Notebook - HDB Flat Resale Price Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Sale Price Prediction of HDB Resale Flat in Singapore"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### By Lee Gang"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of Contents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. [Data Gathering and Processing](#DataGathering)\n",
    "2. [Exploratory Data Analysis](#Exploratory)\n",
    "3. [Model Building and Evaluation](#Model)\n",
    "4. [Feature Importance and Interesting Findings](#Findings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "## 1. Data Gathering and Processing <a name=\"DataGathering\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import Required Libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import json\n",
    "import requests\n",
    "import re\n",
    "import os\n",
    "from datetime import datetime as dt\n",
    "from pandas.io.json import json_normalize\n",
    "import geocoder\n",
    "import matplotlib.pylab as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 Get HDB Flat Resale Data from Data.gov.sg API"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will retrive the HDB Flat resale transaction data from the Data.gov.sg API."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>town</th>\n",
       "      <th>flat_type</th>\n",
       "      <th>flat_model</th>\n",
       "      <th>floor_area_sqm</th>\n",
       "      <th>street_name</th>\n",
       "      <th>resale_price</th>\n",
       "      <th>month</th>\n",
       "      <th>remaining_lease</th>\n",
       "      <th>lease_commence_date</th>\n",
       "      <th>storey_range</th>\n",
       "      <th>_id</th>\n",
       "      <th>block</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>JURONG EAST</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>New Generation</td>\n",
       "      <td>67</td>\n",
       "      <td>JURONG EAST ST 21</td>\n",
       "      <td>270000</td>\n",
       "      <td>2018-07</td>\n",
       "      <td>63 years 08 months</td>\n",
       "      <td>1983</td>\n",
       "      <td>10 TO 12</td>\n",
       "      <td>1</td>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>JURONG EAST</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>New Generation</td>\n",
       "      <td>67</td>\n",
       "      <td>JURONG EAST ST 21</td>\n",
       "      <td>248000</td>\n",
       "      <td>2018-07</td>\n",
       "      <td>63 years 09 months</td>\n",
       "      <td>1983</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>2</td>\n",
       "      <td>211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>JURONG EAST</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>New Generation</td>\n",
       "      <td>84</td>\n",
       "      <td>JURONG EAST ST 24</td>\n",
       "      <td>318000</td>\n",
       "      <td>2018-07</td>\n",
       "      <td>63 years 07 months</td>\n",
       "      <td>1983</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>3</td>\n",
       "      <td>241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>JURONG EAST</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>New Generation</td>\n",
       "      <td>67</td>\n",
       "      <td>JURONG EAST ST 24</td>\n",
       "      <td>305000</td>\n",
       "      <td>2018-07</td>\n",
       "      <td>66 years 11 months</td>\n",
       "      <td>1986</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>4</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>JURONG EAST</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>New Generation</td>\n",
       "      <td>67</td>\n",
       "      <td>JURONG EAST ST 24</td>\n",
       "      <td>258000</td>\n",
       "      <td>2018-07</td>\n",
       "      <td>65 years 11 months</td>\n",
       "      <td>1985</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>5</td>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          town flat_type      flat_model floor_area_sqm        street_name  \\\n",
       "0  JURONG EAST    3 ROOM  New Generation             67  JURONG EAST ST 21   \n",
       "1  JURONG EAST    3 ROOM  New Generation             67  JURONG EAST ST 21   \n",
       "2  JURONG EAST    3 ROOM  New Generation             84  JURONG EAST ST 24   \n",
       "3  JURONG EAST    3 ROOM  New Generation             67  JURONG EAST ST 24   \n",
       "4  JURONG EAST    3 ROOM  New Generation             67  JURONG EAST ST 24   \n",
       "\n",
       "  resale_price    month     remaining_lease lease_commence_date storey_range  \\\n",
       "0       270000  2018-07  63 years 08 months                1983     10 TO 12   \n",
       "1       248000  2018-07  63 years 09 months                1983     01 TO 03   \n",
       "2       318000  2018-07  63 years 07 months                1983     07 TO 09   \n",
       "3       305000  2018-07  66 years 11 months                1986     04 TO 06   \n",
       "4       258000  2018-07  65 years 11 months                1985     01 TO 03   \n",
       "\n",
       "   _id block  \n",
       "0    1   214  \n",
       "1    2   211  \n",
       "2    3   241  \n",
       "3    4   250  \n",
       "4    5   252  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get total number of records\n",
    "url_len = 'https://data.gov.sg/api/action/datastore_search?resource_id=42ff9cfe-abe5-4b54-beda-c88f9bb438ee&limit=1'\n",
    "max_records = requests.get(url_len).json()['result']['total']\n",
    "\n",
    "# Extract all records from API\n",
    "url = 'https://data.gov.sg/api/action/datastore_search?resource_id=42ff9cfe-abe5-4b54-beda-c88f9bb438ee&limit={}'.format(max_records)\n",
    "results = requests.get(url).json()['result']['records']\n",
    "df_hdb = pd.DataFrame(results)\n",
    "df_hdb.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(101994, 12)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A quick look at the dimensions of the data extracted\n",
    "df_hdb.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save **HDB Flat Resale Data** to local drive. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save file to local drive, for backup purposes\n",
    "df_hdb.to_csv(\"./Data/dat_hdb.csv\", index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read **HDB Flat Resale Data** file from local drive if you want to save time from extracting the data from the API."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read file from local drive\n",
    "df_hdb = pd.read_csv(\"./Data/dat_hdb.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Clean and Process HDB Flat Resale Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After looking through the data, we will process it and transform several variables to be usable in our analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>town</th>\n",
       "      <th>flat_type</th>\n",
       "      <th>flat_model</th>\n",
       "      <th>floor_area_sqm</th>\n",
       "      <th>street_name</th>\n",
       "      <th>resale_price</th>\n",
       "      <th>month</th>\n",
       "      <th>remaining_lease</th>\n",
       "      <th>lease_commence_date</th>\n",
       "      <th>storey_range</th>\n",
       "      <th>id</th>\n",
       "      <th>block</th>\n",
       "      <th>reg_date</th>\n",
       "      <th>year</th>\n",
       "      <th>price_sqm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>JURONG EAST</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>New Generation</td>\n",
       "      <td>67.0</td>\n",
       "      <td>JURONG EAST ST 21</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>63.666667</td>\n",
       "      <td>1983</td>\n",
       "      <td>10 TO 12</td>\n",
       "      <td>1</td>\n",
       "      <td>214</td>\n",
       "      <td>2018-07-01</td>\n",
       "      <td>2018</td>\n",
       "      <td>4029.850746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>JURONG EAST</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>New Generation</td>\n",
       "      <td>67.0</td>\n",
       "      <td>JURONG EAST ST 21</td>\n",
       "      <td>248000.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>63.750000</td>\n",
       "      <td>1983</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>2</td>\n",
       "      <td>211</td>\n",
       "      <td>2018-07-01</td>\n",
       "      <td>2018</td>\n",
       "      <td>3701.492537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>JURONG EAST</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>New Generation</td>\n",
       "      <td>84.0</td>\n",
       "      <td>JURONG EAST ST 24</td>\n",
       "      <td>318000.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>63.583333</td>\n",
       "      <td>1983</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>3</td>\n",
       "      <td>241</td>\n",
       "      <td>2018-07-01</td>\n",
       "      <td>2018</td>\n",
       "      <td>3785.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>JURONG EAST</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>New Generation</td>\n",
       "      <td>67.0</td>\n",
       "      <td>JURONG EAST ST 24</td>\n",
       "      <td>305000.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>66.916667</td>\n",
       "      <td>1986</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>4</td>\n",
       "      <td>250</td>\n",
       "      <td>2018-07-01</td>\n",
       "      <td>2018</td>\n",
       "      <td>4552.238806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>JURONG EAST</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>New Generation</td>\n",
       "      <td>67.0</td>\n",
       "      <td>JURONG EAST ST 24</td>\n",
       "      <td>258000.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>65.916667</td>\n",
       "      <td>1985</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>5</td>\n",
       "      <td>252</td>\n",
       "      <td>2018-07-01</td>\n",
       "      <td>2018</td>\n",
       "      <td>3850.746269</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          town flat_type      flat_model  floor_area_sqm        street_name  \\\n",
       "0  JURONG EAST    3 ROOM  New Generation            67.0  JURONG EAST ST 21   \n",
       "1  JURONG EAST    3 ROOM  New Generation            67.0  JURONG EAST ST 21   \n",
       "2  JURONG EAST    3 ROOM  New Generation            84.0  JURONG EAST ST 24   \n",
       "3  JURONG EAST    3 ROOM  New Generation            67.0  JURONG EAST ST 24   \n",
       "4  JURONG EAST    3 ROOM  New Generation            67.0  JURONG EAST ST 24   \n",
       "\n",
       "   resale_price  month  remaining_lease  lease_commence_date storey_range  id  \\\n",
       "0      270000.0    7.0        63.666667                 1983     10 TO 12   1   \n",
       "1      248000.0    7.0        63.750000                 1983     01 TO 03   2   \n",
       "2      318000.0    7.0        63.583333                 1983     07 TO 09   3   \n",
       "3      305000.0    7.0        66.916667                 1986     04 TO 06   4   \n",
       "4      258000.0    7.0        65.916667                 1985     01 TO 03   5   \n",
       "\n",
       "  block   reg_date  year    price_sqm  \n",
       "0   214 2018-07-01  2018  4029.850746  \n",
       "1   211 2018-07-01  2018  3701.492537  \n",
       "2   241 2018-07-01  2018  3785.714286  \n",
       "3   250 2018-07-01  2018  4552.238806  \n",
       "4   252 2018-07-01  2018  3850.746269  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Format column names\n",
    "df_hdb.rename(columns = {'_id':'id'}, inplace = True)\n",
    "\n",
    "# Format columns 'floor_area_sqm' and 'resale_price' to float\n",
    "df_hdb[['floor_area_sqm', 'resale_price']] = df_hdb[['floor_area_sqm', 'resale_price']].astype(float)\n",
    "\n",
    "# Split column 'month' to 'year' and 'month'\n",
    "df_hdb['reg_date'] = pd.to_datetime(df_hdb['month'], format = '%Y-%m')\n",
    "df_hdb['year'] = df_hdb['month'].str[0:4].astype(float)\n",
    "df_hdb['month'] = df_hdb['month'].str[5:7].astype(float)\n",
    "\n",
    "# Format column 'year' and 'lease_commencement_date' to integer\n",
    "df_hdb[['year', 'lease_commence_date']] = df_hdb[['year', 'lease_commence_date']].astype(int)\n",
    "\n",
    "# Convert 'remaining lease' into float\n",
    "df_hdb['remaining_years'] = df_hdb['remaining_lease'].str[:2].astype(int)\n",
    "df_hdb['remaining_months'] = df_hdb['remaining_lease'].str[9:11].apply(lambda x: pd.to_numeric(x, errors = 'coerce'))\n",
    "df_hdb.loc[df_hdb['remaining_months'].isna(), 'remaining_months'] = 0\n",
    "df_hdb['remaining_lease'] = df_hdb['remaining_years'] + (df_hdb['remaining_months']/12)\n",
    "\n",
    "# Derive 'price_sqm' \n",
    "df_hdb['price_sqm'] = df_hdb['resale_price']/df_hdb['floor_area_sqm']\n",
    "\n",
    "# Drop extra columns\n",
    "df_hdb.drop(['remaining_years', 'remaining_months'], axis = 1, inplace = True)\n",
    "\n",
    "df_hdb.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3 Map and Process Street Name for Geocoding Purposes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to retrieve all nearby venues for each location, we will need to geocode the address of the location. After a few trial and errors with different geocoders, we will use the Nominatim-OSM geocoder and the following transformation of the *'street_name'* variable so that all locations will be able to be succesfully geocoded.\n",
    "\n",
    "Only *'street_name'* is used. This is due to the large amount of unique full address that will consume excessive resources for the geocoding and Foursquare API queries (less than 100k queries per day limit)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Format \"street_name\" column for geocoding purposes\n",
    "df_hdb['street_name'] = df_hdb['street_name'].str.replace('UPP ', 'UPPER ', regex = True)\n",
    "df_hdb['street_name'] = df_hdb['street_name'].str.replace(' CTRL', ' CENTRAL', regex = True)\n",
    "df_hdb['street_name'] = df_hdb['street_name'].str.replace('JLN ', 'JALAN ', regex = True)\n",
    "df_hdb['street_name'] = df_hdb['street_name'].str.replace('BT ', 'BUKIT ', regex = True)\n",
    "df_hdb['street_name'] = df_hdb['street_name'].str.replace('LOR ', 'LORONG ', regex = True)\n",
    "df_hdb['street_name'] = df_hdb['street_name'].str.replace(' NTH', ' NORTH', regex = True)\n",
    "df_hdb['street_name'] = df_hdb['street_name'].str.replace('NTH BRIDGE', 'NORTH BRIDGE', regex = True)\n",
    "df_hdb['street_name'] = df_hdb['street_name'].str.replace('KG', 'KAMPONG', regex = True)\n",
    "df_hdb['street_name'] = df_hdb['street_name'].str.replace(' DR', ' DRIVE', regex = True)\n",
    "df_hdb['street_name'] = df_hdb['street_name'].str.replace('TG PAGAR', 'TANJONG PAGAR', regex = True)\n",
    "df_hdb['street_name'] = df_hdb['street_name'].str.replace(\"C'WEALTH\", 'COMMONWEALTH', regex = True)\n",
    "df_hdb['street_name'] = df_hdb['street_name'].str.replace(\"WOODLANDS DRIVE 75\", 'WOODLAND DRIVE 75', regex = True)\n",
    "df_hdb['street_name'] = df_hdb['street_name'].str.replace(\"TAMAN HO SWEE\", 'JALAN BUKIT HO SWEE', regex = True)\n",
    "df_hdb['street_name'] = df_hdb['street_name'].str.replace(\"YUNG PING RD\", '157 CORPORATION DRIVE', regex = True)\n",
    "df_hdb['street_name'] = df_hdb['street_name'].str.replace(\"YUNG LOH RD\", '157 CORPORATION DRIVE', regex = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4 Extract Coordinates via Nominatim/OSM Geocoding (For Initial Run Only)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The geocoding of the **HDB Flat Resale Data** will take more than 30 minutes. tTe codes below are for the initial run only. The result will be saved in a **Coordinates Dataframe**, which will be used for subsequent runs. Only addresses not in the saved **Coordinates Dataframe** will be identified and geocoded for subsequent runs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create unique list of all street names in HDB Flat Resale Data\n",
    "df_coord = pd.DataFrame(df_hdb['street_name'].unique())\n",
    "df_coord.columns = ['street_name']\n",
    "length = len(df_coord['street_name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up loop to geocode each unique address\n",
    "latitude = np.zeros(length)\n",
    "longitude = np.zeros(length)\n",
    "address = df_coord['street_name']\n",
    "\n",
    "for n in range(0,len(address)):\n",
    "    geo = geocoder.osm(address[n] + ', SG')\n",
    "    if geo.x is None:\n",
    "        latitude[n] = 0\n",
    "        longitude[n] = 0\n",
    "    else:\n",
    "        latitude[n] = geo.y\n",
    "        longitude[n] = geo.x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully Geocoded 538 Rows\n",
      "Unsuccessfully Geocoded 1 Rows\n"
     ]
    }
   ],
   "source": [
    "# Compile results into Coordinates Dataframe\n",
    "df_coord['Latitude'] = latitude\n",
    "df_coord['Longitude'] = longitude\n",
    "df_coord.loc[df_coord['Latitude'].isna(), 'Latitude'] = 0\n",
    "df_coord.loc[df_coord['Longitude'].isna(), 'Longitude'] = 0\n",
    "\n",
    "# Check geocoding results\n",
    "print('Successfully Geocoded ' + str(df_coord[df_coord[\"Longitude\"]!=0].shape[0]) + ' Rows')\n",
    "print('Unsuccessfully Geocoded ' + str(df_coord[df_coord[\"Longitude\"]==0].shape[0]) + ' Rows')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save **Coordinates Dataframe** to Local Drive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_coord.to_csv('./Data/dat_coord.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.5 Extract Coordinates Data & Geocode Missing Coordinates (For Subsequent Runs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Retrieve previously saved **Coordinates Dataframe** from local drive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_coord = pd.read_csv('./Data/dat_coord.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check **Coordinates Data** with **HDB Flat Resale Data** on any missing coordinates for all addresses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>street_name</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [street_name, Latitude, Longitude]\n",
       "Index: []"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_coord_new = pd.DataFrame(df_hdb['street_name'].unique())\n",
    "df_coord_new.columns = ['street_name']\n",
    "df_coord_new = df_coord_new.merge(df_coord, how = 'left', on = 'street_name')\n",
    "df_check = df_coord_new[df_coord_new['Latitude']==0]\n",
    "df_check.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Retrieve coordinates for all addresses with missing coordinates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "if (df_check.shape[0]>0):\n",
    "    length = len(df_coord['street_name'])\n",
    "    latitude = np.zeros(length)\n",
    "    longitude = np.zeros(length)\n",
    "    address = df_coord['street_name']\n",
    "    \n",
    "    for n in range(0,len(address)):\n",
    "        geo = geocoder.osm(address[n] + ', SG')\n",
    "        if geo.x is None:\n",
    "            latitude[n] = 0\n",
    "            longitude[n] = 0\n",
    "        else:\n",
    "            latitude[n] = geo.y\n",
    "            longitude[n] = geo.x\n",
    "    df_check['Latitude'] = latitude\n",
    "    df_check['Longitude'] = longitude\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Combined additional coordinates data to **Coordinates Dataframe**."
   ]
  },
  {
   "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>street_name</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>JURONG EAST ST 21</td>\n",
       "      <td>1.333969</td>\n",
       "      <td>103.745903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>JURONG EAST ST 24</td>\n",
       "      <td>1.342401</td>\n",
       "      <td>103.743049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>JURONG EAST ST 32</td>\n",
       "      <td>1.344607</td>\n",
       "      <td>103.733691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PANDAN GDNS</td>\n",
       "      <td>1.319415</td>\n",
       "      <td>103.748373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TEBAN GDNS RD</td>\n",
       "      <td>1.320053</td>\n",
       "      <td>103.744970</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         street_name  Latitude   Longitude\n",
       "0  JURONG EAST ST 21  1.333969  103.745903\n",
       "1  JURONG EAST ST 24  1.342401  103.743049\n",
       "2  JURONG EAST ST 32  1.344607  103.733691\n",
       "3        PANDAN GDNS  1.319415  103.748373\n",
       "4      TEBAN GDNS RD  1.320053  103.744970"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combined additional coordinates data to main Coordinates Dataframe\n",
    "df_coord = df_coord_new.append(df_check)\n",
    "df_coord.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save updated **Coordinates Dataframe** to local drive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_coord.to_csv('./Data/dat_coord.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.6 Retrieve Nearby Venues for Each Street Using Foursquare API"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setup API credentials."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "d1 = dt.today().strftime(\"%Y%m%d\")\n",
    "CLIENT_ID = '(Foursquare Client ID): ' \n",
    "CLIENT_SECRET = '(Foursquare Secret ID)' \n",
    "VERSION = d1 # Foursquare API version"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define **'getNearbyVenues'** function to extract nearby venues from json. We set the default radius to 500m and limit to 100 venues due to computing constraints and Foursquare API limits. \n",
    "\n",
    "As Singapore is a tight and highly dense city state, there will be quite a lot of venues within a 500m radius. Venues within 500m will most probably have more weightage in terms of the impact on the resale price of a property, as compared to venues further away. As such, we will only use these parameters for this study."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getNearbyVenues(names, latitudes, longitudes, radius=500, limit = 100):\n",
    "    \n",
    "    venues_list=[]\n",
    "    for name, lat, lng in zip(names, latitudes, longitudes):\n",
    "             \n",
    "        # create the API request URL\n",
    "        url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(\n",
    "            CLIENT_ID, \n",
    "            CLIENT_SECRET, \n",
    "            VERSION, \n",
    "            lat, \n",
    "            lng, \n",
    "            radius, \n",
    "            limit)\n",
    "            \n",
    "        # make the GET request\n",
    "        results = requests.get(url).json()[\"response\"]['groups'][0]['items']\n",
    "        \n",
    "        # return only relevant information for each nearby venue\n",
    "        venues_list.append([(\n",
    "            name, \n",
    "            lat, \n",
    "            lng, \n",
    "            v['venue']['name'], \n",
    "            v['venue']['location']['lat'], \n",
    "            v['venue']['location']['lng'],  \n",
    "            v['venue']['categories'][0]['name']) for v in results])\n",
    "        \n",
    "    nearby_venues = pd.DataFrame([item for venue_list in venues_list for item in venue_list])\n",
    "    nearby_venues.columns = ['street_name', \n",
    "                  'Latitude', \n",
    "                  'Longitude', \n",
    "                  'Venue', \n",
    "                  'Venue Latitude', \n",
    "                  'Venue Longitude', \n",
    "                  'Venue Category']\n",
    "    \n",
    "    return(nearby_venues)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Retrieve all nearby venues and compile into a **Nearby Venues Dataframe**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_venues = getNearbyVenues(names = df_coord['street_name'],\n",
    "                            latitudes = df_coord['Latitude'],\n",
    "                            longitudes = df_coord['Longitude'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12771, 7)"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A quick look of the number of nearby venues extracted\n",
    "df_venues.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save **Nearby Venues Dataframe** to local drive for easy retrieval."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_venues.to_csv(\"./Data/dat_venues.csv\", index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Retrieve **Nearby Venues Dataframe** from local drive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_venues = pd.read_csv(\"./Data/dat_venues.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.7 Feature Engineering Using Nearby Venues Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>street_name</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Venue</th>\n",
       "      <th>Venue Latitude</th>\n",
       "      <th>Venue Longitude</th>\n",
       "      <th>Venue Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>JURONG EAST ST 21</td>\n",
       "      <td>1.333969</td>\n",
       "      <td>103.745903</td>\n",
       "      <td>Platinum Movie Suites</td>\n",
       "      <td>1.333425</td>\n",
       "      <td>103.744113</td>\n",
       "      <td>Movie Theater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>JURONG EAST ST 21</td>\n",
       "      <td>1.333969</td>\n",
       "      <td>103.745903</td>\n",
       "      <td>Hai Di Lao 海底捞火锅 (Hai Di Lao)</td>\n",
       "      <td>1.334577</td>\n",
       "      <td>103.746118</td>\n",
       "      <td>Hotpot Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>JURONG EAST ST 21</td>\n",
       "      <td>1.333969</td>\n",
       "      <td>103.745903</td>\n",
       "      <td>Song Fa Bak Kut Teh 松發肉骨茶</td>\n",
       "      <td>1.333394</td>\n",
       "      <td>103.743420</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>JURONG EAST ST 21</td>\n",
       "      <td>1.333969</td>\n",
       "      <td>103.745903</td>\n",
       "      <td>UNIQLO</td>\n",
       "      <td>1.333175</td>\n",
       "      <td>103.743160</td>\n",
       "      <td>Clothing Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>JURONG EAST ST 21</td>\n",
       "      <td>1.333969</td>\n",
       "      <td>103.745903</td>\n",
       "      <td>J-Walk</td>\n",
       "      <td>1.333946</td>\n",
       "      <td>103.744433</td>\n",
       "      <td>Trail</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         street_name  Latitude   Longitude                          Venue  \\\n",
       "0  JURONG EAST ST 21  1.333969  103.745903          Platinum Movie Suites   \n",
       "1  JURONG EAST ST 21  1.333969  103.745903  Hai Di Lao 海底捞火锅 (Hai Di Lao)   \n",
       "2  JURONG EAST ST 21  1.333969  103.745903      Song Fa Bak Kut Teh 松發肉骨茶   \n",
       "3  JURONG EAST ST 21  1.333969  103.745903                         UNIQLO   \n",
       "4  JURONG EAST ST 21  1.333969  103.745903                         J-Walk   \n",
       "\n",
       "   Venue Latitude  Venue Longitude      Venue Category  \n",
       "0        1.333425       103.744113       Movie Theater  \n",
       "1        1.334577       103.746118   Hotpot Restaurant  \n",
       "2        1.333394       103.743420  Chinese Restaurant  \n",
       "3        1.333175       103.743160      Clothing Store  \n",
       "4        1.333946       103.744433               Trail  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A quick view of the nearby venues dataset\n",
    "df_venues.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Streamline Venue Categories"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Only major venue categories that we believe might affect property prices are grouped. Feel free to include additional venue categories on your own (and let me know if there is any interesting findings)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bus Stops\n",
    "df_venues['Venue Category'] = df_venues['Venue Category'].replace(\n",
    "    ['Bus Station', 'Bus Stop', 'Bus Line'], \n",
    "    'Bus Stop', regex = True)\n",
    "\n",
    "# Train Stations\n",
    "df_venues['Venue Category'] = df_venues['Venue Category'].replace(\n",
    "    ['Train Station', 'Metro Station', 'Light Rail Station'], \n",
    "    'Train Station', regex = True)\n",
    "\n",
    "# Supermarkets\n",
    "df_venues['Venue Category'] = df_venues['Venue Category'].replace(\n",
    "    ['Supermarket', 'Department Store', 'Convenience Store', 'Grocery Store'], \n",
    "    'Supermarket', regex = True)\n",
    "\n",
    "# Parks\n",
    "df_venues['Venue Category'] = df_venues['Venue Category'].replace(\n",
    "    ['Park', 'Lake', 'Garden', 'Other Great Outdoors'], \n",
    "    'Park', regex = True)\n",
    "\n",
    "# Shopping Malls\n",
    "df_venues['Venue Category'] = df_venues['Venue Category'].replace(\n",
    "    ['Shopping Mall', 'Shopping Plaza'], \n",
    "    'Shopping Mall', regex = True)\n",
    "\n",
    "# Gyms\n",
    "df_venues['Venue Category'] = df_venues['Venue Category'].replace(\n",
    "    ['Gym', 'Gym / Fitness Center', 'Climbing Gym', 'College Gym'], \n",
    "    'Gym', regex = True)\n",
    "\n",
    "# Theaters\n",
    "df_venues['Venue Category'] = df_venues['Venue Category'].replace(\n",
    "    ['Multiplex', 'Theater', 'Movie Theater', 'Indie Theater', 'College Theater'], \n",
    "    'Theater', regex = True)\n",
    "\n",
    "# Museums\n",
    "df_venues.loc[df_venues['Venue Category'].str.contains('Museum', case=False), 'Venue Category'] = 'Museum'\n",
    "\n",
    "# Restaurants\n",
    "df_venues.loc[df_venues['Venue Category'].str.contains('Restaurant', case=False), 'Venue Category'] = 'Restaurant'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filter Nearby Venues Dataframe with only Selected Venue Categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create list of selected Venue Categories\n",
    "venue_list = ['Bus Stop', 'Train Station', 'Supermarket', 'Park', 'Shopping Mall',\n",
    "              'Gym', 'Theater', 'Restaurant', 'High School', 'General College & University',\n",
    "              'Museum', 'Food Court']\n",
    "\n",
    "# Filter venue dataframe with venue list\n",
    "df_vcat = df_venues.loc[df_venues['Venue Category'].isin(venue_list),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Venue Category</th>\n",
       "      <th>street_name</th>\n",
       "      <th>Bus Stop</th>\n",
       "      <th>Food Court</th>\n",
       "      <th>General College &amp; University</th>\n",
       "      <th>Gym</th>\n",
       "      <th>High School</th>\n",
       "      <th>Museum</th>\n",
       "      <th>Park</th>\n",
       "      <th>Restaurant</th>\n",
       "      <th>Shopping Mall</th>\n",
       "      <th>Supermarket</th>\n",
       "      <th>Theater</th>\n",
       "      <th>Train Station</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>157 CORPORATION DRIVE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ADMIRALTY DRIVE</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ADMIRALTY LINK</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AH HOOD RD</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ALJUNIED CRES</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Venue Category            street_name  Bus Stop  Food Court  \\\n",
       "0               157 CORPORATION DRIVE       0.0         1.0   \n",
       "1                     ADMIRALTY DRIVE       1.0         1.0   \n",
       "2                      ADMIRALTY LINK       1.0         1.0   \n",
       "3                          AH HOOD RD       0.0         4.0   \n",
       "4                       ALJUNIED CRES       0.0         2.0   \n",
       "\n",
       "Venue Category  General College & University  Gym  High School  Museum  Park  \\\n",
       "0                                        0.0  0.0          0.0     0.0   0.0   \n",
       "1                                        0.0  0.0          0.0     0.0   0.0   \n",
       "2                                        0.0  0.0          0.0     0.0   0.0   \n",
       "3                                        0.0  0.0          0.0     0.0   1.0   \n",
       "4                                        0.0  0.0          0.0     0.0   0.0   \n",
       "\n",
       "Venue Category  Restaurant  Shopping Mall  Supermarket  Theater  Train Station  \n",
       "0                      0.0            0.0          1.0      0.0            0.0  \n",
       "1                      4.0            1.0          1.0      0.0            0.0  \n",
       "2                      0.0            0.0          0.0      0.0            0.0  \n",
       "3                     12.0            1.0          1.0      0.0            0.0  \n",
       "4                     12.0            0.0          2.0      0.0            1.0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Format Venue Categories Dataframe to tidy format\n",
    "df_vcat = pd.DataFrame(df_vcat.groupby(['street_name', 'Venue Category'])['Venue'].count())\n",
    "df_vcat = df_vcat.reset_index()\n",
    "df_vcat = df_vcat.pivot(index='street_name',columns='Venue Category',values='Venue')\n",
    "df_vcat = df_vcat.reset_index()\n",
    "df_vcat.fillna(0, inplace = True)\n",
    "df_vcat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Venue Category\n",
       "street_name                     0\n",
       "Bus Stop                        0\n",
       "Food Court                      0\n",
       "General College & University    0\n",
       "Gym                             0\n",
       "High School                     0\n",
       "Museum                          0\n",
       "Park                            0\n",
       "Restaurant                      0\n",
       "Shopping Mall                   0\n",
       "Supermarket                     0\n",
       "Theater                         0\n",
       "Train Station                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for missing values\n",
    "df_vcat.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merge Venue Category And Coordinates Dataframe with HDB Dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform One-Hot Encoding for columns \"town\", \"flat_type\", 'flat_model\" and \"storey_range\" and merge with **HDB Flat Resale Data** to form a **Main Dataframe** (for interpretation/ reference purposes) and a **Final Dataframe** (to be used in model building)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>floor_area_sqm</th>\n",
       "      <th>month</th>\n",
       "      <th>remaining_lease</th>\n",
       "      <th>year</th>\n",
       "      <th>price_sqm</th>\n",
       "      <th>Bus Stop</th>\n",
       "      <th>Food Court</th>\n",
       "      <th>General College &amp; University</th>\n",
       "      <th>Gym</th>\n",
       "      <th>High School</th>\n",
       "      <th>...</th>\n",
       "      <th>storey_range_22 TO 24</th>\n",
       "      <th>storey_range_25 TO 27</th>\n",
       "      <th>storey_range_28 TO 30</th>\n",
       "      <th>storey_range_31 TO 33</th>\n",
       "      <th>storey_range_34 TO 36</th>\n",
       "      <th>storey_range_37 TO 39</th>\n",
       "      <th>storey_range_40 TO 42</th>\n",
       "      <th>storey_range_43 TO 45</th>\n",
       "      <th>storey_range_46 TO 48</th>\n",
       "      <th>storey_range_49 TO 51</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>67.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>63.666667</td>\n",
       "      <td>2018</td>\n",
       "      <td>4029.850746</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>67.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>63.750000</td>\n",
       "      <td>2018</td>\n",
       "      <td>3701.492537</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>63.583333</td>\n",
       "      <td>2018</td>\n",
       "      <td>3785.714286</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>67.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>66.916667</td>\n",
       "      <td>2018</td>\n",
       "      <td>4552.238806</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>67.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>65.916667</td>\n",
       "      <td>2018</td>\n",
       "      <td>3850.746269</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 87 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   floor_area_sqm  month  remaining_lease  year    price_sqm  Bus Stop  \\\n",
       "0            67.0    7.0        63.666667  2018  4029.850746       0.0   \n",
       "1            67.0    7.0        63.750000  2018  3701.492537       0.0   \n",
       "2            84.0    7.0        63.583333  2018  3785.714286       0.0   \n",
       "3            67.0    7.0        66.916667  2018  4552.238806       0.0   \n",
       "4            67.0    7.0        65.916667  2018  3850.746269       0.0   \n",
       "\n",
       "   Food Court  General College & University  Gym  High School  ...  \\\n",
       "0         0.0                           0.0  0.0          0.0  ...   \n",
       "1         0.0                           0.0  0.0          0.0  ...   \n",
       "2         2.0                           0.0  1.0          0.0  ...   \n",
       "3         2.0                           0.0  1.0          0.0  ...   \n",
       "4         2.0                           0.0  1.0          0.0  ...   \n",
       "\n",
       "   storey_range_22 TO 24  storey_range_25 TO 27  storey_range_28 TO 30  \\\n",
       "0                      0                      0                      0   \n",
       "1                      0                      0                      0   \n",
       "2                      0                      0                      0   \n",
       "3                      0                      0                      0   \n",
       "4                      0                      0                      0   \n",
       "\n",
       "   storey_range_31 TO 33  storey_range_34 TO 36  storey_range_37 TO 39  \\\n",
       "0                      0                      0                      0   \n",
       "1                      0                      0                      0   \n",
       "2                      0                      0                      0   \n",
       "3                      0                      0                      0   \n",
       "4                      0                      0                      0   \n",
       "\n",
       "   storey_range_40 TO 42  storey_range_43 TO 45  storey_range_46 TO 48  \\\n",
       "0                      0                      0                      0   \n",
       "1                      0                      0                      0   \n",
       "2                      0                      0                      0   \n",
       "3                      0                      0                      0   \n",
       "4                      0                      0                      0   \n",
       "\n",
       "   storey_range_49 TO 51  \n",
       "0                      0  \n",
       "1                      0  \n",
       "2                      0  \n",
       "3                      0  \n",
       "4                      0  \n",
       "\n",
       "[5 rows x 87 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_main = df_hdb.merge(df_vcat, how = 'left', on = 'street_name')\n",
    "df_main = df_main.fillna(0)\n",
    "df_onehot = pd.get_dummies(df_main[['town', 'flat_type', 'flat_model', 'storey_range']])\n",
    "df_final = df_main.join(df_onehot)\n",
    "df_final.drop(['town', 'flat_type','flat_model','storey_range', 'street_name', \n",
    "               'resale_price','id','block','lease_commence_date','reg_date'], \n",
    "              axis = 1, inplace = True)\n",
    "df_final.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Exploratory Data Analysis <a name=\"Exploratory\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Explore dataset and identify high colinearity variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As most models are not robust to high co-linearity between variables, we will identify variables with high co-linearity by plotting a correlation plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correlation between property detail variables\n",
    "\n",
    "df_corr1 = df_final[['price_sqm',  'floor_area_sqm', 'month', 'year', 'remaining_lease', 'Bus Stop',\n",
    "                     'Food Court', 'General College & University', 'Gym', 'High School',\n",
    "                     'Museum', 'Park', 'Restaurant', 'Shopping Mall', 'Supermarket',\n",
    "                     'Theater', 'Train Station']].copy()\n",
    "\n",
    "corr1 = df_corr1.corr()\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "ax = sns.heatmap(corr1,\n",
    "                 center = 0,\n",
    "                 cmap='Blues')\n",
    "\n",
    "\n",
    "ax.set_xticklabels(\n",
    "    ax.get_xticklabels(),\n",
    "    rotation=45,\n",
    "    horizontalalignment='right');\n",
    "\n",
    "ax.set_title(\"Correlation Plot Between Variables\", size =16);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save figure in local drive\n",
    "fig = ax.get_figure()\n",
    "fig.savefig(\"./Images/Correlation_Plot.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that there are some degrees of co-linearity between the number of Restaurants, Shopping Mall, Supermarket and Theaters. However, there are no strong co-linearity between the variables. As such, we will not remove any variables from the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Check on Zero and Near Zero Variance Variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Firstly, let's list out all variables with zero variance."
   ]
  },
  {
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [0]\n",
       "Index: []"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_selection import VarianceThreshold\n",
    "\n",
    "var0_filter = VarianceThreshold(threshold=0.0)\n",
    "var0_filter.fit(df_final, y= df_final['price_sqm'])\n",
    "\n",
    "pd.DataFrame(df_final.columns[var0_filter.get_support()==False])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset does not contain any zero variance variables. Now let's see how many variables have less than 5% variance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>General College &amp; University</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>High School</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Museum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>town_ANG MO KIO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>town_BEDOK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>town_BISHAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>town_BUKIT BATOK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>town_BUKIT MERAH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>town_BUKIT PANJANG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>town_BUKIT TIMAH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>town_CENTRAL AREA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>town_CHOA CHU KANG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>town_CLEMENTI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>town_GEYLANG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>town_HOUGANG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>town_JURONG EAST</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>town_KALLANG/WHAMPOA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>town_MARINE PARADE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>town_PASIR RIS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>town_QUEENSTOWN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>town_SEMBAWANG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>town_SERANGOON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>town_TOA PAYOH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>flat_type_1 ROOM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>flat_type_2 ROOM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>flat_type_MULTI-GENERATION</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>flat_model_2-room</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>flat_model_Adjoined flat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>flat_model_Apartment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>flat_model_DBSS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>flat_model_Improved-Maisonette</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>flat_model_Maisonette</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>flat_model_Model A-Maisonette</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>flat_model_Model A2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>flat_model_Multi Generation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>flat_model_Premium Apartment Loft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>flat_model_Premium Maisonette</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>flat_model_Simplified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>flat_model_Standard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>flat_model_Terrace</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>flat_model_Type S1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>flat_model_Type S2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>storey_range_16 TO 18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>storey_range_19 TO 21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>storey_range_22 TO 24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>storey_range_25 TO 27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>storey_range_28 TO 30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>storey_range_31 TO 33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>storey_range_34 TO 36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>storey_range_37 TO 39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>storey_range_40 TO 42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>storey_range_43 TO 45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>storey_range_46 TO 48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>storey_range_49 TO 51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    0\n",
       "0        General College & University\n",
       "1                         High School\n",
       "2                              Museum\n",
       "3                     town_ANG MO KIO\n",
       "4                          town_BEDOK\n",
       "5                         town_BISHAN\n",
       "6                    town_BUKIT BATOK\n",
       "7                    town_BUKIT MERAH\n",
       "8                  town_BUKIT PANJANG\n",
       "9                    town_BUKIT TIMAH\n",
       "10                  town_CENTRAL AREA\n",
       "11                 town_CHOA CHU KANG\n",
       "12                      town_CLEMENTI\n",
       "13                       town_GEYLANG\n",
       "14                       town_HOUGANG\n",
       "15                   town_JURONG EAST\n",
       "16               town_KALLANG/WHAMPOA\n",
       "17                 town_MARINE PARADE\n",
       "18                     town_PASIR RIS\n",
       "19                    town_QUEENSTOWN\n",
       "20                     town_SEMBAWANG\n",
       "21                     town_SERANGOON\n",
       "22                     town_TOA PAYOH\n",
       "23                   flat_type_1 ROOM\n",
       "24                   flat_type_2 ROOM\n",
       "25         flat_type_MULTI-GENERATION\n",
       "26                  flat_model_2-room\n",
       "27           flat_model_Adjoined flat\n",
       "28               flat_model_Apartment\n",
       "29                    flat_model_DBSS\n",
       "30     flat_model_Improved-Maisonette\n",
       "31              flat_model_Maisonette\n",
       "32      flat_model_Model A-Maisonette\n",
       "33                flat_model_Model A2\n",
       "34        flat_model_Multi Generation\n",
       "35  flat_model_Premium Apartment Loft\n",
       "36      flat_model_Premium Maisonette\n",
       "37              flat_model_Simplified\n",
       "38                flat_model_Standard\n",
       "39                 flat_model_Terrace\n",
       "40                 flat_model_Type S1\n",
       "41                 flat_model_Type S2\n",
       "42              storey_range_16 TO 18\n",
       "43              storey_range_19 TO 21\n",
       "44              storey_range_22 TO 24\n",
       "45              storey_range_25 TO 27\n",
       "46              storey_range_28 TO 30\n",
       "47              storey_range_31 TO 33\n",
       "48              storey_range_34 TO 36\n",
       "49              storey_range_37 TO 39\n",
       "50              storey_range_40 TO 42\n",
       "51              storey_range_43 TO 45\n",
       "52              storey_range_46 TO 48\n",
       "53              storey_range_49 TO 51"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var5_filter = VarianceThreshold(threshold=0.05)\n",
    "var5_filter.fit(df_final, y= df_final['price_sqm'])\n",
    "\n",
    "pd.DataFrame(df_final.columns[var5_filter.get_support()==False])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are a total of 54 variables with less than 5% variance. We can see that most of the variables are due to the one-hot encoding of variables *'town'*, *'flat_model'*, *'flat_type'* and *'storey_range'*. These are still variables that might have important information for the model. As such we will not exclude these columns.\n",
    "\n",
    "As for *'General College & University'*, *'High School'* and *'Musuem'* columns, we will remove them from the dataset as these venues seem to be rare around residences and might introduce more noise than information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(101994, 84)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop 'General College & University', 'High School', and 'Museum' columns from dataset\n",
    "df_final = df_final.drop(['General College & University', 'High School', 'Museum'], axis = 1)\n",
    "df_final.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save **Final Dataframe** to local drive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final.to_csv(\"./Data/dat_final.csv\", index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read **Final Dataframe** from local drive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final = pd.read_csv('./Data/dat_final.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Model Building and Evaluation <a name=\"Model\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Preprocess Data for Model Building"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split Dataset into Features Set and Target Set ( X, y). The **price_sqm** will be our dependent variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df_final['price_sqm']\n",
    "X = df_final.drop(['price_sqm'], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split Dataset into training and testing sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split datasets into train and test datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size =0.25, random_state = 2020)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Standardize datasets using StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standardize Dataset\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler().fit(X_train)\n",
    "X_trains = scaler.transform(X_train)\n",
    "X_tests = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Model Building: Lasso Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we have a sparse dataset with 83 independent variables, we will use lasso regression instead of simple linear regression. Lasso regression introduces a regularisation term that reduces the magnitude of the coefficients (feature selection)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Perform Grid Search 5-Fold Cross-Validation to Select Best *alpha***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use grid serach to fit the **Lasso Regression** model to different *alpha* (L1 regularisation) values, and select the best *alpha* value based on the cross-validated model's *mean square error*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.preprocessing import normalize\n",
    "from scipy import stats\n",
    "import math\n",
    "import statistics\n",
    "\n",
    "# Initialize GridSearch Cross-Validation for lasso across different alpha (l1 reg) values\n",
    "lasso = Lasso()\n",
    "param = {'alpha':[0.001, 0.01, 0.1, 0.3, 0.5, 0.7, 1, 1.5, 2]}\n",
    "\n",
    "lasso_cv = GridSearchCV(lasso,\n",
    "                       param,\n",
    "                       scoring = 'neg_mean_squared_error',\n",
    "                       cv = 5,\n",
    "                       verbose = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\admin\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 77282178.39172363, tolerance: 8783354.30542167\n",
      "  positive)\n",
      "C:\\Users\\admin\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 76406905.72206116, tolerance: 8838137.291203499\n",
      "  positive)\n",
      "C:\\Users\\admin\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 273113278.2540722, tolerance: 8817133.988249918\n",
      "  positive)\n",
      "C:\\Users\\admin\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 279832003.2884178, tolerance: 8819282.877957458\n",
      "  positive)\n",
      "C:\\Users\\admin\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 80575374.77511597, tolerance: 8802445.041222978\n",
      "  positive)\n",
      "C:\\Users\\admin\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 77811511.67357635, tolerance: 8783354.30542167\n",
      "  positive)\n",
      "C:\\Users\\admin\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 76937031.14512062, tolerance: 8838137.291203499\n",
      "  positive)\n",
      "C:\\Users\\admin\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 83062835.6885128, tolerance: 8817133.988249918\n",
      "  positive)\n",
      "C:\\Users\\admin\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 81874050.57654381, tolerance: 8819282.877957458\n",
      "  positive)\n",
      "C:\\Users\\admin\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 81109331.2999115, tolerance: 8802445.041222978\n",
      "  positive)\n",
      "C:\\Users\\admin\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13005614.345830917, tolerance: 8783354.30542167\n",
      "  positive)\n",
      "C:\\Users\\admin\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13010359.158334732, tolerance: 8838137.291203499\n",
      "  positive)\n",
      "C:\\Users\\admin\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13445387.541139603, tolerance: 8817133.988249918\n",
      "  positive)\n",
      "C:\\Users\\admin\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13165720.580392838, tolerance: 8819282.877957458\n",
      "  positive)\n",
      "C:\\Users\\admin\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13205749.332897186, tolerance: 8802445.041222978\n",
      "  positive)\n",
      "C:\\Users\\admin\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 100901952.16036606, tolerance: 11015115.179274742\n",
      "  positive)\n"
     ]
    }
   ],
   "source": [
    "# Run GridSearchCV using Training Dataset to find best alpha value\n",
    "lasso_cv.fit(X_trains,y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-268295.44825499854\n",
      "{'alpha': 0.01}\n"
     ]
    }
   ],
   "source": [
    "best_alpha = pd.Series(lasso_cv.best_params_)[0]\n",
    "\n",
    "print(lasso_cv.best_score_)\n",
    "print(lasso_cv.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Build Final Lasso Regression Model Using Best *alpha* Value**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\admin\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 100901952.16036606, tolerance: 11015115.179274742\n",
      "  positive)\n"
     ]
    }
   ],
   "source": [
    "# Fit final lasso model with best alpha value\n",
    "lasso = Lasso(alpha = best_alpha, \n",
    "              random_state = 2020)\n",
    "lasso = lasso.fit(X_trains, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Model Diagnosis: Lasso Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now use the testing data to evaluate our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------- Lasso Regression Model ------------\n",
      "MAE: 406.28898811955264\n",
      "RMSE: 527.0607772319033\n",
      "R^2 Score: 0.8142585093070762\n"
     ]
    }
   ],
   "source": [
    "yhat = lasso.predict(X_tests)\n",
    "MAE_lasso = round(mean_absolute_error(y_test, yhat),2)\n",
    "RMSE_lasso = round(math.sqrt(mean_squared_error(y_test, yhat)),2)\n",
    "R2_lasso = round(r2_score(y_test, yhat),2)\n",
    "\n",
    "\n",
    "print('-------------- Lasso Regression Model ------------')\n",
    "print('MAE: ' + str(mean_absolute_error(y_test, yhat)))\n",
    "print('RMSE: '+ str(math.sqrt(mean_squared_error(y_test, yhat))))\n",
    "print('R^2 Score: ' + str(r2_score(y_test, yhat)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>Metrics</th>\n",
       "      <th>Lasso Regression</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MAE</td>\n",
       "      <td>406.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RMSE</td>\n",
       "      <td>527.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>R2 Score</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Metrics  Lasso Regression\n",
       "0       MAE            406.29\n",
       "1      RMSE            527.06\n",
       "2  R2 Score              0.81"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create Results Dataframe and store model performance metrics in dataframe\n",
    "df_results = pd.DataFrame()\n",
    "df_results['Metrics'] = ['MAE', 'RMSE', 'R2 Score']\n",
    "df_results['Lasso Regression'] = [MAE_lasso, RMSE_lasso, R2_lasso]\n",
    "df_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08993262718655008"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MAE_lasso/statistics.mean(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize the residuals of the **Lasso Regression** model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate residuals\n",
    "lasso_res = yhat - y_test\n",
    "lasso_res_st = stats.zscore(lasso_res)\n",
    "\n",
    "# Setup subplots and plot graphs\n",
    "fig1, axes = plt.subplots(1,2, figsize = (14,6))\n",
    "sns.distplot(lasso_res, ax = axes[0])\n",
    "sns.scatterplot(y = lasso_res_st, x = yhat, alpha = 0.5, ax = axes[1])\n",
    "axes[0].set_title(\"Histogram of Residuals of Lasso Regression Model\", size =12)\n",
    "axes[1].set_title(\"Residual Plot of Lasso Regression Model\", size =12);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the residuals histogram, the residuals have a normal distribution. As for the residual plot, the residuals seem to be distributed randomly over the horizontal axis. Overall, the residuals distribution does not suggest any major violation of the assumptions of the **Lasso Regression** model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save figure in local drive\n",
    "fig1.savefig(\"./Images/Lasso_Residuals.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4 Model Building: Extreme Gradient Boosted Trees (XGBoost)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**XGBoost** is used as it is one of the top performing machine learning algorithms used in competitions. It is more robust when dealing with high dimensions, as well as it has faster performance as compared to other complex and ensemble algorithms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "from sklearn.model_selection import RandomizedSearchCV, RepeatedKFold\n",
    "\n",
    "# Convert datasets to XGBDMatrix\n",
    "dtrain = xgb.DMatrix(X_trains, label = y_train)\n",
    "dtest = xgb.DMatrix(X_tests)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Build Default Baseline Model**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define Model Parameters and perform cross-validation to get best 'num_boost_rounds'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttrain-rmse:2407.9+1.14391\ttest-rmse:2408.32+6.59549\n",
      "[10]\ttrain-rmse:414.106+7.75005\ttest-rmse:421.967+7.37412\n",
      "[20]\ttrain-rmse:341.551+1.71697\ttest-rmse:353.132+2.08011\n",
      "[30]\ttrain-rmse:310.225+2.07543\ttest-rmse:325.373+3.82845\n",
      "[40]\ttrain-rmse:290.43+1.4601\ttest-rmse:308.857+3.62427\n",
      "[50]\ttrain-rmse:278.165+1.2267\ttest-rmse:299.135+2.97099\n",
      "[60]\ttrain-rmse:267.468+0.925297\ttest-rmse:290.789+2.79074\n",
      "[70]\ttrain-rmse:258.724+0.942736\ttest-rmse:284.49+2.31703\n",
      "[80]\ttrain-rmse:251.378+1.13481\ttest-rmse:279.691+2.0723\n",
      "[90]\ttrain-rmse:245.281+0.919015\ttest-rmse:276.016+2.535\n",
      "[100]\ttrain-rmse:239.513+0.63391\ttest-rmse:272.815+2.57721\n",
      "[110]\ttrain-rmse:234.97+0.552045\ttest-rmse:270.185+2.09791\n",
      "[120]\ttrain-rmse:230.475+0.850855\ttest-rmse:267.731+1.92338\n",
      "[130]\ttrain-rmse:226.508+1.07034\ttest-rmse:265.762+1.91121\n",
      "[140]\ttrain-rmse:222.391+1.0819\ttest-rmse:263.587+1.92365\n",
      "[150]\ttrain-rmse:219.274+1.00303\ttest-rmse:262.389+2.123\n",
      "[160]\ttrain-rmse:216.086+1.09584\ttest-rmse:260.867+2.45202\n",
      "[170]\ttrain-rmse:212.617+1.17766\ttest-rmse:259.523+2.422\n",
      "[180]\ttrain-rmse:209.245+1.5135\ttest-rmse:258.192+2.22855\n",
      "[190]\ttrain-rmse:206.552+1.66433\ttest-rmse:257.454+2.32321\n",
      "[200]\ttrain-rmse:203.813+1.65924\ttest-rmse:256.596+2.31777\n",
      "[210]\ttrain-rmse:200.938+1.65528\ttest-rmse:255.388+2.06852\n",
      "[220]\ttrain-rmse:198.445+1.70433\ttest-rmse:254.303+1.95433\n",
      "[230]\ttrain-rmse:196.25+1.49789\ttest-rmse:253.492+1.83299\n",
      "[240]\ttrain-rmse:193.892+1.36838\ttest-rmse:252.696+1.82951\n",
      "[250]\ttrain-rmse:191.802+1.4696\ttest-rmse:252.217+1.65612\n",
      "[260]\ttrain-rmse:189.576+1.49787\ttest-rmse:251.599+1.78708\n",
      "[270]\ttrain-rmse:187.63+1.67461\ttest-rmse:251.05+1.7104\n",
      "[280]\ttrain-rmse:185.694+1.64063\ttest-rmse:250.454+1.59474\n",
      "[290]\ttrain-rmse:183.684+1.67568\ttest-rmse:249.839+1.57638\n",
      "[300]\ttrain-rmse:181.71+1.66788\ttest-rmse:249.276+1.71586\n",
      "[310]\ttrain-rmse:179.931+1.53523\ttest-rmse:248.702+1.73975\n",
      "[320]\ttrain-rmse:178.146+1.68639\ttest-rmse:248.284+1.70755\n",
      "[330]\ttrain-rmse:176.446+1.58746\ttest-rmse:247.716+1.79975\n",
      "[340]\ttrain-rmse:174.736+1.65616\ttest-rmse:247.4+1.83048\n",
      "[350]\ttrain-rmse:173.146+1.58627\ttest-rmse:247.049+1.88498\n",
      "[360]\ttrain-rmse:171.428+1.62308\ttest-rmse:246.682+1.8077\n",
      "[370]\ttrain-rmse:169.967+1.59526\ttest-rmse:246.42+1.81152\n",
      "[380]\ttrain-rmse:168.531+1.46095\ttest-rmse:245.952+1.85078\n",
      "[390]\ttrain-rmse:166.857+1.31113\ttest-rmse:245.539+1.87628\n",
      "[400]\ttrain-rmse:165.073+1.19963\ttest-rmse:245.109+1.90195\n",
      "[410]\ttrain-rmse:163.728+0.942408\ttest-rmse:244.78+2.01368\n",
      "[420]\ttrain-rmse:162.305+1.0295\ttest-rmse:244.556+2.01629\n",
      "[430]\ttrain-rmse:160.942+1.09185\ttest-rmse:244.323+2.13273\n",
      "[440]\ttrain-rmse:159.474+1.03946\ttest-rmse:243.976+2.02008\n",
      "[450]\ttrain-rmse:158.194+1.15451\ttest-rmse:243.681+2.01291\n",
      "[460]\ttrain-rmse:156.959+1.20812\ttest-rmse:243.427+2.04547\n",
      "[470]\ttrain-rmse:155.561+1.26438\ttest-rmse:243.143+2.13296\n",
      "[480]\ttrain-rmse:154.464+1.43479\ttest-rmse:242.872+2.10665\n",
      "[490]\ttrain-rmse:153.39+1.53078\ttest-rmse:242.662+2.12046\n",
      "[499]\ttrain-rmse:152.272+1.37468\ttest-rmse:242.552+2.1799\n"
     ]
    },
    {
     "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>train-rmse-mean</th>\n",
       "      <th>train-rmse-std</th>\n",
       "      <th>test-rmse-mean</th>\n",
       "      <th>test-rmse-std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>152.271869</td>\n",
       "      <td>1.374683</td>\n",
       "      <td>242.551694</td>\n",
       "      <td>2.179905</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     train-rmse-mean  train-rmse-std  test-rmse-mean  test-rmse-std\n",
       "499       152.271869        1.374683      242.551694       2.179905"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define model parameters\n",
    "xgb_params = {'obj':'reg:squarederror',\n",
    "              'eval_metric':'rmse',\n",
    "              'max_depth':6, \n",
    "              'eta':0.5,\n",
    "              'min_child_weight':1,\n",
    "              'subsample':1,\n",
    "              'colsample_bytree':1}\n",
    "\n",
    "# Find model parameters 'num_boost_round' using cv\n",
    "cv_res = xgb.cv(xgb_params, \n",
    "                dtrain, \n",
    "                num_boost_round=500, \n",
    "                nfold=5, \n",
    "                seed=2020, \n",
    "                stratified=False,\n",
    "                early_stopping_rounds=25, \n",
    "                verbose_eval=10, \n",
    "                show_stdv=True)\n",
    "\n",
    "# Get best num_boost_round\n",
    "nrounds = cv_res[cv_res['test-rmse-mean'] == cv_res['test-rmse-mean'].min()].index[0]\n",
    "cv_res[cv_res['test-rmse-mean'] == cv_res['test-rmse-mean'].min()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build Baseline XGBoost Model using model parameters and best 'num_boost_rounds' from cv."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### Build xgb model\n",
    "\n",
    "xgb_def = xgb.train(params = xgb_params,\n",
    "                   num_boost_round = nrounds,\n",
    "                   dtrain = dtrain)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluate Model Performance Using Testing Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------- XGB Model: Baseline Parameters ------------\n",
      "MAE: 169.52395869270322\n",
      "RMSE: 234.37297135978602\n",
      "R^2 Score: 0.9632715515497843\n"
     ]
    }
   ],
   "source": [
    "# Evaluate Default Model Performance Using test set\n",
    "\n",
    "yhat = xgb_def.predict(dtest)\n",
    "print('-------------- XGB Model: Baseline Parameters ------------')\n",
    "print('MAE: ' + str(mean_absolute_error(y_test, yhat)))\n",
    "print('RMSE: '+ str(math.sqrt(mean_squared_error(y_test, yhat))))\n",
    "print('R^2 Score: ' + str(r2_score(y_test, yhat)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the baseline **XGBoost** model already outperforms **Lasso Regression** significantly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Perform Randomized Grid Search for Hyper Parameter Tuning**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To optimize the computing resources used, we will use randomized grid search for tuning the hyper-parameters of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define and setup Randomized Grid Search for Hyperparaneter Tuning\n",
    "\n",
    "grid_params = {'max_depth': [8, 10, 12, 13],\n",
    "               'min_child_weight': [1, 2, 3, 4],\n",
    "               'learning_rate': [0.3, 0.4, 0.5, 0.6, 0.7], #eta\n",
    "               'gamma': [0.5, 1, 1.5, 2],\n",
    "               'subsample': [0.8, 0.9, 1.0],\n",
    "               'colsample_bytree': [0.8, 0.9, 1.0]\n",
    "              }\n",
    "\n",
    "xgb_tune = xgb.XGBRegressor(objective = 'reg:squarederror',\n",
    "                            eval_metric= 'rmse',\n",
    "                            n_estimators = 200,\n",
    "                            seed = 2020)\n",
    "\n",
    "rkf = RepeatedKFold(n_splits=5, random_state = 2020)\n",
    "\n",
    "random_search = RandomizedSearchCV(xgb_tune, \n",
    "                                   param_distributions=grid_params, \n",
    "                                   n_iter=10, \n",
    "                                   scoring='neg_root_mean_squared_error', \n",
    "                                   n_jobs=4, \n",
    "                                   cv=rkf.split(X_trains,y_train), \n",
    "                                   verbose=3, \n",
    "                                   random_state=2020)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 50 folds for each of 10 candidates, totalling 500 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  24 tasks      | elapsed: 10.8min\n",
      "[Parallel(n_jobs=4)]: Done 120 tasks      | elapsed: 59.7min\n",
      "[Parallel(n_jobs=4)]: Done 280 tasks      | elapsed: 177.6min\n",
      "[Parallel(n_jobs=4)]: Done 500 out of 500 | elapsed: 303.2min finished\n",
      "C:\\Users\\admin\\Anaconda3\\lib\\site-packages\\xgboost\\core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
      "  if getattr(data, 'base', None) is not None and \\\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'subsample': 1.0,\n",
       " 'min_child_weight': 1,\n",
       " 'max_depth': 13,\n",
       " 'learning_rate': 0.3,\n",
       " 'gamma': 2,\n",
       " 'colsample_bytree': 1.0}"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run randomized Grid Search\n",
    "random_search.fit(X_trains, y_train);\n",
    "\n",
    "# Show best parameters from randomized grid search\n",
    "best_params = random_search.best_params_\n",
    "best_params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save tuning results to local drive just in case. We do not want to spend a few hours again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "        168.45867692, 117.10977341, 133.11216463, 152.20385117,\n",
       "        168.78275886,  97.06441286]),\n",
       " 'std_fit_time': array([1.11830978, 0.73273093, 1.26767458, 1.25609669, 0.98319251,\n",
       "        0.52050316, 3.83804184, 2.85648627, 2.79414294, 1.47089036]),\n",
       " 'mean_score_time': array([0.52263805, 0.51095347, 1.57844553, 1.47662493, 1.48932211,\n",
       "        0.49109525, 0.95577505, 1.30863798, 1.4556285 , 0.54487116]),\n",
       " 'std_score_time': array([0.01691211, 0.01405513, 0.06113785, 0.07616722, 0.06549898,\n",
       "        0.01566941, 0.07955839, 0.05550543, 0.07219437, 0.10881179]),\n",
       " 'param_subsample': masked_array(data=[0.8, 1.0, 1.0, 0.8, 1.0, 1.0, 0.9, 1.0, 1.0, 1.0],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_min_child_weight': masked_array(data=[1, 3, 1, 2, 2, 3, 1, 3, 3, 3],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_max_depth': masked_array(data=[8, 8, 13, 12, 12, 8, 10, 12, 12, 8],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_learning_rate': masked_array(data=[0.6, 0.6, 0.3, 0.7, 0.7, 0.4, 0.6, 0.4, 0.7, 0.7],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_gamma': masked_array(data=[2, 1, 2, 0.5, 1.5, 0.5, 0.5, 1.5, 0.5, 2],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_colsample_bytree': masked_array(data=[0.8, 0.8, 1.0, 1.0, 0.9, 1.0, 0.8, 0.8, 0.9, 0.8],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'params': [{'subsample': 0.8,\n",
       "   'min_child_weight': 1,\n",
       "   'max_depth': 8,\n",
       "   'learning_rate': 0.6,\n",
       "   'gamma': 2,\n",
       "   'colsample_bytree': 0.8},\n",
       "  {'subsample': 1.0,\n",
       "   'min_child_weight': 3,\n",
       "   'max_depth': 8,\n",
       "   'learning_rate': 0.6,\n",
       "   'gamma': 1,\n",
       "   'colsample_bytree': 0.8},\n",
       "  {'subsample': 1.0,\n",
       "   'min_child_weight': 1,\n",
       "   'max_depth': 13,\n",
       "   'learning_rate': 0.3,\n",
       "   'gamma': 2,\n",
       "   'colsample_bytree': 1.0},\n",
       "  {'subsample': 0.8,\n",
       "   'min_child_weight': 2,\n",
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       "   'learning_rate': 0.7,\n",
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       "  {'subsample': 1.0,\n",
       "   'min_child_weight': 2,\n",
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       "   'learning_rate': 0.7,\n",
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       "   'learning_rate': 0.4,\n",
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       "   'learning_rate': 0.6,\n",
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       "   'learning_rate': 0.4,\n",
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       "  {'subsample': 1.0,\n",
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       "   'learning_rate': 0.7,\n",
       "   'gamma': 0.5,\n",
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       "  {'subsample': 1.0,\n",
       "   'min_child_weight': 3,\n",
       "   'max_depth': 8,\n",
       "   'learning_rate': 0.7,\n",
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       "        -252.17158499, -239.27841508, -241.15507563, -228.34099086,\n",
       "        -249.33331144, -253.88544141]),\n",
       " 'split1_test_score': array([-253.39377377, -249.64837025, -226.08578777, -264.2828238 ,\n",
       "        -255.70791707, -243.2230721 , -244.37303921, -233.96385624,\n",
       "        -257.27557692, -255.60619038]),\n",
       " 'split2_test_score': array([-255.96949528, -251.9240325 , -227.43516704, -264.96788618,\n",
       "        -259.14704941, -244.69663645, -248.16511624, -236.64116532,\n",
       "        -257.43086237, -261.3695825 ]),\n",
       " 'split3_test_score': array([-249.21637985, -244.24637366, -220.53432735, -253.59931617,\n",
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       "        -250.92162794, -249.49027247]),\n",
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       "        -254.2389544 , -242.71333468, -246.19494025, -234.48132374,\n",
       "        -255.58860702, -251.2121634 ]),\n",
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       "        -248.81115218, -237.98289809, -240.58760708, -230.0327328 ,\n",
       "        -250.32225328, -251.26732545]),\n",
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       "        -249.27961145, -240.83585812, -245.43610255, -229.23107286,\n",
       "        -247.42807188, -253.62983336]),\n",
       " 'split27_test_score': array([-246.42535728, -245.70232312, -220.45861186, -258.23713799,\n",
       "        -246.12221226, -238.58103016, -237.79534874, -224.93707304,\n",
       "        -243.9096868 , -247.52720998]),\n",
       " 'split28_test_score': array([-255.14498614, -248.87312232, -225.71588796, -259.53226931,\n",
       "        -253.24829452, -242.82044457, -243.71097381, -229.70828051,\n",
       "        -252.18340534, -254.99874544]),\n",
       " 'split29_test_score': array([-257.11761045, -248.98514993, -226.92875485, -262.76808421,\n",
       "        -256.43352694, -241.89194859, -251.02095707, -234.21279092,\n",
       "        -257.59977607, -258.9895609 ]),\n",
       " 'split30_test_score': array([-252.3258844 , -246.31318232, -224.8977358 , -260.45698513,\n",
       "        -249.20672312, -240.80801595, -248.46700486, -233.00348582,\n",
       "        -249.44582045, -253.44055544]),\n",
       " 'split31_test_score': array([-249.31832066, -243.96018959, -221.52326932, -262.03199152,\n",
       "        -254.41471388, -237.10656684, -245.20331741, -228.22075491,\n",
       "        -251.16692025, -247.54268633]),\n",
       " 'split32_test_score': array([-252.65124107, -248.72523167, -226.24173615, -258.35881307,\n",
       "        -252.8142602 , -242.93963511, -245.60395677, -230.23786448,\n",
       "        -253.79803297, -257.55823988]),\n",
       " 'split33_test_score': array([-254.42815219, -243.62327642, -222.86753733, -257.16788181,\n",
       "        -250.04802703, -239.38606833, -246.00652353, -229.72689665,\n",
       "        -250.23696333, -251.10937978]),\n",
       " 'split34_test_score': array([-255.97639506, -248.87620392, -225.80677722, -262.46094146,\n",
       "        -256.53301581, -246.21872915, -242.62544279, -234.01393183,\n",
       "        -255.06619418, -257.92290372]),\n",
       " 'split35_test_score': array([-253.43428622, -249.85842698, -221.78197142, -257.74617164,\n",
       "        -249.52353898, -240.81492207, -239.09999085, -231.99412596,\n",
       "        -251.65329036, -252.40011027]),\n",
       " 'split36_test_score': array([-253.87866836, -251.38091155, -226.56387072, -268.94192546,\n",
       "        -257.70237517, -241.64048973, -250.10183151, -234.88894693,\n",
       "        -258.74575761, -254.9024396 ]),\n",
       " 'split37_test_score': array([-252.70653189, -247.00362914, -221.06649463, -259.76147275,\n",
       "        -255.91808581, -239.17785617, -243.38288031, -230.21952719,\n",
       "        -253.74601883, -252.04094782]),\n",
       " 'split38_test_score': array([-250.92350835, -246.77186901, -222.70366286, -257.55178027,\n",
       "        -245.07231479, -241.50390634, -242.48224252, -227.90435216,\n",
       "        -248.01754569, -254.82914268]),\n",
       " 'split39_test_score': array([-249.57065728, -247.36056575, -224.13135592, -258.5727878 ,\n",
       "        -253.43536714, -243.27117544, -243.51640499, -229.96570274,\n",
       "        -253.10906537, -253.63660914]),\n",
       " 'split40_test_score': array([-254.07847884, -248.56356583, -222.57032764, -259.82359825,\n",
       "        -252.35645781, -240.31232172, -244.68002098, -232.78784734,\n",
       "        -253.09703687, -252.38999129]),\n",
       " 'split41_test_score': array([-254.96271329, -246.02904643, -225.57274158, -262.97004   ,\n",
       "        -254.25097849, -239.52708016, -244.74636206, -229.10697682,\n",
       "        -255.83924254, -253.01556664]),\n",
       " 'split42_test_score': array([-253.95964985, -248.44003396, -223.71492048, -257.93294272,\n",
       "        -257.47135075, -242.88010638, -243.5316591 , -231.32989075,\n",
       "        -250.46898982, -254.79252242]),\n",
       " 'split43_test_score': array([-255.35570791, -249.41290706, -226.91917583, -260.33937563,\n",
       "        -255.72961397, -242.8179557 , -242.39910609, -231.72530284,\n",
       "        -252.8828249 , -256.53032345]),\n",
       " 'split44_test_score': array([-250.73913603, -247.08433267, -224.56955843, -254.72941883,\n",
       "        -250.06892152, -239.10086708, -242.23509184, -229.52044444,\n",
       "        -255.07770873, -252.62925948]),\n",
       " 'split45_test_score': array([-252.13720949, -251.10101154, -229.02417691, -263.52575281,\n",
       "        -253.34170219, -240.39453415, -246.40537153, -232.19637772,\n",
       "        -252.51264965, -256.0944262 ]),\n",
       " 'split46_test_score': array([-248.84990731, -246.5101855 , -221.74934855, -259.1733587 ,\n",
       "        -252.33383938, -240.38256886, -241.79341254, -228.89576044,\n",
       "        -253.90028084, -250.19854281]),\n",
       " 'split47_test_score': array([-246.75878662, -244.98370147, -219.30526243, -257.49216194,\n",
       "        -250.12466142, -238.80628254, -243.04933147, -227.68266755,\n",
       "        -249.79023545, -253.3990751 ]),\n",
       " 'split48_test_score': array([-252.55386757, -245.47664677, -220.77793287, -255.27566083,\n",
       "        -250.77801948, -237.34463194, -238.61625686, -227.38008929,\n",
       "        -246.72952096, -249.01614864]),\n",
       " 'split49_test_score': array([-258.64070513, -249.55158949, -226.69618724, -261.26801529,\n",
       "        -256.15317581, -244.86385925, -248.89714511, -232.86538463,\n",
       "        -252.80851087, -259.23366561]),\n",
       " 'mean_test_score': array([-251.85042776, -247.6647775 , -223.65737812, -259.67373028,\n",
       "        -252.21506148, -241.03005931, -244.26155719, -230.25364096,\n",
       "        -252.2283491 , -253.53848009]),\n",
       " 'std_test_score': array([3.06280795, 2.3198439 , 2.75087006, 3.63299682, 3.85280747,\n",
       "        2.47960479, 3.21710634, 2.91578375, 3.63229569, 3.89757569]),\n",
       " 'rank_test_score': array([ 6,  5,  1, 10,  7,  3,  4,  2,  8,  9])}"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get full Grid Search Results and save results to csv\n",
    "pd.DataFrame(random_search.cv_results_).to_csv(\"./Data/xgb_cv_results.csv\", index = False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Perform Cross-Validation to Find Best Number of Trees**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now fit in the tuned hyper-parameters into a 5-fold cross-validation model to get the best 'num_boost_rounds'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttrain-rmse:3280.56+1.44227\ttest-rmse:3280.97+6.73718\n",
      "[10]\ttrain-rmse:290.423+4.46502\ttest-rmse:323.892+1.54562\n",
      "[20]\ttrain-rmse:203.836+4.38013\ttest-rmse:262.806+2.66416\n",
      "[30]\ttrain-rmse:175.761+3.45236\ttest-rmse:248.765+2.10898\n",
      "[40]\ttrain-rmse:156.839+4.09864\ttest-rmse:241.765+2.49276\n",
      "[50]\ttrain-rmse:140.173+3.99963\ttest-rmse:236.955+2.3286\n",
      "[60]\ttrain-rmse:128.227+2.8083\ttest-rmse:234.188+2.19593\n",
      "[70]\ttrain-rmse:116.868+2.56887\ttest-rmse:231.477+2.49898\n",
      "[80]\ttrain-rmse:106.13+2.16773\ttest-rmse:229.501+2.31015\n",
      "[90]\ttrain-rmse:97.7946+1.57815\ttest-rmse:228.151+2.35716\n",
      "[100]\ttrain-rmse:89.742+1.77458\ttest-rmse:227.105+2.44442\n",
      "[110]\ttrain-rmse:82.7636+1.58486\ttest-rmse:226.3+2.51419\n",
      "[120]\ttrain-rmse:77.1238+1.94923\ttest-rmse:225.691+2.44239\n",
      "[130]\ttrain-rmse:70.8248+1.6308\ttest-rmse:225.143+2.53853\n",
      "[140]\ttrain-rmse:65.7769+1.7602\ttest-rmse:224.802+2.55124\n",
      "[150]\ttrain-rmse:61.9922+1.77557\ttest-rmse:224.529+2.57663\n",
      "[160]\ttrain-rmse:58.3585+1.51318\ttest-rmse:224.384+2.62943\n",
      "[170]\ttrain-rmse:54.5784+1.58008\ttest-rmse:224.204+2.56355\n",
      "[180]\ttrain-rmse:51.5227+1.67485\ttest-rmse:224.08+2.57601\n",
      "[190]\ttrain-rmse:48.5924+1.50322\ttest-rmse:223.977+2.56406\n",
      "[200]\ttrain-rmse:46.2536+1.40543\ttest-rmse:223.888+2.58726\n",
      "[210]\ttrain-rmse:44.258+1.34917\ttest-rmse:223.872+2.61595\n",
      "[220]\ttrain-rmse:42.5078+0.970616\ttest-rmse:223.861+2.61728\n",
      "[230]\ttrain-rmse:40.9403+0.958173\ttest-rmse:223.892+2.61723\n"
     ]
    }
   ],
   "source": [
    "# Modify best params dict to fit to native xgb format\n",
    "best_params['eta'] = best_params['learning_rate']\n",
    "del best_params['learning_rate']\n",
    "\n",
    "# Fit xgb model with best parameters to find best 'num_boost_round' using cv\n",
    "cv_tuned = xgb.cv(best_params, \n",
    "                  dtrain, \n",
    "                  num_boost_round=500, \n",
    "                  nfold=5, \n",
    "                  seed=2020, \n",
    "                  stratified=False,\n",
    "                  early_stopping_rounds=10, \n",
    "                  verbose_eval=0, \n",
    "                  show_stdv=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "221"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get best num_boost_round\n",
    "cv_tuned[cv_tuned['test-rmse-mean'] == cv_tuned['test-rmse-mean'].min()]\n",
    "nrounds = cv_tuned[cv_tuned['test-rmse-mean'] == cv_tuned['test-rmse-mean'].min()].index[0]\n",
    "nrounds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Build Final XGBoost Model with Tuned Hyper-Parameters and Number of Trees**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build Final xgb model using tuned parameters\n",
    "xgb_final = xgb.train(params = best_params,\n",
    "                      num_boost_round = nrounds,\n",
    "                      dtrain = dtrain)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save final **XGBoost** model to local drive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_final.save_model(\"./Models/xgb_model_final.model\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load final **XGBoost** model from local drive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_final = xgb.Booster()\n",
    "xgb_final.load_model(\"./Models/xgb_model_final.model\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.5 Model Diagnosis: XGBoost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's evaluate our final **XGBoost** model using the testing dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------- XGB Model: Tuned Parameters ------------\n",
      "MAE: 121.92094416811968\n",
      "RMSE: 206.7730132608173\n",
      "R^2 Score: 0.9714125614665722\n"
     ]
    }
   ],
   "source": [
    "# Evaluate Final Model Performance Using test set\n",
    "yhat1 = xgb_final.predict(dtest)\n",
    "\n",
    "MAE_xgb = round(mean_absolute_error(y_test, yhat1),2)\n",
    "RMSE_xgb = round(math.sqrt(mean_squared_error(y_test, yhat1)),2)\n",
    "R2_xgb = round(r2_score(y_test, yhat1),2)\n",
    "\n",
    "print('-------------- XGB Model: Tuned Parameters ------------')\n",
    "print('MAE: ' + str(mean_absolute_error(y_test, yhat1)))\n",
    "print('RMSE: '+ str(math.sqrt(mean_squared_error(y_test, yhat1))))\n",
    "print('R^2 Score: ' + str(r2_score(y_test, yhat1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save **XGBoost** model performance metrics to **Results Dataframe**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Metrics</th>\n",
       "      <th>Lasso Regression</th>\n",
       "      <th>XGBoost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MAE</td>\n",
       "      <td>406.29</td>\n",
       "      <td>121.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RMSE</td>\n",
       "      <td>527.06</td>\n",
       "      <td>206.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>R2 Score</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Metrics  Lasso Regression  XGBoost\n",
       "0       MAE            406.29   121.92\n",
       "1      RMSE            527.06   206.77\n",
       "2  R2 Score              0.81     0.97"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Store model performance metrics in Results Dataframe\n",
    "\n",
    "df_results['XGBoost'] = [MAE_xgb, RMSE_xgb, R2_xgb]\n",
    "df_results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In terms of the performance metrics, **XGBoost** outperforms **Lasso Regression** significantly. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save **Results Dataframe** to local drive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_results.to_csv('./Data/dat_results.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the residual plot and residuals histogram for the **XGBoost** Model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xgb_res = yhat1 - y_test\n",
    "xgb_res_sd = statistics.stdev(xgb_res)\n",
    "xgb_res_st = stats.zscore(xgb_res)\n",
    "\n",
    "fig2, axes = plt.subplots(1,2, figsize = (14,6))\n",
    "sns.distplot(xgb_res, ax = axes[0])\n",
    "sns.scatterplot(y = xgb_res_st, x = yhat1, alpha = 0.5, ax = axes[1])\n",
    "axes[0].set_title(\"Histogram of Residuals of XGBoost Model\", size =12)\n",
    "axes[1].set_title(\"Residual Plot of XGBoost Model\", size =12);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The residuals of the **XGBoost** model seems to have a normal distribution and is distributed randomly around the horizontal axis in the residuals plot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save figure to local drive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save figure in local drive\n",
    "fig2.savefig(\"./Images/XGB_Residuals.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.6 Model Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both **Lasso Regression** and **XGBoost** model are valid models that showed good predictive abilities on the HDB Flat Resale Price. However, in terms of performance, the **XGBoost** model outperforms the **Lasso Regression** model significantly, with only a mean absolute error of around **2.7%** of the mean of HDB Flat Resale Price (price per sqm). As such, we will be using the **XGBoost** model for the predictive application deployment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error of 2.7% of mean price per sqm\n"
     ]
    }
   ],
   "source": [
    "print('Mean absolute error of ' + str(round(statistics.mean(abs(xgb_res))/statistics.mean(y_test)*100,2)) +'% of mean price per sqm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a final look aat our predicted values vs. actual values merged back in the **HDB Flat Resale Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>resale_price</th>\n",
       "      <th>pred_resale_price</th>\n",
       "      <th>price_sqm</th>\n",
       "      <th>pred_price</th>\n",
       "      <th>town</th>\n",
       "      <th>flat_type</th>\n",
       "      <th>flat_model</th>\n",
       "      <th>floor_area_sqm</th>\n",
       "      <th>street_name</th>\n",
       "      <th>month</th>\n",
       "      <th>remaining_lease</th>\n",
       "      <th>lease_commence_date</th>\n",
       "      <th>storey_range</th>\n",
       "      <th>id</th>\n",
       "      <th>block</th>\n",
       "      <th>reg_date</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>67654</th>\n",
       "      <td>426000.0</td>\n",
       "      <td>424631.0</td>\n",
       "      <td>4580.645161</td>\n",
       "      <td>4565.923340</td>\n",
       "      <td>PUNGGOL</td>\n",
       "      <td>4 ROOM</td>\n",
       "      <td>Premium Apartment</td>\n",
       "      <td>93.0</td>\n",
       "      <td>PUNGGOL CENTRAL</td>\n",
       "      <td>1.0</td>\n",
       "      <td>89.500000</td>\n",
       "      <td>2010</td>\n",
       "      <td>04 TO 06</td>\n",
       "      <td>67643</td>\n",
       "      <td>301C</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66804</th>\n",
       "      <td>600000.0</td>\n",
       "      <td>597799.0</td>\n",
       "      <td>5084.745763</td>\n",
       "      <td>5066.089844</td>\n",
       "      <td>CLEMENTI</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>Improved</td>\n",
       "      <td>118.0</td>\n",
       "      <td>CLEMENTI WEST ST 2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>60.083333</td>\n",
       "      <td>1981</td>\n",
       "      <td>19 TO 21</td>\n",
       "      <td>66801</td>\n",
       "      <td>724</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101798</th>\n",
       "      <td>433888.0</td>\n",
       "      <td>431828.0</td>\n",
       "      <td>3337.600000</td>\n",
       "      <td>3321.753418</td>\n",
       "      <td>WOODLANDS</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>Improved</td>\n",
       "      <td>130.0</td>\n",
       "      <td>WOODLANDS ST 31</td>\n",
       "      <td>1.0</td>\n",
       "      <td>71.916667</td>\n",
       "      <td>1993</td>\n",
       "      <td>01 TO 03</td>\n",
       "      <td>101799</td>\n",
       "      <td>301</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67637</th>\n",
       "      <td>375588.0</td>\n",
       "      <td>374900.0</td>\n",
       "      <td>5523.352941</td>\n",
       "      <td>5513.233398</td>\n",
       "      <td>PUNGGOL</td>\n",
       "      <td>3 ROOM</td>\n",
       "      <td>Premium Apartment</td>\n",
       "      <td>68.0</td>\n",
       "      <td>PUNGGOL DRIVE</td>\n",
       "      <td>1.0</td>\n",
       "      <td>95.166667</td>\n",
       "      <td>2016</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>67619</td>\n",
       "      <td>667A</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69199</th>\n",
       "      <td>428000.0</td>\n",
       "      <td>435935.0</td>\n",
       "      <td>4652.173913</td>\n",
       "      <td>4738.428223</td>\n",
       "      <td>YISHUN</td>\n",
       "      <td>4 ROOM</td>\n",
       "      <td>Model A</td>\n",
       "      <td>92.0</td>\n",
       "      <td>YISHUN AVE 9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>94.750000</td>\n",
       "      <td>2015</td>\n",
       "      <td>13 TO 15</td>\n",
       "      <td>69215</td>\n",
       "      <td>317A</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101499</th>\n",
       "      <td>710000.0</td>\n",
       "      <td>688264.0</td>\n",
       "      <td>4797.297297</td>\n",
       "      <td>4650.434082</td>\n",
       "      <td>SERANGOON</td>\n",
       "      <td>EXECUTIVE</td>\n",
       "      <td>Maisonette</td>\n",
       "      <td>148.0</td>\n",
       "      <td>SERANGOON NORTH AVE 4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>71.750000</td>\n",
       "      <td>1992</td>\n",
       "      <td>10 TO 12</td>\n",
       "      <td>101497</td>\n",
       "      <td>525</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68152</th>\n",
       "      <td>570000.0</td>\n",
       "      <td>573616.0</td>\n",
       "      <td>5135.135135</td>\n",
       "      <td>5167.714844</td>\n",
       "      <td>SENGKANG</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>Premium Apartment</td>\n",
       "      <td>111.0</td>\n",
       "      <td>ANCHORVALE CRES</td>\n",
       "      <td>1.0</td>\n",
       "      <td>94.916667</td>\n",
       "      <td>2015</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>68173</td>\n",
       "      <td>335B</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100542</th>\n",
       "      <td>850000.0</td>\n",
       "      <td>849683.0</td>\n",
       "      <td>6910.569106</td>\n",
       "      <td>6907.992676</td>\n",
       "      <td>BUKIT TIMAH</td>\n",
       "      <td>5 ROOM</td>\n",
       "      <td>Standard</td>\n",
       "      <td>123.0</td>\n",
       "      <td>FARRER RD</td>\n",
       "      <td>1.0</td>\n",
       "      <td>53.250000</td>\n",
       "      <td>1974</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>100541</td>\n",
       "      <td>5</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100825</th>\n",
       "      <td>465000.0</td>\n",
       "      <td>466943.0</td>\n",
       "      <td>5054.347826</td>\n",
       "      <td>5075.472656</td>\n",
       "      <td>JURONG EAST</td>\n",
       "      <td>4 ROOM</td>\n",
       "      <td>Model A</td>\n",
       "      <td>92.0</td>\n",
       "      <td>TEBAN GDNS RD</td>\n",
       "      <td>1.0</td>\n",
       "      <td>92.666667</td>\n",
       "      <td>2013</td>\n",
       "      <td>31 TO 33</td>\n",
       "      <td>100825</td>\n",
       "      <td>21</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68116</th>\n",
       "      <td>420000.0</td>\n",
       "      <td>420164.0</td>\n",
       "      <td>4565.217391</td>\n",
       "      <td>4566.997559</td>\n",
       "      <td>SENGKANG</td>\n",
       "      <td>4 ROOM</td>\n",
       "      <td>Model A</td>\n",
       "      <td>92.0</td>\n",
       "      <td>FERNVALE RD</td>\n",
       "      <td>1.0</td>\n",
       "      <td>94.500000</td>\n",
       "      <td>2015</td>\n",
       "      <td>07 TO 09</td>\n",
       "      <td>68116</td>\n",
       "      <td>453B</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        resale_price  pred_resale_price    price_sqm   pred_price  \\\n",
       "67654       426000.0           424631.0  4580.645161  4565.923340   \n",
       "66804       600000.0           597799.0  5084.745763  5066.089844   \n",
       "101798      433888.0           431828.0  3337.600000  3321.753418   \n",
       "67637       375588.0           374900.0  5523.352941  5513.233398   \n",
       "69199       428000.0           435935.0  4652.173913  4738.428223   \n",
       "101499      710000.0           688264.0  4797.297297  4650.434082   \n",
       "68152       570000.0           573616.0  5135.135135  5167.714844   \n",
       "100542      850000.0           849683.0  6910.569106  6907.992676   \n",
       "100825      465000.0           466943.0  5054.347826  5075.472656   \n",
       "68116       420000.0           420164.0  4565.217391  4566.997559   \n",
       "\n",
       "               town  flat_type         flat_model  floor_area_sqm  \\\n",
       "67654       PUNGGOL     4 ROOM  Premium Apartment            93.0   \n",
       "66804      CLEMENTI     5 ROOM           Improved           118.0   \n",
       "101798    WOODLANDS     5 ROOM           Improved           130.0   \n",
       "67637       PUNGGOL     3 ROOM  Premium Apartment            68.0   \n",
       "69199        YISHUN     4 ROOM            Model A            92.0   \n",
       "101499    SERANGOON  EXECUTIVE         Maisonette           148.0   \n",
       "68152      SENGKANG     5 ROOM  Premium Apartment           111.0   \n",
       "100542  BUKIT TIMAH     5 ROOM           Standard           123.0   \n",
       "100825  JURONG EAST     4 ROOM            Model A            92.0   \n",
       "68116      SENGKANG     4 ROOM            Model A            92.0   \n",
       "\n",
       "                  street_name  month  remaining_lease  lease_commence_date  \\\n",
       "67654         PUNGGOL CENTRAL    1.0        89.500000                 2010   \n",
       "66804      CLEMENTI WEST ST 2    1.0        60.083333                 1981   \n",
       "101798        WOODLANDS ST 31    1.0        71.916667                 1993   \n",
       "67637           PUNGGOL DRIVE    1.0        95.166667                 2016   \n",
       "69199            YISHUN AVE 9    1.0        94.750000                 2015   \n",
       "101499  SERANGOON NORTH AVE 4    1.0        71.750000                 1992   \n",
       "68152         ANCHORVALE CRES    1.0        94.916667                 2015   \n",
       "100542              FARRER RD    1.0        53.250000                 1974   \n",
       "100825          TEBAN GDNS RD    1.0        92.666667                 2013   \n",
       "68116             FERNVALE RD    1.0        94.500000                 2015   \n",
       "\n",
       "       storey_range      id block   reg_date  year  \n",
       "67654      04 TO 06   67643  301C 2020-01-01  2020  \n",
       "66804      19 TO 21   66801   724 2020-01-01  2020  \n",
       "101798     01 TO 03  101799   301 2020-01-01  2020  \n",
       "67637      07 TO 09   67619  667A 2020-01-01  2020  \n",
       "69199      13 TO 15   69215  317A 2020-01-01  2020  \n",
       "101499     10 TO 12  101497   525 2020-01-01  2020  \n",
       "68152      07 TO 09   68173  335B 2020-01-01  2020  \n",
       "100542     07 TO 09  100541     5 2020-01-01  2020  \n",
       "100825     31 TO 33  100825    21 2020-01-01  2020  \n",
       "68116      07 TO 09   68116  453B 2020-01-01  2020  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge Predicted values to Testing Dataset\n",
    "df_pred = pd.DataFrame.copy(df_hdb.drop(['price_sqm'], axis = 1))\n",
    "df_pred = df_pred.merge(y_test, how = 'right', right_index = True, left_index = True)\n",
    "df_pred['pred_price'] = yhat1\n",
    "df_pred['pred_resale_price'] = round(df_pred['pred_price']*df_pred['floor_area_sqm'],0)\n",
    "\n",
    "# Sort columns\n",
    "cols = df_pred.columns.tolist()\n",
    "cols = cols[5:6] + cols[-1:] + cols[-3:-1] + cols[0:5] + cols[6:14]\n",
    "df_pred = df_pred[cols]\n",
    "df_pred = df_pred.sort_values(by = ['reg_date'], ascending = False)\n",
    "df_pred.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_pred.to_csv('./Data/dat_pred.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Feature Importance and Interesting Findings <a name=\"Findings\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, we will investigate the relationship of some of the important predictors that might be interesting to the target users. We will not attempt to uncover the relationship of each of the top predictors with the target variable, as well as the relationship between each other. I might cover that in a separate notebook perhaps not too long down the road. here we will just highlight general trends."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 Top Predictors from XGBoost Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the top 10 most important predictors in the **XGBoost** model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile feature importance into a dataframe\n",
    "df_fi = pd.DataFrame(pd.Series(xgb_final.get_fscore()).sort_values(ascending = False)).reset_index()\n",
    "df_fi['index'] = df_fi['index'].str.extract(r'(\\d+)').astype(int)\n",
    "df_labels = pd.DataFrame(X_test.columns).reset_index()\n",
    "df_fi = df_fi.merge(df_labels, how = 'left', on = 'index').set_index(['index'])\n",
    "df_fi.columns = ['F_score', 'Feature Name']\n",
    "df_fi10 = df_fi[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Feature Importance Plot\n",
    "\n",
    "plt.figure(figsize = (12,8))\n",
    "ax5 = sns.barplot(y = df_fi10['F_score'], x=df_fi10['Feature Name'])\n",
    "\n",
    "ax5.set_title(\"Top 10 Predictors of XGBoost Model\", size = 16)\n",
    "ax5.set_ylabel('Feature Importance Score (F_score)')\n",
    "\n",
    "ax5.set_xticklabels(\n",
    "    ax5.get_xticklabels(),\n",
    "    rotation=30,\n",
    "    horizontalalignment='right');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save figure in local drive\n",
    "ax5 = ax5.get_figure()\n",
    "ax5.savefig(\"./Images/Top_Predictors.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Remaining_lease*, *floor_area_sqm*, and *month* (transaction registration month) are the top three most important features in the **XGBoost** model. The sum of the F scores for the top three features are more than 60% of the total F score for all features. The fourth most important feature is the *year* of the transaction registration date (HDB Flat Resale Application Date), while the number of *Restaurants* within a 500m radius of the property is the fifth most important feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "63.50094431498721"
      ]
     },
     "execution_count": 335,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fi['F_score'][0:3].sum()/df_fi['F_score'].sum()*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resale Price (per sqm) Over Remaining Lease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,8))\n",
    "fig5 = sns.lineplot(x = df_main['remaining_lease'], y = df_main['price_sqm'], alpha = 0.1)\n",
    "fig5.set_title(\"Resale Price per sqm Over Remaining Lease\", size = 16);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save figure in local drive\n",
    "fig5 = fig5.get_figure()\n",
    "fig5.savefig(\"./Images/Resale_Price_Remaining_Lease.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resale Price (per sqm) Over Floor Area (sqm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,8))\n",
    "fig6 = sns.lineplot(x = df_main['floor_area_sqm'], y = df_main['price_sqm'], alpha = 0.1)\n",
    "fig6.set_title(\"Resale Price per sqm Over Floor Area\", size = 16);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save figure in local drive\n",
    "fig6 = fig6.get_figure()\n",
    "fig6.savefig(\"./Images/Resale_Price_Floor_Area.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resale Price (per sqm) Over HDB Resale Application Date, Year and Month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Z/1RZICLXtL97z83FiNUsrz0n7gSmgLdVB6RVF9W1Gp0THwH+I/Zi5vuw6Vi1d4LqEltq7x7sHco7jTHJi57QmKzYib776jz8O7AVSaq3F8XecXsJNv3niTqPO4D9W1JveyeN5muvCTqyrQZWcJv6P2NHTN9b9aMvAs8DDhhj9tf5KtRsxxhjHsN+AIPNcQR7C7p61ALsBKzVRjK/gk2fuKrB8x9r+cV25svYW44rNSv5InAD9sO73j6nmniet9V8/4PYz5BK5YfKCNRUg+eoDbZbZow5YYz5fWxeaOU4fgN7zGonYL6j0+frgqexI+4X7ZuI3AZcTc0f6x4Yw959qNbO5MKKE8H/K8emMkHttjrPW6YqYBKRV9Ni+oEx5l7snak/wKZmNTWCGaSVfQWbavaGehepwcXCF4G3ycWVXn4Am6++Wl3slZ5/Ghtg/U2dIL+RyufaaM3yyjlTe77/cPD/f21x93pxTkS4NMCsBNXVF197sce1acaWV/wy9m/HDwAfM01UixGRLdjRfIDXmfrVRMAe99cHk9orj/1O7Pv3rqplDjZd7DXAm82FUq/1trdTRF5V9dh12MmhbZ9jarDoyLYaaMaYu0TkIeCXReTDwR/F9wPfwlYu+DB25GID9sP8WmPMj4ttRvE/sWkiz2CDsXdjg+tKfdMvAm8RkT/EVjd4EXYyTb2Rtep9Oioivwt8OKh+8DXs6M2V2NSVPzftVWdoizHmqyLyGeAPglux92LrwL4Sm6t4H7ZSwNuBrwev92lsBYgbgFcYY5opMbVPRP4Cmxe5F1vN42vGmHuC/bhPRP4W+L8i8gfYY+RjJzR9L/CrxpjDrb4+sWXc7sIG2GlsDuotBLffjTFfEZF/Az4qIpu5UI3kpvpb7B9jTFlE3o/d109h0zd2Yn+XR2ivgkPLu1H17y8C7xKRJ7Dvk+/n0sC4FV/ATlr8mIj8BjYo/RXscav2RWwFlE8E59Re7CTeM7Tuf2Pf6/Ncmrd/CRHZig20Y9gmMd9Rk5b/aNUF+wewF3N/JyJ/gj2XPwT8X2PMw1XbHMOe42DfU4jIDwTfH69zF+uHsX9/W0khOYz9/PpxsfWaC8DTxpgDwfvuA0EQ+wB2HsN/Bf7WXFrZpZ7ac+JXReTXsO/hV2OD1nY9hB1x/1AQgBawgyfxmvXuxr6+T4rI72PTw/4bNl+81YHBP8WmGJWAj6+2clBl5EvY4/vj2Lka1Q1lnqoa5f4QtnLOXSLy29jR+N/D/q6q5xb8CXZA4reAjFxc1vJ01ej9Xdhz7FMi8p+xd0vfhx1A+b1mX7AacN2cfalf+tXsFxdmrF9X52eVCYDVFUKuwJZfOoNNJziH/QP6zuDnW7F/yA4DWeyt1q9hc+Yq23CwlQPOBut8DXgBNnj/RJ1921WzXz8CPIidVJnGjrB9mFVmvRNUI1llnTuoU3kg+NkHqJnFj/3D/V+C11vE5p1/Hri+ap0N2KD7WLDOLDYd5z80uS/fj60qsYSt2PI31FRRCH6nv4jNvc1jA6/HsX80poJ1drFKVYyabf4u8GiwrQw26P6FmnW2YCc0pYL9+yT2NnK9aiT/VvPYuvvDhWoLDSu1BOutVI3kkvM5+Pk7g99LAXub/6+A7TXrHAc+1cx5ERyX06vs543BY99ZtWwz9uIpEXz9NTZP1gDvXm37we+z9rV/JzbAygbn4zupU40Em5d7DNtQ5CFsF76Lttfo9dZsZ3uwzoeaPJ8q22z0Vfs+fyU2GMpj70r8ETDW4Byq9/WJOvvwOPBEM/tb87ifxo7Ge9XnNvbi+oPYUeRS8P8PAtFVtjcabOfXa5Z9BPsZksIORFxDTcUTGrw/GhzrfcGxTWOD5/9I/c+xt2En++ax6RXvqN0ezVXVcbGfFX/f5O91peN30WdIsP7N2L83Gez75hNcWh3r+Arb+0DNuhuxFwWL2PfNPcAtrZ4f+jW4XxIcaKWUqksuNNR4nTHm7lVWHxhV+/1dxo7uX9ZE5E3Y0b5XGFv9ZE0QkZ/CTorcay50n1VNCO4APg78iDGmtprJ0BLblfPL2Iu0e/q9P0ppGolSSq1hwe3wF2IbnpzC3o0ZekF5tt3YVIN/1EC7eUF+8ouw6QppbOrI0BOR3djJq38IPKKBthoUOkFSKaXWtp/EplSdxd6dqJ0UPKz+FJujfRhbaUg17/XYaioR7OhvJ3W+B8l/xc4bKGDLwyo1EDSNRCmllFJKqS7RkW2llFJKKaW6RINtpZRSSimlumTNTpDcvHmz2bVrV793QymllFJKrXEPP/zwvDFmS72frdlge9euXezf3+vOx0oppZRS6nIjIica/UzTSJRSSimllOoSDbaVUkoppZTqEg22lVJKKaWU6hINtpVSSimllOoSDbaVUkoppZTqEg22lVJKKaWU6hINtpVSSimllOoSDbaVUkoppZTqEg22lVJKKaWU6hINtpVSSimllOoSDbaVUkoppZTqEg22lVJKKaWU6hINtpVSSimllOoSDbaVUkoppZTqEg22lVJKKaWU6hINtpVSSimllOoSDbaVUkoppZTqEg221yBjDGXf9Hs3lFJKKaUuexpsr0HnlvKcXMz0ezeUUkoppS57GmyvMcYYTi5mWcyU+r0rSimllFKXvUi/d0CFK5nzyJU8imUH3zc4jvR7l5RSSimlLls6sr3GnF3OEY+4GGPIe+V+745SSiml1GVNg+01pOCVmUnmGY9HMECuqMG2UkoppVQ/abC9hiykigjgiBBxHFJ5r9+7pJRSSil1WdNge40wxnAykWUiHgUgHnFYyhX7vFdKKaWUUpc3DbbXiGTeI1csE4vYQxqLOKRyHsYMd73tfKnMzHKu37uhlFJKKdUWDbbXiHPLOeKRC4fTEaFsDPmS38e96ty55RzH5rP93g2llFJKqbZosL0GFD2f88t2YmQ1wY4MDyuv7HM6kSPnlSloZRWllFJKDSENtteAhXQBsKPZ1USEdGF4J0kuZop4ZYODVlZRSiml1HDSYHvIVTpGTsQv7U9kJ0kObyfJU4kc47EIgpAZ4osGpZRSSl2+NNgecsm8R65UJh5xL/lZPOKynC0O5STJdMEjmSsxGnOJRx0Ws8N70aCUUkqpy5cG20Pu/HKOqFP/MLqOUPYNBW/4JkmeX84Rde3rikdclob0okEppZRSlzcNtodYZWLkxMilKSTVhm2SZKnsc3Yp/1xqTOWiYdgrqyillFLq8qPB9hBbSBfwMZdMjKwmMnz5zolMEd8YXOfi15UtDtfrUEoppZTSYHtIGWM4ncgyGXSMbCTmOiznhyvf+eRClvHYxaP1a6X9vFfW0XmllFLqcqLB9pBKFTzShfoTI6vFIw7LQzS5MJUvkSqUGIle/LriEYfF7HC3n88WPZ44s6y550oppdRlRIPtIXV+Of/cBMKVRFyHgudTHJJJkueW80TdepVVbPv5sj+8gepytsRsskBGa4YrpZRSlw0NtodQqexzbjnH5CoTIysEyA3BJMmi1/h1iQjGmKF4HY2cT+ZBYGnIR+iVUkop1TwNtofQYrqAMZd2jFxJbggmF676umR4J0kWPZ/lXImNYzFmkoV+745SSimlekSD7SFT6RhZO4FwJbGIy/KAd5I0xnCiQSfMipjrsDRE+efVUsEk1ZGoSzJfouAN7wi9UkoppZqnwfaQSRc80gXvkgmEK4lHBj9IXakTZsVI1GUpM5wpGHOpAvEgF12A9BqorLIWzKXyzKXy/d4NpZRSa5gG20PmfIMJhCuJug65Unmgy86dW6ETZkXUdciWypQG+HXU4/uG+XSB0Zg9bnHXZS6lqSSDYClbYi6tx0IppVT3aLA9REpln7PLuRVTLVYyqJMLC165qU6YFdkhq+aRKnh4/oUmPaMxl/l0AX+IK6usFamCRyIz2Hd9lFJKDTcNtodIIlPE97mks2KzBjXYXkgVEZqb8ClAdgg7YrpVr811BM83pId0sudaYYwhk/coej75AX1vKKWUGn4abA+Rk6tMIFxJzB3M5jbGGE4mskys0gmzIh5xSQxZ6bzzyTzjNcfNFRna/PO1ouD5+MYgMnx3S5RSSg0PDbaHRCpfIpW/tLNis+IDWpEkmfPIlzxikeZOxZGoS2IALxoayRXL5EvlSxoQjcZcZpI6Ma+fCkGjp4jjsJzTCx+llFLdocH2kJhJtj4xslrUFTKF8sB1YDyzlCPuNj9a7zqCVx6e2/6Ngrh4xCUTBOKqPwqlMgYYiTospofnAk4ppdRw0WB7CJTKPmeX8m2nkIDtwAhmoIK7fKnMbCrPeLz1i4jckNz2n0nmGYs2Pm4pLQHYN8l8iajjEHMd0sXSQFfrUUopNbw02B4CiUwR35i2J0ZWGAZrkuR8UHJNWuiEWVm/0iRmkJXKPolsiZFo/bdZPOJqjec+Shds+pKIYAxkB+i9oZRSau3QYHsItNoxspGI45AckLxt3zecWsyybqS5iZHVRoZkkmRl1LrRxcRYzGU+XRy41J7LgTGGVN57Lpd+GKvcKKWUGg4abA+4dMEjlW+tY2Qj8YjD0oAE28u5EvmSf8nEwWbEow7JnDfwdarnUnliK7w+RwTfGNIa5PVcsexTrqp9Ho+4LGp1GKWUUl2gwfaAO7+cJ9Jh+khFPOKQzg9GkHpmKdf2BUQlSM17g3vb3/cNc6kCY6vckXBFSGiQ13OVSiQVI1GXxUwJY/r/3lBKKbW2aLA9wLyyz7nlHJNtpFrUIyL4fv+D1HypzFyqwHis/dF6w2DXRk4XL+4a2ch4PKIlAPugULo42HYdoeybS4JwpZRSqlMabA+wxUzxolvdoZD+V/KYTeZxpPWJkdWijsPSAOdtL2WKTXXEjLoOuWK578fkcpPKl4g4F3/8GQwZTelRSikVMg22B9ipRG7FsnHtcEX6GlD4vuHkYuej9fGow9IAN7c5t5xvflKrMBTVVdaSVN67JJ/eNrfR46CUUipcGmwPqHTBI5UrMdpBqkU9tt15/wKKpVwJz29vYmS1mOuQKXoDWRu5MlLdbFfM0ajLTKrQ5b1S1VL5S7uWjkQdEhkNtpVSSoVLg+0BNbOcDzd9JBCPOiTz/ZsIdiqRZTSEyiqV2siDVDe8Ipkr0kqGzEjUJZEpDuSFw1pU9Hw837/k/aXNbZRSSnVDT4JtEXFF5FER+VzwvYjIb4nIYRE5KCK/ECy/Q0SWReSx4Ov9Vdu4U0SeFpFnROT/7cV+94sxhrMhToys5ojg+5dWY+iFXLHMYrq4aoWOVgxibeTzyUJLlVa0BGBvFRpMENbmNkoppboh3ITgxn4ROAisC75/N3AlcIMxxheRrVXrft0Y84bqB4uIC/wJ8DrgNPCQiNxljHmq63veB/mSjx/2xMiLGHLFcii1u1sxmwyvjCFcSImZnhoNbZudsl0ji2wci7X0uIjjsJAusr7Fx6nWrXShWWlu006zJaWUUqqero9si8gVwOuBP69a/LPAbxpjfABjzOwqm3kJ8Iwx5lljTBH4NPDmbuzvIMgWPbqZ5CEiPR9FLfuGU4ksE/Hwru/iEWfgOkmu1jWykbGYy2wqr3WeeyCd9y6pRFKhzW2UUkqFrRdpJH8E/ApQPZy0G3i7iOwXkS+IyJ6qn71cRB4Plu8Llu0ETlWtczpYtialVggGwhCP9L7qQiJbxPMNkQ4nRlaLug4Fz6c4QLWR59OFFbtGNhJ1HQolfyBz0NeaVMEj6ta/GNLmNkoppcLW1WBbRN4AzBpjHq75URzIG2NuBT4GfDxY/ghwtTHmFuCPgX+sbKrO5i/5aygi7wkC+P1zc3OhvIZ+WMoViTdZyaId8Yjb82D79GI4EyNrCf2vG17h+4bZZL79nHSBpJae67p0vtTwgkib2yillApbt0e2bwfeJCLHsakfrxaRT2FHpj8TrPNZ4HkAxpikMSYd/PvzQFRENgfrX1m13SuAs7VPZoz5M2PMrcaYW7ds2dKll9Rdvm9I5S4tSxYm1xFKZb/hRLGwZQoeS7lSqBMjKwQhXRiMALXZrpGNjEZd7SbZZV7ZJ+/5K95h0eY2SimlwtTVYNsY8z5jzBXGmF3AO4B7jTHvxI5YvzpY7VXAYQAR2SZBsquIvCTYvwXgITELVboAACAASURBVGCPiFwjIrFgW3d1c9/7Je+VKfumqe6DnRAgX+zN6N1MsjtlDMGWMlwckOY2S5kibgfHbTRqJ3yWtPRc1+Q9v+5tsmra3EYppVSYelWNpNbvAH8tIr8EpIGfDJb/APCzIuIBOeAdxiZPeiLyc8CXABf4uDHmQB/2u+tyxXL9pJmQSdBJcmqsu1UXvLLPmaUck/HuPE884rKULWKM6aj9exhmOkkh4cKkynTeY8O4ViXphkITOfEjUYfFTJFrh/PmmFJKqQHTs2DbGHMfcF/w7yVshZLadT4MfLjB4z8PfL57ezgYuj05siLmOizli+ygu2XzFjNFyl0sY+g6tm54vuSH3m2zFflSmUyhzKaJzt5SUcdhPl3QYLtLMgVv1bsPMddhMWubDIU5oVcppdTlSf+SDJhuT46siEccktnu56WeTGQZi3b3ms5g+l7FYzlbDOWOhC0BWNBqGF1Sr017rcodBm1uo5RSKgwabA+QXkyOrIi4Dnmv3NX84HTBI5Xzuj7iHHGcvlfxmEkVQqm2EnEdSmWfzIBUWFlrbNm/1d9fAmTyOklSKaVU5zTYHiC9mhxZrZsjwueWck0FNp2KR+xt/37xgq6RYZU2FIKRchWqsm/Il8pNnZPa3EYppVRYNNgeIL2aHHnRc3apxFnR8zm7nAu1Y2Qj8YhDKudR9vuTepHKexjTetfIRsZiEWZShVC2pS7Il8pNv71Ggsowms6jlFKqUxpsD5BeTY6siLsuy126VT6fKmAMXZsYWU1EMKZ/edtz6QLREI9bpcPnIHXGXAsKnn9pJ6wGKs1t8iU9BkoppTqjwfYASWR7MzmyIh51WOrCrXLfN5xczHat3F9dAtli73NsjTHMJguMhziCLyIIkMprrecwZYseTgu3jgymL+eUUkqptUWD7QHh+6apSglhiroO2VIZL+RJksu5ErlSuaevJeY6LPehuU264OGV/dBH8GOuLQGowpPMl1o6J7W5jVJKqTBosD0g8l4ZY3o7OfLCc4cbbJ9MZEObLNiskahLog8T2paypa6kyozFIsylCvh9ykNfi9ItVvqpNLdRSimlOqHB9oDIFctN55N247nDki16JNJFxnrcYKYySt/rVufnl3NdKW3oOoLnGzKaxhCKsm/IlXwiLVwYxSPuc3culFJKqXZpsD0gkvlSTydHVkQdh+VceKN355byuK70rXV6tof1qfOlMplimXikOxcWrghLfUiNWYsKXhkwbZ2XWvNcKaVUJzTYHhCJbKmnkyMr4lGH5ZA6SZbKPmeWcr2dGFlFgGyXShnW0+1GOqMxl5lkvqvPcbkotFlVRJvbKKWU6pQG2wPA9w3pvNeXYDvmOqQLXii5wYvpAmXf9KTcXz3xiEsixFH61cyG1DWykXjEJZX3yGvb8I7ZVKnWz0tbb1vztpVSSrVPg+0BUJkc2Y/UC/ucndeoNsZwfCHL5Ej3m9g0YidJ9ibtwiv7LGQKjPRgImi6h6P1a1WrlUgq4hEbbGtzG6WUUu3SYHsAZPs4ORLAN523bU/mPHIlr2v5y81wHcEr+z0ZCa50jex29ZiRqMtsSlNJOpUueMSaaNNeqzJRVZvbKKWUapcG2wMgmevP5MiKqOt03EDl9FKWuNu/Ue0KQ7jVVRpZyBR6csxGoy4LqaKWAOyAH1R1ibrtXxhpcxullFLt0mB7ACzl+jM5sqLSHrxduWKZuVSB8Xj/RrUrHJGup10YY5hJFhjvQXlD1xHKxpDSVJK2Fcs+xtB2mlbEcbQqjFJKqbZpsN1n/ZwcWRGLOKRyXtt5qTPJPK70r9xftZGIy2Kmu50XM8UyJc8n0kZaQjscEZa0uUrb8qVyG1MjLxjVSZJKKaU6oMF2n/VzcmSFI4Jv2stL9co+pxNZJkf6U+6vVjzqkMyFU12lkUSmiNPDiivjsYiWAOxApzn8sYijzW2UUkq1TYPtPssWywxCOq6hvUmSi5kiXh/L/dV67sLB617e9kwy39N29LGIQ7ZYXnMlAPOlMvM9mPyZyntEQ7gLoc1tlFJKtUOD7T5L5kqhBAKdckVItzhJ0hjDyYUs47H+T4ysZuheJ8l8qUy64PWk5N9FpPtNdHrt2bk0T8+ku15WL5nz2ir7V80R0eY2Siml2tL/KO8y1+/JkRXxiMtSi8FcMu+R6kfguQo7oa07ObapPgVcIxGX2VR3c9F7KZkvcT5ZoOj5XZ38aYwhW+x8ZDsecVjUvG2llFJt6H+Udxnr1uTIU4tZ/BZHC2MRh2Su1NIo45lEbiAuFGqNRLtXPWImmWekD7XER2Mui5ki5UHIOeqQMYZnZ9OMRl2irsNcsnsXEQXPp+ybjuuha3MbpZRS7Rq8SOkykiuV8UOeHHlsPs17/+YR7n9mvqXHuY5Q9g0Fr7lJYPlSmdlUnon4YKWQgG1BnymGP6HNK/ssZoqM9qDkX61KLnp6DaQyJLIlEtkiE/EI4zGXc8v5rk1oLZR8wnh7uY7ga3MbpZRSbdBgu4867dpYz4PPLgLw1LlkW49vdhLebDKP0H7t4m4SEUwIXTFrpQu2PGK3u0Y2EnEcFrpc1rDbfN9wdDbFRNxWr4m4Dp7fvVSSfMkLrTurATLa3EYppVSLNNjuo250jtx/wgbbR2bSLT9WEDJNBD1l33ByMce6ASn310g25ABuIV3E7WOnz7GYy2wXUy56YT5dIF0oX5TnH3Ec5ruUj54qeERDOmbdnAuglFJq7dJgu48S2XAnRy5lixyZsbmwR+fSlFpMo4g3meucyBYp+eWeNXVpR9x1SYSYt22M4Xwy35OukY1EXYe8Vx7a1uFe2eeZufQlF2ndTCVJ5juvRFIxGnW1k6RSSqmWDW60tMZ1Y3LkIycTGOANz9uO5xuOzWdaenyzFUmOL2QYjw72qHazFw7NSuY8SuXedY1cyWyyMJQT9c4n8xQ9/5Lgt1upJMYYMoXwgu1Kc5tWL2KVUkpd3vofOVymbD5xuJMjHzqeYMNYlDv3bQPgyEyqpce7jlAq+xRXmCSZypdI5by+TBJsRWUUeKXX0gzfN5xazPLIyURPG9k0sn40xrH5DE+cWR6qJjdFz+fYXIapBqlH3UglKZbDqURSq1s13JVSSq1NGmz3Sa5UDm3iFtg86kdPJnjR1RvYMhlnw1iUw23lba88sfDsUm4gmvA0Q4BcB4FRuuDxyKkER+fSbBiLMTYAzXtcR9g8ESeZ83jo2CKzQ9LG/XTClqNsdGdgIh7h3HIu1FSSZivrtEKb2yillGrVcERNa1DYkyMPnkuSKZa59eqNiAh7pyc5PNvayHZFrkFOcMErc245z+RI/4POZghCutB6KknZN5xcyLD/+CKeZ9g0Hh+YdvQVU6NRxuMRDpxd5uC5ZMcj+N2UK5Y5uZhlajTWcB3XETzfhNo0qBsj/9rcRimlVKs02O6TsCdH7j+xiOsIL7hqPQB7pic5ncg1VV2kWizSeBLYXHCbv1+l71oVjzostpi3ncqXePREgqNzGdaPxhgfwDriFVHXYdN4nPlUgf0nFge2UsaJxQxR11n1giXiOMylwxupT+e90Kv9aHMbpZRSrdJguw+6MTly//EE+7avey7VYe/WCQCemW0tlSQecepOkvR9w8nF7MCX+6sWj7gsNRkYlX3DifkM+48nKBvD5onBG82uR0RYPxYj6jg8fCLB0dl06M18OpHKlzi3lGOyiYuWiXiE8yFWJUnlPWIhpzxpcxullFKt0mC7D8KeHDmbynNiMcutuzY8t2zP1kkADrc4STLqOuRL5UsqLizlShQ9f2jytaESGK2eu1sZzT62kBmY3OxWjURdNk/EOZXI8ujJBKn8YJSoOzafYSQSaepcDzuVJJUvhVaJpJpvtLmNUkqp5g1P5LSGhD05cv/xBAC37tr43LKJkQg714+2n7ddk+96ciEzENU4WmUwDatHlH3Dsfk0Dx1fpGwGMze7FY4Im8bj+AYePpHg1GK2a23Qm7GULTKfLjLRQo5/WKkkRc/H801XjmfM1eY2SimlmqfBdh8sZ8OdHLn/xCLT6+JcsX70ouV7pifaqkgCF08uSxc8EtlSX0Z8v3lsgUQHgU3EcUjWSYtJ5ks8fGKREwtZNo7Fh3I0u5GxWIT1ozGOzqX59pnljiqytMsYwzOz6ZabAIWVSlLwuveaR6IuiYwG20oppZqjwXYfLGVLjETD+dUXvDKPn17mxUEVkmp7t06ymCmykG6tfnHMdViumlh4frk/5f4OnF3mg/9ykP/78Om2t1FbPcIr+xybT/Pw8UWMYehHsxtxHTvKnS14fOvYAjPL+Z5O6ptPF0gVvJYvYmytd0OywzSYbuZUxyIOmeKlqVZKKaVUPRps91jZN6QL4U3ceuLMMkXPvyiFpOL6bTZv++kW87bjEZflYDS46PmcWcox0eOqHMYYPn7/McAG3e2KRxxSOQ/fNyznSjx8ImFHs8fX1mh2I5MjUSZHohw4Z0sEdnPEt6Ls21HtZiZF1hNzHWY7bHCTKYRfiaSaoM1tlFJKNUeD7R7Lhzw5cv/xBLGIw807py752TWbx4k40nIqSaUtddk3zKcKGEPPR3/vP7rA4Zk0V24c49h8puUShhUigjGGI7MpHj6+iGBHfIelfGEYoq7DlokRFjNFHjq+2PKdjlbNLOcpeD7xSHs5/uPxCLPJPOUOUklSBY+o271jLCKkB2QSqlJKqcGmwXaPZYvhTY40xvDQ8UVuuWKqbtWFqOtwzebxltu2V2SLHicXs0zGe1vur1T2+eQ3jnP1xjF+6juvwTdw8Hyy7e1FXOH8cp5NE/GBbzPfTVOjMUYiLo+fXuLITKorJQJLZZ9n59MdlYi8UJWk/WA2mSu1Hew3w6YnabCtlFJqdRps99hyrhja7e3TiRyzqQIvrpNCUrF3epIjs+m2RglnknlypXJXyqet5AtPnufccp53376LG7evw3WEA2faD7anRmNsvMxGsxuJR1w2j8c5u5zj4ROJjnOja51N5PB803GOf9RpP5WkVPYplf2u3o1ppYa7Ukqpy5sG2z22nPVCmxz50PFFAF509YaG6+ydniBXKnNmKdfStiOOw0yy0PNyf5mCx6cfOsktV0zxoqs2MBJ12bN1oqO8bXUxEWHjWByAh48nOLmQCaVEYL5U5tiC7bzZqU5SSVarqx4G1xHKZXNJiUyllFKqlgbbPRT25MiHTyS4euMYWydHGq6zZ7q95jajUZd03mOsx2kXn3nkNKm8x7tvu+a5vPZ9O6Y4Mpu+qByh6txYLMKGsRhH5zKhlAg8tZjFdSSUEWXXEUptppL07DwRnSSplFJqdRps91CYnSMzBY8D55J1q5BU27l+lLGY23KwHYs47Fg/GtpEzmbMpQr802NnuWPvFq4L2s0D3LRjHZ5vWn4NanWuI2yesCUCHzq+wGyyvYYymYLH6USuo1ztWnHXYaaN/ckWPNwenLdRR5vbKKWUWp0G2z2UK5bxQ5oe+dipJcq+4cW7GqeQgO0quGfrBEfabG7TS3/9zRP4xvAjL7v6ouU3bl+HAAfOtp+3rVY2ORJlIh7lyTO2RGCxxVSMY/NpYq4Tal78WCzCbKrQcipJKu81Nc8gXyrziQeOkW2z9fpI1GUxrcG2UkqplWmw3UPLuSIxJ5y0jP0nFhmPu9ywbd2q6+6dnuTYQqblAKqXjs1nuPfQLG+8ZQdb112cFjMej3DNlnGe1Lztroq6Dpsn4synCuw/sXhRY6OVLOdKzKYKrBsNt2qN6whl39TtALqSZL7UVKrWY6eW+MwjZ/jWscW29i8WcciWtLmNUkqplWmw3UPLWY94CJMjfWPYfyLBC6/a0FR+7J7pScq+4dn5wR3d/sQDxxiPR3jbi66s+/Obdkxx6HxqaAMbYwy//PeP84kHjvd7V1YkIqwfixF1HB4+meDY/MqVbIwxHJ1LM96lBkG2wU3zqSRe2afg+USaCLYr2+0kPUmAbEHztpVSSjWmwXaPhDk58uhsmqVsiVuvXjlfu2JvkP/canObXnn0ZIJHTi7x9luvZGKkftC2b8c6ip7P0dnBfA2refp8iqdnUtx9cKajZi29MhJ12TQe48RClkdPJRo2FVrMFFnOFrvWjbPVVJKC59NsIstM0pYW7OR9ISKkC1pvWymlVGMabPdIrlTGmHAmR+4/kUBYueRftU0TcTaNxwZygqFvDJ944DhbJ+O8/nnbG663b4ftkPnkkOZt331wBrApF8NSxtAR222z5NnmSeeWchfVlfaDtuwTXWx61GoqScFrflZEZfLls/Pptu+YaHMbpZRSq9Fgu0dyxTJND7mtYv+JRfZOTzLVQo7s3unJgQy273t6jmfnM/zIy65esRHK1GiUKzeMDk2gWi1fKvP1Z+a5/brNxCIO9x9d6PcutWQiHmFqJMrB8ykOnF1+rrTebND0aKTLtdhbSSXJFLymJ2nOpgpEHKFUNhybz7S1byNRbW6jlFJqZRps98hyrkg0hM6RS9kiR2bS3LpKFZJae6cnObec76gFdtiKns9fPXiC67ZM8Mq9W1Zdf9+OKZ46lxyKNIxqDz67QLZY5vU3b+fWqzfwjaPzQ/caIq7Dlok4yzmP/ccXmU3meabDtuzNGo9HmEkWmmovnyo0NzkS7MVC5e7QkTYvRB3R5jZKKaVWpsF2jyxlS6FMjnzkZAIDTedrV+ydtnnbg1QC8HPfPst8usCP3b6rqdHIfTvWkS2WOb7Q3ihkv9x9cIbpdXH27VjH7bs3k8iWOHR+ONNh1o1EGY1GePLsMuVy523Zm+GI4BtDMr96ib50rrmyf+m8R6ZYZt+OdWwYi/J0J3d9tLmNUkqpFWiw3QNhTo586HiCDWNRrt0y3tLjrts6gQCHZwcjlSSZK/F3+09x69UbeN4V65t6zE07bd72MKWSzCbzfPv0Mq+5YRpHhFt3bSDqCvc/M9/vXWtbLOKwZWKEjePxnj1n3HVXbXBT9g3ZUrmpC4CZIC1let1IkGLV/kVo1HFYSBfafrxSSqm1TYPtHqjcYu50cqRX9nn0ZIJbr97YcvOQsViEKzaODUze9t/tP0WuVObdt+1q+jGbJ+JsWzfCk2eGZ1T4nkOzALzmhq2APQ4vvGoDDxxdwNc836aNxV3mUiunkhS8cguVSGywvXXSBttnlnKkmxg5r2c8HuHMUl4DbqWUUnVpsN0DuZBuMR86nyJTLDddhaTW3qCTZL8nc51P5vmXJ87xmhunuXpTayP0+3as48DZ5b6/hmb4xnDPoRmed8XURY16br9uMwuZIofPD8aFzzBoJpWkUGq+oshsUPZv27oRrp+eBNq/6+M6wtRIlANnl9vuRqmUUmrt0mC7B8KaHLn/xCIRR3jBVc2lXdTaOz3JUq7EXKq/I3B/9Y0TOI7wwy+5quXH3rRjimTe43Qi14U9C9eBs0lmkgVec+P0RctfsmsjEUe4/+jwppL0w2qpJNmihzQ5tj2TyjMWcxmPuxdSrDq46xOLOMRclyfPLA9t4yWllFLd0ZNgW0RcEXlURD4XfC8i8lsiclhEDorIL1Qt/18i8oyIfFtEXli1jXeJyJHg61292O+whDU58qHjCb5jx7q2G4jsfW4Er3+TJI/MpPjXI3O85fk72TTRes7vvp22Pf0wtG6/++AMYzGXl1+76aLl4/EIz79yPfcfXRiKEfpBsVoqSSrvEW1iciTYNJKtk3FEhPF4hCs2jHacYjUej5Av+Rw+n8IfsmozSimluqdXI9u/CBys+v7dwJXADcaYG4FPB8v/HbAn+HoP8BEAEdkI/AbwUuAlwG+ISHu5FD0W1uTI2WSek4tZbm0zhQTg6k1jRF3pW962MYaP33+MqdEob33hzra2sW3dCBvHYwOft50tetz/zDyvuG5z3TrUt1+3mblUgSND2hGzHyqpJMsNGty08j6bTRaYrkrtqUyS7PTiZ8NYjNl0npOLw1UxRymlVPd0PdgWkSuA1wN/XrX4Z4HfNMb4AMaY2WD5m4FPGutBYL2IbAe+B/iKMWbRGJMAvgLc2e19D0NYkyP3n0gAcOuu1kr+VYu6DtdunuhbsL3/RIInzyb5oRdf2fbovIhw0xDkbT/wzAIFz+e1NSkkFS+9ZiOuIzygqSQtiUfqp5L4viFTKBN1V3+fGWOYTV0abC/nSsyGkGK1cSzO0fkMc0024lFKKbW29WJk+4+AXwGq7/3uBt4uIvtF5AsisidYvhM4VbXe6WBZo+UDL6wJUw8dX2TbuhGuWD/a0Xb2Tk/wzGy6501Vyr7hLx44zo6pEb5n37aOtrVvxxQLmSIzycGt/nD3oRl2rh/l+m2TdX8+ORLlliumuP8ZTSVpxVjMZS5duCQvuuD5gGnqojaZ98iVymydvJDG9FyKVQgXoo4IG0ZjPHUuRbqgEyaVUupy19VgW0TeAMwaYx6u+VEcyBtjbgU+Bny88pA6mzErLK99vvcEAfz+ubm5DvY8PMlcqePJkQWvzLfPLHPr1Rs6HiHfOz1JwfM5uZjtaDutuvvgDKcWs7zrtl1EOkyp2bdjsPO2zy7lOHA2yWtu3Lri8bpt92bOJ/M822ar8MuRI4Ix9n1VreA1X/FnNnmhxnbFrk1jxFyHp0OqEBN1HUYiDk+eWabo6YRJpZS6nHV7ZPt24E0ichybl/1qEfkUdmT6M8E6nwWeF/z7NDaXu+IK4OwKyy9ijPkzY8ytxphbt2xZvf13LyxlS3VzdlvxRPAHu5MUkoowR/CalS+V+ZtvnuSGbZOXTBZsx5Ubx5gciQxsc5t7Ds3iCLz6+q0rrveyazfhCEPd4KYf6qWS5Ftolz4TpIpMr7swsh1xHXZvGQ918vBYLIJX9jl4LqkTJpVS6jLW1WDbGPM+Y8wVxphdwDuAe40x7wT+EXh1sNqrgMPBv+8CfjSoSvIyYNkYcw74EvDdIrIhmBj53cGygVaZHNlMHulK9h9PEI843Bx0UOzE9qkRJuIRjvQw2P6nx86wmC3y47df0/HIPNjRTVtve/AmSZZ9w72HZnjBVRtWrbYyNRrl5p1T3P/MvKaStKBeKkky7xFzm7uona1qaFNt7/QkR2fTKzbOadXUaIxEtqh3L5RS6jLWrzrbvwO8VUSeAH4b+Mlg+eeBZ4FnsOkl7wUwxiwC/x14KPj6zWDZQAtjcqQxhoeOL3LLFeuJNVnWbCUiwt7piZ6V/0tki3zmkTO8/NpN3Lh9XWjb3bdjinPLg9e179unl5hPF5/rGLma26/bzNnlPCcWepvWM8zqpZKkch7RSLM1tgtMxCOMxy+epHv9tkmKZZ/jIR+LDWMxTi5mmFnWCZNKKXU56lmwbYy5zxjzhuDfS8aY1xtjbjbGvNwY83iw3Bhj/r0xZnfws/1Vj/+4Mea64OsverXfnQhjcuTpRI7ZVIFbd4VX6XDP9CQnFjIt3Xpv16cfOkXBK/Oul+8Kdbs37bCj/IM2un33wVkm4hFeek1z6TIvu3YTAtrgpkUjEZdzQfBqjCFdLDVd9m8mmWfrukvvOuwJUqyOtNlJspELEyaTJPP1yxYqpZRau7SDZBctZ5sPABp56LgdwG+3RXs9e7dO4hs4Otfd0e3TiSxffPIcd960nZ0bOquiUuuazeOMRt2BmiSZLng8+OwCr9q7pem7EBvGYuzbsY77jy50ee/WlrGYy3yQSlLwfDDS9B2k2WSe6ZoUEoDpyThTo9HQJklWi7gO47EIT5xe7slFrlJKqcGhwXYXLedKxCOdTY7cfyLBrk1jl+SXdmLP9AQAR2a6G2x/8hsniEdcfujFV66+cotcR7hx+2DlbX/9yBzFcuPa2o3cft1mTi1me14hZphVAuvlXIlCyZb9a4YxhplU4aLJkdXb3LO1e3XoR2P2s+DguWTPS28qpZTqHw22u8Qr+2SKnU2OzBQ8njqX5EVXd16FpNqGsRhbJ+McDvl2ebXDMym+8ewCb33hTtaPxbryHDftWMfJxWzDjoK9ds/BWa7eOMbuLeMtPa5SoUUb3LRmJOJyfjlPvuQ1GWrDUq5E0fMbXrxev22S04kcmS7Vx143EmU5V+LoXEonxSql1GVCg+0uyZXKGNPZ5MjHTi1R9g0vDjFfu2LP9GRXy//9w6NnGI+5vPGWHV17jn1BdZanzvV/dPvUYpanZ1K89sbplo/5pok4N25fpyUAW1RJJVnIFFtq0w4X19iutnd6EgM808UJxBvHYpxJ5Di3pBMmlVLqcqDBdpfkSuW6nXha8dDxRcbjLjdsC6+KR8XerRPMJAtdGRWeSeb5xtF57rxpW9tt2ZuxZ+sEMdfhwJn+523ffXAG1xHuuL69+u63797E8YUsZxK5kPds7bqQSuIRbWFyJFA3jQTsfAaAp7t4ISoibBiLc2gmyXJ2MO7KKKWU6h4NtrtkOVtqOgCoxzeGh08meOFVG3CdzmtT16o0t+lGve27Hj+LiPCG53VvVBtsl77rt032PW+77Bu++vQst169oe2UmZfv1lSSdoxEXJZzpaYnpM6k6tfYrpgYibBz/WjXmz65jjAZj/LEmSWdMKmUUmucBttdspQtdjQ58uhsmqVsiVtDzteu2L1lAkfCH8HLFDy+8tQMr7huM5tXaeoShn071vHsfDqUMovteuRkgkS2xGtanBhZbevkCNdPT2oJwBaNxVwm4hGcpiuRFFg3EnlusmI9e6ftJMlu51SPRF0cEZ48uxxqIx2llFKDRYPtLvDKPtlSuaMmNPtPJBDCLflXbTTmctXGMQ6HXJHkSwfOkyuVefPzd4a63UZu2jGFb+Dgud51xKx198EZpkajvLjDY3Xb7k0cnctwXpufNE1EmBqNNr2+rbG9cmWfvdOTJLIl5tPFTndvVZMjUTIFjyOzaZ0wqZRSa1T3EmovY5XJkZ3Yf2KRvdOTLQUSrdozPcmDRxcwxoTSRt0r+/zzt89xpgqiWAAAIABJREFU884prts6EcIeru76bZO4jnDg7HLXLkxWspwr8a1ji7z+5u1EOqypftt1m/mLB47zwNF5vv+FV4S0h6rabKrArs0rV4uppFgdnkmxZbL7d2c2jMY4v5xnNplHBFzHwRFwRXAc+1X5tyu2SY7riF1XHFyH59aZHI0yEdePdaWUGiQ6st0FuQ5zMJeyRY7MpEPtGlnP3q2TpAoe55PhjKQ+cHSB+XSBt/RoVBvsrfjrtkzwZJ/ytr92eA7PNy3X1q5n27oRrtsywQPa4KYrfGOYTeWZXiWAvmbzOBFHujpJspqIsHkizsbxOFOjMcZjEUYiLhHXQRB831D0fHKFMqm8x1K2xHyqyGyywJmlHCcXczw7l+Gps0lOLmZ6ss9KKaWap8F2F3TaOfLhEwkMdC1fu2Jv0NwmjFQSYwyffewMO9ePdv0iodZNO6c4MpOi4PV+otk9h2a4bsvEqqOlzbrtuk08PZNiNqWpJGFLZIqUymbVNJKo67B7S/ea26ykMmodcR2irkMs4hCPuIxEXUZjLmOxCOPxCBMjESZHokyN2q8NYzE2jMdI5/o3d0EppVR9Gmx3QaeTIx8+mWDDWJRrW2yO0qqrN40TizihBBVPnUvyzGyaNz9/R9OT1cJy0451eL7pSpvtlRybT/PsXIbX3Lg1tG3evnszAN/Q0e3QzaYqNbZXTw3ZOz3BM7Ppoer0GHGEnFfGH6J9Vkqpy4EG2yGznSPbnxxpjOHA2SQ371zf9aDVdYTrtkyEUv7vs4+eYXIkwnddH17g2awbt69DoOclAO8+OEvEEV61t73a2vXsWD/KNZvHuV+D7dA9V2O7Qdm/anunJyl4/lClZYgIGKGolU2UUmqgaLAdsmLZ76iZzVyqwGKmyHdsnwxtn1ayd3qCo3OZjkqPnV3K8a1ji3zvTdsZibY/ot+u8XiEa7aM8+TZ3jW3KZV97nt6lpdeu4nJkXAnsd62exMHzyVZSBdC3e7lbiYY2W5m0uOFSZLd6yTZHYZCSYNtpZQaJBpsD5iDQSrEDdvD7xpZz97pSYplnxOL2ba38U+Pn8V1hNffvD3EPWvNTTumOHQ+RalHo3oPHV8kmfd4bYgpJBXPpZI8q6PbYZpN5lk/Fm3qgnD71AiT8UjPJkmGxQCFsjbJUUqpQaLB9oA5dC7JSNRh16bu5mtX7Kkqc9aOVL7E3QdnuOP6LWwYb697Yhj27VhH0fM5Otubkch7Ds6ycSzGC64MfzLolRvHuHLjGPc/ow1uwjSTzDeVQgI2JWPP9CSHezwPoFMRxyGd10mSSik1SDTYHjAHzyfZOz3ZlRbt9UxPxlk3EuFIm7fLv/jkeYqe39Nyf/Xs2zEF0JMSgIlMkf0nFvmuG7Z27TjdvnsTB84mSWS731jlcjGbKjQ1ObLi+ukJTi5m+9qdtFUx1yGlwbZSSg0UDbYHSK5Y5th8hhu39SaFBOwI3t7pybZul5fKPp/79jlecOV6ru7RSHwjU6NRrtwwyoEe5G1/9elZfENXUkgqbt+9GQM8qKkkoSj7hrlUga1NjmwD7N02iYGe3S0JQ9SVobo4UEqpy4EG2wPkyGwK38ANPZocWbF3epJTbYzgff3IHIvZIm95QX9HtSv27ZjiqXPJrpZrM8Zw96FZbtg2yRUbxrr2PFdvGmPn+lFNJQnJYqaI5xu2tjCyvWerfR8+PUSTJCOuQ8HzO5rwrJRSKlwabA+Q5yZHTvduZBtgz/REyyN4xhg+++gZrto4xguuXN+9nWvBvh3ryBbLHF/oXrm2I7NpTi1mec0NnXeMXImIcNvuTTxxZpnlXKmrz3U5qDQJml6loU21qdEo26dG+tLcplNa/k8ppQaHBtsD5NC5JFduHGNiJNLT562M4B1uIdj+9ullji9kecvzd9j6vgOgkrfdzVSSuw/OEIs4vGLP5q49R8Xt123GN5pKEoaZZNDQpoU0ErDvjWELtgW0/J9SSg0QDbYHhG8Mh86nuHFbb1NIwI7gbVvX2gjeZx87w/qxKHf0oYlNI1sm40yvi/Pkme5Mkix6Pv96ZI7brt3EeLz7F0TXbh5n27oRHjiqqSSdqjS0aabGdrXrt02wkCkOWc1zIV/S8n9KKTUoNNgeEGcSOdIFjxv6EGyDbW7TbAOPk4tZHj6R4PU3byfqDtYptG/HFAfOLmNM+HnbDz67QKZQ5rU3djeFpEJEuP26TTx+eplUXlNJOjGbyrNxPNZyZ9e9HZbG7IeY65Au6CRJpZQaFIMVKV3GDp23o7G9amZTa8/0JPNp271yNXc9doaY6/DvbupfE5tGbtqxjmTe43QiF/q27zk0w5bJODdfMRX6thu5bfdmyr7hm8cWe/aca9FMssB0i6PaANduniDiyJBNkhRSGmwrpdTA0GB7QBw8n2IyHmHn+tG+PP/1wQjekdmVR/CWskXufXqWV9+wlanRcNuUh+FCve1w87bn0wUePbnEq2/YitPDHPU9WyfYMhnXqiQdmknm2drC5MiKWMRh1+ZxjgzTyHbEIaPBtlJKDQwNtgfEoXNJrt822dNArtq1W8ZxhFVTSb7w5HlKZcObn7+jR3vWmu1TI2wci3Eg5OY29x6axQCv7XIVkloiwu27N/HYqSUNoNpU9g3z6UJLlUiqXT89yZHZdFdLSobJEaHsG4qeTpJUSqlBoMH2AEjlS5xK5LgxpBSSbNEj2WKObzzismvz+Iq5qQWvzL88cY4X79rQ1RrTnRAR9u1cx5NnwsvbLvuGew7OcNOOdWybai9g68Ttuzfj+YZvHddUknbMpwv4Bra2kUYCdj5DrlTmdCIb8p51V8HTSZJKKTUINNgeAE8H9bXDqkSSK5UptVFnd+/WyaCxTv0g9b6n51jOlfi+PrdmX82+HVMsZIrMpDqvIDGXKvC+zz7B2eU833tzf3LU926bZNN4TFNJ2jQbVCLZ1ubI9jBOkgQo6Mi2UkoNBA22B8DB8ykcsZMUwyDBf62O7O6dniBTKHN26dLJhb4x/NNjZ7h2yzg37ezdBMF23LTD3iE4cKazvO1vHVvgFz/9KMfnM/zyd1/PK/ZsCWP3WuYEDW4eOZnQVtxtqFx0tdI9stqO9aOMx9yhmiTpiJAr6si2UkoNAg22B8Chc0mu3fz/s3fm8XGc9f3/PHPuKWkPST502PIdO7eTkDgkkHKWIxTaAi38oJRCD/qjB4XCq/3RFkpPWsrRFtpS0ou2QEspAUIggRwEYjt2nMO3bPnSrZX2nNk5nt8fsyuvZa12Zndmd0Z63q+XEmu1mp3V7s585/t8vp9PDCGRb3lbhkkh8ASxkOC4s3W5g3d1UfHUuQzOZ0p43Q0bfRNiU4/BZARxWWhat60ZJv7+0VF85P6j6O2S8Yk33oC7t3em0K6yb2samkFx4Gymo/sRRCazCgiAdKy5YpsjBNv6gxVuI/Ecciqzi2QwGAw/wIrtDmOYFMcnc9i53j0JSTIqIRkVHRfbA4kIQiK3rPPC/xy+hFRUwp1bvU9ObBWOEFyzoaspR5KJBQXv/8oR/M/Tl/Dq69bjz3/yemzokENMLTvXdSEREfE4C7hxzFRWRSomt+QJv6M/jrHZQmDCYiSBQ77EVkEYDAbDD7Biu8OcmSlA1U3sWufOcKSqG0hGRHSFROims2Kb5wi29sZwYon935mZPA6fn8err9vguxCbeuzZ0I3xBcVR8t9jp2bw3v84hPGFEj70yp14911bfPN8eY7g9i1pHBjLBKbg8wuTOQX9TUpIqmzvj8GkwOnpYEhJBI6gpBswA+KgwmAwGKsZf1QSa5jLYTYuJUdSIBoSEZGaixPf3h/H6HThigHLrx6+hJDI4RW717mzj21gd1W3bUNKUtZN/PX3TuFPvnUMg4kI/uqNN+L2Lf7r4N+5NY2ybuKj9z+/rK6esTyTWbVpJ5IqVYlVdZjZ7xBCAEpQbmJQmsFgMBjuwortDnN0PIdUVEJvk3rSWkxKwXMEEZFHSOTAc8SxN/D2/jh0k+LMTAEAMJtX8ciJabxkVz9ioeYK+E4w0htDWOQbSkkuZIr4zS8dxjefncDrb9yIP379tU37MXvNtRu78csv2oITk3n86hcP4UsHz0NnxdSKaIaJuULzHttVeiIS+uIyTkwFo7MNABSUOZIwGAyGDwhO9bRKOTaRxc71Xa4MHSqage6ICI6zttUTkVBUdUdd7m39MQDAyckctvfHcf8z4zBMinuv97fd31J4jmDX+q4VO9sPHZvC33z/FCSew4dfcw32DifbuIfN8co963HrpiQ++8go/umJMTxyYhrvefE27HDJNnK1UfXY7o+3fgG1vT+O4wEakgSqXtv+S3plMBiMtQTrbHeQ2byKqZyKnS4VSqpuIhWVFr9PRESomrPOVm9MRk9ExInJPBTNwDefncALRlIdCXNplT0bunBuroiF0pWuDIpm4BPfOYG//M4JbOmN4ZNvujEQhXaVVEzGh358Fz7047uQU3T81pefxmcfOc1sAZdhKmtp9lvVbAPWkOR0TkWmUG55W+1A4DjkFfaeYDAYjE7DOtsd5NhimI07w5EmpYiFLnex4rIICmcyEkIItvfFcWIqh+8em0Je1fG6G4PV1a6yu+IH/vx4FrePpAAAZ2cK+NMHjuFCpoQ33TKIN90yBJ7zt5VhPW4fSeH6gW788xNjuP/IOH44OotfvHsLbtuc6vSu+YbJnBVo0+eCNGh75aL4xFQuEH9jiedQYF7bDAaD0XFYZ7uDHJvIQuQJRnqjLW+rGmATlS57dYckzmGpbbG9P4YLmRL+66kL2NEfdy3Zst1s64tB4jk8V4luf+C5Cfzml55GXtXxkXv34GdvGw5soV0lIgl4991b8Kc/eR2ikoCP3n8Uf/TNo45cWFYzk1kVHGneY7uWkXQUHAnOkKTIE+QV5rXNYDAYnYZ1tjvI0fEctvXFXbGXU3UTcVmEULMtWeAhCxw0w3T0GFXnhamcirffscn3ITb1EHkOO9bFcfj8PP7828fxyMkZ3DDYg9946XYkIlLjDQSIneu68Ik33oD/PnQRX9x/DofPz+Ptd2zCy3evAxfQ188NprIK0jHZlYuqkMhjUzqKkwEZkhR4DqqiQTfMK44LDAaDwWgv7AjcIcq6idPTeexyyfJP0QykYlcXkImI5Fi3va3P2qe+uIw7fGiB54TdG7owNlfEY6dm8H9eMIzff+3uVVdoVxF4Dj+1dxCffvNN2NoXw19/7zR++ytHMDZb6PSudYzJXOtOJLVs77OSJE0aDP9qAjD7PwaDwegwrNjuEKem89BNip0u6bUNStEdvtp1IBERoRrOdJuxkIDXXLceP3/n5sDLLO7e3osbBnvwsZ+4Fj+1d3BNdHk39ITx0Xv34Ndfsg0X5kv4tf84jH/50RjKa9AGbjKrtOyxXcuO/jiKZQMXM8HxOXd6sc1gMBgMd2Eykg5xbLwSZuOSHppSICLzV90ekZt7id9115ZWd8kXDCQi+Mi9ezq9G22HEIJ7dvbj5uEk/uGxUfzH/vN47OQMfuXFW3FtZXB0tWN5bJfd7WxXhyQncxhMRlzbrlcQQljiKIPBYHQY1tnuEEcnsljfHUKPC5IGzTARlnjIwjLFdsVjmwZk2ZvhLt1hEb/x0h34g9fuhmFSfOi/n8EnHzqJvLr6LeHctP2rsrEnjLDIB8ZvW+DImnitGQwGw8+wYrsDUEpxbDznmuWfohlX+GvXwnMEcVlkus01zo1DCXzqzTfiDTcN4LtHJ/En3zoWGN1xs1Rt/9zsbPMcwbb+GE4EpNgWeQ45VmwzGAxGR2HFdgeYyCqYL2nY6dJwZNkwkahTbANAT0SEwnSba56QyOPtd2zCL79oKw6fn8fXnr7U6V3ylGpnu8+F9MhadvTHcXa2WEln9Dciz6HAim0Gg8HoKKzY7gBHx90NsyEAoitEsndHROgmK7YZFi+7ph+3bU7ivh+cxZmZ1etUMplVIHAEyRUuRJthW38chkkxOu3/vx3PERgmXZPDsQwGg+EXWLHdAY5NZBGReFcGrAyTguc4hMT6L2VE4tFUug1jVUIIwa/esw3xkICPf/v4qi3EpnIKeuPueGzXsqP/8pBkUAhCF57BYDBWK6zY7gBHx7PY3h93pQhQNAPJmLhi8ExY5MFzJJAaXZOyrpwXdIdFvPfHtmNsroj7njjb6d3xhMms6qrtX5VkVEI6JgWm2KYU7DPEYDAYHYQV222mWNYxNlt0LQJd0Q2kGjiaEELQFRYDaQE2V1QxXyp3ejdWJTcPJ/Dq69bja09fwlPnMp3eHdeZzCnoc3E4spbt/fHAOJLwHEGxHLzPPoPBYKwWWLHdZk5M5kEB7Fzvjl4bAKKhq8NslpKMSlAD1t2aL5WRjsmIygI05qbiCW+/YxOGkhH81XdOYqGktfWxvbSjVHUD80XNVSeSWnb0xzGZVdv+N2sGieeQU/2/nwwGg7FaYcV2mzk6ngXBZd1nK5iUgiMEEfFqf+2lxENCoLy2S2UDIkewo78LfXGZdeY8QhZ4vO9l25FVNHzm4VNte48cG8/iHfftx6Mnpz3Z/qLHtgcyEsAakgSCoduWBA5FlX1+GK2RU7RAro4yGH6AFdtt5thEFsOpCKJNJjvWomomusMiOBva77DEB2ZGUjdMFMo6dm/shiRwSEYlGMxNxTM2p2N46wuG8cToLB48Oun54z19YR6/+7VnMZMv41vPTnjyGFWPba9kJFt7Y+AIAiElETiCQlmHaQblCMDwI+fnipjLM0kfg9EMrNhuIyalODaRw06XLP9U3UAqZs/WTBZ4yAIH3edyDEopMsUydq2LI16Rx8RkASAsBdNLXnfjRlw30I2/e3QUl+ZLnj3Ok2fm8Pv/+xz64iG8fPc6PHtpAfNF90/gXne2wxKPoWQEJwNQbBNCAEpYsBWjaSilmCtoyCpMjsRgNAMrttvI+bkiimUDu1wKszEpXSxI7dATkaD4XLc9VyxjIBnGup7w4m0Cz6EnLKHEljA9gyMEv/6S7RA4Dh9/8LgnF2WPnpzGx755FMOpKP7oJ67Fq65dD5MCPxydc/2xJrMKRJ6sGPYEAPPFMjJNFvvb++PWDEYgLgJp4GY2GP5B1U2omoH5AMwoMBh+hBXbbaQaZuNWZ5ui4qFtk0RE9LXfbl7REQ8J2NJ79cVIf5cc+GKbUopi2b9pfumYjF958VacmMzjPw6cd3Xb335+An/2wHHsXBfHH75uD7rCIjalItjQHcLjp2dcfSwAmMyp6IuHwK1giQkABqVNW2Ju748jr+q4NK809fvthIJ5bTOap1g2wPMEimb4fnWUwfAjrNhuI0cnsugOi1jf3bqOVNUNxGQBIm//JYy4oBP3irJuQqcmrlnfvaz/eFdYDHwwT07RkVN0X5+s7tyaxj07+/CfB87j6HjWlW1+7emL+NRDp3DDYA9+7zW7EamknRJCsG9rGkcuzLvu6jGVVRx5bDfTnV4Mt5nyv5RE4DjkFf9e6DH8TbakQeCsc43fV0cZDD/SlmKbEMITQg4RQr5e+f4LhJAzhJDDla8bKre/iBCyUHP7/6vZxisIIccJIacIIb/djv12m2PjWexcF18xgMYuimba1mtXseNa0gkMk2JB0bBnQzfCdTr1YZGHJPpfc74SZcPA+p4Qcqq/i5533zWC3riMjz94vKVOPKUU/3HgPP7u0TO4fSSF3331NQgteQ/u25qGSYEfnZltdbevYDJrz2ObgEAWuKb0zIPJCEIihxMT/i+2RZ6gwBx9GE0yVyxDFjhQUJTY+4jBcEy7OtvvBXB0yW2/RSm9ofJ1uOb2R2tu/wPAKtYBfAbAKwFcA+DNhJBr2rLnLrFQ0nBpQcEul/y1DdNET9hZsS3wHGKy4Ls0uUxJxZZ0dEV9LSEk0BaApbKBeFjEUDLi+yTPiCTgN166A9M5FZ99ZLSpbVBKcd8TY/iXH47hxTt68YFX7Fx2FWYkHcW6rhAeP+VesV0qG8gqOvq7Gne2KShSMQmK5vwzwXMEW3tjgXAkkXgOeTbcxmgCw6TIlXTIAgeR45Fj7yMGwzGeF9uEkAEArwLw9y1s5lYApyilo5TSMoB/B3CvG/vXLo5NWEvyO11KjqQAIrLzTnVPxF9JktXgmsFkpOF9k1EZWkAtAIuajk2pKGKygLDI+14/e836LvzU3kE8dGwKj51ypqk2KcXfPjKKrzx1Aa/csw6/9pLty0qDgKqUJIWnL8y7JnOYqtj+9cdX7mwbJoXIc9b7qskVk+39cZyZKfg+dEngOai6GeiVIUZnKGkGKCgIsVaBmCMJg+GcdnS2PwHg/QCWHuX/kBByhBDyl4SQ2hbU7YSQpwkh3ySE7K7cthFA7cTWhcptV0AIeRch5AAh5MD0tDdhGc1ydDxndcL6Yi1vSzNMhAQestBMsS35pmCtDa6x4xUeD1la32C4P1xGM0wIHIdERAIhBAOJMPI+l5IAwJv2DmJ7fwyfefgUZvKqrd8xTIq/+s5JfOOZcbz+xo34pbu3NBxSvGNLGoZJXZOSTFZs//oadLZ10/ocRSQezQq7tvfHoZsUo9OFJrfQPgjQFvs/RTN8PQjMcEahpriWBA45RQ/cMZjB6DSeFtuEkFcDmKKUHlzyow8C2AngFgBJAB+o3P4UgGFK6fUAPgXgq9VNLbP5qz7tlNLPUUr3Ukr39vb2uvEUXOPYRBZbeqNNFchLUTQDSYd67SphsfnCwk2WBtfYQeQ5dIXFwFmYZRUNw6nIYnc3FfPG+9ltBJ7Db750B3TTxF9+50RD+YtmmPiTbx3DQ8en8JbbhvD2OzbZmk/Y1hdDb1x2zZVksbPdQLOtGxRhmUNY5MFzpCl5z47KStXhC/POd7QDqE3IZZwysaB46tXOaC/zJQ0yb523OEJgmgjcMZjB6DRed7b3AXgtIeQsLOnHPYSQf6GUjlMLFcA/wpKJgFKapZTmK//+BgCREJKG1ckerNnuAIBLHu+7a2iGiZOTedcs/8qGiUSkhWKbNFdYuMVywTV26Y/LgeqaVf/OtV3WkMijOywG4nls6AnjF144giMXFvDVQxfr3k/RDHz0/qN4YnQW77xzM954y5DtQWBCCPZtSeHQuXkUXOj4T2YVSAKHnvDK7y3dpIiIAggh1kVcE4VoKirhpqEe/OeB87iQKTa7y22jHfKlyawSiJUbhj3mCmXIYm2pQH0lRWQwgoCjYpsQch0h5LWEkNdXv1a6P6X0g5TSAUrpJgBvAvAQpfQthJD1le0RAK8D8Gzl+3WV20AIubWyf7MA9gPYRgjZTAiRKtv6mqNn2kHOzBRQNkzXhiNBgWgTem0A4DiCrrDQlg5XPZYLrrFLd0RCkFYwc4qO9d2hq1Y0NvaEA+Mb/tJd/XjBSBL//MMxjE7nr/p5sazj9/73ORw6l8F7XrwV995wlcKrIfu2pKGbFPvPth5wM5lV0R+XGxb7hmkuut8koxKUJgpRQgj+7z3bIPMcPv7gCV9rokXekgB4SalsoKgazGZwlaDqBhTNvGK4mRCCohqMYxeD4RdsF9uEkM8D+DyANwB4TeXr1U0+7r8SQp4B8AyANICPVm7/SQDPEkKeBvBJAG+qdMB1AO8B8AAsV5P/pJQ+1+Rjtx03hyMNk4LnCcIt2PglI80VFm6QU7S6wTV2iEo8BJ7AMINRcWuGiQ3LXFRUnVf87kwCWCfX97x4G7pCIv78wRNXdEezJQ2/89VncWwih/e9bAdevntdU4+xfV0cqajkipRkKmfP9o8Ci0VEPCQ0rUNNVcKATk3l8e/73Q0DchOR5zy3ncwpGghnrRr4zfWI4ZxS2cDSa1aJ5zCvNJe6ymCsVZyknLyAUtq03R6l9HsAvlf59z117vNpAJ+u87NvAPhGs4/fSY6O59Abl5F2QauraAaSlUG7ZomHxI4UeWXdhEFp3eAaOxBC0BuXMZfXEAv5N6QHsDq+3RFxWamMyHPo7wphvuD/5wEA3WER733JNnz4a8/hCz84i3fftQWZQhm/+z/P4tJCCR965U7cujnV9PY5QnDHlhS+9dwEimV9MfimGSazKrb3N76YI8TynwaAsMS3lJm0rxIG9KWD53HzcMK9VSwXEXkOedVbJ4mpnIqQwEPRDai6YXseg+FPcop+1YCzJHDIltjKBeNKKKWgFLbMDtYiTo6ETwTN29ovHJvIYpdLln+KbjgOs1lKuAX3hWaxE1xjl3RMhmr4fxmzpBkYXsHScF1XCEoAnkeVm4YSeO31G/D1I+N44LkJfOC/jmAiq+DDr97dUqFdZd/WNDSD4uBYpultFFQdeVXHOhudbVCy2NmWBR6y0Fpo0rvvGkE6JuMvHjzhSz0+zxFPO86GSTFXKC9+vtkQXfCZK5QRWiKBE3mOxbYzruLSvIKDYxlX5m5WI06K7ftgFdzHK5Z9zxBCjni1Y6uF6ZyKmXzZteFIAIi2GLseEnkIPNdWKcZcUcXW3pWDa+wSCwm+121rhglJ4FYcZO0KixC54EhiAOBtt2/CUDKCTz98CtmSho/cuwfXD/a4su2d67qQiIh43KGvdy1Tuartnx0ZCb1Ci5qISC1FUVthQNsxlVPw94+eaXo7XuPVkGRe0WFSCo4Q8ISgyE66gcY0KbIlbclw5GWCMnPC8J6ybmJ0Jo+ybmL/2TlMMDeiq3BSbH8ewFsBvAKX9dqv8WKnVhNVvbYby8qUUhCQlpbYqyTaGG4zXyqjL24vuMYOssAjHhJ8HQyTUzQMJyMrLqnxHMH67nCgEtkkgcP7X74DNw8n8Ic/ca2rcgmeI7h9SxoHxjJNvzcns5btX198ZclWNdCmVs6UiIgtv6d2b+jGG24awINHJ/EDl6wM3carznamWAZfkRxIAocsG5IMNIpuLF48LfvzDg7ZM/zF+HwJhknRFRbRHRLx/HgWxyaybPWjBifF9jlK6desoudRAAAgAElEQVQopWcopWPVL8/2bJVwdDwLWeCwKdV6oaloJrojQtN651oSEaktxWqxrEPkCLb3d7WkM19KX1xGyafR7dVOdW+DBEMA6O2SA9XZBoDhVBS/95rd2NLbekDTUvZtSUHVzaalJLY9tiuBNrVEWlwxqvLmW4ewtTeGTz98CnMFfw2ScYSg6NHnZjKrLK66STwXqItIxtUUVKPuHIPAsdeXYaFoBs7OFtATtlZxBZ5DOiZjckHBU+cyzAa0gpNi+xgh5N8IIW+2a/3HAI5O5LC9Pw6Bb31QSNEMpKLuBKJEZKGlgTA76IaJYtlwFFxjl56oBMOnWpK8qmNjImzrOcdlASGJZ84NFXZv6EZ3WGzalWQyqyIkcuhqMHRaDbSpJSIJIKT1hFKR5/AbL9sOVTfxV9894au0PYnnkPNgSFLRDCsRtnKcY/HwwWehVIbILX8MkwUO8yVWbDOAC5mSJR2raQISQpCMyqAUOFCRlfjpONgJnFRAYQAqgJehdeu/NYGiGRidzrti+QcAJii6HIbA1CPSovuCHeZLGnY2EVxjh5gkgCf+1Dvrpon13fY8xAkh2NgT9twlIijwHMELRlLYf3auqZWXyayC/nio4SpKNdBm6WPHJNGVSPPBRATv2LcZT52bx/3PjLe8PbcQec4Tj+RsSbsq55eADUkGmbl8GaE6FrOSwKHAYtvXPMWyjvNzRXTVCRCLSAK6QyKOTmRxfDIHbQ1ffNsutimlP7fM1zu83Lmgc3IqD5O6o9euEmkyzGYpIs8hKnrXUdUNEyJPbA2qNQPHEaRiku+GdAqqjkRUcjTEmo7Jvh/4bCd3bk1D0Uw8dc55BPpUTr0irbMetYE2tSSiomta1B/fsw43Dyfwj4+fxfk5f6RLijxBoex+kVS1/KuFghXbQUUzTBS1+taNHCEwKXt91zrn5ooQea6urh+wVrlS0YqsZGztykqchNp8cqUvL3cyqBwbt4Yjd9jw/G1EWTcRk4Ur3BNaJRH1Tre9oGgYTkVd0ZfXozce8t2QZEkzMJRwps8PSzziYdG3GvR2s2dDF+IhAT9w6EpCKV3sbDe8L7DsZ6k7LEI33SkgCCF47z3bEBI5/PmDx33R1SGEgJruFklLLf+qcCAo+dACkdEYO7p+ymLb1zQ5RcOlTKmhZA+4LCsBgP1n1qasxEnlFgJwE4CTla8bABgADla+GEs4OpHFxp5w3SUWJ5Q0A0kXbPNq6YlIriyZL8WkFASw1WFshXhIgOdaGAeUdRNhkUd3E6/3YCKMosYKE8DqhLxgJIUfnZlzVKAWVAPFstFwOLJKNdCmlogkuOpBn4hKeM892zA6XcAXnzzn4pabhxB3i+1ay79aRMH7eHiGNxSWCbNZCgfCPJXXMGdmCgiLgiPjg4gkoCdsyUqOTawtWYmTYnsbgBdTSj9FKf0UgB8DcAOl9D5K6X3e7F5woZTi2EQOu9a7o9fWTRM9K3g2N0PEo3CbbEnDxkQYsuCO5KUeIZFHWOJ984HNqRqGUyvb/dWj+tqutav9euzbkkZJM3DIgZRksuJEYucij4As29kOiRw4jriasHr7SAovvaYfXz54Ac9dWnBtu81iyTvc60jWWv7VIvHcml0yDjqZYhlygwFvSeCwwBxJ1iQLRQ0zebWp9OOqrGQqu7ZkJU6K7Q0AaivHWOU2xjJcmleQU3RXw2wiLSYvLiUs8taysouFBaUUumliQ4+9AcFW6e8K+aK7YphWZy/dwN+5HpLAoTcuo8CkJACA6wa6EZV5R64klz22nQfaVCGEoDvsvgf9L9w5gnXdIfzFgyc6/n4VOHeHJGst/2qp6sNNHw4xM+pDKa0U2yufbySBQ7bY+WMvo71QSnF6Jo9oC3kfS2Ul42tAVuKk2P5jAIcIIV8ghHwBwFMAPubJXq0CjroYZqMZJmSBqzsZ3iwcRyrhMO51hgtlA+mY7Erwjh0SEX9YAOZUDRt7wi1p6td3h5kGsoLIc7htcwo/OjNre+ViKmulRzaKal8u0KaWRESC6nJgR1ji8Rsv2Y6ZvIrPPTLq6radIvIEOZcK/qWWf7UQQgBKPJGqMbxD1U0YJhrO24g8B0U3fLOyyGgPmaKGhWLZlXP8WpKVOHEj+UcAtwH478rX7Uw+Up9j41lEZR4DidY7vKpmIhXzRv+cjEiuFniKZmAoGXVte42IhYTKZHznCm5KKQyDYn1Pa84r3WERIu9PO8NOsG9LGgXVwJEL9qQXk1kFEYlHtIFjz3KBNrXEQwJMD4YBdq7vwk/vHcRDx6fwWAuR9EuZK5TxpYPn8f6vHMHJyVzD+0s8h7xLy//LWf5dCXX9woXhLQVVB3Xw/mcNgrWDaVKcmsohJrtn5yvwHNI1spJOr/x5hRM3kn0AcpTS/4ElJ3k/IWTYsz0LOEcncti5rqvhkIkdVMNAwmW9dpV4WHStM6xoBuKygK5we7ragNV9SUTdvWBwSqFsIBWTWr7S5zmCdd2hNaNha8SNQz0Ii/alJJM5BX1xubHH9jKBNrWEJd4zK8Y37h3Etr4YPvPwKczm1aa3Y5gUT56Zw0fvfx4/94Un8U9PjOHoeBZPjM42/N1q4IwbF3XLWf7VQgEobPA3UGQVDUKdMJvlYLHta4eZvIqCari+yl4rKzk4Nof5or+Sd93AyZr33wAoEkKuB/BbAMYA/JMnexVwCqqOc3NF7HIpzIYADbt1zRKReNccPfJlDcPpiKux7Hbo67DftqrrGEw6s/urR288tKqX0pxgSUmS+OHpWVtJhFNZ1ZYTyXKBNrXIAo+QyHnyOgg8h9986Q5oholPfPek4xWZiQUF//zDMbzjvv34yP3P4/hkDj9x40b87c/ejE2pCM7MFGxtxwqcae0zU8/yrxYrsZIV20FiLq81HI6sInIcFkqrrzBiXI1hUpyazrsWrLccEUlARBJw6Nw8JhcUzx6nEzhpxemUUkoIuRfAJyml/0AIeZtXOxZkTk9bJ7ydLui1DZOC4wjCLl9JVpEFDkJFutCKJ7alK+cXr07bSTwseuKqYgdVNxAWhabs/pajKyQsFnpueqoHlTu2pvG9E9N49lIWNwz21L0fpRSTOQXXr3CfKvUCbWpJRCRkCponr8HGRBg/f+dm/PX3TuPrRy7htddvXPH+Zd3ED0dn8e3nJ/D0hQVwBLhpKIFfvHsLbhlOQKjs4+Z0FE+ft+92omomWlkwq2f5V4vIc8gy+7/AYJgUOVVD0uYbQxLY67tWmFxQoGom4i5KSJZDFnjwYYLnxhcsWWqq/Q08L3BSbOcIIR8E8BYAdxFCeADe/tUDyqmpPDgCbO9rvbOt6gaSEcmzNxshBImIhLyqtySDyCoatvbFPA2xqUdY5CEJnSlQc4qOa9bHXXt9qvHtZ2eLnkmHgsRNQz0IiRwePzWzYrGdVXQomol+G7Z/9QJtaklERExkFcQcHSLt84rd67D/7By+8IOzuH6gB8Opq+ccxmYL+Pbzk3j42BRyqo6+uIy33DaEH9vVj/QyMxwj6RgePj6NTLFs673Tame7nuVfLVYxpoFSuipOmKudYiWEyO5rJQkcsiX2+q52NMPE6EzetaZSIwSeQzIi4/RMAYpuYGtfvCO1hZs4qUzeCEAF8POU0gkAGwH8mSd7FXBOTuWxKR1t2D2zg6JZemAvSUSklhxJDJOCEHuWa15ACEFvXLaVeuYmhkkh8MT14dV0XO7owKefkAUet2xK4onR2RU1xlMObP+A5QNtaoksY2XnJoQQ/N97tiEqCfj4gycWJSvFso4HnpvA+770NN7zxUP4xjPjuGGoBx+5dw/+7v/sxRtvGVq20AaAzb1WwW5HSiLyrQfO1LP8q4Uj1qoZcyQJBkWHkh+OEBiUMt32Kmd8vrTo4tQueI4gHZUwvqDg2YsLgZdX2j6jVArsv6j5/hxqNNuEkCcopbe7u3vBQzdMjE7n8WO7+l3ZHgUantBaJRoSWvK4zKkaBnoikGzq/LwgFZVxMVNq62PmFA0DyfDiMr5bRCQBMVmAork/iBJE9m1J49GTM3j+0gKuHVi+uz2Zs4YN7XS26wXa1FJd5fGyY9cTkfCr92zDR+5/Hp95+BQ4juDRk9NQNBNDyQjeeedmvGhHn+1u0kj6crF901BixfuKLWqpq5Z/kZi9Y5Oqm56HXDFaJ1PUHL9OBNb7wY3mEsN/qLqBs7MFdIfbv9JKCEEqKmO+WMbT5+axZ6A7sOdEN6u4zrQ1fcbJqTwU3cROF4YjKaUgIJ57VodFHs1eM1JKYZi0bSE29YiFBFB4WxzVQimFQSnWdXnzvAcSYRybyAX2wOImNw8nIAkcHjs9W7fYdtLZrhdoUwvPEcRlEWXD2yLx1s1JvGL3OnzruQmERA4v3NaLl13Tjx39zqVJ8ZCI3riM0Wl7ne282rz9X2PLvytRNZOdIQJAplhe0V1mOQgIimUdiSiTva1GLsyVAJCOyjh6IhLyio4DY3O4fqAHcQ+HNL3CzSqOrXsDOHzeipd2YzhS1U10hQXP3+SSwCEs8k1pnvOqjr54qONdDZHn0BMRoWiNh9/coKAa6I3Lnj1WIiq19eLBz4REHrcMJ/DE6Rm864Ujy34eJrIKYrLQcBXIMCkErn6gTS2JqIhL84rnHdl33TWCWzYlsWdjV8sX1iPpKM7M5Bvej+cIdJOirJtNrUg1svyrReA4FFQdvU2mqzLag6obUHXTsYeyJHBYKGnYuPJiCiOAlMoGzs0VkfTBhVQsJKBUNvDUuXns2dDlWfaIVzC7A5c5dG4e3WER/S6cWEqagVSb3uTJJr2qFd3AQLKzXe0qfbEQSm3y9FV0AwM97tj9LYcs8EhHpbbr0P3Kvq1pZIoajlWSWZcylVPRZ0NCopumbWef7rAI3fReJyjyHG7dnHRlBWtzOoqL8yXbn+VmtNSGSTGbX9nyrxY3EysZ3lFUjaZcneRKsc1YfYzNFSAJnCt5IW4QlnjEJAFPn5/HpTbLRlvFzWLbH69Ghzl8fh7b+mKudCNNStHVpunfnojo+MRbKhvoDkue+m46oSsitmV5RdEMxGTe8/CeDT1hKC06RqwWbh5OQOItV5LlmMoq6LchIWkUaFNLRBICd1AbSUdhUuDcXNHW/dUmLrDzipUwaPcE7GZiJcM7corW1HmrGpIU9AE2xpXkVR3j8yXEPZ4Zc4okcEhGZRybzGJ0Ot/SvFk7cVRsE0KGCSEvqfw7TAipFSa/1dU9CyCqbmAkHcWeDa1LSKp4rddu5XEKZR2bUt51d50SlXgIHOd53HmhrGM4FfVc3tEdFsFzLL4dsN6fNw334AenZ69yarE8tlVbw5GNAm1qCYkcuID9/Tf3xgDAlm6bI6SplRM7ln+1VIsxO8FEjM4x14ReuxYW2766ODuThyzwvpQx8pw1ODk2V8DR8Wwgji1O4tp/AcCXAXy2ctMAgK9Wf04pfdbdXQsessDjb996M168s6/lbZV1E1GJb5vDR1jkQUBsXyVqhomQyPnKC5oQgr4uedEr1gt0w4TAkbZo2ASeQ39XCAW2BA/AciWZLZRxYiJ3xe3zJQ1l3bSVHmkn0KYKIQQ9EallP+p20h+XEZF4jNrQbYs819R7y47l31KsxEr/nxDXKqZJkS3pkMXmzzedTPFluMtCScNUTvX1ICJHCNLREKZzKp65uOD747STT9avANgHIAsAlNKTAFqvKhnLUtKMtqYx8hxBPCzYPiFmFQ2bUlFwPjOaT0UlTz19c6qOoWTEdbu/evTHQ9BMfx9E2sUtm5IQOILHT18pJZnKWrZ/9pxIGgfa1JKIiJaTRkAghGBzOmrLa1viOeQdFttVyz+ng9QUrPPpZ0qaAdogDXQlRM4Kt2EEH0opRqfztlcAO00yamVsHDo372mjrVWcHDFVSmm5+g0hRABzIPEM3TTRE2nvVWUiItoqtqvR7mkfugvEQ97ptqs2h302OqhuEQ8JEHme6SFh+c3fONSDx0/PXrECM1mx/bMjIwEaB9rUEpdF0IAd5kbSUZydLTSUv4g8QaGsO9I8OrX8q9KsZIXRHoplo6V3OYttXz1kihoWSmXP8z3cpCskAhQ4OJbx7bCuk2L7+4SQDwEIE0JeCuBLAP7Xm91igHofZrOUrpA994WcomEgEW57NLodJIFDlyx60kXLqzrW94Ta6n3NcQQDPWEmJamwb0sa0zkVJ6cuyyQmc/Y9tu0E2tQSlviAldpWbLuimZhYUFa8HyEEoM7kHU4s/2qReA65Fny9Gd4yXyxDauF4Lgsc8ooOM0DzDYyrMU2K01M5RCX/ykfqEZUFhAQeT/m04Hby6fptANMAngHwbgDfAPA7XuzUWkc3TMgi1/ZAEztDkpRS6CbF+m5/2P0tR1+X7EmxrehGR8J70nE5UEN6XnLb5hR4juAHNVKSqayKrpBgS4ttJ9CmFkngEBKCtbJQjW23o9sG7BfbhkkxV7Bv+VeLJHDIl9gFo1/JFMotnW8IseZ9mC7fGedmC/jh6CyOjWcxuVBCVtE6eqyZyavIq8FNLg6JvLWK5sPmlJPWaRjA5ymlfwcAhBC+cps9jymGbUqa0RGJRkjkFt0v6oV+5JT2d3ed0hMRYbhsB5RXdXSFxY7YHEZlAVGZh6obaz7yOhYScP1ADx4/NYu33b4JhBBMZhVb0h4r0MZ5EloiKmIur/lyJWc5hpIR8BzBmZkCXritd8X7UqAyWNT4fZ1XdJhN6noFjiCnmDBN6rs5j7VOWTdR1AykXHC+YrHt9qnGoEckAZmihomKHI4CiIg8ElEJ3SEBEVlAWOQ9nxMyTIrT03nEQ8GRjwQJJ6/ed2EV11XCAL7j7u4wAKvT1InEJjvuC2XDxMYOR7M3IioJ4Ik7lm2UUswVVHAE2LnOPUtHp2zsiTApSYV9W1OYyCoYrQwBTuVUW04kVqCN8xNJT1iEagRHbyzyHIaSEZy2Yf8ncByKqr3n5tTyrxbLPox1Pv1IySUtPSHE8cDtWubSvBWDLgs8YrKAVFRGKiojHZUh8hxmc2Ucncjh4FgGj52awZOjszgxmcNUTkFO0Vy3u5vKKlB1c803dLzCyZknRCldXJeklOYJIf4xWV5FcIQg1qHhhGRERCZfxnKOfsWyjkRE9LUdEGDpnNNxGfNFraW/o2aYmC+WsSERxpbeWEc7m8mYBHOSxbcDlpTkM+QUHj81g83pKCazCm7bnGz4e4ZJEQ87P5FEAjQoVGVzOopD5zIN7+ck3bEZy7+lqDrrfPqNvKq5khAoC8yRxC6KZmBstoie8PJNNZHnIPIcYjUlmmaYmMmplSIdIMRqLHWHRXSFRXCEgCOo/J+A4y7/mxDLcax6n6XnEM0wcXom75uAutWIkyNngRByE6X0KQAghNwMIFh5mQFAN0xIAumYTCO2gvtCsWxge3982Z/5jd64jIkFpeliu6DqUA0Tuzd0o7+7fe4j9QiJPJIxCaWy0bagI7/SHRZx3UAPHj81g1ddux66SW11tjWDNjXcZyVJkkBd6Iyko3jo2BQyhTISK6yS2U13rFr+RWKtvfeY/Z//mCtqkF3Ic5B4DlmWFGqLC5kSeIeStmoBXktZNzGdU3ExUwLIlUZB1bM4Wfp95X48x0HgCASOAwWFYVCIoWBI5YKIkyPnrwH4EiHkUuX79QDe6P4urW06pdeuUs99odqRarcdYbPEZAHN1EWUUswVy4hJAq4dSPjK/mhjTxjPXlxY88U2ANyxJYW//t5pPHl2DoA1FNsIwzQRaaKrynMEsZDlQe/nWYVaRtLWkOSZmcKKxbbAc1AVbcU5DaB5y79aRJ5DTtGxrru17TDcg1KK+WIZcbn147rAc1AqA35BmW/oBKWygfNzRVekopLANRV8RymFSQGTUlAKUApfBdStRmy/SpTS/QB2AvglAL8MYBel9KBXO7ZW6ZReu0o994W8qmNzKhKYzl5I5BGVeJQdaEQ1w8R0XsVATxg3DPX4qtAGrI4uR8hVceVrkdtHUuAI8NVDFwFY4T+NoACkJvWIyag9D3q/sDldiW23EW5jpTuu3HFu1vKvFpHnbEtWGO1B0ayhVadDw/UgYEmSjTg3V4DIE1ekO81CiNVVF3mrWA+J/oxlX000LLYJIfdU/v96AK8BsB3ANgCvqdzGcBGOEEQ73LlMRK9MzatGlKdi/guxWYm+eAgFm4lSOUVDQdVx/UA3tvbH25YQ6QSR59DfJSPPwiPQE5GwZ0M3LlW8pO10tgFngTa12PWg9wuxkIC+uIwzNuz/KLDiRWkrln+1SIIVD+8kRIfhLcWy7rqPPJMK1adY1nFpXmHa6DWInYri7sr/X7PM16s92q81iW6YEHjS8QGipe4LWUVra0S5W/REpYZdYJNSzORVhCUeezclkbbRIe0kGxMRgFghFGu9aLljaxqAZfVoZ4LeaaBNLUGU7mxOR213tlcqkFqx/KuFqzgElQPkWb7aWShpEDj3jusstn1lxmaLEHmOdZHXIA3PIJTSDxNCOADfpJT+Zxv2ac1S0gz0+iACvdZ9waTWuGQ7I8rdIiYL4DlSt1BQdQNZRcfmdATDyWgg/H9jsoBbNiVxejqPiYUSusPSmtVH3j6Swme/f9qWhARwHmhTix0Per8xko7iyTNzULSVQyoaaalbsfxbilXYM3sxv5ApaAiJ7h0/ZJHDQpGtvC1HXtUxsVBCKtr5czyj/dj6lFFKTQDv8Xhf1jxlo7N67SrVLh6l1Aqx6fZ3iE09eI4gEZGW7dplFQ2KbuDGwR5sTscCUWhXkQQOu9Z3YfeGbhRUfc06ACSjEl68ow83Dyca3rfZQJsqdjzo/cbm3hgorG7aSog8t6LcajKruNbZvxyiw+g0umEiX9ZaimlfilR5L7HY9qsZmy1AFpg2eq3i5FP2ICHkfYSQQUJIsvrl2Z6tUTqt1wasIjUuWwNhmmFa0oWA0heXrxjYMUyKmYKCeEjA3uHkik4NfqevK4RbNicRDwmYzquuhxwEgV9/6Xa8+dahhvdrNtCmlmREdDRw22mqjiSNYttFnqs7B1C1/GvG8WA5BK7+YzHaS1EzQOnVnsutwGLblyeraJjMqh3Lz2B0Hiev/DtgNSZ+ecntI+7tztpFr9glubmk1wqJqIhTU3n0d4cCfYCoDeBRNAM5VcO2vjg29oQD1c2uR0jkce3GbozPKzgxlVtMI2NcSbOBNrXEZDFQTjB9cRlRiceZBrptniPQTYqybl5VVLth+VeLVJGsMDpP0SNnGApLEtnp2SM/cXamgDDraq9pnFR21wD4DICnARwG8CkAu73YqbWIopnojcu++TB2h0VQCgwng9vVBizf8JDAY7agQjdN3DycxGAysioK7SqEEGxIhHHLpiQkgWC2oLoSVb+aaDbQppZ6HvR+hRBiDUnaiG0HsOzgolPLP8OkuP+Z8boDl5LAsUhvnzBXLHuinecIQYG9xosslDTM5FXEQqwJspZxUmzfB2AXgE/CKrR3VW5juIBqGL4ylY/KAgYSYXSHg29RtK47hL64jJuHk6vi+dQjKgu4cTCBkXQUc0UVRZu2h2uBZgNtaqn60S71oPczI70xnJ0t2Lr4UpcUyM1Y/u0/O4e//f5pPDE6u+zPeY5AM8xA/Q1XK5mChpBL8qBaWGz7ZSilGJ3OI9KihI0RfJy8A3ZQSq+v+f5hQsjTbu/QWsZPy/8hkcc1G7p802lvheEAhfG0CscRDKWi6IlKOHopi7mCip6I1NEABV9Amg+0qSUZlTCbKwfGAWYkHYWqmxhfKGFghdkLjhAUywZSNbc1Y/l3YCwDADg/t/JQpqqzlMFOomiGZTVr0++5rJv40ZlZ3Lk13fBYKgs85lmxDcDqameKGnoDllHBcB8nR7tDhJAXVL8hhNwG4HH3d2nt4Te9dpXVUqCulufhhK6QiJuHE9iYiGC2oK75oAlKAaHJQJtaeiIiykZw/pYjvZUhyQZSEpHnrlr6d2r5RynFwbE5AMC5RsX2Gn8/dppS2dnf/+HjU/jTB47j5FTjkKTq6kWQhom9gFKKU9N5RJl2nQFnxfZtAH5ACDlLCDkL4AkAdxNCniGEHPFk79YIimYiHZPWZFHI8A6B57C1L4abhhIoG+aaDsIhIK5YnEUkIVC67YFEBAJHGobbSPzVWmqnln9js0XM5MsQebJiZ7vaRWd0jpyiOVqxOD6ZAwCcrPzfDsoat3jMFDXkSlogA7Gckld0/NkDx3Hkwnynd8W3OHkXvMKzvVjjqIaBZDTW6d1grFJ6ItJiEM74Qgk9azEIhzQfaFNLWORBYNmbBeHiWOQ5DCUjDWPbRZ4gU9IWn1fV8i8Vs3+KqEpIXrSjD989OgnNWF4qIvEcciqTGXSS2ULZUXZCtcg+Nd24s11FKRtrNpacUorTUznE5LXx/P/1R2N45OQ0fnB6Br/18h24Y0u607vkO2yffSilYyt9ebmTawE/6bUZq49qEM61G60gnNwaCsIxTAqeNB9oUwvPEcTDQqB8hO3EthNCAIrF59WM5d+BsTmMpKO4YaAHJgUuZkrL3k8SOORKbHi3U5imFVYm2xyOVDRjURZ0yoaMBLAuqBY80m2XddP3bkszeRX58srJrauFszMFfOPZcdyzow9b+2L4k28dwwPPTXR6t3zHGmtv+Q+/6rUZq5PeuBWEY1K6ZkJw3Ai0qSUZkQJVbI/0RjFf1JAplBvet/q8nFr+5RUdR8ez2LspicFkGABwPrO8lETkOZQ0w/cF02qlqBkwTfsrM6MzBZgU2JSK4Nxc0db8hyRwyHpwQUUpxZGL8zhyYd63cyimSTE6XUBsDchHKKX43KOjiEoC3vnCzfjIvXtww2ACn374FL588MKalS0uB6vwOgzTazPaTUjkMZyKrpmYd8OkCMvuHeq6wiJ0MzjF9ua0JVFr1N2muNw1dGr5d+h8BiYF9g4nsJdKUncAACAASURBVKEnDI6sPCRJwGLbO0WxrDtatTg1ZUlIXrlnPUxqdTIbUZ0BcDu2fTavIqfoKKoG9p+ds3UB2W5m8iqKmr4mutqPn57FMxcX8NbbhxEPiQiJPH7nVbtw17Ze3PfEWXz+8bOs4K7Aiu0OY+m1mS0Qo72s6w6B48ia6G67EWhTS0SydNtBYbPN2HaBsxxJmrX8i8sCtvfHIQs8+rtCOF9HRlJF1Vb/e8+PzBc1R8PCJyfzSEUl3Lo5CcCebttqHlFXhyRNk+J0pWPcFRYRFnkcOj+PsZmC60V9sxgmxenpPLpk/2RmeIWiGfiHx85gczqKl12zbvF2kefwmy/bjldfux5fPXwRf/Xdk2wVC6zY9gVMr81oNyLPYdMa6W67EWhTiyxw4DkE5gQSkwX0xeWGse0iT5BTdceWfyaleGosgxuHEou6+KFkZEVHkuoQJqP9zDkdjpzKY1t/DKmohJ6waMv+D7BWShQXL6hm8ipK2mUdtCzwSEYknJ7J49lLC75YKZnOKlA0E5IHYUF+48tPXcBMXsW77xq5ah6GIwTvumsEP3PrEL57bAp/9M2jvnh9Osnqf0f4GMOkTK/N6Bj9XSEQQgJTNDaNS4E2i5sjBD0RKVAnj5HexrHtIs+hoOiOLf9OT+UxX9Kwd1Ni8baBRASX5kt1V05EjkPO40jvss68npdS1k0ommHbmSev6rg4X8K2vjgIIdjSF8Npm8W2m7Ht1Y5xfIm7B88R9MZCyJY0HDib8Wwo0w66YeL0TGFVpxRXmcgq+K+nLuDu7b3YvaF72fsQQvDmW4fwi3dvwZNn5vB7X3vOtfdDEGFVXgcplQ2kokyvzegMksBhUyqC+ZL/dI9u4lagTS2JiBisIcl0DJfmSyt2k0Weg6Jbln9OOnMHxjIgAG4aulxsDyXD0E2K8ayy/GMJxHNHkrOzBTx5ZhbzxdX9/nZCqWw4EkBVC+utfbHF/5/P2BuSlAXOtSTJ6awCVa/fMe4OS5B4DgfH5nAhU+yITngqq6K8RpJRP//YGfAcwc/dsanhfV917Xq872U7cHQihw/99zPIrNHP4+p/V/gYxdCRYjGujA6yrjsMfpV3t90KtKklJouBGvzZnI6CwipAV4IAoMTZ8zowNoft/fErOnqDlWj4C3WkJBLPoVDWPPsbGibFVFaByHN4aiyDc7P+0fV2kpyqOZo3OFEZjtxWLbZ7YzApGkqSAEvmkXWh2K52jBt5dodEHsmIjBMTeRwdz0Jr4zyKZpgYncmvia72oXMZPDE6i5++edB2/XLX9l787quuwcX5Ej7wlSOYqHMRvpppS7FNCOEJIYcIIV+vfP8FQsgZQsjhytcNldsJIeSThJBThJAjhJCbarbxNkLIycrX29qx317DgTC9NqOjSIIVerKqu9suBdrUEpb4QCVJjlSGJBsVSYQQxCT7BcN8sYyTk/krJCSAJSMBgHN1hiQJIaA1vt5uk1M06CZFRBKQjMo4PV3AMxcX1rxOfK5QhuxAtnhyMo/13SHEK4Vutei247ftVmz7ZFatG5C03GP2xmXM5Mt4aixzVSqqV0wsKNBN948zfkM3THzu0VGs7w7hdTdudPS7Nw8n8NF79yCn6PjAl49grMGF/2qjXe+M9wI4uuS236KU3lD5Oly57ZUAtlW+3gXgbwCAEJIE8GFYkfG3AvgwISSBAGOYFALTazN8wPqKVdtq7G67GWhTiyRwCIt8W7tnrdAblxGThYa67UREcjQ899S5eVAAe4eTV9welnj0xeUVhyQB74rtmbwKgbOOrTxHkI7JyKs6DozNrVlZCaUUCyUNsoP5hZNT+cUCGwCSUQk9EdF2uA0BUGrhAkczTJyZyTtOokxELDeQ/WfmMDG/sitOq5R1E2dnC+heA2mZX39mHBcyJbzzzpGmLix2ru/CH7/+WoAAH/ivIzg6nvVgL/2J55UeIWQAwKsA/L2Nu98L4J+oxQ8B9BBC1gN4OYAHKaVzlNIMgAcR8Ph4ptdm+AWrux3FwirsbrsdaFNLIioFplNKCMFIOmpr+d8JB8fm0BMRMdIbvepnA4mVHUkovPHaNk2KiQXlqlXDrpCIkMDjqXP+sovzmqqk5uC5jHXxafPCM1MsYyavYltffPE2Qgi29sZsx7ZTAEq5+e7yxIKyaCTglIgkoCcs4vnxLE5MZj2zOZ1YKMGkVvNsNZMplvHFJ8/h5uEEbtnUfK9zOBXFn77hOnSFRPzO/zyLA2NzLu6lf2nHu+MTAN4PYOk7/Q8rUpG/JIRUhT8bAZyvuc+Fym31bg8sTK/N8BPre0IAVl932+1Am1p6IiLKAelsA5Zu+8xswbXX2DApDp7L4OahxLKe3EPJMC5kSnUfT+A45BX3l/lzig69TlFZtYsbnVn9shJFMzA2U8ATp2fw/HgWpgGkHGQ6nJy0Cupt/bErbt/aF8MFm0OSEs8h2+RrXNZNnJ0poDvcvGe1wHNIx2SMzys4fH7eCvRxEVU3Kl3t1e+r/U9PnEVZN/HOOze33CTs7wrhT95wHQYSYXz0/qP4/olpd3bSx3habBNCXg1gilJ6cMmPPghgJ4BbACQBfKD6K8tshq5w+9LHexch5AAh5MD0tL9fPA4EUXn1J0wxgoEs8BhKRVad77bbgTa1RCRhmaOQfxnpjaKsm7jk0rL6sYksCqqBWzYll/35YDKCsmFiOqcu+3NZ4JDz4P02nVcWJSTLcYWsxKcphM1CKcV8sYznLi3gidOzGJsrIioJSEVlR4mgAHByKgeOAFt6ryy2t/VZQ5KNEkkB67jSrB3fxfkiTGq/E18PQgiSURmaQXHg7Bxmcu4N512aL4FSuC5T8xsnJnP4ztEpvPb6DYvzGK2SiEj42Ouuxa51cXz828fx9SOXXNmuX/G6s70PwGsJIWcB/DuAewgh/0IpHa9IRVQA/whLhw1YHevBmt8fAHBphduvgFL6OUrpXkrp3t7eXvefjUtYS3mW5pPB8AsbesKglK6q7rbbgTa1RES+MugXjL+X3dh2uxwcy4AjwA2DPcv+fKg6JFlHSiLyHHIud7brSUiWo6sSL/3UuQzOzOQDLSvRDBPj8yU8eWYOh89nkC3qSEUlJCJS0/KGU1N5DCYiV2n4q8V3NcZ9JUSeoKAajv+2imbg3Gyxpa72UmKygJgs4umLCxidzqOsmzBMCtOkTX2GFc3AmMv76EdMSvHZR04jERHxxlsGG/+CA6KygN9/7R7cujmJzz4yin/70VhgjqdO8dQKg1L6QVhdbBBCXgTgfZTStxBC1lNKx4m1FvE6AM9WfuVrAN5DCPl3WMOQC5X7PQDgYzVDkS+rbjeIlMoG0jGm12b4i2p3+/xcaXHAKOhQuBtoUwvHEcTDAlTddDRU2CkGEmEIHMGZmTzu3t56M+LAWAbXrO9CtE5hO5C0iu3zmeJi1HctPEegmxTlFfyTnbKShGQ5ZIFHOsZhbLaIhZKOnevigXgtq+RVHRMLJVysdFhjsoBUNNTydimlODmVX1abm4rJSEYkW0OStbHtToKSLmRK4DwYbBZ5DumojLHZ4uJFIAEBBQVHrCAeQgj4yr85jlT+b92P56x/cyBQddOT4Wu/8dCxKZyYzOPXX7Ld0WtoF0ng8MFX7sKnHz6JL+4/j7JB8bbbh1ddfdQp37l/JYT0wpKHHAbwi5XbvwHgxwGcAlAE8HMAQCmdI4R8BMD+yv3+gFIaWFW9ahhIxWKN78hgtJn13WGMzRYdDVL5HbcDbWpJRiRcyJQCUaCJPIehVKShI4kdZvMqzswU8PYVQi1isoBkRLLhSOIsRGclGklIloMjBKmojGxJw/6zc9i9oRvJqH8vNk2TIlMs4/xcEZmiBoEj6A5Jrn5ep3MqFkraFcORtWzpi9p2JKGwGkx2CzVFM3AhU/Tsgp8jloxoKZRSUFghWLX/NimFoVdvs7qu1Ub9avfVLqg67nviLHaui+NFO7xTC/Acwa/esw0iz+ErT12AYVK8Y9+mVVVwt63YppR+D8D3Kv++p859KIBfqfOzzwP4vEe7114omF6b4UtCIo/BStR2zyrobnsRaFNLV1iEHiC/2JF0FPvPZkApbelEdmAsAwDYO7yyK8FgMozzmcb2f8uXdM6wJCRq09kFXWERZd3E4fMZbEpFsSkVBeejC05FMzCdUzE2W4RmWPKo5YpGNzi5JDlyKdv64jhwNoNS2WioBecrse12DQHOzRUgVDrK7YSQStzP5f+sef59/zksFDX8v1dd4/nrwRGCX7p7C3hC8NXDF2FS6sowpl9Y3V41PqTqr8302gy/sjERhkEpzNWgnfMg0KaWiMQ7SuTrNJvTMSyUNGSKrQ0mHhibQzomYyi58rDUYNKSJdXTYfKEoOhS8EhO1aGbZksdXkngkKrIDJ6+MO8bt5KFooYfjs7i9HR+scj2Ykm/ysmpHASOYHP6aktHwNJtUwCjM42725LAYcGmNr9Y1nFpXlkM0WF0jvNzRfzvkXG89Jp+bOt343K4MYQQvOuuEbz2+g342tOX8LlHRleNhpsV222mpDG9NsPfhEQeA4mIK1HLncSrQJtaZIEDzwUn7r6aJGmnSKqHZph4+vwCbtmUaHgcG0xEUNIMzOSXd/yQBPeGJGdyqmMJyXJUZQalsoH9Z+eQKSzvptJOJnMKRM66EGhHSuHJyTw2paN1H2urgyRJWeCxYPPibmy2CIHj2t7VZlwJpRR/9+goQgKHt75guK2PTQjBO+/cjNfdsBFff2Ycf/P906ui8cOK7Taj6gaSseAvzzNWNwOroLvtZaBNFUIIEhHRk3AWL6h2Ks+0oNt+/lIWJc1oKCEBrM42gLpSEpHnkHOhs22aFOMLCqIuOs/EQyJknsdpFzTurUApxVRW9cxVZykmpTg1fWVy5FKSUQnJqL0hSWsQ1mz4GakOe3aFOjVKxqjyozNzOHR+Hj9z23BH5ISEELxj3ya84aYBfPPZCfz1w6cCfS4CWLHdfioT4wyGn7G62+FA+257GWhTSyIieRY77jZRWUB/l9yS/d+BsTkIHMF1A8tb/tVSlZnUG5IUeQ6KZrS8MlCVkLid4heWeOQUvaMXU4Wy4clzq8fF+RKKZQPb6wxHVtnWZz9JkgBQtJU/I2dn8pAFnq36dhhVN/D3j41iKBnBj+9Z17H9IITgbbcP46f3DuKB5yfxqYdOBmYFcTlYsd1GmF6bESQGEhHoRnC7214G2tQSDQmBOgmMpGMtxbYfGMvg2o3dthxYusMiukLCio4kBK3Hts/mVfAeFWmEwJOkS7tki1pbpwLqJUcuZUtvDBczJVupjI1i27OKhulcmTWifMBXD13EZFbFu1440vEIekII3nLbEN50yyC+c3QKn/xucAtuVmy3kZJmIMX02oyAUO1ue5Hy1w68DLSppSskIBmVAvN3GumN4tJ8CaWy8wJ3YkHBhUwJe5fxX67HYDKCc5mVUyvVBl3PlaDUkpB4VajJPI+ZfOd025M5xXM5VC0np3IIiVzDpMBtfZUhSRsyG5nnVxySPDNTQEhkXe1OM51T8Z8HL+COLSlcXyesqt0QQvCztw3jZ24dwkPHp/CJ75wIZMHNiu02oupWsc1gBIWBRARaQLvbXgba1EIIwbb+GDQzGOmbI+koKICxJiwLD4xZ8QZ7h5ePaF+OwUQE5+eKdV0FCIit7mg9cqoOzfBOZhGWeEzn1I4kTGqGifmShpDYvlP1qak8tvTGGg4Wb3EwJCkJXN2B64Wihtl885aNDPf4xx+cASjwjn2bO70rV/HmW4fw1hcM43snpvEXDx4PxLG2FlZstxOm12YEjLDEY0NPKDBd26V4GWhTS0QSsKU3ikxxedcNP9FKbPuBsQw2dIewoSds+3cGkxHkVR3zdYotsUVHEi8lJEBlwM+gKLRwQdAsOUUHAdrW8dUNE6PThRWHI6skIhLSMcmWbrsa2760QKKU4vRMHlEPbQwZ9njm4gIePTmDn7x5AP1draeQesFP7x3E227fhEdOzuDPvn0cuhGMWRmAFdttg+m1GUFlKBkNZHfb60CbpWzoDiMeElrq0raDdExCXBYcF9uKZuCZCwvYu8l+VxtoPCQp8RzyTTqSVCUk9SLj3YLjSEesMGfzKkQX7Aztcm6uiLJh1k2OXMqW3piz2PYlvuXzRQ0LxbKnnuGMxhgmxeceOY2+uIzX37Sx07uzIj958wDesW8THj81gz994Di0gBTcrNhuE0yvzQgql7vb/i4il0LhbaDNUjiOYMe6OApl3dcXJoQQbO79/+zdeZgk91kn+O8v7oy8Myuz7qOrq7vVraPV6rZsSZaNbbCNMTLYeGxgwRwL++zCYGCBhZ3ZWXZ2vIxhn4UZs8MDOzuLzWHhgeEcG8Yey7aMJVnduqVWq7ql6rvrPvKM87d/ZGarjsysjMyIzKyq9/M8dreyqiujoo548xfv7/uG8XqLSRI1L11fh+m4ON1C5N9m48nKKnjjRBKGgmm3NbwiZ9gwbTfwr3NIFjGf627fNuccC7nuRf4Bu0+O3O5INlJNL2lxk+SmYptzjksLeYQVGmDTa3//0k3MLRfxEw8dgtqF1rtOff+pMfzUw4fwxOvL+PTfv7onCm4qtrvEsB2kw9SvTfam8ZQO23V9m+Zl2A6WC0ZgbReOyyEJwQ60qSeqyTg0EMZan7eTTA+EcXm56Knv8enLq1AlAXeNxD09VyqsQFdEXG2wSZIxBnDWVnziSt7sygAUTRaxUbJgdjHisWg6gfai1zM7n0NElTAcb62NoNa3famVvO3q2PaalYKJnGHvOu6dBOvKShF//NQV3DMWx4OH070+nJY9cnIU/907pvHUGyv4jS+d7/uCm4rtbuFAhML6yR6lKxKG4ho2Oljd5pwjX7axXCjDcfntHN8gVoEdl3c1wWGz8aQOVRb7ZtR3PYcGIjAdF9fXmqeE1HDOcXZuBSfHElAkb5cNxtjtTZJNnsFzIkmlhaTU1X0w7ba7tKPVqYt+ml3IYyYbafkO7EymukmyhbskqiRitfo5uS7HxYU87WHqkYVcGX/xzDV88tFn8TN/+gwsx8VPPzy95+68f/CeEfz37zyMp+dW8akvnu/qi2Gv6Du9C6hfm+wH40kdN9fK4Jx7+qVsOy42yhZcDgzGVIwmYoiFJDDGsFYysV6yfb/o2q6LaI9e3EqigDuGonjmSmUluB8vYLfHti/mb/dUN3NttYSFnIEfOD3W1vNNpHQ8XU0yqYejlrXdektB3rBhWC4ianfaEGRBwFLeQKpLdygX8mXoXXzBaNgO5pYL+Mh9rX+NE7qCgYjaciJJbaP1Ut5A0XQwEFHbPt69znZcPPXGCkYSIUyk9MDvwq0VTfzjxSV8fXYJ529uAACODUbx3779EB4+kuna97XfPnD3MESB4Xcfu4h/9Z9fwf/wHTO9PqS6qNjuAurXJvtBWJUwnNCwlDMRD+1e4BRNGyXLgSQIODQQRjam7RiEko1pWMit+15sWw5vaehKUBK6gtFECAsbRk/GHe9mLBmCJDC8sVTAdxzb/f1rkX9e+7U3P9+Xz1vIlS1EtZ3fO7IgYKNsIeshBWElb0LoYpuQXo0APOJh5bddluNirWgh1cXvnTcWC3A5cGSwtc2RNUeyrW2SrIxt5yiZDi4t5nv2Yrhf/NVzN/DZJ+YAAJos4Gg2iqODURwbqvwv6cPXvmDYeOL1ZXzjtUU8f20NLgcmUzp+5G2TeMeRDIZabBfqd++7cwgCAz7z1Yv47a+8hv/7h071+pB2ONjf7V1S6dcO9/owCOlYZXW7BM6lugWH43LkyhZslyOpy5jJRpDQlYarNrFq4eV1tXw3Ludd3VhWz6GBCBZzBkzb9dx6ETRJFDCZ1ltOJDl7eRWTKR3ZaHsX59uJJKslnBjeWWwrkrdEEs45bnS5hUQSBVhlC0XTCTz9pDaxspsLNK9VC+ajLW6OrDmcjeCJ15dRMOyWzsvVlSLKXbwj0Y9yZQt/fu4qTo0n8O47srhwK4dX53P4y+eu395HkY2quKNaeB8bjGE6E25pI3DZcvD03Aq+MbuIs3OrsF2OwZiKj9w3hncezWAyvT9rke86MQSBMfzbr87i6blVHMp4+z4OGhXbXcBA/dpkfwirEobiIazkTcQ2rW4btoNc2YYoMIwkQhiOay1deBVJQCqsoGQ6vsZ/uZx3ZaBNM4ok4NhQFC9dX8dApP9WkKYHIvj23MquL3SKpo2Xb2zg++4dafu5xjfF/50Yju14uywKyHvIcu92C8lm60Ur8GJ7uWBA6mLkH1CZHJnSFaQ9tnYcqRY1lxbzuGes+dRBBuDWRqmlO2P72X88dw1F08FPvv0QJtNhfMexLIDK79FLiwVcuLWBC7dyeOXmBr4xuwQAkASGw5kIjg1FK0X4YBSZqArGGCzHxXNX1/CN1xbx1BsrKFkOkrqMD9w9jHccyeDoYPB3Y/rBe44PYiQRwsNHBnp9KDtQBRgwx+UQBerXJvvHeErHrfUSXC6hYNgwbBdhVcSJ4ShSEdVzDNtQTMMrNzd8z9rt1kCbZgYiKjJRFWtFq+8KjEMDYXz5/DxWCmbTAuu5q2twXI7THqZGbpeJqlAlAVcabJKstRi0ehdgtWB2PWkGAHRZwmK+jJFk60N9vOKcY37DQLjLd2Zm5/M4Muh9NXDzJMndim1dkVC2nK5GcvabhVwZf/fCDbz7juyOVWZVEnFiOLblBelS3sCFWzlcmM/hwq0c/v7lW/ib528AAJJ6Jf1odj6PnFHZ+/LwkQG842gGd43Ee/Iz0mv9OpCHiu2AlSwHqYh8IF5VkoMhokoYjGmY3yhjMBbCaDKEmFa/raQVsZAMvwNJuj3QpuFxMIaZbBRPvb4Mu8sxbruZzlQu9G8sFZoW22cvryKsiDg+5K2XdzOBMYwlQ7skklRW9nYrtjnnuLEW/CCbejRZwErRhOUEl+1dNB1YtgupTm97UIqmjetrJbzrWMbzv42HZGSiakuJJJos9nQvRT/4k6euAAB+6K0TLb3/QETFwIyKh2Yqq7W242JuuVgtvjfw+mIBpyaSeOfRAZyaSB7oFzL9jIrtgBm2gwHq1yb7zEw2iulMxJcLpyaLSOgyypbj24W42wNtmtFkEUcHo3h1fgMD4f5ZdTlUSyRZKjScCsk5x7m5Vdw7kez4hcJ4SsdL1zeavk/ZcrFbW3jBdFC2g++brocxBoZKT3UyoPSGXNmq9Ft00cXbw2zae0E10+IkyYNubqmAx15dwPedGm17/4MkCpjJRjCTjeB77h72+QhJUPrjarSPMaAnFwVCgqRIgq8rVEMxDQWfxpz3aqBNM4MxDYmQ0tWc5t3oSmV4SbNNkm8sFbBSNHGmzRSSzSaSejXyrf45kARhy9CTRlbyBsQe3ikUBQHLheCmSc5vlLveduh1cuR2M9kIbq6Xb2/sJPV99ok56KqIj7YZoUn2Liq2A+S4HILAep6KQEi/S+hKJWzZB70caNOIIDAcHYyibDmepjYG7dBAGG80uf3/9OVVAMDpic6L7domyWsNJknKIkNul2K7kkLSmxaSmloEoF/TVDezHBerRav7xfZ8DoMxte19BbUi/VILrSQH1YvX13H28io+enq8bvwl2d+o2A4Q5WsT0pqQIiKsitXBJp2xXRea3H+/2sKqhMOZMNZK/TPKfXogjJvr5YarzefmVjCTifjSMjGerBTbjTZJKi0kkhRMp+cb7GRRgGm7KAUwIbQXkX9AZWX7SJstJIC3SZIHEeccn/3WHNJhBR+8h1o/DqL+uyLtI6btYCB8cCdkEeLFcCLUUhvBbiyHI9Snd5NGkzpCsoiS2R+j3A8NRMABXF7eWQBvlCxcmM/h9FTnq9oAMBTXIAkM11brF9uSKMCwXdhO45HLK3kDQh8sXnAAuZL/49RXCmbXI//WSxYWqsN62hULychG1dvtKGSrJ15fxoX5HH74rRNQexxJSnqDiu2AUb82Ia1J6oovqST9MNCmEVFguGM4hrxpw/W5DcGwHc93BmqJJPX6tp+9Wpk495YOIv82E4VKIkmjlW2gssfFsBsX2zfXywj7HBHZjpAsYj7nb9825xwLuXLXv3dn53MAvE+O3G4mG8ElKrZ3sB0Xn3viMsZTOt59x2CvD4f0CBXbAaF+bUK80ZVKLJjVZGWzFS7vnySSeuIhGRPJENaKnbeTOC7HesnCUr4M2+WeN2CmwwqimoTX69z+P3t5BTFNanvTXD3jKR1XV+r3bAOVFeNGxXbBsFEyd48G7IaQLGKtaDVdhfeqZDkwrOAiBRuZXciDATic6Sw1ayYbwa2NciVNhdz25fPzuL5WwicemOyrTduku3r/W2ufKlO/NiGeMMYwHPcnlUTug4KsmcmBcLVtor12kqJpY7lgIGdYGIqrODOVwv1TKYiMedqAyRjD9EB4x8q243I8c3kV900mfS0QxpM65jfKDT9vAQylBl//lYIJoU+KFcYYXM5RMPxrB9oodT/yDwBem89hLKV3PFRq5vYkycbpNgdN2XLw+W9fwfHhGO5vEK9JDob+viLtYYbtIK0Hk8NKyH6ViihwnM7aK4Q+GWjTjCwKuGMoio2y3XKqRSWpwsRy3oAqCbhrNI4HptOYyUYR1WQIAkM6qnjeuHdoIILLy4UtRfrsQg4bZRtnfGohqRlP6eAArjdKJJEE5BrEx91YK/VFC0mNJAhYKfrXSrKQM7qeQsI5x8WFfEf92jW1OyCzC7mOP9Z+8dfP38Bq0cKPPzi17xfeTNv1Zc/NftXfV6Q9jAOIULwPIZ5EVAmyJHR0e97to4E2zaQjKoZiKjaa3HbnnCNftrGUN1CyHEyldbx1Oo17J5IYiKg7Bs1kIhrKtrcL3nQmDMvhWzYunr28CoEBp8abj9/2arw65rxpIkmdC3Y/tZDU6IqI+Q1/im27+kKqau9MOgAAIABJREFU28X2Yt7AWsnCUR+K7agmYzCmUt921XrJwl+cu4a3TadwfNP49f2Ic461sgnbdftm83e/6Z/fXPuI43KI1K9NiGeMseqAm/Z+YffjQJtmpjMRcI4dfeqG7WC5YGClaCKuyzg1kcAD02lMpMNNk1YiqgTGvX3u0wNvjm2vOTe3imODUcTayF3eKFkN4w1HEiEIDLjaJGu7aDpwt7XCrBRM9NvCoCwKKFejCDuVN2xw3v3Iv04nR243k41SIknVF85ehWE7+NG3TfX6UAK3XrYwmgjhnvEECqbd8b6b/YiK7QBYDkc6TP3ahLRjIKrCdtv7Zd2PA22aqY1yXytZWzY7cs5xbDCKBw6ncWIkhoSutNSvHGpjk+loIgRZZLf7tlcLJi4u5huOcN+N5boN+8ZlUcBIIoSrDVa2GWPgnMPcdvy31kv9mezEqr3WHVrOdz/yDwBm5/MQBYZDA51tjqyZyUSwkDN8OSd72a2NMr744k185/HB28Oc9ivLcQFeGZAV02ScGI5hrWT5nra011GxHQCBMaR9GAJByEEUVSVIgreNfjX9OtCmmWxMRTqsYKP85mbHM1MpDCdCbWXyDkQVFD3cGZBEAZPp8O2V7XNXKlMj2xnR7rgcu70mGE/quNoga7tm82px0bRRMJ2+zCfWpMo0yU4t5oye3AmdXchhKq371p5T6/0+6MNt/uTJyxAYww/dP9HrQwncesnCkWzk9s/nYFzDRErHqg9pS/vJ3roq7RGaIlC/NiFtEgSGbExra7NNPw+0aYQxhjtHYnjw8JubHTu5K5YOe78zMD0QxuuLeXDOcXZuBSldaWu1s2jaGIxpTV8sjad03FgrNV1931xsrxTMXoR0tCSkiFgumG29MKwpmjbKdvenYrq3N0f600ICAIdriSQHuJXk0mIeX3ttEY+cHEE6sr+H2hUMG7GQhGxM2/L49EAYSV3Beh9Ny+01KrYDEFYk6tcmpAOZiAqrjVYSx+3fgTbNSKKwY7Nju2rtFq2mnACVi+NG2cZCzsCzV9dweirZVsFvOi4yUQ1RTWpYTI8nQ3B5JV2kHlncmkhyc61PW0hQuYvpcu/55pvlSlZPXkzcXCujYDo4MuhfjnpEkzAc1w503/bnnphDVJXwkdNjvT6UQLmco2Q5ODoY3dHiJggMx4djkEQBRR+iXPcDKrZ9JosCxpIh6tcmpAOxkHy7kPGC75EkkiApkoCoJjedxLjdoeqK5BdfvImi6bTVQlIr7qOa1PT5J6o9rI03SQrIVYvXfm4hqREYw3oHt8zncwa0LqeQAG9G9Pm5sg1UVrcvBdRGYtouzt/c8PRCspuev7qGZ66s4Z+cGUekT18g+mWtZGIyrSPa4C6+Uo0nLdsubZgEFdu+k0Vhxy0VQog3osCQiaootjE0pN8H2nRDNqp6SsmYSlcK4C+9dAuSwHBvG5F/JctBUpchi5Viv1Ery2gyBAY03CSpSAIKRiV/fLXQ/7ehw4qE+Y1yW//WdlysFa2uR/4BlcmRiiTcfvGzm6Jpw2zhBdyRbGWT5HoAmyT/wz++gV/5ixfw6X+40NHdhCC4nOMPvzWHTFTFB+4e7vXhBMqotj3ttvkzoko4MRzFask88Bsm6apECOlL2agKw/FWbO+FgTbdENdlOB4ubrpSuf1fshycGIm1NU2wbDkYqi40qE02qaqSiMGY1nCTpFCdgmk6Lm6ul/tqkE09iiSg0GYEYN6w4XLekzuhs/M5HM5EWo7JLFkOcsbuBfThbDB927fWy/j7l29hOhPGk68v45OPPovzNzd8fY5O/OPFJVxczOO/eetEX+XBByFXrmSzt3IXMRPVMD0Q9nUA1F60v78jCCF7Vi3j2cstYxeVnO2DLqxInttwannb7bSQAADnQCxUSWHSdmn7GE81jv+rWS9ayJftnrRYeMU52lppXS32JvLPcTkuLRV8mRy5XW2T5KzPrSR/8u3LEAWGf/E9J/DpD98DxoBf/U8v4M+evtLRBlU/WI6LP3ryMqbSOt55NNvTYwnaeslCJqpiINr6HfzJVBjZiIa1A5xQQsU2IaQvyaKAgYja8vjx2kAbvzYa7mWiwJAKK55WW2tFUjsj2k3bha6It5NgFEmALAoNi6CJlI5rq6WmRdLChoF2dw4u5w38X1++gNe7FEGnye1FAM6v9yby78pKAabttlxs244LRRTAwHZ98RtRK3dJ/FzZnlsq4OsXFvG99wwjHVFxbCiKf/OxU3j7TAZ//NQV/PO/ehFL+d6tnP6Xl2/h5noZn3hgas8M1GqH43LYrut5CJIgMBwdikKVhQM70p2uSoSQvjUY01rOjN5rA22Clol4K7a/++5h/C/fc7ytIRxFy8ZQfOtKV0SVGvb4jiV12C5v2OssCQJWS2bbLSRffOkWHruwiF/+8xfwX165FfiGupAsYiln7Jh82UzJdGDYbk829NbSQlrdHGnYLhJhGdGQ1NLG2yPZiK+JJH/81GXoioiP3PdmwkdYlfBL7z2Kn3/PEVxczOPnPv8snnx92bfnbFXRtPHo01dx10gMp9u8K7RXrJVMzGQjbd1tksXKhknTcVrq/d9vqNgmhPStWEgCWGutJHtxoE2QoiEZXmrMiCrh/kPptp7LcTkS2wZ5xUPyjkmQNbVNeVcatJKoktB2C4nLOb52YQHHh2M4MRLDZ756Eb/zX2d9GaveiCgwOJyj4CHmbKNkgqM37Q+z83mEFRHDidZaAUzHRVyTkdKVlortw5kIlvKGL20D529u4Kk3VvDh+8Z2JF8wxvCe44P4nX9yCtmYik998Tx+7+uXYNjBfa23++vnbmCtZOHHHjy0r1PIiqaNsCJiJB5q+2PoioQ7R+JYL1s9b/3pNroyEUL6liqJSIRklK3dL/B7caBNkEKyCFkSYAccu1Vr34lsW4UOqxIst37RM5asXLAb9W1rsojhNi/qr9zYwELOwHffNYRf/9478fG3jOOxVxfwy3/+PK43iBv0g8gY1oqtJ3As5s2epJAAldi/mWwEQovFocs5wqqEWKhxysxmfk2S5Jzjc0/MIaHLeOTkSMP3G02G8Fs/cBLfd+8ovvjiTfyPX3gel5cLHT13K1aLJv7y2et46HAax4b8jVDsJ5xzFEwHR4diOzK1vUpHVMxkwlg5YP3bVGwTQvraSFxD0dp9xXCvDrQJCmPV+EQPo9vbUTRtZGPajouwJld6fOvRFQkDERVXmoxtb7f39bELC9BkAQ9MpyEKDD/81kn8+vfeieWCiV/4wnP45sWltj7ubjRZrPSZt8BxOZbzZk82f5q2i7nloud8bU0WKz9fLSxITvs0SfLZK2t46cYGPnZmfNdzJYsCfvLth/Dr33sn1ksWfvELz+NLL90MtIXoC09fhWE7+JG3TQX2HP1grWRhLKkhHvJnMvZ4SsdQTMVK4eAklFCxTQjpa3Fd2f2dQANt6kmHlbYmcXphOi4G6oyl3q04Gk+GcG3F35Vm03bxjxeX8MB0esvz3zeZxL/52ClMpnV8+u9fxe9/45LvgzY0WUTOsFpqYciXbQC85ZVlP72xVIDj8pYnR9buXGiyCK16t2S3FoCwKmE0Eeqob9vlHJ97cg7ZqIr33TnU8r87PZnEv/34Kdw5EsO/+9ol/MaXXkWu7H/m9831Er708i28784hjCbbb63od5bjQmDAVNq/5BrGGI4ORhFWpOrPwv5HVyZCSF/TZBERVWqp55YG2mwV0YLdMLp5auR2stg8kWQ8pePqatHXYRffnltBwXTwrmM749cyURX/x/ffjUdOjuDvXriJX/tPL2Ih194wmmZaKR5WikZPCm3A++RIw3a2vOBNhuWWfhY7nST5rUvLuLRYwA+/ddLzi+hkWMGvP3InfvzBKTw9t4Kfe/Q5vHxjve1jqeePnrwMSWD4+FsmfP24/Wa9ZOHoYNT37HBJFHDnaBw2d7vaY98rdGUihPS94biG4i6bz2igzU6qVHmhEtTFrGQ5SIWVhsVQLCQ3fO6JlA7DdtuKzGvksVcXkAoruGes/gRMWRTwUw9P41fffweurBTx848+h7OXV3x7fkUUsNzC1MuFDQPhHo3znp3PI6HLGIi0dsfIsF0kNrUPVDZJ7v79dCQbwVLexGobvbmOy/HHT17GRErHO49mPP97oDIc6cP3jeE3P3IPZJHhf/7LF/GnT11ua2Oe43KsFExcXMjj6bkV/NVz1/H47BK+795RpMKtnce9KF+2kQoryER33rnyQ0gRcfdoHBul/b9hknKyCCF9LxlWdm0VpYE29WWjKuaWi1B3GTTTjpLl3B71Xk9ck7BWMFGvE6gWMXh1pYjBWOsDMhpZL1k4d2UVj5wc2bXf+6GZARwaCOM3vnQe/9vfvoKPnRnHD94/0XFGsq5IWNgwcCQbaZhMUTIdlEwHeqRHxfZCrunxbcc5R2TTCwNdlVrKUNk8SfLMlLfs9q+cn8f1tRL+2QeOd/w1OTIYxe987F78/tdfx+efvooXrq/jF7/rKLJRDZbjYrVgYqVoVv+0tv135c/1koXtteBQTMOH7xvt6Nj6meNylG0H94zHA01ZSegKjg5F8dp8DgNhdd8mulCxTQjpe7oiISSLMG237u1MGmjTWDykwOUBJTPw5j31uio1HBs/XkskWS16LsbqeXx2EY7L67aQ1DOSCOH//OhJ/P7XX8efnb2K87c28EvvPYZki3sE6hEFBttxUTCdLQXqZrmy1fawnk4VTRvXVkt4+IiX1WK2JeVHr/bC813GzB/OVCaSznostk3bxaNPX8GxwSjeeqjz7wug8vvjF77rKO6dSOD3vnYJP/Onz0AWBOTqDFgRWCW2MhlWkNIVHM5EkNKV6n+/+XgqrOzr3zdrJRPTA+Gu3IEZTYSQN2zMr5eRCgezit5rVGwTQvaE4biGy8tFKNLOYogG2jQW0Sqj23crjrwybAdhVWy6EbLZ26KajIQu46pPmyQfu7CAQwNhHKqOnW+FKon4ufccwYnhGH7v65fw848+h19+3zHcNRpv+zgYAzaKVsNieyFn7DrOPiiXFvLgQMuTI13OIQiV3PMaSRQqA4sct+ndEl2pbJL02rf9xRdvYilv4he/86jvq5zvOpbFHUNR/Pm5a5BEYUvxXPszFpL39RTIVpQtB5osdm3jJ2MMR7JRFA0b6yUT8dD+a82hqxMhZE9IRdSGF27bdRsWNwedKDAkdLnSutDmRMZ6SqaDqV0KW00SmrYcTCT1hoNtvLi2WsRr83n8xENTbf377zwxiMPZMP71l17FP/urF/GjD0zhw6dG2yr2dEXCfK6MkTqFSq33168INa9uT44cbHFzpOUipkk7zkNSV3Bzvbxra9JMNoKXrre+MbFo2vjCuas4NZ7A3Q367js1HA/hn777SCAfez/gnCNnWDg1nuzqyr0oMNw1msCL19ewWjQ7usPUj/bvPRBCyL4SViqrqPUi22igTXPZqIqSzxMUHc6R0JsXjZIoQGsyWKeWSNJpFvJjFxYhMOCdR1trIann0EAEv/2xe/HAdBp/+K05fOqL59uKJVMlAeslq+73ab5sV1aLe5ZEkkc2qrZc7Bu2g0SdVcaE3tpwm5lsBMuFSt9zK/7y2evIlW386ANTLb0/8d9G2cJwPIRkDzZ+KpKAe8YSiIdkLO+zDG4qtgkhewJjDENxDYU6fZY00Ka57WOuO1XpkRdaupsQ0xqPbR9PhlA0Hay0WIzVUxvPfnIs0XEyhK5I+J/efwd+6uFpnL28ip//wrO4seatzaW2CpyrU6ivFk2IPdwAVtsc2SqH87rxkboitdR2PpNpfZLkesnCXz93Aw8dTmPGwzES/9iOC5dzT61YfpNFAXeOxJCOKFgq+B/N2StUbBNC9oxUWK274Y4G2jSnKyIkYfdhJK0qGDYGY60lB0Q1CabdeGUbAK52MEa9Np79XXe0v6q9GWMMj5wcwb/+8N1YLVr42+dveP4YsiDUnY43v1H2tZXHi/WShfkNo+UWEgAAR907RposQBDYrt9P05kwGICLLQy3+cLZyjTGH37bZOvHR3y1VjZxJBvtyWTTzSRRwInhOAajGpbyRqBTQLuFrk6EkD0jqkqQGlzkaaBNY5XR7QpKPo1ut9z6UyPrCTdLJKkW2530bW8ez+6nO4ZiuGc0jrOXVz1f7HVFxPzG1iKhbDkoW47vw0FaVSt4W13Z5pxDEFjdzZyMMSRayNvWFQmjydCuxfZCrowvvngT7zk+iPFk4yhJEpyCYSOmyb7EcPpBFBjuGIphOKFhubj3C266OhFC9gxBqLSS5Le1kjCABtrsIh1RYTidF9uVVJP6UyPr0WQRjXZJJkIyoqqEq20W243Gs/vlzFQKtzbKuLHm7Xa2JAqwbBfFTS9uNspWS/nUQZldyIEBLbdoGLZbSbJpkMyR0mUYDe5YbDaTjexabD/67atgDPjBfT6NsV+5nKNk2Tg6GG349e4FQWA4mo1iPKFjqWD6Om222+jqRAjZUwYi6o7NWRyggTa78CutpTI1Um05qUCVhIa50oyx25sk29FsPLsfTk8mAaC9KZMM2ChZt/9zqYeRf0BlcuRoMtRyG8v2yZHbRVS5peJnJhPBStHEcr7+hrerK0X811fn8YG7hgObVEiaWykaGE+Ffd/b4QdBYDicjWAqrWO5YOzZgpuysgghe0pUq+TgOi6//ScNtNmdJotNBwO1qmjZnjZQSaJwO0WmXl/9eDKEJ15fbutYdhvP3qmhmIaxZAjnLq/iQ/d6mxYYkkUs5AwMJ0JwXI6lfHci/8qWgysrRVxeLuDychGXV4qYWy5grWjh3R762m3XRazJ8epqay8caivplxbzSNdpPfrjpy5DlUR89Mx4y8dG/OG4HCtFE0Mxrekk2F5jjOHQQBgiY7i0lEdKV/dcFnpXim3GmAjgLIDrnPMPbnr8MwB+nHMeqf73jwH4LQDXq+/yu5zzf1992ycA/PPq4/+Kc/7Zbhw7IaS/iAJDNqpiOW8iqsk00MaDbEzF9dVyR8U2A2tahNUTVSXkDbt+sZ3S8Q+vzGO9ZHkqRr2MZ+/E6Ykk/vOLN28P+mhVSBaxWjJvT5T0O/LPcTlurJcqBXW1sJ5bLuDWevl2u4oiCphI6Tg9kcRUOoyHjwy0/PFZ9XNoRBYF6E1eRNVMD0Rub5K8/9DWvvrZ+Ry+dWkZP3T/RM+yxw8qy3GxVjJxeCCCibTe92PSGWOYHAhDEBhem88hHd5bBXe3rlCfBHAeQKz2AGPsDIB6yxF/xjn/2c0PMMZSAP5XAGdQuWN8jjH2N5zz1eAOmRDSrzJRDTfXK320NNCmdUldwZXl9jcjGnZlDLnX/uhYSMZywUS9ORWbN0ne7WFyo9fx7O06M5XCXz9/Ay9cW8f9HsaHM8bAOZA3bKwXrY4K7fWShYsL+TeL6pUCrq4UYTmVslpglWEthwbCeNexLCbTOqbSYQzGtLYKkspmNLbr1zkZVrCYM5oW2yFFxFhKvz1QZ7PPPXkZMU3Ch+4d8XyMpH1F00bZdnH3aByZaH9siGzVeEqHJDC8cnMDKV3ZM3c0A79CMcbGAHwPgE8B+MXqYyIqK9g/BOD7W/gw7wPwZc75SvXffxnA+wF8PohjJoT0t8pUu8rGHpsG2rQsokrgQNuj24um01YGb1iVGvZaTlSL7Wur3ortdsazt+POkRg0WcDZyyueim0AkAQBy3kTKwUD4TYj/25tlPFzn3/29lCilK5gMq3jnrtHMJXWMZkOYzwV2nWaoxem4yKsirsW6gldwfUWcshnMmE8d3Vty/fd89fW8NzVNfzk2w/1LA7xINooWxBYZT/CXl2kGE6EIDCGl2+sI6EreyL2tRtn+ncA/AqAzeGePwvgbzjnN+v8wv8IY+wdAF4D8Auc86sARgFc3fQ+16qPbcEY+2kAPw0AExO0q5mQ/UoSBaTDKvJlGzYNtGmZJApI6DLKltvWCxTX3X1qZD2qJDQcgpIOKwjJoqf4v07Hs3shiwJOjiVwrhoB6OVFSiUCsAzTcZEOt3e5/cZriyhZDv7FB0/g2GDUcwtPOwzLRTa2+2bFVn/uZrJRPHZhESsFE+mICs45PvfEHAYiKj5w13CHR0tawXmlPzuhKzg+HPX1xVkvDMY1iALw4vUNxDS5Z5GarQr06BhjHwSwwDk/t+mxEQAfBfCZOv/kbwFMcc7vAfAVALW+7Hq/3XYsk3DO/4BzfoZzfiaTyXR8/ISQ/jUU11C2HRpo41EmoqJkeR9DbjsuZKm1qZHbabLYMPaukkgS8hT/9zUfxrN7cXoyiYWcgWseh+/IotBwemarHp9dxPGhKN4ylepKoQ1UctTjLbyoCskiRMZ2TYiobZKsTZJ88o0VvDafxw/eP973RdJ+4LgcSwUDIwkNd4/G93yhXTMQ1XByLI6NsrVr5nuvBf1d/hCARxhjcwAeBfBuAC8DmAFwsfq4zhi7CACc82XOeS0f6P8BcLr692sANm9VHgPgfawXIWTfiG2KqaKBNq2LheS28p4LptPy1MjtRIEhVN1MV894UsfVldYKWZdzPObTePZWnZ6oRACeu+x9m5AsCG23kFxdLWJuuYi3H+n+4lGzzZE1glDZLGtYzV9QTA+EITBgdiEPx+X4oycvYzQRwnvuGPTrcPck23Eb/kz4xbRdrBQMHB2M4kg2uqc2FbYiFVFx30QSecNG2erfgjvQKxTn/Nc452Oc8ykAHwfwVc55knM+xDmfqj5e5JzPAABjbPP9pEdQ2VQJAP8A4L2MsSRjLAngvdXHCCEHlCIJSOoyLMelgTYehBXpdmSiF7aHqZH1REONx7ZPpHSsFM0dw4rqOX/T3/HsrcjGNIyndJy74r3YjoXktgfufHN2CQzAQ4f9nY7ZilaKbQBIhRWUd1lV1GQRY0kdlxby+PprC7i6UsSPvG1y3xV+XnDOsVqyULJsLOUNrJcszz+TuykYNgqmhVMTSYwl+z9xpF1xXcZ9k8nbU1r7Ub91x/8cY+wRADaAFQA/BgCc8xXG2P8O4Onq+/3L2mZJQsjBNRTTsFa0aKCNB4LAkI4oWC/aLbeE1GLrOtlQFddkLG2YCNep18eqI7qvrRRxx3Bs5zts8tVXgxnPvpszk0n87fM3UDKdrmzI5ZzjG7OLuGs0XjefOiiW40KXxZZTHqKa1NIo7ZlsBOcur+LKShEzmQge7MELiH6SM2wMxVTcMRRDzrAxv1HG/Ea5GmVaycTvpDheK5pQJAFnplIHYgNqTJNxajKJZ9u4+9QNXVsO4px/bXPG9qbHI5v+/muc8zs55yc55+/inL+66W3/gXM+U/3f/9et4yaE9K+4riCxh+Kf+kUmonnqcSyZDlLhzs5zSJHgNmhgqSWSXNllkmTQ49mbOT2ZhO1yvHB9rSvPN7dcxLXVkqdsbD8YlouEh/ackNK4H3+zmUwE6yULCzkDP/LA5L5dZW2F43LYjovpTASCwBAPyTg6GMUD02ncPRpHSBGxXKik2HjtRXY5x3LBQCIs49RE8kAU2jURVcJbDqWQ7FJ7mRcH56tACNl3NFnEiZHmK6Fkp4gq1dli3ljZdjAd7SxiT5MbJ5JkoioUSdh1k2TQ49mbOTEcQ0gWcXZuFW89FPyq7OOzlU2gDx7ucrHtOIhrrZcGqiRClYRdh9scqW6SvGc0jlPjwUz83CvWSiZmspEdLxglUUA6oiIdUVG2HKzkTVxfK2K5YEBgDFFVavqC13ZcrJZMTKTClT75A3jHr9svwltFxTYhZE+jJBLvQorYdIR6PZ0mYVQSEFjd+DxRYBhLhnB1l7SPoMezNyOLAk6Ox3HuivcIQK845/jmxSXcM5boyWTFkMd2oVRYwUreavq9dDgbwXtPDOKRkyMHelW7ZDrQFRHD8VDT99NkESPJEEaSIeQNG4u5Mq6vlmC5HJooQlfFLYOSypaDvGHjxFAMQ4nmH5t0H12lCCHkAMpEVRTN3W9Rl632pkZuV0kkEWA32ARWSSRpvLJdG8/+zqOZnm2sOz2RwmLO8JQJ3o5LiwXcXC93vYUEqNzw0DxGwyV1BYbT/HtJFgX803cfwWQ62CFE/Yxzjrxp4dhgzNP3cESVcGggggcPD+DesQTiuozVoomlvIGS6SBXjb67bzJJhXafomKbEEIOoFRYge3uHjtWshwMxf0Z6RwLyTAaJJKMp3Qs5CrFQz3dGs/ezOnJ9iMAvXh8dhGiwLq+CdRyXGiS4Dn7Wt+jkwi7bb1sYSQRainDvB5BYEiGFZwYieHBwwO4cyQGSWJQ5MpGyF7cBSGtoWKbEEIOoEi1L3e3JAmXcyR0fzYcRbUm8X/JyorctQabJLs1nr2ZTFTFZEoPtNiutZCcGk8gqnW3eDLt1obZbBeSRbBqixCpz3JcgMO3719FEpCNabhvIokzk6m+7VUmFVRsE0LIASSLAqJa45VmoDo1UhQQ9inqrpKM0KCNpJpIUq9vuzae/V3Hej8Z+MxUEq/c3EDR9D6FsxUX5nNYyBk9aSExHRcJzfsLK1FgiIakpt9LB916ubIpcr9MbyTeULFNCCEHVDaqNh0CUTAdDMU03za0NVt9G46HIAmsbt92t8ezN3N6ohIB+PzVYCIAH59dgiyyriSebMc5R7jNlpCUrvTtQJFeK5o2opqMwZg/7Vhk76FimxBCDqi4LsNpcuvfdl2kI/5l1qqSADRoNxAFhpFECFe3tZH0Yjx7M8erEYBBtJK41RaS05PJtoveTnAAmtJeWRALNf9eOqhczlEwbBwdjB7IKD5SQcU2IYQcUBFFgsAY3DpFUm1qpJ99w4LAEFZFWE7jVpLtSR+9GM/ejCQKuHc8gbOXV33vUX7lxgZWCiYenul+u4zjcsii0Habg66IDXPUD7L1koWJlI5Yl/vvSX+hYpsQQg4oQWBIhZW6CSAl08FARPE9Zi8/iBmlAAAfxklEQVQWkptukpzfKG95+2M9Gs/ezJmpJJYLJi4v+xsB+PjFJSiSgLdMpXz9uK0wbAeJDtIstOqId6dBtONBZDkuBAZMHOC4Q1JBxTYhhBxgmYhSdyR0yXKQifrfYxpVJVgNIgfHUzpcDlxfq2ySNG0X3+zRePZmTk9UIgDP+thK4rgc37q4hLdMpRDyaUOqF4bldjy4KBmWqW97k/WShaODUc9RimT/oe8AQgg5wKJNCqxYyP++4ZAiNmy/GE9WE0mqrSRP93A8ezPpiIpDA2Gcu7zi28d86fo61koWHp7pfgoJALjgiHTYJ57S679wO4jyho1kWEEmqvb6UEgfoGKbEEIOsFD19r/tvLnaXLYcxEJyIDFlmiw2CP8DRpMhCAy4Ut0k+diF3o1n383piSTO38qhYPgTAfj47CJCsogzU0lfPp5nHB2vqOuq1PBre5C4nMOwHcxkIwd6ND15ExXbhBBygDHGkN02ur1o2hiKBbMip4gCBFY/kUQWBQzHQ7i6UsR6ycLZy70dz97MmakkHJfjOR8iAG3HxbcuLeP+Q6me5DA7LocosmpaTPv0aqvPQR9us1Y0MZnWO75TQPYPKrYJIeSAS4WVLX3UHPBtauR2gsAQViSYTv2+7bFkCFdXS30xnr2ZO4ZiCCsizl3pvG/7uWtryBk23tGDQTZApTc+pskdr8JKooCoKjf82h4Ehu1AlgSMVVuiCAGo2CaEkAOvNrodqCQoqJIAPcBNetGQBMuuv/o5kdJxY62Er5yf7/l49mZEgeHeiSTO+RAB+PjsEsKKiFMTvWkhMWwHyTbGtNdT2SR5cIvtXNnC0WwEskjlFXkTfTcQQsgBp0oiIqoEw3ZQNB1ko/5Njawnpkkwnfob6cZTOhyX49JioS/GszdzZiKJlYKJueVC2x/Dclw89foy3jad7lmB5nCOiE850PGQDLtB2sx+t1GyMBBVkY7QpkiyFRXbhBBCbvdt266LgYCLhZDceCNdLZGk0/Hspu3CCrid4b7JagTgXPutJM9cWUXBdPDwkd69sGCobJT1g65IB3K4jeNyWK6LwxnaFEl2omKbEEII4roCx3UhCmxLW0kQVFkAa1CSjSVDYEDH49k3yhbWS1agBXcqrGA6E+6ob/vx2SVENQknx+I+HlnrapNCNdmfckCTBQgCC2y4zVrRxHrJCuRjd2KtZGJ6IAxdoU2RZCcqtgkhhCCiSmBgSIf9nxq5nSoJEBjqjonXZBE//Y5pfOLBqbY/fmX0OMNdozGslcxAC+4zkymcv7mBfBsRgGXLwbffWMGD02lIPWohMW0XEU3ybTWWMYZkQHnbhu1AEhliIQmLeWNLXGUvlS0HmixilDZFkgao2CaEEAJRYMjGVGRj/k+N3I4xBl2RGhbBH7xnBIczkbY/ft6wMRwPIRPVcNdIHKtFM7CV1tOTSbgcbUUAnru8ipLV2xYSw3aR8GlzZE1luI3/hXCubGMmG8Hdo3GcGIpio2xho8er3Jxz5AwLxwajfRlRSfoDFduEEEIAAIezEaQCivzbLhaSYAZQkAGA7brIVHPCszENx4diWC4YgRTcxwajCKsizs55nyb5+OwiEiEZd432poUEqGzQjPq0ObImrEm+Z20XTRtxXcZARAVjDEOJEO4/lEZE6+0qd86wMRTTkOyg5Ynsf1RsE0IIAVBJJRG6tDpXSSTxv0CqRRdGNw0UGUmGcCQbwWrRqNu60glRYLhvIolnrqx6+tgl08HTl1fx0MxAT1dEGfNvc2SNrjSeEtoOzjmKprNj82FIEXHPWBzHq6vcuXJ3V7ltx4XtuJju4C4MORio2CaEENJ1mhxMakXBsDGaCO3oQZ5IhzGZDmO5YPq+6np6IonVooXXF1uPAPz23ApM28XDPRpkA1SKWAbme7EtiwJ0WfStV36jbGMoriEe2rkCzxjDcHWVO6xKWOriKvd62cLhbASaz+eP7D9UbBNCCOk61af0i+0clzfMOT40EMZYUsNywfT1OWsRgF5SSR6fXUQ6rOD4cMzXY/HCdFxEVCmQuxnJsIKy1fkmScflsBwXU+nmw41qq9zHBqNYL1vIl71vWPWiaNoIKyJG4qFAn4fsD1RsE0II6TpVqkTE+dnWYdgOwqqIsFo/fo0xhplMFENxDcsFw7fnTeoKZjIRnGuxb7tg2DhXbSERepjJbFgu4nowUXUJXfGlTWi9ZGJqQEeohYmmjDGMJEO4/1AKmiJgqVD2tU/fcTk2ShaWCwYM28XRoVjX2q7I3kaBkIQQQrqOMYawWtkk6ddt+Lxh42g22vR9BIHh6GAUtutiOW8gHfZngM/pqST+49mryJWtXTccPvXGMmyX97SFBAAs10U8FMzGvrAqdtwmZDkuBIFhNOEtUk9XJJwcS+DGegkXF/JQRbHt7Piy5aBoVlbJRUHAYKwyITKqSTSSnbSMvlMIIYT0REzzP5EkFdm9eBQFhuNDMSR0BWslf1pKzky0HgH4jdklZKMqjg02f2HQDa2sGLdDk0QIrLM7FxtlC4cHIlAk76WKIDCMJXW8ZSoFVWYtp9E4Lke+bGO5YGCpYEAQGGayEZyeSuGhmTSODEaRCitUaBNPaGWbEEJIT8Q0GddWS758rJLpIBaSW14ll0QBJ4ZjeOHaGjbKFmIdxt8dGYwiqko4O7faNDd7o2Thuatr+NDJkZ6P9WYM0NooZFshCAyxkAzDctsq6GuDYgbjneW+h1UJ944ncWO9hNn5PDRZRGRbm5FhOyiZDhzOIQkMmaiKgUgEUU1uq9AnZDsqtgkhhPSEKnfealBTtGwcT3tbKVYkAXePxfHclTXky3ZHY+pFgeHUpgjARr3YT7y+DMflPR1kA1QmR4YkMdDJlamwgjeWCm0V23nDwj1jCV9iEWur3Eldwau3NrCUN6BJIsrVKZdRTcLUQBgJXUZYCWbDKDnYqNgmhBDSE5os+JLHzDkHB5BoY7CIKok4OZ7AM1dWUTRt6Er7l8UzU0l8Y3YRlxbyONKgReSbF5cwHNdwONM8XSNohu1gIOpPv3ojUU1qq42kYNhI6ApSPg+KCasSTo0ncX2thJxh4Ug0gogqUXQfCRzdHyGEENITiihAFFjHiREly0E6rECV2iuaNFnEybEELMdFyWw/ru6+iSQYGkcArhVNvHBtDQ8fyfS8hcR0XMQ7WMlvRUgR4bXW5pyjaDk4nI0Eco4EgWE8pePEcBwDEZUKbdIVVGwTQgjpCcYYoprU8fCTkuVgJNFZ3nFYlXByPIGSZbedDx0PyTgyGMHZufrF9rcuLcPlwMMzvU0hqQk1iEj0iyqJCHkcbpMzbIwktI576AnpJ1RsE0II6ZmoKsPoIJHE5RyMoe50Qc/Hosm4dzyJvGG3nZJyeiKJ1+ZzWC/tHB3++OwixpMhTKa9RdkFxe/JkfWkIgoMq7Vz6bgcZgsDbAjZa6jYJoQQ0jPRkATbbb/YLhoOslHNtyi2uC7jnrE41stmWyvuZ6ZS4ACe3dZKspw38PKNjb5oIbGcSrZ5N+LrEroM02ntTsF62cShdJhaO8i+Q8U2IYSQnum0sDIcB0OxzuLhtktHVNw1Esdq0fTcTz6TjSCmSTv6tv/x0hI4gLf3eJANABi2i4QPdwJaoStSS5tgLceFyBhGkzT+nOw/VGwTQgjpGVUSPG+iq3HcSi5yLIDCMRvTcHwo5nnojcAY7ptM4pnLq1uSOB6fXcJUWsd4svctJKbtIKF3qdiWRTAw8F2+yBtlC4czERoWQ/Yl+q4mhBDSM6okQhbbSyTJGzaG4povWcz1DMY1SG2kpZyeSGKjbOPiQh4AsJAr49VbuZ5na28W6iDi0IvKcBupaV9+2XIQkkUM+nyHgpB+QcU2IYSQnopqclsbEm3XRSYaXIEmVqcJeo0DrEUAnp1bAQB8c3YJAPBwH7SQAIDLKxnn3ZIOK00TXvKGhSODURomQ/YtKrYJIYT0VFSTYHrcjGg7LmRRQDTg+LpMVIPR4ga/mlhIxtHB6O2+7ccvLmEmG8FwvPf9yLbjQpGEtjPJ2xHV5IbDbfJlG6mwimSX2loI6QUqtgkhhPRUVJM9J5LkTRujiVDgq6FRTQJj2LXneLvTk0nMzudx4VYOFxfyfZOtbTrd2xxZ02hcO+ccJcvGdCbc84QWQoJExTYhhJCeaqelwXF54OPGAUAWBaTDKkoeB92cmUyCA/jdx2YB9EcKCQAYltu1zZE1mixCEoUdve8bZQujyRCiNMCG7HNUbBNCCOkpry0Npu0iJIsIN1gx9dtgTEPRY9/24WwEiZCMueUi7hiKIhtgb7kXHBzhgFtv6kmG5S19247LYbsckzTAhhwAVGwTQgjpKUUSINdZ+Wwkb1gYSYS61noQC1VaSbwQGMN9E0kA/bMxsqYXQ2NSuoKy/WaxvV4yMT1AA2zIwUDFNiGEkJ6LqFLLiSQu50hHlICP6E2qJCIekj2nkrzzWAZJXcbbZ/oj8s9xOUSBQZW6f+nfvJpuOS5EkWE40fsNo4R0AxXbhBBCei4ekltKJClbDqKaDL1LOdE1QzENRcv29G/um0jicz/xVqTC3Xth0IxhO4jrSk82I4Zk8fZG0/WyiSM0wIYcIPSdTgghpOcimtRSIkmxmkLSbQldQUtzx/tYN8e0byeJAiKKjI2yjYgiBZqPTki/oWKbEEJIz7XS2sA5h8uBZA9WikOKiLAqwrC9tZL0E5f3ZnNkTTIsI1e2MJOlATbkYKFimxBCSM+1slGuZDlIheWebaobioc8p5L0m1APNyQmdAUTKb0nL5YI6aXevcQlhBBCqmTxzUQSscGqZ8lycGigd1FxybCCS4v5rj9v3rBh2g4Ahkp4HyrpKNW2ls3dLaz6/4xVElEYKn+CASJjXR3Tvl0qrPSsjYWQXqJimxBCSF+IhWQUDbvu5sfaBMeE3rtV0bAiQpUEWNVR8d1SthycmkhAk0VwXmkHcastNbzOn7UMa8flcLgL160kkeiK2PNJjdQ+Qg4iKrYJIYT0hbgmYa1gol49XTAdZKMqlB7E1tUwxjAU13BtpdS1or9o2ojrck9fZBBCOkM924QQQvqCrkpweP3ID8N2MBTvfS5zKqw2PMYgFE0Hkym9a89HCPEfFduEEEL6QqONj7U+7pjW+5uxUVWCJLCWp112wnJcqLKAJK1qE7KnUbFNCCGkL2gNWkQKho1sVIXUB0NQBIEhG9NQMLwNuGlHrmxhIqVTnzMhe1xXfnMxxkTG2LOMsb/b9vhnGGP5Tf+tMsb+jDF2kTH2FGNsatPbfq36+AXG2Pu6cdyEEEK6RxIFqJIAe9skSdNxMRjrnyEomYgKq4UBPJ1wq60qmaga6PMQQoLXrWWCTwI4v/kBxtgZAIlt7/eTAFY55zMAfhvAp6vvewLAxwHcCeD9AP4dY6x3YaGEEEICEdO2jm23HReyyBDT+icyLqpJEBi7XRAHIVe2MZwIQZXoUkfIXhd4sc0YGwPwPQD+/abHRAC/BeBXtr37hwB8tvr3PwfwHlbJKfoQgEc55wbn/A0AFwHcH/SxE0II6a6oJsGw3yy284aN4Xior1opJFHAQEQJdMCN5bgY6cFYekKI/7qxsv07qBTVm++5/SyAv+Gc39z2vqMArgIA59wGsA4gvfnxqmvVxwghhOwjYVXasmLscI5MrP9aKbIxLbDR7UXTRlKXEenhaHVCiH8CLbYZYx8EsMA5P7fpsREAHwXwmXr/pM5jvMnj25/vpxljZxljZxcXF9s8akIIIb2iyeLtX/iW40KVBET7sOistbXwAFpJipaDiXTvJmUSQvwV9Mr2QwAeYYzNAXgUwLsBvAxgBsDF6uM6Y+xi9f2vARgHAMaYBCAOYGXz41VjAG5sfzLO+R9wzs9wzs9kMplAPiFCCCHBUSXh9kpKwbAxmgj1fOphPYokIKnLKFn+rm7XXmDQWHNC9o9Ai23O+a9xzsc451OobHD8Kuc8yTkf4pxPVR8vVjdEAsDfAPhE9e8/UH1/Xn3849W0kkMAjgD4dpDHTgghpPskUYAmi7AcF47LkY70XwtJzVBM873YzpUtTKXCfdWjTgjpTL/dm/t/AfxRdaV7BZUCHZzzlxljXwDwCgAbwM9wzoPbmUIIIaRnYpqEpZyJsCoi3IctJDVxn4fN1HrVByjuj5B9pWu/xTjnXwPwtTqPRzb9vYxKP3e9f/8pAJ8K6PAIIYT0iagm4+JCHjPZVK8PpSlNFhFRJZQtp+H0Sy9yZRujyRCUBsN9CCF7E/1EE0II6SthVUJMk5GK9P+Y8uG4hqLpzzRJy3ExHKe4P0L2Gyq2CSGE9JWQLGI4ofmyWhy0hK7Aj0CSWtxfP7fNEELaQ8U2IYSQvhJSRBwbivX6MFoSViVoSmVDZyeKloNJivsjZF+iYpsQQgjpwHBMQ95ov5XEtF1okoA4xf0Rsi9RsU0IIYR0IBlRtky99CpnWJikuD9C9i0qtgkhhJAORFUJkiDAbqOVxHE5GKO4P0L2Myq2CSGEkA4wxjAUV1EwvY9/yBsWRhM6xf0Rso/RTzchhBDSoUxEg+16X9muxP1pARwRIaRfULFNCCGEdCiiSRAFBsdtvXe7YNhIRVSK+yNkn6NimxBCCOmQKDAMRFSUPLSSlCwHE0k9wKMihPQDKrYJIYQQHwzGNBhOa8W2absIKSISOsX9EbLfUbFNCCGE+CCqVdpBeAsxgJW4Px2MUdwfIfsdFduEEEKID2RRQDqiomQ1X912XA6BMYr7I+SAoGKbEEII8clgdPdiO2dYGE2EIIt0CSbkIKCfdEIIIcQncV1Gsy4Szjlsh2M4QXF/hBwUVGwTQgghPlElEXFdRrnB6nbBdDAQUaArFPdHyEFBxTYhhBDio+GYhoJp132bYdsYT1HcHyEHCRXbhBBCiI8SulL3ccN2EJIlxEMU90fIQULFNiGEEOKjkCIirIgw7a3j23NlG1Npivsj5KChYpsQQgjx2VA8hOKmVhLH5ZBEhlSE4v4IOWio2CaEEEJ8lgwrcDfFkuTKFsaSFPdHyEFEP/WEEEKIz8KKCEUSYDkuOOdwOMdQLNTrwyKE9AAV24QQQojPGGMYjGkomg4KhoNMVEVIEXt9WISQHqBimxBCCAlAOqLCdl2UbQdjCYr7I+SgolR9QgghJABRVYIoMGiSgFiILreEHFT0008IIYQEQBAYhuMhxDSJ4v4IOcCo2CaEEEICcjgTpkKbkAOOerYJIYSQgFChTQihYpsQQgghhJCAULFNCCGEEEJIQKjYJoQQQgghJCBUbBNCCCGEEBIQKrYJIYQQQggJCBXbhBBCCCGEBISKbUIIIYQQQgJCxTYhhBBCCCEBoWKbEEIIIYSQgFCxTQghhBBCSECo2CaEEEIIISQgVGwTQgghhBASECq2CSGEEEIICQgV24QQQgghhASEim1CCCGEEEICQsU2IYQQQgghAaFimxBCCCGEkIAwznmvjyEQjLFFAJd79PQDAJZ69Nz9hs7FVnQ+tqLzUUHnYSc6J1vR+aig87ATnZOtenU+JjnnmXpv2LfFdi8xxs5yzs/0+jj6AZ2Lreh8bEXno4LOw050Trai81FB52EnOidb9eP5oDYSQgghhBBCAkLFNiGEEEIIIQGhYjsYf9DrA+gjdC62ovOxFZ2PCjoPO9E52YrORwWdh53onGzVd+eDerYJIYQQQggJCK1sE0IIIYQQEhAqtgEwxsYZY48xxs4zxl5mjH2y+niKMfZlxths9c9k9fE7GGNPMMYMxtgvbfo4xxhjz2363wZj7OcbPOf7GWMXGGMXGWO/uunxn60+xhljA0F/7tuOqZ/Owx8yxt7Y9DHuDfrzb3B8/XRO3s0Ye4Yx9hJj7LOMMSnoz7/OsflyPqpv+4Xqx3iJMfZ5xpjW4Dk/Uf24s4yxT2x6/FOMsauMsXyQn3ODY+qn8/C16vdL7XsrG+Tn3kifnZOPMcZeqH6M3wzy827E5/Pxyeq5eLnR743q++3b60r1bZ2eh311Xam+rdNzshevKz9c/fl+gTH2LcbYyd0+zzrP2d3rCuf8wP8PwDCA+6p/jwJ4DcAJAL8J4Ferj/8qgE9X/54F8BYAnwLwSw0+pgjgFiq5i/XedgnANAAFwPMATlTfdgrAFIA5AAMH+Dz8IYAfoO+NyjlB5YXxVQBHq+/3LwH85F49HwBGAbwBIFT97y8A+LE6z5cC8Hr1z2T178nq295WPZ78AT8PXwNwZr/8rHR6TgCkAVwBkKm+32cBvGcPn4+7ALwEQAcgAfgKgCN1nm9fX1d8Og9/iH10Xen0nGDvXlcexJu//74bwFO7fe23PV/Xryu0sg2Ac36Tc/5M9e85AOdR+YX/IVR+UaP65/dV32eBc/40AKvJh30PgEuc83qDde4HcJFz/jrn3ATwaPW5wDl/lnM+1/ln5V0/nYd+0UfnJA3A4Jy/Vn2/LwP4SEefXBt8Ph8SgFB1JUUHcKPO+7wPwJc55yuc81VUPu/3Vz/2k5zzm759ch7003noF310TqYBvMY5X6y+31ewt39WjgN4knNe5JzbAL4O4PvrPOV+v650fB76RR+dk716XflW9WceAJ4EMFb9e6tf+65fV6jY3oYxNoXKKsBTAAZrJ736p5fbsx8H8PkGbxtF5dVkzbXqY32jT87Dp6q3iX6bMaZ6eM5A9PicLAGQGWO1oP4fADDu4Tl918n54JxfB/D/t3f3oXqXdRzH3x983FyZxxIXWVNSjoJCBeksT0UibA21IZbp8AnFDKz/hqaQBWEPJAkFi4n0sEZIo8xoTBbhA1tO55zMTWYujZDNzfksMfHTH9d16s7us81zP//O5wU35+b6/e7r/l3fc677+v5+1/U79w8pVyCfB162vabNro3uK12Mw111WvwWSZpmU7pmwDF5GhiXNK8m6xcywn2FcuVyQtKxkmYDC2nfnkb3FboXhyaNK53GpAnjytXAn+rzg+0Dfe8rSbZbSJoD/Bb4hu1XOqjncOB84O6pdmlTNjT/FmZI4nAjME6ZOhsDlk73OLph0DGxbUqSfrukh4FXgbemexyd6jQede3dBcCJwAeBoyRd1m7XNmWN6StdisOltk8HzqmPJe/2OLpp0DGpV6q+CvwGeICydGJk+4rtrcD3KFffVlOmxtu1p9F9pUtxaNS40mlMRn1ckfQ5SrI9+Xs82D7Q976SZLuSdBjll7zC9qpavFPS3Lp9LrDrIKtbAGy0vbO+9oSWGzKuo5xFtZ49foj206R9NyxxqNNKtv0v4C7K9NBADFFM1tk+x/YngfuB7Z22bTq6FI9zgR22X7C9D1gFnC3pzJZ4nE/z+0rHcahXgienX3/N6PeVbsTkD7bPtD0feIrR7ivYvtP2x21PAC8C22fiuNJpHJo4rnQhJiM5rkg6A1gOXGB7Ty1u285hGFf6ftfpMKrTrncCW23/qGXTPcDlwG315+8PsspLaFkmYPsfwH/ueq5TmydLOhH4J+XM8iudtKEbhikOkubafr4e04WU6bK+G7KYHGd7V536XEq5WaavuhiP54Cz6tTnm5R17I/Y/iv/G48x4Lv1aifAeZSrUwM1LHGofy/vs727DlaLKGuU+25YYlK3TfaVY4DrgYs7bd+71c3Pjpb2fBhYDMyvV/Bn1LjSaRyaOK50ISYjN67Utq4Clvi/680BNtCmnba3MOhxxQO+K3cYHsCnKVMIm4FN9bGQcvPAWsqZ3lpgrO5/POXM6BXgpfr8vXXbbGAPcPQB3nMh5Y7bvwHfbCm/odb3FuVMa/kMjcOfgScoH4a/Aubkb4MfUG4ceYoyzTbq8bgV2FZ/x78EjpjiPa+irMN9Griypfz7tb63689vzbQ4AEcBj9bj2AL8GDgkfxusBJ6sjy83IB4P1LY8zn7+swrNH1c6jUMTx5VOYzKK48pyYG/Lvo8cqJ1t3rOv40q+QTIiIiIiokeyZjsiIiIiokeSbEdERERE9EiS7YiIiIiIHkmyHRERERHRI0m2IyIiIiJ6JMl2RERERESPJNmOiIgpSfq7pPcfYJ+b+nU8ERGjJsl2REQDqBjUZ3qS7YiIKSTZjogYUZLmSdoq6afARmCJpHWSNkq6W9Kcut9CSdskPSjpDkn37qfOYyWtkfSYpGWAWrb9TtKjkrZIuraW3QbMkrRJ0opadpmkh2vZMkmH9DIOERHDLN8gGRExoiTNA54BzqZ87fAqYIHt1yUtBY6gfP3wdmDC9g5JK4H32F40RZ13ALttf1vSF4B7gQ/Y3i1pzPaLkmYBG4DP2N4j6TXbk4n9qfU9F9veV08E1tv+Re8iERExvA4d9AFERERHnrW9XtIi4DTgIUkAhwPrgHHgGds76v4rgWv3U98EsBjA9h8l7W3ZdoOkL9bnJwAnA3ve8frPA58ANtTjmAXsmmbbIiJGXpLtiIjR9nr9KeA+25e0bpT0sWnU+X9TnpI+C5wLzLf9hqS/AEe2ea2An9u+cRrvGxHROFmzHRHRDOuBT0n6KICk2ZJOAbYBJ9UlJwBfOkA99wOX1joWAMfU8qOBvTXRHgfOannNPkmH1edrgYskHVfrGJP0kY5aFhExwpJsR0Q0gO0XgCuAlZI2U5LvcdtvAtcDqyU9COwEXt5PVbcCE5I2AucBz9Xy1cChte7v1Pon/QzYLGmF7SeBm4E1dd/7gLldamZExMjJDZIREQ0naY7t11QWUf8E2G779kEfV0TETJAr2xERzXeNpE3AFspykGUDPp6IiBkjV7YjImYgSVcCX39H8UO2vzaI44mIaKok2xERERERPZJlJBERERERPZJkOyIiIiKiR5JsR0RERET0SJLtiIiIiIgeSbIdEREREdEj/wbQMCYAUg7U/AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,8))\n",
    "fig4 = sns.lineplot(x = df_main['reg_date'], y = df_main['price_sqm'])\n",
    "fig4.set_title(\"Resale Price per sqm from January 2017 to January 2020\", size = 16);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save figure in local drive\n",
    "fig4 = fig4.get_figure()\n",
    "fig4.savefig(\"./Images/Resale_Price_Trend.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig3, axes = plt.subplots(1,2, figsize = (14,6))\n",
    "sns.lineplot(x = df_final['year'], y = df_final['price_sqm'], ax = axes[0])\n",
    "sns.lineplot(x = df_final['month'], y = df_final['price_sqm'], ax = axes[1])\n",
    "axes[0].set_title(\"Resale Price per sqm by Year\")\n",
    "axes[1].set_title(\"Resale Price per sqm by Month\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save figure in local drive\n",
    "fig3.savefig(\"./Images/Annual_Monthly_Price_Trend.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resale Price (per sqm) Over Number of Nearby Restaurants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,8))\n",
    "fig7 = sns.boxplot(x = df_main['Restaurant'], y = df_main['price_sqm'])\n",
    "fig7.set_title(\"Resale Price per sqm Over Number of Nearby Restaurants\", size = 16)\n",
    "fig7.set_xticklabels(\n",
    "    fig7.get_xticklabels(),\n",
    "    rotation=90,\n",
    "    horizontalalignment='right');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save figure in local drive\n",
    "fig7 = fig7.get_figure()\n",
    "fig7.savefig(\"./Images/Resale_Price_Restaurants.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Top Towns with Highest Impact to Resale Price"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Even though **Lasso Regression** is not the selected model, it still have good predictive abilities and is useful for us to gain some insights into the relative relationship of a feature to the target variable in an all else equal situation. By looking at the coefficients, we can also deduce its relative importance and impact to the target variable. \n",
    "\n",
    "We will look at the values of the coefficients for the different *towns*, and look at the top 10 most premium locations (positive coefficient) and top 10 least desired locations (negative coefficient)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merge coefficients with feature name and filter out all non-town variables\n",
    "df_town = pd.DataFrame()\n",
    "df_town['Town'] = X_train.columns\n",
    "df_town['Coef'] = lasso.coef_\n",
    "df_town = df_town.sort_values(by = ['Coef'], ascending =False)\n",
    "df_town = df_town.loc[df_town['Town'].str.contains(\"town\"),:].reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Town</th>\n",
       "      <th>Coef</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>town_BUKIT MERAH</td>\n",
       "      <td>367.933657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>town_QUEENSTOWN</td>\n",
       "      <td>332.485091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>town_KALLANG/WHAMPOA</td>\n",
       "      <td>229.663523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>town_BISHAN</td>\n",
       "      <td>224.852378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>town_TOA PAYOH</td>\n",
       "      <td>203.511156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>town_MARINE PARADE</td>\n",
       "      <td>201.281916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>town_BEDOK</td>\n",
       "      <td>169.617459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>town_GEYLANG</td>\n",
       "      <td>168.927703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>town_CLEMENTI</td>\n",
       "      <td>156.029283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>town_ANG MO KIO</td>\n",
       "      <td>146.527187</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Town        Coef\n",
       "0      town_BUKIT MERAH  367.933657\n",
       "1       town_QUEENSTOWN  332.485091\n",
       "2  town_KALLANG/WHAMPOA  229.663523\n",
       "3           town_BISHAN  224.852378\n",
       "4        town_TOA PAYOH  203.511156\n",
       "5    town_MARINE PARADE  201.281916\n",
       "6            town_BEDOK  169.617459\n",
       "7          town_GEYLANG  168.927703\n",
       "8         town_CLEMENTI  156.029283\n",
       "9       town_ANG MO KIO  146.527187"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display Top 10 Expensive Towns\n",
    "df_town.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>Town</th>\n",
       "      <th>Coef</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>town_WOODLANDS</td>\n",
       "      <td>-190.133716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>town_SENGKANG</td>\n",
       "      <td>-170.330177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>town_SEMBAWANG</td>\n",
       "      <td>-161.438349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>town_CHOA CHU KANG</td>\n",
       "      <td>-161.337509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>town_JURONG WEST</td>\n",
       "      <td>-118.172564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>town_YISHUN</td>\n",
       "      <td>-104.822364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>town_PUNGGOL</td>\n",
       "      <td>-91.568452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>town_BUKIT PANJANG</td>\n",
       "      <td>-82.632655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>town_BUKIT BATOK</td>\n",
       "      <td>-15.160744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>town_PASIR RIS</td>\n",
       "      <td>-1.212532</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Town        Coef\n",
       "25      town_WOODLANDS -190.133716\n",
       "24       town_SENGKANG -170.330177\n",
       "23      town_SEMBAWANG -161.438349\n",
       "22  town_CHOA CHU KANG -161.337509\n",
       "21    town_JURONG WEST -118.172564\n",
       "20         town_YISHUN -104.822364\n",
       "19        town_PUNGGOL  -91.568452\n",
       "18  town_BUKIT PANJANG  -82.632655\n",
       "17    town_BUKIT BATOK  -15.160744\n",
       "16      town_PASIR RIS   -1.212532"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display Top 10 Least Expensive Towns\n",
    "df_town.sort_values(by = \"Coef\")[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig8, axes = plt.subplots(1,2, figsize = (14,6))\n",
    "sns.barplot(x = df_town['Town'][0:10], y = df_town['Coef'], ax = axes[0])\n",
    "sns.barplot(x = df_town.sort_values(by = \"Coef\")['Town'][0:10], y = abs(df_town['Coef']), ax = axes[1])\n",
    "axes[0].set_title(\"Top 10 Most Expensive Towns\")\n",
    "axes[1].set_title(\"Top 10 Least Expensive Towns\")\n",
    "\n",
    "axes[0].set_xticklabels(\n",
    "    axes[0].get_xticklabels(),\n",
    "    rotation=20,\n",
    "    horizontalalignment='right')\n",
    "\n",
    "axes[1].set_xticklabels(\n",
    "    axes[1].get_xticklabels(),\n",
    "    rotation=20,\n",
    "    horizontalalignment='right');\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save figure in local drive\n",
    "fig8.savefig(\"./Images/Top_Towns.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**End of Notebook (last edited 04 March 2020 by Lee Gang)**"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}