYCA5Ofmuz+Xj48O0adPu+jwiIiIiIvLoUmKaT8HBwRgMBvr27Zujrl+/fhgMBoKDg+99YCIiIiIiIg8oJaa3wdvbm0WLFnH+/HlL2YULF/j6668pV67cHY19+fLlOw1PRERERETkgaLE9DY0aNCAcuXKsXTpUkvZ0qVL8fb2pn79+paylStX8tRTT+Hh4YGnpyft2rUjNTXVUn9tS+6SJUsICAjAwcGB2bNn4+bmxrfffms15//+9z+cnZ05c+ZMjniuXLlCr169qFChAo6OjlStWpVPPvnEqk1wcDAdO3Zk0qRJeHl54enpSf/+/a0S4RMnTtC+fXscHR2pUKECCxcuvONnJSIiIiIicitKTG/T66+/TmRkpOXzvHnz6Nmzp1WbzMxMBg8eTFJSEmvXrsXOzo5OnTqRnZ1t1W7EiBGEhISQkpJCp06d6Natm9XYAJGRkbzwwgu4urrmiCU7O5uyZcuyZMkS9u3bx9ixYxk1ahRLliyxahcbG0tqaiqxsbFER0cTFRVFVFSUpT44OJjDhw+zbt06vv32Wz777DNOnDhx0+dw8eJFMjIyrC4REREREZH80Km8t+nVV18lNDTUsuq5ceNGFi1aRFxcnKXN888/b9Vn7ty5lChRgn379lGrVi1L+aBBg+jcubPlc+/evWnatClHjx6ldOnS/P3336xYsYLVq1fnGkvhwoUJDw+3fK5QoQKJiYksWbKELl26WMqLFi3Kp59+ir29PdWqVaNt27asXbuWPn368Msvv/Djjz+yefNmnnjiCUu81atXv+lziIiIsJr7mloX5mJndrppXxEREbmJkd/bOgJ5RB2e0NbWIcgjSCumt6l48eK0bduW6OhoIiMjadu2LcWLF7dqk5qayssvv0zFihVxc3OjQoUKAKSlpVm1a9SokdXnxo0bU7NmTebPnw/AggULKFeuHH5+fjeMZ9asWTRq1IjHHnsMFxcX5syZk2OemjVrYm9vb/ns5eVlWRFNSUmhUKFCVrFUq1YNDw+Pmz6H0NBQTCaT5UpPT79pexERERERkespMb0DPXv2JCoqiujo6BzbeAHat2/PyZMnmTNnDlu2bGHLli0AXLp0yaqds7Nzjr69e/e2bOeNjIzk9ddfx2DI/XdBlyxZwjvvvEPPnj356aefSE5O5vXXX88xT+HCha0+GwwGy7Zis9lsKcsPo9GIm5ub1SUiIiIiIpIfSkzvQOvWrbl06RKXLl0iKCjIqu7kyZOkpKTw7rvv0qJFC6pXr86pU6fyPPYrr7xCWloa06dPZ+/evbz22ms3bJuQkEDTpk3p168f9evXx9fX1+qQpbyoXr06WVlZbNu2zVJ24MABTp8+na9xRERERERE8kvvmN4Be3t7UlJSLH//W9GiRfH09GT27Nl4eXmRlpbGyJEj8zx20aJF6dy5M8OGDeOZZ56hbNmyN2zr6+vL/PnzWbVqFRUqVGDBggUkJSVZtg7nRdWqVWndujV9+vRh9uzZFCpUiEGDBuHo6JjnMURERERERG6HEtM7dKOtq3Z2dixatIiQkBBq1apF1apVmT59OgEBAXkeu1evXnz11Ve5bhP+t759+5KcnEzXrl0xGAy89NJL9OvXjx9//DE/t0JkZCS9e/fG39+fkiVL8v777zNmzJh8jXHNnvAgbesVEREREZE8MZivvVwo952FCxcycOBAjh49SpEiRWwdTp5kZGTg7u6OyWRSYioiIiIi8gjLT26gFdP70Llz5zh06BARERG8+eabD0xSKiIiIiIicjt0+NF96KOPPqJevXqULFmS0NBQW4cjIiIiIiJyV2krrxQobeUVERERERHIX26gFVMRERERERGxKSWmIiIiIiIiYlNKTEVERERERMSmlJiKiIiIiIiITSkxFREREREREZtSYioiIiIiIiI2VcjWAchDKqIsGA22jkJERETuF2EmW0cgIvcxrZg+JIKDg+nYseNN2/j4+DBt2rR7Eo+IiIiIiEhe2TQxDQ4OxmAwMGHCBKvymJgYDIZ7u9pmMBiIiYnJUZ6XhO9BkZSUxBtvvGHrMERERERERKzYfMXUwcGBiRMncurUKVuH8tB77LHHcHJyumH95cuX72E0IiIiIiIiV9k8MW3ZsiWlSpUiIiLipu0SExPx8/PD0dERb29vQkJCyMzMBGDGjBnUrl3b0vbaiuvMmTMtZUFBQYSGht5xvCtXruSpp57Cw8MDT09P2rVrR2pqqqX+8OHDGAwGlixZQvPmzXF0dOTxxx/nl19+ISkpiUaNGuHi4kLr1q3566+/LP2urcyGh4dTokQJ3NzcePPNN7l06ZKlzbfffkvt2rVxdHTE09OTli1bWp7BNZMmTcLLywtPT0/69+9vlWxev5XXYDAwa9YsOnTogLOzM++//z4A//vf/2jYsCEODg5UrFiR8PBwsrKy7vjZiYiIiIiI5Mbmiam9vT0ffvghM2bM4Pfff8+1ze7duwkKCqJz587s2rWLxYsXs2HDBt5++20AAgIC2Lt3L3///TcA8fHxFC9enPj4eACysrJITEzE39//juPNzMxk8ODBJCUlsXbtWuzs7OjUqRPZ2dlW7d577z3effddduzYQaFChXjppZcYPnw4n3zyCQkJCaSmpjJ27FirPmvXriUlJYXY2Fi+/vprli1bRnh4OADHjh3jpZdeomfPnqSkpBAXF0fnzp0xm82W/rGxsaSmphIbG0t0dDRRUVFERUXd9H7ee+89OnTowO7du+nZsyerVq3ilVdeISQkhH379vHFF18QFRXFBx98kGv/ixcvkpGRYXWJiIiIiIjkh8H878zmHgsODub06dPExMTQpEkTatSowdy5c4mJiaFTp06WpKtHjx44OjryxRdfWPpu2LABf39/MjMzMRqNlChRglmzZvH8889Tv359unbtytSpU/nzzz/ZtGkTfn5+nDp1ChcXl1xjMRgMODg4YG9vb1V+8eJF2rZtm+v7pwB//fUXJUqUYPfu3dSqVYvDhw9ToUIF/vOf/9CrVy8AFi1axEsvvcTatWt5+umnAZgwYQJRUVHs37/f8iz+97//kZ6ebtluO2vWLIYNG4bJZCI5OZmGDRty+PBhypcvn+uzjIuLIzU11XIPXbp0wc7OjkWLFgFXV0wHDRrEoEGDLPc8aNAgpk6dahnHz8+PZ5991mp1+csvv2T48OEcPXo0x7xhYWGW5PnfvActwc54423DIiIiIlIwDk9oa+sQRHKVkZGBu7s7JpMJNze3m7a1+YrpNRMnTiQ6Opp9+/blqNu+fTtRUVG4uLhYrqCgILKzszl06BAGgwE/Pz/i4uI4ffo0e/fupW/fvly5csWyutigQYMbJqXXTJ06leTkZKvrueees2qTmprKyy+/TMWKFXFzc6NChQoApKWlWbWrU6eO5e+SJUsCWG03LlmyJCdOnLDqU7duXat3QJs0acLZs2dJT0+nbt26tGjRgtq1a/Piiy8yZ86cHO/l1qxZ0yqx9vLyyjHH9Ro1amT1efv27YwbN87qWffp04djx45x7ty5HP1DQ0MxmUyWKz09/abziYiIiIiIXO+++R1TPz8/goKCGDVqFMHBwVZ12dnZvPnmm4SEhOToV65cOeDqdt7Zs2eTkJBA3bp18fDwwM/Pj/j4eOLi4ggICLhlDKVKlcLX19eqzNXVldOnT1s+t2/fHm9vb+bMmUPp0qXJzs6mVq1aVu+CAhQuXNjy97UThq8vu377740YDAbs7e1ZvXo1iYmJ/PTTT8yYMYPRo0ezZcsWS3L87/HzOoezs7PV5+zsbMLDw+ncuXOOtg4ODjnKjEYjRqMxT/chIiIiIiKSm/smMYWr21vr1atHlSpVrMobNGjA3r17cySN/xYQEMDAgQP59ttvLUmov78/a9asITExkYEDB95xfCdPniQlJYUvvviC5s2bA1e3FBeUn3/+mfPnz+Po6AjA5s2bcXFxoWzZssDVRLNZs2Y0a9aMsWPHUr58eZYtW8bgwYMLLIYGDRpw4MCBmz5rERERERGRgnRfJaa1a9eme/fuzJgxw6p8xIgRPPnkk/Tv358+ffrg7OxMSkoKq1evtrStVasWnp6eLFy4kP/+97/A1WR1yJAhADz11FN3HF/RokXx9PRk9uzZeHl5kZaWxsiRI+943GsuXbpEr169ePfddzly5Ajvvfceb7/9NnZ2dmzZsoW1a9fyzDPPUKJECbZs2cJff/1F9erVC2x+gLFjx9KuXTu8vb158cUXsbOzY9euXezevdtyaq+IiIiIiEhBuq8SU4Dx48ezZMkSq7I6deoQHx/P6NGjad68OWazmUqVKtG1a1dLG4PBgL+/PzExMZbVzDp16uDu7m55H/ROXTtIKCQkhFq1alG1alWmT5+ep23CedGiRQsqV66Mn58fFy9epFu3boSFhQHg5ubG+vXrmTZtGhkZGZQvX57Jkyfz7LPPFsjc1wQFBbFixQrGjRvHRx99ROHChalWrRq9e/fO1zh7woMK5JmLiIiIiMjDz6an8sr/+fcJxQ+y/Jy8JSIiIiIiD68H8lReEREREREReTQpMRURERERERGbuu/eMX1URUVF2ToEERERERERm9CKqYiIiIiIiNiUElMRERERERGxKSWmIiIiIiIiYlNKTEVERERERMSmlJiKiIiIiIiITSkxFREREREREZvSz8XI3RFRFowGW0chIiIieRVmsnUEIvII04qpjR0+fBiDwUBycvIdj+Xj48O0adPy3D4uLg6DwcDp06fveG4REREREZHb9cAnprNmzcLV1ZWsrCxL2dmzZylcuDDNmze3apuQkIDBYOCXX365oznzktB999132Nvbk5aWlmt9tWrVCAkJwdvbm2PHjlGrVq07igkgKSmJN954I8/tmzZtyrFjx3B3d7/juUVERERERG7XA5+YBgYGcvbsWbZt22YpS0hIoFSpUiQlJXHu3DlLeVxcHKVLl6ZKlSp3Pa7nnnsOT09PoqOjc9Rt3LiRAwcO0KtXL+zt7SlVqhSFCuW+q9psNlsl3Tfz2GOP4eTklOcYixQpQqlSpTAYtOVWRERERERs54FPTKtWrUrp0qWJi4uzlMXFxdGhQwcqVapEYmKiVXlgYCAAly5dYvjw4ZQpUwZnZ2eeeOIJqzGOHDlC+/btKVq0KM7OztSsWZMffviBw4cPW8YoWrQoBoOB4ODgHHEVLlyYV199laioKMxms1XdvHnzaNiwIXXr1s2xlffaauyqVato1KgRRqORhIQEzpw5Q/fu3XF2dsbLy4upU6cSEBDAoEGDLONev5XXYDDwn//8h06dOuHk5ETlypVZvny51fP498rvyZMneemllyhbtixOTk7Url2br7/+Oh//GiIiIiIiIvn3wCemAAEBAcTGxlo+x8bGEhAQgL+/v6X80qVLbNq0yZJUvv7662zcuJFFixaxa9cuXnzxRVq3bs3BgwcB6N+/PxcvXmT9+vXs3r2biRMn4uLigre3N9999x0ABw4c4NixY3zyySe5xtWrVy9+++034uPjLWWZmZksWbKEXr163fSehg8fTkREBCkpKdSpU4fBgwezceNGli9fzurVq0lISGDHjh23fDbh4eF06dKFXbt20aZNG7p3784///yTa9sLFy7QsGFDVqxYwZ49e3jjjTd49dVX2bJlyw3Hv3jxIhkZGVaXiIiIiIhIfjwUp/IGBATwzjvvkJWVxfnz59m5cyd+fn5cuXKF6dOnA7B582bOnz9PYGAgqampfP311/z++++ULl0agKFDh7Jy5UoiIyP58MMPSUtL4/nnn6d27doAVKxY0TJfsWLFAChRogQeHh43jKtGjRo88cQTREZGEhAQAMCSJUu4cuUKL7300k3vady4cbRq1QqAM2fOEB0dzVdffUWLFi0AiIyMtMR+M8HBwZa5PvzwQ2bMmMHWrVtp3bp1jrZlypRh6NChls8DBgxg5cqVfPPNNzzxxBO5jh8REUF4eHiO8loX5mJnzvu2YhEREbGxkd/bOgK5zx2e0NbWIchD7KFYMQ0MDCQzM5OkpCQSEhKoUqUKJUqUwN/fn6SkJDIzM4mLi6NcuXJUrFiRHTt2YDabqVKlCi4uLpYrPj6e1NRUAEJCQnj//fdp1qwZ7733Hrt27bqt2Hr16sW3337LmTNngKvbeDt37nzThBagUaNGlr9/++03Ll++TOPGjS1l7u7uVK1a9Zbz16lTx/K3s7Mzrq6unDhxIte2V65c4YMPPqBOnTp4enri4uLCTz/9dMMDnABCQ0MxmUyWKz09/ZYxiYiIiIiI/NtDsWLq6+tL2bJliY2N5dSpU/j7+wNQqlQpKlSowMaNG4mNjeXpp58GIDs7G3t7e7Zv3469vb3VWC4uLgD07t2boKAgvv/+e3766SciIiKYPHkyAwYMyFds3bp145133mHx4sUEBASwYcMGxo0bd8t+zs7Olr+vvaN6/SFF17+7mpvChQtbfTYYDGRnZ+fadvLkyUydOpVp06ZRu3ZtnJ2dGTRoEJcuXbrh+EajEaPReMs4REREREREbuShWDGFq6umcXFxxMXFWbbNAvj7+7Nq1So2b95seb+0fv36XLlyhRMnTuDr62t1lSpVytLX29ubvn37snTpUoYMGcKcOXOAq6fZwtUVxltxdXXlxRdfJDIyknnz5lGxYkWr+PKiUqVKFC5cmK1bt1rKMjIyLO/DFpSEhAQ6dOjAK6+8Qt26dalYsWKBzyEiIiIiInK9hyox3bBhA8nJyZYVU7iamM6ZM4cLFy5YEtMqVarQvXt3evTowdKlSzl06BBJSUlMnDiRH374AYBBgwaxatUqDh06xI4dO1i3bh3Vq1cHoHz58hgMBlasWMFff/3F2bNnbxpbr169SExM5PPPP6dnz575/nkWV1dXXnvtNYYNG0ZsbCx79+6lZ8+e2NnZFehPvfj6+rJ69WoSExNJSUnhzTff5Pjx4wU2voiIiIiISG4eiq28cDUxPX/+PNWqVaNkyZKWcn9/f86cOUOlSpXw9va2lEdGRvL+++8zZMgQ/vjjDzw9PWnSpAlt2rQBrq6G9u/fn99//x03Nzdat27N1KlTgauHBIWHhzNy5Ehef/11evToQVRU1A1je+qpp6hatSoHDx7ktddeu637mzJlCn379qVdu3a4ubkxfPhw0tPTcXBwuK3xcjNmzBgOHTpEUFAQTk5OvPHGG3Ts2BGTyZTvsfaEB+Hm5lZgsYmIiIiIyMPLYM7Li4py38nMzKRMmTJMnjz5lj89cy9lZGTg7u6OyWRSYioiIiIi8gjLT27w0KyYPux27tzJ/v37ady4MSaTyXKAUocOHWwcmYiIiIiIyJ1RYvoAmTRpEgcOHKBIkSI0bNiQhIQEihcvbuuwRERERERE7ogS0wdE/fr12b59u63DEBERERERKXAPzam8IiIiIiIi8mBSYioiIiIiIiI2pcRUREREREREbEqJqYiIiIiIiNiUElMRERERERGxKSWmIiIiIiIiYlP6uRi5OyLKgtFg6yhERERE5FbCTLaOQEQrpo8yg8FATEyMrcMQEREREZFHnBLTuyg4OBiDwcCECROsymNiYjAYtJooIiIiIiICSkzvOgcHByZOnMipU6dsHYqIiIiIiMh9SYnpXdayZUtKlSpFRETEDdskJibi5+eHo6Mj3t7ehISEkJmZCUBoaChPPvlkjj516tThvffeAyApKYlWrVpRvHhx3N3d8ff3Z8eOHVbtDx48iJ+fHw4ODtSoUYPVq1fnGHPEiBFUqVIFJycnKlasyJgxY7h8+fKd3L6IiIiIiMgtKTG9y+zt7fnwww+ZMWMGv//+e4763bt3ExQUROfOndm1axeLFy9mw4YNvP322wB0796dLVu2kJqaaumzd+9edu/eTffu3QE4c+YMr732GgkJCWzevJnKlSvTpk0bzpw5A0B2djadO3fG3t6ezZs3M2vWLEaMGJEjFldXV6Kioti3bx+ffPIJc+bMYerUqTe9v4sXL5KRkWF1iYiIiIiI5IfBbDabbR3Ewyo4OJjTp08TExNDkyZNqFGjBnPnziUmJoZOnTphNpvp0aMHjo6OfPHFF5Z+GzZswN/fn8zMTBwcHKhbty4vvPACY8aMAWDUqFGsWbOGrVu35jrvlStXKFq0KF999RXt2rXjp59+ok2bNhw+fJiyZcsCsHLlSp599lmWLVtGx44dcx3n448/ZvHixWzbtu2G9xgWFkZ4eHiOcu9BS7AzOuX1UYmIiIjIQ+LwhLa2DkHuExkZGbi7u2MymXBzc7tpW62Y3iMTJ04kOjqaffv2WZVv376dqKgoXFxcLFdQUBDZ2dkcOnQIuLpqunDhQgDMZjNff/21ZbUU4MSJE/Tt25cqVarg7u6Ou7s7Z8+eJS0tDYCUlBTKlStnSUoBmjRpkiPGb7/9lqeeeopSpUrh4uLCmDFjLGPcSGhoKCaTyXKlp6ff3gMSEREREZFHln7H9B7x8/MjKCiIUaNGERwcbCnPzs7mzTffJCQkJEefcuXKAfDyyy8zcuRIduzYwfnz50lPT6dbt26WdsHBwfz1119MmzaN8uXLYzQaadKkCZcuXQKuJrPXu/5U4M2bN9OtWzfCw8MJCgrC3d2dRYsWMXny5Jvel9FoxGg05vk5iIiIiIiIXE+J6T00YcIE6tWrR5UqVSxlDRo0YO/evfj6+t6wX9myZfHz82PhwoWcP3+eli1bUrJkSUt9QkICn332GW3atAEgPT2dv//+21Jfo0YN0tLSOHr0KKVLlwZg06ZNVnNs3LiR8uXLM3r0aEvZkSNH7uyGRURERERE8kBbee+h2rVr0717d2bMmGEpGzFiBJs2baJ///4kJydz8OBBli9fzoABA6z6du/enUWLFvHNN9/wyiuvWNX5+vqyYMECUlJS2LJlC927d8fR0dFS37JlS6pWrUqPHj34+eefSUhIsEpAr42RlpbGokWLSE1NZfr06SxbtuwuPAURERERERFrWjG9x8aPH8+SJUssn+vUqUN8fDyjR4+mefPmmM1mKlWqRNeuXa36vfjiiwwYMAB7e/schxXNmzePN954g/r161OuXDk+/PBDhg4daqm3s7Nj2bJl9OrVi8aNG+Pj48P06dNp3bq1pU2HDh145513ePvtt7l48SJt27ZlzJgxhIWF3dZ97gkPuuULziIiIiIiIqBTeaWA5efkLREREREReXjpVF4RERERERF5YCgxFREREREREZtSYioiIiIiIiI2pcRUREREREREbEqJqYiIiIiIiNiUElMRERERERGxKSWmIiIiIiIiYlNKTEVERERERMSmlJiKiIiIiIiITRWydQDykIooC0aDraMQERERkRsJM9k6AhELrZjeY1FRUXh4eFg+h4WFUa9ePas2YWFhlCxZEoPBQExMzF2Nx8fHh2nTpt3VOURERERERG5GiWk+HT9+nAEDBlCxYkWMRiPe3t60b9+etWvX3tZ4Q4cOteqbkpJCeHg4X3zxBceOHePZZ58tqNBzlZSUxBtvvHFX5xAREREREbkZbeXNh8OHD9OsWTM8PDz46KOPqFOnDpcvX2bVqlX079+f/fv353tMFxcXXFxcLJ9TU1MB6NChAwbD7W+FvXz5MoULF75lu8cee+y25xARERERESkIWjHNh379+mEwGNi6dSsvvPACVapUoWbNmgwePJjNmzcDMGXKFGrXro2zszPe3t7069ePs2fP3nDMf2/lDQsLo3379gDY2dlZEtPs7GzGjRtH2bJlMRqN1KtXj5UrV1rGOHz4MAaDgSVLlhAQEICDgwNffvklwcHBdOzYkUmTJuHl5YWnpyf9+/fn8uXLlr7Xb+XNb/wiIiIiIiJ3SolpHv3zzz+sXLmS/v374+zsnKP+2nujdnZ2TJ8+nT179hAdHc26desYPnx4nuYYOnQokZGRABw7doxjx44B8MknnzB58mQmTZrErl27CAoK4rnnnuPgwYNW/UeMGEFISAgpKSkEBQUBEBsbS2pqKrGxsURHRxMVFUVUVNQNY8hv/BcvXiQjI8PqEhERERERyQ9t5c2jX3/9FbPZTLVq1W7abtCgQZa/K1SowPjx43nrrbf47LPPbjmHi4uLJcEtVaqUpXzSpEmMGDGCbt26ATBx4kRiY2OZNm0aM2fOtJq7c+fOVmMWLVqUTz/9FHt7e6pVq0bbtm1Zu3Ytffr0KZD4IyIiCA8Pz1Fe68Jc7MxOt7xnEREREbGRkd/f9SkOT2h71+eQh4NWTPPIbDYD3PK9z9jYWFq1akWZMmVwdXWlR48enDx5kszMzNuaNyMjg6NHj9KsWTOr8mbNmpGSkmJV1qhRoxz9a9asib29veWzl5cXJ06cKLD4Q0NDMZlMlis9PT0/tyciIiIiIqLENK8qV66MwWDIkQz+25EjR2jTpg21atXiu+++Y/v27ZYVzX+/13k7rk+IzWZzjrLcthhffwCSwWAgOzs71zluJ36j0Yibm5vVJSIiIiIikh9KTPOoWLFiBAUFMXPmzFxXD0+fPs22bdvIyspi8uTJPPnkk1SpUoWjR4/e0bxubm6ULl2aDRs2WJUnJiZSvXr1Oxr7encjfhERERERkVtRYpoPn332GVeuXKFx48Z89913HDx4kJSUFKZPn06TJk2oVKkSWVlZzJgxg99++40FCxYwa9asO5532LBhTJw4kcWLF3PgwAFGjhxJcnIyAwcOLIC7+j93K34REREREZGb0eFH+VChQgV27NjBBx98wJAhQzh27BiPPfYYDRs25PPPP6devXpMmTKFiRMnEhoaip+fHxEREfTo0eOO5g0JCSEjI4MhQ4Zw4sQJatSowfLly6lcuXIB3dlVBRn/nvAgbesVEREREZE8MZivneojUgAyMjJwd3fHZDIpMRUREREReYTlJzfQVl4RERERERGxKSWmIiIiIiIiYlNKTEVERERERMSmlJiKiIiIiIiITSkxFREREREREZtSYioiIiIiIiI2pcRUREREREREbEqJqYiIiIiIiNiUElMRERERERGxqUK2DkAeUhFlwWiwdRQiIiIiUtDCTLaOQB5CWjEVERERERERm3rkE9Pg4GAMBgMTJkywKo+JicFguLcrfgaDgZiYmHs6p4iIiIiIiK098okpgIODAxMnTuTUqVO2DuWOXLp0ydYhiIiIiIiI5JsSU6Bly5aUKlWKiIiIm7ZLTEzEz88PR0dHvL29CQkJITMzE4AZM2ZQu3ZtS9trK64zZ860lAUFBREaGprnuP744w+6du1K0aJF8fT0pEOHDhw+fNhSHxwcTMeOHYmIiKB06dJUqVIFgM8++4zKlSvj4OBAyZIleeGFFyx9zGYzH330ERUrVsTR0ZG6devy7bffWup8fX2ZNGmSVRx79uzBzs6O1NTUPMcuIiIiIiKSV0pMAXt7ez788ENmzJjB77//nmub3bt3ExQUROfOndm1axeLFy9mw4YNvP322wAEBASwd+9e/v77bwDi4+MpXrw48fHxAGRlZZGYmIi/v3+eYjp37hyBgYG4uLiwfv16NmzYgIuLC61bt7ZaGV27di0pKSmsXr2aFStWsG3bNkJCQhg3bhwHDhxg5cqV+Pn5Wdq/++67REZG8vnnn7N3717eeecdXnnlFeLj4zEYDPTs2ZPIyEirWObNm0fz5s2pVKlSjjgvXrxIRkaG1SUiIiIiIpIfBrPZbLZ1ELYUHBzM6dOniYmJoUmTJtSoUYO5c+cSExNDp06duPZ4evTogaOjI1988YWl74YNG/D39yczMxOj0UiJEiWYNWsWzz//PPXr16dr165MnTqVP//8k02bNuHn58epU6dwcXHJNRaDwcCyZcvo2LEj8+bN46OPPiIlJcXyruulS5fw8PAgJiaGZ555huDgYFauXElaWhpFihQBYOnSpbz++uv8/vvvuLq6Wo2fmZlJ8eLFWbduHU2aNLGU9+7dm3PnzvHVV19x7NgxvL29SUxMpHHjxly+fJkyZcrw8ccf89prr+WIOSwsjPDw8Bzl3oOWYGd0yue/hoiIiIjc7w5PaGvrEOQBkZGRgbu7OyaTCTc3t5u21Yrpv0ycOJHo6Gj27duXo2779u1ERUXh4uJiuYKCgsjOzubQoUMYDAb8/PyIi4vj9OnT7N27l759+3LlyhVSUlKIi4ujQYMGN0xKc5vv119/xdXV1TJfsWLFuHDhgtWW2tq1a1uSUoBWrVpRvnx5KlasyKuvvsrChQs5d+4cAPv27ePChQu0atXK6j7mz59vGdPLy4u2bdsyb948AFasWMGFCxd48cUXc40zNDQUk8lkudLT0/P2sEVERERERP4//Y7pv/j5+REUFMSoUaMIDg62qsvOzubNN98kJCQkR79y5coBV7fzzp49m4SEBOrWrYuHhwd+fn7Ex8cTFxdHQEBAnmPJzs6mYcOGLFy4MEfdY489Zvnb2dnZqs7V1ZUdO3YQFxfHTz/9xNixYwkLCyMpKYns7GwAvv/+e8qUKWPVz2g0Wv7u3bs3r776KlOnTiUyMpKuXbvi5JT76qfRaLTqKyIiIiIikl9KTK8zYcIE6tWrZzlI6JoGDRqwd+9efH19b9g3ICCAgQMH8u2331qSUH9/f9asWUNiYiIDBw7McxwNGjRg8eLFlChR4pbL3tcrVKgQLVu2pGXLlrz33nt4eHiwbt06WrVqhdFoJC0t7abvurZp0wZnZ2c+//xzfvzxR9avX5+v+UVERERERPJDW3mvU7t2bbp3786MGTOsykeMGMGmTZvo378/ycnJHDx4kOXLlzNgwABLm1q1auHp6cnChQstiWlAQAAxMTGcP3+ep556Ks9xdO/eneLFi9OhQwcSEhI4dOgQ8fHxDBw48IYHNMHVrbfTp08nOTmZI0eOMH/+fLKzs6latSqurq4MHTqUd955h+joaFJTU9m5cyczZ84kOjraMoa9vT3BwcGEhobi6+tr9T6qiIiIiIhIQdOKaS7Gjx/PkiVLrMrq1KlDfHw8o0ePpnnz5pjNZipVqkTXrl0tbQwGA/7+/sTExNC8eXNLP3d3dypWrHjTlc9r22wLFbr6T+Lk5MT69esZMWIEnTt35syZM5QpU4YWLVrcdBwPDw+WLl1KWFgYFy5coHLlynz99dfUrFnTcm8lSpQgIiKC3377DQ8PDxo0aMCoUaOsxunVqxcffvghPXv2zMeT+z97woPyvdIrIiIiIiKPpkf+VN77xfHjx/Hy8iIpKYlGjRrZOhw2btxIQEAAv//+OyVLlsxzv/ycvCUiIiIiIg+v/OQGWjG1MbPZzJEjR5g0aRIlS5akVq1aNo3n4sWLpKenM2bMGLp06ZKvpFREREREROR26B1TGzOZTFStWpUNGzawaNEiHBwcbBrP119/TdWqVTGZTHz00Uc2jUVERERERB4N2sorBUpbeUVEREREBPKXG2jFVERERERERGxKiamIiIiIiIjYlBJTERERERERsSklpiIiIiIiImJTSkxFRERERETEppSYioiIiIiIiE0VsnUA8pCKKAtGg62jEBERERFbCzPZOgJ5AGjF9D4VFhZGvXr1bB2GiIiIiIjIXafE9B5JTEzE3t6e1q1b56n90KFDWbt27V2OSkRERERExPaUmN4j8+bNY8CAAWzYsIG0tLQbtjObzWRlZeHi4oKnp+c9jFBERERERMQ2lJjeA5mZmSxZsoS33nqLdu3aERUVZamLi4vDYDCwatUqGjVqhNFoJCEhIcdWXoPBkOPy8fGx1MfHx9O4cWOMRiNeXl6MHDmSrKwsS31AQAAhISEMHz6cYsWKUapUKcLCwqzinDJlCrVr18bZ2Rlvb2/69evH2bNn79JTERERERERuUqJ6T2wePFiqlatStWqVXnllVeIjIzEbDZbtRk+fDgRERGkpKRQp06dHGMcO3bMcv3666/4+vri5+cHwB9//EGbNm14/PHH+fnnn/n888+ZO3cu77//vtUY0dHRODs7s2XLFj766CPGjRvH6tWrLfV2dnZMnz6dPXv2EB0dzbp16xg+fPhN7+3ixYtkZGRYXSIiIiIiIvlhMF+fIUmBa9asGV26dGHgwIFkZWXh5eXF119/TcuWLYmLiyMwMJCYmBg6dOhg6RMWFkZMTAzJyclWY5nNZp5//nnS0tJISEjA0dGR0aNH891335GSkoLBcPUk3M8++4wRI0ZgMpmws7MjICCAK1eukJCQYBmrcePGPP3000yYMCHXuL/55hveeust/v777xveW1hYGOHh4TnKvQctwc7olJ/HJCIiIiKSw+EJbW0dgtymjIwM3N3dMZlMuLm53bStVkzvsgMHDrB161a6desGQKFChejatSvz5s2zateoUaM8jTdq1Cg2bdpETEwMjo6OAKSkpNCkSRNLUgpXk+GzZ8/y+++/W8quX4n18vLixIkTls+xsbG0atWKMmXK4OrqSo8ePTh58iSZmZk3jCc0NBSTyWS50tPT83QfIiIiIiIi1+h3TO+yuXPnkpWVRZkyZSxlZrOZwoULc+rUKUuZs7PzLcf68ssvmTp1KnFxcZQtW9ZqvH8npdfKAKvywoULW7UxGAxkZ2cDcOTIEdq0aUPfvn0ZP348xYoVY8OGDfTq1YvLly/fMCaj0YjRaLxl7CIiIiIiIjeixPQuysrKYv78+UyePJlnnnnGqu75559n4cKF1KpVK09jbdq0id69e/PFF1/w5JNPWtXVqFGD7777zipBTUxMxNXV1Sohvplt27aRlZXF5MmTsbO7upC+ZMmSPPUVERERERG5E9rKexetWLGCU6dO0atXL2rVqmV1vfDCC8ydOzdP4xw/fpxOnTrRrVs3goKCOH78OMePH+evv/4CoF+/fqSnpzNgwAD279/Pf//7X9577z0GDx5sSTJvpVKlSmRlZTFjxgx+++03FixYwKxZs2773kVERERERPJKK6Z30dy5c2nZsiXu7u456p5//nk+/PBDduzYcctx9u/fz59//kl0dDTR0dGW8vLly3P48GHKlCnDDz/8wLBhw6hbty7FihWjV69evPvuu3mOtV69ekyZMoWJEycSGhqKn58fERER9OjRI89j/Nue8KBbvuAsIiIiIiICOpVXClh+Tt4SEREREZGHl07lFRERERERkQeGElMRERERERGxKSWmIiIiIiIiYlNKTEVERERERMSmlJiKiIiIiIiITSkxFREREREREZtSYioiIiIiIiI2pcRUREREREREbEqJqYiIiIiIiNhUIVsHIA+piLJgNNg6ChERERF50IWZbB2B3ANaMRURERERERGbUmJaANq3b0/Lli1zrdu0aRMGg4EdO3bc46juTHBwMB07drR1GCIiIiIi8ghQYloAevXqxbp16zhy5EiOunnz5lGvXj0aNGiQrzEvXbpUUOGJiIiIiIjc15SYFoB27dpRokQJoqKirMrPnTvH4sWL6dWrF4mJifj5+eHo6Ii3tzchISFkZmZa2vr4+PD+++8THByMu7s7ffr0ISoqCg8PD1asWEHVqlVxcnLihRdeIDMzk+joaHx8fChatCgDBgzgypUrlrFOnTpFjx49KFq0KE5OTjz77LMcPHjQUn9t3FWrVlG9enVcXFxo3bo1x44dAyAsLIzo6Gj++9//YjAYMBgMxMXF3dVnKCIiIiIijy4lpgWgUKFC9OjRg6ioKMxms6X8m2++4dKlS9StW5egoCA6d+7Mrl27WLx4MRs2bODtt9+2Gufjjz+mVq1abN++nTFjxgBXk9vp06ezaNEiVq5cSVxcHJ07d+aHH37ghx9+YMGCBcyePZtvv/3WMk5wcDDbtm1j+fLlbNq0CbPZTJs2bbh8+bKlzblz55g0aRILFixg/fr1pKWlMXToUACGDh1Kly5dLMnqsWPHaNq0aa73fvHiRTIyMqwuERERERGR/DCY/51JyW3bv38/1atXZ926dQQGBgLg7+9PmTJlKFSoEI6OjnzxxReW9hs2bMDf35/MzEwcHBzw8fGhfv36LFu2zNImKiqK119/nV9//ZVKlSoB0LdvXxYsWMCff/6Ji4sLAK1bt8bHx4dZs2Zx8OBBqlSpwsaNGy3J5MmTJ/H29iY6OpoXX3wx13E/++wzxo0bx/Hjx4Grye3p06eJiYm56X2HhYURHh6eo9x70BLsjE63+TRFRERERO4/hye0tXUID5SMjAzc3d0xmUy4ubndtK1WTAtItWrVaNq0KfPmzQMgNTWVhIQEevbsyfbt24mKisLFxcVyBQUFkZ2dzaFDhyxjNGrUKMe4Tk5OluQRoGTJkvj4+FiS0mtlJ06cACAlJYVChQrxxBNPWOo9PT2pWrUqKSkpNxzXy8vLMkZ+hIaGYjKZLFd6enq+xxARERERkUebfse0APXq1Yu3336bmTNnEhkZSfny5WnRogXZ2dm8+eabhISE5OhTrlw5y9/Ozs456gsXLmz12WAw5FqWnZ0NwI0WwM1mMwbD//2uaG5j3M7iudFoxGg05rufiIiIiIjINVoxLUBdunTB3t6er776iujoaF5//XUMBgMNGjRg7969+Pr65riKFClSoDHUqFGDrKwstmzZYik7efIkv/zyC9WrV8/zOEWKFLE6UElERERERORuUWJagFxcXOjatSujRo3i6NGjBAcHAzBixAg2bdpE//79SU5O5uDBgyxfvpwBAwYUeAyVK1emQ4cO9OnThw0bNvDzzz/zyiuvUKZMGTp06JDncXx8fNi1axcHDhzg77//tjo4SUREREREpCBpK28B69WrF3PnzuWZZ56xbNOtU6cO8fHxjB49mubNm2M2m6lUqRJdu3a9KzFERkYycOBA2rVrx6VLl/Dz8+OHH37IsX33Zvr06UNcXByNGjXi7NmzxMbGEhAQkOf+e8KDbvmCs4iIiIiICOhUXilg+Tl5S0REREREHl46lVdEREREREQeGEpMRURERERExKaUmIqIiIiIiIhNKTEVERERERERm1JiKiIiIiIiIjalxFRERERERERsSompiIiIiIiI2JQSUxEREREREbEpJaYiIiIiIiJiU4VsHYA8pCLKgtFg6yhERERERO5/YSZbR2BzWjG1sbCwMOrVq2ez+QMCAhg0aJDN5hcREREREVFieoeOHz/OgAEDqFixIkajEW9vb9q3b8/atWttHZqIiIiIiMgDQVt578Dhw4dp1qwZHh4efPTRR9SpU4fLly+zatUq+vfvz/79++9JHJcvX6Zw4cL3ZC4REREREZGCphXTO9CvXz8MBgNbt27lhRdeoEqVKtSsWZPBgwezefNmANLS0ujQoQMuLi64ubnRpUsX/vzzzxuOmZ2dzbhx4yhbtixGo5F69eqxcuVKS/3hw4cxGAwsWbKEgIAAHBwc+PLLLzl58iQvvfQSZcuWxcnJidq1a/P1119bjZ2ZmUmPHj1wcXHBy8uLyZMn55j/1KlT9OjRg6JFi+Lk5MSzzz7LwYMHC+iJiYiIiIiI5KTE9Db9888/rFy5kv79++Ps7Jyj3sPDA7PZTMeOHfnnn3+Ij49n9erVpKam0rVr1xuO+8knnzB58mQmTZrErl27CAoK4rnnnsuRHI4YMYKQkBBSUlIICgriwoULNGzYkBUrVrBnzx7eeOMNXn31VbZs2WLpM2zYMGJjY1m2bBk//fQTcXFxbN++3Wrc4OBgtm3bxvLly9m0aRNms5k2bdpw+fLlXOO9ePEiGRkZVpeIiIiIiEh+aCvvbfr1118xm81Uq1bthm3WrFnDrl27OHToEN7e3gAsWLCAmjVrkpSUxOOPP56jz6RJkxgxYgTdunUDYOLEicTGxjJt2jRmzpxpaTdo0CA6d+5s1Xfo0KGWvwcMGMDKlSv55ptveOKJJzh79ixz585l/vz5tGrVCoDo6GjKli1r6XPw4EGWL1/Oxo0badq0KQALFy7E29ubmJgYXnzxxRzxRkREEB4enqO81oW52JmdbvhsRERERETkqsO2DuA+oBXT22Q2mwEwGG78kygpKSl4e3tbklKAGjVq4OHhQUpKSo72GRkZHD16lGbNmlmVN2vWLEf7Ro0aWX2+cuUKH3zwAXXq1MHT0xMXFxd++ukn0tLSAEhNTeXSpUs0adLE0qdYsWJUrVrVKt5ChQrxxBNPWMo8PT2pWrVqrvEChIaGYjKZLFd6evoNn4eIiIiIiEhulJjepsqVK2MwGG6YsMHV5DW3xPVG5ddcX5db++u3D0+ePJmpU6cyfPhw1q1bR3JyMkFBQVy6dMkyxq3cqM3N4jUajbi5uVldIiIiIiIi+aHE9DYVK1aMoKAgZs6cSWZmZo7606dPU6NGDdLS0qxWEfft24fJZKJ69eo5+ri5uVG6dGk2bNhgVZ6YmJhr+39LSEigQ4cOvPLKK9StW5eKFStavZfq6+tL4cKFLYcywdWDjn755RfL5xo1apCVlWX1XurJkyf55Zdfbjm/iIiIiIjI7VJiegc+++wzrly5QuPGjfnuu+84ePAgKSkpTJ8+nSZNmtCyZUvq1KlD9+7d2bFjB1u3bqVHjx74+/vn2Ip7zbBhw5g4cSKLFy/mwIEDjBw5kuTkZAYOHHjTWHx9fVm9ejWJiYmkpKTw5ptvcvz4cUu9i4sLvXr1YtiwYaxdu5Y9e/YQHByMnd3/fQUqV65Mhw4d6NOnDxs2bODnn3/mlVdeoUyZMnTo0KFgHpqIiIiIiMh1dPjRHahQoQI7duzggw8+YMiQIRw7dozHHnuMhg0b8vnnn2MwGIiJiWHAgAH4+flhZ2dH69atmTFjxg3HDAkJISMjgyFDhnDixAlq1KjB8uXLqVy58k1jGTNmDIcOHSIoKAgnJyfeeOMNOnbsiMlksrT5+OOPOXv2LM899xyurq4MGTLEqh4gMjKSgQMH0q5dOy5duoSfnx8//PBDvn8ndU94kLb1ioiIiIhInhjMeXn5UCSPMjIycHd3x2QyKTEVEREREXmE5Sc30FZeERERERERsSklpiIiIiIiImJTSkxFRERERETEppSYioiIiIiIiE0pMRURERERERGbUmIqIiIiIiIiNqXEVERERERERGxKiamIiIiIiIjYlBJTERERERERsalCtg5AHlIRZcFosHUUIiIiIiL3VpjJ1hE8kLRiKgBERUXh4eFh6zBEREREROQRpMT0ARIQEMCgQYNylMfExGAwXF2dDAsLo169evc2MBERERERkTugxFRERERERERsSonpQyQqKorw8HB+/vlnDAYDBoOBqKgoAKZMmULt2rVxdnbG29ubfv36cfbs2RxjrFq1iurVq+Pi4kLr1q05duzYPb4LERERERF51CgxfYh07dqVIUOGULNmTY4dO8axY8fo2rUrAHZ2dkyfPp09e/YQHR3NunXrGD58uFX/c+fOMWnSJBYsWMD69etJS0tj6NChN53z4sWLZGRkWF0iIiIiIiL5oVN5HyKOjo64uLhQqFAhSpUqZVX373dTK1SowPjx43nrrbf47LPPLOWXL19m1qxZVKpUCYC3336bcePG3XTOiIgIwsPDc5TXujAXO7PTHdyNiIiIiMgDaOT3Npv68IS2Npv7TmnF9BERGxtLq1atKFOmDK6urvTo0YOTJ0+SmZlpaePk5GRJSgG8vLw4ceLETccNDQ3FZDJZrvT09Lt2DyIiIiIi8nBSYvoAcXNzw2TK+btIp0+fxs3N7Yb9jhw5Qps2bahVqxbfffcd27dvZ+bMmcDVVdJrChcubNXPYDBgNptvGpPRaMTNzc3qEhERERERyQ9t5X2AVKtWjR9//DFHeVJSElWrVgWgSJEiXLlyxap+27ZtZGVlMXnyZOzsrv6/iCVLltz9gEVERERERPJAK6YPkH79+pGamkr//v35+eef+eWXX5g5cyZz585l2LBhAPj4+HDo0CGSk5P5+++/uXjxIpUqVSIrK4sZM2bw22+/sWDBAmbNmmXjuxEREREREblKK6YPEB8fHxISEhg9ejTPPPMMFy5coEqVKkRFRfHiiy8C8Pzzz7N06VICAwM5ffo0kZGRBAcHM2XKFCZOnEhoaCh+fn5ERETQo0ePuxbrnvAgbesVEREREZE8MZhv9RKhSD5kZGTg7u6OyWRSYioiIiIi8gjLT26grbwiIiIiIiJiU0pMRURERERExKaUmIqIiIiIiIhN6fAjKVDXXlnOyMiwcSQiIiIiImJL13KCvBxrpMRUCtTJkycB8Pb2tnEkIiIiIiJyPzhz5gzu7u43baPEVApUsWLFAEhLS7vll0+kIGVkZODt7U16erpOhJZ7Rt87sRV998RW9N2T/DCbzZw5c4bSpUvfsq0SUylQdnZXX1t2d3fXf6zEJtzc3PTdk3tO3zuxFX33xFb03ZO8yutilQ4/EhEREREREZtSYioiIiIiIiI2pcRUCpTRaOS9997DaDTaOhR5xOi7J7ag753Yir57Yiv67sndYjDn5exeERERERERkbtEK6YiIiIiIiJiU0pMRURERERExKaUmIqIiIiIiIhNKTEVERERERERm1JiKgXms88+o0KFCjg4ONCwYUMSEhJsHZI8ZCIiInj88cdxdXWlRIkSdOzYkQMHDli1MZvNhIWFUbp0aRwdHQkICGDv3r02ilgeRhERERgMBgYNGmQp0/dO7pY//viDV155BU9PT5ycnKhXrx7bt2+31Ou7J3dDVlYW7777LhUqVMDR0ZGKFSsybtw4srOzLW303ZOCpsRUCsTixYsZNGgQo0ePZufOnTRv3pxnn32WtLQ0W4cmD5H4+Hj69+/P5s2bWb16NVlZWTzzzDNkZmZa2nz00UdMmTKFTz/9lKSkJEqVKkWrVq04c+aMDSOXh0VSUhKzZ8+mTp06VuX63sndcOrUKZo1a0bhwoX58ccf2bdvH5MnT8bDw8PSRt89uRsmTpzIrFmz+PTTT0lJSeGjjz7i448/ZsaMGZY2+u5JgTOLFIDGjRub+/bta1VWrVo188iRI20UkTwKTpw4YQbM8fHxZrPZbM7OzjaXKlXKPGHCBEubCxcumN3d3c2zZs2yVZjykDhz5oy5cuXK5tWrV5v9/f3NAwcONJvN+t7J3TNixAjzU089dcN6fffkbmnbtq25Z8+eVmWdO3c2v/LKK2azWd89uTu0Yip37NKlS2zfvp1nnnnGqvyZZ54hMTHRRlHJo8BkMgFQrFgxAA4dOsTx48etvotGoxF/f399F+WO9e/fn7Zt29KyZUurcn3v5G5Zvnw5jRo14sUXX6REiRLUr1+fOXPmWOr13ZO75amnnmLt2rX88ssvAPz8889s2LCBNm3aAPruyd1RyNYByIPv77//5sqVK5QsWdKqvGTJkhw/ftxGUcnDzmw2M3jwYJ566ilq1aoFYPm+5fZdPHLkyD2PUR4eixYtYseOHSQlJeWo0/dO7pbffvuNzz//nMGDBzNq1Ci2bt1KSEgIRqORHj166Lsnd82IESMwmUxUq1YNe3t7rly5wgcffMD/a+9OQ6Jq3ziO/yaX0VSCLJssl4Ys28iNSIMWiooHjIJ20cIoIguzbKFeJJTTBtE+IbTQYmUlrRQJlVgEkjRYFkiL1IsoIirQGqM5z4s//+E/jPb0f3I6ld8PnBde932P15GLg9fcM+fMmTNHEtc9BAaNKTqMxWLx+dkwDL8Y0FGWLl2q+vp63b5922+MWkRHevnypQoLC3X9+nWFhYW1O4+6Q0fzeDzKyMiQw+GQJKWmpqqhoUFOp1N5eXneedQeOtrp06d1/PhxlZeXa8iQIXK5XFq+fLliY2M1b9487zxqDx2Jj/Lih/Xo0UNBQUF+u6Nv3rzxeycN6AjLli3TxYsXdfPmTfXt29cbt9lskkQtokPV1dXpzZs3Sk9PV3BwsIKDg1VdXa3du3crODjYW1vUHTpa7969NXjwYJ/YoEGDvDcW5JqHQFm1apXWrl2r2bNna9iwYcrNzVVRUZE2b94sidpDYNCY4oeFhoYqPT1dVVVVPvGqqiplZWWZlBX+RIZhaOnSpaqsrNSNGzfUr18/n/F+/frJZrP51GJra6uqq6upRfxr48eP14MHD+RyubxHRkaGcnJy5HK5ZLfbqTsExKhRo/weidXY2KiEhARJXPMQOC0tLerSxbdNCAoK8j4uhtpDIPBRXnSIFStWKDc3VxkZGcrMzFRZWZlevHihxYsXm50a/iAFBQUqLy/XhQsXFBUV5X2ntlu3bgoPD/c+W9LhcCgpKUlJSUlyOBzq2rWr5s6da3L2+F1FRUV5v8f8XxEREYqOjvbGqTsEQlFRkbKysuRwODRz5kzV1taqrKxMZWVlksQ1DwGTnZ2t0tJSxcfHa8iQIbp//7527Nih/Px8SdQeAsTEOwLjD7Nv3z4jISHBCA0NNdLS0ryP8AA6iqQ2j8OHD3vneDweY8OGDYbNZjOsVqsxevRo48GDB+YljT/S/z4uxjCoOwTOpUuXjKFDhxpWq9VITk42ysrKfMapPQTCx48fjcLCQiM+Pt4ICwsz7Ha7sX79esPtdnvnUHvoaBbDMAwzG2MAAAAAQOfGd0wBAAAAAKaiMQUAAAAAmIrGFAAAAABgKhpTAAAAAICpaEwBAAAAAKaiMQUAAAAAmIrGFAAAAABgKhpTAAAAAICpaEwBAEC7xo4dq+XLlwf89+Tm5srhcPjFm5qaVFJS4hd3u92Kj49XXV1dwHMDAAQejSkAAJ3A/PnzZbFYZLFYFBISIrvdruLiYjU3N39zXWVlpTZu3BjQ3Orr63XlyhUtW7bsu9dYrVYVFxdrzZo1AcwMAPCz0JgCANBJTJ48Wa9evdKzZ8+0adMm7d+/X8XFxW3O/fLliySpe/fuioqKCmhee/fu1YwZM3x+z/PnzzVt2jSNHDlS27ZtU3JyshYvXuyzLicnRzU1NXr8+HFA8wMABB6NKQAAnYTVapXNZlNcXJzmzp2rnJwcnT9/XpJUUlKilJQUHTp0SHa7XVarVYZh+H2U1+12a/Xq1YqLi5PValVSUpIOHjzoHX/06JH++usvRUZGqlevXsrNzdXbt2/bzcnj8ejMmTOaMmWKTzwvL0+vX7+W0+nU/PnztWvXLkVHR/vMiY6OVlZWlk6ePPnjfxwAgKloTAEA6KTCw8O9O6OS9OTJE1VUVOjcuXNyuVxtrsnLy9OpU6e0e/duPX78WAcOHFBkZKQk6dWrVxozZoxSUlJ07949Xbt2Ta9fv9bMmTPbzaG+vl7v379XRkaGT/z+/fsqKChQamqqYmJiNGnSJJWWlvqtHzFihGpqav7F2QMAfiXBZicAAAB+vtraWpWXl2v8+PHeWGtrq44dO6aePXu2uaaxsVEVFRWqqqrShAkTJEl2u9077nQ6lZaW5nMTo0OHDikuLk6NjY0aMGCA32s2NTUpKChIMTExPvFRo0Zp586d8ng83zyPPn36qKmp6R/PFwDwa2PHFACATuLy5cuKjIxUWFiYMjMzNXr0aO3Zs8c7npCQ0G5TKkkul0tBQUEaM2ZMm+N1dXW6efOmIiMjvUdycrIk6enTp22u+fTpk6xWqywWi0/8xIkTGjlypNatW6fS0lJlZmbq7NmzfuvDw8PV0tLyj+cOAPi1sWMKAEAnMW7cODmdToWEhCg2NlYhISE+4xEREd9cHx4e/s1xj8ej7Oxsbd261W+sd+/eba7p0aOHWlpa1NraqtDQUJ/4nj17tHLlSm3ZskWJiYmaNWuWrl69qokTJ3rnvXv37pvNNADg98COKQAAnURERIT69++vhIQEv6b0ewwbNkwej0fV1dVtjqelpamhoUGJiYnq37+/z9Fe05uSkiLpPzdNao/NZtPatWuVkpLi933Shw8fKjU19f8+FwDAr4XGFAAAfJfExETNmzdP+fn5On/+vJ4/f65bt26poqJCklRQUKB3795pzpw5qq2t1bNnz3T9+nXl5+fr69evbb5mz549lZaWptu3b/vEFyxYoNraWjU3N8vtdquyslINDQ1KT0/3mVdTU+OzgwoA+D3RmAIAgO/mdDo1ffp0LVmyRMnJyVq4cKGam5slSbGxsbpz546+fv2qSZMmaejQoSosLFS3bt3UpUv7/3IsWrRIJ06c8InFxMQoPz9fI0aM0Pbt21VcXKyNGzdq6tSp3jl3797Vhw8fNH369ICcKwDg57EYhmGYnQQAAOi8Pn/+rIEDB+rUqVPKzMz0GWtqatKRI0dUUlLit27GjBlKTU3VunXrflKmAIBAYccUAACYKiwsTEePHtXbt2+/e43b7dbw4cNVVFQUwMwAAD8LO6YAAAAAAFOxYwoAAAAAMBWNKQAAAADAVDSmAAAAAABT0ZgCAAAAAExFYwoAAAAAMBWNKQAAAADAVDSmAAAAAABT0ZgCAAAAAExFYwoAAAAAMNXfXTErJiEvyUMAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# The next bit simply reorders the index by increasing average of weekday and weekend prices\n",
+    "# Compare the index order you get from\n",
+    "# state_price_means.index\n",
+    "# with\n",
+    "# state_price_means.mean(axis=1).sort_values(ascending=False).index\n",
+    "# See how this expression simply sits within the reindex()\n",
+    "(state_price_means.reindex(index=state_price_means.mean(axis=1)\n",
+    "    .sort_values(ascending=False)\n",
+    "    .index)\n",
+    "    .plot(kind='barh', figsize=(10, 10), title='Average ticket price by State'))\n",
+    "plt.xlabel('Price ($)');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "The figure above represents a dataframe with two columns, one for the average prices of each kind of ticket. This tells you how the average ticket price varies from state to state. But can you get more insight into the difference in the distributions between states?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 2.6.3.5.2 Distribution of weekday and weekend price by state<a id='2.6.3.5.2_Distribution_of_weekday_and_weekend_price_by_state'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, you can transform the data into a single column for price with a new categorical column that represents the ticket type."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Code task 15#\n",
+    "#Use the pd.melt function, pass in the ski_data columns 'state', 'AdultWeekday', and 'Adultweekend' only,\n",
+    "#specify 'state' for `id_vars`\n",
+    "#gather the ticket prices from the 'Adultweekday' and 'AdultWeekend' columns using the `value_vars` argument,\n",
+    "#call the resultant price column 'Price' via the `value_name` argument,\n",
+    "#name the weekday/weekend indicator column 'Ticket' via the `var_name` argument\n",
+    "ticket_prices = pd.melt(ski_data[['state', 'AdultWeekday', 'AdultWeekend']], \n",
+    "                        id_vars='state', \n",
+    "                        var_name='Ticket', \n",
+    "                        value_vars=['AdultWeekday', 'AdultWeekend'], \n",
+    "                        value_name='Price')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>AdultWeekday</td>\n",
+       "      <td>65.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>AdultWeekday</td>\n",
+       "      <td>47.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>AdultWeekday</td>\n",
+       "      <td>30.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>AdultWeekday</td>\n",
+       "      <td>89.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>AdultWeekday</td>\n",
+       "      <td>74.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     state        Ticket  Price\n",
+       "0   Alaska  AdultWeekday   65.0\n",
+       "1   Alaska  AdultWeekday   47.0\n",
+       "2   Alaska  AdultWeekday   30.0\n",
+       "3  Arizona  AdultWeekday   89.0\n",
+       "4  Arizona  AdultWeekday   74.0"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ticket_prices.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is now in a format we can pass to [seaborn](https://seaborn.pydata.org/)'s [boxplot](https://seaborn.pydata.org/generated/seaborn.boxplot.html) function to create boxplots of the ticket price distributions for each ticket type for each state."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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FhT788MOAYceTJk1SaWmpnn32WR100EG699579e2337Zb95AhQ/TSSy/puOOOk9Pp1P777y+pNWjfuXOnFi5cqD/84Q++8uPHj9esWbPkcDg0e/Zs3/Z58+bpiiuuUM+ePTVlyhQ1NTVp5cqV2rZtm0pLSzVjxgzdfffdOvvss3XLLbcoPz9fdXV1WrZsma655hrl5+f7nqtfv356+eWXNXHiRF1wwQV64oknlJmZqZKSkg7fg4MPPliPP/64xowZo4aGBl1zzTXKyspq87ovvvhinXHGGfJ4PJo5c2Z4f6guSIol315//XV9/PHHuvjiizssO2rUKHXr1k1r164NWea6667T9u3bfbcvv/wyms0FAAAAkGpMi9QS45tpibhZCxYs0EknndQmYJdae9pXr16tYcOG6cYbb9SvfvUrjR49Wl988UXAJG1S63JmM2fO1EUXXaTx48dr6NChHa6hfs899+iFF17Q4MGDNXLkSN/2oUOHqrCwUN99953Gjx/v2z5o0CAVFBTI7XYHPPfFF1+sRx55RIsXL9aIESM0fvx4LV68WEOHDpXUOofZa6+9poKCAk2bNk2HHnqoZs+ercbGxqA97/3799fLL7+sNWvWaMaMGfJ4PJo+fXqH78HChQu1bds2jRw5Uj/+8Y91xRVX+FL7/Z100kkaMGCATjnlFA0cOLDd9ygaLNOZRQBjwLIsVVVV6Zxzzmmzb9asWfrggw+0cuXKDp/ngw8+0IgRI/Tqq6/qhBNOCKvuhoYG9erVS9u3b2833QIAAABAanK73Vq/fr2GDh0akN3r8Xg07Ufnavu2b+LSjl7799GyPy31jeeG/ezatUsDBw7UwoUL2wzf3leoz5UUfhya0PT4HTt2aN26db7769ev1+rVq9WnTx8VFBRIan0hS5cu1T333NPm8Z9++qkqKip02mmnKTc3Vx999JGuuuoqjRw5Uscdd1zcXgcAAACA1ORwOLTsT0vbTNIWK5ZlEbDbVEtLizZt2qR77rlHvXr10llnnRWXehMatK9cuTIgLaK0tFSSNHPmTC1evFhS65p8xhhdcMEFbR7fvXt3vfTSSyovL9eOHTs0ePBgnX766brpppv4oAMAAACICmILSFJdXZ2GDh2q/Px8LV68WJmZ8QmnbZMen0ikxwMAAADprb00ZqCzopEenxQT0QEAAAAAkI4I2gEAAAAAsCmCdgAAAAAAbIqgHQAAAAAAmyJoBwAAAADApgjaAQAAAACwKYJ2AAAAAEgz8+bN09FHHx3RY4YMGaL77rsvJu3pqlmzZumcc86Je70TJkzQlVdeGdM6CNoBAAAAoB0ej0fNzc1xuXk8nk63s6amRg6HQ6eeemoUX31olmXp6aef9t3/97//Lcuy9PbbbweUGzdunJxOp3bt2uXbtnv3bmVnZ+v3v/99XNqazDIT3QAAAAAAsCuPx6Pp507T1m+2x6W+3D699OTSZXI4HBE/duHChbr88sv1yCOPqK6uTgUFBTFoYWjf+973NGDAAL3yyisaN26cJGnHjh169913lZeXp5qaGp100kmSpLfffluNjY2aOHFiXNuYjAjaAQAAACAEY4y2frNdfxhfL4cV27o8Rrrk1dY6I7Vz50499dRTqq2t1aZNm7R48WLdeOONvv133XWX7r33Xu3atUvnnXeeDjjggIDHT5gwQUcffXRA+vs555yj3r17a/HixW3qGzJkiCRp6tSpkqTCwkJ9/vnnmjBhgpYvX65f//rXkqTXX39dw4cP1/jx47V8+XJf0L58+XINGjRIw4YNkyQtWrRI8+fP1/r16zVkyBBdccUV+sUvfuGr7z//+Y9KS0v1j3/8QxkZGTr++ONVXl7ua8e+3nnnHU2ZMkUlJSW6/vrrtX37dl1zzTV6+umn5Xa7NWbMGN1777066qijJLUOF3j66ad11VVX6YYbbtC2bds0ZcoU/eEPf1BOTo7vPb700ku1bNky5eTk6Oqrrw7zr9M1pMcDAAAAQAcclpSZEdtbVy4KPPnkkzrkkEN0yCGH6MILL9SiRYt8wf9TTz2lm266SbfffrtWrlypAQMG6KGHHurS+1FbWyupNdjeuHGj7/7EiRO1YsUKNTc3S5JeeeUVTZgwQePHj9crr7zie/wrr7zi62X/wx/+oOuvv1633367/vWvf+mOO+7QDTfcoEcffVSStGvXLk2cOFE9evTQa6+9phUrVqhHjx469dRTtXv37jZtW758uU488UTdfPPNuv7662WM0emnn65Nmzbpueee0zvvvKNRo0bpxBNP1DfffON73Keffqqnn35azzzzjJ555hm9+uqruuuuu3z7r7nmGr3yyiuqqqrSP/7xDy1fvlzvvPNOl97HcBC0AwAAAECSW7BggS688EJJ0qmnnqodO3bopZdekiTdd999mj17ti6++GIdcsghuu2223TYYYd1qT5vT33v3r3Vv39/3/0JEyZo586dviB++fLlGj9+vMaPH6+VK1dq165d2r17t9566y1f0H7rrbfqnnvu0bRp0zR06FBNmzZNc+fO1e9+9ztJ0hNPPKGMjAw98sgjGjFihA499FAtWrRIdXV1Wr58eUC7/vKXv+iss87Sww8/rEsvvVRS6wWCNWvWaOnSpRozZoyGDRum//u//1Pv3r31pz/9yffYlpYWLV68WEcccYR+8IMf6Mc//rHvPdyxY4cWLFig//u//9PJJ5+sESNG6NFHH+3SHAThIj0eAAAAgK0ZY+R2u33/b2pqkiQ5nU5ZliWXyyXLsiIumyo+/vhj/fOf/9SyZcskSZmZmZo+fboWLlyok046Sf/617/085//POAxxx57bEDPd7QMGzZM+fn5Wr58uQ4//HC9++67Gj9+vPr166ehQ4fqjTfekNPpVGNjoyZNmqSvv/5aX375pebMmaNLLrnE9zzNzc3q1auXpNZU93Xr1vnS1L3cbrc+/fRT3/23335bzzzzjJYuXepL2/c+fseOHerbt2/A4xsbGwMeP2TIkIA6BgwYoC1btkhq7YXfvXu3jj32WN/+Pn366JBDDunK2xUWgnYAAAAAtuZ2uzVlypSQ+6urq5WVlRVx2VSxYMECNTc3a9CgQb5txhh169ZN27ZtC+s5MjIy2oyl37NnT6faM2HCBL3yyis68sgjNWzYMPXr10+SfCnyTqdThYWFGjJkiDZv3iypNUXeO3mdl3cyvpaWFo0ePVoVFRVt6vIfm3/QQQepb9++WrhwoU4//XR1797d9/gBAwa06ZWXWjMFvLp16xawz7IstbS0SOrcPAPRQno8AAAAACSp5uZmPfbYY7rnnnu0evVq3+29995TYWGhKioqdOihh+qtt94KeNy+9w844ABt3LjRd9/j8eiDDz5ot+5u3boFTQ+fOHGiampq9MILL2jChAm+7d7J6JYvX65JkyZJkvLy8jRo0CB99tlnOvjggwNuQ4cOlSSNGjVKa9euVb9+/dqU8fbGS1Jubq5efvllffrpp5o+fbrvosOoUaO0adMmZWZmtnl8bm5uGO+ydPDBB6tbt24B79u2bdv0ySefhPX4rqCnHQAAAICtuVwuVVdXS2rtSfemPldVVcnlcsnlcnWqbCp45plntG3bNs2ZMycggJWkH/3oR1qwYIF+/etfa+bMmRozZoyOP/54VVRU6MMPP9SBBx7oKztp0iSVlpbq2Wef1UEHHaR7771X3377bbt1DxkyRC+99JKOO+44OZ1O7b///pJag/adO3dq4cKF+sMf/uArP378eM2aNUsOh0OzZ8/2bZ83b56uuOIK9ezZU1OmTFFTU5NWrlypbdu2qbS0VDNmzNDdd9+ts88+W7fccovy8/NVV1enZcuW6ZprrlF+fr7vufr166eXX35ZEydO1AUXXKAnnnhCJ510ko499lidc845+t///V8dcsgh+uqrr/Tcc8/pnHPO0ZgxYzp8n3v06KE5c+bommuuUd++fZWXl6frr79eGRmx7wenpx0AAACArVmWpaysLGVlZbUJ0LOysgLGqEdSNhIeIzW3xPbm6UQG9oIFC3TSSSe1Cdgl6Yc//KFWr16tYcOG6cYbb9SvfvUrjR49Wl988YVvkjav2bNna+bMmbrooos0fvx4DR06tMM11O+55x698MILGjx4sEaOHOnbPnToUBUWFuq7777T+PHjfdsHDRqkgoICud3ugOe++OKL9cgjj2jx4sUaMWKExo8fr8WLF/t62rOzs/Xaa6+poKBA06ZN06GHHqrZs2ersbFRPXv2bNOu/v376+WXX9aaNWs0Y8YMtbS06LnnntMJJ5yg2bNna/jw4Tr//PP1+eefKy8vL7w3WtLdd9+tE044QWeddZZOOukkHX/88Ro9enTYj+8syyQyOd8mGhoa1KtXL23fvj3oHx0AAACAPTQ2NvrGrHc0Pj2Ssm63W+vXr9fQoUMDgn2Px6Pp507T1m+2R+kVtC+3Ty89uXSZbzw3kluoz5UUfhxKejwAAAAAhOBwOPTk0mVxm4jMsiwCdgQgaAcAAACAdhBEI5EY0w4AAAAAgE0RtAMAAAAAYFME7QAAAAAA2BRBOwAAAAD8F4trIZqi8XkiaAcAAACQ9rp16yZJ2rVrV4JbglTi/Tx5P1+dwezxAAAAANKew+FQ7969tWXLFklSdna2LMtKcKuQrIwx2rVrl7Zs2aLevXt3aQUCgnYAAAAAkNS/f39J8gXuQFf17t3b97nqLIJ2AAAAAJBkWZYGDBigfv36ac+ePYluDpJct27dutTD7kXQDgAAAAB+HA5HVIItIBqYiA4AAAAAAJsiaAcAAAAAwKYI2gEAAAAAsCmCdgAAAAAAbIqgHQAAAAAAmyJoBwAAAADApgjaAQAAAACwKYJ2AAAAAABsiqAdAAAAAACbImgHAAAAAMCmCNoBAAAAALApgnYAAAAAAGyKoB0AAAAAAJsiaAcAAAAAwKYI2tGhmpoaTZ8+XTU1NYluCgAAAACkFYJ2tMvtdqusrEybN29WWVmZ3G53opsEAAAAAGmDoB3tqqioUH19vSSpvr5elZWVCW4RAAAAAKQPgnaEtGHDBlVWVsoYI0kyxqiyslIbNmxIcMsAAAAAID0QtCMoY4zKy8tDbvcG8uEId0w8Y+cBAAAAIBBBO4Kqq6tTbW2tPB5PwHaPx6Pa2lrV1dWF9Tzhjoln7DwAAAAAtEXQjqAKCgo0duxYORyOgO0Oh0PHHHOMCgoKwnqecMfEM3YeAAAAANoiaEdQlmWppKQk5HbLsjp8jnDHxDN2HgAAAACCI2hHSPn5+SouLvYF6JZlqbi4WIMGDerwseGOiY/m2HkAAAAASDUE7WjXjBkz1LdvX0lSbm6uiouLw3pcuGPiozV2HgAAAABSEUE72uVyuVRaWqq8vDzNnTtXLpcrrMeFOyY+WmPnAQAAACAVEbSjQ0VFRXryySdVVFQU9mPCHRMfjbHzAAAAAJCqCNoRM+GOie/K2HkAAAAASGUE7YipcMfEd3bsPAAAAACkMoJ2xFS4Y+I7O3YeAAAAAFJZZqIbgNRXVFQU1nj4cMsBAAAAQLqgpx0AAAAAAJsiaAcAAAAAwKYI2gEAAAAAsCmCdgAAAAAAbIqgHQAAAAAAmyJoBwAAAADApgjaAQAAAACwKYJ2AAAAAABsiqAdAAAAAACbImgHAAAAAMCmCNoBAAAAALApgnYAAAAAAGyKoB0AAAAAAJsiaAcAAAAAwKYI2gEAAAAAsCmCdgAAAAAAbCqhQftrr72mM888UwMHDpRlWXr66acD9s+aNUuWZQXcvv/97weUaWpq0uWXX67c3Fztt99+Ouuss7Rhw4Y4vgoAAAAAAGIjoUH7zp07ddRRR+nBBx8MWebUU0/Vxo0bfbfnnnsuYP+VV16pqqoqPfHEE1qxYoV27NihM844Qx6PJ9bNBwAAAAAgpjITWfmUKVM0ZcqUdss4nU71798/6L7t27drwYIFevzxx3XSSSdJkpYsWaLBgwfrxRdf1CmnnBL1NqeKmpoalZeXq6SkREVFRYluDgAAAAAgCNuPaV++fLn69eun4cOH65JLLtGWLVt8+9555x3t2bNHkydP9m0bOHCgjjjiCNXU1IR8zqamJjU0NATc0onb7VZZWZk2b96ssrIyud3uRDcJAAAAABCErYP2KVOmqKKiQi+//LLuuece1dbWatKkSWpqapIkbdq0Sd27d9f+++8f8Li8vDxt2rQp5PPeeeed6tWrl+82ePDgmL4Ou6moqFB9fb0kqb6+XpWVlQluEQAAAAAgGFsH7dOnT9fpp5+uI444Qmeeeaaqq6v1ySef6Nlnn233ccYYWZYVcv91112n7du3+25ffvlltJtuWxs2bFBlZaWMMZJa36vKykom7wMAAAAAG7J10L6vAQMGqLCwUGvXrpUk9e/fX7t379a2bdsCym3ZskV5eXkhn8fpdKpnz54Bt3RgjFF5eXnI7d5AHgAAAABgD0kVtNfX1+vLL7/UgAEDJEmjR49Wt27d9MILL/jKbNy4UR988AGTqwVRV1en2traNjPrezwe1dbWqq6uLkEtAwAAAAAEk9DZ43fs2KF169b57q9fv16rV69Wnz591KdPH82bN08//OEPNWDAAH3++ef6n//5H+Xm5mrq1KmSpF69emnOnDm66qqr1LdvX/Xp00dXX321RowY4ZtNHnsVFBRo7NixWrVqVUDg7nA4NHr0aBUUFCSwdQAAAACAfSW0p33lypUaOXKkRo4cKUkqLS3VyJEjdeONN8rhcGjNmjU6++yzNXz4cM2cOVPDhw/Xm2++qZycHN9z3HvvvTrnnHN03nnn6bjjjlN2drb+9re/yeFwJOpl2ZZlWSopKQm5vb15AAAAAAAA8ZfQnvYJEya0O476+eef7/A5XC6XHnjgAT3wwAPRbFrKys/PV3FxsZYsWeKbsK+4uFiDBg1KdNMAAAAAAPtIqjHtiI4ZM2aob9++kqTc3FwVFxcnuEUAAAAAgGAI2tOQy+VSaWmp8vLyNHfuXLlcrkQ3CQAAAAAQRELT45E4RUVFzLAPAAAAADZHTzsAAAAAADZF0A4AAAAAgE0RtAMAAAAAYFME7QAAAAAA2BRBOwAAAAAANkXQDgAAAACATRG0AwAAAABgUwTtAAAAAADYFEE7AAAAAAA2RdAOAAAAAIBNEbQDAAAAAGBTBO0AAAAAANgUQTsAAAAAADZF0A4AAAAAgE0RtAMAAAAAYFME7QAAAAAA2BRBOwAAAAAANkXQDgAAAACATRG0AwAAAABgUwTtAAAAAADYFEE7AAAAAAA2RdAOAAAAAIBNEbQDAAAAAGBTBO2IuZqaGk2fPl01NTVRKQcAAAAA6YKgHTHldrtVVlamzZs3q6ysTG63u0vlAAAAACCdELQjpioqKlRfXy9Jqq+vV2VlZZfKAQAAAEA6IWhHzGzYsEGVlZUyxkiSjDGqrKzUhg0bOlUOAAAAANINQTtiwhij8vLykNv9A/RwygEAAABAOiJoR0zU1dWptrZWHo8nYLvH41Ftba3q6uoiKgcAAAAA6YigHTFRUFCgsWPHyuFwBGx3OBw65phjVFBQEFE5AAAAAEhHBO2ICcuyVFJSEnK7ZVkRlQMAAACAdETQjpjJz89XcXFxQIBeXFysQYMGdaocAAAAAKQbgnbE1IwZM9S3b19JUm5uroqLi7tUDgAAAADSCUE7Ysrlcqm0tFR5eXmaO3euXC5Xl8oBAAAAQDrJTHQDkPqKiopUVFQUtXIAAAAAkC7oaQcAAAAAwKYI2gEAAAAAsCmCdgAAAAAAbIqgHQAAAAAAm2IiOgAAkogxRm632/f/pqYmSZLT6ZRlWZJaV+SwLKvDst5yAADAvgjaAQBIIm63W1OmTGm3THV1tbKysjos6y0HAADsi/R4AAAAAABsip52AACSiMvlUnV1taTWXvepU6dKkqqqquRyuXxlwinrLQcAAOyLoB0AgCRiWVbQlHaXy9VmeyRlAQCAPZEeDwAAAACATRG0AwAAAABgUwTtAAAAAADYFEE7AAAAAAA2RdAOAAAAAIBNEbQDAAAAAGBTBO0AAAAAANgUQTsAAAAAADZF0A4AAAAAgE0RtAMAAAAAYFME7QAAAAAA2BRBOwAAAAAANkXQDgAAAACATRG0AwAAAABgUwTtAAAAAADYFEE7AAAAAAA2RdAOAAAAAIBNEbQDAAAAAGBTBO0AAAAAANgUQTsAAAAAADZF0A4AAAAAgE0RtAMAAAAAYFME7QAAAAAA2BRBOwAAAAAANkXQDgAAAACATRG0AwAAAABgUwTt6FBNTY2mT5+umpqaRDcFAAAAANJKQoP21157TWeeeaYGDhwoy7L09NNP+/bt2bNHv/rVrzRixAjtt99+GjhwoC666CJ99dVXAc8xYcIEWZYVcDv//PPj/EpSl9vtVllZmTZv3qyysjK53e5ENwkAAAAA0kZCg/adO3fqqKOO0oMPPthm365du7Rq1SrdcMMNWrVqlZYtW6ZPPvlEZ511Vpuyl1xyiTZu3Oi7/e53v4tH89NCRUWF6uvrJUn19fWqrKxMcIsAAAAAIH1kJrLyKVOmaMqUKUH39erVSy+88ELAtgceeEDHHHOM6urqVFBQ4NuenZ2t/v37x7Styc4Y4+slN8aoqalJkuR0OmVZliTJ5XL5/i9JGzZsUGVlpYwxvsdVVlZq8uTJys/Pj/MrAAAAAID0k9CgPVLbt2+XZVnq3bt3wPaKigotWbJEeXl5mjJlim666Sbl5OSEfJ6mpiZf0CpJDQ0NsWqybbjd7pAXSLyqq6uVlZUlqTVALy8vb1PGu33+/PkBAT4AAAAAIPqSJmh3u9369a9/reLiYvXs2dO3fcaMGRo6dKj69++vDz74QNddd53ee++9Nr30/u68807dfPPN8Wh20qqrq1NtbW2b7R6PR7W1taqrq1NhYWECWgYAAAAA6SMpgvY9e/bo/PPPV0tLix566KGAfZdcconv/0cccYSGDRumMWPGaNWqVRo1alTQ57vuuutUWlrqu9/Q0KDBgwfHpvE24XK5VF1dLan1AsjUqVMlSVVVVXK5XL4yXgUFBRo7dqxWrVolj8fj2+5wODR69OiA4QkAAAAAgNiw/ZJve/bs0Xnnnaf169frhRdeCOhlD2bUqFHq1q2b1q5dG7KM0+lUz549A26pzrIsZWVlKSsrKyA4d7lcvu3+6e6WZamkpCTo85SUlJAaDwAAAABxYOug3Ruwr127Vi+++KL69u3b4WM+/PBD7dmzRwMGDIhDC1Nbfn6+iouLfQG6ZVkqLi7WoEGDEtwyAAAAAEgPCU2P37Fjh9atW+e7v379eq1evVp9+vTRwIED9aMf/UirVq3SM888I4/Ho02bNkmS+vTpo+7du+vTTz9VRUWFTjvtNOXm5uqjjz7SVVddpZEjR+q4445L1MtKKTNmzFB1dbW2bt2q3NxcFRcXJ7pJAAAAAJA2EtrTvnLlSo0cOVIjR46UJJWWlmrkyJG68cYbtWHDBv31r3/Vhg0bdPTRR2vAgAG+W01NjSSpe/fueumll3TKKafokEMO0RVXXKHJkyfrxRdflMPhSORLSxkul0ulpaXKy8vT3LlzA1LrAQAAAACxldCe9gkTJvjWAA+mvX2SNHjwYL366qvRbhb2UVRUpKKiokQ3AwAAAADSjq3HtAMAAAAAkM4I2gEAAAAAsCmCdgAAAAAAbIqgHQAAAAAAmyJoBwAAAADApgjaAQAAAACwKYJ2AAAAAABsiqAdAAAAAACbImgHAAAAAMCmCNoBAAAAIIgFCxZo0qRJWrBgQVTLApEgaAcAAACAfXz77beqqKhQS0uLKioq9O2330alLBApgnYAAAAA2McNN9yglpYWSVJLS4tuvPHGqJQFIkXQDgAAAAB+Vq5cqTVr1gRse//997Vy5coulQU6g6AdAAAAAP6rpaVFt9xyS9B9t9xyi69HPdKyQGcRtAMAAADAf7399ttqaGgIuq+hoUFvv/12p8oCnUXQDgAAAAD/NW7cOPXs2TPovl69emncuHGdKgt0FkE7AAAAAPxXRkZGyInkbrrpJmVkZHSqLNBZfIoAAAAAwM+YMWM0YsSIgG1HHnmkRo0a1aWyQGcQtAMAAADAPm699VZfT3lGRkbICeciLQtEiqAdAAAAAPbRu3dvzZgxQxkZGZoxY4Z69+4dlbJApDIT3QAAAAAA6ApjjNxud5vt/tuC7Xe5XLIsK+TzzpkzR3PmzAmrDZGUBSJB0A4AAAAgqbndbk2ZMqXdMlOnTm2zrbq6WllZWbFqFhAVpMcDAAAAAGBT9LQDAAAASBkPHv+NnA4jSTJG2t3Sur17hmRZUpPH0i9X9ElgC4HIELQDAAAASBlOh5HTsfe+q00JE8fWAF1H0A4AAADAloJNMBdscrlgk8wBqYKgHQAAAIAtdTTBXLDJ5YBUw0R0AAAAAADYFD3tAAAAAGxvx9EXyGRkts4u19LcujEjU7IsWXvc6rFmaWIbCMQIQTsAAAAA2zMZmZKj23/vdQ/c59kT/wYBcUJ6PAAAAAAANkXQDgAAAACATRG0AwAAAABgUwTtAAAAAADYFEE7AAAAAAA2RdAOAAAAAIBNEbQDAAAAAGBTBO0AAAAAANgUQTsAAAAAADZF0A4AAAAAgE0RtAMAAAAAYFME7QAAAAAA2BRBOwAAAAAANpWZ6AYAAADEizFGbrfb9/+mpiZJktPplGVZcrlcsiwroGywcpICygIAECsE7QAAIG243W5NmTIl5P7q6mplZWVFXBYAgFghPR4AAAAAAJuipx0AAKQNl8ul6upqSa096VOnTpUkVVVVyeVyyeVytSkbrJx3PwAAsUbQDgAA0oZlWUFT2l0uV5vtwcoGKwcAQCyRHg8AAAAAgE3R0w4AQArxnx3dn/+2YPuZCR0AAHsiaAcAIIV0NOO5JN/4bH/MhA4AgD2RHg8AAAAAgE3R0w4AQIp68Phv5HQYSZIx0u6W1u3dMyTLkpo8ln65ok8CWwgAADpC0A4AQIpyOoycjr332y5QZuLYGgAA0BmkxwMAAAAAYFME7QAAAAAA2BRBOwAAAAAANkXQDgAAAACATRG0AwAAAABgUwTtAAAAAADYFEu+AUgZxhi53W7f/5uamiRJTqdTlmVJklwulyzL6rCstxwAAACQSATtAFKG2+3WlClT2i1TXV2trKysDst6ywEAAACJFFHQ/vHHH+uPf/yjXn/9dX3++efatWuXDjjgAI0cOVKnnHKKfvjDH8rpdMaqrQAAAAAApJWwgvZ3331X1157rV5//XUVFRXpmGOO0TnnnKOsrCx98803+uCDD3T99dfr8ssv17XXXqsrr7yS4B1A3LlcLlVXV0tq7XWfOnWqJKmqqkoul8tXJpyy3nIAAABAIoUVtJ9zzjm65ppr9OSTT6pPnz4hy7355pu69957dc899+h//ud/otZIAAiHZVlBU9pdLleb7ZGUBQAAABIlrKB97dq16t69e4fljj32WB177LHavXt3lxsGAAAAAEC6C2vJt3AC9q6UBwAAAAAAbXV59vgXX3xRr7/+usaMGaMzzzwzGm0CAAAAAAAKs6fd6xe/+IVuuOEG3/0///nPOvXUU/Xss89q+vTpKisri3oDAQAAAABIVxEF7a+88opOOOEE3/2ysjLdcccdWrlypZYsWaKHHnoo6g0EAAAAACBdhZUef/PNN0uS6urq9Je//EVvvvmmjDGqra3VUUcdpVtuuUVut1t1dXW65ZZbJEk33nhj7FoNAAAAAEAaCCtonzVrliTpt7/9rU4++WQdffTRev3119W/f3/9+te/ljFGO3fu1P33369Zs2bJGBPLNgMAAAAAkBbCCtoLCwslSd///vd1991367LLLtMDDzygqVOnqqCgQJJUW1uroUOH+u4DAAAAAICuiWhM+7333ivLsvTTn/5Uffr00U033eTb97vf/Y7Z4wEAAAAAiKKIgvYhQ4bo9ddf13fffafq6mr16dPHt++RRx7RHXfcEVHlr732ms4880wNHDhQlmXp6aefDthvjNG8efM0cOBAZWVlacKECfrwww8DyjQ1Nenyyy9Xbm6u9ttvP5111lnasGFDRO0AAAAAAMCOIgrao23nzp066qij9OCDDwbdP3/+fJWVlenBBx9UbW2t+vfvr5NPPlnfffedr8yVV16pqqoqPfHEE1qxYoV27NihM844Qx6PJ14vAwCAmDPGqLGxMeDmdrt9+91ud5ttAAAg+YU1pv2uu+7SFVdcoezs7A7Lvv3229q6datOP/30DstOmTJFU6ZMCbrPGKP77rtP119/vaZNmyZJevTRR5WXl6fKykr97Gc/0/bt27VgwQI9/vjjOumkkyRJS5Ys0eDBg/Xiiy/qlFNOCeflAQBge263O+RvpiRNnTo1jq0BAADxElZP+0cffaSCggJdeumlqq6u1tdff+3b19zcrPfff18PPfSQioqKdP7556tnz55dbtj69eu1adMmTZ482bfN6XRq/PjxqqmpkSS988472rNnT0CZgQMH6ogjjvCVAQAAQHKrqanR9OnTwzq/i6QsACSDsHraH3vsMb3//vv6zW9+oxkzZmj79u1yOBxyOp3atWuXJGnkyJH66U9/qpkzZ8rpdHa5YZs2bZIk5eXlBWzPy8vTF1984SvTvXt37b///m3KeB8fTFNTk5qamnz3GxoautxeAADiZcfRF8hkZErGSC3NrRszMiXLkrXHrR5rlia2gUAUud1ulZWVaevWrSorK9OoUaPkcrm6XBYAkkXYY9qPPPJI/e53v1N9fb1WrVqlpUuX6g9/+IOef/55bd68WStXrtRPf/rTqATs/izLCrhvjGmzbV8dlbnzzjvVq1cv323w4MFRaSsAAPFgMjIlRzcps7vUPbv1ltldcnSTyXAkunlAVFVUVKi+vl6SVF9fr8rKyqiUBYBkEVZPuz/LsnTUUUfpqKOOikV7fPr37y+ptTd9wIABvu1btmzx9b73799fu3fv1rZt2wJ627ds2aKioqKQz33dddeptLTUd7+hoYHAHQCALjDGyO12yxjjy2ZzOp2+i+gul6vDi+7AvjZs2KDKykoZYyS1fs4qKys1efJk5efnd7oskG68x2jv//c9Tvsfozme20/EQXu8DB06VP3799cLL7ygkSNHSpJ2796tV199Vf/7v/8rSRo9erS6deumF154Qeedd54kaePGjfrggw80f/78kM/tdDqjnhEAAOmqpqZG5eXlKikpafeCKVJbRxPlVVdXKysrK44tQrIzxqi8vDzk9vnz5wcEGeGWBdJRJMdojuf2k9CgfceOHVq3bp3v/vr167V69Wr16dNHBQUFuvLKK3XHHXdo2LBhGjZsmO644w5lZ2eruLhYktSrVy/NmTNHV111lfr27as+ffro6quv1ogRI3yzyQMAYofxowBipa6uTrW1tW22ezwe1dbWqq6uToWFhRGXBYBkk9CgfeXKlZo4caLvvjdlfebMmVq8eLGuvfZaNTY26he/+IW2bdumcePG6R//+IdycnJ8j7n33nuVmZmp8847T42NjTrxxBO1ePFiORyM6QOAWAs2fnT27NkJbhUSweVyqbq6Wm6327f8XFVVle8iDhdzEKmCggKNHTtWq1atksfj8W13OBwaPXq0CgoKOlUWSEfeY7SkoMdp/2M0x3P7CXsiuliYMGGCjDFtbosXL5bUOn5+3rx52rhxo9xut1599VUdccQRAc/hcrn0wAMPqL6+Xrt27dLf/vY3xqcDQByEGj+6YcOGBLcMiWBZlrKystqc+GVlZSkrK4vUZETMsiyVlJSE3O7/mYqkLJCOvMfoUMfpfb9PHM/tpdNB+7p16/T888+rsbFRknwnbQCA1NfR+FF+EwBEQ35+voqLi31BgmVZKi4u1qBBg7pUFgCSScRBe319vU466SQNHz5cp512mjZu3ChJuvjii3XVVVdFvYEAAPvxjh/1T0OVAsePAkA0zJgxQ3379pUk5ebm+uY26mpZAEgWEQftc+fOVWZmpurq6pSdne3bPn36dP3973+PauMAAPbkHT+67/whDodDxxxzDONHgX3U1NRo+vTpqqmpSXRTko7L5VJpaany8vI0d+7cdsfTRlIWAJJFxBPR/eMf/9Dzzz/fZr3LYcOG6YsvvohawwAA9uUdJzpz5syg2xnvBuzFKgtdV1RUFPaSkpGUBYBkEHFP+86dOwN62L22bt3K2ucAkEYYPwqEJ9gqCwAAhCvioP2EE07QY4895rtvWZZaWlp09913ByzfBgBIfYwfBdoXzioLxhg1NjaqsbFRu3bt0rZt27Rt2zbt2rXLt53JHQEgfUWcHn/33XdrwoQJWrlypXbv3q1rr71WH374ob755hu98cYbsWgjAMCmvONHy8vLVVJSQsov4KejVRbmz58vy7Lkdrs1ZcqUdp+rurpaWVlZsWoqAMDGIg7aDzvsML3//vt6+OGH5XA4tHPnTk2bNk2XXXaZBgwYEIs2AgBsjPGjQHDeVRb25b/KQmFhYQJaBgBIJhEH7ZLUv39/3XzzzdFuCwAAQMrwrrKwatWqgOURHQ6HRo8e7VtlweVyqbq6WlLrpHVTp06VJFVVVfmyV8hiAYD0FfGY9kWLFmnp0qVtti9dulSPPvpoVBoFAACQ7LyrKYTa7j+JY1ZWlrKysgKCc5fL5dvOigwAkL4iDtrvuusu5ebmttner18/3XHHHVFpFAAAQCpglQUAQFdFHLR/8cUXGjp0aJvthYWFqquri0qjAAAAUgWrLAAAuiLioL1fv356//3322x/7733fD9IAAAAaOVdZSEvL09z585lfDoAICIRT0R3/vnn64orrlBOTo5OOOEESdKrr76qkpISnX/++VFvIAAAQLJjlQUAQGdFHLTfdttt+uKLL3TiiScqM7P14S0tLbrooosY0w4AAAAAQBRFHLR3795dTz75pG699Va99957ysrK0ogRI1hnFAAAAACAKOvUOu2SNHz4cA0fPjyabQEAAAAAIGI1NTUqLy9XSUlJyg1HCitoLy0t1a233qr99ttPpaWl7ZYtKyuLSsMAAAAAAOiI2+1WWVmZtm7dqrKyMo0aNSqlJv0MK2h/9913tWfPHknSqlWrfGuN7ivUdgAAAAAAYqGiokL19fWSpPr6elVWVmr27NkJblX0hBW0v/LKK77/L1++PFZtAQAAAACkAWOM3G637/9NTU2SJKfTKcuy5HK5wuoU3rBhgyorK2WM8T1XZWWlJk+erPz8/IC6gtUjKey6EiWiMe3Nzc1yuVxavXq1jjjiiFi1CQAAAACQwtxut6ZMmRJyf3V1tbKystp9DmOMysvLQ26fP3++LMuKSl2JlBFJ4czMTBUWFsrj8cSqPQAAAAAAdKiurk61tbVt4lOPx6Pa2lrV1dUlqGXRFfHs8f/v//0/XXfddVqyZIn69OkTizYBAIAk4J/a6OV/f999kv1TEAEgXB2ld0sc89rjcrlUXV0tqfX3YurUqZKkqqoquVyusCaSKygo0NixY7Vq1aqAwN3hcGj06NEqKCgIqCtYPd79dhZx0H7//fdr3bp1GjhwoAoLC7XffvsF7F+1alXUGgcAAOyro3RD74mRP7unIAJAuDo6Bkoc89pjWVbQ98blcoX9nlmWpZKSEs2cOTPodu8Fk2B1RVKPv0iWlovWMnQRB+1nn302V4sAAAAApCVvD3uwbKJ9ecvQ4x47+fn5Ki4u1pIlS2SMkWVZKi4u1qBBg6JeVyRLy0VzGbqIg/Z58+Z1qiIAAJC6Hjz+GzkdRsZIu1tat3XPkCxLavJY+uUKhtQBSA3h9LB7eTOO6HGPrRkzZqi6ulpbt25Vbm6uiouLY1JPJEvLRXMZurAnotu1a5cuu+wyDRo0SP369VNxcbG2bt3aqUoBAEBqcTqMnA7JlSn17N56c2VKTkfrPgAAYsXlcqm0tFR5eXmaO3duTMaoh1pabsOGDV0qG46we9pvuukmLV68WDNmzJDL5dIf//hHXXrppVq6dGmnKgYAAACAZEaWkX0UFRV1adx4e8JdWi7SsuEKO2hftmyZFixYoPPPP1+SdOGFF+q4446Tx+ORw+GIqFIAAAAASHbeLCNJatu3S5ZRqvAuLbcv/6XlCgsLIy4brrCD9i+//FI/+MEPfPePOeYYZWZm6quvvtLgwYMjqhR7eSey6GiZCLsvKRGqfcF0795du3fvbvP/YLyvj8k7AAAAEA8dnZ97U56RPsJdWi7SsuEKO2j3eDzq3r174IMzM9Xc3Bxxpdiro4ksvJNW2H1JiUgm5OgMJu8AAABAPHR0XltVVRXH1sAOwl1aLtKy4Qo7aDfGaNasWXI6nb5tbrdbP//5zwPWal+2bFnEjQAAAACARPHPGu1oKbdwlnpD6olkabloL0MXdtC+75UCqXVcO7rG5XKpurpabrfbtyREVVWVb8ZD/3+rq6slqcOyibbjqPPbHcFjeZrV44M/SZIeOO4bWVbw0k0eS1e9yeQdAAAAiK1IskYvuOCCGLcmtflfIPHyvx/soohdhspGsrRcNJehCztoX7RoUacrQWiWZbVJ+3a5XG22BSsXqmyiGUc3ydEt9P7du3z/d2XunbxjX00exgsBAAB7qqmpUXl5uUpKSmI2YzWQijq6QOLtnPRnl6Gy3qXlvN/99jpNIynbkbCDdgAAAACtQUdZWZm2bt2qsrIyjRo1yjYZj+i6HUdfIJORKRkjtfx3/q6MzNZ13CRZe9zqsYZlr9NVJEvLRWsZOoJ2AAAAIAIVFRWqr6+XJNXX16uyslKzZ89OcKsQLSYj0y9rtHvb/Z498W1QCmOd+/BkJLoBAAAAQLLYsGGDKisrfct+GWNUWVmpDRs2JLhlQPLxrnPvypR6dm+9uTIlp6N1H1oRtAMAAABhMMaovLw85HbW7wYQC6THAwAAAGGoq6tTbW1tm+0ej0e1tbWqq6tTYWFhAlqWugIuhLSXlt68d5+7uf3nbPKEeH7ApgjabS6Zl0QAAABIJQUFBRo7dqxWrVolj2dv5OdwODR69GgVFBQksHWpqampyff/nPeeCOsxl7/RN6Lnz87OjrhdQDwRtNtcMi+JAAAAkEosy1JJSYlmzpwZdDudJgBigaAdAAAACFN+fr6Ki4u1ZMkSGWNkWZaKi4s1aNCgRDctJTmdTt//vzvqfL9Z3ffRtEs5H/5ZkvTAcfVytRPlNHmkX67o2+b5AbsiaE+ASFLe/f/PkggAAACJN2PGDFVXV2vr1q3Kzc1VcXFxopuUsgKyFxzdQgftmXu3e2cfj/j5AZsiaO8i/wDcGOMbd+N0OmVZVtDx5Z1JeZf2LokgSa62LelM8wGgU7zHvmDHPSl2c2vU1NSovLxcJSUlKioqivrzI1BHv3FM4NS+RH1PEHsul0ulpaW+45HL1fbMDACihaC9izoKwBlfDiAVJeLY53a7VVZWpq1bt6qsrEyjRo3iRDnGOvo7V1VVxbE1yYdzhNRWVFTExUMAcUHQnmA7jr5AJiNTMkZq+e/6FBmZrTnvkqw9bvVYszSBLQQAe6ioqFB9fb0kqb6+XpWVlZo9e3aCW2VDfr3f/ssaBcOyRwAA2B9Bexe5XC5VV1dLar2i7k1tr6qqksvl6rAXyGRk+o3N6d52f3vrUcZIZ1L+AaQX77Ev2HHPu789kaYNb9iwQZWVlb7A0hijyspKTZ48Wfn5+VF/fUmtZW8k7p1oKRzBlj3q6DeOQL99Xf2eAAAgEbR3mWVZQVPbXC6XLVLeOrPOuzFGp512WsjnJJ0PQLBjXyTHvUjSho0xKi8vb1PGu33+/PlcSIyRjn7jGhsbE9Cq5NHV7wkAABJBu/2FmeYYKsWxM5PeMUYRgJ3U1dWptra2zXaPx6Pa2lrV1dWpsLAwAS2zqYy9UyY/eHx9uzMos+wRAAD2R9Bud51IcwyW4hgJY4wvcHe73brgggskSX/84x996ZDe3hVS5QF0RiRpwwUFBRo7dqxWrVolj2fvMdHhcGj06NEqKCiIb+Ptzu+Y7HSw7BEAAMmOoD2NtDfpnf+Ed9OmTQv6eG/w7o9UeQCdEUnasGVZKikp0cyZM4NuJ9gEAACpjKDd7sJMcwwnxbG9Se8SMeEdAIQrPz9fxcXFWrJkiYwxsixLxcXFGjRoUKKbBgAAEFME7XbXiTTHrvY6PXj8N3I6vDM0S7tbWrd3z2htTpPH0i9X9OlSHQAQqRkzZqi6ulpbt25Vbm6uiouLE90kAACAmMtIdANgP06H8V0gcGVKPbu33lyZ3gsHLPEDIP5cLpdKS0uVl5enuXPnslwWAABR4p2zyv+274pT++5n2c/4oacdAJA0ioqKVFRUlOhmdKimpkbl5eUqKSnpsL2RlAUAIBY6s+IUc1vFDz3tCRBwVcqzp4Nbs9/jEtDYMEX0mpr3jp93N7eOxw91C/r8AGBjbrdbZWVl2rx5s8rKygJ6KrpS1o7MPsuScjwHACD66GlPgKamJt//c957IuzH7W6R7JoM2tnXdPkb8VnGDgDipaKiQvX19ZKk+vp6VVZWavbs2V0ua0f+x/54LUsKAIgt7/xWzG1lHwTtSaTJY6nJE/oLhNgxxvh6wIwxvhNVp9Mpy7IC1qv3lg1WTmJt+0Ty/zv623fM1r4S/TcL9zMVyecUsbFhwwZVVlb6epONMaqsrNTkyZOVn5/f6bIAAMSLd34rKViHIdlSiUDQngD+S7J9d9T5fsuwBbG7UTkf/EmSdNWb9r2qFdFratqlnA//LEl64Lh6uUJ8CsNZxi5eOhrn4z+mJ5KyiK+O/jaSPcdshfuZ4rOXWMYYlZeXh9w+f/78gIt74Za1M/9jc1eXJQUAAMERtCdAwImYo1v7Aa4jOdZPj+g1Ze7d552RPqLnBwAbqqurU21tbZvtHo9HtbW1qqurU2FhYcRl7cxKwLKkAACkG4J2u8vY+yeqqqqSy+WS2+329QZ6t/ljGaToc7lcqq6ulqSg77//e+4tG+rvxN/HHrzjtSTZfsxWuJ+pSD6niL6CggKNHTtWq1atksezd+Y1h8Oh0aNHq6CgoFNlU5l3SIfb7VZLS4saGhpCls3JyZHD4ZDH49F3330XslzPnj2VkZHh+8xzkQBAONpMqtyefSZVDoVJOBEtBO1253ey4XK52qS2BtuG6LMsK+j7HOz9D1aWv5P9+I/Xkuw9Zivcz1Qkn1NEn2VZKikp0cyZM4Nu9w8eIymbysIZstIVDAkBEC4mVYadseQbAABRkp+fr+LiYl/QbVmWiouLNWjQoC6VlSJZWjM5lgoFAKQnlguNHD3tABADkaT99uzZU9nZ2aTypogZM2aourpaW7duVW5uroqLi6NStjO9QJ1ZKjSSVRa6uq68/9CPWKXHewV7XcFeU7CVFzpaYcL7Wvj+Askr1SdVthOWC40cQTsAxEBn0n5J5U0NLpdLpaWlKi8vV0lJSbtzCURSNl46u8pCZ3iHdHg/9337hnfylpubG3FdHb2ucF9TqHJ8f4HkxqTKsDOCdgAAoqyoqEhFRUVRLRt2L5DfUqHdGQQHALAZlguNHEE7AMRAJGm//unxQChh9wL5LRXa1Y6dHUdfIONdxcQYqeW/4+UzMiXLkrXHrR5rlnatkgTwva4OXpN3lYlgK0xI9lplAgCSBcuFRo6gPYkEG0Po/3/G0wH20dm030TyH/MbbFwvx5j0YzIy97k40D1wf0fLItlU4OsK/Zr8V5kIfkktvSdGAgDEB0F7glktza0/+UGu9nv3ewUbR+e/jfF0ALqiozG/HGMAAADij6A9wXqs/mOimwAAAAAAsCmC9iRSVVUll8sVNG1VEuNhAXSJdxy+1Nrr7s3k8R57OMYASHfeYUTtnYsxjAh2Ec8lPBFbBO0J4H9i7BXsBDnY47w/BOm8TiGA2PCOw9+Xy+UiLR4AxDAiJJd4LuGJ2CJoT4BQJ8ZeCTlBNnsn02nytF/Uf78xTMIDAKkk4Lje0URznr3zrvBzAABAbBC0o1XL3kjcux5iOJqamuj1B4AU4k35laSc954I+3G7W0LNsA6kDv/lPINlSDKMCHaVqkt4pguCdgAAACAMwbIlGUKU3po8liQjY1ovXkpS94zWhaBa99lDqi7hmS5sH7QPGTJEX3zxRZvtv/jFL/Sb3/xGs2bN0qOPPhqwb9y4cXrrrbfi1cTUkOHw/ffB4+t969IG0+TZ2xvvdDpj3TIAQBz5H9e/O+r8fU7y9rG7UTkf/ElS60kqAKSbX67ok+gmIA3YPmivra2Vx7M3dfuDDz7QySefrHPPPde37dRTT9WiRYt897t3D7xyhDD4zXTqdKjdoD3wYfa5gggA6LqA47qjW/tBu2Nvzww/BwAAxIbtg/YDDjgg4P5dd92lgw46SOPHj/dtczqd6t+/f7ybBgAAkFT8l4AKtmwZS5YBYcjYG0J55zToaCUo5jtAV9g+aPe3e/duLVmyRKWlpQE/KMuXL1e/fv3Uu3dvjR8/Xrfffrv69esX8nmampoCJtppaGiIabsBAADsgCXLgCjwi0OCzWnAPAeItqQagfb000/r22+/1axZs3zbpkyZooqKCr388su65557VFtbq0mTJgUE5fu688471atXL99t8ODBcWg9AAAAAACRSaqe9gULFmjKlCkaOHCgb9v06dN9/z/iiCM0ZswYFRYW6tlnn9W0adOCPs91112n0tJS3/2GhgYCdwAAkPK8S5ZJCprOSwovANhP0gTtX3zxhV588UUtW7as3XIDBgxQYWGh1q5dG7KM0+m0zazn3rFl3vFlkgL+z9gyAAC6zhiz9057Sxs1793nbm7/OZv2zpMb+Pw2FmzJMol0XgCws6QJ2hctWqR+/frp9NNPb7dcfX29vvzySw0YMCBOLeuaYGPLvFe9JcaWAQAQDf7D5nLeeyKsx1z+Rt+Inj87OzvidgEA0JGkGNPe0tKiRYsWaebMmcrM3HudYceOHbr66qv15ptv6vPPP9fy5ct15plnKjc3NyDwBQAAAIBwWC3NrRk5zbul3btab827W7d59rTuB+IoKXraX3zxRdXV1Wn27NkB2x0Oh9asWaPHHntM3377rQYMGKCJEyfqySefVE5OToJaGxnv2LJgy6549wMAgK7xHxb33VHnh15/vmmXcj78syTpgePq5WrnTKnJI/1yRd82zw8gufVY/cewy3qHtTLUFbGUFEH75MmTg44Vy8rK0vPPP5+AFkWP/9gy0uoAAIiNgJNmR7fQQXvm3u2uTMnp6MTzA0gbwbJ7GeqKaEuKoB0AAAAAYsV/ZQWvYCsshNoPxBJBe4oLe7Zcz96xOUkyAS4AAAAQFaFWVvAKtsKCf6DPUNe9vKtjhXpPkmW1DTshaE9xnZktd3eLlH6HFwAAACB8+wb6DHVtFWx1LH9VVVVxbE1qSIrZ4wEAAAAASEf0tKe4sGfL3d2onA/+JEnq3sVLOVZLs3xJL8ZI3mUxMjIly5LV4vGVbfJYkoyMae3h99ZvWd59AAAAAJKFd9hAqDkBSI+PHEF7igt7tlzH3vHuXZ0AN5JlMn65ok/XKgMAAABgG8HmB/CfE6CxsdG3nQ688BC0AwAAAAA6zTv5nL9Qa9f7/58OvPAQtCMqgi2TIQVfKiPYTJIdLamRjjNvAgCAxIskGPFyuVyB2Y5Aiuto8jmWxusagnZERUfLZEiBaTHtza4ZbEmNROGHGgCA+PP//W1vKS3Lsjos29Xf5c4EI9XV1bY5lwHsyr+jjg689hG0A+3ghxoAgPjr6PdX2vt721FZfpeB+Npx9AUyGZkhJqRu9s1/Faqjzk4deHZB0A4AAJDCgmWNSWSOdcaDx38jpyP0pFmMzwXUGrD7Jr/uHrgv/s1JCQTtQJjCvWoIAICdhNNrbbfMMf+5ckKlzfr/217ZaKbVOh1GTsd/29hmL+EIgNggaO8ErlinJ64aAgCiiXlTQgs1V06wtNlIygbT0Zh41pROP97PRKjvY7p8D2EfBO2dkIxXrBFjfj/o3oN6RydeEgd9AEhniZg3xZveLYkU7//q6O9QVVUVx9bADoJ9Jvy/j5zTI94I2oFo8KbLK/hJVqhlLjjoAwDiyT+9WyLFGwCSAUF7F3HFGgAAdBUTnCVOR2PiSY9PP97PRHvLDcaDf5p+S0uLGhoaQpbt2bOnMjIyfPM4JEMmpzFGjY2NkoJnqCbL64gHgvYu4oo19tXeiZfEyRcAoC0mOEucjsbEe4MKpA//z0R2dnbC2hHOkNxgkiWTs6mpqd0M1WR5HfFA0A5EWfsnXhInXwAAAADCRdAOAEAUeNMY20un9M5E3d5M1f5lAQDpy5um35n0+GTgdDp9Q1OC/R4my+uIB4J2AACioKM0Rm+aXzjpjqQEAgC8afre34O+ffsmuEXRte/QlEQORbA7gna00eSx5E3hDjUhDgAASA7+E6k1edov67+fCdgAwB4I2tEGk6QBQOT80xj3nX3au9+/nBR8pmr/skA0eFNOJemXK8LvqWtqaqLnCwBsgKAdQNLzHyPsFWzpkH3LpIpwX78/xkxHX7AZqL2zT3dULlRZAAAAgna0ytj7UfDv7QnVC+RFbxDsoKMxwsGWE0klnXn9jJkG0ofT6fT9/8Hj6wOWqt1Xk2dvb7z/4wAAiUPQjlZ+PW6henvoBQIAIPn4Z9U4HWo3aA/1OABA4hC0B9HRcjxMzJJ8OvqbkiqcOnYcfYFMRmbrLIotza0bMzIly5K1x60ea5YmtoEx9uDx38jpMCEnkWTOCgB2Fu5wn1DLJcZiaFC4E/kxiV9w3r/prl272l2yTGpdtiw7O5vzMmAfBO1BdJRqWlVVFcfWIBrCXYoJyc9kZEqObv+91z1wn2dP/BsUZ06H8fWitR28wkkkAHuL5nCnaA0N6sxEfkzit1c4y1zui/MyIBBBOwAAAAAgpXRmol7JnpP1ErQH0dFyPKQ8JZ+O/qZMqAcASAdNHkverJtQw2jsItzhTt5hQVLo19SVoUHhTuTHJH7Bec/BIk2PB7qqs5k7dsz0IGgPoqPleBobGxPQKnQFSywBAKCkmtci3OFO/sOCpOgPDerMRH5266VLJO85WFZWlvr2DW94AYBABO0AAAAAgJTV3kS9kv0n6yVoBwAASGGhhogF4x025n0cAKSC9ifqlew+WS9BOwAAQArzHyLmH8Cn8xKowSaokoJPUhVqsioAewXM+dXeaj1++5gnLHwE7QCAsKTSLKxAutp3jpd0XZYsnGXIIlleDkh3/ksj5rz3RNiPSddjUKQI2gEAYUmlWVgBAACSBUE7kpq35y9Yip/3//5X/qSOewbpFQQAIH34lpaTOlxeDkgmYaesS1Lz3v3u5tDFmjzBn99/mcPvjjrfb+WHfXj2+HriWRoxfATtSGod9fxVVVW1m94WbB+9gkDHkn0WVgDwClxaTmpveTkgmXQmZV2SLn8jvKX5/NPbAzq8HN1CB+1+6CQLH0E7ACBiyT4LK6KvyWNJCn4hp3UfkPz4nANIBIJ2JDXvLLj+S9j4L1fjn7bTXs8gvYIA0DUcQ5EO+JwjmYSdsi5JTbuU8+GfJUkPHFcvV4gosckj/XJF3zbPj9giaEdS23cWXKk1kPdua2xs9G1vv2eQXkEAAACkjohS1jP37nNlynfOHPbzI6YI2gEAQOdk7D2N8GY5hcp88tr3PmB7fM4BJBhBOwAA6By/Xhb/LKf2tgFJh885gAQjaEfS8S7z5i/UMm7+6fH+S1TsK9TyFQASy//7HmppR5ZpBBAvxhg1NjaGPO/geATET0fnCKl0Tk/QjqTT0TJvoZZ4806a0RH/5SsAJFZH33eJZRoBxE9TU1Ob8wz/+xyP7ME7y78kZvpPYeEs/ZwqCNoBAACiJJJssH3LAYgOZvnvGpY2tB+CdiS1HUdfIJOR2XoZtaW5dWNGpmRZslqa1WP1H31lHzy+PuRMmCxfAdiTd1lHSSEnfmLCJ9hJZ7PBkBycTqeqq6vbHa4DJLtkuejR0TkC6fGATZiMTL/lK7oH7tunrNPB8hXoGm8PmtvtVktLixoaGkKW7dmzpzIyMuRyuRjj2AXBlnWUmPgJqYd5V1oFvD7PntAFm/fucze3/5zRfP/8j0kMpbOZILP8S6Ev+Hol8kIL87Z0TUfnCP5zW3VGqL9PMPuOo/f+P1p/U4J2AAhTOOOrg2GMY+QiSTH24sQGdtNeNpgkWU071OPD1jGXzLvSyv+kOOe9J8J6zOVvhPfeeZ8/ld+/tNbBLP/tbU8UW83bwtKGbXT2vK8jnfmbErQDAGynMynGXByB3bSXDSZJxgoj/QsA4oGlDW2NoB0AwuQdO9WZ9HgAaCNjb9DOvCut/F/fd0ed73fRYx9Nu5Tz4Z8lSQ8cVy9XO2e06fT+eXWUdu2fmeQtS3p2/DFvS/LYcdT5bYbe+rMk9fhvdlBHmQqd+ZsStANAmLxjp7xXmvv2DT8lE5334PHfyOkIPYttskyYA7ThFwgx70qrgNfn6BY6aM/cu92VGd571+b5U1hH2Ur+mUmRlEV0MW9L8jDtHY8kGb85OGKRqUDQDgCwNafD+E7I216bTu1JuUKxWppbX3nQlTP2zrrFWsUAACQ/gnYAAJKM/3KW7SELAUhPHaVd+6fn+g/9Ij0bsCeCdqAdYS8949m73kyKr8YDAABsLpK062BlSc8G7IWgHWhHZ5ae2d0SLIUXiB3/i0us9RxfkSxNF2ySp0iWsfPvOfN/zL49Y6Emk0r3pXsAAIgZv/Mq7+95OL/x4SJoB4Ak539xibWe46szS9NFUtZ/AqhQPWde/j1jHf1t6UUDACCKWvZm3Qb7PQ/1Gx8ugnY/xhg1NjYGbAt2hSTYlRLs5d/zFOz9C7bMSKgrUYleZiTspWd2Nyrngz9Jap3gCQAAAED0hZvllkoxG0G7H7fbrbPPPjvk/kh6TNJZqJ4n7/vX0TIj/u9zopcZCXvpGcfe8e5pspoMbMT/4hJrPSfOjqMvkMnIDDqjuyRZe9zqsWapJJaxAwCgs6KZ5RYLsfiNJ2gHgCRnsdazLZiMTL8Le93b7vebzJJl7AAASE2x+I0naA+hvR4T/94StOU/WVKwCZGCLTMSauKkaE6M1LDbktMR/ItiTPIFMP6pQe29fwRnAAAECnt1mH32M4knoiGSSUy9kumczmppbg1Ng8VRfmO/oyFdYjaC9hDa6zExHR3cbSpeX6B9J0tqb0Ik/7KxnhTrqjdTK920o9QgKfHDCwB/kZykpNI4NAD205nVYbyPs/MknuEeZzuzmoWUXIGjnXUmvTuZzul6rP5j3OpKxZgtGIL2NBLPL1DaCXPJrX33c8Ue6cTuY9AAINlF6zgbqlwyBY5AKiFoR1pYtmxZu1eGu5yS37I3Eg93yS2pa1fs/YchhFp/mXWXAQBoK+zVYSTJs8fXG88knoi2dtO7W5qTptPN/7zUK9T5qf9jEB6C9hTHF6hVVlZWyl0ZDrVmM+svIxlEMtM6AERb2KvDtPc4mwt3rG97M11LrGgRa+2md8e/OZ0W6rzUKyHnpymUCUvQnuJs+QVKRRl7p+tub8ktiWW3ACmymdYBAJELd6xv+zNdS8kVOgJ+EpAJGysE7UA0dGLJrdaHJc8VewAAAADxR9DeGSmUagEAAAAAKSeFMmEJ2jsjhVItAAAAYq2mpkbl5eUqKSlRUVFRopsDIB2kUCZsRqIbAABILGOMGhsbA277rte77zYACJfb7VZZWZk2b96ssrIyjiUAECF62jsjhVItAID10wHEUkVFherr6yVJ9fX1qqys1OzZsxPcKgBIHgTtnZFCqRZIX8YYX2+HMUZNTU2S9q5Z73K5fJ/ZSMraGvNRAEBcbdiwQZWVlb7jqDFGlZWVmjx5svLz8xPcOgCx4D1v3DdrzytpzhtthKAdSFMd9a5WV1f7lgOMpKytMR9Fh8Jd1xcAOmKMUXl5ecjt8+fP58Q9ygIuMre3dGbz3n3u5vafk4vYiFSw80b/rL2kOW+0EYJ2AIBPuOv6Ij1YLc17V2gOdiGnpYOzfaS1uro61dbWttnu8XhUW1ururo6FRYWJqBlqcubCSdJOe89EdZjLn+Di9iA3RG0A2nK5XKpurpaUusVUe8V0KqqKrlcLrlcrk6VtTXmowAi0mP1HxPdBCSxgoICjR07VqtWrZLHs7e71uFwaPTo0SooKEhg64DECTsjwm9fMmU5eM8bgw2p9O5HZGwdtM+bN08333xzwLa8vDxt2rRJUuuH9+abb9bvf/97bdu2TePGjdNvfvMbHX744YloLpBULMsKmprkcrnabI+krK0xHwUAxI1lWSopKdHMmTODbufYGn3+F5m/O+p8v8ypfTTtUs6Hf5YkPXBcvVztRARcxI6+zmREJFOWg/95Y7K02e5sHbRL0uGHH64XX3zRd9/h2HuWPX/+fJWVlWnx4sUaPny4brvtNp188sn6+OOPlZOTk4jmAgCQ1Pwza/wFy7LZ93HAvvLz81VcXKwlS5bIGCPLslRcXKxBgwYlumkpKeBCiKNb6KA9c+92VyYXsQG7s33QnpmZqf79+7fZbozRfffdp+uvv17Tpk2TJD366KPKy8tTZWWlfvazn8W7qUBK8Z8x3l+omUC9mBEUSG6hMmv8JV2WDRJqxowZqq6u1tatW5Wbm6vi4uJENykAczekHmOMGhsbJQU/b0n0uUrYGRGePb6eeLIc0pvtg/a1a9dq4MCBcjqdGjdunO644w4deOCBWr9+vTZt2qTJkyf7yjqdTo0fP141NTXtBu1NTU0BaSkNDQ0xfQ1AMupoxngp+PrdzAgKAPDncrlUWlqq8vJylZSU2C4rg7kbUk9TU1PQcxTvtkSfq4SdERHqMUg7tg7ax40bp8cee0zDhw/X5s2bddttt6moqEgffvihb1x7Xl5ewGPy8vL0xRdftPu8d955Z5ux8gAAAIiNoqIiFRUVJboZAFJA+BP57c2SSaJ5/IKyddDu38s3YsQIHXvssTrooIP06KOP6vvf/76ktledvOOl2nPdddeptLTUd7+hoUGDBw+OYsuB1PLg8d/I6Wg92hkj7W5p3d49o3VutyaPpV+u6JPAFgIAED7mbkhtTqfT9/cNNoN5Ov4dU2kYSGcm8tvdIiXzX93WQfu+9ttvP40YMUJr167VOeecI0natGmTBgwY4CuzZcuWNr3v+3I6nYwLASLgdJiASWraHvSS/PJlmgv7inXz3n3uDn7bm/au7pTQZWr852YIdeLmvdAbSVkkN9/Ja9AT170f3iaPJckEvVi5d3+gsL9PUkr1AiUb5m5Ibfv+fZnBnGEgnRHR8TzMc6TOnh8lVdDe1NSkf/3rX/rBD36goUOHqn///nrhhRc0cuRISdLu3bv16quv6n//938T3FIASB6duWJ9+Rt9I3r+RJ0wdTQ3g/+4xkjKIrmFe/LamQyiznyfpOTvBQLiof0LbsnTU4yuCXsiv92NyvngT5JaL7hGqrPH83DPkZqamgJWRmuPrYP2q6++WmeeeaYKCgq0ZcsW3XbbbWpoaNDMmTNlWZauvPJK3XHHHRo2bJiGDRumO+64Q9nZ2bablRSwk2CzwgebWdU766oUeFUwGLv0qgKRCjXDcDD+++l1B2B3scwoSRR6iyOTqsNAwp7Iz7G39zvZf7JtHbRv2LBBF1xwgbZu3aoDDjhA3//+9/XWW2+psLBQknTttdeqsbFRv/jFL7Rt2zaNGzdO//jHP1ijHWhHR72JwWZb/eWK5OhVTSXhXlzZ9/+dEfYV66Zdyvnwz5KkB46rl6udX5Amz97PTSKHI/mfsAQ7SWlpadFpp50W1nP5fzeee+45ZWdn2yp1Du0LdvIa7DMRbGhERye43uf3Psar3e+T1OVeIKA9scwoQXJgGEjX+B/Pd4w4VybD0ToiNOAiWOt/rT1u9fjo6Yifv7k5vAwRWwftTzzRfhqCZVmaN2+e5s2bF58GAUCcdObiSmeFfcU6c+92V6YC5jkI+/njLNQJi/ckZdu2bZ16Xu/FqXikznERLDo6Onn1P3Ft7z3v6AQ3oqWcUqgXCIiVcC+47fsYoKv8j+c91iyN6fN3xNZBO4C2IumB9QqVyrvj6AtkMjKDp87tblSP//YAPXh8fbsBml16VZFC/HqY2xueQa80AOwVr4ySeIrkghtgB97vTzgXl/bs6SBL778I2oEk05ke2FATaJmMTL9eoO6B+/x6gJyO5OhVTVXtXlxpaU7NMX5+4y3DHZ7RmV7pzl5k8j4uolToMIcXcBEMQGfFK6MESAedyfLwPm7f8+GufqcI2v2EPTYxCZdo8e+dDdUr6/2AdVSWCZiA+Gr34kr8m5NSOnss8z4uolToTgwv4FgLAKnNjjPie2OBjmKGVGenLA+Cdj+dGZuYLEu0hOqd9e+V9fbGdlSWZY/s48Hjv5HTEXzG1yaPFbXJZbwzy0oKWRcQVRl7I9r2hmd0tVc61ER1wfhfUWe8JAAgGuyYLRcsFggWMyB+CNqBJOZ0GF8w0zaEiF4fLDPLIu78ruCHOzyjM1f9w5lZ14t0UQAAkAgE7X7CHpuYhEu0+PcmBZt8xFsmnLL0MCFW/IdmtPc5TYeUrM5qb73ddMqI6MyEjU6nk2MfEobvbueRDYZkY/cZ8b3t6yhmQPwQtPsJe2xiEi7Rsm9vUnuTj0RSFiksY+/hwf/HI5Y/Kh1NsieRktURsiJaRWPCRo59iCe+u52XqPeupqZG5eXlKikpUVFRUULagNiLxd/ZTmOlg/FvH7+F9pAk/cQA4s7vipT3xyMrKysgKPff7r3RCw4ASHVut1tlZWXavHmzysrKgmbvIPnxd04dTR5LTR7J3Sw17G69uZtb58Zpvdn7/JWedgC2EWpSMCYAC0+8MiKSTVoumYfkECSjie9umBKQDeavoqJC9fX1kqT6+npVVlZq9uzZUXlu2Ad/59SR7NlMBO1IOuEvzbd3n0mWtfnioDPvX0tLixobGyXFdhnAUOliiU4TSxah3qdYvH/tjb/du98eWDIvPlgiqBOCZDT5C/e729F8ICn53nfw3rW3vas2bNigyspK3++pMUaVlZWaPHmy8vPzo15fZ0SyfC/f3eCS4e+M9EHQjqTTmaX5mpqaGJPzX515/xoaGnTBBRe02c4ygOmrM1esw75gJEmevWvTcs0tObBEUOJ0NIcD7330GGNUXl4ecvv8+fNtEeBGsnwv3922kuXvjA50IptJsmdGE0E7ACAuOnPBSJK+22PJsphVG0Di1dXVqba2ts12j8ej2tpa1dXVqbCwMAEtQzTxd04RUcpmsgOCdiSdsJfm8+zxBQb+j+ksq6W5NZU26LhYj6+c3dOGO/P+9ezZk6WwkLAr1le9mRzj0No7RrTuD+84kehjRGexRFDidDQfSHvvfTgzY4dKvw+me/fu2r17t3bt2qWmpibf3//dd99VRUWFZsyYoZEjR0pq/W3Jzs5OqvTrgoICjRgxQmvWrGmz78gjj1RBQUECWtVWJMv38t1tq6CgQGPHjtWqVavk8ew9djscDo0ePdo2f2ekD4J2JJ2wl+YL9ZhOCneyKrtPdNGZ9y8jI4OlsNDlK9ZhXzCSpN2NyvngT51vawJEMqGd3Y8TncESQYnT2flAvDNjb926VWVlZRo1alTQAC2c5TjDdf/997fZlirp13aaP6ezS/3y3W1lWZZKSko0c+bMoNuT5SITUgdBexd5e0skpVSPCQBEW0QXjLrtPfllVm0gNpgZO3J1dXVBe9klac2aNaRNp5D8/HwVFxdryZIlMsbIsiwVFxdr0KBBiW4a0hBBexelYm8J9vJPL/MKFjgESylL1okuEsE/9dIr1Cy2XsmUTolO6GKvfrxWmQj3GOF9/kiPExwjECuRzIzt/93YccSP2l11wTJGPT78syTp4Ycf1vbt2/U///M/amlp8ZXJyMjQHXfcoWHDhvnS45OFN236nXfeafOaxowZQ9p0ipkxY4aqq6u1detW5ebmqri4OKrP39Es/xLnO2hF0I60FM4YPil0yqGXf+DQXkpZMk10kQgdpV76z2LrlSrplIiNeK0yEckxQuI4gejq7AVPSRHNjO3/feoRwbCVvLw8LVy4sE3AYVmW/vznPyflDNyh0qYzMjJIm05BLpdLpaWlvnPGaF9g6miWf4nzHbQiaO+MIJMxSaF7V7yS6UpyKgt3DB8AAHbW2QueW7ZsicvM2Bs2bOhSPe1PANsc8nGxRtp0eikqKmq3gweIB4L2zuggbbO97Ug8xvDZ24PHfyOnI/Ss2gxJQTgStcoEkAwinRk7okkk/b5TBx10UJdm4I5kcsd4i3XaNNJDR7P8e8sABO1IapFehY9kDB8Sw+kwcjpa/9/2Z8o+M/PC3hK1ygSQKDuOvkAmIzPk76F/ABzpzNid+T5Je1PGU3EG7linTaOtjsZ/J+PY70hm+e+qVHz/4sn7/nU090Cs5ikgaEdSi+QqvHesXqjtyTi2rqvsmnoIAOiY/wRxvv9bVpugOli5eKV4R1pPJJM7+j8mEUibjq+Oxn8z9rt9vH9dE+z9Czb3QKzmKSBoR9qoq6uLyxi+ZGLn1EMAQPu6OuFivFK8I6kn0skdASAdELQj6XT2KnykY/gAAEhlnUnxbi9Dy7s/GvUA++po/Defq/bx/nWN9/3raO6BWM1TQNCOpNOVq/CpOrYuEsmUeggACC0aEy5GmuLd2QwtUsnRVfEc/52KeP+6xv/9a++9i9X7nBGVZwGShHdsnTdAT8dlWrwHE/+bf1Duvejhf0uXCxoAkEyCThAX6hbsMQCApEBPO9IOy7RAal0+zjsbfajl5QAgWcRyYtHOZGh5HwcA6DqCdqQdO46ta2lp0fbt2yUFjn8JVdbL3c55WNPeYfsBMwcngnf5i1Bje/xfk3+79xXN15SK670bY9TY2CiJ5Vz8sUoC0kEsJxZN9cnhWAoLgN0RtCMt2W1s3fbt2wOWggjX5W/0Dauc/2zBiRBq+QuvP/5x78nmL1ckx2uyo6ampqCfo3RfzoVVEgC0h6WwANgdQTuA9JGx95Dnn87JRHwAkpE3bd3/GBaM/3GN4xkAJB+CdsAG/Gfz3XHEj9Re4rfl2aMe//qLJOmB4+rlCvEtbvLs7bXed7bgePBPNzTGqKqqSm63WxdccIGk1t5178mjf3p8uDr1mvzSG0OlcyZzmqfT6WQ5l/9KxCoJ3s98sPRa7/NbltVhKq5/WaA93rT1cJZyitbnKdzPebTq2ff5O/Od6qhNLIUFwO4I2gEb8D+ZMN2zQi/bI8ns3uX7vytTcjoie/546Sgl3hu8R8obbHES1RbLueyViDG4wT7z/r2f3hTbjlJx/csC4Yjndz/cz3ks6glVV1fT2zl2ArA7gvYQmLgIsKdk7gmPRMBEe549oQv67Uv0hIMAAACIPoL2EJi4CKku1rPl+geQO0acK5PhaF1hLeAiWOt/rRaPeqxZKklatmyZsrKyQs40ny497P4rCOS890TYj6GHKHG8KbYdfXY7SsX1L4vk1l4HgHd/sgn3cx6teqSOvyektwNIdQTtQJqK9Wy5/kGnNyAPh3+aIgEokkm4n11ScdNHKnYAxOsYHcn3hO8UgFRH0O4nERMXAV3R5LEkGRkj7f7vXG7dM1o7cVr3IVn5T7T33VHnh57nwLPH1xOfiAkHAQAAEFsE7X4SMXER0BW/XNGn04+NdTph2EGnROAZRMDQBEe39t+/YI8BkBCd6QDwPg4AgGAI2oE0Fet0ws4EnW0eB3RRrOduAPZFBwAAINoI2oFkk7H3a+vtsWEYBxBcrOduAAAAiDWCdiDZ+PUKBuuxoRcHAAAASB0E7QCAlMVSUAAQfd6hR263Wy0tLWpoaAhZtmfPnsrIyJDL5WJIEtBJBO0AgJTFUlAAEH2hhh51hCFJQOdkJLoBAAAAAAAgOHraAQC21t7s7xIzwANAvHmHHnUmPR5A5AjagSRGMINos1qaZSTJGKmluXVjRqZkWbK89+PMO9N7qG2kWwJAfHmHHnmPvX379k1wi4DURtAOJDGCGURbj9V/THQTAAAA4IegHQAQd+316Hv3e1VVVcnlcgWd/V0S6ZYAACClEbR3kXfJCyl4ijLpyYglghlEg/+yaF5ut9uXteH9nO37mK6IpEff5XL5MkaY/R0AAKQbgvYuCrXkhfdkl/RkxBLBDKJh32XR9uX/OQMAtKqpqVF5eblKSkpUVFSU6OYASGEE7QBiLpJUaKSuzvToex8HAHbidrtVVlamrVu3qqysTKNGjeJYBSBmCNq7yP8kNFiKMgdwgMnN0IoefQCpoqKiQvX19ZKk+vp6VVZWavbs2QluFYBURdDeRfuehJKiDAAAkLo2bNigyspKGWMktXbaVFZWavLkycrPz09w64D0YselamOBoB2wmVRJJY9nKrT3xEmS5NkTuqDfvoDHAAAQBmOMysvLQ26fP38+ExADcZQu2ZwE7YDNpMrBJ56p0N5hKZKU894TYT+GzBgAQCTq6upUW1vbZrvH41Ftba3q6upUWFiYgJYBSGUE7UAYIlnaz1s2WLl9ywIAgORRUFCgsWPHatWqVfJ4PL7tDodDo0ePVkFBQQJbB6SHRCxVm2gE7UAYIlnaL1hZb7l9y3oxq3bXOJ1O3/+/O+p8ydEteEHPHl9PvP9jAAAIh2VZKikp0cyZM4Nu56I8EHvpOLFtRqIbAGDvwcf/5h+Qew8++944OWgV8D44urV/C/YYAADClJ+fr+LiYt/viGVZKi4u1qBBgxLcMgCpip52IAyRLO3nLRusnHc/AABIXjNmzFB1dbW2bt2q3NxcFRcXJ7pJAFIYPe1AGPx7wrOzs7X//vtr//33V3Z2dpseb2/ZYOXoHQcAwN4WLFigSZMmacGCBSHLuFwulZaWKi8vT3PnzuWCPICYoqcdAAAAkPTtt9+qoqJCLS0tqqio0A9/+EP17t07aNmioiIVFRXFt4EA0hI97QAAAICkG264QS0tLZKklpYW3XjjjQluEQDQ0x5UJMt7IbFYXg2dZYxRY2OjJL7nQCj8HsZHR++zxHsdDytXrtSaNWsCtr3//vtauXKlxowZk6BWAQBBe1CRLO+FxOrM8mpIbVZLs4wkGSO1NLduzMiULEuW976kpqamgM+KF99zYC9+D+Ojo/dZ4r2OtZaWFt1yyy1B991yyy16+umnlZFBgiqAxCBoB5BSeqz+Y6KbAABIMm+//bYaGhqC7mtoaNDbb7+tY489Ns6tAoBWBO1BRLK8FxKL5dWSgx2HMTidTr7nQAf4PYyPjt5nbxnEzrhx49SzZ8+ggXuvXr00bty4BLQKAFoRtAfhXbLLKzs7O4GtQXv8/1b8newr1sMYvCe8brc7aMq7v6qqKrlcrjYXCvj8AG3xexgfvM+Jl5GRoRtvvFFXX311m3033XQTqfEAEoojEICk5z3hDacnyuVyKSsriwmdAAABxowZoxEjRgRsO/LIIzVq1KgEtQgAWtHTDiDm4jWMgRRTAEBX3HrrrZo2bZpaWlqUkZERcnI6AIgngnYAMRevYQykmAIAuqJ3796aMWOGKioqNGPGDPXu3TvRTQIAgnYAAADAa86cOZozZ06imwEAPoxpBwAAAADApgjaAQAAAACwKYJ2AAAAAABsiqAdAAAAAACbImgHAAAAAMCmbB2033nnnRo7dqxycnLUr18/nXPOOfr4448DysyaNUuWZQXcvv/97yeoxQimpqZG06dPV01NTVTKAQAAAEC6sHXQ/uqrr+qyyy7TW2+9pRdeeEHNzc2aPHmydu7cGVDu1FNP1caNG3235557LkEtxr7cbrfKysq0efNmlZWVye12d6kcAAAAAKQTW6/T/ve//z3g/qJFi9SvXz+98847OuGEE3zbnU6n+vfvH+/mIQwVFRWqr6+XJNXX16uyslKzZ8/udDkAAAAASCe27mnf1/bt2yVJffr0Cdi+fPly9evXT8OHD9cll1yiLVu2JKJ52MeGDRtUWVkpY4wkyRijyspKbdiwoVPl0oUxRo2NjQHZBm63W42NjWpsbPS9T0gfVkuz5NkjNe+Wdu9qvTXvljx7WvcBAAAgpGQ/v7Z1T7s/Y4xKS0t1/PHH64gjjvBtnzJlis4991wVFhZq/fr1uuGGGzRp0iS98847cjqdQZ+rqalJTU1NvvsNDQ0xb3+6McaovLw85Pb58+fLsqywy6UTt9utKVOmBGybOnWq7//V1dXKysqKd7OQQD1W/zHRTQAAAEhayX5+nTRB+y9/+Uu9//77WrFiRcD26dOn+/5/xBFHaMyYMSosLNSzzz6radOmBX2uO++8UzfffHNM25vu6urqVFtb22a7x+NRbW2t6urqVFhYGHY5AAAAAEhHSRG0X3755frrX/+q1157Tfn5+e2WHTBggAoLC7V27dqQZa677jqVlpb67jc0NGjw4MFRay+kgoICjR07VqtWrZLH4/FtdzgcGj16tAoKCiIql05cLpeqq6tljPFlhDidTl/GgcvlSmTzECfez4E/t9vtuypcVVXV5rPAZwMAAKCtZD+/tnXQbozR5ZdfrqqqKi1fvlxDhw7t8DH19fX68ssvNWDAgJBlnE5nyNR5RIdlWSopKdHMmTODbvd+QcItl04sy/Kl52RnZye4NUgU/89BMC6Xy9ZpXAAAAHaR7OfXtp6I7rLLLtOSJUtUWVmpnJwcbdq0SZs2bVJjY6MkaceOHbr66qv15ptv6vPPP9fy5ct15plnKjc3N2CMAhIjPz9fxcXFAQF6cXGxBg0a1KlyAAAAAJBubB20P/zww9q+fbsmTJigAQMG+G5PPvmkpNYU6jVr1ujss8/W8OHDNXPmTA0fPlxvvvmmcnJyEtx6SNKMGTPUt29fSVJubq6Ki4u7VA4AAAAA0ont0+Pbk5WVpeeffz5OrUFnuFwulZaWqry8XCUlJSHHi4RbDgAAAADSia2DdqSGoqIiFRUVRa0cAAAAAKQLW6fHAwAAAACQzgjaAQAAAACwKYJ2AAAAAABsiqAdAAAAAACbImgHAAAAAMCmCNoBAAAAALApgnYAAAAAAGyKoB0AAAAAAJvKTHQDAADtM8bI7XbL7Xb7tvn/3+VyybKstG9TuvC+95I6fP87KpuOf6dI3pOufs75ngAAooGgHQBszu12a8qUKQHbpk6d6vt/dXW1srKy0r5N6SLYey8Ff/87KpuOf6dI3pOufs75ngAAooH0eAAAAAAAbIqedgCwOZfLperqahlj1NTUJElyOp2+tFqXy0Wb0oj3vZfU4fvfUdl0/DtF8p509XPO9wQAEA0E7QBgc5Zl+VJos7OzE9yaVnZsU7rwf++l9t//SMqmi86+f5157/ieAACigfR4AAAAAABsiqAdAAAAAACbImgHACSNmpoaTZ8+XTU1NYluCgAAQFwQtAMAkoLb7VZZWZk2b96ssrKygPWuAQAAUhVBOwAgKVRUVKi+vl6SVF9fr8rKygS3CAAAIPYI2gEAcWeMUWNjY0BvudvtVmNjoxobG2WMCSi/YcMGVVZW+rYbY1RZWakNGzbEtd0AAMAevOcSoc4n9j2XSGYs+QYAiDu3260pU6YEbJs6darv/9XV1b6lsowxKi8vb/Mc3u3z58/3rXsNAADSQ7BzCWnv+YT/uUSyo6cdAGBrdXV1qq2tlcfjCdju8XhUW1ururq6BLUMAAAg9uhpBwDEncvlUnV1tYwxampqkiQ5nU5fj7nL5fKVLSgo0NixY7Vq1aqAwN3hcGj06NEqKCiIb+MBAEDCec8lJAU9n/A/l0h2BO0AgLizLMuXspadnd1h2ZKSEs2cOTPodlLjAQBIP/7nElLH5xPJjPR4AIDt5efnq7i42BegW5al4uJiDRo0KMEtAwAAiC2CdgBAUpgxY4b69u0rScrNzVVxcXGCWwQAABB7BO0AAEn2XzrF5XKptLRUeXl5mjt3bkqNVQMAAAjFMok+C7OBhoYG9erVS9u3b1fPnj0T3RwASIjGxsagS6d4pdLSKQAAAIkWbhxKTzsAAAAAADbF7PEAAEnptXQKAABAsiBoBwBISq+lUwAAAJIF6fEAAAAAANgUQTsAAAAAADZF0A4AAAAAgE0RtAMAAAAAYFME7QAAAAAA2BRBOwAAAAAANkXQDgAAAACATRG0AwAAAABgUwTtAAAAAADYFEE7AAAAAAA2RdAOAAAAAIBNEbQDAAAAAGBTBO0AAAAAANgUQTsAAAAAADZF0A4AAAAAgE0RtAMAAAAAYFME7QAAAAAA2BRBOwAAAAAANkXQDgAAAACATRG0AwAAAABgUwTtAAAAAADYFEE7AAAAAAA2RdAOAAAAAIBNEbQDAAAAAGBTmYlugB0YYyRJDQ0NCW4JAAAAACAdeONPbzwaCkG7pO+++06SNHjw4AS3BAAAAACQTr777jv16tUr5H7LdBTWp4GWlhZ99dVXysnJkWVZYT2moaFBgwcP1pdffqmePXvGtH3xqovXRF2JqieedfGaqCtR9cSzLl4TdSWqnnjWxWuirkTVE8+6eE2pXZcxRt99950GDhyojIzQI9fpaZeUkZGh/Pz8Tj22Z8+eMf8AxLsuXhN1JaqeeNbFa6KuRNUTz7p4TdSVqHriWReviboSVU886+I1pW5d7fWwezERHQAAAAAANkXQDgAAAACATRG0d5LT6dRNN90kp9OZMnXxmqgrUfXEsy5eE3Ulqp541sVroq5E1RPPunhN1JWoeuJZF6+JuiQmogMAAAAAwLboaQcAAAAAwKYI2gEAAAAAsCmCdgAAAAAAbIqgHQAAdIrb7Y7Zc69fvz5mz51Iu3fvDrlv69atcWwJACBZELQDAICwtbS06NZbb9WgQYPUo0cPffbZZ5KkG264QQsWLIhaPQcffLAmTpyoJUuWxPTigL+vv/5aK1as0BtvvKGvv/46JnWcd955amlpabN98+bNmjBhQlTr2rlzp5577jn99re/1f333x9wA+Jh3bp1ev7559XY2ChJSub5r1977TU1Nze32d7c3KzXXnstAS1COmH2+Ah99NFHqqura3Ol/KyzzopJfY2NjdqzZ0/Atp49eyZtXR6PR08//bT+9a9/ybIsHXrooTr77LPlcDiiVsekSZO0bNky9e7dO2B7Q0ODzjnnHL388stRqwvJye12y+VyJboZQFK65ZZb9Oijj+qWW27RJZdcog8++EAHHnignnrqKd1777168803o1LPBx98oIULF6qiokJNTU2aPn265syZo2OOOSYqz+9v586duvzyy/X444/L4/FIkhwOhy666CI98MADys7Ojlpd48aN02GHHaZFixb5tm3atEkTJ07U4Ycfrj/96U9Rqefdd9/Vaaedpl27dmnnzp3q06ePtm7dquzsbPXr1893sSUWNmzYIMuyNGjQoJjVkcymTZsWdtlly5ZFrV6Px6N7771XTz31VNBz2W+++SZqddXX12v69Ol6+eWXZVmW1q5dqwMPPFBz5sxR7969dc8990StrnhxOBzauHGj+vXrF7C9vr5e/fr18x07oiVeMccnn3yi5cuXa8uWLW0uKN54441Rq6e2tlZLly4N+pqi+TlPVZmJbkCy+OyzzzR16lStWbNGlmX5rhRaliVJUf2i7tq1S9dee62eeuop1dfXt9mfrHWtW7dOp59+ujZs2KBDDjlExhh98sknGjx4sJ599lkddNBBUaln+fLlQdMP3W63Xn/99ajU4e/VV1/V//3f/wVciLjmmmv0gx/8IOp1+fN4PFqzZo0KCwu1//77x6SOXbt2BT24HnnkkUlXT0tLi26//Xb99re/1ebNm/XJJ5/owAMP1A033KAhQ4Zozpw5nX7uSHqtrrjiik7Xs69Vq1apW7duGjFihCTpL3/5ixYtWqTDDjtM8+bNU/fu3aNWV0tLi9atWxf0R/2EE06IWj1e8b5AGms7d+7Uq6++GvQ1RfMzEQ+PPfaYfv/73+vEE0/Uz3/+c9/2I488Uv/+97+jVs8RRxyhsrIyzZ8/X3/729+0ePFiHX/88Ro2bJjmzJmjH//4xzrggAOiUldpaaleffVV/fWvf9Vxxx0nSVqxYoWuuOIKXXXVVXr44YejUo8kPffcczrhhBM0d+5c3XvvvfrPf/6jSZMm6aijjtITTzwRtXrmzp2rM888Uw8//LB69+6tt956S926ddOFF16okpKSqNXj1dLSottuu0333HOPduzYIUnKycnRVVddpeuvv14ZGdFJ7ty5c6fuuusuvfTSS0GPR7G6GBHNjo1evXr5/m+MUVVVlXr16qUxY8ZIkt555x19++23EQX34bj55pv1yCOPqLS0VDfccIOuv/56ff7553r66aejGpxJrZ+/zMxM1dXV6dBDD/Vtnz59uubOnZuUQbsxxnfe76++vl777bdf1OqJZ8zxhz/8QZdeeqlyc3PVv3//gNdnWVbUPhdPPPGELrroIk2ePFkvvPCCJk+erLVr12rTpk2aOnVqVOrwt3nzZl199dW+48S+fdTReg/333//oJ8Jy7Lkcrl08MEHa9asWfrJT37S9coMwnLGGWeYs88+22zZssX06NHDfPTRR+b11183xxxzjHnttdeiWtcvfvELc+ihh5qlS5earKwss3DhQnPrrbea/Px8s2TJkqSta8qUKebUU0819fX1vm1bt241p556qjnttNO6/Pzvvfeeee+994xlWeaVV17x3X/vvffMqlWrzB133GEKCwu7XI+/xx9/3GRmZprzzjvPlJeXm/vuu8+cd955plu3bqaioiKqdZWUlJhHHnnEGGNMc3OzOe6444xlWWa//fYzr7zySlTr2rJlizn99NNNRkZG0Fuy1WOMMTfffLM58MADzZIlS0xWVpb59NNPjTHGPPnkk+b73/9+l557yJAhAbf99tvPWJZl9t9/f7P//vv7/k5Dhw6NxkvxGTNmjPnTn/5kjDHm008/NS6Xy1xwwQXm4IMPNiUlJVGr58033zRDhw41GRkZxrKsgFu0/06ffvqpOfLII33P7V9PNOqaOnWq2b59u+//7d2iZdWqVaZ///6mZ8+exuFwmAMOOCBmn4nm5mZz9913m7Fjx5q8vDzfZ9B7iwaXy2U+//xzY4wxPXr08H2XPvzwQ7PffvtFpY5g3G63KSsrM06n01iWZbp3725+/OMfm6+++qrLz923b9+gx9GXX37Z5Obmdvn59/Xll1+awsJCc+WVV5phw4aZ6dOnm+bm5qjW0atXL/Pvf//b9/+PPvrIGGPMW2+9ZQ455JCo1mWMMb/+9a/NAQccYB566CHz3nvvmdWrV5vf/OY35oADDjD/8z//E7V6zj//fDNgwABz7bXXmnvvvdfcd999Abdo2rlzp7nsssvMAQccELPfqGuvvdZcfPHFAX//5uZm89Of/tRcffXVUanD68ADDzTPPPOMMab1u7tu3TpjjDHl5eXmggsuiGpdeXl5ZvXq1b66vMeJzz77LOrHiU2bNpkLL7zQDBgwwDgcjqj/nby/CRkZGea0004L+J0466yzzJAhQ8wpp5wShVfSKp4xR0FBgbnrrrui+pzBjBgxwjz44IPGmL2fh5aWFnPJJZeYG2+8Mer1nXrqqeawww4zDz30kKmqqjJPP/10wC1aysrKTN++fc2FF15o7r//flNeXm4uvPBCk5uba26//XZz8cUXG6fTaX7/+993uS6C9jD17dvXvPfee8YYY3r27On7IXzppZfM0UcfHdW6Bg8e7Dt5yMnJMWvXrjXGGPPYY4+ZKVOmJG1d2dnZ5v3332+zffXq1VE5gPuf2O8bWFiWZbKzs82CBQu6XI+/733ve6asrKzN9nvuucd873vfi2pdgwYNMrW1tcYYY6qqqszAgQPNxx9/bK6//npTVFQU1bqKi4tNUVGR+ec//2n2228/849//MM8/vjj5pBDDvH94CdTPcYYc9BBB5kXX3zRGBN4AvGvf/3L9O7dO2r1VFRUmOOOO853jDDGmH//+9/mBz/4QdQvhPXs2dN30nXXXXeZyZMnG2OMWbFihcnPz49aPUcddZQ599xzzUcffWS2bdtmvv3224BbNMX6ZGXWrFmmoaHB9//2btEyfvx4c8kll5jm5mbfZ6+urs6ccMIJ5s9//nPU6jHGmBtuuMEMGDDA3H333cblcplbb73VzJkzx/Tt29eUl5dHpY7Ro0ebxx9/3BgT+F2aN2+eOf7446NSh7/a2lpz6aWXmv3339/k5+eb66+/3nz22WdmxYoVZtKkSWbs2LFdriMrK8sX1Pr74IMPTHZ2dpefP5hPPvnE9OvXz8yYMcO0tLRE/flzc3PNxx9/bIwxZvjw4ebvf/+7Mab1mJeVlRX1+gYMGGD+8pe/tNn+9NNPm4EDB0atnl69epkVK1ZE7fnaE4+Ojdzc3IDfC69///vfpk+fPlGpwys7O9t88cUXxhhj+vfvb9555x1jTOvF0p49e0a1rh49ephPPvnE93/vceKf//xn1F9XrAM072+CZVlm+vTpAb8TP/3pT80dd9xhvv766yi8klbxjDlycnJ8f5tYys7ONuvXrzfGtL4+bzzw0Ucfmf79+0e9vh49eph333036s+7r2nTppmHH364zfbf/va3Ztq0acYYY+6//35zxBFHdLkugvYw9e7d2/ehPvDAA83LL79sjDFm3bp1Uf/x22+//Xy9GIMGDTJvv/22MSY2VyfjWdf+++9v3njjjTbbV6xYEZUeoM8//9ysX7/eWJZlamtrzeeff+67ffXVV1HvxTDGmO7du/sudPhbu3atcTqdUa3L6XSaL7/80hhjzCWXXOLrSf3ss89MTk5OVOvq37+/77OQk5PjO/H7y1/+Yo477rikq8eY+PUOHnjggWbVqlVttq9cudIMGTIkavUY0/qeeU+KTjrpJF8v0xdffGFcLlfU6snOzg76OY+FeJ2stLS0mM8//9zs3Lkzas8ZSjx7POPRk/bXv/7V9OrVy9x1110mOzvb3H333ebiiy823bt3N//4xz+iUocxrRc/jzjiCNOtWzdz9tlnm7/97W/G4/EElFm7dq1xOBxdrmvSpEnm3HPPNY2Njb5tu3btMueee6458cQTu/z8vXv3bpP1sP/++xun02l69uwZ9WwIY4w5+eSTfRlfP/vZz8wxxxxjlixZYk455RRzzDHHRK0eL6fT6TuG+/v3v/8d1ePRkCFDgl5giYV4dGz07t3bVFVVtdleVVUV1QvKxrRevHnrrbeMMcYcf/zx5s477zTGGPPEE0+YAw44IKp1nXbaaeb//b//Z4xpPRZ99tlnxuPxmHPPPdf88Ic/jGpd8QrQ5s2bZ3bs2BHzeuIZc8yePTto0Blt+fn5vkD9yCOPNJWVlcYYY2pqaqJ+wcgYYw499NCg52LRtt9++4WMA7znluvWrYvKxV/GtIfpiCOO0Pvvv68DDzxQ48aN0/z589W9e3f9/ve/14EHHhjVug488EB9/vnnKiws1GGHHaannnpKxxxzjP72t7+1mVwtmeo644wz9NOf/lQLFizwTST09ttv6+c//3lUxqkWFhZKUtBZeWNl8ODBeumll3TwwQcHbH/ppZc0ePDgqNaVl5enjz76SAMGDNDf//53PfTQQ5Jax4NHcyI/qXXMoHeilT59+ujrr7/W8OHDNWLECK1atSrp6pGkww8/XK+//rrvc+K1dOlSjRw5Mmr1bNy4sc24R6l1/NTmzZujVo8kjRkzRrfddptOOukkvfrqq75xt+vXr1deXl7U6hk3bpzWrVvX5nMeCx6PRz169JAk5ebm6quvvtIhhxyiwsJCffzxx1GrxxijYcOG6cMPP9SwYcOi9rzBdOvWzTfmLS8vzzfGs1evXqqrq4tqXZs2bfLNcdCjRw9t375dUuvx94YbbohKHWeeeaaefPJJ3XHHHb4xj6NGjdLf/vY3nXzyyVGpQ5IefvhhzZ49Wz/5yU/Uv3//oGUKCv5/e/cZFdX1tQH8maFIl2LBQkcRFBFU7BRFIDYssaKoYA+CBo2a2FBji4LGEiuCDexdwYIgCIp0EJCqEHvDgigwnPcD78wfBNQ4Zy6i57fWrBXvkLvPMMOde9re2lQy1m/cuBGOjo5o2bIlzMzMwOPxkJiYCDk5OYSEhIh9/g0bNoh9jv9q5cqVePPmDQBg+fLlGD9+PKZPnw5DQ8MqSfBoMTMzw+bNm6vl+Ni8eTPMzMyoxVm+fDkWL16MgIAAqgkCa/LixQvo6ekBqNi/LkzU1rNnT0yfPp1KjIkTJ8LV1RXZ2dno2rUrAODGjRtYvXo1nX2wlQwZMgRXrlxBly5d4OnpidGjR2P37t3Iz8/H7Nmzqcb666+/YGNjg9jYWJSUlOC3337D7du38eLFC1y/fp1qLC0tLU6y0i9ZskTiMQBu+xyGhoZYtGgRbty4AVNTU8jIyFR5nlbOlV69euHSpUswNTXFiBEj4OnpidDQUFy6dAl9+vShEqOyDRs2YP78+di+fTt0dXWpn19IXV0dZ86cqfb3c+bMGairqwOouNdVVlYWP5jY3f4fRHBwsGgZY05ODjE2NiY8Ho80atRItOSWFh8fH9EyxtDQUCIvL09kZWUJn8+nvl+Ly1gvX74kgwYNEu1FFMYZPHgw1SW2AQEBn3zQtHXrViIrK0umTZtG9u7dS/bt20emTp1KGjRoQLZt20Y11pIlS0jDhg1JmzZtiLa2Nnn//j0hhJDdu3eLvSf7Y506dRItpXRyciLjxo0j//77L/ntt9+Ivr5+vYtDCHezgwMGDCDt27cnt27dEi15vXXrFunQoQMZOHAgtTiEVGwtadeuHVFRUSFLly4VHXd3d6e6P/H48ePExMSE7Nmzh8TGxlbJFyGcFaelZ8+eolmn0aNHE0dHRxIZGUlcXFxI27ZtqcYyMTEh0dHRVM9ZEy5nPCU9k1ZaWkqWLl1K8vPzxT7Xt+bdu3dkx44d5NdffyWzZ88mO3fuJO/evaMao7S0lPj7+5OHDx9SPe+3ICwsjCgqKhJjY2Pi6upK3NzciLGxMVFSUqK6D7dDhw5EWVmZKCkpkXbt2hFzc/MqD5pMTU1JWFgYIaTi79jLy4sQUrFypUWLFlRiCAQCsmbNGtK8eXPRdr7mzZuTNWvWSGSFYGU3btwg69evr3FbAw0PHz4kixYtIv379yc//fQT+eOPP6jkoPhYSEgIsbe3Fy2/lhRJ750X+lSf48qVK9TiEFI9J0/lB82cK8+fPyf3798nhPzvMz9w4EAye/Zs8uLFC2pxhFRVVUX9DCUlJYnkdyGEkB07dhApKSkycOBAsnz5crJixQoyaNAgIi0tLcpDtW7dOjJixAixY7GSb2J48eKFKGt3TZkDacnPz0dsbCwMDAyojlbXVaysrCxkZGSAEAITExPqs3cfZ1IvLS3Fu3fvICsrCwUFBaolTQDgxIkTWL9+PdLT0wFAlD3eycmJahwAOHr0KAoKCjB8+HC0bNkSABAQEABVVVWq8Q4cOIDS0lJMmDABCQkJcHBwwPPnzyErKwt/f3+MHDmyXsURCgkJwcqVKxEXF4fy8nJYWFhg8eLFsLe3pxbj6dOnGD9+PIKDg0Uj1mVlZXBwcIC/v3+1UjGS8P79e0hLS0Nams5iqpqyPgsz2vJ4PKqZbENCQlBUVIShQ4ciNzcXAwYMQEZGBjQ0NBAUFER1RP7cuXNYvXo1/vnnH7Rr147aeT8WGxuLN2/ewNbWVvT5iIyMFM140rzWzp8/HyoqKvj9999x9OhRjB49Grq6uqKZtNWrV4sdQ0lJCampqRKdvRCKiIjA9u3bkZOTg6NHj6JFixbYt28f9PT00LNnT4nHlwQFBQWkp6dXW/XzPXjw4AG2bNlS5Tt+xowZaN68ObUY3t7en3ye5myor68vpKSk4OHhgatXr6J///4QCAQoKyuDj48P9Sz8r1+/BiC50r7Xrl1D9+7dq303lJWVISoqSiKVQCTl46zdRUVFKCsrg4KCQrXZYlr3fT/99BPy8/Ph7u6OZs2aVbv3l8R9n5CwzyHJ/sb3JCAg4JPPjx8/nlqs69evY/Pmzbhz5w4IIWjTpg1mzpyJ7t27U4sBsDrtX2zVqlVYsGBBtePl5eVwdnZGYGBgHbSK+VJZWVmYPn065s6dCwcHh7puTr3z7t07ZGRkQFtbG40aNar3cbiQmZkpunE1NjZG69atqcfQ19fHrVu3oKGhUeV4YWEhLCwsqJU+unfv3iefl3TnQ1I3K2pqanj37h3KysogKysLeXn5anHruxs3biAqKgqGhobUyuUNHjwYgwcPxoQJE6icrzbHjh3DuHHj4OzsjH379iEtLQ36+vrYunUrzp49i/Pnz1OLtWrVKjRt2hSurq5Vjvv5+eHp06eYN28etVi2trbw9PTE4MGDqZ2zJlyVPPqRcDmJIglc1xkXDrrl5ubiyJEjVAfdPtcpq4xWB01ZWRkRERHo0KEDlfN9a8hHpeXE9fr1a9EAlHBAqjaSGqj6nrBO+xdq2rQpli9fjilTpoiOCQQCjBo1CqmpqaJZVlquXLlSaw1SPz8/sc79999/Y8qUKZCTk/tsfWlx97L8+uuvX/yzPj4+YsX6nNjYWIwdO5ZqHWGhkpKSGt8rbW1tsc7L5XtVF7KysiS+n/hjknqv6gKfz8ejR4+q3YA9fvwYWlpa1eqB1weurq7YuHFjtf1fRUVFmDlzptjXv8q4HIn/nmzfvh1Lly6Fs7MzOnbsWK0+Ma3BAXNzc8yePRsuLi5QVlZGUlIS9PX1kZiYCEdHRzx69IhKHADQ1dXFwYMHq82M3Lx5E6NGjUJeXh61WEeOHMH8+fMxe/bsGn9/7du3pxKHi1nB5ORktGvXDnw+H8nJyZ/8WVqvSyguLg7p6eng8XgwMTGhmpuES1wOrvD5fDx+/BiNGzeucjwzMxOdOnX6bMfqv+By0I0rJiYmOHDggEQ+a0OHDoW/vz9UVFQwdOjQT/7s8ePHqcbeu3cv/vrrL2RlZQEAWrdujblz52LcuHFinbfyIBGfz69xMIDmir26GiQoLy9HdnZ2jfeWNFevsE77F4qLi4OdnR22b9+OESNGoLS0FCNHjkRGRgZCQ0NrTZLzNby9vbFs2TJ06tSpxi/aEydOiHV+PT09xMbGQkNDQ5RgpSY8Hk/smTpbW9sq/46Li4NAIICRkRGAii8KKSkpdOzYEaGhoWLF+pyEhARYW1tT/VLKysqCq6sroqKiqhyndRHi8r2qTCAQwN/fv9aBI1rvFZ/PR7NmzWBtbQ1ra2vY2NiIPhu0Sfq9EuLid3f69GkAFTOeAQEBaNiwYZX4V65cwaVLl6gmbgOAtLQ05OfnVxsMoNVJA2qfCXr27Bk0NTVRVlZGLRZXJH1TLvw8fAka71VN2yWEaP4tKSgoIC0tDbq6ulU67bm5uTAxMcH79++pxAEAOTk5pKenV7vOSiIWV9tNuJgVrDxwKLwpr+m2kubrevLkCUaNGoWwsDCoqqqCEIJXr17B1tYWQUFB1Tqk/xXXg+VcDK4IO4GnTp2Co6MjGjRoIHpOIBAgOTkZRkZGCA4OFjuWEJeDbpUVFxdXSwZLq4N28eJFrF+/XiLJzSZOnIi///4bysrKn01ASDORpI+PDxYtWgR3d3f06NEDhBBcv34dW7ZswYoVK8RKUBgeHo4ePXpAWloa4eHhn/xZa2vrr44jxPUgAVCxmm3MmDG4d+9etWsf7e2DLHv8F+rYsSNOnDgBJycnNGjQALt370ZOTg6uXr1KNUszAGzbtg3+/v5ij3DVpvKMAc3Zg5pcvXpV9N8+Pj5QVlZGQECAaN/5y5cvMXHiRPTq1YtazI9vYAkhePjwITZv3owePXpQiwMAEyZMgLS0NM6ePVvjl624uHyvKvP09IS/vz/69++Pdu3aSWwP1cOHDxEaGorw8HD4+vpi+vTpaNq0qagDP23aNGqxJP1eCXHxu6u8rPbj2WAZGRno6upi/fr11OLl5uZiyJAhSElJqXJTLnxttEbISUUZUrx58wZycnKi5wQCAc6fP089H8DnMrfTWn0xYcIE5OfnY9GiRRL57H28zLqmjhPN94qrCh3NmjVDdnZ2tZvjyMhI6hmUtbS0cP369Wqd9uvXr1Pdjw1wdy3nIqN2Xl6eqJPM1euaOXMmXr9+jdu3b8PY2BhAxYDi+PHj4eHhIfZ2RV9fXzg7O0NOTg6+vr61/hyPx6PSaY+MjJT44IpwYJcQAmVl5SpbgWRlZdG1a1dMnjyZasw7d+7UOMuooqKCwsJCqrGKioowb948HD58GM+fP6/2PK2O08iRI/Hu3TsYGBhQ3ztfuSMuieoOtdm0aRP++ecfuLi4iI45OTmhbdu2WLp0qVid9sodcRqd8s8JDQ0VZWyv3P+QpGnTpqFTp044d+6cRO8tAbDs8f/VqVOniLS0NDE1NSVPnz6VSAx1dXVRbd3vSfPmzUlqamq14ykpKaRZs2bU4gizrwoffD6fNG3alIwePZp61lIFBQWSnp5O9Zxfory8XJSZXBI0NDTIuXPnJHb+2mRlZZHx48cTaWlpqplYCeHuveLyd6erqyux61BlAwYMIE5OTuTJkydESUmJpKWlkYiICGJpaUktK7Twb7W2h5SUFFmxYgWVWF8akxauaggTQsilS5eIhYUFCQ4OJq9evSKvX78mwcHBpFOnTlSrJHBhzZo1xMTEhNy4cYMoKyuTiIgIsn//ftK4cWOyadMmqrFWr15NNDQ0iJ+fH7l79y65e/cu2b17N9HQ0CArV66kGosrXGXU5pqKigqJiYmpdvzmzZukYcOG3DdITFzVkyaEuzrjhFTUF7906RIhpOIaKKw7HhAQQIyNjanGmjFjBjE2NiZHjhwh8vLyxM/Pjyxfvpy0bNmS7N+/n1ocf3//Tz7qowYNGtRYZzwzM5M0aNCAaqyXL1+SkJAQsm/fPolWduKSgoJCjb8/SWAz7Z9Q256Sxo0bQ1VVtcr+dpr7SyZNmoSDBw9Sq6n7KVwtgwYqZtIeP36Mtm3bVjn+5MkTUS1ZGris025iYoJnz55xFk9S+44+Jisry0lN7rdv3yIyMhJhYWEIDw9HYmIijI2NMXPmTOqjsly9V1z97oCKrTQ11f4sKSlBUFBQlZFzcURHRyM0NBSNGzcGn88Hn89Hz549sWrVKnh4eCAhIUHsGFevXgUhBL1798axY8dEo+VAxe9UR0eH+oznx+0uLS1FQkICfHx88Oeff1KLw1UNYQCYNWsWtm3bViXJk4ODAxQUFDBlypSvzr9SF/k1fvvtN9Gy5/fv38PKygoNGjTAnDlz4O7uTiVG5VgvXrzAjBkzRNs/5OTkMG/evBqT0IorJycHGzZsEO3JNjY2hqenJwwMDKjFkOSsYG0yMzMRFhZW4/3E4sWLqcQoLy+v9lqAilVGXH7/08JVPWngf5n1nz59ijt37oDH46F169ZibymoydSpU+Hp6Qk/Pz/weDw8ePAA0dHRmDNnDrXPgtCZM2ewd+9e2NjYwNXVFb169YKhoSF0dHRw4MABODs7U4nDVZ4TLvMcGBoa4vDhw/j999+rHD906BDVnENnzpyBs7OzqGZ55RlpHo9H7X5FKDg4GEpKSqLvwi1btmDnzp0wMTHBli1bqlWa+lpdunRBdnY2J/d9bE/7J3xuT0llNJeyeHp6Yu/evWjfvj3at29f7cuJZsI2d3d30VLempZ1fGpp2H/l4uKC8PBwrF+/Hl27dgVQsRdk7ty5sLKy+k+ZQOtS5T3xsbGxWLhwIVauXAlTU9Nq7xXNRBeS3Hf0sfXr1yM3NxebN2+W6FIfGRkZqKurY9y4cbC1tUXPnj2r7M+mKTQ0lJP3iqvfHcBdJmA1NTXExcVBX18fBgYG2LVrF2xtbZGTkwNTU1O8e/eOShygIlO9lpbWJ/dNS9q5c+fw119/ISwsjMr5JLkP8mPy8vKIiYmBqalplePJycno0qULiouLv+q8dZVfA6ioKpGWloby8nKYmJhASUmJ6vkre/v2LdLT0yEvL49WrVpV2ftLS0hICAYNGoQOHTqIruVRUVFISkrCmTNn0LdvXypxuE60uHPnTkyfPh2NGjWCpqZmtZvy+Ph4KnGcnJxQWFiIwMBA0UDe/fv34ezsDDU1NbHz/lTGxcRG5SoWkh5ceffuHdzd3bF3717Ra5GSkoKLiws2bdoEBQUFarEA4I8//oCvr68oJ4Rw0G358uVU4ygpKeH27dvQ0dFBy5Ytcfz4cVhaWiIvLw+mpqZ4+/YttVgCgQAnT56skgRx0KBBkJKSohaDy9Jyx44dw8iRI2FnZ4cePXqAx+MhMjISV65cweHDhzFkyBAqcVq3bo1+/fph5cqV1D9nNTE1NcWaNWvQr18/pKSkoFOnTvDy8kJoaCiMjY2p9dtOnDiBhQsXYu7cuTXeW9JMwMk67d+gj5O3Vcbj8ajOfjdq1Ah79+5Fv379qJ2zNu/evcOcOXPg5+cnShIiLS0NNzc3/PXXX9Uy6H6tn3/+GZ06dcL8+fOrHP/rr78QExODI0eOiHX+j5NbkP9PalEZoZzoAqi4afb29q42GhkQEIClS5dS3U84ZMgQXL16Ferq6mjbtm21ixCtlSWDBw9GZGQkpKSkYGNjI3oI9ynSJOwESvq94up3B9SeCTgpKQm2trbUbvZ69eoFLy8vDB48GGPGjMHLly+xcOFC7NixA3FxcUhNTaUSR6iwsBC7d++uclPk6uoqsQGdj2VlZaFDhw4oKiqicj4ub8qtrKwgIyOD/fv3o1mzZgCAR48eYdy4cSgpKflsMqBv2evXrxEaGgojIyOJXCO4Ym5uDgcHB6xevbrK8fnz5+PixYvUOrdc09HRwYwZM6iWx6tJQUEBnJyckJqaCi0tLfB4POTn58PU1BSnTp1Cy5YtqcXiYmKDy8GVqVOn4vLly1Vy/ERGRsLDwwN9+/bFP//8Qy2WEBeDbu3bt8emTZtgbW0Ne3t7tG/fHuvWrcPff/+NtWvX4t9//6USJzs7G/369cP9+/dhZGQEQggyMzOhpaWFc+fOUVspw3Vpubi4OPj6+iI9PR2EEJiYmMDLy4tqlnxFRUWkpKRQz0dSGyUlJaSmpkJXVxdLly5Famoqjh49ivj4ePTr149aIkSuEosCrNP+xYqLi0EIEY0O3bt3DydOnICJiQns7e3ruHVfr3nz5ggLC5NIDenaFBUVIScnB4QQGBoaUuusCzVu3BihoaHVZppSUlJgZ2eHx48fi3X+/3LTS3OJt5ycHFJTU6stwcnKyoKpqSnV7MZcZi4FKmYBw8PDER4ejoiICPB4PNjY2CAoKIhaDC4ylwLc/O7Mzc3B4/GQlJSEtm3bQlr6fzudBAIB8vLy4OjoiMOHD4sdC6iYGSwqKsLQoUORm5uLAQMGICMjAxoaGjh06BB69+5NJQ5QsXrFwcEB8vLysLS0BCEEsbGxKC4uxsWLF2FhYUEt1seVJMj/J61cunQpMjIykJiYSCUOlzfl2dnZGDJkCO7cuSNKpJefn4/WrVvj5MmTnG3doGHEiBGwsrKCu7s7iouL0aFDB+Tl5YEQgqCgIAwbNkys89dViSU5OTmkpKRUW3qamZmJ9u3bi3Utr8u6yCoqKkhMTOTspvzSpUvIyMgQdTLs7Oyox+ByYoMLjRo1wtGjR2FjY1Pl+NWrVzFixAg8ffpUYrElOejm6+sLKSkpeHh44OrVq+jfvz8EAgHKysrg4+MDT09PKnH69esHQggOHDgg2sL1/PlzjB07Fnw+H+fOnaMSR5Kl5erK0KFDMWrUKIwYMYKTeOrq6oiMjISJiQl69uwJFxcXTJkyBXfv3oWJiQm1FYL37t375PM6OjpU4gAse/wXc3JywtChQzFt2jQUFhbC0tISsrKyePbsGXx8fDB9+nSJxP3333/B4/HQokULiZzfy8sLGzdu5GQpr5CioiL1eq2VvX37FrKystWOy8jIUCn3xkUGzJpwte8I4DZzKVAxSi4QCFBaWooPHz4gODiYeh1Srt43Ln53wmzhiYmJcHBwqDJzISsrC11dXbE7NJU5ODiI/ltfXx9paWl48eIF1NTUqF83Zs+ejUGDBmHnzp2iwYiysjJMmjQJs2bNwrVr16jFUlVVrXHlhZaWFtUBIy7rvRsaGiI5ObnGDg2t94qrXCjXrl3DH3/8AaBiCWJ5eTkKCwsREBCAFStWiP0Zb9iwoeh3wtUqDqBiYDkxMbHadTsxMVHsCglqamqiLTM1fb4ByawEA4Dhw4fj4sWLVKt+fErfvn2pbSWoDZc5SgDJlisDKma9a6p41KRJE6rbnIDqg26dO3emOuhWWeXtgba2tsjIyEBsbCwMDAxgZmZGLU54eDhu3LhRJeeKhoYGVq9eTbU6EZd5DrjaZte/f3/MnTsXaWlpNS4jp1k6FgB69uyJX3/9FT169EBMTAwOHToEoGJwlOZqHJqd8s9hM+1fqFGjRggPD0fbtm2xa9cubNq0CQkJCTh27BgWL1781cl9alJeXo4VK1Zg/fr1on04ysrK8PLywh9//EF1ryeXS3kB4NatWzhy5EiNtZ5pxercuTMGDhxYLdHJ0qVLcebMGcTFxVGJU9m7d+9qfE00Bye42nfEJV9fX4SFhSEiIgJv3rxBhw4dROXerKysxL5ZSU5ORrt27cDn85GcnPzJn5XkQJKkBAQEYOTIkVXKo9V38vLySEhIQJs2baocT0tLQ6dOnajeWH68+oLP56Nx48YwNDSssnqBJknflHOBq1wo8vLyoqWnLi4uaN68OVavXo38/HyYmJhQ3afKpWXLlsHX1xfz589H9+7dRdfyNWvWwMvLCwsXLvzqc3NdF7lyUsKioiL4+PigX79+NebjoZWgsLZEiDweD3JycjA0NISVlRWVPcZc5CjhqlwZAPTp0wcaGhrYu3ev6HujuLgY48ePx4sXL3D58mVqsTQ1NRESEgIzMzMcPHgQS5YsQVJSEgICArBjxw4qCUyF9u7di5EjR1bLQUE7Kau6ujrOnj2L7t27Vzl+/fp1DBw4kNpWJy63VPH5fDx69Khap/3BgwcwMDD46jwoNcWpjSQGEfPz8zFjxgwUFBTAw8MDbm5uACoGeAQCwWcTqn7K6dOn8dNPP0FGRqZamemP0RyMYJ32L6SgoICMjAxoa2tjxIgRaNu2LZYsWYKCggIYGRlRvZFcsGABdu/eDW9v7yoJx5YuXYrJkydTzWrM5TJo4YXT3t4ely5dgr29PbKysvDo0SMMGTKEWqzTp09j2LBhGDNmjGjZ7pUrVxAYGIgjR45Uq2ksjqdPn2LixIm4cOFCjc/Tvghxse9I6OjRozh8+HCNgxG09lx26tRJtI+dRif9Y5W/jIS5CGq65NH+wuDidydUWFiIo0ePIicnB3PnzoW6ujri4+PRtGlTait0hgwZUuMNa+Wb5DFjxsDIyEjsWE2bNsW+ffuqbTsKCQmBi4uL2Ntb6gKXN+XCeOHh4TV+/mh0nLhaMty6dWusWLEC/fv3h56eHoKCgtC7d28kJSWhT58+VKtB3L59u1plE6Hg4GA4OjqKHWPYsGHYsWMH1NXVsWHDBqxfvx4PHjwAULFVbe7cufDw8OBs1RsNn0pKWBnNBIV6enp4+vQp3r17BzU1NRBCUFhYCAUFBSgpKeHJkyfQ19fH1atXoaWlJVYsLiY2fvnlF1y9ehXLli2Di4sLtmzZgvv372P79u1YvXo1tcznQMU2wZ9++gnv37+HmZkZeDweEhMTIScnh5CQkFr/Br4Gl4NuXM0Wu7i4ID4+Hrt374alpSUA4ObNm5g8eTI6duwIf39/KnG42FIl7LTOnj0by5cvr7JiTyAQ4Nq1a7h79y7VwZXvxcf3lrWhPhgh2Ypy3w9TU1OyceNGkp+fT1RUVEhUVBQhhJDY2FjStGlTqrGaNWtGTp06Ve34yZMnSfPmzanFKS0tJf7+/uThw4fUzvkppqamZPPmzYSQ/9XsLC8vJ5MnTyaLFy+mGuvs2bOke/fuREFBgWhoaBBbW1sSFhZGNQYhhIwZM4Z0796dxMTEEEVFRXLx4kWyb98+YmRkRM6ePUs9Hlc2btxIlJSUyC+//EJkZWXJ1KlTiZ2dHWnYsCH5/fff67p5X+zu3buievbC2su1PWjh8neXlJREGjduTAwNDYm0tLSoDu7ChQvJuHHjqMUZP348adiwIdHR0SFDhw4lQ4YMIbq6ukRVVZWMGDGCGBkZkQYNGpDIyEixY82cOZO0bNmSBAUFkfz8fFJQUEACAwNJy5Ytiaenp/gv5iPZ2dnE3d2d9OnTh9jZ2ZGZM2eS7OxsqjG4qiFMCCHx8fFEU1OTqKioECkpKdK4cWPC4/GIoqIi0dPToxKjWbNm5M6dO1TO9Slbtmwh0tLSRFVVlZiZmRGBQEAIIeTvv/8mNjY2VGPJycmRv//+u8qx9+/fk19++YXIyclRidGtWzfStGlTcvr0adGx169fk9evX1M5PyEV14QvfUjK06dPybNnzyR2/oMHDxIbG5sqf6dZWVmkd+/eJCgoiBQUFJAePXqQYcOGiR1rwoQJn3zQoKWlRa5evUoIIURZWVlU83nv3r3kp59+ohKjsnfv3pEdO3aQX3/9lcyePZvs3LmTvHv3jnqcVq1akUOHDpG3b9+Sxo0bkytXrhBCCElMTCQaGhpUY/F4PPLkyZNqxxMTE4mamhq1OC9fviSDBg0iPB6PyMrKEllZWcLn88ngwYNJYWEhtThc0NXVJbq6uoTH4xEtLS3Rv3V1dUnr1q2Jvb09uXHjRl03UywCgYDcuXOHREREkPDw8CqP+oh12r/QkSNHiIyMDOHz+aRv376i4ytXriSOjo5UYzVo0KDGG6KMjAxqNw9C8vLyVDssn6KgoEDy8vIIIYRoaGiQ5ORkQgghaWlpRFNTk5M20KapqUlu3rxJCKn4shW+b6dOnSI9evSgHo+rC5CRkRE5ePAgIeR/AyyEELJo0SLyyy+/UI318uVLsm7dOuLm5kYmTZpE1q9fX+++/Crj8nfXu3dvMnfu3Gqxrl+/TnR0dKjFmTdvHpk+fbqo00RIxWfR3d2dLFiwgJSXl5MpU6ZQ+cx/+PCBeHh4iG6G+Hw+adCgAZk1axZ5//692OevLDg4mMjKyhJLS0sye/ZsMmvWLGJpaUkaNGhALl68SC0Olzfl1tbWZPLkyaSsrEz0mcjPzydWVlbk2LFjVGKsW7eOzJgxQzQgJkmxsbHk+PHj5M2bN6JjZ8+epTJAVNmxY8eIhoYGcXR0JA8fPiQJCQnE2NiYGBsbk7i4OCoxysvLydq1a4m8vDxxdXWt8ppo4fF4hM/nEx6P98kHn8+nGvfly5dkxowZRENDQ/R3q6GhQX755Rfq13N9fX2SkJBQ7Xh8fLxoYOr69ev15r5CUVFRdB/WokUL0T1Fbm4uUVRUpBpr3759tT43Z84cqrG4GHTr0KEDMTc3J3w+n5iamhJzc3PRo3379kRZWZkMHz6cSqzKMjMzyenTp8mpU6dE13NxvXr1qsp/f+pBk42NDXnx4gXVcwpt3LiRFBcXi/77Uw/aoqOjiZ6eXo3XQ9rXP66w5fH/waNHj/Dw4UOYmZmJlkPExMRARUWl2v5LcXTp0gVdunSptt9i5syZuHXrFm7cuEEtlq2tLTw9PakuGa+NlpYWzp8/D1NTU5iZmWH+/PkYPXo0oqOj4ejoiFevXkm8DbSpqKggOTkZurq60NXVxYEDB9CjRw/k5eWhbdu2VLdN3LhxA2PGjMG9e/eqLfGmvQRHQUEB6enp0NHRQZMmTXDp0iWYmZkhKysLXbt2rXGJ79fgMlO4UFpaWo3LhmntO+LqdwdUJM+Kj4+HgYEBlJWVkZSUBH19fdy7dw9GRkbUKgo0btwY169fr1ZlIjMzE927d8ezZ8+QkpKCXr16obCwkErMd+/eVakyIYm6rlyV3uKyhrCqqipu3rwJIyMjqKqqIjo6GsbGxrh58ybGjx+PjIyMrzrvx9nVQ0NDJbpkuLS0FEZGRjh79ixMTEzEPt+XePDgAcaPH4+EhAQUFRVh4sSJWL9+PeTl5anGycjIwMSJE/Hw4UN4eHhUy58gzhaGz2UyroxWAqUXL16gW7duolrpxsbGIIQgPT0dBw8ehJaWFqKioqCmpkYlnoKCAq5du4ZOnTpVOX7r1i1YW1vj3bt3uHv3Ltq1a1cv8h5wVa4MqLg+7N+/HwMGDKhyfPbs2QgKCsLDhw+pxQIqvuMLCgrQt29f0fLrc+fOQVVVlUriNm9vbxBCsGzZMnh5edWalLWm5MTfmspL/D8uLSxEJJREUlL09PQQGxsLDQ2NT26lobl9RqhDhw5o3bo1vL29a8y9QjP5aExMDMLCwmpMzOrj40MtDsse/x9oampCU1OzyjHhnhaa1q5di/79++Py5cvo1q0beDweoqKiUFBQgPPnz1ONNWPGDHh5eeHff/9Fx44dq5Vfo5mcq1evXrh06RJMTU0xYsQIeHp6IjQ0FJcuXUKfPn3EOre6ujoyMzPRqFGjz2a0ppnAw8jICHfu3IGuri46dOggyvS5bds2UY1kWqZNm4ZOnTrh3LlzNV6AaNLU1MTz58+ho6MDHR0d3LhxA2ZmZqLMr7RwmSk8NzcXQ4YMQUpKSpW97cLfI60vQa5+d0BF6aiaKiLcuXOnWu12cZSVlSEjI6Napz0jI0P0e5OTk6P6mVRQUICpqSlev36NixcvSqRMUHp6eo1l8VxdXbFhwwZqcfT19XH37l3o6OjAxMQEhw8fhqWlJc6cOQNVVVVqcYCKKhnC96Fp06bIz8+HsbExGjZsiPz8/K8+78c3OJJOfCkjI4MPHz5wur9bIBCgpKQEAoEAAoEAmpqa1RJb0dCmTRu4ublh2rRp8PX1rdJp5/F4YnXaucxkLLRs2TLIysoiJyenWmbyZcuWwd7eXpR8jwZbW1tMnToVu3btEuVzSUhIwPTp00V5bFJSUr54v/3nSDpHycSJE5GUlARra2ssWLAA/fv3x6ZNm0TlymgKCgrCqFGjcPr0aVhZWQGomBA6fvw4rl69SjUWUJG3Rji4IhAIkJKSgu7du1MbwFmyZAmAimtsTYnoaCOE4OjRo7h69WqNHTRxBiyFA6EAJPJe1ObXX3+t8XjlnDVOTk5VMuZ/qby8vBr/mwtZWVk4evSoxKs/rFy5EgsXLoSRkRGaNm1a5TuL+vdX3Uzw108xMTFk7ty5ZOTIkWTIkCFVHrT9+++/5PfffxftH/3jjz/I/fv3qcepbdmcJJaPPH/+XPQaBAIBWbNmDRk4cCCZPXu22Etz/P39RUtn9+zZQ/z9/Wt90LR//36yZ88eQkjF0rzGjRsTPp9P5OTkSFBQENVYCgoK1JZhfY6bmxtZunQpIYSQf/75h8jLyxM7OzuiqqpKXF1dqcWRk5Mj6enp1Y7fvn2byMvLU4tDCCEDBgwgTk5O5MmTJ0RJSYmkpaWRiIgIYmlpSa5du0YtDle/O0IImTx5Mhk8eDApKSkhSkpKJDc3l9y7d4+Ym5tT3f89c+ZM0qhRI+Lj40MiIiJIZGQk8fHxIY0aNSIeHh6EEEJ27txJZXn88OHDyaZNmwghFXsvW7VqRWRkZIi0tDQ5evSo2OevrGXLluTw4cPVjh86dIhoaWlRi+Pj4yNa/hcaGkrk5eVFy/83bNhALQ4hhPTt25ccOHCAEELI1KlTiaWlJdm/fz9xcHAglpaWVGNJ2qpVq8j48eNJaWmpxGMFBgYSVVVVMnDgQPLkyRNy8eJF0qJFC9K9e3fRthMaHj16RAYMGEBUVVWpfx/VhIucDTo6OiQ4OLjW5y9cuEB1u87Dhw+JnZ1dtX3Fffv2JY8ePSKEVPydhYSEiB2rLvK73Lt3jxw7dowkJiZK5PyBgYFETU2N3Lp1i0yfPp00b95cIjkqPD09ya5duwghhJSVlZEePXqI8msItwuJS3iv+vFDVVWVdOnShdqWIKGZM2eSBg0aEEdHRzJ+/HiJ5Djgmo2NDVFRUSGKiorEwsKCmJubEyUlJdKwYUPSpUsXoqqqStTU1Mjt27fruqn/ia2tLblw4YLE4zRp0kTUD5A01mn/QoGBgURGRob079+fyMrKkgEDBhAjIyPSsGHDevuHSgh3ybm4TnpXV4qKikhcXBx5+vQp9XNzdQEipGJQpfKN8qFDh8jMmTPJxo0byYcPH6jFadKkSY03VsHBwaRJkybU4hBSkUdBmHxJRUWFZGRkEEIIuXLlCunQoQO1OFz97gip2PfWo0cPoqqqSqSkpIiWlhaRkZEhVlZW5O3bt9TilJWVkRUrVhBNTU3R4J6mpib5888/SVlZGSGk4kazoKBA7FhNmzYV3aweOHCAGBoakqKiIrJ161aq7xMhhHh7exNVVVWyevVqcu3aNRIREUFWrVpFVFVVyfLly6nGqkySN+W3bt0ioaGhhBBCnjx5Qn766SeirKxMzM3NqcV79+4dKSoqEv377t27xNfXl0onqbLBgwcTZWVl0qxZM2Jvby/RwXIFBQWydevWKsdevHhBhg8fTpSVlanECAwMJBoaGsTOzo7k5+dTOeencJWzQVZW9pN/+wUFBaRBgwbU4gmlp6eTU6dOkZMnT4qu57RxmaOES1u3biUNGjQgLVu2lNhkQIsWLcitW7cIIYScOHFCNDjwxx9/kO7du1OJceLECXLy5MlqD39/fzJjxgwiLy9f48Ds11JTUyPnzp2jdr7a1JY8Mjk5mWRmZlLN7+Lr60uGDh1abU/9zz//TDZs2ECKioqIk5MTsbe3FyvO7Nmza3z8+uuv5Pfffyd+fn7k+fPn4r4ckePHjxMTExOyZ88eEhsbK7FEnJqamiQzM5Pa+T6F7Wn/Qu3bt8fUqVPxyy+/iPaO6unpYerUqWjWrBm8vb2pxdLX14e1tTW2bdtWZanPs2fPYGlpSX3fB1cq7/WVJK5Kf3DtxIkTWLhwIebOnQtTU9Nq+0jrY51xDw8PnDhxAuvWratSr3ju3LkYNmwY1SXKampqiIuLg76+PgwMDLBr1y7Y2toiJycHpqamVPMPcC00NBTx8fEoLy+HhYUF7OzsJBZLuBxfUrXFuSwTRAjhpPQWVzWEuWJvb4+hQ4di2rRpKCwshJGREWRlZfHs2TP4+Phg+vTpVOJwWZL0zp07tZYs3LdvH8aNGyd2DEVFRaxevRozZ84U+1xfgqucDS1atMChQ4fQs2fPGp+PiIjAqFGjcP/+fSrxhEpKSpCXlwcDA4NqeQFokVSOkv9SI1rcMo21LX8+evQozM3NYWBgIDpGczm+nJwcsrOz0bJlS0yZMgUKCgrYsGED8vLyYGZmVuPWLtq2bNmCvXv34ubNm1TOp6enhwsXLlDNY1WT2va0C8nIyGDkyJHYvn075OTkxIrVokULXLp0qVrukNu3b8Pe3h73799HfHw87O3txSqzaWtri/j4eAgEAhgZGYEQgqysLEhJSaFNmza4c+eO6B6QRh6TmkqxCbdG0swLsHbtWjx48IDq/WptWKf9CykqKuL27dvQ1dVFo0aNcPXqVZiamiI9PR29e/emmryDz+fD0NAQqqqqOHXqlGhv9OPHj9G8eXPqnc6cnBxs2LAB6enp4PF4MDY2hqenZ5ULOQ1cJb2rXD+xsgcPHsDAwADFxcVinb+2L8Ca0PwClPQFKDk5Ge3atQOfz0dycvInf5bWAEFJSQnmzp2Lbdu2oaysDIQQyMrKYvr06Vi9ejXV/Wm9evWCl5cXBg8ejDFjxuDly5dYuHAhduzYgbi4OKSmpn71uT/3+6pM0oMrhYWF1PdJc4nL2tyVvXnzBgCgrKxM/dxcDiR6e3tj7Nix1K/flTVq1Ajh4eFo27Ytdu3ahU2bNiEhIQHHjh3D4sWLkZ6eLrHYklRWVoawsDDk5ORgzJgxUFZWxoMHD6CiolIlwdXXysrKQqtWrSi09MvIyckhJSWlWszMzEy0b9+eWqJKNzc3ZGdn49KlS9USfn348AEODg4wMDDA7t27qcR79+4dZs6cKaplnZmZCX19fXh4eKB58+aYP38+lThAxSTK0aNHYWFhgc6dO2PSpEmYOnUqLl68iFGjRn11jhwu69vb2tp+cazQ0FCxYlWmo6ODnTt3ok+fPtDT08PWrVsxYMAA3L59Gz179sTLly+pxapNVlYWLC0tqcUKCAhAcHAw/Pz8qCeorOzUqVOYN28e5s6dK0rSe+vWLaxfvx5LlixBWVkZ5s+fj5EjR2LdunVixVJSUsLZs2dhY2NT5XhYWBgGDhyIN2/eIDc3Fx06dBBroGXDhg2IiIjAnj17RIP+r1+/hpubG3r27InJkydjzJgxKC4uRkhIiDgvCcDnk3LSmkAsLy9H//79kZmZCRMTE4kkZhViiei+kLq6uuimrkWLFkhNTYWpqSkKCwupz9DxeDwEBwdjzpw56NSpE06ePInOnTtTjSEUEhKCQYMGoUOHDujRowcIIYiKikLbtm1x5swZ9O3bl1osSSe9E45c83g87Nq1q8pNlkAgwLVr16iMjiYkJFT5d1xcnGjkEKi4gZCSkkLHjh3FjlWZpJN4dOjQQTTY0aFDhyrJ2iqjOUIpKyuLjRs3YtWqVRLPFL5w4UIUFRUBAFasWIEBAwagV69e0NDQQFBQkFjn/tTvqzLaWV/XrFkDXV1djBw5EgAwYsQIHDt2DJqamjh//jzMzMy++twWFha4cuUK1NTUYG5u/slRf1ozdgAwa9YsODs7Q0lJCdra2qIbiWvXrsHU1JRaHAAoLi4GIQQKCgpQVlbGvXv3sHv3bpiYmMDe3p5aHOHA2sf+/fdfqhlsAeDYsWNYtmwZOnfujLFjx2LkyJFUkxICFZ0m4eDGxYsXMXToUPD5fHTt2vU/ZS//EpLuSAvdu3cPjo6OyM/Px4cPH9C3b18oKytj7dq1eP/+PbZt2yZ2DC477EBF1YfExMRqcRMTE6sNIInD29sbnTp1QqtWrfDLL7+IvmfT0tKwdetWfPjwAfv27aMWb8GCBUhKSkJYWBgcHR1Fx+3s7LBkyRKqnfbevXvjzJkzsLCwgJubG2bPno2jR48iNja2WkWF/4LLpFxcJjWrbOLEiRgxYoQoca7wfvLmzZsSn6kWKi4uFnsmurLhw4cjMDAQTZo0ga6ubrUOGq3vwj///BMbN26Eg4OD6Fj79u3RsmVLLFq0CDExMVBUVISXl5fYnXYnJye4urpi/fr16Ny5M3g8HmJiYjBnzhzRJFtMTEy1RLT/1V9//YVLly5VWaWnoqKCpUuXwt7eHp6enli8eDG1716uknLOnDkTV69eha2tLTQ0NCSaPJV12r+QJDOff4wQAiUlJRw/fhwLFiyAtbU1duzYQbUDLTR//nzMnj27xuVz8+bNoxpT2LGovNSL5kyxMDMtIQTbtm2DlJSU6Dlh6Q8aN16VvwB9fHygrKyMgIAAUTbUly9fYuLEiejVq5fYsSqT9AUoLy9PdHMv6RuKL73ZoTlCWfnLT19fH2lpaXjx4sVnqw18Ca6zogpt374d+/fvBwBcunQJly5dwoULF3D48GHMnTsXFy9e/OpzOzk5iVY6cFESUmjGjBmwtLQUlQkSrjDR19fHihUrqMZycnKqsszb0tKS6jJv4WAHj8dDnz59qizhFQgEyMvLq9LpoCE5ORm3b9/GgQMH4OPjg19//RV2dnYYO3YsBg8eTGVAzNDQECdPnsSQIUMQEhKC2bNnAwCePHlCddsEFx1pIU9PT3Tq1AlJSUnQ0NAQHR8yZAgmTZpELQ6XJk+ejClTpiA3N7fK9qM1a9bAy8uLWpyWLVsiOjoaM2bMwIIFC6pU5ujbty82b94MLS0tavFOnjyJQ4cOoWvXrlWu3SYmJsjJyaEWBwB27NghyhA+bdo0qKurIzIyEgMHDsS0adOoxhISZlnX0dGhlmW9LixduhTt2rVDQUEBhg8fLvo+kZKSojqw8ik7d+4UVRigYcKECYiLi8PYsWOrZQqnSfj+f0xHRwcpKSkAKiYLaKzy3b59O2bPno1Ro0ahrKwMACAtLY3x48eL7qvbtGmDXbt2iRXn1atXePLkSbWl70+fPhXN4Kuqqlar0CAOLlYS7927F8eOHUP//v2pnbM2bHn8F3rx4gXev3+P5s2bo7y8HOvWrUNkZCQMDQ2xaNEiqhfWj5dS7t+/H5MnT8bo0aMREBBAdaaOq+VzAHdLVWxtbXH8+HFOvuxatGiBixcvom3btlWOp6amwt7eXrRP9mudPn0aP/30E2RkZHD69OlP/iytOuNc+NxeVSEae1ZdXV2/6Of8/PzEjgUARUVF1VaRSErl/d+enp54//49tm/fjszMTHTp0oWT5YeSwsV+VUkv8xbmOvH29q6zGsLXr1/HwYMHceTIEbx//57KPtKjR49izJgxEAgE6N27Ny5dugQAWLVqFa5du4YLFy6IHQOoGCxSVlbG7t27oaGhgaSkJOjr6yM8PByTJk1CVlYWlThAxWfh+vXrMDIyEuWtEZbqMzExqZc5L7jK2VDZy5cvRe+LoaHhV5WJ+hwFBQWkpqZCX1+/ynuVlJQEKysrvHr1inpMSZo1axZMTU3h5uYGgUAAKysrREdHQ0FBocZly+K6desWjhw5UmMJO5oD5ZW9f/+e6oy3UG3bFV+9eoXY2Fjk5OQgIiKCWsddUVERISEhteZvoMXc3BxmZmbYsWOH6PuhtLQUkydPRlJSEhISEnD9+nWMHTuW2qTB27dvkZubC0IIDAwMqK5kAgBnZ2dER0fXOKPfvXt37Nu3D0FBQVi3bh1iY2PFjlfbSuKkpCSqK4l1dHQQEhLCyeoRNtP+hSp/8fD5fPz222/47bffJBLr43EU4f5ESdTF5Wr5HMDdUhUul4K9fv0ajx8/rtZpf/LkiWg7hTgGDx4sWrL+qdlOGisVPjcoUJm4AwQ0E0h9jr+/P3R0dGBubk69TnpNmjZtihEjRsDV1VXiX+xqamooKCiAlpYWgoODRTPRhBCJJFwsKSmpsTattrY2tRhc7leV9DJvYQ1h4RYGSdy0fo6ioiLk5eUhKytL5ZoEAD///DN69uyJhw8fVtmC0adPH6rfU5GRkbh+/Xq1QQ0dHR3qSc3Ky8tr/Jv5999/JZLnQNLKyspw4MABjB49GrNnz5ZozobK1NTUYGlpKdEYnTt3xrlz50QJ/YSDDzt37kS3bt2oxtLT08PYsWPh7OwssZvyo0ePYuzYsQCAM2fO4O7du8jIyMDevXvxxx9/4Pr169RiCRNf2tvb49KlS7C3t0dWVhYePXpE/R5TIBBg5cqV2LZtGx4/fiy6li9atAi6urpwc3MTO8bH2xWFVFRU4OjoiBkzZlC999TS0pJYEtbKtmzZgkGDBqFly5Zo3749eDwekpOTIRAIcPbsWQBAbm4uZsyYQS2mkpKSRHPucDWjL8TVSuKlS5diyZIl2LNnj0S2dlbBSY76eurVq1df/ODCo0ePSFhYGNVzcl3yiIuascOGDSOrVq2qdnzt2rXk559/phpr3LhxRFtbmxw5coQUFBSQgoICcuTIEaKrq0tcXFyoxpI0YSkv4YPP51f7t/BRn0yfPp2oqakRMzMzsnHjRqolRWpy+vRpMnToUCIrK0tatWpFVq1aRe7fvy+RWL/88gvR0dEhdnZ2RENDg7x584YQQkhQUBAxNzenFufOnTukZ8+e1WrhCj8XNHl4eJCOHTuSiIgIoqioKCqxdOrUKeol30xNTcnGjRtJfn4+UVFRIVFRUYQQQmJjY0nTpk2pxiKEkA8fPpCCggJy7969Kg/acnNzyYoVK4ixsTGRkpIitra2ZOfOnaSwsJB6rPz8fCql/mpSuTZw5XJbERER1EtCjhgxgkyePFkUKzc3l7x584b07t2belnX33//nVy8eLFK2TxJkJeXp1q69Vtx/fp1oqysTKZNm0bk5OSIp6cnsbOzI4qKiiQ2NpZqrPXr15NOnToRHo9HLCwsiK+vL3nw4AHVGA0aNBD9DU2ePJl4enoSQir+jmmVGxQyNTUlmzdvJoT872+qvLycTJ48mSxevJhqLG9vb6Kvr0/2799P5OXlRX+/hw4dIl27dqUaiytnz54lDg4OJC8vT+Kx3rx5Q/755x9RucZt27aR169fU4/z9u1bsnDhQtKtWzdiYGBA9PT0qjxoKCsrI2FhYeT58+fkzZs3JCkpiSQmJoruWSShQYMGNZZiu3PnDtUSlB06dCDKyspESUmJtGvXjpibm1d50MQ67Z/wcUelpockblq5VF5eTnx8fEiLFi1EnbMWLVqQDRs2kPLycqqxuKoZ26hRI5KcnFzteHJyMvUbvaKiIjJ9+nTSoEED0edBVlaWTJ8+nWqdbK5dunSJWFhYkODgYPLq1Svy+vVrEhwcTDp16kT1veLK+/fvycGDB4mdnR1RUFAgw4cPJ8HBwdQ/45U9e/aM+Pj4kPbt2xNpaWnSv39/cuzYsSo13MVVUlJC/vrrL+Lh4UHi4+NFx319fcnOnTupxenevTuxsrIi58+fJwkJCSQxMbHKgyZtbW0SHR1NCKnaUcvKyqJ+A3vkyBEiIyND+Hw+6du3r+j4ypUriaOjI7U4mZmZnA16dO3alfD5fGJmZkbWrl1L/v33X6rnJ4SQ0tJSsnDhQqKioiJ6LSoqKuSPP/4gJSUl1OJw2ZG+f/8+ad26NTE2NibS0tKka9euRENDgxgZGZHHjx9TjeXg4ECUlZWJrKws6dq1K5k/fz65cOEC9RtYGxsbcuLECarn/FakpKQQFxcX0rZtW2JsbEycnZ1r/N6n5c6dO2Tx4sWkdevWRFpamvTt25cEBARQObe2tjYJCQkhZWVlREtLi5w5c4YQQkhqaipRVVWlEkNIQUFB1OHU0NAQ/c7S0tKIpqYm1VgGBgbk8uXLhJCq1/L09HTqr4srqqqqRFZWlvD5fKKkpETU1NSqPGgoKSkhenp6ogFLSRs1ahRp1qwZ+e2334ivry/ZsGFDlQctDRo0ILm5udTO9zktW7Ykhw8frnb80KFDREtLi1qcpUuXfvJBE9vT/gnh4eFf9HMJCQmYNWuWWLG4zNRceZ90ZZJePsdVzVh5eXkkJiZWq7mbkZEBc3NzsUu+1aSoqKhK9nNae5q5rOVaWbt27bBt27Zqy7sjIiIwZcqUelvSCajIreDv74+9e/eitLQUaWlp1PdufWzTpk2YO3cuSkpK0KhRI0ybNg3z58+X/FIqShQVFREXF8fJni2u96s+evRItMxbmPQuJiYGKioq1F5vjx49IC0tjfnz54syKVcmTpb/j/3+++9wdnautmWHpmnTpuHEiRNYtmyZaDlydHQ0li5dCicnJ2oJ4h48eABbW1tISUkhKysLnTp1QlZWFho1aoRr165R38JVXFyMwMBAxMfHo7y8HBYWFnB2dpZIaSeBQICYmBiEh4cjLCwM0dHRKC4uhoWFBW7cuEElxpEjR0RLRCVRsaUulJaWYsqUKVi0aBH09fXrpA03btzA9OnTRcuVxbV06VJs2LABzZo1w7t375CZmYkGDRrAz88PO3fuRHR0NIVWV9DS0sL58+dhamoKMzMzzJ8/H6NHj0Z0dDQcHR2pXl/l5eWRkZEBHR2dKtfytLQ0WFpa4u3bt9RicUW4bas248ePpxKnRYsWuHz5MoyNjamc71NUVVVx7tw59OjRQ6JxOnfujNWrV1NP3l2bZcuWwdfXF/Pnz68xEefChQs5aQdNbE/7J1hbW9f63KtXr3DgwAHs2rULSUlJYnfauczUPGTIEDx69AiNGzeukvRO0nvd0tPTcfjw4WrHXV1dsWHDBmpx2rVrh0OHDmHx4sVVjgcFBVXLWvm1uMp+Ltzn8zk8Ho9qpz0nJ6fGUlQNGzbE3bt3qcWpC8Js3oSQanuzaXr06BH27t2LPXv2ID8/Hz///DPc3Nzw4MEDrF69Gjdu3BAruztQkbX0U1xcXMQ6v5CJiYnE6qN/jMv9qgCgqakJTU1NABU5KkJDQ2FkZER1gCIxMZGzQY+VK1cCkGwiv8DAQAQFBeGnn34SHWvfvj20tbUxatQoap325s2bIzExsUpH2s3NTWIdaXl5ebi6un5x8kpxSElJoVu3blBXV4eamhqUlZVx8uRJqtnPJV2xpS7IyMjgxIkTWLRoEeexY2JicPDgQRw6dAivXr3Czz//TOW8XGZZ57ISUtu2bREREVFtT/mRI0eoZnTnSmlpKcLCwjgZMJo5cybWrFmDXbt2SSwRq5CamppEEkZ+7M8//8ScOXOwfPnyGgcRaecKWLRoEZSVlbF+/XosWLAAQMV3ytKlS6neLwvFxcWJstSbmJhI5DPOZtr/o9DQUPj5+eH48ePQ0dHBsGHDMGzYMGpvjkAgQGRkJNq3by+x7OeamprYuXMnBg4cCD6fj8ePH1Ov41sTLS0t+Pj4YPjw4VWOHz58GHPmzEF+fj6VOKdPn8awYcMwZswY9O7dGwBw5coVBAYG4siRI1QGRbjMfl4XrKysICMjg/3796NZs2YAKjqh48aNQ0lJyRevQvlWfPjwAcePH4efnx8iIyMxYMAATJw4EY6OjqLZVVqOHz+OPXv2ICQkBCYmJpg0aRLGjh0LVVVV0c/cvn0b5ubmYpc2+fgaUVpainfv3kFWVhYKCgp48eLFV5+7cpbx2NhYLFy4ECtXroSpqWm1VTo0v2yjoqLg6OgIZ2dn+Pv7Y+rUqbh9+zaio6MRHh6Ojh07Uos1YsQIWFlZwd3dHcXFxTAzM8Pdu3dBCEFQUBCGDRtGJU7nzp3h6+sr8cSEQMVssbu7u0QT+TVt2hRhYWHVZoHS09NhZWWFp0+fih2DK9euXfuin7OysqIW859//kF4eDjCw8MhEAjQq1cvWFtbw8bGhursN1cVW7g2ceJEmJqa1po5nKbMzEwcOHAABw8exN27d2FrawtnZ2cMHTq0XiYo5LIS0pkzZzBu3DgsWLAAy5Ytg7e3N+7cuYO9e/fi7NmzEiljLGmqqqqIj4+XeKd9yJAhuHLlCpSUlGBqalqtg0szy//+/ftx6tQpBAQESHT1X+V7rcqrzbgYRJTkSuInT55g1KhRCAsLg6qqKgghePXqFWxtbREUFES1f8U67V/g33//hb+/P/z8/FBUVIQRI0Zg27ZtSEpKojZzW5mcnBzS09Ohp6dH/dxAxajusmXLvqjcC80/Ii6Xqpw7dw4rV65EYmIi5OXl0b59eyxZsuSTqyeY/8nOzsaQIUNw584dUWbw/Px8tG7dGidPnoShoWEdt/DLzZgxA0FBQdDW1sbEiRMxduzYKnWYaWvYsCFGjx4NNzc3dO7cucafKS4uxtq1a0XZxWnKysrC9OnTMXfu3Cq16f8rPp9f4xdrZZL6sk1JScG6desQFxcnWqY8b948mJqaUo2jqamJkJAQmJmZ4eDBg1iyZAmSkpIQEBCAHTt21JqZ+L8KDQ3lbNDD09MT169fx4YNG+Do6Ijk5GTo6+vj9OnTWLJkCZXXtGzZMmRkZGDPnj2iWcEPHz7Azc0NrVq1Evtz/SWVLKSlpaGpqYl27dqJVTKv8ue8ttsh2p9xPp+Pxo0bw8vLC9OmTeMkG/X35M8//8S6devQu3dvdOrUqVqHhuYsGp/PR6dOnTBmzBiMGjVKtCqHhi/d/iaJWUGuhISEYOXKlVWu5YsXL4a9vX1dN+2rcDVg9LmJIZoTQubm5qLtnbq6utW+n2htXf3cZA/t+/O8vDyUlZVVq46VlZUFGRkZ6OrqUokzcuRI5OTkYN++faKB7LS0NIwfPx6GhoYIDAykEgdgnfbP6tevn2hmztnZGY6OjpCSkoKMjIzEOu1c7PvIyMhAdnY2Bg0ahD179lSZBazMycmJWkxSBzVj67v/8sXg4+NDNTYhBJcuXUJGRgYIITAxMYGdnV29e5/4fD60tbU/myuCxsh1WVkZduzYgaFDh1K9ufuvYmNjMXbsWGRkZHz1Of7Laor6OhhWuc69i4sLmjdvjtWrVyM/Px8mJibU9lwKZxi4GPTQ0dHBoUOH0LVr1yr7SLOzs2FhYUGlTrtwFqhBgwai/fhJSUkoKSmp9r31NX9X/2X1i6amJg4dOoRevXr95zgAoKGhAWVlZUyYMAHjxo1Do0aNavy5mrYLfa2TJ0/i2rVrCAsLQ1paGszMzGBjYwMbGxv06tWLeo6NtLS0Gmtyi1u6s658akKDx+MhNzeXWqzMzEy0bt2a2vkq+5KJGdqv59WrV7h06RLu3r0LHo8HfX199OnThw0cfSHhgFGfPn1qXOJdHwdYvL29P/m8JCYXuGBtbQ1XV9dqeQb279+PXbt2ISwsjEqchg0b4vLly9UmaWJiYmBvb4/CwkIqcQDWaf8saWlpeHh4YPr06VVGayTZab948SLmzZvHyb4Pb29vzJ07l/OEWJJOeldYWIijR48iNzcXc+bMgbq6OuLj49G0aVO0aNFCIjElwdbW9ot/lsv69PXJhAkTvmiggdbItYKCAtLT0+t06WlCQgKsra2pdNDqQnl5ObKzs2usCU9zmXLr1q2xYsUK9O/fH3p6eggKCkLv3r2RlJSEPn36UNvHz+UMAxeJ/L50exAguS1ChBA8fvwYK1asQFRU1FfPBpWUlODEiRPw8/NDREQE+vXrBzc3Nzg6OnIyQPnq1StERETg6NGjOHjwIHg8Hj58+EDl3Lm5uRgyZAhSUlJEe9mB/w0e1cc97ZU9e/YMPB5Poiunvif79++Hu7t7te+Fhg0bYtu2baIcCLSVlJTUeC0XruKrT7gcMPoeJCcno127duDz+UhOTv7kz9JOjKmiooL4+PhqK0Ozs7PRqVMnap1pZWVlREREoEOHDlWOS+I+jHXaPyM6Ohp+fn44fPgw2rRpg3HjxmHkyJFo3ry5xDrtdbnv43uQnJwMOzs7UdK0O3fuQF9fH4sWLcK9e/c+m7yLqXDlyhVcuXKlxi9bPz+/OmrVt8/W1haenp4STygJVF9GTAjBw4cPsXnzZmhpaeHChQtU4uzZswdKSkrV8lEcOXIE7969o5YxF6jIzDxmzBjcu3ev2nJl2te/rVu3wtPTE0pKStDR0UF8fDz4fD42bdqE48eP18uBMGtra/z888+YOXMmlJWVkZycDD09Pbi7uyM7OxvBwcF13USq7t69izZt2uD9+/din6ugoAB79uxBQEAAPnz4gPHjx8Pb21siiaBevHghyhwfFhaG1NRUaGhowNraGkeOHKESY+DAgZCSksLOnTuhr6+PmJgYPH/+HF5eXli3bt1Xr06oS4WFhfjjjz9w6NAhvHz5EkBFbo9Ro0bhzz//pLIiQk1N7YsHbMTJG8Kl+Ph4dOnSBc7Ozpg9ezbatGkDQgjS0tKwYcMGBAUF4datW1QrWWRlZcHV1RVRUVFVjrN72ZpxWUWKK3w+H48ePUKTJk1EW5Fq6nZK4vPQsGFDhIWFVcs5FhcXBxsbG9HkobicnJxQWFiIwMBANG/eHABw//59ODs7Q01NDSdOnKASB2Cd9i/27t07BAUFwc/PDzExMRAIBPDx8YGrqyv12eJPzcrUt/Jynzs/zVhCdnZ2sLCwwNq1a6vMNEVFRWHMmDH1Lvv5l2Sq5/F4OHbsGLWY3t7eWLZsGTp16lRjiSqaF6HvDZdllj5eRszj8dC4cWP07t0b69evFyURFJeRkRG2bdtWbeVHeHg4pkyZgjt37lCJAwAdOnRA69at4e3tXeNnj+YyZaBiK0FBQQH69u0rWpZ87tw5qKqqUi2BU1hYiN27d1fJLuvq6kr99XCZyO9b8erVK6q/x7y8PLi5uSE8PBxPnz6lnlm5ffv2SEtLg7q6OqysrERL49u1a0c1TqNGjRAaGor27dujYcOGiImJgZGREUJDQ+Hl5UUtZwNXXrx4gW7duoluiI2NjUEIQXp6Og4ePAgtLS1ERUWJnUztc2W9KqM5YClJEydOxNu3b2sdEPr555+hoqJCdUCey1KXXJNEdQ5vb2/Y2dmhR48eEl+yrq6ujszMTDRq1Oizg1TiDEzdu3cP2tra4PF4nCfGHDBgABQUFBAYGAgpKSkAFauLRo4ciaKiImqTGgUFBXByckJqaiq0tLTA4/GQn58PU1NTnDp1Ci1btqQSB2Cd9q9y584d7N69G/v27UNhYSH69u37RYlzvtbH5eXEHY2qvCRe0heGz52fZiyhhg0bIj4+HgYGBlU67ffu3YORkRGVGRku1UWm+mbNmmHt2rUYN24ctXP+KGraj1vfyyzJyckhIyOjWuKWu3fvwtjYGMXFxdRiKSoqIikpqV4lO/yc2NhYODg4QF5eHpaWliCEIDY2FsXFxbh48SIsLCyoxpN0Ir/nz59j8eLFuHr1ao0rcerL7GNlHz58wLFjx+Dn54fo6Gj0798frq6ucHR0pB5r8+bNEumkf0xNTQ1xcXHQ19eHgYEBdu3aBVtbW+Tk5MDU1BTv3r2TaHzaZs2ahStXruDy5cto2rRplecePXoEe3t79OnT54tLpf5IWrduja1bt8LOzq7G5y9fvowZM2YgMzOTWkxFRUXOSl1y5d27d5g5c6bEqnPw+XyYm5uLSlvSHtQVCggIwKhRo9CgQQOJ156fNWsWJk2aJPHr3cfS0tJgZWUFVVVV0aqiiIgIUWlX2u2pKQcUbazTLgaBQIAzZ87Az89PIp12SZeX+141bdoUwcHBMDc3r9Jpv3jxItzc3FBQUFDXTfzmaWhoICYmBgYGBnXdlHrneyyzpK2tjc2bN1dLXHXq1Cn88ssv+Pfff6nF6t27N3777TeJdJaAiuSOy5cvh6Ki4mcTPdJK7tirVy8YGhpi586dolmZsrIyTJo0Cbm5uV9cduxb8dNPPyEnJwdubm5o2rRptVma+jL7CFQkC9qzZw+CgoKgp6eHCRMmYOzYsZzULZbEbF1lvXr1gpeXFwYPHowxY8bg5cuXWLhwIXbs2IG4uDikpqZSjylJurq62L59e61VMYKDgzFt2jSqq+lsbGzg6uqK4cOHQ15entp5uaakpIS0tLRa95Hn5+fD2NgYRUVF1GJyWeqSK5KuzlF5S25paSmGDRsGV1fX/5Tf6FvTpk0bZGVloWPHjpg0aRJGjRrFWeLDBw8eYPPmzUhKShJVknJ3d6d6fc/Ly5NYta+PsU77N4br8nJ1IS4ursoSUdqDEFOmTMHTp09x+PBhqKurIzk5GVJSUhg8eDCsrKywYcMGqvG+R/PmzYOSkhIWLVpU101hPkEgEMDf37/W3AOhoaFU4vz22284fPgw9uzZI0oEFx4eDldXV/z8889Yt24dlThAxdaLhQsXYu7cuTWWRxN3e4GtrS1OnDgBVVXVz94I0drTLi8vj4SEhGozTmlpaejUqROVGc+PS/TVhMfjoaysTOxYysrKiIyMrNfLW4WE1SXGjx//ya0DNDOtFxcXw93dXWKzdUIhISEoKirC0KFDkZubiwEDBiAjIwMaGho4dOgQevfuTSUOVxo0aICcnJxal5v++++/MDQ0pLqazsvLCwcOHEBxcTFGjBgBNzc3dO3aldr5uVJ5b3FNHj9+jObNm1NdCcZlqUuucFGdA6i4Rgi/cyMiIqCrqyvKhE5zubWQpJO/Xr9+HX5+fjhy5AjKy8sxdOhQTJo0iWpi2boiJSUFKysruLm54eeff4acnJzEYrFO+zeEq/JydZVk5cmTJxg1ahTCwsKgqqoKQghevXoFW1tbBAUFoXHjxlTivH79Gv369cPt27fx5s0bNG/eHI8ePUK3bt1w/vz5anuMmeo8PT2xd+9etG/fHu3bt6/2ZUu7vNz3iIsyS+7u7vD390f//v1r3DNIa5loSUkJxo0bhyNHjohmBMvLy+Hi4oJt27aJVSf7Y9/j9oKmTZti37591WoTh4SEwMXFBY8fPxY7xqlTp2p9LioqCps2bQIhhMpWhs6dO2PTpk0S77z88ccfsLGxQY8ePSRW4eRLysvR/txJerbuU168ePGf7gG+JS1atMChQ4dqnbmNiIjAqFGjcP/+fapxBQIBzp49iz179uD8+fMwNDSEq6srxo0bV22Z/tfKycnBnj17kJOTg40bN6JJkyYIDg6GlpYW2rZtK/b5+Xw+AgICal1uXVhYiIkTJ1L9nHNZ6pIrXFTn+Jjws7F37148fPgQffv2xfnz56mdn8vkr+/evcOhQ4ewZ88eREZGwsDAAG5ubqKSqzQFBwdDSUlJdL3YsmULdu7cCRMTE2zZskXs3BdCqamp8PPzw4EDB/DhwweMHDkSrq6u6NKlC5XzV8Y67d8QrsrL1VWSlZEjRyInJwf79u2DsbExgIqOzfjx42FoaIjAwEBqsYCKUd74+HjRnk5J7C/5XnE1A/k94rLMUqNGjbB3717069eP2jk/JTMzU7TMzNTUVCJL/bnYXuDq6vrZn+HxeNi9e7fYsYCK2r0nTpzAunXr0L17d/B4PERGRmLu3LkYNmyYxFb/ZGRkYMGCBThz5gycnZ2xfPlyKmWWbt26hfnz52Px4sVo166dxGbQHB0dERUVhQ8fPsDCwgI2NjawtrZGz549qdcy55KkZ+sEAgFu376NVq1aVVvS/e7dO2RnZ4vKMNUnbm5uyM7OxqVLl6oNFH748AEODg4wMDCg9ndbk6dPn2L79u34888/IRAI0K9fP3h4eIi1aiE8PBw//fQTevTogWvXriE9PR36+vpYu3YtYmJicPToUbHbXReDU1yWuuRKXVXnePv2LQ4cOIDff/8dhYWFVN8nrpO/CuXk5MDPzw///PMP3r59W22CQ1ympqZYs2YN+vXrh5SUFHTq1AleXl4IDQ2FsbEx9XKkZWVlOHPmDPz9/XHhwgW0atUKbm5uGDduHLVJSRDmmxEVFUUmTZpEVFRUiKWlJdm0aRN58uQJkZaWJrdv367r5olNRUWFxMTEVDt+8+ZN0rBhQ+4bxDASMGDAAOLk5ESePHlClJSUSFpaGomIiCCWlpbk2rVrVGM1a9aM3Llzh+o5fwQ8Ho/o6uqSIUOGkMGDB9f6oOXDhw/Ew8ODyMrKEj6fT3g8HmnQoAGZNWsWef/+PbU4Qvfv3yeTJk0iMjIyZMCAASQlJYXq+TMzM0nHjh0Jn8+v8uDxeITP51ONVVZWRqKiosiqVauIg4MDUVFRITIyMqRLly5U43BJXl6e5OTkEEIIUVJSEv13YmIiUVFREfv8e/bsIR07diRlZWXVnisrKyMdO3Yk+/btEzsO1woKCkjTpk2JtrY2WbNmDTl16hQ5deoUWbVqFdHS0iJNmjQh+fn5Eot/8+ZNMm3aNNKwYUOira1NFi9eTCZPnkwUFBSIl5fXV5+3a9euZP369YSQqp+HmJgY0rx5cyptZ8STkJBACKm4T1dWVibTpk0jcnJyxNPTk9jZ2RFFRUUSGxtLPW5YWBhxcXEhioqKREVFhUyaNIlER0dTjaGgoECysrKonvNz3r59S3bv3k169OhBeDweadOmDfUYioqKJC8vjxBCyJIlS8iwYcMIIYTExcWRpk2bUo8n9P79e+Lj40MaNGhAeDwekZWVJePGjSMPHjwQ+9z0M58wX61bt27o1q0bNm7cKCov9+uvv6K8vByXLl2ClpYW9fJylRUXF6O0tLTKMZp7jsrLy6vNyAAVKwk+3kMjLlZj/OvURXm57010dDRCQ0PRuHFj8Pl88Pl89OzZE6tWrYKHhwfVpa9eXl7YuHEjNm/eTH25a10kbBPKzMxEWFhYjX+/ixcvFvv806ZNQ1BQEHJzc+Hq6irxxGOysrLYuHEjVq1ahZycHBBCYGhoSH3J96tXr7By5Ups2rQJHTp0wJUrVyRSi9vZ2RmysrI4ePBgjYnoaJKSkkK3bt2grq4ONTU1KCsr4+TJk8jJyZFYTEnr3Lkzzp07h5kzZwL43yqcnTt3olu3bmKff/fu3ZgzZ46ozFFlUlJS+O2337B582aMHTtW7FhcatmyJaKjozFjxgwsWLCgyiqmvn37YvPmzdDS0qIa88mTJ9i3bx/27NmDrKwsDBw4EEFBQXBwcBC9byNGjMDgwYO/OrdHSkoKDh48WO1448aN8fz5c7HaX9cKCwsRExNT47XcxcWljlr131lYWMDc3ByTJk3C+fPnsXPnThgYGIiqf0RHR1OrzlFQUAB/f3/4+/sjLy8P3bt3x6ZNmzBixAiJbO/s0qULsrOzOanYcu3aNezZs0e0emT48OFYs2YN1fKqQrKysqJ8MZcvXxZ93tTV1anlHqgsNjYWfn5+CAoKgqKiIubMmQM3Nzc8ePAAixcvhpOTE2JiYsSKwTrt3yAFBQW4urrC1dVVVF5u9erVmD9/PvXyckVFRZg3bx4OHz5c45cDzSU4vXv3hqenJwIDA0V7V+7fv4/Zs2ejT58+1OJ8rsY4UztJLYP6kQgEAtHS3UaNGuHBgwcwMjKCjo4OlXrmHw+shIaG4sKFC2jbtm21QbHjx49/dZyEhATRIB6X9Zx37tyJ6dOno1GjRtDU1Kzy98vj8ah02rdu3QpfX18cP34cfn5+WLBgAfr37w83NzfY29tTu2Z8yTJ8gM5A4tq1a7FmzRpoamoiMDAQTk5OYp+zNqmpqUhISICRkZHEYgDAP//8g/DwcISHh0MgEKBXr16wtrbGokWLxE5IWJdWrVoFR0dHpKWloaysDBs3bsTt27cRHR392SXFX+LOnTufzDfQuXNnpKenix2nLujp6eHChQt4+fIlsrKyAACGhoYSG3Rr2bIlDAwM4OrqigkTJtS4zNXS0hKdO3f+6hiqqqp4+PBhtQzUCQkJaNGixVeft64Jt+UUFRVBWVm52rW8PnXahYnU5s+fj9LSUgwdOhR///039WSOffv2xdWrV9G4cWO4uLjA1dVVItfZ5ORk0X/PnDkTXl5eePTokUSSv/77778ICAiAv78/cnJy0KVLF/j6+mLUqFES3ebUs2dP/Prrr+jRowdiYmJw6NAhABWTAjST+fn4+GDPnj3IyMhA//79RVsWhVtS9PT0sH37diqlD9me9npCUuXlfvnlF1y9ehXLli2Di4sLtmzZgvv372P79u1YvXo1nJ2dqcUqKCiAk5MTUlNToaWlBR6Ph/z8fJiamuLUqVPU/ohYjXGmLkm6zNLEiRO/+Gdp79nigo6ODmbMmIF58+ZxFvPevXvw9/fH3r17UVpairS0NCo3E3w+Hzo6OjA3N6+W4KeyEydOUIklLy8POzu7GmdYhcQZyBGysrLC4sWLJZ4nhM/no3HjxvDy8sK0adPqZbbp2qSkpGDdunWIi4sT5V2ZN28eldk6RUVFREdH13qznZycjG7dulEt7/W9ioiIkMhqlcp+++03REdH48iRI2jdujXi4+Px+PFjuLi4wMXFBUuWLJFofElp3bo1+vXrh5UrV0oskSTXJJ3VfdCgQXBzc8OAAQM+eR0Xl7DaSG3fSzSTv0pLS0NDQwPjxo2Dm5ubKKeVpOXn52PGjBkoKCiAh4cH3NzcAACzZ8+GQCDA33//Ldb5hasThHvXJ0yYAE1NzRp/tqSkBIGBgWLnCWOd9h+ctrY29u7dCxsbG6ioqCA+Ph6GhobYt28fAgMDqWaoFLp06RIyMjJACIGJiQn1Gz9WY5ypS58qsxQUFER1VYmkcZ2wDajYkpOYmAh9fX1q5/yc/Px80XLEkpISZGRkUOm0z5gxA0FBQdDW1pb4MvwJEyZ80QoBGgM5R44cwdKlSyVWlk/o5MmTuHbtGsLCwpCWlgYzMzPY2NjAxsYGvXr1qtfJ6CSpQ4cOmDZtGqZNm1bj81u3bsWOHTuQmJjIbcPqoeLiYhBCRJ3Oe/fu4cSJEzAxMalWDeJrlZaWYsKECQgKCgIhBNLS0hAIBBgzZgz8/f0l2nmTJEVFRaSkpHB6LeeSpLO6S9LnEr5WJm7y1+PHj2PQoEGiyjPfCz6fjxYtWsDGxga9e/dG7969JZKctzLWaf/BKSkp4fbt29DR0UHLli1x/PhxWFpaIi8vD6ampnj79q3YMUJDQ+Hu7o4bN25Umyl59eoVunfvjm3btlEbzWY1xplvjaTKLOXl5aGsrKxKtQkAyMrKgoyMDHR1dcU6P5czxUJubm7o3LlzrR0OWj58+CBaHi8stTlx4kQ4OjpSzapdOU5UVJREluFzrS7K8r169QoRERE4evQoDh48CB6Phw8fPlA7Pxfl5biydu1arF27FqGhodUGUJKSktCnTx/89ttv+O233+qohfWHvb09hg4dimnTpqGwsBBt2rSBjIwMnj17Bh8fH0yfPp1arJycHCQkJKC8vBzm5ubVruv1zdChQzFq1CiMGDGirpsiMZLM6s78d69fvxb1Mz63b13clVsREREIDw9HWFgYoqOj8f79e2hra6N3796wtbWFra0t9e0t39ewB/Of6evr4+7du9DR0YGJiQkOHz4MS0tLnDlzBqqqqlRibNiwAZMnT67xD6Rhw4aYOnUqfHx8qHXa379/jx07duDy5cusxjjDGS73LwtNmDABrq6u1W7ubt68iV27diEsLEys83OdsA2o2J+6aNEi3Lhxo8ZZXA8PD7FjVJ4BnzhxIoKCgqChoSH2eWvSoEEDjB49GqNHjxYtw58xYwbVZfhcy8vL4yzWixcvRDdGYWFhSE1NhYaGBvVyUXFxcdi0aZNEy8sJl6R+Co/HQ1lZmVhxZs+ejQsXLqBjx46ws7NDmzZtwOPxkJ6ejsuXL6NHjx6YPXu2WDF+FPHx8fD19QUAHD16FE2bNkVCQgKOHTuGxYsXU+20GxgYcLZCsKSkpMbkcDRKQgr1798fc+fORVpaWo3X8kGDBlGLxbXw8HD4+fnh2LFjkJKSwogRI0TLr+ubgIAANGrUCP379wdQsV1jx44dMDExQWBgoMRnj2lSU1PDw4cP0aRJE6iqqtZ4vaU1uNyrVy/06tULCxcuRGlpKaKjo0XfU4GBgfjw4QMMDQ2p5DISYjPtPzhfX19ISUnBw8MDV69eRf/+/SEQCFBaWgpfX194enqKHUNHRwfBwcG17mPJyMiAvb098vPzxY4FfLrGOI/HQ2hoKJU4DFNZXcxKV97SUll2djY6deqEwsJCsWNwPVP8cTKmyng8HnJzc8WOwefzoa2tDXNz80++Bhr7vyuT1DL871X79u2RlpYGdXV1WFlZiZbGt2vXTiLxBAIBYmJiqsyeFBcXw8LCAjdu3BD7/KdOnar1uaioKGzatAmEEBQXF4sdS/gdfvDgQWRlZYEQgtatW2PMmDGYNWtWtTrnTM0UFBSQkZEBbW1tjBgxAm3btsWSJUtQUFAAIyMjUXbq/+pzFTkqoznRkJWVBVdXV0RFRVU5LolVMp9asSSpFTmSVFNWdzc3N4lldeeKkZER/vnnH/Tu3RvR0dHo06cPNmzYgLNnz0JaWpr696AkhYeHo0WLFjA0NPxsUk/aA79AxXaayMhIhISEYOfOnXj79i3VzznrtDNV5OfnIzY2FoaGhtT2JcrJySE1NbXWchLZ2dkwNTWlcqPCMHWFy/3LQg0bNkRYWBjMzc2rHI+Li4ONjQ3evHlDNZ6kErZxjcv931wtw68LaWlpyM/PR0lJSZXjtGbQNm/eLNFOem3u3LmDsLAwXL58GSdPnoSqqiqePn0qkVgZGRlYsGCBKNP28uXLqc52MuJp3749Jk2ahCFDhqBdu3YIDg5Gt27dEBcXh/79++PRo0dfdd6PJxfi4uIgEAhEmcIzMzMhJSWFjh07Up1o6NGjB6SlpTF//vwaq+uYmZlRi/U94Sqre12oPDA1b948PHz4EHv37sXt27dhY2MjsWufpAj3mguXqNva2oq9VbA279+/R1RUFK5evYqwsDDcunULenp6sLa2hpWVFaytrekukRe70jtTL125coUYGxuTV69eVXuusLCQmJiYkGvXrlGJpa+vT44fP17r88eOHSN6enpUYjFMXXr//j05ePAgsbOzIwoKCmT48OEkODiYlJeXSyRe//79yfDhw0lZWZnoWFlZGRk2bBhxdHSkHu/evXvE29ub6OnpkRYtWpA3b95Qj/E9mT59OlFTUyNmZmZkw4YN5NmzZ3XdJCpycnJI+/btCY/HI3w+n/B4PNF/8/l86vE+fPhAMjIySGlpKfVzC23dupWMHDmSaGpqksaNG5OhQ4eSjRs3kqSkJInEu3//Ppk0aRKRkZEhAwYMICkpKRKJw4jnyJEjREZGhvD5fNK3b1/R8ZUrV1K7xq5fv54MHDiQvHjxQnTsxYsXxMnJiaxbt45KDCEFBQWSnp5O9Zxfori4mPOYNA0cOJCcPHmyynft96Jx48YkPj6eEEJIhw4dSEBAACGEkOzsbKKoqEgtzsSJE8nr16+rHX/79i2ZOHEitTjXrl0jy5cvJ3369CEKCgqEz+cTXV1d4urqSvbt20f+/fdfKnGsrKyIvLw8adeuHZkxYwY5dOgQefToEZVz14bNtP+gBg0aBFtb21r3tf3999+4evUqlaW8M2fOFI1AycnJVXmuuLgYlpaWsLW1Fbv8glBRURFWr16NK1eu1Lhni8byWob5HC5mpdPS0mBlZQVVVVVRToiIiAi8fv0aoaGhVGYouZgp/vXXX7F8+XIoKip+dtlofcpJUVfL8CVt4MCBkJKSws6dO6Gvr4+YmBg8f/4cXl5eWLduHbX8JMXFxXB3d0dAQACAitlHfX19eHh4oHnz5pg/fz6VOAB35eVevXqFlStXYtOmTejQoQPWrFkj8ZJijHgePXqEhw8fwszMTHTNi4mJgYqKCpXayy1atMDFixfRtm3bKsdTU1Nhb2+PBw8eiB1DqHPnzvD19UXPnj2pnbM2AoEAK1euxLZt2/D48WPR3++iRYugq6tbb/eAf2+cnZ2RkZEBc3NzBAYGIj8/HxoaGjh9+jR+//13scvUCklJSYn2m1f27NkzaGpqip3LoyYf7zW/ceMGtb3mMjIyaNasGQYPHgwbGxtYWVmhUaNGlFpeM5aI7geVlJSENWvW1Pq8vb091q1bRyXWwoULcfz4cbRu3Rru7u4wMjISJcXZsmULBAIB/vjjDyqxAGDSpEkIDw/HuHHjalz+xTBc4PF4oozaHw8c0WJiYoLk5GRs3rwZSUlJkJeXh4uLC9zd3akszecqYVtCQgJKS0tF//29cHFx+S6vP9HR0QgNDUXjxo3B5/PB5/PRs2dPrFq1Ch4eHtTew/nz5yMpKQlhYWFwdHQUHbezs8OSJUuodtqPHz+Oa9euISgoCIsXL5ZIebm1a9dizZo10NTURGBgIJycnCi0nJE0TU3NavWXLS0tqZ3/9evXePz4cbVO+5MnT6hscaqcRXvNmjX47bffsHLlyhqTw9EcrPrzzz8REBCAtWvXYvLkyaLjpqam8PX1ZZ32b8SWLVuwcOFCFBQU4NixY6Lv+Li4OIwePVrs879+/RqEEBBC8ObNmyqTdwKBAOfPn6/WkadFRkYGVlZW6Ny5M7p16ybaa56dnS32uQsLCxEREYGwsDCsWbMGo0ePRuvWrWFtbS1KZNq4cWMKr+J/2Ez7D4rrfeb37t3D9OnTERISIkrSxePx4ODggK1bt1Ldb6Kqqopz586hR48e1M7JMF/ie9u//L3OFDPiUVNTQ1xcHPT19WFgYIBdu3bB1tYWOTk5MDU1/erkXB/T0dHBoUOH0LVrVygrKyMpKQn6+vrIzs6GhYXFZ0v6fC1JlZfj8/mQl5eHnZ3dJ2tvs7+nbwcXK/dcXFwQHh6O9evXo2vXrgCAGzduYO7cubCyshKtNPlaH1ctIP+fdK4yIoFEdIaGhti+fTv69OlT5e83IyMD3bp1w8uXL6nFYr5dn6uawePx4O3tTXXyjvO95gDevHmDyMhIUcykpCS0atWK2koFgM20/7BatGiBlJSUWjvtycnJaNasGbV4Ojo6OH/+PF6+fIns7GwQQtCqVSuoqalRiyGkpqYm8QRgDPMxLsuIfezdu3c1JgQTN5kklzPFX1Iyj8fjYffu3Ry0hvmUdu3aITk5Gfr6+ujSpQvWrl0LWVlZ7NixA/r6+tTiPH36tMYZmKKiIol8LiVdXu57XXnxPeNi5d62bdswZ84cjB07VrTiSFpaGm5ubvjrr7/EPv/Vq1fFPsfXuH//fo33mOXl5aLXyXw7JHUfcfXqVRBC0Lt3bxw7dqzK/bmsrCx0dHTQvHlzsWJUZm1tjVu3bsHAwABWVlaYOXMmrK2t0bRpU2oxaqKoqAh1dXWoq6tDTU0N0tLSSE9PpxqDzbT/oLjeZ86l/fv349SpUwgICICCgkJdN4f5QdTFrPTTp08xceJEXLhwocbn61NJnboomcd8nZCQEBQVFWHo0KHIzc3FgAEDkJGRAQ0NDRw6dAi9e/emEsfa2ho///wzZs6cCWVlZSQnJ0NPTw/u7u7Izs5GcHAwlTgA9+XluCAQCODv71/rLDErf/p5XK7cKyoqQk5ODgghMDQ0lEgZsfz8fGhpadU4015QUEC1ckGnTp0wa9YsjB07tspMu7e3Ny5fvoyIiAhqsZiv9/TpU0yYMKHW6ymt+4h79+5BW1tb4gOXXO01Ly8vR2xsLMLCwnD16lVcv34dRUVF1TLX06xzz2baf1Bc7zPn0vr165GTk4OmTZtCV1e32p6t+Pj4OmoZ8z2ri1m0WbNm4eXLl7hx4wZsbW1x4sQJPH78GCtWrMD69es5bYu4pk2bhqCgIOTm5nJWMo/5Og4ODqL/1tfXR1paGl68eAE1NTWqfwOrVq2Co6Mj0tLSUFZWho0bN+L27duIjo7+bA3e/2rKlCn1vpP+MU9PT/j7+6N///5o164dm+X/Clyu3BPO1PF4PInV/dbT06sxGdiLFy+gp6dHpYPm6uqKjRs3YsmSJRg3bhzu37+P8vJyHD9+HHfu3MHevXtx9uxZseMwdMyaNQuFhYUSv49IT09HQUGBKAnili1bsHPnTpiYmGDLli3UVt5ytddcVVUVRUVFaNasGWxsbODj4wNbW1sYGBhQeBU1YzPtPzAu95lzydvb+5PPL1myhKOWMIxkNWvWDKdOnYKlpSVUVFQQGxuL1q1b4/Tp01i7di0iIyPruon/SeWcAFFRUejfvz/c3Nxgb2/POhw/qJSUFKxbtw5xcXEoLy+HhYUF5s2bB1NTU4nEKykpQV5eHgwMDCAtXb/nNRo1aoS9e/eiX79+dd2UeouLlXvl5eWiDtLbt28BAMrKyvDy8sIff/xBNR8Kn8/H48ePq3Va7t27BxMTExQVFYkdo3KW8JCQEKxcubLK3+/ixYthb28vdhyGDq7uI0xNTbFmzRr069cPKSkp6NSpE7y8vBAaGgpjY2Ps2bOHSpyPSWqv+fbt22Fra4vWrVtTaunnsU47w8k+c4Zh6FNRUUFycjJ0dXWhq6uLAwcOoEePHsjLy0Pbtm2pJQSrC1yUzGP+m6FDh37Rz9XHRGpclpfjSvPmzREWFsbpTeX3xtzcXLRkXVIr9xYsWIDdu3fD29sbPXr0ACEE169fx9KlSzF58mT8+eefYscQltLcuHEjJk+eXGUAQiAQ4ObNm5CSksL169fFjsXn8/Ho0SOJZQRn6OLqPkJJSQmpqanQ1dXF0qVLkZqaiqNHjyI+Ph79+vXDo0ePqMT5WHl5OW7duoWrV6/i6tWriIyMxPv37+vV9kGh+j2MzFChpqaGzp0713UzqIuLi0N6ejp4PB5MTExgbm5e101iGKqMjIxw584d6OrqokOHDti+fTt0dXWxbds2qokk6wIXJfOY/6Zhw4ZV/n3w4EEMHDgQysrKddQiergsL8cVLy8vbNy4EZs3b2YrVb7S4MGDJR4jICAAu3btwqBBg0THzMzM0KJFC8yYMYNKp11YhpEQgpSUFMjKyoqek5WVhZmZGebMmSN2HCH2eas/uLqPkJWVFQ0AXL58GS4uLgAAdXV1qpVAPrfXfMuWLbC1taUWj0tspp357jx58gSjRo1CWFgYVFVVQQjBq1evYGtri6CgIOp1Exmmrhw4cAClpaWYMGECEhIS4ODggOfPn0NWVhb+/v4YOXJkXTfxP/neSuZ97yonl6LpcyWCgIpOQVlZGbWYdVVejraPV0OEhoZCXV0dbdu2rTZLXB9XRHyP5OTkkJycXG1FxJ07d9ChQwdqpXcBYOLEidi4cSPVeuwf4/P5aNiw4Wf/hl+8eCGxNjCfl52dDUNDQ87uIwYNGoSSkhL06NEDy5cvR15eHlq0aIGLFy/C3d0dmZmZVOKoqKhU2WtuY2Mj8b3mXGEz7cx3Z+bMmXj9+jVu374NY2NjAEBaWhrGjx8PDw8PBAYG1nELGYYOZ2dn0X+bm5vj7t27yMjIgLa2tkSypUpSXZbMY74tn6oQEBUVhU2bNn2ywsDX4Lq8nKR8vBpiyJAhddSS70tJSUmNGfhpZFs3MzPD5s2bq1Xr2bx5M8zMzMQ+f2WS2jf8MW9v72qfRebb0rp16yqZzu/evSvR+4jNmzdjxowZOHr0KP755x9RnfQLFy5UWd0krr/++ovzveZcYTPtzHenYcOGuHz5crUl/zExMbC3t0dhYWHdNIxhJEwgECAlJQU6Ojr1LjdFXZTMY8QjqZn2mmRkZGDBggU4c+YMnJ2dsXz5cqrlqbgsL8fUH5mZmXBzc0NUVFSV44QQ8Hg8Kvtiw8PD0b9/f2hra6Nbt27g8XiIiopCQUEBzp8/j169eokdQ+hz5RhplAFke9rrh4iICISHhyMsLAzR0dF4//49tLW10bt3b1FHXtixZr4NbKad+e6Ul5dXWwYIVNRuZHtjme/JrFmzYGpqCjc3NwgEAlhZWSE6OhoKCgo4e/YsbGxs6rqJX6wuSuYx374HDx5gyZIlCAgIgIODAxITEyVSlo3L8nJc6d27N44fPw5VVdUqx1+/fo3BgwezOu1fYOLEiZCWlsbZs2fRrFkziVyjrK2tkZmZiS1btiAjIwOEEAwdOhQzZsxA8+bNqcb6eOa+tLQUiYmJSE1Nxfjx46nEYNfx+qFXr17o1asXFi5ciNLSUkRHRyMsLAxhYWEIDAzEhw8fYGhoiDt37lCLmZOTgz179iAnJwcbN25EkyZNEBwcDC0tLbRt25ZanO8Vm2lnvjtOTk4oLCxEYGCg6Avv/v37cHZ2hpqa2ieXXjJMfdKyZUucPHkSnTp1wsmTJ/HLL7/g6tWr2Lt3rygBC8PQcvr06Sr/Hj16NDZs2ICmTZtWOV45odbXevXqFVauXIlNmzahQ4cOWLNmDdUZx5pwXV5O0mqb8Xzy5AlatGiB0tLSOmpZ/aGoqIi4uDi0adOmrpsiUUuXLsXbt2+xbt06sc/FZtrrr+LiYkRGRiIkJAQ7d+7E27dvqWVZDw8Px08//YQePXrg2rVrSE9Ph76+PtauXYuYmBgcPXqUSpzvGeu0M9+dgoICODk5ITU1FVpaWuDxeMjPz4epqSlOnTqFli1b1nUTGYYKOTk5ZGdno2XLlpgyZQoUFBSwYcMG5OXlwczMrN4kz2Lqhy9JBkhjyfDatWuxZs0aaGpqYuXKlXBychLrfD+a5ORkAECHDh1EieiEBAIBgoODsX37dty9e7eOWlh/dO7cGb6+vujZs6dE4xQWFiImJqbGffPCLNuSlJ2dDUtLS5Yc7gfz/v17REVFiWqY37p1C3p6erC2toaVlRWsra2pLZHv1q0bhg8fjl9//bXK1qpbt25h8ODBuH//PpU43zPWaWe+W5cuXRItNTMxMYGdnV1dN4lhqNLR0cHOnTvRp08f6OnpYevWrRgwYABu376Nnj174uXLl3XdRIb5z/h8PuTl5WFnZwcpKalaf47lN6hZ5ez7Nd3iycvLY9OmTXB1deW6afVOaGgoFi5ciJUrV8LU1LTa1jsaWdiFeRqKioqgrKxcZXk5j8fjpCO9b98+zJs3Dw8ePJB4LObbYG1tjVu3bsHAwEDUQbe2tq62cooWJSUlpKSkQE9Pr0qn/e7du2jTpg3ev38vkbjfE7annfluhIaGwt3dHTdu3ICKigr69u2Lvn37AqhYatm2bVts27ZN4kssGYYrEydOxIgRI0R7LYWf95s3b373yzmZ7xeX+Q3qorycpOXl5YEQAn19fcTExFQpcyorK4smTZp8cjCE+R/hYH+fPn2qHKeZiM7Lywuurq5YuXIlFBQUxD7fp3xcEpAQgocPHyI2NhaLFi2SaGzm2xIVFYVmzZrB1tYWNjY2sLKykmjVGVVVVTx8+BB6enpVjickJLCEd1+IddqZ78aGDRswefLkGke+GzZsiKlTp8LHx4d12pnvxtKlS9GuXTsUFBRg+PDhaNCgAQBASkoK8+fPr+PWMczX8ff35yxWXZSXkzQdHR2UlpbCxcUF6urq0NHRqesm1VtXr16VeIz79+/Dw8ND4h12oHpJQD6fDyMjIyxbtgz29vYSj898OwoLCxEREYGwsDCsWbMGo0ePRuvWrWFtbQ0bGxtYW1tXGfAT15gxYzBv3jwcOXIEPB4P5eXluH79OubMmcPJFpDvAVsez3w3dHR0EBwcLKrN/rGMjAzY29sjPz+f45YxDHcKCwurZYtmERAv0QAAE+RJREFUGObLSbq8HFfU1NQQFxfHSUk+5usNHToUo0aNwogRI+q6KcwP7M2bN4iMjBTtb09KSkKrVq2QmppK5fylpaWYMGECgoKCQAiBtLQ0BAIBxowZA39/f7b65wuwmXbmu/H48eMaS70JSUtL4+nTpxy2iGEka82aNdDV1cXIkSMBACNGjMCxY8fQrFkznD9/Hu3bt6/jFjJM/cFVeTmuDB48GCdPnsSvv/5a102p1yIiIrB9+3bk5ubiyJEjaNGiBfbt2wc9Pb2vTlBXuRJD//79MXfuXKSlpdW4b55GNYaPxcXFIT09HTweDyYmJjA3N6ceg6lfFBUVoa6uDnV1daipqUFaWhrp6elinzc7OxuGhoaQkZHBgQMHsGzZMiQkJKC8vBzm5uZo1aoVhdb/GFinnflutGjRAikpKTA0NKzx+eTkZDRr1ozjVjGM5Gzfvh379+8HUJF48dKlS7hw4QIOHz6MOXPm4OLFi3XcQob59n1cXu7KlSvfxTYqQ0NDLF++HFFRUejYsSMUFRWrPO/h4VFHLas/jh07hnHjxsHZ2Rnx8fH48OEDgIpZyZUrV+L8+fNfdd7BgwdXO7Zs2bJqx2jtmxd68uQJRo0ahbCwMKiqqoIQglevXsHW1hZBQUFUl0Mz37by8nLExsYiLCxMVCK2qKgILVq0gK2tLbZs2QJbW1ux47Ru3Vp0zt69e8PW1hY///wzhVfw42HL45nvxsyZM0UlK+Tk5Ko8V1xcDEtLS9ja2uLvv/+uoxYyDF3y8vLIzMyElpYWPD098f79e2zfvh2ZmZno0qULyx7PSMQff/wBGxsb9OjRg5N9uJL0PZeX+zjhU2U8Hg+5ubkctqZ+Mjc3x+zZs+Hi4lIl43ViYiIcHR3x6NGjum7ifzJy5Ejk5ORg3759oq2EaWlpGD9+PAwNDREYGFjHLWS4oqKigqKiIjRr1gw2NjawsbGBra0tDAwMqMaJiIhAeHg4wsLCEB0djffv30NbW1vUgbe1tWWJ6L4Q67Qz343Hjx/DwsICUlJScHd3h5GREXg8HtLT07FlyxYIBALEx8dLrJwFw3CtefPmOHr0KLp37w4jIyOsWLECw4cPx507d9C5c2dWp52RCEdHR0RFReHDhw+wsLAQJS3q2bMnlJSU6rp5/wkrL8d8ioKCAtLS0qCrq1ul056bmwsTExOJl6m6f/8+1Q5Nw4YNcfnyZXTu3LnK8ZiYGNjb26OwsJBaLObbtn37dtja2qJ169acxSwtLUV0dDTCwsIQFhaGGzdu4MOHDzA0NMSdO3c4a0d9xZbHM9+Npk2bIioqCtOnT8eCBQtEGX95PB4cHBywdetW1mFnvitDhw7FmDFj0KpVKzx//hw//fQTACAxMbHWbSIMI67g4GAIBALExMSIZlC2bt2K4uJiWFhY4MaNG3XdxC/GZXm5ulT5+5D5cs2aNUN2djZ0dXWrHI+MjJRogr9Hjx7hzz//xK5du1BcXEztvOXl5TXm/pGRkUF5eTm1OMy3b+rUqZzHlJGRgZWVFTp37oxu3bohJCQEO3fuRHZ2NudtqY9Yp535rujo6OD8+fN4+fIlsrOzQQhBq1atoKamVtdNYxjqfH19oauri4KCAqxdu1Y0y/nw4UPMmDGjjlvHfM+kpKTQrVs3UdIiZWVlnDx5Ejk5OXXdtP+Ey/JydWHv3r3466+/kJWVBaBif+ncuXMxbty4Om5Z/TB16lR4enrCz88PPB4PDx48QHR0NObMmYPFixeLde7CwkL88ssvuHjxImRkZDB//ny4u7tj6dKlWLduHdq2bQs/Pz9Kr6RC79694enpicDAQDRv3hxAxWz+7Nmzq9WiZxha3r9/j6ioKFFm+lu3bkFPTw/W1tb4559/YG1tXddNrBfY8niGYRiGYb7YP//8g/DwcISHh0MgEKBXr16i2r6sYsG3w8fHB4sWLYK7uzt69OgBQgiuX7+OLVu2YMWKFZg9e3ZdN7FeWLhwIXx8fERL4Rs0aIA5c+Zg+fLlYp13xowZOHPmDEaOHIng4GCkp6fDwcEB79+/x5IlSyTSkSkoKICTkxNSU1OhpaUFHo+H/Px8mJqa4tSpU2jZsiX1mMyPzdraGrdu3YKBgQGsrKxgbW0Na2trtvL1K7BOO8MwTD2XlpaG/Px8lJSUVDkuiVJBDMPn89G4cWN4eXlh2rRpUFFRqesmMTXQ09ODt7c3XFxcqhwPCAjA0qVLkZeXV0ct+/a9e/cOc+fOxcmTJ1FaWgpbW1t4eXkBAExMTKjkbtDR0cHu3bthZ2eH3NxcGBoawsPDAxs2bBD73J9z6dIlZGRkgBACExMT2NnZSTwm82OSkZFBs2bNMHjwYNjY2MDKygqNGjWq62bVS6zTzjAMU0/l5uZiyJAhSElJAY/Hq7ZvlWapIIYROnnyJK5du4awsDCkpaXBzMxMlH24V69e9S4Z3fdKTk4Oqamp1fJbZGVlwdTUVOJJ1OqzuXPnYuvWrXB2doa8vDwOHjwIGxsbHDlyhFoMGRkZ3Lt3T7RMXUFBATExMWjXrh21GAxT14qKihARESEqLZeYmIjWrVuLVmdZW1uzUoNfiF/XDWAYhmG+jqenJ/T09PD48WMoKCjg9u3buHbtGjp16oSwsLC6bh7znRo8eDB8fHwQHx+Px48fY9GiRXj8+DGcnJygoaFR181j/p+hoSEOHz5c7fihQ4fQqlWrOmhR/XH8+HHs3r0bO3bswMaNG3Hu3DmcPHmS6kDox0nhpKSkoKioSO38ld28eRMXLlyocmzv3r3Q09NDkyZNMGXKFFENeoahSVFREY6Ojli9ejVu3ryJZ8+eYe3atVBQUMDatWvRsmVLNlD1hVgiOoZhmHoqOjoaoaGhaNy4Mfh8Pvh8Pnr27IlVq1bBw8MDCQkJdd1E5jv14sULUeb4sLAwpKamQkNDgyUU+oZ4e3tj5MiRuHbtGnr06AEej4fIyEhcuXKlxs488z8FBQXo1auX6N+WlpaQlpbGgwcPoKWlRSUGIQQTJkxAgwYNAFQk65o2bVq1jjuNcoNLly6FjY2NqMJISkoK3NzcMGHCBBgbG+Ovv/5C8+bNsXTpUrFjMcynKCoqQl1dXZTEVFpaGunp6XXdrHqBddoZhmHqKYFAIFqK3KhRIzx48ABGRkbQ0dFhNU8ZiWnfvj3S0tKgrq4OKysrTJ48GTY2Nmy25BszbNgw3Lx5E76+vjh58qRo/3JMTAzMzc3runnfNIFAAFlZ2SrHpKWlUVZWRi3G+PHjq/x77Nix1M79scTExCqJ84KCgtClSxfs3LkTAKClpYUlS5awTjtDXXl5OWJjY0XL469fv46ioiK0aNECtra22LJlC2xtbeu6mfUC67QzDMPUU+3atUNycjL09fXRpUsXrF27FrKystixY4dEawgzP7YpU6awTno90bFjR+zfv7+um1HvfDwLDtQ8Ey7OLPiePXvEauN/8fLlyyrZusPDw+Ho6Cj6d+fOnVFQUMBZe5gfh6qqKoqKitCsWTPY2NjAx8cHtra2MDAwqOum1Tus084wDFNPLVy4EEVFRQCAFStWYMCAAejVqxc0NDQQFBRUx61jvlfu7u4AgJKSEuTl5cHAwADS0ux2gvl+fDwLDkh2JlzSmjZtiry8PGhpaaGkpATx8fHw9vYWPf/mzZsq++sZhpa//voLtra2aN26dV03pd5j2eMZhmG+Iy9evICampoogzzD0FZcXAx3d3cEBAQAADIzM6Gvrw8PDw80b94c8+fPr+MW/tj4fP5n//55PB7Vpd7Mt23q1KlISUnBmjVrcPLkSQQEBODBgweiLQAHDhzAhg0bcOvWrTpuKcMwtWFD4wzDMPWMq6vrF/2cn5+fhFvC/Ijmz5+PpKQkhIWFVVlia2dnhyVLlrBOex07ceJErc9FRUVh06ZNYPM1P5YVK1Zg6NChsLa2hpKSEgICAqrs2ffz84O9vX0dtpBhmM9hM+0MwzD1DJ/Ph46ODszNzT958/2pm3eG+Vo6Ojo4dOgQunbtCmVlZSQlJUFfXx/Z2dmwsLDA69ev67qJzEcyMjKwYMECnDlzBs7Ozli+fDm0tbXrulkMx169egUlJSVISUlVOf7ixQsoKSlVS77HMMy3g820MwzD1DPTpk1DUFAQcnNz4erqirFjx0JdXb2um8X8IJ4+fYomTZpUO15UVMS2ZXxjHjx4gCVLliAgIAAODg5ITExkCQR/YA0bNqzxOPv+YJhvH7+uG8AwDMP8N1u3bsXDhw8xb948nDlzBlpaWhgxYgRCQkLYsldG4jp37oxz586J/i3sqO/cuRPdunWrq2Yxlbx69Qrz5s2DoaEhbt++jStXruDMmTOsw84wDFNPseXxDMMw9dy9e/fg7++PvXv3orS0FGlpaaL67QxDW1RUFBwdHeHs7Ax/f39MnToVt2/fRnR0NMLDw9GxY8e6buIPbe3atVizZg00NTWxcuVKODk51XWTGIZhGDGxTjvDMEw9l5+fD39/f/j7+6OkpAQZGRms085IVEpKCtatW4e4uDiUl5fDwsIC8+bNg6mpaV037YfH5/MhLy8POzu7anuXKxOnxjjDMAzDLdZpZxiGqYc+fPiA48ePw8/PD5GRkRgwYAAmTpwIR0dH8Pls5xPD/KgmTJjwRbkF9uzZw0FrGIZhGBpYp51hGKaemTFjBoKCgqCtrY2JEydi7Nix0NDQqOtmMQzDMAzDMBLAOu0MwzD1DJ/Ph7a2NszNzT85o8aWvzI08fn8z87g8ng8lJWVcdQihmEYhvkxsJJvDMMw9YyLiwsrrcVw7sSJE7U+FxUVhU2bNrHqBQzDMAwjAWymnWEYhmGYr5KRkYEFCxbgzJkzcHZ2xvLly6GtrV3XzWIYhmGY7wrLVsQwDMMwzH/y4MEDTJ48Ge3bt0dZWRkSExMREBDAOuwMwzAMIwGs084wDMMwzBd59eoV5s2bB0NDQ9y+fRtXrlzBmTNn0K5du7puGsMwDMN8t9iedoZhGIZhPmvt2rVYs2YNNDU1ERgYCCcnp7puEsMwDMP8ENiedoZhGIZhPovP50NeXh52dnaQkpKq9edY1QKGYRiGoYvNtDMMwzAM81msagHDMAzD1A02084wDMMwDMMwDMMw3yiWiI5hGIZhGIZhGIZhvlGs084wDMMwDMMwDMMw3yjWaWcYhmEYhmEYhmGYbxTrtDMMwzAMwzAMwzDMN4p12hmGYRiGYRiGYRjmG8U67QzDMAzDMAzDMAzzjWKddoZhGIb5gT158gRTp06FtrY2GjRoAE1NTTg4OCA6OhoAwOPxcPLkyf98Xl1dXWzYsIFuYxmGYRjmByRd1w1gGIZhGKbuDBs2DKWlpQgICIC+vj4eP36MK1eu4MWLF3XdNIZhGIZhAPAIIaSuG8EwDMMwDPcKCwuhpqaGsLAwWFtbV3teV1cX9+7dE/1bR0cHd+/eRU5ODn799VfcuHEDRUVFMDY2xqpVq2BnZwcAsLGxQXh4eJVzCW83oqKiMH/+fNy6dQuNGjXCkCFDsGrVKigqKkrwlTIMwzBM/cWWxzMMwzDMD0pJSQlKSko4efIkPnz4UO35W7duAQD27NmDhw8fiv799u1b9OvXD5cvX0ZCQgIcHBwwcOBA5OfnAwCOHz+Oli1bYtmyZXj48CEePnwIAEhJSYGDgwOGDh2K5ORkHDp0CJGRkXB3d+foFTMMwzBM/cNm2hmGYRjmB3bs2DFMnjwZxcXFsLCwgLW1NUaNGoX27dsDqNjTfuLECQwePPiT52nbti2mT58u6oDr6upi1qxZmDVrluhnXFxcIC8vj+3bt4uORUZGwtraGkVFRZCTk6P++hiGYRimvmMz7QzDMAzzAxs2bBgePHiA06dPw8HBAWFhYbCwsIC/v3+t/09RURF+++03mJiYQFVVFUpKSsjIyBDNtNcmLi4O/v7+ohl+JSUlODg4oLy8HHl5eZRfGcMwDMN8H1giOoZhGIb5wcnJyaFv377o27cvFi9ejEmTJmHJkiWYMGFCjT8/d+5chISEYN26dTA0NIS8vDx+/vlnlJSUfDJOeXk5pk6dCg8Pj2rPaWtr03gpDMMwDPPdYZ12hmEYhmGqMDExEZV5k5GRgUAgqPJ8REQEJkyYgCFDhgCo2ON+9+7dKj8jKytb7f+zsLDA7du3YWhoKLG2MwzDMMz3hi2PZxiGYZgf1PPnz9G7d2/s378fycnJyMvLw5EjR7B27Vo4OTkBqNibfuXKFTx69AgvX74EABgaGuL48eNITExEUlISxowZg/Ly8irn1tXVxbVr13D//n08e/YMADBv3jxER0fjl19+QWJiIrKysnD69GnMnDmT2xfOMAzDMPUI67QzDMMwzA9KSUkJXbp0ga+vL6ysrNCuXTssWrQIkydPxubNmwEA69evx6VLl6ClpQVzc3MAgK+vL9TU1NC9e3cMHDgQDg4OsLCwqHLuZcuW4e7duzAwMEDjxo0BAO3bt0d4eDiysrLQq1cvmJubY9GiRWjWrBm3L5xhGIZh6hGWPZ5hGIZhGIZhGIZhvlFspp1hGIZhGIZhGIZhvlGs084wDMMwDMMwDMMw3yjWaWcYhmEYhmEYhmGYbxTrtDMMwzAMwzAMwzDMN4p12hmGYRiGYRiGYRjmG8U67QzDMAzDMAzDMAzzjWKddoZhGIZhGIZhGIb5RrFOO8MwDMMwDMMwDMN8o1innWEYhmEYhmEYhmG+UazTzjAMwzAMwzAMwzDfKNZpZxiGYRiGYRiGYZhvFOu0MwzDMAzDMAzDMMw36v8A4dV2HhgQvtAAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Code task 16#\n",
+    "#Create a seaborn boxplot of the ticket price dataframe we created above,\n",
+    "#with 'state' on the x-axis, 'Price' as the y-value, and a hue that indicates 'Ticket'\n",
+    "#This will use boxplot's x, y, hue, and data arguments.\n",
+    "plt.subplots(figsize=(12, 8))\n",
+    "sns.boxplot(x='state', y='Price', hue='Ticket', data=ticket_prices)\n",
+    "plt.xticks(rotation='vertical')\n",
+    "plt.ylabel('Price ($)')\n",
+    "plt.xlabel('State');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Aside from some relatively expensive ticket prices in California, Colorado, and Utah, most prices appear to lie in a broad band from around 25 to over 100 dollars. Some States show more variability than others. Montana and South Dakota, for example, both show fairly small variability as well as matching weekend and weekday ticket prices. Nevada and Utah, on the other hand, show the most range in prices. Some States, notably North Carolina and Virginia, have weekend prices far higher than weekday prices. You could be inspired from this exploration to consider a few potential groupings of resorts, those with low spread, those with lower averages, and those that charge a premium for weekend tickets. However, you're told that you are taking all resorts to be part of the same market share, you  could argue against further segment the resorts. Nevertheless, ways to consider using the State information in your modelling include:\n",
+    "\n",
+    "* disregard State completely\n",
+    "* retain all State information\n",
+    "* retain State in the form of Montana vs not Montana, as our target resort is in Montana\n",
+    "\n",
+    "You've also noted another effect above: some States show a marked difference between weekday and weekend ticket prices. It may make sense to allow a model to take into account not just State but also weekend vs weekday."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Thus we currently have two main questions you want to resolve:\n",
+    "\n",
+    "* What do you do about the two types of ticket price?\n",
+    "* What do you do about the state information?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.6.4 Numeric Features<a id='2.6.4_Numeric_Features'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Having decided to reserve judgement on how exactly you utilize the State, turn your attention to cleaning the numeric features."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.6.4.1 Numeric data summary<a id='2.6.4.1_Numeric_data_summary'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>summit_elev</th>\n",
+       "      <td>330.0</td>\n",
+       "      <td>4591.818182</td>\n",
+       "      <td>3735.535934</td>\n",
+       "      <td>315.0</td>\n",
+       "      <td>1403.75</td>\n",
+       "      <td>3127.5</td>\n",
+       "      <td>7806.00</td>\n",
+       "      <td>13487.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vertical_drop</th>\n",
+       "      <td>330.0</td>\n",
+       "      <td>1215.427273</td>\n",
+       "      <td>947.864557</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>461.25</td>\n",
+       "      <td>964.5</td>\n",
+       "      <td>1800.00</td>\n",
+       "      <td>4425.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>base_elev</th>\n",
+       "      <td>330.0</td>\n",
+       "      <td>3374.000000</td>\n",
+       "      <td>3117.121621</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>869.00</td>\n",
+       "      <td>1561.5</td>\n",
+       "      <td>6325.25</td>\n",
+       "      <td>10800.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>trams</th>\n",
+       "      <td>330.0</td>\n",
+       "      <td>0.172727</td>\n",
+       "      <td>0.559946</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastEight</th>\n",
+       "      <td>164.0</td>\n",
+       "      <td>0.006098</td>\n",
+       "      <td>0.078087</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastSixes</th>\n",
+       "      <td>330.0</td>\n",
+       "      <td>0.184848</td>\n",
+       "      <td>0.651685</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastQuads</th>\n",
+       "      <td>330.0</td>\n",
+       "      <td>1.018182</td>\n",
+       "      <td>2.198294</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>15.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>quad</th>\n",
+       "      <td>330.0</td>\n",
+       "      <td>0.933333</td>\n",
+       "      <td>1.312245</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>triple</th>\n",
+       "      <td>330.0</td>\n",
+       "      <td>1.500000</td>\n",
+       "      <td>1.619130</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>double</th>\n",
+       "      <td>330.0</td>\n",
+       "      <td>1.833333</td>\n",
+       "      <td>1.815028</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>14.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>surface</th>\n",
+       "      <td>330.0</td>\n",
+       "      <td>2.621212</td>\n",
+       "      <td>2.059636</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>15.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total_chairs</th>\n",
+       "      <td>330.0</td>\n",
+       "      <td>8.266667</td>\n",
+       "      <td>5.798683</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.00</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>10.00</td>\n",
+       "      <td>41.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Runs</th>\n",
+       "      <td>326.0</td>\n",
+       "      <td>48.214724</td>\n",
+       "      <td>46.364077</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>19.00</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>60.00</td>\n",
+       "      <td>341.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TerrainParks</th>\n",
+       "      <td>279.0</td>\n",
+       "      <td>2.820789</td>\n",
+       "      <td>2.008113</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.00</td>\n",
+       "      <td>14.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LongestRun_mi</th>\n",
+       "      <td>325.0</td>\n",
+       "      <td>1.433231</td>\n",
+       "      <td>1.156171</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.50</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SkiableTerrain_ac</th>\n",
+       "      <td>327.0</td>\n",
+       "      <td>739.801223</td>\n",
+       "      <td>1816.167441</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>85.00</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>690.00</td>\n",
+       "      <td>26819.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Snow Making_ac</th>\n",
+       "      <td>284.0</td>\n",
+       "      <td>174.873239</td>\n",
+       "      <td>261.336125</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>50.00</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>200.50</td>\n",
+       "      <td>3379.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>daysOpenLastYear</th>\n",
+       "      <td>279.0</td>\n",
+       "      <td>115.103943</td>\n",
+       "      <td>35.063251</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>97.00</td>\n",
+       "      <td>114.0</td>\n",
+       "      <td>135.00</td>\n",
+       "      <td>305.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>yearsOpen</th>\n",
+       "      <td>329.0</td>\n",
+       "      <td>63.656535</td>\n",
+       "      <td>109.429928</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>50.00</td>\n",
+       "      <td>58.0</td>\n",
+       "      <td>69.00</td>\n",
+       "      <td>2019.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>averageSnowfall</th>\n",
+       "      <td>316.0</td>\n",
+       "      <td>185.316456</td>\n",
+       "      <td>136.356842</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>69.00</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>300.00</td>\n",
+       "      <td>669.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AdultWeekday</th>\n",
+       "      <td>276.0</td>\n",
+       "      <td>57.916957</td>\n",
+       "      <td>26.140126</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>40.00</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>71.00</td>\n",
+       "      <td>179.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AdultWeekend</th>\n",
+       "      <td>279.0</td>\n",
+       "      <td>64.166810</td>\n",
+       "      <td>24.554584</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>47.00</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>77.50</td>\n",
+       "      <td>179.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>projectedDaysOpen</th>\n",
+       "      <td>283.0</td>\n",
+       "      <td>120.053004</td>\n",
+       "      <td>31.045963</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>100.00</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>139.50</td>\n",
+       "      <td>305.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NightSkiing_ac</th>\n",
+       "      <td>187.0</td>\n",
+       "      <td>100.395722</td>\n",
+       "      <td>105.169620</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>40.00</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>114.00</td>\n",
+       "      <td>650.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   count         mean          std    min      25%     50%  \\\n",
+       "summit_elev        330.0  4591.818182  3735.535934  315.0  1403.75  3127.5   \n",
+       "vertical_drop      330.0  1215.427273   947.864557   60.0   461.25   964.5   \n",
+       "base_elev          330.0  3374.000000  3117.121621   70.0   869.00  1561.5   \n",
+       "trams              330.0     0.172727     0.559946    0.0     0.00     0.0   \n",
+       "fastEight          164.0     0.006098     0.078087    0.0     0.00     0.0   \n",
+       "fastSixes          330.0     0.184848     0.651685    0.0     0.00     0.0   \n",
+       "fastQuads          330.0     1.018182     2.198294    0.0     0.00     0.0   \n",
+       "quad               330.0     0.933333     1.312245    0.0     0.00     0.0   \n",
+       "triple             330.0     1.500000     1.619130    0.0     0.00     1.0   \n",
+       "double             330.0     1.833333     1.815028    0.0     1.00     1.0   \n",
+       "surface            330.0     2.621212     2.059636    0.0     1.00     2.0   \n",
+       "total_chairs       330.0     8.266667     5.798683    0.0     5.00     7.0   \n",
+       "Runs               326.0    48.214724    46.364077    3.0    19.00    33.0   \n",
+       "TerrainParks       279.0     2.820789     2.008113    1.0     1.00     2.0   \n",
+       "LongestRun_mi      325.0     1.433231     1.156171    0.0     0.50     1.0   \n",
+       "SkiableTerrain_ac  327.0   739.801223  1816.167441    8.0    85.00   200.0   \n",
+       "Snow Making_ac     284.0   174.873239   261.336125    2.0    50.00   100.0   \n",
+       "daysOpenLastYear   279.0   115.103943    35.063251    3.0    97.00   114.0   \n",
+       "yearsOpen          329.0    63.656535   109.429928    6.0    50.00    58.0   \n",
+       "averageSnowfall    316.0   185.316456   136.356842   18.0    69.00   150.0   \n",
+       "AdultWeekday       276.0    57.916957    26.140126   15.0    40.00    50.0   \n",
+       "AdultWeekend       279.0    64.166810    24.554584   17.0    47.00    60.0   \n",
+       "projectedDaysOpen  283.0   120.053004    31.045963   30.0   100.00   120.0   \n",
+       "NightSkiing_ac     187.0   100.395722   105.169620    2.0    40.00    72.0   \n",
+       "\n",
+       "                       75%      max  \n",
+       "summit_elev        7806.00  13487.0  \n",
+       "vertical_drop      1800.00   4425.0  \n",
+       "base_elev          6325.25  10800.0  \n",
+       "trams                 0.00      4.0  \n",
+       "fastEight             0.00      1.0  \n",
+       "fastSixes             0.00      6.0  \n",
+       "fastQuads             1.00     15.0  \n",
+       "quad                  1.00      8.0  \n",
+       "triple                2.00      8.0  \n",
+       "double                3.00     14.0  \n",
+       "surface               3.00     15.0  \n",
+       "total_chairs         10.00     41.0  \n",
+       "Runs                 60.00    341.0  \n",
+       "TerrainParks          4.00     14.0  \n",
+       "LongestRun_mi         2.00      6.0  \n",
+       "SkiableTerrain_ac   690.00  26819.0  \n",
+       "Snow Making_ac      200.50   3379.0  \n",
+       "daysOpenLastYear    135.00    305.0  \n",
+       "yearsOpen            69.00   2019.0  \n",
+       "averageSnowfall     300.00    669.0  \n",
+       "AdultWeekday         71.00    179.0  \n",
+       "AdultWeekend         77.50    179.0  \n",
+       "projectedDaysOpen   139.50    305.0  \n",
+       "NightSkiing_ac      114.00    650.0  "
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 17#\n",
+    "#Call ski_data's `describe` method for a statistical summary of the numerical columns\n",
+    "#Hint: there are fewer summary stat columns than features, so displaying the transpose\n",
+    "#will be useful again\n",
+    "ski_data.describe().T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Recall you're missing the ticket prices for some 16% of resorts. This is a fundamental problem that means you simply lack the required data for those resorts and will have to drop those records. But you may have a weekend price and not a weekday price, or vice versa. You want to keep any price you have."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    82.424242\n",
+       "2    14.242424\n",
+       "1     3.333333\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "missing_price = ski_data[['AdultWeekend', 'AdultWeekday']].isnull().sum(axis=1)\n",
+    "missing_price.value_counts()/len(missing_price) * 100"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Just over 82% of resorts have no missing ticket price, 3% are missing one value, and 14% are missing both. You will definitely want to drop the records for which you have no price information, however you will not do so just yet. There may still be useful information about the distributions of other features in that 14% of the data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.6.4.2 Distributions Of Feature Values<a id='2.6.4.2_Distributions_Of_Feature_Values'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that, although we are still in the 'data wrangling and cleaning' phase rather than exploratory data analysis, looking at distributions of features is immensely useful in getting a feel for whether the values look sensible and whether there are any obvious outliers to investigate. Some exploratory data analysis belongs here, and data wrangling will inevitably occur later on. It's more a matter of emphasis. Here, we're interesting in focusing on whether distributions look plausible or wrong. Later on, we're more interested in relationships and patterns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x1000 with 25 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Code task 18#\n",
+    "#Call ski_data's `hist` method to plot histograms of each of the numeric features\n",
+    "#Try passing it an argument figsize=(15,10)\n",
+    "#Try calling plt.subplots_adjust() with an argument hspace=0.5 to adjust the spacing\n",
+    "#It's important you create legible and easy-to-read plots\n",
+    "ski_data.hist(figsize=(15,10))\n",
+    "plt.subplots_adjust(hspace=0.5);\n",
+    "#Hint: notice how the terminating ';' \"swallows\" some messy output and leads to a tidier notebook"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What features do we have possible cause for concern about and why?\n",
+    "\n",
+    "* SkiableTerrain_ac because values are clustered down the low end,\n",
+    "* Snow Making_ac for the same reason,\n",
+    "* fastEight because all but one value is 0 so it has very little variance, and half the values are missing,\n",
+    "* fastSixes raises an amber flag; it has more variability, but still mostly 0,\n",
+    "* trams also may get an amber flag for the same reason,\n",
+    "* yearsOpen because most values are low but it has a maximum of 2019, which strongly suggests someone recorded calendar year rather than number of years."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 2.6.4.2.1 SkiableTerrain_ac<a id='2.6.4.2.1_SkiableTerrain_ac'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "39    26819.0\n",
+       "Name: SkiableTerrain_ac, dtype: float64"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 19#\n",
+    "#Filter the 'SkiableTerrain_ac' column to print the values greater than 10000\n",
+    "ski_data.SkiableTerrain_ac[ski_data.SkiableTerrain_ac > 10000]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q: 2** One resort has an incredibly large skiable terrain area! Which is it?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>39</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Name</th>\n",
+       "      <td>Silverton Mountain</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Region</th>\n",
+       "      <td>Colorado</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state</th>\n",
+       "      <td>Colorado</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>summit_elev</th>\n",
+       "      <td>13487</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vertical_drop</th>\n",
+       "      <td>3087</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>base_elev</th>\n",
+       "      <td>10400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>trams</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastEight</th>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastSixes</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastQuads</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>quad</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>triple</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>double</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>surface</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total_chairs</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Runs</th>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TerrainParks</th>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LongestRun_mi</th>\n",
+       "      <td>1.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SkiableTerrain_ac</th>\n",
+       "      <td>26819.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Snow Making_ac</th>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>daysOpenLastYear</th>\n",
+       "      <td>175.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>yearsOpen</th>\n",
+       "      <td>17.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>averageSnowfall</th>\n",
+       "      <td>400.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AdultWeekday</th>\n",
+       "      <td>79.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AdultWeekend</th>\n",
+       "      <td>79.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>projectedDaysOpen</th>\n",
+       "      <td>181.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NightSkiing_ac</th>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                   39\n",
+       "Name               Silverton Mountain\n",
+       "Region                       Colorado\n",
+       "state                        Colorado\n",
+       "summit_elev                     13487\n",
+       "vertical_drop                    3087\n",
+       "base_elev                       10400\n",
+       "trams                               0\n",
+       "fastEight                         0.0\n",
+       "fastSixes                           0\n",
+       "fastQuads                           0\n",
+       "quad                                0\n",
+       "triple                              0\n",
+       "double                              1\n",
+       "surface                             0\n",
+       "total_chairs                        1\n",
+       "Runs                              NaN\n",
+       "TerrainParks                      NaN\n",
+       "LongestRun_mi                     1.5\n",
+       "SkiableTerrain_ac             26819.0\n",
+       "Snow Making_ac                    NaN\n",
+       "daysOpenLastYear                175.0\n",
+       "yearsOpen                        17.0\n",
+       "averageSnowfall                 400.0\n",
+       "AdultWeekday                     79.0\n",
+       "AdultWeekend                     79.0\n",
+       "projectedDaysOpen               181.0\n",
+       "NightSkiing_ac                    NaN"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 20#\n",
+    "#Now you know there's only one, print the whole row to investigate all values, including seeing the resort name\n",
+    "#Hint: don't forget the transpose will be helpful here\n",
+    "ski_data[ski_data.SkiableTerrain_ac > 10000].T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**A: 2** Your answer here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But what can you do when you have one record that seems highly suspicious?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can see if your data are correct. Search for \"silverton mountain skiable area\". If you do this, you get some [useful information](https://www.google.com/search?q=silverton+mountain+skiable+area)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Silverton Mountain information](images/silverton_mountain_info.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can spot check data. You see your top and base elevation values agree, but the skiable area is very different. Your suspect value is 26819, but the value you've just looked up is 1819. The last three digits agree. This sort of error could have occured in transmission or some editing or transcription stage. You could plausibly replace the suspect value with the one you've just obtained. Another cautionary note to make here is that although you're doing this in order to progress with your analysis, this is most definitely an issue that should have been raised and fed back to the client or data originator as a query. You should view this \"data correction\" step as a means to continue (documenting it carefully as you do in this notebook) rather than an ultimate decision as to what is correct."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "26819.0"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 21#\n",
+    "#Use the .loc accessor to print the 'SkiableTerrain_ac' value only for this resort\n",
+    "ski_data.loc[39, 'SkiableTerrain_ac']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Code task 22#\n",
+    "#Use the .loc accessor again to modify this value with the correct value of 1819\n",
+    "ski_data.loc[39, 'SkiableTerrain_ac'] = 1819"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1819.0"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 23#\n",
+    "#Use the .loc accessor a final time to verify that the value has been modified\n",
+    "ski_data.loc[39, 'SkiableTerrain_ac']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**NB whilst you may become suspicious about your data quality, and you know you have missing values, you will not here dive down the rabbit hole of checking all values or web scraping to replace missing values.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What does the distribution of skiable area look like now?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ski_data.SkiableTerrain_ac.hist(bins=30)\n",
+    "plt.xlabel('SkiableTerrain_ac')\n",
+    "plt.ylabel('Count')\n",
+    "plt.title('Distribution of skiable area (acres) after replacing erroneous value');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You now see a rather long tailed distribution. You may wonder about the now most extreme value that is above 8000, but similarly you may also wonder about the value around 7000. If you wanted to spend more time manually checking values you could, but leave this for now. The above distribution is plausible."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 2.6.4.2.2 Snow Making_ac<a id='2.6.4.2.2_Snow_Making_ac'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "11    3379.0\n",
+       "18    1500.0\n",
+       "Name: Snow Making_ac, dtype: float64"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ski_data['Snow Making_ac'][ski_data['Snow Making_ac'] > 1000]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>11</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Name</th>\n",
+       "      <td>Heavenly Mountain Resort</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Region</th>\n",
+       "      <td>Sierra Nevada</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state</th>\n",
+       "      <td>California</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>summit_elev</th>\n",
+       "      <td>10067</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vertical_drop</th>\n",
+       "      <td>3500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>base_elev</th>\n",
+       "      <td>7170</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>trams</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastEight</th>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastSixes</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastQuads</th>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>quad</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>triple</th>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>double</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>surface</th>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total_chairs</th>\n",
+       "      <td>28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Runs</th>\n",
+       "      <td>97.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TerrainParks</th>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LongestRun_mi</th>\n",
+       "      <td>5.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SkiableTerrain_ac</th>\n",
+       "      <td>4800.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Snow Making_ac</th>\n",
+       "      <td>3379.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>daysOpenLastYear</th>\n",
+       "      <td>155.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>yearsOpen</th>\n",
+       "      <td>64.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>averageSnowfall</th>\n",
+       "      <td>360.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AdultWeekday</th>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AdultWeekend</th>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>projectedDaysOpen</th>\n",
+       "      <td>157.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NightSkiing_ac</th>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                         11\n",
+       "Name               Heavenly Mountain Resort\n",
+       "Region                        Sierra Nevada\n",
+       "state                            California\n",
+       "summit_elev                           10067\n",
+       "vertical_drop                          3500\n",
+       "base_elev                              7170\n",
+       "trams                                     2\n",
+       "fastEight                               0.0\n",
+       "fastSixes                                 2\n",
+       "fastQuads                                 7\n",
+       "quad                                      1\n",
+       "triple                                    5\n",
+       "double                                    3\n",
+       "surface                                   8\n",
+       "total_chairs                             28\n",
+       "Runs                                   97.0\n",
+       "TerrainParks                            3.0\n",
+       "LongestRun_mi                           5.5\n",
+       "SkiableTerrain_ac                    4800.0\n",
+       "Snow Making_ac                       3379.0\n",
+       "daysOpenLastYear                      155.0\n",
+       "yearsOpen                              64.0\n",
+       "averageSnowfall                       360.0\n",
+       "AdultWeekday                            NaN\n",
+       "AdultWeekend                            NaN\n",
+       "projectedDaysOpen                     157.0\n",
+       "NightSkiing_ac                          NaN"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ski_data[ski_data['Snow Making_ac'] > 3000].T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can adopt a similar approach as for the suspect skiable area value and do some spot checking. To save time, here is a link to the website for [Heavenly Mountain Resort](https://www.skiheavenly.com/the-mountain/about-the-mountain/mountain-info.aspx). From this you can glean that you have values for skiable terrain that agree. Furthermore, you can read that snowmaking covers 60% of the trails."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What, then, is your rough guess for the area covered by snowmaking?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2880.0"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    ".6 * 4800"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is less than the value of 3379 in your data so you may have a judgement call to make. However, notice something else. You have no ticket pricing information at all for this resort. Any further effort spent worrying about values for this resort will be wasted. You'll simply be dropping the entire row!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 2.6.4.2.3 fastEight<a id='2.6.4.2.3_fastEight'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Look at the different fastEight values more closely:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0    163\n",
+       "1.0      1\n",
+       "Name: fastEight, dtype: int64"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ski_data.fastEight.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Drop the fastEight column in its entirety; half the values are missing and all but the others are the value zero. There is essentially no information in this column."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Code task 24#\n",
+    "#Drop the 'fastEight' column from ski_data. Use inplace=True\n",
+    "ski_data.drop(columns='fastEight', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What about yearsOpen? How many resorts have purportedly been open for more than 100 years?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "34      104.0\n",
+       "115    2019.0\n",
+       "Name: yearsOpen, dtype: float64"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 25#\n",
+    "#Filter the 'yearsOpen' column for values greater than 100\n",
+    "ski_data.yearsOpen[ski_data.yearsOpen > 100]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Okay, one seems to have been open for 104 years. But beyond that, one is down as having been open for 2019 years. This is wrong! What shall you do about this?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What does the distribution of yearsOpen look like if you exclude just the obviously wrong one?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Code task 26#\n",
+    "#Call the hist method on 'yearsOpen' after filtering for values under 1000\n",
+    "#Pass the argument bins=30 to hist(), but feel free to explore other values\n",
+    "ski_data.yearsOpen[ski_data.yearsOpen < 1000].hist(bins=30)\n",
+    "plt.xlabel('Years open')\n",
+    "plt.ylabel('Count')\n",
+    "plt.title('Distribution of years open excluding 2019');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The above distribution of years seems entirely plausible, including the 104 year value. You can certainly state that no resort will have been open for 2019 years! It likely means the resort opened in 2019. It could also mean the resort is due to open in 2019. You don't know when these data were gathered!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's review the summary statistics for the years under 1000."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    328.000000\n",
+       "mean      57.695122\n",
+       "std       16.841182\n",
+       "min        6.000000\n",
+       "25%       50.000000\n",
+       "50%       58.000000\n",
+       "75%       68.250000\n",
+       "max      104.000000\n",
+       "Name: yearsOpen, dtype: float64"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ski_data.yearsOpen[ski_data.yearsOpen < 1000].describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The smallest number of years open otherwise is 6. You can't be sure whether this resort in question has been open zero years or one year and even whether the numbers are projections or actual. In any case, you would be adding a new youngest resort so it feels best to simply drop this row."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ski_data = ski_data[ski_data.yearsOpen < 1000]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 2.6.4.2.4 fastSixes and Trams<a id='2.6.4.2.4_fastSixes_and_Trams'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The other features you had mild concern over, you will not investigate further. Perhaps take some care when using these features."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.7 Derive State-wide Summary Statistics For Our Market Segment<a id='2.7_Derive_State-wide_Summary_Statistics_For_Our_Market_Segment'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You have, by this point removed one row, but it was for a resort that may not have opened yet, or perhaps in its first season. Using your business knowledge, you know that state-wide supply and demand of certain skiing resources may well factor into pricing strategies. Does a resort dominate the available night skiing in a state? Or does it account for a large proportion of the total skiable terrain or days open?\n",
+    "\n",
+    "If you want to add any features to your data that captures the state-wide market size, you should do this now, before dropping any more rows. In the next section, you'll drop rows with missing price information. Although you don't know what those resorts charge for their tickets, you do know the resorts exists and have been open for at least six years. Thus, you'll now calculate some state-wide summary statistics for later use."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Many features in your data pertain to chairlifts, that is for getting people around each resort. These aren't relevant, nor are the features relating to altitudes. Features that you may be interested in are:\n",
+    "\n",
+    "* TerrainParks\n",
+    "* SkiableTerrain_ac\n",
+    "* daysOpenLastYear\n",
+    "* NightSkiing_ac\n",
+    "\n",
+    "When you think about it, these are features it makes sense to sum: the total number of terrain parks, the total skiable area, the total number of days open, and the total area available for night skiing. You might consider the total number of ski runs, but understand that the skiable area is more informative than just a number of runs."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A fairly new groupby behaviour is [named aggregation](https://pandas-docs.github.io/pandas-docs-travis/whatsnew/v0.25.0.html). This allows us to clearly perform the aggregations you want whilst also creating informative output column names."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state</th>\n",
+       "      <th>resorts_per_state</th>\n",
+       "      <th>state_total_skiable_area_ac</th>\n",
+       "      <th>state_total_days_open</th>\n",
+       "      <th>state_total_terrain_parks</th>\n",
+       "      <th>state_total_nightskiing_ac</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2280.0</td>\n",
+       "      <td>345.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>580.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1577.0</td>\n",
+       "      <td>237.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>80.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>California</td>\n",
+       "      <td>21</td>\n",
+       "      <td>25948.0</td>\n",
+       "      <td>2738.0</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>587.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Colorado</td>\n",
+       "      <td>22</td>\n",
+       "      <td>43682.0</td>\n",
+       "      <td>3258.0</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>428.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Connecticut</td>\n",
+       "      <td>5</td>\n",
+       "      <td>358.0</td>\n",
+       "      <td>353.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>256.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         state  resorts_per_state  state_total_skiable_area_ac  \\\n",
+       "0       Alaska                  3                       2280.0   \n",
+       "1      Arizona                  2                       1577.0   \n",
+       "2   California                 21                      25948.0   \n",
+       "3     Colorado                 22                      43682.0   \n",
+       "4  Connecticut                  5                        358.0   \n",
+       "\n",
+       "   state_total_days_open  state_total_terrain_parks  \\\n",
+       "0                  345.0                        4.0   \n",
+       "1                  237.0                        6.0   \n",
+       "2                 2738.0                       81.0   \n",
+       "3                 3258.0                       74.0   \n",
+       "4                  353.0                       10.0   \n",
+       "\n",
+       "   state_total_nightskiing_ac  \n",
+       "0                       580.0  \n",
+       "1                        80.0  \n",
+       "2                       587.0  \n",
+       "3                       428.0  \n",
+       "4                       256.0  "
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 27#\n",
+    "#Add named aggregations for the sum of 'daysOpenLastYear', 'TerrainParks', and 'NightSkiing_ac'\n",
+    "#call them 'state_total_days_open', 'state_total_terrain_parks', and 'state_total_nightskiing_ac',\n",
+    "#respectively\n",
+    "#Finally, add a call to the reset_index() method (we recommend you experiment with and without this to see\n",
+    "#what it does)\n",
+    "state_summary = ski_data.groupby('state').agg(\n",
+    "    resorts_per_state=pd.NamedAgg(column='Name', aggfunc='size'), #could pick any column here\n",
+    "    state_total_skiable_area_ac=pd.NamedAgg(column='SkiableTerrain_ac', aggfunc='sum'),\n",
+    "    state_total_days_open=pd.NamedAgg(column='daysOpenLastYear', aggfunc='sum'),\n",
+    "    state_total_terrain_parks=pd.NamedAgg(column='TerrainParks', aggfunc='sum'),\n",
+    "    state_total_nightskiing_ac=pd.NamedAgg(column='NightSkiing_ac', aggfunc='sum')\n",
+    ").reset_index()\n",
+    "state_summary.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.8 Drop Rows With No Price Data<a id='2.8_Drop_Rows_With_No_Price_Data'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You know there are two columns that refer to price: 'AdultWeekend' and 'AdultWeekday'. You can calculate the number of price values missing per row. This will obviously have to be either 0, 1, or 2, where 0 denotes no price values are missing and 2 denotes that both are missing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    82.317073\n",
+       "2    14.329268\n",
+       "1     3.353659\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "missing_price = ski_data[['AdultWeekend', 'AdultWeekday']].isnull().sum(axis=1)\n",
+    "missing_price.value_counts()/len(missing_price) * 100"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "About 14% of the rows have no price data. As the price is your target, these rows are of no use. Time to lose them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Code task 28#\n",
+    "#Use `missing_price` to remove rows from ski_data where both price values are missing\n",
+    "ski_data = ski_data[missing_price != 2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.9 Review distributions<a id='2.9_Review_distributions'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x1000 with 25 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ski_data.hist(figsize=(15, 10))\n",
+    "plt.subplots_adjust(hspace=0.5);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These distributions are much better. There are clearly some skewed distributions, so keep an eye on `fastQuads`, `fastSixes`, and perhaps `trams`. These lack much variance away from 0 and may have a small number of relatively extreme values.  Models failing to rate a feature as important when domain knowledge tells you it should be is an issue to look out for, as is a model being overly influenced by some extreme values. If you build a good machine learning pipeline, hopefully it will be robust to such issues, but you may also wish to consider nonlinear transformations of features."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.10 Population data<a id='2.10_Population_data'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Population and area data for the US states can be obtained from [wikipedia](https://simple.wikipedia.org/wiki/List_of_U.S._states). Listen, you should have a healthy concern about using data you \"found on the Internet\". Make sure it comes from a reputable source. This table of data is useful because it allows you to easily pull and incorporate an external data set. It also allows you to proceed with an analysis that includes state sizes and populations for your 'first cut' model. Be explicit about your source (we documented it here in this workflow) and ensure it is open to inspection. All steps are subject to review, and it may be that a client has a specific source of data they trust that you should use to rerun the analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Code task 29#\n",
+    "#Use pandas' `read_html` method to read the table from the URL below\n",
+    "states_url = 'https://simple.wikipedia.org/w/index.php?title=List_of_U.S._states&oldid=7168473'\n",
+    "usa_states = pd.read_html(states_url)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "list"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(usa_states)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(usa_states)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Name &amp; postal abbs. [1]</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Cities</th>\n",
+       "      <th>Established[A]</th>\n",
+       "      <th>Population [B][3]</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Total area[4]</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Land area[4]</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Water area[4]</th>\n",
+       "      <th>Number of Reps.</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>Name &amp; postal abbs. [1]</th>\n",
+       "      <th>Name &amp; postal abbs. [1].1</th>\n",
+       "      <th>Capital</th>\n",
+       "      <th>Largest[5]</th>\n",
+       "      <th>Established[A]</th>\n",
+       "      <th>Population [B][3]</th>\n",
+       "      <th>mi2</th>\n",
+       "      <th>km2</th>\n",
+       "      <th>mi2</th>\n",
+       "      <th>km2</th>\n",
+       "      <th>mi2</th>\n",
+       "      <th>km2</th>\n",
+       "      <th>Number of Reps.</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>AL</td>\n",
+       "      <td>Montgomery</td>\n",
+       "      <td>Birmingham</td>\n",
+       "      <td>Dec 14, 1819</td>\n",
+       "      <td>4903185</td>\n",
+       "      <td>52420</td>\n",
+       "      <td>135767</td>\n",
+       "      <td>50645</td>\n",
+       "      <td>131171</td>\n",
+       "      <td>1775</td>\n",
+       "      <td>4597</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>AK</td>\n",
+       "      <td>Juneau</td>\n",
+       "      <td>Anchorage</td>\n",
+       "      <td>Jan 3, 1959</td>\n",
+       "      <td>731545</td>\n",
+       "      <td>665384</td>\n",
+       "      <td>1723337</td>\n",
+       "      <td>570641</td>\n",
+       "      <td>1477953</td>\n",
+       "      <td>94743</td>\n",
+       "      <td>245384</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>AZ</td>\n",
+       "      <td>Phoenix</td>\n",
+       "      <td>Phoenix</td>\n",
+       "      <td>Feb 14, 1912</td>\n",
+       "      <td>7278717</td>\n",
+       "      <td>113990</td>\n",
+       "      <td>295234</td>\n",
+       "      <td>113594</td>\n",
+       "      <td>294207</td>\n",
+       "      <td>396</td>\n",
+       "      <td>1026</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Arkansas</td>\n",
+       "      <td>AR</td>\n",
+       "      <td>Little Rock</td>\n",
+       "      <td>Little Rock</td>\n",
+       "      <td>Jun 15, 1836</td>\n",
+       "      <td>3017804</td>\n",
+       "      <td>53179</td>\n",
+       "      <td>137732</td>\n",
+       "      <td>52035</td>\n",
+       "      <td>134771</td>\n",
+       "      <td>1143</td>\n",
+       "      <td>2961</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>California</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>Sacramento</td>\n",
+       "      <td>Los Angeles</td>\n",
+       "      <td>Sep 9, 1850</td>\n",
+       "      <td>39512223</td>\n",
+       "      <td>163695</td>\n",
+       "      <td>423967</td>\n",
+       "      <td>155779</td>\n",
+       "      <td>403466</td>\n",
+       "      <td>7916</td>\n",
+       "      <td>20501</td>\n",
+       "      <td>53</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Name & postal abbs. [1]                                 Cities               \\\n",
+       "  Name & postal abbs. [1] Name & postal abbs. [1].1      Capital   Largest[5]   \n",
+       "0                 Alabama                        AL   Montgomery   Birmingham   \n",
+       "1                  Alaska                        AK       Juneau    Anchorage   \n",
+       "2                 Arizona                        AZ      Phoenix      Phoenix   \n",
+       "3                Arkansas                        AR  Little Rock  Little Rock   \n",
+       "4              California                        CA   Sacramento  Los Angeles   \n",
+       "\n",
+       "  Established[A] Population [B][3] Total area[4]          Land area[4]  \\\n",
+       "  Established[A] Population [B][3]           mi2      km2          mi2   \n",
+       "0   Dec 14, 1819           4903185         52420   135767        50645   \n",
+       "1    Jan 3, 1959            731545        665384  1723337       570641   \n",
+       "2   Feb 14, 1912           7278717        113990   295234       113594   \n",
+       "3   Jun 15, 1836           3017804         53179   137732        52035   \n",
+       "4    Sep 9, 1850          39512223        163695   423967       155779   \n",
+       "\n",
+       "           Water area[4]         Number of Reps.  \n",
+       "       km2           mi2     km2 Number of Reps.  \n",
+       "0   131171          1775    4597               7  \n",
+       "1  1477953         94743  245384               1  \n",
+       "2   294207           396    1026               9  \n",
+       "3   134771          1143    2961               4  \n",
+       "4   403466          7916   20501              53  "
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "usa_states = usa_states[0]\n",
+    "usa_states.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note, in even the last year, the capability of `pd.read_html()` has improved. The merged cells you see in the web table are now handled much more conveniently, with 'Phoenix' now being duplicated so the subsequent columns remain aligned. But check this anyway. If you extract the established date column, you should just get dates. Recall previously you used the `.loc` accessor, because you were using labels. Now you want to refer to a column by its index position and so use `.iloc`. For a discussion on the difference use cases of `.loc` and `.iloc` refer to the [pandas documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Code task 30#\n",
+    "#Use the iloc accessor to get the pandas Series for column number 4 from `usa_states`\n",
+    "#It should be a column of dates\n",
+    "established = usa_states.iloc[:, 4]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "established"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Extract the state name, population, and total area (square miles) columns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state</th>\n",
+       "      <th>state_population</th>\n",
+       "      <th>state_area_sq_miles</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>4903185</td>\n",
+       "      <td>52420</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>731545</td>\n",
+       "      <td>665384</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>7278717</td>\n",
+       "      <td>113990</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Arkansas</td>\n",
+       "      <td>3017804</td>\n",
+       "      <td>53179</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>California</td>\n",
+       "      <td>39512223</td>\n",
+       "      <td>163695</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        state  state_population  state_area_sq_miles\n",
+       "0     Alabama           4903185                52420\n",
+       "1      Alaska            731545               665384\n",
+       "2     Arizona           7278717               113990\n",
+       "3    Arkansas           3017804                53179\n",
+       "4  California          39512223               163695"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 31#\n",
+    "#Now use the iloc accessor again to extract columns 0, 5, and 6 and the dataframe's `copy()` method\n",
+    "#Set the names of these extracted columns to 'state', 'state_population', and 'state_area_sq_miles',\n",
+    "#respectively.\n",
+    "usa_states_sub = usa_states.iloc[:, [0,5,6]].copy()\n",
+    "usa_states_sub.columns = ['state','state_population','state_area_sq_miles']\n",
+    "usa_states_sub.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Do you have all the ski data states accounted for?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Massachusetts', 'Pennsylvania', 'Rhode Island', 'Virginia'}"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 32#\n",
+    "#Find the states in `state_summary` that are not in `usa_states_sub`\n",
+    "#Hint: set(list1) - set(list2) is an easy way to get items in list1 that are not in list2\n",
+    "missing_states = set(state_summary.state) - set(usa_states_sub.state)\n",
+    "missing_states"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "No?? "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you look at the table on the web, you can perhaps start to guess what the problem is. You can confirm your suspicion by pulling out state names that _contain_ 'Massachusetts', 'Pennsylvania', or 'Virginia' from usa_states_sub:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "20    Massachusetts[C]\n",
+       "37     Pennsylvania[C]\n",
+       "38     Rhode Island[D]\n",
+       "45         Virginia[C]\n",
+       "47       West Virginia\n",
+       "Name: state, dtype: object"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "usa_states_sub.state[usa_states_sub.state.str.contains('Massachusetts|Pennsylvania|Rhode Island|Virginia')]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Delete square brackets and their contents and try again:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "20    Massachusetts\n",
+       "37     Pennsylvania\n",
+       "38     Rhode Island\n",
+       "45         Virginia\n",
+       "47    West Virginia\n",
+       "Name: state, dtype: object"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 33#\n",
+    "#Use pandas' Series' `replace()` method to replace anything within square brackets (including the brackets)\n",
+    "#with the empty string. Do this inplace, so you need to specify the arguments:\n",
+    "#to_replace='\\[.*\\]' #literal square bracket followed by anything or nothing followed by literal closing bracket\n",
+    "#value='' #empty string as replacement\n",
+    "#regex=True #we used a regex in our `to_replace` argument\n",
+    "#inplace=True #Do this \"in place\"\n",
+    "usa_states_sub.state.replace(to_replace='\\[.*\\]', value='', regex=True, inplace=True)\n",
+    "usa_states_sub.state[usa_states_sub.state.str.contains('Massachusetts|Pennsylvania|Rhode Island|Virginia')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "set()"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 34#\n",
+    "#And now verify none of our states are missing by checking that there are no states in\n",
+    "#state_summary that are not in usa_states_sub (as earlier using `set()`)\n",
+    "missing_states = set(state_summary.state) - set(usa_states_sub.state)\n",
+    "missing_states"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Better! You have an empty set for missing states now. You can confidently add the population and state area columns to the ski resort data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state</th>\n",
+       "      <th>resorts_per_state</th>\n",
+       "      <th>state_total_skiable_area_ac</th>\n",
+       "      <th>state_total_days_open</th>\n",
+       "      <th>state_total_terrain_parks</th>\n",
+       "      <th>state_total_nightskiing_ac</th>\n",
+       "      <th>state_population</th>\n",
+       "      <th>state_area_sq_miles</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2280.0</td>\n",
+       "      <td>345.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>580.0</td>\n",
+       "      <td>731545</td>\n",
+       "      <td>665384</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1577.0</td>\n",
+       "      <td>237.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>7278717</td>\n",
+       "      <td>113990</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>California</td>\n",
+       "      <td>21</td>\n",
+       "      <td>25948.0</td>\n",
+       "      <td>2738.0</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>587.0</td>\n",
+       "      <td>39512223</td>\n",
+       "      <td>163695</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Colorado</td>\n",
+       "      <td>22</td>\n",
+       "      <td>43682.0</td>\n",
+       "      <td>3258.0</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>428.0</td>\n",
+       "      <td>5758736</td>\n",
+       "      <td>104094</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Connecticut</td>\n",
+       "      <td>5</td>\n",
+       "      <td>358.0</td>\n",
+       "      <td>353.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>256.0</td>\n",
+       "      <td>3565278</td>\n",
+       "      <td>5543</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         state  resorts_per_state  state_total_skiable_area_ac  \\\n",
+       "0       Alaska                  3                       2280.0   \n",
+       "1      Arizona                  2                       1577.0   \n",
+       "2   California                 21                      25948.0   \n",
+       "3     Colorado                 22                      43682.0   \n",
+       "4  Connecticut                  5                        358.0   \n",
+       "\n",
+       "   state_total_days_open  state_total_terrain_parks  \\\n",
+       "0                  345.0                        4.0   \n",
+       "1                  237.0                        6.0   \n",
+       "2                 2738.0                       81.0   \n",
+       "3                 3258.0                       74.0   \n",
+       "4                  353.0                       10.0   \n",
+       "\n",
+       "   state_total_nightskiing_ac  state_population  state_area_sq_miles  \n",
+       "0                       580.0            731545               665384  \n",
+       "1                        80.0           7278717               113990  \n",
+       "2                       587.0          39512223               163695  \n",
+       "3                       428.0           5758736               104094  \n",
+       "4                       256.0           3565278                 5543  "
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 35#\n",
+    "#Use 'state_summary's `merge()` method to combine our new data in 'usa_states_sub'\n",
+    "#specify the arguments how='left' and on='state'\n",
+    "state_summary = state_summary.merge(usa_states_sub, how='left', on='state')\n",
+    "state_summary.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Having created this data frame of summary statistics for various states, it would seem obvious to join this with the ski resort data to augment it with this additional data. You will do this, but not now. In the next notebook you will be exploring the data, including the relationships between the states. For that you want a separate row for each state, as you have here, and joining the data this soon means you'd need to separate and eliminate redundances in the state data when you wanted it."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.11 Target Feature<a id='2.11_Target_Feature'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, what will your target be when modelling ticket price? What relationship is there between weekday and weekend prices?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Code task 36#\n",
+    "#Use ski_data's `plot()` method to create a scatterplot (kind='scatter') with 'AdultWeekday' on the x-axis and\n",
+    "#'AdultWeekend' on the y-axis\n",
+    "ski_data.plot(x='AdultWeekday', y='AdultWeekend', kind='scatter');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A couple of observations can be made. Firstly, there is a clear line where weekend and weekday prices are equal. Weekend prices being higher than weekday prices seem restricted to sub $100 resorts. Recall from the boxplot earlier that the distribution for weekday and weekend prices in Montana seemed equal. Is this confirmed in the actual data for each resort? Big Mountain resort is in Montana, so the relationship between these quantities in this state are particularly relevant."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>AdultWeekend</th>\n",
+       "      <th>AdultWeekday</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>141</th>\n",
+       "      <td>42.0</td>\n",
+       "      <td>42.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>142</th>\n",
+       "      <td>63.0</td>\n",
+       "      <td>63.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>143</th>\n",
+       "      <td>49.0</td>\n",
+       "      <td>49.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>144</th>\n",
+       "      <td>48.0</td>\n",
+       "      <td>48.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>145</th>\n",
+       "      <td>46.0</td>\n",
+       "      <td>46.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>146</th>\n",
+       "      <td>39.0</td>\n",
+       "      <td>39.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>147</th>\n",
+       "      <td>50.0</td>\n",
+       "      <td>50.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148</th>\n",
+       "      <td>67.0</td>\n",
+       "      <td>67.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>149</th>\n",
+       "      <td>47.0</td>\n",
+       "      <td>47.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>150</th>\n",
+       "      <td>39.0</td>\n",
+       "      <td>39.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>151</th>\n",
+       "      <td>81.0</td>\n",
+       "      <td>81.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     AdultWeekend  AdultWeekday\n",
+       "141          42.0          42.0\n",
+       "142          63.0          63.0\n",
+       "143          49.0          49.0\n",
+       "144          48.0          48.0\n",
+       "145          46.0          46.0\n",
+       "146          39.0          39.0\n",
+       "147          50.0          50.0\n",
+       "148          67.0          67.0\n",
+       "149          47.0          47.0\n",
+       "150          39.0          39.0\n",
+       "151          81.0          81.0"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 37#\n",
+    "#Use the loc accessor on ski_data to print the 'AdultWeekend' and 'AdultWeekday' columns for Montana only\n",
+    "ski_data.loc[ski_data.state == 'Montana', ['AdultWeekend', 'AdultWeekday']]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Is there any reason to prefer weekend or weekday prices? Which is missing the least?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AdultWeekend    4\n",
+       "AdultWeekday    7\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ski_data[['AdultWeekend', 'AdultWeekday']].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Weekend prices have the least missing values of the two, so drop the weekday prices and then keep just the rows that have weekend price."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/qy/n5ybkbq12ps0vpdz3748rqx80000gn/T/ipykernel_1253/3052103842.py:1: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ski_data.drop(columns='AdultWeekday', inplace=True)\n",
+      "/var/folders/qy/n5ybkbq12ps0vpdz3748rqx80000gn/T/ipykernel_1253/3052103842.py:2: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ski_data.dropna(subset=['AdultWeekend'], inplace=True)\n"
+     ]
+    }
+   ],
+   "source": [
+    "ski_data.drop(columns='AdultWeekday', inplace=True)\n",
+    "ski_data.dropna(subset=['AdultWeekend'], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(277, 25)"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ski_data.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Perform a final quick check on the data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.11.1 Number Of Missing Values By Row - Resort<a id='2.11.1_Number_Of_Missing_Values_By_Row_-_Resort'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Having dropped rows missing the desired target ticket price, what degree of missingness do you have for the remaining rows?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>%</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>329</th>\n",
+       "      <td>5</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>5</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>141</th>\n",
+       "      <td>5</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>86</th>\n",
+       "      <td>5</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>5</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>146</th>\n",
+       "      <td>5</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>184</th>\n",
+       "      <td>4</td>\n",
+       "      <td>16.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>108</th>\n",
+       "      <td>4</td>\n",
+       "      <td>16.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>198</th>\n",
+       "      <td>4</td>\n",
+       "      <td>16.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>4</td>\n",
+       "      <td>16.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     count     %\n",
+       "329      5  20.0\n",
+       "62       5  20.0\n",
+       "141      5  20.0\n",
+       "86       5  20.0\n",
+       "74       5  20.0\n",
+       "146      5  20.0\n",
+       "184      4  16.0\n",
+       "108      4  16.0\n",
+       "198      4  16.0\n",
+       "39       4  16.0"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "missing = pd.concat([ski_data.isnull().sum(axis=1), 100 * ski_data.isnull().mean(axis=1)], axis=1)\n",
+    "missing.columns=['count', '%']\n",
+    "missing.sort_values(by='count', ascending=False).head(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These seem possibly curiously quantized..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.,  4.,  8., 12., 16., 20.])"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "missing['%'].unique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Yes, the percentage of missing values per row appear in multiples of 4."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0     107\n",
+       "4.0      94\n",
+       "8.0      45\n",
+       "12.0     15\n",
+       "16.0     10\n",
+       "20.0      6\n",
+       "Name: %, dtype: int64"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "missing['%'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is almost as if values have been removed artificially... Nevertheless, what you don't know is how useful the missing features are in predicting ticket price. You shouldn't just drop rows that are missing several useless features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 277 entries, 0 to 329\n",
+      "Data columns (total 25 columns):\n",
+      " #   Column             Non-Null Count  Dtype  \n",
+      "---  ------             --------------  -----  \n",
+      " 0   Name               277 non-null    object \n",
+      " 1   Region             277 non-null    object \n",
+      " 2   state              277 non-null    object \n",
+      " 3   summit_elev        277 non-null    int64  \n",
+      " 4   vertical_drop      277 non-null    int64  \n",
+      " 5   base_elev          277 non-null    int64  \n",
+      " 6   trams              277 non-null    int64  \n",
+      " 7   fastSixes          277 non-null    int64  \n",
+      " 8   fastQuads          277 non-null    int64  \n",
+      " 9   quad               277 non-null    int64  \n",
+      " 10  triple             277 non-null    int64  \n",
+      " 11  double             277 non-null    int64  \n",
+      " 12  surface            277 non-null    int64  \n",
+      " 13  total_chairs       277 non-null    int64  \n",
+      " 14  Runs               274 non-null    float64\n",
+      " 15  TerrainParks       233 non-null    float64\n",
+      " 16  LongestRun_mi      272 non-null    float64\n",
+      " 17  SkiableTerrain_ac  275 non-null    float64\n",
+      " 18  Snow Making_ac     240 non-null    float64\n",
+      " 19  daysOpenLastYear   233 non-null    float64\n",
+      " 20  yearsOpen          277 non-null    float64\n",
+      " 21  averageSnowfall    268 non-null    float64\n",
+      " 22  AdultWeekend       277 non-null    float64\n",
+      " 23  projectedDaysOpen  236 non-null    float64\n",
+      " 24  NightSkiing_ac     163 non-null    float64\n",
+      "dtypes: float64(11), int64(11), object(3)\n",
+      "memory usage: 56.3+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "ski_data.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are still some missing values, and it's good to be aware of this, but leave them as is for now."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.12 Save data<a id='2.12_Save_data'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(277, 25)"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ski_data.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Save this to your data directory, separately. Note that you were provided with the data in `raw_data` and you should saving derived data in a separate location. This guards against overwriting our original data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Directory ../data was created.\n",
+      "Writing file.  \"../data/ski_data_cleaned.csv\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "# save the data to a new csv file\n",
+    "datapath = '../data'\n",
+    "save_file(ski_data, 'ski_data_cleaned.csv', datapath)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing file.  \"../data/state_summary.csv\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "# save the state_summary separately.\n",
+    "datapath = '../data'\n",
+    "save_file(state_summary, 'state_summary.csv', datapath)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.13 Summary<a id='2.13_Summary'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q: 3** Write a summary statement that highlights the key processes and findings from this notebook. This should include information such as the original number of rows in the data, whether our own resort was actually present etc. What columns, if any, have been removed? Any rows? Summarise the reasons why. Were any other issues found? What remedial actions did you take? State where you are in the project. Can you confirm what the target feature is for your desire to predict ticket price? How many rows were left in the data? Hint: this is a great opportunity to reread your notebook, check all cells have been executed in order and from a \"blank slate\" (restarting the kernel will do this), and that your workflow makes sense and follows a logical pattern. As you do this you can pull out salient information for inclusion in this summary. Thus, this section will provide an important overview of \"what\" and \"why\" without having to dive into the \"how\" or any unproductive or inconclusive steps along the way."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**A: 3** Your answer here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "In this notebook, we worked on step 2 of the Data Science Method where we used the .CSV file to data wrangle. \n",
+    "First we cleaned the data in order to prepare it for analysis. It originally contained 330 rows amd 27 columns,\n",
+    "where it listed the ski resorts and different variables respectively. \n",
+    "\n",
+    "Looking at the AdultWeekend and AdultWeekday columns, About 14% of the rows have no price data. \n",
+    "As the price is your target, these rows are of no use.\n",
+    "we see the former has less missing values, so we decide to drop\n",
+    "AdultWeekday column completely and remove N/A values in the AdultWeekend column.\n",
+    "The fastEight column was dropped completely as well since half the column had no data.\n",
+    "\n",
+    "The final cleaned dataframe contains 25 columns and 277 rows. There are still some missing values but we shouldn't\n",
+    "remove or mess with the rows without doing more exploration.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": true
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From 40ffe40fa92f6a99cd24edc1868a8dda6dacc7a0 Mon Sep 17 00:00:00 2001
From: brianbui0 <buibrian96@gmail.com>
Date: Mon, 26 Aug 2024 16:39:12 -0700
Subject: [PATCH 5/9] Add files via upload

---
 CapstoneSteps/3.ipynb | 4532 +++++++++++++++++++++++++++++++++++++++++
 1 file changed, 4532 insertions(+)
 create mode 100644 CapstoneSteps/3.ipynb

diff --git a/CapstoneSteps/3.ipynb b/CapstoneSteps/3.ipynb
new file mode 100644
index 000000000..d713640d5
--- /dev/null
+++ b/CapstoneSteps/3.ipynb
@@ -0,0 +1,4532 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 3 Exploratory Data Analysis<a id='3_Exploratory_Data_Analysis'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.1 Contents<a id='3.1_Contents'></a>\n",
+    "* [3 Exploratory Data Analysis](#3_Exploratory_Data_Analysis)\n",
+    "  * [3.1 Contents](#3.1_Contents)\n",
+    "  * [3.2 Introduction](#3.2_Introduction)\n",
+    "  * [3.3 Imports](#3.3_Imports)\n",
+    "  * [3.4 Load The Data](#3.4_Load_The_Data)\n",
+    "    * [3.4.1 Ski data](#3.4.1_Ski_data)\n",
+    "    * [3.4.2 State-wide summary data](#3.4.2_State-wide_summary_data)\n",
+    "  * [3.5 Explore The Data](#3.5_Explore_The_Data)\n",
+    "    * [3.5.1 Top States By Order Of Each Of The Summary Statistics](#3.5.1_Top_States_By_Order_Of_Each_Of_The_Summary_Statistics)\n",
+    "      * [3.5.1.1 Total state area](#3.5.1.1_Total_state_area)\n",
+    "      * [3.5.1.2 Total state population](#3.5.1.2_Total_state_population)\n",
+    "      * [3.5.1.3 Resorts per state](#3.5.1.3_Resorts_per_state)\n",
+    "      * [3.5.1.4 Total skiable area](#3.5.1.4_Total_skiable_area)\n",
+    "      * [3.5.1.5 Total night skiing area](#3.5.1.5_Total_night_skiing_area)\n",
+    "      * [3.5.1.6 Total days open](#3.5.1.6_Total_days_open)\n",
+    "    * [3.5.2 Resort density](#3.5.2_Resort_density)\n",
+    "      * [3.5.2.1 Top states by resort density](#3.5.2.1_Top_states_by_resort_density)\n",
+    "    * [3.5.3 Visualizing High Dimensional Data](#3.5.3_Visualizing_High_Dimensional_Data)\n",
+    "      * [3.5.3.1 Scale the data](#3.5.3.1_Scale_the_data)\n",
+    "        * [3.5.3.1.1 Verifying the scaling](#3.5.3.1.1_Verifying_the_scaling)\n",
+    "      * [3.5.3.2 Calculate the PCA transformation](#3.5.3.2_Calculate_the_PCA_transformation)\n",
+    "      * [3.5.3.3 Average ticket price by state](#3.5.3.3_Average_ticket_price_by_state)\n",
+    "      * [3.5.3.4 Adding average ticket price to scatter plot](#3.5.3.4_Adding_average_ticket_price_to_scatter_plot)\n",
+    "    * [3.5.4 Conclusion On How To Handle State Label](#3.5.4_Conclusion_On_How_To_Handle_State_Label)\n",
+    "    * [3.5.5 Ski Resort Numeric Data](#3.5.5_Ski_Resort_Numeric_Data)\n",
+    "      * [3.5.5.1 Feature engineering](#3.5.5.1_Feature_engineering)\n",
+    "      * [3.5.5.2 Feature correlation heatmap](#3.5.5.2_Feature_correlation_heatmap)\n",
+    "      * [3.5.5.3 Scatterplots of numeric features against ticket price](#3.5.5.3_Scatterplots_of_numeric_features_against_ticket_price)\n",
+    "  * [3.6 Summary](#3.6_Summary)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.2 Introduction<a id='3.2_Introduction'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "At this point, you should have a firm idea of what your data science problem is and have the data you believe could help solve it. The business problem was a general one of modeling resort revenue. The data you started with contained some ticket price values, but with a number of missing values that led to several rows being dropped completely. You also had two kinds of ticket price. There were also some obvious issues with some of the other features in the data that, for example, led to one column being completely dropped, a data error corrected, and some other rows dropped. You also obtained some additional US state population and size data with which to augment the dataset, which also required some cleaning.\n",
+    "\n",
+    "The data science problem you subsequently identified is to predict the adult weekend ticket price for ski resorts."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.3 Imports<a id='3.3_Imports'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2020-10-07T07:04:19.124917Z",
+     "iopub.status.busy": "2020-10-07T07:04:19.124711Z",
+     "iopub.status.idle": "2020-10-07T07:04:19.128523Z",
+     "shell.execute_reply": "2020-10-07T07:04:19.128112Z",
+     "shell.execute_reply.started": "2020-10-07T07:04:19.124888Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from sklearn.decomposition import PCA\n",
+    "from sklearn.preprocessing import scale\n",
+    "\n",
+    "from library.sb_utils import save_file"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.4 Load The Data<a id='3.4_Load_The_Data'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.4.1 Ski data<a id='3.4.1_Ski_data'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ski_data = pd.read_csv('../data/ski_data_cleaned.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 277 entries, 0 to 276\n",
+      "Data columns (total 25 columns):\n",
+      " #   Column             Non-Null Count  Dtype  \n",
+      "---  ------             --------------  -----  \n",
+      " 0   Name               277 non-null    object \n",
+      " 1   Region             277 non-null    object \n",
+      " 2   state              277 non-null    object \n",
+      " 3   summit_elev        277 non-null    int64  \n",
+      " 4   vertical_drop      277 non-null    int64  \n",
+      " 5   base_elev          277 non-null    int64  \n",
+      " 6   trams              277 non-null    int64  \n",
+      " 7   fastSixes          277 non-null    int64  \n",
+      " 8   fastQuads          277 non-null    int64  \n",
+      " 9   quad               277 non-null    int64  \n",
+      " 10  triple             277 non-null    int64  \n",
+      " 11  double             277 non-null    int64  \n",
+      " 12  surface            277 non-null    int64  \n",
+      " 13  total_chairs       277 non-null    int64  \n",
+      " 14  Runs               274 non-null    float64\n",
+      " 15  TerrainParks       233 non-null    float64\n",
+      " 16  LongestRun_mi      272 non-null    float64\n",
+      " 17  SkiableTerrain_ac  275 non-null    float64\n",
+      " 18  Snow Making_ac     240 non-null    float64\n",
+      " 19  daysOpenLastYear   233 non-null    float64\n",
+      " 20  yearsOpen          277 non-null    float64\n",
+      " 21  averageSnowfall    268 non-null    float64\n",
+      " 22  AdultWeekend       277 non-null    float64\n",
+      " 23  projectedDaysOpen  236 non-null    float64\n",
+      " 24  NightSkiing_ac     163 non-null    float64\n",
+      "dtypes: float64(11), int64(11), object(3)\n",
+      "memory usage: 54.2+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "ski_data.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Region</th>\n",
+       "      <th>state</th>\n",
+       "      <th>summit_elev</th>\n",
+       "      <th>vertical_drop</th>\n",
+       "      <th>base_elev</th>\n",
+       "      <th>trams</th>\n",
+       "      <th>fastSixes</th>\n",
+       "      <th>fastQuads</th>\n",
+       "      <th>quad</th>\n",
+       "      <th>...</th>\n",
+       "      <th>TerrainParks</th>\n",
+       "      <th>LongestRun_mi</th>\n",
+       "      <th>SkiableTerrain_ac</th>\n",
+       "      <th>Snow Making_ac</th>\n",
+       "      <th>daysOpenLastYear</th>\n",
+       "      <th>yearsOpen</th>\n",
+       "      <th>averageSnowfall</th>\n",
+       "      <th>AdultWeekend</th>\n",
+       "      <th>projectedDaysOpen</th>\n",
+       "      <th>NightSkiing_ac</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alyeska Resort</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>3939</td>\n",
+       "      <td>2500</td>\n",
+       "      <td>250</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1610.0</td>\n",
+       "      <td>113.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>669.0</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>550.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Eaglecrest Ski Area</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>2600</td>\n",
+       "      <td>1540</td>\n",
+       "      <td>1200</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>640.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>53.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Hilltop Ski Area</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>2090</td>\n",
+       "      <td>294</td>\n",
+       "      <td>1796</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>152.0</td>\n",
+       "      <td>30.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Arizona Snowbowl</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>11500</td>\n",
+       "      <td>2300</td>\n",
+       "      <td>9200</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>777.0</td>\n",
+       "      <td>104.0</td>\n",
+       "      <td>122.0</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>260.0</td>\n",
+       "      <td>89.0</td>\n",
+       "      <td>122.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Sunrise Park Resort</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>11100</td>\n",
+       "      <td>1800</td>\n",
+       "      <td>9200</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.2</td>\n",
+       "      <td>800.0</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>115.0</td>\n",
+       "      <td>49.0</td>\n",
+       "      <td>250.0</td>\n",
+       "      <td>78.0</td>\n",
+       "      <td>104.0</td>\n",
+       "      <td>80.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 25 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  Name   Region    state  summit_elev  vertical_drop  \\\n",
+       "0       Alyeska Resort   Alaska   Alaska         3939           2500   \n",
+       "1  Eaglecrest Ski Area   Alaska   Alaska         2600           1540   \n",
+       "2     Hilltop Ski Area   Alaska   Alaska         2090            294   \n",
+       "3     Arizona Snowbowl  Arizona  Arizona        11500           2300   \n",
+       "4  Sunrise Park Resort  Arizona  Arizona        11100           1800   \n",
+       "\n",
+       "   base_elev  trams  fastSixes  fastQuads  quad  ...  TerrainParks  \\\n",
+       "0        250      1          0          2     2  ...           2.0   \n",
+       "1       1200      0          0          0     0  ...           1.0   \n",
+       "2       1796      0          0          0     0  ...           1.0   \n",
+       "3       9200      0          1          0     2  ...           4.0   \n",
+       "4       9200      0          0          1     2  ...           2.0   \n",
+       "\n",
+       "   LongestRun_mi  SkiableTerrain_ac  Snow Making_ac  daysOpenLastYear  \\\n",
+       "0            1.0             1610.0           113.0             150.0   \n",
+       "1            2.0              640.0            60.0              45.0   \n",
+       "2            1.0               30.0            30.0             150.0   \n",
+       "3            2.0              777.0           104.0             122.0   \n",
+       "4            1.2              800.0            80.0             115.0   \n",
+       "\n",
+       "   yearsOpen  averageSnowfall  AdultWeekend  projectedDaysOpen  NightSkiing_ac  \n",
+       "0       60.0            669.0          85.0              150.0           550.0  \n",
+       "1       44.0            350.0          53.0               90.0             NaN  \n",
+       "2       36.0             69.0          34.0              152.0            30.0  \n",
+       "3       81.0            260.0          89.0              122.0             NaN  \n",
+       "4       49.0            250.0          78.0              104.0            80.0  \n",
+       "\n",
+       "[5 rows x 25 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ski_data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.4.2 State-wide summary data<a id='3.4.2_State-wide_summary_data'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "state_summary = pd.read_csv('../data/state_summary.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 35 entries, 0 to 34\n",
+      "Data columns (total 8 columns):\n",
+      " #   Column                       Non-Null Count  Dtype  \n",
+      "---  ------                       --------------  -----  \n",
+      " 0   state                        35 non-null     object \n",
+      " 1   resorts_per_state            35 non-null     int64  \n",
+      " 2   state_total_skiable_area_ac  35 non-null     float64\n",
+      " 3   state_total_days_open        35 non-null     float64\n",
+      " 4   state_total_terrain_parks    35 non-null     float64\n",
+      " 5   state_total_nightskiing_ac   35 non-null     float64\n",
+      " 6   state_population             35 non-null     int64  \n",
+      " 7   state_area_sq_miles          35 non-null     int64  \n",
+      "dtypes: float64(4), int64(3), object(1)\n",
+      "memory usage: 2.3+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "state_summary.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state</th>\n",
+       "      <th>resorts_per_state</th>\n",
+       "      <th>state_total_skiable_area_ac</th>\n",
+       "      <th>state_total_days_open</th>\n",
+       "      <th>state_total_terrain_parks</th>\n",
+       "      <th>state_total_nightskiing_ac</th>\n",
+       "      <th>state_population</th>\n",
+       "      <th>state_area_sq_miles</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2280.0</td>\n",
+       "      <td>345.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>580.0</td>\n",
+       "      <td>731545</td>\n",
+       "      <td>665384</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1577.0</td>\n",
+       "      <td>237.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>7278717</td>\n",
+       "      <td>113990</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>California</td>\n",
+       "      <td>21</td>\n",
+       "      <td>25948.0</td>\n",
+       "      <td>2738.0</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>587.0</td>\n",
+       "      <td>39512223</td>\n",
+       "      <td>163695</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Colorado</td>\n",
+       "      <td>22</td>\n",
+       "      <td>43682.0</td>\n",
+       "      <td>3258.0</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>428.0</td>\n",
+       "      <td>5758736</td>\n",
+       "      <td>104094</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Connecticut</td>\n",
+       "      <td>5</td>\n",
+       "      <td>358.0</td>\n",
+       "      <td>353.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>256.0</td>\n",
+       "      <td>3565278</td>\n",
+       "      <td>5543</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         state  resorts_per_state  state_total_skiable_area_ac  \\\n",
+       "0       Alaska                  3                       2280.0   \n",
+       "1      Arizona                  2                       1577.0   \n",
+       "2   California                 21                      25948.0   \n",
+       "3     Colorado                 22                      43682.0   \n",
+       "4  Connecticut                  5                        358.0   \n",
+       "\n",
+       "   state_total_days_open  state_total_terrain_parks  \\\n",
+       "0                  345.0                        4.0   \n",
+       "1                  237.0                        6.0   \n",
+       "2                 2738.0                       81.0   \n",
+       "3                 3258.0                       74.0   \n",
+       "4                  353.0                       10.0   \n",
+       "\n",
+       "   state_total_nightskiing_ac  state_population  state_area_sq_miles  \n",
+       "0                       580.0            731545               665384  \n",
+       "1                        80.0           7278717               113990  \n",
+       "2                       587.0          39512223               163695  \n",
+       "3                       428.0           5758736               104094  \n",
+       "4                       256.0           3565278                 5543  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "state_summary.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.5 Explore The Data<a id='3.5_Explore_The_Data'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.5.1 Top States By Order Of Each Of The Summary Statistics<a id='3.5.1_Top_States_By_Order_Of_Each_Of_The_Summary_Statistics'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What does the state-wide picture for your market look like?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "state_summary_newind = state_summary.set_index('state')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.5.1.1 Total state area<a id='3.5.1.1_Total_state_area'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "state\n",
+       "Alaska        665384\n",
+       "California    163695\n",
+       "Montana       147040\n",
+       "New Mexico    121590\n",
+       "Arizona       113990\n",
+       "Name: state_area_sq_miles, dtype: int64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "state_summary_newind.state_area_sq_miles.sort_values(ascending=False).head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Your home state, Montana, comes in at third largest."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.5.1.2 Total state population<a id='3.5.1.2_Total_state_population'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "state\n",
+       "California      39512223\n",
+       "New York        19453561\n",
+       "Pennsylvania    12801989\n",
+       "Illinois        12671821\n",
+       "Ohio            11689100\n",
+       "Name: state_population, dtype: int64"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "state_summary_newind.state_population.sort_values(ascending=False).head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "California dominates the state population figures despite coming in second behind Alaska in size (by a long way). The resort's state of Montana was in the top five for size, but doesn't figure in the most populous states. Thus your state is less densely populated."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.5.1.3 Resorts per state<a id='3.5.1.3_Resorts_per_state'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "state\n",
+       "New York        33\n",
+       "Michigan        28\n",
+       "Colorado        22\n",
+       "California      21\n",
+       "Pennsylvania    19\n",
+       "Name: resorts_per_state, dtype: int64"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "state_summary_newind.resorts_per_state.sort_values(ascending=False).head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "New York comes top in the number of resorts in our market. Is this because of its proximity to wealthy New Yorkers wanting a convenient skiing trip? Or is it simply that its northerly location means there are plenty of good locations for resorts in that state?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.5.1.4 Total skiable area<a id='3.5.1.4_Total_skiable_area'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "state\n",
+       "Colorado      43682.0\n",
+       "Utah          30508.0\n",
+       "California    25948.0\n",
+       "Montana       21410.0\n",
+       "Idaho         16396.0\n",
+       "Name: state_total_skiable_area_ac, dtype: float64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "state_summary_newind.state_total_skiable_area_ac.sort_values(ascending=False).head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "New York state may have the most resorts, but they don't account for the most skiing area. In fact, New York doesn't even make it into the top five of skiable area. Good old Montana makes it into the top five, though. You may start to think that New York has more, smaller resorts, whereas Montana has fewer, larger resorts. Colorado seems to have a name for skiing; it's in the top five for resorts and in top place for total skiable area."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.5.1.5 Total night skiing area<a id='3.5.1.5_Total_night_skiing_area'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "state\n",
+       "New York        2836.0\n",
+       "Washington      1997.0\n",
+       "Michigan        1946.0\n",
+       "Pennsylvania    1528.0\n",
+       "Oregon          1127.0\n",
+       "Name: state_total_nightskiing_ac, dtype: float64"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "state_summary_newind.state_total_nightskiing_ac.sort_values(ascending=False).head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "New York dominates the area of skiing available at night. Looking at the top five in general, they are all the more northerly states. Is night skiing in and of itself an appeal to customers, or is a consequence of simply trying to extend the skiing day where days are shorter? Is New York's domination here because it's trying to maximize its appeal to visitors who'd travel a shorter distance for a shorter visit? You'll find the data generates more (good) questions rather than answering them. This is a positive sign! You might ask your executive sponsor or data provider for some additional data about typical length of stays at these resorts, although you might end up with data that is very granular and most likely proprietary to each resort. A useful level of granularity might be \"number of day tickets\" and \"number of weekly passes\" sold."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.5.1.6 Total days open<a id='3.5.1.6_Total_days_open'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "state\n",
+       "Colorado         3258.0\n",
+       "California       2738.0\n",
+       "Michigan         2389.0\n",
+       "New York         2384.0\n",
+       "New Hampshire    1847.0\n",
+       "Name: state_total_days_open, dtype: float64"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "state_summary_newind.state_total_days_open.sort_values(ascending=False).head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The total days open seem to bear some resemblance to the number of resorts. This is plausible. The season will only be so long, and so the more resorts open through the skiing season, the more total days open we'll see. New Hampshire makes a good effort at making it into the top five, for a small state that didn't make it into the top five of resorts per state. Does its location mean resorts there have a longer season and so stay open longer, despite there being fewer of them?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.5.2 Resort density<a id='3.5.2_Resort_density'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are big states which are not necessarily the most populous. There are states that host many resorts, but other states host a larger total skiing area. The states with the most total days skiing per season are not necessarily those with the most resorts. And New York State boasts an especially large night skiing area. New York had the most resorts but wasn't in the top five largest states, so the reason for it having the most resorts can't be simply having lots of space for them. New York has the second largest population behind California. Perhaps many resorts have sprung up in New York because of the population size? Does this mean there is a high competition between resorts in New York State, fighting for customers and thus keeping prices down? You're not concerned, per se, with the absolute size or population of a state, but you could be interested in the ratio of resorts serving a given population or a given area.\n",
+    "\n",
+    "So, calculate those ratios! Think of them as measures of resort density, and drop the absolute population and state size columns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state</th>\n",
+       "      <th>resorts_per_state</th>\n",
+       "      <th>state_total_skiable_area_ac</th>\n",
+       "      <th>state_total_days_open</th>\n",
+       "      <th>state_total_terrain_parks</th>\n",
+       "      <th>state_total_nightskiing_ac</th>\n",
+       "      <th>resorts_per_100kcapita</th>\n",
+       "      <th>resorts_per_100ksq_mile</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2280.0</td>\n",
+       "      <td>345.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>580.0</td>\n",
+       "      <td>0.410091</td>\n",
+       "      <td>0.450867</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1577.0</td>\n",
+       "      <td>237.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>0.027477</td>\n",
+       "      <td>1.754540</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>California</td>\n",
+       "      <td>21</td>\n",
+       "      <td>25948.0</td>\n",
+       "      <td>2738.0</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>587.0</td>\n",
+       "      <td>0.053148</td>\n",
+       "      <td>12.828736</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Colorado</td>\n",
+       "      <td>22</td>\n",
+       "      <td>43682.0</td>\n",
+       "      <td>3258.0</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>428.0</td>\n",
+       "      <td>0.382028</td>\n",
+       "      <td>21.134744</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Connecticut</td>\n",
+       "      <td>5</td>\n",
+       "      <td>358.0</td>\n",
+       "      <td>353.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>256.0</td>\n",
+       "      <td>0.140242</td>\n",
+       "      <td>90.203861</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         state  resorts_per_state  state_total_skiable_area_ac  \\\n",
+       "0       Alaska                  3                       2280.0   \n",
+       "1      Arizona                  2                       1577.0   \n",
+       "2   California                 21                      25948.0   \n",
+       "3     Colorado                 22                      43682.0   \n",
+       "4  Connecticut                  5                        358.0   \n",
+       "\n",
+       "   state_total_days_open  state_total_terrain_parks  \\\n",
+       "0                  345.0                        4.0   \n",
+       "1                  237.0                        6.0   \n",
+       "2                 2738.0                       81.0   \n",
+       "3                 3258.0                       74.0   \n",
+       "4                  353.0                       10.0   \n",
+       "\n",
+       "   state_total_nightskiing_ac  resorts_per_100kcapita  resorts_per_100ksq_mile  \n",
+       "0                       580.0                0.410091                 0.450867  \n",
+       "1                        80.0                0.027477                 1.754540  \n",
+       "2                       587.0                0.053148                12.828736  \n",
+       "3                       428.0                0.382028                21.134744  \n",
+       "4                       256.0                0.140242                90.203861  "
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# The 100_000 scaling is simply based on eyeballing the magnitudes of the data\n",
+    "state_summary['resorts_per_100kcapita'] = 100_000 * state_summary.resorts_per_state / state_summary.state_population\n",
+    "state_summary['resorts_per_100ksq_mile'] = 100_000 * state_summary.resorts_per_state / state_summary.state_area_sq_miles\n",
+    "state_summary.drop(columns=['state_population', 'state_area_sq_miles'], inplace=True)\n",
+    "state_summary.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With the removal of the two columns that only spoke to state-specific data, you now have a Dataframe that speaks to the skiing competitive landscape of each state. It has the number of resorts per state, total skiable area, and days of skiing. You've translated the plain state data into something more useful that gives you an idea of the density of resorts relative to the state population and size."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "How do the distributions of these two new features look?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "state_summary.resorts_per_100kcapita.hist(bins=30)\n",
+    "plt.xlabel('Number of resorts per 100k population')\n",
+    "plt.ylabel('count');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "state_summary.resorts_per_100ksq_mile.hist(bins=30)\n",
+    "plt.xlabel('Number of resorts per 100k square miles')\n",
+    "plt.ylabel('count');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So they have quite some long tails on them, but there's definitely some structure there."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.5.2.1 Top states by resort density<a id='3.5.2.1_Top_states_by_resort_density'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "state\n",
+       "Vermont          2.403889\n",
+       "Wyoming          1.382268\n",
+       "New Hampshire    1.176721\n",
+       "Montana          1.122778\n",
+       "Idaho            0.671492\n",
+       "Name: resorts_per_100kcapita, dtype: float64"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "state_summary.set_index('state').resorts_per_100kcapita.sort_values(ascending=False).head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "state\n",
+       "New Hampshire    171.141299\n",
+       "Vermont          155.990017\n",
+       "Massachusetts    104.225886\n",
+       "Connecticut       90.203861\n",
+       "Rhode Island      64.724919\n",
+       "Name: resorts_per_100ksq_mile, dtype: float64"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "state_summary.set_index('state').resorts_per_100ksq_mile.sort_values(ascending=False).head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Vermont seems particularly high in terms of resorts per capita, and both New Hampshire and Vermont top the chart for resorts per area. New York doesn't appear in either!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.5.3 Visualizing High Dimensional Data<a id='3.5.3_Visualizing_High_Dimensional_Data'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You may be starting to feel there's a bit of a problem here, or at least a challenge. You've constructed some potentially useful and business relevant features, derived from summary statistics, for each of the states you're concerned with. You've explored many of these features in turn and found various trends. Some states are higher in some but not in others. Some features will also be more correlated with one another than others.\n",
+    "\n",
+    "One way to disentangle this interconnected web of relationships is via [principle components analysis](https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html#sklearn.decomposition.PCA) (PCA). This technique will find linear combinations of the original features that are uncorrelated with one another and order them by the amount of variance they explain. You can use these derived features to visualize the data in a lower dimension (e.g. 2 down from 7) and know how much variance the representation explains. You can also explore how the original features contribute to these derived features."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The basic steps in this process are:\n",
+    "\n",
+    "1. scale the data (important here because our features are heterogenous)\n",
+    "2. fit the PCA transformation (learn the transformation from the data)\n",
+    "3. apply the transformation to the data to create the derived features\n",
+    "4. (optionally) use the derived features to look for patterns in the data and explore the coefficients"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.5.3.1 Scale the data<a id='3.5.3.1_Scale_the_data'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You only want numeric data here, although you don't want to lose track of the state labels, so it's convenient to set the state as the index."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>resorts_per_state</th>\n",
+       "      <th>state_total_skiable_area_ac</th>\n",
+       "      <th>state_total_days_open</th>\n",
+       "      <th>state_total_terrain_parks</th>\n",
+       "      <th>state_total_nightskiing_ac</th>\n",
+       "      <th>resorts_per_100kcapita</th>\n",
+       "      <th>resorts_per_100ksq_mile</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Alaska</th>\n",
+       "      <td>3</td>\n",
+       "      <td>2280.0</td>\n",
+       "      <td>345.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>580.0</td>\n",
+       "      <td>0.410091</td>\n",
+       "      <td>0.450867</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Arizona</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1577.0</td>\n",
+       "      <td>237.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>0.027477</td>\n",
+       "      <td>1.754540</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>California</th>\n",
+       "      <td>21</td>\n",
+       "      <td>25948.0</td>\n",
+       "      <td>2738.0</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>587.0</td>\n",
+       "      <td>0.053148</td>\n",
+       "      <td>12.828736</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Colorado</th>\n",
+       "      <td>22</td>\n",
+       "      <td>43682.0</td>\n",
+       "      <td>3258.0</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>428.0</td>\n",
+       "      <td>0.382028</td>\n",
+       "      <td>21.134744</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Connecticut</th>\n",
+       "      <td>5</td>\n",
+       "      <td>358.0</td>\n",
+       "      <td>353.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>256.0</td>\n",
+       "      <td>0.140242</td>\n",
+       "      <td>90.203861</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             resorts_per_state  state_total_skiable_area_ac  \\\n",
+       "state                                                         \n",
+       "Alaska                       3                       2280.0   \n",
+       "Arizona                      2                       1577.0   \n",
+       "California                  21                      25948.0   \n",
+       "Colorado                    22                      43682.0   \n",
+       "Connecticut                  5                        358.0   \n",
+       "\n",
+       "             state_total_days_open  state_total_terrain_parks  \\\n",
+       "state                                                           \n",
+       "Alaska                       345.0                        4.0   \n",
+       "Arizona                      237.0                        6.0   \n",
+       "California                  2738.0                       81.0   \n",
+       "Colorado                    3258.0                       74.0   \n",
+       "Connecticut                  353.0                       10.0   \n",
+       "\n",
+       "             state_total_nightskiing_ac  resorts_per_100kcapita  \\\n",
+       "state                                                             \n",
+       "Alaska                            580.0                0.410091   \n",
+       "Arizona                            80.0                0.027477   \n",
+       "California                        587.0                0.053148   \n",
+       "Colorado                          428.0                0.382028   \n",
+       "Connecticut                       256.0                0.140242   \n",
+       "\n",
+       "             resorts_per_100ksq_mile  \n",
+       "state                                 \n",
+       "Alaska                      0.450867  \n",
+       "Arizona                     1.754540  \n",
+       "California                 12.828736  \n",
+       "Colorado                   21.134744  \n",
+       "Connecticut                90.203861  "
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 1#\n",
+    "#Create a new dataframe, `state_summary_scale` from `state_summary` whilst setting the index to 'state'\n",
+    "state_summary_scale = state_summary.set_index('state')\n",
+    "#Save the state labels (using the index attribute of `state_summary_scale`) into the variable 'state_summary_index'\n",
+    "state_summary_index = state_summary_scale.index\n",
+    "#Save the column names (using the `columns` attribute) of `state_summary_scale` into the variable 'state_summary_columns'\n",
+    "state_summary_columns = state_summary_scale.columns\n",
+    "state_summary_scale.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The above shows what we expect: the columns we want are all numeric and the state has been moved to the index. Although, it's not necessary to step through the sequence so laboriously, it is often good practice even for experienced professionals. It's easy to make a mistake or forget a step, or the data may have been holding out a surprise! Stepping through like this helps  validate both your work and the data!\n",
+    "\n",
+    "Now use `scale()` to scale the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "state_summary_scale = scale(state_summary_scale)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note, `scale()` returns an ndarray, so you lose the column names. Because you want to visualise scaled data, you already copied the column names. Now you can construct a dataframe from the ndarray here and reintroduce the column names."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>resorts_per_state</th>\n",
+       "      <th>state_total_skiable_area_ac</th>\n",
+       "      <th>state_total_days_open</th>\n",
+       "      <th>state_total_terrain_parks</th>\n",
+       "      <th>state_total_nightskiing_ac</th>\n",
+       "      <th>resorts_per_100kcapita</th>\n",
+       "      <th>resorts_per_100ksq_mile</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-0.806912</td>\n",
+       "      <td>-0.392012</td>\n",
+       "      <td>-0.689059</td>\n",
+       "      <td>-0.816118</td>\n",
+       "      <td>0.069410</td>\n",
+       "      <td>0.139593</td>\n",
+       "      <td>-0.689999</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-0.933558</td>\n",
+       "      <td>-0.462424</td>\n",
+       "      <td>-0.819038</td>\n",
+       "      <td>-0.726994</td>\n",
+       "      <td>-0.701326</td>\n",
+       "      <td>-0.644706</td>\n",
+       "      <td>-0.658125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.472706</td>\n",
+       "      <td>1.978574</td>\n",
+       "      <td>2.190933</td>\n",
+       "      <td>2.615141</td>\n",
+       "      <td>0.080201</td>\n",
+       "      <td>-0.592085</td>\n",
+       "      <td>-0.387368</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.599351</td>\n",
+       "      <td>3.754811</td>\n",
+       "      <td>2.816757</td>\n",
+       "      <td>2.303209</td>\n",
+       "      <td>-0.164893</td>\n",
+       "      <td>0.082069</td>\n",
+       "      <td>-0.184291</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-0.553622</td>\n",
+       "      <td>-0.584519</td>\n",
+       "      <td>-0.679431</td>\n",
+       "      <td>-0.548747</td>\n",
+       "      <td>-0.430027</td>\n",
+       "      <td>-0.413557</td>\n",
+       "      <td>1.504408</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   resorts_per_state  state_total_skiable_area_ac  state_total_days_open  \\\n",
+       "0          -0.806912                    -0.392012              -0.689059   \n",
+       "1          -0.933558                    -0.462424              -0.819038   \n",
+       "2           1.472706                     1.978574               2.190933   \n",
+       "3           1.599351                     3.754811               2.816757   \n",
+       "4          -0.553622                    -0.584519              -0.679431   \n",
+       "\n",
+       "   state_total_terrain_parks  state_total_nightskiing_ac  \\\n",
+       "0                  -0.816118                    0.069410   \n",
+       "1                  -0.726994                   -0.701326   \n",
+       "2                   2.615141                    0.080201   \n",
+       "3                   2.303209                   -0.164893   \n",
+       "4                  -0.548747                   -0.430027   \n",
+       "\n",
+       "   resorts_per_100kcapita  resorts_per_100ksq_mile  \n",
+       "0                0.139593                -0.689999  \n",
+       "1               -0.644706                -0.658125  \n",
+       "2               -0.592085                -0.387368  \n",
+       "3                0.082069                -0.184291  \n",
+       "4               -0.413557                 1.504408  "
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 2#\n",
+    "#Create a new dataframe from `state_summary_scale` using the column names we saved in `state_summary_columns`\n",
+    "state_summary_scaled_df = pd.DataFrame(state_summary_scale, columns=state_summary_columns)\n",
+    "state_summary_scaled_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 3.5.3.1.1 Verifying the scaling<a id='3.5.3.1.1_Verifying_the_scaling'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is definitely going the extra mile for validating your steps, but provides a worthwhile lesson."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First of all, check the mean of the scaled features using panda's `mean()` DataFrame method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "resorts_per_state              1.189525e-17\n",
+       "state_total_skiable_area_ac   -1.982541e-17\n",
+       "state_total_days_open          1.744636e-17\n",
+       "state_total_terrain_parks      1.903239e-17\n",
+       "state_total_nightskiing_ac    -3.489272e-17\n",
+       "resorts_per_100kcapita         5.075305e-17\n",
+       "resorts_per_100ksq_mile        3.172066e-17\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 3#\n",
+    "#Call `state_summary_scaled_df`'s `mean()` method\n",
+    "state_summary_scaled_df.mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is pretty much zero!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Perform a similar check for the standard deviation using pandas's `std()` DataFrame method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "resorts_per_state              1.014599\n",
+       "state_total_skiable_area_ac    1.014599\n",
+       "state_total_days_open          1.014599\n",
+       "state_total_terrain_parks      1.014599\n",
+       "state_total_nightskiing_ac     1.014599\n",
+       "resorts_per_100kcapita         1.014599\n",
+       "resorts_per_100ksq_mile        1.014599\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 4#\n",
+    "#Call `state_summary_scaled_df`'s `std()` method\n",
+    "state_summary_scaled_df.std()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Well, this is a little embarrassing. The numbers should be closer to 1 than this! Check the documentation for [scale](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.scale.html) to see if you used it right. What about [std](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.std.html), did you mess up there? Is one of them not working right?\n",
+    "\n",
+    "The keen observer, who already has some familiarity with statistical inference and biased estimators, may have noticed what's happened here. `scale()` uses the biased estimator for standard deviation (ddof=0). This doesn't mean it's bad! It simply means it calculates the standard deviation of the sample it was given. The `std()` method, on the other hand, defaults to using ddof=1, that is it's normalized by N-1. In other words, the `std()` method default is to assume you want your best estimate of the population parameter based on the given sample. You can tell it to return the biased estimate instead:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "resorts_per_state              1.0\n",
+       "state_total_skiable_area_ac    1.0\n",
+       "state_total_days_open          1.0\n",
+       "state_total_terrain_parks      1.0\n",
+       "state_total_nightskiing_ac     1.0\n",
+       "resorts_per_100kcapita         1.0\n",
+       "resorts_per_100ksq_mile        1.0\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 5#\n",
+    "#Repeat the previous call to `std()` but pass in ddof=0 \n",
+    "state_summary_scaled_df.std(ddof=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There! Now it agrees with `scale()` and our expectation. This just goes to show different routines to do ostensibly the same thing can have different behaviours. Good practice is to keep validating your work and checking the documentation!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.5.3.2 Calculate the PCA transformation<a id='3.5.3.2_Calculate_the_PCA_transformation'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Fit the PCA transformation using the scaled data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "state_pca = PCA().fit(state_summary_scale)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the cumulative variance ratio with number of components."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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uuMIEBQWZxx9/3Nxwww1mzpw5ZsaMGaZixYqmUaNGTp+JS/3sL1++3DzxxBPmu+++M8uXLzc//vij6dOnj/H19TV//fWXY7ysZWnVqlXNzTffbGbNmmXmzJljEhISTN++fU14eLjJyMhwemy33HKLqVKlygW/iM7r+bzQ59mYwllvuNRM56+vpaenm06dOhkPDw/z5JNPmrlz55pZs2aZ5557zvFl3cqVK43NZjO33XabmTt3rlmyZImZMmWKGTBgQK7P3YWU+uIUFxdnJJnbbrst39O4Wpwefvhhp/H69OljJJnx48c7DW/SpIlp1qyZ4/dLLU7nyszMNOnp6Y5vKf744w/HbRfaP/X8xzRr1iwjySxcuNAxLCMjw1SpUsXcdNNNjmH333+/KVOmjNm3b5/T/LK+fb7QPqrp6emmYsWK5vbbb3cafn45O5/dbjfp6elm+vTpxt3d3Rw7dsxxW8eOHR1b/86XU3HKab5jx441wcHBTgvWrC0U5z7OM2fOmKCgIHP//fc7hg0ZMsR4eno6/jDnZNy4ccbNzc2sW7fOafh3331nJJm5c+fmOm1ej/FcBfVeePbZZ40ks2bNGqfxhg0bZmw2m9m5c2euGWJiYoyHh0e2z8bJkydNpUqVzK233uqUt3v37qZcuXJm69atpl69eqZOnTo5fmPu6+vrtAUvIyPD1KlTx9SsWdMxLD/HOGVkZJjk5GTj7+/vtLKdW3HK6Zu/7t27m9q1azt+//LLL40k8/333zuNt27dOiPJTJgwwRjz/y8IHn/8cafxZsyYkec3tsb8f/lQpUoVc+bMGcfwpKQkExQUZK677jrHsKxl1KZNmxzD1q5dm20FPCdZz8XXX39t0tPTzenTp82vv/5qatasadzd3R3vqzp16phKlSpdcF7nGzt2rJFk5s+fb4wxZs+ePcZms+XrD13WlrTnn38+13GynuMHHnjAafiaNWucSp8x//9crV+/3jEsISHBuLu7G19fX6cVpc2bN2f7cidr2Zzb6/n5559fdKbzP3v16tVz2jLhyjJFkqlYsaJJSkpyDIuLizNubm5m3LhxjmG5HX+QNc/NmzcbV2VtQTlX1kr3G2+84TT866+/NpKcikRERIRxd3e/4DIny/r1640kM3PmzAuOl99jnDIyMkx6erq59tprTd++fR3DL3R8T9euXU21atWyHSv50EMPGR8fH6e/XTNnzjTu7u5OW4yyljlZWz6y5HcZk5/XatKkSTku115//fVs6wCSTGBgoFPuvJ6DnPTs2dM0adLkguO4WpwqVark9Ldi5syZRpJp0qSJ09/yrOPszt0adKmf/fNlZGSYtLQ0U6tWLaflQday9Kqrrso2TdZtP/74o2PYwYMHjYeHR7Yvcs+Xn+czv8c4FdR6w6VmOn99bfr06UaS+eSTT3KdX9Z654kTJy54v/nFWfUug/PPCFW3bl1JUo8ePbIN37dvX4Hd7549e3T77berUqVKcnd3l6enpzp27ChJOZ71Kj+6deumSpUqacqUKY5hCxYs0KFDhzRkyBDHsDlz5qhTp06qUqWKMjIyHD/dunWTJC1fvjzX+/Dw8NCdd96pH374QYmJiZIku92u//73v7rhhhsUHBzsGHfTpk3q3bu3goODHY/xrrvukt1u199//+003/Lly+uaa67J1+NcsmSJrrvuOgUGBjrm++KLLyohIUHx8fFO4zZp0kTh4eGO3318fHTFFVc4vZbz5s1Tp06dHK99TubMmaMGDRqoSZMmTs9Z165d830mt9weY2G8F5YsWaJ69eqpVatWTsMHDRokY4yWLFmS67QLFixQRkaG7rrrLqfH6uPjo44dOzo9VpvNpunTp6ts2bJq0aKFoqOj9c0338jf3z/bfK+99lpVrFjR8bu7u7v69eun3bt368CBA7nmSU5O1jPPPKOaNWvKw8NDHh4eKlOmjE6dOpWv58dms2U7/qBRo0ZO74E5c+aoXLly6tWrl9NjbtKkiSpVquR4zEuXLpUk3XHHHU7zu/XWW13a3/3GG2+Uj4+P4/eyZcuqV69e+vXXX2W32yVJ/fv3V4UKFfTRRx85xvvggw8UGhqqfv365et++vXrJ09PT/n5+emqq66S3W7Xd999p0aNGuU767mMMZoyZYrCwsLUuXNnSVJUVJSuvvpqff/990pKSrrg9PPmzZMkPfjgg7mOk/Ucn3+2p1atWqlu3bpavHix0/DKlSurefPmjt+DgoJUoUIFNWnSRFWqVHEMz/p857Qcz+31zMriaqZKlSpl++zl9J5zZZnSqVMnlS1b1vF7xYoVVaFChXz9XWrSpIm8vLx03333adq0adqzZ0+e01xI1vLj/Ofjlltukb+/f7bno1GjRrriiivynG/NmjVVvnx5PfPMM5o0aZK2b9/ucrZJkyapWbNm8vHxkYeHhzw9PbV48eJ8LStSUlK0ePFi9e3bV35+fk6vS/fu3ZWSkqLVq1c7xv/+++/VoUMHhYaGZpvXTTfd5PR7fpcx+XmtlixZIn9/f918881Ow7Nej/Of/2uuuUbly5fP8/FfSKtWrfTHH3/ogQce0IIFC/L8rOdHp06dnP5WZH1Gu3Xr5nQm4tw+u5fy2c/IyNCrr76qevXqycvLSx4eHvLy8tKuXbtyfK+c/3pKZ48Dbdy4sdMyetKkSbLZbLrvvvsu+Ngv9fksjPWGgn6N582bJx8fH6f1z/O1bNlS0tll7jfffKODBw9e0n2W+uIUEhIiPz8/RUdHF9p9BAUFOf3u5eWV6/CCOqAyOTlZHTp00Jo1a/Tyyy9r2bJlWrdunX744QdJZw+cvRgeHh4aMGCAfvzxR8epIqdOnarKlSura9eujvEOHz6s2bNny9PT0+mnfv36kqSjR49e8H6GDBmilJQUffXVV5LOrmjHxsZq8ODBjnFiYmLUoUMHHTx4UO+9955+++03rVu3zrGAOf8xVq5cOV+Pce3aterSpYsk6ZNPPtGKFSu0bt06Pf/88znO99wil8Xb29tpvCNHjjgdFJ+Tw4cPa8uWLdmes7Jly8oYk+dzJuX8GAvrvZCQkJDj/WX9MUlISMh12sOHD0s6u0A7//F+/fXX2R5rcHCwevfurZSUFF1//fVq2LBhjvOtVKlSrsMulOf222/Xhx9+qHvuuUcLFizQ2rVrtW7dOoWGhubr+fHz83MqKdLZ98C5n+fDhw/rxIkT8vLyyvaY4+LiHI85K+f5j8XDwyPH91pucnsu0tLSlJyc7Mh4//3364svvtCJEyd05MgRffPNN7rnnnucTpJzIa+//rrWrVunjRs3KiYmRnv27FGfPn0ct4eHh+vIkSM6depUvua3ZMkSRUdH65ZbblFSUpJOnDihEydO6NZbb9Xp06f15ZdfXnD6I0eOyN3dPcfHnyXrOc7t/Xv+e+X8ZbV0dnmd27I9p+V4bq9n1n25mik/yx1Xlyn5mWduatSooV9++UUVKlTQgw8+qBo1aqhGjRp677338pw2JwkJCfLw8MhWGGw2mypVqpTt+cjv8j0wMFDLly9XkyZN9Nxzz6l+/fqqUqWKRo0apfT09DynHz9+vIYNG6bWrVvr+++/1+rVq7Vu3Tpdf/31+XqeEhISlJGRoQ8++CDb69K9e3dJ///7mJ6ertmzZ+e4Qu3n56eAgACnYfldxuTntUpISFClSpWyXeakQoUK8vDwuOjn/0JGjBiht956S6tXr1a3bt0UHBysa6+9VuvXr7/oebqy/iVl/+xeymd/+PDhGjlypPr06aPZs2drzZo1WrdunRo3bpzjeyW35/CRRx7R4sWLtXPnTqWnp+uTTz7RzTfffMFlnHRpz2dhrTcU9Gt85MgRValSRW5uudeZq666SjNnznR8WVutWjU1aNAgz78luSnep+soAO7u7rr22ms1b948HThwIM+VW+nsH5LU1NRswy+0YnYxslbEzr+v/KxAL1myRIcOHdKyZcsc3xBIKpBrbwwePFhvvvmmvvrqK/Xr10+zZs3SY489Jnd3d8c4ISEhatSokV555ZUc53HuNzU5ydqSMWXKFN1///2aMmWKqlSp4ig0kjRz5kydOnVKP/zwgyIiIhzDN2/enOM883udq6+++kqenp6aM2eO08rwpVzTIzQ09IJbPKSzz5mvr68mT56c6+15yekxFtZ7ITg4WLGxsdmGHzp0SNKF82bd9t133zm9drlZtGiRJk6cqFatWunHH3/U999/n+PKRFxcXK7DcisdiYmJmjNnjkaNGqVnn33WMTw1NbVAr98WEhKi4ODgXM/QmfVNf1bOuLg4Va1a1XF7RkaGS8uY3J4LLy8vlSlTxjFs2LBheu211zR58mSlpKQoIyNDQ4cOzff9VK9eXS1atMj19q5du2rhwoWaPXu2brvttjzn99lnn0k6u4I6fvz4HG+///77c50+NDRUdrtdcXFxua6IZD3HsbGx2Zb5hw4dytdnzVW5vZ5ZWQojU0EsU1zRoUMHdejQQXa7XevXr9cHH3ygxx57TBUrVszXa3+u4OBgZWRk6MiRI07lyRijuLg4x7fIWVy5jmHDhg311VdfyRijLVu2aOrUqRo7dqx8fX2dlgE5+fzzz3X11Vdr4sSJTsNPnjyZr/suX7683N3dNWDAgFy3ikZFRUmSfvnlFyUmJqpv377Zxsnp8eZ3GSPl/VoFBwdrzZo1MsY43Vd8fLwyMjKyvXcu9jqS5/Lw8NDw4cM1fPhwnThxQr/88ouee+45de3aVfv373d8QZXT+ld+1osut88//1x33XWXXn31VafhR48eVbly5bKNn9tzePvtt+uZZ57RRx99pDZt2iguLu6CW9Sz5Of5zE1hrTdcSqachIaG6vfff1dmZuYFy9MNN9ygG264QampqVq9erXGjRun22+/XZGRkWrbtq1L91nqtzhJZxuwMUb33nuv0tLSst2e9a1PlsjISG3ZssVpnCVLlji+xS0okZGRkpTtvmbNmpXntFkfwPO/Nf7Pf/6TbdyscfL7DULdunXVunVrTZkyRV988YVSU1OdtgRJZ3dP3Lp1q2rUqKEWLVpk+8mrOElnC9qaNWv0+++/a/bs2Ro4cKBTOcvpMRpj9Mknn+TrceQm6xSg597XmTNn9N///vei59mtWzctXbpUO3fuzHWcnj176p9//lFwcHCOz1nW+8FVhfVeuPbaa7V9+3Zt3LjRafj06dNls9nUqVOnXKft2rWrPDw89M8//+T4WM9dEY+NjdWdd96pjh07auXKlerdu7fuvvvuHLcSL1682LE1Szq7i+fXX3+tGjVq5PqliM1mkzEm2/Pz6aefOnZpKwg9e/ZUQkKC7HZ7jo+3du3akuS4uN+MGTOcpv/mm2+UkZGR7/v74YcfnL79PHnypGbPnq0OHTo4vbcrV66sW265RRMmTNCkSZPUq1cvp11PL9Xdd9+tSpUq6emnn851F4msbzGPHz+uH3/8Ue3bt9fSpUuz/dxxxx1at26dtm7dmuv9Ze0OfP6K7bmydmf9/PPPnYavW7dOO3bs0LXXXuvSY8yP3F7PrNe7MDIVxjIlP8sId3d3tW7d2rH1//xlRH5kPd7zn4/vv/9ep06dKpDXyGazqXHjxnrnnXdUrlw5p5y5bWmz2WzZlhVbtmzRqlWrnIbl9jz5+fmpU6dO2rRpkxo1apTj65JVor///nu1adPGqXBfSH6XMefK7bW69tprlZycnO0Lw+nTpztuz4ur6xbnKleunG6++WY9+OCDOnbsmPbu3Svp7HpRfHy803I+LS1NCxYscPk+CltO75Wff/7Z5V3FfHx8HLtVjh8/Xk2aNFH79u1dmkduz2dur1FhrkNebKacdOvWTSkpKZo6dWq+7tPb21sdO3bU66+/Luns4R6uKvVbnCSpbdu2mjhxoh544AE1b95cw4YNU/369ZWenq5Nmzbp448/VoMGDRzHMAwYMEAjR47Uiy++qI4dO2r79u368MMPFRgYWKC5KlWqpOuuu07jxo1T+fLlFRERocWLFztWMi6kXbt2Kl++vIYOHapRo0bJ09NTM2bM0B9//JFt3Kzdnl5//XV169ZN7u7uatSokWPTc06GDBmi+++/X4cOHVK7du2yLZDHjh2rRYsWqV27dnrkkUdUu3ZtpaSkaO/evZo7d64mTZqU59a9/v37a/jw4erfv79SU1Oz7eveuXNneXl5qX///nr66aeVkpKiiRMn6vjx43k+PxfSo0cPjR8/Xrfffrvuu+8+JSQk6K233sr3rks5GTt2rObNm6errrpKzz33nBo2bKgTJ05o/vz5Gj58uOrUqaPHHntM33//va666io9/vjjatSokTIzMxUTE6OFCxfqiSeeUOvWrV2+78J6Lzz++OOaPn26evToobFjxyoiIkI///yzJkyYoGHDhl3weIPIyEiNHTtWzz//vPbs2aPrr79e5cuX1+HDh7V27Vr5+/trzJgxstvt6t+/v2w2m7744gu5u7tr6tSpatKkifr166fff//dKVtISIiuueYajRw5Uv7+/powYYL++usvxy6fOQkICNBVV12lN998UyEhIYqMjNTy5cv12Wef5fit4MW67bbbNGPGDHXv3l2PPvqoWrVqJU9PTx04cEBLly7VDTfcoL59+6pu3bq688479e6778rT01PXXXedtm7dqrfeeivbrjkX4u7urs6dO2v48OHKzMzU66+/rqSkJI0ZMybbuI8++qjjvXXu8YsFITAwUD/99JN69uyppk2b6qGHHlLbtm0d+/p//vnn+uOPP3TjjTdqxowZSklJ0SOPPJLj1eGDg4M1Y8YMffbZZ3rnnXdyvL8OHTpowIABevnll3X48GH17NlT3t7e2rRpk/z8/PTwww+rdu3auu+++/TBBx/Izc1N3bp10969ezVy5EiFhYXp8ccfL9DnQDpbDj08PNS5c2dt27ZNI0eOVOPGjXXrrbdKUqFkKoxlStYy4r333tPAgQPl6emp2rVra8aMGVqyZIl69Oih8PBwpaSkOLZ0XXfddS5n79y5s7p27apnnnlGSUlJat++vbZs2aJRo0apadOmGjBggMvzlM4eBzRhwgT16dNH1atXlzFGP/zwg06cOOE4pi7rcS5btkyzZ89W5cqVVbZsWdWuXVs9e/bUSy+9pFGjRqljx47auXOnxo4dq6ioKKcvNsqWLauIiAj99NNPuvbaaxUUFORYvrz33nu68sor1aFDBw0bNkyRkZE6efKkdu/erdmzZ2vJkiWy2+366aef8twCdq78LmMmTZqU52t111136aOPPtLAgQO1d+9eNWzYUL///rteffVVde/ePV+v6YWeg5z06tVLDRo0UIsWLRQaGqp9+/bp3XffVUREhGrVqiXp7DGVL774om677TY99dRTSklJ0fvvv1+gX3IVlJ49e2rq1KmqU6eOGjVqpA0bNujNN9/M155N53vggQf0xhtvaMOGDfr000/zNU1+ns/cPs+Ftd5wKZnO3WKapX///poyZYqGDh2qnTt3qlOnTsrMzNSaNWtUt25d3XbbbXrxxRd14MABXXvttapWrZpOnDih9957z+mYLZcUyCkmSojNmzebgQMHmvDwcOPl5eU47emLL77odEab1NRU8/TTT5uwsDDj6+trOnbsaDZv3pzrWfXOP6NR1tlfzj29rDE5n1koNjbW3HzzzSYoKMgEBgaaO++803FWoLzOqpd17QE/Pz8TGhpq7rnnHrNx48Zs06amppp77rnHhIaGGpvN5nQ2k9yuG5OYmGh8fX0veDaTI0eOmEceecRERUUZT09PExQUZJo3b26ef/75fJ+a+PbbbzeScjxlsjHGzJ4923GtlqpVq5qnnnrKzJs3L9uZz7Kux5CTnM6qN3nyZFO7dm3j7e1tqlevbsaNG2c+++yzbGd6ybp+SH7muX//fjNkyBBTqVIlxzVJbr31VnP48GHHOMnJyeaFF15wXIMj65S1jz/+uNPZ4nJ7HLk9xsJ6L+zbt8/cfvvtJjg42Hh6epratWubN998M9/XcZo5c6bp1KmTCQgIMN7e3iYiIsLcfPPNjusNPf/888bNzS3bmQJXrlxpPDw8zKOPPuoYJp29ZseECRNMjRo1jKenp6lTp46ZMWOG07Q5nRnvwIED5qabbnJct+v66683W7duzfaYL3Qdp/Pl9JlMT083b731luM9W6ZMGVOnTh1z//33O11DJDU11TzxxBOmQoUKxsfHx7Rp08asWrXK5es4jRkzxlSrVs14eXmZpk2bmgULFuQ6XWRkpKlbt+4F532u3E5Hnpu4uDjzzDPPmPr16xs/Pz/j7e1tatasae6//37z559/GmPOnlm0QoUKJjU1Ndf5tGnTxoSEhFxwHLvdbt555x3ToEEDx+eobdu2Ttckyrpm0hVXXGE8PT1NSEiIufPOO3O9ZtL5cvvsZ70Ps2S9DzZs2GB69eplypQpY8qWLWv69+/v9NkviEw5nXEsv8uU83Of+zjPf8+NGDHCVKlSxbi5uTk+D6tWrTJ9+/Y1ERERxtvb2wQHB5uOHTuaWbNmZZtnTrlz+gydOXPGPPPMMyYiIsJx7bthw4bleh2n/Pjrr79M//79TY0aNYyvr68JDAw0rVq1MlOnTnUab/PmzaZ9+/bGz8/PSP+/jlNqaqp58sknTdWqVY2Pj49p1qyZmTlzZo7P/S+//GKaNm1qvL29s50RMzo62gwZMsRxncPQ0FDTrl078/LLLzumlWT27NmT7+fLmPwtY/L7WiUkJJihQ4eaypUrGw8PDxMREWFGjBiR63WccnKh5+B8b7/9tmnXrp0JCQkxXl5eJjw83Nx9993Zrqc3d+5c06RJE+Pr62uqV69uPvzwwwtex+lcWcvHN99802l4TsuzS/3sHz9+3Nx9992mQoUKxs/Pz1x55ZXmt99+y7Z+kN9l6dVXX22CgoKcLl1xIfl9PnP6PBtTOOsNl5opp3WrM2fOmBdffNFx/cbg4GBzzTXXOC69MWfOHNOtWzdTtWpV4+XlZSpUqGC6d+9ufvvtt3w9j+ezGWOM63ULAIoWm82mBx98UB9++KHVUYqdLVu2OM7c9MADD1gdp0QZPXq0xowZoyNHjhTKsVMomR544AGtWbNGGzZssDoKioD4+HhFRETo4Ycf1htvvGF1nFKNXfUAoJT6559/tG/fPj333HOqXLlytt1hAVhjwoQJVkdAEXDgwAHt2bNHb775ptzc3PToo49aHanU4+QQAFBKvfTSS+rcubOSk5P17bffunxGIwBA4fn000919dVXa9u2bZoxY0a+TxSCwsOuegAAAACQB7Y4AQAAAEAeKE4AAAAAkAeKEwAAAADkodSdVS8zM1OHDh1S2bJlHVdGBgAAAFD6GGN08uRJValSRW5uF96mVOqK06FDhxQWFmZ1DAAAAABFxP79+1WtWrULjlPqilPZsmUlnX1yAgICLE4DAAAAwCpJSUkKCwtzdIQLKXXFKWv3vICAAIoTAAAAgHwdwsPJIQAAAAAgDxQnAAAAAMgDxQkAAAAA8kBxAgAAAIA8UJwAAAAAIA8UJwAAAADIA8UJAAAAAPJAcQIAAACAPFCcAAAAACAPFCcAAAAAyAPFCQAAAADyQHECAAAAgDxQnAAAAAAgDxQnAAAAAMiDpcXp119/Va9evVSlShXZbDbNnDkzz2mWL1+u5s2by8fHR9WrV9ekSZMKPygAAACAUs3S4nTq1Ck1btxYH374Yb7Gj46OVvfu3dWhQwdt2rRJzz33nB555BF9//33hZwUAAAAQGnmYeWdd+vWTd26dcv3+JMmTVJ4eLjeffddSVLdunW1fv16vfXWW7rpppsKKSUAAACAnBhjZM80SrcbpdkzlW7PVFrG2X/P/t+c/deeqfSMf/+1nx3Wrkawyvl5Wf0Q8s3S4uSqVatWqUuXLk7Dunbtqs8++0zp6eny9PTMNk1qaqpSU1MdvyclJRV6TgAAAOBSGWMcJSOrjJxbPNIy/l9IHMPOLy528//CknHOsJzmec7v/5/Hefd1zrip//5uzMU9vpkPtlcTilPhiIuLU8WKFZ2GVaxYURkZGTp69KgqV66cbZpx48ZpzJgxlysiAAAAioHMzLNbSPIsHhkXLi7/35piHNOknjN91paYc+eZnpHDsBy32FxkI7GYl7ubPN1t8vJwk6f72Z+z/7ed8383+XgWr/PUFaviJEk2m83pd/NvxT1/eJYRI0Zo+PDhjt+TkpIUFhZWeAEBAAAg6f/lJDXjbBlIzbA7tlykZfz/J/W8351uz7ZlJfsWk3O3rOR3i4k9s/iVEpvtbCnx8nD7t5y4ydPj3zJyTiHJKije5xSXs4Xl/+N6/nub13llxuu8eXqee1/nz/ffMnTufXu42XJdLy/uilVxqlSpkuLi4pyGxcfHy8PDQ8HBwTlO4+3tLW9v78sRDwAAwFIZdufSkZpbCfm3xKTmVFTO+T3b9P/+7ihAedxHRjEqJ1mlwFEyztticrZA5FAyzh923pYVL/dzy4vtvNKTvXicW17OLy7ubiWzkBQXxao4tW3bVrNnz3YatnDhQrVo0SLH45sAAAAKizFGGZnGuUBkZCrNfk4hyaFU5HTbuUUkt3GyF5+s8eyO4UW9p3h5uMk7q1D8++Pt8f+CcXaYu7zcsw/3PKdMXGiLSU5bVvLaYuLp5iY3SgnyYGlxSk5O1u7dux2/R0dHa/PmzQoKClJ4eLhGjBihgwcPavr06ZKkoUOH6sMPP9Tw4cN17733atWqVfrss8/05ZdfWvUQAABAEXHsVJp2HT55TqE4W2Jy3TJyfhGxZyo1PWtYzruUOZWaSzgo/nJws+mc4uGeQ0Fx/r/3uf86jePuXHRymN77/CJ03jRZW2ZK6i5cKB0sLU7r169Xp06dHL9nHYs0cOBATZ06VbGxsYqJiXHcHhUVpblz5+rxxx/XRx99pCpVquj999/nVOQAAJRCCcmpWhN9TGv2JGj1nmPaefikpXnc3Ww5FgmnrSc5bFk5f6uL07g5lZNzS8l5t507Hw/34nXgPVDU2Ywpyt+VFLykpCQFBgYqMTFRAQEBVscBAAD5dORkqtZEJ2jNnmNavSdBu+KTs40THuQnf28Px5YRb8/ct7DkvPXk/6XEO6dxzt9Kc06J4fgToPhxpRsUq2OcAABA6RF/MsVRklbvSdA/R05lG6d2xbJqUz1IrasHq1VUkELKcEIoAIWD4gQAAIqEw0kp/5akY1oTnaA9ORSlOpXKqk31YLWpHqRWUcEK8i8+F88EULxRnAAAgCViE884tiitiT6m6KPORclmk+pWClCb6sFqXT1IrSKDVJ6iBMAiFCcAAHBZHDxx5t8TOZwtSvsSTjvdbrNJ9asEqHVUsNpUD1aryCAF+nG5EQBFA8UJAAAUigPHT5/d7W5PglZHJ2j/sTNOt7vZpAZVA9U6KkhtqgerRWSQAn0pSgCKJooTAAC4ZMYYHTh+Rqv2/P+sdwdPOBcldzebGlQNVJt/i1LzyPIK8KEoASgeKE4AAMBlxhjFHDvtdIxSTkWpYdVAx8kcWkQGqYw3qx4AiieWXgAAIE/GGO1NOO10jFJsYorTOB5uNjWqFvjvyRyC1TyiPEUJQInB0gwAAGRjjNGeo6fO2aKUoMNJqU7jeLrb1LhaOcdZ75pHlJefF6sWAEomlm4AAEDGGP1z5JTjYrNroo/pyEnnouTl7qYmYeUcF5xtFl5evl7uFiUGgMuL4gQAQClkjNHu+OSzRSn6mNbsOaajyecVJQ83NQ37/xalZuHl5eNJUQJQOlGcAAAoBTIzjXb9W5TWRJ89813CqTSncbw93NQsvLxaVz971rsmYeUoSgDwL4oTAAAlUGam0c7DJ/89mcMxrYlO0PHT6U7j+Hi6qXlEeccFZxuHBcrbg6IEADmhOAEAUAJkZhrtiEtynMxh7d5jOnFeUfL1dFeLyPKOC842qlZOXh5uFiUGgOKF4gQAQDFkzzTaEZv078kcjmnd3mNKPONclPy83NU8orzjOkoNq1KUAOBiUZwAACgG7JlG2w8lOc56t3bvMZ1MyXAax9/LXS0igxwnc2hYNVCe7hQlACgIFCcAAIqgDHumtv1blNZEH9O66GM6mepclMp4e6hlZHnHBWcbVAmQB0UJAAoFxQkAgCIg3Z6prQcTHSdyWL/3uJLPK0plvT3UKur/W5TqVaYoAcDlQnECAMAC6fZMbTmQ6NiitH7vMZ1OszuNE+DjoVZRZ49PalM9WHUrB8jdzWZRYgAo3ShOAABcBmkZmdpy4ITWRJ896936vcd1Jt25KAX6eqp1VJBa/3syhzqVKEoAUFRQnAAAKASpGXb9sT/x7HWUohO0Yd9xpaRnOo1T3s9TraOCHRecrV2xrNwoSgBQJFGcAAAoACnpdm3ef8JxHaWNMceVmuFclIL8vRzXUGpTPVi1KpShKAFAMUFxAgDgIqSk27Up5sS/xyglaGPMCaWdV5RCynip9b/HKLX+tyjZbBQlACiOKE4AAOTDmTS7NsUcP3sdpehj2hxzQml256IUWtb7nC1KQaoRSlECgJKC4gQAQA5Op2Vo477/b1HavP+E0u3GaZwKZb0du921rh6k6iH+FCUAKKEoTgAASDqVmqEN+45rTXSCVu85pi0HshelSgE+jt3u2lQPVmSwH0UJAEoJihMAoFQ6lZqhdXuPOU4P/ueBRGVkOhelKoE+jq1JbaoHKzyIogQApRXFCQBQquyOP6kpK/bqh40Hs11HqWo5X0dRals9WNXK+1KUAACSKE4AgFIgM9No2d/xmrJir37bddQxvGo5X7Wt8e8xSlFBCgvyszAlAKAoozgBAEqskynp+m7DAU1buVd7E05Lkmw2qXPdihrcPkptqgexRQkAkC8UJwBAibP36ClNXblX3204oOTUDElSWR8P3dYyTHe1jWTLEgDAZRQnAECJYIzR77uPauqKvVqyM17m3/M81Aj116D2UbqxaVX5e/NnDwBwcfgLAgAo1k6nZeiHjQc1beVe7YpPdgzvVDtUg9tH6cqaIXJzY3c8AMCloTgBAIqlA8dP67+r9unLtTFKSjm7O56/l7tuaRGmu9pGqHpoGYsTAgBKEooTAKDYMMZoTfQxTV2xVwu3xynrsksRwX4a2DZSN7eopgAfT2tDAgBKJIoTAKDIS0m3a9YfhzRlxV7tiE1yDL+yZogGtYtUpzoV5M7ueACAQkRxAgAUWXGJKfp89T59sTZGx06lSZJ8PN10Y7NqGtQuUldULGtxQgBAaUFxAgAUKcYYbdp/QlNW7NW8P2OV8e/+eFXL+equthHq1zJM5fy8LE4JAChtKE4AgCIhLSNTc/+M1ZQV0frjQKJjeKuoIA1uF6nO9SrKw93NwoQAgNKM4gQAsNSRk6n6Yk2MPl+zT0dOpkqSvNzd1LtJFQ1qF6kGVQMtTggAAMUJAGCRrQcTNXlFtOb8Eas0e6YkqUJZbw1oE6H+rcMVUsbb4oQAAPwfxQkAcNlk2DO1YNthTVkRrfX7jjuGNw0vp0HtItWtQWV5ebA7HgCg6KE4AQAK3fFTafpyXYz+u2qfYhNTJEkebjb1aFRZg9tHqUlYOWsDAgCQB4oTAKDQ/BWXpKkr9urHTQeVmnF2d7xgfy/d0Tpcd7SJUMUAH4sTAgCQPxQnAECBsmcaLd5xWFNX7tXKfxIcw+tXCdDg9lHq2aiyfDzdLUwIAIDrKE4AgAKReCZd367fr2mr9mr/sTOSJHc3m7rWr6jB7aPUIqK8bDabxSkBALg4FCcAwCX550iypq7Yq+83HtDpNLskKdDXU/1bhWtA2whVLedrcUIAAC4dxQkA4LLMTKPlu45o6oq9Wv73EcfwKyqW0eD2UerTpKp8vdgdDwBQclCcAAD5lpyaoe83HNC0lXu15+gpSZLNJl1bp6KGtI9U2xrB7I4HACiRKE4AgDzFJJzWtFV79c26/TqZmiFJKuvtoVtbhmlg20iFB/tZnBAAgMJFcQIA5MgYo5X/JGjKir1a/NdhGXN2ePUQfw1qH6kbm1VTGW/+jAAASgf+4gEAnJxJs+vHTQc1dWW0/j6c7Bje8YpQDWofqY61QuXmxu54AIDSheIEAJAkHTxxRv9dtU9frYvRidPpkiQ/L3fd3Lya7mobqZoVylicEAAA61CcAKAUM8Zo3d7jmroyWgu2HZY98+z+eGFBvhrYNlK3tAhToK+nxSkBALAexQkASqGUdLvmbInVlBXR2nYoyTG8XY1gDWoXqWvrVpQ7u+MBAOBAcQKAUuRwUopmrN6nGWtilHAqTZLk7eGmG5tV1cB2kapTKcDihAAAFE0UJwAoBTbvP6EpK6L185ZYZfy7O17lQB8NaBuh/i3DVd7fy+KEAAAUbRQnACih0jIyNW9rrKas2KvN+084hreMLK9B7aLUtX5Febi7WRcQAIBihOIEACVMQnKqvlgTo/+u3qf4k6mSJC93N/VqXEWD2kWqYbVAixMCAFD8UJwAoITYdihRU1bs1aw/DiktI1OSFFrWW3e2jtDtrcMVWtbb4oQAABRfFCcAKMYy7JlatP2wpqzYq7V7jzmGN64WqMHto9S9YWV5ebA7HgAAl4riBADF0InTafpq3X79d9U+HTxxRpLk4WZTt4aVNbh9pJqFl7c4IQAAJQvFCQCKkb8Pn9SUFXv146YDSkk/uztekL+Xbm8VrjvbRKhSoI/FCQEAKJkoTgBQxGVmGi35K15TVkZrxe4Ex/C6lQM0uH2kejeuIh9PdwsTAgBQ8lGcAKCISkpJ17frD2j6qr3al3BakuRmk7rUq6TB7SPVKipINpvN4pQAAJQOFCcAKGL2HEnWtJV79d2GAzqVZpckBfh4qH+rcA1oG6Fq5f0sTggAQOlDcQKAIsAYo193HdXUFdFauvOIY3itCmU0qH2k+jatKj8vFtkAAFiFv8IAYKFTqRn6YeMBTV25V/8cOSVJstmka2pX0OD2UWpfM5jd8QAAKAIoTgBggf3HTmvayr36ev1+nUzJkCSV8fbQLS2qaWDbSEWG+FucEAAAnIviBACXiTFGq/YkaOqKvfplx2FlmrPDI4P9NKhdpG5qXk1lfTytDQkAAHJEcQKAQpaSbtdPmw9qyoq9+ivupGN4h1ohGtI+Sh2vCJWbG7vjAQBQlFGcAKCQxCae0X9X7dOXa2N0/HS6JMnX0103Na+qQe0iVbNCWYsTAgCA/KI4AUABMsZow77jmrJyr+ZvjZP93/3xqpX31cC2kbq1RZgC/dgdDwCA4obiBAAFIDXDrp+3xGrKir3682CiY3ib6kEa1C5KnetVlDu74wEAUGxRnADgEsSfTNGM1TGasSZGR5NTJUleHm7q26SqBraLVL0qARYnBAAABYHiBAAXYcuBE5qyYq/mbDmkdPvZ3fEqBfhoQNsI9W8VriB/L4sTAgCAgkRxAoB8Srdnav7WOE1ZEa2NMSccw5tHlNegdpG6vkElebq7WRcQAAAUGooTAOThTJpdk1dE67+r9ikuKUWS5OluU89GVTSoXaQah5WzNiAAACh0FCcAuIC0jEwNmbpOq/YkSJJCynjpjtYRuqNNuCqU9bE4HQAAuFwoTgCQC2OMnv7uD63akyB/L3eNuaGBejWuLG8Pd6ujAQCAy4ziBAC5eHPBTs3cfEgebjZNvLO5rroi1OpIAADAIhzFDAA5+Hz1Pk1Y9o8kadyNDSlNAACUchQnADjPL9sP68WftkqSHr/uCt3SIsziRAAAwGoUJwA4xx/7T+jhLzcp00j9WoTpkWtrWh0JAAAUARQnAPjXvoRTGjJ1nc6k23XVFaF6uW8D2Ww2q2MBAIAiwPLiNGHCBEVFRcnHx0fNmzfXb7/9dsHxP/roI9WtW1e+vr6qXbu2pk+ffpmSAijJjp1K06Ap65RwKk31qwRowh3NuJgtAABwsPSsel9//bUee+wxTZgwQe3bt9d//vMfdevWTdu3b1d4eHi28SdOnKgRI0bok08+UcuWLbV27Vrde++9Kl++vHr16mXBIwBQEqSk23XPtHWKPnpKVcv5asqglirjzUlHAQDA/9mMMcaqO2/durWaNWumiRMnOobVrVtXffr00bhx47KN365dO7Vv315vvvmmY9hjjz2m9evX6/fff8/XfSYlJSkwMFCJiYkKCAi49AcBoFizZxo9MGODFmw7rAAfD/3wQDvVrFDW6lgAAOAycKUbWLYfSlpamjZs2KAuXbo4De/SpYtWrlyZ4zSpqany8fFxGubr66u1a9cqPT0912mSkpKcfgBAOnuB25fmbNeCbYfl5e6mTwe2pDQBAIAcWVacjh49KrvdrooVKzoNr1ixouLi4nKcpmvXrvr000+1YcMGGWO0fv16TZ48Wenp6Tp69GiO04wbN06BgYGOn7AwTisM4KzPfo/W1JV7JUlv39pYraKCrA0EAACKLMuPfD7/jFXGmFzPYjVy5Eh169ZNbdq0kaenp2644QYNGjRIkuTu7p7jNCNGjFBiYqLjZ//+/QWaH0DxNGfLIb388w5J0vPd66pX4yoWJwIAAEWZZcUpJCRE7u7u2bYuxcfHZ9sKlcXX11eTJ0/W6dOntXfvXsXExCgyMlJly5ZVSEhIjtN4e3srICDA6QdA6bZmT4KGf/2HJGlQu0jd0yHK4kQAAKCos6w4eXl5qXnz5lq0aJHT8EWLFqldu3YXnNbT01PVqlWTu7u7vvrqK/Xs2VNubpZvPANQDOyOP6l7p69Xmj1TXetX1Mie9bhWEwAAyJOl59sdPny4BgwYoBYtWqht27b6+OOPFRMTo6FDh0o6u5vdwYMHHddq+vvvv7V27Vq1bt1ax48f1/jx47V161ZNmzbNyocBoJiIT0rRwMnrlJSSoWbh5fTebU3l7kZpAgAAebO0OPXr108JCQkaO3asYmNj1aBBA82dO1cRERGSpNjYWMXExDjGt9vtevvtt7Vz5055enqqU6dOWrlypSIjIy16BACKi+TUDA2euk4HT5xRVIi/Ph3YUj6eOR8bCQAAcD5Lr+NkBa7jBJQ+6fZM3TNtvZb/fUTB/l764YF2igj2tzoWAACwWLG4jhMAXA7GGL3w41Yt//uIfDzd9NmglpQmAADgMooTgBLt/cW79fX6/XKzSR/2b6YmYeWsjgQAAIohihOAEuvb9fv1zi9/S5LG3tBA19XL+VIHAAAAeaE4ASiRfv37iEb88KckadjVNXRnmwiLEwEAgOKM4gSgxNl2KFHDPt+gjEyjPk2q6Kkuta2OBAAAijmKE4AS5eCJMxo8ZZ1OpdnVtnqw3ri5sdy4VhMAALhEFCcAJUbi6XQNmrxW8SdTVbtiWU0a0FxeHizmAADApWONAkCJkJph133/Xa9d8cmqFOCjKYNbKtDX0+pYAACghKA4ASj2MjONnvx2i9ZEH1MZbw9NGdxSVcr5Wh0LAACUIBQnAMXe6wv+0uw/DsnDzaZJdzZX3coXvvI3AACAqyhOAIq16av26j/L90iSXr+pka6sFWJxIgAAUBJRnAAUWwu3xWn0rG2SpCe7XKGbmlezOBEAACipKE4AiqWNMcf1yFeblGmk/q3C9GCnmlZHAgAAJRjFCUCxs/foKd0zbb1S0jPVqXaoXrqhgWw2rtUEAAAKD8UJQLGSkJyqgVPW6tipNDWsGqgPb28mD3cWZQAAoHCxtgGg2DiTZtfd09ZrX8JpVSvvq88GtZC/t4fVsQAAQClAcQJQLNgzjR75apM27z+hcn6emjaklSqU9bE6FgAAKCUoTgCKPGOMRs/apkXbD8vLw02f3tVCNULLWB0LAACUIhQnAEXef37do/+u3iebTXqvXxO1iAyyOhIAAChlLqo4/fPPP3rhhRfUv39/xcfHS5Lmz5+vbdu2FWg4APhp80G9Nu8vSdILPeqpW8PKFicCAAClkcvFafny5WrYsKHWrFmjH374QcnJyZKkLVu2aNSoUQUeEEDpteqfBD317RZJ0pD2Ubr7yiiLEwEAgNLK5eL07LPP6uWXX9aiRYvk5eXlGN6pUyetWrWqQMMBKL3+PnxS9/13vdLsmeresJJe6FHX6kgAAKAUc7k4/fnnn+rbt2+24aGhoUpISCiQUABKt8NJKRo0ea1OpmSoRUR5jb+1idzcuMAtAACwjsvFqVy5coqNjc02fNOmTapatWqBhAJQep1MSdegKet0KDFF1UP99cldLeTj6W51LAAAUMq5XJxuv/12PfPMM4qLi5PNZlNmZqZWrFihJ598UnfddVdhZARQSqTbM/XAjI3aEZukkDLemja4lcr7e+U9IQAAQCFzuTi98sorCg8PV9WqVZWcnKx69erpqquuUrt27fTCCy8URkYApYAxRiN++FO/7ToqX093TR7UQmFBflbHAgAAkCTZjDHmYibcs2ePNm7cqMzMTDVt2lS1atUq6GyFIikpSYGBgUpMTFRAQIDVcQD8a/yiv/X+4l1yd7Pp07taqFOdClZHAgAAJZwr3cDjYu+kevXqql69+sVODgAOX6+L0fuLd0mSXu7TgNIEAACKHJd31bv55pv12muvZRv+5ptv6pZbbimQUABKj6U74/Xcj1slSQ9fU1P9W4VbnAgAACC7i7oAbo8ePbINv/766/Xrr78WSCgApcPWg4l6cMZG2TONbmxWVcM7X2F1JAAAgBy5XJySk5OdLnybxdPTU0lJSQUSCkDJt//YaQ2ask6n0+y6smaIXruxkWw2rtUEAACKJpeLU4MGDfT1119nG/7VV1+pXr16BRIKQMl24nSaBk1Zq6PJqapTqawm3NlMXh4uL44AAAAuG5dPDjFy5EjddNNN+ueff3TNNddIkhYvXqwvv/xS3377bYEHBFCypKTbdd/0DfrnyClVDvTR1MGtFODjaXUsAACAC3K5OPXu3VszZ87Uq6++qu+++06+vr5q1KiRfvnlF3Xs2LEwMgIoITIzjZ749g+t3XtMZX08NHVwK1UK9LE6FgAAQJ4u+jpOxRXXcQKs88rP2/XJb9HydLdp2pBWalcjxOpIAACgFLss13FKS0tTfHy8MjMznYaHh3MqYQDZTVkRrU9+i5YkvXVLY0oTAAAoVlwuTrt27dKQIUO0cuVKp+HGGNlsNtnt9gILB6BkmL81VmPnbJckPX19bd3QpKrFiQAAAFzjcnEaNGiQPDw8NGfOHFWuXJnTBwO4oA37junRrzbLGOnONuEa1rGG1ZEAAABc5nJx2rx5szZs2KA6deoURh4AJcieI8m6Z9p6pWZk6rq6FTS6V32+bAEAAMWSyxdOqVevno4ePVoYWQCUIEdOpmrglLU6fjpdjasF6v3+TeXhzrWaAABA8eTyWszrr7+up59+WsuWLVNCQoKSkpKcfgDgdFqG7p62TvuPnVF4kJ8+G9RSfl4XfS4aAAAAy7m8JnPddddJkq699lqn4ZwcAoAkZdgz9fAXm7TlQKLK+3lq2pBWCinjbXUsAACAS+JycVq6dGlh5ABQAhhj9OKsbVr8V7y8Pdz06cCWigrxtzoWAADAJXO5OHXs2LEwcgAoASYs+0dfrImRzSa9d1tTNY8ob3UkAACAAnHRBx2cPn1aMTExSktLcxreqFGjSw4FoPj5cdMBvblgpyRpVM96ur5BJYsTAQAAFByXi9ORI0c0ePBgzZs3L8fbOcYJKH1W7j6qp7/bIkm676rqGtQ+yuJEAAAABcvls+o99thjOn78uFavXi1fX1/Nnz9f06ZNU61atTRr1qzCyAigCPsrLkn3/3eD0u1GPRtV1rPXc403AABQ8ri8xWnJkiX66aef1LJlS7m5uSkiIkKdO3dWQECAxo0bpx49ehRGTgBFUGziGQ2esk4nUzPUKipIb93SWG5uXOAWAACUPC5vcTp16pQqVKggSQoKCtKRI0ckSQ0bNtTGjRsLNh2AIispJV2Dp6xTbGKKalYoo48HNJePp7vVsQAAAAqFy8Wpdu3a2rnz7AHgTZo00X/+8x8dPHhQkyZNUuXKlQs8IICiJy0jU8M+36C/4k4qtKy3pg5uqXJ+XlbHAgAAKDQu76r32GOPKTY2VpI0atQode3aVTNmzJCXl5emTp1a0PkAFDHGGD37/Rat2J0gfy93TRnUUtXK+1kdCwAAoFDZjDHmUmZw+vRp/fXXXwoPD1dISEhB5So0SUlJCgwMVGJiogICAqyOAxQ7by3YqQ+X7pa7m02TB7VUxytCrY4EAABwUVzpBhd9Hacsfn5+atas2aXOBkAx8MWaGH24dLckaVzfhpQmAABQauSrOA0fPlwvvfSS/P39NXz48AuOO378+AIJBqBoWfLXYb0w809J0qPX1tKtLcMsTgQAAHD55Ks4bdq0Senp6ZKkjRs3ymbL+XTDuQ0HULxtOXBCD87YpEwj3dK8mh67rpbVkQAAAC6rSz7GqbjhGCfANTEJp3XjxBU6mpymDrVCNHlQS3m6u3xCTgAAgCLHlW7g0tpPRkaGPDw8tHXr1ksKCKB4OH4qTYOmrNXR5DTVqxygiXc2pzQBAIBSyaU1IA8PD0VERMhutxdWHgBFREq6XfdMX689R0+pajlfTRncUmW8L/l8MgAAAMWSy18dv/DCCxoxYoSOHTtWGHkAFAH2TKPHv96sDfuOK8DHQ1MHt1TFAB+rYwEAAFjG5a+P33//fe3evVtVqlRRRESE/P39nW7fuHFjgYUDYI1Xft6heVvj5OXupo/vaqFaFctaHQkAAMBSLhenPn36FEIMAEXFp7/t0eQV0ZKkt25trDbVgy1OBAAAYD2Xi9OoUaMKIweAImDun7F6Ze4OSdKIbnXUu3EVixMBAAAUDZweC4Akad3eY3rs680yRhrYNkL3XVXd6kgAAABFhstbnOx2u9555x198803iomJUVpamtPtnDQCKH52xyfrnmnrlZaRqS71KurFXvW5oDUAAMA5XN7iNGbMGI0fP1633nqrEhMTNXz4cN14441yc3PT6NGjCyEigMIUfzJFg6asVeKZdDUNL6f3bmsqdzdKEwAAwLlcLk4zZszQJ598oieffFIeHh7q37+/Pv30U7344otavXp1YWQEUEhOpWZoyNR1OnD8jCKD/fTpXS3k6+VudSwAAIAix+XiFBcXp4YNG0qSypQpo8TERElSz5499fPPPxdsOgCFJsOeqQe/2KitB5MU5O+lqYNbKbiMt9WxAAAAiiSXi1O1atUUGxsrSapZs6YWLlwoSVq3bp28vVnpAooDY4xemLlVy3YekY+nmz4b2EKRIf55TwgAAFBKuVyc+vbtq8WLF0uSHn30UY0cOVK1atXSXXfdpSFDhhR4QAAF78Mlu/XVuv1ys0kf9G+mpuHlrY4EAABQpNmMMeZSZrBmzRqtWLFCNWvWVO/evQsqV6FJSkpSYGCgEhMTFRAQYHUc4LL7bsMBPfntH5Kkl26orwFtI60NBAAAYBFXuoHLpyM/ffq0/Pz8HL+3bt1arVu3dj0lgMvut11H9Oz3WyRJQzvWoDQBAADkk8u76lWoUEF33nmnFixYoMzMzMLIBKAQbD+UpGGfb1RGplHvxlX0dNfaVkcCAAAoNlwuTtOnT1dqaqr69u2rKlWq6NFHH9W6desKIxuAAnLoxBkNnrpWyakZalM9SG/e0khuXKsJAAAg31wuTjfeeKO+/fZbHT58WOPGjdOOHTvUrl07XXHFFRo7dmxhZARwCRLPpGvQlLU6nJSqKyqW0X8GtJC3B9dqAgAAcMUlnxxCkrZv36477rhDW7Zskd1uL4hchYaTQ6A0Sc2wa+DktVq955gqBnjrhwfaq2o5X6tjAQAAFAmudAOXtzhlSUlJ0TfffKM+ffqoWbNmSkhI0JNPPnmxswNQwDIzjZ7+botW7zmmMt4emjyoJaUJAADgIrl8Vr2FCxdqxowZmjlzptzd3XXzzTdrwYIF6tixY2HkA3CR3ly4Uz9tPiQPN5sm3tlM9asEWh0JAACg2HK5OPXp00c9evTQtGnT1KNHD3l6ehZGLgCX4L+r92nisn8kSa/d1EgdaoVanAgAAKB4c7k4xcXFcWwQUIQt2n5Yo37aKkka3vkK3dy8msWJAAAAij+Xj3GiNAFF1+b9J/TwlxuVaaTbWobp4WtqWh0JAACgRLjok0MAKFr2JZzS3VPXKSU9U1fXDtXLfRrIZuNaTQAAAAWB4gSUAAnJqRo4ea0STqWpQdUAfXR7M3m48/EGAAAoKKxZAcXcmTS77pm+XnsTTqtaeV9NHtRS/t4uH74IAACAC7ik4nTgwAEdPHiwoLIAcJE90+jRrzZpU8wJBfp6aurgVqpQ1sfqWAAAACWOy8UpMzNTY8eOVWBgoCIiIhQeHq5y5crppZdeUmZmZmFkBJADY4zGzt6mhdsPy8vDTZ8ObKGaFcpYHQsAAKBEcrk4Pf/88/rwww/12muvadOmTdq4caNeffVVffDBBxo5cqTLASZMmKCoqCj5+PioefPm+u233y44/owZM9S4cWP5+fmpcuXKGjx4sBISEly+X6C4++S3PZq2ap9sNumdW5uoZWSQ1ZEAAABKLJsxxrgyQZUqVTRp0iT17t3bafhPP/2kBx54wKVd977++msNGDBAEyZMUPv27fWf//xHn376qbZv367w8PBs4//+++/q2LGj3nnnHfXq1UsHDx7U0KFDVatWLf3444/5us+kpCQFBgYqMTGRU6uj2Jr1xyE98uUmSdILPerqng7VLU4EAABQ/LjSDVze4nTs2DHVqVMn2/A6dero2LFjLs1r/Pjxuvvuu3XPPfeobt26evfddxUWFqaJEyfmOP7q1asVGRmpRx55RFFRUbryyit1//33a/369a4+DKDYWr0nQU9+84ckaXD7SN19ZZTFiQAAAEo+l4tT48aN9eGHH2Yb/uGHH6px48b5nk9aWpo2bNigLl26OA3v0qWLVq5cmeM07dq104EDBzR37lwZY3T48GF999136tGjR673k5qaqqSkJKcfoLjadfik7pu+Xmn2TF1fv5Je6FGPazUBAABcBi6fs/iNN95Qjx499Msvv6ht27ay2WxauXKl9u/fr7lz5+Z7PkePHpXdblfFihWdhlesWFFxcXE5TtOuXTvNmDFD/fr1U0pKijIyMtS7d2998MEHud7PuHHjNGbMmHznAoqqw0kpGjRlnZJSMtQ8orzeva2J3N0oTQAAAJeDy1ucOnbsqL///lt9+/bViRMndOzYMd14443auXOnOnTo4HKA878tN8bk+g369u3b9cgjj+jFF1/Uhg0bNH/+fEVHR2vo0KG5zn/EiBFKTEx0/Ozfv9/ljIDVklMzNHjKOh08cUbVQ/z16V0t5OPpbnUsAACAUuOirpJZpUoVvfLKK5d0xyEhIXJ3d8+2dSk+Pj7bVqgs48aNU/v27fXUU09Jkho1aiR/f3916NBBL7/8sipXrpxtGm9vb3l7e19SVsBK6fZMPTBjo7bHJimkjJemDm6l8v5eVscCAAAoVfJVnLZs2aIGDRrIzc1NW7ZsueC4jRo1ytcde3l5qXnz5lq0aJH69u3rGL5o0SLdcMMNOU5z+vRpeXg4R3Z3P/utu4snBwSKBWOMnvvhT/369xH5errrs4EtFR7sZ3UsAACAUidfxalJkyaKi4tThQoV1KRJE9lsthyLis1mk91uz/edDx8+XAMGDFCLFi3Utm1bffzxx4qJiXHsejdixAgdPHhQ06dPlyT16tVL9957ryZOnKiuXbsqNjZWjz32mFq1aqUqVark+36B4uK9xbv07YYDcrNJH97eVI3DylkdCQAAoFTKV3GKjo5WaGio4/8FpV+/fkpISNDYsWMVGxurBg0aaO7cuYqIiJAkxcbGKiYmxjH+oEGDdPLkSX344Yd64oknVK5cOV1zzTV6/fXXCywTUFR8s36/3v1llyTppT4NdG3dnHdhBQAAQOFz+QK4v/76q9q1a5dtl7mMjAytXLlSV111VYEGLGhcABfFwfK/j2jI1HWyZxo92KmGnuqa/dppAAAAuDSFegHcTp065Xih28TERHXq1MnV2QE4z9aDiXrg8w2yZxr1bVpVT3apbXUkAACAUs/l4pTb6cITEhLk7+9fIKGA0urA8dMaPHWdTqXZ1b5msF6/qREXuAUAACgC8n068htvvFHS2RNADBo0yOkU33a7XVu2bFG7du0KPiFQSiSeTtegKet05GSq6lQqq4l3NpeXh8vfbQAAAKAQ5Ls4BQYGSjq7xals2bLy9fV13Obl5aU2bdro3nvvLfiEQCmQmmHXvf9dr93xyaoU4KMpg1sqwMfT6lgAAAD4V76L05QpUyRJkZGRevLJJ9ktDyggmZlGT3zzh9ZGH1NZbw9NHdJSlQN9854QAAAAl02+i1OWUaNGFUYOoNR6ff5fmrMlVp7uNk0a0Fx1KnG2RwAAgKLG5eIkSd99952++eYbxcTEKC0tzem2jRs3FkgwoDSYtnKv/vPrHknS6zc1UvuaIRYnAgAAQE5cPvL8/fff1+DBg1WhQgVt2rRJrVq1UnBwsPbs2aNu3boVRkagRJq/NU6jZ2+TJD3VtbZubFbN4kQAAADIjcvFacKECfr444/14YcfysvLS08//bQWLVqkRx55RImJiYWREShxNuw7rke/2iRjpNtbh+uBq2tYHQkAAAAX4HJxiomJcZx23NfXVydPnpQkDRgwQF9++WXBpgNKoOijp3TPtHVKzcjUtXUqaGzv+lyrCQAAoIhzuThVqlRJCQkJkqSIiAitXr1akhQdHS1jTMGmA0qYo8mpGjRlrY6fTlejaoH64Pam8nDnWk0AAABFnctrbNdcc41mz54tSbr77rv1+OOPq3PnzurXr5/69u1b4AGBkuJ0WobunrZe+xJOKyzIV58NbCk/r4s6PwsAAAAuM5txcTNRZmamMjMz5eFxdoXvm2++0e+//66aNWtq6NCh8vLyKpSgBSUpKUmBgYFKTExUQACnfcblkWHP1NDPN+iXHfEq5+ep74e1U43QMlbHAgAAKNVc6QYuFaeMjAy98sorGjJkiMLCwi45qBUoTrjcjDEa+dNWfb46Rt4ebvri3tZqHhFkdSwAAIBSz5Vu4NKueh4eHnrzzTdlt9svKSBQmkxavkefr46RzSa9d1sTShMAAEAx5PIxTtddd52WLVtWCFGAkuenzQf1+vy/JEkv9qyn6xtUtjgRAAAALobLR6Z369ZNI0aM0NatW9W8eXP5+/s73d67d+8CCwcUZyv/Oaonv/1DknTPlVEa3D7K4kQAAAC4WC6fHMLNLfeNVDabrcjvxscxTrgcdsad1M2TVupkSoZ6NKysD/o3lZsb12oCAAAoSlzpBi5vccrMzLzoYEBpEJeYokFT1upkSoZaRpbX27c2pjQBAAAUc1x5EyhA6fZM3TN9nWITU1Qj1F+f3NVCPp7uVscCAADAJaI4AQXo89X7tPVgksr5eWrq4FYq51e0r2sGAACA/KE4AQUkITlV4xf9LUl6umsdhQX5WZwIAAAABYXiBBSQtxb+rZMpGapfJUD9WhbPC0QDAAAgZxQnoABsPZior9bFSJJG964vd04GAQAAUKJcVHH6559/9MILL6h///6Kj4+XJM2fP1/btm0r0HBAcWCM0ahZ22SMdEOTKmoZGWR1JAAAABQwl4vT8uXL1bBhQ61Zs0Y//PCDkpOTJUlbtmzRqFGjCjwgUNT9tPmQNuw7Lj8vd43oVtfqOAAAACgELhenZ599Vi+//LIWLVokL6//nzGsU6dOWrVqVYGGA4q6U6kZGjdvhyTpwU41VSnQx+JEAAAAKAwuF6c///xTffv2zTY8NDRUCQkJBRIKKC4+Wrpbh5NSFR7kp7uvjLI6DgAAAAqJy8WpXLlyio2NzTZ806ZNqlq1aoGEAoqDvUdP6dPfoiVJI3vW40K3AAAAJZjLxen222/XM888o7i4ONlsNmVmZmrFihV68skndddddxVGRqBIevnn7UqzZ+qqK0J1Xd0KVscBAABAIXK5OL3yyisKDw9X1apVlZycrHr16umqq65Su3bt9MILLxRGRqDIWbYzXr/siJeHm00v9qwnm43TjwMAAJRkHq5O4OnpqRkzZmjs2LHatGmTMjMz1bRpU9WqVasw8gFFTlpGpsbO2S5JGtQuUjUrlLE4EQAAAAqby8Vp+fLl6tixo2rUqKEaNWoURiagSJu2cq/2HDmlkDJeeuQ6vjAAAAAoDVzeVa9z584KDw/Xs88+q61btxZGJqDIij+ZovcW75IkPX19HQX4eFqcCAAAAJeDy8Xp0KFDevrpp/Xbb7+pUaNGatSokd544w0dOHCgMPIBRcob83cqOTVDjasF6uZm1ayOAwAAgMvE5eIUEhKihx56SCtWrNA///yjfv36afr06YqMjNQ111xTGBmBImFTzHF9t+HsFwSje9eXmxsnhAAAACgtXC5O54qKitKzzz6r1157TQ0bNtTy5csLKhdQpGRmGo2effaEEDc1q6am4eUtTgQAAIDL6aKL04oVK/TAAw+ocuXKuv3221W/fn3NmTOnILMBRcb3Gw/oj/0nVMbbQ89cX9vqOAAAALjMXD6r3nPPPacvv/xShw4d0nXXXad3331Xffr0kZ+fX2HkAyyXlJKu1+fvlCQ9cm1NVQjwsTgRAAAALjeXi9OyZcv05JNPql+/fgoJCSmMTECR8sHiXTqanKrqIf4a1C7K6jgAAACwgMvFaeXKlYWRAyiSdscna8qKvZKkkb3qycvjkg4LBAAAQDGVr+I0a9YsdevWTZ6enpo1a9YFx+3du3eBBAOsZozR2DnblZFpdG2dCupUu4LVkQAAAGCRfBWnPn36KC4uThUqVFCfPn1yHc9ms8lutxdUNsBSi3fE69e/j8jL3U0je9azOg4AAAAslK/ilJmZmeP/gZIqJd2usXPOnn787g5RigzxtzgRAAAArOTyARvTp09XampqtuFpaWmaPn16gYQCrPbZ79GKOXZaFQO89VCnmlbHAQAAgMVcLk6DBw9WYmJituEnT57U4MGDCyQUYKW4xBR9tHS3JOnZbnXk7+3yOVQAAABQwrhcnIwxstls2YYfOHBAgYGBBRIKsNJr83bodJpdzcLLqU+TqlbHAQAAQBGQ76/SmzZtKpvNJpvNpmuvvVYeHv+f1G63Kzo6Wtdff32hhAQul/V7j2nm5kOy2aQxvRvk+CUBAAAASp98F6ess+lt3rxZXbt2VZkyZRy3eXl5KTIyUjfddFOBBwQuF3um0ahZ2yRJt7UMU8NqbEEFAADAWfkuTqNGjZIkRUZGql+/fvLx8Sm0UIAVvl63X9sOJamsj4ee7FLb6jgAAAAoQlw+6n3gwIGFkQOwVOLpdL21cKck6fHrrlBwGW+LEwEAAKAocbk42e12vfPOO/rmm28UExOjtLQ0p9uPHTtWYOGAy+WdX/7WsVNpqlWhjAa0jbA6DgAAAIoYl8+qN2bMGI0fP1633nqrEhMTNXz4cN14441yc3PT6NGjCyEiULh2xp3Uf1fvkySN6lVfnu4ufywAAABQwrm8hjhjxgx98sknevLJJ+Xh4aH+/fvr008/1YsvvqjVq1cXRkag0BhjNGb2Ntkzja6vX0lX1gqxOhIAAACKIJeLU1xcnBo2bChJKlOmjONiuD179tTPP/9csOmAQjZ/a5xW/pMgLw83Pd+jrtVxAAAAUES5XJyqVaum2NhYSVLNmjW1cOFCSdK6devk7c0B9Sg+UtLtevnnHZKkoVdVV1iQn8WJAAAAUFS5XJz69u2rxYsXS5IeffRRjRw5UrVq1dJdd92lIUOGFHhAoLD8Z/keHTxxRlUCfTTs6ppWxwEAAEAR5vJZ9V577TXH/2+++WZVq1ZNK1euVM2aNdW7d+8CDQcUlgPHT2vCst2SpOd61JWvl7vFiQAAAFCUuVycztemTRu1adOmILIAl824uX8pNSNTraOC1KNhZavjAAAAoIjLV3GaNWtWvmfIVicUdSv/Oaqf/4yVm00a3bu+bDab1ZEAAABQxOWrOPXp0ydfM7PZbLLb7ZeSByhUGfZMjZ29XZJ0R+sI1a0cYHEiAAAAFAf5Kk6ZmZmFnQO4LL5YG6O/4k6qnJ+nhne+wuo4AAAAKCZcPqseUFwdO5Wmtxf+LUl6okttlff3sjgRAAAAiguXTw4xduzYC97+4osvXnQYoDC9vXCnEs+kq06lsrq9VbjVcQAAAFCMuFycfvzxR6ff09PTFR0dLQ8PD9WoUYPihCJp26FEfbE2RtLZE0K4u3FCCAAAAOSfy8Vp06ZN2YYlJSVp0KBB6tu3b4GEAgqSMUZjZm2XMVLPRpXVpnqw1ZEAAABQzBTIMU4BAQEaO3asRo4cWRCzAwrU7C2xWrv3mHw83fRc97pWxwEAAEAxVGAnhzhx4oQSExMLanZAgTidlqFXf94hSXrw6pqqUs7X4kQAAAAojlzeVe/99993+t0Yo9jYWP33v//V9ddfX2DBgIIwYek/iktKUbXyvrr3qupWxwEAAEAx5XJxeuedd5x+d3NzU2hoqAYOHKgRI0YUWDDgUsUknNbHv+2RJL3Qo558PN0tTgQAAIDiyuXiFB0dXRg5gAL38s/blZaRqStrhqhr/YpWxwEAAEAxxgVwUSL9+vcRLdx+WO5uNo3qVU82G6cfBwAAwMVzeYtTSkqKPvjgAy1dulTx8fHKzMx0un3jxo0FFg64GOn2TI2ZvU2SdFfbCNWqWNbiRAAAACjuXC5OQ4YM0aJFi3TzzTerVatWfJOPImfayr3658gpBfl76bHrrrA6DgAAAEoAl4vTzz//rLlz56p9+/aFkQe4JEeTU/XeL7skSU93ra1AX0+LEwEAAKAkcPkYp6pVq6psWXZ9QtH05vydOpmaoYZVA3VLizCr4wAAAKCEcLk4vf3223rmmWe0b9++wsgDXLQ/9p/QNxv2S5JG964ndzd2IwUAAEDBcHlXvRYtWiglJUXVq1eXn5+fPD2dd4U6duxYgYUD8isz02j07G0yRurbtKqaRwRZHQkAAAAliMvFqX///jp48KBeffVVVaxYkZNDoEj4cdNBbYo5IT8vdz3brY7VcQAAAFDCuFycVq5cqVWrVqlx48aFkQdwWXJqhl6b/5ck6eFraqligI/FiQAAAFDSuHyMU506dXTmzJnCyAJclA+W7NKRk6mKDPbTkCsjrY4DAACAEsjl4vTaa6/piSee0LJly5SQkKCkpCSnH+By2nMkWZN/j5Ykvdirnrw93C1OBAAAgJLI5V31rr/+eknStdde6zTcGCObzSa73V4wyYB8eGnOdqXbja6uHapr6lS0Og4AAABKKJeL09KlSwsjB+CyJX8d1tKdR+TpbtPInvWsjgMAAIASzOXi1LFjx8LIAbgkNcOul+bskCQNaR+lGqFlLE4EAACAkszl4vTrr79e8ParrrrqosMA+TVlxV5FHz2l0LLeeuiamlbHAQAAQAnncnG6+uqrsw0791pOrh7jNGHCBL355puKjY1V/fr19e6776pDhw45jjto0CBNmzYt2/B69epp27ZtLt0viq/DSSn6YPEuSdIz19dRWR/PPKYAAAAALo3LZ9U7fvy40098fLzmz5+vli1bauHChS7N6+uvv9Zjjz2m559/Xps2bVKHDh3UrVs3xcTE5Dj+e++9p9jYWMfP/v37FRQUpFtuucXVh4Fi7PV5f+lUml1NwsrpxqZVrY4DAACAUsBmjDEFMaNff/1Vjz/+uDZs2JDvaVq3bq1mzZpp4sSJjmF169ZVnz59NG7cuDynnzlzpm688UZFR0crIiIiX/eZlJSkwMBAJSYmKiAgIN9ZUTRs2HdcN01cKUn66cH2ahxWztpAAAAAKLZc6QYub3HKTWhoqHbu3Jnv8dPS0rRhwwZ16dLFaXiXLl20cuXKfM3js88+03XXXXfB0pSamsq1pkqIzEyj0bPO7pJ5a4tqlCYAAABcNi4f47Rlyxan340xio2N1WuvvabGjRvnez5Hjx6V3W5XxYrO196pWLGi4uLi8pw+NjZW8+bN0xdffHHB8caNG6cxY8bkOxeKrm837NefBxNV1ttDT3WtY3UcAAAAlCIuF6cmTZrIZrPp/D382rRpo8mTJ7sc4NwTS0j/v5BuXqZOnapy5cqpT58+FxxvxIgRGj58uOP3pKQkhYWFuZwT1ko8k6435p/dovnodbUUWtbb4kQAAAAoTVwuTtHR0U6/u7m5KTQ0VD4+Pi7NJyQkRO7u7tm2LsXHx2fbCnU+Y4wmT56sAQMGyMvL64Ljent7y9ublezi7r1fdinhVJpqhPrrrraRVscBAABAKeNyccrvSRjy4uXlpebNm2vRokXq27evY/iiRYt0ww03XHDa5cuXa/fu3br77rsLJAuKtl2HT2r6qr2SpFG96svLo8AOzQMAAADyJd9roEuWLFG9evVyPLlCYmKi6tevr99++82lOx8+fLg+/fRTTZ48WTt27NDjjz+umJgYDR06VNLZ3ezuuuuubNN99tlnat26tRo0aODS/aH4McZozOztysg06lyvoq66ItTqSAAAACiF8r3F6d1339W9996b42n6AgMDdf/992v8+PG5Xrw2J/369VNCQoLGjh2r2NhYNWjQQHPnznVs1YqNjc12TafExER9//33eu+99/J9Pyi+Fm4/rN93H5WXh5tG9qhndRwAAACUUvm+jlNERITmz5+vunXr5nj7X3/9pS5duuR68dqigus4FR8p6XZdN365Dhw/owc71eBMegAAAChQhXIdp8OHD8vT0zPX2z08PHTkyJH8pwTy8Mmve3Tg+BlVCvDRA1fXtDoOAAAASrF8F6eqVavqzz//zPX2LVu2qHLlygUSCjh04owmLPtHkjSiex35e7t8HhMAAACgwOS7OHXv3l0vvviiUlJSst125swZjRo1Sj179izQcCi9xs37S2fS7WoZWV69G1exOg4AAABKuXwf43T48GE1a9ZM7u7ueuihh1S7dm3ZbDbt2LFDH330kex2uzZu3JjnNZisxjFORd+aPQnq9/Fq2WzS7IeuVIOqgVZHAgAAQAnkSjfI9/5PFStW1MqVKzVs2DCNGDFCWX3LZrOpa9eumjBhQpEvTSj6MuyZGjVrmySpf6twShMAAACKBJcOHImIiNDcuXN1/Phx7d69W8YY1apVS+XLly+sfChlvly3X3/FnVSAj4ee7FLb6jgAAACAJBeLU5by5curZcuWBZ0FpdyJ02l6e+FOSdITXWoryN/L4kQAAADAWfk+OQRQ2MYv+lsnTqerdsWyuqN1uNVxAAAAAAeKE4qEHbFJ+nz1PknSqN715OHOWxMAAABFB2unsJwxRqNnbVOmkbo3rKR2NUKsjgQAAAA4oTjBcnP/jNOa6GPy9nDTc93rWh0HAAAAyIbiBEudSbPrlZ+3S5KGXV1D1cr7WZwIAAAAyI7iBEtNXP6PDiWmqGo5X91/VQ2r4wAAAAA5ojjBMvuPndak5f9Ikp7vUVe+Xu4WJwIAAAByRnGCZV75eYfSMjLVtnqwujWoZHUcAAAAIFcUJ1hixe6jmr8tTu5uNo3qXU82m83qSAAAAECuKE647NLtmRoze5skaUCbCNWpFGBxIgAAAODCKE647D5fvU9/H05WeT9PPX7dFVbHAQAAAPJEccJllZCcqvGL/pYkPdm1tgL9PC1OBAAAAOSN4oTL6q2FO3UyJUP1KgfotpbhVscBAAAA8oXihMtm68FEfbVuvyRpzA315e7GCSEAAABQPFCccFkYYzRq1jYZI93QpIpaRgZZHQkAAADIN4oTLoufNh/Shn3H5evprme71bE6DgAAAOASihMKXXJqhl6du0OS9NA1NVU50NfiRAAAAIBrKE4odB8t3a34k6kKD/LT3VdGWR0HAAAAcBnFCYVq79FT+uy3aEnSyJ715OPpbnEiAAAAwHUUJxSql3/erjR7pjrUCtF1dStYHQcAAAC4KBQnFJqlO+P1y454ebjZNKpXPdlsnH4cAAAAxRPFCYUiLSNTL83eLkka1C5SNSuUtTgRAAAAcPEoTigU01bu1Z6jpxRSxkuPXFfL6jgAAADAJaE4ocDFn0zRe4t3SZKevr6OAnw8LU4EAAAAXBqKEwrcG/N3Kjk1Q42rBermZtWsjgMAAABcMooTCtSmmOP6bsMBSdKo3vXl5sYJIQAAAFD8UZxQYDIzjUbP2iZJuqlZNTULL29xIgAAAKBgUJxQYL7feEB/HEhUGW8PPXN9bavjAAAAAAWG4oQCkZSSrtfn75QkPXJtTVUI8LE4EQAAAFBwKE4oEB8s3qWjyamqHuKvQe2irI4DAAAAFCiKEy7Z7vhkTVmxV5I0slc9eXnwtgIAAEDJwhouLokxRmPnbFdGptG1dSqoU+0KVkcCAAAAChzFCZdk8Y54/fr3EXm5u2lkz3pWxwEAAAAKBcUJFy0l3a6xc7ZLkoZcGaXIEH+LEwEAAACFg+KEi/bZ79GKOXZaFcp666FralodBwAAACg0FCdclNjEM/pwyW5J0ojudVTG28PiRAAAAEDhoTjhorw27y+dSberWXg59WlS1eo4AAAAQKGiOMFl6/ce00+bD8lmk8b0biCbzWZ1JAAAAKBQUZzgEnum0ahZ2yRJ/VqEqWG1QIsTAQAAAIWP4gSXfL1uv7YdSlJZHw892bW21XEAAACAy4LihHxLPJ2uNxf8JUl6/LorFFLG2+JEAAAAwOVBcUK+vfPL3zp+Ol21KpTRgLYRVscBAAAALhuKE/JlZ9xJ/Xf1PknSqF715enOWwcAAAClB2u/yJMxRmNmb5M906hr/Yq6slaI1ZEAAACAy4rihDzN3xqnlf8kyMvDTS/0qGd1HAAAAOCyozjhglLS7Xr55x2SpKFXVVdYkJ/FiQAAAIDLj+KEC/rP8j06eOKMqgT6aNjVNa2OAwAAAFiC4oRcHTh+WhOW7ZYkjeheV75e7hYnAgAAAKxBcUKuxs39S6kZmWoVFaSejSpbHQcAAACwDMUJOVr5z1H9/Ges3GzS6F71ZbPZrI4EAAAAWIbihGwy7JkaM2u7JOmO1hGqVyXA4kQAAACAtShOyOaLtTHaefikyvl5anjnK6yOAwAAAFiO4gQnx06l6e2Ff0uSnuh8hcr7e1mcCAAAALAexQlO3l64U4ln0lWnUln1bxVudRwAAACgSKA4wWHrwUR9sTZGkjS6d315uPP2AAAAACSKE/5ljNGY2dtkjNSzUWW1qR5sdSQAAACgyKA4QZI0e0us1u09Lh9PNz3Xva7VcQAAAIAiheIEnU7L0Ks/75AkPXB1TVUp52txIgAAAKBooThBE5b+o7ikFFUr76v7rqpudRwAAACgyKE4lXIxCaf18W97JEkv9KgnH093ixMBAAAARQ/FqZR7+eftSsvI1JU1Q9S1fkWr4wAAAABFEsWpFPv17yNauP2w3N1sGtWrnmw2m9WRAAAAgCKJ4lRKpdszNWb2NknSXW0jVKtiWYsTAQAAAEUXxamUmrZyr/45ckpB/l567LorrI4DAAAAFGkUp1LoyMlUvffLLknS011rK9DX0+JEAAAAQNFGcSqF3lqwUydTM9SwaqBuaRFmdRwAAACgyKM4lTJ/7D+hbzbslySN7l1P7m6cEAIAAADIC8WpFMnMNBo9e5uMkfo2rarmEUFWRwIAAACKBYpTKfLjpoPaFHNCfl7uerZbHavjAAAAAMUGxamUSE7N0Gvz/5IkPXxNLVUM8LE4EQAAAFB8UJxKiQ+W7NKRk6mKDPbTkCsjrY4DAAAAFCsUp1Jgz5FkTf49WpI0smc9eXu4W5wIAAAAKF4oTqXAS3O2K91udHXtUF1Tp4LVcQAAAIBih+JUwi3567CW7jwiT3ebRvasJ5uN048DAAAArqI4lWCpGXaNnb1dkjSkfZRqhJaxOBEAAABQPFGcSrApK/Zqb8JphZTx1kPX1LQ6DgAAAFBsUZxKqMNJKfpg8S5J0rPd6qisj6fFiQAAAIDii+JUQr0+7y+dSrOrSVg53di0qtVxAAAAgGLN8uI0YcIERUVFycfHR82bN9dvv/12wfFTU1P1/PPPKyIiQt7e3qpRo4YmT558mdIWDxv2HdcPmw5Kksb0ri83N04IAQAAAFwKDyvv/Ouvv9Zjjz2mCRMmqH379vrPf/6jbt26afv27QoPD89xmltvvVWHDx/WZ599ppo1ayo+Pl4ZGRmXOXnRlZlpNHrWNknSLc2rqXFYOWsDAQAAACWAzRhjrLrz1q1bq1mzZpo4caJjWN26ddWnTx+NGzcu2/jz58/Xbbfdpj179igoKOii7jMpKUmBgYFKTExUQEDARWcvqr5eF6Nnvv9TZb09tOTJqxVa1tvqSAAAAECR5Eo3sGxXvbS0NG3YsEFdunRxGt6lSxetXLkyx2lmzZqlFi1a6I033lDVqlV1xRVX6Mknn9SZM2dyvZ/U1FQlJSU5/ZRUiWfS9cb8nZKkR6+rRWkCAAAACohlu+odPXpUdrtdFStWdBpesWJFxcXF5TjNnj179Pvvv8vHx0c//vijjh49qgceeEDHjh3L9TincePGacyYMQWevyh675ddSjiVphqh/rqrbaTVcQAAAIASw/KTQ9hszicuMMZkG5YlMzNTNptNM2bMUKtWrdS9e3eNHz9eU6dOzXWr04gRI5SYmOj42b9/f4E/hqJg1+GTmrZqryRpVK/68vKw/KUFAAAASgzLtjiFhITI3d0929al+Pj4bFuhslSuXFlVq1ZVYGCgY1jdunVljNGBAwdUq1atbNN4e3vL27tk77JmjNGY2dtlzzS6rm5FXXVFqNWRAAAAgBLFss0SXl5eat68uRYtWuQ0fNGiRWrXrl2O07Rv316HDh1ScnKyY9jff/8tNzc3VatWrVDzFmULtx/W77uPysvdTSN71rU6DgAAAFDiWLo/1/Dhw/Xpp59q8uTJ2rFjhx5//HHFxMRo6NChks7uZnfXXXc5xr/99tsVHByswYMHa/v27fr111/11FNPaciQIfL19bXqYVgqJd2ul+ZslyTde1WUIoL9LU4EAAAAlDyWXsepX79+SkhI0NixYxUbG6sGDRpo7ty5ioiIkCTFxsYqJibGMX6ZMmW0aNEiPfzww2rRooWCg4N166236uWXX7bqIVjuk1/36MDxM6oU4KMHrq5pdRwAAACgRLL0Ok5WKEnXcTp04oyueXuZUtIz9d5tTXRDk6pWRwIAAACKjWJxHSdcunHz/lJKeqZaRpZX78ZVrI4DAAAAlFgUp2JqzZ4Ezf7jkGy2s6cfz+0U7gAAAAAuHcWpGMqwZ2rUrG2SpP6twtWgamAeUwAAAAC4FBSnYujLdfv1V9xJBfh46Mkuta2OAwAAAJR4FKdi5sTpNL29cKckaXjnKxTk72VxIgAAAKDkozgVM+MX/a0Tp9N1RcUyurNNhNVxAAAAgFKB4lSM7IhN0uer90mSRveqLw93Xj4AAADgcmDNu5gwxmj0rG3KNFL3hpXUrmaI1ZEAAACAUoPiVEz8/Ges1kQfk7eHm57rXtfqOAAAAECpQnEqBs6k2fXqzzskSUM71lC18n4WJwIAAABKF4pTMTBx+T86lJiiquV8NbRjDavjAAAAAKUOxamI23/stCYt/0eS9HyPuvL1crc4EQAAAFD6UJyKuFd+3qG0jEy1rR6sbg0qWR0HAAAAKJUoTkXYit1HNX9bnNzdbBrVu55sNpvVkQAAAIBSieJURKXbMzVm9jZJ0p2tw1WnUoDFiQAAAIDSi+JURH2+ep/+Ppys8n6eerzzFVbHAQAAAEo1ilMRlJCcqvGL/pYkPdm1tsr5eVmcCAAAACjdKE5F0FsLd+pkSobqVQ7QbS3DrY4DAAAAlHoUpyLmzwOJ+mrdfknS6N715e7GCSEAAAAAq1GcihBjjEbP3iZjpN6Nq6hVVJDVkQAAAACI4lSk/LT5kDbsOy5fT3eN6F7H6jgAAAAA/kVxKiKSUzP06twdkqSHrqmpyoG+FicCAAAAkIXiVER8tHS34k+mKjzIT3dfGWV1HAAAAADnoDgVAXuPntJnv0VLkl7oUVc+nu4WJwIAAABwLopTEfDyz9uVZs9Uh1oh6lyvotVxAAAAAJyH4mSxpTvj9cuOeHm42TSqVz3ZbJx+HAAAAChqKE4WSsvI1Euzt0uSBrWLVM0KZS1OBAAAACAnFCcLTV0ZrT1HTymkjJceua6W1XEAAAAA5MLD6gClWYMqgapZoYzu61BdAT6eVscBAAAAkAuKk4Xa1QzRvEc7yJ3jmgAAAIAijeJkMU939pYEAAAAijrW2gEAAAAgDxQnAAAAAMgDxQkAAAAA8kBxAgAAAIA8UJwAAAAAIA8UJwAAAADIA8UJAAAAAPJAcQIAAACAPFCcAAAAACAPFCcAAAAAyAPFCQAAAADyQHECAAAAgDxQnAAAAAAgDxQnAAAAAMgDxQkAAAAA8kBxAgAAAIA8UJwAAAAAIA8eVge43IwxkqSkpCSLkwAAAACwUlYnyOoIF1LqitPJkyclSWFhYRYnAQAAAFAUnDx5UoGBgRccx2byU69KkMzMTB06dEhly5aVzWazOo6SkpIUFham/fv3KyAgwOo4KAC8piUPr2nJxOta8vCalky8riVPUXpNjTE6efKkqlSpIje3Cx/FVOq2OLm5ualatWpWx8gmICDA8jcOChavacnDa1oy8bqWPLymJROva8lTVF7TvLY0ZeHkEAAAAACQB4oTAAAAAOSB4mQxb29vjRo1St7e3lZHQQHhNS15eE1LJl7XkofXtGTidS15iutrWupODgEAAAAArmKLEwAAAADkgeIEAAAAAHmgOAEAAABAHihOAAAAAJAHipOFJkyYoKioKPn4+Kh58+b67bffrI6ES/Drr7+qV69eqlKlimw2m2bOnGl1JFyicePGqWXLlipbtqwqVKigPn36aOfOnVbHwiWaOHGiGjVq5LjwYtu2bTVv3jyrY6EAjRs3TjabTY899pjVUXCRRo8eLZvN5vRTqVIlq2OhABw8eFB33nmngoOD5efnpyZNmmjDhg1Wx8oXipNFvv76az322GN6/vnntWnTJnXo0EHdunVTTEyM1dFwkU6dOqXGjRvrww8/tDoKCsjy5cv14IMPavXq1Vq0aJEyMjLUpUsXnTp1yupouATVqlXTa6+9pvXr12v9+vW65pprdMMNN2jbtm1WR0MBWLdunT7++GM1atTI6ii4RPXr11dsbKzj588//7Q6Ei7R8ePH1b59e3l6emrevHnavn273n77bZUrV87qaPnC6cgt0rp1azVr1kwTJ050DKtbt6769OmjcePGWZgMBcFms+nHH39Unz59rI6CAnTkyBFVqFBBy5cv11VXXWV1HBSgoKAgvfnmm7r77rutjoJLkJycrGbNmmnChAl6+eWX1aRJE7377rtWx8JFGD16tGbOnKnNmzdbHQUF6Nlnn9WKFSuK7V5WbHGyQFpamjZs2KAuXbo4De/SpYtWrlxpUSoAeUlMTJR0diUbJYPdbtdXX32lU6dOqW3btlbHwSV68MEH1aNHD1133XVWR0EB2LVrl6pUqaKoqCjddttt2rNnj9WRcIlmzZqlFi1a6JZbblGFChXUtGlTffLJJ1bHyjeKkwWOHj0qu92uihUrOg2vWLGi4uLiLEoF4EKMMRo+fLiuvPJKNWjQwOo4uER//vmnypQpI29vbw0dOlQ//vij6tWrZ3UsXIKvvvpKGzduZK+NEqJ169aaPn26FixYoE8++URxcXFq166dEhISrI6GS7Bnzx5NnDhRtWrV0oIFCzR06FA98sgjmj59utXR8sXD6gClmc1mc/rdGJNtGICi4aGHHtKWLVv0+++/Wx0FBaB27dravHmzTpw4oe+//14DBw7U8uXLKU/F1P79+/Xoo49q4cKF8vHxsToOCkC3bt0c/2/YsKHatm2rGjVqaNq0aRo+fLiFyXApMjMz1aJFC7366quSpKZNm2rbtm2aOHGi7rrrLovT5Y0tThYICQmRu7t7tq1L8fHx2bZCAbDeww8/rFmzZmnp0qWqVq2a1XFQALy8vFSzZk21aNFC48aNU+PGjfXee+9ZHQsXacOGDYqPj1fz5s3l4eEhDw8PLV++XO+//748PDxkt9utjohL5O/vr4YNG2rXrl1WR8ElqFy5crYvqOrWrVtsTo5GcbKAl5eXmjdvrkWLFjkNX7Rokdq1a2dRKgDnM8booYce0g8//KAlS5YoKirK6kgoJMYYpaamWh0DF+naa6/Vn3/+qc2bNzt+WrRooTvuuEObN2+Wu7u71RFxiVJTU7Vjxw5VrlzZ6ii4BO3bt892WY+///5bERERFiVyDbvqWWT48OEaMGCAWrRoobZt2+rjjz9WTEyMhg4danU0XKTk5GTt3r3b8Xt0dLQ2b96soKAghYeHW5gMF+vBBx/UF198oZ9++klly5Z1bCUODAyUr6+vxelwsZ577jl169ZNYWFhOnnypL766istW7ZM8+fPtzoaLlLZsmWzHXvo7++v4OBgjkkspp588kn16tVL4eHhio+P18svv6ykpCQNHDjQ6mi4BI8//rjatWunV199VbfeeqvWrl2rjz/+WB9//LHV0fKF4mSRfv36KSEhQWPHjlVsbKwaNGiguXPnFpvGjezWr1+vTp06OX7P2gd74MCBmjp1qkWpcCmyLhdw9dVXOw2fMmWKBg0adPkDoUAcPnxYAwYMUGxsrAIDA9WoUSPNnz9fnTt3tjoagH8dOHBA/fv319GjRxUaGqo2bdpo9erVrCcVcy1bttSPP/6oESNGaOzYsYqKitK7776rO+64w+po+cJ1nAAAAAAgDxzjBAAAAAB5oDgBAAAAQB4oTgAAAACQB4oTAAAAAOSB4gQAAAAAeaA4AQAAAEAeKE4AAAAAkAeKEwAAAADkgeIEAAAAAHmgOAEALllcXJwefvhhVa9eXd7e3goLC1OvXr20ePFiq6MVKYMGDVKfPn1cmubIkSPy9PTU6dOnlZGRIX9/f8XExBROQABArjysDgAAKN727t2r9u3bq1y5cnrjjTfUqFEjpaena8GCBXrwwQf1119/WR2xWFu1apWaNGkiPz8/rVmzRkFBQQoPD7c6FgCUOmxxAgBckgceeEA2m01r167VzTffrCuuuEL169fX8OHDtXr1asd4MTExuuGGG1SmTBkFBATo1ltv1eHDhx23jx49Wk2aNNHkyZMVHh6uMmXKaNiwYbLb7XrjjTdUqVIlVahQQa+88orT/dtsNk2cOFHdunWTr6+voqKi9O233zqN8+eff+qaa66Rr6+vgoODdd999yk5Odlxe9aWoLfeekuVK1dWcHCwHnzwQaWnpzvGSUtL09NPP62q/2vn7kKabt84gH9/W4bogmnEfMG2ypfUoQUuDcGIGXqUEYbUalIkVNiRnnQQ0kFU0EFFGnTQJiZYkdZBpkHooAkqjZXpsCYrI6oVUlJGuO16DqIf7SldvvDw/8P3A4PdL7uue9vRxX3/7vR0JCYmori4GP39/eq40+mEXq9Hb28vcnNzodPpUFlZibdv36rfr7W1FXfv3oWiKFAUJerzcxkYGEBpaSkA4NGjR+p7IiL6b3HHiYiIFm1qago9PT04ffo0EhMTfxvX6/UAABHBrl27kJiYCJfLhVAohGPHjqGmpiaqeJiYmMD9+/fR09ODiYkJVFdXIxAIIDs7Gy6XCwMDAzh06BCsVitKSkrUz508eRJnz57FxYsX0dbWhr1798JsNiM3NxczMzOorKxESUkJhoeHEQwGcfjwYdTX18PpdKox+vr6kJqair6+Pvj9ftTU1GDTpk2oq6sDABw8eBAvX75ER0cH0tLS0NXVhcrKSoyMjCArKwsAMDMzg/Pnz6OtrQ0ajQb79+9HY2Mj2tvb0djYCJ/Ph+npaTgcDgBAcnLyH3/XyclJFBQUqDG1Wi2cTie+ffsGRVGg1+uxb98+tLS0LPq/IyKiBRIiIqJFGhwcFADS2dk577wHDx6IVquVyclJtW90dFQAyNDQkIiINDU1SUJCgkxPT6tzKioqxGQySTgcVvtycnLkzJkzahuAHDlyJCpfcXGxHD16VERErl69KklJSfLlyxd1/N69e6LRaOTdu3ciIlJbWytGo1FCoZA6Z8+ePVJTUyMiIn6/XxRFkTdv3kTlsVqtcuLECRERcTgcAkD8fr863tzcLAaDQW3X1tZKVVXVvL+ViMjs7KwEAgF58uSJxMXFidfrFb/fLzqdTlwulwQCAfnw4UPMOEREtHy440RERIsmIgB+HJebj8/nQ0ZGBjIyMtS+vLw86PV6+Hw+WCwWAIDJZMKqVavUOQaDAVqtFhqNJqovGAxGxd+6detvba/Xq+YuLCyM2hErLS1FJBLB+Pg4DAYDACA/Px9arVadk5qaipGREQCAx+OBiCA7Ozsqz/fv37F69Wq1nZCQgA0bNkTF+Pda/8aKFStgMplw8+ZNWCwWFBYWwu12w2AwoKysbMHxiIho6Vg4ERHRomVlZUFRFPh8vnlvixORPxZX/+6Pi4uLGlcU5Y99kUgk5tp+xp0r969z5sr9M08kEoFWq8Xjx4+jiisA0Ol088b4WVwuRH5+Pl69eoXZ2VlEIhHodDqEQiGEQiHodDoYjUaMjo4uOC4RES0eL4cgIqJFS05ORkVFBZqbm/H169ffxj99+gTgx+7S5OQkXr9+rY6NjY3h8+fPyM3NXfI6fr2E4md748aNam6v1xu1PrfbDY1G89sO0lw2b96McDiMYDCIzMzMqFdKSspfr3PlypUIh8Mx53V3d8Pr9SIlJQXXr1+H1+uF2WzGhQsX4PV60d3d/dc5iYhoebBwIiKiJWlpaUE4HMaWLVtw+/ZtvHjxAj6fD5cuXVKP0JWXl6OgoAA2mw0ejwdDQ0Ow2+3Ytm0bioqKlryGW7du4dq1a3j+/DmampowNDSE+vp6AIDNZkN8fDxqa2vx7Nkz9PX14fjx4zhw4IB6TC+W7Oxs2Gw22O12dHZ2IhAIYHh4GOfOnVtQEWMymfD06VOMj4/j48ePUbf2/cpoNEKn0+H9+/eoqqrC2rVrMTY2ht27dyMzMxNGo/GvcxIR0fJg4UREREuybt06eDwebN++HQ0NDTCbzdixYwcePnyIK1euAPhxZO3OnTtISkpCWVkZysvLsX79ety4cWNZ1nDq1Cl0dHSgoKAAra2taG9vR15eHoAfzx319vZiamoKFosF1dXVsFqtuHz58oJyOBwO2O12NDQ0ICcnBzt37sTg4GDUc1ux1NXVIScnB0VFRVizZg3cbvecc/v7+2GxWBAfH4/BwUGkp6cjLS1tQWsmIqLlo8hiDl8TERH9j1AUBV1dXfM+Y0VERLRU3HEiIiIiIiKKgYUTERERERFRDLyOnIiI/q/xxDkREf0XuONEREREREQUAwsnIiIiIiKiGFg4ERERERERxcDCiYiIiIiIKAYWTkRERERERDGwcCIiIiIiIoqBhRMREREREVEMLJyIiIiIiIhi+AcYtvn6jsWT1wAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Code task 6#\n",
+    "#Call the `cumsum()` method on the 'explained_variance_ratio_' attribute of `state_pca` and\n",
+    "#create a line plot to visualize the cumulative explained variance ratio with number of components\n",
+    "#Set the xlabel to 'Component #', the ylabel to 'Cumulative ratio variance', and the\n",
+    "#title to 'Cumulative variance ratio explained by PCA components for state/resort summary statistics'\n",
+    "#Hint: remember the handy ';' at the end of the last plot call to suppress that untidy output\n",
+    "plt.subplots(figsize=(10, 6))\n",
+    "plt.plot(state_pca.explained_variance_ratio_.cumsum())\n",
+    "plt.xlabel('Component #')\n",
+    "plt.ylabel('Cumulative ratio variance')\n",
+    "plt.title('Cumulative variance ratio explained by PCA components for state/resort summary statistics');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The first two components seem to account for over 75% of the variance, and the first four for over 95%."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Note:** It is important to move quickly when performing exploratory data analysis. You should not spend hours trying to create publication-ready figures. However, it is crucially important that you can easily review and summarise the findings from EDA. Descriptive axis labels and titles are _extremely_ useful here. When you come to reread your notebook to summarise your findings, you will be thankful that you created descriptive plots and even made key observations in adjacent markdown cells."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Apply the transformation to the data to obtain the derived features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Code task 7#\n",
+    "#Call `state_pca`'s `transform()` method, passing in `state_summary_scale` as its argument\n",
+    "state_pca_x = state_pca.transform(state_summary_scale)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(35, 7)"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "state_pca_x.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the first two derived features (the first two principle components) and label each point with the name of the state."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Take a moment to familiarize yourself with the code below. It will extract the first and second columns from the transformed data (`state_pca_x`) as x and y coordinates for plotting. Recall the state labels you saved (for this purpose) for subsequent calls to `plt.annotate`. Grab the second (index 1) value of the cumulative variance ratio to include in your descriptive title; this helpfully highlights the percentage variance explained\n",
+    "by the two PCA components you're visualizing. Then create an appropriately sized and well-labelled scatterplot\n",
+    "to convey all of this information."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = state_pca_x[:, 0]\n",
+    "y = state_pca_x[:, 1]\n",
+    "state = state_summary_index\n",
+    "pc_var = 100 * state_pca.explained_variance_ratio_.cumsum()[1]\n",
+    "plt.subplots(figsize=(10,8))\n",
+    "plt.scatter(x=x, y=y)\n",
+    "plt.xlabel('First component')\n",
+    "plt.ylabel('Second component')\n",
+    "plt.title(f'Ski states summary PCA, {pc_var:.1f}% variance explained')\n",
+    "for s, x, y in zip(state, x, y):\n",
+    "    plt.annotate(s, (x, y))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.5.3.3 Average ticket price by state<a id='3.5.3.3_Average_ticket_price_by_state'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here, all point markers for the states are the same size and colour. You've visualized relationships between the states based on features such as the total skiable terrain area, but your ultimate interest lies in ticket prices. You know ticket prices for resorts in each state, so it might be interesting to see if there's any pattern there."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "state\n",
+       "Alaska         57.333333\n",
+       "Arizona        83.500000\n",
+       "California     81.416667\n",
+       "Colorado       90.714286\n",
+       "Connecticut    56.800000\n",
+       "Name: AdultWeekend, dtype: float64"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 8#\n",
+    "#Calculate the average 'AdultWeekend' ticket price by state\n",
+    "state_avg_price = ski_data.groupby('state')['AdultWeekend'].mean()\n",
+    "state_avg_price.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "state_avg_price.hist(bins=30)\n",
+    "plt.title('Distribution of state averaged prices')\n",
+    "plt.xlabel('Mean state adult weekend ticket price')\n",
+    "plt.ylabel('count');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.5.3.4 Adding average ticket price to scatter plot<a id='3.5.3.4_Adding_average_ticket_price_to_scatter_plot'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "At this point you have several objects floating around. You have just calculated average ticket price by state from our ski resort data, but you've been looking at principle components generated from other state summary data. We extracted indexes and column names from a dataframe and the first two principle components from an array. It's becoming a bit hard to keep track of them all. You'll create a new DataFrame to do this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PC1</th>\n",
+       "      <th>PC2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Alaska</th>\n",
+       "      <td>-1.336533</td>\n",
+       "      <td>-0.182208</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Arizona</th>\n",
+       "      <td>-1.839049</td>\n",
+       "      <td>-0.387959</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>California</th>\n",
+       "      <td>3.537857</td>\n",
+       "      <td>-1.282509</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Colorado</th>\n",
+       "      <td>4.402210</td>\n",
+       "      <td>-0.898855</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Connecticut</th>\n",
+       "      <td>-0.988027</td>\n",
+       "      <td>1.020218</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  PC1       PC2\n",
+       "state                          \n",
+       "Alaska      -1.336533 -0.182208\n",
+       "Arizona     -1.839049 -0.387959\n",
+       "California   3.537857 -1.282509\n",
+       "Colorado     4.402210 -0.898855\n",
+       "Connecticut -0.988027  1.020218"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 9#\n",
+    "#Create a dataframe containing the values of the first two PCA components\n",
+    "#Remember the first component was given by state_pca_x[:, 0],\n",
+    "#and the second by state_pca_x[:, 1]\n",
+    "#Call these 'PC1' and 'PC2', respectively and set the dataframe index to `state_summary_index`\n",
+    "pca_df = pd.DataFrame({'PC1': state_pca_x[:,0], 'PC2': state_pca_x[:,1]}, index=state_summary_index)\n",
+    "pca_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "That worked, and you have state as an index."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "state\n",
+       "Alaska         57.333333\n",
+       "Arizona        83.500000\n",
+       "California     81.416667\n",
+       "Colorado       90.714286\n",
+       "Connecticut    56.800000\n",
+       "Name: AdultWeekend, dtype: float64"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# our average state prices also have state as an index\n",
+    "state_avg_price.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>AdultWeekend</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Alaska</th>\n",
+       "      <td>57.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Arizona</th>\n",
+       "      <td>83.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>California</th>\n",
+       "      <td>81.416667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Colorado</th>\n",
+       "      <td>90.714286</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Connecticut</th>\n",
+       "      <td>56.800000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             AdultWeekend\n",
+       "state                    \n",
+       "Alaska          57.333333\n",
+       "Arizona         83.500000\n",
+       "California      81.416667\n",
+       "Colorado        90.714286\n",
+       "Connecticut     56.800000"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# we can also cast it to a dataframe using Series' to_frame() method:\n",
+    "state_avg_price.to_frame().head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now you can concatenate both parts on axis 1 and using the indexes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PC1</th>\n",
+       "      <th>PC2</th>\n",
+       "      <th>AdultWeekend</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Alaska</th>\n",
+       "      <td>-1.336533</td>\n",
+       "      <td>-0.182208</td>\n",
+       "      <td>57.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Arizona</th>\n",
+       "      <td>-1.839049</td>\n",
+       "      <td>-0.387959</td>\n",
+       "      <td>83.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>California</th>\n",
+       "      <td>3.537857</td>\n",
+       "      <td>-1.282509</td>\n",
+       "      <td>81.416667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Colorado</th>\n",
+       "      <td>4.402210</td>\n",
+       "      <td>-0.898855</td>\n",
+       "      <td>90.714286</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Connecticut</th>\n",
+       "      <td>-0.988027</td>\n",
+       "      <td>1.020218</td>\n",
+       "      <td>56.800000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  PC1       PC2  AdultWeekend\n",
+       "state                                        \n",
+       "Alaska      -1.336533 -0.182208     57.333333\n",
+       "Arizona     -1.839049 -0.387959     83.500000\n",
+       "California   3.537857 -1.282509     81.416667\n",
+       "Colorado     4.402210 -0.898855     90.714286\n",
+       "Connecticut -0.988027  1.020218     56.800000"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 10#\n",
+    "#Use pd.concat to concatenate `pca_df` and `state_avg_price` along axis 1\n",
+    "# remember, pd.concat will align on index\n",
+    "pca_df = pd.concat([pca_df, state_avg_price], axis=1)\n",
+    "pca_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You saw some range in average ticket price histogram above, but it may be hard to pick out differences if you're thinking of using the value for point size. You'll add another column where you seperate these prices into quartiles; that might show something."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PC1</th>\n",
+       "      <th>PC2</th>\n",
+       "      <th>AdultWeekend</th>\n",
+       "      <th>Quartile</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Alaska</th>\n",
+       "      <td>-1.336533</td>\n",
+       "      <td>-0.182208</td>\n",
+       "      <td>57.333333</td>\n",
+       "      <td>(53.8, 61.0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Arizona</th>\n",
+       "      <td>-1.839049</td>\n",
+       "      <td>-0.387959</td>\n",
+       "      <td>83.500000</td>\n",
+       "      <td>(77.8, 93.0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>California</th>\n",
+       "      <td>3.537857</td>\n",
+       "      <td>-1.282509</td>\n",
+       "      <td>81.416667</td>\n",
+       "      <td>(77.8, 93.0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Colorado</th>\n",
+       "      <td>4.402210</td>\n",
+       "      <td>-0.898855</td>\n",
+       "      <td>90.714286</td>\n",
+       "      <td>(77.8, 93.0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Connecticut</th>\n",
+       "      <td>-0.988027</td>\n",
+       "      <td>1.020218</td>\n",
+       "      <td>56.800000</td>\n",
+       "      <td>(53.8, 61.0]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  PC1       PC2  AdultWeekend      Quartile\n",
+       "state                                                      \n",
+       "Alaska      -1.336533 -0.182208     57.333333  (53.8, 61.0]\n",
+       "Arizona     -1.839049 -0.387959     83.500000  (77.8, 93.0]\n",
+       "California   3.537857 -1.282509     81.416667  (77.8, 93.0]\n",
+       "Colorado     4.402210 -0.898855     90.714286  (77.8, 93.0]\n",
+       "Connecticut -0.988027  1.020218     56.800000  (53.8, 61.0]"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pca_df['Quartile'] = pd.qcut(pca_df.AdultWeekend, q=4, precision=1)\n",
+    "pca_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PC1             float64\n",
+       "PC2             float64\n",
+       "AdultWeekend    float64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Note that Quartile is a new data type: category\n",
+    "# This will affect how we handle it later on\n",
+    "pca_df.dtypes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This looks great. But, let's have a healthy paranoia about it. You've just created a whole new DataFrame by combining information. Do we have any missing values? It's a narrow DataFrame, only four columns, so you'll just print out any rows that have any null values, expecting an empty DataFrame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PC1</th>\n",
+       "      <th>PC2</th>\n",
+       "      <th>AdultWeekend</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Rhode Island</th>\n",
+       "      <td>-1.843646</td>\n",
+       "      <td>0.761339</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   PC1       PC2  AdultWeekend\n",
+       "state                                         \n",
+       "Rhode Island -1.843646  0.761339           NaN"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pca_df[pca_df.isnull().any(axis=1)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Ah, Rhode Island. How has this happened? Recall you created the original ski resort state summary dataset in the previous step before removing resorts with missing prices. This made sense because you wanted to capture all the other available information. However, Rhode Island only had one resort and its price was missing. You have two choices here. If you're interested in looking for any pattern with price, drop this row. But you are also generally interested in any clusters or trends, then you'd like to see Rhode Island even if the ticket price is unknown. So, replace these missing values to make it easier to handle/display them."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Because `Quartile` is a category type, there's an extra step here. Add the category (the string 'NA') that you're going to use as a replacement."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PC1                -1.843646\n",
+       "PC2                 0.761339\n",
+       "AdultWeekend       64.124388\n",
+       "Quartile        (61.0, 77.8]\n",
+       "Name: Rhode Island, dtype: object"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pca_df['AdultWeekend'].fillna(pca_df.AdultWeekend.mean(), inplace=True)\n",
+    "pca_df['Quartile'] = pca_df['Quartile'].cat.add_categories('NA')\n",
+    "pca_df['Quartile'].fillna('NA', inplace=True)\n",
+    "pca_df.loc['Rhode Island']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note, in the above Quartile has the string value 'NA' that you inserted. This is different to `numpy`'s NaN type.\n",
+    "\n",
+    "You now have enough information to recreate the scatterplot, now adding marker size for ticket price and colour for the discrete quartile."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice in the code below how you're iterating over each quartile and plotting the points in the same quartile group as one. This gives a list of quartiles for an informative legend with points coloured by quartile and sized by ticket price (higher prices are represented by larger point markers)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = pca_df.PC1\n",
+    "y = pca_df.PC2\n",
+    "price = pca_df.AdultWeekend\n",
+    "quartiles = pca_df.Quartile\n",
+    "state = pca_df.index\n",
+    "pc_var = 100 * state_pca.explained_variance_ratio_.cumsum()[1]\n",
+    "fig, ax = plt.subplots(figsize=(10,8))\n",
+    "for q in quartiles.cat.categories:\n",
+    "    im = quartiles == q\n",
+    "    ax.scatter(x=x[im], y=y[im], s=price[im], label=q)\n",
+    "ax.set_xlabel('First component')\n",
+    "ax.set_ylabel('Second component')\n",
+    "plt.legend()\n",
+    "ax.set_title(f'Ski states summary PCA, {pc_var:.1f}% variance explained')\n",
+    "for s, x, y in zip(state, x, y):\n",
+    "    plt.annotate(s, (x, y))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, you see the same distribution of states as before, but with additional information about the average price. There isn't an obvious pattern. The red points representing the upper quartile of price can be seen to the left, the right, and up top. There's also a spread of the other quartiles as well. In this representation of the ski summaries for each state, which accounts for some 77% of the variance, you simply do not seeing a pattern with price."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The above scatterplot was created using matplotlib. This is powerful, but took quite a bit of effort to set up. You have to iterate over the categories, plotting each separately, to get a colour legend. You can also tell that the points in the legend have different sizes as well as colours. As it happens, the size and the colour will be a 1:1 mapping here, so it happily works for us here. If we were using size and colour to display fundamentally different aesthetics, you'd have a lot more work to do. So matplotlib is powerful, but not ideally suited to when we want to visually explore multiple features as here (and intelligent use of colour, point size, and even shape can be incredibly useful for EDA).\n",
+    "\n",
+    "Fortunately, there's another option: seaborn. You saw seaborn in action in the previous notebook, when you wanted to distinguish between weekend and weekday ticket prices in the boxplot. After melting the dataframe to have ticket price as a single column with the ticket type represented in a new column, you asked seaborn to create separate boxes for each type."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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WLVt06NAhc9usWbNUs2ZNVaxY0dyWHZ+fu9m1a5eeeeYZFSpUyHzdF198UcnJyTp8+LBFX19fXz355JMWbZUrV7b4b+XXX39V2bJl1bhx43THXLFihby8vNSmTRuLnx9Vq1aVr69vhjPkR48e1T///KNu3bpJksX5LVu21Llz5yzeyzfeeEOtWrVSly5dNHv2bE2ZMkWVKlVKc113d/c0t6107dpVKSkp2rhxY7r1SNJXX32l6tWry8nJyfzzdO3atZn6WSopzbiVK1eW9H+33GT25+2hQ4d09uxZde3a1WIjyRIlSqhu3bqZqgUAHhShH4DVCgoK0ltvvaUffvhBZ8+e1euvv64TJ05owoQJFv0OHz6sbdu2qUWLFhb/oM+qF154QTNnztTJkyf13HPPydvbW7Vq1dKaNWvueW5KSoqaNm2qpUuX6s0339TatWu1fft2bd26VZLuubRWks6fPy9Jqlmzpuzt7S2+Fi5caP4lhMlk0tq1a9WsWTNNmDBB1atXV5EiRfTaa6/p+vXr6V7/fs+7l4IFC1q8dnBwSLf9bvfHZ+V86f/u975x44bq1aunbdu26cMPP9SGDRsUERGhpUuXSkr7nru4uMjDwyPN+NWqVVO9evX0xRdfSLodnk6cOKFXX301g+/a0u+//66IiAjt3r1bly5d0ubNm/X4449Lur0k2sXFRZGRkZm+3syZM2UYhjp06KBr167p2rVrSkxM1DPPPKM//vjD4j7u+5XZIJ+YmKg5c+aoSpUqCgoKyvI4s2bNUr9+/fTSSy9p4sSJd+3j6upqvg/98uXLGjJkiCZPnqwCBQpo4MCBunLlio4ePaoFCxZo6NChWr9+fYZjduvWTY6OjuZl6QcOHFBERIR69uxp7pNdn587nTp1SvXq1dOZM2f02WefmX+Bmfr5uvO6hQoVSnMNR0dHi34XL15U8eLFMxz3/PnzunbtmhwcHNL8/IiKisrwl5ipP3uGDh2a5tz+/ftLksX5JpNJoaGhio+Pl6+vr8W9/P/l4+OTps3X11fS7V/WpmfSpEl65ZVXVKtWLS1ZskRbt25VRESEmjdvnqmfpVLa9zX1FpHU8zP78za1ztS67/a9AEBOS7vLFQBYIXt7e40YMUKffvqp9u3bZ3GsTp066tixo/mRZ9OmTcvUhn9307NnT/Xs2VM3b97Uxo0bNWLECLVu3VqHDx82z9rfzb59+7Rnzx6FhYWpR48e5vbUe7szo3DhwpKkxYsXZziWdHuWKTW0HT58WIsWLdLIkSOVkJCQ4UxoZs5zdHTUrVu30pyb0T/S88K6det09uxZbdiwwTw7Kynd/Qkyetzba6+9po4dO2rnzp2aOnWqypYta348XWZUqVLF/Pd3J1tbWzVq1Ei//vqrTp8+fc/wlpKSYg6r7du3v2uf1H0O7ldCQoLmzp2rGjVqqGrVqhn2XbFihS5cuKD33nsvy+PMmjVLffr0UY8ePfTVV19l6pF7Q4YMUY0aNdSlSxdJt2e5Z82aJU9PT9WsWVNNmzbVypUr1aBBg3SvUaBAAbVt21Zz5szRhx9+qFmzZsnJycl8TSl7Pz//tXz5ct28eVNLly61+O/4XptsZqRIkSI6ffp0hn1SN6xbtWrVXY+7u7tneK4kDRs2LN3PXLly5cx/PnfunAYMGKCqVatq//79Gjp0qD7//PM056QG6/+KioqSdPdfdqT67rvvFBISomnTplm0P8gvJ++U2Z+3qXWm1v1fd2sDgJxA6Adgdc6dOyc/P7807anLOosWLZrmWI8ePeTq6qquXbvq5s2bmj17tmxtbe+7BldXV7Vo0UIJCQl69tlntX//fpUoUSLNbFGq1EBw54Zjd24Q998+d16jWbNmsrOz07FjxzK9jFiSypYtq3fffVdLlizRzp07H/i8gIAA/f333xZ9161bl60byGWHrLzn99KuXTs99thjGjJkiMLDw/Xpp59m6zPhhw0bppUrV6pv37768ccfzasWUiUmJmrVqlVq06aNfvvtN50+fVoDBgxQhw4d0lzr1Vdf1Zw5czRmzJi7PuEiM3766SddunRJo0ePvmffGTNmyMnJybz0O7PCwsLUp08fde/eXdOnT8/U+7l+/Xr98MMPFr/YMwzDYjf9GzduyDCMe16rZ8+eWrRokVauXKnvvvtO7dq1M98qIWXv5+e/7nZdwzD07bff3vc1W7Rooffff1/r1q1Tw4YN79qndevWWrBggZKTk1WrVq0sXb9cuXIqU6aM9uzZY96sND3Jycnq0qWLTCaTfv31V82bN09Dhw5VSEhIml8YXL9+XT/99JPFUvvvv/9eNjY2ql+/frpjmEymNH8vf//9t/78888sbYaZkcz+vC1Xrpz8/Pw0f/58DR482Pz3e/LkSW3ZsuWu/38EANmN0A/A6jRr1kzFixdXmzZtVL58eaWkpGj37t365JNP5Obmlu7Ozx06dJCLi4s6dOiguLg4zZ8/P024ykjfvn3l7Oysp556Sn5+foqKitLYsWPNM4ySzLcPfPPNN3J3d5eTk5MCAwNVvnx5lSpVSm+//bYMw1DBggX1888/3/XWgNR7Xz/77DP16NFD9vb2KleunAICAjR69GgNHz5cx48fV/PmzVWgQAGdP39e27dvl6urq0aNGqW///5br776qjp27KgyZcrIwcFB69at099//62333473e8vs+e98MILeu+99/T+++8rODhYBw4c0NSpU+Xp6Znp9zI31K1bVwUKFNDLL7+sESNGyN7eXvPmzdOePXuyfC1bW1sNGDBAb731llxdXS0eTZgdUp/q0L9/f9WoUUOvvPKKnnjiCSUmJmrXrl365ptvVLFiRbVp00YzZsyQnZ2d3nnnnbsGin79+um1117TL7/8Yr4/3mQyWdyHfC8zZsyQs7OzunbtmmG/s2fPatWqVerUqZMKFChw1z5z5sxRr169NHPmTL344ouSpB9++EG9e/dW1apV1a9fP23fvt3inGrVqqUJdbdu3VK/fv00cuRIBQYGmtubNWum0aNHy8PDQ0eOHNHatWv15ptv3vN7bNq0qYoXL67+/fsrKirKYmm/lL2fn/9q0qSJHBwc1KVLF7355puKj4/XtGnTdPXq1fu+5qBBg7Rw4UK1bdtWb7/9tp588knFxcUpPDxcrVu3VoMGDdS5c2fNmzdPLVu21MCBA/Xkk0/K3t5ep0+f1vr169W2bVu1a9cu3TG+/vprtWjRQs2aNVNoaKiKFStm3mth586d5j0vRowYoU2bNmn16tXy9fU1/6Ksd+/eqlatmsXfXaFChfTKK6/o1KlTKlu2rFauXKlvv/1Wr7zyih577LF0a2ndurU++OADjRgxQsHBwTp06JBGjx6twMBAJSUl3ff7+F+Z/XlrY2OjDz74QH369FG7du3Ut29fXbt2TSNHjmR5P4Dck6fbCAJADli4cKHRtWtXo0yZMoabm5thb29vPPbYY8YLL7xgHDhwwKJv6u79/7V+/XrDzc3NaN68uREbG2sYRuZ27589e7bRoEEDw8fHx3BwcDCKFi1qPP/882l2pZ48ebIRGBho2NraGpKMWbNmGYZhGAcOHDCaNGliuLu7GwUKFDA6duxonDp16q5jDxs2zChatKhhY2OT5mkAy5cvNxo0aGB4eHgYjo6ORokSJYwOHTqYn0pw/vx5IzQ01Chfvrzh6upquLm5GZUrVzY+/fRTIykpKd3vL7Pn3bp1y3jzzTcNf39/w9nZ2QgODjZ2796d7u79ERERFuOk7mh/8eJFi/YePXoYrq6u5tepu/dPnDjRot/69esNScYPP/xg0X638bZs2WLUqVPHcHFxMYoUKWL06dPH2Llzp8Xfy93GvpsTJ04YkoyXX345w36Z+V7Ts3v3bqNHjx7GY489Zjg4OBiurq5GtWrVjPfff9+4cOGCcfHiRcPBwcF49tln073G1atXDWdnZ6NNmzaGYdzebVxZeDrAqVOnDBsbG+PFF1+8Z9+PPvrIkGSsW7cu3T6pfy93vt/6/0/fuNvX3Z6K8O677xpVqlRJs3v8hQsXjA4dOhienp6Gv79/lp5a8c477xiSDH9/f4vd51Nlx+fnbrv3//zzz0aVKlUMJycno1ixYsYbb7xh/Prrr2n+Ww8ODjaeeOKJTF3z6tWrxsCBA43HHnvMsLe3N7y9vY1WrVoZ//zzj7lPYmKi8fHHH5vHdnNzM8qXL2/069fPOHLkyD3frz179hjPP/+84e3tbdjb2xu+vr5Gw4YNja+++sowDMNYvXq1YWNjk+bn2eXLl43HHnvMqFmzpvkpGKnf24YNG4ygoCDD0dHR8PPzM9555500f8d3/oy8deuWMXToUKNYsWKGk5OTUb16dWP58uV3fV/uPDe9n0upP1fufPLKvX7eppo+fbpRpkwZw8HBwShbtqwxc+bMez65AQCyi8kwMrHGDQAAZGjKlCl67bXXtG/fPotnyT/sVq5cqdatW2vPnj133UEdyAshISG6dOlSmj1YAABZx/J+AAAewK5duxQZGanRo0erbdu2+SrwS7fvg+/cuTOBHwAAK8VMPwAADyAgIEBRUVGqV6+e5s6dy326QDZgph8Asg+hHwAAAAAAK3V/D6IGAAAAAAAPPUI/AAAAAABWitAPAAAAAICVeqR2709JSdHZs2fl7u4uk8mU1+UAAAAAAKycYRi6fv26ihYtKhub3J93f6RC/9mzZ+Xv75/XZQAAAAAAHjH//vuvihcvnuvjPlKh393dXdLtN9vDwyOPqwEAAAAAWLuYmBj5+/ub82hue6RCf+qSfg8PD0I/AAAAACDX5NUt5mzkBwAAAACAlSL0AwAAAABgpQj9AAAAAABYqUfqnn4AAAAAyE6GYSgpKUnJycl5XQryiK2trezs7B7ax8IT+gEAAADgPiQkJOjcuXOKjY3N61KQx1xcXOTn5ycHB4e8LiUNQj8AAAAAZFFKSooiIyNla2urokWLysHB4aGd6UXOMQxDCQkJunjxoiIjI1WmTBnZ2Dxcd9ET+gEAAAAgixISEpSSkiJ/f3+5uLjkdTnIQ87OzrK3t9fJkyeVkJAgJyenvC7JwsP1KwgAAAAAyEcetlld5I2H+XPw8FYGAAAAAAAeCKEfAAAAAAArRegHAAAAAKSxYcMGmUwmXbt2TZIUFhYmLy+vPK0JWUfoBwAAAIA89u+//6p3797mJwGUKFFCAwcO1OXLl3Nl/JCQEA0aNMiirW7dujp37pw8PT1zpQbkDEI/AAAAAOSh48ePKygoSIcPH9b8+fN19OhRffXVV1q7dq3q1KmjK1eu5NjYiYmJ6R5zcHCQr68vjyLM5wj9AAAAAJCHBgwYIAcHB61evVrBwcF67LHH1KJFC/3+++86c+aMhg8fLkkymUxavny5xbleXl4KCwszv37rrbdUtmxZubi4qGTJknrvvfcsgv3IkSNVtWpVzZw5UyVLlpSjo6N69Oih8PBwffbZZzKZTDKZTDpx4kSa5f138/PPP6tGjRpycnJSyZIlNWrUKCUlJWXn24MHlG9C/8iRI80fwNQvX1/fvC4LAAAAAO7blStX9Ntvv6l///5ydna2OObr66tu3bpp4cKFMgwjU9dzd3dXWFiYDhw4oM8++0zffvutPv30U4s+R48e1aJFi7RkyRLt3r1bn3/+uerUqaO+ffvq3LlzOnfunPz9/e851m+//abu3bvrtdde04EDB/T1118rLCxMH330UebfAOQ4u7wuICueeOIJ/f777+bXtra2eVgNAAAAADyYI0eOyDAMVahQ4a7HK1SooKtXr+rixYuZut67775r/nNAQICGDBmihQsX6s033zS3JyQkaO7cuSpSpIi5zcHBQS4uLlmaWP3oo4/09ttvq0ePHpKkkiVL6oMPPtCbb76pESNGZPo6yFn5KvTb2dkxuw8AAADgkZE6w+/g4JCp/osXL9bkyZN19OhR3bhxQ0lJSfLw8LDoU6JECYvAf7927NihiIgIi5n95ORkxcfHKzY2Vi4uLg88Bh5cvlneL93+LVjRokUVGBiozp076/jx4xn2v3XrlmJiYiy+AAAAAOBhUbp0aZlMJh04cOCux//55x8VKVJEXl5eMplMaZb5//d+/a1bt6pz585q0aKFVqxYoV27dmn48OFKSEiwOMfV1TVbak9JSdGoUaO0e/du89fevXt15MgROTk5ZcsYeHD5Zqa/Vq1amjNnjsqWLavz58/rww8/VN26dbV//34VKlTorueMHTtWo0aNyuVKAQAAACBzChUqpCZNmujLL7/U66+/bnFff1RUlObNm6cBAwZIkooUKaJz586Zjx85ckSxsbHm13/88YdKlChh3vhPkk6ePJmpOhwcHJScnJyl2qtXr65Dhw6pdOnSWToPuSvfzPS3aNFCzz33nCpVqqTGjRvrl19+kSTNnj073XOGDRum6Oho89e///6bW+UCAAAAQKZMnTpVt27dUrNmzbRx40b9+++/WrVqlZo0aaKyZcvq/ffflyQ1bNhQU6dO1c6dO/XXX3/p5Zdflr29vfk6pUuX1qlTp7RgwQIdO3ZMn3/+uZYtW5apGgICArRt2zadOHFCly5dUkpKyj3Pef/99zVnzhyNHDlS+/fv18GDB7Vw4UKLfQWQ9/JN6L+Tq6urKlWqpCNHjqTbx9HRUR4eHhZfAAAAAPAwKVOmjCIiIlSyZEk9//zzKlGihFq0aKGyZcvqjz/+kJubmyTpk08+kb+/v+rXr6+uXbtq6NChFvfNt23bVq+//rpeffVVVa1aVVu2bNF7772XqRqGDh0qW1tbPf744ypSpIhOnTp1z3OaNWumFStWaM2aNapZs6Zq166tSZMmqUSJEvf3RiBHmIzMPvvhIXPr1i2VKlVKL730kvk3X/cSExMjT09PRUdH8wsAAAAAAPctPj5ekZGRCgwMzJH710eMGKFJkyZp9erVqlOnTrZfH9kro89DXufQfDPTP3ToUIWHhysyMlLbtm1Thw4dFBMTY348BAAAAPBfbdq0UePGje967M8//5TJZNLOnTtzuaoHExoaqmeffTavy0AuGDVqlD7//HNt27YtU0vtgfTkm438Tp8+rS5duujSpUsqUqSIateura1bt7J0BAAAAHfVu3dvtW/fXidPnkzzb8aZM2eqatWqql69epaumZCQkOlHpwEPqmfPnnldAqxAvpnpX7Bggc6ePauEhASdOXNGS5Ys0eOPP57XZQEAAOAh1bp1a3l7eyssLMyiPTY2VgsXLlTv3r21ZcsW1a9fX87OzvL399drr72mmzdvmvsGBAToww8/VGhoqDw9PdW3b1+FhYXJy8tLK1asULly5eTi4qIOHTro5s2bmj17tgICAlSgQAH973//s9gN/erVq3rxxRdVoEABubi4qEWLFhb7U6Ve97ffflOFChXk5uam5s2bm3drHzlypGbPnq0ff/xRJpNJJpNJGzZsyNH3EED+l29CPwAAAJAVdnZ2evHFFxUWFmbxbPMffvhBCQkJqlKlipo1a6b27dvr77//1sKFC7V582a9+uqrFteZOHGiKlasqB07dpg3RYuNjdXnn3+uBQsWaNWqVdqwYYPat2+vlStXauXKlZo7d66++eYbLV682Hyd0NBQ/fXXX/rpp5/0559/yjAMtWzZ0uI567Gxsfr44481d+5cbdy4UadOndLQoUMl3b7d9fnnnzf/IuDcuXOqW7duTr6FAKxAvt3I737k9QYKAAAAyDlGUpISzpxR/N59iv3rL5ns7HTGv7iCQkO1bt06NWjQQJIUHBysYsWKyc7OTs7Ozvr666/N19i8ebOCg4N18+ZNOTk5KSAgQNWqVbN47FlYWJh69uypo0ePqlSpUpKkl19+WXPnztX58+fNO603b95cAQEB+uqrr3TkyBHzTuypQf3y5cvy9/fX7Nmz1bFjx7te98svv9To0aMVFRUl6fYvDq5du6bly5fn+PuJjOX0Rn7IXx7mjfzyzT39AAAAQHqSb97UjbXrdO7992XEx5vbXSRVc3PTNxMmKKRePR0/eVKbNm3S6tWrNXDgQB09elTz5s0z9zcMQykpKYqMjFSFChUkSUFBQWnGc3FxMQdzSfLx8VFAQIA58Ke2XbhwQZJ08OBB2dnZqVatWubjhQoVUrly5XTw4MF0r+vn52e+BgDcD0I/AAAA8r2bW7bo7Jtv3vVYezc3ffTbb7qwfbtmrVypEiVKqFGjRkpJSVG/fv302muvpTnnscceM//Z1dU1zXF7e3uL1yaT6a5tqbuup7e41jAMmUymDK/7CC3MBZADCP0AAADI1xKjonT+gw/SPd7c3UNjz1/Q9CFDNPvff9X3pZdkMplUvXp17d+/X6VLl87xGh9//HElJSVp27ZtFsv7Dx8+bF5RkBkODg4WmwMCwL2wkR8AAADytYSTJ5V04WK6x11tbNTCw10Ttm/X2XPnFBoaKkl666239Oeff2rAgAHavXu3jhw5op9++kn/+9//sr3GMmXKqG3bturbt682b96sPXv2qHv37ipWrJjatm2b6esEBATo77//1qFDh3Tp0iWLTQAB4G4I/QAAAMjXkq9evWef5zy9FJOSogY1a5qX7leuXFnh4eE6cuSI6tWrp2rVqum9996Tn59fjtQ5a9Ys1ahRQ61bt1adOnVkGIZWrlyZZkl/Rvr27aty5copKChIRYoU0R9//JEjtcL6Xb58Wd7e3jpx4kRel/LQCwsLMz8mc9CgQXldTpaxez8AAADytetr1+r0gFfv3VFSiXnfyaVGjRyuCI+C/L57/9ChQ3X16lXNmDEjzbHLly+rSpUqOnPmjK5evSovLy9Jt7/nl19+WTt27NDBgwfVunXrTD9J4pdfftHo0aP1999/y9XVVfXr19fSpUszXe+GDRvMT+D4r4MHD6p8+fKSpKVLl2rMmDE6evSoEhMTVaZMGQ0ZMkQvvPBChtf+7bffNGLECO3fv19OTk6qX7++Pv74YwUGBkqS4uLiFB0drfbt2+vJJ5/U5MmT01yD3fsBAACAHGL/2GOSra10j3vdbQsWlH0OzeIDDyI5xdD2yCu6cD1e3u5OejKwoGxtTPc+8T7FxcVpxowZWrly5V2P9+7dW5UrV9aZM2cs60xOlrOzs1577TUtWbIk0+MtWbJEffv21ZgxY9SwYUMZhqG9e/feV+2HDh2yCM5FihQx/7lgwYIaPny4ypcvLwcHB61YsUI9e/aUt7e3mjVrdtfrHT9+XG3bttXgwYM1b948RUdH6/XXX1f79u21a9cuSZKzs7OcnZ3l4OBwXzXnNUI/AAAA8jUHf395tG6tmB9/zLBf4VdekX3RorlUFZA5q/ad06ifD+hc9P89atLP00kj2jyu5hVz5pdUv/76q+zs7FSnTp00x6ZNm6Zr167p/fff16+//mpxzNXVVdOmTZMk/fHHH7p27do9x0pKStLAgQM1ceJE9e7d29xerly5+6rd29vbvPLgTiEhIRavBw4cqNmzZ2vz5s3phv6dO3cqOTlZH374oWxsbt/9PnToULVt21aJiYlZuv3mYcU9/QAAAMjXbJycVOR/r8rxiSfS7ePeooXcm9/9H/1AXlm175xe+W6nReCXpKjoeL3y3U6t2ncuR8bduHGjgoKC0rQfOHBAo0eP1pw5c8wB+EHt3LlTZ86ckY2NjapVqyY/Pz+1aNFC+/fvv6/rpV6jUaNGWr9+fbr9DMPQ2rVrdejQIdWvXz/dfkFBQbK1tdWsWbOUnJys6OhozZ07V02bNrWKwC8R+gEAAGAFHIoXV/Epn8vvow9l95/ZfMfy5VXs88/k+84w2f9nGTCQ15JTDI36+YDutsFaatuonw8oOSX7t2A7ceKEit6x6uXWrVvq0qWLJk6caN7sMjscP35ckjRy5Ei9++67WrFihQoUKKDg4GBduXIl09fx8/PTN998oyVLlmjp0qUqV66cGjVqpI0bN1r0i46OlpubmxwcHNSqVStNmTJFTZo0Sfe6AQEBWr16td555x05OjrKy8tLp0+f1oIFC+7vG34IsbwfAAAAVsGhaFE5PPecXOvVU8r165LJJNsCBWRXoEBelwaksT3ySpoZ/v8yJJ2Ljtf2yCuqU6pQto4dFxeXZrO5YcOGqUKFCurevXu2jpWSkiJJGj58uJ577jlJt59kUbx4cf3www/q169fpq5Trlw5i1sC6tSpo3///Vcff/yxxUy+u7u7du/erRs3bmjt2rUaPHiwSpYsmWbpf6qoqCj16dNHPXr0UJcuXXT9+nW9//776tChg9asWSOTKef2VsgthH4AAABYFXtvb8nbO6/LADJ04Xr6gf9++mVF4cKFdfWOR12uW7dOe/fu1eLFiyXdXh6f2nf48OEaNWrUfY2V+gjMxx9/3Nzm6OiokiVL6tSpU/d1zVS1a9fWd999Z9FmY2Oj0qVLS5KqVq2qgwcPauzYsemG/i+++EIeHh6aMGGCue27776Tv7+/tm3bptq1az9QjQ8DQj8AAAAA5DJv98w95i+z/bKiWrVqacLykiVLFBcXZ34dERGhXr16adOmTSpVqtR9j1WjRg05Ojrq0KFDevrppyVJiYmJOnHihEqUKHHf15WkXbt2mX+pkB7DMHTr1q10j8fGxsrW1taiLfV16iqF/I7QDwAAAAC57MnAgvLzdFJUdPxd7+s3SfL1vP34vuzWrFkzDRs2TFevXlWB/3/7y53B/tKlS5KkChUqWOyWf+DAASUkJOjKlSu6fv26du/eLen2rLokbd++XS+++KLWrl2rYsWKycPDQy+//LJGjBghf39/lShRQhMnTpQkdezYMdM1T548WQEBAXriiSeUkJCg7777TkuWLLF4dODYsWMVFBSkUqVKKSEhQStXrtScOXPMTxyQpKlTp2rZsmVau3atJKlVq1b69NNPNXr0aPPy/nfeeUclSpRQtWrVMl3fw4zQDwAAAAC5zNbGpBFtHtcr3+2USbII/ql3kY9o87hsbbL/nvJKlSopKChIixYtyvQ99alatmypkydPml+nBuPU2wFiY2N16NAhJSYmmvtMnDhRdnZ2euGFFxQXF6datWpp3bp15l84SLc31AsNDdXIkSPvOm5CQoKGDh2qM2fOyNnZWU888YR++eUXtWzZ0tzn5s2b6t+/v06fPi1nZ2eVL19e3333nTp16mTuc+nSJR07dsz8umHDhvr+++81YcIETZgwQS4uLqpTp45WrVolZ2fnLL03DyuTkfq38wiIiYmRp6enoqOj5eHhkdflAAAAAMin4uPjFRkZqcDAwDSb4mXFqn3nNOrnAxab+vl5OmlEm8fVvGLGS9cfxMqVKzV06FDt27cv2x7Pd7/i4uJUsGBBrVy5Ug0aNMjTWjISEhKiqlWravLkyWmOZfR5yOscykw/AAAAAOSR5hX91ORxX22PvKIL1+Pl7X57SX9OzPD/V8uWLXXkyBGdOXNG/v7+OTrWvYSHh6thw4YPbeCfN2+e+vXrp7i4OPNtDPkJM/0AAAAAkEXZNdOPh9/169d1/vx5SZKXl5cKFy6cpg8z/QAAAAAA5EPu7u5yd3fP6zLuW97evAEAAAAAAHIMoR8AAAAAACtF6AcAAAAAwEoR+gEAAAAAsFKEfgAAAAAArBShHwAAAAAAK0XoBwAAAIBHzOXLl+Xt7a0TJ07kdSkPvbCwMJlMJplMJg0aNCivy8kyQj8AAAAA5KWUZClyk7R38e3/TUnO8SHHjh2rNm3aKCAgwNyWGmz/+/XVV1+Zjx86dEgNGjSQj4+PnJycVLJkSb377rtKTEzMcKzDhw+rbdu2Kly4sDw8PPTUU09p/fr1Wa751q1bGj58uEqUKCFHR0eVKlVKM2fONB/fv3+/nnvuOQUEBMhkMmny5MmZuu7evXsVHBwsZ2dnFStWTKNHj5ZhGObjnTp10rlz51SnTp0s1/wwsMvrAgAAAADgkXXgJ2nVW1LM2f9r8ygqNR8vPf5MjgwZFxenGTNmaOXKlWmOzZo1S82bNze/9vT0NP/Z3t5eL774oqpXry4vLy/t2bNHffv2VUpKisaMGZPueK1atVLZsmW1bt06OTs7a/LkyWrdurWOHTsmX1/fTNf9/PPP6/z585oxY4ZKly6tCxcuKCkpyXw8NjZWJUuWVMeOHfX6669n6poxMTFq0qSJGjRooIiICB0+fFihoaFydXXVkCFDJEnOzs5ydnaWg4NDpmt9mBD6AQAAACAvHPhJWvSiJMOyPebc7fbn5+RI8P/1119lZ2d315lrLy+vdIN4yZIlVbJkSfPrEiVKaMOGDdq0aVO6Y126dElHjx7VzJkzVblyZUnSuHHj9OWXX2r//v2ZDv2rVq1SeHi4jh8/roIFC0qSxSoFSapZs6Zq1qwpSXr77bczdd158+YpPj5eYWFhcnR0VMWKFXX48GFNmjRJgwcPlslkytR1HmYs7wcAAACA3JaSfHuG/87AL/1f26q3c2Sp/8aNGxUUFHTXY6+++qoKFy6smjVr6quvvlJKSkq61zl69KhWrVql4ODgdPsUKlRIFSpU0Jw5c3Tz5k0lJSXp66+/lo+Pj2rUqJHpmn/66ScFBQVpwoQJKlasmMqWLauhQ4cqLi4u09e4mz///FPBwcFydHQ0tzVr1kxnz561mv0OmOkHAAAAgNx2covlkv40DCnmzO1+gfWydegTJ06oaNGiado/+OADNWrUSM7Ozlq7dq2GDBmiS5cu6d1337XoV7duXe3cuVO3bt3SSy+9pNGjR6c7lslk0po1a9S2bVu5u7vLxsZGPj4+WrVqlby8vDJd8/Hjx7V582Y5OTlp2bJlunTpkvr3768rV65Y3NefVVFRUWlWDPj4+JiPBQYG3ve1HxaEfgAAAADIbTfOZ2+/LIiLi5OTk1Oa9v+G+6pVq0qSRo8enSb0L1y4UNevX9eePXv0xhtv6OOPP9abb75517EMw1D//v3l7e2tTZs2ydnZWdOnT1fr1q0VEREhPz+/TNWckpIik8mkefPmmfcZmDRpkjp06KAvvvhCzs7OmbrO3dy5hD91Ez9rWNovEfoBAAAAIPe5+WRvvywoXLiwrl69es9+tWvXVkxMjM6fP2+e/ZYkf39/SdLjjz+u5ORkvfTSSxoyZIhsbW3TXGPdunVasWKFrl69Kg8PD0nSl19+qTVr1mj27NmZvvfez89PxYoVs9hYsEKFCjIMQ6dPn1aZMmUydZ07+fr6KioqyqLtwoULkmTxPedn3NMPAAAAALmtRN3bu/Qrvdlkk+RR7Ha/bFatWjUdOHDgnv127dolJyenDJfhG4ahxMREi0fc/VdsbKwkycbGMnra2NhkuF/AnZ566imdPXtWN27cMLcdPnxYNjY2Kl68eKavc6c6depo48aNSkhIMLetXr1aRYsWTbPsP78i9AMAAABAbrOxvf1YPklpg///f9183O1+2axZs2bav3+/xWz/zz//rG+//Vb79u3TsWPHNH36dA0fPlwvvfSSeZO7efPmadGiRTp48KCOHz+uH374QcOGDVOnTp1kZ3d7Efn27dtVvnx5nTlzRtLtUF2gQAH16NFDe/bs0eHDh/XGG28oMjJSrVq1ynTNXbt2VaFChdSzZ08dOHBAGzdu1BtvvKFevXqZl/YnJCRo9+7d2r17txISEnTmzBnt3r1bR48eNV9n6tSpatSokcV1HR0dFRoaqn379mnZsmUaM2aM1ezcLxH6AQAAACBvPP7M7cfyedxxX7tH0Rx7XJ8kVapUSUFBQVq0aJG5zd7eXl9++aXq1KmjypUr67PPPtPo0aP1ySefmPvY2dlp/PjxevLJJ1W5cmWNHDlSAwYM0PTp0819YmNjdejQISUmJkq6fSvBqlWrdOPGDTVs2FBBQUHavHmzfvzxR1WpUsV8XkBAgEaOHJluzW5ublqzZo2uXbumoKAgdevWTW3atNHnn39u7nP27FlVq1ZN1apV07lz5/Txxx+rWrVq6tOnj7nPpUuXdOzYMfNrT09PrVmzRqdPn1ZQUJD69++vwYMHa/Dgwff35j6ETEZ66zCsUExMjDw9PRUdHW2+nwQAAAAAsio+Pl6RkZEKDAy866Z4WZKSfHuX/hvnb9/DX6Jujszw/9fKlSs1dOhQ7du3L83S+9wWFxenggULauXKlWrQoEGe1pKRkJAQVa1aVZMnT05zLKPPQ17nUDbyAwAAAIC8ZGOb7Y/lu5eWLVvqyJEjOnPmjHljvrwSHh6uhg0bPrSBf968eerXr5/i4uLMTzXIT5jpBwAAAIAsytaZfjzUrl+/rvPnbz860cvLS4ULF07Th5l+AAAAAADyIXd3d7m7u+d1GfeNjfwAAAAAALBShH4AAAAAAKwUoR8AAAAAACtF6AcAAAAAwEoR+gEAAAAAsFKEfgAAAAAArBShHwAAAAAeMZcvX5a3t7dOnDiR16U89MLCwmQymWQymTRo0KC8LifLCP0AAAAAkIeSU5IVERWhlcdXKiIqQskpyTk+5tixY9WmTRsFBARYtIeFhaly5cpycnKSr6+vXn31VfOx+Ph4hYaGqlKlSrKzs9Ozzz6bqbGuXr2qF154QZ6envL09NQLL7yga9euZane0NBQc/D+79cTTzxh7hMSEnLXPq1atcrw2r/99ptq164td3d3FSlSRM8995wiIyPNxzt16qRz586pTp06War5YWGX1wUAAAAAwKPq95O/a9z2cTofe97c5uPio7effFuNSzTOkTHj4uI0Y8YMrVy50qJ90qRJ+uSTTzRx4kTVqlVL8fHxOn78uPl4cnKynJ2d9dprr2nJkiWZHq9r1646ffq0Vq1aJUl66aWX9MILL+jnn3/O9DU+++wzjRs3zvw6KSlJVapUUceOHc1tS5cuVUJCgvn15cuX0/S50/Hjx9W2bVsNHjxY8+bNU3R0tF5//XW1b99eu3btkiQ5OzvL2dlZDg4Oma73YULoBwAAAIA88PvJ3zV4w2AZMizaL8Re0OANgzUpZFKOBP9ff/1VdnZ2FjPXV69e1bvvvquff/5ZjRo1Mrf/dybd1dVV06ZNkyT98ccfmZqtP3jwoFatWqWtW7eqVq1akqRvv/1WderU0aFDh1SuXLlM1Zy6SiDV8uXLdfXqVfXs2dPcVrBgQYtzFixYIBcXlwxD/86dO5WcnKwPP/xQNja3F8IPHTpUbdu2VWJiouzt7TNV38OM5f0AAAAAkMuSU5I1bvu4NIFfkrlt/PbxObLUf+PGjQoKCrJoW7NmjVJSUnTmzBlVqFBBxYsX1/PPP69///33gcb6888/5enpaQ78klS7dm15enpqy5Yt933dGTNmqHHjxipRokSGfTp37ixXV9d0+wQFBcnW1lazZs1ScnKyoqOjNXfuXDVt2tQqAr9E6AcAAACAXLfzwk6LJf13MmQoKjZKOy/szPaxT5w4oaJFi1q0HT9+XCkpKRozZowmT56sxYsX68qVK2rSpInFkvmsioqKkre3d5p2b29vRUVF3dc1z507p19//VV9+vRJt8/27du1b9++DPtIUkBAgFavXq133nlHjo6O8vLy0unTp7VgwYL7qu1hROgHAAAAgFx2MfZitvbLiri4ODk5OVm0paSkKDExUZ9//rmaNWum2rVra/78+Tpy5IjWr1//QOOZTKY0bYZh3LU9M8LCwuTl5ZXhRoIzZsxQxYoV9eSTT2Z4raioKPXp00c9evRQRESEwsPD5eDgoA4dOsgw0q7CyI+4px8AAAAAclkRlyLZ2i8rChcurKtXr1q0+fn5SZIef/zx/xu7SBEVLlxYp06duu+xfH19df582hUNFy9elI+PT5avZxiGZs6cqRdeeCHdjfViY2O1YMECjR49+p7X++KLL+Th4aEJEyaY27777jv5+/tr27Ztql27dpZrfNgw0w8AAAAAuay6d3X5uPjIpLvPdptkkq+Lr6p7V8/2satVq6YDBw5YtD311FOSpEOHDpnbrly5okuXLmV43/y91KlTR9HR0dq+fbu5bdu2bYqOjlbdunWzfL3w8HAdPXpUvXv3TrfPokWLdOvWLXXv3v2e14uNjZWtra1FW+rrlJSULNf3MCL0AwAAAEAus7Wx1dtPvi1JaYJ/6uu3nnxLtja2ac59UM2aNdP+/fstZvvLli2rtm3bauDAgdqyZYv27dunHj16qHz58mrQoIG534EDB7R7925duXJF0dHR2r17t3bv3m0+vn37dpUvX15nzpyRJFWoUEHNmzdX3759tXXrVm3dulV9+/ZV69atM71z/3/NmDFDtWrVUsWKFTPs8+yzz6pQoUJpjk2dOtXi6QStWrVSRESERo8erSNHjmjnzp3q2bOnSpQooWrVqmW5vocRoR8AAAAA8kDjEo01KWSSvF0sN7rzcfHJscf1SVKlSpUUFBSkRYsWWbTPmTNHtWrVUqtWrRQcHCx7e3utWrXKYhf7li1bqlq1avr555+1YcMGVatWzSIcx8bG6tChQ0pMTDS3zZs3T5UqVVLTpk3VtGlTVa5cWXPnzrUYOyAgQCNHjsyw7ujoaC1ZsiTDWf7Dhw9r8+bN6fa5dOmSjh07Zn7dsGFDff/991q+fLmqVaum5s2by9HRUatWrZKzs3OG9eQXJsNadifIhJiYGHl6eio6OloeHh55XQ4AAACAfCo+Pl6RkZEKDAxMsyleViWnJGvnhZ26GHtRRVyKqLp39RyZ4f+vlStXaujQodq3b5/5+fR5JS4uTgULFtTKlSstVhU8bEJCQlS1alVNnjw5zbGMPg95nUPZyA8AAAAA8pCtja1q+tbM1TFbtmypI0eO6MyZM/L398/Vse8UHh6uhg0bPrSBf968eerXr5/i4uJUtWrVvC4ny5jpBwAAAIAsys6Zfjzcrl+/bn4CgZeXlwoXLpymDzP9AAAAAADkQ+7u7nJ3d8/rMu4bG/kBAAAAAGClCP0AAAAAAFgpQj8AAAAAAFaK0A8AAAAAgJUi9AMAAAAAYKUI/QAAAAAAWClCPwAAAAA8Yi5fvixvb2+dOHEir0t56IWFhclkMslkMmnQoEF5XU6WEfoBAAAAIA8Zycm6uW27olf8opvbtstITs7xMceOHas2bdooICBAkmWwvfPrwoULkqSRI0fe9birq2uGYx0+fFht27ZV4cKF5eHhoaeeekrr16/Pcs1ffPGFKlSoIGdnZ5UrV05z5syxOL506VIFBQXJy8tLrq6uqlq1qubOnXvP6+7du1fBwcFydnZWsWLFNHr0aBmGYT7eqVMnnTt3TnXq1MlyzQ8Du7wuAAAAAAAeVTGrV+v8mLFKiooyt9n5+srnnWHyaNo0R8aMi4vTjBkztHLlSnNbp06d1Lx5c4t+oaGhio+Pl7e3tyRp6NChevnlly36NGrUSDVr1sxwvFatWqls2bJat26dnJ2dNXnyZLVu3VrHjh2Tr69vpmqeNm2ahg0bpm+//VY1a9bU9u3b1bdvXxUoUEBt2rSRJBUsWFDDhw9X+fLl5eDgoBUrVqhnz57y9vZWs2bN7nrdmJgYNWnSRA0aNFBERIQOHz6s0NBQubq6asiQIZIkZ2dnOTs7y8HBIVO1PmwI/QAAAACQB2JWr9aZgYOk/8wqS1LS+fO32z+bnCPB/9dff5WdnZ3FzHVqsE118eJFrVu3TjNmzDC3ubm5yc3Nzfx6z549OnDggL766qt0x7p06ZKOHj2qmTNnqnLlypKkcePG6csvv9T+/fszHfrnzp2rfv36qVOnTpKkkiVLauvWrRo/frw59IeEhFicM3DgQM2ePVubN29ON/TPmzdP8fHxCgsLk6OjoypWrKjDhw9r0qRJGjx4sEwmU6bqe5ixvB8AAAAAcpmRnKzzY8amCfy3D95uOz9mbI4s9d+4caOCgoIy7DNnzhy5uLioQ4cO6faZPn26ypYtq3r16qXbp1ChQqpQoYLmzJmjmzdvKikpSV9//bV8fHxUo0aNTNd869YtOTk5WbQ5Oztr+/btSkxMTNPfMAytXbtWhw4dUv369dO97p9//qng4GA5Ojqa25o1a6azZ89azX4HhH4AAAAAyGWxf+2wWNKfhmEoKSpKsX/tyPaxT5w4oaJFi2bYZ+bMmeratavF7P9/3bp1S/PmzVPv3r0zvI7JZNKaNWu0a9cuubu7y8nJSZ9++qlWrVolLy+vTNfcrFkzTZ8+XTt27JBhGPrrr780c+ZMJSYm6tKlS+Z+0dHRcnNzk4ODg1q1aqUpU6aoSZMm6V43KipKPj4+Fm2pr6My+vvJR1jeDwAAAAC5LOnixWztlxVxcXFpZs3/688//9SBAwfSbJT3X0uXLtX169f14osvZjiWYRjq37+/vL29tWnTJjk7O2v69Olq3bq1IiIi5Ofnl6ma33vvPUVFRal27doyDEM+Pj4KDQ3VhAkTZGtra+7n7u6u3bt368aNG1q7dq0GDx6skiVLpln6/193LuFP3cTPGpb2S8z0AwAAAECusytSJFv7ZUXhwoV19erVdI9Pnz5dVatWzXD5fWpwv9c9+evWrdOKFSu0YMECPfXUU6pevbq+/PJLOTs7a/bs2Zmu2dnZWTNnzlRsbKxOnDihU6dOKSAgQO7u7ipcuLC5n42NjUqXLq2qVatqyJAh6tChg8aOHZvudX19fdPM6Kc+reDOFQD5FaEfAAAAAHKZS1AN2fn6SunNJptMsvP1lUtQ5u97z6xq1arpwIEDdz1248YNLVq0KMNl+5GRkVq/fv09l/ZLUmxsrKTbYfy/bGxslJKSkoWqb7O3t1fx4sVla2urBQsWqHXr1mmu/V+GYejWrVvpHq9Tp442btyohIQEc9vq1atVtGhR8+MM8ztCPwAAAADkMpOtrXzeGfb/X9wR/P//a593hsn0n6Xr2aVZs2bav3//XWf7Fy5cqKSkJHXr1i3d82fOnCk/Pz+1aNEizbHt27erfPnyOnPmjKTbobpAgQLq0aOH9uzZo8OHD+uNN95QZGSkWrVqlemaDx8+rO+++05HjhzR9u3b1blzZ+3bt09jxowx9xk7dqzWrFmj48eP659//tGkSZM0Z84cde/e3dxn6tSpatSokfl1165d5ejoqNDQUO3bt0/Lli3TmDFjrGbnfonQDwAAAAB5wqNpUxX7bLLs7lhGbufjo2I59Lg+SapUqZKCgoK0aNGiNMdmzJih9u3bq0CBAnc9NyUlRWFhYQoNDbW4lz5VbGysDh06ZN5Rv3Dhwlq1apVu3Lihhg0bKigoSJs3b9aPP/6oKlWqmM8LCAjQyJEj0605OTlZn3zyiapUqaImTZooPj5eW7ZssZiNv3nzpvr3768nnnhCdevW1eLFi/Xdd9+pT58+5j6XLl3SsWPHzK89PT21Zs0anT59WkFBQerfv78GDx6swYMHp1tLfmMyjLs9I8I6xcTEyNPTU9HR0fLw8MjrcgAAAADkU/Hx8YqMjFRgYGCGm+JlhpGcfHs3/4sXZVekiFyCauTIDP9/rVy5UkOHDtW+ffsyXB6fG+Li4lSwYEGtXLlSDRo0yNNaMhISEqKqVatq8uTJaY5l9HnI6xzK7v0AAAAAkIdMtrZyrfVkro7ZsmVLHTlyRGfOnJG/v3+ujn2n8PBwNWzY8KEN/PPmzVO/fv0UFxenqlWr5nU5WcZMPwAAAABkUXbO9OPhdv36dZ0/f16S5OXlZfG0gFTM9AMAAAAAkA+5u7vL3d09r8u4b2zkBwAAAACAlSL0AwAAAABgpQj9AAAAAABYKUI/AAAAAABWitAPAAAAAICVIvQDAAAAAGClCP0AAAAAAFgpQj8AAAAA5KGUFENnDl3V4YgonTl0VSkpRo6OFxoaKpPJpHHjxlm0L1++XCaTKU3/cuXKycHBQWfOnMnRupAz7PK6AAAAAAB4VB3bdUGbFh7RzWu3zG2uXo6q16mMSlXzzrFxnZycNH78ePXr108FChRIt9/mzZsVHx+vjh07KiwsTMOHD8+xmpAzmOkHAAAAgDxwbNcFrfp6n0Xgl6Sb125p1df7dGzXhRwbu3HjxvL19dXYsWMz7Ddjxgx17dpVL7zwgmbOnCnDyNlVCMh+hH4AAAAAyGUpKYY2LTySYZ/Ni47k2FJ/W1tbjRkzRlOmTNHp06fv2uf69ev64Ycf1L17dzVp0kQ3b97Uhg0bcqQe5BxCPwAAAADksnNHrqWZ4b/Tjau3dO7ItRyroV27dqpatapGjBhx1+MLFixQmTJl9MQTT8jW1ladO3fWjBkzcqwe5AxCPwAAAADkspsxGQf+rPa7X+PHj9fs2bN14MCBNMdmzJih7t27m193795dS5cu1bVr13K0JmQvQj8AAAAA5DJXD8ds7Xe/6tevr2bNmumdd96xaD9w4IC2bdumN998U3Z2drKzs1Pt2rUVFxen+fPn52hNyF7s3g8AAAAAucyvjJdcvRwzXOLvVsBRfmW8cryWcePGqWrVqipbtqy5bcaMGapfv76++OILi75z587VjBkz9Morr+R4XcgezPQDAAAAQC6zsTGpXqcyGfZ5+vkysrEx5XgtlSpVUrdu3TRlyhRJUmJioubOnasuXbqoYsWKFl99+vTRjh07tGfPnhyvC9kj34b+sWPHymQyadCgQXldCgAAAABkWalq3mrer6JcvSyX8LsVcFTzfhVVqpp3rtXywQcfmB/H99NPP+ny5ctq165dmn5lypRRpUqV2NAvH8mXy/sjIiL0zTffqHLlynldCgAAAADct1LVvBVYpcjt3fxjbsnV4/aS/pyc4Q8LC0vTVqJECcXHx5tfJycnp3v+33//nRNlIYfku5n+GzduqFu3bvr2229VoECBDPveunVLMTExFl8AAAAA8DCxsTGpWLkCKlvTV8XKFciVJf14dOS70D9gwAC1atVKjRs3vmffsWPHytPT0/zl7++fCxUCAAAAAPBwyFehf8GCBdq5c6fGjh2bqf7Dhg1TdHS0+evff//N4QoBAAAAAHh45Jt7+v/9918NHDhQq1evlpOTU6bOcXR0lKNjzj7XEgAAAACAh1W+Cf07duzQhQsXVKNGDXNbcnKyNm7cqKlTp+rWrVuytbXNwwoBAAAAAHi45JvQ36hRI+3du9eirWfPnipfvrzeeustAj8AAAAAAHfIN6Hf3d1dFStWtGhzdXVVoUKF0rQDAAAAAIB8tpEfAAAAAADIvHwd+jds2KDJkyfndRkAAAAAkKtGjhypqlWrZumcgICAhzY/hYaG6tlnn831cUNCQjRo0KBcHzc35evQDwAAAADWYsuWLbK1tVXz5s1zZTyTyaTly5ebX//zzz8ymUzatm2bRb9atWrJ0dFRsbGx5raEhAS5uLjom2++yZVacf8I/QAAAADwEJg5c6b+97//afPmzTp16lSuj1++fHn5+flp/fr15rYbN25o165d8vb21pYtW8zt27ZtU1xcnBo0aJDrdSJrCP0AAAAAkMdu3rypRYsW6ZVXXlHr1q0VFhZmcXzcuHHy8fGRu7u7evfurfj4eIvjd1um/uyzzyo0NPSu4wUEBEiS2rVrJ5PJZH4dEhKiDRs2mPtt2rRJZcuW1TPPPGPRvmHDBhUrVkxlypSRJM2aNUsVKlSQk5OTypcvry+//NJivDNnzqhTp04qUKCAChUqpLZt2+rEiRPpvh87duyQt7e3PvroI0lSdHS0XnrpJXl7e8vDw0MNGzbUnj17zP1Tb3eYO3euAgIC5Onpqc6dO+v69evmPjdv3tSLL74oNzc3+fn56ZNPPkl3fGtC6AcAAACAPLZw4UKVK1dO5cqVU/fu3TVr1iwZhiFJWrRokUaMGKGPPvpIf/31l/z8/NKE6qyKiIiQdDusnzt3zvy6QYMG2rx5s5KSkiRJ69evV0hIiIKDgy1WAKxfv948y//tt99q+PDh+uijj3Tw4EGNGTNG7733nmbPni1Jio2NVYMGDeTm5qaNGzdq8+bNcnNzU/PmzZWQkJCmtg0bNqhRo0YaNWqUhg8fLsMw1KpVK0VFRWnlypXasWOHqlevrkaNGunKlSvm844dO6bly5drxYoVWrFihcLDwzVu3Djz8TfeeEPr16/XsmXLtHr1am3YsEE7dux4oPcxPyD0AwAAAEAemzFjhrp37y5Jat68uW7cuKG1a9dKkiZPnqxevXqpT58+KleunD788EM9/vjjDzRekSJFJEleXl7y9fU1vw4JCdHNmzfNvwTYsGGDgoODFRwcrL/++kuxsbFKSEjQ1q1bzaH/gw8+0CeffKL27dsrMDBQ7du31+uvv66vv/5akrRgwQLZ2Nho+vTpqlSpkipUqKBZs2bp1KlTFqsHJOnHH3/UM888o2nTpumVV16RdPsXDHv37tUPP/ygoKAglSlTRh9//LG8vLy0ePFi87kpKSkKCwtTxYoVVa9ePb3wwgvm9/DGjRuaMWOGPv74YzVp0kSVKlXS7NmzlZyc/EDvY35gl9cFAAAAAMCj7NChQ9q+fbuWLl0qSbKzs1OnTp00c+ZMNW7cWAcPHtTLL79scU6dOnUsZt6zS5kyZVS8eHFt2LBBTzzxhHbt2qXg4GB5e3srMDBQf/zxhxwdHRUXF6eGDRvq4sWL+vfff9W7d2/17dvXfJ2kpCR5enpKur1U/+jRo3J3d7cYKz4+XseOHTO/3rZtm1asWKEffvhB7dq1M7fv2LFDN27cUKFChSzOj4uLszg/ICDAYgw/Pz9duHBB0u1VAAkJCapTp475eMGCBVWuXLkHebvyBUI/AAAAAOShGTNmKCkpScWKFTO3GYYhe3t7Xb16NVPXsLGxMd8OkCoxMfG+6gkJCdH69etVuXJllSlTRt7e3pJkXuLv6OioEiVKKCAgQOfPn5d0e4l/rVq1LK5ja2sr6fYMfI0aNTRv3rw0Y6WuMJCkUqVKqVChQpo5c6ZatWolBwcH8/l+fn5pVgVIt1cqpLK3t7c4ZjKZlJKSIklp3ptHCcv7AQAAACCPJCUlac6cOfrkk0+0e/du89eePXtUokQJzZs3TxUqVNDWrVstzrvzdZEiRXTu3Dnz6+TkZO3bty/Dse3t7e+6vL1BgwbasmWL1qxZo5CQEHN7cHCwNmzYoA0bNqhhw4aSJB8fHxUrVkzHjx9X6dKlLb4CAwMlSdWrV9eRI0fk7e2dpk/qagBJKly4sNatW6djx46pU6dO5l9aVK9eXVFRUbKzs0tzfuHChTPxLkulS5eWvb29xft29epVHT58OFPn52eEfgAAAADIIytWrNDVq1fVu3dvVaxY0eKrQ4cOmjFjhgYOHKiZM2dq5syZOnz4sEaMGKH9+/dbXKdhw4b65Zdf9Msvv+iff/5R//79de3atQzHDggI0Nq1axUVFWWxoqBBgwa6efOmZs6cqeDgYHN76n39/72fX7q9c/7YsWP12Wef6fDhw9q7d69mzZqlSZMmSZK6deumwoULq23bttq0aZMiIyMVHh6ugQMH6vTp0xY1eXt7a926dfrnn3/UpUsXJSUlqXHjxqpTp46effZZ/fbbbzpx4oS2bNmid999V3/99Vem3mc3Nzf17t1bb7zxhtauXat9+/YpNDRUNjbWH4mt/zsEAAAAgIfUjBkz1LhxY4sZ71TPPfecdu/erTJlyuj999/XW2+9pRo1aujkyZPmTe5S9erVSz169NCLL76o4OBgBQYGWgTzu/nkk0+0Zs0a+fv7q1q1aub2wMBAlShRQtevX7cI/cWKFdNjjz2m+Ph4i2v36dNH06dPV1hYmCpVqqTg4GCFhYWZZ/pdXFy0ceNGPfbYY2rfvr0qVKigXr16KS4uTh4eHmnq8vX11bp167R3715169ZNKSkpWrlyperXr69evXqpbNmy6ty5s06cOCEfH5/MvdGSJk6cqPr16+uZZ55R48aN9fTTT6tGjRqZPj+/MhmP0M0NMTEx8vT0VHR09F0/XAAAAACQGfHx8YqMjFRgYKCcnJzyuhzksYw+D3mdQ5npBwAAAADAShH6AQAAAACwUoR+AAAAAACsFKEfAAAAAAArRegHAAAAAMBKEfoBAAAAALBShH4AAAAAAKwUoR8AAAAAACtF6AcAAAAAwEoR+gEAAAAAsFKEfgAAAAB4hIwcOVImk8niy9fX13zcMAyNHDlSRYsWlbOzs0JCQrR///48rBgPgtAPAAAAAI+YJ554QufOnTN/7d2713xswoQJmjRpkqZOnaqIiAj5+vqqSZMmun79eh5WjPtll9cFAAAAAMCjbPv27Vq6dKnOnj2rokWLqn379nryySdzdEw7OzuL2f1UhmFo8uTJGj58uNq3by9Jmj17tnx8fPT999+rX79+OVoXsh8z/QAAAACQR6ZMmaL+/ftr/fr1OnDggNavX6/+/ftrypQpOTrukSNHVLRoUQUGBqpz5846fvy4JCkyMlJRUVFq2rSpua+jo6OCg4O1ZcuWHK0JOYPQDwAAAAB5YPv27Zo9e7YkKTk52eJ/Z8+erYiIiBwZt1atWpozZ45+++03ffvtt4qKilLdunV1+fJlRUVFSZJ8fHwszvHx8TEfQ/7C8n4AAAAAyANLly6Vra2tOej/l62trZYuXaqaNWtm+7gtWrQw/7lSpUqqU6eOSpUqpdmzZ6t27dqSJJPJZHGOYRhp2pA/MNMPAAAAAHng7Nmzdw380u0Z/zNnzuRKHa6urqpUqZKOHDlivs//zln9CxcupJn9R/5A6AcAAACAPFC0aFHZ2tre9Zitra2KFSuWK3XcunVLBw8elJ+fnwIDA+Xr66s1a9aYjyckJCg8PFx169bNlXqQvQj9AAAAAJAH2rdvn+FMf+ru+dlt6NChCg8PV2RkpLZt26YOHTooJiZGPXr0kMlk0qBBgzRmzBgtW7ZM+/btU2hoqFxcXNS1a9ccqQc5i3v6AQAAACAPPPnkk+rRo4dmz55tvrc/9X979OiRI/fzS9Lp06fVpUsXXbp0SUWKFFHt2rW1detWlShRQpL05ptvKi4uTv3799fVq1dVq1YtrV69Wu7u7jlSD3KWyTAMI6+LyC0xMTHy9PRUdHS0PDw88rocAAAAAPlUfHy8IiMjFRgYKCcnpwe6VkREhJYuXaozZ86oWLFiat++fY4FfuSMjD4PeZ1DmekHAAAAgDxUs2ZNQj5yDPf0AwAAAABgpQj9AAAAAABYKUI/AAAAAABWitAPAAAAAICVIvQDAAAAAGClCP0AAAAAAFgpQj8AAAAAAFaK0A8AAAAAgJUi9AMAAAAAYKUI/QAAAADwiDlz5oy6d++uQoUKycXFRVWrVtWOHTvMxw3D0MiRI1W0aFE5OzsrJCRE+/fvz8OKcb8I/QAAAACQh27duqW1a9dq4cKFWrt2rW7dupWj4129elVPPfWU7O3t9euvv+rAgQP65JNP5OXlZe4zYcIETZo0SVOnTlVERIR8fX3VpEkTXb9+PUdrQ/azy+sCAAAAAOBRtXjxYk2dOlU3btyQyWSSYRhyc3PTq6++qg4dOuTImOPHj5e/v79mzZplbgsICDD/2TAMTZ48WcOHD1f79u0lSbNnz5aPj4++//579evXL0fqQs5gph8AAAAA8sDixYs1btw43bhxQ9LtsC1JN27c0Lhx47R48eIcGfenn35SUFCQOnbsKG9vb1WrVk3ffvut+XhkZKSioqLUtGlTc5ujo6OCg4O1ZcuWHKkJOYfQDwAAAAC5LD4+XlOnTs2wz9SpU3Nkqf/x48c1bdo0lSlTRr/99ptefvllvfbaa5ozZ44kKSoqSpLk4+NjcZ6Pj4/5GPIPQj8AAAAA5LI//vjDPMOfnhs3bmjz5s3ZPnZKSoqqV6+uMWPGqFq1aurXr5/69u2radOmWfQzmUwWrw3DSNOGhx+hHwAAAABy2aVLl+4ZoE0mky5dupTtY/v5+enxxx+3aKtQoYJOnTolSfL19ZWkNLP6Fy5cSDP7j4cfoR8AAAAAclnhwoXN9/CnxzAMFS5cONvHfuqpp3To0CGLtsOHD6tEiRKSpMDAQPn6+mrNmjXm4wkJCQoPD1fdunWzvR7kLHbvBwAAAIBc9vTTT8vNzS3DJf5ubm56+umns33s119/XXXr1tWYMWP0/PPPa/v27frmm2/0zTffSLq9wmDQoEEaM2aMypQpozJlymjMmDFycXFR165ds70e5Cxm+gEAAAAglzk6OurVV1/NsM+rr74qR0fHbB+7Zs2aWrZsmebPn6+KFSvqgw8+0OTJk9WtWzdznzfffFODBg1S//79FRQUpDNnzmj16tVyd3fP9nqQs0zGvdaUWJGYmBh5enoqOjpaHh4eeV0OAAAAgHwqPj5ekZGRCgwMlJOT031fZ/HixZo6dapu3Lghk8kkwzDk5uamV199VR06dMjGipGTMvo85HUOZXk/AAAAAOSRDh06qE2bNtq8ebMuXbqkwoUL6+mnn86RGX48mgj9AAAAAJCHHB0d1ahRo7wuA1aKe/oBAAAAALBShH4AAAAAAKwUoR8AAADIQaGhoXr22Wcz7BMQEKDJkyfnSj0AHi2EfgAAAKQRGhoqk8mkcePGWbQvX75cJpMpV2sxmUxavnx5mvbMhOn8IiIiQi+99FJelwHAChH6AQAAcFdOTk4aP368rl69mtelWL0iRYrIxcUl3eOJiYm5WA0Aa0LoBwAAwF01btxYvr6+Gjt2bIb9tmzZovr168vZ2Vn+/v567bXXdPPmTUnSlClTVKlSJXPf1JUCX3zxhbmtWbNmGjZs2APXu2rVKj399NPy8vJSoUKF1Lp1ax07dsx8/MSJEzKZTFq0aJHq1asnZ2dn1axZU4cPH1ZERISCgoLk5uam5s2b6+LFi+bzUlcUjBo1St7e3vLw8FC/fv2UkJBg7rN48WJVqlRJzs7OKlSokBo3bmx+D1J9/PHH8vPzU6FChTRgwACLIH/n8n6TyaSvvvpKbdu2laurqz788ENJ0s8//6waNWrIyclJJUuW1KhRo5SUlPTA7x0A60XoBwAAwF3Z2tpqzJgxmjJlik6fPn3XPnv37lWzZs3Uvn17/f3331q4cKE2b96sV199VZIUEhKi/fv369KlS5Kk8PBwFS5cWOHh4ZKkpKQkbdmyRcHBwQ9c782bNzV48GBFRERo7dq1srGxUbt27ZSSkmLRb8SIEXr33Xe1c+dO2dnZqUuXLnrzzTf12WefadOmTTp27Jjef/99i3PWrl2rgwcPav369Zo/f76WLVumUaNGSZLOnTunLl26qFevXjp48KA2bNig9u3byzAM8/nr16/XsWPHtH79es2ePVthYWEKCwvL8PsZMWKE2rZtq71796pXr1767bff1L17d7322ms6cOCAvv76a4WFhemjjz564PcOgBUzHiHR0dGGJCM6OjqvSwEAAHjoJCQlGGdvnDXOXD9jdO3e1Wjbtq1hGIZRu3Zto1evXoZhGMayZcuM//4T8oUXXjBeeukli+ts2rTJsLGxMeLi4oyUlBSjcOHCxuLFiw3DMIyqVasaY8eONby9vQ3DMIwtW7YYdnZ2xvXr19OtS5Lh5ORkuLq6WnzZ2dmZa7ybCxcuGJKMvXv3GoZhGJGRkYYkY/r06eY+8+fPNyQZa9euNbeNHTvWKFeunPl1jx49jIIFCxo3b940t02bNs1wc3MzkpOTjR07dhiSjBMnTty1jh49ehglSpQwkpKSzG0dO3Y0OnXqZH5dokQJ49NPP7X4ngcNGmRxnXr16hljxoyxaJs7d67h5+eX7nuAnBMXF2ccOHDAiIuLy+tS8BDI6POQ1zmUmX4AAIBHXFJKkg5ePqix28eq7fK2arG0hf4896cuxV3SlbgrGj9+vGbPnq0DBw6kOXfHjh0KCwuTm5ub+atZs2ZKSUlRZGSkTCaT6tevrw0bNujatWvav3+/Xn75ZSUnJ5tnxatXry43N7cMa/z000+1e/dui69nnnnGos+xY8fUtWtXlSxZUh4eHgoMDJQknTp1yqJf5cqVzX/28fGRJItbEHx8fHThwgWLc6pUqWJxz32dOnV048YN/fvvv6pSpYoaNWqkSpUqqWPHjvr222/T7IPwxBNPyNbW1vzaz88vzRh3CgoKsni9Y8cOjR492uK97tu3r86dO6fY2NgMrwX8V0BAgEwmU5qvAQMGSJIMw9DIkSNVtGhROTs7m1fsIH+yy+sCAAAAkHdSjBRtPbtV/1v3PyUZ/3dv+NX4q7oUe0mf7/pcr9V8Tc2aNdM777yj0NBQy/NTUtSvXz+99tpraa792GOPSbq9xP+bb77Rpk2bVKVKFXl5eal+/foKDw/Xhg0bFBIScs86fX19Vbp0aYs2d3d3Xbt2zfy6TZs28vf317fffquiRYsqJSVFFStWtLj3XpLs7e3Nf059EsGdbXfeEpAek8kkW1tbrVmzRlu2bNHq1as1ZcoUDR8+XNu2bTP/4uG/18/sGK6urhavU1JSNGrUKLVv3z5NXycnp0zVi4fP6dOntXDhQv3666+KiYmRh4eHWrRooU6dOql48eI5MmZERISSk5PNr/ft26cmTZqoY8eOkqQJEyZo0qRJCgsLU9myZfXhhx+qSZMmOnTokNzd3XOkJuQcZvoBAAAeYaeun9Lg8MEWgf+/lhxZot0Xd2vcuHH6+eeftWXLFovj1atX1/79+1W6dOk0Xw4ODpL+777+xYsXmwN+cHCwfv/992y7n//y5cs6ePCg3n33XTVq1EgVKlTI1qcO7NmzR3FxcebXW7dulZubmzmUmUwmPfXUUxo1apR27dolBwcHLVu2LNvGl26/14cOHbrre21jwz/r86MtW7aoY8eOWrRoka5du6aUlBRdu3ZNixYt0vPPP5/mv7fsUqRIEfn6+pq/VqxYoVKlSik4OFiGYWjy5MkaPny42rdvr4oVK2r27NmKjY3V999/nyP1IGfx0wEAAOARtv/SfsUlxWXY5+u/v9ZjZR9Tt27dNGXKFItjb731lv78808NGDBAu3fv1pEjR/TTTz/pf//7n7lPxYoVVahQIc2bN88c+kNCQrR8+XLFxcXp6aeffuDvo0CBAipUqJC++eYbHT16VOvWrdPgwYMf+LqpEhIS1Lt3bx04cEC//vqrRowYoVdffVU2Njbatm2bxowZo7/++kunTp3S0qVLdfHiRVWoUCHbxpek999/X3PmzNHIkSO1f/9+HTx4UAsXLtS7776breMgd5w+fVpDhgxRUlKSxay7JCUnJysxMVFDhw5NdxPN7JKQkKDvvvtOvXr1kslkUmRkpKKiotS0aVNzH0dHRwUHB+fYLyGQswj9AAAAj7C/zv91zz4HLx/U9YTr+uCDDyx2pJdu3x8fHh6uI0eOqF69eqpWrZree+89+fn5mfuYTCbzbH69evXM53l6eqpatWry8PB44O/DxsZGCxYs0I4dO1SxYkW9/vrrmjhx4gNfN1WjRo1UpkwZ1a9fX88//7zatGmjkSNHSpI8PDy0ceNGtWzZUmXLltW7776rTz75RC1atMi28aXbjzZcsWKF1qxZo5o1a6p27dqaNGmSSpQoka3jIHcsXLhQKSkpaf6bSmUYhpKTk7Vo0aIcrWP58uW6du2a+dadqKgoSf+330UqHx8f8zHkLyYjvU+ZFYqJiZGnp6eio6Oz5f9cAAAA8rvx28fru4PfZdjHzsZOv7T7RUXdiuZSVQ+X0NBQXbt2TcuXL8/rUvAQiY+PV2RkpAIDA+9rT4XGjRtb7EmRHi8vL/3+++/3UWHmNGvWTA4ODvr5558l3b7l4KmnntLZs2ctfnnXt29f/fvvv1q1alWO1ZKfZfR5yOscykw/AADAIyzEP+SefZoHNFch50I5XwzwCImJicnWfvfj5MmT+v3339WnTx9zm6+vrySlmdW/cOFCmtl/5A+EfgAAgEdYSc+SKulZMt3jtiZbdavQTY62jrlYFWD9Mjvjm5Mzw7NmzZK3t7datWplbgsMDJSvr6/WrFljbktISFB4eLjq1q2bY7Ug5xD6AQAAHmFFXIpocoPJCvQMTHPM3sZen4Z8qnIFyuVBZQ+PsLAwlvYj27Vo0UK2trYZ9rG1tVXLli1zZPyUlBTNmjVLPXr0kJ3d/z3J3WQyadCgQRozZoyWLVumffv2KTQ0VC4uLuratWuO1IKcZXfvLgAAALBmgZ6Bmt5kuo5HH9eak2sUmxirJ/2eVFXvqvJ395edDf9kBLJbp06dtGTJknQ38zOZTLK1tdXzzz+fI+P//vvvOnXqlHr16pXm2Jtvvqm4uDj1799fV69eVa1atbR69Wq5u7vnSC3IWWzkBwAAAABZ9KAb+Um3N80bOnSokpOTLR7bZ2trK1tbW3388ccsqc8n2MgPAAAAAGChbt26WrRokTp16iQvLy/Z2NjIy8tLnTp10qJFiwj8yBas1QIAAACAPFK8eHENHjxYgwcPzutSYKWY6QcAAAAAwEoR+gEAAAAAsFKEfgAAAAAArBShHwAAAAAAK0XoBwAAAADAShH6AQAAAACwUoR+AAAAAACsFKEfAAAAAAArRegHAAAAgDx0/vx5LVq0SNOnT9eiRYt0/vz5HB0vKSlJ7777rgIDA+Xs7KySJUtq9OjRSklJMfcxDEMjR45U0aJF5ezsrJCQEO3fvz9H60LOsMvrAgAAAADgUXT58mWNGzdO4eHhMgxDNjY2SklJ0ccff6yQkBC99dZbKlSoULaPO378eH311VeaPXu2nnjiCf3111/q2bOnPD09NXDgQEnShAkTNGnSJIWFhals2bL68MMP1aRJEx06dEju7u7ZXhNyDjP9AAAAAJDLLl++rNDQUG3cuFEpKSkyDEPJyckyDEMpKSkKDw9XaGioLl++nO1j//nnn2rbtq1atWqlgIAAdejQQU2bNtVff/0l6fYs/+TJkzV8+HC1b99eFStW1OzZsxUbG6vvv/8+2+tBziL0AwAAAEAuGzdunC5cuKDk5OS7Hk9OTtaFCxc0fvz4bB/76aef1tq1a3X48GFJ0p49e7R582a1bNlSkhQZGamoqCg1bdrUfI6jo6OCg4O1ZcuWbK8HOYvl/QAAAACQi86fP68NGzbIMIwM+yUnJ2vDhg06f/68fHx8sm38t956S9HR0SpfvrxsbW2VnJysjz76SF26dJEkRUVFSVKaMX18fHTy5MlsqwO5g5l+AAAAAMhF4eHhme5rGEaW+mfGwoUL9d133+n777/Xzp07NXv2bH388ceaPXu2RT+TyZSmljvb8PBjph8AAAAAclFMTIxsbGzSXdr/XzY2NoqJicnW8d944w29/fbb6ty5sySpUqVKOnnypMaOHasePXrI19dX0u0Zfz8/P/N5Fy5cyNYVB8gdzPQDAAAAQC7y8PCweDxeRlJSUuTh4ZGt48fGxsrGxjIK2trammsKDAyUr6+v1qxZYz6ekJCg8PBw1a1bN1trQc4j9AMAAABALgoODs70MnmTyaTg4OBsHb9Nmzb66KOP9Msvv+jEiRNatmyZJk2apHbt2pnHHDRokMaMGaNly5Zp3759Cg0NlYuLi7p27ZqttSDnsbwfAAAAAHKRj4+PgoODtXHjxgyX+Nva2io4ODjbl9RPmTJF7733nvr3768LFy6oaNGi6tevn95//31znzfffFNxcXHq37+/rl69qlq1amn16tVyd3fP1lqQ80zGvbaMtCIxMTHy9PRUdHR0ti+RAQAAAPDoiI+PV2RkpAIDA+Xk5JTl8y9fvqzQ0NB0H9tna2srb29vhYWFqVChQtlRMnJQRp+HvM6hLO8HAAAAgFxWqFAhhYWFKTg4WDY2NjKZTLK1tZXJZJKNjY2Cg4MJ/MgWLO8HAAAAgDxQqFAhTZgwQefPn1d4eLhiYmLk4eGRI0v68egi9AMAAABAHvLx8dHzzz+f12XASrG8HwAAAAAAK5VvQv+0adNUuXJleXh4yMPDQ3Xq1NGvv/6a12UBAAAAAPDQyjehv3jx4ho3bpz++usv/fXXX2rYsKHatm2r/fv353VpAAAAAAA8lPLNPf1t2rSxeP3RRx9p2rRp2rp1q5544ok8qgoAAAAAgIdXvgn9/5WcnKwffvhBN2/eVJ06ddLtd+vWLd26dcv8OiYmJjfKAwAAAADgoZBvlvdL0t69e+Xm5iZHR0e9/PLLWrZsmR5//PF0+48dO1aenp7mL39//1ysFgAAAACAvJWvQn+5cuW0e/dubd26Va+88op69OihAwcOpNt/2LBhio6ONn/9+++/uVgtAAAAAAB5K1+FfgcHB5UuXVpBQUEaO3asqlSpos8++yzd/o6Ojubd/lO/AAAAAOBhcfToUY0fP17NmjXT008/rWbNmmn8+PE6evRojo15/fp1DRo0SCVKlJCzs7Pq1q2riIgI83HDMDRy5EgVLVpUzs7OCgkJYQP1fCxfhf47GYZhcc8+AAAAAOQHycnJ+uSTT9S5c2ctXbpUly9fVnx8vC5fvqylS5eqc+fO+uSTT5ScnJztY/fp00dr1qzR3LlztXfvXjVt2lSNGzfWmTNnJEkTJkzQpEmTNHXqVEVERMjX11dNmjTR9evXs70W5Lx8E/rfeecdbdq0SSdOnNDevXs1fPhwbdiwQd26dcvr0gAAAAAgSyZPnqz58+dLUppgn/p6/vz5mjx5craOGxcXpyVLlmjChAmqX7++SpcurZEjRyowMFDTpk2TYRiaPHmyhg8frvbt26tixYqaPXu2YmNj9f3332drLcgd+Sb0nz9/Xi+88ILKlSunRo0aadu2bVq1apWaNGmS16UBAAAAQKYdPXrUHPjvZf78+dm61D8pKUnJyclycnKyaHd2dtbmzZsVGRmpqKgoNW3a1HzM0dFRwcHB2rJlS7bVgdyTbx7ZN2PGjLwuAQAAAAAe2JIlS2Rra5uppfu2trZasmSJ3nrrrWwZ293dXXXq1NEHH3ygChUqyMfHR/Pnz9e2bdtUpkwZRUVFSZJ8fHwszvPx8dHJkyezpQbkrnwz0w8AAAAA1mDdunWZvlc/OTlZ69aty9bx586dK8MwVKxYMTk6Ourzzz9X165dZWtra+5jMpkszjEMI00b8gdCPwAAAADkops3b+Zo/3spVaqUwsPDdePGDf3777/avn27EhMTFRgYKF9fX0kyz/inunDhQprZf+QPhH4AAAAAyEWurq452j8r1/Xz89PVq1f122+/qW3btubgv2bNGnO/hIQEhYeHq27dujlSB3IWoR8AAAAAclHDhg0tltJnxNbWVg0bNszW8X/77TetWrVKkZGRWrNmjRo0aKBy5cqpZ8+eMplMGjRokMaMGaNly5Zp3759Cg0NlYuLi7p27ZqtdSB35JuN/AAAAADAGjz33HP64YcfMtU3OTlZHTp0yNbxo6OjNWzYMJ0+fVoFCxbUc889p48++kj29vaSpDfffFNxcXHq37+/rl69qlq1amn16tVyd3fP1jqQO0yGYRh5XURuiYmJkaenp6Kjo+Xh4ZHX5QAAAADIp+Lj4xUZGanAwMA0j7/LjE8++SRTj+3r0qWLhgwZcj8lIhdl9HnI6xzK8n4AAAAAyGWDBg1Sly5dJCnNUv/U1126dNGgQYNyuzRYGZb3AwAAAEAus7W11ZAhQ9S2bVstWbJE69at082bN+Xq6qqGDRuqQ4cOKlWqVF6XCStA6AcAAACAPFK6dGm99dZbeuutt/K6FFgplvcDAAAAAGClCP0AAAAAcJ8eoX3RkYGH+XNA6AcAAACALEp9vF1sbGweV4KHQernIPVz8TDhnn4AAAAAyCJbW1t5eXnpwoULkiQXFxeZTKY8rgq5zTAMxcbG6sKFC/Ly8krzJIaHAaEfAAAAAO6Dr6+vJJmDPx5dXl5e5s/Dw4bQDwAAAAD3wWQyyc/PT97e3kpMTMzrcpBH7O3tH8oZ/lSEfgAAAAB4ALa2tg916MOjjY38AAAAAACwUoR+AAAAAACsFKEfAAAAAAArRegHAAAAAMBKEfoBAAAAALBShH4AAAAAAKwUoR8AAAAAACtF6AcAAAAAwEoR+gEAAAAAsFKEfgAAAAAArBShHwAAAAAAK0XoBwAAAADAShH6AQAAAACwUoR+AAAAAACsFKEfAAAAAAArRegHAAAAAMBKEfoBAAAAALBShH4AAAAAAKwUoR8AAAAAACtF6AcAAAAAwEoR+gEAAAAAsFKEfgAAAAAArBShHwAAAAAAK0XoBwAAAADAShH6AQAAAACwUoR+AAAAAACsFKEfAAAAAAArRegHAAAAAMBKEfoBAAAAALBShH4AAAAAAKwUoR8AAAAAACtF6AcAAAAAwEoR+gEAAAAAsFKEfgAAAAAArBShHwAAAAAAK0XoBwAAAADAShH6AQAAAACwUoR+AAAAAACsFKEfAAAAAAArRegHAAAAAMBKEfoBAAAAALBShH4AAAAAAKwUoR8AAAAAACtF6AcAAAAAwEoR+gEAAAAAsFKEfgAAAAAArBShHwAAAAAAK0XoBwAAAADAShH6AQAAAACwUoR+AAAAAACsFKEfAAAAAAArRegHAAAAAMBKEfoBAAAAALBShH4AAAAAAKwUoR8AAAAAACtF6AcAAAAAwEoR+gEAAAAAsFKEfgAAAAAArBShHwAAAAAAK0XoBwAAAADAShH6AQAAAACwUoR+AAAAAACsFKEfAAAAAAArRegHAAAAAMBKEfoBAAAAALBShH4AAAAAAKwUoR8AAAAAACtF6AcAAAAAwEoR+gEAAAAAsFKEfgAAAAAArBShHwAAAAAAK0XoBwAAAADAShH6AQAAAACwUoR+AAAAAACsFKEfAAAAAAArRegHAAAAAMBKEfoBAAAAALBShH4AAAAAAKwUoR8AAAAAACtF6AcAAAAAwEoR+gEAAAAAsFKEfgAAAAAArBShHwAAAAAAK0XoBwAAAADASuWb0D927FjVrFlT7u7u8vb21rPPPqtDhw7ldVkAAAAAADy08k3oDw8P14ABA7R161atWbNGSUlJatq0qW7evJnXpQEAAAAA8FAyGYZh5HUR9+PixYvy9vZWeHi46tevn6lzYmJi5OnpqejoaHl4eORwhQAAAACAR11e51C7XB8xm0RHR0uSChYsmG6fW7du6datW+bXMTExOV4XAAAAAAAPi3yzvP+/DMPQ4MGD9fTTT6tixYrp9hs7dqw8PT3NX/7+/rlYJQAAAAAAeStfLu8fMGCAfvnlF23evFnFixdPt9/dZvr9/f1Z3g8AAAAAyBUs78+i//3vf/rpp5+0cePGDAO/JDk6OsrR0TGXKgMAAAAA4OGSb0K/YRj63//+p2XLlmnDhg0KDAzM65IAAAAAAHio5ZvQP2DAAH3//ff68ccf5e7urqioKEmSp6ennJ2d87g6AAAAAAAePvnmnn6TyXTX9lmzZik0NDRT18jreykAAAAAAI+WvM6h+WamP5/8bgIAAAAAgIdGvnxkHwAAAAAAuDdCPwAAAAAAVorQDwAAAACAlSL0AwAAAABgpQj9AAAAAABYKUI/AAAAAABWitAPAAAAAICVIvQDAAAAAGClCP0AAAAAAFgpQj8AAAAAAFaK0A8AAAAAgJUi9AMAAAAAYKUI/QAAAAAAWClCPwAAAAAAVorQDwAAAACAlSL0AwAAAABgpbIc+vfs2aMPP/xQX375pS5dumRxLCYmRr169cq24gAAAAAAwP0zGYZhZLbz6tWr1aZNG5UpU0bXr19XbGysFi1apAYNGkiSzp8/r6JFiyo5OTnHCn4QMTEx8vT0VHR0tDw8PPK6HAAAAACAlcvrHJqlmf6RI0dq6NCh2rdvn06cOKE333xTzzzzjFatWpVT9QEAAAAAgPtkl5XO+/fv19y5cyVJJpNJb7zxhooXL64OHTpo/vz5evLJJ3OkSAAAAAAAkHVZCv2Ojo66du2aRVuXLl1kY2Ojzp0765NPPsnO2gAAAAAAwAPIUuivWrWq1q9frxo1ali0d+rUSSkpKerRo0e2FgcAAAAAAO5flkL/K6+8oo0bN971WJcuXSRJ33zzzYNXBQAAAAAAHliWdu/P7/J610QAAAAAwKMlr3Nolnbvv3r1qqZMmaKYmJg0x6Kjo9M9BgAAAAAAcl+WQv/UqVO1cePGu/52wtPTU5s2bdKUKVOyrTgAAAAAAHD/shT6lyxZopdffjnd4/369dPixYsfuCgAAAAAAPDgshT6jx07pjJlyqR7vEyZMjp27NgDFwUAAAAAAB5clkK/ra2tzp49m+7xs2fPysYmS5cEAAAAAAA5JEsJvVq1alq+fHm6x5ctW6Zq1ao9aE0AAAAAACAb2GWl86uvvqrOnTurePHieuWVV2RraytJSk5O1pdffqlPP/1U33//fY4UCgAAAAAAssZkGIaRlROGDx+usWPHyt3dXSVLlpTJZNKxY8d048YNvfHGGxo3blxO1frA8vr5iAAAAACAR0te59Ash35JioiI0Lx583TkyBEZhqGyZcuqa9euevLJJ3OixmyT1282AAAAAODRktc5NEvL+2NjY/XGG29o+fLlSkxMVKNGjTRlyhQVLlw4p+oDAAAAAAD3KUsb+Y0YMUJhYWFq1aqVunTpot9//12vvPJKTtUGAAAAAAAeQJZm+pcuXaoZM2aoc+fOkqRu3brpqaeeUnJysnlTPwAAAAAA8HDI0kz/v//+q3r16plfP/nkk7Kzs9PZs2ezvTAAAAAAAPBgshT6k5OT5eDgYNFmZ2enpKSkbC0KAAAAAAA8uCwt7zcMQ6GhoXJ0dDS3xcfH6+WXX5arq6u5benSpdlXIQAAAAAAuC9ZCv09evRI09a9e/dsKwYAAAAAAGSfLIX+WbNm5VQdAAAAAAAgm2Xpnn4AAAAAAJB/EPoBAAAAALBShH4AAAAAAKwUoR8AAAAAACtF6AcAAAAAwEoR+gEAAAAAsFKEfgAAAAAArBShHwAAAAAAK0XoBwAAAADAShH6AQAAAACwUoR+AAAAAACsFKEfAAAAAAArRegHAAAAAMBKEfoB4BFjMpm0fPnyvC4DAAAAuYDQDwAP6KuvvpK7u7uSkpLMbTdu3JC9vb3q1atn0XfTpk0ymUw6fPhwbpdpdu7cObVo0SLPxgcAAEDuIfQDwANq0KCBbty4ob/++svctmnTJvn6+ioiIkKxsbHm9g0bNqho0aIqW7ZsXpQqSfL19ZWjo2OejQ8AAIDcQ+gHgAdUrlw5FS1aVBs2bDC3bdiwQW3btlWpUqW0ZcsWi/aQkBCVLl1aH3/8scV19u3bJxsbGx07dkySdOrUKbVt21Zubm7y8PDQ888/r/Pnz5v7jxw5UlWrVtXMmTP12GOPyc3NTa+88oqSk5M1YcIE+fr6ytvbWx999JHFOP9d3n/ixAmZTCYtXbpUDRo0kIuLi6pUqaI///zT4pxvv/1W/v7+cnFxUbt27TRp0iR5eXllw7sHAACAnEToB4BsEBISovXr15tfr1+/XiEhIQoODja3JyQk6M8//1TDhg3Vq1cvzZo1y+IaM2fOVL169VSqVCkZhqFnn31WV65cUXh4uNasWaNjx46pU6dOFuccO3ZMv/76q1atWqX58+dr5syZatWqlU6fPq3w8HCNHz9e7777rrZu3Zph/cOHD9fQoUO1e/dulS1bVl26dDHfrvDHH3/o5Zdf1sCBA7V79241adIkzS8SAAAA8HCyy+sCACDfMgwpJVmytVNISIhef/11JSUlKS4uTrt27VL9+vWVnJyszz//XJK0detWxcXFqUGDBnJ2dtb777+v7du368knn1RiYqK+++47TZw4UZL0+++/6++//1ZkZKT8/f0lSXPnztUTTzyhiIgI1axZU/+PvfuOz+n6Azj+ebL3ECMJSRASiSAIRUhiNWqPonasVtHYSlWtn9qjlFItSVVRGmmp2mLFiBAzVipixI7s/dzfH6mnHonV0hDf9+v1vF55zjn33HNvVH3vPed7ANRqNcuXL8fc3Bx3d3caNmzI+fPn2bx5Mzo6Ori6ujJjxgzCwsKoU6fOEy9l5MiRtGjRAoBJkyZRuXJlLl26RKVKlVi4cCHvvfceI0eOBMDFxYXw8HA2bdr0ym6tEEIIIYR4OeRNvxBCvKiMZIg7BL8OgpVt4ci3NPQsT2pqKhEREezbtw8XFxdKliyJr68vERERpKamEhYWhqOjI+XLl8fOzo4WLVqwfPlyADZt2kRGRgYdO3YEIDo6GgcHB03AD+Du7o6VlRXR0dGasrJly2Jubq75XqpUKdzd3dHR0dEqu3379lMvqWrVqpqf7ezsADTHnD9/ntq1a2u1f/y7EEIIIYR4PcmbfiGEeBHZGXByDWwe+XdZ7D4qmJWijL0tu3fvJiEhAV9fXyAvaV65cuU4cOAAu3fvplGjRprD+vXrR48ePZg3bx4rVqygc+fOmJiYAKAoCiqVKt/pHy/X19fXqlepVAWWqdXqp17Wo8c87P/hMQWNRVGUp/YnhBBCCCFeD/KmXwghXkTCZdjyaf7ylFs0dLUibPcuTbK+h3x9fdm6dSuHDh2iYcOGmvLmzZtjamrKN998wx9//EGfPn00de7u7sTFxXH16lVN2dmzZ0lMTMTNze2VXNqTVKpUiSNHjmiVPbpTgRBCCCGEeH3Jm34hhHgRCbF56/gL0NDqBoO2xJKdnaN50w95Qf/HH39MRkaGVtCvq6tLQEAAY8eOpUKFCtStW1dT16RJE6pWrUq3bt2YP38+OTk5DBw4EF9fX7y8vF7Z5RXkk08+wcfHh7lz59KqVSt27drFH3/8UeBMBCGEEEII8XqRN/1CCPFCnhzoNnQ2Ij09gwoVKlCqVClNua+vL8nJyTg7O2ut0Qfo27cvWVlZWm/54e9t9aytrfHx8aFJkyaUL1+etWvXvtzLeQ7e3t4sWbKEuXPnUq1aNbZs2cKwYcMwMjL6z8cihBBCCCFejEp5ixZmJiUlYWlpSWJiIhYWFoU9HCHEm+jOBVjiDblZ+evqDIKmk0H3+SdRHThwAD8/P65du6b1oOB1179/f86dO8e+ffsKeyhCCCGEEK+1wo5D5U2/EEK8COty0GYRPD613bos1Or73AF/ZmYmly5dYvz48XTq1Om1D/hnz57NiRMnuHTpEgsXLiQ4OJhevXoV9rCEEEIIIcQzyJp+IYR4EXr64NYKiu+Gc5sh8RpUbAKlvcDa6bm7Wb16NX379sXT05OVK1e+wgG/HEeOHGHmzJkkJydTvnx5FixYQL9+/Qp7WEIIIYQQ4hlker8QQgghhBBCCPGKFHYcKtP7hRBCCCGEEEKIIkqCfiGEEEIIIYQQooiSoF8IIYQQQgghhCiiJOgXQgghhBBCCCGKKAn6hRBCCCGEEEKIIkqCfiGEEEIIIYQQooiSoF8IIYQQQgghhCiiJOgXQgghhBBCCCGKKAn6hRBCCCGEEEKIIkqCfiGEEEIIIYQQooiSoF8IIYQQQgghhCiiJOgXQgghhBBCCCGKKAn6hRBCCCGEEEKIIkqCfiGEEEIIIYQQooiSoF8IIYQQQgghhCiiJOgXQgghhBBCCCGKKAn6hRBCCCGEEEKIIkqCfiGEEEIIIYQQooiSoF8IIYQQQgghhCiiJOgXQgghhBBCCCGKKAn6hRBCCCGEEEKIIkqCfiGEEEIIIYQQooiSoF8IUaQEBQVhZWVV2MMQQgghhBDitSBBvxBvoZs3b/LJJ59Qvnx5DA0NcXBwoFWrVuzcubOwh/ZCypYty/z587XKOnfuzIULF17aOWJjY1GpVERFRb20PoUQQgghhPiv6BX2AIQQ/63Y2Fi8vb2xsrJi5syZVK1alezsbLZu3cqgQYM4d+5cYQ/xXzE2NsbY2LiwhyGEEEIIIcRrQd70C/GWGThwICqViiNHjvD+++/j4uJC5cqVGT58OIcOHQIgLi6ONm3aYGZmhoWFBZ06deLWrVuaPiZOnIinpycrV66kbNmyWFpa8sEHH5CcnKxp4+fnR2BgIKNHj6ZYsWLY2toyceJErbEkJiby4YcfUrJkSSwsLGjUqBEnTpzQavPbb7/h5eWFkZERxYsXp3379pr+r1y5wrBhw1CpVKhUKqDg6f1P6gNApVIRGhqq1d7KyoqgoCAAypUrB0D16tVRqVT4+fm90P0WQgghhBCiMEnQL8Rb5P79+2zZsoVBgwZhamqar97KygpFUWjbti33799nz549bN++nZiYGDp37qzVNiYmhtDQUDZt2sSmTZvYs2cP06dP12oTHByMqakphw8fZubMmUyePJnt27cDoCgKLVq04ObNm2zevJnIyEhq1KhB48aNuX//PgC///477du3p0WLFhw/fpydO3fi5eUFQEhICGXKlGHy5MnEx8cTHx9f4DU/rY/nceTIEQB27NhBfHw8ISEhz32sEEIIIYQQhU2m9wvxFrl06RKKolCpUqUnttmxYwcnT57k8uXLODg4ALBy5UoqV65MREQEtWrVAkCtVhMUFIS5uTkAPXr0YOfOnUydOlXTV9WqVZkwYQIAFStW5Ouvv2bnzp00bdqU3bt3c+rUKW7fvo2hoSEAs2fPJjQ0lPXr1/Phhx8ydepUPvjgAyZNmqTps1q1agAUK1YMXV1dzM3NsbW1feL1PK2P51GiRAkAbGxsnnoe8eoEBATw4MGDfDMyhBBCCCHEs8mbfiGKutR7EH8SbkShpOa9QX84Fb4g0dHRODg4aAJ+AHd3d6ysrIiOjtaUlS1bVhPwA9jZ2XH79m2tvqpWrar1/dE2kZGRpKSkYGNjg5mZmeZz+fJlYmJiAIiKiqJx48b/8MJ5aX28qQICAlCpVAwYMCBf3cNlHgEBAf/9wN5AD5e0PK6g5SFCCCGEEK8TedMvRFGlKHD9GGwMhFunAahoVBaVSkX02TO0bdv2CYcpBT4UeLxcX19fq16lUqFWq7XKntZGrVZjZ2dHWFhYvnM9XJP/MhLyPasPlUqFoihaZdnZ2f/6vK8LBwcH1qxZw7x58zT3IiMjg9WrV+Po6FjIoxNCCCGEEK+avOkXoqi6cw5+aKUJ+AGKZcTi76zHoq8XkJqamu+QBw8e4O7uTlxcHFevXtWUnz17lsTERNzc3F7a8GrUqMHNmzfR09OjQoUKWp/ixYsDeTMFnraNoIGBAbm5uU89z7P6KFGihFY+gIsXL5KWlqZ1DuCZ53ld1ahRA0dHR61cBCEhITg4OFC9enVN2ZYtW6hfvz5WVlbY2NjQsmVLzYwLgKysLAYPHoydnR1GRkaULVuWadOmaeonTpyIo6MjhoaG2NvbExgYqKn78ccf8fLy0izF6Nq1a75ZIWfOnKFFixZYWFhgbm5OgwYNtM4Pecs/7OzssLGxYdCgQVoPZ56VkPFZ439aUsmgoCAmTZrEiRMnNEkjg4KCKFu2LADt2rVDpVJpvp84cYKGDRtibm6OhYUFNWvW5OjRo8/6VQkhhBBCvBJvVNC/d+9eWrVqhb29vUypFOJZLm6DrPyB/eLmhuRmplG7dm1++eUXLl68SHR0NAsWLKBu3bo0adKEqlWr0q1bN44dO8aRI0fo2bMnvr6+L5QA71maNGlC3bp1adu2LVu3biU2Npbw8HA+//xzTYA0YcIEVq9ezYQJE4iOjubUqVPMnDlT00fZsmXZu3cv169f5+7duwWe51l9NGrUiK+//ppjx45x9OhRBgwYoDVDoWTJkhgbG7NlyxZu3bpFYmLiS7sH/5XevXuzYsUKzffly5fTp08frTapqakMHz6ciIgIdu7ciY6ODu3atdPMzFiwYAG//fYbP//8M+fPn+fHH3/UBLnr169n3rx5LF26lIsXLxIaGkqVKlU0fWdlZTFlyhROnDhBaGgoly9f1lpWcP36dXx8fDAyMmLXrl1ERkbSp08fcnJyNG12795NTEwMu3fvJjg4mKCgIE1A/zyeNv5nJZXs3LkzI0aMoHLlypqkkZ07dyYiIgKAFStWEB8fr/nerVs3ypQpQ0REBJGRkYwZMybfrBchhBBCiP/KGzW9PzU1lWrVqtG7d286dOhQ2MMR4vV2eV+BxeWsdTg2tgpTYzwYMWIE8fHxlChRgpo1a/LNN99oHqh98skn+Pj4oKOjQ7NmzVi4cOFLHZ5KpWLz5s2MGzeOPn36cOfOHWxtbfHx8aFUqVJA3rZ869atY8qUKUyfPh0LCwt8fHw0fUyePJmPPvoIZ2dnMjMz803Tf54+5syZQ+/evfHx8cHe3p6vvvqKyMhITb2enh4LFixg8uTJfPHFFzRo0KDAJQmvFXUu6Ohqvvbo0YOxY8cSGxuLSqXiwIEDrFmzRus6Hv879fvvv6dkyZKcPXsWDw8P4uLiqFixIvXr10elUuHk5KRpGxcXh62tLU2aNEFfXx9HR0dq166tqX/0AUP58uVZsGABtWvXJiUlBTMzMxYtWoSlpSVr1qzRBMcuLi5a47G2tubrr79GV1eXSpUq0aJFC3bu3En//v2f65Y8bfzPk1TSzMwMPT09rWSOD5dLWFlZaZXHxcUxatQoTcLMihUrPtcYhRBCCCFeBZVS0L+S3wAqlYoNGzY8cV1yQZKSkrC0tCQxMRELC4tXNzghXge7psLemQXX1egFLedpBYbiDZeTBbfPwqn1cOsUlK1PwKIDPMiE0NBQOnToQNWqVVEUhdOnT7N+/Xratm2rmQIfExPD+PHjOXToEHfv3kWtVpOamsrvv/9O8+bNOXbsGE2bNsXGxoZmzZrRsmVL3n33XQCuXr2Kt7c3iqLQrFkzmjdvTqtWrdDTy3uufPz4cSZOnEhUVBT3799HrVaTlpbGmTNncHd3p3nz5pQoUYLg4OACLy0gIIA7d+7w+++/a8qGDBnCqVOn2LVrF1Dw/xOsrKyYP38+AQEBTx3/rFmzGDNmTL78D+np6YwcOZIZM2YwceJEQkNDiYqK0mpT0HknTpzI1KlT8fX1pUmTJnTs2BFnZ+d/9GsVQgghxJuvsOPQN2p6/4vKzMwkKSlJ6yPEW8OtFegUMJlHpcoL+iXgLzoUBS7tgGUN4eBC+DMMdv0PLm6F9AQg7217UFAQwcHB+ab2A7Rq1Yp79+6xbNkyDh8+zOHDh4G8qfmQlxvg8uXLTJkyhfT0dDp16sT7778P5CULPH/+PIsWLcLY2JiBAwfi4+NDdnY2qampvPvuu5iZmfHjjz8SERHBhg0btPp+noSNz0oc+ayEjE8b/8OkklFRUVqf8+fPM2rUqGeO7XETJ07U5CjYtWsX7u7ummsWQgghhPivvVHT+1/UtGnTtPbmFuKtUtINuqyFDR9C2r28MiMraL0AbD0KdWjiJUu4AqEDQNHePQF1Ltw8Bal3adasmSbI9vf312p27949oqOjWbp0KQ0aNABg//79+U5jYWFB586d6dy5M++//z7NmjXj/v37FCtWDGNjY1q3bk3r1q0ZNGgQlSpV4tSpUyiKwt27d5k+fbpmG8jHk9pVrVqV4OBgsrOz//Ha92clZHza+B9NKvlwnf/jnpQ0Ul9fv8ByFxcXXFxcGDZsGF26dGHFihW0a9fuH12bEEIIIcS/UaSD/rFjxzJ8+HDN96SkJK29x4Uo0nT1oWIT+DAMHlzNCwitHMDKKe9tvyg6HlyBjCckGMxMhsRr6JoWJzo6GgBdXe1ZHtbW1tjY2PDtt99iZ2dHXFwcY8aM0Wozb9487Ozs8PT0REdHh3Xr1mFra6tZHpCbm8s777yDiYkJK1euxNjYGCcnJ9RqNQYGBixcuJABAwZw+vRppkyZotX34MGDWbhwIR988AFjx47F0tKSQ4cOUbt2bVxdXZ/rFjxMyFinTh3UajWffvqp1gOEp43/0aSSM2bMwNXVlRs3brB582batm2Ll5cXZcuW5fLly0RFRVGmTBnMzc0xNDSkbNmy7Ny5E29vbwwNDTEyMmLUqFG8//77lCtXjmvXrhERESF5aIQQQghRaIr09H5DQ0MsLCy0PkK8dawcoaw3lGsA1mUl4C+KHn/Dn68+b9r7k/4e1NHRYc2aNURGRuLh4cGwYcOYNWuWVhszMzNmzJiBl5cXtWrVIjY2ls2bN6Ojo4OVlRXLli3D29tbs0Xixo0bsbGxoUSJEgQFBbFu3Trc3d2ZPn06s2fP1urbxsaGXbt2kZKSgq+vLzVr1mTZsmUv9NZ/zpw5ODg44OPjQ9euXRk5ciQmJibPNf6HSSV9fHzo06cPLi4ufPDBB8TGxmqSSnbo0IFmzZrRsGFDSpQowerVqzXn3b59u2YLRF1dXe7du0fPnj1xcXGhU6dOvPfeezLrTAghhBCFRhL5CSHEm+5eDCypD9lp+eusy0GfrWBe6r8flxBCCCGEKPQ49I1605+SkqJJsARoplrGxcUV7sCEEKIwWZfN243hcTp6eTkcJOAXQgghhHhrvVFv+sPCwmjYsGG+8l69ehEUFPTM4wv7CYsQQrwyWalw8zQc+Rbunocy70DNnlCyMugW6fQtQgghhBCvtcKOQ9+ooP/fKuybLYQQr1xuFmRngL6JBPtCCCGEEK+Bwo5D5V+EQghRlOga5H2EEEIIIYTgDVvTL4QQQgghhBBCiOcnQb8QQgghhBBCCFFESdAvhBBCCCGEEEIUURL0CyGEEEIIIYQQRZQE/UIIIYQQQgghRBElQb8QQgghhBBCCFFESdAvhBBCCCGEEEIUURL0CyGEEEIIIYQQRZQE/eI/pVKpCA0Nfen9+vn5MXTo0Jfe75OULVuW+fPnv/LzBAQE0LZt21d+HiGEEEIIIUTRJEG/eCkCAgJQqVSoVCr09PRwdHTk448/JiEhobCH9tz+q0BeCCGEEEIIIf4rEvSLl6ZZs2bEx8cTGxvLd999x8aNGxk4cGBhD0sIIYQQQggh3loS9IuXxtDQEFtbW8qUKcO7775L586d2bZtW752d+/epV27dpiYmFCxYkV+++03rfo9e/ZQu3ZtDA0NsbOzY8yYMeTk5GjqU1NT6dmzJ2ZmZtjZ2TFnzpx858jKymL06NGULl0aU1NT3nnnHcLCwl7oeiZOnIijoyOGhobY29sTGBj4xLZz586lSpUqmJqa4uDgwMCBA0lJSdHUBwUFYWVlxdatW3Fzc8PMzEzzkOSh3Nxchg8fjpWVFTY2NowePRpFUV5ozEIIIYQQQgjxKAn6xSvx559/smXLFvT19fPVTZo0iU6dOnHy5EmaN29Ot27duH//PgDXr1+nefPm1KpVixMnTvDNN9/w/fff87///U9z/KhRo9i9ezcbNmxg27ZthIWFERkZqXWO3r17c+DAAdasWcPJkyfp2LEjzZo14+LFi881/vXr1zNv3jyWLl3KxYsXCQ0NpUqVKk9sr6Ojw4IFCzh9+jTBwcHs2rWL0aNHa7VJS0tj9uzZrFy5kr179xIXF8fIkSM19XPmzGH58uV8//337N+/n/v377Nhw4bnGq8QQgghhBBCFEh5iyQmJiqAkpiYWNhDKTLupt1V4lPilW49uim6urqKqampYmRkpAAKoMydO1erPaB8/vnnmu8pKSmKSqVS/vjjD0VRFOWzzz5TXF1dFbVarWmzaNEixczMTMnNzVWSk5MVAwMDZc2aNZr6e/fuKcbGxsqQIUMURVGUS5cuKSqVSrl+/brWuRs3bqyMHTv2idfi5OSkzJs3T1EURZkzZ47i4uKiZGVlPbNtQX7++WfFxsZG833FihUKoFy6dEnrukqVKqX5bmdnp0yfPl3zPTs7WylTpozSpk2bJ55HCCGEEEII8Xor7DhUrzAfOIg3142UG+y7to+gM0EkZCZwP/4+NevVZNnSZZAN3333HRcuXOCTTz7Jd2zVqlU1P5uammJubs7t27cBiI6Opm7duqhUKk0bb29vUlJSuHbtGgkJCWRlZVG3bl1NfbFixXB1ddV8P3bsGIqi4OLionXezMxMbGxsnuv6OnbsyPz58ylfvjzNmjWjefPmtGrVCj29gv+T2b17N19++SVnz54lKSmJnJwcMjIySE1NxdTUFAATExOcnZ01x9jZ2WmuOzExkfj4eK3r0tPTw8vLS6b4CyGEEEIIIf4xmd4vXlh8Sjyj9ozif4f/x7WUa6Rmp3In/Q7RKdGc1zlPpcqVWLBgAZmZmUyaNCnf8Y9P+VepVKjVagAURdEK+B+WPWz3PAGwWq1GV1eXyMhIoqKiNJ/o6Gi++uqr57pGBwcHzp8/z6JFizA2NmbgwIH4+PiQnZ2dr+2VK1do3rw5Hh4e/PLLL0RGRrJo0SIArfYFXbcE9EIIIYQQQohXSYJ+8cIibkZw8u7JAuu+PPIl15KvATBhwgRmz57NjRs3nrtvd3d3wsPDtYLh8PBwzM3NKV26NBUqVEBfX59Dhw5p6hMSErhw4YLme/Xq1cnNzeX27dtUqFBB62Nra/vcYzE2NqZ169YsWLCAsLAwDh48yKlTp/K1O3r0KDk5OcyZM4c6derg4uLyQtcMYGlpiZ2dndZ15eTk5MtVIIQQQgghhBAvQoJ+8UKSM5NZGb3yifU56hxiEmMA8PPzo3Llynz55ZfP3f/AgQO5evUqn3zyCefOnePXX39lwoQJDB8+HB0dHczMzOjbty+jRo1i586dnD59moCAAHR0/v6j7OLiQrdu3ejZsychISFcvnyZiIgIZsyYwebNm59rHEFBQXz//fecPn2aP//8k5UrV2JsbIyTk1O+ts7OzuTk5LBw4UJN2yVLljz3NT80ZMgQpk+fzoYNGzh37hwDBw7kwYMHL9yPEEIIIYQQQjwkQb94IdlKNilZKU9t82j98OHDWbZsGVevXn2u/kuXLs3mzZs5cuQI1apVY8CAAfTt25fPP/9c02bWrFn4+PjQunVrmjRpQv369alZs6ZWPytWrKBnz56MGDECV1dXWrduzeHDh3FwcHiucVhZWbFs2TK8vb2pWrUqO3fuZOPGjQXmBPD09GTu3LnMmDEDDw8PVq1axbRp057rPI8aMWIEPXv2JCAggLp162Jubk67du1euB8hhBBCCCGEeEilvEWLipOSkrC0tCQxMRELC4vCHs4bKTs3m6mHp/LLxV+e2Ca4WTA1StX4D0clhBBCCCGEEK+nwo5D5U2/eCH6uvp0dOmIrkq3wHr3Yu44WeSfAi+EEEIIIYQQ4r8nQb94YS7WLixotAALA+2nVFWLV2Wm70xsjJ9vWzwhhBBCCCGEEK9WwZuOC/EU+rr6NCjdgJ9b/szV5Kuk56RjY2yDo4UjVoZWhT08IYQQQgghhBB/kaBf/CMqlYrS5qUpbV66sIcihBBCCCGEEOIJZHq/EEIIIYQQQghRREnQL4QQQgghhBBCFFES9AshhBBCCCGEEEWUBP1CCCGEEEIIIUQRJUG/EEIIIYQQQghRREnQL4QQQgghhBBCFFES9AshhBBCCCGEEEWUBP1CCCGEEEIIIUQRJUG/EEIIIYQQQghRREnQL4QQb5DY2FhUKhVRUVGFPRQhhBBCCPEGkKBfCCFesYCAAFQqFQMGDMhXN3DgQFQqFQEBAc/Vl4ODA/Hx8Xh4eLzkUQohhBBCiKJIgn4hhPgPODg4sGbNGtLT0zVlGRkZrF69GkdHx+fuR1dXF1tbW/T09F7FMIUQQgghRBEjQb8QQvwHatSogaOjIyEhIZqykJAQHBwcqF69uqZsy5Yt1K9fHysrK2xsbGjZsiUxMTGa+sen94eFhaFSqdi5cydeXl6YmJhQr149zp8/r3X+jRs3UrNmTYyMjChfvjyTJk0iJyfn1V60EEIIIYQodBL0CyHEf6R3796sWLFC83358uX06dNHq01qairDhw8nIiKCnTt3oqOjQ7t27VCr1U/te9y4ccyZM4ejR4+ip6en1e/WrVvp3r07gYGBnD17lqVLlxIUFMTUqVNf7gUKIYQQQojXjgT9QgjxH+nRowf79+8nNjaWK1eucODAAbp3767VpkOHDrRv356KFSvi6enJ999/z6lTpzh79uxT+546dSq+vr64u7szZswYwsPDycjI0NSNGTOGXr16Ub58eZo2bcqUKVNYunTpK7tWIYQQQgjxepBFoUII8bKl3IH7MZAQC4YWkJEEikLx4sVp0aIFwcHBKIpCixYtKF68uNahMTExjB8/nkOHDnH37l3NG/64uLinJu+rWrWq5mc7OzsAbt++jaOjI5GRkURERGi92c/NzSUjI4O0tDRMTExe4sULIYQQQojXiQT9QgjxMt2PhZD+cO3I32UXsqBYNcjJok+fPgwePBiARYsW5Tu8VatWODg4sGzZMuzt7VGr1Xh4eJCVlfXU0+rr62t+VqlUAJoHBmq1mkmTJtG+fft8xxkZGb3oFQohhBBCiDeIBP1CCPGyZKbAtnHaAT+AOheuH4M752jWrJkmgPf399dqdu/ePaKjo1m6dCkNGjQAYP/+/f96WDVq1OD8+fNUqFDhX/clhBBCCCHeLBL0CyHEy/LgCpz/veA6RYGzv6JrV5Xo6Gggb/u9R1lbW2NjY8O3336LnZ0dcXFxjBkz5l8P64svvqBly5Y4ODjQsWNHdHR0OHnyJKdOneJ///vfv+5fCCGEEEK8viSRnxBCvCzpCXnB/ZNcjwS1GgsLCywsLPJV6+josGbNGiIjI/Hw8GDYsGHMmjXrXw/L39+fTZs2sX37dmrVqkWdOnWYO3cuTk5O/7pvIYQQQgjxelMpytP+hVq0JCUlYWlpSWJiYoH/4BZCiH/l5ilYUv/J9fWGwLuT/7vxCCGEEEKIQlfYcai86RdCiJfFqixUfLfgOpUKPNr9p8MRQgghhBBCgn4hhHhZjMyh2Qwo9djWejp60P57KOleOOMSQgghhBBvLUnkJ4QQL5NNeegeAncvwJ1zYGwNtlXAujzo6T/7eCGEEEIIIV4iCfqFEOJlMy+V9ynXoLBHIoQQQggh3nIyvV8IIYQQQgghhCiiJOgXQgghirCAgABUKhUDBgzIVzdw4EBUKhUBAQEv7XwTJ07E09PzpfUnhBBCiH9Hgn4hhBCiiHNwcGDNmjWkp6dryjIyMli9ejWOjo6FODIhhBBCvGoS9AshhBBFXI0aNXB0dCQkJERTFhISgoODA9WrV9eUZWZmEhgYSMmSJTEyMqJ+/fpERERo6sPCwlCpVOzcuRMvLy9MTEyoV68e58+fByAoKIhJkyZx4sQJVCoVKpWKoKAgAObOnUuVKlUwNTXFwcGBgQMHkpKSouk7KCgIKysrtm7dipubG2ZmZjRr1oz4+HhNm4iICJo2bUrx4sWxtLTE19eXY8eOvarbJoQQQhQJEvQLIYQQb4HevXuzYsUKzffly5fTp08frTajR4/ml19+ITg4mGPHjlGhQgX8/f25f/++Vrtx48YxZ84cjh49ip6enqafzp07M2LECCpXrkx8fDzx8fF07twZAB0dHRYsWMDp06cJDg5m165djB49WqvftLQ0Zs+ezcqVK9m7dy9xcXGMHDlSU5+cnEyvXr3Yt28fhw4domLFijRv3pzk5OSXeq+EEEKIokSCfiGEEOIt0KNHD/bv309sbCxXrlzhwIEDdO/eXVOfmprKN998w6xZs3jvvfdwd3dn2bJlGBsb8/3332v1NXXqVHx9fXF3d2fMmDGEh4eTkZGBsbExZmZm6OnpYWtri62tLcbGxgAMHTqUhg0bUq5cORo1asSUKVP4+eeftfrNzs5myZIleHl5UaNGDQYPHszOnTs19Y0aNaJ79+64ubnh5ubG0qVLSUtLY8+ePa/wzgkhhBBvNtmyTwghhChCFEXh+oN0MrPVWJvqa8qLFy9OixYtCA4ORlEUWrRoQfHixTX1MTExZGdn4+3trSnT19endu3aREdHa52jatWqmp/t7OwAuH379lPzA+zevZsvv/ySs2fPkpSURE5ODhkZGaSmpmJqagqAiYkJzs7OWn3fvn1b8/327dt88cUX7Nq1i1u3bpGbm0taWhpxcXEvepuEEEKIt4YE/UIIIUQRcSc5gw3Hr/P1rkskZeTgamuGXnImuooCQJ8+fRg8eDAAixYt0jpW+auNSqXKV/54mb7+3w8THtap1eonjuvKlSs0b96cAQMGMGXKFIoVK8b+/fvp27cv2dnZBfb7sO+H44K8nQju3LnD/PnzcXJywtDQkLp165KVlfX0GyOEEEK8xWR6vxBCCFEEqNUK6yKv8eXmcyRl5ABw/mYK+y7eISUz73uzZs3IysoiKysLf39/reMrVKiAgYEB+/fv15RlZ2dz9OhR3NzcnnscBgYG5ObmapUdPXqUnJwc5syZQ506dXBxceHGjRsvfI379u0jMDCQ5s2bU7lyZQwNDbl79+4L9yOEEEK8TeRNvxBCCFEEXHuQzqJdl/KVqxW4kZC3VZ+urq5mqr6urq5WO1NTUz7++GNGjRpFsWLFcHR0ZObMmaSlpdG3b9/nHkfZsmW5fPkyUVFRlClTBnNzc5ydncnJyWHhwoW0atWKAwcOsGTJkhe+xgoVKrBy5Uq8vLxISkpi1KhRmpwBQgghhCiYvOkXQgghioD0rBxSs3ILrEvJzCU7N2/6vYWFBRYWFgW2mz59Oh06dKBHjx7UqFGDS5cusXXrVqytrZ97HB06dKBZs2Y0bNiQEiVKsHr1ajw9PZk7dy4zZszAw8ODVatWMW3atBe+xuXLl5OQkED16tXp0aOHZntBIYQQQjyZSnl0sVwRl5SUhKWlJYmJiU/8B48QQgjxJrqVlEGHb8K59tdb/UeNb+FG3wblC2FUQgghhCjsOFTe9AshhBBFQCkLI6a08eCxnHvYWhjRyE3ehgshhBBvK1nTL4QQQhQR9ZxtCPm4HsHhV4i9l8q7lUvxXmVbyhU3K+yhCSGEEKKQSNAvhBBCFBGG+rpUd7SmSmlLsnLVmBjI/+aFEEKIt538a0AIIYQoYvR0ddDTlRV8QgghhJA1/UIIIYQQQgghRJElQb8QQgghhBBCCFFESdAvhBBCCCGEEEIUURL0CyGEEEIIIYQQRZQE/UIIIYQQQgghRBElQb8QQgghhBBCCFFESdAvhBBCCCGEEEIUURL0CyGEEEIIIYQQRZQE/UIIIYQQQgghRBElQb8QQgghhBBCCFFESdAvhBBCCCGEEEIUURL0CyGEEEIIIYQQRZQE/UIIIYQQQgghRBElQb8QQgghhBBCCFFESdAvhBBCCCGEEEIUURL0CyGEEEIIIYQQRZQE/UIIIYQQQgghRBElQb8QQgjxkvn5+TF06NDnbh8WFoZKpeLBgwevbExCCCGEeDtJ0C+EEEK8oICAANq2bVvYwxBCCCGEeCYJ+oUQ4i0kQasQQgghxNtBgn4hxFsrICAAlUrF9OnTtcpDQ0NRqVT/6VhUKhUqlYpDhw5plWdmZmJjY4NKpSIsLOylne+rr74iKCjopfX3NktNTaVnz56YmZlhZ2fHnDlz8rX58ccf8fLywtzcHFtbW7p27crt27fztYuMjMTLywsTExPq1avH+fPnteq/+eYbnJ2dMTAwwNXVlZUrV76y6xJCCCFE0SBBvxDirWZkZMSMGTNISEgo7KHg4ODAihUrtMo2bNiAmZnZSz+XpaUlVlZWL73ft9GoUaPYvXs3GzZsYNu2bYSFhREZGanVJisriylTpnDixAlCQ0O5fPkyAQEB+foaN24cc+bM4ejRo+jp6dGnTx9N3YYNGxgyZAgjRozg9OnTfPTRR/Tu3Zvdu3e/6ksUQgghxBtMgn4hxFutSZMm2NraMm3atKe2Cw8Px8fHB2NjYxwcHAgMDCQ1NRWAhQsXUqVKFU3bhzMFFi1apCnz9/dn7NixTz1Hr169WLNmDenp6Zqy5cuX06tXr3xtr1+/TufOnbG2tsbGxoY2bdoQGxsLwLlz5zAxMeGnn37StA8JCcHIyIhTp04B+af3q9VqZsyYQYUKFTA0NMTR0ZGpU6dq6k+dOkWjRo0wNjbGxsaGDz/8kJSUlKdez9sgJSWF77//ntmzZ9O0aVOqVKlCcHAwubm5Wu369OnDe++9R/ny5alTpw4LFizgjz/+yHcPp06diq+vL+7u7owZM4bw8HAyMjIAmD17NgEBAQwcOBAXFxeGDx9O+/btmT179n92vUIIIYR480jQL4R4q+nq6vLll1+ycOFCrl27VmCbU6dO4e/vT/v27Tl58iRr165l//79DB48GMjL1H7mzBnu3r0LwJ49eyhevDh79uwBICcnh/DwcHx9fZ86lpo1a1KuXDl++eUXAK5evcrevXvp0aOHVru0tDQaNmyImZkZe/fuZf/+/ZiZmdGsWTOysrKoVKkSs2fPZuDAgVy5coUbN27Qv39/pk+frvVw4lFjx45lxowZjB8/nrNnz/LTTz9RqlQpzfmaNWuGtbU1ERERrFu3jh07dmiu/61w5wJEBsGmEXB8FWQkARATE0NWVhZ169bVNC1WrBiurq5ahx8/fpw2bdrg5OSEubk5fn5+AMTFxWm1q1q1quZnOzs7AM0ygOjoaLy9vbXae3t7Ex0d/VIuUQghhBBFkwT9QhQxsbGxqFQqoqKi/nVfZcuWZf78+c/d/k3adkytqDU/t2vXDk9PTyZMmFBg21mzZtG1a1eGDh1KxYoVqVevHgsWLOCHH34gIyMDDw8PbGxsNEF+WFgYI0aM0HyPiIggIyOD+vXrP3NcvXv3Zvny5QCsWLGC5s2bU6JECa02a9asQUdHh++++44qVarg5ubGihUriIuL06z7HzhwIPXr16dHjx707NmTmjVrMmTIkALPmZyczFdffcXMmTPp1asXzs7O1K9fn379+gGwatUq0tPT+eGHH/Dw8KBRo0Z8/fXXrFy5klu3bj3zmt54147CsoawcQgc/Q5+HQgXt0FGEoqiPPPw1NRU3n33XczMzPjxxx+JiIhgw4YNQN60/0fp6+trfn6YV0KtVucre0hRlP88/4QQQggh3iwS9AvxmCVLlmBubk5OTo6mLCUlBX19fRo0aKDVdt++fahUKi5cuPCvzvk8wfIvv/yCrq5uvjeDD1WqVInAwEAcHByIj4/Hw8PjX40J8oLVDz/88Lnb16tXj/j4eCwtLf/1uV+F+xn3ibgZwef7P2fA9gFcSrhESnYKaiVvantwcDBnz57Nd1xkZCRBQUGYmZlpPv7+/qjVai5fvoxKpcLHx4ewsDAePHjAmTNnGDBgALm5uURHRxMWFkaNGjWea21+9+7dOXjwIH/++SdBQUFaa7ofHc+lS5cwNzfXjKdYsWJkZGQQExOjabd8+XJOnjzJsWPHCAoKemJwGB0dTWZmJo0bN35ifbVq1TA1NdWUeXt7o1ar8yWaK3JS7kLoQMh6bClDbhbcOk2F0sXR19fXSsCYkJCg9XfCuXPnuHv3LtOnT6dBgwZUqlSpwCR+z+Lm5sb+/fu1ysLDw3Fzc3vhvoQQQgjx9tAr7AEI8bpp2LAhKSkpHD16lDp16gB5wb2trS0RERGkpaVhYmIC5AXr9vb2uLi4vPJxtW7dGhsbG4KDgxk/frxW3YEDBzh//jxr165FV1cXW1vbJ/ajKAq5ubno6T37P//H3zA/i4GBwVPPXZjupt1lbuRcNv65UVN27d41lHSFiJsR1G9QH39/fz777LN8CdbUajUfffQRgYGB+fp1dHQE8qb4f/vtt+zbt49q1aphZWWFj48Pe/bsISwsTDOd+1lsbGxo2bIlffv2JSMjg/fee4/k5OR846lZsyarVq3Kd/yjv7MTJ06QmpqKjo4ON2/exN7evsBzGhsbP3VMT3ubXOTfMiddhbtPeLCRnoCZOpG+ffsyatQobGxsKFWqFOPGjUNH5+9n6o6OjhgYGLBw4UIGDBjA6dOnmTJlygsPZdSoUXTq1IkaNWrQuHFjNm7cSEhICDt27PinVyeEEEKIt4C86RfiMa6urtjb22ttjxYWFkabNm1wdnYmPDxcq7xhw4ZA3jTd0aNHU7p0aUxNTXnnnXe0+rhy5QqtWrXC2toaU1NTKleuzObNm4mNjdX0YW1tjUqlKjCrt76+Pj169CAoKCjflOLly5dTs2ZNqlWrlm96/8NZBFu3bsXLywtDQ0P27dtHcnIy3bp1w9TUFDs7O+bNm4efnx9Dhw7V9Pv49H6VSsV3331Hu3btMDExoWLFivz2229a9+PRGQv37t2jS5culClTBhMTE6pUqcLq1atf4Lfx8hy5eUQr4H9Iraj5ZNcnXEu+xvTp09m4caPW7xigRo0anDlzhgoVKuT7GBgYAH+v61+/fr0mwPf19WXHjh3PtZ7/UX369CEsLIyePXuiq6ubr75GjRpcvHiRkiVL5hvPw1kW9+/fJyAggHHjxtG7d2+6deumlSDwURUrVsTY2JidO3cWWO/u7k5UVJQmcSHkPWjS0dH5Tx54FapHloE8qX7WrFn4+PjQunVrmjRpQv369alZs6amSYkSJQgKCmLdunW4u7szffr0f5R8r23btnz11VfMmjWLypUrs3TpUlasWPHcD5SEEEII8ZZS3iKJiYkKoCQmJhb2UMRrKvvOHSXrxg2ly/vvK++++66mvFatWsq6deuUjz/+WPnss88URVGUzMxMxdjYWPnuu+8URVGUrl27KvXq1VP27t2rXLp0SZk1a5ZiaGioXLhwQVEURWnRooXStGlT5eTJk0pMTIyyceNGZc+ePUpOTo7yyy+/KIBy/vx5JT4+Xnnw4EGB4ztz5owCKLt379aUpaSkKGZmZsrixYsVRVGUy5cvK4By/PhxRVEUZffu3QqgVK1aVdm2bZty6dIl5e7du0q/fv0UJycnZceOHcqpU6eUdu3aKebm5sqQIUM0fTs5OSnz5s3TfAeUMmXKKD/99JNy8eJFJTAwUDEzM1Pu3bunda6EhARFURTl2rVryqxZs5Tjx48rMTExyoIFCxRdXV3l0KFD//h39E/cT7+vtApppXgEeWh9rLytFPPq5opHkIeyLXaboiiK0qNHD8XIyEh59K/HEydOKMbGxsrAgQOV48ePKxcuXFB+/fVXZfDgwZo2arVaKV68uKKrq6ts2rRJURRFiYqKUnR1dRVdXd1n/r0DKBs2bND0defOHSUzM1NRFEVJSEjQ+r2npqYqFStWVPz8/JS9e/cqf/75pxIWFqYEBgYqV69eVRRFUTp27Ki88847SnZ2tpKamqq4uroqAwcO1JyvV69eSps2bTTfJ06cqFhbWyvBwcHKpUuXlIMHD2r+bKempip2dnZKhw4dlFOnTim7du1Sypcvr/Tq1evFfxlvmqQbijK3sqJMsMj/WVRHUVLuFPYIhRBCCPGaK+w4VN70CwFkXr7MvR9+ILZzZ/5s2QqPCxc5sHcv6XFxJCcnc/z4cXx8fPD19dW8vT906BDp6ek0bNiQmJgYVq9ezbp162jQoAHOzs6MHDmS+vXra/Zdj4uLw9vbmypVqlC+fHlatmyJj48Purq6FCtWDICSJUtia2v7xDXx7u7uvPPOO1p7uf/888/k5ubSpUuXp17j5MmTadq0Kc7OzhgYGBAcHMzs2bNp3LgxHh4erFixIt82YwUJCAigS5cuVKhQgS+//JLU1FSOHDlSYNvSpUszcuRIPD09KV++PJ988gn+/v6sW7fumed5mdJz0rmafPWpbeKS8nIlTJkyJd9MiqpVq7Jnzx4uXrxIgwYNqF69OuPHj9dkV4e8WRAP3+Y/zP1QtWpVLC0tqV69OhYWFs89XpVKRfHixTWzCB5nYmLC3r17cXR0pH379ri5udGnTx/S09OxsLDghx9+YPPmzaxcuRI9PT1MTExYtWoV3333HZs3by6wz/HjxzNixAi++OIL3Nzc6Ny5s2bduYmJCVu3buX+/fvUqlWL999/n8aNG/P1118/9zW9scztoM0i0NXXLtczgpbzwLR44YxLCCGEEOI5yZp+8dbLOHeOuN59yE1I0JTVyMoiNSOD33v2RL9PH1xcXChZsiS+vr706NGD1NRUwsLCcHR0pHz58qxbtw5FUfJNdc7MzMTGxgaAwMBAPv74Y7Zt20aTJk3o0KGD1vZcz6tv374MHTqUr7/+GnNzc5YvX0779u2xsrJ66nFeXl6an//880+ys7OpXbu2pszS0jLfNmMFeXTMpqammJubPzEpWW5uLtOnT2ft2rVcv36dzMxMMjMztRLC/RcMdQ0pZVqK6ynXtcrL9C+j+dneLG+9u5OTk2Zf9EfVqlWLbdu2PfU869ev1/quUqm4d+/ec43x8QcNj7KysspXb2trS3BwcIHte/bsSc+ePbXKatasSWZmpuZ7UFCQVr2Ojg7jxo1j3LhxBfZZpUoVdu3a9bRLKLqcvKHfLjj9C1yPBMe6ULktlJAEekIIIYR4/UnQL95q2bduc23QIK2AH8DJwABbPT32nzlDxsKv8flrqzVbW1vKlSvHgQMH2L17N40aNQLyEqvp6uoSGRmZbw32w4zt/fr1w9/fn99//51t27Yxbdo05syZwyeffPJCY/7ggw8YNmwYa9euxc/Pj/379zN58uRnHvdooP0wgCxo+69neXRLsYd9PLql2KPmzJnDvHnzmD9/PlWqVMHU1JShQ4fm26bsVbMxtqF/lf5MPDixwHoDHQMqFav0n45JvEF09cCuat4nNzv/W38hhBBCiNeYTO8Xr7VXve971uU/yb5+o8C62iYmRKSlsfJYJJcf2ZbM19eXrVu3cujQIU0CvurVq5Obm8vt27fzJVZ7NJu9g4MDAwYMICQkhBEjRrBs2TIAzTTu55leb25uTseOHVmxYgXLly+nfPnyL5zIy9nZGX19fa1p+UlJSVy8ePGF+nmWffv20aZNG7p37061atUoX778Sz/H86pfuj7e9t75ynVVusz2nY2DuUMhjEq8cSTgF0IIIcQbRoJ+8Uy3b9/mo48+wtHREUNDQ2xtbfH39+fgwYMv9TyPZ47/N1QqleZjampKxYoVCQgIIDIyUqtd2mPfH1XbxIRj6emkq9XYmZlryn19fVm2bBkZGRmaoN/FxYVu3brRs2dPQkJCuHz5MhEREcyYMYMmTZrQtm1bhg4dytatW7l8+TLHjh1j165dmv21nZycUKlUbNq0iTt37pCSklLgmB7q27cv4eHhfPPNN/Tp0+eFt00zNzenV69ejBo1it27d3PmzBn69OmDjo7OS92CrUKFCmzfvp3w8HCio6P56KOPuHnz5kvr/0WUMi3FFO8pfN3oa+rZ1aNSsUr0cu/F6harqV+6Pno6MvFJCCGEEEIUPW9c0L948WLKlSuHkZERNWvWZN++fYU9pCKvQ4cOnDhxguDgYC5cuMBvv/2Gn58f9+/fL+yhPdWKFSuIj4/nzJkzLFq0iJSUFN555x1++OEHTRsl98nbcb1jYkKGomCoUmFiZKgp9/X1JTk5GWdnZxwc/n47vGLFCnr27MmIESNwdXWldevWHD58WDOtPjc3l0GDBuHm5kazZs1wdXVl8eLFQF7Cu0mTJjFmzBhKlSrF4MGDn3pt9evXx9XVlaSkJHr16vWP7s/cuXOpW7cuLVu2pEmTJnh7e+Pm5oaRkdE/6q8g48ePp0aNGvj7++Pn54etrS1t27Z9af2/qBImJfB18GVBowUs91/OsJrDcLNxQ1/e3gohhBBCiKKqUPYM+IfWrFmj6OvrK8uWLVPOnj2rDBkyRDE1NVWuXLnyXMcX9lYJb6KHW4WFhYU9td2VK1eU1q1bK6ampoq5ubnSsWNH5ebNm5r6x7cHUxRFGTJkiOLr66upB7Q+ly9f1mwBt2PHDqVmzZqKsbGxUrduXeXcuXNPHQ+PbH/2qJ49eyrm5ubK/fv3FUVRlNiNG5Xm5uZKKT09xUilUioaGCqz7OyVs66VNJ9axibKoB49NX388ccfioWFhRIcHKwoiqKcPHlSadiwoWJkZKQUK1ZM6d+/v5KcnKwoiqJMmDAh33U93HZt9OjRSsWKFRVjY2OlXLlyyueff65kZWU99bpepZSUFMXS0lKzTZsQQgghhBDi3yvsOPSNetM/d+5c+vbtS79+/XBzc2P+/Pk4ODjwzTffFPbQiiwzMzPMzMwIDQ3Vyvz9KEVRaNu2Lffv32fPnj1s376dmJgYOnfu/Nzn+eqrr6hbty79+/cnPj6e+Ph4rbfo48aNY86cORw9ehQ9PT369Onzj65n2LBhJCcns3379ryx29nhUcyGxaXL8GvZcnS0smRM/A1OpKdrjtG1skTXPC8Z35o1a+jUqRM//PADPXv2JC0tjWbNmmFtbU1ERATr1q1jx44dmjf1I0eOpFOnTjRr1kxzXfXq1QPyptgHBQVx9uxZvvrqK5YtW8a8efP+0XX9E8ePH2f16tXExMRw7NgxunXrBkCbNm3+szEIIYQQQgghXq03ZhFrVlYWkZGRjBkzRqv83XffJTw8vMBjHm4P9lBSUtIrHWNRpKenR1BQEP3792fJkiXUqFEDX19fPvjgA83WbTt27ODkyZNcvnxZE6ivXLmSypUrExERQa1atZ55HktLSwwMDDAxMdFKfPfQ1KlTNXugjxkzhhYtWpCRkfHCU9ErVcrL0B4bGwtA2Zo1mbAhhLi+/VDS0uhuUIz9qalsTU6mmrExeqVLo29liUpfn8WLF/PZZ5/x66+/atbyr1q1ivT0dH744QfNNP6vv/6aVq1aMWPGDEqVKoWxsTGZmZn5ruvzzz/X/Fy2bFlGjBjB2rVrGT169Atd078xe/Zszp8/j4GBgWa5TPHisu+4EEIIIYQQRcUb86b/7t275ObmUqpUKa3yUqVKPTEx2LRp07C0tNR8Hn1zLJ7tQVoWt5MyaNO2HTdu3OC3337D39+fsLAwatSoodnnOzo6GgcHB637O3PmTABmzZql1WdoaOg/ShT36N7wdnZ2AE/cG/5xKpWK0NBQIP9Wdbm5ucz9/Xc6pqbgHReH18ULhKemclNHRckxn+K4/Ht0jIz45ZdfGDp0KNu2bdME/JB37dWqVdPaDs/b2xu1Ws35RzL+F2T9+vXUr18fW1tbzMzMGD9+PHFxcc91TS9D9erViYyMJCUlhfv377N9+3aqVKnyn51fCCGEEEII8eq9MUH/QwXtK/6kIHLs2LEkJiZqPlevXv0vhvjGu5+ayaaTN/jg20O0+no/X26O5kZyDk2bNuWLL74gPDycgIAAJkyYADz9d/D777+TkJCAjo5Ovj3gs7Ozn3tMj+4N//BcT9ob/mmio6MBKFeuHJC3j/z8+fP5dPx4du3bS8TevTRt1Aj9WrWwCQjA0MkJRVHw9PSkRIkSrFixQus6nnbtT3u4cejQIT744APee+89Nm3axPHjxxk3btx/vn+9EEIIIYQQomh7Y4L+4sWLo6urm++t/u3bt/O9/X/I0NAQCwsLrY94upxcNeuOXmPwT8c5dzOZW0mZLD8QS7fvDhN3P1XTzt3dndTUVM3PcXFxWg9VHjx4AECJEiWYNm0aJUqUID4+XutcUVFRAISHh+Pj48O+ffv47rvvCAwM1PQdEhKidUxoaCjVq1fXKvP392fs2LHPdX3z58/H3NycVatWYW1tzeeff46pqSn169fHs2ZNKtWty4GjR4mIimLatGnY29tz+PBhnJ2d6devH8uWLUNPT49SpUrx/vvv4+7uTlRUFCkpKcycOZPy5ctTokQJAM2bfn19fbZv387s2bM14zhw4AB2dnaMHz8ea2trKlasyJUrV57rGoQQQgghhBDieb0xQf/DNccPE7A9tH37dk1iNPHvXUtIZ/6Oi5rvuelJ3Fz9GRfD/2DDjnAuX77MunXrmDlzpibhW5MmTahatSrdunXj2LFjHDlyhP3792NjY8O8efNYuHAhVapU4ejRo+zevRuACRMmcPr0aVJTU/H396d9+/a8//77ODo6snPnTvr164darcbT0xOAe/fuAbBnzx6srKw048vJySE8PFyz3v9RDx480DwkioqK4v333+enn37CxMSEYsWKsXfvXrp06cLdu3fx9fXlxIkTfPTRR6Snp3Pnzh2io6M1U95v3brF1KlTmTFjBjY2NjRu3BgfHx+6deuGkZERXl5eLFmyhAEDBmBnZ4e3tzeDBw9mz549lCtXDgMDA5YsWcLdu3fJzs6mQoUK3LhxA1dXVwAWLFjAhg0bXvrvUwghhBBCCPF2UymPz7l+ja1du5YePXqwZMkS6taty7fffsuyZcs4c+YMTk5Ozzw+KSkJS0tLEhMT5a3/E0RdfUDbRQc035WcbB4cWEXG5ePopNxGR8nFwcGBjh078tlnn2FsbAzA5QsXCBw4kN3h4ejo6FCsWDEqubuzZcsW6tati7u7O2XKlGHBggU8ePCAYcOGkZ2dzbp162jTpg1Lly7lwoUL9OrVi+PHj5OZmcm5c+e4ceMGjRo1Ijg4mJ49e1K9enUaNGjAwoULuXz5MvHx8fj4+JCQkICZmZlm3I9Prbe1tcXf3x8nJyfWrl1LdHQ0KpWK+/fvExAQwMaNG7GysmLw4MGsXr2aa9eukZSUhIGBAX5+fpiZmbFv3z6uXbvGtWvX8PPzo3v37syZM4fDhw9Tr1499PT0MDMzo0OHDsydO5ehQ4eSlpbGV199xfvvv8/evXsB2L17N97e3lhaWqKrqwtAixYtqFOnDhMnTtTMkhBCCCGEEEK8+Qo7Dn2jgn6AxYsXM3PmTOLj4/Hw8GDevHn4+Pg817GFfbPfBLF3U3nvq32kZ+fmq/vqA0/aeJbOV55x/gJ3v1lM8rbt8Nc6+8/TUsl0cuLXrVs5cPQojRo14uTJk1y4cIF27dpp1sVXrlyZS5cuaa3ZVxSFtLQ0zp49i5ubGx06dMDe3p4pU6ZQsmRJbt++TYUKFdi3bx+hoaGEhoZy+PDhJ16TSqViw4YNtG3blkGDBrF06dJ8Wf/T0tJYtGgRH3/8MQEBAVy/fl1rVklycjLe3t7Ex8fTrFkzmjVrRrt27TAxMSEiIoLatWtrJfODvB0nqlevrhlbmzZtsLOzY8mSJWzYsIFevXpx8+ZNTExMnvVrEUIIIYQQQryhCjsOfWO27Hto4MCBDBw4sLCHUWSVsTZmWNOKfLn5nFa5vaUR1R2t87XPiI7mSo+eqFNStMrViYmkR0WRvGUr9Vu1xN/fn88++4yAgADtdmo1H330EYGBgfn6dnR0BMDPz49vv/2Wffv2Ua1aNaysrPDx8WHPnj2EhYXh5+f33NenVqupWbMmq1atylf3cC0+kC+ANzc359ixY4SFhbFt2za++OILJk6cSEREhCah4O+//07p0toPRQwNDTU/9+vXjx49ejBv3jxWrFhB586dJeAXQgghhBBCvFJvXNAvXi09XR06ejlQxtqEr3ddIiEtixZV7eha2xHHYtoBam5SEjenfpkv4H9U/IQJGFeryvTp0/H09MTFxUWrvkaNGpw5c4YKFSo8sQ8/Pz+GDBnC+vXrNQG+r68vO3bsIDw8nCFDhjz39dWoUYO1a9dSsmTJF37KpqenR5MmTWjSpAkTJkzAysqKXbt20bRpUwwNDYmLiyswt8BDzZs3x9TUlG+++YY//vhDM91fCCGEEEIIIV4VCfpFPtYmBjSvYoe3c3GycnOxNjFATzd/zsesq9dIP3r06Z1lZ5N+7DhVOr5Pt27dWLhwoVb1p59+Sp06dRg0aBD9+/fH1NRUk0DvYVsPDw9sbGxYtWoVv/76K5D3IGDEiBEA1K9f/7mvrVu3bsyaNYs2bdowefJkypQpQ1xcHCEhIYwaNYoyZcoUeNymTZv4888/8fHxwdrams2bN6NWq3F1dcXc3JyRI0cybNgw1Go19evXJykpifDwcMzMzOjVqxcAurq6BAQEMHbsWCpUqEDdunWfe9xCCCGEEEII8U+8Mdn7xX/P0kSfEuZGBQb8ALn37z1XP2knTwAwZcoUHk8hUbVqVfbs2cPFixdp0KAB1atXZ/z48djZ2WnaqFQqzRv0Bg0aaI6ztLSkevXqT31j/3DqvZ5e3vMtExMT9u7di6OjI+3bt8fNzY0+ffqQnp7+1H6srKwICQmhUaNGuLm5sWTJElavXk3lypU11/bFF18wbdo03Nzc8Pf3Z+PGjZQrV46wsDBUKhUPHjygb9++ZGVl0adPn+e6dwUJCAigbdu2//h4IYQQQgghxNtD3vSLf0yl9+Q/Pl/a2Wt+1jXJWx/v5ORERkZGvra1atVi27ZtTz3X+vXrtc+tUmm28Xua27dvA3nZ+x+ytbUlODj4iccEBQXlK6tfvz5hYWFPPEalUuHl5cXFixdp2rQpW7Zs0dQ9elx8fDx6enr07NnzmWMXQgghhBBCiH9L3vSLf0y/dGl0TJ+diM6sUcP/YDTaFEUhNjaW//3vf5QqVQoPD49Xfs7ly5fzySefsH//fuLi4vLV//nnn4wfP55OnTpRqlSpVz4eIYQQQgghhJCgX/xj+mXKYPPhR09tY1ChAgbly/9HI/pbYmIirq6u7N+/nzVr1uTbou9lS01N5eeff+bjjz+mZcuWBc4WqFWrFomJiYwdO5YuXbpQpkwZTExMqFKlCqtXr9Zqu379eqpUqYKxsTE2NjY0adKE1NTUAs8dGRlJyZIlmTp1KgBbtmyhfv36WFlZYWNjQ8uWLYmJiXnp1yyEEEIIIYR4/UnQL/4xlY4Olu3bYdmhQ4H1+k5OlFnwFfqPbIX3X7GysiIzM5OoqKgX2tLvn1q7di2urq64urrSvXt3VqxYkS9/wb1794iMjMTa2pqaNWuyadMmTp8+zYcffkiPHj04fPgwkLcEoEuXLvTp04fo6GjCwsJo3759vv4gb+lA48aNmTRpEuPGjQPyHkAMHz6ciIgIdu7ciY6ODu3atdPkNxBCCCGEEEK8PVRKQZFEEZWUlISlpSWJiYkvvF2beLKcBw/IuhzLg5AQMi9cQNfSAusuXTBydUXf3v7ZHRQB3t7edOrUiSFDhpCTk4OdnR2rV6+mSZMmhIWF0bBhQxISErCysirweJVKRevWrfn11185duwYNWvWJDY2Ficnp3xtAwICePDgAb1796ZHjx4sXbqULl26PHFsd+7coWTJkpw6deo/WeYghBBCCCGE+Fthx6Hypl/8a3pWVphU98Ru0kScVizHYdEizBs2fK0C/oCAAFQqFdOnT9cqDw0NRaVSvXiH2emQcAUSrnD+zCmOHDnCBx98AOTtFNC5c2eWL19e4KG5ublMnTqVqlWrYmNjg5mZGQB3794FoFq1ajRu3JgqVarQsWNHli1bRkJCglYfhw8fpkOHDgQHB+cL+GNiYujatSvly5fHwsKCcuXKARSYZ0AIIYQQQghRtEn2fvHSqHR0UJk8O7FfYTEyMmLGjBl89NFHWFtb//OObp+FvXPg7AZAxffHHcjJyaF06dKaJoqioK+vny9YB5gzZw7z5s1j/vz5VKlSBVNTUypWrEhOTg4Aurq6bN++nfDwcLZt28bChQsZN24chw8f1gTwzs7O2NjYsHz5clq0aIGBgYGm/1atWuHg4MCyZcuwt7dHrVbj4eFBVlbWP79mIYQQQgghxBtJ3vSLt0aTJk2wtbVl2rRpT2wTHh6Oj48PxsbGODg4EBgYqEmgN3bsWOp4ecKK5nB6PahzycnJZt6vJ2ha0ZiosN/48ccfqVWrFhYWFmRnZ1O7dm0uXLigdY4//vgDPT09+vXrR5cuXQpMsjdmzBh69+7NrFmzSElJISMjQ2vbwuLFi7Nr1y5iYmLo3Lkz2dnZQF7egOjoaD7//HMaN26Mm5tbgQ8ehBBCCCGEEG8HCfrFSxEQEEDbtm0LexhactPTybpxg+zr11Gys9HV1eXLL79k4cKFXLt2LV/7U6dO4e/vT/v27Tl58iRr165l//79DB48GIBuXbtyOPIEMdfvaY5ZHJFFjgLT/HTwMLhGyZIlGTRoEOHh4fTr14/79+8zZswYTXu1Ws2pU6d48OABS5YsYezYsXTr1k1rHIcPH+bYsWN8+umnbN++nY4dO5KcnEx0dLRWu5IlS7Jr1y7OnTtHly5dyMnJwdraGhsbG7799lsuXbrErl27GD58+Mu8rUIIIYQQQog3iAT9osjJTUkh7ehRbowaTYx/My6960/qoUPk3LtHK18/PD09mTBhQr7jZs2aRdeuXRk6dCgVK1akXr16LFiwgB9++IGMjAw8XMpStbQpP53K1hyz8EgWloZQ014Xzv5KI98GdO/eHTc3Nz766CPu379PWlqapv2OHTt48OABvr6+fPLJJ4wcOZJ3331XaxwWFhbo6uoyduxYGjduTGhoKG3atOHkyZP5xmxra8uuXbs4deoU3bp1Q1EU1qxZQ2RkJB4eHgwbNoxZs2a9xLsrhBBCCCGEeJNI0C9euszMTAIDAylZsiRGRkbUr1+fiIgIAGJjY1GpVFpvn9u2bYuenh5JSUkA3Lx5E5VKxfnz5wH48ccf8fLywtzcHFtbW7p27crt27e1zjlx4kQ8PT3JTUnhwfr1XOnegzpLl/DDrVuQm0vu3bukR0VxZ+ECvhw3juDgYM6ePavVR2RkJMuXL0elUmFqaoqZmRn+/v6o1WouX74MOgZ0q1+WVX8F/YqikKMGFxsd2q5JAzM7bt+5x4ABA3BxcaFhw4aYmpqSlZXFokWLsLKyIjo6GkdHR7Zu3UpycjK3bt3im2++AfKWDwC4ubnRr18/XFxcsLKy4vr162zZskWTiC8oKIjQ0FDNuO3s7Dh//jxr165FV1eXJk2acPbsWTIyMjhx4gS+vr4oivLazcQQQgghhBBCvHoS9L9FHmawHzBgQL66gQMHolKpCAgI+NfnGT16NL/88gvBwcEcO3aMChUq4O/vz/379zVtjh49CuQFzvv27cPa2pr9+/cDsHv3bmxtbXF1dQVg9uzZ5ObmcuLECUJDQ7l8+bJmnAcPHkSlUtGoUSN27txJ5vnz3J4+44lje7B6DTX19PH39+ezzz7TqlOr1Xz44YeEh4cTFRVFVFQUJ06c4OLFizg7O4O+IV0HjeXCPTXH4nMJv5rL1USFVe2NCWprDDV7EdCnD5GRkcyfP1/Tj42NjSaJXkE7ZD6+e8ChQ4f44IMPeO+999i0aRPHjx9n3LhxkohPCCGEEEII8cIke/9bxsHBgTVr1jBv3jyMjY0ByMjIYPXq1Tg6Ov6rvtVqNampqXzzzTcEBQXx3nvvAbBs2TK2b9/O999/T8eOHQE4fvy4Zn27rq4u3bt3JywsjObNmxMWFoavr6+m34kTJ9K+fXt0dXWpU6cOCxYsoHbt2qSkpLB8+XI8PT3x8fEhNz2d+OAfCh7bI8H2nYUL+d+YMXj5+eHi4qIpr1GjBufOnaNu3bpPvMYyNZrhU8WRVSdvkp6j0KS8LhVtdMHvM7Ctwr59+1i8eDHNmzcH4OrVq5qt+ADc3d2Ji4vjxo0b2P+1peHBgwe1znHgwAGcnJwYN26cpuzKlStPv/lCCCGEEEIIUQB50/+WqVGjBo6OjoSEhADg5+dHu3btcHBwoHr16pp2W7ZsoX79+lhZWWFjY0PLli21ssw/nKb/888/4+fnx8qVK4mNjaVUqVJkZ2fj7e2t1detW7e01qSnpqZy/Phxdu/ejampKatWrWL27Nm4urqyYcMGraB/8ODBKIpCpUqV0NHRoXbt2gCcPn2atWvX0rdvX0aNGoV1yZJU+HohTf+MoUfcFe7n5nIwLRXfS5fYnpICwPH0NFrs3EHdJk2wsrJi/vz5AERFRfHpp5+yf/9+VCoV+/bt4+LFiwwZMgRDQ0O2bt2Km5sbZqWcuJtjwqoYM9ZdMqR7zz4ERPvSds4+MLaiQoUKzJs3j5o1a2Jubk758uXR0dHRBP5NmjTB1dWVnj17cuLECfbt26cV3ANUqFCBuLg41qxZQ0xMDAsWLGDDhg0v49cvhBBCCCGEeMtI0P+aUqlUT/08bRr+06bxnzhxglOnTjFq1CgAQkJCSE9Pp0+fPlrtUlNTGT58OBEREezcuRMdHR3atWuHWq3Wajdy1GgGDhpM27ZtsbOzw9/fXzP+h1asWIG9vb3WXvKurq6EhYWxZ88e3NzcWL16NXp6enz44YfcuXOH9PR0zTju3LmDSqVCR0eHjRs3MmnSJACmTZtGVlYW3bp1IzQ0lOzsbFaUK8d8+9JczswiQ1G4m5PLdw4O1DUxIUdRGHjtGi6GhhwMDWXOnDnk5ORoxlS1alW++uorAN577z2qV69OSEgIubm5zJ49m5UrV7J3715y1Ap37j0gLUtN2+FzwbgY6OZNmlm+fDlJSUmcPHkSGxsbvvzySwwNDVm+fDlqtRodHR02bNhAZmYmtWvXpl+/fkydOlXrnrZp04Zhw4YxePBgPD09CQ8PZ/z48U/8fQshhBBCCCHEk8j0/tdUfHy85ue1a9fyxRdfaBLbAZqp+U/ycBr/zE8/xSAnB5WuHjmZmVy/fp0yZcpw48YNzdv6w4cPs379esLCwjTHd+jQQau/77//npIlS3L27Fnc3CtzPC5v7/dst/eINnJD19AE49xcAgMDCQkJYePGjQwaNIi7d++yadMmLC0tcXNz0/Tn5eXF7t27OXz4MGFhYVSuXJnKlStz8uRJjI2NCQ8PZ8DgARw4doCsrCxKlSrFrVu3MDExoXz58gDs27eP9u3bc+fOHS5dukQFZ2e8ff1IPXCA6sbG7ExNobm5ORUNDfm6jANrHiRwMiODadWr41KzJtVLliQnJ4f+/ftrxlWpUiUArl27hpWVFUFBQfTu3ZslS5bkresHAgMDmTx5Mjdv3sx336tXr86lS5e0ygICAjT3zsPDAxcXF/bt26fV5vG1/jNnzmTmzJlaZUOHDn3i71sIIYQQQgghCiJB/2vK1tZW87OlpSUqlUqrbOPGjUycOJEzZ85gb29Pr169GDduHHp6eb/Sq1evYmdtTb0qVYhJS6Oknh4GJiYYGRhQw9OTrKwsypUrR8mSJVEUBUdHR4yMjHBwcKBixYrExcWhUqnyMtTn5Gje8NevX5/0jAxy/vqjk3TtPFO6NUSdcg8dHRU7duwA8gLjLVu2sH//frKzs7l79y6jRo3SbE9Xs2ZNhg8fjqIoeHp6oq+vT1ZWFlFRUQBs+n0TpsamWFSwAODWrVtYWVsxfvx47ty5A0BCQgJ9+vQhOjo6L+O+mRnF+vYh9cABzHV10AV0H5lxEJuVhauhIaU/+QT9kiUBNEsFnsbExEQT8ENetvzHdw94VExMDOPHj+fQoUPcvXtXc+/i4uLw8PB45vmEEEIIIYQQ4mWR6f1voK1bt9K9e3cCAwM5e/YsS5cuJSgoSDNNXP3XvvApiYlkZmezoWw5fEzNiElMpERWFjm3b1P6ryRyd+7cwcvLi6NHj5Kbm8vJkyeZPHkyjo6OlCtXDoD//e9/rFq1CoDc3Fxadu5F8dajAUi/cBAb/0EYlnQCoEuXLnz66afo6uqyadMmHjx4gL29vSZT/q5duwDYu3ev5u22SqWiQ4cOmu/GpsaoTdXoGuuiX14fHdO8P6YPEh5w4MABJk+eDICBgQGNGzfWektu5O5Osd4Bef0+dt8UQNfKGtP69f8uKyCb/uP09fW1vj98GPIkrVq14t69eyxbtozDhw9z+PBhAMm+L4QQQgghhPjPSdD/mrmZepPjt49z/NZxridfLzC4nDp1KmPGjKFXr16UL1+epk2bMmXKFJYuXYqiKGRdvQpAe0tL4rKz0VNBJysrFMBYR4eMs9HYmJoCoKurS82aNXF3d6dUqVJAXrK/S5cuYWBgwMSJExk7dixff/01AB07dmTvlt/QtykDgI6xBUZlPVFlJjPuiwksW7aMTz/9FPh7Xf/hw4dp2rSp1jVs3rxZs37ey8uLlStX/p38rxSos9UYlzPG7gM7dE110bPUo+K0igCcPnsaMzMz7OzsUKlUuLm5oSgKaWlp6FlZYfPhh+RUqULOI+fTL1MGz06duJCZgdrKSlP+cOvAl+XevXtER0fz+eef07hxY9zc3EhISHip5xBCCCGEEEKI5yXT+18Tqdmp7L++n+lHpnM3PS/Tu4WBBUNrDCUtO02rbWRkJBEREVoJ4HJzc8nIyCDx0iUyL+atKa9mbMyN7GxCE5NQUFABeRPNFXITE4G8afYPg/OSJUty8eJF6tati4GBAadPn2bSpElMnjyZjIwMIC8pH4D3/aNcB5TcbG4s7UtO8j30dVTk5ORgbW1N27ZtWbduHSqVirFjx+bbDjAzM5MxY8YAeVvWGRsbk52dDUDGjQxUqEiNTuXsR2dRZ+aNOmZi3u4BU6dMRVEUKleuDOStw7e1teXq1ascPnwYPT09jly8iI6ODjYf9qdcrwB0rSzpZ2jI1HXr+PDDDxkzZgxxcXHMnj0b0E48CHlJ/YYPH47VIw8Inoe1tTU2NjZ8++232NnZERcXp7lOIYQQQgghhPivyZv+18SR+COM3DNSE/ADJGUlMfnQZE7fPa3VVq1WM2nSJKKiooiKimLfwSOs2bKfuev3cDjFAMUtLxjWQ0V7SytCkxL5NSlJ65ede/9+Xhu9v5/7PJzGvmDBAtq2bYtarSYzM5MyZcpQ/68p8SNGjMDe3p5TW3/KO0adgRF509bnzZuHj48P2dnZtGzZEsibPr9582aWLl0KQFra3w8wHmbo9/f3R09PTzOrQclSUGeoUemrcJ7snPdoSh9MK5tiVNYIRVFQqVRERkYyaNAg4uLi6Ny5MwYGBjRo0IB69epp1tL/tHkzSZYW6JcogYWFBRs3biQqKgpPT0/GjRvHF198AYCRkZHWPd61axcffvjhi/wKAdDR0WHNmjVERkbi4eHBsGHDmDVr1gv3I4QQQgghhBAvgwT9r4E7aXeYGTHzifXbr2xH4e9p/jVq1OD8+fNUqFCBEqWd2HpVxZA/bvJVRDIfrT3NwbS/t8arb2pKtqKQrSiY6OjQ3do6r0In/69+3rx5ecfUr8/atWupU6cOKpWKGTNm4OzsTJs2bZg9ezYVK1ZEUasxNzdn2JAhLPpr6r+fnx8HDx7kxIkTmr3nO3bsyIYNGyhevDgAf/75J0lJSVrb9y1ZsoT79+/TsmVLjEyMcJnhgp61HuoMNemX09E10UXfSh9dY12ybmVhamrKp59+yoYNGwgKCmLLli3Mnz+fxMRElixZQmhoKBcvXuTgwYMYGhpqbW9Yr149Tpw4QWZmJkePHkWtVqOvr6+ZieDn54eiKFSoUAETExMCAgJ48OCB1n1q27at1rKLoKAgQkNDNd+bNGnC2bNnycjI4MSJE/j6+qIoCm3btn3i71gIIYQQQgghXgUJ+l8Dd9LvcC3l2hPr03PSUStqzfcvvviCH374gYkTJ7LjwFEWhuwhNXovCXtXApCalddW39EJXZWKTWXLsalsOa0+H2avf9TevXsBOHv2LFeuXMHLywtFUdixYwd//vknp06d4ssvv+TixYuULl2atLQ00tPTqVGjBnZ2doSGhmJgYMD69eu5du0aKpWKbt264eTkRKNGjYC8pQnx8fFaa/wnTpxI5cqVsbCwADWYlDTBxNkEVHBz7U2UXAUlVyE7IRtddLEuZk2pUqVo2bIlLVq04MMPP9QE3Y0aNaJ58+ZERUUxduxYjh8/zh9//MHOnTsB+OGHH9i/fz+LFy/GwcGB7t27Y2BgwOLFi7XuRdmyZZk/f77WGB0dHTE0NMTe3p7AwMCn/k5fZwEBAW/cA4iH20s+3N0hLCwMlUqV74GMEEIIIYQQQpsE/W8gf39/Nm3axPbt2+naqhHxK0eQFBGKnkUJrXa55fK2mTPT1cVMV1dTrtLXR8fMLF+/5ubmQF4Gfjc3N/bu3cvYsWM5ceIE4eHhXL58mQkTJnDjxg1u3rxJ7969OXToEN7e3ty+fRsjIyN0dXWZMWMGkLfVXe/evTV9Qd42e9WqVePWrVua865atYqEhAR27dpFVlYWfw77k6RjSaCAOlONOlVNzv0cMmIzKFWiFDqqv//Y2tnZaV3DmTNnAOjcuTOHDx9G568ZDT169CAnJ4ebN2/SqVMnBg0aREpKCr169WLOnDmMHz+eoKCgAu/3+vXrmTdvHkuXLuXixYuEhoZSpUqVZ/+iniEgIACVSsX06dO1ykNDQ/PlGPgnHg+U/62srCxmzpxJtWrVMDExoXjx4nh7e7NixQpNPob/Sr169YiPj8fS0vI/Pa8QQgghhBBvGknk9xooZVIKJwsnriRdKbC+XJNyrJ2/VqvM398ff39/wi/dpet3h7XqircYRtcRU2kWsYpHN4k7XNEFXSsrhn3/HcZ/JcF7VO/evenduzcAaVk5xN5NJfZeGu/2GsqiicPJTk8hNDQUPz8/PD09td6Et23bFisrK4KCgqhTpw7R0dHk5uYSFRWFo6MjSUlJWFpaMn/+fNq2bUtsbCzlypUjIiICLy8vfv75Z3r16sXChQupUasG4yaOI/JYJLmGuRiaGVKrRi3Mcs3ITs3WCmIfDY5TU1Pp2bMnAMOHD6d3797ExcXh7+9PfHw8ly5dYvTo0Zw4cYI7d+6wbds2zbExMTHMmjVLaynAQ3Fxcdja2tKkSRPNUoDatWsX+Lt6UUZGRsyYMYOPPvoI64dLL16Cl709YFZWFv7+/pw4cYIpU6bg7e2NhYUFhw4dYvbs2VSvXh1PT89/1Hd2dna+bRGfxcDAAFtb2390PiGEEEIIId4m8qb/NWBjbMOYWk/O8D7SayT2ZvYF1jmXMKOyvYVWmZ6OioENK2I/oD/W3bth6OaGcfXq2M2YjtPaNQUG/I/KzM7l16gbNF+wn4GrjhGwIoLd52+Tkpnz1OMgL/A+efIkqampNG7cWPP2/ODBgwC0a9eOrVu30qpVKwCOHz/OxIkTGThwIPXq1WPgwIHUrV2XXZt3kXgzkZQrKdw7c48DoQcw1c/bZjAjI4O5c+diaGjIsmXLgLzdC86dO8f9vxIUxsbGsnz5ctq1a6cZ2+3btwGIjo5GpVJRpUoVTE1NcXBw4NSpU1y8eJHc3Nx819SxY0fS09MpX748/fv3Z8OGDeTkPPtePI8mTZpga2vLtGnTntrul19+oXLlyhgaGlK2bFnmzJmjVV+2bFn+97//ERAQgKWlJf3796dcubwlHdWrV0elUuHn56d1zOzZs7Gzs8PGxoZBgwY99W39/Pnz2bt3Lzt37mTQoEF4enpSvnx5unbtyuHDh6lYMW87xS1btlC/fn2srKywsbGhZcuWxMTEaPp5OPvg559/xs/PDyMjI3788UfUajWTJ0+mTJkyGBoa4unpyZYtW544nsen9wcFBWFlZcXWrVtxc3PDzMyMZs2aER8frzkmIiKCpk2bUrx4cSwtLfH19eXYsWNPve9CCCGEEEK86STof0142XqxuPFiypiX0ZSVNCnJzAYzaeTQ6InHlbI04pvuNRjX3I0qpS1p62nPLx/Xo3IZK4yrVKHU2LE4/RCM44rlWLVpg6GT0zPHEnsvlXEbTmmVZWSribmdQlrW04PdtWvX4u7ujqurK1euXGHp0qXs3btXk9gPYPTo0YwePRpAEywaGhpy9OhRflz/K5u278TNzQ09PT3c3NwwMjKidOnSeePIyOD27ds4Ojpy4sQJTa6AdevW4ejoqEkQuHHjRq5evUqpUqU05z1y5AiQt6OAjo4OCxYs4PTp0wQHB3Py5MkCA34ABwcHzp8/z6JFizA2NmbgwIGaXQr+LV1dXb788ksWLlzItWsF53WIjIykU6dOfPDBB5w6dYqJEycWuBxh1qxZeHh4EBkZyfjx4zXXu2PHDuLj4wkJCdG03b17NzExMezevZvg4GCCgoKeuLwB8pZgNGnShOrVq+er09fXx9Q074FMamoqw4cPJyIigp07d6Kjo0O7du1Qq9Vax3z66acEBgYSHR2Nv78/X331FXPmzGH27NmcPHkSf39/WrduzcWLF5/nNgJ5O0PMnj2blStXsnfvXuLi4hg5cqSmPjk5mV69erFv3z4OHTpExYoVad68OcnJyc99DiGEEEIIId44ylskMTFRAZTExMTCHsoT3Um9o5y+c1o5eeekEp8S/0LHpqRnK1k5uf96DDujbypOn27S+ph6NFZMKtZRrt1PVXx9fZXAwEAlMzZWSfxji3Jz+gylWWUPpdt7zZW6tWop8+fPV86fP694e3srgFKmTBlly5YtCqAASmhoqHL58mUFUI4fP65MmDBBcXWvrFTyaaXoGJoqusZmikM5Z8W5QgXF0tJSMTMzUwYNGqT06tVLcXFxUfT09JS5c+cqiqIoQ4YMUQDFyMhIyc3NVb766isFUFQqlVK3bl3lt99+05y3S5cuiqIoSteuXZWmTZtqXXPr1q0VXV1dzXcnJydl3rx5Bd6fc+fOKYASGRn5j+7vvfR7SnxKvNKtRzelTZs2iqIoSp06dZQ+ffooiqIoGzZsUB79T7Og8Y4aNUpxd3fXGm/btm212jx6jx/Vq1cvxcnJScnJydGUdezYUencufMTx2xsbKwEBga+0HUqiqLcvn1bAZRTp05pjWn+/Pla7ezt7ZWpU6dqldWqVUsZOHBggdeye/duBVASEhIURVGUFStWKIBy6dIlzfGLFi1SSpUq9cSx5eTkKObm5srGjRtf+LqEEEIIIYR4XoUdh8qa/tdMcZPiFDcp/o+ONTV6Ob9OM8P8/RRvMQx7SyP09XTYtXUrqfv28We79ihpaQDMBS6fi6ZNbCw/zZ5NWRcX9u/fz+DBg7l//z7+/v7s3r2bhg0b4uXlRenSpTXb3v28PoQ7yVmYt/oIh7ofAXDv93kUS43F2NiYsmXLoqenR1BQEO3bt+f69euatfzz58/nq6++IiMjg2vXrtG6dWuGDBlCx44dWbs2Lw9CQkIC1tbWJCQkADBixAhq1aqFs7MzKSkpJCYmkpmZCeS9qX741vqhoKAgcnNzeeeddzAxMWHlypUYGxvj9ByzJh4VnxLPgesHWHFmBQkZCdy9eRc7HTtSs1OZMWMGjRo1YsSIEfmOi46Opk2bNlpl3t7ezJ8/n9zcXHT/StLo5eX13GOpXLmy5jjIS4h46tSpJ7ZXFOW5kgvGxMQwfvx4Dh06xN27dzVv+OPi4vDw8NC0e3SsSUlJ3LhxA29vb62+vL29OXHixHNfk4mJCc7OzlrX9HBJB+Qt7/jiiy/YtWsXt27dIjc3l7S0NOLi4p77HEIIIYQQQrxpJOgX+ZS1McXNzpzoeO1pz6ObVaKkuRGphw9z7ZNAeGSveoBfHiSSoyhU8PWFvwJKRVHQ19fXBNxAvqA6NTOH5MwczB8py35wk6vXLxMefoAxY/7Od/C04PPRcj29/H+0Hz5ksLGxQVdXlwcPHpCUlETJkiXx9vZm3bp1BU7Zt7KyYvr06QwfPpzc3FyqVKnCxo0bsbGxKXAcBbmZepMx+8Zw7Pbfa8jvpN3hZtpNNsVsom29tvj7+/PZZ5/lSyZY0DX3798/33KER+/rw0SJT/J44jyVSpVvCv6jXFxciI6OfmL9Q61atcLBwYFly5Zhb2+PWq3Gw8MjX2LBx/8MPBzDo573QcNDBV2T8sif0YCAAO7cucP8+fNxcnLC0NCQunXrvvSkh0IIIYQQQrxOZE2/yKekhRHfdK9J3/rlKG5mQMWSZizuVoOGriXISXjArRkz8wX8OYrCr0mJjC5RkpCy5dg1+lOOHzvGiRMncHJyYtWqVU88n66OCt1HgrvM69Fkxl9gxP/mUadOHcLCwjQ7Bbi7u1O6dGmGDBmiab9o0SLMzc0pXbo0ZcuWxdfXlxIl/t6+0MrKijZt2miyvR89ehRFUbhz5w7Z2dlcv36dqlWrao0pNjaWoUOHAnk7Exw6dIjExERSUlI4ePAgjRs3fqF7GnkrUivgf9S0I9O4lnKN6dOns3HjRk0CO3t7ewwMDLhw4QLfffcd9+7d+/seZWZSsmRJrbf1j3JwcOD48eMAT8xV8CK6du3Kjh07NH0+Kicnh9TUVO7du0d0dDSff/45jRs3xs3NTethz5NYWFhgb2/P/v37tcrDw8Nxc3P712N/aN++fQQGBtK8eXNNUsS7d+++tP6FEEIIIYR4HUnQLwpU1saUse9V4vdPGvDzR3VpXsUOSxMDsq9fI/Ps2Xztw1JSSFKr6WBpSUVDQ0pt20alEiXw8PDg/fff5/vvv3/iucyN9ChlYQRAbkoCtzdMpZRnI7q0a8nNmze5efMmd+7cAWDgwIFcvXqVTz75hHPnzvHrr78yYcIEhg8fjo7O8/1xdnZ2Jicnh4ULF/Lnn3+ycuVKlixZ8g/u0vNJzkrmh7M/PLE+V8nl4oOLVKlShdatW7N06VIAVq9ezaVLl5g+fTpXr17F1dWVI0eOEBwcTHJyMg0bNnxin7q6unh4eGBsbMyWLVu4desWiYmJ//gahg4dire3N40bN2bRokWcOHGCP//8k59//pl33nmHixcvYm1tjY2NDd9++y2XLl1i165dDB8+/Ln6HzVqFDNmzGDt2rWcP3+eMWPGEBUVpfVw59+qUKECK1euJDo6msOHD9OtWzeMjY1fWv9CCCGEEEK8jiToF0+kp6tDKUsjrE0NNGXqlNQC24YkPqCuiQnmf715VqekoP5rnXyHDh2Iiop64vZoKpUKCyM91n1UlzblFNSpD7gVuZWa7s7Y2dlhZ2dHrVq1AChdujSbN2/myJEjVKtWjQEDBtC3b18+//zz574uT09P5s6dy4wZM/Dw8GDVqlXP3DLv38hR55CSlfLUNsmZeUspHn0z7uvri6OjI5988gnLli3j/v371K1bly+++AIrKys8PDzo06cP5ubmXLt2jfDwcM2xsbGx6OvrM3LkSJYuXYq9vT2+vr7Url2blStXsmXLFsaMGfPcWw8aGhqyfft2Ro8ezdKlS6lTpw61atViwYIFBAYG4uHhgY6ODmvWrCEyMhIPDw+GDRvGrFmznqv/wMBARowYwYgRI6hSpQpbtmzht99+0+zu8DIsX76chIQEqlevTo8ePQgMDKRkyZIvrX8hhBBCCCFeRypFeWyedhGWlJSEpaUliYmJWFhYPPsAkU/aiRNc6fzBM9upjIwov/l3DOzt/4NRvd5ycnOYFjGNn8///MQ2K/xXUN6gPMWLF2fq1KmMHTs2X5sPP/yQ9evXc+/ePcqVK0dycjJTpkzh3XffZf369YwbN44zZ85QqVIlzZr+48eP4+npyfXr13FxcSEgIEAzS6J///4MGjSIiRMnvsKrF0IIIYQQ4u1W2HGovOkXL8SgTBkMypZ9ZjvrDz5AX96iAqCnq0eHih3QVRW8/r6CVQWcLJy4ePEiiqI8cR37wzXyD5c6NG/enIEDB1KhQgU+/fRTihcvTlhYWIHHLl68GAcHB77++msqVapE27ZtmTRpEnPmzHlqAj8hhBBCCCHEm02CfvFC9GxsKDnm06e2URkZYdmuLaoCMui/rSpaVWRBowVYGGg/2atWohrzfJaQlWlKckb+nQMe9XBSzsOM9o8mH1SpVNja2mptUfeo6Oho6tatq5UN39vbm5SUFK5du/aPrkkIIYQQQgjx+pOoTLwwEy8vbCdP4uakyfBYZngdMzMclizB0MWlkEb3etLX1adB6QasbbmWuOQ40rPTKWVaitwMez79+TxHryRQwUKNSqXizJkztG3bNl8f586dw9ramuLFi+f1+QLb7hW0/d3jDxGEEEIIIYQQRY8E/eKF6ZqZYdmmDSbVq5N6+DAp+w+go6+PRYsWGLm7YeDoWNhDfC2pVCrKmJehjHkZAGLvpdL6+/0kpecl0zv/QIVxueosXLSY4cOHa2WWv3nzJqtWraJnz57/KEh3d3fnl19+0Qr+w8PDNVsdCiGEEEIIIYommd4v/hEdQ0MMK1akWPfuOHyzmDILF2DRzF8C/hcQezdVE/A/ZNX4I9LTM/D392fv3r1cvXqVLVu20LRpU0qXLs3UqVP/0blexlaHQgghhBBCiDeP/Gtf/GsyPfyf0SngvukXK8281X/g7OxM586dcXZ25sMPP6Rhw4YcPHiQYsWK/aNzvYytDoUQQgghhBBvHtmyT4hCEnc/lXaLwrmXmqUpMzHQ5bfB9alQ0qwQRyaEEEIIIYR4WQo7DpU3/UIUEsdipvzUvw7vedhiZaJPg4rFWftRHQn4hRBvlLCwMFQqFQ8ePCjsoTzTmzRWIYQQ4mWRoF+IQuRqa86cTtXYMqQBS7rXpEppq8IekhDiLbZkyRLMzc3Jyfk730hKSgr6+vo0aNBAq+2+fftQqVTY29sTHx+PpaXlfz3cF1avXr03ZqxCCCHEyyJBvxCFzMRAD1tLY0wNZTMNIUThatiwISkpKRw9elRTtm/fPmxtbYmIiCAtLU1THhYWhr29PS4uLtja2r4R+V0MDAzemLEKIYQQL4sE/UIIIYQAwNXVFXt7e8LCwjRlYWFhtGnTBmdnZ8LDw7XKGzZsmG/K/JUrV2jVqhXW1taYmppSuXJlNm/erDnuzJkztGjRAgsLC8zNzWnQoAExMTEAqNVqJk+eTJkyZTA0NMTT05MtW7Zojo2NjUWlUhESEkLDhg0xMTGhWrVqHDx4UNPmaed/fKxBQUFYWVmxdetW3NzcMDMzo1mzZsTHx7/sWyuEEEIUGgn6hRBCCKHh5+fH7t27Nd93796Nn58fvr6+mvKsrCwOHjxIw4YN8x0/aNAgMjMz2bt3L6dOnWLGjBmYmeXlKrl+/To+Pj4YGRmxa9cuIiMj6dOnj2Y5wVdffcWcOXOYPXs2J0+exN/fn9atW3Px4kWtc4wbN46RI0cSFRWFi4sLXbp00fTxtPMXJC0tjdmzZ7Ny5Ur27t1LXFwcI0eO/Hc3UQghhHiNyHxiIYQQQmj4+fkxbNgwcnJySE9P5/jx4/j4+JCbm8uCBQsAOHToEOnp6TRs2JC4uDit4+Pi4ujQoQNVqlQBoHz58pq6RYsWYWlpyZo1a9DX1wfAxcVFUz979mw+/fRTPvjgAwBmzJjB7t27mT9/PosWLdK0GzlyJC1atABg0qRJVK5cmUuXLlGpUqWnnr8g2dnZHDp0CC8vLwAGDx7M5MmTX/zGCSGEEK8pedMvhBBCFBI/Pz+GDh1aeAPISocbx2Hn/+DXQRC9kYZelUhNTSUiIoJ9+/bh4uJCyZIl8fX1JSIigtTUVMLCwnB0dCwwoA4MDOR///sf3t7eTJgwgZMnT2rqoqKiaNCggSbgf1RSUhI3btzA29tbq9zb25uffvpJ6z5VrVoVgNDQUCpXrgzkPSDw9PR86vkLYmBggK6urua7nZ0dt2/ffva9E0IIId4QEvQLIYQQL1FAQAAqlYoBAwbkqxs4cCAqlYqAgAAAQkJCmDJlyn88wr/kZMHZDfCtH+ybBcd/hLXdqXDkc8rY27F79252796Nr68vALa2tpQrV47FixczYcIE6tevX2C3/fr1488//6RHjx6cOnUKLy8vFi5cCICxsfEzh/V4kj1FUfK1KeihwcN2Tzt/QR4N+B+ev6BzCiGEEG8qCfqFEEKIl8zBwYE1a9aQnp6uKcvIyGD16tU4OjpqyooVK4a5uXlhDBESLsPGwPzl8VE0rOpIWFgYYWFh+Pn5aaoevu0H8m3h9ygHBwcGDBhASEgII0aMYNmyZUDeG/p9+/aRnZ2d7xgLCwvs7e3Zv3+/Vnl4eDgmJibPvJzg4GBOnDiBSqXC0dERIyMjQkJCaNCgAaNGjcLU1JSOHTsCedsQPu5hMr+HSwskmZ8QQoiiQoJ+IYQQ4iWrUaMGjo6OhISEaMpCQkJwcHCgevXqmrLHp/eXLVuWL7/8kj59+mBubo6joyPffvutpv55stdDXqDs4+ODsbExDg4OBAYGkpqaqqlfvHgxFWs1wmjSPUrNTub9n//eii8zR+HG5bNs376do0ePMnv2bE2g7+7uzrp16wD4+OOPUalUTJ8+HYAdO3ZQv359DA0NMTc3p1GjRvz666/s2rULNzc3IG+9fFJSEh988AFHjx7l4sWLrFy5kvPnzwMwatQoZsyYwdq1azl39iyjhw4lKiqK0qVLP/Oed+rUicqVK9OvXz9++ukn3nnnHY4dO8alS5eoW7cup0+fZuzYsQBMmDBB69hHk/lNnToVQJL5CSGEKDIk6BdCCCFegd69e7NixQrN9+XLl9OnT59nHjdnzhy8vLw4fvw4AwcO5OOPP+bcuXNabZ6Wvf7UqVP4+/vTvn17Tp48ydq1a9m/fz+DBw8G4OjRowQGBjI5sAfnB5uxpZsJPk5/T3EfvT2TUzfyZiiUL18ed3d3/P39uX//Pu3atdO0O3/+PPHx8Zp+09LSGD58OJ06dcLa2po9e/bQoUMHXFxcWLx4MQA2Njbs2rWLlJQUfH19qVmzJsuWLdNM1w8MDGTYJ58wfMgQqlatyqbly1lSpQq6N2+Sffs2OX9ttVcQY2Nj9PT0MDIyYvz48VSvXp1mzZrh6+vL+vXrKVeuHDVq1ABgw4YNWsdmZ2ezZMkSvLy8cHZ2BmDnzp3P/F0JIYQQbwTlLZKYmKgASmJiYmEPRQghRBHVq1cvpU2bNsqdO3cUQ0ND5fLly0psbKxiZGSk3LlzR2nTpo3Sq1cvRVEUxdfXVxkyZIjmWCcnJ6V79+6a72q1WilZsqTyzTffKIqiKJcvX1YA5bvvvtO0OXPmjAIo0dHRiqIoSo8ePZQPP/xQa0z79u1TdHR0lPT0dOWXX35RLCwslKQ/IxVlcnFFmWCh+aSMNVf0dVBWTe6rOTYrK0uxt7dXZs6cqSiKouzevVsBlISEhKfeh9u3byuAcurUqee+d9kJCcqdRYuVs66VtD61jI2VHtbWStwngUrWjXhN+w0bNigP/ykzYcIEpVq1avn63LVrl9KkSRPF3t5eMTMzU4yMjBRASUlJURRFUVasWKGYmJhoHRMSEqKoVKrnHrcQQgjxNIUdh8qbfiGEEOIlyMzOJTs3V/O9ePHitGjRguDgYFasWEGLFi0oXrz4M/t5mJke8pLK2dra5ssm/2gbOzs7AE2byMhIgoKCMDMz03z8/f1Rq9VcvnyZpk2b4uTkRPna/vQ44saqU9mkZeclrotJUJOtBu82f89I0NfXp3bt2kRHRz913DExMXTt2pXy5ctjYWFBuXLlAPJt6fckiqKQvGUrd/7aFvBRZjq6JOeqSdm2jTtffUXuX2vyHzx4gIWFxRP7vHLlCs2bN8fDw4NffvmFyMhIzdZ/j+YVeDwxoCTzE0IIUZToFfYAhBBCiDfZ7aQMDl++z6rDVzDW1+VWUgY6uWoA+vTpo5n+/ug+809TUACqVquf2OZhtvuHbdRqNR999BGBgfmT9Dk6OmJgYMCxY8cICwtj2x+b+eKXy0w8nEHE3C4oNdxgyUBUVmW0jlMUJV9W/ce1atUKBwcHli1bhr29PWq1Gg8PD7Kysp7rurOvXef23LkF1pUzMGDfXzkJEkNDse7ZE2N3NyIiInB1dQXytt7LfeShC+QtZcjJyWHOnDno6OS95/j555+fazxCCCFEUSFBvxBCCPEPJaVnM2vredZFXtOU3b14FxdrHXLVCs2aNdMEvf7+/v/JmGrUqMGZM2eoUKHCE9vo6enRpEkTmjRpwoTJU7CysmKXnh/+jf0xMBjK/v376dq1K5D3Rvzo0aOahIMGBgYAWgH2vXv3iI6OZunSpZqs/o9n4X+WrCuxqJOSCqzrYm3FTw8SmHLrJh0trUha9zNHixfn+++/Z+XKlUBeEsTLly8TFRVFmTJlMDc3x9nZmZycHBYuXEirVq04cOAAS5YseaFxCSGEEG86md4vhBBC/ENx99O0Av6HLt5K4WpCGrq6ukRHRxMdHZ1vP/hX5dNPP+XgwYMMGjSIqKgoLl68yG+//cYnn3wCwKZNm1iwYAFRUVFcuXKFH374AbVajaurK6ampnz88ceMGjWKLVu2cPbsWfr3709aWhp9+/YFwMnJCZVKxaZNm7hz5w4pKSlYW1tjY2PDt99+y6VLl9i1axfDhw9/oXHn3Lz1xLrS+gasdHTialY2/a9dpemXXxIUFERQUJBmG74OHTrQrFkzGjZsSIkSJVi9ejWenp7MnTuXGTNm4OHhwapVq5g2bdo/vLNCCCHEm0ne9AshhBD/0N2UzALLcxWFxLRssOGpa85fhapVq7Jnzx7GjRtHgwYNUBQFZ2dnOnfuDICVlRUhISFMnDiRjIwMKlasyOrVq6lcuTIA06dPR61W06NHD5KTk/Hy8mLr1q1YW1sDULp0aSZNmsSYMWPo3bs3PXv2JCgoiDVr1hAYGIiHhweurq4sWLAAPz+/5x63ysT4qfWVjYz41sEBAMv27bH/cqpWvaGhIevXr8933LBhwxg2bJhWWY8ePTQ/BwQEEBAQoFXftm1bWdMvhBCiyFApb9H/1ZKSkrC0tCQxMfE//0eYEEKIouf09URaLsw/jd3EQJfNgQ0oW9y0EEb1ZkqPjia2Xfvnalvm26WY+/i84hEJIYQQL0dhx6EyvV8IIYT4hxxtTOhcyyFf+ZhmlXAoZlIII3pzGTg4YOrt/cx2usWKYfiUfAXixQQFBWFlZfWfn7ds2bLMnz//Pz+vEEK8jSToF0II8f/27jyqqnr///jzgMyTCg4gKILzgORQVy2G1MjMHG7ldFXKq+Vsall5NdNKvYqR5lB9vxfI2V8qea0sNTBNvY44oiIOWGoWKlMi0/n94ddzO4JDKh48vB5rneXZn89n7/3m7LWE9/lMcpfcHe0Y06Eec3s/QutATzo0rMbCAY/S9ZEa2NrcerV7MWfr6kq18W9ToWqVm7YxODhQY85s7H18HmBktxYZGYnBYMBgMGBnZ0dAQABjx44l5/92G5CS7dy5k0GDBlk6DBGRckFz+kVERO5BVXdHOgX50KFRtWuJn62+T79bDgEB1IyL42JMLBmrV2PMz79WYTDgEhJClRHDcWzY0LJBluDpp58mJiaG/Px8Nm/ezN///ndycnKYP3++pUMrs6pUufmXOyIicn/pLxMREZH7wL6CrRL++8Chdm2qTfgHtb+Mp2ZsDH4x/6J2/GpqzIrCqXFjDDZl7zN2cHCgevXq+Pn50bt3b/r06UN8fDxGo5F//vOfBAQE4OTkRLNmzcwWG0xMTMRgMLBx40ZatmyJs7Mzbdq04ejRo6Y2kyZNIjg4mIULF+Lv74+Hhwc9e/YkKyvL1OaLL76gadOmODk54enpSfv27cnJyeGHH37Azs6O8+fPm8U7ZswYQkpYE+Ho0aMYDAaOHDliVj5r1iz8/f0xGo0UFhYyYMAAateujZOTE/Xr1+ejjz4yax8ZGUnXrl2ZOXMm3t7eeHp6MnToUPKvf4lD8eH9s2bNomnTpri4uODn58eQIUPIzs7+cw9CRERKVPZ+c4qIiEi5ZmNnh0NAAC5/+QuurVvjWL8+ti4Pz6KITk5O5Ofn849//IOYmBjmz5/PoUOHeO211/jb3/7Gpk2bzNqPHz+eqKgodu3aRYUKFXj55ZfN6lNTU4mPj2ft2rWsXbuWTZs2MW3aNADOnTtHr169ePnll0lOTiYxMZHu3btjNBoJCQkhICCAhQsXmq5VUFDAokWLeOmll4rFXb9+fVq0aMHixYvNypcsWULv3r0xGAwUFRXh6+vLihUrOHz4MBMnTuTtt99mxYoVZuckJCSQmppKQkICcXFxpi0Wb8bGxobZs2dz8OBB4uLi+P7773njjTfu6PMWEZHbMJYjGRkZRsCYkZFh6VBERETkIZV5NdN4Lvuc8eKVi8b+/fsbu3TpYqr7z3/+Y/T09DQ+//zzRkdHR+PWrVvNzh0wYICxV69eRqPRaExISDACxg0bNpjqv/rqKyNgvHLlitFoNBrfeecdo7OzszEzM9PU5vXXXzc+9thjRqPRaNy9e7cRMJ46darEWKdPn25s2LCh6Tg+Pt7o6upqzM7ONhqNRmNMTIzRw8PDVD9r1ixjQECA6fjo0aNGwHjo0KGbfh5Dhgwx/vWvfzUd9+/f31irVi1jQUGBqeyFF14w9ujRw3Rcq1Yt44cffnjTa65YscLo6el503oRkYeJpfNQ9fSLiIiI3IHMvEy2/LyF4RuH0/3L7vRf158zmWdYu3Ytrq6uODo60rp1a0JCQhg7diy5ubl06NABV1dX0+vzzz8nNTXV7LpBQUGm997e3gBcuHDBVObv74+bm5tZm+v1zZo1o127djRt2pQXXniBzz77jEuXLpnaRkZGcvz4cbZv3w7Av/71L1588UVcbjJyomfPnpw+fdrUfvHixQQHB9OoUSNTmwULFtCyZUuqVKmCq6srn332GWlpaWbXady4Mba2tiXGXJKEhAQ6dOhAjRo1cHNzo1+/fqSnp2tBRBGR+0BJv4iIiMhtXMm/whdHv2DwhsHsvrCbrPwsTmacZM+FPfg08+H7bd9z9OhRcnNzWbVqlem8r776iqSkJNPr8OHDZvP6Aezs7EzvDYZruz4UFRWVWH+9zfV6W1tb1q9fzzfffEOjRo2YM2cO9evX5+TJkwBUrVqVzp07ExMTw4ULF/j666+LTR/4I29vb8LDw1myZAkAS5cu5W9/+5upfsWKFbz22mu8/PLLfPfddyQlJfHSSy+Rl5d305/pxphvdPr0aZ555hmaNGnCypUr2b17N3PnzgUwWwdARETujlbvFxEREbmNM1lniN4TXWLdZeNl8irlUcu3lqmsUaNGODg4kJaWRmhoaKnGZjAYaNu2LW3btmXixInUqlWL1atXM3r0aAD+/ve/07NnT3x9fQkMDKRt27a3vF6fPn0YN24c6oFSvQAAMXZJREFUvXr1IjU1lZ49e5rqNm/eTJs2bRgyZIip7MaRC3/Wrl27KCgoICoqCpv/W6jxxjUCRETk7qmnX0REROQ2DqYfxIjxpvXLjy2nsKjQdOzm5sbYsWN57bXXiIuLIzU1lb179zJ37lzi4uLuW1z/+c9/+OCDD9i1axdpaWmsWrWKX3/9lYZ/2NowIiICDw8P3nvvvRIX8LtR9+7dyczMZPDgwYSHh1OjRg1TXZ06ddi1axfffvstx44dY8KECezcufOefobAwEAKCgqYM2cOJ06cYOHChSxYsOCerikiIv+lpF9ERETkNjKvZt62vqCowKxsypQpTJw4kalTp9KwYUMiIiL497//Te3ate9bXO7u7vzwww8888wz1KtXj3/84x9ERUXRsWNHUxsbGxsiIyMpLCykX79+d3TNzp07s2/fPvr06WNW9+qrr9K9e3d69OjBY489Rnp6ulmv/90IDg5m1qxZTJ8+nSZNmrB48WKmTp16T9cUEZH/MhiNxpt/bW1lMjMz8fDwICMjA3d3d0uHIyIiIg+J7We3M3D9wJvWj2kxhsgmkQ8uoD9p4MCB/PLLL6xZs8bSoYiIlDuWzkM1p19ERETkNgIqBhDgEcCJjBPF6pwqOPGE7xMWiOr2MjIy2LlzJ4sXL+bLL7+0dDgiImIBGt4vIiIichtVnavyUfhHBHkFmZVXd6nOZx0+I7BioIUiu7UuXbrw3HPP8corr9ChQwdLhyMiIhag4f0iIiIidygjN4O0rDQuXb2Ei50Lfm5+VHWuaumwRESkDLN0HqqefhERsXpnzpxhwIAB+Pj4YG9vT61atRg5ciTp6emWDk0eMh6OHjSt0pQQ3xBaVGuhhF9ERMo8Jf0iImLVTpw4QcuWLTl27BhLly7l+PHjLFiwgI0bN9K6dWsuXrxY4nl5eXkPOFIRERGR+09Jv4iIWLWhQ4dib2/Pd999R2hoKDVr1qRjx45s2LCBn3/+mfHjxwPg7+/Pe++9R2RkJB4eHgwceG2l9q1btxISEoKTkxN+fn6MGDGCnJwc0/XPnTtHp06dcHJyonbt2ixZsgR/f3+io6NNbdLS0ujSpQuurq64u7vz4osv8ssvv5jqJ02aRHBwMAsXLsTf3x8PDw969uxJVlbWg/mQRERExGop6RcREat18eJFvv32W4YMGYKTk5NZXfXq1enTpw/Lly/n+vI2M2bMoEmTJuzevZsJEyZw4MABIiIi6N69O/v372f58uVs2bKFYcOGma7Tr18/zp49S2JiIitXruTTTz/lwoULpnqj0UjXrl25ePEimzZtYv369aSmptKjRw+zeFJTU4mPj2ft2rWsXbuWTZs2MW3atFL8dERERKQ80JZ9IiJitVJSUjAajTRs2LDE+oYNG3Lp0iV+/fVXAJ588knGjh1rqu/Xrx+9e/dm1KhRANStW5fZs2cTGhrK/PnzOXXqFBs2bGDnzp20bNkSgP/5n/+hbt26pmts2LCB/fv3c/LkSfz8/ABYuHAhjRs3ZufOnbRq1QqAoqIiYmNjcXNzA6Bv375s3LiR999///5+KCIiIlKuqKdfRESsS34uXDwJ6cch99bD46/38BsMBgBT4n7d7t27iY2NxdXV1fSKiIigqKiIkydPcvToUSpUqEDz5s1N59SpU4dKlSqZjpOTk/Hz8zMl/ACNGjWiYsWKJCcnm8r8/f1NCT+At7e32YgBERGR8uD6lDdLCQsLM33Zby2U9IuIiPX49SisHQkft4Q5LaizdwoGg4HDBw+U2PzIkSNUqlQJLy8vAFxcXMzqi4qKeOWVV0hKSjK99u3bR0pKCoGBgdxs19s/lhuNRtOXCje2+WO5nZ2dWb3BYKCoqOjOfm4REZEy4vz58wwfPpyAgAAcHBzw8/Ojc+fObNy40dKhlVsa3i8iItYh/TjEdYbs/y6Q53k5iQ4BtsybO4fXxow1m9d//vx5Fi9eTL9+/UpMygGaN2/OoUOHqFOnTon1DRo0oKCggL1799KiRQsAjh8/zuXLl01tGjVqRFpaGmfOnDH19h8+fJiMjIybTjsQERF5GJ06dYq2bdtSsWJF/vnPfxIUFER+fj7ffvstQ4cO5ciRIw8kjvz8/GJfppdn6ukXERHrkLLBLOG/7uOODlzNvkxEh3b88MMPnDlzhnXr1tGhQwdq1Khxyznz48aNY9u2bQwdOpSkpCRSUlJYs2YNw4cPB64l/e3bt2fQoEHs2LGDvXv3MmjQIJycnExfJLRv356goCD69OnDnj172LFjB/369SM0NLTYdAIREZGH2ZAhQzAYDOzYsYPnn3+eevXq0bhxY0aPHs327duB2+9oc6OioiImT56Mr68vDg4OBAcHs27dOlP9qVOnMBgMrFixgrCwMBwdHVm0aBHp6en06tULX19fnJ2dadq0KUuXLjW7dk5ODv369cPV1RVvb2+ioqKK3f/SpUv069ePSpUq4ezsTMeOHUlJSblPn9iDoaRfREQefnnZcGB5iVV1PW3ZNcCBQD9vevToQWBgIIMGDSI8PJxt27ZRuXLlm142KCiITZs2kZKSwhNPPMEjjzzChAkT8Pb2NrX5/PPPqVatGiEhIXTr1o2BAwfi5uaGo6MjcG2Yfnx8PJUqVSIkJIT27dsTEBDA8uUlxysiIvIwKMrNJTclhewtW8je8iNnt29n3bp1DB06tNh0OYCKFSve8Y42f/TRRx8RFRXFzJkz2b9/PxERETz33HPFEu9x48YxYsQIkpOTiYiIIDc3lxYtWrB27VoOHjzIoEGD6Nu3L//5z39M57z++uskJCSwevVqvvvuOxITE9m9e7fZdSMjI9m1axdr1qxh27ZtGI1GnnnmGfLz8+/xE3xwDMabTUi0QpmZmXh4eJCRkYG7u7ulwxERkfslPxcWPw+nNpdcb2MLw3ZB5YBSD+Wnn37Cz8+PDRs20K5du1K/n4iIyIN2NTWV3+bNJ3PdOigsBGD/lSv0TDvNstmzeXHYsBKnzq1fv56OHTua7Whz+PBhGjduzI4dO2jVqhWTJk0iPj6epKQkAGrUqMHQoUN5++23Tdd59NFHadWqFXPnzuXUqVPUrl2b6OhoRo4cecu4O3XqRMOGDZk5cybZ2dl4enry+eefm750uHjxIr6+vgwaNIjo6GhSUlKoV68eP/74I23atAEgPT0dPz8/4uLieOGFF+7o87J0Hqo5/SIi8vCzc4RHB9486W/8V3DzKZVbf//992RnZ9O0aVPOnTvHG2+8gb+/PyEhIaVyPxEREUu6mprK6b79KLx40az8ek/yrx9GkxsWjlPTJsXOvd2ONte3sb0uMzOTs2fP0rZtW7Pytm3bsm/fPrOyG6fMFRYWMm3aNJYvX87PP//M1atXuXr1qmkUQmpqKnl5ebRu3dp0TuXKlalfv75ZvBUqVOCxxx4zlXl6elK/fn2zHXjKOg3vFxER6+DbCgKeLF7uUgVCxl77YqAU5Ofn8/bbb9O4cWO6detGlSpVSExM1AJCIiJidYpycvhl2vRiCT9ALXt7DEBqVhZn33idgvT0Ym3udEebG91YV1L7G6cUREVF8eGHH/LGG2/w/fffk5SUREREBHl5eaZr3M6tdum5VbxljZJ+ERGxDu4+0GUuvBAHNduAdzA89QG8tA6q1L/t6XcrIiKCgwcP8vvvv/PLL7+wevVqatWqVWr3ExERsZS8tDPkbC55VF1FW1vauriw9PIlLqeeIO90mln95cuXzXa0ue5WO9q4u7vj4+PDli1bzMq3bt162x1wNm/eTJcuXfjb3/5Gs2bNCAgIMFsHoE6dOtjZ2ZkWGIRri/YdO3bMdNyoUSMKCgrM1gFIT0/n2LFjD9UOPBreLyIi1sPDBzy6Qr0IKCwARzdLRyQiImI18s+evWX9xKrV6J12mh6nT/GPuFjaurlSUFDA+vXrmT9/PocPHzbtaBMdHU1BQQFDhgy55Y42r7/+Ou+88w6BgYEEBwcTExNDUlISixcvvmUsderUYeXKlWzdupVKlSoxa9Yszp8/b0rWXV1dGTBgAK+//jqenp5Uq1aN8ePHY2Pz337xunXr0qVLFwYOHMgnn3yCm5sbb775JjVq1KBLly5/8tOzHCX9IiJifeycQKPrRURE7iujseiW9b729qz0r80n6b8xcfFiflmwgCpVqtCiRQvmz59v2tFm+PDhhISEYGNjw9NPP82cOXNues0RI0aQmZnJmDFjuHDhAo0aNWLNmjXUrVv3lrFMmDCBkydPEhERgbOzM4MGDaJr165kZGSY2syYMYPs7Gyee+453NzcGDNmjFk9QExMDCNHjuTZZ58lLy+PkJAQvv7664dqGp9W7xcREREREZHb+n3PHk737nNHbX0/WYBbaGgpR/RwsHQeqjn9IiIiIiIiclv2NWti7+9/23Y2Hh44BAaWfkByR5T0i4iIiIiIyG1V8PKi+qR3wObWaWT1CROwq1HjAUUlt6OkX0RERERERO6IU/Pm+M6fh00Jw9QNTk54T5uKa1joQ7WlnbXTQn4iIiIiIiJyR2zs7XENCaH2qpVcPXaM33fsxFhUiHPwIzg0boR9jRoYKijNLEv0NEREREREROSOGQwG7H19sff1xe3JJy0djtzGQzO8//3336dNmzY4OztTsWJFS4cjIiIiIiIiUuY9NEl/Xl4eL7zwAoMHD7Z0KCIiIiIiIiIPhYdmeP+7774LQGxsrGUDEREREREREXlIPDRJ/924evUqV69eNR1nZmZaMBoRERERERGRB+uhGd5/N6ZOnYqHh4fp5efnZ+mQRERERERERB4Yiyb9kyZNwmAw3PK1a9euu77+W2+9RUZGhul15syZ+xi9iIiIiIiISNlm0eH9w4YNo2fPnrds4+/vf9fXd3BwwMHB4a7PFxEREREREXmYWTTp9/LywsvLy5IhiIiIiIiIiFith2Yhv7S0NC5evEhaWhqFhYUkJSUBUKdOHVxdXS0bnIiIiIiIiEgZ9NAk/RMnTiQuLs50/MgjjwCQkJBAWFiYhaISERERERERKbsMRqPRaOkgHpTMzEw8PDzIyMjA3d3d0uGIiIiIiIiIlbN0HmrVW/aJiIiIiIiIlGdK+kVERERERESslJJ+ERERERGxGmFhYYwaNeqO2iYmJmIwGLh8+fJN20yaNIng4OD7EpuIJSjpFxERERGRMi0yMhKDwcCrr75arG7IkCEYDAYiIyMBWLVqFVOmTLlv9x47diwbN268b9cTedCU9IuIiIiISJnn5+fHsmXLuHLliqksNzeXpUuXUrNmTVNZ5cqVcXNzu2/3dXV1xdPT875dT+RBU9IvIiIiIiJlXvPmzalZsyarVq0yla1atQo/Pz/Tdt5QfHj/1atXeeONN/Dz88PBwYG6devyv//7v2bX3r17Ny1btsTZ2Zk2bdpw9OhRU92Nw/sLCgoYMWIEFStWxNPTk3HjxtG/f3+6du1qarNu3Toef/xxU5tnn32W1NRUU/2pU6cwGAysWrWK8PBwnJ2dadasGdu2bbsPn5SIOSX9IiIiIiLyUHjppZeIiYkxHf/rX//i5ZdfvuU5/fr1Y9myZcyePZvk5GQWLFiAq6urWZvx48cTFRXFrl27qFChwi2vOX36dBYvXkxMTAw//vgjmZmZxMfHm7XJyclh9OjR7Ny5k40bN2JjY0O3bt0oKioqdt+xY8eSlJREvXr16NWrFwUFBXf4aYjcmQqWDkBERERERORO9O3bl7feesvUU/7jjz+ybNkyEhMTS2x/7NgxVqxYwfr162nfvj0AAQEBxdq9//77hIaGAvDmm2/SqVMncnNzcXR0LNZ2zpw5vPXWW3Tr1g2Ajz/+mK+//tqszV//+lez4//93/+latWqHD58mCZNmpjKx44dS6dOnQB49913ady4McePH6dBgwZ3+ImI3J56+kVEREREpMzJKyzk8u955BUUmsq8vLzo1KkTcXFxxMTE0KlTJ7y8vG56jaSkJGxtbU0J/c0EBQWZ3nt7ewNw4cKFYu0yMjL45ZdfePTRR01ltra2tGjRwqxdamoqvXv3JiAgAHd3d2rXrg1AWlraXd1X5F6op19ERERERMqMwiIjR85nErf1FHvSLtO0hgeXcvIwGo0AvPzyywwbNgyAuXPn3vJaTk5Od3RPOzs703uDwQBQbCj+H11vc9312K7r3Lkzfn5+fPbZZ/j4+FBUVESTJk3Iy8u7p/uK3A319IuIiIiISJlx6GwG3eZuZcWunzh+IZvVe38m4egFsnKvzXV/+umnycvLIy8vj4iIiFteq2nTphQVFbFp06b7EpuHhwfVqlVjx44dprLCwkL27t1rOk5PTyc5OZl//OMftGvXjoYNG3Lp0qX7cn+Ru6GefhERERERKRNyrhYQveEYeYXmvd1FRkj5JZus3HzcHO1ITk4Grg2tvxV/f3/69+/Pyy+/zOzZs2nWrBmnT5/mwoULvPjii3cV4/Dhw5k6dSp16tShQYMGzJkzh0uXLpl66itVqoSnpyeffvop3t7epKWl8eabb97VvUTuB/X0i4iIiIhImZBxJZ8dJ0vuFb98JY/MK/kAuLu74+7ufkfXnD9/Ps8//zxDhgyhQYMGDBw4kJycnLuOcdy4cfTq1Yt+/frRunVrXF1diYiIMC36Z2Njw7Jly9i9ezdNmjThtddeY8aMGXd9P5F7ZTDeOAHFimVmZuLh4UFGRsYd/ychIiIiIiIPxuXf8+j12XaSz2UVq6vt5cIXr7bG09XBApHdXFFREQ0bNuTFF19kypQplg5HyiBL56Hq6RcRERERkTKhorM9Y56qX2Ld6xH17zrhj42NpWLFiqbjSZMmERwcbNZm0qRJVKtWDYPBQHx8/E2vdfr0aT777DOOHTvGgQMHGDx4MCdPnqR37953HI+/vz/R0dF/7ocQuUtK+kVEREREpMx41L8y0T2Cqep2LcH3crVnfHh14udNISAgAAcHB/z8/OjcuTMbN268q3uMHTvW7Nzk5GTeffddPvnkE86dO0fHjh1veq6NjQ2xsbG0atWKtm3bcuDAATZs2EDDhg3v+P47d+5k0KBBdxW7yJ+lhfxERERERKTMcHeyo+sjNXgsoDLZuQVc/OVnundsR8WKFfnnP/9JUFAQ+fn5fPvttwwdOpQjR4786Xu4urri6upqOk5NTQWgS5cuxbbju5Gfnx8//vhjiXX5+flm2/DdTJUqVf5EtCL3Rj39IiIiIiJS5nh7OFG3mhvvvjkag8HAjh07eP7556lXrx6NGzdm9OjRbN++HYBZs2bRtGlTXFxc8PPzY8iQIWRnZ9/02n8c3j9p0iQ6d+4MXOvFv570FxUVMXnyZHx9fXFwcCA4OJh169aZrnHq1CkMBgMrVqwgLCwMR0dHFi1aRGRkJF27dmXmzJl4e3vj6enJ0KFDyc/PN5174/D+Pxu/yJ+hpF9ERERERMqkixcvsm7dOoYOHYqLi0ux+uvz9G1sbJg9ezYHDx4kLi6O77//njfeeOOO7jF27FhiYmIAOHfuHOfOnQPgo48+IioqipkzZ7J//34iIiJ47rnnSElJMTt/3LhxjBgxguTkZCIiIgBISEggNTWVhIQE4uLiiI2NJTY29qYx3Ev8Irej4f0iIiIiIlImFOXlkX/mJ/LOpGHMz+fg2bMYjUYaNGhwy/NGjRplel+7dm2mTJnC4MGDmTdv3m3v6erqavryoHr16qbymTNnMm7cOHr27AnA9OnTSUhIIDo6mrlz55rdu3v37mbXrFSpEh9//DG2trY0aNCATp06sXHjRgYOHHjf4xe5HSX9IiIiIiJicfnnz5MeE8OlJUvh/4bCn71yBYCrx49jzM/HcJP58gkJCXzwwQccPnyYzMxMCgoKyM3NJScnp8QRAreTmZnJ2bNnadu2rVl527Zt2bdvn1lZy5Yti53fuHFjbG1tTcfe3t4cOHDgpve73/GL/JGG94uIiIiIiEUVpKdzfsp7XIr73JTwA9Syt8cA/GdmFFcOHizx3NOnT/PMM8/QpEkTVq5cye7du0098X+cR383blzUz2g0FisrKSm/cTE/g8FAUVFRifcozfhFQEm/iIiIiIhY2NXUE2SXsP1eRVtb2rq4sPRiOiffnUzh5ctm9ZcvX2bXrl0UFBQQFRXFX/7yF+rVq8fZs2fvKR53d3d8fHzYsmWLWfnWrVv/1NZ8d6I04hf5IyX9IiIiIiJiMcb8fC4tW3bT+olVq1FoNNLlm69Z8a9/kZKSQnJyMrNnz6Z169YEBgZSUFDAnDlzOHHiBAsXLmTBggX3HNfrr7/O9OnTWb58OUePHuXNN98kKSmJkSNH3vO1/6i04he5Tkm/iIiIiIhYTNGVK+Slpt603tfenpX+tXnM2Zk3Z8ygSZMmdOjQgY0bNzJ//nyCg4OZNWsW06dPp0mTJixevJipU6fec1wjRoxgzJgxjBkzhqZNm7Ju3TrWrFlD3bp17/naf1Ra8YtcZzAajUZLB/GgZGZm4uHhQUZGBu7u7pYOR0RERESk3CvKzeXMoEH8vmPnbdvWWrQQ5xIWzhMpyyydh6qnX0RERERELMbG0ZFKffrctl2FKlWwq1HjAUQkYl2U9IuIiIiIiEU5Nm5MherVb9mmypjR2Hl7P6CIRMqG2NhYKlaseE/XUNIvIiIiIiIWZe/ri9//fHbTnvwqo0bhGh7+gKOSh0VkZCQGg4Fp06aZlcfHxxfbYrG0HDt2DGdnZ5YsWWJWXlRURIcOHR5IDDdTwaJ3FxERERERARzr1KHWokVcPX6cjH//m8LMDJybt8A1JAS7mn7YOjtbOkQpwxwdHZk+fTqvvPIKlSpVeuD3r1evHtOmTWP48OGEh4fj/X+jUqKiojhx4sRdXTM/P/++xKaefhERERERKRPsvKvj+sTj1PjndGouWIDXoIE4NqivhF9uq3379lSvXv22Ox9s3bqVkJAQnJyc8PPzY8SIEeTk5AAwZ84cmjZtamp7faTA3LlzTWURERG89dZbJV57+PDhBAcHM3DgQACOHDnCxIkT+eijjwCYPn06vr6+ODg4EBwczLp160znnjp1CoPBwIoVKwgLC8PR0ZFFixYVu0d6ejqPPvoozz33HLm5uXf02SjpFxERERERkYeara0tH3zwAXPmzOGnn34qsc2BAweIiIige/fu7N+/n+XLl7NlyxaGDRsGQFhYGIcOHeK3334DYNOmTXh5ebFp0yYACgoK2Lp1K6GhoSVe32AwEBMTw+bNm/nss8+IjIykR48ePPvsswB8/PHHzJw5k/379xMREcFzzz1HSkqK2TXGjRvHiBEjSE5OJiIiwqzup59+4oknnqBBgwasWrUKR0fHO/pslPSLiIhIia73OiQlJd20zf1YYEhEROR+6NatG8HBwbzzzjsl1s+YMYPevXszatQo6tatS5s2bZg9ezaff/45ubm5NGnSBE9PT1OSn5iYyJgxY0zHO3fuJDc3l8cff/ymMdSsWZPo6GheffVVzp49a+rlBxg5ciQ9e/akfv36TJ8+neDgYKKjo83OHzVqFN27d6d27dr4+PiYyo8dO0bbtm1p3749cXFxVKhw5zP1lfSLiIg8JBYsWICbmxsFBQWmsuzsbOzs7HjiiSfM2m7evBmDwcCxY8dKNaYePXqUyj38/f2L/SEkIiICQGEBpKfC2aRr/xqLTFXTp08nLi6Ow4cPFztt9+7dxMbG4urqanpFRERQVFTEyZMnMRgMhISEkJiYyOXLlzl06BCvvvoqhYWFJCcnk5iYSPPmzXF1db1leC+99BLe3t6MGDECDw8PMjMzAfjLX/5i1q5t27YkJyeblbVs2bLY9a5cucLjjz9O165dmT179p9enFBJv4iIyEMiPDyc7Oxsdu3aZSrbvHkz1atXZ+fOnfz++++m8sTERHx8fKhXr16pxuTk5ETVqlVL9R4iIiImmWfh+/dgQVv4NPTav+f2Q/61+e0hISFERETw9ttvFzu1qKiIV155haSkJNNr3759pKSkEBgYCFwb4p+YmMjmzZtp1qwZFStWJCQkhE2bNpGYmEhYWNgdhVmhQoVivfE3JutGo7FYmYuLS7FrOTg40L59e7766qubTl24FSX9IiIiD4n69evj4+NDYmKiqSwxMZEuXboQGBjI1q1bzcrDw8NZtGgRLVu2xM3NjerVq9O7d28uXLhganfp0iX69OlDlSpVcHJyom7dusTExJjd98SJE4SHh+Ps7EyzZs3Ytm2bqe7G4f2TJk0iODiYhQsX4u/vj4eHBz179iQrK8vUJisriz59+uDi4oK3tzcffvghYWFhjBo1Crj2B9fp06d57bXXMBgMZn8QrVy5ksaNG+Pg4IC/vz9RUVFmsfr7+/PBBx/w8ssv4+bmRs2aNfn000/v6vMWEZEy5moWbJwCP34I+VeuleVfgQuH4dcjkHvtd820adP497//bfZ7EaB58+YcOnSIOnXqFHvZ29sD/53X/8UXX5gS/NDQUDZs2HDL+fy34u7uDmD2+xOuLSrYsGHD255vY2PDwoULadGiBU8++SRnz579U/dX0i8iIvIQCQsLIyEhwXSckJBAWFgYoaGhpvK8vDy2bdtGeHg4eXl5TJkyhX379hEfH8/JkyeJjIw0nT9hwgQOHz7MN998Q3JyMvPnz8fLy8vsnuPHj2fs2LEkJSVRr149evXqZTbF4EapqanEx8ezdu1a1q5dy6ZNm8z2Th49ejQ//vgja9asYf369WzevJk9e/aY6letWoWvry+TJ0/m3LlznDt3Drg2LPPFF1+kZ8+eHDhwgEmTJjFhwgRiY2PN7h8VFUXLli3Zu3cvQ4YMYfDgwRw5cuRPf9YiIlLGXDoF+5aUXJf5M1w+BUDTpk3p06cPc+bMMWsybtw4tm3bxtChQ0lKSiIlJYU1a9YwfPhwU5vr8/oXL15sSvrDwsKIj483DbO/Wx999BHLly/n6NGjvPnmmyQlJTFy5Mg7OtfW1pbFixfTrFkznnzySc6fP3/H973z2f8iIiJiEYVFhVwtvIq9rT1hYWG89tprFBQUcOXKFfbu3UtISAiFhYXMnj0bgO3bt3PlyhXCw8MJCAgwXScgIIDZs2fz6KOPkp2djaurK2lpaTzyyCOmOYT+/v7F7j927Fg6deoEwLvvvkvjxo05fvw4DRo0KDHeoqIiYmNjcXNzA6Bv375s3LiR999/n6ysLOLi4liyZAnt2rUDICYmxmyxosqVK2Nra2sanXDdrFmzaNeuHRMmTACu7Yl8+PBhZsyYYfZFxjPPPMOQIUOAa3/gffjhhyQmJt40XhEReUhknbt1feZZqH5ty70pU6awYsUKs+qgoCA2bdrE+PHjeeKJJzAajQQGBtKjRw9TG4PBQGhoKPHx8ab1coKCgvDw8CAgIMDUa383hg0bxpgxY7hw4QKNGjVizZo11K1b947Pr1ChAkuXLqVHjx48+eSTJCYm3tEUOyX9IiIiZVTW1SxSM1JZfXw1xy4dw9vFm9aNWpOTk8POnTu5dOkS9erVo2rVqoSGhtK3b19ycnJITEykZs2aBAQEsHfvXiZNmkRSUhIXL16kqOjaYkdpaWk0atSIwYMH89e//pU9e/bw1FNP0bVrV9q0aWMWR1BQkOm9t7c3ABcuXLhpEu3v729K+K+fc31KwYkTJ8jPz+fRRx811Xt4eFC/fv3bfh7Jycl06dLFrKxt27ZER0dTWFiIra1tsXgNBgPVq1c3m9IgIiIPKTvnEotjuzpde2P/3/patWqVuI99q1at+O677255my+++MLs2GAwkJ6e/qdCPXXqVLGycePG8f7775fY3t/fH6PRWKw8MjLS7IvtChUqsHLlyj8Vi5J+ERGRMijzaiaLkhcxf998U9nB3w6ynvW4VnFl7XdrycvOM80trF69OrVr1+bHH38kISGBJ598kpycHJ566imeeuopFi1aRJUqVUhLSyMiIoK8vDwAOnbsyOnTp/nqq6/YsGED7dq1Y+jQocycOdN0Xzs7O9P76/Prr395UJI/tr9+zvX21/+gKWkxo9spacGjks671f1FROQhVqk2uFWHrBKGtrtWu1YvxWhOv4iISBl0OP2wWcL/RxXqVmDt+rXFVhEODQ3l22+/Zfv27YSHh3PkyBF+++03pk2bxhNPPEGDBg1K7PGuUqUKkZGRLFq0iOjo6FJd+C4wMBA7Ozt27NhhKsvMzCQlJcWsnb29PYWFhWZljRo1YsuWLWZlW7dupV69eqZefhERsWIeNeDFReDgZl5u7wo9FoKHr2XiKuPU0y8iIlLG/J7/OzEHY25a79LQhYMLD2JTZGO2inBoaCiDBw8mNzeX8PBwHB0dsbe3Z86cObz66qscPHiQKVOmmF1r4sSJtGjRgsaNG3P16lXWrl17RysJ3y03Nzf69+/P66+/TuXKlalatSrvvPMONjY2Zr34/v7+/PDDD/Ts2RMHBwe8vLwYM2YMrVq1YsqUKfTo0YNt27bx8ccfM2/evFKLV0REyhi/VjAoEX7eAxeOQNUG4NMcvOpYOrIySz39IiIiZUxWXhYH0w/etN6lgQtFeUXUCqhFtWrVTOWhoaFkZWURGBiIn58fVapUITY2lv/3//4fjRo1Ytq0aWbD9uFaj/pbb71FUFAQISEh2NrasmzZslL72eDagnytW7fm2WefpX379rRt25aGDRvi6OhoajN58mROnTpFYGAgVapUAa5ttbRixQqWLVtGkyZNmDhxIpMnTzab6ygiIuWAZx0IehHaT7z2rxL+WzIY72QSnZXIzMzEw8ODjIyMe1p1UUREpDSlX0mnz9d9+Dn755u2sTXYsqbrGmq613yAkZWOnJwcatSoQVRUFAMGDLB0OCIiIveVpfNQ9fSLiIiUMZ5OnvRp2OeWbTr6d6SaS7Vbtimr9u7dy9KlS0lNTWXPnj306XPtZ71xZX4RERG5d0r6RUREyqAQ3xCqOZec1DtVcOKlJi/hYOvwgKO6f2bOnEmzZs1o3749OTk5bN68GS8vL0uHJSIiYnU0vF9ERKSMOplxkrlJc1l/ej1Fxmtbzj1a/VHGtBxDw8oNi21fJyIiImWPpfNQJf0iIiJlWG5BLj9l/URWfhbOFZzxcfXBzd7t9ieKiIhImWDpPFRb9omIiJRhjhUcqVNJqxKLiIjI3dGcfhERERERERErpaRfRERERERExEop6RcRERERERGxUkr6RURERERERKyUkn4RERERERERK6WkX0RERERERMRKKekXERERERERsVJK+kVERERERESslJJ+ERERERERESulpF9ERERERETESinpFxEREREREbFSSvpFRERERERErJSSfhERERERERErpaRfRERERERExEop6RcRERERERGxUkr6RURERERERKyUkn4RERERERERK6WkX0RERERERMRKKekXERERERERsVJK+kVERERERESslJJ+ERERERERESulpF9ERERERETESinpFxEREREREbFSSvpFRERERERErJSSfhERERERERErpaRfRERERERExEop6RcRERERERGxUkr6RURERERERKxUBUsH8CAZjUYAMjMzLRyJiIiIiIiIlAfX88/r+eiDVq6S/qysLAD8/PwsHImIiIiIiIiUJ1lZWXh4eDzw+xqMlvq6wQKKioo4e/Ysbm5uGAwGS4djMZmZmfj5+XHmzBnc3d0tHY6UMj3v8kPPunzR8y4/9KzLFz3v8kPPuvwwGo1kZWXh4+ODjc2Dn2Ffrnr6bWxs8PX1tXQYZYa7u7v+gylH9LzLDz3r8kXPu/zQsy5f9LzLDz3r8sESPfzXaSE/ERERERERESulpF9ERERERETESinpL4ccHBx45513cHBwsHQo8gDoeZcfetbli553+aFnXb7oeZcfetbyoJSrhfxEREREREREyhP19IuIiIiIiIhYKSX9IiIiIiIiIlZKSb+IiIiIiIiIlVLSLyIiIiIiImKllPSXY6dOnWLAgAHUrl0bJycnAgMDeeedd8jLy7N0aFJK3n//fdq0aYOzszMVK1a0dDhyn82bN4/atWvj6OhIixYt2Lx5s6VDklLwww8/0LlzZ3x8fDAYDMTHx1s6JCklU6dOpVWrVri5uVG1alW6du3K0aNHLR2WlIL58+cTFBSEu7s77u7utG7dmm+++cbSYckDMnXqVAwGA6NGjbJ0KGKllPSXY0eOHKGoqIhPPvmEQ4cO8eGHH7JgwQLefvttS4cmpSQvL48XXniBwYMHWzoUuc+WL1/OqFGjGD9+PHv37uWJJ56gY8eOpKWlWTo0uc9ycnJo1qwZH3/8saVDkVK2adMmhg4dyvbt21m/fj0FBQU89dRT5OTkWDo0uc98fX2ZNm0au3btYteuXTz55JN06dKFQ4cOWTo0KWU7d+7k008/JSgoyNKhiBXTln1iZsaMGcyfP58TJ05YOhQpRbGxsYwaNYrLly9bOhS5Tx577DGaN2/O/PnzTWUNGzaka9euTJ061YKRSWkyGAysXr2arl27WjoUeQB+/fVXqlatyqZNmwgJCbF0OFLKKleuzIwZMxgwYIClQ5FSkp2dTfPmzZk3bx7vvfcewcHBREdHWzossULq6RczGRkZVK5c2dJhiMifkJeXx+7du3nqqafMyp966im2bt1qoahE5H7LyMgA0O9pK1dYWMiyZcvIycmhdevWlg5HStHQoUPp1KkT7du3t3QoYuUqWDoAKTtSU1OZM2cOUVFRlg5FRP6E3377jcLCQqpVq2ZWXq1aNc6fP2+hqETkfjIajYwePZrHH3+cJk2aWDocKQUHDhygdevW5Obm4urqyurVq2nUqJGlw5JSsmzZMvbs2cPOnTstHYqUA+rpt0KTJk3CYDDc8rVr1y6zc86ePcvTTz/NCy+8wN///ncLRS53426et1gng8Fgdmw0GouVicjDadiwYezfv5+lS5daOhQpJfXr1ycpKYnt27czePBg+vfvz+HDhy0dlpSCM2fOMHLkSBYtWoSjo6Olw5FyQD39VmjYsGH07Nnzlm38/f1N78+ePUt4eDitW7fm008/LeXo5H77s89brI+Xlxe2trbFevUvXLhQrPdfRB4+w4cPZ82aNfzwww/4+vpaOhwpJfb29tSpUweAli1bsnPnTj766CM++eQTC0cm99vu3bu5cOECLVq0MJUVFhbyww8/8PHHH3P16lVsbW0tGKFYGyX9VsjLywsvL687avvzzz8THh5OixYtiImJwcZGgz8eNn/meYt1sre3p0WLFqxfv55u3bqZytevX0+XLl0sGJmI3Auj0cjw4cNZvXo1iYmJ1K5d29IhyQNkNBq5evWqpcOQUtCuXTsOHDhgVvbSSy/RoEEDxo0bp4Rf7jsl/eXY2bNnCQsLo2bNmsycOZNff/3VVFe9enULRialJS0tjYsXL5KWlkZhYSFJSUkA1KlTB1dXV8sGJ/dk9OjR9O3bl5YtW5pG7aSlpfHqq69aOjS5z7Kzszl+/Ljp+OTJkyQlJVG5cmVq1qxpwcjkfhs6dChLlizhyy+/xM3NzTSax8PDAycnJwtHJ/fT22+/TceOHfHz8yMrK4tly5aRmJjIunXrLB2alAI3N7dia3O4uLjg6empNTukVCjpL8e+++47jh8/zvHjx4sNF9ROjtZp4sSJxMXFmY4feeQRABISEggLC7NQVHI/9OjRg/T0dCZPnsy5c+do0qQJX3/9NbVq1bJ0aHKf7dq1i/DwcNPx6NGjAejfvz+xsbEWikpKw/UtOG/8/zkmJobIyMgHH5CUml9++YW+ffty7tw5PDw8CAoKYt26dXTo0MHSoYmIFTAYld2JiIiIiIiIWCVN4BYRERERERGxUkr6RURERERERKyUkn4RERERERERK6WkX0RERERERMRKKekXERERERERsVJK+kVERERERESslJJ+ERERERERESulpF9ERERERETESinpFxEREREREbFSSvpFRETKqcjISAwGAwaDATs7OwICAhg7diw5OTmmNitXriQsLAwPDw9cXV0JCgpi8uTJXLx4EYBz587Ru3dv6tevj42NDaNGjbLQTyMiIiIlUdIvIiJSjj399NOcO3eOEydO8N577zFv3jzGjh0LwPjx4+nRowetWrXim2++4eDBg0RFRbFv3z4WLlwIwNWrV6lSpQrjx4+nWbNmlvxRREREpAQGo9FotHQQIiIi8uBFRkZy+fJl4uPjTWUDBw5k7dq1fPnllzz22GNER0czcuTIYudevnyZihUrmpWFhYURHBxMdHR06QYuIiIid0w9/SIiImLi5OREfn4+ixcvxtXVlSFDhpTY7saEX0RERMomJf0iIiICwI4dO1iyZAnt2rUjJSWFgIAA7OzsLB2WiIiI3AMl/SIiIuXY2rVrcXV1xdHRkdatWxMSEsKcOXMwGo0YDAZLhyciIiL3qIKlAxARERHLCQ8PZ/78+djZ2eHj42Pq2a9Xrx5btmwhPz9fvf0iIiIPMfX0i4iIlGMuLi7UqVOHWrVqmSX3vXv3Jjs7m3nz5pV43uXLlx9QhCIiInIv1NMvIiIixTz22GO88cYbjBkzhp9//plu3brh4+PD8ePHWbBgAY8//rhpVf+kpCQAsrOz+fXXX0lKSsLe3p5GjRpZ8CcQERER0JZ9IiIi5VZJW/bdaMWKFcydO5e9e/dSVFREYGAgzz//PMOHDzet4F/S3P9atWpx6tSp0glcRERE7piSfhERERERERErpTn9IiIiIiIiIlZKSb+IiIiIiIiIlVLSLyIiIiIiImKllPSLiIiIiIiIWCkl/SIiIiIiIiJWSkm/iIiIiIiIiJVS0i8iIiIiIiJipZT0i4iIiIiIiFgpJf0iIiIiIiIiVkpJv4iIiIiIiIiVUtIvIiIiIiIiYqX+P4pvPAt31lA0AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 1200x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Code task 11#\n",
+    "#Create a seaborn scatterplot by calling `sns.scatterplot`\n",
+    "#Specify the dataframe pca_df as the source of the data,\n",
+    "#specify 'PC1' for x and 'PC2' for y,\n",
+    "#specify 'AdultWeekend' for the pointsize (scatterplot's `size` argument),\n",
+    "#specify 'Quartile' for `hue`\n",
+    "#specify pca_df.Quartile.cat.categories for `hue_order` - what happens with/without this?\n",
+    "x = pca_df.PC1\n",
+    "y = pca_df.PC2\n",
+    "state = pca_df.index\n",
+    "plt.subplots(figsize=(12, 10))\n",
+    "# Note the argument below to make sure we get the colours in the ascending\n",
+    "# order we intuitively expect!\n",
+    "sns.scatterplot(x=x, y=y, size='AdultWeekend', hue='Quartile', \n",
+    "                hue_order=pca_df.Quartile.cat.categories, data=pca_df)\n",
+    "#and we can still annotate with the state labels\n",
+    "for s, x, y in zip(state, x, y):\n",
+    "    plt.annotate(s, (x, y))   \n",
+    "plt.title(f'Ski states summary PCA, {pc_var:.1f}% variance explained');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Seaborn does more! You should always care about your output. What if you want the ordering of the colours in the legend to align intuitively with the ordering of the quartiles? Add a `hue_order` argument! Seaborn has thrown in a few nice other things:\n",
+    "\n",
+    "* the aesthetics are separated in the legend\n",
+    "* it defaults to marker sizes that provide more contrast (smaller to larger)\n",
+    "* when starting with a DataFrame, you have less work to do to visualize patterns in the data\n",
+    "\n",
+    "The last point is important. Less work means less chance of mixing up objects and jumping to erroneous conclusions. This also emphasizes the importance of getting data into a suitable DataFrame. In the previous notebook, you `melt`ed the data to make it longer, but with fewer columns, in order to get a single column of price with a new column representing a categorical feature you'd want to use. A **key skill** is being able to wrangle data into a form most suited to the particular use case."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Having gained a good visualization of the state summary data, you can discuss and follow up on your findings."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the first two components, there is a spread of states across the first component. It looks like Vermont and New Hampshire might be off on their own a little in the second dimension, although they're really no more extreme than New York and Colorado are in the first dimension. But if you were curious, could you get an idea what it is that pushes Vermont and New Hampshire up?\n",
+    "\n",
+    "The `components_` attribute of the fitted PCA object tell us how important (and in what direction) each feature contributes to each score (or coordinate on the plot). **NB we were sensible and scaled our original features (to zero mean and unit variance)**. You may not always be interested in interpreting the coefficients of the PCA transformation in this way, although it's more likely you will when using PCA for EDA as opposed to a preprocessing step as part of a machine learning pipeline. The attribute is actually a numpy ndarray, and so has been stripped of helpful index and column names. Fortunately, you thought ahead and saved these. This is how we were able to annotate the scatter plots above. It also means you can construct a DataFrame of `components_` with the feature names for context:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>resorts_per_state</th>\n",
+       "      <th>state_total_skiable_area_ac</th>\n",
+       "      <th>state_total_days_open</th>\n",
+       "      <th>state_total_terrain_parks</th>\n",
+       "      <th>state_total_nightskiing_ac</th>\n",
+       "      <th>resorts_per_100kcapita</th>\n",
+       "      <th>resorts_per_100ksq_mile</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.486079</td>\n",
+       "      <td>0.318224</td>\n",
+       "      <td>0.489997</td>\n",
+       "      <td>0.488420</td>\n",
+       "      <td>0.334398</td>\n",
+       "      <td>0.187154</td>\n",
+       "      <td>0.192250</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-0.085092</td>\n",
+       "      <td>-0.142204</td>\n",
+       "      <td>-0.045071</td>\n",
+       "      <td>-0.041939</td>\n",
+       "      <td>-0.351064</td>\n",
+       "      <td>0.662458</td>\n",
+       "      <td>0.637691</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.177937</td>\n",
+       "      <td>0.714835</td>\n",
+       "      <td>0.115200</td>\n",
+       "      <td>0.005509</td>\n",
+       "      <td>-0.511255</td>\n",
+       "      <td>0.220359</td>\n",
+       "      <td>-0.366207</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.056163</td>\n",
+       "      <td>-0.118347</td>\n",
+       "      <td>-0.162625</td>\n",
+       "      <td>-0.177072</td>\n",
+       "      <td>0.438912</td>\n",
+       "      <td>0.685417</td>\n",
+       "      <td>-0.512443</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-0.209186</td>\n",
+       "      <td>0.573462</td>\n",
+       "      <td>-0.250521</td>\n",
+       "      <td>-0.388608</td>\n",
+       "      <td>0.499801</td>\n",
+       "      <td>-0.065077</td>\n",
+       "      <td>0.399461</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>-0.818390</td>\n",
+       "      <td>-0.092319</td>\n",
+       "      <td>0.238198</td>\n",
+       "      <td>0.448118</td>\n",
+       "      <td>0.246196</td>\n",
+       "      <td>0.058911</td>\n",
+       "      <td>-0.009146</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>-0.090273</td>\n",
+       "      <td>-0.127021</td>\n",
+       "      <td>0.773728</td>\n",
+       "      <td>-0.613576</td>\n",
+       "      <td>0.022185</td>\n",
+       "      <td>-0.007887</td>\n",
+       "      <td>-0.005631</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   resorts_per_state  state_total_skiable_area_ac  state_total_days_open  \\\n",
+       "0           0.486079                     0.318224               0.489997   \n",
+       "1          -0.085092                    -0.142204              -0.045071   \n",
+       "2          -0.177937                     0.714835               0.115200   \n",
+       "3           0.056163                    -0.118347              -0.162625   \n",
+       "4          -0.209186                     0.573462              -0.250521   \n",
+       "5          -0.818390                    -0.092319               0.238198   \n",
+       "6          -0.090273                    -0.127021               0.773728   \n",
+       "\n",
+       "   state_total_terrain_parks  state_total_nightskiing_ac  \\\n",
+       "0                   0.488420                    0.334398   \n",
+       "1                  -0.041939                   -0.351064   \n",
+       "2                   0.005509                   -0.511255   \n",
+       "3                  -0.177072                    0.438912   \n",
+       "4                  -0.388608                    0.499801   \n",
+       "5                   0.448118                    0.246196   \n",
+       "6                  -0.613576                    0.022185   \n",
+       "\n",
+       "   resorts_per_100kcapita  resorts_per_100ksq_mile  \n",
+       "0                0.187154                 0.192250  \n",
+       "1                0.662458                 0.637691  \n",
+       "2                0.220359                -0.366207  \n",
+       "3                0.685417                -0.512443  \n",
+       "4               -0.065077                 0.399461  \n",
+       "5                0.058911                -0.009146  \n",
+       "6               -0.007887                -0.005631  "
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(state_pca.components_, columns=state_summary_columns)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For the row associated with the second component, are there any large values?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It looks like `resorts_per_100kcapita` and `resorts_per_100ksq_mile` might count for quite a lot, in a positive sense. Be aware that sign matters; a large negative coefficient multiplying a large negative feature will actually produce a large positive PCA score."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>17</th>\n",
+       "      <th>29</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>state</th>\n",
+       "      <td>New Hampshire</td>\n",
+       "      <td>Vermont</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resorts_per_state</th>\n",
+       "      <td>16</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state_total_skiable_area_ac</th>\n",
+       "      <td>3427.0</td>\n",
+       "      <td>7239.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state_total_days_open</th>\n",
+       "      <td>1847.0</td>\n",
+       "      <td>1777.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state_total_terrain_parks</th>\n",
+       "      <td>43.0</td>\n",
+       "      <td>50.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state_total_nightskiing_ac</th>\n",
+       "      <td>376.0</td>\n",
+       "      <td>50.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resorts_per_100kcapita</th>\n",
+       "      <td>1.176721</td>\n",
+       "      <td>2.403889</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resorts_per_100ksq_mile</th>\n",
+       "      <td>171.141299</td>\n",
+       "      <td>155.990017</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                        17          29\n",
+       "state                        New Hampshire     Vermont\n",
+       "resorts_per_state                       16          15\n",
+       "state_total_skiable_area_ac         3427.0      7239.0\n",
+       "state_total_days_open               1847.0      1777.0\n",
+       "state_total_terrain_parks             43.0        50.0\n",
+       "state_total_nightskiing_ac           376.0        50.0\n",
+       "resorts_per_100kcapita            1.176721    2.403889\n",
+       "resorts_per_100ksq_mile         171.141299  155.990017"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "state_summary[state_summary.state.isin(['New Hampshire', 'Vermont'])].T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>17</th>\n",
+       "      <th>29</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>resorts_per_state</th>\n",
+       "      <td>0.839478</td>\n",
+       "      <td>0.712833</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state_total_skiable_area_ac</th>\n",
+       "      <td>-0.277128</td>\n",
+       "      <td>0.104681</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state_total_days_open</th>\n",
+       "      <td>1.118608</td>\n",
+       "      <td>1.034363</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state_total_terrain_parks</th>\n",
+       "      <td>0.921793</td>\n",
+       "      <td>1.233725</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state_total_nightskiing_ac</th>\n",
+       "      <td>-0.245050</td>\n",
+       "      <td>-0.747570</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resorts_per_100kcapita</th>\n",
+       "      <td>1.711066</td>\n",
+       "      <td>4.226572</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resorts_per_100ksq_mile</th>\n",
+       "      <td>3.483281</td>\n",
+       "      <td>3.112841</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                   17        29\n",
+       "resorts_per_state            0.839478  0.712833\n",
+       "state_total_skiable_area_ac -0.277128  0.104681\n",
+       "state_total_days_open        1.118608  1.034363\n",
+       "state_total_terrain_parks    0.921793  1.233725\n",
+       "state_total_nightskiing_ac  -0.245050 -0.747570\n",
+       "resorts_per_100kcapita       1.711066  4.226572\n",
+       "resorts_per_100ksq_mile      3.483281  3.112841"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "state_summary_scaled_df[state_summary.state.isin(['New Hampshire', 'Vermont'])].T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So, yes, both states have particularly large values of `resorts_per_100ksq_mile` in absolute terms, and these put them more than 3 standard deviations from the mean. Vermont also has a notably large value for `resorts_per_100kcapita`. New York, then, does not seem to be a stand-out for density of ski resorts either in terms of state size or population count."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.5.4 Conclusion On How To Handle State Label<a id='3.5.4_Conclusion_On_How_To_Handle_State_Label'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can offer some justification for treating all states equally, and work towards building a pricing model that considers all states together, without treating any one particularly specially. You haven't seen any clear grouping yet, but you have captured potentially relevant state data in features most likely to be relevant to your business use case. This answers a big question!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.5.5 Ski Resort Numeric Data<a id='3.5.5_Ski_Resort_Numeric_Data'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After what may feel a detour, return to examining the ski resort data. It's worth noting, the previous EDA was valuable because it's given us some potentially useful features, as well as validating an approach for how to subsequently handle the state labels in your modeling."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Name</th>\n",
+       "      <td>Alyeska Resort</td>\n",
+       "      <td>Eaglecrest Ski Area</td>\n",
+       "      <td>Hilltop Ski Area</td>\n",
+       "      <td>Arizona Snowbowl</td>\n",
+       "      <td>Sunrise Park Resort</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Region</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>Arizona</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>Arizona</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>summit_elev</th>\n",
+       "      <td>3939</td>\n",
+       "      <td>2600</td>\n",
+       "      <td>2090</td>\n",
+       "      <td>11500</td>\n",
+       "      <td>11100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vertical_drop</th>\n",
+       "      <td>2500</td>\n",
+       "      <td>1540</td>\n",
+       "      <td>294</td>\n",
+       "      <td>2300</td>\n",
+       "      <td>1800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>base_elev</th>\n",
+       "      <td>250</td>\n",
+       "      <td>1200</td>\n",
+       "      <td>1796</td>\n",
+       "      <td>9200</td>\n",
+       "      <td>9200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>trams</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastSixes</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastQuads</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>quad</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>triple</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>double</th>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>surface</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total_chairs</th>\n",
+       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Runs</th>\n",
+       "      <td>76.0</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>65.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TerrainParks</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LongestRun_mi</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SkiableTerrain_ac</th>\n",
+       "      <td>1610.0</td>\n",
+       "      <td>640.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>777.0</td>\n",
+       "      <td>800.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Snow Making_ac</th>\n",
+       "      <td>113.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>104.0</td>\n",
+       "      <td>80.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>daysOpenLastYear</th>\n",
+       "      <td>150.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>122.0</td>\n",
+       "      <td>115.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>yearsOpen</th>\n",
+       "      <td>60.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>49.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>averageSnowfall</th>\n",
+       "      <td>669.0</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>260.0</td>\n",
+       "      <td>250.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AdultWeekend</th>\n",
+       "      <td>85.0</td>\n",
+       "      <td>53.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>89.0</td>\n",
+       "      <td>78.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>projectedDaysOpen</th>\n",
+       "      <td>150.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>152.0</td>\n",
+       "      <td>122.0</td>\n",
+       "      <td>104.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NightSkiing_ac</th>\n",
+       "      <td>550.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>80.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                0                    1                 2  \\\n",
+       "Name               Alyeska Resort  Eaglecrest Ski Area  Hilltop Ski Area   \n",
+       "Region                     Alaska               Alaska            Alaska   \n",
+       "state                      Alaska               Alaska            Alaska   \n",
+       "summit_elev                  3939                 2600              2090   \n",
+       "vertical_drop                2500                 1540               294   \n",
+       "base_elev                     250                 1200              1796   \n",
+       "trams                           1                    0                 0   \n",
+       "fastSixes                       0                    0                 0   \n",
+       "fastQuads                       2                    0                 0   \n",
+       "quad                            2                    0                 0   \n",
+       "triple                          0                    0                 1   \n",
+       "double                          0                    4                 0   \n",
+       "surface                         2                    0                 2   \n",
+       "total_chairs                    7                    4                 3   \n",
+       "Runs                         76.0                 36.0              13.0   \n",
+       "TerrainParks                  2.0                  1.0               1.0   \n",
+       "LongestRun_mi                 1.0                  2.0               1.0   \n",
+       "SkiableTerrain_ac          1610.0                640.0              30.0   \n",
+       "Snow Making_ac              113.0                 60.0              30.0   \n",
+       "daysOpenLastYear            150.0                 45.0             150.0   \n",
+       "yearsOpen                    60.0                 44.0              36.0   \n",
+       "averageSnowfall             669.0                350.0              69.0   \n",
+       "AdultWeekend                 85.0                 53.0              34.0   \n",
+       "projectedDaysOpen           150.0                 90.0             152.0   \n",
+       "NightSkiing_ac              550.0                  NaN              30.0   \n",
+       "\n",
+       "                                  3                    4  \n",
+       "Name               Arizona Snowbowl  Sunrise Park Resort  \n",
+       "Region                      Arizona              Arizona  \n",
+       "state                       Arizona              Arizona  \n",
+       "summit_elev                   11500                11100  \n",
+       "vertical_drop                  2300                 1800  \n",
+       "base_elev                      9200                 9200  \n",
+       "trams                             0                    0  \n",
+       "fastSixes                         1                    0  \n",
+       "fastQuads                         0                    1  \n",
+       "quad                              2                    2  \n",
+       "triple                            2                    3  \n",
+       "double                            1                    1  \n",
+       "surface                           2                    0  \n",
+       "total_chairs                      8                    7  \n",
+       "Runs                           55.0                 65.0  \n",
+       "TerrainParks                    4.0                  2.0  \n",
+       "LongestRun_mi                   2.0                  1.2  \n",
+       "SkiableTerrain_ac             777.0                800.0  \n",
+       "Snow Making_ac                104.0                 80.0  \n",
+       "daysOpenLastYear              122.0                115.0  \n",
+       "yearsOpen                      81.0                 49.0  \n",
+       "averageSnowfall               260.0                250.0  \n",
+       "AdultWeekend                   89.0                 78.0  \n",
+       "projectedDaysOpen             122.0                104.0  \n",
+       "NightSkiing_ac                  NaN                 80.0  "
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ski_data.head().T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.5.5.1 Feature engineering<a id='3.5.5.1_Feature_engineering'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Having previously spent some time exploring the state summary data you derived, you now start to explore the resort-level data in more detail. This can help guide you on how (or whether) to use the state labels in the data. It's now time to merge the two datasets and engineer some intuitive features. For example, you can engineer a resort's share of the supply for a given state."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state</th>\n",
+       "      <th>resorts_per_state</th>\n",
+       "      <th>state_total_skiable_area_ac</th>\n",
+       "      <th>state_total_days_open</th>\n",
+       "      <th>state_total_terrain_parks</th>\n",
+       "      <th>state_total_nightskiing_ac</th>\n",
+       "      <th>resorts_per_100kcapita</th>\n",
+       "      <th>resorts_per_100ksq_mile</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2280.0</td>\n",
+       "      <td>345.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>580.0</td>\n",
+       "      <td>0.410091</td>\n",
+       "      <td>0.450867</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1577.0</td>\n",
+       "      <td>237.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>0.027477</td>\n",
+       "      <td>1.754540</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>California</td>\n",
+       "      <td>21</td>\n",
+       "      <td>25948.0</td>\n",
+       "      <td>2738.0</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>587.0</td>\n",
+       "      <td>0.053148</td>\n",
+       "      <td>12.828736</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Colorado</td>\n",
+       "      <td>22</td>\n",
+       "      <td>43682.0</td>\n",
+       "      <td>3258.0</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>428.0</td>\n",
+       "      <td>0.382028</td>\n",
+       "      <td>21.134744</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Connecticut</td>\n",
+       "      <td>5</td>\n",
+       "      <td>358.0</td>\n",
+       "      <td>353.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>256.0</td>\n",
+       "      <td>0.140242</td>\n",
+       "      <td>90.203861</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         state  resorts_per_state  state_total_skiable_area_ac  \\\n",
+       "0       Alaska                  3                       2280.0   \n",
+       "1      Arizona                  2                       1577.0   \n",
+       "2   California                 21                      25948.0   \n",
+       "3     Colorado                 22                      43682.0   \n",
+       "4  Connecticut                  5                        358.0   \n",
+       "\n",
+       "   state_total_days_open  state_total_terrain_parks  \\\n",
+       "0                  345.0                        4.0   \n",
+       "1                  237.0                        6.0   \n",
+       "2                 2738.0                       81.0   \n",
+       "3                 3258.0                       74.0   \n",
+       "4                  353.0                       10.0   \n",
+       "\n",
+       "   state_total_nightskiing_ac  resorts_per_100kcapita  resorts_per_100ksq_mile  \n",
+       "0                       580.0                0.410091                 0.450867  \n",
+       "1                        80.0                0.027477                 1.754540  \n",
+       "2                       587.0                0.053148                12.828736  \n",
+       "3                       428.0                0.382028                21.134744  \n",
+       "4                       256.0                0.140242                90.203861  "
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "state_summary.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Name</th>\n",
+       "      <td>Alyeska Resort</td>\n",
+       "      <td>Eaglecrest Ski Area</td>\n",
+       "      <td>Hilltop Ski Area</td>\n",
+       "      <td>Arizona Snowbowl</td>\n",
+       "      <td>Sunrise Park Resort</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Region</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>Arizona</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>Arizona</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>summit_elev</th>\n",
+       "      <td>3939</td>\n",
+       "      <td>2600</td>\n",
+       "      <td>2090</td>\n",
+       "      <td>11500</td>\n",
+       "      <td>11100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vertical_drop</th>\n",
+       "      <td>2500</td>\n",
+       "      <td>1540</td>\n",
+       "      <td>294</td>\n",
+       "      <td>2300</td>\n",
+       "      <td>1800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>base_elev</th>\n",
+       "      <td>250</td>\n",
+       "      <td>1200</td>\n",
+       "      <td>1796</td>\n",
+       "      <td>9200</td>\n",
+       "      <td>9200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>trams</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastSixes</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastQuads</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>quad</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>triple</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>double</th>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>surface</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total_chairs</th>\n",
+       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Runs</th>\n",
+       "      <td>76.0</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>65.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TerrainParks</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LongestRun_mi</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SkiableTerrain_ac</th>\n",
+       "      <td>1610.0</td>\n",
+       "      <td>640.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>777.0</td>\n",
+       "      <td>800.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Snow Making_ac</th>\n",
+       "      <td>113.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>104.0</td>\n",
+       "      <td>80.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>daysOpenLastYear</th>\n",
+       "      <td>150.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>122.0</td>\n",
+       "      <td>115.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>yearsOpen</th>\n",
+       "      <td>60.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>49.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>averageSnowfall</th>\n",
+       "      <td>669.0</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>260.0</td>\n",
+       "      <td>250.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AdultWeekend</th>\n",
+       "      <td>85.0</td>\n",
+       "      <td>53.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>89.0</td>\n",
+       "      <td>78.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>projectedDaysOpen</th>\n",
+       "      <td>150.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>152.0</td>\n",
+       "      <td>122.0</td>\n",
+       "      <td>104.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NightSkiing_ac</th>\n",
+       "      <td>550.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>80.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resorts_per_state</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state_total_skiable_area_ac</th>\n",
+       "      <td>2280.0</td>\n",
+       "      <td>2280.0</td>\n",
+       "      <td>2280.0</td>\n",
+       "      <td>1577.0</td>\n",
+       "      <td>1577.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state_total_days_open</th>\n",
+       "      <td>345.0</td>\n",
+       "      <td>345.0</td>\n",
+       "      <td>345.0</td>\n",
+       "      <td>237.0</td>\n",
+       "      <td>237.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state_total_terrain_parks</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state_total_nightskiing_ac</th>\n",
+       "      <td>580.0</td>\n",
+       "      <td>580.0</td>\n",
+       "      <td>580.0</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>80.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resorts_per_100kcapita</th>\n",
+       "      <td>0.410091</td>\n",
+       "      <td>0.410091</td>\n",
+       "      <td>0.410091</td>\n",
+       "      <td>0.027477</td>\n",
+       "      <td>0.027477</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resorts_per_100ksq_mile</th>\n",
+       "      <td>0.450867</td>\n",
+       "      <td>0.450867</td>\n",
+       "      <td>0.450867</td>\n",
+       "      <td>1.75454</td>\n",
+       "      <td>1.75454</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                          0                    1  \\\n",
+       "Name                         Alyeska Resort  Eaglecrest Ski Area   \n",
+       "Region                               Alaska               Alaska   \n",
+       "state                                Alaska               Alaska   \n",
+       "summit_elev                            3939                 2600   \n",
+       "vertical_drop                          2500                 1540   \n",
+       "base_elev                               250                 1200   \n",
+       "trams                                     1                    0   \n",
+       "fastSixes                                 0                    0   \n",
+       "fastQuads                                 2                    0   \n",
+       "quad                                      2                    0   \n",
+       "triple                                    0                    0   \n",
+       "double                                    0                    4   \n",
+       "surface                                   2                    0   \n",
+       "total_chairs                              7                    4   \n",
+       "Runs                                   76.0                 36.0   \n",
+       "TerrainParks                            2.0                  1.0   \n",
+       "LongestRun_mi                           1.0                  2.0   \n",
+       "SkiableTerrain_ac                    1610.0                640.0   \n",
+       "Snow Making_ac                        113.0                 60.0   \n",
+       "daysOpenLastYear                      150.0                 45.0   \n",
+       "yearsOpen                              60.0                 44.0   \n",
+       "averageSnowfall                       669.0                350.0   \n",
+       "AdultWeekend                           85.0                 53.0   \n",
+       "projectedDaysOpen                     150.0                 90.0   \n",
+       "NightSkiing_ac                        550.0                  NaN   \n",
+       "resorts_per_state                         3                    3   \n",
+       "state_total_skiable_area_ac          2280.0               2280.0   \n",
+       "state_total_days_open                 345.0                345.0   \n",
+       "state_total_terrain_parks               4.0                  4.0   \n",
+       "state_total_nightskiing_ac            580.0                580.0   \n",
+       "resorts_per_100kcapita             0.410091             0.410091   \n",
+       "resorts_per_100ksq_mile            0.450867             0.450867   \n",
+       "\n",
+       "                                            2                 3  \\\n",
+       "Name                         Hilltop Ski Area  Arizona Snowbowl   \n",
+       "Region                                 Alaska           Arizona   \n",
+       "state                                  Alaska           Arizona   \n",
+       "summit_elev                              2090             11500   \n",
+       "vertical_drop                             294              2300   \n",
+       "base_elev                                1796              9200   \n",
+       "trams                                       0                 0   \n",
+       "fastSixes                                   0                 1   \n",
+       "fastQuads                                   0                 0   \n",
+       "quad                                        0                 2   \n",
+       "triple                                      1                 2   \n",
+       "double                                      0                 1   \n",
+       "surface                                     2                 2   \n",
+       "total_chairs                                3                 8   \n",
+       "Runs                                     13.0              55.0   \n",
+       "TerrainParks                              1.0               4.0   \n",
+       "LongestRun_mi                             1.0               2.0   \n",
+       "SkiableTerrain_ac                        30.0             777.0   \n",
+       "Snow Making_ac                           30.0             104.0   \n",
+       "daysOpenLastYear                        150.0             122.0   \n",
+       "yearsOpen                                36.0              81.0   \n",
+       "averageSnowfall                          69.0             260.0   \n",
+       "AdultWeekend                             34.0              89.0   \n",
+       "projectedDaysOpen                       152.0             122.0   \n",
+       "NightSkiing_ac                           30.0               NaN   \n",
+       "resorts_per_state                           3                 2   \n",
+       "state_total_skiable_area_ac            2280.0            1577.0   \n",
+       "state_total_days_open                   345.0             237.0   \n",
+       "state_total_terrain_parks                 4.0               6.0   \n",
+       "state_total_nightskiing_ac              580.0              80.0   \n",
+       "resorts_per_100kcapita               0.410091          0.027477   \n",
+       "resorts_per_100ksq_mile              0.450867           1.75454   \n",
+       "\n",
+       "                                               4  \n",
+       "Name                         Sunrise Park Resort  \n",
+       "Region                                   Arizona  \n",
+       "state                                    Arizona  \n",
+       "summit_elev                                11100  \n",
+       "vertical_drop                               1800  \n",
+       "base_elev                                   9200  \n",
+       "trams                                          0  \n",
+       "fastSixes                                      0  \n",
+       "fastQuads                                      1  \n",
+       "quad                                           2  \n",
+       "triple                                         3  \n",
+       "double                                         1  \n",
+       "surface                                        0  \n",
+       "total_chairs                                   7  \n",
+       "Runs                                        65.0  \n",
+       "TerrainParks                                 2.0  \n",
+       "LongestRun_mi                                1.2  \n",
+       "SkiableTerrain_ac                          800.0  \n",
+       "Snow Making_ac                              80.0  \n",
+       "daysOpenLastYear                           115.0  \n",
+       "yearsOpen                                   49.0  \n",
+       "averageSnowfall                            250.0  \n",
+       "AdultWeekend                                78.0  \n",
+       "projectedDaysOpen                          104.0  \n",
+       "NightSkiing_ac                              80.0  \n",
+       "resorts_per_state                              2  \n",
+       "state_total_skiable_area_ac               1577.0  \n",
+       "state_total_days_open                      237.0  \n",
+       "state_total_terrain_parks                    6.0  \n",
+       "state_total_nightskiing_ac                  80.0  \n",
+       "resorts_per_100kcapita                  0.027477  \n",
+       "resorts_per_100ksq_mile                  1.75454  "
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# DataFrame's merge method provides SQL-like joins\n",
+    "# here 'state' is a column (not an index)\n",
+    "ski_data = ski_data.merge(state_summary, how='left', on='state')\n",
+    "ski_data.head().T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Having merged your state summary features into the ski resort data, add \"state resort competition\" features:\n",
+    "\n",
+    "* ratio of resort skiable area to total state skiable area\n",
+    "* ratio of resort days open to total state days open\n",
+    "* ratio of resort terrain park count to total state terrain park count\n",
+    "* ratio of resort night skiing area to total state night skiing area\n",
+    "\n",
+    "Once you've derived these features to put each resort within the context of its state,drop those state columns. Their main purpose was to understand what share of states' skiing \"assets\" is accounted for by each resort."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ski_data['resort_skiable_area_ac_state_ratio'] = ski_data.SkiableTerrain_ac / ski_data.state_total_skiable_area_ac\n",
+    "ski_data['resort_days_open_state_ratio'] = ski_data.daysOpenLastYear / ski_data.state_total_days_open\n",
+    "ski_data['resort_terrain_park_state_ratio'] = ski_data.TerrainParks / ski_data.state_total_terrain_parks\n",
+    "ski_data['resort_night_skiing_state_ratio'] = ski_data.NightSkiing_ac / ski_data.state_total_nightskiing_ac\n",
+    "\n",
+    "ski_data.drop(columns=['state_total_skiable_area_ac', 'state_total_days_open', \n",
+    "                       'state_total_terrain_parks', 'state_total_nightskiing_ac'], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.5.5.2 Feature correlation heatmap<a id='3.5.5.2_Feature_correlation_heatmap'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A great way to gain a high level view of relationships amongst the features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/qy/n5ybkbq12ps0vpdz3748rqx80000gn/T/ipykernel_1636/570658913.py:5: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
+      "  sns.heatmap(ski_data.corr());\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1200x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Code task 12#\n",
+    "#Show a seaborn heatmap of correlations in ski_data\n",
+    "#Hint: call pandas' `corr()` method on `ski_data` and pass that into `sns.heatmap`\n",
+    "plt.subplots(figsize=(12,10))\n",
+    "sns.heatmap(ski_data.corr());"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There is a lot to take away from this. First, summit and base elevation are quite highly correlated. This isn't a surprise. You can also see that you've introduced a lot of multicollinearity with your new ratio features; they are negatively correlated with the number of resorts in each state. This latter observation makes sense! If you increase the number of resorts in a state, the share of all the other state features will drop for each. An interesting observation in this region of the heatmap is that there is some positive correlation between the ratio of night skiing area with the number of resorts per capita. In other words, it seems that when resorts are more densely located with population, more night skiing is provided.\n",
+    "\n",
+    "Turning your attention to your target feature, `AdultWeekend` ticket price, you see quite a few reasonable correlations. `fastQuads` stands out, along with `Runs` and `Snow Making_ac`. The last one is interesting. Visitors would seem to value more guaranteed snow, which would cost in terms of snow making equipment, which would drive prices and costs up. Of the new features, `resort_night_skiing_state_ratio` seems the most correlated with ticket price. If this is true, then perhaps seizing a greater share of night skiing capacity is positive for the price a resort can charge.\n",
+    "\n",
+    "As well as `Runs`, `total_chairs` is quite well correlated with ticket price. This is plausible; the more runs you have, the more chairs you'd need to ferry people to them! Interestingly, they may count for more than the total skiable terrain area. For sure, the total skiable terrain area is not as useful as the area with snow making. People seem to put more value in guaranteed snow cover rather than more variable terrain area.\n",
+    "\n",
+    "The vertical drop seems to be a selling point that raises ticket prices as well."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.5.5.3 Scatterplots of numeric features against ticket price<a id='3.5.5.3_Scatterplots_of_numeric_features_against_ticket_price'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Correlations, particularly viewing them together as a heatmap, can be a great first pass at identifying patterns. But correlation can mask relationships between two variables. You'll now create a series of scatterplots to really dive into how ticket price varies with other numeric features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define useful function to create scatterplots of ticket prices against desired columns\n",
+    "def scatterplots(columns, ncol=None, figsize=(15, 8)):\n",
+    "    if ncol is None:\n",
+    "        ncol = len(columns)\n",
+    "    nrow = int(np.ceil(len(columns) / ncol))\n",
+    "    fig, axes = plt.subplots(nrow, ncol, figsize=figsize, squeeze=False)\n",
+    "    fig.subplots_adjust(wspace=0.5, hspace=0.6)\n",
+    "    for i, col in enumerate(columns):\n",
+    "        ax = axes.flatten()[i]\n",
+    "        ax.scatter(x = col, y = 'AdultWeekend', data=ski_data, alpha=0.5)\n",
+    "        ax.set(xlabel=col, ylabel='Ticket price')\n",
+    "    nsubplots = nrow * ncol    \n",
+    "    for empty in range(i+1, nsubplots):\n",
+    "        axes.flatten()[empty].set_visible(False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Code task 13#\n",
+    "#Use a list comprehension to build a list of features from the columns of `ski_data` that\n",
+    "#are _not_ any of 'Name', 'Region', 'state', or 'AdultWeekend'\n",
+    "features = [col for col in ski_data.columns if col not in ['Name', 'Region', 'state', 'AdultWeekend']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1500x1500 with 28 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "scatterplots(features, ncol=4, figsize=(15, 15))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the scatterplots you see what some of the high correlations were clearly picking up on. There's a strong positive correlation with `vertical_drop`. `fastQuads` seems very useful. `Runs` and `total_chairs` appear quite similar and also useful. `resorts_per_100kcapita` shows something interesting that you don't see from just a headline correlation figure. When the value is low, there is quite a variability in ticket price, although it's capable of going quite high. Ticket price may drop a little before then climbing upwards as the number of resorts per capita increases. Ticket price could climb with the number of resorts serving a population because it indicates a popular area for skiing with plenty of demand. The lower ticket price when fewer resorts serve a population may similarly be because it's a less popular state for skiing. The high price for some resorts when resorts are rare (relative to the population size) may indicate areas where a small number of resorts can benefit from a monopoly effect. It's not a clear picture, although we have some interesting signs."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, think of some further features that may be useful in that they relate to how easily a resort can transport people around. You have the numbers of various chairs, and the number of runs, but you don't have the ratio of chairs to runs. It seems logical that this ratio would inform you how easily, and so quickly, people could get to their next ski slope! Create these features now."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ski_data['total_chairs_runs_ratio'] = ski_data.total_chairs / ski_data.Runs\n",
+    "ski_data['total_chairs_skiable_ratio'] = ski_data.total_chairs / ski_data.SkiableTerrain_ac\n",
+    "ski_data['fastQuads_runs_ratio'] = ski_data.fastQuads / ski_data.Runs\n",
+    "ski_data['fastQuads_skiable_ratio'] = ski_data.fastQuads / ski_data.SkiableTerrain_ac"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1500x800 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "scatterplots(['total_chairs_runs_ratio', 'total_chairs_skiable_ratio', \n",
+    "              'fastQuads_runs_ratio', 'fastQuads_skiable_ratio'], ncol=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "At first these relationships are quite counterintuitive. It seems that the more chairs a resort has to move people around, relative to the number of runs, ticket price rapidly plummets and stays low. What we may be seeing here is an exclusive vs. mass market resort effect; if you don't have so many chairs, you can charge more for your tickets, although with fewer chairs you're inevitably going to be able to serve fewer visitors. Your price per visitor is high but your number of visitors may be low. Something very useful that's missing from the data is the number of visitors per year.\n",
+    "\n",
+    "It also appears that having no fast quads may limit the ticket price, but if your resort covers a wide area then getting a small number of fast quads may be beneficial to ticket price."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.6 Summary<a id='3.6_Summary'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q: 1** Write a summary of the exploratory data analysis above. What numerical or categorical features were in the data? Was there any pattern suggested of a relationship between state and ticket price? What did this lead us to decide regarding which features to use in subsequent modeling? What aspects of the data (e.g. relationships between features) should you remain wary of when you come to perform feature selection for modeling? Two key points that must be addressed are the choice of target feature for your modelling and how, if at all, you're going to handle the states labels in the data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**A: 1** Your answer here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "The exploratory data analysis part of the DSM aims to summarize main characteristics of the dataset and includes visuals\n",
+    "There wasn't see a clear or obvious pattern between the resort state and the price of the AdultWeekend ticket\n",
+    "The data at first had lots of dimensionality so reducing it made it easier to see relationships with the parameter\n",
+    "of AdultWeekend price\n",
+    "From the heatmap, we do see there is some correlation of more guarantee snow, which provides good reason to provide\n",
+    "better equipment like the ski chairs since more runs are likely to happen."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Name</th>\n",
+       "      <td>Alyeska Resort</td>\n",
+       "      <td>Eaglecrest Ski Area</td>\n",
+       "      <td>Hilltop Ski Area</td>\n",
+       "      <td>Arizona Snowbowl</td>\n",
+       "      <td>Sunrise Park Resort</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Region</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>Arizona</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>Arizona</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>summit_elev</th>\n",
+       "      <td>3939</td>\n",
+       "      <td>2600</td>\n",
+       "      <td>2090</td>\n",
+       "      <td>11500</td>\n",
+       "      <td>11100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vertical_drop</th>\n",
+       "      <td>2500</td>\n",
+       "      <td>1540</td>\n",
+       "      <td>294</td>\n",
+       "      <td>2300</td>\n",
+       "      <td>1800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>base_elev</th>\n",
+       "      <td>250</td>\n",
+       "      <td>1200</td>\n",
+       "      <td>1796</td>\n",
+       "      <td>9200</td>\n",
+       "      <td>9200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>trams</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastSixes</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastQuads</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>quad</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>triple</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>double</th>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>surface</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total_chairs</th>\n",
+       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Runs</th>\n",
+       "      <td>76.0</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>65.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TerrainParks</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LongestRun_mi</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SkiableTerrain_ac</th>\n",
+       "      <td>1610.0</td>\n",
+       "      <td>640.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>777.0</td>\n",
+       "      <td>800.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Snow Making_ac</th>\n",
+       "      <td>113.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>104.0</td>\n",
+       "      <td>80.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>daysOpenLastYear</th>\n",
+       "      <td>150.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>122.0</td>\n",
+       "      <td>115.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>yearsOpen</th>\n",
+       "      <td>60.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>49.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>averageSnowfall</th>\n",
+       "      <td>669.0</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>260.0</td>\n",
+       "      <td>250.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AdultWeekend</th>\n",
+       "      <td>85.0</td>\n",
+       "      <td>53.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>89.0</td>\n",
+       "      <td>78.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>projectedDaysOpen</th>\n",
+       "      <td>150.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>152.0</td>\n",
+       "      <td>122.0</td>\n",
+       "      <td>104.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NightSkiing_ac</th>\n",
+       "      <td>550.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>80.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resorts_per_state</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resorts_per_100kcapita</th>\n",
+       "      <td>0.410091</td>\n",
+       "      <td>0.410091</td>\n",
+       "      <td>0.410091</td>\n",
+       "      <td>0.027477</td>\n",
+       "      <td>0.027477</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resorts_per_100ksq_mile</th>\n",
+       "      <td>0.450867</td>\n",
+       "      <td>0.450867</td>\n",
+       "      <td>0.450867</td>\n",
+       "      <td>1.75454</td>\n",
+       "      <td>1.75454</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resort_skiable_area_ac_state_ratio</th>\n",
+       "      <td>0.70614</td>\n",
+       "      <td>0.280702</td>\n",
+       "      <td>0.013158</td>\n",
+       "      <td>0.492708</td>\n",
+       "      <td>0.507292</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resort_days_open_state_ratio</th>\n",
+       "      <td>0.434783</td>\n",
+       "      <td>0.130435</td>\n",
+       "      <td>0.434783</td>\n",
+       "      <td>0.514768</td>\n",
+       "      <td>0.485232</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resort_terrain_park_state_ratio</th>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resort_night_skiing_state_ratio</th>\n",
+       "      <td>0.948276</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.051724</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total_chairs_runs_ratio</th>\n",
+       "      <td>0.092105</td>\n",
+       "      <td>0.111111</td>\n",
+       "      <td>0.230769</td>\n",
+       "      <td>0.145455</td>\n",
+       "      <td>0.107692</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total_chairs_skiable_ratio</th>\n",
+       "      <td>0.004348</td>\n",
+       "      <td>0.00625</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.010296</td>\n",
+       "      <td>0.00875</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastQuads_runs_ratio</th>\n",
+       "      <td>0.026316</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.015385</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastQuads_skiable_ratio</th>\n",
+       "      <td>0.001242</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00125</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                 0                    1  \\\n",
+       "Name                                Alyeska Resort  Eaglecrest Ski Area   \n",
+       "Region                                      Alaska               Alaska   \n",
+       "state                                       Alaska               Alaska   \n",
+       "summit_elev                                   3939                 2600   \n",
+       "vertical_drop                                 2500                 1540   \n",
+       "base_elev                                      250                 1200   \n",
+       "trams                                            1                    0   \n",
+       "fastSixes                                        0                    0   \n",
+       "fastQuads                                        2                    0   \n",
+       "quad                                             2                    0   \n",
+       "triple                                           0                    0   \n",
+       "double                                           0                    4   \n",
+       "surface                                          2                    0   \n",
+       "total_chairs                                     7                    4   \n",
+       "Runs                                          76.0                 36.0   \n",
+       "TerrainParks                                   2.0                  1.0   \n",
+       "LongestRun_mi                                  1.0                  2.0   \n",
+       "SkiableTerrain_ac                           1610.0                640.0   \n",
+       "Snow Making_ac                               113.0                 60.0   \n",
+       "daysOpenLastYear                             150.0                 45.0   \n",
+       "yearsOpen                                     60.0                 44.0   \n",
+       "averageSnowfall                              669.0                350.0   \n",
+       "AdultWeekend                                  85.0                 53.0   \n",
+       "projectedDaysOpen                            150.0                 90.0   \n",
+       "NightSkiing_ac                               550.0                  NaN   \n",
+       "resorts_per_state                                3                    3   \n",
+       "resorts_per_100kcapita                    0.410091             0.410091   \n",
+       "resorts_per_100ksq_mile                   0.450867             0.450867   \n",
+       "resort_skiable_area_ac_state_ratio         0.70614             0.280702   \n",
+       "resort_days_open_state_ratio              0.434783             0.130435   \n",
+       "resort_terrain_park_state_ratio                0.5                 0.25   \n",
+       "resort_night_skiing_state_ratio           0.948276                  NaN   \n",
+       "total_chairs_runs_ratio                   0.092105             0.111111   \n",
+       "total_chairs_skiable_ratio                0.004348              0.00625   \n",
+       "fastQuads_runs_ratio                      0.026316                  0.0   \n",
+       "fastQuads_skiable_ratio                   0.001242                  0.0   \n",
+       "\n",
+       "                                                   2                 3  \\\n",
+       "Name                                Hilltop Ski Area  Arizona Snowbowl   \n",
+       "Region                                        Alaska           Arizona   \n",
+       "state                                         Alaska           Arizona   \n",
+       "summit_elev                                     2090             11500   \n",
+       "vertical_drop                                    294              2300   \n",
+       "base_elev                                       1796              9200   \n",
+       "trams                                              0                 0   \n",
+       "fastSixes                                          0                 1   \n",
+       "fastQuads                                          0                 0   \n",
+       "quad                                               0                 2   \n",
+       "triple                                             1                 2   \n",
+       "double                                             0                 1   \n",
+       "surface                                            2                 2   \n",
+       "total_chairs                                       3                 8   \n",
+       "Runs                                            13.0              55.0   \n",
+       "TerrainParks                                     1.0               4.0   \n",
+       "LongestRun_mi                                    1.0               2.0   \n",
+       "SkiableTerrain_ac                               30.0             777.0   \n",
+       "Snow Making_ac                                  30.0             104.0   \n",
+       "daysOpenLastYear                               150.0             122.0   \n",
+       "yearsOpen                                       36.0              81.0   \n",
+       "averageSnowfall                                 69.0             260.0   \n",
+       "AdultWeekend                                    34.0              89.0   \n",
+       "projectedDaysOpen                              152.0             122.0   \n",
+       "NightSkiing_ac                                  30.0               NaN   \n",
+       "resorts_per_state                                  3                 2   \n",
+       "resorts_per_100kcapita                      0.410091          0.027477   \n",
+       "resorts_per_100ksq_mile                     0.450867           1.75454   \n",
+       "resort_skiable_area_ac_state_ratio          0.013158          0.492708   \n",
+       "resort_days_open_state_ratio                0.434783          0.514768   \n",
+       "resort_terrain_park_state_ratio                 0.25          0.666667   \n",
+       "resort_night_skiing_state_ratio             0.051724               NaN   \n",
+       "total_chairs_runs_ratio                     0.230769          0.145455   \n",
+       "total_chairs_skiable_ratio                       0.1          0.010296   \n",
+       "fastQuads_runs_ratio                             0.0               0.0   \n",
+       "fastQuads_skiable_ratio                          0.0               0.0   \n",
+       "\n",
+       "                                                      4  \n",
+       "Name                                Sunrise Park Resort  \n",
+       "Region                                          Arizona  \n",
+       "state                                           Arizona  \n",
+       "summit_elev                                       11100  \n",
+       "vertical_drop                                      1800  \n",
+       "base_elev                                          9200  \n",
+       "trams                                                 0  \n",
+       "fastSixes                                             0  \n",
+       "fastQuads                                             1  \n",
+       "quad                                                  2  \n",
+       "triple                                                3  \n",
+       "double                                                1  \n",
+       "surface                                               0  \n",
+       "total_chairs                                          7  \n",
+       "Runs                                               65.0  \n",
+       "TerrainParks                                        2.0  \n",
+       "LongestRun_mi                                       1.2  \n",
+       "SkiableTerrain_ac                                 800.0  \n",
+       "Snow Making_ac                                     80.0  \n",
+       "daysOpenLastYear                                  115.0  \n",
+       "yearsOpen                                          49.0  \n",
+       "averageSnowfall                                   250.0  \n",
+       "AdultWeekend                                       78.0  \n",
+       "projectedDaysOpen                                 104.0  \n",
+       "NightSkiing_ac                                     80.0  \n",
+       "resorts_per_state                                     2  \n",
+       "resorts_per_100kcapita                         0.027477  \n",
+       "resorts_per_100ksq_mile                         1.75454  \n",
+       "resort_skiable_area_ac_state_ratio             0.507292  \n",
+       "resort_days_open_state_ratio                   0.485232  \n",
+       "resort_terrain_park_state_ratio                0.333333  \n",
+       "resort_night_skiing_state_ratio                     1.0  \n",
+       "total_chairs_runs_ratio                        0.107692  \n",
+       "total_chairs_skiable_ratio                      0.00875  \n",
+       "fastQuads_runs_ratio                           0.015385  \n",
+       "fastQuads_skiable_ratio                         0.00125  "
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ski_data.head().T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing file.  \"../data/ski_data_step3_features.csv\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Save the data \n",
+    "\n",
+    "datapath = '../data'\n",
+    "save_file(ski_data, 'ski_data_step3_features.csv', datapath)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From 7dffe246a4592b52dde8018dfc4652635d014433 Mon Sep 17 00:00:00 2001
From: brianbui0 <buibrian96@gmail.com>
Date: Thu, 29 Aug 2024 21:43:03 +0700
Subject: [PATCH 6/9] Add files via upload

---
 .../04_preprocessing_and_training (1).ipynb   | 4039 +++++++++++++++++
 1 file changed, 4039 insertions(+)
 create mode 100644 CapstoneSteps/04_preprocessing_and_training (1).ipynb

diff --git a/CapstoneSteps/04_preprocessing_and_training (1).ipynb b/CapstoneSteps/04_preprocessing_and_training (1).ipynb
new file mode 100644
index 000000000..d385f7ab4
--- /dev/null
+++ b/CapstoneSteps/04_preprocessing_and_training (1).ipynb	
@@ -0,0 +1,4039 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 4 Pre-Processing and Training Data<a id='4_Pre-Processing_and_Training_Data'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4.1 Contents<a id='4.1_Contents'></a>\n",
+    "* [4 Pre-Processing and Training Data](#4_Pre-Processing_and_Training_Data)\n",
+    "  * [4.1 Contents](#4.1_Contents)\n",
+    "  * [4.2 Introduction](#4.2_Introduction)\n",
+    "  * [4.3 Imports](#4.3_Imports)\n",
+    "  * [4.4 Load Data](#4.4_Load_Data)\n",
+    "  * [4.5 Extract Big Mountain Data](#4.5_Extract_Big_Mountain_Data)\n",
+    "  * [4.6 Train/Test Split](#4.6_Train/Test_Split)\n",
+    "  * [4.7 Initial Not-Even-A-Model](#4.7_Initial_Not-Even-A-Model)\n",
+    "    * [4.7.1 Metrics](#4.7.1_Metrics)\n",
+    "      * [4.7.1.1 R-squared, or coefficient of determination](#4.7.1.1_R-squared,_or_coefficient_of_determination)\n",
+    "      * [4.7.1.2 Mean Absolute Error](#4.7.1.2_Mean_Absolute_Error)\n",
+    "      * [4.7.1.3 Mean Squared Error](#4.7.1.3_Mean_Squared_Error)\n",
+    "    * [4.7.2 sklearn metrics](#4.7.2_sklearn_metrics)\n",
+    "        * [4.7.2.0.1 R-squared](#4.7.2.0.1_R-squared)\n",
+    "        * [4.7.2.0.2 Mean absolute error](#4.7.2.0.2_Mean_absolute_error)\n",
+    "        * [4.7.2.0.3 Mean squared error](#4.7.2.0.3_Mean_squared_error)\n",
+    "    * [4.7.3 Note On Calculating Metrics](#4.7.3_Note_On_Calculating_Metrics)\n",
+    "  * [4.8 Initial Models](#4.8_Initial_Models)\n",
+    "    * [4.8.1 Imputing missing feature (predictor) values](#4.8.1_Imputing_missing_feature_(predictor)_values)\n",
+    "      * [4.8.1.1 Impute missing values with median](#4.8.1.1_Impute_missing_values_with_median)\n",
+    "        * [4.8.1.1.1 Learn the values to impute from the train set](#4.8.1.1.1_Learn_the_values_to_impute_from_the_train_set)\n",
+    "        * [4.8.1.1.2 Apply the imputation to both train and test splits](#4.8.1.1.2_Apply_the_imputation_to_both_train_and_test_splits)\n",
+    "        * [4.8.1.1.3 Scale the data](#4.8.1.1.3_Scale_the_data)\n",
+    "        * [4.8.1.1.4 Train the model on the train split](#4.8.1.1.4_Train_the_model_on_the_train_split)\n",
+    "        * [4.8.1.1.5 Make predictions using the model on both train and test splits](#4.8.1.1.5_Make_predictions_using_the_model_on_both_train_and_test_splits)\n",
+    "        * [4.8.1.1.6 Assess model performance](#4.8.1.1.6_Assess_model_performance)\n",
+    "      * [4.8.1.2 Impute missing values with the mean](#4.8.1.2_Impute_missing_values_with_the_mean)\n",
+    "        * [4.8.1.2.1 Learn the values to impute from the train set](#4.8.1.2.1_Learn_the_values_to_impute_from_the_train_set)\n",
+    "        * [4.8.1.2.2 Apply the imputation to both train and test splits](#4.8.1.2.2_Apply_the_imputation_to_both_train_and_test_splits)\n",
+    "        * [4.8.1.2.3 Scale the data](#4.8.1.2.3_Scale_the_data)\n",
+    "        * [4.8.1.2.4 Train the model on the train split](#4.8.1.2.4_Train_the_model_on_the_train_split)\n",
+    "        * [4.8.1.2.5 Make predictions using the model on both train and test splits](#4.8.1.2.5_Make_predictions_using_the_model_on_both_train_and_test_splits)\n",
+    "        * [4.8.1.2.6 Assess model performance](#4.8.1.2.6_Assess_model_performance)\n",
+    "    * [4.8.2 Pipelines](#4.8.2_Pipelines)\n",
+    "      * [4.8.2.1 Define the pipeline](#4.8.2.1_Define_the_pipeline)\n",
+    "      * [4.8.2.2 Fit the pipeline](#4.8.2.2_Fit_the_pipeline)\n",
+    "      * [4.8.2.3 Make predictions on the train and test sets](#4.8.2.3_Make_predictions_on_the_train_and_test_sets)\n",
+    "      * [4.8.2.4 Assess performance](#4.8.2.4_Assess_performance)\n",
+    "  * [4.9 Refining The Linear Model](#4.9_Refining_The_Linear_Model)\n",
+    "    * [4.9.1 Define the pipeline](#4.9.1_Define_the_pipeline)\n",
+    "    * [4.9.2 Fit the pipeline](#4.9.2_Fit_the_pipeline)\n",
+    "    * [4.9.3 Assess performance on the train and test set](#4.9.3_Assess_performance_on_the_train_and_test_set)\n",
+    "    * [4.9.4 Define a new pipeline to select a different number of features](#4.9.4_Define_a_new_pipeline_to_select_a_different_number_of_features)\n",
+    "    * [4.9.5 Fit the pipeline](#4.9.5_Fit_the_pipeline)\n",
+    "    * [4.9.6 Assess performance on train and test data](#4.9.6_Assess_performance_on_train_and_test_data)\n",
+    "    * [4.9.7 Assessing performance using cross-validation](#4.9.7_Assessing_performance_using_cross-validation)\n",
+    "    * [4.9.8 Hyperparameter search using GridSearchCV](#4.9.8_Hyperparameter_search_using_GridSearchCV)\n",
+    "  * [4.10 Random Forest Model](#4.10_Random_Forest_Model)\n",
+    "    * [4.10.1 Define the pipeline](#4.10.1_Define_the_pipeline)\n",
+    "    * [4.10.2 Fit and assess performance using cross-validation](#4.10.2_Fit_and_assess_performance_using_cross-validation)\n",
+    "    * [4.10.3 Hyperparameter search using GridSearchCV](#4.10.3_Hyperparameter_search_using_GridSearchCV)\n",
+    "  * [4.11 Final Model Selection](#4.11_Final_Model_Selection)\n",
+    "    * [4.11.1 Linear regression model performance](#4.11.1_Linear_regression_model_performance)\n",
+    "    * [4.11.2 Random forest regression model performance](#4.11.2_Random_forest_regression_model_performance)\n",
+    "    * [4.11.3 Conclusion](#4.11.3_Conclusion)\n",
+    "  * [4.12 Data quantity assessment](#4.12_Data_quantity_assessment)\n",
+    "  * [4.13 Save best model object from pipeline](#4.13_Save_best_model_object_from_pipeline)\n",
+    "  * [4.14 Summary](#4.14_Summary)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4.2 Introduction<a id='4.2_Introduction'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In preceding notebooks, performed preliminary assessments of data quality and refined the question to be answered. You found a small number of data values that gave clear choices about whether to replace values or drop a whole row. You determined that predicting the adult weekend ticket price was your primary aim. You threw away records with missing price data, but not before making the most of the other available data to look for any patterns between the states. You didn't see any and decided to treat all states equally; the state label didn't seem to be particularly useful.\n",
+    "\n",
+    "In this notebook you'll start to build machine learning models. Before even starting with learning a machine learning model, however, start by considering how useful the mean value is as a predictor. This is more than just a pedagogical device. You never want to go to stakeholders with a machine learning model only to have the CEO point out that it performs worse than just guessing the average! Your first model is a baseline performance comparitor for any subsequent model. You then build up the process of efficiently and robustly creating and assessing models against it. The development we lay out may be little slower than in the real world, but this step of the capstone is definitely more than just instructional. It is good practice to build up an understanding that the machine learning pipelines you build work as expected. You can validate steps with your own functions for checking expected equivalence between, say, pandas and sklearn implementations."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4.3 Imports<a id='4.3_Imports'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import pickle\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from sklearn import __version__ as sklearn_version\n",
+    "from sklearn.decomposition import PCA\n",
+    "from sklearn.preprocessing import scale\n",
+    "from sklearn.model_selection import train_test_split, cross_validate, GridSearchCV, learning_curve\n",
+    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
+    "from sklearn.dummy import DummyRegressor\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "from sklearn.impute import SimpleImputer\n",
+    "from sklearn.feature_selection import SelectKBest, f_regression\n",
+    "import datetime\n",
+    "\n",
+    "from library.sb_utils import save_file"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4.4 Load Data<a id='4.4_Load_Data'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
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+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Name</th>\n",
+       "      <td>Alyeska Resort</td>\n",
+       "      <td>Eaglecrest Ski Area</td>\n",
+       "      <td>Hilltop Ski Area</td>\n",
+       "      <td>Arizona Snowbowl</td>\n",
+       "      <td>Sunrise Park Resort</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Region</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>Arizona</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>Arizona</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>summit_elev</th>\n",
+       "      <td>3939</td>\n",
+       "      <td>2600</td>\n",
+       "      <td>2090</td>\n",
+       "      <td>11500</td>\n",
+       "      <td>11100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vertical_drop</th>\n",
+       "      <td>2500</td>\n",
+       "      <td>1540</td>\n",
+       "      <td>294</td>\n",
+       "      <td>2300</td>\n",
+       "      <td>1800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>base_elev</th>\n",
+       "      <td>250</td>\n",
+       "      <td>1200</td>\n",
+       "      <td>1796</td>\n",
+       "      <td>9200</td>\n",
+       "      <td>9200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>trams</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastSixes</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastQuads</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>quad</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>triple</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>double</th>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>surface</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total_chairs</th>\n",
+       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Runs</th>\n",
+       "      <td>76.0</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>65.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TerrainParks</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LongestRun_mi</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SkiableTerrain_ac</th>\n",
+       "      <td>1610.0</td>\n",
+       "      <td>640.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>777.0</td>\n",
+       "      <td>800.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Snow Making_ac</th>\n",
+       "      <td>113.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>104.0</td>\n",
+       "      <td>80.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>daysOpenLastYear</th>\n",
+       "      <td>150.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>122.0</td>\n",
+       "      <td>115.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>yearsOpen</th>\n",
+       "      <td>60.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>49.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>averageSnowfall</th>\n",
+       "      <td>669.0</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>260.0</td>\n",
+       "      <td>250.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AdultWeekend</th>\n",
+       "      <td>85.0</td>\n",
+       "      <td>53.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>89.0</td>\n",
+       "      <td>78.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>projectedDaysOpen</th>\n",
+       "      <td>150.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>152.0</td>\n",
+       "      <td>122.0</td>\n",
+       "      <td>104.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NightSkiing_ac</th>\n",
+       "      <td>550.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>80.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resorts_per_state</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resorts_per_100kcapita</th>\n",
+       "      <td>0.410091</td>\n",
+       "      <td>0.410091</td>\n",
+       "      <td>0.410091</td>\n",
+       "      <td>0.027477</td>\n",
+       "      <td>0.027477</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resorts_per_100ksq_mile</th>\n",
+       "      <td>0.450867</td>\n",
+       "      <td>0.450867</td>\n",
+       "      <td>0.450867</td>\n",
+       "      <td>1.75454</td>\n",
+       "      <td>1.75454</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resort_skiable_area_ac_state_ratio</th>\n",
+       "      <td>0.70614</td>\n",
+       "      <td>0.280702</td>\n",
+       "      <td>0.013158</td>\n",
+       "      <td>0.492708</td>\n",
+       "      <td>0.507292</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resort_days_open_state_ratio</th>\n",
+       "      <td>0.434783</td>\n",
+       "      <td>0.130435</td>\n",
+       "      <td>0.434783</td>\n",
+       "      <td>0.514768</td>\n",
+       "      <td>0.485232</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resort_terrain_park_state_ratio</th>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resort_night_skiing_state_ratio</th>\n",
+       "      <td>0.948276</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.051724</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total_chairs_runs_ratio</th>\n",
+       "      <td>0.092105</td>\n",
+       "      <td>0.111111</td>\n",
+       "      <td>0.230769</td>\n",
+       "      <td>0.145455</td>\n",
+       "      <td>0.107692</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total_chairs_skiable_ratio</th>\n",
+       "      <td>0.004348</td>\n",
+       "      <td>0.00625</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.010296</td>\n",
+       "      <td>0.00875</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastQuads_runs_ratio</th>\n",
+       "      <td>0.026316</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.015385</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastQuads_skiable_ratio</th>\n",
+       "      <td>0.001242</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00125</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                 0                    1  \\\n",
+       "Name                                Alyeska Resort  Eaglecrest Ski Area   \n",
+       "Region                                      Alaska               Alaska   \n",
+       "state                                       Alaska               Alaska   \n",
+       "summit_elev                                   3939                 2600   \n",
+       "vertical_drop                                 2500                 1540   \n",
+       "base_elev                                      250                 1200   \n",
+       "trams                                            1                    0   \n",
+       "fastSixes                                        0                    0   \n",
+       "fastQuads                                        2                    0   \n",
+       "quad                                             2                    0   \n",
+       "triple                                           0                    0   \n",
+       "double                                           0                    4   \n",
+       "surface                                          2                    0   \n",
+       "total_chairs                                     7                    4   \n",
+       "Runs                                          76.0                 36.0   \n",
+       "TerrainParks                                   2.0                  1.0   \n",
+       "LongestRun_mi                                  1.0                  2.0   \n",
+       "SkiableTerrain_ac                           1610.0                640.0   \n",
+       "Snow Making_ac                               113.0                 60.0   \n",
+       "daysOpenLastYear                             150.0                 45.0   \n",
+       "yearsOpen                                     60.0                 44.0   \n",
+       "averageSnowfall                              669.0                350.0   \n",
+       "AdultWeekend                                  85.0                 53.0   \n",
+       "projectedDaysOpen                            150.0                 90.0   \n",
+       "NightSkiing_ac                               550.0                  NaN   \n",
+       "resorts_per_state                                3                    3   \n",
+       "resorts_per_100kcapita                    0.410091             0.410091   \n",
+       "resorts_per_100ksq_mile                   0.450867             0.450867   \n",
+       "resort_skiable_area_ac_state_ratio         0.70614             0.280702   \n",
+       "resort_days_open_state_ratio              0.434783             0.130435   \n",
+       "resort_terrain_park_state_ratio                0.5                 0.25   \n",
+       "resort_night_skiing_state_ratio           0.948276                  NaN   \n",
+       "total_chairs_runs_ratio                   0.092105             0.111111   \n",
+       "total_chairs_skiable_ratio                0.004348              0.00625   \n",
+       "fastQuads_runs_ratio                      0.026316                  0.0   \n",
+       "fastQuads_skiable_ratio                   0.001242                  0.0   \n",
+       "\n",
+       "                                                   2                 3  \\\n",
+       "Name                                Hilltop Ski Area  Arizona Snowbowl   \n",
+       "Region                                        Alaska           Arizona   \n",
+       "state                                         Alaska           Arizona   \n",
+       "summit_elev                                     2090             11500   \n",
+       "vertical_drop                                    294              2300   \n",
+       "base_elev                                       1796              9200   \n",
+       "trams                                              0                 0   \n",
+       "fastSixes                                          0                 1   \n",
+       "fastQuads                                          0                 0   \n",
+       "quad                                               0                 2   \n",
+       "triple                                             1                 2   \n",
+       "double                                             0                 1   \n",
+       "surface                                            2                 2   \n",
+       "total_chairs                                       3                 8   \n",
+       "Runs                                            13.0              55.0   \n",
+       "TerrainParks                                     1.0               4.0   \n",
+       "LongestRun_mi                                    1.0               2.0   \n",
+       "SkiableTerrain_ac                               30.0             777.0   \n",
+       "Snow Making_ac                                  30.0             104.0   \n",
+       "daysOpenLastYear                               150.0             122.0   \n",
+       "yearsOpen                                       36.0              81.0   \n",
+       "averageSnowfall                                 69.0             260.0   \n",
+       "AdultWeekend                                    34.0              89.0   \n",
+       "projectedDaysOpen                              152.0             122.0   \n",
+       "NightSkiing_ac                                  30.0               NaN   \n",
+       "resorts_per_state                                  3                 2   \n",
+       "resorts_per_100kcapita                      0.410091          0.027477   \n",
+       "resorts_per_100ksq_mile                     0.450867           1.75454   \n",
+       "resort_skiable_area_ac_state_ratio          0.013158          0.492708   \n",
+       "resort_days_open_state_ratio                0.434783          0.514768   \n",
+       "resort_terrain_park_state_ratio                 0.25          0.666667   \n",
+       "resort_night_skiing_state_ratio             0.051724               NaN   \n",
+       "total_chairs_runs_ratio                     0.230769          0.145455   \n",
+       "total_chairs_skiable_ratio                       0.1          0.010296   \n",
+       "fastQuads_runs_ratio                             0.0               0.0   \n",
+       "fastQuads_skiable_ratio                          0.0               0.0   \n",
+       "\n",
+       "                                                      4  \n",
+       "Name                                Sunrise Park Resort  \n",
+       "Region                                          Arizona  \n",
+       "state                                           Arizona  \n",
+       "summit_elev                                       11100  \n",
+       "vertical_drop                                      1800  \n",
+       "base_elev                                          9200  \n",
+       "trams                                                 0  \n",
+       "fastSixes                                             0  \n",
+       "fastQuads                                             1  \n",
+       "quad                                                  2  \n",
+       "triple                                                3  \n",
+       "double                                                1  \n",
+       "surface                                               0  \n",
+       "total_chairs                                          7  \n",
+       "Runs                                               65.0  \n",
+       "TerrainParks                                        2.0  \n",
+       "LongestRun_mi                                       1.2  \n",
+       "SkiableTerrain_ac                                 800.0  \n",
+       "Snow Making_ac                                     80.0  \n",
+       "daysOpenLastYear                                  115.0  \n",
+       "yearsOpen                                          49.0  \n",
+       "averageSnowfall                                   250.0  \n",
+       "AdultWeekend                                       78.0  \n",
+       "projectedDaysOpen                                 104.0  \n",
+       "NightSkiing_ac                                     80.0  \n",
+       "resorts_per_state                                     2  \n",
+       "resorts_per_100kcapita                         0.027477  \n",
+       "resorts_per_100ksq_mile                         1.75454  \n",
+       "resort_skiable_area_ac_state_ratio             0.507292  \n",
+       "resort_days_open_state_ratio                   0.485232  \n",
+       "resort_terrain_park_state_ratio                0.333333  \n",
+       "resort_night_skiing_state_ratio                     1.0  \n",
+       "total_chairs_runs_ratio                        0.107692  \n",
+       "total_chairs_skiable_ratio                      0.00875  \n",
+       "fastQuads_runs_ratio                           0.015385  \n",
+       "fastQuads_skiable_ratio                         0.00125  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ski_data = pd.read_csv('../data/ski_data_step3_features.csv')\n",
+    "ski_data.head().T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4.5 Extract Big Mountain Data<a id='4.5_Extract_Big_Mountain_Data'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Big Mountain is your resort. Separate it from the rest of the data to use later."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "big_mountain = ski_data[ski_data.Name == 'Big Mountain Resort']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>124</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Name</th>\n",
+       "      <td>Big Mountain Resort</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Region</th>\n",
+       "      <td>Montana</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state</th>\n",
+       "      <td>Montana</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>summit_elev</th>\n",
+       "      <td>6817</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vertical_drop</th>\n",
+       "      <td>2353</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>base_elev</th>\n",
+       "      <td>4464</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>trams</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastSixes</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastQuads</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>quad</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>triple</th>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>double</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>surface</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total_chairs</th>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Runs</th>\n",
+       "      <td>105.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TerrainParks</th>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LongestRun_mi</th>\n",
+       "      <td>3.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SkiableTerrain_ac</th>\n",
+       "      <td>3000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Snow Making_ac</th>\n",
+       "      <td>600.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>daysOpenLastYear</th>\n",
+       "      <td>123.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>yearsOpen</th>\n",
+       "      <td>72.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>averageSnowfall</th>\n",
+       "      <td>333.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AdultWeekend</th>\n",
+       "      <td>81.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>projectedDaysOpen</th>\n",
+       "      <td>123.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NightSkiing_ac</th>\n",
+       "      <td>600.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resorts_per_state</th>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resorts_per_100kcapita</th>\n",
+       "      <td>1.122778</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resorts_per_100ksq_mile</th>\n",
+       "      <td>8.161045</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resort_skiable_area_ac_state_ratio</th>\n",
+       "      <td>0.140121</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resort_days_open_state_ratio</th>\n",
+       "      <td>0.129338</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resort_terrain_park_state_ratio</th>\n",
+       "      <td>0.148148</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resort_night_skiing_state_ratio</th>\n",
+       "      <td>0.84507</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total_chairs_runs_ratio</th>\n",
+       "      <td>0.133333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total_chairs_skiable_ratio</th>\n",
+       "      <td>0.004667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastQuads_runs_ratio</th>\n",
+       "      <td>0.028571</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastQuads_skiable_ratio</th>\n",
+       "      <td>0.001</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                    124\n",
+       "Name                                Big Mountain Resort\n",
+       "Region                                          Montana\n",
+       "state                                           Montana\n",
+       "summit_elev                                        6817\n",
+       "vertical_drop                                      2353\n",
+       "base_elev                                          4464\n",
+       "trams                                                 0\n",
+       "fastSixes                                             0\n",
+       "fastQuads                                             3\n",
+       "quad                                                  2\n",
+       "triple                                                6\n",
+       "double                                                0\n",
+       "surface                                               3\n",
+       "total_chairs                                         14\n",
+       "Runs                                              105.0\n",
+       "TerrainParks                                        4.0\n",
+       "LongestRun_mi                                       3.3\n",
+       "SkiableTerrain_ac                                3000.0\n",
+       "Snow Making_ac                                    600.0\n",
+       "daysOpenLastYear                                  123.0\n",
+       "yearsOpen                                          72.0\n",
+       "averageSnowfall                                   333.0\n",
+       "AdultWeekend                                       81.0\n",
+       "projectedDaysOpen                                 123.0\n",
+       "NightSkiing_ac                                    600.0\n",
+       "resorts_per_state                                    12\n",
+       "resorts_per_100kcapita                         1.122778\n",
+       "resorts_per_100ksq_mile                        8.161045\n",
+       "resort_skiable_area_ac_state_ratio             0.140121\n",
+       "resort_days_open_state_ratio                   0.129338\n",
+       "resort_terrain_park_state_ratio                0.148148\n",
+       "resort_night_skiing_state_ratio                 0.84507\n",
+       "total_chairs_runs_ratio                        0.133333\n",
+       "total_chairs_skiable_ratio                     0.004667\n",
+       "fastQuads_runs_ratio                           0.028571\n",
+       "fastQuads_skiable_ratio                           0.001"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "big_mountain.T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(277, 36)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ski_data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ski_data = ski_data[ski_data.Name != 'Big Mountain Resort']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(276, 36)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ski_data.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4.6 Train/Test Split<a id='4.6_Train/Test_Split'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So far, you've treated ski resort data as a single entity. In machine learning, when you train your model on all of your data, you end up with no data set aside to evaluate model performance. You could keep making more and more complex models that fit the data better and better and not realise you were overfitting to that one set of samples. By partitioning the data into training and testing splits, without letting a model (or missing-value imputation) learn anything about the test split, you have a somewhat independent assessment of how your model might perform in the future. An often overlooked subtlety here is that people all too frequently use the test set to assess model performance _and then compare multiple models to pick the best_. This means their overall model selection process is  fitting to one specific data set, now the test split. You could keep going, trying to get better and better performance on that one data set, but that's  where cross-validation becomes especially useful. While training models, a test split is very useful as a final check on expected future performance."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What partition sizes would you have with a 70/30 train/test split?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(193.2, 82.8)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(ski_data) * .7, len(ski_data) * .3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(ski_data.drop(columns='AdultWeekend'), \n",
+    "                                                    ski_data.AdultWeekend, test_size=0.3, \n",
+    "                                                    random_state=47)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((193, 35), (83, 35))"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train.shape, X_test.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((193,), (83,))"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((193, 32), (83, 32))"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 1#\n",
+    "#Save the 'Name', 'state', and 'Region' columns from the train/test data into names_train and names_test\n",
+    "#Then drop those columns from `X_train` and `X_test`. Use 'inplace=True'\n",
+    "names_list = ['Name', 'state', 'Region']\n",
+    "names_train = X_train[names_list]\n",
+    "names_test = X_test[names_list]\n",
+    "X_train.drop(columns=names_list, inplace=True)\n",
+    "X_test.drop(columns=names_list, inplace=True)\n",
+    "X_train.shape, X_test.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "summit_elev                             int64\n",
+       "vertical_drop                           int64\n",
+       "base_elev                               int64\n",
+       "trams                                   int64\n",
+       "fastSixes                               int64\n",
+       "fastQuads                               int64\n",
+       "quad                                    int64\n",
+       "triple                                  int64\n",
+       "double                                  int64\n",
+       "surface                                 int64\n",
+       "total_chairs                            int64\n",
+       "Runs                                  float64\n",
+       "TerrainParks                          float64\n",
+       "LongestRun_mi                         float64\n",
+       "SkiableTerrain_ac                     float64\n",
+       "Snow Making_ac                        float64\n",
+       "daysOpenLastYear                      float64\n",
+       "yearsOpen                             float64\n",
+       "averageSnowfall                       float64\n",
+       "projectedDaysOpen                     float64\n",
+       "NightSkiing_ac                        float64\n",
+       "resorts_per_state                       int64\n",
+       "resorts_per_100kcapita                float64\n",
+       "resorts_per_100ksq_mile               float64\n",
+       "resort_skiable_area_ac_state_ratio    float64\n",
+       "resort_days_open_state_ratio          float64\n",
+       "resort_terrain_park_state_ratio       float64\n",
+       "resort_night_skiing_state_ratio       float64\n",
+       "total_chairs_runs_ratio               float64\n",
+       "total_chairs_skiable_ratio            float64\n",
+       "fastQuads_runs_ratio                  float64\n",
+       "fastQuads_skiable_ratio               float64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 2#\n",
+    "#Check the `dtypes` attribute of `X_train` to verify all features are numeric\n",
+    "X_train.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "summit_elev                             int64\n",
+       "vertical_drop                           int64\n",
+       "base_elev                               int64\n",
+       "trams                                   int64\n",
+       "fastSixes                               int64\n",
+       "fastQuads                               int64\n",
+       "quad                                    int64\n",
+       "triple                                  int64\n",
+       "double                                  int64\n",
+       "surface                                 int64\n",
+       "total_chairs                            int64\n",
+       "Runs                                  float64\n",
+       "TerrainParks                          float64\n",
+       "LongestRun_mi                         float64\n",
+       "SkiableTerrain_ac                     float64\n",
+       "Snow Making_ac                        float64\n",
+       "daysOpenLastYear                      float64\n",
+       "yearsOpen                             float64\n",
+       "averageSnowfall                       float64\n",
+       "projectedDaysOpen                     float64\n",
+       "NightSkiing_ac                        float64\n",
+       "resorts_per_state                       int64\n",
+       "resorts_per_100kcapita                float64\n",
+       "resorts_per_100ksq_mile               float64\n",
+       "resort_skiable_area_ac_state_ratio    float64\n",
+       "resort_days_open_state_ratio          float64\n",
+       "resort_terrain_park_state_ratio       float64\n",
+       "resort_night_skiing_state_ratio       float64\n",
+       "total_chairs_runs_ratio               float64\n",
+       "total_chairs_skiable_ratio            float64\n",
+       "fastQuads_runs_ratio                  float64\n",
+       "fastQuads_skiable_ratio               float64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 3#\n",
+    "#Repeat this check for the test split in `X_test`\n",
+    "X_test.dtypes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You have only numeric features in your X now!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4.7 Initial Not-Even-A-Model<a id='4.7_Initial_Not-Even-A-Model'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A good place to start is to see how good the mean is as a predictor. In other words, what if you simply say your best guess is the average price?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "63.811088082901556"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 4#\n",
+    "#Calculate the mean of `y_train`\n",
+    "train_mean = y_train.mean()\n",
+    "train_mean"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`sklearn`'s `DummyRegressor` easily does this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[63.81108808]])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 5#\n",
+    "#Fit the dummy regressor on the training data\n",
+    "#Hint, call its `.fit()` method with `X_train` and `y_train` as arguments\n",
+    "#Then print the object's `constant_` attribute and verify it's the same as the mean above\n",
+    "dumb_reg = DummyRegressor(strategy='mean')\n",
+    "dumb_reg.fit(X_train, y_train)\n",
+    "dumb_reg.constant_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "How good is this? How closely does this match, or explain, the actual values? There are many ways of assessing how good one set of values agrees with another, which brings us to the subject of metrics."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.7.1 Metrics<a id='4.7.1_Metrics'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 4.7.1.1 R-squared, or coefficient of determination<a id='4.7.1.1_R-squared,_or_coefficient_of_determination'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "One measure is $R^2$, the [coefficient of determination](https://en.wikipedia.org/wiki/Coefficient_of_determination). This is a measure of the proportion of variance in the dependent variable (our ticket price) that is predicted by our \"model\". The linked Wikipedia articles gives a nice explanation of how negative values can arise. This is frequently a cause of confusion for newcomers who, reasonably, ask how can a squared value be negative?\n",
+    "\n",
+    "Recall the mean can be denoted by $\\bar{y}$, where\n",
+    "\n",
+    "$$\\bar{y} = \\frac{1}{n}\\sum_{i=1}^ny_i$$\n",
+    "\n",
+    "and where $y_i$ are the individual values of the dependent variable.\n",
+    "\n",
+    "The total sum of squares (error), can be expressed as\n",
+    "\n",
+    "$$SS_{tot} = \\sum_i(y_i-\\bar{y})^2$$\n",
+    "\n",
+    "The above formula should be familiar as it's simply the variance without the denominator to scale (divide) by the sample size.\n",
+    "\n",
+    "The residual sum of squares is similarly defined to be\n",
+    "\n",
+    "$$SS_{res} = \\sum_i(y_i-\\hat{y})^2$$\n",
+    "\n",
+    "where $\\hat{y}$ are our predicted values for the depended variable.\n",
+    "\n",
+    "The coefficient of determination, $R^2$, here is given by\n",
+    "\n",
+    "$$R^2 = 1 - \\frac{SS_{res}}{SS_{tot}}$$\n",
+    "\n",
+    "Putting it into words, it's one minus the ratio of the residual variance to the original variance. Thus, the baseline model here, which always predicts $\\bar{y}$, should give $R^2=0$. A model that perfectly predicts the observed values would have no residual error and so give $R^2=1$. Models that do worse than predicting the mean will have increased the sum of squares of residuals and so produce a negative $R^2$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Code task 6#\n",
+    "#Calculate the R^2 as defined above\n",
+    "def r_squared(y, ypred):\n",
+    "    \"\"\"R-squared score.\n",
+    "    \n",
+    "    Calculate the R-squared, or coefficient of determination, of the input.\n",
+    "    \n",
+    "    Arguments:\n",
+    "    y -- the observed values\n",
+    "    ypred -- the predicted values\n",
+    "    \"\"\"\n",
+    "    ybar = np.sum(y) / len(y) #yes, we could use np.mean(y)\n",
+    "    sum_sq_tot = np.sum((y - ybar)**2) #total sum of squares error\n",
+    "    sum_sq_res = np.sum((y - ypred)**2) #residual sum of squares error\n",
+    "    R2 = 1.0 - sum_sq_res / sum_sq_tot\n",
+    "    return R2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Make your predictions by creating an array of length the size of the training set with the single value of the mean."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([63.81108808, 63.81108808, 63.81108808, 63.81108808, 63.81108808])"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_tr_pred_ = train_mean * np.ones(len(y_train))\n",
+    "y_tr_pred_[:5]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Remember the `sklearn` dummy regressor? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([63.81108808, 63.81108808, 63.81108808, 63.81108808, 63.81108808])"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_tr_pred = dumb_reg.predict(X_train)\n",
+    "y_tr_pred[:5]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can see that `DummyRegressor` produces exactly the same results and saves you having to mess about broadcasting the mean (or whichever other statistic we used - check out the [documentation](https://scikit-learn.org/stable/modules/generated/sklearn.dummy.DummyRegressor.html) to see what's available) to an array of the appropriate length. It also gives you an object with `fit()` and `predict()` methods as well so you can use them as conveniently as any other `sklearn` estimator."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r_squared(y_train, y_tr_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Exactly as expected, if you use the average value as your prediction, you get an $R^2$ of zero _on our training set_. What if you use this \"model\" to predict unseen values from the test set? Remember, of course, that your \"model\" is trained on the training set; you still use the training set mean as your prediction."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Make your predictions by creating an array of length the size of the test set with the single value of the (training) mean."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-0.0031235200417913944"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_te_pred = train_mean * np.ones(len(y_test))\n",
+    "r_squared(y_test, y_te_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Generally, you can expect performance on a test set to be slightly worse than on the training set. As you are getting an $R^2$ of zero on the training set, there's nowhere to go but negative!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$R^2$ is a common metric, and interpretable in terms of the amount of variance explained, it's less appealing if you want an idea of how \"close\" your predictions are to the true values. Metrics that summarise the difference between predicted and actual values are _mean absolute error_ and _mean squared error_."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 4.7.1.2 Mean Absolute Error<a id='4.7.1.2_Mean_Absolute_Error'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is very simply the average of the absolute errors:\n",
+    "\n",
+    "$$MAE = \\frac{1}{n}\\sum_i^n|y_i - \\hat{y}|$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Code task 7#\n",
+    "#Calculate the MAE as defined above\n",
+    "def mae(y, ypred):\n",
+    "    \"\"\"Mean absolute error.\n",
+    "    \n",
+    "    Calculate the mean absolute error of the arguments\n",
+    "\n",
+    "    Arguments:\n",
+    "    y -- the observed values\n",
+    "    ypred -- the predicted values\n",
+    "    \"\"\"\n",
+    "    abs_error = np.abs(y - ypred)\n",
+    "    mae = np.mean(abs_error)\n",
+    "    return mae"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "17.92346371714677"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mae(y_train, y_tr_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "19.136142081278486"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mae(y_test, y_te_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Mean absolute error is arguably the most intuitive of all the metrics, this essentially tells you that, on average, you might expect to be off by around \\\\$19 if you guessed ticket price based on an average of known values."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 4.7.1.3 Mean Squared Error<a id='4.7.1.3_Mean_Squared_Error'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Another common metric (and an important one internally for optimizing machine learning models) is the mean squared error. This is simply the average of the square of the errors:\n",
+    "\n",
+    "$$MSE = \\frac{1}{n}\\sum_i^n(y_i - \\hat{y})^2$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "#Code task 8#\n",
+    "#Calculate the MSE as defined above\n",
+    "def mse(y, ypred):\n",
+    "    \"\"\"Mean square error.\n",
+    "    \n",
+    "    Calculate the mean square error of the arguments\n",
+    "\n",
+    "    Arguments:\n",
+    "    y -- the observed values\n",
+    "    ypred -- the predicted values\n",
+    "    \"\"\"\n",
+    "    sq_error = (y - ypred)**2\n",
+    "    mse = np.mean(sq_error)\n",
+    "    return mse"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "614.1334096969046"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mse(y_train, y_tr_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "581.4365441953483"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mse(y_test, y_te_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So here, you get a slightly better MSE on the test set than you did on the train set. And what does a squared error mean anyway? To convert this back to our measurement space, we often take the square root, to form the _root mean square error_ thus:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([24.78171523, 24.11299534])"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.sqrt([mse(y_train, y_tr_pred), mse(y_test, y_te_pred)])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.7.2 sklearn metrics<a id='4.7.2_sklearn_metrics'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Functions are good, but you don't want to have to define functions every time we want to assess performance. `sklearn.metrics` provides many commonly used metrics, included the ones above."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 4.7.2.0.1 R-squared<a id='4.7.2.0.1_R-squared'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.0, -0.0031235200417913944)"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r2_score(y_train, y_tr_pred), r2_score(y_test, y_te_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 4.7.2.0.2 Mean absolute error<a id='4.7.2.0.2_Mean_absolute_error'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(17.92346371714677, 19.136142081278486)"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_absolute_error(y_train, y_tr_pred), mean_absolute_error(y_test, y_te_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 4.7.2.0.3 Mean squared error<a id='4.7.2.0.3_Mean_squared_error'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(614.1334096969046, 581.4365441953483)"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_squared_error(y_train, y_tr_pred), mean_squared_error(y_test, y_te_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.7.3 Note On Calculating Metrics<a id='4.7.3_Note_On_Calculating_Metrics'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When calling functions to calculate metrics, it is important to take care in the order of the arguments. Two of the metrics above actually don't care if the arguments are reversed; one does. Which one cares?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In a Jupyter code cell, running `r2_score?` will bring up the docstring for the function, and `r2_score??` will bring up the actual code of the function! Try them and compare the source for `sklearn`'s function with yours. Feel free to explore what happens when you reverse the order of the arguments and compare behaviour of `sklearn`'s function and yours."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.0, -3.041041349306602e+30)"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# train set - sklearn\n",
+    "# correct order, incorrect order\n",
+    "r2_score(y_train, y_tr_pred), r2_score(y_tr_pred, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(-0.0031235200417913944, 0.0)"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# test set - sklearn\n",
+    "# correct order, incorrect order\n",
+    "r2_score(y_test, y_te_pred), r2_score(y_te_pred, y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.0, -3.041041349306602e+30)"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# train set - using our homebrew function\n",
+    "# correct order, incorrect order\n",
+    "r_squared(y_train, y_tr_pred), r_squared(y_tr_pred, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/qy/n5ybkbq12ps0vpdz3748rqx80000gn/T/ipykernel_12919/1803819837.py:15: RuntimeWarning: divide by zero encountered in double_scalars\n",
+      "  R2 = 1.0 - sum_sq_res / sum_sq_tot\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(-0.0031235200417913944, -inf)"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# test set - using our homebrew function\n",
+    "# correct order, incorrect order\n",
+    "r_squared(y_test, y_te_pred), r_squared(y_te_pred, y_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can get very different results swapping the argument order. It's worth highlighting this because data scientists do this too much in the real world! Don't be one of them! Frequently the argument order doesn't matter, but it will bite you when you do it with a function that does care. It's sloppy, bad practice and if you don't make a habit of putting arguments in the right order, you will forget!\n",
+    "\n",
+    "Remember:\n",
+    "* argument order matters,\n",
+    "* check function syntax with `func?` in a code cell"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4.8 Initial Models<a id='4.8_Initial_Models'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.8.1 Imputing missing feature (predictor) values<a id='4.8.1_Imputing_missing_feature_(predictor)_values'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Recall when performing EDA, you imputed (filled in) some missing values in pandas. You did this judiciously for exploratory/visualization purposes. You left many missing values in the data. You can impute missing values using scikit-learn, but note that you should learn values to impute from a train split and apply that to the test split to then assess how well your imputation worked."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 4.8.1.1 Impute missing values with median<a id='4.8.1.1_Impute_missing_values_with_median'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There's missing values. Recall from your data exploration that many distributions were skewed. Your first thought might be to impute missing values using the median."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 4.8.1.1.1 Learn the values to impute from the train set<a id='4.8.1.1.1_Learn_the_values_to_impute_from_the_train_set'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "summit_elev                           2215.000000\n",
+       "vertical_drop                          750.000000\n",
+       "base_elev                             1300.000000\n",
+       "trams                                    0.000000\n",
+       "fastSixes                                0.000000\n",
+       "fastQuads                                0.000000\n",
+       "quad                                     1.000000\n",
+       "triple                                   1.000000\n",
+       "double                                   1.000000\n",
+       "surface                                  2.000000\n",
+       "total_chairs                             7.000000\n",
+       "Runs                                    28.000000\n",
+       "TerrainParks                             2.000000\n",
+       "LongestRun_mi                            1.000000\n",
+       "SkiableTerrain_ac                      170.000000\n",
+       "Snow Making_ac                          96.500000\n",
+       "daysOpenLastYear                       109.000000\n",
+       "yearsOpen                               57.000000\n",
+       "averageSnowfall                        120.000000\n",
+       "projectedDaysOpen                      115.000000\n",
+       "NightSkiing_ac                          70.000000\n",
+       "resorts_per_state                       15.000000\n",
+       "resorts_per_100kcapita                   0.248243\n",
+       "resorts_per_100ksq_mile                 22.902162\n",
+       "resort_skiable_area_ac_state_ratio       0.051458\n",
+       "resort_days_open_state_ratio             0.071225\n",
+       "resort_terrain_park_state_ratio          0.069444\n",
+       "resort_night_skiing_state_ratio          0.077081\n",
+       "total_chairs_runs_ratio                  0.200000\n",
+       "total_chairs_skiable_ratio               0.040323\n",
+       "fastQuads_runs_ratio                     0.000000\n",
+       "fastQuads_skiable_ratio                  0.000000\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# These are the values we'll use to fill in any missing values\n",
+    "X_defaults_median = X_train.median()\n",
+    "X_defaults_median"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 4.8.1.1.2 Apply the imputation to both train and test splits<a id='4.8.1.1.2_Apply_the_imputation_to_both_train_and_test_splits'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Code task 9#\n",
+    "#Call `X_train` and `X_test`'s `fillna()` method, passing `X_defaults_median` as the values to use\n",
+    "#Assign the results to `X_tr` and `X_te`, respectively\n",
+    "X_tr = X_train.fillna(X_defaults_median)\n",
+    "X_te = X_test.fillna(X_defaults_median)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 4.8.1.1.3 Scale the data<a id='4.8.1.1.3_Scale_the_data'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As you have features measured in many different units, with numbers that vary by orders of magnitude, start off by scaling them to put them all on a consistent scale. The [StandardScaler](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html) scales each feature to zero mean and unit variance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Code task 10#\n",
+    "#Call the StandardScaler`s fit method on `X_tr` to fit the scaler\n",
+    "#then use it's `transform()` method to apply the scaling to both the train and test split\n",
+    "#data (`X_tr` and `X_te`), naming the results `X_tr_scaled` and `X_te_scaled`, respectively\n",
+    "scaler = StandardScaler()\n",
+    "scaler.fit(X_tr)\n",
+    "X_tr_scaled = scaler.transform(X_tr)\n",
+    "X_te_scaled = scaler.transform(X_te)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 4.8.1.1.4 Train the model on the train split<a id='4.8.1.1.4_Train_the_model_on_the_train_split'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lm = LinearRegression().fit(X_tr_scaled, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 4.8.1.1.5 Make predictions using the model on both train and test splits<a id='4.8.1.1.5_Make_predictions_using_the_model_on_both_train_and_test_splits'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Code task 11#\n",
+    "#Call the `predict()` method of the model (`lm`) on both the (scaled) train and test data\n",
+    "#Assign the predictions to `y_tr_pred` and `y_te_pred`, respectively\n",
+    "y_tr_pred = lm.predict(X_tr_scaled)\n",
+    "y_te_pred = lm.predict(X_te_scaled)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 4.8.1.1.6 Assess model performance<a id='4.8.1.1.6_Assess_model_performance'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.8177988515690603, 0.7209725843435145)"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# r^2 - train, test\n",
+    "median_r2 = r2_score(y_train, y_tr_pred), r2_score(y_test, y_te_pred)\n",
+    "median_r2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Recall that you estimated ticket price by simply using a known average. As expected, this produced an $R^2$ of zero for both the training and test set, because $R^2$ tells us how much of the variance you're explaining beyond that of using just the mean, and you were using just the mean. Here we see that our simple linear regression model explains over 80% of the variance on the train set and over 70% on the test set. Clearly you are onto something, although the much lower value for the test set suggests you're overfitting somewhat. This isn't a surprise as you've made no effort to select a parsimonious set of features or deal with multicollinearity in our data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(8.547850301825427, 9.407020118581316)"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 12#\n",
+    "#Now calculate the mean absolute error scores using `sklearn`'s `mean_absolute_error` function\n",
+    "# as we did above for R^2\n",
+    "# MAE - train, test\n",
+    "median_mae = mean_absolute_error(y_train, y_tr_pred), mean_absolute_error(y_test, y_te_pred)\n",
+    "median_mae"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using this model, then, on average you'd expect to estimate a ticket price within \\\\$9 or so of the real price. This is much, much better than the \\\\$19 from just guessing using the average. There may be something to this machine learning lark after all!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(111.8958125365848, 161.7315645119227)"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 13#\n",
+    "#And also do the same using `sklearn`'s `mean_squared_error`\n",
+    "# MSE - train, test\n",
+    "median_mse = mean_squared_error(y_train, y_tr_pred), mean_squared_error(y_test, y_te_pred)\n",
+    "median_mse"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 4.8.1.2 Impute missing values with the mean<a id='4.8.1.2_Impute_missing_values_with_the_mean'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You chose to use the median for filling missing values because of the skew of many of our predictor feature distributions. What if you wanted to try something else, such as the mean?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 4.8.1.2.1 Learn the values to impute from the train set<a id='4.8.1.2.1_Learn_the_values_to_impute_from_the_train_set'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "summit_elev                           4074.554404\n",
+       "vertical_drop                         1043.196891\n",
+       "base_elev                             3020.512953\n",
+       "trams                                    0.103627\n",
+       "fastSixes                                0.072539\n",
+       "fastQuads                                0.673575\n",
+       "quad                                     1.010363\n",
+       "triple                                   1.440415\n",
+       "double                                   1.813472\n",
+       "surface                                  2.497409\n",
+       "total_chairs                             7.611399\n",
+       "Runs                                    41.188482\n",
+       "TerrainParks                             2.434783\n",
+       "LongestRun_mi                            1.293122\n",
+       "SkiableTerrain_ac                      448.785340\n",
+       "Snow Making_ac                         129.601190\n",
+       "daysOpenLastYear                       110.100629\n",
+       "yearsOpen                               56.559585\n",
+       "averageSnowfall                        162.310160\n",
+       "projectedDaysOpen                      115.920245\n",
+       "NightSkiing_ac                          86.384615\n",
+       "resorts_per_state                       16.264249\n",
+       "resorts_per_100kcapita                   0.424802\n",
+       "resorts_per_100ksq_mile                 40.957785\n",
+       "resort_skiable_area_ac_state_ratio       0.097205\n",
+       "resort_days_open_state_ratio             0.126014\n",
+       "resort_terrain_park_state_ratio          0.116022\n",
+       "resort_night_skiing_state_ratio          0.155024\n",
+       "total_chairs_runs_ratio                  0.271441\n",
+       "total_chairs_skiable_ratio               0.070483\n",
+       "fastQuads_runs_ratio                     0.010401\n",
+       "fastQuads_skiable_ratio                  0.001633\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 14#\n",
+    "#As we did for the median above, calculate mean values for imputing missing values\n",
+    "# These are the values we'll use to fill in any missing values\n",
+    "X_defaults_mean = X_train.mean()\n",
+    "X_defaults_mean"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By eye, you can immediately tell that your replacement values are much higher than those from using the median."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 4.8.1.2.2 Apply the imputation to both train and test splits<a id='4.8.1.2.2_Apply_the_imputation_to_both_train_and_test_splits'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_tr = X_train.fillna(X_defaults_mean)\n",
+    "X_te = X_test.fillna(X_defaults_mean)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 4.8.1.2.3 Scale the data<a id='4.8.1.2.3_Scale_the_data'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler = StandardScaler()\n",
+    "scaler.fit(X_tr)\n",
+    "X_tr_scaled = scaler.transform(X_tr)\n",
+    "X_te_scaled = scaler.transform(X_te)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 4.8.1.2.4 Train the model on the train split<a id='4.8.1.2.4_Train_the_model_on_the_train_split'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lm = LinearRegression().fit(X_tr_scaled, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 4.8.1.2.5 Make predictions using the model on both train and test splits<a id='4.8.1.2.5_Make_predictions_using_the_model_on_both_train_and_test_splits'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_tr_pred = lm.predict(X_tr_scaled)\n",
+    "y_te_pred = lm.predict(X_te_scaled)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 4.8.1.2.6 Assess model performance<a id='4.8.1.2.6_Assess_model_performance'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.8170154093990025, 0.7163814716959962)"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r2_score(y_train, y_tr_pred), r2_score(y_test, y_te_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(8.536884040670977, 9.416375625789271)"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_absolute_error(y_train, y_tr_pred), mean_absolute_error(y_test, y_te_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(112.37695054778277, 164.39269309524354)"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_squared_error(y_train, y_tr_pred), mean_squared_error(y_test, y_te_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These results don't seem very different to when you used the median for imputing missing values. Perhaps it doesn't make much difference here. Maybe your overtraining dominates. Maybe other feature transformations, such as taking the log, would help. You could try with just a subset of features rather than using all of them as inputs.\n",
+    "\n",
+    "To perform the median/mean comparison, you copied and pasted a lot of code just to change the function for imputing missing values. It would make more sense to write a function that performed the sequence of steps:\n",
+    "1. impute missing values\n",
+    "2. scale the features\n",
+    "3. train a model\n",
+    "4. calculate model performance\n",
+    "\n",
+    "But these are common steps and `sklearn` provides something much better than writing custom functions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.8.2 Pipelines<a id='4.8.2_Pipelines'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "One of the most important and useful components of `sklearn` is the [pipeline](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html). In place of `panda`'s `fillna` DataFrame method, there is `sklearn`'s `SimpleImputer`. Remember the first linear model above performed the steps:\n",
+    "\n",
+    "1. replace missing values with the median for each feature\n",
+    "2. scale the data to zero mean and unit variance\n",
+    "3. train a linear regression model\n",
+    "\n",
+    "and all these steps were trained on the train split and then applied to the test split for assessment.\n",
+    "\n",
+    "The pipeline below defines exactly those same steps. Crucially, the resultant `Pipeline` object has a `fit()` method and a `predict()` method, just like the `LinearRegression()` object itself. Just as you might create a linear regression model and train it with `.fit()` and predict with `.predict()`, you can wrap the entire process of imputing and feature scaling and regression in a single object you can train with `.fit()` and predict with `.predict()`. And that's basically a pipeline: a model on steroids."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 4.8.2.1 Define the pipeline<a id='4.8.2.1_Define_the_pipeline'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pipe = make_pipeline(\n",
+    "    SimpleImputer(strategy='median'), \n",
+    "    StandardScaler(), \n",
+    "    LinearRegression()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sklearn.pipeline.Pipeline"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(pipe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(True, True)"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hasattr(pipe, 'fit'), hasattr(pipe, 'predict')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 4.8.2.2 Fit the pipeline<a id='4.8.2.2_Fit_the_pipeline'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here, a single call to the pipeline's `fit()` method combines the steps of learning the imputation (determining what values to use to fill the missing ones), the scaling (determining the mean to subtract and the variance to divide by), and then training the model. It does this all in the one call with the training data as arguments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;simpleimputer&#x27;, SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                (&#x27;standardscaler&#x27;, StandardScaler()),\n",
+       "                (&#x27;linearregression&#x27;, LinearRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;simpleimputer&#x27;, SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                (&#x27;standardscaler&#x27;, StandardScaler()),\n",
+       "                (&#x27;linearregression&#x27;, LinearRegression())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='median')),\n",
+       "                ('standardscaler', StandardScaler()),\n",
+       "                ('linearregression', LinearRegression())])"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 15#\n",
+    "#Call the pipe's `fit()` method with `X_train` and `y_train` as arguments\n",
+    "pipe.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 4.8.2.3 Make predictions on the train and test sets<a id='4.8.2.3_Make_predictions_on_the_train_and_test_sets'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_tr_pred = pipe.predict(X_train)\n",
+    "y_te_pred = pipe.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 4.8.2.4 Assess performance<a id='4.8.2.4_Assess_performance'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.8177988515690603, 0.7209725843435145)"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r2_score(y_train, y_tr_pred), r2_score(y_test, y_te_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And compare with your earlier (non-pipeline) result:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.8177988515690603, 0.7209725843435145)"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "median_r2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(8.547850301825427, 9.407020118581316)"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_absolute_error(y_train, y_tr_pred), mean_absolute_error(y_test, y_te_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (200383607.py, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  Cell \u001b[0;32mIn[62], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m    Compare with your earlier result:\u001b[0m\n\u001b[0m            ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+     ]
+    }
+   ],
+   "source": [
+    "Compare with your earlier result:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(8.547850301825427, 9.407020118581316)"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "median_mae"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(111.8958125365848, 161.7315645119227)"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_squared_error(y_train, y_tr_pred), mean_squared_error(y_test, y_te_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Compare with your earlier result:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(111.8958125365848, 161.7315645119227)"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "median_mse"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These results confirm the pipeline is doing exactly what's expected, and results are identical to your earlier steps. This allows you to move faster but with confidence."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4.9 Refining The Linear Model<a id='4.9_Refining_The_Linear_Model'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You suspected the model was overfitting. This is no real surprise given the number of features you blindly used. It's likely a judicious subset of features would generalize better. `sklearn` has a number of feature selection functions available. The one you'll use here is `SelectKBest` which, as you might guess, selects the k best features. You can read about SelectKBest \n",
+    "[here](https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.SelectKBest.html#sklearn.feature_selection.SelectKBest). `f_regression` is just the [score function](https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.f_regression.html#sklearn.feature_selection.f_regression) you're using because you're performing regression. It's important to choose an appropriate one for your machine learning task."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.9.1 Define the pipeline<a id='4.9.1_Define_the_pipeline'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Redefine your pipeline to include this feature selection step:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Code task 16#\n",
+    "#Add `SelectKBest` as a step in the pipeline between `StandardScaler()` and `LinearRegression()`\n",
+    "#Don't forget to tell it to use `f_regression` as its score function\n",
+    "pipe = make_pipeline(\n",
+    "    SimpleImputer(strategy='median'), \n",
+    "    StandardScaler(),\n",
+    "    SelectKBest(f_regression),\n",
+    "    LinearRegression()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.9.2 Fit the pipeline<a id='4.9.2_Fit_the_pipeline'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;simpleimputer&#x27;, SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                (&#x27;standardscaler&#x27;, StandardScaler()),\n",
+       "                (&#x27;selectkbest&#x27;,\n",
+       "                 SelectKBest(score_func=&lt;function f_regression at 0x7f82947acee0&gt;)),\n",
+       "                (&#x27;linearregression&#x27;, LinearRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;simpleimputer&#x27;, SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                (&#x27;standardscaler&#x27;, StandardScaler()),\n",
+       "                (&#x27;selectkbest&#x27;,\n",
+       "                 SelectKBest(score_func=&lt;function f_regression at 0x7f82947acee0&gt;)),\n",
+       "                (&#x27;linearregression&#x27;, LinearRegression())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SelectKBest</label><div class=\"sk-toggleable__content\"><pre>SelectKBest(score_func=&lt;function f_regression at 0x7f82947acee0&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='median')),\n",
+       "                ('standardscaler', StandardScaler()),\n",
+       "                ('selectkbest',\n",
+       "                 SelectKBest(score_func=<function f_regression at 0x7f82947acee0>)),\n",
+       "                ('linearregression', LinearRegression())])"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pipe.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.9.3 Assess performance on the train and test set<a id='4.9.3_Assess_performance_on_the_train_and_test_set'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_tr_pred = pipe.predict(X_train)\n",
+    "y_te_pred = pipe.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.7674914326052744, 0.6259877354190837)"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r2_score(y_train, y_tr_pred), r2_score(y_test, y_te_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(9.501495079727484, 11.201830190332053)"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_absolute_error(y_train, y_tr_pred), mean_absolute_error(y_test, y_te_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This has made things worse! Clearly selecting a subset of features has an impact on performance. `SelectKBest` defaults to k=10. You've just seen that 10 is worse than using all features. What is the best k? You could create a new pipeline with a different value of k:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.9.4 Define a new pipeline to select a different number of features<a id='4.9.4_Define_a_new_pipeline_to_select_a_different_number_of_features'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Code task 17#\n",
+    "#Modify the `SelectKBest` step to use a value of 15 for k\n",
+    "pipe15 = make_pipeline(\n",
+    "    SimpleImputer(strategy='median'), \n",
+    "    StandardScaler(),\n",
+    "    SelectKBest(f_regression, k=15),\n",
+    "    LinearRegression()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.9.5 Fit the pipeline<a id='4.9.5_Fit_the_pipeline'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;simpleimputer&#x27;, SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                (&#x27;standardscaler&#x27;, StandardScaler()),\n",
+       "                (&#x27;selectkbest&#x27;,\n",
+       "                 SelectKBest(k=15,\n",
+       "                             score_func=&lt;function f_regression at 0x7f82947acee0&gt;)),\n",
+       "                (&#x27;linearregression&#x27;, LinearRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;simpleimputer&#x27;, SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                (&#x27;standardscaler&#x27;, StandardScaler()),\n",
+       "                (&#x27;selectkbest&#x27;,\n",
+       "                 SelectKBest(k=15,\n",
+       "                             score_func=&lt;function f_regression at 0x7f82947acee0&gt;)),\n",
+       "                (&#x27;linearregression&#x27;, LinearRegression())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SelectKBest</label><div class=\"sk-toggleable__content\"><pre>SelectKBest(k=15, score_func=&lt;function f_regression at 0x7f82947acee0&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='median')),\n",
+       "                ('standardscaler', StandardScaler()),\n",
+       "                ('selectkbest',\n",
+       "                 SelectKBest(k=15,\n",
+       "                             score_func=<function f_regression at 0x7f82947acee0>)),\n",
+       "                ('linearregression', LinearRegression())])"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pipe15.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.9.6 Assess performance on train and test data<a id='4.9.6_Assess_performance_on_train_and_test_data'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_tr_pred = pipe15.predict(X_train)\n",
+    "y_te_pred = pipe15.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.7924096060483825, 0.6376199973170797)"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r2_score(y_train, y_tr_pred), r2_score(y_test, y_te_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(9.211767769307114, 10.488246867294354)"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_absolute_error(y_train, y_tr_pred), mean_absolute_error(y_test, y_te_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You could keep going, trying different values of k, training a model, measuring performance on the test set, and then picking the model with the best test set performance. There's a fundamental problem with this approach: _you're tuning the model to the arbitrary test set_! If you continue this way you'll end up with a model works well on the particular quirks of our test set _but fails to generalize to new data_. The whole point of keeping a test set is for it to be a set of that new data, to check how well our model might perform on data it hasn't seen.\n",
+    "\n",
+    "The way around this is a technique called _cross-validation_. You partition the training set into k folds, train our model on k-1 of those folds, and calculate performance on the fold not used in training. This procedure then cycles through k times with a different fold held back each time. Thus you end up building k models on k sets of data with k estimates of how the model performs on unseen data but without having to touch the test set."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.9.7 Assessing performance using cross-validation<a id='4.9.7_Assessing_performance_using_cross-validation'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cv_results = cross_validate(pipe15, X_train, y_train, cv=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.63760862, 0.72831381, 0.74443537, 0.5487915 , 0.50441472])"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cv_scores = cv_results['test_score']\n",
+    "cv_scores"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Without using the same random state for initializing the CV folds, your actual numbers will be different."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.6327128053007867, 0.09502487849877678)"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.mean(cv_scores), np.std(cv_scores)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These results highlight that assessing model performance in inherently open to variability. You'll get different results depending on the quirks of which points are in which fold. An advantage of this is that you can also obtain an estimate of the variability, or uncertainty, in your performance estimate."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.44, 0.82])"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.round((np.mean(cv_scores) - 2 * np.std(cv_scores), np.mean(cv_scores) + 2 * np.std(cv_scores)), 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.9.8 Hyperparameter search using GridSearchCV<a id='4.9.8_Hyperparameter_search_using_GridSearchCV'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pulling the above together, we have:\n",
+    "* a pipeline that\n",
+    "    * imputes missing values\n",
+    "    * scales the data\n",
+    "    * selects the k best features\n",
+    "    * trains a linear regression model\n",
+    "* a technique (cross-validation) for estimating model performance\n",
+    "\n",
+    "Now you want to use cross-validation for multiple values of k and use cross-validation to pick the value of k that gives the best performance. `make_pipeline` automatically names each step as the lowercase name of the step and the parameters of the step are then accessed by appending a double underscore followed by the parameter name. You know the name of the step will be 'selectkbest' and you know the parameter is 'k'.\n",
+    "\n",
+    "You can also list the names of all the parameters in a pipeline like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['memory', 'steps', 'verbose', 'simpleimputer', 'standardscaler', 'selectkbest', 'linearregression', 'simpleimputer__add_indicator', 'simpleimputer__copy', 'simpleimputer__fill_value', 'simpleimputer__keep_empty_features', 'simpleimputer__missing_values', 'simpleimputer__strategy', 'simpleimputer__verbose', 'standardscaler__copy', 'standardscaler__with_mean', 'standardscaler__with_std', 'selectkbest__k', 'selectkbest__score_func', 'linearregression__copy_X', 'linearregression__fit_intercept', 'linearregression__n_jobs', 'linearregression__positive'])"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 18#\n",
+    "#Call `pipe`'s `get_params()` method to get a dict of available parameters and print their names\n",
+    "#using dict's `keys()` method\n",
+    "pipe.get_params().keys()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The above can be particularly useful as your pipelines becomes more complex (you can even nest pipelines within pipelines)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "k = [k+1 for k in range(len(X_train.columns))]\n",
+    "grid_params = {'selectkbest__k': k}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now you have a range of `k` to investigate. Is 1 feature best? 2? 3? 4? All of them? You could write a for loop and iterate over each possible value, doing all the housekeeping oyurselves to track the best value of k. But this is  a common task so there's a built in function in `sklearn`. This is [`GridSearchCV`](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html).\n",
+    "This takes the pipeline object, in fact it takes anything with a `.fit()` and `.predict()` method. In simple cases with no feature selection or imputation or feature scaling etc. you may see the classifier or regressor object itself directly passed into `GridSearchCV`. The other key input is the parameters and values to search over. Optional parameters include the cross-validation strategy and number of CPUs to use."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lr_grid_cv = GridSearchCV(pipe, param_grid=grid_params, cv=5, n_jobs=-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5,\n",
+       "             estimator=Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                        SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                                       (&#x27;standardscaler&#x27;, StandardScaler()),\n",
+       "                                       (&#x27;selectkbest&#x27;,\n",
+       "                                        SelectKBest(score_func=&lt;function f_regression at 0x7f82947acee0&gt;)),\n",
+       "                                       (&#x27;linearregression&#x27;,\n",
+       "                                        LinearRegression())]),\n",
+       "             n_jobs=-1,\n",
+       "             param_grid={&#x27;selectkbest__k&#x27;: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,\n",
+       "                                            12, 13, 14, 15, 16, 17, 18, 19, 20,\n",
+       "                                            21, 22, 23, 24, 25, 26, 27, 28, 29,\n",
+       "                                            30, ...]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5,\n",
+       "             estimator=Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                        SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                                       (&#x27;standardscaler&#x27;, StandardScaler()),\n",
+       "                                       (&#x27;selectkbest&#x27;,\n",
+       "                                        SelectKBest(score_func=&lt;function f_regression at 0x7f82947acee0&gt;)),\n",
+       "                                       (&#x27;linearregression&#x27;,\n",
+       "                                        LinearRegression())]),\n",
+       "             n_jobs=-1,\n",
+       "             param_grid={&#x27;selectkbest__k&#x27;: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,\n",
+       "                                            12, 13, 14, 15, 16, 17, 18, 19, 20,\n",
+       "                                            21, 22, 23, 24, 25, 26, 27, 28, 29,\n",
+       "                                            30, ...]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;simpleimputer&#x27;, SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                (&#x27;standardscaler&#x27;, StandardScaler()),\n",
+       "                (&#x27;selectkbest&#x27;,\n",
+       "                 SelectKBest(score_func=&lt;function f_regression at 0x7f82947acee0&gt;)),\n",
+       "                (&#x27;linearregression&#x27;, LinearRegression())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SelectKBest</label><div class=\"sk-toggleable__content\"><pre>SelectKBest(score_func=&lt;function f_regression at 0x7f82947acee0&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "GridSearchCV(cv=5,\n",
+       "             estimator=Pipeline(steps=[('simpleimputer',\n",
+       "                                        SimpleImputer(strategy='median')),\n",
+       "                                       ('standardscaler', StandardScaler()),\n",
+       "                                       ('selectkbest',\n",
+       "                                        SelectKBest(score_func=<function f_regression at 0x7f82947acee0>)),\n",
+       "                                       ('linearregression',\n",
+       "                                        LinearRegression())]),\n",
+       "             n_jobs=-1,\n",
+       "             param_grid={'selectkbest__k': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,\n",
+       "                                            12, 13, 14, 15, 16, 17, 18, 19, 20,\n",
+       "                                            21, 22, 23, 24, 25, 26, 27, 28, 29,\n",
+       "                                            30, ...]})"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lr_grid_cv.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "score_mean = lr_grid_cv.cv_results_['mean_test_score']\n",
+    "score_std = lr_grid_cv.cv_results_['std_test_score']\n",
+    "cv_k = [k for k in lr_grid_cv.cv_results_['param_selectkbest__k']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'selectkbest__k': 8}"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 19#\n",
+    "#Print the `best_params_` attribute of `lr_grid_cv`\n",
+    "lr_grid_cv.best_params_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Code task 20#\n",
+    "#Assign the value of k from the above dict of `best_params_` and assign it to `best_k`\n",
+    "best_k = lr_grid_cv.best_params_['selectkbest__k']\n",
+    "plt.subplots(figsize=(10, 5))\n",
+    "plt.errorbar(cv_k, score_mean, yerr=score_std)\n",
+    "plt.axvline(x=best_k, c='r', ls='--', alpha=.5)\n",
+    "plt.xlabel('k')\n",
+    "plt.ylabel('CV score (r-squared)')\n",
+    "plt.title('Pipeline mean CV score (error bars +/- 1sd)');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The above suggests a good value for k is 8. There was an initial rapid increase with k, followed by a slow decline. Also noticeable is the variance of the results greatly increase above k=8. As you increasingly overfit, expect greater swings in performance as different points move in and out of the train/test folds."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Which features were most useful? Step into your best model, shown below. Starting with the fitted grid search object, you get the best estimator, then the named step 'selectkbest', for which you can its `get_support()` method for a logical mask of the features selected."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "selected = lr_grid_cv.best_estimator_.named_steps.selectkbest.get_support()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Similarly, instead of using the 'selectkbest' named step, you can access the named step for the linear regression model and, from that, grab the model coefficients via its `coef_` attribute:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "vertical_drop        10.767857\n",
+       "Snow Making_ac        6.290074\n",
+       "total_chairs          5.794156\n",
+       "fastQuads             5.745626\n",
+       "Runs                  5.370555\n",
+       "LongestRun_mi         0.181814\n",
+       "trams                -4.142024\n",
+       "SkiableTerrain_ac    -5.249780\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 21#\n",
+    "#Get the linear model coefficients from the `coef_` attribute and store in `coefs`,\n",
+    "#get the matching feature names from the column names of the dataframe,\n",
+    "#and display the results as a pandas Series with `coefs` as the values and `features` as the index,\n",
+    "#sorting the values in descending order\n",
+    "coefs = lr_grid_cv.best_estimator_.named_steps.linearregression.coef_\n",
+    "features = X_train.columns[selected]\n",
+    "pd.Series(coefs, index=features).sort_values(ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These results suggest that vertical drop is your biggest positive feature. This makes intuitive sense and is consistent with what you saw during the EDA work. Also, you see the area covered by snow making equipment is a strong positive as well. People like guaranteed skiing! The skiable terrain area is negatively associated with ticket price! This seems odd. People will pay less for larger resorts? There could be all manner of reasons for this. It could be  an effect whereby larger resorts can host more visitors at any one time and so can charge less per ticket. As has been mentioned previously, the data are missing information about visitor numbers. Bear in mind,  the coefficient for skiable terrain is negative _for this model_. For example, if you kept the total number of chairs and fastQuads constant, but increased the skiable terrain extent, you might imagine the resort is worse off because the chairlift capacity is stretched thinner."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4.10 Random Forest Model<a id='4.10_Random_Forest_Model'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A model that can work very well in a lot of cases is the random forest. For regression, this is provided by `sklearn`'s `RandomForestRegressor` class.\n",
+    "\n",
+    "Time to stop the bad practice of repeatedly checking performance on the test split. Instead, go straight from defining the pipeline to assessing performance using cross-validation. `cross_validate` will perform the fitting as part of the process. This uses the default settings for the random forest so you'll then proceed to investigate some different hyperparameters."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.10.1 Define the pipeline<a id='4.10.1_Define_the_pipeline'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Code task 22#\n",
+    "#Define a pipeline comprising the steps:\n",
+    "#SimpleImputer() with a strategy of 'median'\n",
+    "#StandardScaler(),\n",
+    "#and then RandomForestRegressor() with a random state of 47\n",
+    "RF_pipe = make_pipeline(\n",
+    "    SimpleImputer(strategy='median'),\n",
+    "    StandardScaler(),\n",
+    "    RandomForestRegressor(random_state=47)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.10.2 Fit and assess performance using cross-validation<a id='4.10.2_Fit_and_assess_performance_using_cross-validation'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Code task 23#\n",
+    "#Call `cross_validate` to estimate the pipeline's performance.\n",
+    "#Pass it the random forest pipe object, `X_train` and `y_train`,\n",
+    "#and get it to use 5-fold cross-validation\n",
+    "rf_default_cv_results = cross_validate(RF_pipe, X_train, y_train, cv=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.69249204, 0.78061953, 0.77546915, 0.62190924, 0.61742339])"
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rf_cv_scores = rf_default_cv_results['test_score']\n",
+    "rf_cv_scores"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.6975826707112506, 0.07090742940774528)"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.mean(rf_cv_scores), np.std(rf_cv_scores)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.10.3 Hyperparameter search using GridSearchCV<a id='4.10.3_Hyperparameter_search_using_GridSearchCV'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Random forest has a number of hyperparameters that can be explored, however here you'll limit yourselves to exploring some different values for the number of trees. You'll try it with and without feature scaling, and try both the mean and median as strategies for imputing missing values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'randomforestregressor__n_estimators': [10,\n",
+       "  12,\n",
+       "  16,\n",
+       "  20,\n",
+       "  26,\n",
+       "  33,\n",
+       "  42,\n",
+       "  54,\n",
+       "  69,\n",
+       "  88,\n",
+       "  112,\n",
+       "  143,\n",
+       "  183,\n",
+       "  233,\n",
+       "  297,\n",
+       "  379,\n",
+       "  483,\n",
+       "  615,\n",
+       "  784,\n",
+       "  1000],\n",
+       " 'standardscaler': [StandardScaler(), None],\n",
+       " 'simpleimputer__strategy': ['mean', 'median']}"
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n_est = [int(n) for n in np.logspace(start=1, stop=3, num=20)]\n",
+    "grid_params = {\n",
+    "        'randomforestregressor__n_estimators': n_est,\n",
+    "        'standardscaler': [StandardScaler(), None],\n",
+    "        'simpleimputer__strategy': ['mean', 'median']\n",
+    "}\n",
+    "grid_params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Code task 24#\n",
+    "#Call `GridSearchCV` with the random forest pipeline, passing in the above `grid_params`\n",
+    "#dict for parameters to evaluate, 5-fold cross-validation, and all available CPU cores (if desired)\n",
+    "rf_grid_cv = GridSearchCV(RF_pipe, param_grid=grid_params, cv=5, n_jobs=-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5,\n",
+       "             estimator=Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                        SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                                       (&#x27;standardscaler&#x27;, StandardScaler()),\n",
+       "                                       (&#x27;randomforestregressor&#x27;,\n",
+       "                                        RandomForestRegressor(random_state=47))]),\n",
+       "             n_jobs=-1,\n",
+       "             param_grid={&#x27;randomforestregressor__n_estimators&#x27;: [10, 12, 16, 20,\n",
+       "                                                                 26, 33, 42, 54,\n",
+       "                                                                 69, 88, 112,\n",
+       "                                                                 143, 183, 233,\n",
+       "                                                                 297, 379, 483,\n",
+       "                                                                 615, 784,\n",
+       "                                                                 1000],\n",
+       "                         &#x27;simpleimputer__strategy&#x27;: [&#x27;mean&#x27;, &#x27;median&#x27;],\n",
+       "                         &#x27;standardscaler&#x27;: [StandardScaler(), None]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-26\" type=\"checkbox\" ><label for=\"sk-estimator-id-26\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5,\n",
+       "             estimator=Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                        SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                                       (&#x27;standardscaler&#x27;, StandardScaler()),\n",
+       "                                       (&#x27;randomforestregressor&#x27;,\n",
+       "                                        RandomForestRegressor(random_state=47))]),\n",
+       "             n_jobs=-1,\n",
+       "             param_grid={&#x27;randomforestregressor__n_estimators&#x27;: [10, 12, 16, 20,\n",
+       "                                                                 26, 33, 42, 54,\n",
+       "                                                                 69, 88, 112,\n",
+       "                                                                 143, 183, 233,\n",
+       "                                                                 297, 379, 483,\n",
+       "                                                                 615, 784,\n",
+       "                                                                 1000],\n",
+       "                         &#x27;simpleimputer__strategy&#x27;: [&#x27;mean&#x27;, &#x27;median&#x27;],\n",
+       "                         &#x27;standardscaler&#x27;: [StandardScaler(), None]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-27\" type=\"checkbox\" ><label for=\"sk-estimator-id-27\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;simpleimputer&#x27;, SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                (&#x27;standardscaler&#x27;, StandardScaler()),\n",
+       "                (&#x27;randomforestregressor&#x27;,\n",
+       "                 RandomForestRegressor(random_state=47))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-28\" type=\"checkbox\" ><label for=\"sk-estimator-id-28\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-29\" type=\"checkbox\" ><label for=\"sk-estimator-id-29\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-30\" type=\"checkbox\" ><label for=\"sk-estimator-id-30\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(random_state=47)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "GridSearchCV(cv=5,\n",
+       "             estimator=Pipeline(steps=[('simpleimputer',\n",
+       "                                        SimpleImputer(strategy='median')),\n",
+       "                                       ('standardscaler', StandardScaler()),\n",
+       "                                       ('randomforestregressor',\n",
+       "                                        RandomForestRegressor(random_state=47))]),\n",
+       "             n_jobs=-1,\n",
+       "             param_grid={'randomforestregressor__n_estimators': [10, 12, 16, 20,\n",
+       "                                                                 26, 33, 42, 54,\n",
+       "                                                                 69, 88, 112,\n",
+       "                                                                 143, 183, 233,\n",
+       "                                                                 297, 379, 483,\n",
+       "                                                                 615, 784,\n",
+       "                                                                 1000],\n",
+       "                         'simpleimputer__strategy': ['mean', 'median'],\n",
+       "                         'standardscaler': [StandardScaler(), None]})"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 25#\n",
+    "#Now call the `GridSearchCV`'s `fit()` method with `X_train` and `y_train` as arguments\n",
+    "#to actually start the grid search. This may take a minute or two.\n",
+    "rf_grid_cv.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'randomforestregressor__n_estimators': 69,\n",
+       " 'simpleimputer__strategy': 'median',\n",
+       " 'standardscaler': None}"
+      ]
+     },
+     "execution_count": 108,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 26#\n",
+    "#Print the best params (`best_params_` attribute) from the grid search\n",
+    "rf_grid_cv.best_params_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It looks like imputing with the median helps, but scaling the features doesn't."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.6951357 , 0.79430697, 0.77170917, 0.62254707, 0.66499334])"
+      ]
+     },
+     "execution_count": 110,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rf_best_cv_results = cross_validate(rf_grid_cv.best_estimator_, X_train, y_train, cv=5)\n",
+    "rf_best_scores = rf_best_cv_results['test_score']\n",
+    "rf_best_scores"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.7097384501425082, 0.06451341966873386)"
+      ]
+     },
+     "execution_count": 111,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.mean(rf_best_scores), np.std(rf_best_scores)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You've marginally improved upon the default CV results. Random forest has many more hyperparameters you could tune, but we won't dive into that here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Code task 27#\n",
+    "#Plot a barplot of the random forest's feature importances,\n",
+    "#assigning the `feature_importances_` attribute of \n",
+    "#`rf_grid_cv.best_estimator_.named_steps.randomforestregressor` to the name `imps` to then\n",
+    "#create a pandas Series object of the feature importances, with the index given by the\n",
+    "#training data column names, sorting the values in descending order\n",
+    "plt.subplots(figsize=(10, 5))\n",
+    "imps = rf_grid_cv.best_estimator_.named_steps.randomforestregressor.feature_importances_\n",
+    "rf_feat_imps = pd.Series(imps, index=X_train.columns).sort_values(ascending=False)\n",
+    "rf_feat_imps.plot(kind='bar')\n",
+    "plt.xlabel('features')\n",
+    "plt.ylabel('importance')\n",
+    "plt.title('Best random forest regressor feature importances');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Encouragingly, the dominant top four features are in common with your linear model:\n",
+    "* fastQuads\n",
+    "* Runs\n",
+    "* Snow Making_ac\n",
+    "* vertical_drop"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4.11 Final Model Selection<a id='4.11_Final_Model_Selection'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Time to select your final model to use for further business modeling! It would be good to revisit the above model selection; there is undoubtedly more that could be done to explore possible hyperparameters.\n",
+    "It would also be worthwhile to investigate removing the least useful features. Gathering or calculating, and storing, features adds business cost and dependencies, so if features genuinely are not needed they should be removed.\n",
+    "Building a simpler model with fewer features can also have the advantage of being easier to sell (and/or explain) to stakeholders.\n",
+    "Certainly there seem to be four strong features here and so a model using only those would probably work well.\n",
+    "However, you want to explore some different scenarios where other features vary so keep the fuller \n",
+    "model for now. \n",
+    "The business is waiting for this model and you have something that you have confidence in to be much better than guessing with the average price.\n",
+    "\n",
+    "Or, rather, you have two \"somethings\". You built a best linear model and a best random forest model. You need to finally choose between them. You can calculate the mean absolute error using cross-validation. Although `cross-validate` defaults to the $R^2$ [metric for scoring](https://scikit-learn.org/stable/modules/model_evaluation.html#scoring) regression, you can specify the mean absolute error as an alternative via\n",
+    "the `scoring` parameter."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.11.1 Linear regression model performance<a id='4.11.1_Linear_regression_model_performance'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 'neg_mean_absolute_error' uses the (negative of) the mean absolute error\n",
+    "lr_neg_mae = cross_validate(lr_grid_cv.best_estimator_, X_train, y_train, \n",
+    "                            scoring='neg_mean_absolute_error', cv=5, n_jobs=-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10.499032338015294, 1.6220608976799682)"
+      ]
+     },
+     "execution_count": 114,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lr_mae_mean = np.mean(-1 * lr_neg_mae['test_score'])\n",
+    "lr_mae_std = np.std(-1 * lr_neg_mae['test_score'])\n",
+    "lr_mae_mean, lr_mae_std"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "11.793465668669324"
+      ]
+     },
+     "execution_count": 115,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_absolute_error(y_test, lr_grid_cv.best_estimator_.predict(X_test))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.11.2 Random forest regression model performance<a id='4.11.2_Random_forest_regression_model_performance'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rf_neg_mae = cross_validate(rf_grid_cv.best_estimator_, X_train, y_train, \n",
+    "                            scoring='neg_mean_absolute_error', cv=5, n_jobs=-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(9.644639167595688, 1.3528565172191818)"
+      ]
+     },
+     "execution_count": 117,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rf_mae_mean = np.mean(-1 * rf_neg_mae['test_score'])\n",
+    "rf_mae_std = np.std(-1 * rf_neg_mae['test_score'])\n",
+    "rf_mae_mean, rf_mae_std"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9.537730050637332"
+      ]
+     },
+     "execution_count": 118,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_absolute_error(y_test, rf_grid_cv.best_estimator_.predict(X_test))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.11.3 Conclusion<a id='4.11.3_Conclusion'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The random forest model has a lower cross-validation mean absolute error by almost \\\\$1. It also exhibits less variability. Verifying performance on the test set produces performance consistent with the cross-validation results."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4.12 Data quantity assessment<a id='4.12_Data_quantity_assessment'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, you need to advise the business whether it needs to undertake further data collection. Would more data be useful? We're often led to believe more data is always good, but gathering data invariably has a cost associated with it. Assess this trade off by seeing how performance varies with differing data set sizes. The `learning_curve` function does this conveniently."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fractions = [.2, .25, .3, .35, .4, .45, .5, .6, .75, .8, 1.0]\n",
+    "train_size, train_scores, test_scores = learning_curve(pipe, X_train, y_train, train_sizes=fractions)\n",
+    "train_scores_mean = np.mean(train_scores, axis=1)\n",
+    "train_scores_std = np.std(train_scores, axis=1)\n",
+    "test_scores_mean = np.mean(test_scores, axis=1)\n",
+    "test_scores_std = np.std(test_scores, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.subplots(figsize=(10, 5))\n",
+    "plt.errorbar(train_size, test_scores_mean, yerr=test_scores_std)\n",
+    "plt.xlabel('Training set size')\n",
+    "plt.ylabel('CV scores')\n",
+    "plt.title('Cross-validation score as training set size increases');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This shows that you seem to have plenty of data. There's an initial rapid improvement in model scores as one would expect, but it's essentially levelled off by around a sample size of 40-50."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4.13 Save best model object from pipeline<a id='4.13_Save_best_model_object_from_pipeline'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Code task 28#\n",
+    "#This may not be \"production grade ML deployment\" practice, but adding some basic\n",
+    "#information to your saved models can save your bacon in development.\n",
+    "#Just what version model have you just loaded to reuse? What version of `sklearn`\n",
+    "#created it? When did you make it?\n",
+    "#Assign the pandas version number (`pd.__version__`) to the `pandas_version` attribute,\n",
+    "#the numpy version (`np.__version__`) to the `numpy_version` attribute,\n",
+    "#the sklearn version (`sklearn_version`) to the `sklearn_version` attribute,\n",
+    "#and the current datetime (`datetime.datetime.now()`) to the `build_datetime` attribute\n",
+    "#Let's call this model version '1.0'\n",
+    "best_model = rf_grid_cv.best_estimator_\n",
+    "best_model.version = 1.0\n",
+    "best_model.pandas_version = pd.__version__\n",
+    "best_model.numpy_version = np.__version__\n",
+    "best_model.sklearn_version = sklearn_version\n",
+    "best_model.X_columns = [col for col in X_train.columns]\n",
+    "best_model.build_datetime = datetime.datetime.now()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "A file already exists with this name.\n",
+      "\n",
+      "Do you want to overwrite? (Y/N)Y\n",
+      "Writing file.  \"../models/ski_resort_pricing_model.pkl\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "# save the model\n",
+    "\n",
+    "modelpath = '../models'\n",
+    "save_file(best_model, 'ski_resort_pricing_model.pkl', modelpath)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4.14 Summary<a id='4.14_Summary'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q: 1** Write a summary of the work in this notebook. Capture the fact that you gained a baseline idea of performance by simply taking the average price and how well that did. Then highlight that you built a linear model and the features that found. Comment on the estimate of its performance from cross-validation and whether its performance on the test split was consistent with this estimate. Also highlight that a random forest regressor was tried, what preprocessing steps were found to be best, and again what its estimated performance via cross-validation was and whether its performance on the test set was consistent with that. State which model you have decided to use going forwards and why. This summary should provide a quick overview for someone wanting to know quickly why the given model was chosen for the next part of the business problem to help guide important business decisions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**A: 1** Your answer here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (2809940350.py, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  Cell \u001b[0;32mIn[123], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m    In this notebook, we worked on preprocessing the dataset and then training the dataset to to help make prediction\u001b[0m\n\u001b[0m       ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+     ]
+    }
+   ],
+   "source": [
+    "In this notebook, we worked on preprocessing the dataset and then training the dataset to to help make prediction\n",
+    "for the AdultWeekend prices and create modeling pipelines. Some models we used was the absolute_mean_error and the \n",
+    "r_squared formula. We tested using 2 models which were linear and the random forest. The top positive features were\n",
+    "Vertical drop, Snow making_ac, total_chairs, and fastquads. For the random forest model, the top features to \n",
+    "consider are fastquads, runs, snow making_ac, and vertical drop so very similar to linear model. \n",
+    "The random forest model has a lower cross-validation mean absolute error by almost $1. \n",
+    "It also exhibits less variability."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": true
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From 5cb78813e61dd27ee71b3b020395f36fa2e05e96 Mon Sep 17 00:00:00 2001
From: brianbui0 <buibrian96@gmail.com>
Date: Sat, 31 Aug 2024 17:08:48 +0700
Subject: [PATCH 7/9] Add files via upload

---
 CapstoneSteps/05_modeling.ipynb | 1493 +++++++++++++++++++++++++++++++
 1 file changed, 1493 insertions(+)
 create mode 100644 CapstoneSteps/05_modeling.ipynb

diff --git a/CapstoneSteps/05_modeling.ipynb b/CapstoneSteps/05_modeling.ipynb
new file mode 100644
index 000000000..8dca6fa6f
--- /dev/null
+++ b/CapstoneSteps/05_modeling.ipynb
@@ -0,0 +1,1493 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 5 Modeling<a id='5_Modeling'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5.1 Contents<a id='5.1_Contents'></a>\n",
+    "* [5 Modeling](#5_Modeling)\n",
+    "  * [5.1 Contents](#5.1_Contents)\n",
+    "  * [5.2 Introduction](#5.2_Introduction)\n",
+    "  * [5.3 Imports](#5.3_Imports)\n",
+    "  * [5.4 Load Model](#5.4_Load_Model)\n",
+    "  * [5.5 Load Data](#5.5_Load_Data)\n",
+    "  * [5.6 Refit Model On All Available Data (excluding Big Mountain)](#5.6_Refit_Model_On_All_Available_Data_(excluding_Big_Mountain))\n",
+    "  * [5.7 Calculate Expected Big Mountain Ticket Price From The Model](#5.7_Calculate_Expected_Big_Mountain_Ticket_Price_From_The_Model)\n",
+    "  * [5.8 Big Mountain Resort In Market Context](#5.8_Big_Mountain_Resort_In_Market_Context)\n",
+    "    * [5.8.1 Ticket price](#5.8.1_Ticket_price)\n",
+    "    * [5.8.2 Vertical drop](#5.8.2_Vertical_drop)\n",
+    "    * [5.8.3 Snow making area](#5.8.3_Snow_making_area)\n",
+    "    * [5.8.4 Total number of chairs](#5.8.4_Total_number_of_chairs)\n",
+    "    * [5.8.5 Fast quads](#5.8.5_Fast_quads)\n",
+    "    * [5.8.6 Runs](#5.8.6_Runs)\n",
+    "    * [5.8.7 Longest run](#5.8.7_Longest_run)\n",
+    "    * [5.8.8 Trams](#5.8.8_Trams)\n",
+    "    * [5.8.9 Skiable terrain area](#5.8.9_Skiable_terrain_area)\n",
+    "  * [5.9 Modeling scenarios](#5.9_Modeling_scenarios)\n",
+    "    * [5.9.1 Scenario 1](#5.9.1_Scenario_1)\n",
+    "    * [5.9.2 Scenario 2](#5.9.2_Scenario_2)\n",
+    "    * [5.9.3 Scenario 3](#5.9.3_Scenario_3)\n",
+    "    * [5.9.4 Scenario 4](#5.9.4_Scenario_4)\n",
+    "  * [5.10 Summary](#5.10_Summary)\n",
+    "  * [5.11 Further work](#5.11_Further_work)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5.2 Introduction<a id='5.2_Introduction'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this notebook, we now take our model for ski resort ticket price and leverage it to gain some insights into what price Big Mountain's facilities might actually support as well as explore the sensitivity of changes to various resort parameters. Note that this relies on the implicit assumption that all other resorts are largely setting prices based on how much people value certain facilities. Essentially this assumes prices are set by a free market.\n",
+    "\n",
+    "We can now use our model to gain insight into what Big Mountain's ideal ticket price could/should be, and how that might change under various scenarios."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5.3 Imports<a id='5.3_Imports'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import pickle\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from sklearn import __version__ as sklearn_version\n",
+    "from sklearn.model_selection import cross_validate"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5.4 Load Model<a id='5.4_Load_Model'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Expected model version doesn't match version loaded\n"
+     ]
+    }
+   ],
+   "source": [
+    "# This isn't exactly production-grade, but a quick check for development\n",
+    "# These checks can save some head-scratching in development when moving from\n",
+    "# one python environment to another, for example\n",
+    "expected_model_version = '1.0'\n",
+    "model_path = '../models/ski_resort_pricing_model.pkl'\n",
+    "if os.path.exists(model_path):\n",
+    "    with open(model_path, 'rb') as f:\n",
+    "        model = pickle.load(f)\n",
+    "    if model.version != expected_model_version:\n",
+    "        print(\"Expected model version doesn't match version loaded\")\n",
+    "    if model.sklearn_version != sklearn_version:\n",
+    "        print(\"Warning: model created under different sklearn version\")\n",
+    "else:\n",
+    "    print(\"Expected model not found\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5.5 Load Data<a id='5.5_Load_Data'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ski_data = pd.read_csv('../data/ski_data_step3_features.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "big_mountain = ski_data[ski_data.Name == 'Big Mountain Resort']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>124</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Name</th>\n",
+       "      <td>Big Mountain Resort</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Region</th>\n",
+       "      <td>Montana</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state</th>\n",
+       "      <td>Montana</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>summit_elev</th>\n",
+       "      <td>6817</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vertical_drop</th>\n",
+       "      <td>2353</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>base_elev</th>\n",
+       "      <td>4464</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>trams</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastSixes</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastQuads</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>quad</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>triple</th>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>double</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>surface</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total_chairs</th>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Runs</th>\n",
+       "      <td>105.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TerrainParks</th>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LongestRun_mi</th>\n",
+       "      <td>3.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SkiableTerrain_ac</th>\n",
+       "      <td>3000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Snow Making_ac</th>\n",
+       "      <td>600.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>daysOpenLastYear</th>\n",
+       "      <td>123.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>yearsOpen</th>\n",
+       "      <td>72.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>averageSnowfall</th>\n",
+       "      <td>333.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AdultWeekend</th>\n",
+       "      <td>81.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>projectedDaysOpen</th>\n",
+       "      <td>123.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NightSkiing_ac</th>\n",
+       "      <td>600.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resorts_per_state</th>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resorts_per_100kcapita</th>\n",
+       "      <td>1.122778</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resorts_per_100ksq_mile</th>\n",
+       "      <td>8.161045</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resort_skiable_area_ac_state_ratio</th>\n",
+       "      <td>0.140121</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resort_days_open_state_ratio</th>\n",
+       "      <td>0.129338</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resort_terrain_park_state_ratio</th>\n",
+       "      <td>0.148148</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resort_night_skiing_state_ratio</th>\n",
+       "      <td>0.84507</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total_chairs_runs_ratio</th>\n",
+       "      <td>0.133333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total_chairs_skiable_ratio</th>\n",
+       "      <td>0.004667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastQuads_runs_ratio</th>\n",
+       "      <td>0.028571</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastQuads_skiable_ratio</th>\n",
+       "      <td>0.001</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                    124\n",
+       "Name                                Big Mountain Resort\n",
+       "Region                                          Montana\n",
+       "state                                           Montana\n",
+       "summit_elev                                        6817\n",
+       "vertical_drop                                      2353\n",
+       "base_elev                                          4464\n",
+       "trams                                                 0\n",
+       "fastSixes                                             0\n",
+       "fastQuads                                             3\n",
+       "quad                                                  2\n",
+       "triple                                                6\n",
+       "double                                                0\n",
+       "surface                                               3\n",
+       "total_chairs                                         14\n",
+       "Runs                                              105.0\n",
+       "TerrainParks                                        4.0\n",
+       "LongestRun_mi                                       3.3\n",
+       "SkiableTerrain_ac                                3000.0\n",
+       "Snow Making_ac                                    600.0\n",
+       "daysOpenLastYear                                  123.0\n",
+       "yearsOpen                                          72.0\n",
+       "averageSnowfall                                   333.0\n",
+       "AdultWeekend                                       81.0\n",
+       "projectedDaysOpen                                 123.0\n",
+       "NightSkiing_ac                                    600.0\n",
+       "resorts_per_state                                    12\n",
+       "resorts_per_100kcapita                         1.122778\n",
+       "resorts_per_100ksq_mile                        8.161045\n",
+       "resort_skiable_area_ac_state_ratio             0.140121\n",
+       "resort_days_open_state_ratio                   0.129338\n",
+       "resort_terrain_park_state_ratio                0.148148\n",
+       "resort_night_skiing_state_ratio                 0.84507\n",
+       "total_chairs_runs_ratio                        0.133333\n",
+       "total_chairs_skiable_ratio                     0.004667\n",
+       "fastQuads_runs_ratio                           0.028571\n",
+       "fastQuads_skiable_ratio                           0.001"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "big_mountain.T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5.6 Refit Model On All Available Data (excluding Big Mountain)<a id='5.6_Refit_Model_On_All_Available_Data_(excluding_Big_Mountain)'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This next step requires some careful thought. We want to refit the model using all available data. But should we include Big Mountain data? On the one hand, we are _not_ trying to estimate model performance on a previously unseen data sample, so theoretically including Big Mountain data should be fine. One might first think that including Big Mountain in the model training would, if anything, improve model performance in predicting Big Mountain's ticket price. But here's where our business context comes in. The motivation for this entire project is based on the sense that Big Mountain needs to adjust its pricing. One way to phrase this problem: we want to train a model to predict Big Mountain's ticket price based on data from _all the other_ resorts! We don't want Big Mountain's current price to bias this. We want to calculate a price based only on its competitors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = ski_data.loc[ski_data.Name != \"Big Mountain Resort\", model.X_columns]\n",
+    "y = ski_data.loc[ski_data.Name != \"Big Mountain Resort\", 'AdultWeekend']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(276, 276)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(X), len(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;simpleimputer&#x27;, SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                (&#x27;standardscaler&#x27;, None),\n",
+       "                (&#x27;randomforestregressor&#x27;,\n",
+       "                 RandomForestRegressor(n_estimators=69, random_state=47))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;simpleimputer&#x27;, SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                (&#x27;standardscaler&#x27;, None),\n",
+       "                (&#x27;randomforestregressor&#x27;,\n",
+       "                 RandomForestRegressor(n_estimators=69, random_state=47))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">None</label><div class=\"sk-toggleable__content\"><pre>None</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(n_estimators=69, random_state=47)</pre></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='median')),\n",
+       "                ('standardscaler', None),\n",
+       "                ('randomforestregressor',\n",
+       "                 RandomForestRegressor(n_estimators=69, random_state=47))])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cv_results = cross_validate(model, X, y, scoring='neg_mean_absolute_error', cv=5, n_jobs=-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-12.09690217,  -9.30247694, -11.41595784,  -8.10096706,\n",
+       "       -11.04942819])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cv_results['test_score']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10.393146442687748, 1.4712769116280346)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mae_mean, mae_std = np.mean(-1 * cv_results['test_score']), np.std(-1 * cv_results['test_score'])\n",
+    "mae_mean, mae_std"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These numbers will inevitably be different to those in the previous step that used a different training data set. They should, however, be consistent. It's important to appreciate that estimates of model performance are subject to the noise and uncertainty of data!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5.7 Calculate Expected Big Mountain Ticket Price From The Model<a id='5.7_Calculate_Expected_Big_Mountain_Ticket_Price_From_The_Model'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_bm = ski_data.loc[ski_data.Name == \"Big Mountain Resort\", model.X_columns]\n",
+    "y_bm = ski_data.loc[ski_data.Name == \"Big Mountain Resort\", 'AdultWeekend']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bm_pred = model.predict(X_bm).item()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_bm = y_bm.values.item()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Big Mountain Resort modelled price is $95.87, actual price is $81.00.\n",
+      "Even with the expected mean absolute error of $10.39, this suggests there is room for an increase.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f'Big Mountain Resort modelled price is ${bm_pred:.2f}, actual price is ${y_bm:.2f}.')\n",
+    "print(f'Even with the expected mean absolute error of ${mae_mean:.2f}, this suggests there is room for an increase.')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This result should be looked at optimistically and doubtfully! The validity of our model lies in the assumption that other resorts accurately set their prices according to what the market (the ticket-buying public) supports. The fact that our resort seems to be charging that much less that what's predicted suggests our resort might be undercharging. \n",
+    "But if ours is mispricing itself, are others? It's reasonable to expect that some resorts will be \"overpriced\" and some \"underpriced.\" Or if resorts are pretty good at pricing strategies, it could be that our model is simply lacking some key data? Certainly we know nothing about operating costs, for example, and they would surely help."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5.8 Big Mountain Resort In Market Context<a id='5.8_Big_Mountain_Resort_In_Market_Context'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Features that came up as important in the modeling (not just our final, random forest model) included:\n",
+    "* vertical_drop\n",
+    "* Snow Making_ac\n",
+    "* total_chairs\n",
+    "* fastQuads\n",
+    "* Runs\n",
+    "* LongestRun_mi\n",
+    "* trams\n",
+    "* SkiableTerrain_ac"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A handy glossary of skiing terms can be found on the [ski.com](https://www.ski.com/ski-glossary) site. Some potentially relevant contextual information is that vertical drop, although nominally the height difference from the summit to the base, is generally taken from the highest [_lift-served_](http://verticalfeet.com/) point."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It's often useful to define custom functions for visualizing data in meaningful ways. The function below takes a feature name as an input and plots a histogram of the values of that feature. It then marks where Big Mountain sits in the distribution by marking Big Mountain's value with a vertical line using `matplotlib`'s [axvline](https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.axvline.html) function. It also performs a little cleaning up of missing values and adds descriptive labels and a title."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Code task 1#\n",
+    "#Add code to the `plot_compare` function that displays a vertical, dashed line\n",
+    "#on the histogram to indicate Big Mountain's position in the distribution\n",
+    "#Hint: plt.axvline() plots a vertical line, its position for 'feature1'\n",
+    "#would be `big_mountain['feature1'].values, we'd like a red line, which can be\n",
+    "#specified with c='r', a dashed linestyle is produced by ls='--',\n",
+    "#and it's nice to give it a slightly reduced alpha value, such as 0.8.\n",
+    "#Don't forget to give it a useful label (e.g. 'Big Mountain') so it's listed\n",
+    "#in the legend.\n",
+    "def plot_compare(feat_name, description, state=None, figsize=(10, 5)):\n",
+    "    \"\"\"Graphically compare distributions of features.\n",
+    "    \n",
+    "    Plot histogram of values for all resorts and reference line to mark\n",
+    "    Big Mountain's position.\n",
+    "    \n",
+    "    Arguments:\n",
+    "    feat_name - the feature column name in the data\n",
+    "    description - text description of the feature\n",
+    "    state - select a specific state (None for all states)\n",
+    "    figsize - (optional) figure size\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    plt.subplots(figsize=figsize)\n",
+    "    # quirk that hist sometimes objects to NaNs, sometimes doesn't\n",
+    "    # filtering only for finite values tidies this up\n",
+    "    if state is None:\n",
+    "        ski_x = ski_data[feat_name]\n",
+    "    else:\n",
+    "        ski_x = ski_data.loc[ski_data.state == state, feat_name]\n",
+    "    ski_x = ski_x[np.isfinite(ski_x)]\n",
+    "    plt.hist(ski_x, bins=30)\n",
+    "    plt.axvline(x=big_mountain[feat_name].values, c='r', ls='--', alpha=0.8, label='Big Mountain')\n",
+    "    plt.xlabel(description)\n",
+    "    plt.ylabel('frequency')\n",
+    "    plt.title(description + ' distribution for resorts in market share')\n",
+    "    plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.8.1 Ticket price<a id='5.8.1_Ticket_price'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Look at where Big Mountain sits overall amongst all resorts for price and for just other resorts in Montana."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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PT0/z4IMPWmV9+/Y1kszMmTMz7Mf5C9S6deuMJDNq1Kgsj52TcyUzCxYsMJLMxo0bHcqTk5ONv7+/6dSpk7l8+XK2EqVRo0YZNzc363zPTEpKigkLC8uyra6VKFWrVs107NjxqnFc7bUsyQQGBlrn/NWOlZ7UZHWejx071irLTqJkzD+v9cy+LDknSnv27DGSzMCBAx3qpf8A88wzzzgcR5L54YcfHOpWqVLFtGrVKsOxMouzatWqDmUzZswwkszChQsdyl966SUjySxfvtwqu1q7ZiYiIsK4u7ubvXv3OpRn9zU6aNAgU7Ro0aseo3v37sZut5sjR444lLdp08b4+vqa06dPG2P+ef3efvvtGfbx8ccfZ3g/MsaYzZs3G0lm8eLF2Xq8V8oqUQoODjZnz561yuLj442bm5sZP378VfeXHn/79u0dyocMGWIkOSSPxhjTsWNHU7x48Sz3l5KSYpKSkszcuXONu7u7w3Oafp6tWrUqw3bpidLFixfNPffcYwIDA63kxxhjLly4YIoXL54hzpSUFFOzZk1zyy23WGUvv/xyhs/3rPz1119GkpkyZcpV60VERBhvb2+Hc+vSpUumePHiZsCAAVlul5ycbJKSkkzz5s0d3gvSE6Xy5cubxMREh20qVapkateubZKSkhzK27VrZ0JDQx0+l1AwMPQON9x7770nHx8fde/eXZLk7++vLl26aP369dq/f79Vb82aNWrevLmCg4OtMnd3d3Xr1u2Gx3yl5s2ba+XKlZLSLjy9ePGihg4dqpIlS2rFihWS0oYYpA+HkqSlS5eqaNGiat++vZKTk61brVq1FBISYg0t+Oabb5ScnKw+ffo41PP29lZ0dHSGIQjGGA0YMECjR4/WRx99pOHDh1vrLl++rFWrVqlTp07y9fV12N9dd92ly5cva+PGjQ77u/vuux3u16hRQ5J0+PBhSWkXvV++fDnDtSqNGzdWRETEv2jVjPbt26cDBw7ogQcekLe39zXrv/jii+rXr58mTJig1157TW5u/7y9LV26VM2aNVNYWJhDO7Rp00aSFBsb+69i7dmzp8PQqYiICDVu3NgainGle+6555r7Sx/KeOVwTmc5PVecHTt2TJIUFBTkUO7u7q533nlHq1atUnBwsLZu3aoJEybo888/V0pKSqb7CgoKUmpqquLj47M83t69e3Xs2LEs2+pabrnlFn399dd6+umntXbt2uu6ruWOO+5QsWLFsl0/q/M8s+c1N6Xv33lI3y233KLKlSs7DFeSpJCQEN1yyy0OZTVq1LBetzm1evVq+fn56d5773UoT4/H+fg5bdcaNWpkuBYqu6/RW265RadPn1aPHj30+eefZzrb5erVq9W8eXOFh4dniP/ixYsZJs7IzmsyXYUKFVSsWDGNGDFCM2bM0M8//5ztbbPSrFkzh8lUgoODFRQUlO3nz3lGt8qVK0uSdQ3wleUnT550GH63bds23X333SpRooTc3d3l6empPn36KCUlRfv27XPYvlixYrrjjjsyjeHvv//WHXfcoR9//FHffvutNaxYSvucPHnypPr27evw3Kampqp169batGmTNVQ8J4oXL67y5cvr5Zdf1qRJk7Rt27Ysh5vWqlVLN910k3Xf29tbN998c4Y2njFjhurUqSNvb295eHjI09NTq1at0p49ezLs8+6775anp6d1/9dff9Uvv/xivW84f+bGxcVlGMqO/I9ECTfUr7/+qnXr1qlt27Yyxuj06dM6ffq09YF85Zjhv//+WyEhIRn2kVnZjdSiRQsdOXJE+/fv18qVK1W7dm1rLPPKlSt16dIlbdiwQS1atLC2OX78uE6fPi0vLy95eno63OLj460P++PHj0uS6tevn6HeggULMnwpSExM1IIFC1S1alXrC0W6v//+W8nJyXrjjTcy7Ouuu+6SpAz7K1GihMN9u90u6Z+Lrf/++29JmT8Huf28pF+7ld2L7D/44AOVLl3aSsCvdPz4cX3xxRcZ2qFq1aqSMrZDTmXVHuntlc7X1zdbk1X8+eefcnd3v2qb5vRccZb+nGaWhHbv3l2HDh3SO++8oxIlSmjLli269957Va1aNSvBulL6Pq6WvPzbc+f111/XiBEjtHjxYjVr1kzFixdXx44dHX5cuZbQ0NBs171arM7Pa25L339m8YaFhWU4vvPrVkp77V7vJAnp773O100FBQXJw8Mjw/Fz2q6Z1c/ua7R3796aOXOmDh8+rHvuuUdBQUFq0KCB9SNVevxZtV36+uuNPzAwULGxsapVq5aeeeYZVa1aVWFhYRo9enSm189mx799/ooXL+5w38vL66rl6dcaHjlyRE2aNNEff/yh1157TevXr9emTZusa/2cj3+1dtq3b59++OEHtWnTJsNffqS/V917770Znt+XXnpJxhidPHkyW4/1SjabTatWrVKrVq00ceJE1alTR6VKldLjjz+e4e8OstPGkyZN0iOPPKIGDRro008/1caNG7Vp0ya1bt060+fCuT3SH+eTTz6Z4XEOHDhQ0r//rMGN5+HqAPDfMnPmTBlj9Mknn+iTTz7JsH7OnDkaO3as3N3dVaJEiUx/oc6szG63ZzrjTF58oUn/pWzlypVasWKFWrZsaZU/++yzWrdunRISEhwSpZIlS6pEiRJatmxZpvtM/zWxZMmSkqRPPvkkWz006ReQtmrVSi1atNCyZcusX3aLFSsmd3d39e7dO8ueicjIyGw+6jTpHzZZPS+5+R8h6RMrXOsC6XTLli1Tt27d1KRJE61atcqh/UqWLKkaNWroxRdfzHTb9C9Q1yur9nD+cM7uf+WUKlVKKSkpio+Pz/LLSU7Play2P3nyZKbHKFasmLp06aI333xTs2fP1tmzZ1W/fn09//zzmjFjhkPd9C856fvMzLXOnWvx8/PTmDFjNGbMGB0/ftzqXWrfvr1++eWXa24vZb/9rxZXfHy8KlSoYN339vbO9L3nr7/+ump7XE16W8XFxWX4oeDYsWPXvd+cHP+HH36QMcahzU6cOKHk5OQMx89pu2ZWPyev0fvvv1/333+/Lly4oHXr1mn06NFq166d9u3bp4iICJUoUUJxcXEZ9pGe5P/b+KtXr6758+fLGKOdO3dq9uzZev755+Xj46Onn346R/typcWLF+vChQtatGiRw3tIZhMhSFdvp0aNGqlLly564IEHJKVNuJHeq5/e3m+88UaWszteOXIkJyIiIqzJofbt26eFCxcqJiZGiYmJGd6nruWDDz5Q06ZNNX36dIfyrP5jzrk90h/nyJEj1blz50y3qVixYo5iguuRKOGGSUlJ0Zw5c1S+fHm9++67GdYvXbpUr776qr7++mu1a9dOzZo105IlS3T8+HHrTTQlJUULFizIsG3ZsmW1c+dOh7LVq1dnmOEnM1f2mvj4+FyzfmhoqKpUqaJPP/1UW7Zs0bhx4yRJLVu21IABAzRp0iQVKVLEmuVKShsaMX/+fKWkpKhBgwZZ7rtVq1by8PDQgQMHsj0cpHbt2oqNjVWLFi3UtGlTrVixQkFBQfL19VWzZs20bds21ahRw/o18d9o2LChvL299eGHHzrEt2HDBh0+fDhbiVJ2fym9+eabVb58ec2cOVNDhw61nqesREREaP369WrRooWVLEVFRUlKa/+vvvpK5cuXv+oQIecetOyaN2+ehg4dan1wHj58WBs2bFCfPn1ytJ90bdq00fjx4zV9+nQ9//zzmda5nnPlSpUqVZIkHThwwPrVXlKGL8fpatSooZIlSzrM+Jjut99+U4kSJa76ZadixYoKDQ3Nsq1ykqwGBwerX79+2rFjh6ZMmaKLFy/K19c3x6/la8nqPL9yOvXM3nv27dunvXv3Onwhz8m5lT686YMPPnB4H9m0aZP27NmjUaNGXd8DyqbmzZtr4cKFWrx4sTp16mSVp89QeOWwqtyS3dfolfz8/NSmTRslJiaqY8eO2r17tyIiItS8eXN99tlnOnbsmMN5NXfuXPn6+mZrKvbsPF82m001a9bU5MmTNXv2bG3dujVbcecX6a/BK99bjTHZnsHSWd++feXn56eePXtas5u6u7vr1ltvVdGiRfXzzz9r0KBBV93H9b4HS2mfGc8++6w+/fTT63oubDZbhs+ZnTt36vvvv88wjDMzFStWVFRUlHbs2GF9L0DBR6KEG+brr7/WsWPH9NJLL1nT5l6pWrVqmjp1qt577z21a9dOzz77rJYsWaI77rhD//vf/+Tr66s333wz07HMvXv31nPPPaf//e9/io6O1s8//6ypU6cqMDDwmnFVr15dkvTSSy+pTZs2cnd3v2Zi0bx5c73xxhvy8fHRrbfeKimtdyYyMlLLly/X3XffLQ+Pf15e3bt314cffqi77rpLgwcP1i233CJPT0/9/vvvWrNmjTp06KBOnTqpbNmyev755zVq1Cj99ttvat26tYoVK6bjx4/rxx9/tH5Vd1a5cmUrSbj99tu1cuVKlSlTRq+99ppuu+02NWnSRI888ojKli2rc+fO6ddff9UXX3yh1atXX7N9rlSsWDE9+eSTGjt2rB588EF16dJFR48eVUxMTLaH3lWvXl1r167VF198odDQUAUEBGT5K9ubb76p9u3bq2HDhnriiSd000036ciRI/rmm2/04YcfZqgfGhqq2NhYtWrVSrfffrtWrFihatWq6fnnn9eKFSvUuHFjPf7446pYsaIuX76sQ4cO6auvvtKMGTNUpkwZBQQEKCIiQp9//rmaN2+u4sWLq2TJktdMAE+cOKFOnTrpoYce0pkzZzR69Gh5e3tr5MiR2WoTZ02aNFHv3r01duxYHT9+XO3atZPdbte2bdvk6+urxx577LrPlXQNGjSQj4+PNm7c6HBt2uHDh9W9e3c98sgjqlGjhhISErRr1y6NHz9ex44dU4cOHTLsa+PGjYqOjr7qL85ubm564YUX9OCDD1ptdfr06WyfOw0aNFC7du1Uo0YNFStWTHv27NH777+vRo0aydfXV9L1vZavZvPmzQ7n+ahRo1S6dGlrGI2U9t5z3333aeDAgbrnnnt0+PBhTZw40eoRTVe+fHn5+Pjoww8/VOXKleXv76+wsLBME8SKFSvq//7v//TGG2/Izc1Nbdq00aFDh/Tcc88pPDxcTzzxxHU9nuzq06eP3nzzTfXt21eHDh1S9erV9e2332rcuHG66667HHrLc0t2X6MPPfSQ9b4bGhqq+Ph4jR8/XoGBgVZSOXr0aOuap//9738qXry4PvzwQ3355ZeaOHFitj4X0oePvf322woICJC3t7ciIyP1/fffa9q0aerYsaPKlSsnY4wWLVqk06dPW6MLCoqWLVvKy8tLPXr00PDhw3X58mVNnz5dp06duu593nvvvfL19dW9996rS5cuad68efL399cbb7yhvn376uTJk7r33nsVFBSkP//8Uzt27NCff/5p9eKkv4Zfe+019e3bV56enqpYsWKmf4i9c+dODRo0SF26dFFUVJS8vLy0evVq7dy587p69tq1a6cXXnhBo0ePVnR0tPbu3avnn39ekZGR2f5vrbfeektt2rRRq1at1K9fP5UuXVonT57Unj17tHXrVn388cc5jgsu5rJpJPCf07FjR+Pl5WVOnDiRZZ3u3bsbDw8Pa6a77777zjRs2NDY7XYTEhJinnrqKfP2229nmBUnISHBDB8+3ISHhxsfHx8THR1ttm/fnq1Z7xISEsyDDz5oSpUqZWw2W7Zm3Pn888+NJNOyZUuH8oceeshIMq+//nqGbZKSkswrr7xiatasaby9vY2/v7+pVKmSGTBggNm/f79D3cWLF5tmzZqZIkWKGLvdbiIiIsy9997rMJOQ8/Tgxhjz+++/m0qVKpmyZctaU8sePHjQ9O/f35QuXdp4enqaUqVKmcaNGzvM3JXeLh9//LHD/tJn97lypq7U1FQzfvx4Ex4ebry8vEyNGjXMF198kWGWr6xs377d3HrrrcbX19dhprzMnhtj0qYUb9OmjQkMDDR2u92UL1/ePPHEE9Z65+nBjTHm9OnT5tZbbzXFixc3mzZtMsakzWj3+OOPm8jISOPp6WmKFy9u6tata0aNGuUwW9vKlStN7dq1jd1uN5IyndHMud3ef/998/jjj5tSpUoZu91umjRpYjZv3uxQN7Pn68p1zrNhpaSkmMmTJ5tq1aoZLy8vExgYaBo1amS++OILh3rZOVey0rt3b1OlShWHsgsXLpiYmBhzyy23mOLFixtJxs/Pz9SoUcOaqfJKv/76a6YzOmbl3XffNVFRUcbLy8vcfPPNZubMmVnOBnblTHRPP/20qVevnilWrJix2+2mXLly5oknnjB//fWXVedqr2VJ5tFHH800Judjpc96t3z5ctO7d29TtGhRa1p759dqamqqmThxoilXrpzx9vY29erVM6tXr8709TBv3jxTqVIl4+np6XDMzKYHT0lJMS+99JK5+eabjaenpylZsqS57777rCnF02U2a50xmZ9Tmclq+7///ts8/PDDJjQ01Hh4eJiIiAgzcuTIDH+dcLV2zcyVU0k7y85rdM6cOaZZs2YmODjYeHl5mbCwMNO1a1ezc+dOh33t2rXLtG/f3gQGBhovLy9Ts2bNDDMOZvW+l27KlCkmMjLSuLu7W++Dv/zyi+nRo4cpX7688fHxMYGBgeaWW27J8PcWmcnqPM+s/bKaTTE78aefv+nvfekye6/84osvrM+k0qVLm6eeesp8/fXXGd6LszpP0mN1fk7XrFlj/P39TevWra2/v4iNjTVt27Y1xYsXN56enqZ06dKmbdu2GeIfOXKkCQsLM25ubpl+JqQ7fvy46devn6lUqZLx8/Mz/v7+pkaNGmby5MkOf2mR1Tnn/BpNSEgwTz75pCldurTx9vY2derUMYsXL87wvKV/Lr788suZxrVjxw7TtWtXExQUZDw9PU1ISIi54447Mn3/RP5nM8aYG5CPAUChs3btWjVr1kwff/xxhhnCCoLNmzerfv362rhxY5ZDQps2barZs2dn2av23HPPae7cuTpw4IBDLyoAAAUds94BwH9UvXr11LVrV73wwgvXtf3p06f15ptvaty4cSRJAIBCh0QJAP7DXn31VdWvXz/LmZ369eunokWLZrru4MGDGjlypHr27JmHEQIA4BoMvQMAAAAAJ/QoAQAAAIATEiUAAAAAcEKiBAAAAABOCv00RampqTp27JgCAgKu+meIAAAAAAo3Y4zOnTunsLAwubldvc+o0CdKx44dU3h4uKvDAAAAAJBPHD16VGXKlLlqnUKfKAUEBEhKa4wiRYq4OBoAyGWpqdLx42nLwcHSNX4dAwDgv+zs2bMKDw+3coSrKfSJUvpwuyJFipAoASh8Ll2SevVKW16/XvLxcW08AAAUANm5JIefHgEAAADACYkSAAAAADghUQIAAAAAJ4X+GiUAAAAUfCkpKUpKSnJ1GMjn3N3d5eHhkSt/C0SiBAAAgHzt/Pnz+v3332WMcXUoKAB8fX0VGhoqLy+vf7UfEiUAAADkWykpKfr999/l6+urUqVK5UpPAQonY4wSExP1559/6uDBg4qKirrmn8peDYkSABRk7u5Sly7/LANAIZOUlCRjjEqVKiUf/gIB1+Dj4yNPT08dPnxYiYmJ8vb2vu59kSgBQEHm5SWNGOHqKAAgz9GThOz6N71IDvvJlb0AAAAAQCFCjxIAFGTGSKdPpy0XLSrxiysAALmCHiUAKMguX5Zatky7Xb7s6mgAADlw6NAh2Ww2bd++3dWh5Ctly5bVlClTXB0GiRIAAACQ2/r16yebzWbdSpQoodatW2vnzp1WnfDwcMXFxalatWr/6lhly5aVzWbT/PnzM6yrWrWqbDabZs+e/a+OcT1sNpsWL16c4+02bdqk//u//8v9gHKIRAkAAADIA61bt1ZcXJzi4uK0atUqeXh4qF27dtZ6d3d3hYSEyMPj318NEx4erlmzZjmUbdy4UfHx8fLz8/vX+7+RSpUqJV9fX1eHQaIEAACAAujSpaxviYnZr5uQkL2618FutyskJEQhISGqVauWRowYoaNHj+rPP/+UlPnQuyVLligqKko+Pj5q1qyZ5syZI5vNptPp16NmoVevXoqNjdXRo0etspkzZ6pXr14ZErEjR46oQ4cO8vf3V5EiRdS1a1cdP37cWt+vXz917NjRYZshQ4aoadOm1v2mTZvq8ccf1/Dhw1W8eHGFhIQoJibGWl+2bFlJUqdOnWSz2az7Bw4cUIcOHRQcHCx/f3/Vr19fK1eudDiW89A7m82md999V506dZKvr6+ioqK0ZMmSq7ZHbiBRAgAAQMHTpEnWt6eecqzbsmXWdR97zLFu+/aZ1/uXzp8/rw8//FAVKlRQiRIlMq1z6NAh3XvvverYsaO2b9+uAQMGaNSoUdnaf3BwsFq1aqU5c+ZIki5evKgFCxaof//+DvWMMerYsaNOnjyp2NhYrVixQgcOHFC3bt1y/JjmzJkjPz8//fDDD5o4caKef/55rVixQlLa8DlJmjVrluLi4qz758+f11133aWVK1dq27ZtatWqldq3b68jR45c9VhjxoxR165dtXPnTt11113q1auXTp48meOYc4JZ74BcUPbpL/Ns34cmtM2zfQMAgLyzdOlS+fv7S5IuXLig0NBQLV26NMv/+ZkxY4YqVqyol19+WZJUsWJF/fTTT3rxxRezdbz+/ftr2LBhGjVqlD755BOVL19etWrVcqizcuVK7dy5UwcPHlR4eLgk6f3331fVqlW1adMm1a9fP9uPr0aNGho9erQkKSoqSlOnTtWqVavUsmVLlSpVSpJUtGhRhYSEWNvUrFlTNWvWtO6PHTtWn332mZYsWaJBgwZleax+/fqpR48ekqRx48bpjTfe0I8//qjWrVtnO96cIlECAABAwbN+fdbr3N0d7///Xo5MOSctX3xx/TE5adasmaZPny5JOnnypKZNm6Y2bdroxx9/VERERIb6e/fuzZCo3HLLLdk+Xtu2bTVgwACtW7dOM2fOzNCbJEl79uxReHi4lSRJUpUqVVS0aFHt2bMnx4nSlUJDQ3XixImrbnPhwgWNGTNGS5cu1bFjx5ScnKxLly5ds0fpymP5+fkpICDgmsf6t0iUAKAgc3eX0i8Mdv5iAACFmY+P6+teg5+fnypUqGDdr1u3rgIDA/XOO+9o7NixGeobY2Rz+j88Y0y2j+fh4aHevXtr9OjR+uGHH/TZZ59l6xjO5W5ubhmOm5SUlGEbT09Ph/s2m02pqalXjfGpp57SN998o1deeUUVKlSQj4+P7r33XiU6X1eWC8f6t1x6jVJMTIzDtIk2m82ha84Yo5iYGIWFhcnHx0dNmzbV7t27XRgxAOQzXl5STEzazcvL1dEAAK7CZrPJzc1Nl7KYHKJSpUrWtTzpNm/enKNj9O/fX7GxserQoYOKFSuWYX2VKlV05MgRh0kffv75Z505c0aVK1eWlDbrXFxcnMN21/NfT56enkpJSXEoW79+vfr166dOnTqpevXqCgkJ0aFDh3K87xvB5ZM5VK1a1Zo2MS4uTrt27bLWTZw4UZMmTdLUqVO1adMmhYSEqGXLljp37pwLIwYAAACuLSEhQfHx8YqPj9eePXv02GOP6fz582rfvn2m9QcMGKBffvlFI0aM0L59+7Rw4ULr/48y6wXKTOXKlfXXX39lmCo8XYsWLVSjRg316tVLW7du1Y8//qg+ffooOjpa9erVkyTdcccd2rx5s+bOnav9+/dr9OjR+umnn3L8+MuWLatVq1YpPj5ep06dkiRVqFBBixYt0vbt27Vjxw717Nkzz3uGrpfLEyUPDw9r2sSQkBDrwi9jjKZMmaJRo0apc+fOqlatmubMmaOLFy/qo48+cnHUAJBPGPPP1LU5GJ4BAMh7y5YtU2hoqEJDQ9WgQQNt2rRJH3/8scM021eKjIzUJ598okWLFqlGjRqaPn26Neud3W7P9nFLlCghnyyGEKb/CWyxYsV0++23q0WLFipXrpwWLFhg1WnVqpWee+45DR8+XPXr19e5c+fUp0+f7D/w/+/VV1/VihUrFB4ertq1a0uSJk+erGLFiqlx48Zq3769WrVqpTp16uR43zeCzeRk4GMui4mJ0csvv6zAwEDZ7XY1aNBA48aNU7ly5fTbb7+pfPny2rp1q9WwktShQwcVLVrUmvrQWUJCghKumA//7NmzCg8P15kzZ1SkSJE8f0z4b2LWO7jMpUv/TFu7fn2ujq0HgPzg8uXLOnjwoCIjI+Xt7e3qcG64F198UTNmzHAYKoeru9o5c/bsWQUGBmYrN3Bpj1KDBg00d+5cffPNN3rnnXcUHx+vxo0b6++//1Z8fLyktDnhrxQcHGyty8z48eMVGBho3a6c0QMAAADIz6ZNm6ZNmzbpt99+0/vvv6+XX35Zffv2dXVY/0kunfWuTZs21nL16tXVqFEjlS9fXnPmzFHDhg0lZRyPmdVMHelGjhypoUOHWvfTe5QAAACA/G7//v0aO3asTp48qZtuuknDhg3TyJEjXR3Wf1K+mh7cz89P1atX1/79+9WxY0dJUnx8vEJDQ606J06cyNDLdCW73Z6jMZwAAABAfjF58mRNnjzZ1WFA+WAyhyslJCRoz549Cg0NVWRkpEJCQrTiij8IS0xMVGxsrBo3buzCKAEAAAAUdi7tUXryySfVvn173XTTTTpx4oTGjh2rs2fPqm/fvrLZbBoyZIjGjRunqKgoRUVFady4cfL19VXPnj1dGTYAAABuMBfOP4YCJrfOFZcmSr///rt69Oihv/76S6VKlVLDhg21ceNGRURESJKGDx+uS5cuaeDAgTp16pQaNGig5cuXKyAgwJVhAwAA4AZxd3eXlDayKKspr4ErXbx4UVLaH97+Gy5NlObPn3/V9TabTTExMYqJibkxAQFAQePuLjVv/s8yABQyHh4e8vX11Z9//ilPT0+5ueWrK0eQjxhjdPHiRZ04cUJFixa1kuzrla8mcwAA5JCXl/TSS66OAgDyjM1mU2hoqA4ePKjDhw+7OhwUAEWLFlVISMi/3g+JEgAAAPI1Ly8vRUVFKTEx0dWhIJ/z9PT81z1J6UiUAAAAkO+5ubnJ29vb1WHgP4RBngBQkF26JNWrl3a7dMnV0QAAUGiQKAEAAACAExIlAAAAAHBCogQAAAAATkiUAAAAAMAJiRIAAAAAOCFRAgAAAAAn/I8SABRk7u7Srbf+swwAAHIFiRIAFGReXtJrr7k6CgAACh2G3gEAAACAExIlAAAAAHBCogQABdmlS9Jtt6XdLl1ydTQAABQaXKMEAAXd5cuujgAAgEKHHiUAAAAAcEKiBAAAAABOSJQAAAAAwAmJEgAAAAA4IVECAAAAACfMegcABZmbm1Snzj/LAAAgV5AoAUBBZrdLb7/t6igAACh0+PkRAAAAAJyQKAEAAACAExIlACjILl2SWrRIu1265OpoAAAoNLhGCQAKutOnXR0BAACFDj1KAAAAAOCERAkAAAAAnJAoAQAAAIATEiUAAAAAcEKiBAAAAABOmPUOAAoyNzepSpV/lgEAQK4gUQKAgsxul+bOdXUUAAAUOvz8CAAAAABOSJQAAAAAwAmJEgAUZJcvS+3bp90uX3Z1NAAAFBpcowQABZkxUlzcP8sAACBX0KMEAAAAAE5IlAAAAADACYkSAAAAADghUQIAAAAAJyRKAAAAAOCEWe8AoCCz2aRy5f5ZBgAAuYJECQAKMm9vaeFCV0cBAEChw9A7AAAAAHBCogQAAAAATkiUAKAgu3xZ6to17Xb5squjAQCg0OAaJQAoyIyRfvvtn2UAAJAr6FECAAAAACckSgAAAADghEQJAAAAAJyQKAEAAACAExIlAAAAAHDCrHcAUJDZbFJo6D/LAAAgV5AoAUBB5u0tffGFq6MAAKDQYegdAAAAADghUQIAAAAAJyRKAFCQJSRIffqk3RISXB0NAACFBtcoAUBBlpoq/fzzP8sAACBX0KMEAAAAAE7yTaI0fvx42Ww2DRkyxCozxigmJkZhYWHy8fFR06ZNtXv3btcFCQAAAOA/IV8kSps2bdLbb7+tGjVqOJRPnDhRkyZN0tSpU7Vp0yaFhISoZcuWOnfunIsiBQAAAPBf4PJE6fz58+rVq5feeecdFStWzCo3xmjKlCkaNWqUOnfurGrVqmnOnDm6ePGiPvroIxdGDAAAAKCwc3mi9Oijj6pt27Zq0aKFQ/nBgwcVHx+vO++80yqz2+2Kjo7Whg0bstxfQkKCzp4963ADAAAAgJxw6ax38+fP19atW7Vp06YM6+Lj4yVJwcHBDuXBwcE6fPhwlvscP368xowZk7uBAkB+VrSoqyMAAKDQcVmP0tGjRzV48GB98MEH8vb2zrKezWZzuG+MyVB2pZEjR+rMmTPW7ejRo7kWMwDkOz4+0sqVaTcfH1dHAwBAoeGyHqUtW7boxIkTqlu3rlWWkpKidevWaerUqdq7d6+ktJ6l0NBQq86JEycy9DJdyW63y263513gAAAAAAo9l/UoNW/eXLt27dL27dutW7169dSrVy9t375d5cqVU0hIiFasWGFtk5iYqNjYWDVu3NhVYQMAAAD4D3BZj1JAQICqVavmUObn56cSJUpY5UOGDNG4ceMUFRWlqKgojRs3Tr6+vurZs6crQgaA/CchQXrssbTlN96Q6FEHACBXuHQyh2sZPny4Ll26pIEDB+rUqVNq0KCBli9froCAAFeHBgD5Q2qqtHXrP8sAACBX5KtEae3atQ73bTabYmJiFBMT45J4AAAAAPw3ufx/lAAAAAAgvyFRAgAAAAAnJEoAAAAA4IRECQAAAACc5KvJHAAA18Hb29URAABQ6JAoAUBB5uMjffutq6MAAKDQYegdAAAAADghUQIAAAAAJyRKAFCQJSZKgwen3RITXR0NAACFBtcoAUBBlpIifffdP8sAACBX0KMEAAAAAE5IlAAAAADACYkSAAAAADghUQIAAAAAJyRKAAAAAOCEWe+A/7iyT3+ZZ/s+NKFtnu0bAAAgL5EoAUBB5uMjbd7s6igAACh0GHoHAAAAAE5IlAAAAADACYkSABRkiYnSiBFpt8REV0cDAEChQaIEAAVZSoq0alXaLSXF1dEAAFBokCgBAAAAgBMSJQAAAABwQqIEAAAAAE5IlAAAAADACYkSAAAAADghUQIAAAAAJx6uDgAA8C94e0vr1/+zDAAAcgWJEgAUZDab5OPj6igAACh0GHoHAAAAAE7oUQKAgiwxURo3Lm35mWckLy/XxgMAQCFBjxIAFGQpKdLSpWm3lBRXRwMAQKFBogQAAAAATkiUAAAAAMAJiRIAAAAAOCFRAgAAAAAnJEoAAAAA4IRECQAAAACc8D9KAFCQeXtLK1b8swwAAHIFiRIAFGQ2m1SsmKujAACg0GHoHQAAAAA4oUcJAAqyxERp8uS05SeekLy8XBsPAACFBD1KAFCQpaRIH3+cdktJcXU0AAAUGiRKAAAAAOCERAkAAAAAnJAoAQAAAIATEiUAAAAAcEKiBAAAAABOSJQAAAAAwAn/owQABZndLi1Z8s8yAADIFSRKAFCQublJYWGujgIAgEKHoXcAAAAA4IQeJQAoyJKSpGnT0pYHDpQ8PV0bDwAAhQQ9SgBQkCUnS++/n3ZLTnZ1NAAAFBokSgAAAADghEQJAAAAAJzkOFE6ePBgXsQBAAAAAPlGjhOlChUqqFmzZvrggw90+fLlvIgJAAAAAFwqx4nSjh07VLt2bQ0bNkwhISEaMGCAfvzxx7yIDQAAAABcIseJUrVq1TRp0iT98ccfmjVrluLj43XbbbepatWqmjRpkv7888+8iBMAAAAAbpjrnszBw8NDnTp10sKFC/XSSy/pwIEDevLJJ1WmTBn16dNHcXFxuRknACAzdru0cGHazW53dTQAABQa150obd68WQMHDlRoaKgmTZqkJ598UgcOHNDq1av1xx9/qEOHDtfcx/Tp01WjRg0VKVJERYoUUaNGjfT1119b640xiomJUVhYmHx8fNS0aVPt3r37ekMGgMLHzU0qVy7t5sZEpgAA5JYcf6pOmjRJ1atXV+PGjXXs2DHNnTtXhw8f1tixYxUZGalbb71Vb731lrZu3XrNfZUpU0YTJkzQ5s2btXnzZt1xxx3q0KGDlQxNnDhRkyZN0tSpU7Vp0yaFhISoZcuWOnfuXM4fKQAAAABkk0dON5g+fbr69++v+++/XyEhIZnWuemmm/Tee+9dc1/t27d3uP/iiy9q+vTp2rhxo6pUqaIpU6Zo1KhR6ty5syRpzpw5Cg4O1kcffaQBAwbkNHQAKHySkqRZs9KW779f8vR0bTwAABQSOU6U9u/ff806Xl5e6tu3b472m5KSoo8//lgXLlxQo0aNdPDgQcXHx+vOO++06tjtdkVHR2vDhg1ZJkoJCQlKSEiw7p89ezZHcQBAgZKcLL39dtpy794kSgAA5JIcD72bNWuWPv744wzlH3/8sebMmZPjAHbt2iV/f3/Z7XY9/PDD+uyzz1SlShXFx8dLkoKDgx3qBwcHW+syM378eAUGBlq38PDwHMcEAAAA4L8tx4nShAkTVLJkyQzlQUFBGjduXI4DqFixorZv366NGzfqkUceUd++ffXzzz9b6202m0N9Y0yGsiuNHDlSZ86csW5Hjx7NcUwAAAAA/ttyPPTu8OHDioyMzFAeERGhI0eO5DgALy8vVahQQZJUr149bdq0Sa+99ppGjBghSYqPj1doaKhV/8SJExl6ma5kt9tlZ4pcAAAAAP9CjnuUgoKCtHPnzgzlO3bsUIkSJf51QMYYJSQkKDIyUiEhIVqxYoW1LjExUbGxsWrcuPG/Pg4AAAAAZCXHPUrdu3fX448/roCAAN1+++2SpNjYWA0ePFjdu3fP0b6eeeYZtWnTRuHh4Tp37pzmz5+vtWvXatmyZbLZbBoyZIjGjRunqKgoRUVFady4cfL19VXPnj1zGjYAAAAAZFuOE6WxY8fq8OHDat68uTw80jZPTU1Vnz59cnyN0vHjx9W7d2/FxcUpMDBQNWrU0LJly9SyZUtJ0vDhw3Xp0iUNHDhQp06dUoMGDbR8+XIFBATkNGwAAAAAyDabMcZcz4b79u3Tjh075OPjo+rVqysiIiK3Y8sVZ8+eVWBgoM6cOaMiRYq4OhwUUmWf/jLP9n1oQts827dUsGOHpNRU6Zdf0pYrVZLccjyiGgCA/4yc5AY57lFKd/PNN+vmm2++3s0BALnBzU2qUsXVUQAAUOjkOFFKSUnR7NmztWrVKp04cUKpqakO61evXp1rwQEAAACAK+Q4URo8eLBmz56ttm3bqlq1alf9TyMAQB5LSpLmzUtb7tFD8vR0bTwAABQSOU6U5s+fr4ULF+quu+7Ki3gAADmRnCy9/nracpcuJEoAAOSSHF/1e+UfxAIAAABAYZTjRGnYsGF67bXXdJ2T5QEAAABAvpfjoXfffvut1qxZo6+//lpVq1aVp9Mwj0WLFuVacAAAAADgCjlOlIoWLapOnTrlRSwAAAAAkC/kOFGaNWtWXsQBAAAAAPnGdf2Fe3JyslauXKm33npL586dkyQdO3ZM58+fz9XgAAAAAMAVctyjdPjwYbVu3VpHjhxRQkKCWrZsqYCAAE2cOFGXL1/WjBkz8iJOAEBm7Hbprbf+WQYAALkixz1KgwcPVr169XTq1Cn5+PhY5Z06ddKqVatyNTgAwDW4uUl166bd3K5rkAAAAMjEdc16991338nLy8uhPCIiQn/88UeuBQYAAAAArpLjRCk1NVUpKSkZyn///XcFBATkSlAAgGxKTpbS/5ahc2fJI8dv6wAAIBM5HqfRsmVLTZkyxbpvs9l0/vx5jR49WnfddVduxgYAuJakJGnixLRbUpKrowEAoNDI8U+PkydPVrNmzVSlShVdvnxZPXv21P79+1WyZEnNmzcvL2IEAAAAgBsqx4lSWFiYtm/frnnz5mnr1q1KTU3VAw88oF69ejlM7gAAAAAABdV1DWb38fFR//791b9//9yOBwAAAABcLseJ0ty5c6+6vk+fPtcdDAAAAADkBzlOlAYPHuxwPykpSRcvXpSXl5d8fX1JlAAAAAAUeDme9e7UqVMOt/Pnz2vv3r267bbbmMwBAAAAQKGQK3+4ERUVpQkTJui+++7TL7/8khu7BABkh5eXlP6XDU5/BA4AAK5frv0zobu7u44dO5ZbuwMAZIe7u3Tbba6OAgCAQifHidKSJUsc7htjFBcXp6lTp+rWW2/NtcAAAAAAwFVynCh17NjR4b7NZlOpUqV0xx136NVXX82tuAAA2ZGcLH39ddpymzaSR64NFAAA4D8tx5+oqampeREHAOB6JCVJY8akLbdoQaIEAEAuyfGsdwAAAABQ2OX4p8ehQ4dmu+6kSZNyunsAAAAAcLkcJ0rbtm3T1q1blZycrIoVK0qS9u3bJ3d3d9WpU8eqZ7PZci9KAAAAALiBcpwotW/fXgEBAZozZ46KFSsmKe1PaO+//341adJEw4YNy/UgAQAAAOBGyvE1Sq+++qrGjx9vJUmSVKxYMY0dO5ZZ7wAAAAAUCjlOlM6ePavjx49nKD9x4oTOnTuXK0EBAAAAgCvleOhdp06ddP/99+vVV19Vw4YNJUkbN27UU089pc6dO+d6gACAq/DykiZM+GcZAADkihwnSjNmzNCTTz6p++67T0lJSWk78fDQAw88oJdffjnXAwQAXIW7e9r/JwEAgFyV40TJ19dX06ZN08svv6wDBw7IGKMKFSrIz88vL+IDAAAAgBvuuv9wNi4uTnFxcbr55pvl5+cnY0xuxgUAyI6UFGnlyrRbSoqrowEAoNDIcY/S33//ra5du2rNmjWy2Wzav3+/ypUrpwcffFBFixZl5jsAuJESE6Wnn05bXr9e8vFxbTwAABQSOe5ReuKJJ+Tp6akjR47I19fXKu/WrZuWLVuWq8EBAAAAgCvkuEdp+fLl+uabb1SmTBmH8qioKB0+fDjXAgMAAAAAV8lxj9KFCxccepLS/fXXX7Lb7bkSFAAAAAC4Uo4Tpdtvv11z58617ttsNqWmpurll19Ws2bNcjU4AAAAAHCFHA+9e/nll9W0aVNt3rxZiYmJGj58uHbv3q2TJ0/qu+++y4sYAQAAAOCGynGPUpUqVbRz507dcsstatmypS5cuKDOnTtr27ZtKl++fF7ECAAAAAA3VI56lJKSknTnnXfqrbfe0pgxY/IqJgBAdnl6SqNH/7MMAAByRY4SJU9PT/3000+y2Wx5FQ8AICc8PKT27V0dBQAAhU6Oh9716dNH7733Xl7EAgAAAAD5Qo4nc0hMTNS7776rFStWqF69evLz83NYP2nSpFwLDgBwDSkp0vffpy03aiS5u7s2HgAAColsJUo7d+5UtWrV5Obmpp9++kl16tSRJO3bt8+hHkPyAOAGS0yUhgxJW16/XvLxcWk4AAAUFtlKlGrXrq24uDgFBQXp8OHD2rRpk0qUKJHXsQEAAACAS2TrGqWiRYvq4MGDkqRDhw4pNTU1T4MCAAAAAFfKVo/SPffco+joaIWGhspms6levXpyz2Ic/G+//ZarAQIAAADAjZatROntt99W586d9euvv+rxxx/XQw89pICAgLyODUABV/bpL/N0/4cmtM3T/QMAgP+ubM9617p1a0nSli1bNHjwYBIlAAAAAIVWjqcHnzVrVl7EAQAAAAD5Ro4TJQBAPuLpKQ0f/s8yAADIFSRKAFCQeXhIXbu6OgoAAAqdbE0PDgAAAAD/JfQoAUBBlpoqbduWtly7tuTG718AAOQGEiUABVZeTj+e11OP51bs9qQEffzRCElSl54vKcHTzrTpAADkAn56BAAAAAAnJEoAAAAA4MSlidL48eNVv359BQQEKCgoSB07dtTevXsd6hhjFBMTo7CwMPn4+Khp06bavXu3iyIGAAAA8F/g0kQpNjZWjz76qDZu3KgVK1YoOTlZd955py5cuGDVmThxoiZNmqSpU6dq06ZNCgkJUcuWLXXu3DkXRg4AAACgMHPpZA7Lli1zuD9r1iwFBQVpy5Ytuv3222WM0ZQpUzRq1Ch17txZkjRnzhwFBwfro48+0oABA1wRNgAAAIBCLl9do3TmzBlJUvHixSVJBw8eVHx8vO68806rjt1uV3R0tDZs2JDpPhISEnT27FmHGwAAAADkRL6ZHtwYo6FDh+q2225TtWrVJEnx8fGSpODgYIe6wcHBOnz4cKb7GT9+vMaMGZO3waLAyctppAFXSnFz1+y67a1lAACQO/JNojRo0CDt3LlT3377bYZ1NpvN4b4xJkNZupEjR2ro0KHW/bNnzyo8PDx3gwWAfCLZ3UOLqjV3dRgAABQ6+SJReuyxx7RkyRKtW7dOZcqUscpDQkIkpfUshYaGWuUnTpzI0MuUzm63y263523AAAAAAAo1l16jZIzRoEGDtGjRIq1evVqRkZEO6yMjIxUSEqIVK1ZYZYmJiYqNjVXjxo1vdLgAkO/YTKoq/HVEFf46IptJdXU4AAAUGi7tUXr00Uf10Ucf6fPPP1dAQIB1TVJgYKB8fHxks9k0ZMgQjRs3TlFRUYqKitK4cePk6+urnj17ujJ0AMgXvJKTNOnLSZKkLj1fUoInPeoAAOQGlyZK06dPlyQ1bdrUoXzWrFnq16+fJGn48OG6dOmSBg4cqFOnTqlBgwZavny5AgICbnC0AAAAAP4rXJooGWOuWcdmsykmJkYxMTF5HxAAAAAAKJ/9jxIAAAAA5AckSgAAAADghEQJAAAAAJyQKAEAAACAk3zxh7MAgOuT4uaueTVbW8sAACB3kCgBQAGW7O6hebVauzoMAAAKHYbeAQAAAIATepQAIBNln/7S1SFki82kqsyZE5Kk3wODZGz8/gUAQG4gUQKAAswrOUlvfj5BktSl50tK8LS7OCIAAAoHfnoEAAAAACckSgAAAADghEQJAAAAAJyQKAEAAACAExIlAAAAAHBCogQAAAAATpgeHAAKsBQ3d31WtZm1DAAAcgeJEgAUYMnuHppVr4OrwwAAoNBh6B0AAAAAOKFHCQAKMJtJVanzpyRJf/oXk7Hx+xcAALmBRAkACjCv5CS9u+gFSVKXni8pwdPu4ogAACgc+OkRAAAAAJyQKAEAAACAExIlAAAAAHBCogQAAAAATkiUAAAAAMAJiRIAAAAAOGF6cAAowFLd3PRVxdusZQAAkDtIlACgAEty99SMhve6OgwAAAodfn4EAAAAACf0KAFAQWaMiiRckCSdtftJNpuLAwIAoHAgUQKAAsyenKgPFjwrSerS8yUleNpdHBEAAIUDQ+8AAAAAwAmJEgAAAAA4IVECAAAAACckSgAAAADghEQJAAAAAJww6x0AIEfKPv1lnu370IS2ebZvAABygkQJAAqwVDc3rS5f31oGAAC5g0QJAAqwJHdPTbmtl6vDAACg0OHnRwAAAABwQo8SABRkxsienChJSvDwkmw2FwcEAEDhQI8SABRg9uREffzRCH380QgrYQIAAP8eiRIAAAAAOGHoHZDP5eVUzAAAAMgcPUoAAAAA4IRECQAAAACckCgBAAAAgBMSJQAAAABwwmQOAFCApbq5aUNETWsZAADkDhIlACjAktw9NaHp/a4OAwCAQoefHwEAAADACYkSAAAAADghUQKAAsyelKAlc4ZoyZwhsicluDocAAAKDRIlAAAAAHBCogQAAAAATkiUAAAAAMAJiRIAAAAAOCFRAgAAAAAn/OEs8o2yT3/p6hCAQoHXEgAA/x6JEgAUYKlubtpSurK1DAAAcgeJEgAUYEnunhrTYoCrwwAAoNBx6c+P69atU/v27RUWFiabzabFixc7rDfGKCYmRmFhYfLx8VHTpk21e/du1wQLAAAA4D/DpYnShQsXVLNmTU2dOjXT9RMnTtSkSZM0depUbdq0SSEhIWrZsqXOnTt3gyMFAAAA8F/i0qF3bdq0UZs2bTJdZ4zRlClTNGrUKHXu3FmSNGfOHAUHB+ujjz7SgAEMNQEAe1KCPljwnCTpvm4vKMHT7uKIAAAoHPLtlb8HDx5UfHy87rzzTqvMbrcrOjpaGzZsyHK7hIQEnT171uEGAIWZPSVR9pREV4cBAEChkm8Tpfj4eElScHCwQ3lwcLC1LjPjx49XYGCgdQsPD8/TOAEAAAAUPvk2UUpns9kc7htjMpRdaeTIkTpz5ox1O3r0aF6HCAAAAKCQybfTg4eEhEhK61kKDQ21yk+cOJGhl+lKdrtddjtj9AEAAABcv3zboxQZGamQkBCtWLHCKktMTFRsbKwaN27swsgAAAAAFHYu7VE6f/68fv31V+v+wYMHtX37dhUvXlw33XSThgwZonHjxikqKkpRUVEaN26cfH191bNnTxdGDQAAAKCwc2mitHnzZjVr1sy6P3ToUElS3759NXv2bA0fPlyXLl3SwIEDderUKTVo0EDLly9XQECAq0IGgHzF2Nz0U3B5axkAAOQOmzHGuDqIvHT27FkFBgbqzJkzKlKkiKvDwVWUffpLV4cAwMUOTWjr6hAAAIVYTnIDfn4EAAAAACckSgAAAADghEQJAAowe1KCPpg/Sh/MHyV7UoKrwwEAoNDIt/+jBADIniIJF1wdAgAAhQ49SgAAAADghEQJAAAAAJyQKAEAAACAExIlAAAAAHBCogQAAAAATpj1DgAKMGNz0/4S4dYyAADIHSRKAFCAJXp4ali7Ya4OAwCAQoefHwEAAADACYkSAAAAADhh6B0AFGD25ES9uXi8JOnRjiOV4OHl4ogAACgcSJQAoCAzRkEXTlnLAAAgdzD0DgAAAACckCgBAAAAgBMSJQAAAABwQqIEAAAAAE5IlAAAAADACbPeAUBBZrPpSGCItQwAAHIHiRIAFGAJHl4a1PFpV4cBAEChQ6KEbCv79JeuDgEA/pW8fB87NKFtnu0bAHDjcY0SAAAAADihRwkACjB7cqJeXTpJkjSs3VAleHi5OCIAAAoHEiUAKMiM0U1n4q1lAACQOxh6BwAAAABOSJQAAAAAwAmJEgAAAAA44RolAEC+wd8QuAbTpgNARvQoAQAAAIATepQAoCCz2XTCr5i1DAAAcgeJEgAUYAkeXnrw3tGuDgMAgEKHoXcAAAAA4IRECQAAAACcMPQOAAowr+QkjV/2uiRpZOvHlejh6eKIAAAoHEiUbrC8nvqWaViB/xabSVXU30etZQAAkDsYegcAAAAATkiUAAAAAMAJiRIAAAAAOCFRAgAAAAAnJEoAAAAA4IRZ7wCggDtr93N1CAAAFDokSgBQgCV42nVf9xddHQYAAIUOQ+8AAAAAwAmJEgAAAAA4YegdABRgXslJilk5Q5IU0+JhJXp4ujgiAAAKBxIlACjAbCZV1Y4fsJYBAEDuYOgdAAAAADghUQIAAAAAJwy9K2TKPv2lq0MAAOCGycvPvUMT2ubZvuE6nDPILnqUAAAAAMAJiRIAAAAAOGHoHQAUcAnuXq4OAQCAQodECQAKsARPu7rcN9HVYQAAUOgw9A4AAAAAnJAoAQAAAIATht4BQAHmmZKkZ9bMlCSNa9ZfSe6eLo4IAIDCgUQJAAowt9RU1f1jj7UsdxcH9B/G/9gVPgX5OeX/fAonzskbi6F3AAAAAOCERAkAAAAAnBSIRGnatGmKjIyUt7e36tatq/Xr17s6JAAAAACFWL5PlBYsWKAhQ4Zo1KhR2rZtm5o0aaI2bdroyJEjrg4NAAAAQCGV7xOlSZMm6YEHHtCDDz6oypUra8qUKQoPD9f06dNdHRoAAACAQipfz3qXmJioLVu26Omnn3Yov/POO7Vhw4ZMt0lISFBCQoJ1/8yZM5Kks2fP5l2gOZCacNHVIQAoRFKSEnQ+NTVtOeGiUlNTXBwR4CivP3/5XM1cfvnekx/l5TnD+Z61/HJOpsdhjLlm3XydKP31119KSUlRcHCwQ3lwcLDi4+Mz3Wb8+PEaM2ZMhvLw8PA8iREAXO3W9IU3e7syDCBTgVNcHcF/E+3uGrR71vJb25w7d06BgYFXrZOvE6V0NpvN4b4xJkNZupEjR2ro0KHW/dTUVJ08eVIlSpTIcpv87OzZswoPD9fRo0dVpEgRV4dTqNC2eYe2zTu0bd6hbfMObZt3aNu8Q9vmHVe2rTFG586dU1hY2DXr5utEqWTJknJ3d8/Qe3TixIkMvUzp7Ha77Ha7Q1nRokXzKsQbpkiRIrxI8whtm3do27xD2+Yd2jbv0LZ5h7bNO7Rt3nFV216rJyldvp7MwcvLS3Xr1tWKFSscylesWKHGjRu7KCoAAAAAhV2+7lGSpKFDh6p3796qV6+eGjVqpLfffltHjhzRww8/7OrQAAAAABRS+T5R6tatm/7++289//zziouLU7Vq1fTVV18pIiLC1aHdEHa7XaNHj84wnBD/Hm2bd2jbvEPb5h3aNu/QtnmHts07tG3eKShtazPZmRsPAAAAAP5D8vU1SgAAAADgCiRKAAAAAOCERAkAAAAAnJAoAQAAAIATEqV8YPz48apfv74CAgIUFBSkjh07au/evQ51jDGKiYlRWFiYfHx81LRpU+3evdtFERdc48ePl81m05AhQ6wy2vb6/fHHH7rvvvtUokQJ+fr6qlatWtqyZYu1nra9PsnJyXr22WcVGRkpHx8flStXTs8//7xSU1OtOrRt9qxbt07t27dXWFiYbDabFi9e7LA+O+2YkJCgxx57TCVLlpSfn5/uvvtu/f777zfwUeRPV2vbpKQkjRgxQtWrV5efn5/CwsLUp08fHTt2zGEftG3mrnXeXmnAgAGy2WyaMmWKQzltm7nstO2ePXt09913KzAwUAEBAWrYsKGOHDliradtM3ettj1//rwGDRqkMmXKyMfHR5UrV9b06dMd6uS3tiVRygdiY2P16KOPauPGjVqxYoWSk5N155136sKFC1adiRMnatKkSZo6dao2bdqkkJAQtWzZUufOnXNh5AXLpk2b9Pbbb6tGjRoO5bTt9Tl16pRuvfVWeXp66uuvv9bPP/+sV199VUWLFrXq0LbX56WXXtKMGTM0depU7dmzRxMnTtTLL7+sN954w6pD22bPhQsXVLNmTU2dOjXT9dlpxyFDhuizzz7T/Pnz9e233+r8+fNq166dUlJSbtTDyJeu1rYXL17U1q1b9dxzz2nr1q1atGiR9u3bp7vvvtuhHm2buWudt+kWL16sH374QWFhYRnW0baZu1bbHjhwQLfddpsqVaqktWvXaseOHXruuefk7e1t1aFtM3ettn3iiSe0bNkyffDBB9qzZ4+eeOIJPfbYY/r888+tOvmubQ3ynRMnThhJJjY21hhjTGpqqgkJCTETJkyw6ly+fNkEBgaaGTNmuCrMAuXcuXMmKirKrFixwkRHR5vBgwcbY2jbf2PEiBHmtttuy3I9bXv92rZta/r37+9Q1rlzZ3PfffcZY2jb6yXJfPbZZ9b97LTj6dOnjaenp5k/f75V548//jBubm5m2bJlNyz2/M65bTPz448/Gknm8OHDxhjaNruyatvff//dlC5d2vz0008mIiLCTJ482VpH22ZPZm3brVs36702M7Rt9mTWtlWrVjXPP/+8Q1mdOnXMs88+a4zJn21Lj1I+dObMGUlS8eLFJUkHDx5UfHy87rzzTquO3W5XdHS0NmzY4JIYC5pHH31Ubdu2VYsWLRzKadvrt2TJEtWrV09dunRRUFCQateurXfeecdaT9tev9tuu02rVq3Svn37JEk7duzQt99+q7vuuksSbZtbstOOW7ZsUVJSkkOdsLAwVatWjbbOoTNnzshms1m9zrTt9UtNTVXv3r311FNPqWrVqhnW07bXJzU1VV9++aVuvvlmtWrVSkFBQWrQoIHDEDLa9vrddtttWrJkif744w8ZY7RmzRrt27dPrVq1kpQ/25ZEKZ8xxmjo0KG67bbbVK1aNUlSfHy8JCk4ONihbnBwsLUOWZs/f762bt2q8ePHZ1hH216/3377TdOnT1dUVJS++eYbPfzww3r88cc1d+5cSbTtvzFixAj16NFDlSpVkqenp2rXrq0hQ4aoR48ekmjb3JKddoyPj5eXl5eKFSuWZR1c2+XLl/X000+rZ8+eKlKkiCTa9t946aWX5OHhoccffzzT9bTt9Tlx4oTOnz+vCRMmqHXr1lq+fLk6deqkzp07KzY2VhJt+2+8/vrrqlKlisqUKSMvLy+1bt1a06ZN02233SYpf7ath0uOiiwNGjRIO3fu1Lfffpthnc1mc7hvjMlQBkdHjx7V4MGDtXz5cofxxc5o25xLTU1VvXr1NG7cOElS7dq1tXv3bk2fPl19+vSx6tG2ObdgwQJ98MEH+uijj1S1alVt375dQ4YMUVhYmPr27WvVo21zx/W0I22dfUlJSerevbtSU1M1bdq0a9anba9uy5Yteu2117R169YctxNte3XpE+Z06NBBTzzxhCSpVq1a2rBhg2bMmKHo6Ogst6Vtr+3111/Xxo0btWTJEkVERGjdunUaOHCgQkNDM4z4uZIr25YepXzkscce05IlS7RmzRqVKVPGKg8JCZGkDNn0iRMnMvwSCkdbtmzRiRMnVLduXXl4eMjDw0OxsbF6/fXX5eHhYbUfbZtzoaGhqlKlikNZ5cqVrZmBOG+v31NPPaWnn35a3bt3V/Xq1dW7d2898cQTVq8obZs7stOOISEhSkxM1KlTp7Ksg6wlJSWpa9euOnjwoFasWGH1Jkm07fVav369Tpw4oZtuusn6XDt8+LCGDRumsmXLSqJtr1fJkiXl4eFxzc822jbnLl26pGeeeUaTJk1S+/btVaNGDQ0aNEjdunXTK6+8Iil/ti2JUj5gjNGgQYO0aNEirV69WpGRkQ7rIyMjFRISohUrVlhliYmJio2NVePGjW90uAVK8+bNtWvXLm3fvt261atXT7169dL27dtVrlw52vY63XrrrRmmsd+3b58iIiIkcd7+GxcvXpSbm+Pbs7u7u/VrJ22bO7LTjnXr1pWnp6dDnbi4OP3000+09TWkJ0n79+/XypUrVaJECYf1tO316d27t3bu3OnwuRYWFqannnpK33zzjSTa9np5eXmpfv36V/1so22vT1JSkpKSkq762ZYv29YlU0jAwSOPPGICAwPN2rVrTVxcnHW7ePGiVWfChAkmMDDQLFq0yOzatcv06NHDhIaGmrNnz7ow8oLpylnvjKFtr9ePP/5oPDw8zIsvvmj2799vPvzwQ+Pr62s++OADqw5te3369u1rSpcubZYuXWoOHjxoFi1aZEqWLGmGDx9u1aFts+fcuXNm27ZtZtu2bUaSmTRpktm2bZs181p22vHhhx82ZcqUMStXrjRbt241d9xxh6lZs6ZJTk521cPKF67WtklJSebuu+82ZcqUMdu3b3f4bEtISLD2Qdtm7lrnrTPnWe+MoW2zcq22XbRokfH09DRvv/222b9/v3njjTeMu7u7Wb9+vbUP2jZz12rb6OhoU7VqVbNmzRrz22+/mVmzZhlvb28zbdo0ax/5rW1JlPIBSZneZs2aZdVJTU01o0ePNiEhIcZut5vbb7/d7Nq1y3VBF2DOiRJte/2++OILU61aNWO3202lSpXM22+/7bCetr0+Z8+eNYMHDzY33XST8fb2NuXKlTOjRo1y+IJJ22bPmjVrMn1/7du3rzEme+146dIlM2jQIFO8eHHj4+Nj2rVrZ44cOeKCR5O/XK1tDx48mOVn25o1a6x90LaZu9Z56yyzRIm2zVx22va9994zFSpUMN7e3qZmzZpm8eLFDvugbTN3rbaNi4sz/fr1M2FhYcbb29tUrFjRvPrqqyY1NdXaR35rW5sxxuRVbxUAAAAAFERcowQAAAAATkiUAAAAAMAJiRIAAAAAOCFRAgAAAAAnJEoAAAAA4IRECQAAAACckCgBAAAAgBMSJQAAAABwQqIEAPlATEyMatWqlaNtypYtqylTpuRJPLmpadOmGjJkyA0/7vW0z6FDh2Sz2bR9+/Zs1e/Xr586duyY49huJJvNpsWLF+fpMRITE1WhQgV99913GdbNnj1ba9euzVC+a9culSlTRhcuXMjT2ADgepEoAUAe2LBhg9zd3dW6desbdswb8YW4MMksyQkPD1dcXJyqVat2Q2K4ngQ5p+Li4tSmTZs8Pcbbb7+tiIgI3Xrrrdnepnr16rrllls0efLkPIwMAK4fiRIA5IGZM2fqscce07fffqsjR464Ohxkk7u7u0JCQuTh4eHqUP61xMRESVJISIjsdnueHuuNN97Qgw8+6FC2Zs0a3XrrrRo8eLA6deqkOnXqaPr06Q517r//fk2fPl0pKSl5Gh8AXA8SJQDIZRcuXNDChQv1yCOPqF27dpo9e3aGOhMmTFBwcLACAgL0wAMP6PLlyw7rMxuu1rFjR/Xr1y/TY5YtW1aS1KlTJ9lsNuu+s3vuuUePPfaYdX/IkCGy2WzavXu3JCk5OVkBAQH65ptvJEnGGE2cOFHlypWTj4+PatasqU8++cRhnz///LPuuusu+fv7Kzg4WL1799Zff/2VRetIy5YtU2BgoObOnStJ+uOPP9StWzcVK1ZMJUqUUIcOHXTo0CGrfnrPzyuvvKLQ0FCVKFFCjz76qJKSkqw6J06cUPv27eXj46PIyEh9+OGHWR5fSuvJmTNnjj7//HPZbDbZbDatXbs206F3u3fvVtu2bVWkSBEFBASoSZMmOnDgQKb73bJli4KCgvTiiy9Kks6cOaP/+7//U1BQkIoUKaI77rhDO3bskJQ2JG3MmDHasWOHFUNm58qVbTBmzBhrXwMGDLCSISntnBk0aJCGDh2qkiVLqmXLlpIy9jT+/vvv6t69u4oXLy4/Pz/Vq1dPP/zwg7X+iy++UN26deXt7a1y5cppzJgxSk5OzrItt27dql9//VVt27a1yk6fPq0OHTqoatWqevLJJ/Xyyy9r5MiRGbZt1aqV/v77b8XGxma5fwBwFRIlAMhlCxYsUMWKFVWxYkXdd999mjVrlowx1vqFCxdq9OjRevHFF7V582aFhoZq2rRp/+qYmzZtkiTNmjVLcXFx1n1nTZs2dbheJDY2ViVLlrS+qG7atEmXL1+2hlA9++yzmjVrlqZPn67du3friSee0H333WfVj4uLU3R0tGrVqqXNmzdr2bJlOn78uLp27Zrp8efPn6+uXbtq7ty56tOnjy5evKhmzZrJ399f69at07fffit/f3+1bt3aIQlYs2aNDhw4oDVr1mjOnDmaPXu2Q1LRr18/HTp0SKtXr9Ynn3yiadOm6cSJE1m215NPPqmuXbuqdevWiouLU1xcnBo3bpyh3h9//KHbb79d3t7eWr16tbZs2aL+/ftnmjisXbtWzZs315gxYzRq1CgZY9S2bVvFx8frq6++0pYtW1SnTh01b95cJ0+eVLdu3TRs2DBVrVrViqFbt25Zxrxq1Srt2bNHa9as0bx58/TZZ59pzJgxDnXmzJkjDw8Pfffdd3rrrbcy7OP8+fOKjo7WsWPHtGTJEu3YsUPDhw9XamqqJOmbb77Rfffdp8cff1w///yz3nrrLc2ePdtK/DKzbt063XzzzSpSpIhV9uuvv+rcuXMaPXq0wsPDVaFCBXXp0kWPPPKIw7ZeXl6qWbOm1q9fn+X+AcBlDAAgVzVu3NhMmTLFGGNMUlKSKVmypFmxYoW1vlGjRubhhx922KZBgwamZs2a1v3o6GgzePBghzodOnQwffv2te5HRESYyZMnW/clmc8+++yqse3cudPYbDbz559/mpMnTxpPT08zduxY06VLF2OMMePGjTMNGjQwxhhz/vx54+3tbTZs2OCwjwceeMD06NHDGGPMc889Z+68806H9UePHjWSzN69ex0ey5tvvmkCAwPN6tWrrbrvvfeeqVixoklNTbXKEhISjI+Pj/nmm2+MMcb07dvXREREmOTkZKtOly5dTLdu3Ywxxuzdu9dIMhs3brTW79mzx0hyaB9nffv2NR06dHAoO3jwoJFktm3bZowxZuTIkSYyMtIkJiZedR+LFy82AQEB5qOPPrLWrVq1yhQpUsRcvnzZYZvy5cubt956yxhjzOjRox2e96vFWrx4cXPhwgWrbPr06cbf39+kpKQYY9LauVatWhm2vfK8eOutt0xAQID5+++/Mz1OkyZNzLhx4xzK3n//fRMaGpplbIMHDzZ33HGHQ9nZs2dNyZIlzX333WeeeeYZs2bNmiy379Spk+nXr1+W6wHAVehRAoBctHfvXv3444/q3r27JMnDw0PdunXTzJkzrTp79uxRo0aNHLZzvp9XqlWrphIlSig2Nlbr169XzZo1dffdd1s9RGvXrlV0dLSktCF1ly9fVsuWLeXv72/d5s6daw0927Jli9asWeOwvlKlSpLkMDzt008/1ZAhQ7R8+XI1a9bMKt+yZYt+/fVXBQQEWNsXL15cly9fdti+atWqcnd3t+6HhoZaPUZ79uyRh4eH6tWrZ62vVKmSihYt+q/ba/v27WrSpIk8PT2zrPPDDz/onnvu0Zw5c9SjRw+Hx3b+/HmVKFHCoX0OHjyY5dC9q6lZs6Z8fX2t+40aNdL58+d19OhRq+zKNsjq8dSuXVvFixfPdP2WLVv0/PPPO8T70EMPKS4uThcvXsx0m0uXLsnb29uhLCAgQKtXr9bFixf15ptvqn379rr77ru1bdu2DNv7+PhkuW8AcKWCf7UqAOQj7733npKTk1W6dGmrzBgjT09PnTp1SsWKFcvWftzc3ByG60lyuCbnetlsNt1+++1au3atvLy81LRpU1WrVk0pKSnatWuXNmzYYF0blT4c68svv3R4PJKsyQFSU1PVvn17vfTSSxmOFRoaai3XqlVLW7du1axZs1S/fn3ZbDZr+7p162Z6TVGpUqWsZedExWazWfGlt1P6PnOTj4/PNeuUL19eJUqU0MyZM9W2bVt5eXlJSntsoaGhmU6NnRtJXLorH7efn99V617r8aSmpmrMmDHq3LlzhnXOyVC6kiVLateuXRnKq1evrk8//VSzZ8/WxYsX9f3336tZs2bav3+/w3N78uRJlS9f/qpxAYAr0KMEALkkOTlZc+fO1auvvqrt27dbtx07digiIsJKBipXrqyNGzc6bOt8v1SpUoqLi7Pup6Sk6Keffrrq8T09PbM1e1j6dUpr165V06ZNZbPZ1KRJE73yyiu6dOmSdX1SlSpVZLfbdeTIEVWoUMHhFh4eLkmqU6eOdu/erbJly2aoc+WX9vLly2vNmjX6/PPPHSaTqFOnjvbv36+goKAM2wcGBl7zsUhp7ZmcnKzNmzdbZXv37tXp06evup2Xl9c126tGjRpav379VZPUkiVLavXq1Tpw4IC6detm1a1Tp47i4+Pl4eGR4bGVLFky2zGk27Fjhy5dumTd37hxo/z9/VWmTJlsbZ/+eLZv366TJ09mur5OnTrau3dvhngrVKggN7fMvzLUrl1bv/zyS4bE/kpVqlTRtGnTdObMGe3cudNh3U8//aTatWtn+zEAwI1CogQAuWTp0qU6deqUHnjgAVWrVs3hdu+99+q9996TJA0ePFgzZ87UzJkztW/fPo0ePdqadS7dHXfcoS+//FJffvmlfvnlFw0cOPCaX/zLli2rVatWKT4+XqdOncqyXtOmTbV7927t2rVLTZo0sco+/PBD1alTx7ooPyAgQE8++aSeeOIJzZkzRwcOHNC2bdv05ptvas6cOZKkRx99VCdPnlSPHj30448/6rffftPy5cvVv3//DAnAzTffrDVr1ljD8CSpV69eKlmypDp06KD169fr4MGDio2N1eDBg/X7779nq90rVqyo1q1b66GHHtIPP/ygLVu26MEHH7xm70nZsmW1c+dO7d27V3/99VemydCgQYN09uxZde/eXZs3b9b+/fv1/vvva+/evQ71goKCtHr1av3yyy/q0aOHkpOT1aJFCzVq1EgdO3bUN998o0OHDmnDhg169tlnraSubNmyOnjwoLZv366//vpLCQkJWcabmJioBx54QD///LO+/vprjR49WoMGDcoygclMjx49FBISoo4dO+q7777Tb7/9pk8//VTff/+9JOl///uf5s6dq5iYGO3evVt79uzRggUL9Oyzz2a5z2bNmunChQsO5/DWrVsVExOjvXv3Kjk5WadPn9bLL78sb29vValSxap36NAh/fHHH2rRokW2HwMA3CgkSgCQS9577z21aNEi056Qe+65R9u3b9fWrVvVrVs3/e9//9OIESNUt25dHT58OMNsYP3791ffvn3Vp08fRUdHKzIy0uHansy8+uqrWrFihcLDw6/6C321atVUsmRJ1axZ00qKoqOjlZKSYl2flO6FF17Q//73P40fP16VK1dWq1at9MUXXygyMlKSFBYWpu+++04pKSlq1aqVqlWrpsGDByswMDDTL/AVK1bU6tWrNW/ePA0bNky+vr5at26dbrrpJnXu3FmVK1dW//79denSJYdZ1K5l1qxZCg8PV3R0tDp37mxNyX01Dz30kCpWrKh69eqpVKlS+u677zLUKVGihFavXm3NFle3bl298847mV6zFBISotWrV2vXrl3q1auXUlNT9dVXX+n2229X//79dfPNN6t79+46dOiQgoODJaWdF61bt1azZs1UqlQpzZs3L8t4mzdvrqioKN1+++3q2rWr2rdvr5iYmGy3kZTWg7V8+XIFBQXprrvuUvXq1TVhwgTr+q9WrVpp6dKlWrFiherXr6+GDRtq0qRJioiIyHKfJUqUUOfOnR2GT4aGhuro0aNq3bq1Bg4cqB49emjp0qX69NNPHYZkzps3T3feeedV9w8ArmIzV+srBwAALtevXz+dPn3a4f+Q8pNdu3apRYsW1sQcV5o9e7bKli2rpk2bOpQnJCQoKipK8+bNs4Z7AkB+Qo8SAAD4V6pXr66JEyc6/FHwtRw+fFijRo0iSQKQb9GjBABAPpffe5QAoDAiUQIAAAAAJwy9AwAAAAAnJEoAAAAA4IRECQAAAACckCgBAAAAgBMSJQAAAABwQqIEAAAAAE5IlAAAAADACYkSAAAAADj5f7wi10zgQ0nJAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_compare('AdultWeekend', 'Adult weekend ticket price ($)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_compare('AdultWeekend', 'Adult weekend ticket price ($) - Montana only', state='Montana')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.8.2 Vertical drop<a id='5.8.2_Vertical_drop'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_compare('vertical_drop', 'Vertical drop (feet)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Big Mountain is doing well for vertical drop, but there are still quite a few resorts with a greater drop."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.8.3 Snow making area<a id='5.8.3_Snow_making_area'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_compare('Snow Making_ac', 'Area covered by snow makers (acres)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Big Mountain is very high up the league table of snow making area."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.8.4 Total number of chairs<a id='5.8.4_Total_number_of_chairs'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_compare('total_chairs', 'Total number of chairs')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Big Mountain has amongst the highest number of total chairs, resorts with more appear to be outliers."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.8.5 Fast quads<a id='5.8.5_Fast_quads'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_compare('fastQuads', 'Number of fast quads')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Most resorts have no fast quads. Big Mountain has 3, which puts it high up that league table. There are some values  much higher, but they are rare."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.8.6 Runs<a id='5.8.6_Runs'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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fS3l+V6LidiN4+eWX1axZMwUGBsrPz0/+/v5auXKlduzYccGx9ejRQ76+vs7lJk2aSJL27t17wcfs3r27fHz+/nioX7++81xnyi/ft2+fS3nDhg3VtGlTl7IBAwYoLS1NGzdulCR9/PHHatSoka644gplZ2c7p65duxY6EmLHjh1VqVKl88ae383p7O51V199terXr3/RXT7+8Y9/XNT+knTjjTe6LJ9dZ3Xq1FGlSpX06KOP6uWXX9b27duLddyvv/5aGRkZuu2221zKW7durdjY2PPuf/XVV2vu3LmaMmWK1q9fX6BbUnHceOONllqQzo61f//+8vPzO2d3tZKQP2jM2e+Tm2++WSEhIQXeJ1dccYWzJUGSAgMDVbdu3Qu+z1atWqUGDRro6quvdikfMmSIjDEFBrWxel3Pvl8yMjK0cuVK9enTR8HBwS73XPfu3ZWRkeHs3nb11Vfr+++/17Bhw/TZZ58pLS3N5dgnT57UN998o379+qlChQrOcl9fXw0cOFC//vprge6pVu6bK664QgEBAbr33ns1b948/fLLL8XetygX+zl59oij5/pMPPuYq1at0vXXX6/w8HD5+vrK399fTzzxhA4fPlygO3STJk1Ut27dQmNISUlRq1atlJaWpvXr17t8xn722WfKzs7WoEGDXOo2MDBQ7dq1K9bIsoWx8lnUoUMHhYaGOpcjIyNVrVo1l+uRnZ2txMRENWjQQAEBAfLz81NAQIB27dpV6N/Bs98369at05EjRzR48GCX15mbm6sbbrhBycnJBbqDA+5CEgUUw+HDh+VwOFy6l0lStWrV5Ofn5+zudC7Tp0/X/fffr5YtW+p///uf1q9fr+TkZN1www1KT0+/4NgiIiJclvO7IF3MMStXruyyHBAQcM7yjIwMl3KHw1HgmPll+dfqjz/+0JYtW+Tv7+8yhYaGyhhTYOjr4o5oln/8wraPjo4uVl0VJTg42Nk982Kcr87Cw8O1Zs0aXXHFFXrsscfUsGFDRUdHa9KkSedMbPJf27mu/7m8++67Gjx4sF577TW1atVKlStX1qBBg5Samlrs12Z15Lmz4/Lz81NERMRF1VNxHD58WH5+fqpatapLuc1mk8PhKHD+s+tMyqu3C73PDh8+XOR7NH/9maxe17O3P3z4sLKzs/XCCy8UuOe6d+8uSc57bvz48XrmmWe0fv16devWTREREerUqZM2bNggSTp69KiMMW6Lv3bt2lqxYoWqVaum4cOHq3bt2qpdu3aBZ7KsuNjPSSufiWd+Hn777bfq0qWLJOnVV1/VV199peTkZE2YMKHQ85/rOn377bf66aefdMsttxToevzHH39Iklq0aFGgft99990L/ikBK59FxblHHn74YU2cOFG9e/fWRx99pG+++UbJyclq2rRpoXVx9vXIf539+vUr8DqffvppGWOcXecBd+OZKKAYIiIi9M0338gY45JIHTx4UNnZ2apSpcp5j/HWW2+pffv2mjVrlkv5+Z5xKQmBgYE6duxYgfKS/I2eMxX2T3d+Wf4f2ipVqigoKKjIh/PPvqZnJ7BFyT/+gQMHCvyj8fvvvxerropSVAyBgYEuA4zk+/PPPy/4fI0bN9aCBQtkjNGWLVs0d+5cPfnkkwoKCtK4ceMK3Sf/tRd1/c/3O0lVqlTRjBkzNGPGDO3bt08ffvihxo0bp4MHD2rp0qXFiru49XRmXJdddplzOTs7W4cPH3b5h8xutxd6fS8m0YqIiFB2drYOHTrkkkgZY5SamqoWLVpc8LGLe/4DBw4UKP/9998lXfj7v6jtK1Wq5GwpGj58eKH7xMXFScpLZB9++GE9/PDD+uuvv7RixQo99thj6tq1q/bv369KlSrJx8fHrfG3bdtWbdu2VU5OjjZs2KAXXnhBo0aNUmRkpP75z39aOpYnLViwQP7+/vr4449dRiQs6rcEz3WdbrnlFjkcDk2YMEG5ubnOgXGkv6/3Bx98UKxWZysu5LOoKPnPMyUmJrqU//nnn4UOc3/29ch/nS+88EKRI35GRkZaigm4ULREAcXQqVMnnThxosAfvvnz5zvX5yvq22mbzVZgSOItW7YU+I0qd6hZs6Z++uknl39EDx8+rHXr1rnlfNu2bdP333/vUvb2228rNDTU+cB/z549tXv3bkVERKh58+YFpgv9YdSOHTtKyvtjfabk5GTt2LHDLQ8d16xZU1u2bHEp++mnnwp0aboQNptNTZs21XPPPaeKFSs6u0MW5pprrlFgYKD++9//upSvW7fOcrezGjVqaMSIEercubPLOS+m9aUwZ8f63nvvKTs722WglcKu76pVq3TixAmXMiutC/nvg7PfJ//73/908uRJtz+c3qlTJ23fvr1Afc6fP182m00dOnQo0fMFBwerQ4cO2rRpk5o0aVLoPVdYS0LFihXVr18/DR8+XEeOHNGePXsUEhKili1bauHChS7XOjc3V2+99ZaqV69eZJe0MxWnvnx9fdWyZUvniHLnev97o/whuc/sSpienq4333zzgo73+OOPa8aMGXriiSc0fvx4Z3nXrl3l5+en3bt3F1q3zZs3d257ob0VrHwWnesYZ/8dXLJkiX777bdi7d+mTRtVrFhR27dvL/J15rcSAu5GSxRQDIMGDdKLL76owYMHa8+ePWrcuLG+/PJLJSYmqnv37rr++uud2zZu3Fiff/65PvroI0VFRSk0NFT16tVTz5499a9//UuTJk1Su3bttHPnTj355JOKi4u74FHeimvgwIF65ZVXdPvtt+uee+7R4cOHNXXq1BLpmlaY6Oho3XjjjUpISFBUVJTeeustLV++XE8//bRzZK1Ro0bpf//7n6677jo99NBDatKkiXJzc7Vv3z4tW7ZMo0ePVsuWLS2fu169err33nv1wgsvyMfHR926dXOOzhcTE6OHHnqopF+uBg4cqNtvv13Dhg3TP/7xD+3du1dTp04t0FWsuD7++GO99NJL6t27t2rVqiVjjBYuXKi//vpLnTt3LnK/SpUqacyYMZoyZYruvvtu3Xzzzdq/f79zlMRzOXbsmDp06KABAwbo8ssvV2hoqJKTk7V06VL17dvXuV3jxo21cOFCzZo1S1dddZV8fHxc/kGzauHChfLz81Pnzp2do/M1bdpU/fv3d24zcOBATZw4UU888YTatWun7du3a+bMmQVGVWvUqJEkafbs2QoNDVVgYKDi4uIKTQ46d+6srl276tFHH1VaWpratGnjHJ3vyiuv1MCBAy/4NRXHQw89pPnz56tHjx568sknFRsbqyVLluill17S/fffX6wkxKrnn39e1157rdq2bav7779fNWvW1PHjx/Xzzz/ro48+cj6H1atXLzVq1EjNmzdX1apVtXfvXs2YMUOxsbHOUQGTkpLUuXNndejQQWPGjFFAQIBeeukl/fDDD3rnnXeK1fLUuHFjZ1yDBw+Wv7+/6tWrp//+979atWqVevTooRo1aigjI8PZYn3mZ21p0KNHD02fPl0DBgzQvffeq8OHD+uZZ565qB/2HjlypCpUqKB7771XJ06c0H/+8x/VrFlTTz75pCZMmKBffvlFN9xwgypVqqQ//vhD3377rUJCQpzDpedf96efflrdunWTr6+vmjRpUmjycaGfRUXp2bOn5s6dq8svv1xNmjTRd999p2nTpp13ZNR8FSpU0AsvvKDBgwfryJEj6tevn6pVq6ZDhw7p+++/16FDhwr09gDcxlMjWgDe7OzR+Ywx5vDhw+a+++4zUVFRxs/Pz8TGxprx48ebjIwMl+02b95s2rRpY4KDg11GRMvMzDRjxowxl112mQkMDDTNmjUzixcvLjBqmDHWRuebNm1agXWF7T9v3jxTv359ExgYaBo0aGDefffdIkfnO/uY+SMlvf/++y7l+SNXnTnCXv5IZB988IFp2LChCQgIMDVr1iwwipUxxpw4ccI8/vjjpl69eiYgIMCEh4ebxo0bm4ceesikpqa6vJ7hw4ef83qcKScnxzz99NOmbt26xt/f31SpUsXcfvvtZv/+/S7bWR2d7+z3RL7c3FwzdepUU6tWLRMYGGiaN29uVq1aVeTofGdfx7NHr/vxxx/NrbfeamrXrm2CgoJMeHi4ufrqq83cuXPPG2dubq5JSkoyMTExJiAgwDRp0sR89NFHRY5aln/OjIwMc99995kmTZqYsLAwExQUZOrVq2cmTZrkHFHRGGOOHDli+vXrZypWrGhsNptzlK9zvR/PNTrfd999Z3r16mUqVKhgQkNDza233mr++OMPl/0zMzPN2LFjTUxMjAkKCjLt2rUzmzdvLnRUxBkzZpi4uDjj6+vrcs7C7rP09HTz6KOPmtjYWOPv72+ioqLM/fffb44ePeqyXWGj6xlT9KiXZytq/71795oBAwaYiIgI4+/vb+rVq2emTZvmMjLmua5rUc51v6SkpJg777zTXHbZZcbf399UrVrVtG7d2kyZMsW5zbPPPmtat25tqlSpYgICAkyNGjXMXXfdZfbs2eNyrLVr15qOHTuakJAQExQUZK655hrz0UcfuWxT2GfEmcaPH2+io6ONj4+Pc2TFr7/+2vTp08fExsYau91uIiIiTLt27cyHH3543tde1Pu8uJ+TZysq/qI+Owr7nHjjjTdMvXr1jN1uN7Vq1TJJSUnm9ddfLzAyYVHvk/xYz67Td955x/j5+Zk77rjD+Z5ZvHix6dChgwkLCzN2u93Exsaafv36mRUrVjj3y8zMNHfffbepWrWq8x4+e4TEfMX9LCrqPXf2PXr06FFz1113mWrVqpng4GBz7bXXmrVr1xb7szLfmjVrTI8ePUzlypWNv7+/ueyyy0yPHj2K3B5wB5sxZ/3aGwAAAACgSDwTBQAAAAAWkEQBAAAAgAUkUQAAAABgAUkUAAAAAFhAEgUAAAAAFpBEAQAAAIAFZf7HdnNzc/X7778rNDS0WD/+BwAAAKBsMsbo+PHjio6Olo/Phbcnlfkk6vfff1dMTIynwwAAAADgJfbv36/q1atf8P5lPokKDQ2VlHehwsLCPBwNALfIzZX++CNvPjJSuohvlgAAQNmVlpammJgYZ45wocp8EpXfhS8sLIwkCiir0tOl227Lm1+7VgoK8mw8AADAq13sYz58XQsAAAAAFpBEAQAAAIAFJFEAAAAAYEGZfyYKAAAApV9OTo5Onz7t6TDg5Xx9feXn5+f2nzYiiQIAAIBXO3HihH799VcZYzwdCkqB4OBgRUVFKSAgwG3nIIkCAACA18rJydGvv/6q4OBgVa1a1e0tDCi9jDHKysrSoUOHlJKSovj4+Iv6Qd1zIYkCUPr5+ko33/z3PACgzDh9+rSMMapataqC+AkLnEdQUJD8/f21d+9eZWVlKTAw0C3nIYkCUPoFBEiPPurpKAAAbkQLFIrLXa1PLudw+xkAAAAAoAyhJQpA6WeM9NdfefMVK0p8WwkAANyIligApV9GhtS5c96UkeHpaAAAKJY9e/bIZrNp8+bNng7Fq9SsWVMzZszwdBjnRBIFAAAAlLAhQ4bIZrM5p4iICN1www3asmWLc5uYmBgdOHBAjRo1uqhz1axZUzabTQsWLCiwrmHDhrLZbJo7d+5FneNC2Gw2LV682PJ+ycnJuvfee0s+oBJEEgUAAAC4wQ033KADBw7owIEDWrlypfz8/NSzZ0/nel9fXzkcDvn5XfwTNjExMZozZ45L2fr165WamqqQkJCLPv6lVLVqVQUHB3s6jHMiiQIAAEDpk55e9JSVVfxtMzOLt+0FsNvtcjgccjgcuuKKK/Too49q//79OnTokKTCu/N9+OGHio+PV1BQkDp06KB58+bJZrPpr/xnf4tw2223ac2aNdq/f7+z7I033tBtt91WIEnbt2+fbrrpJlWoUEFhYWHq37+//vjjD+f6IUOGqHfv3i77jBo1Su3bt3cut2/fXg8++KDGjh2rypUry+FwKCEhwbm+Zs2akqQ+ffrIZrM5l3fv3q2bbrpJkZGRqlChglq0aKEVK1a4nOvs7nw2m02vvfaa+vTpo+DgYMXHx+vDDz885/VwN5IoAAAAlD5t2xY9PfKI67adOxe97QMPuG7bq1fh212kEydO6L///a/q1KmjiIiIQrfZs2eP+vXrp969e2vz5s0aOnSoJkyYUKzjR0ZGqmvXrpo3b54k6dSpU3r33Xd15513umxnjFHv3r115MgRrVmzRsuXL9fu3bt1yy23WH5N8+bNU0hIiL755htNnTpVTz75pJYvXy4pr0ueJM2ZM0cHDhxwLp84cULdu3fXihUrtGnTJnXt2lW9evXSvn37znmuyZMnq3///tqyZYu6d++u2267TUeOHLEcc0lhdL4ypua4JW479p6nerjt2AAAAGXNxx9/rAoVKkiSTp48qaioKH388cdF/o7Ryy+/rHr16mnatGmSpHr16umHH37Qv//972Kd784779To0aM1YcIEffDBB6pdu7auuOIKl21WrFihLVu2KCUlRTExMZKkN998Uw0bNlRycrJatGhR7NfXpEkTTZo0SZIUHx+vmTNnauXKlercubOqVq0qSapYsaIcDodzn6ZNm6pp06bO5SlTpmjRokX68MMPNWLEiCLPNWTIEN16662SpMTERL3wwgv69ttvdcMNNxQ73pJEEgUAAIDSZ+3aotf5+rou///WkUKdndB89NGFx3SWDh06aNasWZKkI0eO6KWXXlK3bt307bffKjY2tsD2O3fuLJDEXH311cU+X48ePTR06FB98cUXeuONNwq0QknSjh07FBMT40ygJKlBgwaqWLGiduzYYTmJOlNUVJQOHjx4zn1OnjypyZMn6+OPP9bvv/+u7Oxspaenn7cl6sxzhYSEKDQ09LzncieSKACln6+vlP+g7tl/OAEAZVNQkOe3PY+QkBDVqVPHuXzVVVcpPDxcr776qqZMmVJge2OMbGf91qExptjn8/Pz08CBAzVp0iR98803WrRoUbHOcXa5j49PgfOePn26wD7+/v4uyzabTbm5ueeM8ZFHHtFnn32mZ555RnXq1FFQUJD69eunrLOfYyuBc7kTSRSA0i8gQDrjYVYAALyRzWaTj4+P0osYqOLyyy/XJ5984lK2YcMGS+e488479cwzz+iWW25RpUqVCqxv0KCB9u3bp/379ztbo7Zv365jx46pfv36kvJGx/vhhx9c9tu8eXOBROZ8/P39lZOT41K2du1aDRkyRH369JGU94zUnj17LB3XGzCwBAAAAOAGmZmZSk1NVWpqqnbs2KEHHnhAJ06cUK9evQrdfujQofrxxx/16KOP6qefftJ7773n/H2nwlqPClO/fn39+eefBYY7z3f99derSZMmuu2227Rx40Z9++23GjRokNq1a6fmzZtLkjp27KgNGzZo/vz52rVrlyZNmlQgqSqOmjVrauXKlUpNTdXRo0clSXXq1NHChQu1efNmff/99xowYIBHW5QuFEkUgNLPmL+HoLXQ7QEAAHdaunSpoqKiFBUVpZYtWyo5OVnvv/++y1DhZ4qLi9MHH3yghQsXqkmTJpo1a5ZzdD673V7s80ZERCioiG6J+T+AW6lSJV133XW6/vrrVatWLb377rvObbp27aqJEydq7NixatGihY4fP65BgwYV/4X/f88++6yWL1+umJgYXXnllZKk5557TpUqVVLr1q3Vq1cvde3aVc2aNbN8bE+zGSsdLUuhtLQ0hYeH69ixYwoLC/N0OG7H6Hwol9LT/x5+du3aEu3PDgDwrIyMDKWkpCguLk6BgYGeDueS+/e//62XX37Z5fefcG7nes+UVG7AM1EAAACAl3jppZfUokULRURE6KuvvtK0adPOOfQ3PIMkCgAAAPASu3bt0pQpU3TkyBHVqFFDo0eP1vjx4z0dFs5CEgUAAAB4ieeee07PPfecp8PAeTCwBAAAAABYQBIFAAAAr1fGx0JDCboU7xWSKAAAAHgtX19fSVJWVpaHI0FpcerUKUmy/OPAVvBMFIDSz9dX6tTp73kAQJnh5+en4OBgHTp0SP7+/vLxoQ0AhTPG6NSpUzp48KAqVqzoTMDdgSQKQOkXECA9/bSnowAAuIHNZlNUVJRSUlK0d+9eT4eDUqBixYpyOBxuPQdJFAAAALxaQECA4uPj6dKH8/L393drC1Q+jyZRNWvWLPQbhWHDhunFF1+UMUaTJ0/W7NmzdfToUbVs2VIvvviiGjZs6IFoAQAA4Ck+Pj4KDAz0dBiAJA8PLJGcnKwDBw44p+XLl0uSbr75ZknS1KlTNX36dM2cOVPJyclyOBzq3Lmzjh8/7smwAXib9HSpefO8KT3d09EAAIAyzqNJVNWqVeVwOJzTxx9/rNq1a6tdu3YyxmjGjBmaMGGC+vbtq0aNGmnevHk6deqU3n77bU+GDQAAAKAc85rhTbKysvTWW2/pzjvvlM1mU0pKilJTU9WlSxfnNna7Xe3atdO6deuKPE5mZqbS0tJcJgAAAAAoKV6TRC1evFh//fWXhgwZIklKTU2VJEVGRrpsFxkZ6VxXmKSkJIWHhzunmJgYt8UMAAAAoPzxmiTq9ddfV7du3RQdHe1SbrPZXJaNMQXKzjR+/HgdO3bMOe3fv98t8QIAAAAon7xiiPO9e/dqxYoVWrhwobMsf2z31NRURUVFOcsPHjxYoHXqTHa7XXa73X3BAgAAACjXvKIlas6cOapWrZp69OjhLIuLi5PD4XCO2CflPTe1Zs0atW7d2hNhAgAAAIDnW6Jyc3M1Z84cDR48WH5+f4djs9k0atQoJSYmKj4+XvHx8UpMTFRwcLAGDBjgwYgBeB1fX6lNm7/nAQAA3MjjSdSKFSu0b98+3XnnnQXWjR07Vunp6Ro2bJjzx3aXLVum0NBQD0QKwGsFBEjPP+/pKAAAQDlhM8YYTwfhTmlpaQoPD9exY8cUFhbm6XDcrua4JW479p6nepx/IwAAAMBLlVRu4BXPRAEAAABAaUESBaD0S0+Xrr02b0pP93Q0AACgjPP4M1EAUCIyMjwdAQAAKCdoiQIAAAAAC0iiAAAAAMACkigAAAAAsIAkCgAAAAAsIIkCAAAAAAsYnQ9A6efjIzVr9vc8AACAG5FEASj97HZp9mxPRwEAAMoJvrIFAAAAAAtIogAAAADAApIoAKVferp0/fV5U3q6p6MBAABlHM9EASgb/vrL0xEAAIBygpYoAAAAALCAJAoAAAAALCCJAgAAAAALSKIAAAAAwAKSKAAAAACwgNH5AJR+Pj5SgwZ/zwMAALgRSRSA0s9ul+bP93QUAACgnOArWwAAAACwgCQKAAAAACwgiQJQ+mVkSL165U0ZGZ6OBgAAlHE8EwWg9DNGOnDg73kAAAA3oiUKAAAAACwgiQIAAAAAC0iiAAAAAMACkigAAAAAsIAkCgAAAAAsYHQ+AKWfzSbVqvX3PAAAgBuRRAEo/QIDpffe83QUAACgnKA7HwAAAABYQBIFAAAAABaQRAEo/TIypP7986aMDE9HAwAAyjieiQJQ+hkj/fLL3/MAAABuREsUAAAAAFhAEgUAAAAAFpBEAQAAAIAFHk+ifvvtN91+++2KiIhQcHCwrrjiCn333XfO9cYYJSQkKDo6WkFBQWrfvr22bdvmwYgBAAAAlGceTaKOHj2qNm3ayN/fX59++qm2b9+uZ599VhUrVnRuM3XqVE2fPl0zZ85UcnKyHA6HOnfurOPHj3sucAAAAADllkdH53v66acVExOjOXPmOMtq1qzpnDfGaMaMGZowYYL69u0rSZo3b54iIyP19ttva+jQoQWOmZmZqczMTOdyWlqa+14AAO9gs0lRUX/PAwAAuJFHW6I+/PBDNW/eXDfffLOqVaumK6+8Uq+++qpzfUpKilJTU9WlSxdnmd1uV7t27bRu3bpCj5mUlKTw8HDnFBMT4/bXAcDDAgOljz7KmwIDPR0NAAAo4zyaRP3yyy+aNWuW4uPj9dlnn+m+++7Tgw8+qPnz50uSUlNTJUmRkZEu+0VGRjrXnW38+PE6duyYc9q/f797XwQAAACAcsWj3flyc3PVvHlzJSYmSpKuvPJKbdu2TbNmzdKgQYOc29nO6p5jjClQls9ut8tut7svaAAAAADlmkdboqKiotSgQQOXsvr162vfvn2SJIfDIUkFWp0OHjxYoHUKQDmWmSkNGpQ3nfFMJAAAgDt4NIlq06aNdu7c6VL2008/KTY2VpIUFxcnh8Oh5cuXO9dnZWVpzZo1at269SWNFYAXy82Vtm/Pm3JzPR0NAAAo4zzane+hhx5S69atlZiYqP79++vbb7/V7NmzNXv2bEl53fhGjRqlxMRExcfHKz4+XomJiQoODtaAAQM8GToAAACAcsqjSVSLFi20aNEijR8/Xk8++aTi4uI0Y8YM3Xbbbc5txo4dq/T0dA0bNkxHjx5Vy5YttWzZMoWGhnowcgAAAADllc0YYzwdhDulpaUpPDxcx44dU1hYmKfDcbua45a47dh7nurhtmMDFyU9XWrbNm9+7VopKMiz8QAAAK9UUrmBR5+JAgAAAIDShiQKAAAAACzw6DNRAFBiKlb0dAQAAKCcIIkCUPoFBUkrVng6CgAAUE7QnQ8AAAAALCCJAgAAAAALSKIAlH6ZmdK99+ZNmZmejgYAAJRxPBMFoPTLzZU2bvx7HgAAwI1oiQIAAAAAC0iiAAAAAMACkigAAAAAsIAkCgAAAAAsIIkCAAAAAAsYnQ9A2RAY6OkIAABAOUESBaD0CwqSvvzS01EAAIBygu58AAAAAGABSRQAAAAAWEASBaD0y8qSRo7Mm7KyPB0NAAAo43gmCkDpl5MjffXV3/MAAABuREsUAAAAAFhAEgUAAAAAFpBEAQAAAIAFJFEAAAAAYAFJFAAAAABYQBIFAAAAABYwxDmA0i8oSNqwwdNRAACAcoKWKAAAAACwgCQKAAAAACwgiQJQ+mVlSY8+mjdlZXk6GgAAUMaRRAEo/XJypJUr86acHE9HAwAAyjiSKAAAAACwgCQKAAAAACwgiQIAAAAAC0iiAAAAAMACkigAAAAAsIAkCgAAAAAs8PN0AABw0QIDpbVr/54HAABwI5IoAKWfzSYFBXk6CgAAUE54tDtfQkKCbDaby+RwOJzrjTFKSEhQdHS0goKC1L59e23bts2DEQMAAAAo7zz+TFTDhg114MAB57R161bnuqlTp2r69OmaOXOmkpOT5XA41LlzZx0/ftyDEQPwOllZUkJC3pSV5eloAABAGefxJMrPz08Oh8M5Va1aVVJeK9SMGTM0YcIE9e3bV40aNdK8efN06tQpvf322x6OGoBXycmRPv44b8rJ8XQ0AACgjPN4ErVr1y5FR0crLi5O//znP/XLL79IklJSUpSamqouXbo4t7Xb7WrXrp3WrVtX5PEyMzOVlpbmMgEAAABASfFoEtWyZUvNnz9fn332mV599VWlpqaqdevWOnz4sFJTUyVJkZGRLvtERkY61xUmKSlJ4eHhzikmJsatrwEAAABA+eLRJKpbt276xz/+ocaNG+v666/XkiVLJEnz5s1zbmOz2Vz2McYUKDvT+PHjdezYMee0f/9+9wQPAAAAoFzyeHe+M4WEhKhx48batWuXc5S+s1udDh48WKB16kx2u11hYWEuEwAAAACUFK9KojIzM7Vjxw5FRUUpLi5ODodDy5cvd67PysrSmjVr1Lp1aw9GCQAAAKA88+iP7Y4ZM0a9evVSjRo1dPDgQU2ZMkVpaWkaPHiwbDabRo0apcTERMXHxys+Pl6JiYkKDg7WgAEDPBk2AAAAgHLMo0nUr7/+qltvvVV//vmnqlatqmuuuUbr169XbGysJGns2LFKT0/XsGHDdPToUbVs2VLLli1TaGioJ8MG4G0CA6X8VuvAQM/GAgAAyjybMcZ4Ogh3SktLU3h4uI4dO1Yuno+qOW6J246956kebjs2AAAA4G4llRt41TNRAAAAAODtPNqdDwBKRFaW9NxzefMPPSQFBHg2HgAAUKbREgWg9MvJkd5/P2/KyfF0NAAAoIwjiQIAAAAAC0iiAAAAAMACkigAAAAAsIAkCgAAAAAsIIkCAAAAAAtIogAAAADAAn4nCkDpZ7dLH3749zwAAIAbkUQBKP18fKToaE9HAQAAygm68wEAAACABbREASj9Tp+WXnopb37YMMnf37PxAACAMo2WKAClX3a29OabeVN2tqejAQAAZRxJFAAAAABYQBIFAAAAABZYTqJSUlLcEQcAAAAAlAqWk6g6deqoQ4cOeuutt5SRkeGOmAAAAADAa1lOor7//ntdeeWVGj16tBwOh4YOHapvv/3WHbEBAAAAgNexnEQ1atRI06dP12+//aY5c+YoNTVV1157rRo2bKjp06fr0KFD7ogTAAAAALzCBQ8s4efnpz59+ui9997T008/rd27d2vMmDGqXr26Bg0apAMHDpRknABQNLtdeu+9vMlu93Q0AACgjLvgJGrDhg0aNmyYoqKiNH36dI0ZM0a7d+/WqlWr9Ntvv+mmm24qyTgBoGg+PlKtWnmTD4OOAgAA9/KzusP06dM1Z84c7dy5U927d9f8+fPVvXt3+fz/f1zi4uL0yiuv6PLLLy/xYAEAAADA0ywnUbNmzdKdd96pO+64Qw6Ho9BtatSooddff/2igwOAYjl9WpozJ2/+jjskf3/PxgMAAMo0y0nUrl27zrtNQECABg8efEEBAYBl2dnS7Nl58wMHkkQBAAC3svzwwJw5c/T+++8XKH///fc1b968EgkKAAAAALyV5STqqaeeUpUqVQqUV6tWTYmJiSUSFAAAAAB4K8tJ1N69exUXF1egPDY2Vvv27SuRoAAAAADAW1lOoqpVq6YtW7YUKP/+++8VERFRIkEBAAAAgLeynET985//1IMPPqjVq1crJydHOTk5WrVqlUaOHKl//vOf7ogRAAAAALyG5dH5pkyZor1796pTp07y88vbPTc3V4MGDeKZKAAAAABlnuUkKiAgQO+++67+9a9/6fvvv1dQUJAaN26s2NhYd8QHAOdnt0vz5/89DwAA4EaWk6h8devWVd26dUsyFgC4MD4+UoMGno4CAACUE5aTqJycHM2dO1crV67UwYMHlZub67J+1apVJRYcvEvNcUvcevw9T/Vw6/EBAACAkmA5iRo5cqTmzp2rHj16qFGjRrLZbO6ICwCK7/Rp6Z138uZvvVXy9/dsPAAAoEyznEQtWLBA7733nrp37+6OeADAuuxs6T//yZu/+WaSKAAA4FaWhzgPCAhQnTp13BELAAAAAHg9y0nU6NGj9fzzz8sY4454AAAAAMCrWe7O9+WXX2r16tX69NNP1bBhQ/mf1W1m4cKFJRYcAAAAAHgbyy1RFStWVJ8+fdSuXTtVqVJF4eHhLtOFSkpKks1m06hRo5xlxhglJCQoOjpaQUFBat++vbZt23bB5wAAAACAi2W5JWrOnDklHkRycrJmz56tJk2auJRPnTpV06dP19y5c1W3bl1NmTJFnTt31s6dOxUaGlricQAAAADA+VhuiZKk7OxsrVixQq+88oqOHz8uSfr999914sQJy8c6ceKEbrvtNr366quqVKmSs9wYoxkzZmjChAnq27evGjVqpHnz5unUqVN6++23LyRsAAAAALholpOovXv3qnHjxrrppps0fPhwHTp0SFJeq9GYMWMsBzB8+HD16NFD119/vUt5SkqKUlNT1aVLF2eZ3W5Xu3bttG7duiKPl5mZqbS0NJcJQBlnt0uvvJI32e2ejgYAAJRxlpOokSNHqnnz5jp69KiCgoKc5X369NHKlSstHWvBggXauHGjkpKSCqxLTU2VJEVGRrqUR0ZGOtcVJikpyeUZrZiYGEsxASiFfHykq67Km3wuqIEdAACg2C5odL6vvvpKAQEBLuWxsbH67bffin2c/fv3a+TIkVq2bJkCAwOL3M5ms7ksG2MKlJ1p/Pjxevjhh53LaWlpJFIAAAAASozlJCo3N1c5OTkFyn/99VdLgz189913OnjwoK666ipnWU5Ojr744gvNnDlTO3fulJTXIhUVFeXc5uDBgwVap85kt9tlpzsPUL5kZ0v5P6/Qt6/kZ/mjDQAAoNgs93vp3LmzZsyY4Vy22Ww6ceKEJk2apO7duxf7OJ06ddLWrVu1efNm59S8eXPddttt2rx5s2rVqiWHw6Hly5c798nKytKaNWvUunVrq2EDKMtOn5amTs2bTp/2dDQAAKCMs/x17XPPPacOHTqoQYMGysjI0IABA7Rr1y5VqVJF77zzTrGPExoaqkaNGrmUhYSEKCIiwlk+atQoJSYmKj4+XvHx8UpMTFRwcLAGDBhgNWwAAAAAKBGWk6jo6Ght3rxZ77zzjjZu3Kjc3Fzddddduu2221wGmigJY8eOVXp6uoYNG6ajR4+qZcuWWrZsGb8RBQAAAMBjbMYY4+kg3CktLU3h4eE6duyYwsLCPB2O29Uct8TTIVywPU/18HQIKK3S06W2bfPm166VSvgLHQAAUDaUVG5guSVq/vz551w/aNCgCw4GAAAAALyd5SRq5MiRLsunT5/WqVOnFBAQoODgYJIoAAAAAGWa5STq6NGjBcp27dql+++/X4888kiJBFWWlebudgAAAAAuIIkqTHx8vJ566indfvvt+vHHH0vikABQfAEBUv5PL5z1Q+AAAAAlrcR+kdLX11e///57SR0OAIrP11e69lpPRwEAAMoJy0nUhx9+6LJsjNGBAwc0c+ZMtWnTpsQCAwAAAABvZDmJ6t27t8uyzWZT1apV1bFjRz377LMlFRcAFF92tvTpp3nz3bpJfiXWyA4AAFCA5f80cnNz3REHAFy406elyZPz5q+/niQKAAC4lY+nAwAAAACA0sTy17UPP/xwsbedPn261cMDAAAAgFeznERt2rRJGzduVHZ2turVqydJ+umnn+Tr66tmzZo5t7PZbCUXJQAAAAB4CctJVK9evRQaGqp58+apUqVKkvJ+gPeOO+5Q27ZtNXr06BIPEgAAAAC8heVnop599lklJSU5EyhJqlSpkqZMmcLofAAAAADKPMtJVFpamv74448C5QcPHtTx48dLJCgAAAAA8FaWu/P16dNHd9xxh5599lldc801kqT169frkUceUd++fUs8QAA4r4AA6amn/p4HAABwI8tJ1Msvv6wxY8bo9ttv1+nTp/MO4uenu+66S9OmTSvxAAHgvHx9834fCgAA4BKwnEQFBwfrpZde0rRp07R7924ZY1SnTh2FhIS4Iz4AAAAA8CoX/GO7Bw4c0IEDB1S3bl2FhITIGFOScQFA8eXkSCtW5E05OZ6OBgAAlHGWW6IOHz6s/v37a/Xq1bLZbNq1a5dq1aqlu+++WxUrVmSEPgCXXlaWNG5c3vzatVJQkGfjAQAAZZrllqiHHnpI/v7+2rdvn4KDg53lt9xyi5YuXVqiwQEAAACAt7HcErVs2TJ99tlnql69ukt5fHy89u7dW2KBAQAAAIA3stwSdfLkSZcWqHx//vmn7HZ7iQQFAAAAAN7KchJ13XXXaf78+c5lm82m3NxcTZs2TR06dCjR4AAAAADA21juzjdt2jS1b99eGzZsUFZWlsaOHatt27bpyJEj+uqrr9wRIwAAAAB4DcstUQ0aNNCWLVt09dVXq3Pnzjp58qT69u2rTZs2qXbt2u6IEQAAAAC8hqWWqNOnT6tLly565ZVXNHnyZHfFBADW+PtLkyb9PQ8AAOBGlpIof39//fDDD7LZbO6KBwCs8/OTevXydBQAAKCcsNydb9CgQXr99dfdEQsAAAAAeD3LA0tkZWXptdde0/Lly9W8eXOFhIS4rJ8+fXqJBQcAxZKTI339dd58q1aSr69n4wEAAGVasZKoLVu2qFGjRvLx8dEPP/ygZs2aSZJ++uknl+3o5gfAI7KypFGj8ubXrpWCgjwaDgAAKNuKlURdeeWVOnDggKpVq6a9e/cqOTlZERER7o4NAAAAALxOsZ6JqlixolJSUiRJe/bsUW5urluDAgAAAABvVayWqH/84x9q166doqKiZLPZ1Lx5c/kW8czBL7/8UqIBAgAAAIA3KVYSNXv2bPXt21c///yzHnzwQd1zzz0KDQ11d2wAAAAA4HWKPTrfDTfcIEn67rvvNHLkSJIoAAAAAOWS5SHO58yZ4444AAAAAKBUsJxEAYDX8feXxo79ex4AAMCNSKIAlH5+flL//p6OAgAAlBPFGuLcXWbNmqUmTZooLCxMYWFhatWqlT799FPnemOMEhISFB0draCgILVv317btm3zYMQAAAAAyjuPJlHVq1fXU089pQ0bNmjDhg3q2LGjbrrpJmeiNHXqVE2fPl0zZ85UcnKyHA6HOnfurOPHj3sybADeJjdX+u67vInfsQMAAG5mM8YYTwdxpsqVK2vatGm68847FR0drVGjRunRRx+VJGVmZioyMlJPP/20hg4dWqzjpaWlKTw8XMeOHVNYWJg7Qy+WmuOWeDoEr7XnqR6eDgGlVXq61LZt3vzatVJQkGfjAQAAXqmkcgOPtkSdKScnRwsWLNDJkyfVqlUrpaSkKDU1VV26dHFuY7fb1a5dO61bt67I42RmZiotLc1lAgAAAICS4vGBJbZu3apWrVopIyNDFSpU0KJFi9SgQQNnohQZGemyfWRkpPbu3Vvk8ZKSkjR58mS3xgz3cGcrHa1cAAAAKCkeb4mqV6+eNm/erPXr1+v+++/X4MGDtX37dud6m83msr0xpkDZmcaPH69jx445p/3797stdgAAAADlj8dbogICAlSnTh1JUvPmzZWcnKznn3/e+RxUamqqoqKinNsfPHiwQOvUmex2u+x2u3uDBgAAAFBuebwl6mzGGGVmZiouLk4Oh0PLly93rsvKytKaNWvUunVrD0YIAAAAoDzzaEvUY489pm7duikmJkbHjx/XggUL9Pnnn2vp0qWy2WwaNWqUEhMTFR8fr/j4eCUmJio4OFgDBgzwZNgAAAAAyjGPJlF//PGHBg4cqAMHDig8PFxNmjTR0qVL1blzZ0nS2LFjlZ6ermHDhuno0aNq2bKlli1bptDQUE+GDcDb+PlJDz749zwAAIAbed3vRJU0ficKEqPzAQAAoAz+ThQAAAAAlAb0ewFQ+uXmSj/+mDd/+eWSD98PAQAA9yGJAlD6ZWZKgwblza9dKwUFeTYeAABQpvF1LQAAAABYQBIFAAAAABaQRAEAAACABSRRAAAAAGABSRQAAAAAWMDofCgX3P0jx/yYLwAAQPlBEgWg9PPzk+699+95AAAAN+K/DQCln7//30kUAACAm/FMFAAAAABYQEsUgNIvN1fasydvvmZNyYfvhwAAgPuQRAEo/TIzpf798+bXrpWCgjwbDwAAKNP4uhYAAAAALCCJAgAAAAALSKIAAAAAwAKSKAAAAACwgCQKAAAAACwgiQIAAAAACxjiHEDp5+cnDRz49zwAAIAb8d8GgNLP318aOdLTUQAAgHKC7nwAAAAAYAEtUQBKv9xcKTU1b97hkHz4fggAALgPSRSA0i8zU7rxxrz5tWuloCDPxgMAAMo0vq4FAAAAAAtIogAAAADAApIoAAAAALCAJAoAAAAALCCJAgAAAAALSKIAAAAAwAKGOAdQ+vn6Sjff/Pc8AACAG5FEASj9AgKkRx/1dBQAAKCcoDsfAAAAAFhASxSA0s8Y6a+/8uYrVpRsNk9GAwAAyjiSKAClX0aG1Llz3vzatVJQkGfjAQAAZRrd+QAAAADAApIoAAAAALCAJAoAAAAALPBoEpWUlKQWLVooNDRU1apVU+/evbVz506XbYwxSkhIUHR0tIKCgtS+fXtt27bNQxEDAAAAKO88mkStWbNGw4cP1/r167V8+XJlZ2erS5cuOnnypHObqVOnavr06Zo5c6aSk5PlcDjUuXNnHT9+3IORAwAAACivPDo639KlS12W58yZo2rVqum7777TddddJ2OMZsyYoQkTJqhv376SpHnz5ikyMlJvv/22hg4d6omwAQAAAJRjXvVM1LFjxyRJlStXliSlpKQoNTVVXbp0cW5jt9vVrl07rVu3rtBjZGZmKi0tzWUCUMb5+ko9e+ZNvr6ejgYAAJRxXvM7UcYYPfzww7r22mvVqFEjSVJqaqokKTIy0mXbyMhI7d27t9DjJCUlafLkye4NFjhLzXFL3HbsPU/1cNuxy4yAACkhwdNRAACAcsJrWqJGjBihLVu26J133imwzmazuSwbYwqU5Rs/fryOHTvmnPbv3++WeAEAAACUT17REvXAAw/oww8/1BdffKHq1as7yx0Oh6S8FqmoqChn+cGDBwu0TuWz2+2y2+3uDRiAdzFGysjImw8MlIr4kgUAAKAkeLQlyhijESNGaOHChVq1apXi4uJc1sfFxcnhcGj58uXOsqysLK1Zs0atW7e+1OEC8FYZGVLbtnlTfjIFAADgJh5tiRo+fLjefvtt/d///Z9CQ0Odz0CFh4crKChINptNo0aNUmJiouLj4xUfH6/ExEQFBwdrwIABngwdAAAAQDnl0SRq1qxZkqT27du7lM+ZM0dDhgyRJI0dO1bp6ekaNmyYjh49qpYtW2rZsmUKDQ29xNECAAAAgIeTKGPMebex2WxKSEhQAiNvAQAAAPACXjM6HwAAAACUBiRRAAAAAGABSRQAAAAAWOAVvxMFABfF11fq1OnveQAAADciiQJQ+gUESE8/7ekoAABAOUF3PgAAAACwgCQKAAAAACwgiQJQ+qWnS82b503p6Z6OBgAAlHEkUQAAAABgAUkUAAAAAFhAEgUAAAAAFpBEAQAAAIAFJFEAAAAAYAFJFAAAAABY4OfpAADgovn6Sm3a/D0PAADgRiRRAEq/gADp+ec9HQUAACgn6M4HAAAAABaQRAEAAACABSRRAEq/9HTp2mvzpvR0T0cDAADKOJ6JAlA2ZGR4OgIAAFBO0BIFAAAAABaQRAEAAACABSRRAAAAAGABSRQAAAAAWEASBQAAAAAWMDofgNLPx0dq1uzveQAAADciiQJQ+tnt0uzZno4CAACUE3xlCwAAAAAWkEQBAAAAgAUkUQBKv/R06frr86b0dE9HAwAAyjieiQJQNvz1l6cjAAAA5QQtUQAAAABgAUkUAAAAAFhAEgUAAAAAFpBEAQAAAIAFJFEAAAAAYAGj8wEo/Xx8pAYN/p4HAABwI5IoAKWf3S7Nn+/pKAAAQDnh0a9sv/jiC/Xq1UvR0dGy2WxavHixy3pjjBISEhQdHa2goCC1b99e27Zt80ywAAAAACAPJ1EnT55U06ZNNXPmzELXT506VdOnT9fMmTOVnJwsh8Ohzp076/jx45c4UgAAAADI49HufN26dVO3bt0KXWeM0YwZMzRhwgT17dtXkjRv3jxFRkbq7bff1tChQy9lqAC8WUaGdPPNefPvvy8FBno2HgAAUKZ57RPYKSkpSk1NVZcuXZxldrtd7dq107p164rcLzMzU2lpaS4TgDLOGOnAgbzJGE9HAwAAyjivHVgiNTVVkhQZGelSHhkZqb179xa5X1JSkiZPnuzW2IBLqea4JW49/p6nerj1+O6Uf23spzP1/m/HJEk3P/6pMv3tF33s0nxdAACAe3ltS1Q+m83msmyMKVB2pvHjx+vYsWPOaf/+/e4OEQAAAEA54rUtUQ6HQ1Jei1RUVJSz/ODBgwVap85kt9tlt1/8t9AAAAAAUBivbYmKi4uTw+HQ8uXLnWVZWVlas2aNWrdu7cHIAAAAAJRnHm2JOnHihH7++WfnckpKijZv3qzKlSurRo0aGjVqlBITExUfH6/4+HglJiYqODhYAwYM8GDUAAAAAMozjyZRGzZsUIcOHZzLDz/8sCRp8ODBmjt3rsaOHav09HQNGzZMR48eVcuWLbVs2TKFhoZ6KmQA3shm075wh3MeAADAnWzGlO3xgNPS0hQeHq5jx44pLCzM0+G4faQ1wKrSPAqdO++n0nxdAABA4UoqN/DaZ6IAAAAAwBuRRAEAAACABV47xDkAFJc9O0vPfjxdkjS658PK9AvwcEQAAKAsI4kCUPoZoxrHUp3zAAAA7kR3PgAAAACwgCQKAAAAACwgiQIAAAAAC0iiAAAAAMACkigAAAAAsIDR+QCUfjabDoZUcs4DAAC4E0kUgFIv0y9Ad/eb5OkwAABAOUF3PgAAAACwgCQKAAAAACygOx8At6k5bsklOU9A9mklLf2PJGn8DQ8qy8//kpwXAACUTyRRAEo9m8lV/OH9znkAAAB3ojsfAAAAAFhAEgUAAAAAFpBEAQAAAIAFPBMFlHOXavAHAACAsoKWKAAAAACwgJYoAGVCmj3E0yEAAIBygiQKQKmX6W/X7f/8t6fDAAAA5QTd+QAAAADAAlqiAACWuHMwkj1P9XDbsQEAKCkkUQBKvYDs00pY8bIkKeH6+5Tl5+/hiAAAQFlGEgWg1LOZXDX6Y7dzHgAAwJ14JgoAAAAALCCJAgAAAAALSKIAAAAAwAKSKAAAAACwgCQKAAAAACxgdD4AZUKmb4CnQwAAAOUESRSAUi/T366bb5/q6TAAAEA5QRIFAIWoOW6JW4+/56kebj1+acV1BwCUBjwTBQAAAAAW0BIFoNTzzzmtx1a/IUlK7HCnTvv6ezgiAABQlpFEASj1fHJzddVvO5zz8vVwQAAAoEyjOx8AAAAAWEBLFAB4gLsHUEDh3HndGbQCQGnGwD7WlIqWqJdeeklxcXEKDAzUVVddpbVr13o6JAAAAADllNcnUe+++65GjRqlCRMmaNOmTWrbtq26deumffv2eTo0AAAAAOWQ1ydR06dP11133aW7775b9evX14wZMxQTE6NZs2Z5OjQAAAAA5ZBXPxOVlZWl7777TuPGjXMp79Kli9atW1foPpmZmcrMzHQuHzt2TJKUlpbmvkAtyM085ekQgDIn53SmTuTm5s1nnlJubo6HI0J55C1/ZwDgQrj7f1Rv+YzMj8MYc1HH8eok6s8//1ROTo4iIyNdyiMjI5WamlroPklJSZo8eXKB8piYGLfECMA7tMmfeXGgJ8NAORY+w9MRAID38rbPyOPHjys8PPyC9/fqJCqfzWZzWTbGFCjLN378eD388MPO5dzcXB05ckQRERFF7lPS0tLSFBMTo/379yssLOySnBPWUU/ejzoqHain0oF6Kh2op9KBevJ+RdWRMUbHjx9XdHT0RR3fq5OoKlWqyNfXt0Cr08GDBwu0TuWz2+2y2+0uZRUrVnRXiOcUFhbGjVUKUE/ejzoqHain0oF6Kh2op9KBevJ+hdXRxbRA5fPqgSUCAgJ01VVXafny5S7ly5cvV+vWrT0UFQAAAIDyzKtboiTp4Ycf1sCBA9W8eXO1atVKs2fP1r59+3Tfffd5OjQAAAAA5ZDXJ1G33HKLDh8+rCeffFIHDhxQo0aN9Mknnyg2NtbToRXJbrdr0qRJBboVwrtQT96POiodqKfSgXoqHain0oF68n7uriObudjx/QAAAACgHPHqZ6IAAAAAwNuQRAEAAACABSRRAAAAAGABSRQAAAAAWEASVcJeeuklxcXFKTAwUFdddZXWrl3r6ZDKtYSEBNlsNpfJ4XA41xtjlJCQoOjoaAUFBal9+/batm2bByMuH7744gv16tVL0dHRstlsWrx4scv64tRLZmamHnjgAVWpUkUhISG68cYb9euvv17CV1H2na+ehgwZUuD+uuaaa1y2oZ7cKykpSS1atFBoaKiqVaum3r17a+fOnS7bcD95XnHqifvJs2bNmqUmTZo4f5i1VatW+vTTT53ruY+8w/nq6VLeRyRRJejdd9/VqFGjNGHCBG3atElt27ZVt27dtG/fPk+HVq41bNhQBw4ccE5bt251rps6daqmT5+umTNnKjk5WQ6HQ507d9bx48c9GHHZd/LkSTVt2lQzZ84sdH1x6mXUqFFatGiRFixYoC+//FInTpxQz549lZOTc6leRpl3vnqSpBtuuMHl/vrkk09c1lNP7rVmzRoNHz5c69ev1/Lly5Wdna0uXbro5MmTzm24nzyvOPUkcT95UvXq1fXUU09pw4YN2rBhgzp27KibbrrJmShxH3mH89WTdAnvI4MSc/XVV5v77rvPpezyyy8348aN81BEmDRpkmnatGmh63Jzc43D4TBPPfWUsywjI8OEh4ebl19++RJFCElm0aJFzuXi1Mtff/1l/P39zYIFC5zb/Pbbb8bHx8csXbr0ksVenpxdT8YYM3jwYHPTTTcVuQ/1dOkdPHjQSDJr1qwxxnA/eauz68kY7idvVKlSJfPaa69xH3m5/Hoy5tLeR7RElZCsrCx999136tKli0t5ly5dtG7dOg9FBUnatWuXoqOjFRcXp3/+85/65ZdfJEkpKSlKTU11qTO73a527dpRZx5UnHr57rvvdPr0aZdtoqOj1ahRI+ruEvv8889VrVo11a1bV/fcc48OHjzoXEc9XXrHjh2TJFWuXFkS95O3Orue8nE/eYecnBwtWLBAJ0+eVKtWrbiPvNTZ9ZTvUt1Hfhf/EiBJf/75p3JychQZGelSHhkZqdTUVA9FhZYtW2r+/PmqW7eu/vjjD02ZMkWtW7fWtm3bnPVSWJ3t3bvXE+FCKla9pKamKiAgQJUqVSqwDffbpdOtWzfdfPPNio2NVUpKiiZOnKiOHTvqu+++k91up54uMWOMHn74YV177bVq1KiRJO4nb1RYPUncT95g69atatWqlTIyMlShQgUtWrRIDRo0cP5zzX3kHYqqJ+nS3kckUSXMZrO5LBtjCpTh0unWrZtzvnHjxmrVqpVq166tefPmOR80pM6804XUC3V3ad1yyy3O+UaNGql58+aKjY3VkiVL1Ldv3yL3o57cY8SIEdqyZYu+/PLLAuu4n7xHUfXE/eR59erV0+bNm/XXX3/pf//7nwYPHqw1a9Y413MfeYei6qlBgwaX9D6iO18JqVKlinx9fQtksQcPHizwzQU8JyQkRI0bN9auXbuco/RRZ96lOPXicDiUlZWlo0ePFrkNLr2oqCjFxsZq165dkqinS+mBBx7Qhx9+qNWrV6t69erOcu4n71JUPRWG++nSCwgIUJ06ddS8eXMlJSWpadOmev7557mPvExR9VQYd95HJFElJCAgQFdddZWWL1/uUr58+XK1bt3aQ1HhbJmZmdqxY4eioqIUFxcnh8PhUmdZWVlas2YNdeZBxamXq666Sv7+/i7bHDhwQD/88AN150GHDx/W/v37FRUVJYl6uhSMMRoxYoQWLlyoVatWKS4uzmU995N3OF89FYb7yfOMMcrMzOQ+8nL59VQYt95HloahwDktWLDA+Pv7m9dff91s377djBo1yoSEhJg9e/Z4OrRya/To0ebzzz83v/zyi1m/fr3p2bOnCQ0NddbJU089ZcLDw83ChQvN1q1bza233mqioqJMWlqahyMv244fP242bdpkNm3aZCSZ6dOnm02bNpm9e/caY4pXL/fdd5+pXr26WbFihdm4caPp2LGjadq0qcnOzvbUyypzzlVPx48fN6NHjzbr1q0zKSkpZvXq1aZVq1bmsssuo54uofvvv9+Eh4ebzz//3Bw4cMA5nTp1yrkN95Pnna+euJ88b/z48eaLL74wKSkpZsuWLeaxxx4zPj4+ZtmyZcYY7iNvca56utT3EUlUCXvxxRdNbGysCQgIMM2aNXMZvhSX3i233GKioqKMv7+/iY6ONn379jXbtm1zrs/NzTWTJk0yDofD2O12c91115mtW7d6MOLyYfXq1UZSgWnw4MHGmOLVS3p6uhkxYoSpXLmyCQoKMj179jT79u3zwKspu85VT6dOnTJdunQxVatWNf7+/qZGjRpm8ODBBeqAenKvwupHkpkzZ45zG+4nzztfPXE/ed6dd97p/P+tatWqplOnTs4EyhjuI29xrnq61PeRzRhjrLVdAQAAAED5xTNRAAAAAGABSRQAAAAAWEASBQAAAAAWkEQBAAAAgAUkUQAAAABgAUkUAAAAAFhAEgUAAAAAFpBEAQAAAIAFJFEAgItWs2ZNzZgxw9NhFGrIkCHq3bu3p8NwkZCQoMjISNlsNi1evNjT4QAALCKJAoAyxGaznXMaMmTIeffnn3r32rFjhyZPnqxXXnlFBw4cULdu3TwdEgDAIj9PBwAAKDkHDhxwzr/77rt64okntHPnTmdZUFCQJ8Iqc4wxysnJkZ+f9T+ju3fvliTddNNNstlsbj8fAKDk0RIFAGWIw+FwTuHh4bLZbC5lb7/9tmrXrq2AgADVq1dPb775pnPfmjVrSpL69Okjm83mXN69e7duuukmRUZGqkKFCmrRooVWrFhhKa78LnXPPPOMoqKiFBERoeHDh+v06dPObQprBatYsaLmzp0rSdqzZ49sNpvee+89tW3bVkFBQWrRooV++uknJScnq3nz5qpQoYJuuOEGHTp0qEAMkydPVrVq1RQWFqahQ4cqKyvLuc4Yo6lTp6pWrVoKCgpS06ZN9cEHHzjXf/7557LZbPrss8/UvHlz2e12rV27ttDXunXrVnXs2FFBQUGKiIjQvffeqxMnTkjK68bXq1cvSZKPj0+RSVRR5yusa+KoUaPUvn1753L79u314IMPauzYsapcubIcDocSEhJc9klISFCNGjVkt9sVHR2tBx98sNA4AACFI4kCgHJi0aJFGjlypEaPHq0ffvhBQ4cO1R133KHVq1dLkpKTkyVJc+bM0YEDB5zLJ06cUPfu3bVixQpt2rRJXbt2Va9evbRv3z5L51+9erV2796t1atXa968eZo7d64zQbJi0qRJevzxx7Vx40b5+fnp1ltv1dixY/X8889r7dq12r17t5544gmXfVauXKkdO3Zo9erVeuedd7Ro0SJNnjzZuf7xxx/XnDlzNGvWLG3btk0PPfSQbr/9dq1Zs8blOGPHjlVSUpJ27NihJk2aFIjt1KlTuuGGG1SpUiUlJyfr/fff14oVKzRixAhJ0pgxYzRnzhxJea2GZ7YcFuZ85yvKvHnzFBISom+++UZTp07Vk08+qeXLl0uSPvjgAz333HN65ZVXtGvXLi1evFiNGzcu9rEBAJIMAKBMmjNnjgkPD3cut27d2txzzz0u29x8882me/fuzmVJZtGiRec9doMGDcwLL7zgXI6NjTXPPfdckdsPHjzYxMbGmuzsbJdz33LLLec8d3h4uJkzZ44xxpiUlBQjybz22mvO9e+8846RZFauXOksS0pKMvXq1XM5d+XKlc3JkyedZbNmzTIVKlQwOTk55sSJEyYwMNCsW7fO5dx33XWXufXWW40xxqxevdpIMosXLz7HVTFm9uzZplKlSubEiRPOsiVLlhgfHx+TmppqjDFm0aJF5nx/fos63+DBg81NN93kUjZy5EjTrl0753K7du3Mtdde67JNixYtzKOPPmqMMebZZ581devWNVlZWeeMAQBQNFqiAKCc2LFjh9q0aeNS1qZNG+3YseOc+508eVJjx45VgwYNVLFiRVWoUEE//vij5Zaohg0bytfX17kcFRWlgwcPWjqGJJcWmcjISElyaUmJjIwscNymTZsqODjYudyqVSudOHFC+/fv1/bt25WRkaHOnTurQoUKzmn+/PnO55fyNW/e/Jyx7dixQ02bNlVISIizrE2bNsrNzXV5Nq24zne+opzdanXmtb755puVnp6uWrVq6Z577tGiRYuUnZ19QecBgPKKJ1QBoBw5+xkcY8x5Bzd45JFH9Nlnn+mZZ55RnTp1FBQUpH79+rk8U1Qc/v7+BWLJzc11WTbGuGxz5jNThR0nP/azy8487rmcue2SJUt02WWXuay32+0uy2cmR4U51/Us7iAS5zqfj4+P5WuUf+781xkTE6OdO3dq+fLlWrFihYYNG6Zp06ZpzZo1BfYDABSOligAKCfq16+vL7/80qVs3bp1ql+/vnPZ399fOTk5LtvkD2jQp08fNW7cWA6HQ3v27Cnx+KpWreryjNCuXbt06tSpEjn2999/r/T0dOfy+vXrVaFCBVWvXl0NGjSQ3W7Xvn37VKdOHZcpJibG0nkaNGigzZs36+TJk86yr776Sj4+Pqpbt+5Fv46zr5Ekbd682fJxgoKCdOONN+o///mPPv/8c3399dfaunXrRccHAOUFLVEAUE488sgj6t+/v5o1a6ZOnTrpo48+0sKFC11G2qtZs6ZWrlypNm3ayG63q1KlSqpTp44WLlyoXr16yWazaeLEicVu6bGiY8eOmjlzpq655hrl5ubq0UcfLbGWkaysLN111116/PHHtXfvXk2aNEkjRoyQj4+PQkNDNWbMGD300EPKzc3Vtddeq7S0NK1bt04VKlTQ4MGDi32e2267TZMmTdLgwYOVkJCgQ4cO6YEHHtDAgQOdXQ8vRseOHTVt2jTNnz9frVq10ltvvaUffvhBV155ZbGPMXfuXOXk5Khly5YKDg7Wm2++qaCgIMXGxl50fABQXtASBQDlRO/evfX8889r2rRpatiwoV555RXNmTPHZXjsZ599VsuXL1dMTIzzH/PnnntOlSpVUuvWrdWrVy917dpVzZo1K/H4nn32WcXExOi6667TgAEDNGbMGJfnmC5Gp06dFB8fr+uuu079+/dXr169XIb9/te//qUnnnhCSUlJql+/vrp27aqPPvpIcXFxls4THByszz77TEeOHFGLFi3Ur18/derUSTNnziyR19G1a1dNnDhRY8eOVYsWLXT8+HENGjTI0jEqVqyoV199VW3atFGTJk20cuVKffTRR4qIiCiRGAGgPLCZsztXAwAAAACKREsUAAAAAFhAEgUAAAAAFpBEAQAAAIAFJFEAAAAAYAFJFAAAAABYQBIFAAAAABaQRAEAAACABSRRAAAAAGABSRQAAAAAWEASBQAAAAAWkEQBAAAAgAX/D45WfY5SU3ZqAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_compare('Runs', 'Total number of runs')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Big Mountain compares well for the number of runs. There are some resorts with more, but not many."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.8.7 Longest run<a id='5.8.7_Longest_run'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_compare('LongestRun_mi', 'Longest run length (miles)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Big Mountain has one of the longest runs. Although it is just over half the length of the longest, the longer ones are rare."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.8.8 Trams<a id='5.8.8_Trams'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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t2rSpQMvPL7Pvq/bt29v+qy5J9913nyTleLzcTHJysr7//ns9/PDDKlmypK3d1dVVTz75pE6dOlXg4yW3/lu3btX58+fVs2dPu+PTarWqTZs22rlzp+0//g888IDmz5+viRMnavv27UpPT7eb17Zt25SSkpJtlDk8PFwPPvhgtlMFzdb/wAMP6OLFi3rsscf06aefZjvtqyBufJ+Z2X8Wi0Xt2rWzPc/6XRkaGmp37V1AQICCgoKyzXPOnDmqWbOmvLy85ObmJnd3d61bty7H3xkdO3bMdbTx66+/VqNGjdS4cWOtWbNGAQEBttc+//xz+fv7KzY21m7/1qhRQyEhIdlOjc0vM7+L8rONz5w5o2effVbh4eG2bRERESFJOW6PG983n3/+uSwWi5544gm79QwJCVF0dHSB1xMobAQpwEEeffRR1axZU2PGjMn2Aaagrv+DK10LbCVKlJCXl1e29qtXr2abPiQkJMe2rFN9/vzzT0nSsGHD5O7ubvd4/vnnJSnbh6H83sHt3LlzOfYNCwuzvV4UclrmkCFD9PLLL6tz58767LPP9P3332vnzp2Kjo5WSkpKtv45bfe82rO2fePGjbVy5UplZGSoR48eKleunKpXr64lS5bkWfO5c+fk5uamwMBAu/ac9t+N/Pz8tHHjRtWoUUOjR49WtWrVFBYWprFjx5p6H5q5M19Op+Bk1VpU+zWL2ffVjds063SonPb7zVy4cEGGYZhavtk7Ht7YP+sYffjhh7Mdo1OmTJFhGLbT0JYtW6aePXvqnXfeUb169RQQEKAePXooISHBrrbc6r+x9hIlSmQ7BTQvTz75pN577z0dP35cDz30kIKCglSnTh2tWbMm/xvgBn9n/+X2u/LG4zir/frfodOnT9dzzz2nOnXq6JNPPtH27du1c+dOtWnTJsdl57WfV65cqZSUFD333HPZTsf7888/dfHiRXl4eGTbvwkJCQUOo2Z+F91sG1utVrVq1UrLly/X8OHDtW7dOu3YscN2rWF+tseff/4pwzAUHBycbT23b99eKKEbKAxcIwU4iMVi0ZQpU9SyZUu9/fbb2V7P+oN+48XmRfnBM+sD1I1tWX84y5QpI+nadRk3Xl+V5cbrh/J7x7HAwEDFx8dna8+6UDxr2YUtp/qyrjGZNGmSXftff/0lf3//Ql1+p06d1KlTJ6Wmpmr79u2Ki4tT9+7dVb58edWrVy/HaQIDA5WRkaFz587ZfajJaf/lJCoqSkuXLpVhGNq/f7/mz5+vCRMmyNvbWyNHjszXPMzc2TDrw/31smrNqj+39/vf/cDkqPeVJJUuXVouLi6mlm/2jpE39s+a3xtvvJHrne2ygm2ZMmU0c+ZMzZw5UydOnNCqVas0cuRInTlzRl999ZVt3+RW/9+tXZJ69+6t3r17Kzk5WZs2bdLYsWPVoUMH/frrr7YRjOLg/fffV9OmTTV79my79qzrDm+U17aaMWOGli1bprZt22rFihVq1aqV7bWsm2h89dVXOU7r6+tbgOqvKcjvopwcPHhQP/zwg+bPn6+ePXva2rNuhJKTnN7HFotFmzdvzvHarryu9wJuJUakAAdq0aKFWrZsqQkTJmS7m1twcLC8vLy0f/9+u/ZPP/20yOpZsmSJDMOwPT9+/Li2bt1qu1C6cuXKioyM1A8//KCYmJgcHwX9Q968eXP99NNP2rNnj137woULZbFY1KxZswLN19PT0/RogsViyfaHevXq1fk+9a4gPD091aRJE02ZMkWScrxbYpasbfHBBx/YtS9evNjUMi0Wi6KjozVjxgz5+/vbbfuCbLfcXLp0SatWrcpWq4uLixo3bixJtjuK3fh+v3G6rNqk/I0yFNX7Kj98fHxUp04dLV++3K5Wq9Wq999/X+XKldM999xTqMts0KCB/P399dNPP+V6jGaNjF7vrrvu0oABA9SyZUvbtqpXr568vb31/vvv2/U9deqU1q9fn+/bvOfnveTj46O2bdtqzJgxSktL048//pjPNXYOOf3O2L9/v90NUfLLy8tLy5cvV4cOHdSxY0e73/kdOnTQuXPnlJmZmeO+vf4fWQU9hs38LspJVii6cXvMnTs33/Po0KGDDMPQH3/8keN6RkVFmaoJKCqMSAEONmXKFNWqVUtnzpxRtWrVbO1Z54e/9957uvvuuxUdHa0dO3aY/rBsxpkzZ9SlSxc9/fTTSkxM1NixY+Xl5aVRo0bZ+sydO1dt27ZV69at1atXL/3jH//Q+fPn9fPPP2vPnj366KOPCrTsF198UQsXLlT79u01YcIERUREaPXq1Zo1a5aee+65An/gjIqK0rfffqvPPvtMoaGh8vX1zTZqdqMOHTpo/vz5qlKliu677z7t3r1br776qu0W14Xl3//+t06dOqXmzZurXLlyunjxol5//XW5u7vn+WWurVq1UuPGjTV8+HAlJycrJiZG3333nRYtWnTTZX7++eeaNWuWOnfurIoVK8owDC1fvlwXL15Uy5Ytbf0Kst1yExgYqOeee04nTpzQPffcoy+++EL/+9//9Nxzz+muu+6SdO1UvxYtWiguLk6lS5dWRESE1q1bp+XLl2ebX9aHqClTpqht27ZydXXVfffdl2NAKKr3VX7FxcWpZcuWatasmYYNGyYPDw/NmjVLBw8e1JIlSwr9O8tKliypN954Qz179tT58+f18MMPKygoSGfPntUPP/ygs2fPavbs2UpMTFSzZs3UvXt3ValSRb6+vtq5c6e++uor22izv7+/Xn75ZY0ePVo9evTQY489pnPnzmn8+PHy8vLS2LFj81VTVFSUli9frtmzZ6tWrVpycXFRTEyMnn76aXl7e6tBgwYKDQ1VQkKC4uLi5OfnZ7tmp7jo0KGD/vOf/2js2LFq0qSJDh06pAkTJqhChQoF+o4od3d3LVmyRH379tXDDz+shQsX6rHHHtOjjz6qDz74QO3atdOgQYP0wAMPyN3dXadOndKGDRvUqVMndenSRdL/jTwvW7ZMFStWlJeXV64BpKC/i3JSpUoV3X333Ro5cqQMw1BAQIA+++wzU6dsNmjQQM8884x69+6tXbt2qXHjxvLx8VF8fLy2bNmiqKgoPffcc6bqAooCQQpwsPvvv1+PPfZYjgFp2rRpkqSpU6fq8uXLevDBB/X555/f9PtACmrSpEnauXOnevfuraSkJD3wwANaunSp7Xs9pGujITt27NArr7yiwYMH68KFCwoMDFTVqlXVrVu3Ai+7bNmy2rp1q0aNGmW75XDFihU1depUDRkypMDzff3119W/f389+uijunLlipo0aXLTC5WzPkDExcXp8uXLqlmzppYvX65//etfBa4jJ3Xq1NGuXbs0YsQInT17Vv7+/oqJidH69evtQvWNXFxctGrVKg0ZMkRTp05VWlqaGjRooC+++EJVqlTJc5mRkZHy9/fX1KlTdfr0aXl4eKhy5crZTsMpyHbLTUhIiN566y0NGzZMBw4cUEBAgEaPHq3x48fb9Vu0aJEGDhyoESNGKDMz03aL8JiYGLt+3bt313fffadZs2ZpwoQJMgxDR48ezfG4KKr3VX41adJE69ev19ixY9WrVy9ZrVZFR0dr1apV2W5SUlieeOIJ3XXXXZo6dar69eunS5cuKSgoSDVq1LDdOMLLy0t16tTRokWLdOzYMaWnp+uuu+7SiBEjNHz4cNu8Ro0apaCgIP33v//VsmXL5O3traZNm2rSpEl2tz7Py6BBg/Tjjz9q9OjRSkxMlGEYMgxDjRo10vz58/Xhhx/qwoULKlOmjBo2bKiFCxc6/IudzRozZoyuXLmid999V1OnTlXVqlU1Z84crVixosDHjYuLi9599135+vrqiSeeUHJysvr27atVq1bp9ddf16JFixQXFyc3NzeVK1dOTZo0sQtK48ePV3x8vJ5++mldunRJERERuX7nXkF/F+XE3d1dn332mQYNGqR+/frJzc1NLVq00Nq1a23/OMmPuXPnqm7dupo7d65mzZolq9WqsLAwNWjQINvNYwBHsRjXn8cDAAAAALgprpECAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJvE9Urr2LfOnT5+Wr69voX85IgAAAIDiwzAMXbp0SWFhYXJxyX3ciSAl6fTp0woPD3d0GQAAAACcxMmTJ1WuXLlcXydISfL19ZV0bWOVKlXKcYVYrdKff177OThYyiMBAwAAACh8SUlJCg8Pt2WE3BCkJNvpfKVKlXJskEpJkR5//NrPmzdL3t6OqwUAAAC4g93skh+GPAAAAADAJIIUAAAAAJhEkAIAAAAAk7hGCgAAAMWeYRjKyMhQZmamo0uBk3N1dZWbm9vf/tojghQAAACKtbS0NMXHx+vKlSuOLgXFRIkSJRQaGioPD48Cz4MgBQAAgGLLarXq6NGjcnV1VVhYmDw8PP72SANuX4ZhKC0tTWfPntXRo0cVGRmZ55fu5oUg5UxcXaV//vP/fgYAAECe0tLSZLVaFR4erhIlSji6HBQD3t7ecnd31/Hjx5WWliYvL68CzYcg5Uw8PKQRIxxdBQAAQLFT0FEF3JkK4/3COw4AAAAATGJEypkYhnTx4rWf/f0lzu8FAAAAnBIjUs7k6lWpZctrj6tXHV0NAAAAHOzYsWOyWCzat2+fo0txKuXLl9fMmTMdWgNBCgAAAHCAXr16yWKx2B6BgYFq06aN9u/fb+sTHh6u+Ph4Va9e/W8tq3z58rJYLFq6dGm216pVqyaLxaL58+f/rWUUhMVi0cqVK01Pt3PnTj3zzDOFX5AJBCkAAADAQdq0aaP4+HjFx8dr3bp1cnNzU4cOHWyvu7q6KiQkRG5uf/+KnPDwcM2bN8+ubfv27UpISJCPj8/fnv+tVLZsWYffpZEgBQAAgNtTSkruj7S0/PdNTc1f3wLw9PRUSEiIQkJCVKNGDY0YMUInT57U2bNnJeV8at+qVasUGRkpb29vNWvWTAsWLJDFYtHFrGvtc/H4449r48aNOnnypK3tvffe0+OPP54tqJ04cUKdOnVSyZIlVapUKXXr1k1//vmn7fVevXqpc+fOdtMMHjxYTZs2tT1v2rSpXnjhBQ0fPlwBAQEKCQnRuHHjbK+XL19ektSlSxdZLBbb8yNHjqhTp04KDg5WyZIlVbt2ba1du9ZuWTee2mexWPTOO++oS5cuKlGihCIjI7Vq1ao8t8ffRZACAADA7alRo9wfL71k37dly9z7Dhxo3zc2Nud+f9Ply5f1wQcfqFKlSgoMDMyxz7Fjx/Twww+rc+fO2rdvn/r166cxY8bka/7BwcFq3bq1FixYIEm6cuWKli1bpj59+tj1MwxDnTt31vnz57Vx40atWbNGR44c0SOPPGJ6nRYsWCAfHx99//33mjp1qiZMmKA1a9ZIunZ6niTNmzdP8fHxtueXL19Wu3bttHbtWu3du1etW7dWbGysTpw4keeyxo8fr27dumn//v1q166dHn/8cZ0/f950zfnFXfuc0P4/EvXPf32pVHfPIpn/scnti2S+AAAAMOfzzz9XyZIlJUnJyckKDQ3V559/nuv3HM2ZM0eVK1fWq6++KkmqXLmyDh48qFdeeSVfy+vTp4+GDh2qMWPG6OOPP9bdd9+tGjVq2PVZu3at9u/fr6NHjyo8PFyStGjRIlWrVk07d+5U7dq1871+9913n8aOHStJioyM1Jtvvql169apZcuWKlu2rCTJ399fISEhtmmio6MVHR1tez5x4kStWLFCq1at0oABA3JdVq9evfTYY49JkiZNmqQ33nhDO3bsUJs2bfJdrxkEKQAAANyeNm/O/TVXV/vn/3+UJEc3hprPPit4TTdo1qyZZs+eLUk6f/68Zs2apbZt22rHjh2KiIjI1v/QoUPZgswDDzyQ7+W1b99e/fr106ZNm/Tee+9lG42SpJ9//lnh4eG2ECVJVatWlb+/v37++WfTQep6oaGhOnPmTJ7TJCcna/z48fr88891+vRpZWRkKCUl5aYjUtcvy8fHR76+vjdd1t9BkHImrq5Shw5av/ZXWfl2bgAAgL/H29vxfW/Cx8dHlSpVsj2vVauW/Pz89L///U8TJ07M1t8wDFlu+K5RwzDyvTw3Nzc9+eSTGjt2rL7//nutWLEiX8u4sd3FxSXbctPT07NN4+7ubvfcYrHIarXmWeNLL72kr7/+Wq+99poqVaokb29vPfzww0q78bq2QljW3+HQT+txcXGqXbu2fH19FRQUpM6dO+vQoUN2fW68LaTFYlHdunXt+qSmpmrgwIEqU6aMfHx81LFjR506depWrkrh8PCQxo3TzIaPK93V/eb9AQAAcFuxWCxycXFRSi43r6hSpYrtWqIsu3btMrWMPn36aOPGjerUqZNKly6d7fWqVavqxIkTdjel+Omnn5SYmKh7771X0rW75sXHx9tNV5DvunJ3d1dmZqZd2+bNm9WrVy916dJFUVFRCgkJ0bFjx0zPu6g5NEht3LhR/fv31/bt27VmzRplZGSoVatWSk5Otut3/W0h4+Pj9cUXX9i9PnjwYK1YsUJLly7Vli1bdPnyZXXo0CHbTgEAAACcSWpqqhISEpSQkKCff/5ZAwcO1OXLlxUbG5tj/379+umXX37RiBEj9Ouvv+rDDz+0ff9TTqNIObn33nv1119/ZbsVepYWLVrovvvu0+OPP649e/Zox44d6tGjh5o0aaKYmBhJ0oMPPqhdu3Zp4cKFOnz4sMaOHauDBw+aXv/y5ctr3bp1SkhI0IULFyRJlSpV0vLly7Vv3z798MMP6t69e5GOLBWUQ4PUV199pV69eqlatWqKjo7WvHnzdOLECe3evduu3/W3hQwJCVFAQIDttcTERL377ruaNm2aWrRoofvvv1/vv/++Dhw4kO02iU7PMKSUFHmmp177GQAAALe1r776SqGhoQoNDVWdOnW0c+dOffTRR3a3Eb9ehQoV9PHHH2v58uW67777NHv2bNtd+zw983+jssDAQHnncopi1pfkli5dWo0bN1aLFi1UsWJFLVu2zNandevWevnllzV8+HDVrl1bly5dUo8ePfK/4v/ftGnTtGbNGoWHh+v++++XJM2YMUOlS5dW/fr1FRsbq9atW6tmzZqm513ULIaZkyqL2G+//abIyEgdOHDA9u3NvXr10sqVK+Xh4SF/f381adJEr7zyioKCgiRJ69evV/PmzXX+/Hm7ocno6Gh17txZ48ePz7ac1NRUpV73fQBJSUkKDw9XYmKiSpUqVcRrmYeUFKlRo2t37es+hbv2AQAA3MTVq1d19OhRVahQQV5eXo4uxyFeeeUVzZkzx+5UPOQtr/dNUlKS/Pz8bpoNnOaOBoZhaMiQIWrYsKEtRElS27Zt9cEHH2j9+vWaNm2adu7cqQcffNAWhBISEuTh4ZHt/M7g4GAlJCTkuKy4uDj5+fnZHtffkQQAAABwZrNmzdLOnTv1+++/a9GiRXr11VfVs2dPR5d1x3Gau/YNGDBA+/fv15YtW+zar//ir+rVqysmJkYRERFavXq1unbtmuv8crvbiCSNGjVKQ4YMsT3PGpECAAAAnN3hw4c1ceJEnT9/XnfddZeGDh2qUaNGObqsO45TBKmBAwdq1apV2rRpk8qVK5dn39DQUEVEROjw4cOSpJCQEKWlpenChQt2o1JnzpxR/fr1c5yHp6enqXNIAQAAAGcxY8YMzZgxw9Fl3PEcemqfYRgaMGCAli9frvXr16tChQo3nebcuXM6efKkQkNDJV271767u7vWXPclavHx8Tp48GCuQQoAAAAA/g6Hjkj1799fixcv1qeffipfX1/bNU1+fn7y9vbW5cuXNW7cOD300EMKDQ3VsWPHNHr0aJUpU0ZdunSx9X3qqac0dOhQBQYGKiAgQMOGDVNUVJRatGjhyNUDAADALeJE909DMVAY7xeHBqnZs2dLUrbbO86bN0+9evWSq6urDhw4oIULF+rixYsKDQ1Vs2bNtGzZMvn6+tr6z5gxQ25uburWrZtSUlLUvHlzzZ8/X66urrdydQAAAHCLubu7S5KuXLmS6+28gRtduXJF0v+9fwrCoUHqZknQ29tbX3/99U3n4+XlpTfeeENvvPFGYZXmGK6uUvPm2rrxiKwuTnNDRQAAAKfl6uoqf39/nTlzRpJUokSJfH8xLe48hmHoypUrOnPmjPz9/f/WwItT3GwC/5+HhzRliiaPXO3oSgAAAIqNkJAQSbKFKeBm/P39be+bgiJIAQAAoFizWCwKDQ1VUFCQ0tPTHV0OnJy7u3uhXAJEkAIAAMBtwdXVlWvkcctwIY4zSUmRYmK0asFgeaanOroaAAAAALkgSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFLOxNVVatBAu/9xr6wu7BoAAADAWfFp3Zl4eEivv67xLfop3dXd0dUAAAAAyAVBCgAAAABMIkgBAAAAgEkEKWeSkiI1bKiP3h8uz/RUR1cDAAAAIBcEKWdz9ao8M9McXQUAAACAPBCkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMcnN0AbiOi4tUs6YOZpyQYSHjAgAAAM6KIOVMPD2lt9/W6JGrHV0JAAAAgDww7AEAAAAAJhGkAAAAAMAkgpQzSUmRWrTQ+0vHyDM91dHVAAAAAMgFQcrZXLyoUqnJjq4CAAAAQB4IUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJrk5ugBcx8VFqlpVh6+ekmEh4wIAAADOiiDlTDw9pYULNXTkakdXAgAAACAPDHsAAAAAgEkEKQAAAAAwiSDlTK5elWJj9c7H4+WZkeboagAAAADkgmuknIlhSPHxCkpOvPYzAAAAAKfEiBQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEnctc+ZWCxSxYo6cTn+2s8AAAAAnBJBypl4eUkffqgBI1c7uhIAAAAAeeDUPgAAAAAwiSAFAAAAACYRpJzJ1atSt256c+VkeWakOboaAAAAALngGilnYhjS77/rrsTEaz8DAAAAcEqMSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmMRd+5yJxSKFhurMRZdrPwMAAABwSgQpZ+LlJX32mfqOXO3oSgAAAADkgVP7AAAAAMAkghQAAAAAmESQciapqVKPHpr2+TR5ZKQ7uhoAAAAAueAaKWditUo//aTIc4myGFZHVwMAAAAgF4xIAQAAAIBJDg1ScXFxql27tnx9fRUUFKTOnTvr0KFDdn0Mw9C4ceMUFhYmb29vNW3aVD/++KNdn9TUVA0cOFBlypSRj4+POnbsqFOnTt3KVQEAAABwB3FokNq4caP69++v7du3a82aNcrIyFCrVq2UnJxs6zN16lRNnz5db775pnbu3KmQkBC1bNlSly5dsvUZPHiwVqxYoaVLl2rLli26fPmyOnTooMzMTEesFgAAAIDbnMUwDMPRRWQ5e/asgoKCtHHjRjVu3FiGYSgsLEyDBw/WiBEjJF0bfQoODtaUKVPUr18/JSYmqmzZslq0aJEeeeQRSdLp06cVHh6uL774Qq1bt77pcpOSkuTn56fExESVKlWqSNcxTykpUqNG2v9Hov7ZfYpS3T2LZDHHJrcvkvkCAAAAxV1+s4FTXSOVmJgoSQoICJAkHT16VAkJCWrVqpWtj6enp5o0aaKtW7dKknbv3q309HS7PmFhYapevbqtz41SU1OVlJRk9wAAAACA/HKaIGUYhoYMGaKGDRuqevXqkqSEhARJUnBwsF3f4OBg22sJCQny8PBQ6dKlc+1zo7i4OPn5+dke4eHhhb06BefvryRPH0dXAQAAACAPThOkBgwYoP3792vJkiXZXrNYLHbPDcPI1najvPqMGjVKiYmJtsfJkycLXnhh8vaW1q7VE4++UmSn9QEAAAD4+5wiSA0cOFCrVq3Shg0bVK5cOVt7SEiIJGUbWTpz5oxtlCokJERpaWm6cOFCrn1u5OnpqVKlStk9AAAAACC/HBqkDMPQgAEDtHz5cq1fv14VKlSwe71ChQoKCQnRmjVrbG1paWnauHGj6tevL0mqVauW3N3d7frEx8fr4MGDtj4AAAAAUJjcHLnw/v37a/Hixfr000/l6+trG3ny8/OTt7e3LBaLBg8erEmTJikyMlKRkZGaNGmSSpQooe7du9v6PvXUUxo6dKgCAwMVEBCgYcOGKSoqSi1atHDk6pmXmioNHKhJO05oXItnlebm7uiKAAAAAOTAoUFq9uzZkqSmTZvatc+bN0+9evWSJA0fPlwpKSl6/vnndeHCBdWpU0fffPONfH19bf1nzJghNzc3devWTSkpKWrevLnmz58vV1fXW7UqhcNqlfbsUfU/E2UxrI6uBgAAAEAunOp7pByF75ECAAAAIBXT75ECAAAAgOKAIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQcjZeXkp19XB0FQAAAADy4NDbn+MG3t7Sli3658jVjq4EAAAAQB4YkQIAAAAAkwhSAAAAAGASQcqZpKVJgwZp7Nq5cs9Md3Q1AAAAAHLBNVLOJDNT+u471fojUS5Wq+Tq6IIAAAAA5IQRKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGAStz93Jt7e0q5d6jhytaMrAQAAAJAHRqQAAAAAwCSCFAAAAACYRJByJmlp0ogRGvntPLlnpju6GgAAAAC5IEg5k8xMad061T/+g1ysVkdXAwAAACAXBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgkpujC8B1vLykzZv1z399qVQ3D0dXAwAAACAXBClnYrFI3t5Kdfd0dCUAAAAA8sCpfQAAAABgEkHKmaSlSePGafCWD+Seme7oagAAAADkgiDlTDIzpc8/14NHdsrFanV0NQAAAAByQZACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJrk5ugBcx8tLWrNGT4z/RqluHo6uBgAAAEAuCFLOxGKRSpdWkldJR1cCAAAAIA+c2gcAAAAAJhGknElamjRlip7d/rHcM9MdXQ0AAACAXBCknElmpvTRR2p3aItcrFZHVwMAAAAgFwQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYJKbowvAdTw9pVWr1PeVtUpzc3d0NQAAAAByQZByJi4uUliYzvgGOroSAAAAAHng1D4AAAAAMIkg5UzS06XXX1fvXZ/KLTPD0dUAAAAAyAVByplkZEiLFqnLjxvkas10dDUAAAAAckGQAgAAAACTCFIAAAAAYJLpIHX06NGiqAMAAAAAig3TQapSpUpq1qyZ3n//fV29erUoagIAAAAAp2Y6SP3www+6//77NXToUIWEhKhfv37asWNHUdQGAAAAAE7JdJCqXr26pk+frj/++EPz5s1TQkKCGjZsqGrVqmn69Ok6e/ZsUdQJAAAAAE6jwDebcHNzU5cuXfThhx9qypQpOnLkiIYNG6Zy5cqpR48eio+PL8w67wyentKHH6p/p5FKc3N3dDUAAAAAclHgILVr1y49//zzCg0N1fTp0zVs2DAdOXJE69ev1x9//KFOnTrddB6bNm1SbGyswsLCZLFYtHLlSrvXe/XqJYvFYveoW7euXZ/U1FQNHDhQZcqUkY+Pjzp27KhTp04VdLUcy8VFqlhRJ/1DZFi4oSIAAADgrEx/Wp8+fbqioqJUv359nT59WgsXLtTx48c1ceJEVahQQQ0aNNDcuXO1Z8+em84rOTlZ0dHRevPNN3Pt06ZNG8XHx9seX3zxhd3rgwcP1ooVK7R06VJt2bJFly9fVocOHZSZyRfaAgAAACgabmYnmD17tvr06aPevXsrJCQkxz533XWX3n333ZvOq23btmrbtm2efTw9PXNdTmJiot59910tWrRILVq0kCS9//77Cg8P19q1a9W6deub1uBU0tOlefP02L4D+iiqhTJcTe8eAAAAALeA6U/qhw8fvmkfDw8P9ezZs0AF3ejbb79VUFCQ/P391aRJE73yyisKCgqSJO3evVvp6elq1aqVrX9YWJiqV6+urVu35hqkUlNTlZqaanuelJRUKLX+bRkZ0ttv67E/ErW8WjOCFAAAAOCkTJ/aN2/ePH300UfZ2j/66CMtWLCgUIrK0rZtW33wwQdav369pk2bpp07d+rBBx+0haCEhAR5eHiodOnSdtMFBwcrISEh1/nGxcXJz8/P9ggPDy/UugEAAADc3kwHqcmTJ6tMmTLZ2oOCgjRp0qRCKSrLI488ovbt26t69eqKjY3Vl19+qV9//VWrV6/OczrDMGSxWHJ9fdSoUUpMTLQ9Tp48Wah1AwAAALi9mQ5Sx48fV4UKFbK1R0RE6MSJE4VSVG5CQ0MVERFhO70wJCREaWlpunDhgl2/M2fOKDg4ONf5eHp6qlSpUnYPAAAAAMgv00EqKChI+/fvz9b+ww8/KDAwsFCKys25c+d08uRJhYaGSpJq1aold3d3rVmzxtYnPj5eBw8eVP369Yu0FgAAAAB3LtN3M3j00Uf1wgsvyNfXV40bN5Ykbdy4UYMGDdKjjz5qal6XL1/Wb7/9Znt+9OhR7du3TwEBAQoICNC4ceP00EMPKTQ0VMeOHdPo0aNVpkwZdenSRZLk5+enp556SkOHDlVgYKACAgI0bNgwRUVF2e7iBwAAAACFzXSQmjhxoo4fP67mzZvLze3a5FarVT169DB9jdSuXbvUrFkz2/MhQ4ZIknr27KnZs2frwIEDWrhwoS5evKjQ0FA1a9ZMy5Ytk6+vr22aGTNmyM3NTd26dVNKSoqaN2+u+fPny9XV1eyqAQAAAEC+WAzDMAoy4a+//qoffvhB3t7eioqKUkRERGHXdsskJSXJz89PiYmJjr1eymqVfvlFLaZv1JHAcjIsps+8zJdjk9sXyXwBAACA4i6/2aDAX1R0zz336J577ino5MiJi4tUtap+K3PU0ZUAAAAAyIPpIJWZman58+dr3bp1OnPmjKxWq93r69evL7TiAAAAAMAZmQ5SgwYN0vz5823f75TX9zXBpPR0ackSdT24T6vubaIM1wIPGAIAAAAoQqY/qS9dulQffvih2rVrVxT13NkyMqT//le9/kjU6soNCVIAAACAkzJ9NwMPDw9VqlSpKGoBAAAAgGLBdJAaOnSoXn/9dRXwZn8AAAAAUOyZPndsy5Yt2rBhg7788ktVq1ZN7u7udq8vX7680IoDAAAAAGdkOkj5+/urS5cuRVELAAAAABQLpoPUvHnziqIOAAAAACg2TF8jJUkZGRlau3at5s6dq0uXLkmSTp8+rcuXLxdqcQAAAADgjEyPSB0/flxt2rTRiRMnlJqaqpYtW8rX11dTp07V1atXNWfOnKKo887g6SnNnavRb2xRmpv7zfsDAAAAcAjTI1KDBg1STEyMLly4IG9vb1t7ly5dtG7dukIt7o7j4iLVqqWDIZVkWAo0WAgAAADgFijQXfu+++47eXh42LVHRETojz/+KLTCAAAAAMBZmR72sFqtyszMzNZ+6tQp+fr6FkpRd6yMDOnDD9X+581ytWbfxgAAAACcg+kg1bJlS82cOdP23GKx6PLlyxo7dqzatWtXmLXdedLTpalT1W/HJ3LLzHB0NQAAAAByYfrUvhkzZqhZs2aqWrWqrl69qu7du+vw4cMqU6aMlixZUhQ1AgAAAIBTMR2kwsLCtG/fPi1ZskR79uyR1WrVU089pccff9zu5hMAAAAAcLsyHaQkydvbW3369FGfPn0Kux4AAAAAcHqmg9TChQvzfL1Hjx4FLgYAAAAAigPTQWrQoEF2z9PT03XlyhV5eHioRIkSBCkAAAAAtz3Td+27cOGC3ePy5cs6dOiQGjZsyM0mAAAAANwRTAepnERGRmry5MnZRqtgkoeHNHOmJjR/WumuBbp8DQAAAMAtUChBSpJcXV11+vTpwprdncnVVWrYULvKVZPVxdXR1QAAAADIhelhj1WrVtk9NwxD8fHxevPNN9WgQYNCKwwAAAAAnJXpINW5c2e75xaLRWXLltWDDz6oadOmFVZdd6aMDOnLL9X8t536tmKMMhmVAgAAAJyS6SBltVqLog5IUnq6NH68Bv2RqC0RNQhSAAAAgJMqtGukAAAAAOBOYXpEasiQIfnuO336dLOzBwAAAACnZzpI7d27V3v27FFGRoYqV64sSfr111/l6uqqmjVr2vpZLJbCqxIAAAAAnIjpIBUbGytfX18tWLBApUuXlnTtS3p79+6tRo0aaejQoYVeJAAAAAA4E9PXSE2bNk1xcXG2ECVJpUuX1sSJE7lrHwAAAIA7gukglZSUpD///DNb+5kzZ3Tp0qVCKQoAAAAAnJnpINWlSxf17t1bH3/8sU6dOqVTp07p448/1lNPPaWuXbsWRY13Dg8PafJkTWnSS+mups+6BAAAAHCLmP60PmfOHA0bNkxPPPGE0tPTr83EzU1PPfWUXn311UIv8I7i6iq1aKHv1qY6uhIAAAAAeTAdpEqUKKFZs2bp1Vdf1ZEjR2QYhipVqiQfH5+iqA8AAAAAnE6Bv5A3Pj5e8fHxuueee+Tj4yPDMAqzrjtTZqa0dq0aHNsnF2umo6sBAAAAkAvTQercuXNq3ry57rnnHrVr107x8fGSpL59+3Lr878rLU0aOVIjNs6Xe2aGo6sBAAAAkAvTQerFF1+Uu7u7Tpw4oRIlStjaH3nkEX311VeFWhwAAAAAOCPT10h98803+vrrr1WuXDm79sjISB0/frzQCgMAAAAAZ2V6RCo5OdluJCrLX3/9JU9Pz0IpCgAAAACcmekg1bhxYy1cuND23GKxyGq16tVXX1WzZs0KtTgAAAAAcEamT+179dVX1bRpU+3atUtpaWkaPny4fvzxR50/f17fffddUdQIAAAAAE7F9IhU1apVtX//fj3wwANq2bKlkpOT1bVrV+3du1d33313UdQIAAAAAE7F1IhUenq6WrVqpblz52r8+PFFVdOdy91dGjtWry/YqQxX04OFAAAAAG4RU5/W3d3ddfDgQVkslqKq587m5ibFxmrddwX+nmQAAAAAt4DpT+w9evTQu+++WxS1AAAAAECxYPr8sbS0NL3zzjtas2aNYmJi5OPjY/f69OnTC624O05mprRtm2JO/ag9YVVkdXF1dEUAAAAAcpCvILV//35Vr15dLi4uOnjwoGrWrClJ+vXXX+36ccrf35SWJg0erH//kah/dp+iVIIUAAAA4JTyFaTuv/9+xcfHKygoSMePH9fOnTsVGBhY1LUBAAAAgFPK1zVS/v7+Onr0qCTp2LFjslqtRVoUAAAAADizfI1IPfTQQ2rSpIlCQ0NlsVgUExMjV9ecTzv7/fffC7VAAAAAAHA2+QpSb7/9trp27arffvtNL7zwgp5++mn5+voWdW0AAAAA4JTyfde+Nm3aSJJ2796tQYMGEaQAAAAA3LFM3/583rx5RVEHAAAAABQbpr+QF0XI3V0aPlxzH3hIGa6mMy4AAACAW4RP687EzU3q1k2r9/jcvC8AAAAAh2FECgAAAABMIkg5E6tV2r1b1RN+k8Xgu7oAAAAAZ0WQciapqVK/fpr09ZvyyEh3dDUAAAAAckGQAgAAAACTCFIAAAAAYJJDg9SmTZsUGxursLAwWSwWrVy50u51wzA0btw4hYWFydvbW02bNtWPP/5o1yc1NVUDBw5UmTJl5OPjo44dO+rUqVO3cC0AAAAA3GkcGqSSk5MVHR2tN998M8fXp06dqunTp+vNN9/Uzp07FRISopYtW+rSpUu2PoMHD9aKFSu0dOlSbdmyRZcvX1aHDh2UmZl5q1YDAAAAwB3God8j1bZtW7Vt2zbH1wzD0MyZMzVmzBh17dpVkrRgwQIFBwdr8eLF6tevnxITE/Xuu+9q0aJFatGihSTp/fffV3h4uNauXavWrVvfsnUBAAAAcOdw2mukjh49qoSEBLVq1crW5unpqSZNmmjr1q2SpN27dys9Pd2uT1hYmKpXr27rk5PU1FQlJSXZPQAAAAAgv5w2SCUkJEiSgoOD7dqDg4NtryUkJMjDw0OlS5fOtU9O4uLi5OfnZ3uEh4cXcvUF5OYmvfCC5teKVaaLq6OrAQAAAJALpw1SWSwWi91zwzCytd3oZn1GjRqlxMRE2+PkyZOFUuvf5u4u9eih5dWbK8PVoWddAgAAAMiD0wapkJAQSco2snTmzBnbKFVISIjS0tJ04cKFXPvkxNPTU6VKlbJ7AAAAAEB+OW2QqlChgkJCQrRmzRpbW1pamjZu3Kj69etLkmrVqiV3d3e7PvHx8Tp48KCtT7FitUo//aRKf52QxbA6uhoAAAAAuXDo+WOXL1/Wb7/9Znt+9OhR7du3TwEBAbrrrrs0ePBgTZo0SZGRkYqMjNSkSZNUokQJde/eXZLk5+enp556SkOHDlVgYKACAgI0bNgwRUVF2e7iV6ykpko9emj6H4n6Z/cpSnX3dHRFAAAAAHLg0CC1a9cuNWvWzPZ8yJAhkqSePXtq/vz5Gj58uFJSUvT888/rwoULqlOnjr755hv5+vrappkxY4bc3NzUrVs3paSkqHnz5po/f75cXblZAwAAAICiYTEMw3B0EY6WlJQkPz8/JSYmOvZ6qZQUqVEj7S/iEaljk9sXyXwBAACA4i6/2cBpr5ECAAAAAGdFkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIORM3N+mZZ7Qkuo0yXbjrIAAAAOCsHHr7c9zA3f1akPp9taMrAQAAAJAHRqQAAAAAwCSClDOxWqXff1f4xQRZDKujqwEAAACQC4KUM0lNlbp101ufTpZHRrqjqwEAAACQC4IUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkg5Uzc3KQnn9SKas2U6eLq6GoAAAAA5MLN0QXgOu7u0qBBmhe/2tGVAAAAAMgDI1IAAAAAYBJByplYrdLp0wq6dE4Ww+roagAAAADkgiDlTFJTpY4d9c7y/8gjI93R1QAAAADIBUEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQciaurtI//6kvKjeU1YVdAwAAADgrN0cXgOt4eEgjRmjOhdWOrgQAAABAHhj2AAAAAACTCFLOxDCkCxdU6urlaz8DAAAAcEoEKWdy9arUsqXeX/YveWakOboaAAAAALkgSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFLOxNVV6tBB6++uLasLuwYAAABwVm6OLgDX8fCQxo3TzKurHV0JAAAAgDww7AEAAAAAJhGknIlhSCkp8kxPvfYzAAAAAKdEkHImV69KjRrpo8Uj5JmR5uhqAAAAAOSCIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkg5E1dXqXlzbY2IltWFXQMAAAA4Kz6tOxMPD2nKFE1u2lvpru6OrgYAAABALghSAAAAAGASQQoAAAAATCJIOZOUFCkmRqsWDJZneqqjqwEAAACQC4IUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkg5UxcXaUGDbT7H/fK6sKuAQAAAJwVn9adiYeH9PrrGt+in9Jd3R1dDQAAAIBcOHWQGjdunCwWi90jJCTE9rphGBo3bpzCwsLk7e2tpk2b6scff3RgxQAAAADuBE4dpCSpWrVqio+Ptz0OHDhge23q1KmaPn263nzzTe3cuVMhISFq2bKlLl265MCKAQAAANzunD5Iubm5KSQkxPYoW7aspGujUTNnztSYMWPUtWtXVa9eXQsWLNCVK1e0ePFiB1ddQCkpUsOG+uj94fJMT3V0NQAAAABy4fRB6vDhwwoLC1OFChX06KOP6vfff5ckHT16VAkJCWrVqpWtr6enp5o0aaKtW7fmOc/U1FQlJSXZPZzG1avyzExzdBUAAAAA8uDUQapOnTpauHChvv76a/3vf/9TQkKC6tevr3PnzikhIUGSFBwcbDdNcHCw7bXcxMXFyc/Pz/YIDw8vsnUAAAAAcPtx6iDVtm1bPfTQQ4qKilKLFi20evVqSdKCBQtsfSwWi900hmFka7vRqFGjlJiYaHucPHmy8IsHAAAAcNty6iB1Ix8fH0VFRenw4cO2u/fdOPp05syZbKNUN/L09FSpUqXsHgAAAACQX8UqSKWmpurnn39WaGioKlSooJCQEK1Zs8b2elpamjZu3Kj69es7sEoAAAAAtzs3RxeQl2HDhik2NlZ33XWXzpw5o4kTJyopKUk9e/aUxWLR4MGDNWnSJEVGRioyMlKTJk1SiRIl1L17d0eXDgAAAOA25tRB6tSpU3rsscf0119/qWzZsqpbt662b9+uiIgISdLw4cOVkpKi559/XhcuXFCdOnX0zTffyNfX18GVF5CLi1Szpg5mnJBhKVaDhQAAAMAdxWIYhuHoIhwtKSlJfn5+SkxMdIrrpcqPXF2k8z82uX2Rzh8AAAAorvKbDRj2AAAAAACTCFIAAAAAYBJBypmkpEgtWuj9pWPkmZ7q6GoAAAAA5IIg5WwuXlSp1GRHVwEAAAAgDwQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACT3BxdAK7j4iJVrarDV0/JsJBxAQAAAGdFkHImnp7SwoUaOnK1oysBAAAAkAeGPQAAAADAJIIUAAAAAJhEkHImV69KsbF65+Px8sxIc3Q1AAAAAHLBNVLOxDCk+HgFJSde+xkAAACAU2JECgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJO7a50wsFqliRZ24HH/tZwAAAABOiSDlTLy8pA8/1ICRqx1dCQAAAIA8cGofAAAAAJhEkAIAAAAAkwhSzuTqValbN725crI8M9IcXQ0AAACAXHCNlDMxDOn333VXYuK1nwEAAAA4JUakAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABM4q59zsRikUJDdeaiy7WfAQAAADglgpQz8fKSPvtMfUeudnQlAAAAAPLAqX0AAAAAYBJBCgAAAABMIkg5k9RUqUcPTft8mjwy0h1dDQAAAIBccI2UM7FapZ9+UuS5RFkMq6OrAQAAAJALRqQAAAAAwCSCFAAAAACYRJACAAAAAJO4RgoACqh8EX7n27HJ7Yts3gAA4O9jRAoAAAAATCJIORt/fyV5+ji6CgAAAAB54NQ+Z+LtLa1dqyeK8HQhAAAAAH8fI1IAAAAAYBJBCgAAAABMIkg5k9RU6ZlnNOmrN+SRke7oagAAAADkgmuknInVKu3Zo+p/JspiWB1dDQAAAIBcMCIFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQcrZeHkp1dXD0VUAAAAAyAO3P3cm3t7Sli3658jVjq4EAAAAQB4YkQIAAAAAkwhSAAAAAGASQcqZpKVJgwZp7Nq5cs9Md3Q1AAAAAHLBNVLOJDNT+u471fojUS5Wq+Tq6IIAAAAA5IQgBQAAAOCmyhfxDdGOTW5fpPMvbJzaBwAAAAAm3TZBatasWapQoYK8vLxUq1Ytbd682dElAQAAALhN3RZBatmyZRo8eLDGjBmjvXv3qlGjRmrbtq1OnDjh6NIAAAAA3IZui2ukpk+frqeeekp9+/aVJM2cOVNff/21Zs+erbi4OAdXBwAAHKkor+sobtd0ACg8xT5IpaWlaffu3Ro5cqRde6tWrbR169Ycp0lNTVVqaqrteWJioiQpKSmp6ArNj5QUKTNTl61WZaZekdWaWSSLcfh6ArcJa+qVIps3xylQeDhWgcJRlMeS5DzHU1YdhmHk2a/YB6m//vpLmZmZCg4OtmsPDg5WQkJCjtPExcVp/Pjx2drDw8OLpMYCeevJIpu138wimzWAQsJxChQPHKtA4XG24+nSpUvy8/PL9fViH6SyWCwWu+eGYWRryzJq1CgNGTLE9txqter8+fMKDAzMdZpbJSkpSeHh4Tp58qRKlSrl0FpQeNivtx/26e2HfXp7Yr/eftintydn2q+GYejSpUsKCwvLs1+xD1JlypSRq6trttGnM2fOZBulyuLp6SlPT0+7Nn9//6IqsUBKlSrl8DcRCh/79fbDPr39sE9vT+zX2w/79PbkLPs1r5GoLMX+rn0eHh6qVauW1qxZY9e+Zs0a1a9f30FVAQAAALidFfsRKUkaMmSInnzyScXExKhevXp6++23deLECT377LOOLg0AAADAbei2CFKPPPKIzp07pwkTJig+Pl7Vq1fXF198oYiICEeXZpqnp6fGjh2b7dRDFG/s19sP+/T2wz69PbFfbz/s09tTcdyvFuNm9/UDAAAAANgp9tdIAQAAAMCtRpACAAAAAJMIUgAAAABgEkEKAAAAAEwiSDnArFmzVKFCBXl5ealWrVravHlznv03btyoWrVqycvLSxUrVtScOXNuUaXILzP79Ntvv5XFYsn2+OWXX25hxcjLpk2bFBsbq7CwMFksFq1cufKm03CcOj+z+5Vj1fnFxcWpdu3a8vX1VVBQkDp37qxDhw7ddDqOV+dVkH3Kser8Zs+erfvuu8/2Zbv16tXTl19+mec0xeE4JUjdYsuWLdPgwYM1ZswY7d27V40aNVLbtm114sSJHPsfPXpU7dq1U6NGjbR3716NHj1aL7zwgj755JNbXDlyY3afZjl06JDi4+Ntj8jIyFtUMW4mOTlZ0dHRevPNN/PVn+O0eDC7X7NwrDqvjRs3qn///tq+fbvWrFmjjIwMtWrVSsnJyblOw/Hq3AqyT7NwrDqvcuXKafLkydq1a5d27dqlBx98UJ06ddKPP/6YY/9ic5wauKUeeOAB49lnn7Vrq1KlijFy5Mgc+w8fPtyoUqWKXVu/fv2MunXrFlmNMMfsPt2wYYMhybhw4cItqA5/lyRjxYoVefbhOC1+8rNfOVaLnzNnzhiSjI0bN+bah+O1eMnPPuVYLZ5Kly5tvPPOOzm+VlyOU0akbqG0tDTt3r1brVq1smtv1aqVtm7dmuM027Zty9a/devW2rVrl9LT04usVuRPQfZplvvvv1+hoaFq3ry5NmzYUJRloohxnN7eOFaLj8TERElSQEBArn04XouX/OzTLByrxUNmZqaWLl2q5ORk1atXL8c+xeU4JUjdQn/99ZcyMzMVHBxs1x4cHKyEhIQcp0lISMixf0ZGhv76668iqxX5U5B9GhoaqrfffluffPKJli9frsqVK6t58+batGnTrSgZRYDj9PbEsVq8GIahIUOGqGHDhqpevXqu/Thei4/87lOO1eLhwIEDKlmypDw9PfXss89qxYoVqlq1ao59i8tx6uboAu5EFovF7rlhGNnabtY/p3Y4jpl9WrlyZVWuXNn2vF69ejp58qRee+01NW7cuEjrRNHhOL39cKwWLwMGDND+/fu1ZcuWm/bleC0e8rtPOVaLh8qVK2vfvn26ePGiPvnkE/Xs2VMbN27MNUwVh+OUEalbqEyZMnJ1dc02UnHmzJlsqTtLSEhIjv3d3NwUGBhYZLUifwqyT3NSt25dHT58uLDLwy3CcXrn4Fh1TgMHDtSqVau0YcMGlStXLs++HK/Fg5l9mhOOVefj4eGhSpUqKSYmRnFxcYqOjtbrr7+eY9/icpwSpG4hDw8P1apVS2vWrLFrX7NmjerXr5/jNPXq1cvW/5tvvlFMTIzc3d2LrFbkT0H2aU727t2r0NDQwi4PtwjH6Z2DY9W5GIahAQMGaPny5Vq/fr0qVKhw02k4Xp1bQfZpTjhWnZ9hGEpNTc3xtWJznDroJhd3rKVLlxru7u7Gu+++a/z000/G4MGDDR8fH+PYsWOGYRjGyJEjjSeffNLW//fffzdKlChhvPjii8ZPP/1kvPvuu4a7u7vx8ccfO2oVcAOz+3TGjBnGihUrjF9//dU4ePCgMXLkSEOS8cknnzhqFXCDS5cuGXv37jX27t1rSDKmT59u7N271zh+/LhhGBynxZXZ/cqx6vyee+45w8/Pz/j222+N+Ph42+PKlSu2PhyvxUtB9inHqvMbNWqUsWnTJuPo0aPG/v37jdGjRxsuLi7GN998YxhG8T1OCVIO8NZbbxkRERGGh4eHUbNmTbtbevbs2dNo0qSJXf9vv/3WuP/++w0PDw+jfPnyxuzZs29xxbgZM/t0ypQpxt133214eXkZpUuXNho2bGisXr3aAVUjN1m30r3x0bNnT8MwOE6LK7P7lWPV+eW0PyUZ8+bNs/XheC1eCrJPOVadX58+fWyfk8qWLWs0b97cFqIMo/gepxbD+P9XbgEAAAAA8oVrpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAE7v2LFjslgs2rdvn6NLsfnll19Ut25deXl5qUaNGo4uBwBwixGkAAA31atXL1ksFk2ePNmufeXKlbJYLA6qyrHGjh0rHx8fHTp0SOvWrcuxT9OmTTV48OBbWxgA4JYgSAEA8sXLy0tTpkzRhQsXHF1KoUlLSyvwtEeOHFHDhg0VERGhwMDAAs/HMAxlZGQUeHoAgGMQpAAA+dKiRQuFhIQoLi4u1z7jxo3LdprbzJkzVb58edvzXr16qXPnzpo0aZKCg4Pl7++v8ePHKyMjQy+99JICAgJUrlw5vffee9nm/8svv6h+/fry8vJStWrV9O2339q9/tNPP6ldu3YqWbKkgoOD9eSTT+qvv/6yvd60aVMNGDBAQ4YMUZkyZdSyZcsc18NqtWrChAkqV66cPD09VaNGDX311Ve21y0Wi3bv3q0JEybIYrFo3Lhx2ebRq1cvbdy4Ua+//rosFossFouOHTumb7/9VhaLRV9//bViYmLk6empzZs368iRI+rUqZOCg4NVsmRJ1a5dW2vXrrWbZ/ny5TVx4kT16NFDJUuWVEREhD799FOdPXtWnTp1UsmSJRUVFaVdu3bZpjl+/LhiY2NVunRp+fj4qFq1avriiy9yXG8AQP4RpAAA+eLq6qpJkybpjTfe0KlTp/7WvNavX6/Tp09r06ZNmj59usaNG6cOHTqodOnS+v777/Xss8/q2Wef1cmTJ+2me+mllzR06FDt3btX9evXV8eOHXXu3DlJUnx8vJo0aaIaNWpo165d+uqrr/Tnn3+qW7dudvNYsGCB3Nzc9N1332nu3Lk51vf6669r2rRpeu2117R//361bt1aHTt21OHDh23LqlatmoYOHar4+HgNGzYsx3nUq1dPTz/9tOLj4xUfH6/w8HDb68OHD1dcXJx+/vln3Xfffbp8+bLatWuntWvXau/evWrdurViY2N14sQJu/nOmDFDDRo00N69e9W+fXs9+eST6tGjh5544gnt2bNHlSpVUo8ePWQYhiSpf//+Sk1N1aZNm3TgwAFNmTJFJUuWNLnHAADZGAAA3ETPnj2NTp06GYZhGHXr1jX69OljGIZhrFixwrj+T8nYsWON6Ohou2lnzJhhRERE2M0rIiLCyMzMtLVVrlzZaNSoke15RkaG4ePjYyxZssQwDMM4evSoIcmYPHmyrU96erpRrlw5Y8qUKYZhGMbLL79stGrVym7ZJ0+eNCQZhw4dMgzDMJo0aWLUqFHjpusbFhZmvPLKK3ZttWvXNp5//nnb8+joaGPs2LF5zqdJkybGoEGD7No2bNhgSDJWrlx50zqqVq1qvPHGG7bnERERxhNPPGF7Hh8fb0gyXn75ZVvbtm3bDElGfHy8YRiGERUVZYwbN+6mywIAmMOIFADAlClTpmjBggX66aefCjyPatWqycXl//4EBQcHKyoqyvbc1dVVgYGBOnPmjN109erVs/3s5uammJgY/fzzz5Kk3bt3a8OGDSpZsqTtUaVKFUnXrmfKEhMTk2dtSUlJOn36tBo0aGDX3qBBA9uyCsONdSQnJ2v48OGqWrWq/P39VbJkSf3yyy/ZRqTuu+8+28/BwcGSZLftstqytt0LL7ygiRMnqkGDBho7dqz2799faOsAAHcyghQAwJTGjRurdevWGj16dLbXXFxcbKeUZUlPT8/Wz93d3e65xWLJsc1qtd60nqy7BlqtVsXGxmrfvn12j8OHD6tx48a2/j4+Pjed5/XzzWIYRqHeofDGOl566SV98skneuWVV7R582bt27dPUVFR2W6Icf12yqonp7asbde3b1/9/vvvevLJJ3XgwAHFxMTojTfeKLT1AIA7FUEKAGDa5MmT9dlnn2nr1q127WXLllVCQoJdmCrM737avn277eeMjAzt3r3bNupUs2ZN/fjjjypfvrwqVapk98hveJKkUqVKKSwsTFu2bLFr37p1q+69915T9Xp4eCgzMzNffTdv3qxevXqpS5cuioqKUkhIiI4dO2ZqebkJDw/Xs88+q+XLl2vo0KH63//+VyjzBYA7GUEKAGBaVFSUHn/88WwjG02bNtXZs2c1depUHTlyRG+99Za+/PLLQlvuW2+9pRUrVuiXX35R//79deHCBfXp00fStZsqnD9/Xo899ph27Nih33//Xd9884369OmT7zCT5aWXXtKUKVO0bNkyHTp0SCNHjtS+ffs0aNAgU/MpX768vv/+ex07dkx//fVXniNslSpV0vLly7Vv3z798MMP6t69e75G5G5m8ODB+vrrr3X06FHt2bNH69evNx0IAQDZEaQAAAXyn//8J9tpfPfee69mzZqlt956S9HR0dqxY0eOd7QrqMmTJ2vKlCmKjo7W5s2b9emnn6pMmTKSpLCwMH333XfKzMxU69atVb16dQ0aNEh+fn5212PlxwsvvKChQ4dq6NChioqK0ldffaVVq1YpMjLS1HyGDRsmV1dXVa1aVWXLls12vdP1ZsyYodKlS6t+/fqKjY1V69atVbNmTVPLy0lmZqb69++ve++9V23atFHlypU1a9asvz1fALjTWYwb/woCAAAAAPLEiBQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGDS/wOmXY+PeRsgwgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_compare('trams', 'Number of trams')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The vast majority of resorts, such as Big Mountain, have no trams."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.8.9 Skiable terrain area<a id='5.8.9_Skiable_terrain_area'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_compare('SkiableTerrain_ac', 'Skiable terrain area (acres)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Big Mountain is amongst the resorts with the largest amount of skiable terrain."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5.9 Modeling scenarios<a id='5.9_Modeling_scenarios'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Big Mountain Resort has been reviewing potential scenarios for either cutting costs or increasing revenue (from ticket prices). Ticket price is not determined by any set of parameters; the resort is free to set whatever price it likes. However, the resort operates within a market where people pay more for certain facilities, and less for others. Being able to sense how facilities support a given ticket price is valuable business intelligence. This is where the utility of our model comes in.\n",
+    "\n",
+    "The business has shortlisted some options:\n",
+    "1. Permanently closing down up to 10 of the least used runs. This doesn't impact any other resort statistics.\n",
+    "2. Increase the vertical drop by adding a run to a point 150 feet lower down but requiring the installation of an additional chair lift to bring skiers back up, without additional snow making coverage\n",
+    "3. Same as number 2, but adding 2 acres of snow making cover\n",
+    "4. Increase the longest run by 0.2 mile to boast 3.5 miles length, requiring an additional snow making coverage of 4 acres\n",
+    "\n",
+    "The expected number of visitors over the season is 350,000 and, on average, visitors ski for five days. Assume the provided data includes the additional lift that Big Mountain recently installed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "expected_visitors = 350_000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>vertical_drop</th>\n",
+       "      <th>Snow Making_ac</th>\n",
+       "      <th>total_chairs</th>\n",
+       "      <th>fastQuads</th>\n",
+       "      <th>Runs</th>\n",
+       "      <th>LongestRun_mi</th>\n",
+       "      <th>trams</th>\n",
+       "      <th>SkiableTerrain_ac</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>124</th>\n",
+       "      <td>2353</td>\n",
+       "      <td>600.0</td>\n",
+       "      <td>14</td>\n",
+       "      <td>3</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>3.3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3000.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     vertical_drop  Snow Making_ac  total_chairs  fastQuads   Runs  \\\n",
+       "124           2353           600.0            14          3  105.0   \n",
+       "\n",
+       "     LongestRun_mi  trams  SkiableTerrain_ac  \n",
+       "124            3.3      0             3000.0  "
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_feats = ['vertical_drop', 'Snow Making_ac', 'total_chairs', 'fastQuads', \n",
+    "             'Runs', 'LongestRun_mi', 'trams', 'SkiableTerrain_ac']\n",
+    "big_mountain[all_feats]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Code task 2#\n",
+    "#In this function, copy the Big Mountain data into a new data frame\n",
+    "#(Note we use .copy()!)\n",
+    "#And then for each feature, and each of its deltas (changes from the original),\n",
+    "#create the modified scenario dataframe (bm2) and make a ticket price prediction\n",
+    "#for it. The difference between the scenario's prediction and the current\n",
+    "#prediction is then calculated and returned.\n",
+    "#Complete the code to increment each feature by the associated delta\n",
+    "def predict_increase(features, deltas):\n",
+    "    \"\"\"Increase in modelled ticket price by applying delta to feature.\n",
+    "    \n",
+    "    Arguments:\n",
+    "    features - list, names of the features in the ski_data dataframe to change\n",
+    "    deltas - list, the amounts by which to increase the values of the features\n",
+    "    \n",
+    "    Outputs:\n",
+    "    Amount of increase in the predicted ticket price\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    bm2 = X_bm.copy()\n",
+    "    for f, d in zip(features, deltas):\n",
+    "        bm2[f] += d\n",
+    "    return model.predict(bm2).item() - model.predict(X_bm).item()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.9.1 Scenario 1<a id='5.9.1_Scenario_1'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Close up to 10 of the least used runs. The number of runs is the only parameter varying."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[-1, -2, -3, -4, -5, -6, -7, -8, -9, -10]"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[i for i in range(-1, -11, -1)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "runs_delta = [i for i in range(-1, -11, -1)]\n",
+    "price_deltas = [predict_increase(['Runs'], [delta]) for delta in runs_delta]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0.0,\n",
+       " -0.4057971014492807,\n",
+       " -0.6666666666666714,\n",
+       " -0.6666666666666714,\n",
+       " -0.6666666666666714,\n",
+       " -1.2608695652173907,\n",
+       " -1.2608695652173907,\n",
+       " -1.2608695652173907,\n",
+       " -1.7101449275362341,\n",
+       " -1.8115942028985472]"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "price_deltas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Code task 3#\n",
+    "#Create two plots, side by side, for the predicted ticket price change (delta) for each\n",
+    "#condition (number of runs closed) in the scenario and the associated predicted revenue\n",
+    "#change on the assumption that each of the expected visitors buys 5 tickets\n",
+    "#There are two things to do here:\n",
+    "#1 - use a list comprehension to create a list of the number of runs closed from `runs_delta`\n",
+    "#2 - use a list comprehension to create a list of predicted revenue changes from `price_deltas`\n",
+    "runs_closed = [-1 * runs for runs in runs_delta] #1\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(10, 5))\n",
+    "fig.subplots_adjust(wspace=0.5)\n",
+    "ax[0].plot(runs_closed, price_deltas, 'o-')\n",
+    "ax[0].set(xlabel='Runs closed', ylabel='Change ($)', title='Ticket price')\n",
+    "revenue_deltas = [5 * expected_visitors * rev_changes for rev_changes in price_deltas] #2\n",
+    "ax[1].plot(runs_closed, revenue_deltas, 'o-')\n",
+    "ax[1].set(xlabel='Runs closed', ylabel='Change ($)', title='Revenue');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The model says closing one run makes no difference. Closing 2 and 3 successively reduces support for ticket price and so revenue. If Big Mountain closes down 3 runs, it seems they may as well close down 4 or 5 as there's no further loss in ticket price. Increasing the closures down to 6 or more leads to a large drop. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.9.2 Scenario 2<a id='5.9.2_Scenario_2'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this scenario, Big Mountain is adding a run, increasing the vertical drop by 150 feet, and installing an additional chair lift."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Code task 4#\n",
+    "#Call `predict_increase` with a list of the features 'Runs', 'vertical_drop', and 'total_chairs'\n",
+    "#and associated deltas of 1, 150, and 1\n",
+    "ticket2_increase = predict_increase(['Runs', 'vertical_drop', 'total_chairs'], [1, 150, 1])\n",
+    "revenue2_increase = 5 * expected_visitors * ticket2_increase"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This scenario increases support for ticket price by $1.99\n",
+      "Over the season, this could be expected to amount to $3474638\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f'This scenario increases support for ticket price by ${ticket2_increase:.2f}')\n",
+    "print(f'Over the season, this could be expected to amount to ${revenue2_increase:.0f}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.9.3 Scenario 3<a id='5.9.3_Scenario_3'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this scenario, you are repeating the previous one but adding 2 acres of snow making."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Code task 5#\n",
+    "#Repeat scenario 2 conditions, but add an increase of 2 to `Snow Making_ac`\n",
+    "ticket3_increase = predict_increase(['Runs', 'vertical_drop', 'total_chairs', 'Snow Making_ac'], [1, 150, 1, 2])\n",
+    "revenue3_increase = 5 * expected_visitors * ticket3_increase"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This scenario increases support for ticket price by $1.99\n",
+      "Over the season, this could be expected to amount to $3474638\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f'This scenario increases support for ticket price by ${ticket3_increase:.2f}')\n",
+    "print(f'Over the season, this could be expected to amount to ${revenue3_increase:.0f}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Such a small increase in the snow making area makes no difference!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.9.4 Scenario 4<a id='5.9.4_Scenario_4'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This scenario calls for increasing the longest run by .2 miles and guaranteeing its snow coverage by adding 4 acres of snow making capability."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 6#\n",
+    "#Predict the increase from adding 0.2 miles to `LongestRun_mi` and 4 to `Snow Making_ac`\n",
+    "predict_increase(['LongestRun_mi', 'Snow Making_ac'], [0.2, 4])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "No difference whatsoever. Although the longest run feature was used in the linear model, the random forest model (the one we chose because of its better performance) only has longest run way down in the feature importance list. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5.10 Summary<a id='5.10_Summary'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q: 1** Write a summary of the results of modeling these scenarios. Start by starting the current position; how much does Big Mountain currently charge? What does your modelling suggest for a ticket price that could be supported in the marketplace by Big Mountain's facilities? How would you approach suggesting such a change to the business leadership? Discuss the additional operating cost of the new chair lift per ticket (on the basis of each visitor on average buying 5 day tickets) in the context of raising prices to cover this. For future improvements, state which, if any, of the modeled scenarios you'd recommend for further consideration. Suggest how the business might test, and progress, with any run closures."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**A: 1** Your answer here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "In this notebook, we used modeling to clean, pre-process, and train the dataset. We concluded that the random\n",
+    "forest model should outperform the linear regression model in predicting AdultWeekend prices in Big Mountain\n",
+    "resort. We tested some features like: Runs, vertical_drop, total_chairs, Snow making_ac, and LongestRun_mi.\n",
+    "We had an expectation that 350,000 people and on avereage goes skiing 5 days. Current AdultWeekend prices are\n",
+    "$81 and want to generate $3,474,638, we would need to increase prices by $1.99."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5.11 Further work<a id='5.11_Further_work'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q: 2** What next? Highlight any deficiencies in the data that hampered or limited this work. The only price data in our dataset were ticket prices. You were provided with information about the additional operating cost of the new chair lift, but what other cost information would be useful? Big Mountain was already fairly high on some of the league charts of facilities offered, but why was its modeled price so much higher than its current price? Would this mismatch come as a surprise to the business executives? How would you find out? Assuming the business leaders felt this model was useful, how would the business make use of it? Would you expect them to come to you every time they wanted to test a new combination of parameters in a scenario? We hope you would have better things to do, so how might this model be made available for business analysts to use and explore?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**A: 2** Your answer here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "I believe it would be useful if there were columns relating to the breakdown of operating cost to make a better\n",
+    "prediction on ticket prices. Without it, it makes it difficult to see how we can cut costs of our facility.\n",
+    "We have data on the equipment and landscape of our resort so we can only try to increase prices to justify these\n",
+    "features."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": true
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
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+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From f60441bd7bbff30c4ca148debc1f0488f478bdf3 Mon Sep 17 00:00:00 2001
From: brianbui0 <buibrian96@gmail.com>
Date: Sun, 1 Sep 2024 17:14:36 +0700
Subject: [PATCH 8/9] Add files via upload

---
 .../Guided Capstone Project report.pdf          | Bin 0 -> 55483 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 CapstoneSteps/Guided Capstone Project report.pdf

diff --git a/CapstoneSteps/Guided Capstone Project report.pdf b/CapstoneSteps/Guided Capstone Project report.pdf
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From 940e3c5c5a52dda421324de55063cef6699b17ca Mon Sep 17 00:00:00 2001
From: brianbui0 <buibrian96@gmail.com>
Date: Sun, 1 Sep 2024 20:28:34 +0700
Subject: [PATCH 9/9] Add files via upload

---
 .../Big Mountain Ski Resort Presentation.pdf    | Bin 0 -> 45387 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 CapstoneSteps/Big Mountain Ski Resort Presentation.pdf

diff --git a/CapstoneSteps/Big Mountain Ski Resort Presentation.pdf b/CapstoneSteps/Big Mountain Ski Resort Presentation.pdf
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