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+{
+ "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": 1,
+   "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": 2,
+   "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": 3,
+   "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": 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>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": 4,
+     "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": 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>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": 5,
+     "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": 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>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": 6,
+     "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": 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>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": 7,
+     "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": 8,
+   "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": 8,
+     "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": 9,
+   "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": 9,
+     "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": 10,
+   "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": 10,
+     "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": "markdown",
+   "metadata": {},
+   "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:\n",
+    "There are 2 resorts named Crystal Mountain but are in different regions and states. Thus, this shows us that 'Crystal Mountain' is not a duplicated row. "
+   ]
+  },
+  {
+   "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>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": 11,
+     "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": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "33"
+      ]
+     },
+     "execution_count": 12,
+     "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": 13,
+   "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": 13,
+     "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": 14,
+   "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": 14,
+     "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": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Region    38\n",
+       "state     35\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 15,
+     "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": 16,
+   "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": 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>AdultWeekday</th>\n",
+       "      <th>AdultWeekend</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>47.333333</td>\n",
+       "      <td>57.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Arizona</th>\n",
+       "      <td>81.500000</td>\n",
+       "      <td>83.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>California</th>\n",
+       "      <td>78.214286</td>\n",
+       "      <td>81.416667</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>47.800000</td>\n",
+       "      <td>56.800000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             AdultWeekday  AdultWeekend\n",
+       "state                                  \n",
+       "Alaska          47.333333     57.333333\n",
+       "Arizona         81.500000     83.500000\n",
+       "California      78.214286     81.416667\n",
+       "Colorado        90.714286     90.714286\n",
+       "Connecticut     47.800000     56.800000"
+      ]
+     },
+     "execution_count": 17,
+     "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')[['AdultWeekday', 'AdultWeekend']].mean()\n",
+    "state_price_means.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\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": "markdown",
+   "metadata": {},
+   "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": 19,
+   "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": 20,
+   "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": 20,
+     "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": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\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": "markdown",
+   "metadata": {},
+   "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": 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>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": 22,
+     "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": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    82.424242\n",
+       "2    14.242424\n",
+       "1     3.333333\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 23,
+     "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": 24,
+   "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": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "39    26819.0\n",
+       "Name: SkiableTerrain_ac, dtype: float64"
+      ]
+     },
+     "execution_count": 25,
+     "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": 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>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": 26,
+     "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\n",
+    "\n",
+    "Silverton Mountain has a very large skiable terrain area of 26819.0"
+   ]
+  },
+  {
+   "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": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "26819.0"
+      ]
+     },
+     "execution_count": 27,
+     "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": 28,
+   "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": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1819.0"
+      ]
+     },
+     "execution_count": 29,
+     "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": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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10ll/8+bNoVQqdY4alHVdj/v3338BwOCRw9Ls56SkJOTk5GDcuHGluhu4vNtZls+/ZluuXr1aYj3Lli1Dy5YtYW1tDUtLS8jlcuzevbvE/VacnTt3oqCgAMOGDdPZJmtrawQEBBi8+7ks30uG+m/duhUymQxvvvmmzjqVSiWaNWumt87yfi6A0u2zX3/9FdbW1hg1alSZtkujpO+zlJQUqFQqDBo0SKdf+/btS3U3cln3l7OzMzp37my0Vrlcrn199uxZ/Pvvvxg6dKjO95m9vT0GDBiAlJQUnVPcxrb34cOH2ssmSltvcnIyHjx4oHcHnre3Nzp37lzmU2PAo+/zu3fvYsiQIdi8eXOJd4RrNGnSBK1atUJ0dLS27cyZM/jjjz/03hcbNmxAx44dYW9vr31PrVix4qk+h49LSkrC7du3MXz4cJ39V1RUhG7duuHIkSOlvrzCLENWbm4ubt26BS8vL6N96tSpg127dsHd3R3jx49HnTp1UKdOnRKv83mSp6dnqfsqlUqjbbdu3SrTesvq1q1bBmvV7KMn1+/q6qrzWnPh5eOnlZ50584dCCHKtJ7SmD59Or744gukpKQgNDQUrq6u6NKlC44eParXNzY2FtWrV8fgwYNLtexLly6hU6dOuHr1KhYtWoSDBw/iyJEj+PbbbwHob6/mRorHlWYMr1+/jl9++QVyuVznp3HjxgBQ7BfN/fv30alTJxw+fBizZ8/Gvn37cOTIEe0p1ydrtLW11bvD6vr167h79y6srKz0asjMzNSuv6zrepJmurW1tU57afez5rqp0tw48jTbWZbPv2ZbStr2BQsW4N1330W7du3w888/IyUlBUeOHEG3bt1KnLc4mlM3bdq00dum+Ph4vfeOof1Skic/s9evX4cQAh4eHnrrTElJ0VtneT8Xpd1nN27cgJeXV6n+0WRISd9nmhoNbYehtieVdX8V93vjyWma2ox9rxYVFeHOnTs67SVtb2nrLWnd5fk+Hzp0KFauXImLFy9iwIABcHd3R7t27ZCYmFjivKNGjUJycjL+/PNPAI+eMKBQKDBkyBBtn4SEBAwaNAjVq1dHbGwskpOTceTIEYwaNQoPHz4sc72GaD6TAwcO1Nt/n332GYQQ2lPhJanUdxcas23bNhQWFpZ4sWKnTp3QqVMnFBYW4ujRo1i8eDEmT54MDw+PUv+SLsuztzIzM422aT4Umi90lUql06+0ad8YV1dXXLt2Ta9dc+TBzc3tqZYPPPrXWZUqVSp8PZaWlpg6dSqmTp2Ku3fvYteuXfj444/RtWtXXL58WeeunB07duD1119Hp06dsHv3bvj4+BS77E2bNiE3NxcJCQk6fY8fP26w/5PXVgD6Y2iIm5sbmjZtijlz5hicXtw/CPbs2YN///0X+/bt0x5RAmD0GiFD70k3Nze4urpqrzN8kuZRC2Vdl6H1AI+utXn8i7m0+1lzs8mT118Z8jTbCZT+86/5sizpvRsbG4vAwEAsXbpUp/1pnxWmWe9PP/1U4vsZKNt3krF53NzcIJPJcPDgQYN3tj3ZVt7PRWn3WbVq1XDo0CEUFRWVO2gVR1Ojse0o6WhWWfdXcWP05DRNbca+V6tUqQJnZ+di6ytvvSWt+/HPhLW1td7F9YDh310jR47EyJEjkZubiwMHDiA8PBw9e/bEX3/9Vex7fMiQIZg6dSpiYmIwZ84crFmzBn379tXZ/tjYWPj6+iI+Pl5nXz75O9WQ0v7+1Wz34sWLjd7JX5pwDpjhkaxLly5h2rRpcHJyMnjhsiEWFhZo166d9l/VmlN3pTl6Uxbp6ek4ceKETltcXBwcHBy0F7hqPswnT57U6bdlyxa95SkUilLX1qVLF+0v0MetXr0atra2FfLIBzs7O7Rr1w4JCQk6dRUVFSE2NhY1atRAvXr1nmodVatWxcCBAzF+/Hjcvn1b7y4QHx8f7RdHp06dSrwwU/MhfPyLRgiB5cuXG+x/7949vbGIi4tDlSpV8MorrxhdT8+ePZGWloY6deqgdevWej/FhSxDNQLQuXu2JD179sStW7dQWFhocP2aZ5Q97boaNGgA4NFF7iVtg6H97O/vDycnJyxbtqzEU/6GlHY7H2fs86+hOZXYqFGjYtctk8n09tvJkyeLvSHlcca+b7p27QpLS0v8/fffBrfp8cfYVJSePXtCCIGrV68aXF+TJk10+pf3c1HafRYaGoqHDx8iJibm6TbMiHbt2kGhUGhvztFISUkp9hIJjbLur7KoX78+qlevjri4OJ3PRG5uLn7++WftHYdlUdp6O3ToABsbG8TGxurMf+XKFe0lKBq1atXCX3/9pRNQbt26pXP355Ps7OwQGhqKGTNmID8/H+np6cXW7ezsjL59+2L16tXYunUrMjMz9U4VymQyWFlZ6QSszMzMUt1dWNrfvx07dkTVqlVx+vRpo5/J0j5Xr1IfyUpLS9OeC83KysLBgwcRHR0NCwsLbNy4UfuvYkOWLVuGPXv2oEePHqhZsyYePnyIlStXAoD2IaYODg7w8fHB5s2b0aVLF7i4uMDNza3cTwz38vJC7969ERERAU9PT8TGxiIxMRGfffaZ9kPSpk0b1K9fH9OmTUNBQQGcnZ2xceNGg3eQNGnSBAkJCVi6dClatWqFKlWqGP3CDQ8P114X9Mknn8DFxQVr167Ftm3bMH/+fDg5OZVrm54UFRWF4OBgBAUFYdq0abCyssKSJUuQlpaGdevWletf2b169dI+E61atWq4ePEiFi5cCB8fH9StW1evv6enJ/bv34+uXbvilVdeQWJiotEntQcHB8PKygpDhgzBBx98gIcPH2Lp0qV6h981XF1d8e677+LSpUuoV68etm/fjuXLl+Pdd99FzZo1jW7DrFmzkJiYCH9/f7z33nuoX78+Hj58iAsXLmD79u1YtmyZ0VNk/v7+cHZ2xjvvvIPw8HDI5XKsXbtWL7AXZ/DgwVi7di26d++OSZMmoW3btpDL5bhy5Qr27t2LPn36oF+/fk+9rnbt2sHGxgYpKSk614WUdj/b29vjyy+/xFtvvYVXX30VY8aMgYeHB86fP48TJ07gm2++qZDtLM3nXyMlJQWurq4l/qLs2bMnPv30U4SHhyMgIABnz57FrFmz4OvrW6rHdGiWv2jRIgwfPhxyuRz169dHrVq1MGvWLMyYMQP//PMPunXrBmdnZ1y/fh1//PEH7OzsSnUHYVl07NgRb7/9NkaOHImjR4/ilVdegZ2dHa5du4ZDhw6hSZMmePfdd7X9y/u5KO0+GzJkCKKjo/HOO+/g7NmzCAoKQlFREQ4fPoyGDRuW+syDMS4uLpg6dSqioqLg7OyMfv364cqVK4iMjISnp2eJR8/Kur/KokqVKpg/fz7eeOMN9OzZE2PHjoVKpcLnn3+Ou3fvYt68eWVeZmnrrVq1KmbOnImPP/4Yw4YNw5AhQ3Dr1i1ERkbC2toa4eHh2mUOHToU3333Hd58802MGTMGt27dwvz58/VOXY8ZMwY2Njbo2LEjPD09kZmZiaioKDg5OaFNmzYl1j5q1CjEx8djwoQJqFGjht7ntWfPnkhISMC4ceMwcOBAXL58GZ9++ik8PT1L/Ed3aX//2tvbY/HixRg+fDhu376NgQMHwt3dHTdu3MCJEydw48YNvaOzRpX6EvlnSHMHgObHyspKuLu7i4CAADF37lyRlZWlN8+Td/wlJyeLfv36CR8fH6FQKISrq6sICAgQW7Zs0Zlv165dokWLFkKhUAgA2jsRHr+braR1CfF/dxP99NNPonHjxsLKykrUqlVLLFiwQG/+v/76S4SEhAhHR0dRrVo1MXHiRLFt2za9uwtv374tBg4cKKpWrSpkMpnOOmHgLp5Tp06JXr16CScnJ2FlZSWaNWsmoqOjdfpo7qLYsGGDTrvmzpcn+xty8OBB0blzZ2FnZydsbGxE+/btxS+//GJweaW5u/DLL78U/v7+ws3NTVhZWYmaNWuK0aNHiwsXLmj7GBqPu3fvio4dOwoXFxftnaiG7i785ZdfRLNmzYS1tbWoXr26+M9//iN+/fVXvf0dEBAgGjduLPbt2ydat24tFAqF8PT0FB9//LFQq9U6yzS0/2/cuCHee+894evrK+RyuXBxcRGtWrUSM2bMEPfv3y92HyQlJYkOHToIW1tbUa1aNfHWW2+JY8eO6Y3J8OHDhZ2dncFlqNVq8cUXX2i31d7eXjRo0ECMHTtWnDt3rszrMmbo0KGiUaNGeu2l3c9CCLF9+3YREBAg7OzshK2trWjUqJH47LPPKmw7S/v5LyoqEj4+Pnp39hmiUqnEtGnTRPXq1YW1tbVo2bKl2LRpk8H3nKG7C4UQYvr06cLLy0tUqVJFb79s2rRJBAUFCUdHR6FQKISPj48YOHCg2LVrV6n2iyHFfY8JIcTKlStFu3bttJ/lOnXqiGHDhomjR49q+zzN56Is++zBgwfik08+EXXr1hVWVlbC1dVVdO7cWSQlJWn7GLu7sDTfZ0VFRWL27NmiRo0awsrKSjRt2lRs3bpVNGvWTO/uUGPKsr+eVNJ34qZNm0S7du2EtbW1sLOzE126dBG///67Th9j42nsrrnS1CuEED/88INo2rSpsLKyEk5OTqJPnz4G74hetWqVaNiwobC2thaNGjUS8fHxemO5atUqERQUJDw8PISVlZXw8vISgwYNEidPnjS43U8qLCwU3t7eAoCYMWOGwT7z5s0TtWrVEgqFQjRs2FAsX77c6O/lx98vQpT+968QQuzfv1/06NFDuLi4CLlcLqpXry569Oih934rjkyIchyzJ6IX1tGjR9GmTRukpKSgXbt2pi7nqezevRshISFIT0/XngolXYGBgbh58ybS0tJMXUqFy8jIQIMGDRAeHl7swzKJyoshi4jK7PXXX0dubi62bt1q6lKeSlBQEF566SWj1+jR8xOyTpw4gXXr1sHf3x+Ojo44e/Ys5s+fj5ycHKSlpZX6QmaisqjU12QRUeX05ZdfYsWKFbh3757Z/pHoO3fuICAgQOdp3PT8srOzw9GjR7FixQrcvXsXTk5OCAwMxJw5cxiwSDI8kkVEREQkAbN7hAMRERGROWDIIiIiIpIAQxYRERGRBHjhOx49sfzff/+Fg4NDuR6mSURERM+eEAL37t17qr99KSWGLDz6G03e3t6mLoOIiIjK4fLly6X6w/PPGkMW/u+Pyl6+fLnMf92+OGq1Gr/99htCQkIgl8srbLlU8ThW5oHjZD44VubDnMcqJycH3t7elfZRMgxZ+L8/buvo6FjhIcvW1haOjo5m98Z90XCszAPHyXxwrMzH8zBWlfVSn8p3ApOIiIjoOcCQRURERCQBhiwiIiIiCZg0ZEVFRaFNmzZwcHCAu7s7+vbti7Nnz+r0EUIgIiICXl5esLGxQWBgINLT03X6qFQqTJw4EW5ubrCzs0Pv3r1x5cqVZ7kpRERERDpMGrL279+P8ePHIyUlBYmJiSgoKEBISAhyc3O1febPn48FCxbgm2++wZEjR6BUKhEcHIx79+5p+0yePBkbN27E+vXrcejQIdy/fx89e/ZEYWGhKTaLiIiIyLR3F+7YsUPndXR0NNzd3ZGamopXXnkFQggsXLgQM2bMQP/+/QEAq1atgoeHB+Li4jB27FhkZ2djxYoVWLNmDV599VUAQGxsLLy9vbFr1y507dr1mW8XERERUaV6hEN2djYAwMXFBQCQkZGBzMxMhISEaPsoFAoEBAQgKSkJY8eORWpqKtRqtU4fLy8v+Pn5ISkpyWDIUqlUUKlU2tc5OTkAHt3GqlarK2x7NMuqyGWSNDhW5oHjZD44VubDnMeqstdcaUKWEAJTp07Fyy+/DD8/PwBAZmYmAMDDw0Onr4eHBy5evKjtY2VlBWdnZ70+mvmfFBUVhcjISL323377Dba2tk+9LU9KTEys8GWSNDhW5oHjZD44VubDHMcqLy/P1CUUq9KErAkTJuDkyZM4dOiQ3rQnHzImhCjxwWPF9Zk+fTqmTp2qfa15YmxISEiFP4w0MTERwcHBZvuAtxcFx8o8cJzMB8fKfJjzWGnORFVWlSJkTZw4EVu2bMGBAwd0/vaQUqkE8Oholaenp7Y9KytLe3RLqVQiPz8fd+7c0TmalZWVBX9/f4PrUygUUCgUeu1yuVySN5hUy6WKx7EyDxwn88GxMh/mOFaVvV6T3l0ohMCECROQkJCAPXv2wNfXV2e6r68vlEqlziHM/Px87N+/XxugWrVqBblcrtPn2rVrSEtLMxqyiIiIiKRm0iNZ48ePR1xcHDZv3gwHBwftNVROTk6wsbGBTCbD5MmTMXfuXNStWxd169bF3LlzYWtri7CwMG3f0aNH4/3334erqytcXFwwbdo0NGnSRHu3IREREdGzZtKQtXTpUgBAYGCgTnt0dDRGjBgBAPjggw/w4MEDjBs3Dnfu3EG7du3w22+/6fzF7a+++gqWlpYYNGgQHjx4gC5duiAmJgYWFhbPalOIiIiIdJg0ZAkhSuwjk8kQERGBiIgIo32sra2xePFiLF68uAKrIyIiIio//u1CIiIiIglUirsLn3d+ETuhKiz+kROGXJjXQ4JqiIiI6FngkSwiIiIiCTBkEREREUmAIYuIiIhIAgxZRERERBJgyCIiIiKSAEMWERERkQQYsoiIiIgkwJBFREREJAGGLCIiIiIJMGQRERERSYAhi4iIiEgCDFlEREREEmDIIiIiIpIAQxYRERGRBBiyiIiIiCTAkEVEREQkAYYsIiIiIgkwZBERERFJgCGLiIiISAIMWUREREQSYMgiIiIikgBDFhEREZEEGLKIiIiIJMCQRURERCQBhiwiIiIiCTBkEREREUmAIYuIiIhIAgxZRERERBJgyCIiIiKSAEMWERERkQQYsoiIiIgkwJBFREREJAGThqwDBw6gV69e8PLygkwmw6ZNm3Smy2Qygz+ff/65tk9gYKDe9MGDBz/jLSEiIiLSZdKQlZubi2bNmuGbb74xOP3atWs6PytXroRMJsOAAQN0+o0ZM0an33ffffcsyiciIiIyytKUKw8NDUVoaKjR6UqlUuf15s2bERQUhNq1a+u029ra6vUlIiIiMiWThqyyuH79OrZt24ZVq1bpTVu7di1iY2Ph4eGB0NBQhIeHw8HBweiyVCoVVCqV9nVOTg4AQK1WQ61WV1jNmmUpqoinmp+kp9nX3OeVG8fJfHCszIc5j1Vlr1kmhChfAqhgMpkMGzduRN++fQ1Onz9/PubNm4d///0X1tbW2vbly5fD19cXSqUSaWlpmD59Ol566SUkJiYaXVdERAQiIyP12uPi4mBra/vU20JERETSy8vLQ1hYGLKzs+Ho6GjqcvSYTchq0KABgoODsXjx4mKXk5qaitatWyM1NRUtW7Y02MfQkSxvb2/cvHmzQgdJrVYjMTERM49WgapIVub50yK6VlgtVDzNWAUHB0Mul5u6HDKC42Q+OFbmw5zHKicnB25ubpU2ZJnF6cKDBw/i7NmziI+PL7Fvy5YtIZfLce7cOaMhS6FQQKFQ6LXL5XJJ3mCqIhlUhWUPWeb2Zn8eSPUeoIrFcTIfHCvzYY5jVdnrNYvnZK1YsQKtWrVCs2bNSuybnp4OtVoNT0/PZ1AZERERkWEmPZJ1//59nD9/Xvs6IyMDx48fh4uLC2rWrAng0aHADRs24Msvv9Sb/++//8batWvRvXt3uLm54fTp03j//ffRokULdOzY8ZltBxEREdGTTBqyjh49iqCgIO3rqVOnAgCGDx+OmJgYAMD69eshhMCQIUP05reyssLu3buxaNEi3L9/H97e3ujRowfCw8NhYWHxTLaBiIiIyBCThqzAwECUdN3922+/jbffftvgNG9vb+zfv1+K0oiIiIieillck0VERERkbhiyiIiIiCTAkEVEREQkAYYsIiIiIgkwZBERERFJgCGLiIiISAIMWUREREQSYMgiIiIikgBDFhEREZEEGLKIiIiIJMCQRURERCQBhiwiIiIiCTBkEREREUmAIYuIiIhIAgxZRERERBJgyCIiIiKSAEMWERERkQQYsoiIiIgkwJBFREREJAGGLCIiIiIJMGQRERERSYAhi4iIiEgCDFlEREREEmDIIiIiIpIAQxYRERGRBBiyiIiIiCTAkEVEREQkAYYsIiIiIgkwZBERERFJgCGLiIiISAIMWUREREQSYMgiIiIikgBDFhEREZEEGLKIiIiIJGDSkHXgwAH06tULXl5ekMlk2LRpk870ESNGQCaT6fy0b99ep49KpcLEiRPh5uYGOzs79O7dG1euXHmGW0FERESkz6QhKzc3F82aNcM333xjtE+3bt1w7do17c/27dt1pk+ePBkbN27E+vXrcejQIdy/fx89e/ZEYWGh1OUTERERGWVpypWHhoYiNDS02D4KhQJKpdLgtOzsbKxYsQJr1qzBq6++CgCIjY2Ft7c3du3aha5du1Z4zURERESlYdKQVRr79u2Du7s7qlatioCAAMyZMwfu7u4AgNTUVKjVaoSEhGj7e3l5wc/PD0lJSUZDlkqlgkql0r7OyckBAKjVaqjV6gqrXbMsRRXxVPOT9DT7mvu8cuM4mQ+Olfkw57Gq7DVX6pAVGhqK1157DT4+PsjIyMDMmTPRuXNnpKamQqFQIDMzE1ZWVnB2dtaZz8PDA5mZmUaXGxUVhcjISL323377Dba2thW+HZ+2LirXfE+eGiXpJSYmmroEKgWOk/ngWJkPcxyrvLw8U5dQrEodsl5//XXt//v5+aF169bw8fHBtm3b0L9/f6PzCSEgk8mMTp8+fTqmTp2qfZ2TkwNvb2+EhITA0dGxYorHo4SdmJiImUerQFVkvB5j0iJ4uvNZ0YxVcHAw5HK5qcshIzhO5oNjZT7Meaw0Z6Iqq0odsp7k6ekJHx8fnDt3DgCgVCqRn5+PO3fu6BzNysrKgr+/v9HlKBQKKBQKvXa5XC7JG0xVJIOqsOwhy9ze7M8Dqd4DVLE4TuaDY2U+zHGsKnu9ZvWcrFu3buHy5cvw9PQEALRq1QpyuVznEOe1a9eQlpZWbMgiIiIikppJj2Tdv38f58+f177OyMjA8ePH4eLiAhcXF0RERGDAgAHw9PTEhQsX8PHHH8PNzQ39+vUDADg5OWH06NF4//334erqChcXF0ybNg1NmjTR3m1IREREZAomDVlHjx5FUFCQ9rXmOqnhw4dj6dKlOHXqFFavXo27d+/C09MTQUFBiI+Ph4ODg3aer776CpaWlhg0aBAePHiALl26ICYmBhYWFs98e4iIiIg0TBqyAgMDIYTxxxvs3LmzxGVYW1tj8eLFWLx4cUWWRkRERPRUzOqaLCIiIiJzwZBFREREJAGGLCIiIiIJMGQRERERSYAhi4iIiEgCDFlEREREEmDIIiIiIpIAQxYRERGRBBiyiIiIiCTAkEVEREQkAYYsIiIiIgkwZBERERFJgCGLiIiISAIMWUREREQSYMgiIiIikgBDFhEREZEEGLKIiIiIJMCQRURERCQBhiwiIiIiCTBkEREREUmAIYuIiIhIAgxZRERERBJgyCIiIiKSAEMWERERkQQYsoiIiIgkwJBFREREJAGGLCIiIiIJMGQRERERSYAhi4iIiEgCDFlEREREEmDIIiIiIpIAQxYRERGRBBiyiIiIiCTAkEVEREQkAZOGrAMHDqBXr17w8vKCTCbDpk2btNPUajU+/PBDNGnSBHZ2dvDy8sKwYcPw77//6iwjMDAQMplM52fw4MHPeEuIiIiIdJk0ZOXm5qJZs2b45ptv9Kbl5eXh2LFjmDlzJo4dO4aEhAT89ddf6N27t17fMWPG4Nq1a9qf77777lmUT0RERGSUpSlXHhoaitDQUIPTnJyckJiYqNO2ePFitG3bFpcuXULNmjW17ba2tlAqlZLWSkRERFQWJg1ZZZWdnQ2ZTIaqVavqtK9duxaxsbHw8PBAaGgowsPD4eDgYHQ5KpUKKpVK+zonJwfAo1OUarW6wurVLEtRRTzV/CQ9zb7mPq/cOE7mg2NlPsx5rCp7zTIhRPkSQAWTyWTYuHEj+vbta3D6w4cP8fLLL6NBgwaIjY3Vti9fvhy+vr5QKpVIS0vD9OnT8dJLL+kdBXtcREQEIiMj9drj4uJga2v71NtCRERE0svLy0NYWBiys7Ph6Oho6nL0mEXIUqvVeO2113Dp0iXs27ev2B2ZmpqK1q1bIzU1FS1btjTYx9CRLG9vb9y8ebNCB0mtViMxMREzj1aBqkhW5vnTIrpWWC1UPM1YBQcHQy6Xm7ocMoLjZD44VubDnMcqJycHbm5ulTZkVfrThWq1GoMGDUJGRgb27NlT4k5s2bIl5HI5zp07ZzRkKRQKKBQKvXa5XC7JG0xVJIOqsOwhy9ze7M8Dqd4DVLE4TuaDY2U+zHGsKnu9lTpkaQLWuXPnsHfvXri6upY4T3p6OtRqNTw9PZ9BhURERESGmTRk3b9/H+fPn9e+zsjIwPHjx+Hi4gIvLy8MHDgQx44dw9atW1FYWIjMzEwAgIuLC6ysrPD3339j7dq16N69O9zc3HD69Gm8//77aNGiBTp27GiqzSIiIiIybcg6evQogoKCtK+nTp0KABg+fDgiIiKwZcsWAEDz5s115tu7dy8CAwNhZWWF3bt3Y9GiRbh//z68vb3Ro0cPhIeHw8LC4pltBxEREdGTTBqyAgMDUdx19yVdk+/t7Y39+/dXdFlERERET41/u5CIiIhIAgxZRERERBJgyCIiIiKSAEMWERERkQQYsoiIiIgkwJBFREREJAGGLCIiIiIJMGQRERERSYAhi4iIiEgCDFlEREREEmDIIiIiIpIAQxYRERGRBBiyiIiIiCTAkEVEREQkAYYsIiIiIgmUK2TVrl0bt27d0mu/e/cuateu/dRFEREREZm7coWsCxcuoLCwUK9dpVLh6tWrT10UERERkbmzLEvnLVu2aP9/586dcHJy0r4uLCzE7t27UatWrQorjoiIiMhclSlk9e3bFwAgk8kwfPhwnWlyuRy1atXCl19+WWHFEREREZmrMoWsoqIiAICvry+OHDkCNzc3SYoiIiIiMndlClkaGRkZFV0HERER0XOlXCELAHbv3o3du3cjKytLe4RLY+XKlU9dGBEREZE5K1fIioyMxKxZs9C6dWt4enpCJpNVdF1EREREZq1cIWvZsmWIiYnB0KFDK7oeIiIioudCuZ6TlZ+fD39//4quhYiIiOi5Ua6Q9dZbbyEuLq6iayEiIiJ6bpTrdOHDhw/x/fffY9euXWjatCnkcrnO9AULFlRIcURERETmqlwh6+TJk2jevDkAIC0tTWcaL4InIiIiKmfI2rt3b0XXQURERPRcKdc1WURERERUvHIdyQoKCir2tOCePXvKXRARERHR86BcIUtzPZaGWq3G8ePHkZaWpveHo4mIiIheROUKWV999ZXB9oiICNy/f/+pCiIiIiJ6HlToNVlvvvkm/24hERERESo4ZCUnJ8Pa2rrU/Q8cOIBevXrBy8sLMpkMmzZt0pkuhEBERAS8vLxgY2ODwMBApKen6/RRqVSYOHEi3NzcYGdnh969e+PKlSsVsTlERERE5Vau04X9+/fXeS2EwLVr13D06FHMnDmz1MvJzc1Fs2bNMHLkSAwYMEBv+vz587FgwQLExMSgXr16mD17NoKDg3H27Fk4ODgAACZPnoxffvkF69evh6urK95//3307NkTqampsLCwKM/mERERET21coUsJycnnddVqlRB/fr1MWvWLISEhJR6OaGhoQgNDTU4TQiBhQsXYsaMGdpQt2rVKnh4eCAuLg5jx45FdnY2VqxYgTVr1uDVV18FAMTGxsLb2xu7du1C165dy7N5RERERE+tXCErOjq6ouvQk5GRgczMTJ3QplAoEBAQgKSkJIwdOxapqalQq9U6fby8vODn54ekpCSjIUulUkGlUmlf5+TkAHh0l6Rara6wbdAsS1FFPNX8JD3NvuY+r9w4TuaDY2U+zHmsKnvN5QpZGqmpqThz5gxkMhkaNWqEFi1aVFRdyMzMBAB4eHjotHt4eODixYvaPlZWVnB2dtbro5nfkKioKERGRuq1//bbb7C1tX3a0vV82rqoXPNt3769giuhkiQmJpq6BCoFjpP54FiZD3Mcq7y8PFOXUKxyhaysrCwMHjwY+/btQ9WqVSGEQHZ2NoKCgrB+/XpUq1atwgp88qGnQogS/z5iSX2mT5+OqVOnal/n5OTA29sbISEhcHR0fLqCH6NWq5GYmIiZR6tAVVT2v+mYFsHTnc+KZqyCg4P1/uA5VR4cJ/PBsTIf5jxWmjNRlVW5QtbEiRORk5OD9PR0NGzYEABw+vRpDB8+HO+99x7WrVv31IUplUoAj45WeXp6atuzsrK0R7eUSiXy8/Nx584dnaNZWVlZ8Pf3N7pshUIBhUKh1y6XyyV5g6mKZFAVlj1kmdub/Xkg1XuAKhbHyXxwrMyHOY5VZa+3XI9w2LFjB5YuXaoNWADQqFEjfPvtt/j1118rpDBfX18olUqdw5f5+fnYv3+/NkC1atUKcrlcp8+1a9eQlpZWbMgiIiIiklq5jmQVFRUZTI9yuRxFRaW//uj+/fs4f/689nVGRgaOHz8OFxcX1KxZE5MnT8bcuXNRt25d1K1bF3PnzoWtrS3CwsIAPLrLcfTo0Xj//ffh6uoKFxcXTJs2DU2aNNHebUhERERkCuUKWZ07d8akSZOwbt06eHl5AQCuXr2KKVOmoEuXLqVeztGjRxEUFKR9rblOavjw4YiJicEHH3yABw8eYNy4cbhz5w7atWuH3377TfuMLODRn/ixtLTEoEGD8ODBA3Tp0gUxMTF8RhYRERGZVLlC1jfffIM+ffqgVq1a8Pb2hkwmw6VLl9CkSRPExsaWejmBgYEQwvjjDWQyGSIiIhAREWG0j7W1NRYvXozFixeXZROIiIiIJFWukOXt7Y1jx44hMTERf/75J4QQaNSoEU/REREREf1/Zbrwfc+ePWjUqJH2lsng4GBMnDgR7733Htq0aYPGjRvj4MGDkhRKREREZE7KFLIWLlyIMWPGGHyWlJOTE8aOHYsFCxZUWHFERERE5qpMIevEiRPo1q2b0ekhISFITU196qKIiIiIzF2ZQtb169eLffCXpaUlbty48dRFEREREZm7MoWs6tWr49SpU0annzx5Uufp7EREREQvqjKFrO7du+OTTz7Bw4cP9aY9ePAA4eHh6NmzZ4UVR0RERGSuyvQIh//+979ISEhAvXr1MGHCBNSvXx8ymQxnzpzBt99+i8LCQsyYMUOqWomIiIjMRplCloeHB5KSkvDuu+9i+vTp2geJymQydO3aFUuWLNH+8WYiIiKiF1mZH0bq4+OD7du3486dOzh//jyEEKhbty6cnZ2lqI+IiIjILJXrie8A4OzsjDZt2lRkLURERETPjTJd+E5EREREpcOQRURERCQBhiwiIiIiCTBkEREREUmAIYuIiIhIAgxZRERERBJgyCIiIiKSAEMWERERkQQYsoiIiIgkwJBFREREJAGGLCIiIiIJMGQRERERSYAhi4iIiEgCDFlEREREEmDIIiIiIpIAQxYRERGRBBiyiIiIiCTAkEVEREQkAYYsIiIiIgkwZBERERFJgCGLiIiISAIMWUREREQSYMgiIiIikkClD1m1atWCTCbT+xk/fjwAYMSIEXrT2rdvb+KqiYiI6EVnaeoCSnLkyBEUFhZqX6elpSE4OBivvfaatq1bt26Ijo7WvraysnqmNRIRERE9qdKHrGrVqum8njdvHurUqYOAgABtm0KhgFKpfNalERERERlV6UPW4/Lz8xEbG4upU6dCJpNp2/ft2wd3d3dUrVoVAQEBmDNnDtzd3Y0uR6VSQaVSaV/n5OQAANRqNdRqdYXVq1mWoop4qvlJepp9zX1euXGczAfHynyY81hV9pplQojyJQAT+PHHHxEWFoZLly7By8sLABAfHw97e3v4+PggIyMDM2fOREFBAVJTU6FQKAwuJyIiApGRkXrtcXFxsLW1lXQbiIiIqGLk5eUhLCwM2dnZcHR0NHU5eswqZHXt2hVWVlb45ZdfjPa5du0afHx8sH79evTv399gH0NHsry9vXHz5s0KHSS1Wo3ExETMPFoFqiJZyTM8IS2ia4XVQsXTjFVwcDDkcrmpyyEjOE7mg2NlPsx5rHJycuDm5lZpQ5bZnC68ePEidu3ahYSEhGL7eXp6wsfHB+fOnTPaR6FQGDzKJZfLJXmDqYpkUBWWPWSZ25v9eSDVe4AqFsfJfHCszIc5jlVlr7fSP8JBIzo6Gu7u7ujRo0ex/W7duoXLly/D09PzGVVGREREpM8sQlZRURGio6MxfPhwWFr+38G3+/fvY9q0aUhOTsaFCxewb98+9OrVC25ubujXr58JKyYiIqIXnVmcLty1axcuXbqEUaNG6bRbWFjg1KlTWL16Ne7evQtPT08EBQUhPj4eDg4OJqqWiIiIyExCVkhICAxdn29jY4OdO3eaoCIiIiKi4pnF6UIiIiIic8OQRURERCQBhiwiIiIiCTBkEREREUmAIYuIiIhIAgxZRERERBJgyCIiIiKSAEMWERERkQQYsoiIiIgkwJBFREREJAGGLCIiIiIJMGQRERERSYAhi4iIiEgCDFlEREREEmDIIiIiIpIAQxYRERGRBBiyiIiIiCTAkEVEREQkAYYsIiIiIgkwZBERERFJgCGLiIiISAIMWUREREQSYMgiIiIikgBDFhEREZEEGLKIiIiIJMCQRURERCQBhiwiIiIiCTBkEREREUmAIYuIiIhIAgxZRERERBJgyCIiIiKSAEMWERERkQQYsoiIiIgkwJBFREREJIFKHbIiIiIgk8l0fpRKpXa6EAIRERHw8vKCjY0NAgMDkZ6ebsKKiYiIiB6p1CELABo3boxr165pf06dOqWdNn/+fCxYsADffPMNjhw5AqVSieDgYNy7d8+EFRMRERGZQciytLSEUqnU/lSrVg3Ao6NYCxcuxIwZM9C/f3/4+flh1apVyMvLQ1xcnImrJiIiohedpakLKMm5c+fg5eUFhUKBdu3aYe7cuahduzYyMjKQmZmJkJAQbV+FQoGAgAAkJSVh7NixRpepUqmgUqm0r3NycgAAarUaarW6wmrXLEtRRTzV/CQ9zb7mPq/cOE7mg2NlPsx5rCp7zTIhRPkSwDPw66+/Ii8vD/Xq1cP169cxe/Zs/Pnnn0hPT8fZs2fRsWNHXL16FV5eXtp53n77bVy8eBE7d+40utyIiAhERkbqtcfFxcHW1laSbSEiIqKKlZeXh7CwMGRnZ8PR0dHU5eip1CHrSbm5uahTpw4++OADtG/fHh07dsS///4LT09PbZ8xY8bg8uXL2LFjh9HlGDqS5e3tjZs3b1boIKnVaiQmJmLm0SpQFcnKPH9aRNcKq4WKpxmr4OBgyOVyU5dDRnCczAfHynyY81jl5OTAzc2t0oasSn+68HF2dnZo0qQJzp07h759+wIAMjMzdUJWVlYWPDw8il2OQqGAQqHQa5fL5ZK8wVRFMqgKyx6yzO3N/jyQ6j1AFYvjZD44VubDHMeqstdb6S98f5xKpcKZM2fg6ekJX19fKJVKJCYmaqfn5+dj//798Pf3N2GVRERERJX8SNa0adPQq1cv1KxZE1lZWZg9ezZycnIwfPhwyGQyTJ48GXPnzkXdunVRt25dzJ07F7a2tggLCzN16URERPSCq9Qh68qVKxgyZAhu3ryJatWqoX379khJSYGPjw8A4IMPPsCDBw8wbtw43LlzB+3atcNvv/0GBwcHE1dOREREL7pKHbLWr19f7HSZTIaIiAhEREQ8m4KIiIiISsmsrskiIiIiMhcMWUREREQSYMgiIiIikgBDFhEREZEEGLKIiIiIJMCQRURERCQBhiwiIiIiCTBkEREREUmAIYuIiIhIAgxZRERERBJgyCIiIiKSAEMWERERkQQYsoiIiIgkwJBFREREJAGGLCIiIiIJMGQRERERSYAhi4iIiEgCDFlEREREEmDIIiIiIpIAQxYRERGRBBiyiIiIiCTAkEVEREQkAUtTF0DG1fpo21PNf2FejwqqhIiIiMqKR7KIiIiIJMCQRURERCQBhiwiIiIiCTBkEREREUmAIYuIiIhIAgxZRERERBJgyCIiIiKSAEMWERERkQQYsoiIiIgkwJBFREREJAGGLCIiIiIJVOqQFRUVhTZt2sDBwQHu7u7o27cvzp49q9NnxIgRkMlkOj/t27c3UcVEREREj1TqkLV//36MHz8eKSkpSExMREFBAUJCQpCbm6vTr1u3brh27Zr2Z/v27SaqmIiIiOgRS1MXUJwdO3bovI6Ojoa7uztSU1PxyiuvaNsVCgWUSuWzLo+IiIjIqEodsp6UnZ0NAHBxcdFp37dvH9zd3VG1alUEBARgzpw5cHd3N7oclUoFlUqlfZ2TkwMAUKvVUKvVFVavZlmKKqLCllme9VPJNPuK+6xy4ziZD46V+TDnsarsNcuEEKZJAGUkhECfPn1w584dHDx4UNseHx8Pe3t7+Pj4ICMjAzNnzkRBQQFSU1OhUCgMLisiIgKRkZF67XFxcbC1tZVsG4iIiKji5OXlISwsDNnZ2XB0dDR1OXrMJmSNHz8e27Ztw6FDh1CjRg2j/a5duwYfHx+sX78e/fv3N9jH0JEsb29v3Lx5s0IHSa1WIzExETOPVoGqSFZhy30W0iK6mrqEZ0ozVsHBwZDL5aYuh4zgOJkPjpX5MOexysnJgZubW6UNWWZxunDixInYsmULDhw4UGzAAgBPT0/4+Pjg3LlzRvsoFAqDR7nkcrkkbzBVkQyqQvMKWeb2QasoUr0HqGJxnMwHx8p8mONYVfZ6K3XIEkJg4sSJ2LhxI/bt2wdfX98S57l16xYuX74MT0/PZ1AhERERkWGV+hEO48ePR2xsLOLi4uDg4IDMzExkZmbiwYMHAID79+9j2rRpSE5OxoULF7Bv3z706tULbm5u6Nevn4mrJyIiohdZpT6StXTpUgBAYGCgTnt0dDRGjBgBCwsLnDp1CqtXr8bdu3fh6emJoKAgxMfHw8HBwQQVExERET1SqUNWSdfk29jYYOfOnc+oGiIiIqLSq9SnC4mIiIjMFUMWERERkQQYsoiIiIgkwJBFREREJAGGLCIiIiIJMGQRERERSYAhi4iIiEgCDFlEREREEmDIIiIiIpIAQxYRERGRBBiyiIiIiCTAkEVEREQkAYYsIiIiIgkwZBERERFJwNLUBRA9rtZH28o974V5PSqwEiIioqfDI1lEREREEuCRLDKIR5SIiIieDo9kEREREUmAIYuIiIhIAgxZRERERBJgyCIiIiKSAEMWERERkQQYsoiIiIgkwEc4UIV7msc/mIpfxE7Mb/vov6pCWZnm5SMriIjIEB7JIiIiIpIAQxYRERGRBBiyiIiIiCTAa7LoufE014IpLEyz3qe5not/+oiIqHLjkSwiIiIiCTBkEREREUmApwuJTMgcH3dBRESlwyNZRERERBJgyCIiIiKSwHMTspYsWQJfX19YW1ujVatWOHjwoKlLIiIiohfYc3FNVnx8PCZPnowlS5agY8eO+O677xAaGorTp0+jZs2api6PqNLh4x+IiKT3XBzJWrBgAUaPHo233noLDRs2xMKFC+Ht7Y2lS5eaujQiIiJ6QZn9kaz8/Hykpqbio48+0mkPCQlBUlKSiaoien6Z41Ewc3xgrDl60Y5ymuNn4Wm8aNtbEcw+ZN28eROFhYXw8PDQaffw8EBmZqbBeVQqFVQqlfZ1dnY2AOD27dtQq9UVVptarUZeXh4s1VVQWCSrsOVSxbMsEsjLK+JYSezWrVtPNb/mM3Xr1i3I5fJSz2dZkFvudT5NzU+zXnP0+L4q71iZE1O9rypaaceqMm7vvXv3AABCCEmW/7TMPmRpyGS6vxiFEHptGlFRUYiMjNRr9/X1laQ2Mg9hpi7gBeD2pakrKDtzrNlUuK9K70XbV1Jv77179+Dk5CTtSsrB7EOWm5sbLCws9I5aZWVl6R3d0pg+fTqmTp2qfV1UVITbt2/D1dXVaDArj5ycHHh7e+Py5ctwdHSssOVSxeNYmQeOk/ngWJkPcx4rIQTu3bsHLy8vU5dikNmHLCsrK7Rq1QqJiYno16+ftj0xMRF9+vQxOI9CoYBCodBpq1q1qmQ1Ojo6mt0b90XFsTIPHCfzwbEyH+Y6VpXxCJaG2YcsAJg6dSqGDh2K1q1bo0OHDvj+++9x6dIlvPPOO6YujYiIiF5Qz0XIev3113Hr1i3MmjUL165dg5+fH7Zv3w4fHx9Tl0ZEREQvqOciZAHAuHHjMG7cOFOXoUOhUCA8PFzv1CRVPhwr88BxMh8cK/PBsZKOTFTW+x6JiIiIzNhz8cR3IiIiosqGIYuIiIhIAgxZRERERBJgyCIiIiKSAEOWRJYsWQJfX19YW1ujVatWOHjwoKlLeq5FRUWhTZs2cHBwgLu7O/r27YuzZ8/q9BFCICIiAl5eXrCxsUFgYCDS09N1+qhUKkycOBFubm6ws7ND7969ceXKFZ0+d+7cwdChQ+Hk5AQnJycMHToUd+/elXoTn0tRUVGQyWSYPHmyto3jVHlcvXoVb775JlxdXWFra4vmzZsjNTVVO51jVTkUFBTgv//9L3x9fWFjY4PatWtj1qxZKCoq0vbhWJmIoAq3fv16IZfLxfLly8Xp06fFpEmThJ2dnbh48aKpS3tude3aVURHR4u0tDRx/Phx0aNHD1GzZk1x//59bZ958+YJBwcH8fPPP4tTp06J119/XXh6eoqcnBxtn3feeUdUr15dJCYmimPHjomgoCDRrFkzUVBQoO3TrVs34efnJ5KSkkRSUpLw8/MTPXv2fKbb+zz4448/RK1atUTTpk3FpEmTtO0cp8rh9u3bwsfHR4wYMUIcPnxYZGRkiF27donz589r+3CsKofZs2cLV1dXsXXrVpGRkSE2bNgg7O3txcKFC7V9OFamwZAlgbZt24p33nlHp61Bgwbio48+MlFFL56srCwBQOzfv18IIURRUZFQKpVi3rx52j4PHz4UTk5OYtmyZUIIIe7evSvkcrlYv369ts/Vq1dFlSpVxI4dO4QQQpw+fVoAECkpKdo+ycnJAoD4888/n8WmPRfu3bsn6tatKxITE0VAQIA2ZHGcKo8PP/xQvPzyy0anc6wqjx49eohRo0bptPXv31+8+eabQgiOlSnxdGEFy8/PR2pqKkJCQnTaQ0JCkJSUZKKqXjzZ2dkAABcXFwBARkYGMjMzdcZFoVAgICBAOy6pqalQq9U6fby8vODn56ftk5ycDCcnJ7Rr107bp3379nBycuL4lsH48ePRo0cPvPrqqzrtHKfKY8uWLWjdujVee+01uLu7o0WLFli+fLl2Oseq8nj55Zexe/du/PXXXwCAEydO4NChQ+jevTsAjpUpPTdPfK8sbt68icLCQnh4eOi0e3h4IDMz00RVvViEEJg6dSpefvll+Pn5AYB23xsal4sXL2r7WFlZwdnZWa+PZv7MzEy4u7vrrdPd3Z3jW0rr16/HsWPHcOTIEb1pHKfK459//sHSpUsxdepUfPzxx/jjjz/w3nvvQaFQYNiwYRyrSuTDDz9EdnY2GjRoAAsLCxQWFmLOnDkYMmQIAH6uTIkhSyIymUzntRBCr42kMWHCBJw8eRKHDh3Sm1aecXmyj6H+HN/SuXz5MiZNmoTffvsN1tbWRvtxnEyvqKgIrVu3xty5cwEALVq0QHp6OpYuXYphw4Zp+3GsTC8+Ph6xsbGIi4tD48aNcfz4cUyePBleXl4YPny4th/H6tnj6cIK5ubmBgsLC71Un5WVpfevCKp4EydOxJYtW7B3717UqFFD265UKgGg2HFRKpXIz8/HnTt3iu1z/fp1vfXeuHGD41sKqampyMrKQqtWrWBpaQlLS0vs378fX3/9NSwtLbX7kONkep6enmjUqJFOW8OGDXHp0iUA/ExVJv/5z3/w0UcfYfDgwWjSpAmGDh2KKVOmICoqCgDHypQYsiqYlZUVWrVqhcTERJ32xMRE+Pv7m6iq558QAhMmTEBCQgL27NkDX19fnem+vr5QKpU645Kfn4/9+/drx6VVq1aQy+U6fa5du4a0tDRtnw4dOiA7Oxt//PGHts/hw4eRnZ3N8S2FLl264NSpUzh+/Lj2p3Xr1njjjTdw/Phx1K5dm+NUSXTs2FHvMSh//fUXfHx8APAzVZnk5eWhShXdX+cWFhbaRzhwrEzIBBfbP/c0j3BYsWKFOH36tJg8ebKws7MTFy5cMHVpz613331XODk5iX379olr165pf/Ly8rR95s2bJ5ycnERCQoI4deqUGDJkiMFbmGvUqCF27doljh07Jjp37mzwFuamTZuK5ORkkZycLJo0acJbmJ/C43cXCsFxqiz++OMPYWlpKebMmSPOnTsn1q5dK2xtbUVsbKy2D8eqchg+fLioXr269hEOCQkJws3NTXzwwQfaPhwr02DIksi3334rfHx8hJWVlWjZsqX2UQIkDQAGf6Kjo7V9ioqKRHh4uFAqlUKhUIhXXnlFnDp1Smc5Dx48EBMmTBAuLi7CxsZG9OzZU1y6dEmnz61bt8Qbb7whHBwchIODg3jjjTfEnTt3nsFWPp+eDFkcp8rjl19+EX5+fkKhUIgGDRqI77//Xmc6x6pyyMnJEZMmTRI1a9YU1tbWonbt2mLGjBlCpVJp+3CsTEMmhBCmPJJGRERE9DziNVlEREREEmDIIiIiIpIAQxYRERGRBBiyiIiIiCTAkEVEREQkAYYsIiIiIgkwZBERERFJgCGLiAySyWTYtGmT0em1atXCwoULS728mJgYVK1atdg+ERERaN68eamXWZmYc+1EJA2GLKIXVFZWFsaOHYuaNWtCoVBAqVSia9euSE5OLtX8R44cwdtvvy1xlcbFxMRAJpMV+7Nv375nVs+0adOwe/fuZ7Y+Iqr8LE1dABGZxoABA6BWq7Fq1SrUrl0b169fx+7du3H79u1SzV+tWjWJKyze66+/jm7dumlf9+/fH35+fpg1a5a2zcXFpdTLKywshEwm0/tDu/n5+bCysipxfnt7e9jb25d6fUT0/OORLKIX0N27d3Ho0CF89tlnCAoKgo+PD9q2bYvp06ejR48eBueZNWsWPDw8cPz4cQD6pwsXLFiAJk2awM7ODt7e3hg3bhzu37+vt5xNmzahXr16sLa2RnBwMC5fvlxsrdHR0WjYsCGsra3RoEEDLFmyBABgY2MDpVKp/bGysoKtra32tYuLC/773/+ievXqsLOzQ7t27XSObGlOX27duhWNGjWCQqHAxYsXUatWLcyePRsjRoyAk5MTxowZAwD48MMPUa9ePdja2qJ27dqYOXMm1Gq1dnlPni4cMWIE+vbtiy+++AKenp5wdXXF+PHjdeYpTmxsLFq3bg0HBwcolUqEhYUhKytLp096ejp69OgBR0dHODg4oFOnTvj7779LtXwikh5DFtELSHPUZdOmTVCpVMX2FUJg0qRJWLFiBQ4dOmT0uqMqVarg66+/RlpaGlatWoU9e/bggw8+0OmTl5eHOXPmYNWqVfj999+Rk5ODwYMHG1338uXLMWPGDMyZMwdnzpzB3LlzMXPmTKxatarEbRw5ciR+//13rF+/HidPnsRrr72Gbt264dy5czr1REVF4YcffkB6ejrc3d0BAJ9//jn8/PyQmpqKmTNnAgAcHBwQExOD06dPY9GiRVi+fDm++uqrYmvYu3cv/v77b+zduxerVq1CTEwMYmJiSqwdeHQE7dNPP8WJEyewadMmZGRkYMSIEdrpV69exSuvvAJra2vs2bMHqampGDVqFAoKCkq1fCJ6Bkz8B6qJyER++ukn4ezsLKytrYW/v7+YPn26OHHihHY6ALFhwwbx5ptvigYNGojLly/rzO/j4yO++uoro8v/8ccfhaurq/Z1dHS0ACBSUlK0bWfOnBEAxOHDh4UQQoSHh4tmzZppp3t7e4u4uDid5X766aeiQ4cOeusLCAgQkyZNEkIIcf78eSGTycTVq1d1+nTp0kVMnz5dp57jx4/rbVffvn2NbpfG/PnzRatWrbSvn6x9+PDhwsfHRxQUFGjbXnvtNfH666+XuGxD/vjjDwFA3Lt3TwghxPTp04Wvr6/Iz88v1/KISHq8JovoBTVgwAD06NEDBw8eRHJyMnbs2IH58+fjhx9+0B4xmTJlChQKBVJSUuDm5lbs8vbu3Yu5c+fi9OnTyMnJQUFBAR4+fIjc3FzY2dkBACwtLdG6dWvtPA0aNEDVqlVx5swZtG3bVmd5N27cwOXLlzF69GjtKTsAKCgogJOTU7G1HDt2DEII1KtXT6ddpVLB1dVV+9rKygpNmzbVm//xGjV++uknLFy4EOfPn8f9+/dRUFAAR0fHYuto3LgxLCwstK89PT1x6tSpYufR+N///oeIiAgcP34ct2/fRlFREQDg0qVLaNSoEY4fP45OnTpBLpeXanlE9OzxdCHRC0xzXdQnn3yCpKQkjBgxAuHh4drpwcHBuHr1Knbu3Fnsci5evIju3bvDz88PP//8M1JTU/Htt98CgN41SDKZTG9+Q22aULF8+XIcP35c+5OWloaUlJRi6ykqKoKFhQVSU1N15j1z5gwWLVqk7WdjY2Nw3ZpQqJGSkoLBgwcjNDQUW7duxf/+9z/MmDED+fn5xdbxZACSyWTa7SpObm4uQkJCYG9vj9jYWBw5cgQbN24EAO06bWxsSlwOEZkWj2QRkVajRo10no3Vu3dv9OrVC2FhYbCwsDB6/dTRo0dRUFCAL7/8Unt33o8//qjXr6CgAEePHtUetTp79izu3r2LBg0a6PX18PBA9erV8c8//+CNN94o03a0aNEChYWFyMrKQqdOnco0ryG///47fHx8MGPGDG3bxYsXn3q5xvz555+4efMm5s2bB29vbwCP9vHjmjZtilWrVkGtVvNoFlElxSNZRC+gW7duoXPnzoiNjcXJkyeRkZGBDRs2YP78+ejTp49O3379+mHNmjUYOXIkfvrpJ4PLq1OnDgoKCrB48WL8888/WLNmDZYtW6bXTy6XY+LEiTh8+DCOHTuGkSNHon379nqnCjUiIiIQFRWFRYsW4a+//sKpU6cQHR2NBQsWFLt99erVwxtvvIFhw4YhISEBGRkZOHLkCD777DNs3769lHvp/7z00ku4dOkS1q9fj7///htff/219siSFGrWrAkrKyvt/tyyZQs+/fRTnT4TJkzQ3jhw9OhRnDt3DmvWrMHZs2clq4uIyoYhi+gFZG9vj3bt2uGrr77CK6+8Aj8/P8ycORNjxozBN998o9d/4MCBWLVqFYYOHYqEhAS96c2bN8eCBQvw2Wefwc/PD2vXrkVUVJReP1tbW3z44YcICwtDhw4dYGNjg/Xr1xut86233sIPP/yAmJgYNGnSBAEBAYiJiYGvr2+J2xgdHY1hw4bh/fffR/369dG7d28cPnxYe2SoLPr06YMpU6ZgwoQJaN68OZKSkrR3HUqhWrVqiImJwYYNG9CoUSPMmzcPX3zxhU4fV1dX7NmzB/fv30dAQABatWqF5cuX86gWUSUiE0IIUxdBRERE9LzhkSwiIiIiCTBkERE9YwcPHtQ+ENbQDxE9H3i6kIjoGXvw4AGuXr1qdPpLL730DKshIqkwZBERERFJgKcLiYiIiCTAkEVEREQkAYYsIiIiIgkwZBERERFJgCGLiIiISAIMWUREREQSYMgiIiIikgBDFhEREZEE/h+Im9FNWvY0CAAAAABJRU5ErkJggg==\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": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "11    3379.0\n",
+       "18    1500.0\n",
+       "Name: Snow Making_ac, dtype: float64"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ski_data['Snow Making_ac'][ski_data['Snow Making_ac'] > 1000]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "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": 32,
+     "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": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2880.0"
+      ]
+     },
+     "execution_count": 33,
+     "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": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0    163\n",
+       "1.0      1\n",
+       "Name: fastEight, dtype: int64"
+      ]
+     },
+     "execution_count": 34,
+     "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": 35,
+   "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": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "34      104.0\n",
+       "115    2019.0\n",
+       "Name: yearsOpen, dtype: float64"
+      ]
+     },
+     "execution_count": 36,
+     "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": 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": [
+    "#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": 38,
+   "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": 38,
+     "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": 39,
+   "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": 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>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": 40,
+     "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": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    82.317073\n",
+       "2    14.329268\n",
+       "1     3.353659\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 41,
+     "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": 42,
+   "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[ski_data[['AdultWeekend', 'AdultWeekday']].isnull().sum(axis=1) != 2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.9 Review distributions<a id='2.9_Review_distributions'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Xr9ZrHNaChbBmsGXLFsTExODTTz9FcHAw3N3dMW7cOOTm5jZofe7u7jrP7ezsamz/66+/DLq8oVy7dg1CCHh6ekIul+s8Dh48WONt6uuz7AsvvACFQoHk5GQA9863zszM5KkaVC/dunXDI488Il2jo7y8HBs2bMDQoUPh7u6Oa9euAQBmzpyp952cMmUKAOh9p+s6Zbo6169fh7e3d5WnIGpdunQJjz/+OK5evYoPP/wQ+/btQ2ZmpnSNg/ru5O+nnSquVCr1XquqDdAfc03XVPD29kZFRQXy8/NrXLetrS1atWrF0z6oydX03bt79y4ef/xx/PLLL0hISMCePXuQmZmJrVu3AvhfrmmvNzhw4EAkJSWhV69eaN26NV599VUUFhYCgPR78sgjj+j9nmzZsqXG/WNl165dw/Hjx/XW4+zsDCFErfvaui770ksv4erVq0hLSwMA6TTnqq6BSmTJqtqf1XdfXPmYHLh3XF6XY3VD/91BZO7GjBmDFStWYNKkSdi5cyd+/fVXZGZmonXr1jq599FHH2H27NnYtm0bBgwYAHd3dwwbNgznz5+v83u5urpi9OjR+PDDD/HLL7/g+PHj8PT0xPz582u9y6xMJsP48ePx119/QalU6lwbDKj7Prl///7Ytm2b9A9q7dq1Q0BAgHStM7qH/0TXDDw8PLBs2TIsW7YMly5dwnfffYc5c+YgLy8PO3bsgL29PdRqtd5yN27ckM5ZtkQeHh6QyWTYt29flXeNq+lOcvVZ1s3NDUOHDsXnn3+OhIQErF27Fvb29hg9erRhBkJWY8KECZgyZQrOnDmDCxcuICcnRyqoanN17ty51V4YukuXLjrPq7suT121bt0a+/fvR0VFRbXFsG3btqGoqAhbt26Fr6+v1F75otwN0apVKwCo8uC6ugPuymPWriMnJ0ev759//gkbGxu4ubnprbtt27bS87KyMty8eVNaF1FTqem79/PPP+PPP//Enj17dO5UVdWBr6+vL9asWQPg3h2uvvzyS6hUKpSWluKTTz6Rfk++/vprnbxtCA8PDzg4OFR7PcCajjPqs+zAgQPh7e2NtWvXYuDAgVi7di2CgoIs7m63RLWpat/elPviymr7u4PImty5cwfff/89FixYgDlz5kjtarUat27d0unr5OSE+Ph4xMfH49q1a9LssCFDhuDs2bMNev9u3bph1KhRWLZsGX7//Xf07du32r45OTmYOnUqHn74YZw6dQozZ87UudZ3ffbJQ4cOxdChQ6FWq3Hw4EEsXLgQY8aMQYcOHRAcHNygsVgaFsKaWfv27TFt2jT89NNP+M9//gPg3l0jtXec0/r9999x7tw5syiEKRSKBs0qiY6OxqJFi3D16lWMGDGiSZedMGECvvzyS2zfvh0bNmzAM888gwceeKDeMZN1Gz16NOLi4pCcnIwLFy6gbdu2iIyMBHCvyOXv74/ffvsNiYmJDX4PbRG3LjkVFRWFTZs2ITk5udrTI7UH5PcXh4UQ+L//+78Gx6jVpUsXeHl5YdOmTYiLi5PeKzs7GxkZGTqnb9S0jrZt22Ljxo2YOXOmtI6ioiJ888030p0k7/fFF1/onDLy5ZdfoqysrF53rCVqiJq+e1XlGgD861//qnGdnTt3xhtvvIFvvvkGR44cAXCvqGRra4v//ve/jT6FOjo6GomJiWjVqhX8/PyabNkWLVpg7NixWLZsGfbt24dDhw7VOnYic1WffTXQtPvimlT1dweRNZHJZBBC6O2bP/30U5SXl1e7nKenJ8aPH4/ffvsNy5YtQ3FxMRwdHXVy38HBQep/8+ZNODs7S7M076ctotV0XFxeXo7Ro0dDJpPhxx9/xBdffIGZM2ciLCxM+gf2huzPFQoFQkND8cADD2Dnzp04evQoC2H/HwthTezOnTsYMGAAxowZg65du8LZ2RmZmZnYsWOH9KUeO3YsXnzxRUyZMgXPPvsssrOzkZSUhNatWxs5+roJDAzEnj178O9//xteXl5wdnbWm/lSlUcffRT/+Mc/MGHCBBw6dAj9+/eHk5MTcnJysH//fgQGBuKVV14xyLKRkZFo164dpkyZIt2Jh6i+HnjgATzzzDNITk7G7du3MXPmTJ2ZWP/6178QFRWFgQMHYvz48Wjbti1u3bqFM2fO4MiRI/jqq69qfQ/ttQA+/PBDxMTEQC6Xo0uXLnB2dtbrO3r0aKxduxYvv/wyzp07hwEDBqCiogK//PILHnzwQYwaNQoRERGws7PD6NGjMWvWLPz111/4+OOP9U43bAgbGxu88847mDRpEp555hn8/e9/x+3bt6FSqao9NbKqdSQlJeGFF15AdHQ0Jk+eDLVajSVLluD27dtYtGiR3jJbt26Fra0tIiIipDv39ejRo94FdaL6qum7V1hYCDc3N7z88stYsGAB5HI5vvjiC/z222866zh+/DimTZuG559/Hv7+/rCzs8PPP/+M48ePS/9a3aFDB7z99tuYP38+Lly4gKeeegpubm64du0afv31V+lfresiNjYW33zzDfr374/XX38d3bt3R0VFBS5duoTU1FTMmDEDQUFBBln2pZdewuLFizFmzBg4ODhg5MiRDdzSRKatun11dZpyX3y/uvzdQWRNXFxc0L9/fyxZsgQeHh7o0KED0tPTsWbNGr1JEUFBQYiOjkb37t3h5uaGM2fOYP369Tr/KKvN/cWLFyMqKgotWrSQ7hz/2muv4YUXXkBISAhatWqFvLw8bNq0CTt27JBOUazOggULsG/fPqSmpkKpVGLGjBlIT0/HxIkT0bNnT/j5+dV5n/zWW2/hypUrCA8PR7t27XD79m18+OGHOtctJfCukU3tr7/+Ei+//LLo3r27cHFxEQ4ODqJLly5iwYIFoqioSAhx785SSUlJomPHjsLe3l706dNH/Pzzz9XeNbLyXTC0d6CrfAcJ7V2url+/LrUBEFOnTtXpp72b25IlS3Taq3q/qu4qd+zYMfHoo48KR0dHAUAn5rr47LPPRFBQkHBychIODg6iU6dOYty4cTp3y6nubnZ1WVZr3rx5AoDw8fHRuTsdUX2kpqZKd4P5/fff9V7/7bffxIgRI0SbNm2EXC4XSqVSPPHEE+KTTz6R+lSXs1pz584V3t7ewsbGRueuMpV/E4QQoqSkRLz11lvC399f2NnZiVatWoknnnhCZGRkSH3+/e9/ix49egh7e3vRtm1b8c9//lP8+OOPenesqe9dI7U+/fRT6f07d+4sPvvsM711Vfc7o7Vt2zYRFBQk7O3thZOTkwgPDxf/+c9/dPpof9MOHz4shgwZIlq2bCmcnZ3F6NGjxbVr1+odN1Fd1fW7l5GRIYKDg4Wjo6No3bq1mDRpkjhy5IgAINauXSuEEOLatWti/PjxomvXrsLJyUm0bNlSdO/eXXzwwQeirKxM5323bdsmBgwYIFxcXIRCoRC+vr7iueeeE7t27apX/Hfv3hVvvPGG6NKli7CzsxOurq4iMDBQvP766yI3N1fqV9VdrOu6rFZISIgAIF544YV6xUhkbqraV/v6+orBgwdX2b+u++LQ0FDRrVs3veWrW/f9x/Z1+buDyJJV9ffrlStXxLPPPivc3NyEs7OzeOqpp8TJkyf19nlz5swRffr0EW5ubkKhUIiOHTuK119/Xdy4cUPqo1arxaRJk0Tr1q2FTCaT7h57+fJl8cYbb4hHH31UKJVKYWtrK5ydnUVQUJBYvny5zv698l0jU1NThY2NjViwYIHOWG7evCnat28vHnnkEaFWq4UQddsnf//99yIqKkq0bdtW2NnZiTZt2ohBgwaJffv2GXhrmzeZEEI0X9mNiIioYVQqFeLj43H9+nWzOG2ciIiIiIhMD+8aSUREREREREREVoHXCKMmUVZWVuPrNjY21d7ljoiMr6KiAhUVFTX2sbXlLoTIWIQQNV7oF7h3AfvG3p2WiIiIyNKwEkFNQi6X1/io7g53RGQaXnrppVrzuLmpVCoIIXhaJBGA9PT0WnN03bp1xg6TiIiIyOTwGmHUJA4dOlTj69q7dhCRabp48SJu3LhRY58+ffo0UzREVFlhYSHOnTtXYx8/Pz+0atWqmSIiIiIiMg/1LoTt3bsXS5YsweHDh5GTk4OUlBQMGzZMel0Igfj4eKxevRr5+fkICgrCypUr0a1bN6mPWq3GzJkzsWnTJpSUlCA8PByrVq2q8ZaiREREREREREREjVHvUyOLiorQo0cPrFixosrXk5KSsHTpUqxYsQKZmZlQKpWIiIhAYWGh1Cc2NhYpKSnYvHkz9u/fj7t37yI6OrrWa10QERERERERERE1VKNOjZTJZDozwoQQ8Pb2RmxsLGbPng3g3uwvT09PLF68GJMnT8adO3fQunVrrF+/HiNHjgQA/Pnnn/Dx8cH27dsxcODAWt+3oqICf/75J5ydnXkRWLJ4QggUFhbC29vbIm8wwHwma8J8JrIMlp7LAPOZrIel5zNzmaxJXfPZoLf8ysrKQm5uLiIjI6U2hUKB0NBQZGRkYPLkyTh8+DA0Go1OH29vbwQEBCAjI6PKQpharYZarZaeX716FQ899JAhQycyeZcvX7bI04e1hXAia8J8JrIMlprLAPOZrI+l5jNzmaxRbfls0EJYbm4uAMDT01On3dPTE9nZ2VIfOzs7uLm56fXRLl/ZwoULER8fr9f+6aefwtHR0RChE5ms4uJiTJo0Cc7OzsYOpUlox3X58mW4uLhI7RqNBqmpqYiMjDTKHQrNFbdbwzTXdisoKICPj4/V5TPA72ZjcNs1TlNsP0vPZaDmfAas43vJMVqG2sZo6flcWy4D1vE9aG7cpoZXl21a13w2aCFMq/KUSyFErdMwa+ozd+5cxMXFSc+1gxs2bBgcHByQlpaGiIgIq/2CaTQaq98GgOVuh4KCAkyaNMlipzJrx+Xi4qJXCHN0dISLi4tFfZ5NjdutYZp7u1lbPgP8bjYGt13jNOX2s9RcBmrOZ8A6vpcco2Wo6xgtNZ9ry2XAOr4HzY3b1PDqs01ry2eDFsKUSiWAe7O+vLy8pPa8vDxplphSqURpaSny8/N1ZoXl5eUhJCSkyvUqFAooFAq9drlcLm2A+//fWnEb3GNp28GSxkJERERERERkTAa9GqCfnx+USiXS0tKkttLSUqSnp0tFrt69e0Mul+v0ycnJwcmTJ6sthBERERERERERETVWvWeE3b17F3/88Yf0PCsrC8eOHYO7uzvat2+P2NhYJCYmwt/fH/7+/khMTISjoyPGjBkDAHB1dcXEiRMxY8YMtGrVCu7u7pg5cyYCAwPx5JNPGm5kRERERERERERE96l3IezQoUMYMGCA9Fx77a6YmBgkJydj1qxZKCkpwZQpU5Cfn4+goCCkpqbqXKzsgw8+gK2tLUaMGIGSkhKEh4cjOTkZLVq0MMCQ/qfDnB8avY6LiwYbIBIiaizmM5FlCFDthLq8cddhYS4TmY7G5jTzmch0MJ/JWtS7EBYWFgYhRLWvy2QyqFQqqFSqavvY29tj+fLlWL58eX3fnoiIiIiIiIiIqEEMeo0wIiIiIiIiIiIiU8VCGBERERERERERWQUWwoiIiIiIiIiIyCqwEEZERGTm9u7diyFDhsDb2xsymQzbtm3TeV0IAZVKBW9vbzg4OCAsLAynTp3S6aNWqzF9+nR4eHjAyckJTz/9NK5cudKMoyAiIiIianoshBEREZm5oqIi9OjRAytWrKjy9aSkJCxduhQrVqxAZmYmlEolIiIiUFhYKPWJjY1FSkoKNm/ejP379+Pu3buIjo5GeXl5cw2DiIiIiKjJ1fuukURERGRaoqKiEBUVVeVrQggsW7YM8+fPx/DhwwEA69atg6enJzZu3IjJkyfjzp07WLNmDdavX48nn3wSALBhwwb4+Phg165dGDhwYLONhYiIiIioKbEQRkREZMGysrKQm5uLyMhIqU2hUCA0NBQZGRmYPHkyDh8+DI1Go9PH29sbAQEByMjIqLYQplaroVarpecFBQUAAI1GA41Go9NX+1xhIxo9psrrtnTa8VrbuA2lKbYfPwsiIiLzxUIYERGRBcvNzQUAeHp66rR7enoiOztb6mNnZwc3Nze9Ptrlq7Jw4ULEx8frtaempsLR0bHKZd7pU1Gv+Kuyffv2Rq/DHKWlpRk7BLNmyO1XXFxssHURERFR82IhjIiIyArIZDKd50IIvbbKauszd+5cxMXFSc8LCgrg4+ODyMhIuLi46PTVaDRIS0vDm4dsoK6o+X1rc1JlXadqarddREQE5HK5scMxO02x/bSzH4mIiMj8sBBGRERkwZRKJYB7s768vLyk9ry8PGmWmFKpRGlpKfLz83VmheXl5SEkJKTadSsUCigUCr12uVxebcFBXSGDurxxhTBrLQbVtF2pdobcfvwciIiIzBfvGklERGTB/Pz8oFQqdU4LKy0tRXp6ulTk6t27N+RyuU6fnJwcnDx5ssZCGBERERGRueGMMCKyGh3m/NDodVxcNNgAkRAZ1t27d/HHH39Iz7OysnDs2DG4u7ujffv2iI2NRWJiIvz9/eHv74/ExEQ4OjpizJgxAABXV1dMnDgRM2bMQKtWreDu7o6ZM2ciMDBQuoskEREREZElYCGMiIjIzB06dAgDBgyQnmuv2xUTE4Pk5GTMmjULJSUlmDJlCvLz8xEUFITU1FQ4OztLy3zwwQewtbXFiBEjUFJSgvDwcCQnJ6NFixbNPh4iIiIioqbCQhgREZGZCwsLgxCi2tdlMhlUKhVUKlW1fezt7bF8+XIsX768CSIkIiIiIjINvEYYERERERERERFZBRbCiIiIiIiIiIjIKrAQRkREREREREREVoGFMCIiIiIiE7Bw4UI88sgjcHZ2Rps2bTBs2DCcO3dOp48QAiqVCt7e3nBwcEBYWBhOnTql00etVmP69Onw8PCAk5MTnn76aVy5cqU5h0Jk9ZjPRKaLhTAiIiIiIhOQnp6OqVOn4uDBg0hLS0NZWRkiIyNRVFQk9UlKSsLSpUuxYsUKZGZmQqlUIiIiAoWFhVKf2NhYpKSkYPPmzdi/fz/u3r2L6OholJeXG2NYRFaJ+UxkunjXSCIiIiIiE7Bjxw6d52vXrkWbNm1w+PBh9O/fH0IILFu2DPPnz8fw4cMBAOvWrYOnpyc2btyIyZMn486dO1izZg3Wr1+PJ598EgCwYcMG+Pj4YNeuXRg4cGCzj4vIGjGfiUwXC2FERERERCbozp07AAB3d3cAQFZWFnJzcxEZGSn1USgUCA0NRUZGBiZPnozDhw9Do9Ho9PH29kZAQAAyMjKq/cNZrVZDrVZLzwsKCgAAGo0GGo1Gr7+2TWEjGjXGqtZtKrSxmXKMjcUxNt/YmzOfiahmLIQREREREZkYIQTi4uLw2GOPISAgAACQm5sLAPD09NTp6+npiezsbKmPnZ0d3Nzc9Ppol6/KwoULER8fr9eempoKR0fHapd7p09F3QZUje3btzdq+eaQlpZm7BCanDWPsbi4uMnfuznzub5Fbe1rgGUXtpubNRSZm1tdtmldtzcLYUREREREJmbatGk4fvw49u/fr/eaTCbTeS6E0GurrLY+c+fORVxcnPS8oKAAPj4+iIyMhIuLi15/jUaDtLQ0vHnIBuqKmt+7JidVpjujRTvGiIgIyOVyY4fTJDjG/xWKmlJz5nNDi9qAdRS2m5s1FJmbW03btK6FbRbCiIiIiIhMyPTp0/Hdd99h7969aNeundSuVCoB3Jsl4uXlJbXn5eVJs0qUSiVKS0uRn5+vM4skLy8PISEh1b6nQqGAQqHQa5fL5TUWSNQVMqjLG14IM4fiS23bwBJY8xibetzNnc/1LWoD1lHYbm7WUGRubnXZpnUtbLMQRkRERERkAoQQmD59OlJSUrBnzx74+fnpvO7n5welUom0tDT07NkTAFBaWor09HQsXrwYANC7d2/I5XKkpaVhxIgRAICcnBycPHkSSUlJzTsgIitmrHxuaFEbsI7CdnOzhiJzc6tpm9Z1W7MQRkRERERkAqZOnYqNGzfi22+/hbOzs3QNIFdXVzg4OEAmkyE2NhaJiYnw9/eHv78/EhMT4ejoiDFjxkh9J06ciBkzZqBVq1Zwd3fHzJkzERgYKN11joiaHvOZyHSxEEZEREREZAI+/vhjAEBYWJhO+9q1azF+/HgAwKxZs1BSUoIpU6YgPz8fQUFBSE1NhbOzs9T/gw8+gK2tLUaMGIGSkhKEh4cjOTkZLVq0aK6hEFk95jOR6WIhjIiIiIjIBAhR+x3bZDIZVCoVVCpVtX3s7e2xfPlyLF++3IDREVF9MJ+JTJeNsQMgIiIiIiIiIiJqDiyEERERERERERGRVWAhjIiIiIiIiIiIrAILYURULZVKBZlMpvNQKpXS60IIqFQqeHt7w8HBAWFhYTh16pQRIyYiIiIiIiKqHgthRFSjbt26IScnR3qcOHFCei0pKQlLly7FihUrkJmZCaVSiYiICBQWFhoxYiIiIiIiIqKqsRBGRDWytbWFUqmUHq1btwZwbzbYsmXLMH/+fAwfPhwBAQFYt24diouLsXHjRiNHTURERERERKTP1tgBEJFpO3/+PLy9vaFQKBAUFITExER07NgRWVlZyM3NRWRkpNRXoVAgNDQUGRkZmDx5cpXrU6vVUKvV0vOCggIAgEajgUajkdq1/6/9r6JF7begbg73x2iKKm83qpvm2m78XIiIiIiIjIuFMCKqVlBQED7//HN07twZ165dQ0JCAkJCQnDq1Cnk5uYCADw9PXWW8fT0RHZ2drXrXLhwIeLj4/XaU1NT4ejoqNeelpYGAEjq25iRGM727duNHUKdaLcb1U9Tb7fi4uImXT8REREREdWMhTAiqlZUVJT0/4GBgQgODkanTp2wbt069OvXDwAgk8l0lhFC6LXdb+7cuYiLi5OeFxQUwMfHB5GRkXBxcZHaNRoN0tLSEBERAblcjgDVTkMNq1FOqgYaO4QaVd5uVDfNtd20MyCJiIiIiMg4WAgjojpzcnJCYGAgzp8/j2HDhgEAcnNz4eXlJfXJy8vTmyV2P4VCAYVCodcul8urLEBo29Xl1RfXmpO5FJeq255Us6bebvxMiIiIiIiMixfLJ6I6U6vVOHPmDLy8vODn5welUqlzKllpaSnS09MREhJixCiJiIiIiIiIqsYZYURUrZkzZ2LIkCFo37498vLykJCQgIKCAsTExEAmkyE2NhaJiYnw9/eHv78/EhMT4ejoiDFjxhg7dCIiIiIiIiI9LIQRUbWuXLmC0aNH48aNG2jdujX69euHgwcPwtfXFwAwa9YslJSUYMqUKcjPz0dQUBBSU1Ph7Oxs5MiJiIiIiIiI9LEQRkTV2rx5c42vy2QyqFQqqFSq5gmIiIiIiIiIqBF4jTAiIiIiIiIiIrIKBi+EqVQqyGQynYdSqZReF0JApVLB29sbDg4OCAsLw6lTpwwdBhERERERERERkY4mmRHWrVs35OTkSI8TJ05IryUlJWHp0qVYsWIFMjMzoVQqERERgcLCwqYIhYiIiIiIiIiICEATXSPM1tZWZxaYlhACy5Ytw/z58zF8+HAAwLp16+Dp6YmNGzdi8uTJTREOEZHBdJjzQ6PXcXHRYANEQkRERERERPXVJDPCzp8/D29vb/j5+WHUqFG4cOECACArKwu5ubmIjIyU+ioUCoSGhiIjI6MpQiEiIiIiIiIiIgLQBDPCgoKC8Pnnn6Nz5864du0aEhISEBISglOnTiE3NxcA4OnpqbOMp6cnsrOzq12nWq2GWq2WnhcUFAAANBoNbG1tpf+vTNFCNHo8Va3X1GhjNIdYm5KlbgdLGw8RERERERGRsRi8EBYVFSX9f2BgIIKDg9GpUyesW7cO/fr1AwDIZDKdZYQQem33W7hwIeLj4/XaU1NT4ejoCABIS0vTez2pb4OGoGP79u2NX0kzqWobWCNL2w7FxcXGDoGIyKTwFGUiIiIiaqgmuUbY/ZycnBAYGIjz589j2LBhAIDc3Fx4eXlJffLy8vRmid1v7ty5iIuLk54XFBTAx8cHkZGRcHBwQFpaGiIiIiCXy3WWC1DtNOxgGuikamCTrl+j0VS7DayJpW4H7QxIIiIiIiIiImqcJi+EqdVqnDlzBo8//jj8/PygVCqRlpaGnj17AgBKS0uRnp6OxYsXV7sOhUIBhUKh1y6Xy6WCx/3/L713efWzzJpTcxVlqtoG1sjStoMljYXu4WwWam4qlUpvZrWnp6d0yQIhBOLj47F69Wrk5+cjKCgIK1euRLdu3YwRLhERERFRkzH4xfJnzpyJ9PR0ZGVl4ZdffsFzzz2HgoICxMTEQCaTITY2FomJiUhJScHJkycxfvx4ODo6YsyYMYYOhYiIiP6/bt26IScnR3qcOHFCei0pKQlLly7FihUrkJmZCaVSiYiICBQWFhoxYiIiIiIiwzP4jLArV65g9OjRuHHjBlq3bo1+/frh4MGD8PX1BQDMmjULJSUlmDJlivSvzqmpqXB2djZ0KERERPT/2draQqlU6rULIbBs2TLMnz8fw4cPBwCsW7cOnp6e2LhxIyZPntzcoRIRERERNRmDF8I2b95c4+symQwqlQoqlcrQb01ERETVOH/+PLy9vaFQKBAUFITExER07NgRWVlZyM3NRWRkpNRXoVAgNDQUGRkZLIQRERERkUVp8muEERERkXEFBQXh888/R+fOnXHt2jUkJCQgJCQEp06dkq4TVvmmNZ6ensjOzq5xvWq1Gmq1WnquvbmHRqOBRqPR6at9rrARjR6PIVSOz5RpYzWnmE1JU2w/fhZERETmi4UwIiIiCxcVFSX9f2BgIIKDg9GpUyesW7cO/fr1A3Bvxvb9hBB6bZUtXLhQ7yL8AJCamgpHR8cql3mnT0V9w28S27dvN3YI9ZaWlmbsEMyaIbdfcXGxwdZFREREzYuFMCIiIivj5OSEwMBAnD9/HsOGDQMA5ObmwsvLS+qTl5enN0ussrlz5yIuLk56XlBQAB8fH0RGRsLFxUWnr0ajQVpaGt48ZAN1hfHv6nxSNdDYIdSZdttFRETwTsIN0BTbTzv7kYiIiMwPC2FERERWRq1W48yZM3j88cfh5+cHpVKJtLQ09OzZEwBQWlqK9PR0LF68uMb1KBQKKBQKvXa5XF5twUFdIYO63PiFMHMsKNW0Xal2htx+Tfk57N27F0uWLMHhw4eRk5ODlJQUqWAN3JutGR8fj9WrV0s3nlq5ciW6desm9VGr1Zg5cyY2bdqEkpIShIeHY9WqVWjXrl2TxU1ERGQubIwdABERETWtmTNnIj09HVlZWfjll1/w3HPPoaCgADExMZDJZIiNjUViYiJSUlJw8uRJjB8/Ho6OjhgzZoyxQyeyOkVFRejRowdWrFhR5etJSUlYunQpVqxYgczMTCiVSkRERKCwsFDqExsbi5SUFGzevBn79+/H3bt3ER0djfLy8uYaBhHhXmF7yJAh8Pb2hkwmw7Zt23ReF0JApVLB29sbDg4OCAsLw6lTp3T6qNVqTJ8+HR4eHnBycsLTTz+NK1euNOMoiCwPC2FEREQW7sqVKxg9ejS6dOmC4cOHw87ODgcPHoSvry8AYNasWYiNjcWUKVPQp08fXL16FampqXB2djZy5ETWJyoqCgkJCRg+fLjea0IILFu2DPPnz8fw4cMREBCAdevWobi4GBs3bgQA3LlzB2vWrMH777+PJ598Ej179sSGDRtw4sQJ7Nq1q7mHQ2TVWNgmMk08NZKIiMjCbd68ucbXZTIZVCoVVCpV8wRERA2SlZWF3NxcREZGSm0KhQKhoaHIyMjA5MmTcfjwYWg0Gp0+3t7eCAgIQEZGBgYONJ/r4xGZu6ioKJ0b1tyvcmEbANatWwdPT09s3LgRkydPlgrb69evx5NPPgkA2LBhA3x8fLBr1y7mM1EDsRBGRERERGQGcnNzAUDvRhaenp7Izs6W+tjZ2cHNzU2vj3b5qqjVaqjVaum59oYAGo0GGo1Gr7+2TWEjGjAS/fWYIm1sphxjY3GMxht7UxW265vL2tcAy87n5mYNudXc6rJN67q9WQgjIiIiIjIjMpnuDSeEEHptldXWZ+HChYiPj9drT01NhaOjY7XLvdOnopZoa7Z9+/ZGLd8c0tLSjB1Ck7PmMRYXFzdzJPc0VWG7obkMWEc+NzdryK3mVtM2rWs+sxBGRERERGQGlEolgHt/HHt5eUnteXl50h/TSqUSpaWlyM/P1/njOS8vDyEhIdWue+7cuYiLi5OeFxQUwMfHB5GRkXBxcdHrr9FokJaWhjcP2UBd0fA7wZ5Ume6pXdoxRkREWOwdWznG/82YMhZDF7brm8uAdeRzc7OG3Gpuddmmdc1nFsKIiIiIiMyAn58flEol0tLS0LNnTwBAaWkp0tPTsXjxYgBA7969IZfLkZaWhhEjRgAAcnJycPLkSSQlJVW7boVCAYVCodcul8tr/CNOXSGDurzhfzibwx+ItW0DS2DNYzTWuJuqsN3QXAasI5+bmzXkVnOraZvWdVvzrpFERERERCbi7t27OHbsGI4dOwbg3nWEjh07hkuXLkEmkyE2NhaJiYlISUnByZMnMX78eDg6OmLMmDEAAFdXV0ycOBEzZszATz/9hKNHj+LFF19EYGCgdLFtIjK++wvbWtrCtrbIdX9hW0tb2K5phicR1YwzwppBhzk/NHodFxcNNkAkRERERGTKDh06hAEDBkjPtac4xcTEIDk5GbNmzUJJSQmmTJmC/Px8BAUFITU1Fc7OztIyH3zwAWxtbTFixAiUlJQgPDwcycnJaNGiRbOPh8ia3b17F3/88Yf0XFvYdnd3R/v27aXCtr+/P/z9/ZGYmFhtYbtVq1Zwd3fHzJkzWdgmaiQWwoiIiIiITERYWBiEqP7ObTKZDCqVCiqVqto+9vb2WL58OZYvX94EERJRXbGwTWSaWAgjIiIiIiIiMjAWtolME68RRkREREREREREVoGFMCIiIiIiIiIisgo8NZLqhRf+JyIiIiIiIiJzxRlhRERERERERERkFVgIIyIiIiIiIiIiq8BTI4mIzFB1pykrWggk9QUCVDuhLpfVuA6epkxERERERNaGM8KIiIiIiIiIiMgqsBBGRERERERERERWgYUwIiIiIiIiIiKyCiyEERERERERERGRVeDF8omIrFR1F9yvD15wn4iIiIiIzAlnhBERERERERERkVXgjDAiIiIiM8cZnkRERER1w0IYEREREbGYRkRERFaBp0YSEREREREREZFV4Iwwanb8F2ciIiIiIiIiMgbOCCMiIiIiIiIiIqvAQhgREREREREREVkFnhppRQxxSqKpqGosihYCSX2BANVOqMtlta6Dp1cSERERERERWRfOCCMiIiIiIiIiIqvAGWFktXjRfiIiIsMyldnn3D8TERFRdVgIIyKiBmNBmajxasuj+p76T0RERETVYyGMiIiIqIFMZQYUEREREdUNC2FmoqYDbf5LMRERERERERFR7VgIIyIiIqvDmVxERERE1ol3jSQiIiIiIiIiIqvAGWFEjcALhRMREZme+/fPDb2EBPfP1qWqY7r6fnf4nSEiMg+cEUZERERERERERFaBM8KIiMioOLOSiIiIiCxRY29ox2PcpmHUGWGrVq2Cn58f7O3t0bt3b+zbt8+Y4RBRAzGXiSwH85nIcjCfiSwH85nIcIw2I2zLli2IjY3FqlWr8Oijj+Jf//oXoqKicPr0abRv395YYRFRPTGXiSwH85nIcjCfiSwH85ksQWPPAtFet9EQjFYIW7p0KSZOnIhJkyYBAJYtW4adO3fi448/xsKFC40VFhHVE3OZLIm1T19nPhNZDuYzkeVgPhMZllEKYaWlpTh8+DDmzJmj0x4ZGYmMjAy9/mq1Gmq1Wnp+584dAMCtW7dgb2+P4uJi3Lx5E3K5XGc527KiJoje9NhWCBQXV8BWY4Pyiob/AWfuzHU73Lx5s8bXCwsLAQBCiOYIp17qm8tAzfms0Wikdo1Go5Pb1pLPjWWuedBYteVRbbTft8ZuN+Zz1fkMGG4bWyNrzWtDaej2qymfTTmXgabPZ6D5fjebS1XHGfX97pjKWOqj8vGWKQpa+FOjllfYCLzRs6LaMVpaPtc3lwHLy2dTwG2qr7F/z2l/k2v6vaprPhulEHbjxg2Ul5fD09NTp93T0xO5ubl6/RcuXIj4+Hi9dj8/vyaL0dyMMXYAJsIct4PH+3XrV1hYCFdX16YNpp7qm8sA87k5mGMeNFZd86ipMZ/vYT4bnjXmtSE1ZPvVJZ9NMZcB88pnU/n9rk59vjumPhZrVpfP0VLy2Zj7ZuaA4XGb6qrrb3Jt+WzUu0bKZLqVUSGEXhsAzJ07F3FxcdLziooK3Lp1C61atUJhYSF8fHxw+fJluLi4NHnMpqigoMDqtwFgudtBCIHCwkJ4e3sbO5Rq1TWXgZrz+f5lLPXzbGrcbg3TXNvNWvMZ4HezMbjtGqcptp855DLQdPkMWMf3kmO0DLWN0dLyub65DFjH96C5cZsaXl22aV3z2SiFMA8PD7Ro0UKvgp2Xl6dX6QYAhUIBhUKh0/bAAw8A+N8PgouLi9V/wbgN7rHE7WCK/zoF1D+XgZrzuSqW+Hk2B263hmmO7WbN+Qzwu9kY3HaNY+jtZ6q5DDRfPgPW8b3kGC1DTWO0pHxuaC4D1vE9aG7cpoZX2zatSz7bGDKgurKzs0Pv3r2Rlpam056WloaQkBBjhEREDcBcJrIczGciy8F8JrIczGciwzPaqZFxcXEYO3Ys+vTpg+DgYKxevRqXLl3Cyy+/bKyQiKgBmMtEloP5TGQ5mM9EloP5TGRYRiuEjRw5Ejdv3sTbb7+NnJwcBAQEYPv27fD19a3XehQKBRYsWKA3/dOacBvcw+1gHIbK5cr4eTYMt1vDcLvd01T5DHAbNwa3XeNY6/ZrynwGrGO7coyWwRLGyHw2P9ymhmfIbSoTpnqfWCIiIiIiIiIiIgMyyjXCiIiIiIiIiIiImhsLYUREREREREREZBVYCCMiIiIiIiIiIqvAQhgREREREREREVkFsy+ErVq1Cn5+frC3t0fv3r2xb98+Y4fUICqVCjKZTOehVCql14UQUKlU8Pb2hoODA8LCwnDq1CmddajVakyfPh0eHh5wcnLC008/jStXruj0yc/Px9ixY+Hq6gpXV1eMHTsWt2/fbo4h6tm7dy+GDBkCb29vyGQybNu2Tef15hzzpUuXMGTIEDg5OcHDwwOvvvoqSktLm2LYVEeWktsNsXDhQjzyyCNwdnZGmzZtMGzYMJw7d06njyX+JhjSwoULIZPJEBsbK7VxmxmPNeezljXu5xuKxwfmwZLzurbvoLmry3GGufv444/RvXt3uLi4wMXFBcHBwfjxxx+NHZZJsuRcNgZL//0whqb4zTLrQtiWLVsQGxuL+fPn4+jRo3j88ccRFRWFS5cuGTu0BunWrRtycnKkx4kTJ6TXkpKSsHTpUqxYsQKZmZlQKpWIiIhAYWGh1Cc2NhYpKSnYvHkz9u/fj7t37yI6Ohrl5eVSnzFjxuDYsWPYsWMHduzYgWPHjmHs2LHNOk6toqIi9OjRAytWrKjy9eYac3l5OQYPHoyioiLs378fmzdvxjfffIMZM2Y03eCpRpaW2/WVnp6OqVOn4uDBg0hLS0NZWRkiIyNRVFQk9bHE3wRDyczMxOrVq9G9e3eddm4z47D2fL6fte3nG4rHB6bP0vO6tu+guavLcYa5a9euHRYtWoRDhw7h0KFDeOKJJzB06FC9orm1s/RcNgZL//0whib5zRJmrG/fvuLll1/WaevatauYM2eOkSJquAULFogePXpU+VpFRYVQKpVi0aJFUttff/0lXF1dxSeffCKEEOL27dtCLpeLzZs3S32uXr0qbGxsxI4dO4QQQpw+fVoAEAcPHpT6HDhwQAAQZ8+ebYJR1R0AkZKSIj1vzjFv375d2NjYiKtXr0p9Nm3aJBQKhbhz506TjJdqZkm5bQh5eXkCgEhPTxdCWMdvQkMVFhYKf39/kZaWJkJDQ8Vrr70mhOA2Mybm8z3Wvp9vKB4fmCZryuvK30FLVPk4w1K5ubmJTz/91NhhmBRrymVjsIbfD2MwxG+W2c4IKy0txeHDhxEZGanTHhkZiYyMDCNF1Tjnz5+Ht7c3/Pz8MGrUKFy4cAEAkJWVhdzcXJ2xKhQKhIaGSmM9fPgwNBqNTh9vb28EBARIfQ4cOABXV1cEBQVJffr16wdXV1eT22bNOeYDBw4gICAA3t7eUp+BAwdCrVbj8OHDTTpO0meJud1Yd+7cAQC4u7sDsM7fhLqaOnUqBg8ejCeffFKnndvMOJjPurifbzweHxgf89ryVD7OsDTl5eXYvHkzioqKEBwcbOxwTAZzmcyVIX6zbA0VTHO7ceMGysvL4enpqdPu6emJ3NxcI0XVcEFBQfj888/RuXNnXLt2DQkJCQgJCcGpU6ek8VQ11uzsbABAbm4u7Ozs4ObmptdHu3xubi7atGmj995t2rQxuW3WnGPOzc3Vex83NzfY2dmZ3HaxBpaW240lhEBcXBwee+wxBAQEAGje/DAnmzdvxpEjR5CZman3GreZcTCf/4f7ecPg8YHxMa8tS1XHGZbixIkTCA4Oxl9//YWWLVsiJSUFDz30kLHDMhnMZTJHhvrNMttCmJZMJtN5LoTQazMHUVFR0v8HBgYiODgYnTp1wrp169CvXz8ADRtr5T5V9TflbdZcYza37WINLCW3G2vatGk4fvw49u/fr/eaNf4mVOfy5ct47bXXkJqaCnt7+2r7cZsZB/OZ+3lD4/GB8TGvLUNNxxnmrkuXLjh27Bhu376Nb775BjExMUhPT2cxrBLmMpkTQ/1mme2pkR4eHmjRooVetTovL0+vqm2OnJycEBgYiPPnz0t3lapprEqlEqWlpcjPz6+xz7Vr1/Te6/r16ya3zZpzzEqlUu998vPzodFoTG67WANLz+36mD59Or777jvs3r0b7dq1k9qt8TehNocPH0ZeXh569+4NW1tb2NraIj09HR999BFsbW2l8XCbNS/mc/WsfT/fUDw+MD7mteWo7jjDUtjZ2eFvf/sb+vTpg4ULF6JHjx748MMPjR2WyWAuk7kx5G+W2RbC7Ozs0Lt3b6Slpem0p6WlISQkxEhRGY5arcaZM2fg5eUFPz8/KJVKnbGWlpYiPT1dGmvv3r0hl8t1+uTk5ODkyZNSn+DgYNy5cwe//vqr1OeXX37BnTt3TG6bNeeYg4ODcfLkSeTk5Eh9UlNToVAo0Lt37yYdJ+mz9NyuCyEEpk2bhq1bt+Lnn3+Gn5+fzuvW+JtQm/DwcJw4cQLHjh2THn369MELL7yAY8eOoWPHjtxmRsB8rp617+cbiscHxse8Nn+1HWdYKiEE1Gq1scMwGcxlMhdN8pvV4Mvsm4DNmzcLuVwu1qxZI06fPi1iY2OFk5OTuHjxorFDq7cZM2aIPXv2iAsXLoiDBw+K6Oho4ezsLI1l0aJFwtXVVWzdulWcOHFCjB49Wnh5eYmCggJpHS+//LJo166d2LVrlzhy5Ih44oknRI8ePURZWZnU56mnnhLdu3cXBw4cEAcOHBCBgYEiOjq62ccrxL27ux09elQcPXpUABBLly4VR48eFdnZ2UKI5htzWVmZCAgIEOHh4eLIkSNi165dol27dmLatGnNtzFIhyXldkO88sorwtXVVezZs0fk5ORIj+LiYqmPJf4mGNr9d40UgtvMWKw9n7WscT/fUDw+MH2Wnte1fQfNXV2OM8zd3Llzxd69e0VWVpY4fvy4mDdvnrCxsRGpqanGDs2kWHouG4Ol/34YQ1P8Zpl1IUwIIVauXCl8fX2FnZ2d6NWrl9ne9nfkyJHCy8tLyOVy4e3tLYYPHy5OnTolvV5RUSEWLFgglEqlUCgUon///uLEiRM66ygpKRHTpk0T7u7uwsHBQURHR4tLly7p9Ll586Z44YUXhLOzs3B2dhYvvPCCyM/Pb44h6tm9e7cAoPeIiYkRQjTvmLOzs8XgwYOFg4ODcHd3F9OmTRN//fVXUw6famEpud0QVeUFALF27VqpjyX+Jhha5UIYt5nxWHM+a1njfr6heHxgHiw5r2v7Dpq7uhxnmLuXXnpJ+n62bt1ahIeHswhWDUvOZWOw9N8PY2iK3yzZ/18xERERERERERGRRTPba4QRERERERERERHVBwthRERERERERERkFVgIIyIiIiIiIiIiq8BCGBERERERERERWQUWwoiIiIiIiIiIyCqwEEZERERERERERFaBhTAiIiIiIiIiIrIKLIQREREREREREZFVYCGMiIiIiIiIiIisAgthRERERERERERkFVgIIyIiIiIiIiIiq8BCGBERERERERERWQUWwoiIiIiIiIiIyCqwEGZitmzZgm7dusHBwQEymQzHjh0z2Lq3b98OlUpV7etFRUVYtGgRevbsiZYtW6Jly5bo2bMnFi9ejJKSEoPFUV979uyBTCbDnj17jBYDEVWN+UlUfxkZGVCpVLh9+3adl+nQoQPGjx/foPdTqVSQyWQNWpaIatbU+ZWcnAyZTIZDhw7V2nf8+PHo0KFDk8VCRLX76aef0KdPHzg5OUEmk2Hbtm3GDomqwEKYCbl+/TrGjh2LTp06YceOHThw4AA6d+5ssPVv374d8fHxVb527do19OvXD2+//TYGDhyIlJQUpKSk4KmnnkJ8fDweffRR3Lhxw2CxEBERWauMjAzEx8fXqxCWkpKCN998s+mCIiIiokYRQmDEiBGQy+X47rvvcODAAYSGhho7LKqCrbEDoP/5/fffodFo8OKLLzZ7wowbNw5nz57F7t278dhjj0ntERERGDx4MAYMGICJEyfi22+/bda4iIiIrFlJSQkcHBzQs2dPY4dCREREVdBoNJDJZLh27Rpu3bqFZ555BuHh4cYOi2rAGWEmYvz48VIBauTIkZDJZAgLC8OhQ4cwatQodOjQAQ4ODujQoQNGjx6N7OxsneWLi4sxc+ZM+Pn5wd7eHu7u7ujTpw82bdokrX/lypUAAJlMJj0uXryIQ4cOITU1FRMnTtQpgmk99thjeOmll/Ddd9/ht99+AwBcvHgRMpkMycnJev1lMpnOKZh//PEHJkyYAH9/fzg6OqJt27YYMmQITpw4obfs2bNn8dRTT8HR0REeHh54+eWXUVhYqNfv6NGjiI6ORps2baBQKODt7Y3BgwfjypUrddvgRGbshx9+wMMPPwyFQgE/Pz+89957OqdmGDs/iah6KpUK//znPwEAfn5+0v54z5496NChA6Kjo7F161b07NkT9vb20kzuyqdGak9L3rBhA+Li4qBUKuHg4IDQ0FAcPXq0TrFs2bIFwcHBcHJyQsuWLTFw4MA6L0tkjara/1b2119/Ye7cufDz84OdnR3atm2LqVOn6s0Arbw/1qruNOj8/HxMmDAB7u7ucHJywpAhQ3DhwoVaYxZCYNWqVXj44Yfh4OAANzc3PPfcc3ValsiSXL9+Hf/4xz/g4+MDhUKB1q1b49FHH8WuXbsAVJ97YWFhCAsLk55r97/r16/HjBkz0LZtWygUCrz44oto164dAGD27NmQyWTSqcr1Od6+ffs2ZsyYgY4dO0KhUKBNmzYYNGgQzp49K/UpLS1FQkICunbtKo1lwoQJuH79uuE2mIXjjDAT8eabb6Jv376YOnUqEhMTMWDAALi4uOD06dPo0qULRo0aBXd3d+Tk5ODjjz/GI488gtOnT8PDwwMAEBcXh/Xr1yMhIQE9e/ZEUVERTp48iZs3b0rrLyoqwtdff40DBw5I7+vl5SUVy4YNG1ZtfMOGDcPq1auRmpqKHj161Gtsf/75J1q1aoVFixahdevWuHXrFtatW4egoCAcPXoUXbp0AXDv9MzQ0FDI5XKsWrUKnp6e+OKLLzBt2jSd9RUVFSEiIgJ+fn5YuXIlPD09kZubi927d/OPcrJ4P/30E4YOHYrg4GBs3rwZ5eXlSEpKwrVr1xq0PkPnJxHVbNKkSbh16xaWL1+OrVu3wsvLCwDw0EMPAQCOHDmCM2fO4I033oCfnx+cnJxqXN+8efPQq1cvfPrpp7hz5w5UKhXCwsJw9OhRdOzYsdrlEhMT8cYbb2DChAl44403UFpaiiVLluDxxx/Hr7/+KsVDRPfUZf8rhMCwYcPw008/Ye7cuXj88cdx/PhxLFiwAAcOHMCBAwegUCga9P4TJ05EREQENm7ciMuXL+ONN95AWFgYjh8/jgceeKDa5SZPnozk5GS8+uqrWLx4MW7duoW3334bISEh+O233+Dp6dmgeIjMzdixY3HkyBG8++676Ny5M27fvo0jR45Ify/X19y5cxEcHIxPPvkENjY2CAwMxMiRIzF8+HBMnz4dY8aMkfK9rsfbhYWFeOyxx3Dx4kXMnj0bQUFBuHv3Lvbu3YucnBx07doVFRUVGDp0KPbt24dZs2YhJCQE2dnZWLBggTSRxsHBwWDbzWIJMhm7d+8WAMRXX31VbZ+ysjJx9+5d4eTkJD788EOpPSAgQAwbNqzG9U+dOlVU9ZG//PLLAoA4e/ZstcueOXNGABBTp04VQgiRlZUlAIi1a9fq9QUgFixYUOMYSktLhb+/v3j99del9tmzZwuZTCaOHTum0z8iIkIAELt37xZCCHHo0CEBQGzbtq2G0RJZpqCgIOHt7S1KSkqktoKCAuHu7i7ltzHzk4hqt2TJEgFAZGVl6bT7+vqKFi1aiHPnzukt4+vrK2JiYqTn2mOGXr16iYqKCqn94sWLQi6Xi0mTJkltCxYs0Nn/X7p0Sdja2orp06frvEdhYaFQKpVixIgRjRwhkeWpy/53x44dAoBISkrSWXbLli0CgFi9erXUVt3+uHKur127VgAQzzzzjE6///znPwKASEhIkNpiYmKEr6+v9PzAgQMCgHj//fd1lr18+bJwcHAQs2bNqvP4icxdy5YtRWxsbLWvV849rdDQUBEaGio91+5/+/fvr9dXewy+ZMmSGmOp7nj77bffFgBEWlpatctu2rRJABDffPONTntmZqYAIFatWlXje9M9PDXSxN29exezZ8/G3/72N9ja2sLW1hYtW7ZEUVERzpw5I/Xr27cvfvzxR8yZMwd79uwx+F0ehRAA0KC74pSVlSExMREPPfQQ7OzsYGtrCzs7O5w/f15nDLt370a3bt30ZpyNGTNG5/nf/vY3uLm5Yfbs2fjkk09w+vTpBoyIyPwUFRUhMzMTw4cPh729vdTu7OyMIUOGNGidhs5PImqc7t271+tGOWPGjNHZN/v6+iIkJAS7d++udpmdO3eirKwM48aNQ1lZmfSwt7dHaGgo7wJLVEld978///wzAOidXvX888/DyckJP/30U4NjeOGFF3Seh4SEwNfXt8Zc//777yGTyfDiiy/q5LpSqUSPHj2Y62RV+vbti+TkZCQkJODgwYPQaDSNWt+zzz5b5751Pd7+8ccf0blzZzz55JPVruv777/HAw88gCFDhujk9cMPPwylUsm8riMWwkzcmDFjsGLFCkyaNAk7d+7Er7/+iszMTLRu3Vqn2PXRRx9h9uzZ2LZtGwYMGAB3d3cMGzYM58+fr/U92rdvDwDIysqqts/FixcBAD4+PvUeQ1xcHN58800MGzYM//73v/HLL78gMzMTPXr00BnDzZs3oVQq9Zav3Obq6or09HQ8/PDDmDdvHrp16wZvb28sWLCg0T9oRKYsPz8fFRUVdcqTujJ0fhJR42hPlayr6vKyplM9tKdyPfLII5DL5TqPLVu28C7RRJXUdf978+ZN2NraonXr1jp9ZDJZrXlZm4bmuhACnp6eerl+8OBB5jpZlS1btiAmJgaffvopgoOD4e7ujnHjxiE3N7dB66vP/rqux9vXr1+XrjNWnWvXruH27duws7PTy+vc3FzmdR3xGmEm7M6dO/j++++xYMECzJkzR2pXq9W4deuWTl8nJyfEx8cjPj4e165dk2aHDRkyROfCelWJjIzEvHnzsG3bNjz11FNV9tm2bRsA4IknngAA6V/D1Gq1Tr+qdsYbNmzAuHHjkJiYqNN+48YNnWsatGrVqsofoqraAgMDsXnzZgghcPz4cSQnJ+Ptt9+Gg4ODzrYisiRubm6QyWS15omx85OIGq6+M6+ry8tWrVpVu4z2+qJff/01fH196xcgkRWq6/63VatWKCsrw/Xr13WKYUII5Obm4pFHHpHaFAqF3n4aqHpfXfl97m/729/+Vm3cHh4ekMlk2LdvX5XXJmvo9cqIzJGHhweWLVuGZcuW4dKlS/juu+8wZ84c5OXlYceOHbC3t68yJ2/cuCHtN+9Xn/11XY+3W7duXevN3zw8PNCqVSvs2LGjytednZ3rHJc144wwEyaTySCE0NtJffrppygvL692OU9PT4wfPx6jR4/GuXPnUFxcDOB/O7vKp0327t0bAwcOxJo1a/Cf//xHb3379+/HZ599hkcffRR9+vSR3sPe3h7Hjx/X6fvtt99WOY7KY/jhhx9w9epVnbYBAwbg1KlT0p0ptTZu3FjtWGUyGXr06IEPPvgADzzwAI4cOVJtXyJz5+TkhL59+2Lr1q3466+/pPbCwkL8+9//lp6bSn4SUdWq2x83xKZNm6TLFwBAdnY2MjIydO5wVdnAgQNha2uL//73v+jTp0+VDyL6n7ruf8PDwwHc+6P3ft988w2Kioqk14F7d6irvJ/++eefcffu3Spj+OKLL3SeZ2RkIDs7u8Zcj46OhhACV69erTLPAwMDax44kYVq3749pk2bhoiICOnvx6py8vfff8e5c+ca/X51Pd6OiorC77//Lp1mXZXo6GjcvHkT5eXlVea19sL7VDPOCDNhLi4u6N+/P5YsWQIPDw906NAB6enpWLNmjd7dYYKCghAdHY3u3bvDzc0NZ86cwfr16xEcHAxHR0cAkHZ2ixcvRlRUFFq0aIHu3bvDzs4O69atQ3h4OCIjI/Hqq69KO+qff/4ZH374IZRKJbZs2SK9n/Z6A5999hk6deqEHj164Ndff63yj+Lo6GgkJyeja9eu6N69Ow4fPowlS5boTfuMjY3FZ599hsGDByMhIUG6K13lGW3ff/89Vq1ahWHDhqFjx44QQmDr1q24ffs2IiIiGr3diUzZO++8g6eeegoRERGYMWMGysvLsXjxYjg5OUkzRY2Zn0RUO+3++MMPP0RMTAzkcnmDD1zz8vLwzDPP4O9//zvu3LmDBQsWwN7eHnPnzq12mQ4dOuDtt9/G/PnzceHCBTz11FNwc3PDtWvX8Ouvv0qzzInof+qy/42IiMDAgQMxe/ZsFBQU4NFHH5XuGtmzZ0+MHTtWWt/YsWPx5ptv4q233kJoaChOnz6NFStWwNXVtcr3P3ToECZNmoTnn38ely9fxvz589G2bVtMmTKl2pgfffRR/OMf/8CECRNw6NAh9O/fH05OTsjJycH+/fsRGBiIV155xbAbisgE3blzBwMGDMCYMWPQtWtXODs7IzMzEzt27MDw4cMB3MvJF198EVOmTMGzzz6L7OxsJCUl6Z3q3BD1Od7esmULhg4dijlz5qBv374oKSlBeno6oqOjMWDAAIwaNQpffPEFBg0ahNdeew19+/aFXC7HlStXsHv3bgwdOhTPPPNMo2O2eMa7Tj9VVtVdI69cuSKeffZZ4ebmJpydncVTTz0lTp48qXdXizlz5og+ffoINzc3oVAoRMeOHcXrr78ubty4IfVRq9Vi0qRJonXr1kImk+ndseru3bvi3XffFT169BCOjo4CgAAghg4dKm7duqUX7507d8SkSZOEp6encHJyEkOGDBEXL17UuwtOfn6+mDhxomjTpo1wdHQUjz32mNi3b5/eHTiEEOL06dMiIiJC2NvbC3d3dzFx4kTx7bff6tyV7uzZs2L06NGiU6dOwsHBQbi6uoq+ffuK5OTkRm1/InPx3Xffie7duws7OzvRvn17sWjRIr27whkrP4mobubOnSu8vb2FjY2NlEO+vr5i8ODBVfav7q6R69evF6+++qpo3bq1UCgU4vHHHxeHDh3SWbby74PWtm3bxIABA4SLi4tQKBTC19dXPPfcc2LXrl0GHSuRpajL/rekpETMnj1b+Pr6CrlcLry8vMQrr7wi8vPzddalVqvFrFmzhI+Pj3BwcBChoaHi2LFj1d41MjU1VYwdO1Y88MADwsHBQQwaNEicP39eZ52V7xqp9dlnn4mgoCDh5OQkHBwcRKdOncS4ceP0fiuILNVff/0lXn75ZdG9e3fh4uIiHBwcRJcuXcSCBQtEUVGREEKIiooKkZSUJDp27Cjs7e1Fnz59xM8//1ztXSPv/5tdq7q7RtbneDs/P1+89tpron379kIul4s2bdqIwYMHi7Nnz0p9NBqNeO+990SPHj2Evb29aNmypejatauYPHmy3u8CVU0mxH3z6YnuU1BQgNDQUFy7dg379u1Dp06djB0SEVVDpVIhPj4e/Eknsg579uzBgAED8NVXX+G5554zdjhEREREZoPXCKNqubi44Mcff4S9vT3Cw8Nx+fJlY4dERERERERERNRgvEYY1UipVOLChQvGDoOIiIiIiIiIqNF4aiQREREREREREVkFnhpJRERERERERERWgYUwIiIiIiIiIiKyCiyEERERERERERGRVTDLi+VXVFTgzz//hLOzM2QymbHDIWpSQggUFhbC29sbNjaWV7tmPpM1YT4TWQZLz2WA+UzWw9LzmblM1qSu+WyWhbA///wTPj4+xg6DqFldvnwZ7dq1M3YYBsd8JmvEfCayDJaaywDzmayPpeYzc5msUW35bJaFMGdnZwD3Bufi4lJlH41Gg9TUVERGRkIulzdneM2GYzR/dRlfQUEBfHx8pO+9pbHWfOaYzIOhx2Tt+WyJ3xHAMsfFMdXM0nMZsN58rgnHbJljtvR8tvRjbXOOHTDv+E0x9rrms1kWwrRTOl1cXGpMZkdHR7i4uJjMh2JoHKP5q8/4LHUqs7XmM8dkHppqTNaaz5b4HQEsc1wcU91Yai4D1pvPNeGYLXvMlprPln6sbc6xA+YdvynHXls+W95J0ERERERERERERFVgIYyIiIiIiIiIiKwCC2FERERERERERGQVzPIaYfURoNoJdXnDz/e+uGiwAaMhosZgPhNZBuYykeVgPhORIXWY80O9+itaCCT11f0t4u8K1YYzwoiIiIiIiIiIyCqwEEZERERERERERFaBhTAiIiIiIiIiIrIKLIQREREREREREZFVYCGMiIiIiIiIiIisAgthRERERERERERkFVgIIyIiIiIiIiIiq8BCGBERERERERERWQXb+nReuHAhtm7dirNnz8LBwQEhISFYvHgxunTpIvURQiA+Ph6rV69Gfn4+goKCsHLlSnTr1k3qo1arMXPmTGzatAklJSUIDw/HqlWr0K5dO8ONjIiIiCxShzk/NHodFxcNNkAkRERERGRu6jUjLD09HVOnTsXBgweRlpaGsrIyREZGoqioSOqTlJSEpUuXYsWKFcjMzIRSqURERAQKCwulPrGxsUhJScHmzZuxf/9+3L17F9HR0SgvLzfcyIiIiIiIiIiIiO5TrxlhO3bs0Hm+du1atGnTBocPH0b//v0hhMCyZcswf/58DB8+HACwbt06eHp6YuPGjZg8eTLu3LmDNWvWYP369XjyyScBABs2bICPjw927dqFgQMHGmhoRERERERERERE/9Ooa4TduXMHAODu7g4AyMrKQm5uLiIjI6U+CoUCoaGhyMjIAAAcPnwYGo1Gp4+3tzcCAgKkPkRERERERERERIZWrxlh9xNCIC4uDo899hgCAgIAALm5uQAAT09Pnb6enp7Izs6W+tjZ2cHNzU2vj3b5ytRqNdRqtfS8oKAAAKDRaKDRaKpcRtuusBH1HVqV6zFF2thMOcbGsvQx1mV8ljp2IiIiIiIioubW4ELYtGnTcPz4cezfv1/vNZlMpvNcCKHXVllNfRYuXIj4+Hi99tTUVDg6Ota43nf6VNT4em22b9/eqOWbQ1pamrFDaHKWPsaaxldcXNyMkRARERERERFZrgYVwqZPn47vvvsOe/fu1bnTo1KpBHBv1peXl5fUnpeXJ80SUyqVKC0tRX5+vs6ssLy8PISEhFT5fnPnzkVcXJz0vKCgAD4+PoiMjISLi0uVy2g0GqSlpeHNQzZQV9RchKvJSZXpXrNMO8aIiAjI5XJjh9MkLH2MdRmfdgYkERERERERETVOvQphQghMnz4dKSkp2LNnD/z8/HRe9/Pzg1KpRFpaGnr27AkAKC0tRXp6OhYvXgwA6N27N+RyOdLS0jBixAgAQE5ODk6ePImkpKQq31ehUEChUOi1y+XyWosj6goZ1OUNL4SZQ/GlLtvB3Fn6GGsanyWPm4iIiIiIiKg51asQNnXqVGzcuBHffvstnJ2dpWt6ubq6wsHBATKZDLGxsUhMTIS/vz/8/f2RmJgIR0dHjBkzRuo7ceJEzJgxA61atYK7uztmzpyJwMBA6S6SREREREREREREhlavQtjHH38MAAgLC9NpX7t2LcaPHw8AmDVrFkpKSjBlyhTk5+cjKCgIqampcHZ2lvp/8MEHsLW1xYgRI1BSUoLw8HAkJyejRYsWjRsNERERERERERFRNep9amRtZDIZVCoVVCpVtX3s7e2xfPlyLF++vD5vT0RERERERERE1GA2xg6AiIiIiIiIiIioObAQRkREREREREREVoGFMCIiIiIiIiIisgoshBERERERERERkVVgIYyIiIiIiIiIiKwCC2FERERERERERGQVWAgjslJ79+7FkCFD4O3tDZlMhm3btum8LoSASqWCt7c3HBwcEBYWhlOnTun0UavVmD59Ojw8PODk5ISnn34aV65cacZREBEREREREdUdC2FEVqqoqAg9evTAihUrqnw9KSkJS5cuxYoVK5CZmQmlUomIiAgUFhZKfWJjY5GSkoLNmzdj//79uHv3LqKjo1FeXt5cwyAiIiIiIiKqM1tjB0BExhEVFYWoqKgqXxNCYNmyZZg/fz6GDx8OAFi3bh08PT2xceNGTJ48GXfu3MGaNWuwfv16PPnkkwCADRs2wMfHB7t27cLAgQObbSxERERERGRcHeb8YOwQiOqEM8KISE9WVhZyc3MRGRkptSkUCoSGhiIjIwMAcPjwYWg0Gp0+3t7eCAgIkPoQERERERERmRLOCCMiPbm5uQAAT09PnXZPT09kZ2dLfezs7ODm5qbXR7t8VdRqNdRqtfS8oKAAAKDRaKDRaKpcRtuusBH1HEnV6zEF2lhMKabG4pjqvj4iIiIiIjIOFsKIqFoymUznuRBCr62y2vosXLgQ8fHxeu2pqalwdHSscd3v9Kmo8fXabN++vVHLN4W0tDRjh2BwHFP1iouLDbIeIrJce/fuxZIlS3D48GHk5OQgJSUFw4YNk14XQiA+Ph6rV69Gfn4+goKCsHLlSnTr1k3qo1arMXPmTGzatAklJSUIDw/HqlWr0K5dOyOMiIiIyLSwEEZEepRKJYB7s768vLyk9ry8PGmWmFKpRGlpKfLz83VmheXl5SEkJKTadc+dOxdxcXHS84KCAvj4+CAyMhIuLi5VLqPRaJCWloY3D9lAXVFzIa4mJ1Wmc90y7ZgiIiIgl8uNHY5BcEy1086AJCKqjvZmNhMmTMCzzz6r97r2ZjbJycno3LkzEhISEBERgXPnzsHZ2RnAvZvZ/Pvf/8bmzZvRqlUrzJgxA9HR0Th8+DBatGjR3EMiIiIyKSyEEZEePz8/KJVKpKWloWfPngCA0tJSpKenY/HixQCA3r17Qy6XIy0tDSNGjAAA5OTk4OTJk0hKSqp23QqFAgqFQq9dLpfXWmhQV8igLm94IcwUizN1Gbe54ZhqXg8RUU14MxsiIqKmxUIYkZW6e/cu/vjjD+l5VlYWjh07Bnd3d7Rv3x6xsbFITEyEv78//P39kZiYCEdHR4wZMwYA4OrqiokTJ2LGjBlo1aoV3N3dMXPmTAQGBkoH3kRERGQ4td3MZvLkybXezKa6Qlh9r+FpidfvrI0lXguzNtYwZkseGxFVjYUwIit16NAhDBgwQHquPV0xJiYGycnJmDVrFkpKSjBlyhTpGiSpqanSaRcA8MEHH8DW1hYjRoyQrkGSnJzM0y6IiIiaQFPezKah1/C0xOt31sYSr4VZG0seM6/fSWR9WAgjslJhYWEQovp/xZXJZFCpVFCpVNX2sbe3x/Lly7F8+fImiJCIiIiq0hQ3s6nvNTwt8fqdtbHEa2HWxhrGzOt3ElkfFsKIiIiIiMxAU97MpqHX8LTE63fWxhKvhVkbSx6zpY6LiKpnY+wAiIiIiIiodvffzEZLezMbbZHr/pvZaGlvZlNTIYyIiMhasBBGRERERGQi7t69i2PHjuHYsWMA/nczm0uXLkEmk0k3s0lJScHJkycxfvz4am9m89NPP+Ho0aN48cUXeTMbIhOkUqkgk8l0HtqZn8C9U5pVKhW8vb3h4OCAsLAwnDp1yogRE1kGnhpJRERERGQieDMbIuvSrVs37Nq1S3p+f54mJSVh6dKlSE5ORufOnZGQkICIiAicO3dOJ+eJqH5YCCMiIiIiMhG8mQ2RdbG1tdWZBaYlhMCyZcswf/58DB8+HACwbt06eHp6YuPGjZg8eXJzh0pkMVgIIyIiIiIiIjKC8+fPw9vbGwqFAkFBQUhMTETHjh2RlZWF3NxcREZGSn0VCgVCQ0ORkZFRbSFMrVZDrVZLz7V3xdRoNNBoNFUuo22v7vW6UrSovojfVBQ2Que/ANBl/veNXm9z3dHWUNveGEwx9rrGwkIYERGRhVOpVIiPj9dp8/T0RG5uLoB7/+ocHx+P1atXS6darVy5Et26dTNGuERERFYhKCgIn3/+OTp37oxr164hISEBISEhOHXqlLSP1t4RVsvT0xPZ2dnVrnPhwoV6+3wASE1NhaOjY43x3H+TjYZI6tuoxRvlnT4VBl3f9u3bDbq+2jR22xuTKcVeXFxcp34shBEREVkBXoOEiIjItERFRUn/HxgYiODgYHTq1Anr1q1Dv379ANw7Hfp+Qgi9tvvNnTtXurYgcG9GmI+PDyIjI+Hi4lLlMhqNBmlpaYiIiIBcLm/weAJUOxu8bEMpbATe6VOBNw/ZQF1R/Xapr+acEWaIbW8Mphi7dgZkbVgIIyIisgK8BgkREZFpc3JyQmBgIM6fP49hw4YBAHJzc+Hl5SX1ycvL05sldj+FQgGFQqHXLpfLay1W1KVPTdTlhitE1fu9K2QGff/mLuw0dtsbkynFXtc4WAgjIiKyAoa+BglQ/+uQaNvuv46HsRjyehameI2MxuKY6rYuIiJDUqvVOHPmDB5//HH4+flBqVQiLS0NPXv2BACUlpYiPT0dixcvNnKkROaNhTAiIiIL1xTXIAEafh0SQ1/HoyGa4tofpnSNDEPhmKpW12uQEBHVZObMmRgyZAjat2+PvLw8JCQkoKCgADExMZDJZIiNjUViYiL8/f3h7++PxMREODo6YsyYMcYOncissRBGRERk4ZriGiRA/a9Dor2WhKGv49EQhrz2hyleI6OxOKaa1fUaJERENbly5QpGjx6NGzduoHXr1ujXrx8OHjwIX19fAMCsWbNQUlKCKVOmSDezSU1N5fU7iRqJhTAiIiIrY4hrkAANvw6Joa/j0RBNUdwxpWtkGArHVP06iIgaa/PmzTW+LpPJoFKpoFKpmicgIithY+wAiIiIqHlpr0Hi5eWlcw0SLe01SEJCQowYJRERERGR4XFGGBERkYXjNUiIiIiIiO5hIYyIiMjC8Rok+jrM+cEg67m4aLBB1kNEREREzYOFMCIiIgvHa5AQEREREd1T70LY3r17sWTJEhw+fBg5OTlISUmRLrQL3LvLVHx8PFavXi39q/LKlSvRrVs3qY9arcbMmTOxadMmlJSUIDw8HKtWrUK7du0MMigioqoYYgYIZ38QERERERGZr3pfLL+oqAg9evTAihUrqnw9KSkJS5cuxYoVK5CZmQmlUomIiAgUFhZKfWJjY5GSkoLNmzdj//79uHv3LqKjo1FeXt7wkRAREREREREREdWg3jPCoqKiEBUVVeVrQggsW7YM8+fPx/DhwwEA69atg6enJzZu3IjJkyfjzp07WLNmDdavX48nn3wSALBhwwb4+Phg165dGDhwYCOGQ0REREREREREVLV6zwirSVZWFnJzcxEZGSm1KRQKhIaGIiMjAwBw+PBhaDQanT7e3t4ICAiQ+hARERERERERERmaQS+Wn5ubCwDw9PTUaff09ER2drbUx87ODm5ubnp9tMtXplaroVarpecFBQUAAI1GA41GU+Uy2naFjWjASPTXY4q0sZlyjI1l6WOsy/gsdexEREREREREza1J7hopk8l0ngsh9Noqq6nPwoULER8fr9eempoKR0fHGtf7Tp+KWqKt2fbt2xu1fHNIS0szdghNztLHWNP4iouLmzESIiIiIiIiIstl0EKYUqkEcG/Wl5eXl9Sel5cnzRJTKpUoLS1Ffn6+zqywvLw8hISEVLneuXPnIi4uTnpeUFAAHx8fREZGwsXFpcplNBoN0tLS8OYhG6grai7C1eSkynSvWaYdY0REBORyubHDaRKWPsa6jE87A5KIiIiIiIiIGseghTA/Pz8olUqkpaWhZ8+eAIDS0lKkp6dj8eLFAIDevXtDLpcjLS0NI0aMAADk5OTg5MmTSEpKqnK9CoUCCoVCr10ul9daHFFXyKAub3ghzByKL3XZDubO0sdY0/gsedxEREREREREzanehbC7d+/ijz/+kJ5nZWXh2LFjcHd3R/v27REbG4vExET4+/vD398fiYmJcHR0xJgxYwAArq6umDhxImbMmIFWrVrB3d0dM2fORGBgoHQXSSIiIiIiIiIiIkOr910jDx06hJ49e0ozvuLi4tCzZ0+89dZbAIBZs2YhNjYWU6ZMQZ8+fXD16lWkpqbC2dlZWscHH3yAYcOGYcSIEXj00Ufh6OiIf//732jRooWBhkVEhqBSqSCTyXQe2lOggXvX9lOpVPD29oaDgwPCwsJw6tQpI0ZMREREREREVL16zwgLCwuDENXfiVEmk0GlUkGlUlXbx97eHsuXL8fy5cvr+/ZE1My6deuGXbt2Sc/vL1gnJSVh6dKlSE5ORufOnZGQkICIiAicO3dOp/hNREREVJUOc35o9DouLhpsgEiIiMha1HtGGBFZF1tbWyiVSunRunVrAPdmgy1btgzz58/H8OHDERAQgHXr1qG4uBgbN240ctRERERERERE+gx6sXwisjznz5+Ht7c3FAoFgoKCkJiYiI4dOyIrKwu5ubmIjIyU+ioUCoSGhiIjIwOTJ0+ucn1qtRpqtVp6rr0rpkajgUajqXIZbbvCpvrZqM2luhgbuh5Drc8UcEx1Xx8RERERERkHC2FEVK2goCB8/vnn6Ny5M65du4aEhASEhITg1KlTyM3NBQB4enrqLOPp6Yns7Oxq17lw4ULEx8frtaempsLR0bHGeN7pU9GAURjW9u3bDbq+tLQ0g67PFHBM1SsuLjbIesh0dJjzAxQtBJL6AgGqnQ26UzVP6yIiIiJqPiyEEVG1oqKipP8PDAxEcHAwOnXqhHXr1qFfv34A7l0X8H5CCL22+82dOxdxcXHS84KCAvj4+CAyMhIuLi5VLqPRaJCWloY3D9lAXVH/PzIN6aRqoEHWox1TREQE5HK5QdZpbBxT7bQzIImIiIiIyDhYCCOiOnNyckJgYCDOnz+PYcOGAQByc3Ph5eUl9cnLy9ObJXY/hUIBhUKh1y6Xy2stNKgrZA2abWFIhi7w1GXc5oZjqnk9RERERERkPLxYPhHVmVqtxpkzZ+Dl5QU/Pz8olUqdU8ZKS0uRnp6OkJAQI0ZJREREREREVDXOCCOias2cORNDhgxB+/btkZeXh4SEBBQUFCAmJgYymQyxsbFITEyEv78//P39kZiYCEdHR4wZM8bYoRMRERERERHpYSGMiKp15coVjB49Gjdu3EDr1q3Rr18/HDx4EL6+vgCAWbNmoaSkBFOmTEF+fj6CgoKQmpoKZ2dnI0dOREREREREpI+FMCKq1ubNm2t8XSaTQaVSQaVSNU9ARERERJV0mPNDo9fBu7cSEVkPXiOMiIiIiIiIiIisAmeEERHVA//VmYiIiIgsUYBqp9Hv0E7UHDgjjIiIiIiIiIiIrAJnhBERERERERER/X88C8SycUYYERERERERERFZBRbCiIiIiIiIiIjIKrAQRkREREREREREVoGFMCIiIiIiIiIisgq8WD4RERERERERkQHV5YL7ihYCSX2BANVOqMtleq/zgvtNgzPCiIiIiIiIiIjIKrAQRkREREREREREVoGFMCIiIiIiIiIisgoshBERERERERERkVVgIYyIiIiIiIiIiKwCC2FERERERERERGQVWAgjIiIiIiIiIiKrYGvsAIiIrE2HOT9A0UIgqS8QoNoJdbms3uu4uGhwE0RGRERERERk2TgjjIiIiIiIiIiIrAJnhBERERGZuQ5zfmj0OjjTlKxZXXKottnczCEiIvPAQlgteGBJRERERERERGQZeGokERERERERERFZBc4IIyIiIiIiIiIyMTxDrWlwRhgREREREREREVkFzghrBqziEhERkam7/3iltouCNyUe8xAREVFT4owwIiIiIiIiIiKyCpwRRkRERERERERkgXiGmj6jzghbtWoV/Pz8YG9vj969e2Pfvn3GDIeIGoi5TGQ5mM9EloP5TGQ5mM9EhmO0GWFbtmxBbGwsVq1ahUcffRT/+te/EBUVhdOnT6N9+/bGCouI6om5bL4M8a9D92voNYUs7V+YzBnzmchyMJ+JLAfzmSyBKc1MM1ohbOnSpZg4cSImTZoEAFi2bBl27tyJjz/+GAsXLjRWWERUT8xlIsvBfCZTYEoHyuaM+dz8+N2lpsJ8JmOr6vfNmDfWaSyjFMJKS0tx+PBhzJkzR6c9MjISGRkZev3VajXUarX0/M6dOwCAW7duQaPRVPkeGo0GxcXFsNXYoLzCvD6Uqvxt5pd6bQobgTd6VuDh+VuhbqYx/jI3vFneR0v7Od68eRNyubxZ37s51GV8hYWFAAAhRHOGVif1zWWA+axlWyFQXFzR4DHdvHmz8TGUFTV6HTrra+CYDDEWQwha+JNeW31/Z2v7jbT2fLbEXAaYz6bIEMdNNeWzKecywHxuKs2RF6ayT9Sy9GNxwPLy2dqOtc19f2XO8Rsj9tp+I+ucz8IIrl69KgCI//znPzrt7777rujcubNe/wULFggAfPBh1Y/Lly83V4rWWX1zWQjmMx98AMxnPviwlIcp5rIQzGc++GjIw1LymbnMBx+157NR7xopk+lWDYUQem0AMHfuXMTFxUnPKyoqcOvWLbRq1arK/gBQUFAAHx8fXL58GS4uLoYN3ERwjOavLuMTQqCwsBDe3t7NHF3d1TWXAeazFsdkHgw9JmvPZ0v8jgCWOS6OqWbmkMsA89nQOGbLHLOl5bO1HWubc+yAecdvirHXNZ+NUgjz8PBAixYtkJubq9Oel5cHT09Pvf4KhQIKhUKn7YEHHqjTe7m4uJjMh9JUOEbzV9v4XF1dmzGauqtvLgPM58o4JvNgyDExny3zOwJY5rg4puqZai4DzOemxjFbHkvKZ2s91jbn2AHzjt/UYq9LPts0Qxx67Ozs0Lt3b6Slpem0p6WlISQkxBghEVEDMJeJLAfzmchyMJ+JLAfzmcjwjHZqZFxcHMaOHYs+ffogODgYq1evxqVLl/Dyyy8bKyQiagDmMpHlYD4TWQ7mM5HlYD4TGZbRCmEjR47EzZs38fbbbyMnJwcBAQHYvn07fH19DbJ+hUKBBQsW6E0LtSQco/mzhPE1dS4DlrGdKuOYzIMljqkm3Dc3jCWOi2Myf8xnw+OYyViYz9Uz59gB847fnGOXCWGi94klIiIiIiIiIiIyIKNcI4yIiIiIiIiIiKi5sRBGRERERERERERWgYUwIiIiIiIiIiKyCiyEERERERERERGRVbDIQtiqVavg5+cHe3t79O7dG/v27TN2SAajUqkgk8l0Hkql0thhNcrevXsxZMgQeHt7QyaTYdu2bTqvCyGgUqng7e0NBwcHhIWF4dSpU8YJtoFqG+P48eP1Ptd+/foZJ1gjqG/Opqeno3fv3rC3t0fHjh3xySefNFOkdbNw4UI88sgjcHZ2Rps2bTBs2DCcO3euxmX27Nmj9x2QyWQ4e/ZsM0Vds4b89pj659ShQ4cqt/nUqVOr7G/qn5GpYD6b/neF+Wz6n5E5sOTj7cos8fi7Mms4Hrd25rh/Nvd9sDnvby19v2pxhbAtW7YgNjYW8+fPx9GjR/H4448jKioKly5dMnZoBtOtWzfk5ORIjxMnThg7pEYpKipCjx49sGLFiipfT0pKwtKlS7FixQpkZmZCqVQiIiIChYWFzRxpw9U2RgB46qmndD7X7du3N2OExlPfnM3KysKgQYPw+OOP4+jRo5g3bx5effVVfPPNN80cefXS09MxdepUHDx4EGlpaSgrK0NkZCSKiopqXfbcuXM63wN/f/9miLhu6vPbYw6fU2Zmps540tLSAADPP/98jcuZ8mdkbMxnXab8XWE+32PKn5Eps4bj7cos7fi7Mms4Hrdm5rp/toR9sLnuby1+vyosTN++fcXLL7+s09a1a1cxZ84cI0VkWAsWLBA9evQwdhhNBoBISUmRnldUVAilUikWLVoktf3111/C1dVVfPLJJ0aIsPEqj1EIIWJiYsTQoUONEo+x1TdnZ82aJbp27arTNnnyZNGvX78mi7Gx8vLyBACRnp5ebZ/du3cLACI/P7/5AquH+v72mOPn9Nprr4lOnTqJioqKKl839c/IFDCf7zH17wrz2fQ/I1Nn6cfblVn68Xdl1nA8bm0sZf9sbvtgS9rfWtp+1aJmhJWWluLw4cOIjIzUaY+MjERGRoaRojK88+fPw9vbG35+fhg1ahQuXLhg7JCaTFZWFnJzc3U+U4VCgdDQUIv6TIF700nbtGmDzp074+9//zvy8vKMHVKTa0jOHjhwQK//wIEDcejQIWg0miaLtTHu3LkDAHB3d6+1b8+ePeHl5YXw8HDs3r27qUOrl/r89pjb51RaWooNGzbgpZdegkwmq7GvKX9GxsR81mfK3xXm8z2m/BmZKms53q7Mmo6/K7Om43FLZEn7Z3PcB1vC/tYS96sWVQi7ceMGysvL4enpqdPu6emJ3NxcI0VlWEFBQfj888+xc+dO/N///R9yc3MREhKCmzdvGju0JqH93Cz5MwWAqKgofPHFF/j555/x/vvvIzMzE0888QTUarWxQ2tSDcnZ3NzcKvuXlZXhxo0bTRZrQwkhEBcXh8ceewwBAQHV9vPy8sLq1avxzTffYOvWrejSpQvCw8Oxd+/eZoy2evX97TG3z2nbtm24ffs2xo8fX20fU/+MjI35/D+m/l1hPpv+Z2TKrOF4uzJrO/6uzFqOxy2VpeyfzXEfbCn7W0vcr9oaO4CmULlKKYSotXJpLqKioqT/DwwMRHBwMDp16oR169YhLi7OiJE1LUv+TAFg5MiR0v8HBASgT58+8PX1xQ8//IDhw4cbMbLmUd/Pt6r+VbWbgmnTpuH48ePYv39/jf26dOmCLl26SM+Dg4Nx+fJlvPfee+jfv39Th1mrhvz2mNPntGbNGkRFRcHb27vaPqb+GZkK5rPpf1eYz6b/GZkDSz82u5+1Hn9XZk2fuSUy9/2zOe6DLWV/a4n7VYuaEebh4YEWLVroVbbz8vL0KquWwsnJCYGBgTh//ryxQ2kS2rtqWNNnCtyrqPv6+lrs56rVkJxVKpVV9re1tUWrVq2aLNaGmD59Or777jvs3r0b7dq1q/fy/fr1M9nvQG2/Peb0OWVnZ2PXrl2YNGlSvZc15c+ouTGfa2bK3xXm8z2m/BmZEms83q7M0o+/K7PW43FLYQn7Z0vZB5vj/tZS96sWVQizs7ND7969pTsaaKWlpSEkJMRIUTUttVqNM2fOwMvLy9ihNAk/Pz8olUqdz7S0tBTp6ekW+5kCwM2bN3H58mWL/Vy1GpKzwcHBev1TU1PRp08fyOXyJou1PoQQmDZtGrZu3Yqff/4Zfn5+DVrP0aNHTfY7UNtvjzl8Tlpr165FmzZtMHjw4Hova8qfUXNjPtfMlL8rzOd7TPkzMiXWeLxdmaUff1dmrcfjlsKc98+Wtg82x/2txe5Xm/ni/E1u8+bNQi6XizVr1ojTp0+L2NhY4eTkJC5evGjs0AxixowZYs+ePeLChQvi4MGDIjo6Wjg7O5v1+AoLC8XRo0fF0aNHBQCxdOlScfToUZGdnS2EEGLRokXC1dVVbN26VZw4cUKMHj1aeHl5iYKCAiNHXnc1jbGwsFDMmDFDZGRkiKysLLF7924RHBws2rZta1ZjbKjacnbOnDli7NixUv8LFy4IR0dH8frrr4vTp0+LNWvWCLlcLr7++mtjDUHPK6+8IlxdXcWePXtETk6O9CguLpb6VB7XBx98IFJSUsTvv/8uTp48KebMmSMAiG+++cYYQ9BT22+POX5OQghRXl4u2rdvL2bPnq33mrl9RqaA+XyPqX9XmM+m/xmZOks/3q7MEo+/K7OG43FrZq77Z3PfB5v7/taS96sWVwgTQoiVK1cKX19fYWdnJ3r16lXj7VXNzciRI4WXl5eQy+XC29tbDB8+XJw6dcrYYTWK9larlR8xMTFCiHu3bF6wYIFQKpVCoVCI/v37ixMnThg36HqqaYzFxcUiMjJStG7dWsjlctG+fXsRExMjLl26ZOywm01NORsTEyNCQ0N1+u/Zs0f07NlT2NnZiQ4dOoiPP/64mSOuWVWfNQCxdu1aqU/lcS1evFh06tRJ2NvbCzc3N/HYY4+JH374ofmDr0Ztvz3m+DkJIcTOnTsFAHHu3Dm918ztMzIVzGfT/64wn03/MzIHlny8XZklHn9XZg3H49bOHPfP5r4PNvf9rSXvV2VC/P+rrxEREREREREREVkwi7pGGBERERERERERUXVYCCMiIiIiIiIiIqvAQhgREREREREREVkFFsKIiIiIiIiIiMgqsBBGRERERERERERWgYUwIiIiIiIiIiKyCiyEERERERERERGRVWAhjIiIiIiIiIiIrAILYUREREREREREZBVYCCMiIiIiIiIiIqvAQhgREREREREREVkFFsKIiIiIiIiIiMgqsBBGRERERERERERWgYUwI8jIyIBKpcLt27cbtPzGjRuxbNmyRsUQFhaGsLCwRq2jKuPHj0fLli0Nus6mipXIHCUnJ0Mmk0kPW1tbeHl5YdSoUTh//ryxwyMyK/fnUk2PPXv2GDtUyGQyqFSqBi0bFhamMx4HBwf06NEDy5YtQ0VFhcFi1P4+HTp0yGDrJOtlLd+n06dPQ6VS4eLFi3qvjR8/Xid37ezs0KlTJ8ycORMFBQXNH6yBXbx4ETKZDMnJycYOhRrpl19+wTPPPIP27dtDoVDA09MTwcHBmDFjhtSnQ4cOiI6OrnVdDd3fab9P7733Xq19tb8vVeVdbbFZw3GDNbA1dgDWKCMjA/Hx8Rg/fjweeOCBei+/ceNGnDx5ErGxsQaPzRStWrXK2CEQmZy1a9eia9eu+Ouvv/Cf//wH7777Lnbv3o2zZ8/Czc3N2OERmYUDBw7oPH/nnXewe/du/PzzzzrtDz30UHOGVaUDBw6gXbt2DV6+Y8eO+OKLLwAAeXl5+OSTT/D6668jJycHixcvNlSYRFRPp0+fRnx8PMLCwtChQwe91x0cHKTfpNu3b+Prr7/G+++/j+PHjyM1NbWZozUsLy8vHDhwAJ06dTJ2KNQIP/zwA55++mmEhYUhKSkJXl5eyMnJwaFDh7B582a8//779VpfY/d3TcmajhssHQthZPLq8kNSXl6OsrIyKBSKZoiIyPgCAgLQp08fAPdme5SXl2PBggXYtm0bJkyYYOToiMxDv379dJ63bt0aNjY2eu0NVVJSAgcHB712jUYjzeisq8bG5ODgoLOOqKgodO3aFStWrEBCQgLkcnmD160dDxEZXuXfpKeeegoXLlxAWloasrKy4OfnZ8ToGkehUBjs95aMJykpCX5+fti5c6fOfm3UqFFISkqq9/pM+TthTccNlo6nRjYzlUqFf/7znwAAPz8/nemTFRUVSEpKQteuXaFQKNCmTRuMGzcOV65ckZYPCwvDDz/8gOzsbJ3pl1rx8fEICgqCu7s7XFxc0KtXL6xZswZCCIPEv2PHDoSHh8PV1RWOjo548MEHsXDhQr1+f/zxBwYNGoSWLVvCx8cHM2bMgFqt1ulT11grnxqpnfaalJSEhIQE+Pn5QaFQYPfu3aioqEBCQgK6dOkCBwcHPPDAA+jevTs+/PBDg4yfyFRpi2LXrl0DUP0pxePHj9f5F+f7p5EvXboUfn5+aNmyJYKDg3Hw4EGdZS9cuIBRo0bB29tbmvYeHh6OY8eONdWwiIyutLQUCQkJ0r65devWmDBhAq5fv67TT3vKx9atW9GzZ0/Y29sjPj4ee/bsgUwmw/r16zFjxgy0bdsWCoUCf/zxB65fv44pU6bgoYceQsuWLdGmTRs88cQT2Ldvn14clU9x0J7WsXv3brzyyivw8PBAq1atMHz4cPz555+1jksul6N3794oLi7G9evX8ccff2DChAnw9/eHo6Mj2rZtiyFDhuDEiRM6y9U0nqrk5OSgd+/e8Pf3l07f5m8JGcr+/fsRHh4OZ2dnODo6IiQkBD/88INOn/rkilqtxowZM6BUKuHo6Ij+/fvj8OHD6NChA8aPH6/TNzc3F5MnT0a7du1gZ2cHPz8/xMfHo6ysTKffxx9/jB49eqBly5ZwdnZG165dMW/ePCm2559/HgAwYMAA6bi+tlMFK+/zgepPg6oce2N/O+6nUqkgk8lw/PhxPP/883B1dYW7uzvi4uJQVlaGc+fO4amnnoKzszM6dOigVxThqZGW4ebNm/Dw8KiySGNjU3O5YdWqVbC1tcWCBQuktsrf5frsKwGgoqIC7777Ltq3bw97e3v06dMHP/30U53GsmvXLoSHh8PFxQWOjo549NFH67yslqUeN9yvrscMwL2ZrDNmzEDHjh2lGsegQYNw9uzZer2noXFGWDObNGkSbt26heXLl2Pr1q3w8vICcG/W0yuvvILVq1dj2rRpiI6OxsWLF/Hmm29iz549OHLkCDw8PLBq1Sr84x//wH//+1+kpKTorf/ixYuYPHky2rdvDwA4ePAgpk+fjqtXr+Ktt95qVOxr1qzB3//+d4SGhuKTTz5BmzZt8Pvvv+PkyZM6/TQaDZ5++mlMnDgRM2bMwN69e/HOO+/A1dVVJ4bGxvrRRx+hc+fOeO+99+Di4gJ/f38kJSVBpVLhjTfeQP/+/aHRaHD27NkGX4+NyFxkZWUBADp37tyg5VeuXImuXbtK1x988803MWjQIGRlZcHV1RUAMGjQIJSXlyMpKQnt27fHjRs3kJGRwfwii1VRUYGhQ4di3759mDVrFkJCQpCdnY0FCxYgLCwMhw4d0vmX2yNHjuDMmTN444034OfnBycnJxQVFQEA5s6di+DgYHzyySewsbFBmzZtpIPiBQsWQKlU4u7du0hJSUFYWBh++umnOl0fc9KkSRg8eDA2btyIy5cv45///CdefPFFvdM0qvLf//4Xtra2cHNzwx9//IFWrVph0aJFaN26NW7duoV169YhKCgIR48eRZcuXXSWrWo8lZ08eRKDBg1Cu3btcODAAXh4eADgbwkZRnp6OiIiItC9e3esWbMGCoUCq1atwpAhQ7Bp0yaMHDlSp39dcmXChAnYsmULZs2ahSeeeAKnT5/GM888o3c9rtzcXPTt2xc2NjZ466230KlTJxw4cAAJCQm4ePEi1q5dCwDYvHkzpkyZgunTp+O9996DjY0N/vjjD5w+fRoAMHjwYCQmJmLevHlYuXIlevXqBQC1niqYlZUFW1tbdOzYscHbrzG/HZWNGDECL774IiZPnoy0tDQkJSVBo9Fg165dmDJlCmbOnImNGzdi9uzZ+Nvf/obhw4c3OG4yPcHBwfj000/x6quv4oUXXkCvXr1qnWUshMA///lPfPTRR/j000/1Cs33u3XrFoC67ytXrFgBX19f6TqYSUlJiIqKQnp6OoKDg6t9nw0bNmDcuHEYOnQo1q1bB7lcjn/9618YOHAgdu7cifDw8Fq3haUfN2j9+eefdTpmKCwsxGOPPYaLFy9i9uzZCAoKwt27d7F3717k5OSga9eudX5PgxPU7JYsWSIAiKysLKntzJkzAoCYMmWKTt9ffvlFABDz5s2T2gYPHix8fX1rfZ/y8nKh0WjE22+/LVq1aiUqKiqk10JDQ0VoaGidYy4sLBQuLi7iscce01lPZTExMQKA+PLLL3XaBw0aJLp06WKQWLOysgQA0alTJ1FaWqqznujoaPHwww/XeVxE5mbt2rUCgDh48KDQaDSisLBQ7NixQyiVStG/f3+h0WiEENXneExMjM7vhzafAgMDRVlZmdT+66+/CgBi06ZNQgghbty4IQCIZcuWNen4iIwpJiZGODk5Sc83bdokAIhvvvlGp19mZqYAIFatWiW1+fr6ihYtWohz587p9N29e7cAIPr371/r+5eVlQmNRiPCw8PFM888o/MaALFgwQLpufa3oPJxQ1JSkgAgcnJypLbQ0FDRrVs3odFohEajEX/++aeYM2eOACCef/75amMpLS0V/v7+4vXXX6/TeLQxZWZmirS0NOHi4iKee+45UVJSIvXhbwnV1f3fp6r069dPtGnTRhQWFkptZWVlIiAgQLRr1046lqxrrpw6dUoAELNnz9bpp/0diImJkdomT54sWrZsKbKzs3X6vvfeewKAOHXqlBBCiGnTpokHHnigxnF+9dVXAoDYvXu33mva3yRt7t64cUN8/PHHwsbGRudvAyH0fyO0fH19dWKvz29HbRYsWCAAiPfff1+n/eGHHxYAxNatW6U2jUYjWrduLYYPHy61aY9B1q5dW+f3JNNz48YN8dhjjwkAAoCQy+UiJCRELFy4UCc/fX19xeDBg0VxcbF49tlnhaurq9i1a5fe+qr7LmtVt6/Ufp+8vb119jsFBQXC3d1dPPnkk1KbNg+0f48XFRUJd3d3MWTIEJ33Ki8vFz169BB9+/atMhZLPW6or+qOGd5++20BQKSlpTV43U2Fp0aaiN27dwOAXjW8b9++ePDBB+s8JfPnn3/Gk08+CVdXV7Ro0QJyufz/sXfncVFV///AXwMMwyKSoGyKSHzcQSVNFDVABUXR1MytDEtL00xSM81KUAPEXMo1/Zhoalop5paCKZi55Fru6Vc0TZDEBdwA4fz+8Df3wzgDDDAw2+v5ePDQuffcO+fcue9777zn3nPw2WefITs7G1lZWRWu34EDB5CTk4PRo0eX2Q+ITCZDr169VKa1aNECV69e1Wlde/furfZrQ9u2bfHHH39g9OjR2LVrl0mMqEOkSbt27SCXy+Hg4IDu3bujVq1a+Omnn8rVd0BxPXv2hKWlpfS6RYsWACDFrZOTE3x8fDB79mzMnTsXJ06c0Oloc0SGaNu2bXjuuefQq1cvPHnyRPpr1aoV3Nzc1EaFatGiRYl3Zb7yyisapy9duhQvvPACbGxsYGVlBblcjl9++QXnzp3Tqo69e/dWqwMAtXPumTNnIJfLIZfL4eHhgTlz5uC1117D8uXLAQBPnjxBbGwsmjVrBmtra1hZWcHa2hoXL17UWJeS2gMAq1atQo8ePTBixAh8//33sLGxkebxWEK68ODBAxw+fBj9+/dXGa3c0tISQ4cOxfXr13HhwgWVZcqKlbS0NABP724qrn///mrn1m3btiEkJAQeHh4qx4bw8HCVdbVt2xZ3797F4MGD8dNPP+HWrVsVaqsydmvXro13330XAwcOxOeff17udRWn7bFDG8+OBNi0aVPIZDJpewCAlZUV/vOf/1Ro/WTYnJ2d8euvv+LIkSOIj4/Hyy+/jL/++gtTpkyBn5+fyn6fnZ2Nzp074/fff5cebdZGec6V/fr1UznvODg4oFevXti3bx8KCws1rv/AgQO4ffs2IiMjVWK6qKgI3bt3x5EjR6Q7tUpjStcNpdH2muHnn39Go0aN0LVrV63XXV2YCDMQ2dnZACA9Klmch4eHNL80v//+O8LCwgAAy5cvx2+//YYjR45g6tSpAJ52vldRytswtRl5ws7OTuXgAzztDPPx48c6raumbTVlyhR88cUXOHToEMLDw+Hs7IwuXbqY/NDbZH5Wr16NI0eOYM+ePRg5ciTOnTuHwYMHV3h9zs7OKq+VA08oY1Emk+GXX35Bt27dkJCQgBdeeAF16tTB+++/j9zc3Io3hMiA3bx5E3fv3oW1tbX0RVT5l5mZqfalVtN5qbR5c+fOxbvvvouAgABs3LgRhw4dwpEjR9C9e3etz9llxa6Sj48Pjhw5gqNHj+L06dO4e/cu1qxZIz36PH78eHz66afo06cPtm7disOHD+PIkSNo2bKlxrqU1tb169fD1tYWI0aMUPvxjMcS0oU7d+5ACFHidTMAtWvnsmJFWd7V1VWlnJWVldqyN2/exNatW9WOC82bNwcA6dgwdOhQfPPNN7h69SpeeeUVuLi4ICAgACkpKVq31dbWFkeOHMGRI0ewdetWBAcH47vvvkN8fLzW69BE22OHNpycnFReW1tba/w+YG1trfJ9gExLmzZt8NFHH+GHH37AjRs38MEHH+DKlSsqfcP99ddfOHz4MMLDw+Hr66vVest7rnRzc9M4LT8/H/fv39f4Hsr+9vr3768W17NmzYIQQnpEszSmdN1QGm2vGf7991+DHbmSfYQZCOUOmZGRobaz3LhxQ+pXozTr16+HXC7Htm3bVE48mzdvrnT96tSpAwAqHfdXhi7qqunONCsrK4wfPx7jx4/H3bt3sXv3bnz88cfo1q0brl27Bjs7O11Un0jvmjZtKnWWGxISgsLCQvz3v//Fjz/+iP79+8PGxgb37t1TW64iv0YreXl5YcWKFQCeXsh8//33iI6ORn5+PpYuXVrh9RIZKmVHsjt37tQ438HBQeV1aXdMa5q3Zs0aBAcHY8mSJSrTqyIhpOwwuCTKvlFiY2NVpt+6dQvPPfecWvnS2rp27Vp8+umnCAoKQnJyMlq1aqUyn8cSqqxatWrBwsICGRkZavOUnT5rc+1cnPJa/ObNm6hbt640/cmTJ2pJtdq1a6NFixYl3pWlTMYBT/sde/PNN/HgwQPs27cP06ZNQ0REBP766y94eXmVWS8LCwuV2A0NDUXr1q0RExOD1157DZ6engCefpl9dmAqQD0hSFQd5HI5pk2bhnnz5qn0J92+fXu8+uqrGD58OICng0mU1aF+ec+VmZmZGqdZW1ur3EFanPJ4sWDBghJHW3w2SV7SekzluqE02l4z1KlTR2f5A13jHWF6oCnr2rlzZwBPd6rijhw5gnPnzqncNqpQKDRmbJVDqhZ/vOnRo0f49ttvK13nwMBAODo6YunSpToZgbIq66r03HPPoX///hgzZgxu376NK1eu6GzdRIYmISEBtWrVwmeffYaioiI0aNAAf/31l8pFcXZ2Ng4cOKCT92vUqBE++eQT+Pn54fjx4zpZJ5GhiYiIQHZ2NgoLC9GmTRu1v2c7kC8vmUwmXRMo/fnnnzh48GCl1qurumzfvh3//PNPudfl5OSE3bt3o2nTpggJCVEbgbY4HkuoIuzt7REQEIBNmzapXBMXFRVhzZo1qFevXrkHj3nppZcAABs2bFCZ/uOPP6qNBBkREYHTp0/Dx8dH47GheCKseJ3Dw8MxdepU5Ofn48yZMwDKfzeGQqHAokWL8PjxY8ycOVOa3qBBA/z5558qZffs2VPiHTBEuqIpIQ1AekTu2XiIjIzE+vXrsXLlSrzxxhslPq6oVN5z5aZNm1TuPMzNzcXWrVvRqVMnle+exXXo0AHPPfcczp49qzGm27RpA2tr61LrCZjPdYO21wzh4eH466+/KjQIR1XjHWF64OfnBwD48ssvERkZCblcjsaNG+Odd97BggULYGFhgfDwcGnUSE9PT3zwwQcqy2/atAlLlixB69atpV+Kevbsiblz52LIkCF45513kJ2djS+++EJtJ62IGjVqYM6cORgxYgS6du2Kt99+G66urrh06RL++OMPLFy4sFzrq6q69urVC76+vmjTpg3q1KmDq1evYv78+fDy8kLDhg0rtW4iQ1arVi1MmTIFkyZNwrp16zB06FB8/fXXeP311/H2228jOzsbCQkJqFmzZoXW/+eff+K9997Dq6++ioYNG8La2hp79uzBn3/+icmTJ+u4NUSGYdCgQVi7di169OiBcePGoW3btpDL5bh+/Tr27t2Ll19+GX379q3w+iMiIjBjxgxMmzYNQUFBuHDhAqZPnw5vb2+1L95VLSIiAomJiWjSpAlatGiBY8eOYfbs2RV+pMHBwQE7d+5Ev379EBoaii1btiAkJITHEiq3PXv2aPwxMy4uDqGhoQgJCcHEiRNhbW2NxYsX4/Tp0/juu+/K7NP2Wc2bN8fgwYMxZ84cWFpaonPnzjhz5gzmzJkDR0dHlbtWpk+fjpSUFAQGBuL9999H48aN8fjxY1y5cgU7duzA0qVLUa9ePbz99tuwtbVFhw4d4O7ujszMTMTFxcHR0REvvvgiAEiPhy1btgwODg6wsbGBt7e32uNLxQUFBaFHjx5YuXIlJk+eDG9vbwwdOhSffvopPvvsMwQFBeHs2bNYuHCh9PgzUVXp1q0b6tWrh169eqFJkyYoKirCyZMnMWfOHNSoUQPjxo1TW6Z///6ws7ND//798ejRI3z33XclJprKe660tLREaGgoxo8fj6KiIsyaNQs5OTmIiYkpsQ01atTAggULEBkZidu3b6N///7SKI1//PEH/v33X7W7sDQxl+sGba8ZoqKisGHDBrz88suYPHky2rZti0ePHiEtLQ0REREICQmptjo/i4kwPQgODsaUKVOwatUqLF++HEVFRdi7dy+WLFkCHx8frFixAosWLYKjoyO6d++OuLg4lZPhuHHjcObMGXz88ce4d+8ehBAQQqBz58745ptvMGvWLPTq1Qt169bF22+/DRcXF+n208oYPnw4PDw8MGvWLIwYMQJCCDRo0ACRkZHlXldV1TUkJAQbN27Ef//7X+Tk5MDNzQ2hoaH49NNPyxzGl8jYjR07FgsXLsT06dNx7tw5rFq1Suq09Pnnn8e0adOwY8cOtY46teHm5gYfHx8sXrwY165dg0wmw/PPP485c+Zg7Nixum8MkQGwtLTEli1b8OWXX+Lbb79FXFwcrKysUK9ePQQFBUk/bFXU1KlT8fDhQ6xYsQIJCQlo1qwZli5diqSkpArFaWV8+eWXkMvliIuLw/379/HCCy9g06ZN+OSTTyq8TltbW/z0008YMmQIevTogY0bN6JNmzY8llC5fPTRRxqnp6enY8+ePZg2bRqGDRuGoqIitGzZElu2bFHrvF1bK1euhLu7O1asWIF58+ahVatW+P7779G9e3eVx33c3d1x9OhRzJgxA7Nnz8b169fh4OAAb29vaQAbAOjUqRMSExPx/fff486dO6hduzY6duyI1atXS92OeHt7Y/78+fjyyy8RHByMwsJCrFy5Um0ArWfNmjULO3fuxIwZM/DNN9/gww8/RE5ODhITE/HFF1+gbdu2+P777/Hyyy9XaFsQaeuTTz7BTz/9hHnz5iEjIwN5eXlwd3dH165dMWXKFDRt2lTjcj169MCOHTvQq1cvvPzyy9i0aRNsbW3VypX3XPnee+/h8ePHeP/995GVlYXmzZtj+/bt6NChQ6nteP3111G/fn0kJCRg5MiRyM3NhYuLC1q1alVmPCqZy3WDttcMDg4O2L9/P6Kjo7Fs2TLExMSgVq1aePHFF/HOO+9UW301kQldPOdGRERERERkYg4cOIAOHTpg7dq1GDJkiL6rQ0REOsBEGBERERERmb2UlBQcPHgQrVu3hq2tLf744w/Ex8fD0dERf/75p9ooiEREZJz4aCShsLCw1A7wZTJZiR0LEhERERGZgpo1ayI5ORnz589Hbm4uateujfDwcMTFxZlVEqyoqAhFRUWllrGy4tdIIlNjTrHPUSMJXbp0gVwuL/HPx8dH31UkIiIiIqpSAQEB2L9/P27fvo2CggJkZGQgMTER7u7u+q5atZo+fXqp3w3kcjlHYycyQeYU+3w0knDhwgXk5uaWOF+hUFS6Yz8iIiIiIjJ8N27cwI0bN0ot06JFixJH+SMi42ROsc9EGBERERERERERmQU+GklERERERERERGbBKHs6Kyoqwo0bN+Dg4ACZTKbv6hBVKSEEcnNz4eHhAQsL08tdM57JnDCeiUyDqccywHgm82Hq8cxYJnOibTwbZSLsxo0b8PT01Hc1iKrVtWvXUK9ePX1XQ+cYz2SOGM9EpsFUYxlgPJP5MdV4ZiyTOSorno0yEebg4ADgaeNq1qypsUxBQQGSk5MRFhYGuVxendUzWdymuqfNNs3JyYGnp6e035ua0uLZlPY5tsVwVWd7zDme9cWY91fWXX/Kqr+pxzJQdjwb+2esDbbRNJh7PJvTd2e2w/BUd1u0jWejTIQpb+msWbNmqcFsZ2eHmjVrGv3OYyi4TXWvPNvUVG9lLi2eTWmfY1sMlz7aY47xrC/GvL+y7vqjbf1NNZaBsuPZ2D9jbbCNpsHc49mcvjuzHYZHX20pK55N7yFoIiIiIiIiIiIiDZgIIyIiIiIiIiIis8BEGBERERERERERmQWj7COsOjWYvL3S67gS31MHNSEyb4xFItPAWCai4nhMIDIcvtG7kFdY8b7SGItkLHhHGBERERERERERmQXeEUZERERGpTx3kCgsBRLaqv/KzV+tiYiIiMwT7wgjIiIiIiIiIiKzwEQYERERERERERGZBSbCiIiIiIiIiIjILLCPMCIiIiIiqpDKjjJHRERU3XhHGBEREREREZEOxcXF4cUXX4SDgwNcXFzQp08fXLhwQaWMEALR0dHw8PCAra0tgoODcebMGZUyeXl5GDt2LGrXrg17e3v07t0b169fr86mEJkcJsKIiIiIiIiIdCgtLQ1jxozBoUOHkJKSgidPniAsLAwPHjyQyiQkJGDu3LlYuHAhjhw5Ajc3N4SGhiI3N1cqExUVhaSkJKxfvx779+/H/fv3ERERgcLCQn00i8gk8NFIIiIiIiIiIh3auXOnyuuVK1fCxcUFx44dw0svvQQhBObPn4+pU6eiX79+AIBVq1bB1dUV69atw8iRI3Hv3j2sWLEC3377Lbp27QoAWLNmDTw9PbF7925069at2ttFZAp4RxgRERERERFRFbp37x4AwMnJCQCQnp6OzMxMhIWFSWUUCgWCgoJw4MABAMCxY8dQUFCgUsbDwwO+vr5SGSIqP94RRkRERERERFRFhBAYP348OnbsCF9fXwBAZmYmAMDV1VWlrKurK65evSqVsba2Rq1atdTKKJd/Vl5eHvLy8qTXOTk5AICCggIUFBRoXEY5XWEhyts0jevRF+X767selWUq7QCqvy3avg8TYURERERERERV5L333sOff/6J/fv3q82TyVRHXRVCqE17Vmll4uLiEBMTozY9OTkZdnZ2pa53RpuiUueXZceOHZVaXldSUlL0XQWdMJV2ANXXlocPH2pVjokwIiIiIiIioiowduxYbNmyBfv27UO9evWk6W5ubgCe3vXl7u4uTc/KypLuEnNzc0N+fj7u3LmjcldYVlYWAgMDNb7flClTMH78eOl1Tk4OPD09ERYWhpo1a2pcpqCgACkpKfj0qAXyikpPwpXmdLR++yxTtiM0NBRyuVyvdakMU2kHUP1tUd4BWRYmwoiIiIiIiIh0SAiBsWPHIikpCampqfD29laZ7+3tDTc3N6SkpMDf3x8AkJ+fj7S0NMyaNQsA0Lp1a8jlcqSkpGDAgAEAgIyMDJw+fRoJCQka31ehUEChUKhNl8vlZSYi8opkyCuseCLMUJI22rTVGJhKO4Dqa4u278FEGBEREREREZEOjRkzBuvWrcNPP/0EBwcHqU8vR0dH2NraQiaTISoqCrGxsWjYsCEaNmyI2NhY2NnZYciQIVLZ4cOHY8KECXB2doaTkxMmTpwIPz8/aRRJIio/JsKIiIiIiIiIdGjJkiUAgODgYJXpK1euxLBhwwAAkyZNwqNHjzB69GjcuXMHAQEBSE5OhoODg1R+3rx5sLKywoABA/Do0SN06dIFiYmJsLS0rK6mEJkcJsKIiIiIiIiIdEiIskdglMlkiI6ORnR0dIllbGxssGDBAixYsECHtSMybxb6rgAREREREREREVF1YCKMiIiIiIiIiIjMAhNhRERERERERERkFsqdCNu3bx969eoFDw8PyGQybN68WWW+EALR0dHw8PCAra0tgoODcebMGZUyeXl5GDt2LGrXrg17e3v07t0b169fr1RDiIiIiIiIiIiISlPuRNiDBw/QsmVLLFy4UOP8hIQEzJ07FwsXLsSRI0fg5uaG0NBQ5ObmSmWioqKQlJSE9evXY//+/bh//z4iIiJQWFhY8ZYQERERERERERGVotyjRoaHhyM8PFzjPCEE5s+fj6lTp6Jfv34AgFWrVsHV1RXr1q3DyJEjce/ePaxYsQLffvstunbtCgBYs2YNPD09sXv3bnTr1q0SzSEiIiIiIiIiItJMp32EpaenIzMzE2FhYdI0hUKBoKAgHDhwAABw7NgxFBQUqJTx8PCAr6+vVIaIiIiIiIiIiEjXyn1HWGkyMzMBAK6urirTXV1dcfXqVamMtbU1atWqpVZGufyz8vLykJeXJ73OyckBABQUFKCgoEDjMsrpJc3XlsJSVGp5XdTBUOhqm9L/aLNNub2JiIiIiIiIdEOniTAlmUym8loIoTbtWaWViYuLQ0xMjNr05ORk2NnZlbrelJSUMmpbuoS2lVocALBjx47Kr8SAVHabkrrStunDhw+rsSZEREREREREpkuniTA3NzcAT+/6cnd3l6ZnZWVJd4m5ubkhPz8fd+7cUbkrLCsrC4GBgRrXO2XKFIwfP156nZOTA09PT4SFhaFmzZoalykoKEBKSgpCQ0Mhl8sr3Cbf6F0VXlbpdLRp9Humq21K/6PNNlXeAUlERERERERElaPTRJi3tzfc3NyQkpICf39/AEB+fj7S0tIwa9YsAEDr1q0hl8uRkpKCAQMGAAAyMjJw+vRpJCQkaFyvQqGAQqFQmy6Xy8tMyGhTpjR5haXfyaYNU0saVXabkrrStim3NRFVVnR0tNqd1cW7JBBCICYmBsuWLcOdO3cQEBCARYsWoXnz5vqoLhERERFRlSl3Iuz+/fu4dOmS9Do9PR0nT56Ek5MT6tevj6ioKMTGxqJhw4Zo2LAhYmNjYWdnhyFDhgAAHB0dMXz4cEyYMAHOzs5wcnLCxIkT4efnJ40iSURERLrVvHlz7N69W3ptaWkp/T8hIQFz585FYmIiGjVqhJkzZyI0NBQXLlyAg4ODPqpLRERERFQlyp0IO3r0KEJCQqTXykcWIyMjkZiYiEmTJuHRo0cYPXq09KtycnKyyoX0vHnzYGVlhQEDBuDRo0fo0qULEhMTVS7KiYiISHesrKykLgyKE0Jg/vz5mDp1Kvr16wcAWLVqFVxdXbFu3TqMHDmyuqtKRERERFRlLMq7QHBwMIQQan+JiYkAnnaUHx0djYyMDDx+/BhpaWnw9fVVWYeNjQ0WLFiA7OxsPHz4EFu3boWnp6dOGkRERETqLl68CA8PD3h7e2PQoEG4fPkygKd3dmdmZiIsLEwqq1AoEBQUhAMHDuirukREREREVaJKRo0kIiIiwxEQEIDVq1ejUaNGuHnzJmbOnInAwECcOXNG6idMOaiNkqurK65evVrqevPy8pCXlye9Vg7uUVBQgIKCAo3LKCxFZZpSbgoLofKvUkn1MyTKOhpDXZ9lzHUHyq6/sbaLiIiImAgjMlv79u3D7NmzcezYMWRkZCApKQl9+vSR5mvTeXZeXh4mTpyI7777TnrMefHixahXr54eWkREJQkPD5f+7+fnh/bt28PHxwerVq1Cu3btADy9o7s4IYTatGfFxcWpdcIPAMnJybCzs9O4TELb8tZeN2a0KVJ5vWPHDv1UpAJSUlL0XYUKM+a6AyXX/+HDh9Vck//h4BdERESVw0QYkZl68OABWrZsiTfffBOvvPKK2nxtOs+OiorC1q1bsX79ejg7O2PChAmIiIjAsWPH2OcfkQGzt7eHn58fLl68KCXAMzMz4e7uLpXJyspSu0vsWVOmTJH6CgWe3hHm6emJsLAw1KxZU+MyvtG7Kt+AclBYCMxoU4RPj1ogr+h/ib3T0d2qtR4VUVBQgJSUFISGhhrdCMLGXHeg7Por737UFw5+QUREVHFMhBGZqfDwcJW7RIrTpvPse/fuYcWKFfj222+lEV/XrFkDT09P7N69G926Gf6XTCJzlZeXh3PnzqFTp07w9vaGm5sbUlJS4O/vDwDIz89HWloaZs2aVep6FAoFFAqF2nS5XF5i8iOvsPS7zKpKXpFM5b2NKTlT2vY0dMZcd6Dk+uu7TRz8goiIqOKYCCMiNWV1nj1y5EgcO3YMBQUFKmU8PDzg6+uLAwcOMBFGZEAmTpyIXr16oX79+sjKysLMmTORk5ODyMhIyGQyREVFITY2Fg0bNkTDhg0RGxsLOzs7DBkyRN9VJyINlINfKBQKBAQEIDY2Fs8//7xW5++SlLfPP+W0Z/vf04eq6rPN2Pu60wbbaNptJyLNmAgjIjXadJ6dmZkJa2tr1KpVS62McnlNynOhXfzCRRcdbOvzQseULjRNqS1A9bZHX9vs+vXrGDx4MG7duoU6deqgXbt2OHToELy8vAAAkyZNwqNHjzB69GipT6Hk5GQ+RkVkgKpq8IuK9PkHqPe/pw9V3eefsfd1pw1zbqM++/wjIv1gIoyISlSRzrPLKlORC+2UlBSddLBtCJ1jm9KFpim1Baie9ujrYnv9+vWlzpfJZIiOjkZ0dHT1VIiIKqyqBr8ob59/yn7Unu1/Tx+qqs8/Y+/rThtso/77/COi6sdEGBGpUfY7Ulrn2W5ubsjPz8edO3dU7grLyspCYGBgiesuz4V28QsX/8/3VLpd+uwc25QuNE2pLUD1tocX20Ska7oa/KIiff4B6v3v6UNVH7uNva87bZhzG0293USkjokwIlKjTefZrVu3hlwuR0pKCgYMGAAAyMjIwOnTp5GQkFDiuityoS2Xy3VykW0IFzqmdKFpSm0Bqqc9prS9iMgw6GrwC2PWYPL2Sq/jSnxPHdSEiIiMARNhRGbq/v37uHTpkvQ6PT0dJ0+ehJOTE+rXr19m59mOjo4YPnw4JkyYAGdnZzg5OWHixInw8/OTRpEkIiIi3eLgF0RERJXDRBiRmTp69ChCQkKk18rHFSMjI5GYmKhV59nz5s2DlZUVBgwYgEePHqFLly5ITEyEpaVltbeHiIjIHHDwCyIiosphIozITAUHB0OIkkdi1KbzbBsbGyxYsAALFiyoghoSERHRszj4BRERUeUwEVYN2G8BEREREREREZH+Wei7AkRERERERERERNWBiTAiIiIiIiIiIjILTIQREREREREREZFZYCKMiIiIiIiIiIjMAhNhRERERERERERkFjhqJBEREZkdXYzoDHBUZyIiIiJjwzvCiIiIiIiIiIjILPCOMCIyG7q4A4R3fxARERERERkv3hFGREREREREpGP79u1Dr1694OHhAZlMhs2bN6vMF0IgOjoaHh4esLW1RXBwMM6cOaNSJi8vD2PHjkXt2rVhb2+P3r174/r169XYCiLTw0QYERERERERkY49ePAALVu2xMKFCzXOT0hIwNy5c7Fw4UIcOXIEbm5uCA0NRW5urlQmKioKSUlJWL9+Pfbv34/79+8jIiIChYWF1dUMIpPDRyOJiIiIiIiIdCw8PBzh4eEa5wkhMH/+fEydOhX9+vUDAKxatQqurq5Yt24dRo4ciXv37mHFihX49ttv0bVrVwDAmjVr4Onpid27d6Nbt27V1hYiU8I7woiIiIiIiIiqUXp6OjIzMxEWFiZNUygUCAoKwoEDBwAAx44dQ0FBgUoZDw8P+Pr6SmWIqPx4RxgRERERERFRNcrMzAQAuLq6qkx3dXXF1atXpTLW1taoVauWWhnl8s/Ky8tDXl6e9DonJwcAUFBQgIKCAo3LKKcrLEQFWqK+Hn1Rvr++61FZptIOoPrbou37MBFGREREREREpAcymUzltRBCbdqzSisTFxeHmJgYtenJycmws7Mrdb0z2hSVUdvS7dixo1LL60pKSoq+q6ATptIOoPra8vDhQ63KMRFGREREREREVI3c3NwAPL3ry93dXZqelZUl3SXm5uaG/Px83LlzR+WusKysLAQGBmpc75QpUzB+/HjpdU5ODjw9PREWFoaaNWtqXKagoAApKSn49KgF8opKT8KV5nS0fvssU7YjNDQUcrlcr3WpDFNpB1D9bVHeAVkWnSfCoqOj1TLQxW/dFEIgJiYGy5Ytw507dxAQEIBFixahefPmuq4KERERERFRmRpM3q42TWEpkNAW8I3ehbzCspMDV+J7VkXVyER5e3vDzc0NKSkp8Pf3BwDk5+cjLS0Ns2bNAgC0bt0acrkcKSkpGDBgAAAgIyMDp0+fRkJCgsb1KhQKKBQKtelyubzMRERekUyrfb0khpK00aatxsBU2gFUX1u0fY8quSOsefPm2L17t/Ta0tJS+r9yiNjExEQ0atQIM2fORGhoKC5cuAAHB4eqqA4RERERERFRtbp//z4uXbokvU5PT8fJkyfh5OSE+vXrIyoqCrGxsWjYsCEaNmyI2NhY2NnZYciQIQAAR0dHDB8+HBMmTICzszOcnJwwceJE+Pn5SaNIElH5VUkizMrKSrrVszhthoglIiIiIiIiMnZHjx5FSEiI9Fr5yGJkZCQSExMxadIkPHr0CKNHj5aelkpOTla5QWTevHmwsrLCgAED8OjRI3Tp0gWJiYkqN5sQUflYVMVKL168CA8PD3h7e2PQoEG4fPkyAO2GiCUiIiIiIiIydsHBwRBCqP0lJiYCeNpRfnR0NDIyMvD48WOkpaXB19dXZR02NjZYsGABsrOz8fDhQ2zduhWenp56aA2R6dD5HWEBAQFYvXo1GjVqhJs3b2LmzJkIDAzEmTNntBoiVpPKDAFb2WE6FZaVG0JWVwxh6FRTGsbVUGizTbm9iYiIiIiIiHRD54mw8PBw6f9+fn5o3749fHx8sGrVKrRr1w5A+YeIrcwQsJUdpjOhbaUW1xlDGYoWMK1hXA1FadtU2yFgiYiIiIiIiKh0VdJHWHH29vbw8/PDxYsX0adPHwClDxGrSWWGgK3sMJ2+0bsqvKwu6XsoWsC0hnE1FNpsU22HgCUiIiIiIiKi0lV5IiwvLw/nzp1Dp06dtBoiVpPKDAFb2WE6KzN8rC4ZUuLJlIZxNRSlbVNuayIiIiIiIiLd0HkibOLEiejVqxfq16+PrKwszJw5Ezk5OYiMjIRMJitziFgiIiIiY9Fg8vZKr+NKfE8d1ISIiIiItKHzRNj169cxePBg3Lp1C3Xq1EG7du1w6NAheHl5AYBWQ8QSERERERERERHpms4TYevXry91vnKI2OjoaF2/NRERERERERERUYks9F0BIiIiIiIiIiKi6sBEGBERERERERERmQUmwoiIiIiIiIiIyCwwEUZERERERERERGaBiTAiIiIiIiIiIjILTIQREREREREREZFZYCKMiIiIiIiIiIjMAhNhRERERERERERkFqz0XYGq5hu9C3mFMn1Xg4iIiIiIiIiI9MzkE2FERERERERVrcHk7fquAgDgSnxPfVeBiMigMRFGREREpEclfXlWWAoktNXu7nZ+8SUiIiLSDhNhRkIXvzDxIpmIiIiIiIiIzBk7yyciIiIiIiIiIrPARBgREREREREREZkFJsKIiIiIiIiIiMgsMBFGRERERERERERmgYkwIiIiIiIiIiIyC0yEERERERERERGRWWAijIiIiIiIiIiIzIKVvitARGRMGkzeXqHlFJYCCW0B3+hduPB5hI5rRURERERERNpgIoyIiIiIiIiIKqWiPxgXdyW+pw5qQlQ6PhpJRERERERERERmgXeEERERERk5/gpPREREpB3eEUZERERERERERGaBiTAiIiIiIiIiIjILfDSSiKia8REmIiIiIiIi/eAdYUREREREREREZBZ4RxgRERERERER6V1lnpxQWAoktNVhZchk8Y4wIiIiIiIiIiIyC3q9I2zx4sWYPXs2MjIy0Lx5c8yfPx+dOnXSZ5VMWmX7JWKGnUrCWK5+7GeMqgrj2XzxuGJ6GM9EpoPxTKQ7ekuEbdiwAVFRUVi8eDE6dOiAr7/+GuHh4Th79izq16+vr2oRUTkxlolMB+OZyHQwnolMB+O5fHyjdyGvUFapdfCHHdOmt0TY3LlzMXz4cIwYMQIAMH/+fOzatQtLlixBXFycvqpFWtDFgaWyeGAyHIxlItPBeCYyHYxn81WeuzuVT3wYwvV9VTGFp1oYz0S6pZdEWH5+Po4dO4bJkyerTA8LC8OBAwfUyufl5SEvL096fe/ePQDA7du3UVBQoPE9CgoK8PDhQ1gVWKCwyDQP6tXNqkjg4cMig9im2dnZen1/pYC4Xyq1vMJC4BP/ImRnZ0Mul2ssk5ubCwAQQlTqvapCeWMZKF88K+M4OzsbVk8e6Lj21cuQ4keponFU/HMpab81JtXZHnOOZ6XqjmVDjD1tGWPd/zPxewD/O7+1mroJeUZS9+LKOj8bciwD1RPP5nCtbYwxWF7m1EZziWdz/u6sy/1Zn983Telau7rbom086yURduvWLRQWFsLV1VVluqurKzIzM9XKx8XFISYmRm26t7d3ldWRNBui7wr8f7Xn6LsGuqPtNs3NzYWjo2OV1qW8yhvLgHnHs6HEj5IpxZGxYTxXL0OLvfJg3fVHm/obYiwDph3P1c3Y92NtsI1PmUo8m3ss62p/5nWycSsrnvXaWb5MppqlFUKoTQOAKVOmYPz48dLroqIi3L59G87OzhrLA0BOTg48PT1x7do11KxZU7cVN1PcprqnzTYVQiA3NxceHh7VXDvtaRvLQPni2ZT2ObbFcFVne8w5nvXFmPdX1l1/yqq/McQyULXxbOyfsTbYRtNgbvFszt+d2Q7DU91t0Tae9ZIIq127NiwtLdUy2FlZWWqZbgBQKBRQKBQq05577jmt3qtmzZpGv/MYGm5T3Strmxrir1NA+WMZqFg8m9I+x7YYrupqj7nHs74Y8/7KuutPafU31FgGqjeejf0z1gbbaBrMJZ753ZntMETV2RZt4tmiGuqhxtraGq1bt0ZKSorK9JSUFAQGBuqjSkRUAYxlItPBeCYyHYxnItPBeCbSPb09Gjl+/HgMHToUbdq0Qfv27bFs2TL8/fffGDVqlL6qREQVwFgmMh2MZyLTwXgmMh2MZyLd0lsibODAgcjOzsb06dORkZEBX19f7NixA15eXjpZv0KhwLRp09RuC6WK4zbVPVPYplUZy6awfZTYFsNlau2pjKo+N+uDMX++rLv+GHv9AV5r6wLbaBpMoY2MZ+2wHYbHUNsiE4Y6TiwREREREREREZEO6aWPMCIiIiIiIiIiourGRBgREREREREREZkFJsKIiIiIiIiIiMgsMBFGRERERERERERmwSQTYYsXL4a3tzdsbGzQunVr/Prrr/quktGIi4vDiy++CAcHB7i4uKBPnz64cOGCShkhBKKjo+Hh4QFbW1sEBwfjzJkzeqqx8YmLi4NMJkNUVJQ0jdtUM2OI5X379qFXr17w8PCATCbD5s2bVeZr89nm5eVh7NixqF27Nuzt7dG7d29cv369Gluhu9g3hLYsWbIELVq0QM2aNVGzZk20b98eP//8s9G1g8qmzX77rNTUVMhkMrW/8+fPV1Otn4qOjlarg5ubW6nLpKWloXXr1rCxscHzzz+PpUuXVlNt1TVo0EDjdhwzZozG8vrc7ro4TmuyceNGNGvWDAqFAs2aNUNSUlIVtcAwGcM5uqIqcmwxZpquTU3BP//8g9dffx3Ozs6ws7NDq1atcOzYMX1Xy+AYUixrE3vDhg1TO5e0a9dOpYw213F37tzB0KFD4ejoCEdHRwwdOhR3797VWVvKOs/r6nq0qttR1vneWD4PNcLErF+/XsjlcrF8+XJx9uxZMW7cOGFvby+uXr2q76oZhW7duomVK1eK06dPi5MnT4qePXuK+vXri/v370tl4uPjhYODg9i4caM4deqUGDhwoHB3dxc5OTl6rLlx+P3330WDBg1EixYtxLhx46Tp3KbqjCWWd+zYIaZOnSo2btwoAIikpCSV+dp8tqNGjRJ169YVKSkp4vjx4yIkJES0bNlSPHnypNraoavYN4S2bNmyRWzfvl1cuHBBXLhwQXz88cdCLpeL06dPG1U7qGza7LfP2rt3rwAgLly4IDIyMqS/6v5sp02bJpo3b65Sh6ysrBLLX758WdjZ2Ylx48aJs2fPiuXLlwu5XC5+/PHHaqz1/2RlZanUPSUlRQAQe/fu1Vhen9tdF8fpZx04cEBYWlqK2NhYce7cOREbGyusrKzEoUOHqrg1hsFYztEVVZFji7Eq6drU2N2+fVt4eXmJYcOGicOHD4v09HSxe/ducenSJX1XzaAYWixrE3uRkZGie/fuKueS7OxslfVocx3XvXt34evrKw4cOCAOHDggfH19RUREhM7aUtZ5XlfXo1XdjrLO98byeTzL5BJhbdu2FaNGjVKZ1qRJEzF58mQ91ci4ZWVlCQAiLS1NCCFEUVGRcHNzE/Hx8VKZx48fC0dHR7F06VJ9VdMo5ObmioYNG4qUlBQRFBQkXWxwm2pmjLH87BcsbT7bu3fvCrlcLtavXy+V+eeff4SFhYXYuXNntdX9WRWJfUNtixBC1KpVS/z3v/81+nZQ6Z7dbzVRJmTu3LlTfRXTYNq0aaJly5Zal580aZJo0qSJyrSRI0eKdu3a6bhmFTNu3Djh4+MjioqKNM43lO1ekeO0JgMGDBDdu3dXmdatWzcxaNAgndfZEBnjOboytDm2GKOSrk1NwUcffSQ6duyo72oYPEOPZU2xFxkZKV5++eUSl9HmOu7s2bMCgMqPFwcPHhQAxPnz53VS99LO87q6Hq2Odjzr2fO9sXwezzKpRyPz8/Nx7NgxhIWFqUwPCwvDgQMH9FQr43bv3j0AgJOTEwAgPT0dmZmZKttYoVAgKCiI27gMY8aMQc+ePdG1a1eV6dym6kwllrX5bI8dO4aCggKVMh4eHvD19dVrWysS+4bYlsLCQqxfvx4PHjxA+/btjbYdpJ1n99vS+Pv7w93dHV26dMHevXurumoaXbx4ER4eHvD29sagQYNw+fLlEssePHhQ7ZjYrVs3HD16FAUFBVVd1VLl5+djzZo1eOuttyCTyUotawjbvbiKnoNL+jzM4RhhKufo8ijPscWYlHRtagq2bNmCNm3a4NVXX4WLiwv8/f2xfPlyfVfLoBhDLJcUe6mpqXBxcUGjRo3w9ttvIysrS5qnzXXcwYMH4ejoiICAAKlMu3bt4OjoqNO2l3Se19X1aHW1Q6mk872xfB7FmVQi7NatWygsLISrq6vKdFdXV2RmZuqpVsZLCIHx48ejY8eO8PX1BQBpO3Ibl8/69etx/PhxxMXFqc3jNlVnKrGszWebmZkJa2tr1KpVq8Qy1a2isW9IbTl16hRq1KgBhUKBUaNGISkpCc2aNTO6dpD2NO23mri7u2PZsmXYuHEjNm3ahMaNG6NLly7Yt29fNdYWCAgIwOrVq7Fr1y4sX74cmZmZCAwMRHZ2tsbymZmZGvfbJ0+e4NatW9VR5RJt3rwZd+/exbBhw0osYyjb/VkVPQeX9HmYwzHCVM7R2tL22GJsSrs2NQWXL1/GkiVL0LBhQ+zatQujRo3C+++/j9WrV+u7agbD0GO5pNgLDw/H2rVrsWfPHsyZMwdHjhxB586dkZeXB0C767jMzEy4uLiovaeLi4vO2l7aeV5X16PV0Y7iNJ3vjeXzeJZVlaxVz579NVIIUeYvlKTuvffew59//on9+/erzeM21t61a9cwbtw4JCcnw8bGpsRy3KbqTGWbVKQd+myrrmNfH21p3LgxTp48ibt372Ljxo2IjIxEWlqaNN9Y2kHaK22/La5x48Zo3Lix9Lp9+/a4du0avvjiC7z00ktVXU1JeHi49H8/Pz+0b98ePj4+WLVqFcaPH69xGU37rabp1W3FihUIDw+Hh4dHiWUMZbuXpCLHBFM5R1WUubRf22OLMdH22tSYFRUVoU2bNoiNjQXw9G7UM2fOYMmSJXjjjTf0XDvDYqixXFLsDRw4UPq/r68v2rRpAy8vL2zfvh39+vUrcX3PtktTG3XZ9tLO88rO5HVxPVrV7ShO0/neWD6PZ5nUHWG1a9eGpaWlWtYwKytLLdtKpRs7diy2bNmCvXv3ol69etJ05UgX3MbaO3bsGLKystC6dWtYWVnBysoKaWlp+Oqrr2BlZSVtN27T/zGVWNYmXtzc3JCfn487d+6UWKY6VSb2Dakt1tbW+M9//oM2bdogLi4OLVu2xJdffml07SDtlLTfaqtdu3a4ePFiFdRMe/b29vDz8yuxHm5ubhr3WysrKzg7O1dHFTW6evUqdu/ejREjRpR7WUPY7hW9rinp8zCHY4SpnKO1Udlji6Eq69q0sLBQ31WsNHd3dzRr1kxlWtOmTfH333/rqUaGx5BjuTyx5+7uDi8vL+l8os11nJubG27evKm2rn///bfK2l78PK+r69HqbIe253tj+TxMKhFmbW2N1q1bIyUlRWV6SkoKAgMD9VQr4yKEwHvvvYdNmzZhz5498Pb2Vpnv7e0NNzc3lW2cn5+PtLQ0buMSdOnSBadOncLJkyelvzZt2uC1117DyZMn8fzzz3ObPsNUYlmbeGndujXkcrlKmYyMDJw+fbpa26qL2DeUtmgihEBeXp7Rt4NUlbXfauvEiRNwd3fXce3KJy8vD+fOnSuxHu3bt1c7JiYnJ6NNmzaQy+XVUUWNVq5cCRcXF/Ts2bPcyxrCdq/odU1Jn4c5HCNM5RxdGl0dWwxVWdemlpaW+q5ipXXo0AEXLlxQmfbXX3/By8tLTzUyPIYYyxWJvezsbFy7dk06n2hzHde+fXvcu3cPv//+u1Tm8OHDuHfvXpW1vfh5XlfXo9XZDm3P98byeZjcqJHKIWBXrFghzp49K6KiooS9vb24cuWKvqtmFN59913h6OgoUlNTVYZAffjwoVQmPj5eODo6ik2bNolTp06JwYMHlznMOKl6dmQeblN1xhLLubm54sSJE+LEiRMCgJg7d644ceKENOy0Np/tqFGjRL169cTu3bvF8ePHRefOndWGFK5quop9Q2jLlClTxL59+0R6err4888/xccffywsLCxEcnKyUbWDyqbNfjt58mQxdOhQ6fW8efNEUlKS+Ouvv8Tp06fF5MmTBQCxcePGaq37hAkTRGpqqrh8+bI4dOiQiIiIEA4ODtIx7tl6X758WdjZ2YkPPvhAnD17VqxYsULI5XLx448/Vmu9iyssLBT169cXH330kdo8Q9ruujhODx06VGUUtd9++01YWlqK+Ph4ce7cOREfHy+srKxURrwyZcZyjq4obY4tpsbURo38/fffhZWVlfj888/FxYsXxdq1a4WdnZ1Ys2aNvqtmUAwtlsuKvdzcXDFhwgRx4MABkZ6eLvbu3Svat28v6tatW+7ruO7du4sWLVqIgwcPioMHDwo/Pz8RERGhs7aUdZ7X1fVoVbdDiJLP98b0eTzL5BJhQgixaNEi4eXlJaytrcULL7xgckMdVyUAGv9WrlwplSkqKhLTpk0Tbm5uQqFQiJdeekmcOnVKf5U2Qs9ebHCbamYMsbx3716NMRMZGSmE0O6zffTokXjvvfeEk5OTsLW1FREREeLvv/+u1nboKvYNoS1vvfWWtN/UqVNHdOnSRUqCGVM7qGza7LeRkZEiKChIej1r1izh4+MjbGxsRK1atUTHjh3F9u3bq73uAwcOFO7u7kIulwsPDw/Rr18/cebMmRLrLYQQqampwt/fX1hbW4sGDRqIJUuWVHOtVe3atUsAEBcuXFCbZ0jbXRfH6aCgIKm80g8//CAaN24s5HK5aNKkSbUnU/XNGM7RFaXNscXUmFoiTAghtm7dKnx9fYVCoRBNmjQRy5Yt03eVDJIhxXJZsffw4UMRFhYm6tSpI+Ryuahfv76IjIxUu0bT5jouOztbvPbaa8LBwUE4ODiI1157Tdy5c0dnbSnrPK+r69GqbocQJZ/vjenzeJZMiP/f0yoREREREREREZEJM6k+woiIiIiIiIiIiErCRBgREREREREREZkFJsKIiIiIiIiIiMgsMBFGRERERERERERmgYkwIiIiIiIiIiIyC0yEERERERERERGRWWAijIiIiIiIiIiIzAITYUREREREREREZBaYCCMiIiIiIiIiIrPARBgREREREREREZkFJsKIiIiIiIiIiMgsMBFGRERERERERERmgYkwIiIiIiIiIiIyC0yEaeHw4cPo27cv6tevD4VCAVdXV7Rv3x4TJkzQd9W0lpqaCplMBplMhsTERI1lOnfuDJlMhgYNGlToPYKDg+Hr61tmuQYNGmDYsGEVeg+iioqOjoZMJtN3NZCdnY0pU6agWbNmsLOzQ82aNdGuXTssWrQIBQUF+q6eimHDhqFGjRrV9n4HDhxAdHQ07t69K01LT0+Hg4MDXnnlFY3LrFu3DjKZDF9//XU11ZLI8B06dAivvvoq3N3dYW1tDTc3N/Tv3x8HDx7Ud9WISAvXrl3D6NGj0ahRI9ja2sLJyQl+fn54++23ce3aNX1XT6NffvkFbdq0gb29PWQyGTZv3qz1sleuXFH7jpKYmAiZTIYrV67ovK5kOL766ivIZDKtvkMqVWbfUH4nTk1Nlabt2LED0dHRamV9fX3RtGlTtelJSUmQyWRo37692rxvv/0WMpkMW7ZsKXfdtCGTyfDee+9VaFlNbTdnTISVYfv27QgMDEROTg4SEhKQnJyML7/8Eh06dMCGDRv0Xb1yc3BwwIoVK9Smp6enIzU1FTVr1qzyOiQlJeHTTz+t8vchMjTnz5+Hv78/vv76a7z22mvYvn071q9fjxdeeAHjxo1DaGgoHj58qO9q6s2BAwcQExOjkgjz9vbG3LlzsWnTJqxbt06lfGZmJsaOHYtu3bph5MiR1VxbIsO0YMECdOjQAdevX0dCQgJ2796NL774Av/88w86duyIhQsX6ruKRFSK69ev44UXXkBKSgrGjx+PHTt24JtvvsHgwYNx5MgRXL58Wd9VVCOEwIABAyCXy7FlyxYcPHgQQUFB+q4WGYFvvvkGAHDmzBkcPnxYL3XYsWMHYmJi1KaHhITg/PnzyMzMVJmempoKe3t7HD16FLm5uWrzLCws8NJLL1VpnanyrPRdAUOXkJAAb29v7Nq1C1ZW/9tcgwYNQkJCgh5rVjEDBw7Ef//7X1y8eBENGzaUpn/zzTeoW7cu/Pz8cPbs2Sqtg7+/f5Wun8gQFRYW4pVXXkFOTg5+//13NGrUSJrXo0cPBAUFYdCgQRg/fjyWLl2qx5oanrfffhtJSUkYO3YsQkJC4O7uDgAYOXIkhBAak/tV4eHDh7Czs6uW9yJSKiwsxJMnT6BQKMos+9tvvyEqKgo9evRAUlKS2nVL3759MW7cOPj7+6NDhw5VWW0io1SeeKsqy5cvx61bt/D777/D29tbmt6nTx98/PHHKCoq0lvdSnLjxg3cvn0bffv2RZcuXfRdHTISR48exR9//IGePXti+/btWLFiBQICAvRdLUlISAgWLlyI1NRUDBo0SJqempqKESNGYPHixdi/fz/Cw8NV5vn7++O5557TQ42pPHhHWBmys7NRu3ZtlYtJJQsL1c3XoEEDREREYOfOnXjhhRdga2uLJk2aSJnu4k6fPo2XX34ZtWrVgo2NDVq1aoVVq1ZJ84UQcHV1xZgxY6RphYWFqFWrFiwsLHDz5k1p+ty5c2FlZaVyF0VJQkND4enpqVKnoqIirFq1CpGRkWptAoBFixbhpZdegouLC+zt7eHn54eEhAStHuNKSkqCnZ0dRowYgSdPnkjbqfijkcrbNL/77jtMnToVHh4eqFmzJrp27YoLFy6orE8IgdjYWHh5ecHGxgZt2rRBSkoKgoODERwcXGZ9ituwYQPCwsLg7u4OW1tbNG3aFJMnT8aDBw/Uyh4+fBi9evWCs7MzbGxs4OPjg6ioqHK9H1Wf7du3o1WrVlAoFPD29sYXX3yhVkab/XrGjBmwsrLS+BjCW2+9BWdnZzx+/BgAsGfPHgQHB8PZ2Rm2traoX78+XnnlFekOr6SkJJw9exaTJ09WSYIpDRw4EGFhYVixYoX0y5PyUYGEhAR8/vnnqF+/vrTf//LLL2rruHjxIoYMGQIXFxcoFAo0bdoUixYtUilTnnjTxqVLl/Dmm2+iYcOGsLOzQ926ddGrVy+cOnVKpVxRURFmzpyJxo0bw9bWFs899xxatGiBL7/8EsDTR1c//PBDAE/vAlM+yq28fVuZ7HrnnXcAPL31fMuWLVi4cCHq1q0LIQQWL16MVq1awdbWFrVq1UL//v3VfjlPSUnByy+/jHr16sHGxgb/+c9/MHLkSNy6dUulnPJR2uPHj6N///6oVasWfHx8yr19yDj9+uuvUpw8a/Xq1ZDJZDhy5AiApxfyvXv3hpOTE2xsbODv74/vv/9eZZl///0Xo0ePRrNmzVCjRg24uLigc+fO+PXXX1XKFY/5mTNnwtvbGwqFAnv37i0zhgAgLi4OMpkMS5YsUbtusbKywuLFiyGTyRAfHy9NV+7rJ06cQL9+/VCzZk04Ojri9ddfx7///qvW/g0bNqB9+/awt7dHjRo10K1bN5w4cUKljPKx6kuXLqFHjx6oUaMGPD09MWHCBOTl5Wn5KZC50OY88u+//8La2lrjEwXnz5+HTCbDV199JU3LzMzEyJEjUa9ePVhbW8Pb2xsxMTHStShQerw9fvwYEyZMQKtWreDo6AgnJye0b98eP/30k9r73717F8OHD4eTkxNq1KiBnj174vLly5DJZGqPW2lzns7OzoaFhQVcXFw0bq/i1+rlibXbt29j9OjRqFu3LqytrfH8889j6tSpKuVeffVVNG/eXGW5Xr16QSaT4YcffpCmHT9+HDKZDFu3bkV0dDTq1asHAPjoo49UulnR9hqBzJPy2i4+Ph6BgYFYv3692pMRhw4dQocOHWBjYwMPDw9MmTJF43dQTfEGlN0lz7Bhw6QYVF57Kh+7DA4OVnuUMDs7G6dOnULPnj3RunVr7N27V5p37do1XL58GSEhIdI0bWIeAHJycjBx4kR4e3vD2toadevWRVRUlMbvpcUJIfDxxx9DLpdj+fLl0vTz58+je/fusLOzQ+3atTFq1Ci1u9cA7a6Ly3NNZFQElWrEiBECgBg7dqw4dOiQyM/PL7Gsl5eXqFevnmjWrJlYvXq12LVrl3j11VcFAJGWliaVO3/+vHBwcBA+Pj5i9erVYvv27WLw4MECgJg1a5ZUbtCgQaJRo0bS60OHDgkAwtbWVqxdu1aaHh4eLtq2bVtqO/bu3SsAiB9++EF8+umnwsPDQzx58kQIIcTPP/8sZDKZuHTpkujZs6fw8vJSWfaDDz4QS5YsETt37hR79uwR8+bNE7Vr1xZvvvmmSrmgoCDRvHlz6fXcuXOFpaWlmDFjhtp2ioyMVKtbgwYNxGuvvSa2b98uvvvuO1G/fn3RsGFDqZ5CCDFlyhQBQLzzzjti586dYvny5aJ+/frC3d1dBAUFlboNnjVjxgwxb948sX37dpGamiqWLl0qvL29RUhIiEq5nTt3CrlcLlq0aCESExPFnj17xDfffCMGDRpUrvej6rF7925haWkpOnbsKDZt2iR++OEH8eKLL4r69euL4oc8bfbrmzdvCoVCIaZOnaryHtnZ2cLW1lZ8+OGHQggh0tPThY2NjQgNDRWbN28WqampYu3atWLo0KHizp07Qggh3nnnHQFAnDt3rsS6L168WAAQ3333nbReAMLT01N07NhRbNy4UWqPXC4XBw4ckJY9c+aMcHR0FH5+fmL16tUiOTlZTJgwQVhYWIjo6GipXHniLTIyUtjb25e6vdPS0sSECRPEjz/+KNLS0kRSUpLo06ePsLW1FefPn5fKxcXFCUtLSzFt2jTxyy+/iJ07d4r58+dLdbt27ZoYO3asACA2bdokDh48KA4ePCju3bsnreO7774TAERsbKyoVauWeOWVV6R5b7/9tpDL5WLChAli586dYt26daJJkybC1dVVZGZmSuWWLFki4uLixJYtW0RaWppYtWqVaNmypWjcuLHK8X3atGkCgPDy8hIfffSRSElJEZs3by51W5Bp8ff3Fx06dFCb/uKLL4oXX3xRCCHEnj17hLW1tejUqZPYsGGD2Llzpxg2bJgAIFauXCktc/78efHuu++K9evXi9TUVLFt2zYxfPhwYWFhIfbu3SuVU8Z83bp1RUhIiPjxxx9FcnKySE9PLzOGnjx5Iuzs7ERAQECp7Wrbtq2ws7OTYr34vv7hhx+KXbt2iblz5wp7e3vh7++vEheff/65kMlk4q233hLbtm0TmzZtEu3btxf29vbizJkzUrnIyEhhbW0tmjZtKr744guxe/du8dlnnwmZTCZiYmLK/VmQadP2PNK3b1/h6ekpCgsLVZafNGmSsLa2Frdu3RJCCJGRkSE8PT2Fl5eX+Prrr8Xu3bvFjBkzhEKhEMOGDZOWKy3e7t69K4YNGya+/fZbsWfPHrFz504xceJEYWFhIVatWiWto7CwUHTs2FHY2NiI+Ph4kZycLGJiYkTDhg0FADFt2jSprLbn6TVr1ggAIiwsTOzcuVPlPPgsbWPt0aNHokWLFsLe3l588cUXIjk5WXz66afCyspK9OjRQyq3dOlSAUDcuHFDCCFEQUGBcHBwELa2tuLtt9+Wys2aNUtYWVmJnJwcce3aNbFp0ybp+9LBgwfF8ePHy/XZKj+L4sfNlStXCgAiPT29xPaT8Xr48KFwdHSUzqf//e9/BQCRmJgolTlz5oyws7MTzZo1E99995346aefRLdu3aRr+uL7xrPxplTS907luffSpUuif//+AoB07Xnw4EHx+PFjIYQQLVu2VPk+vnHjRmFlZSXu378vPvroI6n+QgixatUqAUBs375dqr82Mf/gwQPRqlUrUbt2bTF37lyxe/du8eWXXwpHR0fRuXNnUVRUpNLOMWPGCCGEePz4sRg0aJBwcHAQP//8s1QmMzNTuLi4iLp164qVK1eKHTt2iNdee03absWvO7S9LtbmmsjYMBFWhlu3bomOHTsKAAKAkMvlIjAwUMTFxYnc3FyVsl5eXsLGxkZcvXpVmvbo0SPh5OQkRo4cKU0bNGiQUCgU4u+//1ZZPjw8XNjZ2Ym7d+8KIf53QFCWmzlzpmjSpIno3bu39GU9Pz9f2Nvbi48//rjUdhRPhF2+fFnIZDKxbds2IYQQr776qggODhZCCI2JsOIKCwtFQUGBWL16tbC0tBS3b9+W5ikTYYWFheK9994T1tbWYs2aNWrrKOmAVPxELIQQ33//vXRQEkKI27dvC4VCIQYOHKhS7uDBgwJAuRNhxRUVFYmCggKRlpYmAIg//vhDmufj4yN8fHzEo0ePKrx+qj4BAQHCw8ND5fPKyckRTk5OKomw4krbryMjI4WLi4vIy8uTps2aNUtYWFhIJ+Aff/xRABAnT54ssV7du3cXAKQTqyY///yzSkJceWFYUnu6du0qTevWrZuoV6+e2gXze++9J2xsbKQ2aRtvyraXlQh71pMnT0R+fr5o2LCh+OCDD6TpERERolWrVqUuO3v27DIvegcMGCAACFdXV/Hvv/8KIf53DJgzZ45K2WvXrglbW1sxadIkjetSxv3Vq1cFAPHTTz9J85TJgc8++6ysJpOJUn4JO3HihDTt999/FwCkL8JNmjQR/v7+oqCgQGXZiIgI4e7urvaFXenJkyeioKBAdOnSRfTt21earox5Hx8ftR/eyoqhzMxMAaDMH2kGDhwoAIibN28KIf63rxePVyGEWLt2rQAgncf//vtvYWVlJcaOHatSLjc3V7i5uYkBAwZI0yIjIwUA8f3336uU7dGjh2jcuHGp9SMq6TyyZcsWAUAkJyerlPXw8FD5YWTkyJGiRo0aKtfjQgjxxRdfCABS0ra0eNNUp4KCAjF8+HDh7+8vTd++fbsAIJYsWaJSPi4uTu2Lubbn6aKiIjFy5EhhYWEhAAiZTCaaNm0qPvjgA7Xzo7axpkxwPVtu1qxZKtv00qVLAoBYvXq1EEKI/fv3CwBi0qRJwtvbW1ouNDRUBAYGSq+V23L27NllbkdNny0TYeZn9erVAoBYunSpEOLpuaRGjRqiU6dOUpmBAwcKW1tblR80nzx5Ipo0aaKzRJgQQowZM6bE7whRUVEqyeGxY8eKdu3aCSGE2LFjh7C0tJRi+s033xSWlpYiJydHCKF9zMfFxQkLCwtx5MgRlXLK7xc7duxQaeeYMWNEdna26Nixo6hbt67a94+PPvpIyGQytemhoaFqbS+utOtiba6JjA0fjSyDs7Mzfv31Vxw5cgTx8fF4+eWX8ddff2HKlCnw8/NTe5ymVatWqF+/vvTaxsYGjRo1wtWrV6Vpe/bsQZcuXeDp6amy7LBhw/Dw4UNpVKeuXbsCAHbv3g3g6a2LoaGh6Nq1K1JSUgAABw8exIMHD6Sy2vD29kZwcDC++eYbZGdn46effsJbb71VYvkTJ06gd+/ecHZ2hqWlJeRyOd544w0UFhbir7/+Uin7+PFj9OnTB2vXrkVycjJee+01revVu3dvldctWrQAAGnbHTp0CHl5eRgwYIBKuXbt2lVopMvLly9jyJAhcHNzk9ql7Njz3LlzAIC//voL//d//4fhw4fDxsam3O9B1evBgwc4cuQI+vXrp/J5OTg4oFevXipltd2vx40bh6ysLOmRgKKiIixZsgQ9e/aU9rtWrVrB2toa77zzDlatWlXhjmyFEACgNrplSe3Zt28fCgsL8fjxY/zyyy/o27cv7Ozs8OTJE+mvR48eePz4MQ4dOqSyzrLiTVtPnjxBbGwsmjVrBmtra1hZWcHa2hoXL16U4ggA2rZtiz/++AOjR4/Grl27kJOTU673UZo+fToA4P3330ft2rUBANu2bYNMJsPrr7+u0nY3Nze0bNlS5Zb2rKwsjBo1Cp6enrCysoJcLoeXlxcAqNRXqaTRKsn0DR48GC4uLiqPMCxYsAB16tTBwIEDcenSJZw/f146zz0bdxkZGSqPGy9duhQvvPACbGxspH3vl19+0bjf9e7dG3K5XGWarmKopOPMs+frAQMGwMrKSnrsY9euXXjy5AneeOMNlbba2NggKChIbRQqmUymdtxt0aJFuY8xZPq0PY+Eh4fDzc0NK1eulKbt2rULN27cULmO3bZtG0JCQuDh4aGyryr78UlLS1N5f03xBgA//PADOnTogBo1akgxu2LFCpU6Kdf17LXp4MGDVV6X5zwtk8mwdOlSXL58GYsXL8abb76JgoICzJs3D82bN1ervzaxtmfPHtjb26N///4q5ZSPjCm7W/Dx8UGDBg1Uvnv4+fnh9ddfR3p6Ov7v//4PeXl52L9/v1bfPbT9bMn8rFixAra2tlLfWzVq1MCrr76KX3/9FRcvXgQA7N27F126dIGrq6u0nKWlJQYOHFht9VQ+5qg8x6Wmpkrd8XTs2BEAsG/fPmlemzZt4ODgUK6Y37ZtG3x9fdGqVSuVct26ddM4ymN6ejrat2+PnJwcHDp0CC1btlSZv3fvXjRv3lxt+pAhQ9Tap+11cVnXRMaIiTAttWnTBh999BF++OEH3LhxAx988AGuXLmi1mG+s7Oz2rIKhQKPHj2SXmdnZ0udPRfn4eEhzQcALy8v+Pj4YPfu3VKCTJkIu379Oi5cuIDdu3fD1tYWgYGB5WrP8OHDsXXrVsydOxe2trZqJ0alv//+G506dcI///yDL7/8UkoKKoOgeLuAp8G0a9cutG/fvtx1enbbKTsqVb6HcrsUPxgqaZpWmvv376NTp044fPgwZs6cidTUVBw5cgSbNm1SeU9l/yjKvg/IsN25cwdFRUVwc3NTm1d8Wnn2a39/f3Tq1Emat23bNly5ckVl6GJlnLq4uGDMmDHw8fGBj4+PSt89ygR5enp6ifVXDgP9bJK8pPbk5+fj/v37yM7OxpMnT7BgwQLI5XKVvx49egCAWtK+rHjT1vjx4/Hpp5+iT58+2Lp1Kw4fPowjR46gZcuWKuuaMmUKvvjiCxw6dAjh4eFwdnZGly5dcPTo0XK9n7Ke1tbW0rSbN29K/So+2/5Dhw5JbS8qKkJYWBg2bdqESZMm4ZdffsHvv/8uXYhoarumYzWZB4VCgZEjR2LdunW4e/cu/v33X3z//fcYMWIEFAqF1FfnxIkT1fa70aNHA/hf3M2dOxfvvvsuAgICsHHjRhw6dAhHjhxB9+7dtd7vyoqh2rVrw87OrtRjDPD0OGNnZwcnJyeV6c8eZ6ysrODs7Cyde5XtffHFF9Xau2HDBrVjjJ2dndoPSAqFQupXkUhJ2/OIlZUVhg4diqSkJKlf3MTERLi7u6Nbt25SuZs3b2Lr1q1q+6my76tn91VN8bZp0yYMGDAAdevWxZo1a3Dw4EEcOXIEb731lso+nJ2dDSsrK7V4eva6tCLnaS8vL7z77rtYsWIFLl68iA0bNuDx48dSf5pK2sRadnY23Nzc1BLgLi4usLKykuIcALp06SIlxnbv3o3Q0FD4+fnB1dUVu3fvxm+//YZHjx5plQjT9rMl83Lp0iXs27cPPXv2hBACd+/exd27d6Xvo8q+rJX77bM0TasqQUFBsLCwwN69e5GdnY3Tp09LN044ODjA398fqamp+Pvvv5Geni4lzsoT8zdv3sSff/6pVs7BwQFCCLVjw++//46//voLAwcO1PgdVdvtVp7r4rKuiYwRR42sALlcjmnTpmHevHk4ffp0uZd3dnZGRkaG2vQbN24AgHSXA/D0ZPTTTz8hLS0NRUVFCA4OhoODAzw8PJCSkoLdu3ejU6dO5d4B+/XrhzFjxiA+Ph5vv/02bG1tNZbbvHkzHjx4gE2bNknZYQA4efKkxvL169fH3Llz0bdvX/Tr1w8//PCDzu6kUn5xLz5QgFJmZma57grbs2cPbty4gdTUVJXhnZ8dcKBOnToAng5lTYavVq1akMlkasMcA1CZVt79+v3338err76K48ePY+HChWjUqBFCQ0NVynTq1AmdOnVCYWEhjh49igULFiAqKgqurq4YNGgQQkNDsWzZMmzevBmTJ0/W+D6bN2+GlZWV2sAPJbXH2toaNWrUgFwuh6WlJYYOHaoywEZxxUee0qU1a9bgjTfeQGxsrMr0W7duqYyYY2VlhfHjx2P8+PG4e/cudu/ejY8//hjdunXDtWvXKjUaY+3atSGTyfDrr79qPBYqp50+fRp//PEHEhMTERkZKc2/dOlSiet+9ksDmZd3330X8fHx+Oabb/D48WM8efIEo0aNAvC/c/WUKVPQr18/jcs3btwYwNM4CQ4OxpIlS1Tma+q4FtC832kTQyEhIdi5cyeuX7+u8eL4+vXrOHbsGMLDw2FpaakyLzMzE3Xr1pVeP3nyBNnZ2dK5V9neH3/8UeW4SVRZ2p5HAODNN9/E7NmzsX79egwcOBBbtmxBVFSUyv5cu3ZttGjRAp9//rnG91P+8KykKd7WrFkDb29vbNiwQWX+sx3QOzs748mTJ7h9+7ZKMuzZ83atWrUqfZ4eMGAA4uLiKvzd4/DhwxBCqLQnKysLT548UfvusWLFCvz+++84fPgwPvnkEwBA586dkZKSgqtXr6JGjRpo165dme9bns+WzMc333wDIQR+/PFH/Pjjj2rzV61ahZkzZ8LZ2bnMa3olhUKhcTCW4kneinB0dJSSXampqbCwsFAZdTkoKAh79+6Fn58fgP/dQVaemK9duzZsbW01DrCnnF/cwIED4ebmhqlTp6KoqEiKUSVtt1t5r4tLuyYyRkyElSEjI0PjL0XKWwWfPZlqo0uXLkhKSsKNGzdUll+9ejXs7OxUTixdu3bFsmXLMH/+fLRr1w4ODg4q6zhy5IjayUUbtra2+Oyzz7Bv3z68++67JZZTniyLf7kUQqiMSvGssLAw7Nq1Cz179kRERAR++ukn2Nvbl7uOzwoICIBCocCGDRtUvnQcOnQIV69eLVciTFO7AODrr79Wed2oUSP4+Pjgm2++wfjx4402420u7O3t0bZtW2zatAmzZ8+WkrC5ubnYunWrVK68+3Xfvn1Rv359TJgwAWlpaZg3b16JCRJLS0sEBASgSZMmWLt2LY4fP45Bgwahb9++aNasGeLj49GvXz+1kSM3bNiA5ORkjBo1Su0Xm5La06lTJ1haWkpfgE+cOIEWLVqo3C1V1WQymVpcbN++Hf/88w/+85//aFzmueeeQ//+/fHPP/8gKioKV65cQbNmzSp8V1pERATi4+Pxzz//qD2e8mxdgbLjnkjJ3d0dr776KhYvXoz8/Hz06tVLuruzcePGaNiwIf74448yz8Oa4uTPP//EwYMH1e4A1UZJMTRlyhT8/PPPGD16NJKSklSSA4WFhXj33XchhMCUKVPU1rl27Vq0bt1aev3999/jyZMnUmK+W7dusLKywv/93//xkWHSqfKcR5o2bYqAgACsXLkShYWFyMvLw5tvvqlSJiIiAjt27ICPjw9q1apV4TpZW1urnOszMzPVRo0MCgpCQkICNmzYoHI9vX79epVy5TlPl/Td4/79+7h27VqFv3t8//332Lx5M/r27StNX716tTS/eFmZTIZPP/0UFhYWeOmllwA8/U7y4Ycf4urVq3jppZc0Pk76rIpcI5BpKywsxKpVq+Dj44P//ve/avO3bduGOXPm4Oeff0ZISAi2bNmCmzdvSndZFhYWYsOGDWrLNWjQAH/++afKtD179uD+/ftl1qn49aemm0NCQkLwxRdfYN26dWjdurX0fRx4egyYN28eNm/eDLlcLiXJyhPzERERiI2NhbOzs9Y/XH/yySdwcHDABx98gAcPHiAuLk6lvgkJCfjjjz9UHo9ct26dyjrKe11c2jWRMWIirAzdunVDvXr10KtXLzRp0gRFRUU4efIk5syZgxo1amDcuHHlXue0adOk/gs+++wzODk5Ye3atdi+fTsSEhLg6Ogole3cuTNkMhmSk5MRExMjTe/atauUuS1P/2DFKX9ZLk1oaCisra0xePBgTJo0CY8fP8aSJUtw586dUpfr2LEjfvnlF3Tv3h1hYWHYsWOHSrsqwsnJCePHj0dcXBxq1aqFvn374vr164iJiYG7u7vKcNJlCQwMRK1atTBq1ChMmzYNcrkca9euxR9//KFWdtGiRejVqxfatWuHDz74APXr18fff/+NXbt2Ye3atZVqE+nejBkz0L17d4SGhmLChAkoLCzErFmzYG9vj9u3bwMo/35taWmJMWPG4KOPPoK9vb3aMMxLly7Fnj170LNnT9SvXx+PHz+WftVRxqelpSU2btyI0NBQtG/fHhMmTED79u2Rl5eHrVu3YtmyZQgKCsKcOXM0vn9oaCjGjx+PoqIizJo1Czk5OSrHhC+//BIdO3ZEp06d8O6776JBgwbIzc3FpUuXsHXrVuzZs6dC27OwsFDjr3X29vYIDw9HREQEEhMT0aRJE7Ro0QLHjh3D7Nmz1e5G6dWrF3x9fdGmTRvUqVMHV69exfz58+Hl5YWGDRsCgPRr2pdffonIyEjI5XI0btxY5YJDkw4dOuCdd97Bm2++iaNHj+Kll16Cvb09MjIysH//fvj5+eHdd99FkyZN4OPjg8mTJ0MIAScnJ2zdulXqc5FIk3HjxiEgIAAAVPomAp5eLIaHh6Nbt24YNmwY6tati9u3b+PcuXM4fvy41LdgREQEZsyYgWnTpiEoKAgXLlzA9OnT4e3tjSdPnmhVD21iqEOHDpg/fz6ioqLQsWNHvPfee9I5a9GiRTh8+DDmz5+vseuCTZs2wcrKCqGhoThz5gw+/fRTtGzZUkouN2jQANOnT8fUqVNx+fJldO/eHbVq1cLNmzfx+++/w97eXuWYRKQtbc8jSm+99RZGjhyJGzduIDAwULrzUmn69OlISUlBYGAg3n//fTRu3BiPHz/GlStXsGPHDixdurTMLi8iIiKwadMmjB49Gv3798e1a9cwY8YMuLu7S/0XAUD37t3RoUMHTJgwATk5OWjdujUOHjwoJZiKX5tqe57+/PPP8dtvv2HgwIFo1aoVbG1tkZ6ejoULFyI7OxuzZ88u9zZ+4403sGjRIkRGRuLKlSvw8/PD/v37ERsbix49eqh8l3BxcYGvry+Sk5MREhIi3bHdtWtX3L59G7dv38bcuXO1et/yfrZk+n7++WfcuHEDs2bNUnsCAgB8fX2xcOFCrFixAjNmzMCWLVvQuXNnfPbZZ7Czs8OiRYvw4MEDteWGDh2KTz/9FJ999hmCgoJw9uxZLFy4UKvvn8rrz1mzZkl3TBdPXikTYUlJSZg4caLKsp06dQIA/PTTTwgMDFS5+UPbmI+KisLGjRvx0ksv4YMPPkCLFi1QVFSEv//+G8nJyZgwYYJ0HVLcuHHjUKNGDbzzzju4f/8+vvrqK8hkMkRFReGbb75Bz549MXPmTLi6umLt2rU4f/68yvIVuS4u7ZrI6Oinj37jsWHDBjFkyBDRsGFDUaNGDSGXy0X9+vXF0KFDxdmzZ1XKenl5iZ49e6qtIygoSG1Ew1OnTolevXoJR0dHYW1tLVq2bKkyUkpx/v7+AoD47bffpGn//POPACCcnZ1VhlQtSfFRI0ujadTIrVu3ipYtWwobGxtRt25d8eGHH0qj2xUfdUI5amRxp0+fFm5ubuKFF16QRngrafSOZ+umaQSZoqIiMXPmTFGvXj1hbW0tWrRoIbZt2yZatmypMvKWNg4cOCDat28v7OzsRJ06dcSIESPE8ePH1d5TiKej0oWHhwtHR0ehUCiEj4+P2ghbZDi2bNkiWrRoIaytrUX9+vVFfHy8NDKakrb7tdKVK1cEADFq1Ci1eQcPHhR9+/YVXl5eQqFQCGdnZxEUFCS2bNmiVvbWrVti8uTJokmTJsLGxkbUqFFDtG3bVixcuFBt1CplDMyaNUvExMRI+72/v7/YtWuX2rrT09PFW2+9JerWrSvkcrmoU6eOCAwMFDNnzpTKlCfelKNRafpTHifu3Lkjhg8fLlxcXISdnZ3o2LGj+PXXX9WOe3PmzBGBgYGidu3a0ucyfPhwceXKFZV6TJkyRXh4eEijZT37WZQ2MtU333wjAgIChL29vbC1tRU+Pj7ijTfeEEePHpXKnD17VoSGhgoHBwdRq1Yt8eqrr4q///5bbbQh5f6iPG6ReWvQoIFo2rSpxnl//PGHGDBggHBxcRFyuVy4ubmJzp07SyNhCSFEXl6emDhxoqhbt66wsbERL7zwgti8ebOIjIxUOeeWtn9rG0NCPD0m9e/fX7i6ugorKyvh4uIi+vXrJw4cOKBWVrmvHzt2TPTq1UvUqFFDODg4iMGDB0sjSxa3efNmERISImrWrCkUCoXw8vIS/fv3F7t375bKlDTi7LPHYSIhtD+PKN27d0/Y2toKAGL58uUa1/nvv/+K999/X3h7ewu5XC6cnJxE69atxdSpU8X9+/eFEGWPdBgfHy8aNGggFAqFaNq0qVi+fLnGffj27dvizTffFM8995yws7MToaGh4tChQwKA+PLLL1XKanOePnTokBgzZoxo2bKlcHJyEpaWlqJOnTqie/fuKqPHCVG+WMvOzhajRo0S7u7uwsrKSnh5eYkpU6ZoHM36gw8+EADE559/rjK9YcOGAoD4888/1dqlaVtq+9ly1Ejz0adPH2FtbS2ysrJKLDNo0CBhZWUlMjMzxW+//SbatWsnFAqFcHNzEx9++KFYtmyZ2r6Rl5cnJk2aJDw9PYWtra0ICgoSJ0+e1GrUyLy8PDFixAhRp04dIZPJ1Nadk5MjrKysBACxbds2tfq2atVKABBTp05Vm6dNzAshxP3798Unn3wiGjduLKytrYWjo6Pw8/MTH3zwgcqomfj/o0YW99133wkrKyvx5ptvSqNVK693bWxshJOTkxg+fLj46aef1Nqu7XVxcaVdExkTmRD/f/ggIiOVnp6OJk2aYNq0afj444/1XR0yUQsWLMD777+P06dPSx3uVrUrV67A29sbs2fPVvsFioiqx59//omWLVti0aJFUif4piQ6OhoxMTH4999/1fohIaKKWbduHV577TX89ttv5R48iojIUJnSNREfjSSj8scff+C7775DYGAgatasiQsXLiAhIQE1a9bE8OHD9V09MkEnTpxAeno6pk+fjpdffrnakmBEpF//93//h6tXr+Ljjz+Gu7u72iPRREQA8N133+Gff/6Bn58fLCwscOjQIcyePRsvvfQSk2BEZBJM8ZqIiTAyKvb29jh69ChWrFiBu3fvwtHREcHBwfj8889VOlEs7UZHmUymNloWUUn69u2LzMxMdOrUCUuXLtV3dYiomsyYMQPffvstmjZtih9++KFSI5sSkelycHDA+vXrMXPmTDx48ED6kjhz5kx9V42ISCdM8ZqIj0aSyWnQoAGuXr1a4vygoCCkpqZWX4WIiIiIiIiIyCDwjjAyOVu3bkVeXl6J88sagY6IiIiIiIiITJNF2UVU7du3D7169YKHhwdkMhk2b96sMl8IgejoaHh4eMDW1hbBwcE4c+aMSpm8vDyMHTsWtWvXhr29PXr37o3r169XqiFESn5+fmjTpk2Jf88Os22uGMtERERERERkbsqdCHvw4AFatmyJhQsXapyfkJCAuXPnYuHChThy5Ajc3NwQGhqK3NxcqUxUVBSSkpKwfv167N+/H/fv30dERAQKCwsr3hIiKhfGMhEREREREZmbSvURJpPJkJSUhD59+gB4egeJh4cHoqKi8NFHHwF4eseIq6srZs2ahZEjR+LevXuoU6cOvv32WwwcOBAAcOPGDXh6emLHjh3o1q1bme9bVFSEGzduwMHBATKZrKLVJzIKQgjk5ubCw8MDFhblzl1rRV+xDDCeybxUVTzv27cPs2fPxrFjx5CRkaESz8r3jYmJwbJly3Dnzh0EBARg0aJFKqOg5uXlYeLEifjuu+/w6NEjdOnSBYsXL0a9evW0rgfjmcxFdZyb9Y3xTObC1OOZsUzmROt4FpUAQCQlJUmv/+///k8AEMePH1cp17t3b/HGG28IIYT45ZdfBABx+/ZtlTItWrQQn332mcb3efz4sbh37570d/bsWQGAf/wzq79r165VJlxLBVRPLAvBeOYf/wDdx/OOHTvE1KlTxcaNGwWgGs9CCBEfHy8cHBzExo0bxalTp8TAgQOFu7u7yMnJkcqMGjVK1K1bV6SkpIjjx4+LkJAQ0bJlS/HkyROt63Ht2jW9b1v+8a86/6ry3KxvjGf+mdufqcYzY5l/5vhXVjzrtLP8zMxMAICrq6vKdFdXV2kUv8zMTFhbW6NWrVpqZZTLPysuLg4xMTFq0//73/+axNCdRKV5+PAhRowYUa2d/FdVLAOMZzJvVRXP4eHhCA8P1zhPCIH58+dj6tSp6NevHwBg1apVcHV1xbp166Q7PFesWIFvv/0WXbt2BQCsWbMGnp6e2L17t9Z3eCrbde3aNdSsWVMHLaOCggIkJycjLCwMcrlc39UxKZXZtjk5OfD09DTpAXiKx7Otra3J7YemFlum1h6g+tpk6vFsjOdmU9uf2Z7qo208V8mokc/ecimEKPM2zNLKTJkyBePHj5deKxvXp08fjcFcUFCAlJQUhIaGGtwHoy22wTAYQhtycnIwYsQIvdzKrOtYBrSPZ0PY9oaM26dkhrxt9BHP6enpyMzMRFhYmDRNoVAgKCgIBw4cwMiRI3Hs2DEUFBSolPHw8ICvry8OHDhQYiIsLy9PZZReZR+Ctra2sLW1raIWmRcrKyvY2dnB1tbW4PZnY1eZbVtQUABA/TxpSpRtq1mzJmxtbWFnZ4eaNWuazH5YUFBgUm0ytfYA1d8mU43n4rFsTIkwU9qf2Z7qV1Y86zQR5ubmBuDpnSLu7u7S9KysLOnOEjc3N+Tn5+POnTsqd5JkZWUhMDBQ43oVCgUUCoXadLlcXuqGL2u+MWAbDIM+26CP962qWAbKH8+msP9UJW6fkhnittFHffRxh2dycjLv8NSxlJQUfVfBZFVk2z58+LAKakJERETVQaeJMG9vb7i5uSElJQX+/v4AgPz8fKSlpWHWrFkAgNatW0MulyMlJQUDBgwAAGRkZOD06dNISEjQZXWIqIIYy0Smpzrv8AwLCzOaX50NnSHf4WjsKrNtc3JyqqhWREREVNXKnQi7f/8+Ll26JL1OT0/HyZMn4eTkhPr16yMqKgqxsbFo2LAhGjZsiNjYWNjZ2WHIkCEAAEdHRwwfPhwTJkyAs7MznJycMHHiRPj5+Un9khBR1WMsE5kHQ7rDkyqO27TqVGTb8rMgIiIyXuVOhB09ehQhISHSa+UvwZGRkUhMTMSkSZPw6NEjjB49WhqiPTk5WaWzsnnz5sHKygoDBgyQhmhPTEyEpaWlDpr0P77Ru5BXWLlnva/E99RRbYgMizHFMqnzjd6FhLaVO87x+GYeeIeneWgweXul18FjApUX9zsiKo7HBDIW5U6EBQcHQwhR4nyZTIbo6GhER0eXWMbGxgYLFizAggULyvv2RKQjjGUi08E7PImIiIiItFMlo0YSERFR9eEdnkRERERE2mEijIiIyMjxDk8iIiIiIu1Y6LsCRERERERERERE1YF3hBERERERERGR3jWYvB0KS1GpQaHY4T6VhXeEERERERERERGRWWAijIiIiIiIiIiIzAITYUREREREREREZBaYCCMiIiIiIiIiIrPARBgREREREREREZkFJsKIiIiIiIiIiMgsMBFGRERERERERERmgYkwIiIiIiIiIiIyC0yEERERERERERGRWWAijIiIiIiIiIiIzAITYUREREREREREZBaYCCMiIiIiIiIiIrPARBgREREREREREZkFJsKIiIiIiIiIiMgsMBFGRERERERERERmgYkwIiIiIiIiIiIyC0yEERERERERERGRWWAijIiIiIiIiIiIzAITYUREREREREREZBaYCCMiIiIiIiIiIrPARBgREREREREREZkFJsKIiIiIiIiIiMgsMBFGRERERERERERmgYkwIiIiIiIiIiIyC0yEERERERERERGRWWAijIiIiIiIiIiIzAITYUREREREREREZBaYCCMiIiIiIiKqZtHR0ZDJZCp/bm5u0nwhBKKjo+Hh4QFbW1sEBwfjzJkzeqwxkWlgIoyIiIiIiIhID5o3b46MjAzp79SpU9K8hIQEzJ07FwsXLsSRI0fg5uaG0NBQ5Obm6rHGRMaPiTAiIiIiIiIiPbCysoKbm5v0V6dOHQBP7wabP38+pk6din79+sHX1xerVq3Cw4cPsW7dOj3Xmsi4Wem7AkRERERERETm6OLFi/Dw8IBCoUBAQABiY2Px/PPPIz09HZmZmQgLC5PKKhQKBAUF4cCBAxg5cqTG9eXl5SEvL096nZOTAwAoKChAQUFBlbZFYSl0sx4LofJveVV1O8tLWR9Dq1dFGXJ7tK0TE2FERERERERE1SwgIACrV69Go0aNcPPmTcycOROBgYE4c+YMMjMzAQCurq4qy7i6uuLq1aslrjMuLg4xMTFq05OTk2FnZ6fbBjwjoa1u1zejTVGFltuxY4duK6IjKSkp+q6CThliex4+fKhVOSbCiIiIiIhILxpM3l7pdVyJ76mDmhBVv/DwcOn/fn5+aN++PXx8fLBq1Sq0a9cOACCTyVSWEUKoTStuypQpGD9+vPQ6JycHnp6eCAsLQ82aNXXcAlW+0bt0sh6FhcCMNkX49KgF8opKbmtVOh3dTWfrKigoQEpKCkJDQyGXy3W2Xn0x5PYo74AsCxNhZeDJmYiIiIiIiKqavb09/Pz8cPHiRfTp0wcAkJmZCXd3d6lMVlaW2l1ixSkUCigUCrXpcrm8ypMWeYW6TVrlFcl0vk5tVcW2qo7PoDoZYnu0rQ87yyciIiIiMhLR0dGQyWQqf25ubtJ8IQSio6Ph4eEBW1tbBAcH48yZM3qsMRFpKy8vD+fOnYO7uzu8vb3h5uam8vhZfn4+0tLSEBgYqMdaEhk/JsKIiIiIiIxI8+bNkZGRIf2dOnVKmpeQkIC5c+di4cKFOHLkCNzc3BAaGorc3Fw91piINJk4cSLS0tKQnp6Ow4cPo3///sjJyUFkZCRkMhmioqIQGxuLpKQknD59GsOGDYOdnR2GDBmi76oTGTU+GklEREREZESsrKxU7gJTEkJg/vz5mDp1Kvr16wcAWLVqFVxdXbFu3boSR5kjIv24fv06Bg8ejFu3bqFOnTpo164dDh06BC8vLwDApEmT8OjRI4wePRp37txBQEAAkpOT4eDgoOeaExk3JsKIiIiIiIzIxYsX4eHhAYVCgYCAAMTGxuL5559Heno6MjMzERYWJpVVKBQICgrCgQMHSk2E5eXlIS8vT3qt7HC4oKAAVlZW0v+LU1gKXTarwp6tV3mWqciyhsjU2gNUX5v0uc3Wr19f6nyZTIbo6GhER0dXT4WIzAQTYURERERERiIgIACrV69Go0aNcPPmTcycOROBgYE4c+YMMjMzAUCtI21XV1dcvXq11PXGxcUhJiZGbXpycjLs7OwAQKWvIgBIaFuZlujOjh07Krzss20ydqbWHqDq2/Tw4cMqXT8RGR6dJ8Kio6PVTqKurq7SiVkIgZiYGCxbtky6vXPRokVo3ry5rqtCRJXEeCYiIjIs4eHh0v/9/PzQvn17+Pj4YNWqVWjXrh2Ap3eRFCeEUJv2rClTpmD8+PHS65ycHHh6eiIsLAy2trZISUlBaGioyohcvtG7dNGkSjsd3a3cyxQUFGhsk7EytfYA1dcm5d2PRGQ+quSOsObNm2P37t3Sa0tLS+n/yg48ExMT0ahRI8ycOROhoaG4cOECn3UmMkCMZyLjx6Q2kemyt7eHn58fLl68iD59+gAAMjMz4e7uLpXJyspSu0vsWQqFAgqFQm26XC6XkhDF/w8AeYWlJ9eqS2WSJM+2ydiZWnuAqm+TqW0vIipblYwaqezAU/lXp04dAOodePr6+mLVqlV4+PAh1q1bVxVVIaJKYjwTmQaOMkdkmvLy8nDu3Dm4u7vD29sbbm5uKo+S5efnIy0tDYGBgXqsJRERkeGokkSYsgNPb29vDBo0CJcvXwaAMjvwJCLDw3gmMg1MahOZhokTJyItLQ3p6ek4fPgw+vfvj5ycHERGRkImkyEqKgqxsbFISkrC6dOnMWzYMNjZ2WHIkCH6rjoREZFB0PmjkVXRgWdpo9hoGuVDOU1hwZFs9Ilt0G0d9EGf8WwI296QKY9vlTnOmeq2NeR9R591qopR5oio+l2/fh2DBw/GrVu3UKdOHbRr1w6HDh2Cl5cXAGDSpEl49OgRRo8eLT3qnJyczC4LiIiI/j+dJ8KqogNPbUax0WRGm6LyVr9KmPtINmxD5ehzJBtDiGdT2H+qwow2yn8rfpyrzLHJGBjivqOveK6qUebK+0MVlZ+2iV2FZeV//DO3z6wySXN9bqv169eXOl8mkyE6OhrR0dHVUyEiIiIjUyWd5Reniw48SxvFpmbNmmrllSOMfHrUAnlF+u/E01xHsmEbdMOQRrKpzng2hG1vyFpP34kZbYoqdZyryLHJGBjyvqOveK6qUeYq+kMVlV9Zid2EtpV/D1NPjpekIklzff5IRURERJVT5YkwZQeenTp1UunA09/fH8D/OvCcNWtWievQZhQbje9dJDOI0WzMfSQbtqHy720o9BHPprD/VAVl8qsyxzlT366GuO8YSn10NcpceX+oovLTNrHrG72r0u9lqsnxklQmaW5IP1IRERFR+eg8ETZx4kT06tUL9evXR1ZWFmbOnKmxA8+GDRuiYcOGiI2NZQeeRAaK8UxkmnSR1AYq/kMVlV+ZP/7p4Ic/c/3MKrK/muu2IiIiMgU6T4SxA08i08F4JjINTGobtgaTt5c4T2EpkND26R1fhnCXOxEREZGx03kijB14EpkOxjORaWBSm4iIiIjoqSrvI4yIiIj0i0ltIiIiIqKnmAgjIjJTpT2Opa0r8T11UBMiMhU8rpA+VGS/e/axY+53RETmg4kwIqJqposvigpLHVSEiIiIiIjIzFjouwJERERERERERETVgYkwIiIiIiIiIiIyC0yEERERERERERGRWWAijIiIiIiIiIiIzAI7y68GHMmGiIiIiIiIiEj/mAgjIiIiIiIiMmO6GNWcyFjw0UgiIiIiIiIiIjILvCOMiIiIiHg3ABEREZkF3hFGRERERERERERmgYkwIiIiIiIiIiIyC3w0koiIiIiIqJJ08XgxR4onIqp6vCOMiIiIiIiIiIjMAhNhRERERERERERkFvhoJBERERERmTWOmkpExfFRZ9PGO8KIiIiIiIiIiMgsMBFGRERERERERERmgYkwIiIiIiIiIiIyC+wjzEjwGWUiIiLDw36FiIiIiIwL7wgjIiIiIiIiIiKzwEQYERERERERERGZBT4aSUREFcbHtomIiIiIyJjwjjAiIiIiIiIiIjILvCOMiIiIiIjIAFT0TmuFpUBCW8A3ehcufB6h41oREZkW3hFGRERERERERERmgYkwIiIiIiIiIiIyC0yEERERERERERGRWWAijIiIiIiIiIiIzAI7yyciKoeKdmJLRERERERE+sc7woiIiIiIiIiIyCwwEUZERERERERERGaBiTAiIiIiIiIiIjILTIQREREREREREZFZYGf5REREZFR0MWjFlfieOqgJERERERkb3hFGRERERERERERmgYkwIiIiIiIiIiIyC0yEERERERERERGRWWAfYURERGR2dNHPGBEREVFJlNcaCkuBhLaAb/Qu5BXKyrUO9mlaNZgIIyIiIiIiIgkHJSEyDIzFqsFEGBERERERkYngHa9ERKVjIsyMMJtMRIaIxyYiIiIiIqouTIRRuZTnC2tJz0LzCyvpC38hJdI/xiERERER6ZNeE2GLFy/G7NmzkZGRgebNm2P+/Pno1KmTPqtERqK6vkhVpmNDbZlCYpCxTGQ6GM9EpoPxTGQ6GM9EumOhrzfesGEDoqKiMHXqVJw4cQKdOnVCeHg4/v77b31ViYgqgLFMZDoYz0Smg/FMZDoYz0S6pbc7wubOnYvhw4djxIgRAID58+dj165dWLJkCeLi4vRVLSIqp+qKZT5ORVWNfZXx3EyGwVDu+mY8ExkGXTyZwXgmc6br82p1PDVVEl3Fsl4SYfn5+Th27BgmT56sMj0sLAwHDhxQK5+Xl4e8vDzp9b179wAAt2/fRkFBgVr5goICPHz4EFYFFigsqt4PRlesigQePiwyyTb8Z+L3lV93pdeg5ftUw+eQnZ1d6vzc3FwAgBCiSt6/Msoby4D28ayM4+zsbMjlclg9eVAFLTBepnCM0KXixxWFhcAn/kVoNXUT8sqxbXRxXGE8l35+BsBYLifGetUpa9uWFs+GHMuA7uPZxsZG5ZysZMzxbGqxZYjtqew1v/J8ros2mVM8V+TcDBhWPBvi/lwZbI/u6OpaWy+JsFu3bqGwsBCurq4q011dXZGZmalWPi4uDjExMWrTvb29q6yOhmCIviugA2xD2WrP0a5cbm4uHB0dq7Yy5VTeWAbMN56rginEV1XR17ZhPD/FeNYtxnrVKW3bahPPhhjLAONZW6YWW6bWHkB3bTKneDaVWDa1/Znt0Q1dXWvrtbN8mUw1eyiEUJsGAFOmTMH48eOl10VFRbh9+zacnZ01ls/JyYGnpyeuXbuGmjVr6r7i1YBtMAyG0AYhBHJzc+Hh4aGX99eGtrEMaB/PhrDtDRm3T8kMeduYazxTxRny/mzsKrNtjSGWAd3Fc25ursnth6YWW6bWHqD62mRq8WwK52ZT25/ZnuqjbTzrJRFWu3ZtWFpaqmWws7Ky1DLdAKBQKKBQKFSmPffcc2W+T82aNQ3ugykvtsEw6LsNhvjrFFD+WAbKH8/63vaGjtunZIa6bcw5nqniDHV/NgUV3baGGsuA7uNZ+eXZFPdDU2uTqbUHqJ42mVI8m9K52dT2Z7anemgTz3oZNdLa2hqtW7dGSkqKyvSUlBQEBgbqo0pEVAGMZSLTwXgmMh2MseOrnAAAQB1JREFUZyLTwXgm0j29PRo5fvx4DB06FG3atEH79u2xbNky/P333xg1apS+qkREFcBYJjIdjGci08F4JjIdjGci3dJbImzgwIHIzs7G9OnTkZGRAV9fX+zYsQNeXl6VXrdCocC0adPUbgk1JmyDYTCFNlS1qoplbvvScfuUjNum4qry3EwVw/256pj6ttVlPJvitjK1NplaewDTbFNFmdv52dQ+e7bH8MiEoY4TS0REREREREREpEN66SOMiIiIiIiIiIioujERRkREREREREREZoGJMCIiIiIiIiIiMgtMhBERERERERERkVkwyUTY4sWL4e3tDRsbG7Ru3Rq//vqrvqsEAIiLi8OLL74IBwcHuLi4oE+fPrhw4YJKGSEEoqOj4eHhAVtbWwQHB+PMmTMqZfLy8jB27FjUrl0b9vb26N27N65fv16dTQHwtD0ymQxRUVHSNGOp/z///IPXX38dzs7OsLOzQ6tWrXDs2DGja4cpM9Q4rkr79u1Dr1694OHhAZlMhs2bN6vMN9f90tSOnUTPio6OhkwmU/lzc3OT5muzf9NTPI5WDWM4J5vDucKYr72VeA1OSsZ+7jO1801Z7Rk2bJja59WuXTuVMobUnjIJE7N+/Xohl8vF8uXLxdmzZ8W4ceOEvb29uHr1qr6rJrp16yZWrlwpTp8+LU6ePCl69uwp6tevL+7fvy+ViY+PFw4ODmLjxo3i1KlTYuDAgcLd3V3k5ORIZUaNGiXq1q0rUlJSxPHjx0VISIho2bKlePLkSbW15ffffxcNGjQQLVq0EOPGjTOq+t++fVt4eXmJYcOGicOHD4v09HSxe/ducenSJaNqhykz5DiuSjt27BBTp04VGzduFABEUlKSynxz3S9N6dhJpMm0adNE8+bNRUZGhvSXlZUlzddm/6aneBzVPWM5J5v6ucKYr72VeA1OxRn7uc/UzjdltScyMlJ0795d5fPKzs5WKWNI7SmLySXC2rZtK0aNGqUyrUmTJmLy5Ml6qlHJsrKyBACRlpYmhBCiqKhIuLm5ifj4eKnM48ePhaOjo1i6dKkQQoi7d+8KuVwu1q9fL5X5559/hIWFhdi5c2e11Ds3N1c0bNhQpKSkiKCgIOlkbCz1/+ijj0THjh1LnG8s7TBlxhTHVeXZExD3y/8x1mMnUUmmTZsmWrZsqXGeNvs3acbjqG4Y6znZlM4Vxn7trcRrcCrOlM59pna+KSkR9vLLL5e4jCG3RxOTejQyPz8fx44dQ1hYmMr0sLAwHDhwQE+1Ktm9e/cAAE5OTgCA9PR0ZGZmqtRfoVAgKChIqv+xY8dQUFCgUsbDwwO+vr7V1sYxY8agZ8+e6Nq1q8p0Y6n/li1b0KZNG7z66qtwcXGBv78/li9fbnTtMFXGFsfVhfvl/xjrsZOoNBcvXoSHhwe8vb0xaNAgXL58GYB2+zdph8eK8jPmc7IpnSuM/dpbidfg9CxTPfeZ6r6cmpoKFxcXNGrUCG+//TaysrKkecbWHpNKhN26dQuFhYVwdXVVme7q6orMzEw91UozIQTGjx+Pjh07wtfXFwCkOpZW/8zMTFhbW6NWrVollqlK69evx/HjxxEXF6c2zxjqDwCXL1/GkiVL0LBhQ+zatQujRo3C+++/j9WrV0t1VNappDoaQjtMlTHFcXXifvmUsR47iUoTEBCA1atXY9euXVi+fDkyMzMRGBiI7OxsrfZv0g6PFeVnrOdkUzpXmMK1txKvwak4Uz73meK+HB4ejrVr12LPnj2YM2cOjhw5gs6dOyMvLw+A8bXHSt8VqAoymUzltRBCbZq+vffee/jzzz+xf/9+tXkVqX91tPHatWsYN24ckpOTYWNjU2I5Q62/UlFREdq0aYPY2FgAgL+/P86cOYMlS5bgjTfekMoZejtMnTHEsT6Y+35pjMdOorKEh4dL//fz80P79u3h4+ODVatWSR3R8pioOzxWlJ+x7X+mcq4wlWtvJV6DU3HmcO4zpX154MCB0v99fX3Rpk0beHl5Yfv27ejXr1+Jyxlqe0zqjrDatWvD0tJSLeOYlZWllo3Vp7Fjx2LLli3Yu3cv6tWrJ01XjpJRWv3d3NyQn5+PO3fulFimqhw7dgxZWVlo3bo1rKysYGVlhbS0NHz11VewsrKS3t9Q66/k7u6OZs2aqUxr2rQp/v77b6mOgOG3w1QZSxxXN+6XxnvsJCove3t7+Pn54eLFi1rt36QdHivKzxjPyaZ0rjCVa28lXoNTaUzp3GcO+7K7uzu8vLxw8eJFAMbXHpNKhFlbW6N169ZISUlRmZ6SkoLAwEA91ep/hBB47733sGnTJuzZswfe3t4q8729veHm5qZS//z8fKSlpUn1b926NeRyuUqZjIwMnD59usrb2KVLF5w6dQonT56U/tq0aYPXXnsNJ0+exPPPP2/Q9Vfq0KGD2nDaf/31F7y8vAAY/udg6gw9jvXFnPdLYz92EpVXXl4ezp07B3d3d632b9IOjxXlZ0znZFM8V5jKtbcSr8GpNKZ07jOHfTk7OxvXrl2Du7s7ACNsT9X3x1+9lEM8r1ixQpw9e1ZERUUJe3t7ceXKFX1XTbz77rvC0dFRpKamqgw7+vDhQ6lMfHy8cHR0FJs2bRKnTp0SgwcP1jjMar169cTu3bvF8ePHRefOnfU2LGnxkWuMpf6///67sLKyEp9//rm4ePGiWLt2rbCzsxNr1qwxqnaYMkOO46qUm5srTpw4IU6cOCEAiLlz54oTJ05IQ9Sb635pisdOouImTJggUlNTxeXLl8WhQ4dERESEcHBwkI552uzf9BSPo7pnLOdkczlXGOO1txKvwak4Yz/3mdr5prT25ObmigkTJogDBw6I9PR0sXfvXtG+fXtRt25dg21PWUwuESaEEIsWLRJeXl7C2tpavPDCC9KwyfoGQOPfypUrpTJFRUVi2rRpws3NTSgUCvHSSy+JU6dOqazn0aNH4r333hNOTk7C1tZWREREiL///ruaW/PUsydjY6n/1q1bha+vr1AoFKJJkyZi2bJlKvONpR2mzFDjuCrt3btX4zEiMjJSCGG++6UpHjuJihs4cKBwd3cXcrlceHh4iH79+okzZ85I87XZv+kpHkerhjGck83lXGGs195KvAYnJWM/95na+aa09jx8+FCEhYWJOnXqCLlcLurXry8iIyPV6mpI7SmLTAghqupuMyIiIiIiIiIiIkNhUn2EERERERERERERlYSJMCIiIiIiIiIiMgtMhBERERERERERkVlgIoyIiIiIiIiIiMwCE2FERERERERERGQWmAgjIiIiIiIiIiKzwEQYERERERERERGZBSbCiIiIiIiIiIjILDARRkREREREREREZoGJMCIiIiIiIiIiMgtMhBERERERERERkVlgIoyIiIiIiIiIiMwCE2FERERERERERGQWmAjTsa+++goymQy+vr5aL5OYmAiZTIYrV66U+/1SU1Mhk8mQmpoqTduxYweio6PVyvr6+qJp06Zq05OSkiCTydC+fXu1ed9++y1kMhm2bNlS7rppQyaT4b333quSdZfXlStXIJPJkJiYqO+qkAGp6v3i4cOHiI6OVolhXdF0fBg2bBhkMpn0Z29vjwYNGqB3795YuXIl8vLydF6Pinrw4AHi4+Ph7++PGjVqwN7eHq1atUJsbCwePHig7+qRAVOeV21sbHD16lW1+cHBwSrn6QYNGmDYsGEVeq9n11WSGzduIDo6GidPntQ4f9euXQgLC4OHhwcUCgU8PDwQHByM+Ph4lXLanDc1Hbcqc61BREREZEqYCNOxb775BgBw5swZHD58WC912LFjB2JiYtSmh4SE4Pz588jMzFSZnpqaCnt7exw9ehS5ublq8ywsLPDSSy9VaZ2JDJW7uzsOHjyInj17Vsn6Hz58iJiYmCpJhJXE1tYWBw8exMGDB7Ft2zZMnz4d9vb2ePvtt9G6dWtcv3692upSkps3b6Jdu3aYPn06unXrhqSkJGzevBnh4eGYOXMm2rVrh5s3b+q7mmTg8vLy8Mknn5RZLikpCZ9++mmV1uXGjRuIiYnRmAhbunQpunfvjpo1a2LhwoXYtWsXZs2ahaZNm+LHH38s93tpOm717NkTBw8ehLu7e2WaQURERGT0rPRdAVNy9OhR/PHHH+jZsye2b9+OFStWICAgQN/VkoSEhGDhwoVITU3FoEGDpOmpqakYMWIEFi9ejP379yM8PFxlnr+/P5577jk91Jioajx69Ai2trZalVUoFGjXrl0V16h6WVhYqLXpjTfewJtvvomIiAj0798fhw4d0lPt/lef8+fPY+/evejYsaM0PTQ0FD179kRISAgiIyOxc+dOPdaSDF337t2xbt06TJw4ES1btiyxnL+/fzXWSl1cXBxeeukltaTX0KFDUVRUVO71aTpu1alTB3Xq1KlUPYmIiIhMAe8I06EVK1YAAOLj4xEYGIj169fj4cOHKmUOHTqEDh06wMbGBh4eHpgyZQoKCgrU1iWTyTQ+3ljW4xvDhg3DokWLpHUo/65cuYLg4GC1x6Sys7Nx6tQp9OzZE61bt8bevXuledeuXcPly5cREhIiTbt48SKGDBkCFxcXKBQKNG3aVHq/4nJycjBx4kR4e3vD2toadevWRVRUVJmPMwkh8PHHH0Mul2P58uXS9A0bNqB9+/awt7dHjRo10K1bN5w4cUKt7TVq1MClS5fQo0cP1KhRA56enpgwYYLa4143btzAgAED4ODgAEdHRwwcOFDtTjkybNHR0ZDJZDhx4gT69euHmjVrwtHREa+//jr+/fdfqVyDBg0QERGBTZs2wd/fHzY2NtIdk6dPn8bLL7+MWrVqwcbGBq1atcKqVatU3qekRyO1jYW7d+9iwoQJeP7556FQKODi4oIePXrg/PnzuHLlivTFNCYmRorX4jGu7fucP38e3bt3h52dHWrXro1Ro0ap3eFZlrCwMLz99ts4fPgw9u3bJ03fsGEDwsLC4O7uDltbWzRt2hSTJ09WiWflY9QHDx5UW+/06dMhl8tx48YNAMCJEycQEREhtcnDwwM9e/aU7kQ7evQokpOTMXz4cJUkmFLHjh3x1ltvYdeuXTh27Jg0XfnI2Ndff41GjRpBoVCgWbNmWL9+vdo6MjMzMXLkSNSrVw/W1tbw9vZGTEwMnjx5IpVRfvZffPEF5s6dC29vb9SoUQPt27fXe6KQtDNp0iQ4Ozvjo48+KrWcpnPrmTNnEBYWBjs7O9SpUwdjxozB9u3b1c6jSkeOHEGnTp1gZ2eH559/HvHx8VISKzU1FS+++CIA4M0335RiXXmez87OLvFOLQuL0i/VNJ03tX00UvlYZ2l1r+j2KMm///6L0aNHo1mzZqhRowZcXFzQuXNn/Prrr2pl8/LyMH36dDRt2hQ2NjZwdnZGSEgIDhw4oPX7ERERET2LiTAdefToEb777ju8+OKL8PX1xVtvvYXc3Fz88MMPUpmzZ8+iS5cuuHv3LhITE7F06VKcOHECM2fO1Fk9Pv30U/Tv3x8ApEeflI9CODk5oUWLFirJrrS0NFhaWiIwMBBBQUEqF7PKcspE2NmzZ/Hiiy/i9OnTmDNnDrZt24aePXvi/fffV3kU8+HDhwgKCsKqVavw/vvv4+eff8ZHH32ExMRE9O7dG0IIjXXPy8vDkCFDsHDhQmzduhVvv/02ACA2NhaDBw9Gs2bN8P333+Pbb79Fbm4uOnXqhLNnz6qso6CgAL1790aXLl3w008/4a233sK8efMwa9YsqcyjR4/QtWtXJCcnIy4uDj/88APc3NwwcODASmx50pe+ffviP//5D3788UdER0dj8+bN6Natm0qC+fjx4/jwww/x/vvvY+fOnXjllVdw4cIFBAYG4syZM/jqq6+wadMmNGvWDMOGDUNCQkKp76ltLOTm5qJjx474+uuv8eabb2Lr1q1YunQpGjVqhIyMDLi7u0t3NA0fPlyKV+UjWtq+z82bNxEUFITTp09j8eLF+Pbbb3H//v0K9b/Xu3dvAFBJhF28eBE9evTAihUrsHPnTkRFReH7779Hr169pDIDBw6Em5ubWpLuyZMn+Prrr9G3b194eHjgwYMHCA0Nxc2bN7Fo0SKkpKRg/vz5qF+/vpS4S0lJAQD06dOnxHoq5ynLKm3ZsgVfffUVpk+fjh9//BFeXl4YPHiwyp02mZmZaNu2LXbt2oXPPvsMP//8M4YPH47/196dh0dRpXsc/zVJp0kgIGsWgRAyIEpYhMgSxbAlEIEZxKuIjqLjhgKC6CgMCgGRCKPozEVQXFiuIPiMgsgiREmiDoNGVFZl8IqCQohGMWFrspz7Bzc9NJ2EBDrp7ft5nn6gT52ues+pOl3pt7b09HTH987Zzo5z2bJlOn78uK677jr99ttvVehReFJ4eLgef/xxbdy4UZs3b67y5w4fPqykpCTt3btXCxYs0NKlS1VYWFjhmMrNzdWtt96qP/7xj1qzZo1SU1M1efJkvf7665Kkrl27atGiRZKkxx9/3DHW7777bklSr1699NZbbyktLU3bt29XSUlJleKsaL9ZHeeL/UL6ozK//PKLJGnatGlat26dFi1apDZt2qhPnz5Of4MUFxcrNTVVTz75pIYMGaJVq1Zp8eLFSkxM1IEDB6q9XAAAAAcDt1i6dKmRZF588UVjjDGFhYWmfv36pnfv3o46I0aMMKGhoSY3N9dRVlxcbNq3b28kmf379zvKJZlp06a5LCcmJsaMGjXK8T4zM9NIMpmZmY6yMWPGmIpW7YQJE4wkc+jQIWOMMePGjTM9e/Y0xhizfv16ExQUZH777TdjjDF33nmnCQoKMgUFBcYYYwYOHGhatGjhmF5m7Nixpm7duuaXX34xxhiTnp5u6tSpY3Jycpzq/eMf/zCSzPr1653aOWbMGJOfn2+uueYac+mll5ovv/zSMf3AgQMmODjYjBs3zmlehYWFJjIy0tx0002OslGjRhlJ5s0333Sqe91115nLLrvM8X7BggVGknnnnXec6t1zzz1Gklm0aFG5fQfvMm3aNCPJPPTQQ07ly5YtM5LM66+/bow5M2aCgoLM3r17nerdfPPNxmazmQMHDjiVp6ammrCwMHP06FFjjDH79+932S6qOhZmzJhhJJmMjIwK2/HTTz9VON6rupzHHnvMWCwWp7FjjDHJycku3w+jRo0y9erVqzCer776ykgy999/f7nTS0tLTVFRkcnOzjaSzPbt2x3Tpk2bZkJCQsyRI0ccZStXrjSSTHZ2tjHGmM8++8xIMqtXr64whtGjRxtJ5uuvv65WnJIq/I793e9+5yi77777TP369c3333/vNM9nnnnGSDK7d+82xvxn3Xfs2NEUFxc76n366adGknnjjTcqjA+etWjRIiPJ5OTkGLvdbtq0aWMSEhJMaWmpMcaYpKQk06FDB0f9c/etf/7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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": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[   Name & postal abbs. [1]                                    Cities  \\\n",
+       "    Name & postal abbs. [1] Name & postal abbs. [1].1         Capital   \n",
+       " 0                  Alabama                        AL      Montgomery   \n",
+       " 1                   Alaska                        AK          Juneau   \n",
+       " 2                  Arizona                        AZ         Phoenix   \n",
+       " 3                 Arkansas                        AR     Little Rock   \n",
+       " 4               California                        CA      Sacramento   \n",
+       " 5                 Colorado                        CO          Denver   \n",
+       " 6              Connecticut                        CT        Hartford   \n",
+       " 7                 Delaware                        DE           Dover   \n",
+       " 8                  Florida                        FL     Tallahassee   \n",
+       " 9                  Georgia                        GA         Atlanta   \n",
+       " 10                 Hawaiʻi                        HI        Honolulu   \n",
+       " 11                   Idaho                        ID           Boise   \n",
+       " 12                Illinois                        IL     Springfield   \n",
+       " 13                 Indiana                        IN    Indianapolis   \n",
+       " 14                    Iowa                        IA      Des Moines   \n",
+       " 15                  Kansas                        KS          Topeka   \n",
+       " 16             Kentucky[C]                        KY       Frankfort   \n",
+       " 17               Louisiana                        LA     Baton Rouge   \n",
+       " 18                   Maine                        ME         Augusta   \n",
+       " 19                Maryland                        MD       Annapolis   \n",
+       " 20        Massachusetts[C]                        MA          Boston   \n",
+       " 21                Michigan                        MI         Lansing   \n",
+       " 22               Minnesota                        MN        St. Paul   \n",
+       " 23             Mississippi                        MS         Jackson   \n",
+       " 24                Missouri                        MO  Jefferson City   \n",
+       " 25                 Montana                        MT          Helena   \n",
+       " 26                Nebraska                        NE         Lincoln   \n",
+       " 27                  Nevada                        NV     Carson City   \n",
+       " 28           New Hampshire                        NH         Concord   \n",
+       " 29              New Jersey                        NJ         Trenton   \n",
+       " 30              New Mexico                        NM        Santa Fe   \n",
+       " 31                New York                        NY          Albany   \n",
+       " 32          North Carolina                        NC         Raleigh   \n",
+       " 33            North Dakota                        ND        Bismarck   \n",
+       " 34                    Ohio                        OH        Columbus   \n",
+       " 35                Oklahoma                        OK   Oklahoma City   \n",
+       " 36                  Oregon                        OR           Salem   \n",
+       " 37         Pennsylvania[C]                        PA      Harrisburg   \n",
+       " 38         Rhode Island[D]                        RI      Providence   \n",
+       " 39          South Carolina                        SC        Columbia   \n",
+       " 40            South Dakota                        SD          Pierre   \n",
+       " 41               Tennessee                        TN       Nashville   \n",
+       " 42                   Texas                        TX          Austin   \n",
+       " 43                    Utah                        UT  Salt Lake City   \n",
+       " 44                 Vermont                        VT      Montpelier   \n",
+       " 45             Virginia[C]                        VA        Richmond   \n",
+       " 46              Washington                        WA         Olympia   \n",
+       " 47           West Virginia                        WV      Charleston   \n",
+       " 48               Wisconsin                        WI         Madison   \n",
+       " 49                 Wyoming                        WY        Cheyenne   \n",
+       " \n",
+       "                    Established[A] Population [B][3] Total area[4]           \\\n",
+       "         Largest[5] Established[A] Population [B][3]           mi2      km2   \n",
+       " 0       Birmingham   Dec 14, 1819           4903185         52420   135767   \n",
+       " 1        Anchorage    Jan 3, 1959            731545        665384  1723337   \n",
+       " 2          Phoenix   Feb 14, 1912           7278717        113990   295234   \n",
+       " 3      Little Rock   Jun 15, 1836           3017804         53179   137732   \n",
+       " 4      Los Angeles    Sep 9, 1850          39512223        163695   423967   \n",
+       " 5           Denver    Aug 1, 1876           5758736        104094   269601   \n",
+       " 6       Bridgeport    Jan 9, 1788           3565278          5543    14357   \n",
+       " 7       Wilmington    Dec 7, 1787            973764          2489     6446   \n",
+       " 8     Jacksonville    Mar 3, 1845          21477737         65758   170312   \n",
+       " 9          Atlanta    Jan 2, 1788          10617423         59425   153910   \n",
+       " 10        Honolulu   Aug 21, 1959           1415872         10932    28313   \n",
+       " 11           Boise    Jul 3, 1890           1787065         83569   216443   \n",
+       " 12         Chicago    Dec 3, 1818          12671821         57914   149995   \n",
+       " 13    Indianapolis   Dec 11, 1816           6732219         36420    94326   \n",
+       " 14      Des Moines   Dec 28, 1846           3155070         56273   145746   \n",
+       " 15         Wichita   Jan 29, 1861           2913314         82278   213100   \n",
+       " 16      Louisville    Jun 1, 1792           4467673         40408   104656   \n",
+       " 17     New Orleans   Apr 30, 1812           4648794         52378   135659   \n",
+       " 18        Portland   Mar 15, 1820           1344212         35380    91633   \n",
+       " 19       Baltimore   Apr 28, 1788           6045680         12406    32131   \n",
+       " 20          Boston    Feb 6, 1788           6892503         10554    27336   \n",
+       " 21         Detroit   Jan 26, 1837           9986857         96714   250487   \n",
+       " 22     Minneapolis   May 11, 1858           5639632         86936   225163   \n",
+       " 23         Jackson   Dec 10, 1817           2976149         48432   125438   \n",
+       " 24     Kansas City   Aug 10, 1821           6137428         69707   180540   \n",
+       " 25        Billings    Nov 8, 1889           1068778        147040   380831   \n",
+       " 26           Omaha    Mar 1, 1867           1934408         77348   200330   \n",
+       " 27       Las Vegas   Oct 31, 1864           3080156        110572   286380   \n",
+       " 28      Manchester   Jun 21, 1788           1359711          9349    24214   \n",
+       " 29          Newark   Dec 18, 1787           8882190          8723    22591   \n",
+       " 30     Albuquerque    Jan 6, 1912           2096829        121590   314917   \n",
+       " 31        New York   Jul 26, 1788          19453561         54555   141297   \n",
+       " 32       Charlotte   Nov 21, 1789          10488084         53819   139391   \n",
+       " 33           Fargo    Nov 2, 1889            762062         70698   183108   \n",
+       " 34        Columbus    Mar 1, 1803          11689100         44826   116098   \n",
+       " 35   Oklahoma City   Nov 16, 1907           3956971         69899   181037   \n",
+       " 36        Portland   Feb 14, 1859           4217737         98379   254799   \n",
+       " 37    Philadelphia   Dec 12, 1787          12801989         46054   119280   \n",
+       " 38      Providence   May 29, 1790           1059361          1545     4001   \n",
+       " 39      Charleston   May 23, 1788           5148714         32020    82933   \n",
+       " 40     Sioux Falls    Nov 2, 1889            884659         77116   199729   \n",
+       " 41       Nashville    Jun 1, 1796           6829174         42144   109153   \n",
+       " 42         Houston   Dec 29, 1845          28995881        268596   695662   \n",
+       " 43  Salt Lake City    Jan 4, 1896           3205958         84897   219882   \n",
+       " 44      Burlington    Mar 4, 1791            623989          9616    24906   \n",
+       " 45  Virginia Beach   Jun 25, 1788           8535519         42775   110787   \n",
+       " 46         Seattle   Nov 11, 1889           7614893         71298   184661   \n",
+       " 47      Charleston   Jun 20, 1863           1792147         24230    62756   \n",
+       " 48       Milwaukee   May 29, 1848           5822434         65496   169635   \n",
+       " 49        Cheyenne   Jul 10, 1890            578759         97813   253335   \n",
+       " \n",
+       "    Land area[4]          Water area[4]         Number of Reps.  \n",
+       "             mi2      km2           mi2     km2 Number of Reps.  \n",
+       " 0         50645   131171          1775    4597               7  \n",
+       " 1        570641  1477953         94743  245384               1  \n",
+       " 2        113594   294207           396    1026               9  \n",
+       " 3         52035   134771          1143    2961               4  \n",
+       " 4        155779   403466          7916   20501              53  \n",
+       " 5        103642   268431           452    1170               7  \n",
+       " 6          4842    12542           701    1816               5  \n",
+       " 7          1949     5047           540    1399               1  \n",
+       " 8         53625   138887         12133   31424              27  \n",
+       " 9         57513   148959          1912    4951              14  \n",
+       " 10         6423    16635          4509   11678               2  \n",
+       " 11        82643   214045           926    2398               2  \n",
+       " 12        55519   143793          2395    6202              18  \n",
+       " 13        35826    92789           593    1537               9  \n",
+       " 14        55857   144669           416    1077               4  \n",
+       " 15        81759   211754           520    1346               4  \n",
+       " 16        39486   102269           921    2387               6  \n",
+       " 17        43204   111898          9174   23761               6  \n",
+       " 18        30843    79883          4537   11750               2  \n",
+       " 19         9707    25142          2699    6990               8  \n",
+       " 20         7800    20202          2754    7134               9  \n",
+       " 21        56539   146435         40175  104052              14  \n",
+       " 22        79627   206232          7309   18930               8  \n",
+       " 23        46923   121531          1508    3907               4  \n",
+       " 24        68742   178040           965    2501               8  \n",
+       " 25       145546   376962          1494    3869               1  \n",
+       " 26        76824   198974           524    1356               3  \n",
+       " 27       109781   284332           791    2048               4  \n",
+       " 28         8953    23187           397    1027               2  \n",
+       " 29         7354    19047          1368    3544              12  \n",
+       " 30       121298   314161           292     757               3  \n",
+       " 31        47126   122057          7429   19240              27  \n",
+       " 32        48618   125920          5201   13471              13  \n",
+       " 33        69001   178711          1698    4397               1  \n",
+       " 34        40861   105829          3965   10269              16  \n",
+       " 35        68595   177660          1304    3377               5  \n",
+       " 36        95988   248608          2391    6191               5  \n",
+       " 37        44743   115883          1312    3397              18  \n",
+       " 38         1034     2678           511    1324               2  \n",
+       " 39        30061    77857          1960    5076               7  \n",
+       " 40        75811   196350          1305    3379               1  \n",
+       " 41        41235   106798           909    2355               9  \n",
+       " 42       261232   676587          7365   19075              36  \n",
+       " 43        82170   212818          2727    7064               4  \n",
+       " 44         9217    23871           400    1035               1  \n",
+       " 45        39490   102279          3285    8508              11  \n",
+       " 46        66456   172119          4842   12542              10  \n",
+       " 47        24038    62259           192     497               3  \n",
+       " 48        54158   140268         11339   29367               8  \n",
+       " 49        97093   251470           720    1864               1  ]"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "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)\n",
+    "usa_states"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "list"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(usa_states)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(usa_states)"
+   ]
+  },
+  {
+   "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 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": 51,
+     "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": 53,
+   "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": 54,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0     Dec 14, 1819\n",
+       "1      Jan 3, 1959\n",
+       "2     Feb 14, 1912\n",
+       "3     Jun 15, 1836\n",
+       "4      Sep 9, 1850\n",
+       "5      Aug 1, 1876\n",
+       "6      Jan 9, 1788\n",
+       "7      Dec 7, 1787\n",
+       "8      Mar 3, 1845\n",
+       "9      Jan 2, 1788\n",
+       "10    Aug 21, 1959\n",
+       "11     Jul 3, 1890\n",
+       "12     Dec 3, 1818\n",
+       "13    Dec 11, 1816\n",
+       "14    Dec 28, 1846\n",
+       "15    Jan 29, 1861\n",
+       "16     Jun 1, 1792\n",
+       "17    Apr 30, 1812\n",
+       "18    Mar 15, 1820\n",
+       "19    Apr 28, 1788\n",
+       "20     Feb 6, 1788\n",
+       "21    Jan 26, 1837\n",
+       "22    May 11, 1858\n",
+       "23    Dec 10, 1817\n",
+       "24    Aug 10, 1821\n",
+       "25     Nov 8, 1889\n",
+       "26     Mar 1, 1867\n",
+       "27    Oct 31, 1864\n",
+       "28    Jun 21, 1788\n",
+       "29    Dec 18, 1787\n",
+       "30     Jan 6, 1912\n",
+       "31    Jul 26, 1788\n",
+       "32    Nov 21, 1789\n",
+       "33     Nov 2, 1889\n",
+       "34     Mar 1, 1803\n",
+       "35    Nov 16, 1907\n",
+       "36    Feb 14, 1859\n",
+       "37    Dec 12, 1787\n",
+       "38    May 29, 1790\n",
+       "39    May 23, 1788\n",
+       "40     Nov 2, 1889\n",
+       "41     Jun 1, 1796\n",
+       "42    Dec 29, 1845\n",
+       "43     Jan 4, 1896\n",
+       "44     Mar 4, 1791\n",
+       "45    Jun 25, 1788\n",
+       "46    Nov 11, 1889\n",
+       "47    Jun 20, 1863\n",
+       "48    May 29, 1848\n",
+       "49    Jul 10, 1890\n",
+       "Name: (Established[A], Established[A]), dtype: object"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "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": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Massachusetts', 'Pennsylvania', 'Rhode Island', 'Virginia'}"
+      ]
+     },
+     "execution_count": 57,
+     "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": 60,
+   "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": 60,
+     "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": 61,
+   "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": 61,
+     "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": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "set()"
+      ]
+     },
+     "execution_count": 62,
+     "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": 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>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": 63,
+     "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, on='state', how='left')\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": 64,
+   "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": 65,
+   "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": 65,
+     "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": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AdultWeekend    4\n",
+       "AdultWeekday    7\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 66,
+     "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": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ski_data.drop(columns='AdultWeekday', inplace=True)\n",
+    "ski_data.dropna(subset=['AdultWeekend'], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(277, 25)"
+      ]
+     },
+     "execution_count": 68,
+     "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": 74,
+   "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": 74,
+     "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": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.,  4.,  8., 12., 16., 20.])"
+      ]
+     },
+     "execution_count": 72,
+     "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": 75,
+   "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": 75,
+     "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": 76,
+   "metadata": {
+    "scrolled": true
+   },
+   "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": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(277, 25)"
+      ]
+     },
+     "execution_count": 77,
+     "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": 78,
+   "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": 79,
+   "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": "markdown",
+   "metadata": {},
+   "source": [
+    "The raw data contains information on ski resorts located in different states in America including the elevation, number of different chairs in the resort and  price information. After loading the data, we simply just looked at the first few rows of the data and checked the data types. Then looked at the data for the resort we're working for - Big Mountain resort which, fortunately, had no missing values. We then checked for missing values of the columns and learned that as high as 50% values are missing for some of the feature variables specifically \"fastEight\" followed by \"NightSkiing_ac\" which had about 43% of the values missing and about 15% of the price information was missing also for the resorts. \n",
+    "\n",
+    "We, then, checked if we could find any dupplicate resort values (observations) and found none. We also looked to discover the difference between state and region columns, if any. We looked at the distribution of the values for the price feature and further examined by plotting prices by states with box plots. And we did find weekday vs. weekend prices disparity in some of the states, but the prices were equal for the state of Montana where Big mountain is located in.\n",
+    "\n",
+    "We moved on to exploring the summary statistics of numerical features and plotting the distribution of each feature to check if there's anything odd or if there are any irregular outliers. Looking at the distribution plots we had some susspicions in some columns that we wanted to look into. \n",
+    "* Observing the distribution of 'SkiableTerrain_ac' column, the values were clustered down the low end, which reveals that there was one large outlier that pulled the mean upwards. And we found an incredibly large value that could have been an error when the data was entered, Thus we searched online to find reliable data and found from the website of the resort that the number was indeed an error and made the appropriate correction. \n",
+    "* 'Snow Making_ac' column also had another large number as an outlier that we looked into. And later, we removed the 'FastEight' column since 50% of the data was missing and most of the data had zero values.  \n",
+    "* We removed  another outlier from the 'yearsOpen' column  - as the value is not in the range that is possible for this feature\n",
+    "\n",
+    "Then we looked at some state-wide summary statistics, to understand the market segment better. To have a better grasp of the states data we loaded population data from a reliable source(Wikipedia), checked if all the columns were matching and merged it to our State-wide Summary Statistics for further investigation later on. \n",
+    "After dropping values with missing prices, which is our target feature, examined it further and we had a final look at the remaining data and those with missing values. \n",
+    "Finally we saved the cleaned ski data and state summarry data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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
+}
diff --git a/Notebooks/03_exploratory_data_analysis_LinaAbdullahi.ipynb b/Notebooks/03_exploratory_data_analysis_LinaAbdullahi.ipynb
new file mode 100644
index 000000000..742c8404b
--- /dev/null
+++ b/Notebooks/03_exploratory_data_analysis_LinaAbdullahi.ipynb
@@ -0,0 +1,4667 @@
+{
+ "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": 1,
+   "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": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ski_data = pd.read_csv('../data/ski_data_cleaned.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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": 4,
+   "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": 4,
+     "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": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "state_summary = pd.read_csv('../data/state_summary.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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": 7,
+   "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": 7,
+     "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": 8,
+   "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": 9,
+   "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": 9,
+     "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": 10,
+   "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": 10,
+     "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": 11,
+   "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": 11,
+     "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": 12,
+   "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": 12,
+     "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": 13,
+   "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": 13,
+     "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": 14,
+   "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": 14,
+     "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": 15,
+   "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": 15,
+     "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": 16,
+   "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": 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_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": 18,
+   "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": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "state_summary.set_index('state').resorts_per_100kcapita.sort_values(ascending=False).head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "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": 19,
+     "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": 20,
+   "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>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": 20,
+     "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": 21,
+   "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": 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",
+       "  </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": 22,
+     "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": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "resorts_per_state             -7.295751e-17\n",
+       "state_total_skiable_area_ac   -4.163336e-17\n",
+       "state_total_days_open          7.692260e-17\n",
+       "state_total_terrain_parks      4.599495e-17\n",
+       "state_total_nightskiing_ac     7.612958e-17\n",
+       "resorts_per_100kcapita         5.075305e-17\n",
+       "resorts_per_100ksq_mile        5.075305e-17\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 23,
+     "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": 24,
+   "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": 24,
+     "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": 25,
+   "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": 25,
+     "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": 26,
+   "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": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\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": 28,
+   "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": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(35, 7)"
+      ]
+     },
+     "execution_count": 44,
+     "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": 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 = 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": 50,
+   "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": 50,
+     "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": 51,
+   "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": 54,
+   "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>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": 54,
+     "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 `AdultWeekend`\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": 55,
+   "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": 55,
+     "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": 57,
+   "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": 57,
+     "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": 58,
+   "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": 58,
+     "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": 61,
+   "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>(53.1, 60.4]</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>(78.4, 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>(78.4, 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>(78.4, 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>(53.1, 60.4]</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  (53.1, 60.4]\n",
+       "Arizona     -1.839049 -0.387959     83.500000  (78.4, 93.0]\n",
+       "California   3.537857 -1.282509     81.416667  (78.4, 93.0]\n",
+       "Colorado     4.402210 -0.898855     90.714286  (78.4, 93.0]\n",
+       "Connecticut -0.988027  1.020218     56.800000  (53.1, 60.4]"
+      ]
+     },
+     "execution_count": 61,
+     "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": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PC1              float64\n",
+       "PC2              float64\n",
+       "AdultWeekend     float64\n",
+       "Quartile        category\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 64,
+     "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": 65,
+   "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>Rhode Island</th>\n",
+       "      <td>-1.843646</td>\n",
+       "      <td>0.761339</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   PC1       PC2  AdultWeekend Quartile\n",
+       "state                                                  \n",
+       "Rhode Island -1.843646  0.761339           NaN      NaN"
+      ]
+     },
+     "execution_count": 65,
+     "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": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PC1             -1.843646\n",
+       "PC2              0.761339\n",
+       "AdultWeekend    64.124388\n",
+       "Quartile               NA\n",
+       "Name: Rhode Island, dtype: object"
+      ]
+     },
+     "execution_count": 66,
+     "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": 67,
+   "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": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\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",
+    "price = pca_df.AdultWeekend\n",
+    "state = pca_df.index\n",
+    "quartiles = pca_df.Quartile\n",
+    "categories = pca_df.Quartile.cat.categories\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=price, hue=quartiles, \n",
+    "                hue_order=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": 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>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": 70,
+     "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": 71,
+   "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": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "state_summary[state_summary.state.isin(['New Hampshire', 'Vermont'])].T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "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": 72,
+     "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": "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": 73,
+   "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": 73,
+     "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": 79,
+   "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": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "state_summary.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "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_x</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_x</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_x</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>resorts_per_state_y</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_y</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_y</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_x                              3                    3   \n",
+       "resorts_per_100kcapita_x                  0.410091             0.410091   \n",
+       "resorts_per_100ksq_mile_x                 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",
+       "resorts_per_state_y                              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_y                  0.410091             0.410091   \n",
+       "resorts_per_100ksq_mile_y                 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_x                                3                 2   \n",
+       "resorts_per_100kcapita_x                    0.410091          0.027477   \n",
+       "resorts_per_100ksq_mile_x                   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",
+       "resorts_per_state_y                                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_y                    0.410091          0.027477   \n",
+       "resorts_per_100ksq_mile_y                   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_x                                   2  \n",
+       "resorts_per_100kcapita_x                       0.027477  \n",
+       "resorts_per_100ksq_mile_x                       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",
+       "resorts_per_state_y                                   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_y                       0.027477  \n",
+       "resorts_per_100ksq_mile_y                       1.75454  "
+      ]
+     },
+     "execution_count": 80,
+     "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": 81,
+   "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": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\aabdu\\AppData\\Local\\Temp\\ipykernel_22896\\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": 83,
+   "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": 84,
+   "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 = [column for column in ski_data.columns if column not in ['Name', 'Region', 'state', 'AdultWeekend']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1500x1500 with 32 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": 86,
+   "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": 87,
+   "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**\n",
+    "\n",
+    "\n",
+    "We began our EDA by checking for some general insights on `population density per state` and `resort density per state` which reveals that: \n",
+    "* The state of Montana, where Big mountain is located in, is third largest in state area and is in the top 5 states having largest skiing area.\n",
+    "* New York has the highest number of resorts in our market. \n",
+    "* New Hampshire and Vermont are rank higher in terms of 'resorts per capita' and  'resorts per area'\n",
+    "\n",
+    "Principal component analysis (PCA) is one of the useful tools we used here to reduce dimensionality since we have high dimensionality in the states_summary dataframe. After scaling and fitting the PCA transformation using the scaled data, We found linear combinations of the features that are uncorrelated with one another. From this, we learned that 75% of the variance is expliained by the first two components/derived features.  We used those derived features to visually convey the r/ship in each state with a scatter plot. The plot didn't exactly reveal an obvious pattern, but we could observe that New Hampshire and Vermont are much higher from the rest with regards to component 2.\n",
+    "Investigating this further shows that `resorts_per_100kcapita` and `resorts_per_100ksq_mile` seem to have heavier positive weights. `New Hampshire` and `Vermont` states have larger values of `resorts_per_100ksq_mile` in absolute terms, `Vermont` also has a large value for resorts_per_100kcapita than the rest. \n",
+    "\n",
+    "Furthermore in our analysis, we added ratio of some features by state to figure out how much each resort contributes to the skiing variables in each state. Then, we created a heatmap to see if these features are related to each other. Interestingly, we found that there's a positive connection between the `ratio of night skiing area` and the number of `resorts per capita`. And from this we can conclude that, when resorts are in more densely populated areas, they tend to offer more night skiing.\n",
+    "\n",
+    "We then used individual scatter plots to further identify correlations between each state features and ticket prices. Notably, the `resort_night_skiing_state_ratio` and `vertical drop` showed a strong positive correlation with ticket prices, suggesting that offering more night skiing capacity might enable resorts to charge higher prices. Additionally, features such as `Runs` and `total_chairs` also show higher correlations with ticket prices, which could mean that having more runs and adequate chairlifts may also positively influence pricing. The plot also reveals that guaranteed snow cover (represented by `snow making area`) is more important for customers compared to larger size of terrain area. \n",
+    "\n",
+    "Another interesting observation was the relationship between the `number of resorts per capita` and ticket prices; while lower ticket prices were associated with fewer resorts serving a population, a higher `number of resorts per capita` could lead to increased ticket prices, possibly due to higher demand. Additionally, it suggests that resorts with a higher ratio of `total_chairs` to `Runs` tends to have lower ticket prices. Moreover, the presence of fast quads also seemed to impact ticket prices, with their absence potentially limiting prices and their inclusion in wide-area resorts being advantageous for pricing.\n",
+    "\n",
+    "\n",
+    "In conclusion, there wasn't any obvious pattern showing a relationship between state and ticket price. However there are key features that are important to consider in our modeling:\n",
+    "* resorts_per_100kcapita\n",
+    "* resorts_per_100ksq_mile\n",
+    "* vertical_drop\n",
+    "* resort_night_skiing_state_ratio\n",
+    "* Runs and total_chairs_runs_ratio\n",
+    "* total_chairs and total_chairs_skiable_ratio\n",
+    "* snow_making_ac\n",
+    "* fastQuads, fastQuads_runs_ratio and fastQuads_skiable_ratio\n",
+    "\n",
+    "The **number of visitors per year** could provide valuable insights in this analysis but the data is not currently available. The `states` labels on the other habd, may not be as useful to consider in our modeling.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "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",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
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+       "</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_x</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_x</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_x</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>resorts_per_state_y</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_y</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_y</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>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_x                              3                    3   \n",
+       "resorts_per_100kcapita_x                  0.410091             0.410091   \n",
+       "resorts_per_100ksq_mile_x                 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",
+       "resorts_per_state_y                              3                    3   \n",
+       "resorts_per_100kcapita_y                  0.410091             0.410091   \n",
+       "resorts_per_100ksq_mile_y                 0.450867             0.450867   \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_x                                3                 2   \n",
+       "resorts_per_100kcapita_x                    0.410091          0.027477   \n",
+       "resorts_per_100ksq_mile_x                   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",
+       "resorts_per_state_y                                3                 2   \n",
+       "resorts_per_100kcapita_y                    0.410091          0.027477   \n",
+       "resorts_per_100ksq_mile_y                   0.450867           1.75454   \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_x                                   2  \n",
+       "resorts_per_100kcapita_x                       0.027477  \n",
+       "resorts_per_100ksq_mile_x                       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",
+       "resorts_per_state_y                                   2  \n",
+       "resorts_per_100kcapita_y                       0.027477  \n",
+       "resorts_per_100ksq_mile_y                       1.75454  \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": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ski_data.head().T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "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)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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
+}
diff --git a/Notebooks/04_preprocessing_and_training_LinaAbdullahi.ipynb b/Notebooks/04_preprocessing_and_training_LinaAbdullahi.ipynb
new file mode 100644
index 000000000..b0e840696
--- /dev/null
+++ b/Notebooks/04_preprocessing_and_training_LinaAbdullahi.ipynb
@@ -0,0 +1,4143 @@
+{
+ "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": 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.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": 2,
+   "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",
+       "      <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_x</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_x</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_x</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>resorts_per_state_y</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_y</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_y</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>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_x                              3                    3   \n",
+       "resorts_per_100kcapita_x                  0.410091             0.410091   \n",
+       "resorts_per_100ksq_mile_x                 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",
+       "resorts_per_state_y                              3                    3   \n",
+       "resorts_per_100kcapita_y                  0.410091             0.410091   \n",
+       "resorts_per_100ksq_mile_y                 0.450867             0.450867   \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_x                                3                 2   \n",
+       "resorts_per_100kcapita_x                    0.410091          0.027477   \n",
+       "resorts_per_100ksq_mile_x                   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",
+       "resorts_per_state_y                                3                 2   \n",
+       "resorts_per_100kcapita_y                    0.410091          0.027477   \n",
+       "resorts_per_100ksq_mile_y                   0.450867           1.75454   \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_x                                   2  \n",
+       "resorts_per_100kcapita_x                       0.027477  \n",
+       "resorts_per_100ksq_mile_x                       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",
+       "resorts_per_state_y                                   2  \n",
+       "resorts_per_100kcapita_y                       0.027477  \n",
+       "resorts_per_100ksq_mile_y                       1.75454  \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": 2,
+     "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": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "big_mountain = ski_data[ski_data.Name == 'Big Mountain Resort']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "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_x</th>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resorts_per_100kcapita_x</th>\n",
+       "      <td>1.122778</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resorts_per_100ksq_mile_x</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>resorts_per_state_y</th>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resorts_per_100kcapita_y</th>\n",
+       "      <td>1.122778</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resorts_per_100ksq_mile_y</th>\n",
+       "      <td>8.161045</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_x                                  12\n",
+       "resorts_per_100kcapita_x                       1.122778\n",
+       "resorts_per_100ksq_mile_x                      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",
+       "resorts_per_state_y                                  12\n",
+       "resorts_per_100kcapita_y                       1.122778\n",
+       "resorts_per_100ksq_mile_y                      8.161045\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": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "big_mountain.T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(277, 39)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ski_data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ski_data = ski_data[ski_data.Name != 'Big Mountain Resort']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(276, 39)"
+      ]
+     },
+     "execution_count": 7,
+     "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": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(193.2, 82.8)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(ski_data) * .7, len(ski_data) * .3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "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": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((193, 38), (83, 38))"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train.shape, X_test.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((193,), (83,))"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((193, 35), (83, 35))"
+      ]
+     },
+     "execution_count": 12,
+     "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": 13,
+   "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_x                     int64\n",
+       "resorts_per_100kcapita_x              float64\n",
+       "resorts_per_100ksq_mile_x             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",
+       "resorts_per_state_y                     int64\n",
+       "resorts_per_100kcapita_y              float64\n",
+       "resorts_per_100ksq_mile_y             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": 13,
+     "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": 14,
+   "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_x                     int64\n",
+       "resorts_per_100kcapita_x              float64\n",
+       "resorts_per_100ksq_mile_x             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",
+       "resorts_per_state_y                     int64\n",
+       "resorts_per_100kcapita_y              float64\n",
+       "resorts_per_100ksq_mile_y             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": 14,
+     "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": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "63.811088082901556"
+      ]
+     },
+     "execution_count": 15,
+     "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": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[63.81108808]])"
+      ]
+     },
+     "execution_count": 16,
+     "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": 17,
+   "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.mean((y - ybar)**2) #total sum of squares error\n",
+    "    sum_sq_res = np.mean((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": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([63.81108808, 63.81108808, 63.81108808, 63.81108808, 63.81108808])"
+      ]
+     },
+     "execution_count": 18,
+     "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": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([63.81108808, 63.81108808, 63.81108808, 63.81108808, 63.81108808])"
+      ]
+     },
+     "execution_count": 19,
+     "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": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0"
+      ]
+     },
+     "execution_count": 20,
+     "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": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-0.0031235200417913944"
+      ]
+     },
+     "execution_count": 21,
+     "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": 22,
+   "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": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "17.92346371714677"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mae(y_train, y_tr_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "19.136142081278486"
+      ]
+     },
+     "execution_count": 24,
+     "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": 25,
+   "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": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "614.1334096969046"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mse(y_train, y_tr_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "581.4365441953483"
+      ]
+     },
+     "execution_count": 27,
+     "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": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([24.78171523, 24.11299534])"
+      ]
+     },
+     "execution_count": 28,
+     "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": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.0, -0.0031235200417913944)"
+      ]
+     },
+     "execution_count": 29,
+     "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": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(17.92346371714677, 19.136142081278486)"
+      ]
+     },
+     "execution_count": 30,
+     "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": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(614.1334096969046, 581.4365441953483)"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_squared_error(y_train, y_tr_pred), mean_squared_error(y_test, y_te_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "r2_score??"
+   ]
+  },
+  {
+   "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": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.0, -3.041041349306602e+30)"
+      ]
+     },
+     "execution_count": 33,
+     "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": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(-0.0031235200417913944, 0.0)"
+      ]
+     },
+     "execution_count": 34,
+     "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": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.0, -3.041041349306602e+30)"
+      ]
+     },
+     "execution_count": 35,
+     "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": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\aabdu\\AppData\\Local\\Temp\\ipykernel_14252\\2966209695.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": 36,
+     "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": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Object `func` not found.\n"
+     ]
+    }
+   ],
+   "source": [
+    "func?"
+   ]
+  },
+  {
+   "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_x                     15.000000\n",
+       "resorts_per_100kcapita_x                 0.248243\n",
+       "resorts_per_100ksq_mile_x               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",
+       "resorts_per_state_y                     15.000000\n",
+       "resorts_per_100kcapita_y                 0.248243\n",
+       "resorts_per_100ksq_mile_y               22.902162\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": [
+       "(0.8177988515690604, 0.7209725843435147)"
+      ]
+     },
+     "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, 9.407020118581318)"
+      ]
+     },
+     "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": [
+       "(111.89581253658478, 161.73156451192256)"
+      ]
+     },
+     "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_x                     16.264249\n",
+       "resorts_per_100kcapita_x                 0.424802\n",
+       "resorts_per_100ksq_mile_x               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",
+       "resorts_per_state_y                     16.264249\n",
+       "resorts_per_100kcapita_y                 0.424802\n",
+       "resorts_per_100ksq_mile_y               40.957785\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, 0.7163814716959966)"
+      ]
+     },
+     "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": [
+       "(112.37695054778276, 164.39269309524326)"
+      ]
+     },
+     "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": [
+       "(0.8177988515690604, 0.7209725843435147)"
+      ]
+     },
+     "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": [
+       "(0.8177988515690604, 0.7209725843435147)"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "median_r2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(8.547850301825427, 9.407020118581318)"
+      ]
+     },
+     "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": "markdown",
+   "metadata": {},
+   "source": [
+    "Compare with your earlier result:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(8.547850301825427, 9.407020118581318)"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "median_mae"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(111.89581253658478, 161.73156451192256)"
+      ]
+     },
+     "execution_count": 63,
+     "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": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(111.89581253658478, 161.73156451192256)"
+      ]
+     },
+     "execution_count": 64,
+     "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": 65,
+   "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": 66,
+   "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 0x000001CADBD2F5B0&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 0x000001CADBD2F5B0&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 0x000001CADBD2F5B0&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 0x000001CADBD2F5B0>)),\n",
+       "                ('linearregression', LinearRegression())])"
+      ]
+     },
+     "execution_count": 66,
+     "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": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_tr_pred = pipe.predict(X_train)\n",
+    "y_te_pred = pipe.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.7674914326052744, 0.6259877354190835)"
+      ]
+     },
+     "execution_count": 68,
+     "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": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(9.501495079727484, 11.201830190332057)"
+      ]
+     },
+     "execution_count": 69,
+     "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": 70,
+   "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": 71,
+   "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 0x000001CADBD2F5B0&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 0x000001CADBD2F5B0&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 0x000001CADBD2F5B0&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 0x000001CADBD2F5B0>)),\n",
+       "                ('linearregression', LinearRegression())])"
+      ]
+     },
+     "execution_count": 71,
+     "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": 72,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_tr_pred = pipe15.predict(X_train)\n",
+    "y_te_pred = pipe15.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.7924096060483825, 0.6376199973170795)"
+      ]
+     },
+     "execution_count": 73,
+     "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": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(9.211767769307114, 10.488246867294356)"
+      ]
+     },
+     "execution_count": 74,
+     "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": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cv_results = cross_validate(pipe15, X_train, y_train, cv=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.63760862, 0.72831381, 0.74443537, 0.5487915 , 0.50441472])"
+      ]
+     },
+     "execution_count": 76,
+     "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": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.6327128053007864, 0.09502487849877675)"
+      ]
+     },
+     "execution_count": 77,
+     "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": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.44, 0.82])"
+      ]
+     },
+     "execution_count": 78,
+     "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": 79,
+   "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": 79,
+     "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": 80,
+   "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": 81,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lr_grid_cv = GridSearchCV(pipe, param_grid=grid_params, cv=5, n_jobs=-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "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 0x000001CADBD2F5B0&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 0x000001CADBD2F5B0&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 0x000001CADBD2F5B0&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 0x000001CADBD2F5B0&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 0x000001CADBD2F5B0>)),\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": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lr_grid_cv.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "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": 84,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'selectkbest__k': 8}"
+      ]
+     },
+     "execution_count": 84,
+     "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": 85,
+   "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": 86,
+   "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": 87,
+   "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": 87,
+     "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": 88,
+   "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": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 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-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 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-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 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-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 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-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" 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;randomforestregressor&#x27;,\n",
+       "                 RandomForestRegressor(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-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" 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;randomforestregressor&#x27;,\n",
+       "                 RandomForestRegressor(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-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" 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-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" 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-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" 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>"
+      ],
+      "text/plain": [
+       "Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='median')),\n",
+       "                ('standardscaler', StandardScaler()),\n",
+       "                ('randomforestregressor',\n",
+       "                 RandomForestRegressor(random_state=47))])"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "RF_pipe"
+   ]
+  },
+  {
+   "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": 90,
+   "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": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.68815377, 0.79319846, 0.7697586 , 0.62607286, 0.62196863])"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rf_cv_scores = rf_default_cv_results['test_score']\n",
+    "rf_cv_scores"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.6998304626738178, 0.07105845764211104)"
+      ]
+     },
+     "execution_count": 92,
+     "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": 93,
+   "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": 93,
+     "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": 96,
+   "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": 97,
+   "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-25\" type=\"checkbox\" ><label for=\"sk-estimator-id-25\" 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-26\" type=\"checkbox\" ><label for=\"sk-estimator-id-26\" 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-27\" type=\"checkbox\" ><label for=\"sk-estimator-id-27\" 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-28\" type=\"checkbox\" ><label for=\"sk-estimator-id-28\" 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-29\" type=\"checkbox\" ><label for=\"sk-estimator-id-29\" 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": 97,
+     "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": 98,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'randomforestregressor__n_estimators': 54,\n",
+       " 'simpleimputer__strategy': 'median',\n",
+       " 'standardscaler': None}"
+      ]
+     },
+     "execution_count": 98,
+     "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": 99,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.6928248 , 0.79990844, 0.77210672, 0.64478369, 0.65575752])"
+      ]
+     },
+     "execution_count": 99,
+     "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": 100,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.7130762336844233, 0.062263724425466904)"
+      ]
+     },
+     "execution_count": 100,
+     "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": 101,
+   "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": 102,
+   "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": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10.499032338015295, 1.6220608976799666)"
+      ]
+     },
+     "execution_count": 103,
+     "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": 104,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "11.793465668669324"
+      ]
+     },
+     "execution_count": 104,
+     "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": 105,
+   "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": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(9.721783475783477, 1.362257714837129)"
+      ]
+     },
+     "execution_count": 106,
+     "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": 107,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9.418440428380189"
+      ]
+     },
+     "execution_count": 107,
+     "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": 108,
+   "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": 109,
+   "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": 110,
+   "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": 111,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Directory ../models was created.\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\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this preprocessing step, we first evaluated the usefulness of the mean value as a predictor. we then compared imputing missing values with both the median and mean approaches and assessed their performance using a linear regression model. Interestingly, the choice of imputation method (median or mean) didn't have a significant impact on the model's accuracy. On average, the model could estimate a ticket price within approximately 9 dollars of the actual price, which was much better than a simple guess based on the average value (with a variance of 19 dollars).\n",
+    "\n",
+    "Next, we employed a pipeline that involved imputing missing values, scaling the data, and performing linear regression in a single process. To identify the most influential/dominant features, we utilized the `select k best` strategy. After experimenting with setting different values of K, we did a hyperparameter search using GridSearchCV and found that 8 was the best value for k (8 number of features). The top 8 features that the linear regression model identified as the most important for predicting ticket prices were: `vertical_drop`, `Snow Making_ac`, `total_chairs`, `fastQuads`, `Runs`, `LongestRun_mi`, `trams`, and `SkiableTerrain_ac`.\n",
+    "\n",
+    "Subsequently, we explored the random forest model using cross-validation and found that it aligned with the linear model, highlighting the importance of four features: `fastQuads`, `Runs`, `Snow Making_ac`, and `vertical_drop`.\n",
+    "Comparing the performance of the `linear regression` and `random forest models`, we observed that the random forest model exhibited a lower cross-validation mean absolute error, almost 1 dollar less. As a result, we conclude that the preferred choice for this project is the `random forest model`, selected for its superior stability and lower cross-validation mean absolute error.\n",
+    "\n",
+    "Validating the performance on the test set yielded consistent results, affirming the reliability and effectiveness of the random forest model for predicting ticket prices."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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
+}
diff --git a/Notebooks/05_modeling_LinaAbdullahi.ipynb b/Notebooks/05_modeling_LinaAbdullahi.ipynb
new file mode 100644
index 000000000..8282755c5
--- /dev/null
+++ b/Notebooks/05_modeling_LinaAbdullahi.ipynb
@@ -0,0 +1,1526 @@
+{
+ "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": [],
+   "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_x</th>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resorts_per_100kcapita_x</th>\n",
+       "      <td>1.122778</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resorts_per_100ksq_mile_x</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>resorts_per_state_y</th>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resorts_per_100kcapita_y</th>\n",
+       "      <td>1.122778</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resorts_per_100ksq_mile_y</th>\n",
+       "      <td>8.161045</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_x                                  12\n",
+       "resorts_per_100kcapita_x                       1.122778\n",
+       "resorts_per_100ksq_mile_x                      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",
+       "resorts_per_state_y                                  12\n",
+       "resorts_per_100kcapita_y                       1.122778\n",
+       "resorts_per_100ksq_mile_y                      8.161045\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=54, 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=54, 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=54, 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=54, 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([-12.2087004 ,  -9.08100673, -11.3460303 ,  -8.0256734 ,\n",
+       "       -11.11924579])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cv_results['test_score']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10.356131325156323, 1.5524968728339152)"
+      ]
+     },
+     "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 $97.96, actual price is $81.00.\n",
+      "Even with the expected mean absolute error of $10.36, 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": 16,
+   "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": 17,
+   "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 ($)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "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": 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('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": 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('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": 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('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": 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('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": 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('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": 24,
+   "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": 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('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": 26,
+   "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": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "expected_visitors = 350_000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "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": 28,
+     "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": 29,
+   "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": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[-1, -2, -3, -4, -5, -6, -7, -8, -9, -10]"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[i for i in range(-1, -11, -1)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "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": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[-0.8888888888888857,\n",
+       " -1.1851851851851904,\n",
+       " -1.4444444444444429,\n",
+       " -1.4444444444444429,\n",
+       " -1.4444444444444429,\n",
+       " -2.2037037037037095,\n",
+       " -2.2037037037037095,\n",
+       " -2.2037037037037095,\n",
+       " -2.9074074074074048,\n",
+       " -2.9074074074074048]"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "price_deltas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "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 * i for i 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 * j for j 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": 34,
+   "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": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This scenario increases support for ticket price by $2.22\n",
+      "Over the season, this could be expected to amount to $3888889\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": 36,
+   "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": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This scenario increases support for ticket price by $2.22\n",
+      "Over the season, this could be expected to amount to $3888889\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": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0"
+      ]
+     },
+     "execution_count": 38,
+     "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": "markdown",
+   "metadata": {},
+   "source": [
+    "The model predicts a ticket price of 97.96 dollars for Big Mountain Resort, with an expected mean absolute error of $10.36. Comparing this to the actual ticket price of 81.00 dollars suggests potential room for an increase. \n",
+    "\n",
+    "Evaluating Big Mountain Resort's position in the market based on key features reveals the following:\n",
+    "* Ticket Price: Big Mountain Resort has the highest ticket price in Montana but ranks mid compared to other resorts across different states.\n",
+    "* Vertical Drop: Big Mountain Resort performs well in vertical drop, although some resorts exceed its drop.\n",
+    "* Snow Making Area: Big Mountain Resort ranks very high in snow making area.\n",
+    "* Total Chairs: Big Mountain Resort has a substantial number of total chairs, with a few resorts having more, which are considered outliers.\n",
+    "* Fast Quads: Most resorts lack fast quads, but Big Mountain Resort stands out with 3, even though some values are higher but rare.\n",
+    "* Number of Runs: Big Mountain Resort compares well in the number of runs, with a few resorts having more.\n",
+    "* Longest Run: While Big Mountain Resort's longest run is over half the length of the longest, longer runs are uncommon.\n",
+    "* Trams: Similar to most resorts, Big Mountain Resort does not have trams.\n",
+    "* Skiable Terrain: Big Mountain Resort boasts one of the largest amounts of skiable terrain, ranking among the top resorts in this aspect.\n",
+    "\n",
+    "\n",
+    "With this model, further investigation was performed on the options the business suggested by building the following scenarios:\n",
+    "\n",
+    "*Scenario 1: Permanently Closing Least Used Runs*\n",
+    " * The analysis indicates that closing 1 or 2 runs has minimal impact, but closing 3 runs reduces support for ticket price and revenue. However, closing 4 or 5 runs does not result in further loss in ticket price. Beyond that, a significant drop in ticket price occurs. Therefore, closing up to 5 of the least used runs could lead to cost savings without significant revenue loss.\n",
+    "\n",
+    "*Scenario 2: Increase Vertical Drop with Additional Chair Lift*\n",
+    " * Adding a run at a lower point and installing an additional chair lift to bring skiers back up increases support for ticket price by 2.22 dollars, which over the season could amount to a revenue increase of $3,888,889. This suggests a positive impact on both revenue and customer experience.\n",
+    "\n",
+    "*Scenario 3: Increase Vertical Drop with Chair Lift and Snow Making*\n",
+    " * The addition of snow making coverage in this scenario does not yield any additional benefits in terms of ticket price or revenue compared to Scenario 2.\n",
+    "\n",
+    "*Scenario 4: Increase Longest Run with Additional Snow Making Coverage*\n",
+    " * Despite increasing the longest run and adding snow making coverage, this scenario does not result in a change in ticket price or revenue. As a result, other scenarios with more potential for positive impact could be prioritized.\n",
+    "\n",
+    "In conclusion, I recommend prioritizing Scenario 2 to increase the vertical drop with an additional chair lift, as it shows a notable positive impact on ticket price and revenue. Additionally, investigationg scenario 1 with more cost data and further analysis could allow the business optimize revenue by identifying potential pricing adjustments and achieve cost savings. The other scenarios do not provide significant benefits based on the analysis.\n"
+   ]
+  },
+  {
+   "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": "markdown",
+   "metadata": {},
+   "source": [
+    "The data used for modeling had a few limitations:\n",
+    "* The absence of detailed cost data beyond ticket prices and the additional operating cost of the new chair lift is one of the limitations of this model. Information on maintenance, personnel, snowmaking expenses, and other operational costs would provide a more accurate model and a better understanding of profitability for Big Mountain Resort.\n",
+    "\n",
+    "* The dataset might lack information on external factors influencing ticket prices, such as local economic conditions, competitor pricing strategies, or demand fluctuations due to events. These factors could affect the pricing dynamics beyond the resort's facilities.\n",
+    "\n",
+    "* Data on visitor demographics, preferences, and spending behavior could enhance the model's accuracy by considering how different customer segments respond to pricing changes.\n",
+    "\n",
+    "The higher modeled price compared to the actual price might come from factors like including the omission of critical cost data, or underestimation of the impact of some features on pricing. If business leaders found the model useful they could use it to simulate and test different pricing scenarios based on proposed changes in facilities or operational costs, and to optimize revenue by identifying potential pricing adjustments.\n",
+    "\n",
+    "To avoid frequent requests for parameter testing, the model could be made available to the business through an interactive dashboard or a tool as it would allow them to input different scenarios and parameters to explore the potential impact on ticket prices and revenue."
+   ]
+  }
+ ],
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