Boston_Housing_Data_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-zKtWHnR3FH6"
   },
   "source": [
    "# **Importing the necessary Libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "q75hcgg12x6o"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "VZiZ9yKm3e1-",
    "outputId": "aec416bc-cb42-4b6a-f30b-c9a6b211ab97"
   },
   "outputs": [
    {
     "data": {
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       "    }\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>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1</td>\n",
       "      <td>296</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD  TAX  PTRATIO  \\\n",
       "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900    1  296     15.3   \n",
       "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671    2  242     17.8   \n",
       "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671    2  242     17.8   \n",
       "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622    3  222     18.7   \n",
       "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622    3  222     18.7   \n",
       "\n",
       "        B  LSTAT  MEDV  \n",
       "0  396.90   4.98  24.0  \n",
       "1  396.90   9.14  21.6  \n",
       "2  392.83   4.03  34.7  \n",
       "3  394.63   2.94  33.4  \n",
       "4  396.90    NaN  36.2  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing the Boston Dataset\n",
    "df = pd.read_csv(\"BostonHousingData.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d2zQoBkk4xFK"
   },
   "source": [
    "The Boston Housing Dataset is a derived from information collected by the U.S. Census Service concerning housing in the area of Boston MA. The following describes the dataset columns:\n",
    "\n",
    "* CRIM - per capita crime rate by town\n",
    "* ZN - proportion of residential land zoned for lots over 25,000 sq.ft.\n",
    "* INDUS - proportion of non-retail business acres per town.\n",
    "* CHAS - Charles River dummy variable (1 if tract bounds river; 0 otherwise)\n",
    "* NOX - nitric oxides concentration (parts per 10 million)\n",
    "* RM - average number of rooms per dwelling\n",
    "* AGE - proportion of owner-occupied units built prior to 1940\n",
    "* DIS - weighted distances to five Boston employment centres\n",
    "* RAD - index of accessibility to radial highways\n",
    "* TAX - full-value property-tax rate per $10,000\n",
    "* PTRATIO - pupil-teacher ratio by town B- 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n",
    "* LSTAT - % lower status of the population\n",
    "\n",
    "* MEDV - Median value of owner-occupied homes in $1000's"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Wxbq8mJ24gra"
   },
   "source": [
    "# **Business Understanding**\n",
    "The Boston housing dataset is majorly focused on predicting housing prices in Boston. The stakeholders to the Boston dataset couldn include Investors, Customers intrested in purchasing houses, job seeekers, housing competitors, and the government.\n",
    "\n",
    "**Problem statement**\n",
    "The goal is to develop a predictive model that accurately estimates housing prices in the Boston area. The problem arises from the complexity of various factors influencing the prices, such as crime rates, property tax, accessibility to highways, average number of rooms, and environmental quality. By building a reliable model, stakeholders can make informed decisions related to real estate investments, policy-making, market competition, and personal home purchases.\n",
    "\n",
    "**Objectives**\n",
    "1. Predicting housing prices using various models\n",
    "2. Identify key factors influencing housing prices\n",
    "3. Support decision nmaking for stakeholders\n",
    "**Research Question**\n",
    "What are the key factors that influence housing prices in Boston, and how accurately can we predict the prices of homes based on these factors?\n",
    "\n",
    "\n",
    "**Access Situation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rQxUruhD4CX3",
    "outputId": "d3f201c2-08e7-462c-a3ac-18c03cbd0b9c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 506 entries, 0 to 505\n",
      "Data columns (total 14 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   CRIM     486 non-null    float64\n",
      " 1   ZN       486 non-null    float64\n",
      " 2   INDUS    486 non-null    float64\n",
      " 3   CHAS     486 non-null    float64\n",
      " 4   NOX      506 non-null    float64\n",
      " 5   RM       506 non-null    float64\n",
      " 6   AGE      486 non-null    float64\n",
      " 7   DIS      506 non-null    float64\n",
      " 8   RAD      506 non-null    int64  \n",
      " 9   TAX      506 non-null    int64  \n",
      " 10  PTRATIO  506 non-null    float64\n",
      " 11  B        506 non-null    float64\n",
      " 12  LSTAT    486 non-null    float64\n",
      " 13  MEDV     506 non-null    float64\n",
      "dtypes: float64(12), int64(2)\n",
      "memory usage: 55.5 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(506, 14)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 523
    },
    "id": "onsPfXCs8yMA",
    "outputId": "891505e8-2961-4f09-a38d-f51444effb61"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CRIM       3.952569\n",
       "ZN         3.952569\n",
       "INDUS      3.952569\n",
       "CHAS       3.952569\n",
       "NOX        0.000000\n",
       "RM         0.000000\n",
       "AGE        3.952569\n",
       "DIS        0.000000\n",
       "RAD        0.000000\n",
       "TAX        0.000000\n",
       "PTRATIO    0.000000\n",
       "B          0.000000\n",
       "LSTAT      3.952569\n",
       "MEDV       0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().mean()*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "kdZojxFJ9J3I"
   },
   "outputs": [],
   "source": [
    "df.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f9PZL7gm9bPX",
    "outputId": "0348c949-6e20-4524-821a-00e45eeb8536"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 394 entries, 0 to 504\n",
      "Data columns (total 14 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   CRIM     394 non-null    float64\n",
      " 1   ZN       394 non-null    float64\n",
      " 2   INDUS    394 non-null    float64\n",
      " 3   CHAS     394 non-null    float64\n",
      " 4   NOX      394 non-null    float64\n",
      " 5   RM       394 non-null    float64\n",
      " 6   AGE      394 non-null    float64\n",
      " 7   DIS      394 non-null    float64\n",
      " 8   RAD      394 non-null    int64  \n",
      " 9   TAX      394 non-null    int64  \n",
      " 10  PTRATIO  394 non-null    float64\n",
      " 11  B        394 non-null    float64\n",
      " 12  LSTAT    394 non-null    float64\n",
      " 13  MEDV     394 non-null    float64\n",
      "dtypes: float64(12), int64(2)\n",
      "memory usage: 46.2 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(394, 14)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>394.000000</td>\n",
       "      <td>394.000000</td>\n",
       "      <td>394.000000</td>\n",
       "      <td>394.000000</td>\n",
       "      <td>394.000000</td>\n",
       "      <td>394.000000</td>\n",
       "      <td>394.000000</td>\n",
       "      <td>394.000000</td>\n",
       "      <td>394.000000</td>\n",
       "      <td>394.000000</td>\n",
       "      <td>394.000000</td>\n",
       "      <td>394.000000</td>\n",
       "      <td>394.000000</td>\n",
       "      <td>394.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.690136</td>\n",
       "      <td>11.460660</td>\n",
       "      <td>11.000863</td>\n",
       "      <td>0.068528</td>\n",
       "      <td>0.553215</td>\n",
       "      <td>6.280015</td>\n",
       "      <td>68.932741</td>\n",
       "      <td>3.805268</td>\n",
       "      <td>9.403553</td>\n",
       "      <td>406.431472</td>\n",
       "      <td>18.537563</td>\n",
       "      <td>358.490939</td>\n",
       "      <td>12.769112</td>\n",
       "      <td>22.359645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.202423</td>\n",
       "      <td>23.954082</td>\n",
       "      <td>6.908364</td>\n",
       "      <td>0.252971</td>\n",
       "      <td>0.113112</td>\n",
       "      <td>0.697985</td>\n",
       "      <td>27.888705</td>\n",
       "      <td>2.098571</td>\n",
       "      <td>8.633451</td>\n",
       "      <td>168.312419</td>\n",
       "      <td>2.166460</td>\n",
       "      <td>89.283295</td>\n",
       "      <td>7.308430</td>\n",
       "      <td>9.142979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.006320</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.389000</td>\n",
       "      <td>3.561000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>1.129600</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>1.730000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.081955</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.130000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.453000</td>\n",
       "      <td>5.879250</td>\n",
       "      <td>45.475000</td>\n",
       "      <td>2.110100</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>280.250000</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>376.707500</td>\n",
       "      <td>7.125000</td>\n",
       "      <td>16.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.268880</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.560000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.538000</td>\n",
       "      <td>6.201500</td>\n",
       "      <td>77.700000</td>\n",
       "      <td>3.199200</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>19.100000</td>\n",
       "      <td>392.190000</td>\n",
       "      <td>11.300000</td>\n",
       "      <td>21.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.435973</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.624000</td>\n",
       "      <td>6.605500</td>\n",
       "      <td>94.250000</td>\n",
       "      <td>5.116700</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>666.000000</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>396.900000</td>\n",
       "      <td>17.117500</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>88.976200</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>27.740000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.871000</td>\n",
       "      <td>8.780000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>12.126500</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>711.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>396.900000</td>\n",
       "      <td>37.970000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CRIM          ZN       INDUS        CHAS         NOX          RM  \\\n",
       "count  394.000000  394.000000  394.000000  394.000000  394.000000  394.000000   \n",
       "mean     3.690136   11.460660   11.000863    0.068528    0.553215    6.280015   \n",
       "std      9.202423   23.954082    6.908364    0.252971    0.113112    0.697985   \n",
       "min      0.006320    0.000000    0.460000    0.000000    0.389000    3.561000   \n",
       "25%      0.081955    0.000000    5.130000    0.000000    0.453000    5.879250   \n",
       "50%      0.268880    0.000000    8.560000    0.000000    0.538000    6.201500   \n",
       "75%      3.435973   12.500000   18.100000    0.000000    0.624000    6.605500   \n",
       "max     88.976200  100.000000   27.740000    1.000000    0.871000    8.780000   \n",
       "\n",
       "              AGE         DIS         RAD         TAX     PTRATIO           B  \\\n",
       "count  394.000000  394.000000  394.000000  394.000000  394.000000  394.000000   \n",
       "mean    68.932741    3.805268    9.403553  406.431472   18.537563  358.490939   \n",
       "std     27.888705    2.098571    8.633451  168.312419    2.166460   89.283295   \n",
       "min      2.900000    1.129600    1.000000  187.000000   12.600000    2.600000   \n",
       "25%     45.475000    2.110100    4.000000  280.250000   17.400000  376.707500   \n",
       "50%     77.700000    3.199200    5.000000  330.000000   19.100000  392.190000   \n",
       "75%     94.250000    5.116700   24.000000  666.000000   20.200000  396.900000   \n",
       "max    100.000000   12.126500   24.000000  711.000000   22.000000  396.900000   \n",
       "\n",
       "            LSTAT        MEDV  \n",
       "count  394.000000  394.000000  \n",
       "mean    12.769112   22.359645  \n",
       "std      7.308430    9.142979  \n",
       "min      1.730000    5.000000  \n",
       "25%      7.125000   16.800000  \n",
       "50%     11.300000   21.050000  \n",
       "75%     17.117500   25.000000  \n",
       "max     37.970000   50.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0ZnbkhJw92iR"
   },
   "source": [
    "**Continuous Data Plots**\n",
    "* Histogram\n",
    "* Scatter Plot\n",
    "* Box Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 488
    },
    "id": "XJQG2xkS9foO",
    "outputId": "108386c8-1f03-48e0-b6cb-869d55d8f431"
   },
   "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>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CRIM</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.188075</td>\n",
       "      <td>0.391552</td>\n",
       "      <td>-0.051970</td>\n",
       "      <td>0.416160</td>\n",
       "      <td>-0.227170</td>\n",
       "      <td>0.341311</td>\n",
       "      <td>-0.365052</td>\n",
       "      <td>0.608667</td>\n",
       "      <td>0.560841</td>\n",
       "      <td>0.265428</td>\n",
       "      <td>-0.386254</td>\n",
       "      <td>0.461906</td>\n",
       "      <td>-0.397230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZN</th>\n",
       "      <td>-0.188075</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.521256</td>\n",
       "      <td>-0.033357</td>\n",
       "      <td>-0.515660</td>\n",
       "      <td>0.343210</td>\n",
       "      <td>-0.568174</td>\n",
       "      <td>0.645359</td>\n",
       "      <td>-0.298773</td>\n",
       "      <td>-0.305768</td>\n",
       "      <td>-0.422164</td>\n",
       "      <td>0.169894</td>\n",
       "      <td>-0.415041</td>\n",
       "      <td>0.406822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INDUS</th>\n",
       "      <td>0.391552</td>\n",
       "      <td>-0.521256</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.049820</td>\n",
       "      <td>0.762737</td>\n",
       "      <td>-0.403068</td>\n",
       "      <td>0.642387</td>\n",
       "      <td>-0.696569</td>\n",
       "      <td>0.591944</td>\n",
       "      <td>0.734204</td>\n",
       "      <td>0.395691</td>\n",
       "      <td>-0.344788</td>\n",
       "      <td>0.598156</td>\n",
       "      <td>-0.510829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHAS</th>\n",
       "      <td>-0.051970</td>\n",
       "      <td>-0.033357</td>\n",
       "      <td>0.049820</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.076661</td>\n",
       "      <td>0.095308</td>\n",
       "      <td>0.072644</td>\n",
       "      <td>-0.095037</td>\n",
       "      <td>0.014102</td>\n",
       "      <td>-0.026513</td>\n",
       "      <td>-0.104995</td>\n",
       "      <td>0.068913</td>\n",
       "      <td>-0.037113</td>\n",
       "      <td>0.173701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOX</th>\n",
       "      <td>0.416160</td>\n",
       "      <td>-0.515660</td>\n",
       "      <td>0.762737</td>\n",
       "      <td>0.076661</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.316563</td>\n",
       "      <td>0.732540</td>\n",
       "      <td>-0.768137</td>\n",
       "      <td>0.628170</td>\n",
       "      <td>0.679824</td>\n",
       "      <td>0.210216</td>\n",
       "      <td>-0.384257</td>\n",
       "      <td>0.593655</td>\n",
       "      <td>-0.459054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RM</th>\n",
       "      <td>-0.227170</td>\n",
       "      <td>0.343210</td>\n",
       "      <td>-0.403068</td>\n",
       "      <td>0.095308</td>\n",
       "      <td>-0.316563</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.248670</td>\n",
       "      <td>0.218713</td>\n",
       "      <td>-0.236057</td>\n",
       "      <td>-0.320561</td>\n",
       "      <td>-0.390686</td>\n",
       "      <td>0.123320</td>\n",
       "      <td>-0.636226</td>\n",
       "      <td>0.723951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGE</th>\n",
       "      <td>0.341311</td>\n",
       "      <td>-0.568174</td>\n",
       "      <td>0.642387</td>\n",
       "      <td>0.072644</td>\n",
       "      <td>0.732540</td>\n",
       "      <td>-0.248670</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.753547</td>\n",
       "      <td>0.443585</td>\n",
       "      <td>0.504472</td>\n",
       "      <td>0.264968</td>\n",
       "      <td>-0.281990</td>\n",
       "      <td>0.601137</td>\n",
       "      <td>-0.407470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DIS</th>\n",
       "      <td>-0.365052</td>\n",
       "      <td>0.645359</td>\n",
       "      <td>-0.696569</td>\n",
       "      <td>-0.095037</td>\n",
       "      <td>-0.768137</td>\n",
       "      <td>0.218713</td>\n",
       "      <td>-0.753547</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.477075</td>\n",
       "      <td>-0.529603</td>\n",
       "      <td>-0.228840</td>\n",
       "      <td>0.285168</td>\n",
       "      <td>-0.505036</td>\n",
       "      <td>0.279547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAD</th>\n",
       "      <td>0.608667</td>\n",
       "      <td>-0.298773</td>\n",
       "      <td>0.591944</td>\n",
       "      <td>0.014102</td>\n",
       "      <td>0.628170</td>\n",
       "      <td>-0.236057</td>\n",
       "      <td>0.443585</td>\n",
       "      <td>-0.477075</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.441949</td>\n",
       "      <td>-0.444135</td>\n",
       "      <td>0.510868</td>\n",
       "      <td>-0.416638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TAX</th>\n",
       "      <td>0.560841</td>\n",
       "      <td>-0.305768</td>\n",
       "      <td>0.734204</td>\n",
       "      <td>-0.026513</td>\n",
       "      <td>0.679824</td>\n",
       "      <td>-0.320561</td>\n",
       "      <td>0.504472</td>\n",
       "      <td>-0.529603</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.446961</td>\n",
       "      <td>-0.435457</td>\n",
       "      <td>0.572218</td>\n",
       "      <td>-0.508864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PTRATIO</th>\n",
       "      <td>0.265428</td>\n",
       "      <td>-0.422164</td>\n",
       "      <td>0.395691</td>\n",
       "      <td>-0.104995</td>\n",
       "      <td>0.210216</td>\n",
       "      <td>-0.390686</td>\n",
       "      <td>0.264968</td>\n",
       "      <td>-0.228840</td>\n",
       "      <td>0.441949</td>\n",
       "      <td>0.446961</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.179816</td>\n",
       "      <td>0.395006</td>\n",
       "      <td>-0.543809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.386254</td>\n",
       "      <td>0.169894</td>\n",
       "      <td>-0.344788</td>\n",
       "      <td>0.068913</td>\n",
       "      <td>-0.384257</td>\n",
       "      <td>0.123320</td>\n",
       "      <td>-0.281990</td>\n",
       "      <td>0.285168</td>\n",
       "      <td>-0.444135</td>\n",
       "      <td>-0.435457</td>\n",
       "      <td>-0.179816</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.383783</td>\n",
       "      <td>0.347256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LSTAT</th>\n",
       "      <td>0.461906</td>\n",
       "      <td>-0.415041</td>\n",
       "      <td>0.598156</td>\n",
       "      <td>-0.037113</td>\n",
       "      <td>0.593655</td>\n",
       "      <td>-0.636226</td>\n",
       "      <td>0.601137</td>\n",
       "      <td>-0.505036</td>\n",
       "      <td>0.510868</td>\n",
       "      <td>0.572218</td>\n",
       "      <td>0.395006</td>\n",
       "      <td>-0.383783</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.743450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEDV</th>\n",
       "      <td>-0.397230</td>\n",
       "      <td>0.406822</td>\n",
       "      <td>-0.510829</td>\n",
       "      <td>0.173701</td>\n",
       "      <td>-0.459054</td>\n",
       "      <td>0.723951</td>\n",
       "      <td>-0.407470</td>\n",
       "      <td>0.279547</td>\n",
       "      <td>-0.416638</td>\n",
       "      <td>-0.508864</td>\n",
       "      <td>-0.543809</td>\n",
       "      <td>0.347256</td>\n",
       "      <td>-0.743450</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CRIM        ZN     INDUS      CHAS       NOX        RM       AGE  \\\n",
       "CRIM     1.000000 -0.188075  0.391552 -0.051970  0.416160 -0.227170  0.341311   \n",
       "ZN      -0.188075  1.000000 -0.521256 -0.033357 -0.515660  0.343210 -0.568174   \n",
       "INDUS    0.391552 -0.521256  1.000000  0.049820  0.762737 -0.403068  0.642387   \n",
       "CHAS    -0.051970 -0.033357  0.049820  1.000000  0.076661  0.095308  0.072644   \n",
       "NOX      0.416160 -0.515660  0.762737  0.076661  1.000000 -0.316563  0.732540   \n",
       "RM      -0.227170  0.343210 -0.403068  0.095308 -0.316563  1.000000 -0.248670   \n",
       "AGE      0.341311 -0.568174  0.642387  0.072644  0.732540 -0.248670  1.000000   \n",
       "DIS     -0.365052  0.645359 -0.696569 -0.095037 -0.768137  0.218713 -0.753547   \n",
       "RAD      0.608667 -0.298773  0.591944  0.014102  0.628170 -0.236057  0.443585   \n",
       "TAX      0.560841 -0.305768  0.734204 -0.026513  0.679824 -0.320561  0.504472   \n",
       "PTRATIO  0.265428 -0.422164  0.395691 -0.104995  0.210216 -0.390686  0.264968   \n",
       "B       -0.386254  0.169894 -0.344788  0.068913 -0.384257  0.123320 -0.281990   \n",
       "LSTAT    0.461906 -0.415041  0.598156 -0.037113  0.593655 -0.636226  0.601137   \n",
       "MEDV    -0.397230  0.406822 -0.510829  0.173701 -0.459054  0.723951 -0.407470   \n",
       "\n",
       "              DIS       RAD       TAX   PTRATIO         B     LSTAT      MEDV  \n",
       "CRIM    -0.365052  0.608667  0.560841  0.265428 -0.386254  0.461906 -0.397230  \n",
       "ZN       0.645359 -0.298773 -0.305768 -0.422164  0.169894 -0.415041  0.406822  \n",
       "INDUS   -0.696569  0.591944  0.734204  0.395691 -0.344788  0.598156 -0.510829  \n",
       "CHAS    -0.095037  0.014102 -0.026513 -0.104995  0.068913 -0.037113  0.173701  \n",
       "NOX     -0.768137  0.628170  0.679824  0.210216 -0.384257  0.593655 -0.459054  \n",
       "RM       0.218713 -0.236057 -0.320561 -0.390686  0.123320 -0.636226  0.723951  \n",
       "AGE     -0.753547  0.443585  0.504472  0.264968 -0.281990  0.601137 -0.407470  \n",
       "DIS      1.000000 -0.477075 -0.529603 -0.228840  0.285168 -0.505036  0.279547  \n",
       "RAD     -0.477075  1.000000  0.900000  0.441949 -0.444135  0.510868 -0.416638  \n",
       "TAX     -0.529603  0.900000  1.000000  0.446961 -0.435457  0.572218 -0.508864  \n",
       "PTRATIO -0.228840  0.441949  0.446961  1.000000 -0.179816  0.395006 -0.543809  \n",
       "B        0.285168 -0.444135 -0.435457 -0.179816  1.000000 -0.383783  0.347256  \n",
       "LSTAT   -0.505036  0.510868  0.572218  0.395006 -0.383783  1.000000 -0.743450  \n",
       "MEDV     0.279547 -0.416638 -0.508864 -0.543809  0.347256 -0.743450  1.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Correlation\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "o5-iM0YB-iqY"
   },
   "source": [
    "**Correlation Interpretation**\n",
    "\n",
    "Correlation ranges from -1 to +1\n",
    "* -1 = Perfect negative Correlation\n",
    "* -0.6 to -0.9 = High Negative Correlation\n",
    "* -0.3 to -0.5 = Moderate negative correlation\n",
    "* -0.1 to -0.2 = Low negative correlation\n",
    "\n",
    "* 0 = No correlation\n",
    "\n",
    "* +1 = Perfect positive Correlation\n",
    "* +0.6 to +0.9 = High positive Correlation\n",
    "* +0.3 to +0.5 = Moderate positive correlation\n",
    "* +0.1 to +0.2 = Low positive correlation\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 699
    },
    "id": "L15AwZsJAxfu",
    "outputId": "6c003a27-ded1-4506-c43a-a351837637bf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15, 8))\n",
    "sns.heatmap(df.corr(), annot = True, cmap = \"YlGnBu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 564
    },
    "id": "tG7jBZuuEFLw",
    "outputId": "11c3e437-3d50-41ac-9307-88eca6f8924c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,6))\n",
    "plt.scatter('RM', 'MEDV', data = df)\n",
    "plt.xlabel(\"Number of Rooms\")\n",
    "plt.ylabel(\"Median Value of Houses\")\n",
    "plt.title(\"Relationship between Number of Rooms and the Median Value of the Houses\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5_VJv0JMOQzF"
   },
   "source": [
    "Y = a + bX\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 564
    },
    "id": "zz0qM-e5FDFx",
    "outputId": "1bfe167f-5042-4e0f-e85b-10ae5bde8cb4"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,6))\n",
    "plt.scatter('DIS', 'NOX', data = df)\n",
    "plt.xlabel(\"Distance to 5 employment centres\")\n",
    "plt.ylabel(\"Nitric Polution\")\n",
    "plt.title(\"Relationship between the distance to 5 employment centers and the Nitric Polution\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 575
    },
    "id": "s4na9bPgJyAi",
    "outputId": "d1beeec5-5b67-4afe-f12d-533ebc79120b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'TAX boxplot'}>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AABRB/AAAAEUQPwAAQBHEDwAAUATxAwAAFEH8AAAARRA/AABAEcQPAABQBPEDAAAUQfwAAABFaBjoAXh5VFWVrVu3DvQYAHvN0/9N8+8b8Ptm6NChqdVqAz3G7x3xU4itW7dm5syZAz0GwEvi5JNPHugRAPaqFStWpKmpaaDH+L3jtjcAAKAIrvwUaMsfvTvVfv7TA/u4qkp2Pvnb3+/XkLg9BNjH1XY+mRE//fJAj/F7zf8BF6jaryEZNHigxwDYC4YM9AAAe0010AMUwG1vAABAEcQPAABQBPEDAAAUQfwAAABFED8AAEARxA8AAFAE8QMAABRB/AAAAEUQPwAAQBHEDwAAUATxAwAAFEH8AAAARRA/AABAEcQPAABQBPEDAAAUQfwAAABFED8AAEARxA8AAFAE8QMAABRB/AAAAEUQPwAAQBHEDwAAUATxAwAAFEH8AAAARRA/AABAEcQPAABQBPEDAAAUQfwAAABFED8AAEARxA8AAFAE8QMAABRB/AAAAEUQPwAAQBHEDwAAUATxAwAAFEH8AAAARRA/AABAEcQPAABQBPEDAAAUQfwAAABFED8AAEARxA8AAFAE8QMAABRB/AAAAEUQPwAAQBHEDwAAUIR+x8///d//5T3veU/GjBmTYcOG5Y/+6I+ybt26+vNVVWXevHlpa2tLU1NTpk6dmvXr1/d5jd7e3lxwwQUZO3Zshg8fnhNPPDEPPfTQiz8bAACAZ9Gv+Nm8eXPe9ra3ZfDgwVmxYkV+/vOf58orr8wrXvGK+pqFCxfmqquuyrXXXpu1a9emtbU1xxxzTLq7u+tr5s6dm+XLl2fZsmW58847s2XLlhx//PHZsWPHXjsxAACAp2voz+Irrrgi7e3tufHGG+v7DjnkkPrvq6rKNddck8svvzynnHJKkmTJkiVpaWnJzTffnHPOOSednZ1ZtGhRbrrppkyfPj1JsnTp0rS3t2fVqlU59thj98JpAQAA9NWvKz+33nprjjrqqJx66qk58MADc+SRR+aGG26oP3///feno6MjM2bMqO9rbGzMlClTsmbNmiTJunXrsn379j5r2traMmHChPqaXfX29qarq6vPBgAA0B/9ip/77rsv1113XcaPH59vfvObOffcc/PBD34wX/ziF5MkHR0dSZKWlpY+x7W0tNSf6+joyJAhQ7L//vs/65pdLViwIM3NzfWtvb29P2MDAAD0L3527tyZN73pTZk/f36OPPLInHPOOfnLv/zLXHfddX3W1Wq1Po+rqtpt366ea82ll16azs7O+rZhw4b+jA0AANC/+DnooINy2GGH9dl36KGH5sEHH0yStLa2JsluV3A2bdpUvxrU2tqabdu2ZfPmzc+6ZleNjY0ZNWpUnw0AAKA/+hU/b3vb2/LLX/6yz75f/epXOfjgg5Mk48aNS2tra1auXFl/ftu2bVm9enUmT56cJJk4cWIGDx7cZ83GjRtzzz331NcAAADsbf36trcPfehDmTx5cubPn59Zs2blhz/8Ya6//vpcf/31SX57u9vcuXMzf/78jB8/PuPHj8/8+fMzbNiwnH766UmS5ubmzJkzJxdddFHGjBmT0aNH5+KLL87hhx9e//Y3AACAva1f8fPmN785y5cvz6WXXpqPfexjGTduXK655pqcccYZ9TWXXHJJenp6ct5552Xz5s2ZNGlSbr/99owcObK+5uqrr05DQ0NmzZqVnp6eTJs2LYsXL86gQYP23pkBAAA8Ta2qqmqgh+ivrq6uNDc3p7Oz0+d/XqCenp7MnDkzSdL9pjOTQYMHeCIAAPrYsT0jf3xTkmTFihVpamoa4IH2Df1pg3595gcAAGBfJX4AAIAiiB8AAKAI4gcAACiC+AEAAIogfgAAgCKIHwAAoAjiBwAAKIL4AQAAiiB+AACAIogfAACgCOIHAAAogvgBAACKIH4AAIAiiB8AAKAI4gcAACiC+AEAAIogfgAAgCKIHwAAoAjiBwAAKIL4AQAAiiB+AACAIogfAACgCOIHAAAogvgBAACKIH4AAIAiiB8AAKAI4gcAACiC+AEAAIogfgAAgCKIHwAAoAjiBwAAKIL4AQAAiiB+AACAIogfAACgCOIHAAAogvgBAACKIH4AAIAiiB8AAKAI4gcAACiC+AEAAIogfgAAgCKIHwAAoAjiBwAAKIL4AQAAiiB+AACAIogfAACgCOIHAAAogvgBAACKIH4AAIAiiB8AAKAI4gcAACiC+AEAAIogfgAAgCKIHwAAoAjiBwAAKIL4AQAAiiB+AACAIogfAACgCOIHAAAogvgBAACKIH4AAIAiiB8AAKAI4gcAACiC+AEAAIogfgAAgCKIHwAAoAjiBwAAKIL4AQAAitCv+Jk3b15qtVqfrbW1tf58VVWZN29e2tra0tTUlKlTp2b9+vV9XqO3tzcXXHBBxo4dm+HDh+fEE0/MQw89tHfOBgAA4Fn0+8rPG97whmzcuLG+3X333fXnFi5cmKuuuirXXntt1q5dm9bW1hxzzDHp7u6ur5k7d26WL1+eZcuW5c4778yWLVty/PHHZ8eOHXvnjAAAAJ5BQ78PaGjoc7XnKVVV5Zprrsnll1+eU045JUmyZMmStLS05Oabb84555yTzs7OLFq0KDfddFOmT5+eJFm6dGna29uzatWqHHvssS/ydAAAAJ5Zv6/83HvvvWlra8u4ceNy2mmn5b777kuS3H///eno6MiMGTPqaxsbGzNlypSsWbMmSbJu3bps3769z5q2trZMmDChvuaZ9Pb2pqurq88GAADQH/2Kn0mTJuWLX/xivvnNb+aGG25IR0dHJk+enEcffTQdHR1JkpaWlj7HtLS01J/r6OjIkCFDsv/++z/rmmeyYMGCNDc317f29vb+jA0AANC/+Jk5c2b+7M/+LIcffnimT5+er3/960l+e3vbU2q1Wp9jqqrabd+unm/NpZdems7Ozvq2YcOG/owNAADw4r7qevjw4Tn88MNz77331j8HtOsVnE2bNtWvBrW2tmbbtm3ZvHnzs655Jo2NjRk1alSfDQAAoD9eVPz09vbmF7/4RQ466KCMGzcura2tWblyZf35bdu2ZfXq1Zk8eXKSZOLEiRk8eHCfNRs3bsw999xTXwMAAPBS6Ne3vV188cU54YQT8gd/8AfZtGlTPvGJT6SrqyuzZ89OrVbL3LlzM3/+/IwfPz7jx4/P/PnzM2zYsJx++ulJkubm5syZMycXXXRRxowZk9GjR+fiiy+u30YHAADwUulX/Dz00EN597vfnUceeSQHHHBA3vrWt+auu+7KwQcfnCS55JJL0tPTk/POOy+bN2/OpEmTcvvtt2fkyJH117j66qvT0NCQWbNmpaenJ9OmTcvixYszaNCgvXtmAAAAT1Orqqoa6CH6q6urK83Nzens7PT5nxeop6cnM2fOTJJ0v+nMZNDgAZ4IAIA+dmzPyB/flCRZsWJFmpqaBnigfUN/2uBFfeYHAABgXyF+AACAIogfAACgCOIHAAAogvgBAACKIH4AAIAiiB8AAKAI4gcAACiC+AEAAIogfgAAgCKIHwAAoAjiBwAAKIL4AQAAiiB+AACAIogfAACgCOIHAAAogvgBAACKIH4AAIAiiB8AAKAI4gcAACiC+AEAAIogfgAAgCKIHwAAoAjiBwAAKIL4AQAAiiB+AACAIogfAACgCOIHAAAogvgBAACKIH4AAIAiiB8AAKAI4gcAACiC+AEAAIogfgAAgCKIHwAAoAjiBwAAKIL4AQAAiiB+AACAIogfAACgCOIHAAAogvgBAACKIH4AAIAiiB8AAKAI4gcAACiC+AEAAIogfgAAgCKIHwAAoAjiBwAAKIL4AQAAiiB+AACAIogfAACgCOIHAAAogvgBAACKIH4AAIAiiB8AAKAI4gcAACiC+AEAAIogfgAAgCKIHwAAoAjiBwAAKIL4AQAAiiB+AACAIogfAACgCOIHAAAogvgBAACKIH4AAIAiiB8AAKAI4gcAACiC+AEAAIrwouJnwYIFqdVqmTt3bn1fVVWZN29e2tra0tTUlKlTp2b9+vV9juvt7c0FF1yQsWPHZvjw4TnxxBPz0EMPvZhRAAAAntMex8/atWtz/fXX541vfGOf/QsXLsxVV12Va6+9NmvXrk1ra2uOOeaYdHd319fMnTs3y5cvz7Jly3LnnXdmy5YtOf7447Njx449PxMAAIDnsEfxs2XLlpxxxhm54YYbsv/++9f3V1WVa665JpdffnlOOeWUTJgwIUuWLMkTTzyRm2++OUnS2dmZRYsW5corr8z06dNz5JFHZunSpbn77ruzatWqvXNWAAAAu9ij+Dn//PNz3HHHZfr06X3233///eno6MiMGTPq+xobGzNlypSsWbMmSbJu3bps3769z5q2trZMmDChvgYAAGBva+jvAcuWLcuPf/zjrF27drfnOjo6kiQtLS199re0tOSBBx6orxkyZEifK0ZPrXnq+F319vamt7e3/rirq6u/YwMAAIXr15WfDRs25K//+q+zdOnSDB069FnX1Wq1Po+rqtpt366ea82CBQvS3Nxc39rb2/szNgAAQP/iZ926ddm0aVMmTpyYhoaGNDQ0ZPXq1fnMZz6ThoaG+hWfXa/gbNq0qf5ca2trtm3bls2bNz/rml1deuml6ezsrG8bNmzoz9gAAAD9i59p06bl7rvvzk9/+tP6dtRRR+WMM87IT3/607z61a9Oa2trVq5cWT9m27ZtWb16dSZPnpwkmThxYgYPHtxnzcaNG3PPPffU1+yqsbExo0aN6rMBAAD0R78+8zNy5MhMmDChz77hw4dnzJgx9f1z587N/PnzM378+IwfPz7z58/PsGHDcvrppydJmpubM2fOnFx00UUZM2ZMRo8enYsvvjiHH374bl+gAAAAsLf0+wsPns8ll1ySnp6enHfeedm8eXMmTZqU22+/PSNHjqyvufrqq9PQ0JBZs2alp6cn06ZNy+LFizNo0KC9PQ4AAECSpFZVVTXQQ/RXV1dXmpub09nZ6Ra4F6inpyczZ85MknS/6cxk0OABnggAgD52bM/IH9+UJFmxYkWampoGeKB9Q3/aYI9+zg8AAMC+RvwAAABFED8AAEARxA8AAFAE8QMAABRB/AAAAEUQPwAAQBHEDwAAUATxAwAAFEH8AAAARRA/AABAEcQPAABQBPEDAAAUQfwAAABFED8AAEARxA8AAFAE8QMAABRB/AAAAEUQPwAAQBHEDwAAUATxAwAAFEH8AAAARRA/AABAEcQPAABQBPEDAAAUQfwAAABFED8AAEARxA8AAFAE8QMAABRB/AAAAEUQPwAAQBHEDwAAUATxAwAAFEH8AAAARRA/AABAEcQPAABQBPEDAAAUQfwAAABFED8AAEARxA8AAFAE8QMAABRB/AAAAEUQPwAAQBHEDwAAUATxAwAAFEH8AAAARRA/AABAEcQPAABQhIaBHoABsGP7QE8AAMCu/D/aS078FGjkfy0b6BEAAOBl57Y3AACgCK78FKj7iNOSQYMHegwAAJ5ux3Z36LzExE+JBg0WPwAAFMdtbwAAQBHEDwAAUATxAwAAFEH8AAAARRA/AABAEcQPAABQBPEDAAAUQfwAAABFED8AAEARxA8AAFAE8QMAABRB/AAAAEUQPwAAQBHEDwAAUATxAwAAFEH8AAAARehX/Fx33XV54xvfmFGjRmXUqFE5+uijs2LFivrzVVVl3rx5aWtrS1NTU6ZOnZr169f3eY3e3t5ccMEFGTt2bIYPH54TTzwxDz300N45GwAAgGfRr/h51atelU9+8pP50Y9+lB/96Ed5xzvekT/90z+tB87ChQtz1VVX5dprr83atWvT2tqaY445Jt3d3fXXmDt3bpYvX55ly5blzjvvzJYtW3L88cdnx44de/fMAAAAnqZf8XPCCSfkne98Z173utflda97Xf7hH/4hI0aMyF133ZWqqnLNNdfk8ssvzymnnJIJEyZkyZIleeKJJ3LzzTcnSTo7O7No0aJceeWVmT59eo488sgsXbo0d999d1atWvWSnCAAAEDyIj7zs2PHjixbtiyPP/54jj766Nx///3p6OjIjBkz6msaGxszZcqUrFmzJkmybt26bN++vc+atra2TJgwob4GAADgpdDQ3wPuvvvuHH300dm6dWtGjBiR5cuX57DDDqvHS0tLS5/1LS0teeCBB5IkHR0dGTJkSPbff//d1nR0dDzre/b29qa3t7f+uKurq79jAwAAhev3lZ8//MM/zE9/+tPcddddef/735/Zs2fn5z//ef35Wq3WZ31VVbvt29XzrVmwYEGam5vrW3t7e3/HBgAACtfv+BkyZEhe+9rX5qijjsqCBQtyxBFH5NOf/nRaW1uTZLcrOJs2bapfDWptbc22bduyefPmZ13zTC699NJ0dnbWtw0bNvR3bAAAoHAv+uf8VFWV3t7ejBs3Lq2trVm5cmX9uW3btmX16tWZPHlykmTixIkZPHhwnzUbN27MPffcU1/zTBobG+tfr/3UBgAA0B/9+szPZZddlpkzZ6a9vT3d3d1ZtmxZvv3tb+e2225LrVbL3LlzM3/+/IwfPz7jx4/P/PnzM2zYsJx++ulJkubm5syZMycXXXRRxowZk9GjR+fiiy/O4YcfnunTp78kJwgAAJD0M35+/etf58wzz8zGjRvT3NycN77xjbnttttyzDHHJEkuueSS9PT05LzzzsvmzZszadKk3H777Rk5cmT9Na6++uo0NDRk1qxZ6enpybRp07J48eIMGjRo754ZAADA09SqqqoGeoj+6urqSnNzczo7O90C9wL19PRk5syZSZLuN52ZDBo8wBMBANDHju0Z+eObkiQrVqxIU1PTAA+0b+hPG7zoz/wAAADsC8QPAABQBPEDAAAUQfwAAABFED8AAEARxA8AAFAE8QMAABRB/AAAAEVoGOgBePnVdj6Zfe4n2wLsqqqSnU/+9vf7NSS12sDOA/Ai1Z76N42XjPgp0IiffnmgRwAAgJed294AAIAiuPJTiKFDh2bFihUDPQbAXrN169acfPLJSZLly5dn6NChAzwRwN7j37SXhvgpRK1WS1NT00CPAfCSGDp0qH/jAHhebnsDAACKIH4AAIAiiB8AAKAI4gcAACiC+AEAAIogfgAAgCKIHwAAoAjiBwAAKIL4AQAAiiB+AACAIogfAACgCOIHAAAogvgBAACKIH4AAIAiiB8AAKAI4gcAACiC+AEAAIogfgAAgCKIHwAAoAjiBwAAKIL4AQAAiiB+AACAIogfAACgCOIHAAAogvgBAACKIH4AAIAiiB8AAKAI4gcAACiC+AEAAIogfgAAgCKIHwAAoAjiBwAAKIL4AQAAiiB+AACAIogfAACgCOIHAAAogvgBAACKIH4AAIAiiB8AAKAI4gcAACiC+AEAAIogfgAAgCKIHwAAoAjiBwAAKIL4AQAAiiB+AACAIogfAACgCOIHAAAogvgBAACKIH4AAIAiiB8AAKAI4gcAACiC+AEAAIogfgAAgCKIHwAAoAjiBwAAKEK/4mfBggV585vfnJEjR+bAAw/MSSedlF/+8pd91lRVlXnz5qWtrS1NTU2ZOnVq1q9f32dNb29vLrjggowdOzbDhw/PiSeemIceeujFnw0AAMCz6Ff8rF69Oueff37uuuuurFy5Mk8++WRmzJiRxx9/vL5m4cKFueqqq3Lttddm7dq1aW1tzTHHHJPu7u76mrlz52b58uVZtmxZ7rzzzmzZsiXHH398duzYsffODAAA4GlqVVVVe3rwb37zmxx44IFZvXp1/uRP/iRVVaWtrS1z587NRz7ykSS/vcrT0tKSK664Iuecc046OztzwAEH5Kabbsq73vWuJMnDDz+c9vb2fOMb38ixxx77vO/b1dWV5ubmdHZ2ZtSoUXs6PgD7sJ6ensycOTNJsmLFijQ1NQ3wRAAMhP60wYv6zE9nZ2eSZPTo0UmS+++/Px0dHZkxY0Z9TWNjY6ZMmZI1a9YkSdatW5ft27f3WdPW1pYJEybU1wAAAOxtDXt6YFVVufDCC/PHf/zHmTBhQpKko6MjSdLS0tJnbUtLSx544IH6miFDhmT//fffbc1Tx++qt7c3vb299cddXV17OjYAAFCoPb7y84EPfCA/+9nP8uUvf3m352q1Wp/HVVXttm9Xz7VmwYIFaW5urm/t7e17OjYAAFCoPYqfCy64ILfeemvuuOOOvOpVr6rvb21tTZLdruBs2rSpfjWotbU127Zty+bNm591za4uvfTSdHZ21rcNGzbsydgAAEDB+hU/VVXlAx/4QP71X/813/rWtzJu3Lg+z48bNy6tra1ZuXJlfd+2bduyevXqTJ48OUkyceLEDB48uM+ajRs35p577qmv2VVjY2NGjRrVZwMAAOiPfn3m5/zzz8/NN9+cf/u3f8vIkSPrV3iam5vT1NSUWq2WuXPnZv78+Rk/fnzGjx+f+fPnZ9iwYTn99NPra+fMmZOLLrooY8aMyejRo3PxxRfn8MMPz/Tp0/f+GQIAAKSf8XPdddclSaZOndpn/4033pizzjorSXLJJZekp6cn5513XjZv3pxJkybl9ttvz8iRI+vrr7766jQ0NGTWrFnp6enJtGnTsnjx4gwaNOjFnQ0AAMCzeFE/52eg+Dk/APg5PwAkL+PP+QEAANhXiB8AAKAIe/xDTgGeSVVV2bp160CPQQGe/vfM3zleLkOHDn3en10I/O4SP8BetXXr1vrnMODlcvLJJw/0CBTC58tg3+a2NwAAoAiu/AB71dChQ7NixYqBHoMCVFWV3t7eJL/9YdhuReLlMHTo0IEeAXgRxA+wV9VqNbeE8LIZNmzYQI8AwD7EbW8AAEARxA8AAFAE8QMAABRB/AAAAEUQPwAAQBHEDwAAUATxAwAAFEH8AAAARRA/AABAEcQPAABQBPEDAAAUQfwAAABFED8AAEARxA8AAFAE8QMAABRB/AAAAEUQPwAAQBEaBnqAPVFVVZKkq6trgCcBAAAG0lNN8FQjPJd9Mn66u7uTJO3t7QM8CQAA8Lugu7s7zc3Nz7mmVr2QRPods3Pnzjz88MMZOXJkarXaQI8DwADp6upKe3t7NmzYkFGjRg30OAAMgKqq0t3dnba2tuy333N/qmefjB8ASH4bP83Nzens7BQ/ADwvX3gAAAAUQfwAAABFED8A7LMaGxvz0Y9+NI2NjQM9CgD7AJ/5AQAAiuDKDwAAUATxAwAAFEH8AAAARRA/AABAEcQPAPukz372sxk3blyGDh2aiRMn5rvf/e5AjwTA7zjxA8A+5ytf+Urmzp2byy+/PD/5yU/y9re/PTNnzsyDDz440KMB8DvMV10DsM+ZNGlS3vSmN+W6666r7zv00ENz0kknZcGCBQM4GQC/y1z5AWCfsm3btqxbty4zZszos3/GjBlZs2bNAE0FwL5A/ACwT3nkkUeyY8eOtLS09Nnf0tKSjo6OAZoKgH2B+AFgn1Sr1fo8rqpqt30A8HTiB4B9ytixYzNo0KDdrvJs2rRpt6tBAPB04geAfcqQIUMyceLErFy5ss/+lStXZvLkyQM0FQD7goaBHgAA+uvCCy/MmWeemaOOOipHH310rr/++jz44IM599xzB3o0AH6HiR8A9jnvete78uijj+ZjH/tYNm7cmAkTJuQb3/hGDj744IEeDYDfYX7ODwAAUASf+QEAAIogfgAAgCKIHwAAoAjiBwAAKIL4AQAAiiB+AACAIogfAACgCOIHAAAogvgBAACKIH4AAIAiiB8AAKAI4gcAACjC/wPfu5vk7CBtbAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,6))\n",
    "plt.title(\"TAX boxplot\")\n",
    "sns.boxplot(df['TAX'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 890
    },
    "id": "jurqSt75AfuK",
    "outputId": "f00ef0f1-af82-42a6-8610-9887616bfb17"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'CRIM'}>, <Axes: title={'center': 'ZN'}>,\n",
       "        <Axes: title={'center': 'INDUS'}>,\n",
       "        <Axes: title={'center': 'CHAS'}>],\n",
       "       [<Axes: title={'center': 'NOX'}>, <Axes: title={'center': 'RM'}>,\n",
       "        <Axes: title={'center': 'AGE'}>, <Axes: title={'center': 'DIS'}>],\n",
       "       [<Axes: title={'center': 'RAD'}>, <Axes: title={'center': 'TAX'}>,\n",
       "        <Axes: title={'center': 'PTRATIO'}>,\n",
       "        <Axes: title={'center': 'B'}>],\n",
       "       [<Axes: title={'center': 'LSTAT'}>,\n",
       "        <Axes: title={'center': 'MEDV'}>, <Axes: >, <Axes: >]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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06dMIDw/Hn3/+iWbNmiEzMxPdunVDbm4uAMDPz0+rv5+fHy5dugQAKCoqgqurK3x9fXX6FBUV1bldpVIJpVKpeV5WVgYAcHd3h7u7e52vdXFxgYeHB9zd3a1+LE3FEXMCmJe9MSYvlUoFwLbGsyNHjuDtt99Gfn4+CgsLkZmZiREjRmiWCyGQmJiI9evXo6SkBH379sWaNWvQvXt3TR+lUom5c+di27ZtqKiowIABA7B27Vq0adPG4Djq+oxmS7+L7A33XcNx3xnGlsYzW2Lo905HfZ8xL/vCvP7H1GOaXRbOiIhMoXPnzjh16hRu3ryJHTt2IDY2Fjk5OZrlNQdcIUS9g7AhfZKTk5GYmKjTnpWVBQ8PD4Niz87ONqifPXHEnADmZW8Myau8vNwCkRjnzp076NWrFyZPnoz/+7//01mempqK5cuXa123MTo6Wuu6jfHx8fjiiy+QkZGhuW7j0KFDjb5uIxEREZEjYeGMiJosV1dXzc0BwsLCkJeXh/feew/z588HcG9Wmb+/v6Z/cXGxZhaaXC5HZWUlSkpKtGadFRcXIyIios7tLly4EAkJCZrn6inFMTExBs2izc7ORnR0tMP8JckRcwKYl70xJq/S0lILRWW4wYMHY/DgwXqXCSGwcuVKLF68GCNHjgRw74Yofn5+2Lp1K+Li4nDr1i2kpaVh8+bNGDhwIABgy5YtCAwMxIEDB4w6/ZyIiIjIkbBwRkT0/wkhoFQqERQUBLlcjuzsbPTu3RsAUFlZiZycHKSkpAAAQkNDIZVKkZ2djdGjRwMACgsLcebMGaSmpta5HZlMBplMptMulUoNLkQY09deOGJOAPOyN4bkZW95m/O6jTVPPVcXFVUqleaUVjX185rtVD/uu4bjvqsb9wsRUf2aTOEsRLEfyirTnuf667IhJl0fEVnOokWLMHjwYAQGBqKsrAwZGRk4fPgw9u3bB4lEgvj4eCQlJSE4OBjBwcFISkqCh4cHxo8fDwDw8fHB1KlTMWfOHLRo0QLNmzfH3Llz0aNHD81sDXMy9ZjG8YzIcamvu2iO6zY25NRzRz3N1xK47xqO+04/Wzz13J7xOyeRY2oyhTMiovtdv34dEyZMQGFhIXx8fNCzZ0/s27cP0dHRAIB58+ahoqIC06dP11xIOysrS+sOLStWrICLiwtGjx6tuZB2eno6rwVERDbJHNdtNObUc0c9zdcSHHnfhSj2m2W9ZxT3Zkk68r4zBVs89ZyIyNawcEZETVJaWlqdyyUSCRQKBRQKRa193NzcsGrVKqxatcrE0RERmY5cLgdgnus2NuTUc0c9zdcSHHHfmXp2jlrN/eSI+84UuE+IiOrnZO0AiIiIiMh87r9uo5r6uo3qotj9121UU1+3sb4bnhARERE5Ms44IyIiIrJzt2/fxs8//6x5XlBQgFOnTqF58+Zo27atzV+3kYiIiMhWsXBGREREZOdOnDiBJ554QvNcfd2x2NhYpKen87qNRERERA3EwhkRERGRnYuKioIQotblvG4jERERUcPwGmdERERERERERER6sHBGRERERERERESkBwtnREREREREREREerBwRkREREREREREpIdRhbN169ahZ8+e8Pb2hre3N8LDw7F3717NciEEFAoFAgIC4O7ujqioKJw9e1ZrHUqlErNmzULLli3h6emJ4cOH48qVK6bJhoiIiIiIiIiIyESMKpy1adMGy5Ytw4kTJ3DixAk8+eST+Nvf/qYpjqWmpmL58uVYvXo18vLyIJfLER0djbKyMs064uPjkZmZiYyMDBw7dgy3b9/G0KFDUVVVZdrMiIiIiIiIiIiIGsGowtmwYcPw17/+FZ06dUKnTp3w1ltvoVmzZjh+/DiEEFi5ciUWL16MkSNHIiQkBBs3bkR5eTm2bt0KALh16xbS0tLw7rvvYuDAgejduze2bNmC06dP48CBA2ZJkIiIiIiIiIiIqCFcGvrCqqoqfPbZZ7hz5w7Cw8NRUFCAoqIixMTEaPrIZDJERkYiNzcXcXFxyM/Ph0ql0uoTEBCAkJAQ5ObmYtCgQXq3pVQqoVQqNc9LS0sBACqVCiqVqs441ctlTqKhqda7bnNTb8dS2zM1e48fsP8cbCF+e913RERERERE1HQZXTg7ffo0wsPD8eeff6JZs2bIzMxEt27dkJubCwDw8/PT6u/n54dLly4BAIqKiuDq6gpfX1+dPkVFRbVuMzk5GYmJiTrtWVlZ8PDwMCjuN8OqDepnjD179ph8nXXJzs626PZMzd7jB+w/B2vGX15ebrVtExERERERETWE0YWzzp0749SpU7h58yZ27NiB2NhY5OTkaJZLJBKt/kIInbaa6uuzcOFCJCQkaJ6XlpYiMDAQMTEx8Pb2rnPdKpUK2dnZeO2EE5TVdcdhrDMK/TPkTE2dQ3R0NKRSqUW2aUr2Hj9g/znYQvzqmaJERERERERE9sLowpmrqyv+8pe/AADCwsKQl5eH9957D/Pnzwdwb1aZv7+/pn9xcbFmFppcLkdlZSVKSkq0Zp0VFxcjIiKi1m3KZDLIZDKddqlUanARQFktgbLKtIUzSxcgjMnXFtl7/ID952DN+O15vxEREREREVHTZNTNAfQRQkCpVCIoKAhyuVzrVLDKykrk5ORoimKhoaGQSqVafQoLC3HmzJk6C2dERERERETk+NatW4eePXvC29sb3t7eCA8Px969ezXLhRBQKBQICAiAu7s7oqKicPbsWa11KJVKzJo1Cy1btoSnpyeGDx+OK1euWDoVInIQRhXOFi1ahKNHj+LXX3/F6dOnsXjxYhw+fBjPPvssJBIJ4uPjkZSUhMzMTJw5cwaTJk2Ch4cHxo8fDwDw8fHB1KlTMWfOHBw8eBAnT57Ec889hx49emDgwIFmSZCIiIiIiIjsQ5s2bbBs2TKcOHECJ06cwJNPPom//e1vmuJYamoqli9fjtWrVyMvLw9yuRzR0dEoKyvTrCM+Ph6ZmZnIyMjAsWPHcPv2bQwdOhRVVVXWSouI7JhRp2pev34dEyZMQGFhIXx8fNCzZ0/s27cP0dHRAIB58+ahoqIC06dPR0lJCfr27YusrCx4eXlp1rFixQq4uLhg9OjRqKiowIABA5Ceng5nZ2fTZkZERERERER2ZdiwYVrP33rrLaxbtw7Hjx9Ht27dsHLlSixevBgjR44EAGzcuBF+fn7YunUr4uLicOvWLaSlpWHz5s2ayRlbtmxBYGAgDhw4gEGDLHOdaiJyHEYVztLS0upcLpFIoFAooFAoau3j5uaGVatWYdWqVcZsmoiIiIiIiJqQqqoqfPbZZ7hz5w7Cw8NRUFCAoqIixMTEaPrIZDJERkYiNzcXcXFxyM/Ph0ql0uoTEBCAkJAQ5Obm1lk4UyqVUCqVmufqm1upVCqoVKpaX6deJnMSDc61vnVbg3rb1ozBHJiXfTEmL3PlbvTNAYiIiIiIiIjM5fTp0wgPD8eff/6JZs2aITMzE926dUNubi4AaG4+p+bn54dLly4BuHezOldXV62b0an7FBUV1bnd5ORkJCYm6rRnZWXBw8Oj3rjfDKuut4+x9uzZY/J1Guv+a5Q7EuZlXwzJq7y83CzbZuGMiIiIiIiIbEbnzp1x6tQp3Lx5Ezt27EBsbCxycnI0yyUSiVZ/IYROW02G9Fm4cCESEhI0z0tLSxEYGIiYmBh4e3vX+jqVSoXs7Gy8dsIJyuq6t2GsMwrrnVqqzis6OhpSqdRqcZga87IvxuSlniVqaiycERERERHVo/2CL82y3l+XDTHLeonsmaurK/7yl78AAMLCwpCXl4f33nsP8+fPB3BvVpm/v7+mf3FxsWYWmlwuR2VlJUpKSrRmnRUXFyMiIqLO7cpkMshkMp12qVRqUCFCWS2Bssq0hTNbKIAYmr+9YV72xZC8zJW3UXfVJCIiIiIiIrIkIQSUSiWCgoIgl8u1TtmqrKxETk6OpigWGhoKqVSq1aewsBBnzpypt3BGRKQPZ5wRERERERGRTVi0aBEGDx6MwMBAlJWVISMjA4cPH8a+ffsgkUgQHx+PpKQkBAcHIzg4GElJSfDw8MD48eMBAD4+Ppg6dSrmzJmDFi1aoHnz5pg7dy569OihucsmEZExWDgjIiIiIiIim3D9+nVMmDABhYWF8PHxQc+ePbFv3z5ER0cDAObNm4eKigpMnz4dJSUl6Nu3L7KysuDl5aVZx4oVK+Di4oLRo0ejoqICAwYMQHp6Opydna2VFhHZMRbOiIiIiIiIyCakpaXVuVwikUChUEChUNTax83NDatWrcKqVatMHB0RNUUsnBERERGRQzHXhfyJiIio6eHNAYiIiIiIiIiIiPRg4YyIiIiIiIiIiEgPFs6IiIiIiIiIiIj0YOGMiJqk5ORk9OnTB15eXmjdujVGjBiBc+fOafURQkChUCAgIADu7u6IiorC2bNntfoolUrMmjULLVu2hKenJ4YPH44rV65YMhUiIiIiIiIyExbOiKhJysnJwYwZM3D8+HFkZ2fj7t27iImJwZ07dzR9UlNTsXz5cqxevRp5eXmQy+WIjo5GWVmZpk98fDwyMzORkZGBY8eO4fbt2xg6dCiqqqqskRYRERERERGZEO+qSURN0r59+7Seb9iwAa1bt0Z+fj4ef/xxCCGwcuVKLF68GCNHjgQAbNy4EX5+fti6dSvi4uJw69YtpKWlYfPmzRg4cCAAYMuWLQgMDMSBAwcwaNAgi+dFREREREREpsMZZ0REAG7dugUAaN68OQCgoKAARUVFiImJ0fSRyWSIjIxEbm4uACA/Px8qlUqrT0BAAEJCQjR9iIiIiIiIyH5xxhkRNXlCCCQkJODRRx9FSEgIAKCoqAgA4Ofnp9XXz88Ply5d0vRxdXWFr6+vTh/16/VRKpVQKpWa56WlpQAAlUoFlUpVZ6zq5TInYUhqBqtvu+ak3rY1YzAH5mVfjMnL0XInIiIiotqxcEZETd7MmTPxww8/4NixYzrLJBKJ1nMhhE5bTfX1SU5ORmJiok57VlYWPDw8DIr5zbBqg/oZas+ePSZdX0NkZ2dbOwSzYF72xZC8ysvLLRAJEREREdkCFs6IqEmbNWsWdu/ejSNHjqBNmzaadrlcDuDerDJ/f39Ne3FxsWYWmlwuR2VlJUpKSrRmnRUXFyMiIqLWbS5cuBAJCQma56WlpQgMDERMTAy8vb3rjFelUiE7OxuvnXCCsrruAp4xziisdz02dU7R0dGQSqVWi8PUmJd9MSYv9SxRIiIiInJ8LJwRUZMkhMCsWbOQmZmJw4cPIygoSGt5UFAQ5HI5srOz0bt3bwBAZWUlcnJykJKSAgAIDQ2FVCpFdnY2Ro8eDQAoLCzEmTNnkJqaWuu2ZTIZZDKZTrtUKjW4EKGslkBZZbrCmS0UQIzJ354wL/tiSF6OmDcRERER6cfCGRE1STNmzMDWrVvx+eefw8vLS3NNMh8fH7i7u0MikSA+Ph5JSUkIDg5GcHAwkpKS4OHhgfHjx2v6Tp06FXPmzEGLFi3QvHlzzJ07Fz169NDcZZOIiIiIiIjsFwtnRNQkrVu3DgAQFRWl1b5hwwZMmjQJADBv3jxUVFRg+vTpKCkpQd++fZGVlQUvLy9N/xUrVsDFxQWjR49GRUUFBgwYgPT0dDg7O1sqFSIiIiIiIjITFs6IqEkSov67UkokEigUCigUilr7uLm5YdWqVVi1apUJoyMiIiIiIiJb4GTtAIiIiIiIiIiIiGwRC2dERERERERERER6GFU4S05ORp8+feDl5YXWrVtjxIgROHfunFYfIQQUCgUCAgLg7u6OqKgonD17VquPUqnErFmz0LJlS3h6emL48OG4cuVK47MhIiIiIiIiIiIyEaMKZzk5OZgxYwaOHz+O7Oxs3L17FzExMbhz546mT2pqKpYvX47Vq1cjLy8Pcrkc0dHRKCsr0/SJj49HZmYmMjIycOzYMdy+fRtDhw5FVVWV6TIjIiIiIiIiIiJqBKNuDrBv3z6t5xs2bEDr1q2Rn5+Pxx9/HEIIrFy5EosXL8bIkSMBABs3boSfnx+2bt2KuLg43Lp1C2lpadi8eTMGDhwIANiyZQsCAwNx4MABDBo0yESpERERERERERERNVyj7qp569YtAEDz5s0BAAUFBSgqKkJMTIymj0wmQ2RkJHJzcxEXF4f8/HyoVCqtPgEBAQgJCUFubq7ewplSqYRSqdQ8Ly0tBQCoVCqoVKo6Y1QvlznVfwc9Y9W3bVNvx1LbMzV7jx+w/xxsIX573XdERERERETUdDW4cCaEQEJCAh599FGEhIQAAIqKigAAfn5+Wn39/Pxw6dIlTR9XV1f4+vrq9FG/vqbk5GQkJibqtGdlZcHDw8OgeN8MqzaonzH27Nlj8nXWJTs726LbMzV7jx+w/xysGX95ebnVtk1ERERE9iE5ORk7d+7Ef/7zH7i7uyMiIgIpKSno3Lmzpo8QAomJiVi/fj1KSkrQt29frFmzBt27d9f0USqVmDt3LrZt24aKigoMGDAAa9euRZs2bayRFhHZsQYXzmbOnIkffvgBx44d01kmkUi0ngshdNpqqqvPwoULkZCQoHleWlqKwMBAxMTEwNvbu871qlQqZGdn47UTTlBW1x2Dsc4oLHNaqTqH6OhoSKVSi2zTlOw9fsD+c7CF+NUzRYmIiIiIaqO+rnafPn1w9+5dLF68GDExMfjxxx/h6ekJ4H/X1U5PT0enTp2wdOlSREdH49y5c/Dy8gJw77raX3zxBTIyMtCiRQvMmTMHQ4cORX5+Ppydna2ZIhHZmQYVzmbNmoXdu3fjyJEjWhV7uVwO4N6sMn9/f017cXGxZhaaXC5HZWUlSkpKtGadFRcXIyIiQu/2ZDIZZDKZTrtUKjW4CKCslkBZZdrCmaULEMbka4vsPX7A/nOwZvz2vN+IiIiIyDJ4XW0isjVG3VVTCIGZM2di586d+OqrrxAUFKS1PCgoCHK5XOt0sMrKSuTk5GiKYqGhoZBKpVp9CgsLcebMmVoLZ0RERERERNT0GHtdbQD1XlebiMgYRs04mzFjBrZu3YrPP/8cXl5emmuS+fj4wN3dHRKJBPHx8UhKSkJwcDCCg4ORlJQEDw8PjB8/XtN36tSpmDNnDlq0aIHmzZtj7ty56NGjh+avAURERERERNS0WfK62kDDb0rnCDekq2vbjnaTL+ZlX4zJy1y5G1U4W7duHQAgKipKq33Dhg2YNGkSAGDevHmoqKjA9OnTNRdqzMrK0pxrDgArVqyAi4sLRo8erblQY3p6Os81JyIiIiIiIgCWva420Pib0jnCDen0sfcbpNWGedkXQ/Iy1w3pjCqcCVF/BV0ikUChUEChUNTax83NDatWrcKqVauM2TwRERERNZBCodD5Qnj/7AtD7lJHRGQplr6uNtDwm9I5wg3p9LGFG4yZA/OyL8bkZa4b0jX4rppEREREZF+6d++OAwcOaJ7fP9vfkLvUERGZmxACs2bNQmZmJg4fPlzndbV79+4N4H/X1U5JSQGgfV3t0aNHA/jfdbVTU1Nr3XZjb0rnCDekqy0GW4jD1JiXfTEkL3PlzcIZERERURPh4uKima1xP0PuUkdEZAm8rjYR2RoWzoiIiIiaiAsXLiAgIAAymQx9+/ZFUlISOnToUO9d6mornBlzIW1LXrRY5mz6C3SbizEXO3a0Cz4D5jtWNfeZI+47U7DF/cLrahORrWHhjIiIiKgJ6Nu3LzZt2oROnTrh+vXrWLp0KSIiInD27FmD7lKnT0MupG2JixanPmL2TZiMMRf+dsQLPpvrWNXcr46470zBXBfSbgxeV5uIbA0LZ0RERERNwODBgzX/79GjB8LDw9GxY0ds3LgR/fr1A2D8XeqMuZC2JS9aHKLYb9b1m5IhF/521As+A+Y7Vur96sj7zhTMdSFtIiJHwsIZERERURPk6emJHj164MKFCxgxYgSAuu9Sp09DLqRtiYsWm/ri3OZkzL5wxAs+m+tY1dxPjrjvTIH7hIiofk7WDoCIiIiILE+pVOKnn36Cv7+/1l3q1NR3qYuIiLBilERERETWxRlnRERERE3A3LlzMWzYMLRt2xbFxcVYunQpSktLERsba9Bd6oiIiIiaIhbOiIiIiJqAK1euYNy4cfj999/RqlUr9OvXD8ePH0e7du0AGHaXOlNrv+BLs62biIiIyBRYOCMiIiJqAjIyMupcbshd6oiIiIiaGl7jjIiIiIiIiIiISA8WzoiIiIiIiIiIiPRg4YyIiIiIiIiIiEgPFs6IiIiIiIiIiIj0YOGMiIiIiIiIiIhIDxbOiIiIiIiIiIiI9GDhjIiIiIiIiIiISA8WzoioyTpy5AiGDRuGgIAASCQS7Nq1S2u5EAIKhQIBAQFwd3dHVFQUzp49q9VHqVRi1qxZaNmyJTw9PTF8+HBcuXLFglkQERERERGRubBwRkRN1p07d9CrVy+sXr1a7/LU1FQsX74cq1evRl5eHuRyOaKjo1FWVqbpEx8fj8zMTGRkZODYsWO4ffs2hg4diqqqKkulQURERERERGbiYu0AiIisZfDgwRg8eLDeZUIIrFy5EosXL8bIkSMBABs3boSfnx+2bt2KuLg43Lp1C2lpadi8eTMGDhwIANiyZQsCAwNx4MABDBo0yGK5EBERERERkelxxhkRkR4FBQUoKipCTEyMpk0mkyEyMhK5ubkAgPz8fKhUKq0+AQEBCAkJ0fQhIiIiIiIi+8UZZ0REehQVFQEA/Pz8tNr9/Pxw6dIlTR9XV1f4+vrq9FG/Xh+lUgmlUql5XlpaCgBQqVRQqVR1xqVeLnMSBmZimPq2a07qbVszBnNgXvbFmLwcLXciIiIiqh0LZ0REdZBIJFrPhRA6bTXV1yc5ORmJiYk67VlZWfDw8DAorjfDqg3qZ6g9e/aYdH0NkZ2dbe0QzIJ52RdD8iovL7dAJETUGO0XfAkAkDkLpD4ChCj2Q1lV9+9vQ/y6bEij10FERPaFhTMiIj3kcjmAe7PK/P39Ne3FxcWaWWhyuRyVlZUoKSnRmnVWXFyMiIiIWte9cOFCJCQkaJ6XlpYiMDAQMTEx8Pb2rjMulUqF7OxsvHbCCcrqxn8BUDujsN712NQ5RUdHQyqVWi0OU2Ne9sWYvNSzRIlMQV3gqYuxxR8Wd4iIiEzH6MLZkSNH8PbbbyM/Px+FhYXIzMzEiBEjNMuFEEhMTMT69etRUlKCvn37Ys2aNejevbumj1KpxNy5c7Ft2zZUVFRgwIABWLt2Ldq0aWOSpIiIGisoKAhyuRzZ2dno3bs3AKCyshI5OTlISUkBAISGhkIqlSI7OxujR48GABQWFuLMmTNITU2tdd0ymQwymUynXSqVGlyIUFZLTPKX8/u3bW3G5G9PmJd9MSQvR8ybiIiIiPQz+uYAd+7cQa9evbB69Wq9y1NTU7F8+XKsXr0aeXl5kMvliI6ORllZmaZPfHw8MjMzkZGRgWPHjuH27dsYOnQoqqqqGp4JEZGRbt++jVOnTuHUqVMA7t0Q4NSpU/jtt98gkUgQHx+PpKQkZGZm4syZM5g0aRI8PDwwfvx4AICPjw+mTp2KOXPm4ODBgzh58iSee+459OjRQ3OXTSIiIiIiIrJfRhfOBg8ejKVLl2LkyJE6y4QQWLlyJRYvXoyRI0ciJCQEGzduRHl5ObZu3QoAuHXrFtLS0vDuu+9i4MCB6N27N7Zs2YLTp0/jwIEDjc+IiMhAJ06cQO/evTUzyhISEtC7d2+8/vrrAIB58+YhPj4e06dPR1hYGK5evYqsrCx4eXlp1rFixQqMGDECo0ePRv/+/eHh4YEvvvgCzs7OVsmJiIiIyN4dOXIEw4YNQ0BAACQSCXbt2qW1XAgBhUKBgIAAuLu7IyoqCmfPntXqo1QqMWvWLLRs2RKenp4YPnw4rly5YsEsiMhRmPQaZwUFBSgqKkJMTIymTSaTITIyErm5uYiLi0N+fj5UKpVWn4CAAISEhCA3NxeDBuleZ8cW70B3/7rNzd7vYGbv8QP2n4MtxG+L+y4qKgpC1D42SCQSKBQKKBSKWvu4ublh1apVWLVqlRkiJCIiImp61Gc5TZ48Gf/3f/+ns1x9llN6ejo6deqEpUuXIjo6GufOndP8gTM+Ph5ffPEFMjIy0KJFC8yZMwdDhw5Ffn4+/8BJREYxaeGsqKgIADQXzlbz8/PDpUuXNH1cXV21LqSt7qN+fU22eAc6wPJ3obP3O5jZe/yA/edgzfh5FzoiIiIiMsTgwYMxePBgvctqnuUEABs3boSfnx+2bt2KuLg4zVlOmzdv1lw+Y8uWLQgMDMSBAwf0TtYgIqqNWe6qKZFoX7BaCKHTVlNdfWzxDnSA5e5CZ+93MLP3+AH7z8EW4udd6IiIiIioscx1lhPQ8DOdHOEsp7q2bYtnjjQG87IvxuRlrtxNWjiTy+UA7s0q8/f317QXFxdrZqHJ5XJUVlaipKREa9ZZcXExIiIi9K7XFu9Ap96+Jdn7HczsPX7A/nOwZvz2vN+IiIiIyDaY6ywnoPFnOjnCWU762PtZN7VhXvbFkLzMdZaTSQtnQUFBkMvlyM7O1lxsu7KyEjk5OUhJSQEAhIaGQiqVIjs7G6NHjwYAFBYW4syZM0hNTTVlOEREREREROSATH2WE9DwM50c4SwnfWzhrBVzYF72xZi8zHWWk9GFs9u3b+Pnn3/WPC8oKMCpU6fQvHlztG3bFvHx8UhKSkJwcDCCg4ORlJQEDw8PjB8/HgDg4+ODqVOnYs6cOWjRogWaN2+OuXPnokePHprzz4mIiIiIiIhqMtdZTkDjz3RyhLOcaovBFuIwNeZlXwzJy1x5Oxn7ghMnTqB3796aGWUJCQno3bs3Xn/9dQDAvHnzEB8fj+nTpyMsLAxXr15FVlaW5u4mALBixQqMGDECo0ePRv/+/eHh4YEvvviCdzchIiIiIiKiWt1/lpOa+iwndVHs/rOc1NRnOdVVOCMi0sfoGWdRUVEQovaLHkokEigUCigUilr7uLm5YdWqVVi1apWxmyciIiIiIiIHxrOciMiWmOWumkREREREREQNceLECTzxxBOa5+rrjsXGxiI9PR3z5s1DRUUFpk+fjpKSEvTt21fvWU4uLi4YPXo0KioqMGDAAKSnp/MsJyIyGgtnREREREREZDN4lhMR2RKjr3FGRERERERERETUFLBwRkREREREREREpAcLZ0RERERERERERHqwcEZERERERERERKQHC2dERERERERERER6sHBGRERERERERESkBwtnREREREREREREerBwRkREREREREREpAcLZ0RERERERERERHqwcEZERERERERERKSHi7UDICIix9V+wZf19pE5C6Q+AoQo9kNZJam3/6/LhpgiNCIih2XI2NsQHH+JiKgp4owzIiIiIiIiIiIiPVg4IyIiIiIiIiIi0oOFMyIiIiIiIiIiIj14jbNGMMf1I3jtCCIiIiKipoPXpCMism2ccUZERERERERERKQHC2dERERERERERER68FRNIiIiIiIiIiJqFHOcei5zFkh9xOSrNQpnnBEREREREREREenBwhkREREREREREZEePFWTiIiIzIZ3iyMiIiIie8YZZ0RERERERERERHpYdcbZ2rVr8fbbb6OwsBDdu3fHypUr8dhjj1kzJKvT95d59cXwQhT7oaySNGi9/Ms8kXnZ+3hmrllBROZi6vesLVx41lbY+3hGRHQ/jmlE1FhWK5x9+umniI+Px9q1a9G/f398+OGHGDx4MH788Ue0bdvWWmERERmN45ll2UuRj4UYskccz4jIkXBMIyJTsFrhbPny5Zg6dSqmTZsGAFi5ciX279+PdevWITk52VphEREZjeMZ1aUxs4VrY65ZxIYUJU0xC5psF8czqou9/OGCSI1jGhGZglUKZ5WVlcjPz8eCBQu02mNiYpCbm6vTX6lUQqlUap7funULAPDHH39ApVLVuS2VSoXy8nK4qJxQVW2fH/BdqgXKy6sblcONGzdMHBXQN/mgQf1kTgJ/712NhxbvhNKKx+CbhQMa/Fr1++jGjRuQSqUmjMoybCH+srIyAIAQwirbNxdjxzOAY1pNphjjbJE58/rL3H+adH1qhnwocPTjZcg4yfHsf4wZz/T9LnK5e8eUKTgsR/25swRT7ztzfKY218+BIbE66ngGWPYzmjk/n5njPWcoW/gOYQ7My3zMMZ7Zwmc0qxTOfv/9d1RVVcHPz0+r3c/PD0VFRTr9k5OTkZiYqNMeFBRkthhtzfhGvr7luyYJo8EaG78pWHsf0D1lZWXw8fGxdhgmY+x4BnBM08cWxghzYF72xdi8OJ5xPLMkR/25swRT7jt7+jxpTKyONp4BjjOm2dN7jshcrP0Zzao3B5BItKvxQgidNgBYuHAhEhISNM+rq6vxxx9/oEWLFnr736+0tBSBgYG4fPkyvL29TRO4hdl7DvYeP2D/OdhC/EIIlJWVISAgwCrbNzdDxzOAY1pNjpgTwLzsjTF5cTz7H2PGM0d971gC913Dcd/VzdHHM8Ayn9Ec9X3GvOwL8zLfmGaVwlnLli3h7OysU+kvLi7W+YsAAMhkMshkMq22Bx54wKhtent72/2bx95zsPf4AfvPwdrxO9pfMgHjxzOAY1ptHDEngHnZG0Pz4nh2T0PGM0d971gC913Dcd/VzhHHM8A6n9Ec9X3GvOxLU8/LHGOak8nXaABXV1eEhoYiOztbqz07OxsRERHWCImIqEE4nhGRo+B4RkSOhGMaEZmK1U7VTEhIwIQJExAWFobw8HCsX78ev/32G1588UVrhURE1CAcz4jIUXA8IyJHwjGNiEzBaoWzMWPG4MaNG3jjjTdQWFiIkJAQ7NmzB+3atTPpdmQyGZYsWaIz5dae2HsO9h4/YP852Hv8ts5S4xngmMfSEXMCmJe9cdS8jGXO8Yz7uOG47xqO+65p43fOxmFe9oV5mY9EOOK9h4mIiIiIiIiIiBrJKtc4IyIiIiIiIiIisnUsnBEREREREREREenBwhkREREREREREZEeLJwRERERERERERHp4fCFs7Vr1yIoKAhubm4IDQ3F0aNHrR2SXsnJyejTpw+8vLzQunVrjBgxAufOndPqI4SAQqFAQEAA3N3dERUVhbNnz1op4rolJydDIpEgPj5e02YP8V+9ehXPPfccWrRoAQ8PDzz00EPIz8/XLLflHO7evYu///3vCAoKgru7Ozp06IA33ngD1dXVmj62HD/Vz17Gs9oYMs5NmjQJEolE69GvXz8rRWwYhUKhE7NcLtcst9efu/bt2+vkJZFIMGPGDAD2cayOHDmCYcOGISAgABKJBLt27dJabsixUSqVmDVrFlq2bAlPT08MHz4cV65csWAWjsPexzBLMMV7tqlytM/SZH3Gjlk5OTkIDQ2Fm5sbOnTogA8++ECnz44dO9CtWzfIZDJ069YNmZmZ5gq/VsbktXPnTkRHR6NVq1bw9vZGeHg49u/fr9UnPT1d7+eFP//809ypaDEmr8OHD+uN+T//+Y9WP3s7Xvo+m0kkEnTv3l3Tx9rHq77fc/rYxM+WcGAZGRlCKpWKjz76SPz4449i9uzZwtPTU1y6dMnaoekYNGiQ2LBhgzhz5ow4deqUGDJkiGjbtq24ffu2ps+yZcuEl5eX2LFjhzh9+rQYM2aM8Pf3F6WlpVaMXNe3334r2rdvL3r27Clmz56tabf1+P/44w/Rrl07MWnSJPHNN9+IgoICceDAAfHzzz9r+thyDkuXLhUtWrQQ//rXv0RBQYH47LPPRLNmzcTKlSs1fWw5fqqbPY1ntTFknIuNjRVPPfWUKCws1Dxu3Lhhxajrt2TJEtG9e3etmIuLizXL7fXnrri4WCun7OxsAUAcOnRICGEfx2rPnj1i8eLFYseOHQKAyMzM1FpuyLF58cUXxYMPPiiys7PFd999J5544gnRq1cvcffuXQtnY98cYQyzBFO8Z5sqR/osTdZn7Jh18eJF4eHhIWbPni1+/PFH8dFHHwmpVCq2b9+u6ZObmyucnZ1FUlKS+Omnn0RSUpJwcXERx48ft1RaRuc1e/ZskZKSIr799ltx/vx5sXDhQiGVSsV3332n6bNhwwbh7e2t9XmgsLDQUikJIYzP69ChQwKAOHfunFbM9/9ut8fjdfPmTa18Ll++LJo3by6WLFmi6WPt41Xf77mabOVny6ELZ4888oh48cUXtdq6dOkiFixYYKWIDFdcXCwAiJycHCGEENXV1UIul4tly5Zp+vz555/Cx8dHfPDBB9YKU0dZWZkIDg4W2dnZIjIyUlM4s4f458+fLx599NFal9t6DkOGDBFTpkzRahs5cqR47rnnhBC2Hz/VzZ7Hs9rUHOeEuFeM+dvf/ma9oBpgyZIlolevXnqXOdLP3ezZs0XHjh1FdXW1EML+jlXND2eGHJubN28KqVQqMjIyNH2uXr0qnJycxL59+ywWuyNwxDHM3BrynqX/sdfP0mQbjB2z5s2bJ7p06aLVFhcXJ/r166d5Pnr0aPHUU09p9Rk0aJAYO3asiaKunynG4m7duonExETN8w0bNggfHx9ThdggxualLpyVlJTUuk5HOF6ZmZlCIpGIX3/9VdNmC8dLzZDCma38bDnsqZqVlZXIz89HTEyMVntMTAxyc3OtFJXhbt26BQBo3rw5AKCgoABFRUVa+chkMkRGRtpUPjNmzMCQIUMwcOBArXZ7iH/37t0ICwvDM888g9atW6N379746KOPNMttPYdHH30UBw8exPnz5wEA33//PY4dO4a//vWvAGw/fqqdvY9ntak5zqkdPnwYrVu3RqdOnfD888+juLjYGuEZ5cKFCwgICEBQUBDGjh2LixcvAnCcn7vKykps2bIFU6ZMgUQi0bTb47FSM+TY5OfnQ6VSafUJCAhASEiIXR0/a3PUMczSHGU8sRR7/SxN1teQMevrr7/W6T9o0CCcOHECKpWqzj6Wev+ZYiyurq5GWVmZzme327dvo127dmjTpg2GDh2KkydPmizu+jQmr969e8Pf3x8DBgzAoUOHtJY5wvFKS0vDwIED0a5dO612ax4vY9nKz5bDFs5+//13VFVVwc/PT6vdz88PRUVFVorKMEIIJCQk4NFHH0VISAgAaGK25XwyMjLw3XffITk5WWeZPcR/8eJFrFu3DsHBwdi/fz9efPFFvPzyy9i0aRMA289h/vz5GDduHLp06QKpVIrevXsjPj4e48aNA2D78VPt7Hk8q42+cQ4ABg8ejE8++QRfffUV3n33XeTl5eHJJ5+EUqm0YrR169u3LzZt2oT9+/fjo48+QlFRESIiInDjxg2H+bnbtWsXbt68iUmTJmna7PFY3c+QY1NUVARXV1f4+vrW2ofq54hjmDU4ynhiCfb6WZpsQ0PGrKKiIr397969i99//73OPpZ6/5liLH733Xdx584djB49WtPWpUsXpKenY/fu3di2bRvc3NzQv39/XLhwwaTx16Yhefn7+2P9+vXYsWMHdu7cic6dO2PAgAE4cuSIpo+9H6/CwkLs3bsX06ZN02q39vEylq38bLmYbE026v6/jAP3fpHWbLM1M2fOxA8//IBjx47pLLPVfC5fvozZs2cjKysLbm5utfaz1fiBe39BCQsLQ1JSEoB7f4E4e/Ys1q1bh4kTJ2r62WoOn376KbZs2YKtW7eie/fuOHXqFOLj4xEQEIDY2FhNP1uNn+rnSMeutnFuzJgxmv+HhIQgLCwM7dq1w5dffomRI0daOkyDDB48WPP/Hj16IDw8HB07dsTGjRs1F8u392OXlpaGwYMHIyAgQNNmj8dKn4YcG3s7frbC3n8ObAX3Y/3s8bM02R5j3yv6+tdst4X3X0Nj2LZtGxQKBT7//HO0bt1a096vXz+tmwP1798fDz/8MFatWoX333/fdIHXw5i8OnfujM6dO2ueh4eH4/Lly3jnnXfw+OOPN2id5tLQGNLT0/HAAw9gxIgRWu22cryMYQs/Ww4746xly5ZwdnbWqTIWFxfrVCNtyaxZs7B7924cOnQIbdq00bSr79Bmq/nk5+ejuLgYoaGhcHJygkQiQU5ODt577z24uLhoYiwqKkJUVJTmr3/q+O/cuYNly5ahd+/eaNasGTw9PfHQQw8hKSkJd+7c0WwnJycHTk5OWLRokU4Mv/zyC5o1a4ZRo0Y1KAd/f39069ZNq61r16747bffANj+MXj11VexYMECjB07Fj169MCECRPwyiuvaGYA2nr8VDt7Hc9qU9s4p4+/vz/atWtnU38Fq3k3IhcXF/j7+2Ps2LG4cOECPD090aNHD1y4cAEJCQkAgD59+mh+yQP/O3ZHjhzRrCc9Pd1KGdXt0qVLOHDggM5fLGuyxWNVF0PGRLlcjsrKSpSUlNTah+rnaGOYtZj69/j7778PiUSiNeu3poKCArz88svo2rUrPD094ebmhvbt2+O5557DoUOHtMa12u7Upn4cPnzY6Bgbwl4/S5PtaMiYJZfL9fZ3cXFBixYt6uxjqfdfY8biTz/9FFOnTsU///lPnUvy1OTk5IQ+ffpY7POAOq+tW7dqjTlLlizBxYsX8cQTTyA5OVnnchLqu6KrZy3169cP58+fR0ZGBh577DFUVVVh4sSJaNOmDQYNGoR//OMfdnO8hBD4+OOPMWHCBLi6utbZ19LHy1i28rPlsIUzV1dXhIaGIjs7W6s9OzsbERERVoqqdkIIzJw5Ezt37sRXX32FoKAgreVBQUGQy+Va+VRWViInJ8cm8hkwYABOnz6NU6dO4Y033tC0BwUF4dSpU+jQoUOt8YeEhKBfv3544403MGjQIGRmZmLXrl0YPHgwli5din79+uH69esAgMjISLz88stITU3Ft99+q1lXdXU1YmNj4eHhgXXr1jUoh/79++vctvz8+fOac8Jt/RiUl5fDyUn7R9rZ2RnV1dUAbD9+qp29jWe1qW+c0+fGjRu4fPky/P39LRChcTZs2ICvv/4aBw4cwMyZM7F79248+uijKCoqwk8//QR/f3+4ublBIpHg6tWr+OqrrwBo/9x9/PHH8Pb2tnImdduwYQNat26NIUOG1NnPlo+VPoaMiaGhoZBKpVp9CgsLcebMGbv62bM2RxnDrM3Uv8c//vhjAMDZs2fxzTff6CzfvXs3evTogd27dyM2NhaZmZnYv38/XnvtNdy4cQNPPvmkZly7n3psrPl4+OGHjY7RGPb+WZpsR0PGrPDwcJ3+WVlZCAsLg1QqrbOPpd5/DR2Lt23bhkmTJmHr1q31fhYA7v0snjp1ymKfB9R5nT17FsD/xqA2bdpgyJAheOihh5CSkoKuXbviwIEDta7n5MmTqKiowLhx49C1a1eEh4fj4YcfxtKlS+Hn54fPP//cLo4XcG+yyc8//4ypU6fWux1LHy9j2czPlsluM2CD1LdvTUtLEz/++KOIj48Xnp6eWneVsBUvvfSS8PHxEYcPH9a6LWx5ebmmz7Jly4SPj4/YuXOnOH36tBg3bpxN3kJ7w4YNAoDw9fUVEolEnDp1Sgjxv/i7d+8u/vKXv2jif/LJJ4WLi4s4evSozrqOHj0qXFxcxKBBgzRt5eXlolOnTqJLly6ioqJCCCFESkqKACB27NjR4Li//fZb4eLiIt566y1x4cIF8cknnwgPDw+xZcsWTR9bPgaxsbHiwQcfFP/6179EQUGB2Llzp2jZsqWYN2+epo8tx091s6fxrDb1jXNlZWVizpw5Ijc3VxQUFIhDhw6J8PBw8eCDD9rUe1Q9xuXl5QkhhJgzZ444fPiwiI+PFwBEr169hJeXl/j1119FZGSk8PPzE87OzuKxxx7T+rm7evWq8PDwEM8//7wAIDZs2GDdxPSoqqoSbdu2FfPnz9dqt5djVVZWJk6ePClOnjwpAIjly5eLkydPam7jbsiY+OKLL4o2bdqIAwcOiO+++048+eSTolevXlq3rKf6OcIYZgmmeM8aIi8vTwAQQ4YMEQDE888/r7X8559/Fh4eHqJPnz7i1q1betdx6NAhzWc8IXTHRktzpM/SZH31jVkLFiwQEyZM0PS/ePGi8PDwEK+88or48ccfRVpampBKpWL79u2aPv/+97+Fs7OzWLZsmfjpp5/EsmXLhIuLizh+/LjN5rV161bh4uIi1qxZo/VzdfPmTU0fhUIh9u3bJ3755Rdx8uRJMXnyZOHi4iK++eYbi+bl7OwsAIh//vOfOnm99NJLwsPDQ3h5eYmioiKxYsUKMWbMGAFAHD16VCxYsEAAEFKpVEycOFEIoXu8kpKSbP54qT333HOib9++etdp7eNV3+85W/3ZcujCmRBCrFmzRrRr1064urqKhx9+WHNLalsDQO/j/i9S1dXVYsmSJUIulwuZTCYef/xxcfr0aesFXQv1B6eePXsKd3d3TdFLHb9UKhUSiUQ8/vjjIiMjQwAQcXFxta7vhRdeEADEiRMnNG25ubnCyclJvPLKK+L06dNCJpOJZ599ttGxf/HFFyIkJETIZDLRpUsXsX79eq3ltnwMSktLxezZs0Xbtm2Fm5ub6NChg1i8eLFQKpWaPrYcP9XPXsaz2tQ3zpWXl4uYmBjRqlUrIZVKRdu2bUVsbKz47bffrBt4DTW/HI4ZM0b4+/trPrB1795dnD17VgghRGRkpOjevbsYOnSoACBcXV01P3fr168XHh4e4uDBgzZbONu/f78AIM6dO6fVbi/HSn27+ZqP2NhYIYRhY2JFRYWYOXOmaN68uXB3dxdDhw61uTzthb2PYZZgivesIV588UUBQJw+fVpEREQILy8vcefOHc3y6dOnG10Es3bhzJE+S5NtqGvMio2NFZGRkVr9Dx8+LHr37i1cXV1F+/btxbp163TW+dlnn4nOnTsLqVQqunTp0qg/+jeUMXlFRkbWOSYJIUR8fLxo27atcHV1Fa1atRIxMTEiNzfXghnd89xzzwkAwsXFRW9e3bp1EwBEYmKiSElJEb6+vgKA8PHxEY8++qjYunWrAKD1x0J7O15CCHHz5k3h7u6u8z1WzdrHq77fc7b6s+XwhTOyvPs/OL333nsCgDh48KBmufqLpBBCJCUlCQBi7969ta5vz549AoBITk7Wap83b55wcnISQUFBIiAgQPzxxx/mSYiI6D61fTlcvXq1zsxX9XhXWloqPD09xdq1azXL+vbtKyZOnKiZ+WGLhTMicjzl5eXCx8dH9OnTRwghxD/+8Q8BQKSnp2v6BAcHC39/f6PWqx4bjx8/LlQqldaDMzSJyNzqK97fvn1bODs7iwEDBgghhFiyZIkAIP773/9q+vzlL38RXl5e4t133xU//fSTqK6utkjsZPsc9hpnZBtefPFFdOjQAfPnz9e6gKya+sL7dV3rSL1M3VctMTERXl5eKCgowPvvvw9fX18TRk5EVLeqqircvXsXt2/fxv79+7F06VI8/vjjGD58uE5fLy8vjBo1SnNNoR9//BHffPMNpkyZYumwiaiJ2759O27duqW59s2YMWPQrFkzpKWlafpcvnxZc43X+1VXV+Pu3buah/o6qvfr168fpFKp1kMmk5kvISIiA3h6eqJly5a4du1arX22bt0KX19fzJkzB127doWPjw+GDRuGzZs36/0uS00HC2dkVq6urli6dClOnDiBf/7znw1ah3qQqnk72Q0bNuDWrVtwcnLSuRggEZG5qb8cenl54amnnoKvry8+//xzuLi46O0/ZcoUnDhxAqdPn0ZaWho6duyodctzIiJLSEtLg7u7O8aOHQsAaNasGZ555hkcPXq03ruqjRw5Uqsg9vLLL+v02bRpE/Ly8rQe+m4+QERkafUVv/r06YOff/4Z+/btw6JFixAeHo6DBw9i4sSJGD58OItnTRgLZ2R2Y8eOxcMPP4zFixdDpVJpLWvbti2Ae7c7r82vv/4KAAgMDNS0Xbx4Ea+++iqefvppvPbaa/jwww/rvEsKEZGpqb8cfvXVV4iLi8NPP/2EcePG1dr/8ccfR3BwMD788ENs3rwZU6ZM0fmDABGROf388884cuQIhgwZAiEEbt68iZs3b2LUqFEA/nenzbZt2+LSpUs6r3/33Xc1xbDadO3aFWFhYVqP0NBQ8yRERGSgO3fu4MaNGwgICKizn1QqxaBBg/DWW29h//79uHz5MqKiovCvf/0Le/futVC0ZGtYOCOzk0gkSElJwS+//IL169drLYuOjgYA7Nq1q9bXq5ep+wohMHnyZLi7u+ODDz7A4sWL0atXL0ybNg1lZWVmyYGIqCb1l8MnnngCH3zwAaZNm4Z9+/Zh+/bttb5m8uTJWLduHf744w/ExsZaMFoionuFMSEEtm/fDl9fX81jyJAhAICNGzeiqqoK0dHRKCwsxIkTJ7Re37FjR00xjIjInnz55ZeoqqpCVFSUUa9r0aIF4uPjAQBnzpwxfWBkF1g4I4sYOHAgoqOj8cYbb+D27dua9rCwMMTExCAtLQ3//ve/dV537NgxfPzxx3jqqac0f6187733cOTIEaxbtw6tW7eGVCpFeno6rl27hldffdViORER3S81NRW+vr54/fXX9V73BwBiY2MxbNgwvPrqq3jwwQctHCERNWVVVVXYuHEjOnbsiEOHDuk85syZg8LCQuzduxevvPIKPDw8MGPGDP5Rkojs3m+//Ya5c+fCx8cHcXFxevuoVCrcuHFD77KffvoJAOqdrUaOS/+FWIjMICUlBaGhoSguLkb37t017Zs2bcLAgQMRExODl19+GQMGDAAAfPXVV3jvvffQpUsXpKenAwDOnz+PRYsWYezYsZrTCgDgoYcewqJFi5CYmIhRo0Zh4MCBFs2NiMjX1xcLFy7EvHnzsHXrVjz33HM6fQICAuqcYUtEZC579+7FtWvXkJKSonfGRUhICFavXo20tDRkZmZi27ZtGDduHHr06IGXXnoJDz/8MGQyGYqLi5GVlQUA8Pb21lnPmTNncPfuXZ32jh07olWrVibPi4jofuox6O7duyguLsbRo0exYcMGODs7IzMzs9Zx6NatW2jfvj2eeeYZDBw4EIGBgbh9+zYOHz6M9957D127dsXIkSMtnA3ZChbOyGJ69+6NcePGYevWrVrtfn5+OH78ON5//33885//xPvvvw8A+Mtf/oJFixYhPj4enp6eqK6uxqRJk+Dj44M1a9borH/x4sXYtWsXpk2bhtOnT8PLy8sieRERqc2aNQurV6/GG2+8Uef1zoiILC0tLQ2urq6YPHmy3uUtW7bE008/je3bt+P69esYPnw4Tp8+jZUrV2LDhg1ITExEdXU15HI5HnnkEWRmZuJvf/ubznpqW/9HH32EadOmmTQnIqKa1GOQq6srHnjgAXTt2hXz58/HtGnT6izee3t7IzExEQcPHsSiRYtw/fp1SCQSBAUFIT4+HvPnz4eHh4el0iAbIxG8NQQREREREREREZEOXuOMiIiIiIiIiIhIDxbOiIiIiIiIiIiI9GDhjIiIiIiIiIiISA8WzoiIiIiIiIiIiPRg4YyIiIiIiIiIiEgPFs6IiIiIiIiIiIj0YOGMiIiIiIiIiIhIDxdrB9AQ1dXVuHbtGry8vCCRSKwdDhEZQAiBsrIyBAQEwMmJNfv7cUwjsi8cz2rH8YzIvnA8qxvHNCL7Yq4xzS4LZ9euXUNgYKC1wyCiBrh8+TLatGlj7TBsCsc0IvvE8UwXxzMi+8TxTD+OaUT2ydRjml0Wzry8vADc2xne3t5Gv16lUiErKwsxMTGQSqWmDo/+P+5n87OnfVxaWorAwEDNzy/9T2PHtIawp/eOLeN+NB172pccz2qnbzyzp2NrLEfODXDs/Bw5N8Dw/Die1c0an9Hq4wjvXeZgGxwxB3ONaXZZOFNPk/X29m5w4czDwwPe3t52+waxB9zP5meP+5jT3HU1dkxrCHt879gi7kfTscd9yfFMl77xzB6PraEcOTfAsfNz5NwA4/PjeKafNT6j1ccR3rvMwTY4cg6mHtN4IjsREREREREREZEeLJwRERERERERERHpwcIZERERERERERGRHnZ5jTNH1n7Bl2ZZ76/LhphlvUREjqIh46/MWSD1ESBEsR/KKt1rKXDsJSKi+pjj87/69xNZDr/HETkuzjgjIiIiIiIiIiLSg4UzIiIiIiIiIiIiPZrEqZo1p83Wd2qNoThtloiIiIiIiIjIcXHGGRERERERERERkR4snBEREREREREREenBwhkREREREREREZEeLJwRERERERERERHpwcIZERERERERERGRHiycERERERERERER6cHCGRERERERERERkR4snBEREREREREREenBwhkRERERERHZjCNHjmDYsGEICAiARCLBrl27tJYLIaBQKBAQEAB3d3dERUXh7NmzWn2USiVmzZqFli1bwtPTE8OHD8eVK1csmAUROQoWzoiIiIiIiMhm3LlzB7169cLq1av1Lk9NTcXy5cuxevVq5OXlQS6XIzo6GmVlZZo+8fHxyMzMREZGBo4dO4bbt29j6NChqKqqslQaROQgXKwdABEREREREZHa4MGDMXjwYL3LhBBYuXIlFi9ejJEjRwIANm7cCD8/P2zduhVxcXG4desW0tLSsHnzZgwcOBAAsGXLFgQGBuLAgQMYNGiQxXIhIvtnlsLZ1atXMX/+fOzduxcVFRXo1KkT0tLSEBoaCuDeYJeYmIj169ejpKQEffv2xZo1a9C9e3dzhENEREREREQOoKCgAEVFRYiJidG0yWQyREZGIjc3F3FxccjPz4dKpdLqExAQgJCQEOTm5tZaOFMqlVAqlZrnpaWlAACVSgWVSlVnXDJn0Zi0alVzu+rn9cVjy5iDbXDEHMyVi8kLZyUlJejfvz+eeOIJ7N27F61bt8Yvv/yCBx54QNNHPbU2PT0dnTp1wtKlSxEdHY1z587By8vL1CERERERERGRAygqKgIA+Pn5abX7+fnh0qVLmj6urq7w9fXV6aN+vT7JyclITEzUac/KyoKHh0edcaU+YlD4RtuzZ4/e9uzsbPNs0IKYg21wpBzKy8vNsn6TF85SUlIQGBiIDRs2aNrat2+v+b8hU2uJiIiIiIiIaiORSLSeCyF02mqqr8/ChQuRkJCgeV5aWorAwEDExMTA29u7znWHKPYbELXxzii0Z8epVCpkZ2cjOjoaUqnULNs0N+ZgGxwxB/UsUVMzeeFs9+7dGDRoEJ555hnk5OTgwQcfxPTp0/H8888DMGxqLREREREREVFNcrkcwL1ZZf7+/pr24uJizSw0uVyOyspKlJSUaM06Ky4uRkRERK3rlslkkMlkOu1SqbTewoKyqu6iXUPVtl1DYrJ1zME2OFIO5srD5IWzixcvYt26dUhISMCiRYvw7bff4uWXX4ZMJsPEiRMNmlpbU2PONQd0zzeXOQmtfxvKHOfPWurceEtwhHOmbZ097WN7iJHI1Nov+NIs6/112RCzrJeIiMjWBQUFQS6XIzs7G7179wYAVFZWIicnBykpKQCA0NBQSKVSZGdnY/To0QCAwsJCnDlzBqmpqVaLnYjsk8kLZ9XV1QgLC0NSUhIAoHfv3jh79izWrVuHiRMnavoZM7W2MeeaA7Wfb/5mWHW9r61LbeebN4alz423BEc4Z9rW2cM+Ntf55kRERETkWG7fvo2ff/5Z87ygoACnTp1C8+bN0bZtW8THxyMpKQnBwcEIDg5GUlISPDw8MH78eACAj48Ppk6dijlz5qBFixZo3rw55s6dix49emjusklEZCiTF878/f3RrVs3rbauXbtix44dAAybWltTY841B3TPN5c5CbwZVo3XTjhBWd3wKbU1zzc3BUudG28JjnDOtK2zp31srvPNiYiIiMixnDhxAk888YTmufq7YGxsLNLT0zFv3jxUVFRg+vTpKCkpQd++fZGVlaV1o7kVK1bAxcUFo0ePRkVFBQYMGID09HQ4OztbPB8ism8mL5z1798f586d02o7f/482rVrB8CwqbU1NeZcc6D2882V1ZJGnYtujkKFpc+NtwRHOGfa1tnDPrb1+IiIiIjINkRFRUGI2i9hI5FIoFAooFAoau3j5uaGVatWYdWqVWaIkIiaEpMXzl555RVEREQgKSkJo0ePxrfffov169dj/fr1AO4NcvVNrSUiIiIiIiIiIrI2kxfO+vTpg8zMTCxcuBBvvPEGgoKCsHLlSjz77LOaPoZMrSUiIiIiIiIiIrImkxfOAGDo0KEYOnRorcsNmVpLRERERERERERkTWYpnFHT0H7Bl3UulzkLpD5y74YHxly77ddlQxobGhERUZNy5MgRvP3228jPz0dhYSEyMzMxYsQIzXIhBBITE7F+/XrNbP81a9age/fumj5KpRJz587Ftm3bNBfSXrt2Ldq0aWOFjIiIiIhsg5O1AyAiIiKixrlz5w569eqF1atX612empqK5cuXY/Xq1cjLy4NcLkd0dDTKyso0feLj45GZmYmMjAwcO3YMt2/fxtChQ1FVVWWpNIiIiIhsDmecNRH1zQ4jaoo4Q4OIHMXgwYMxePBgvcuEEFi5ciUWL16MkSNHAgA2btwIPz8/bN26FXFxcbh16xbS0tKwefNmDBw4EACwZcsWBAYG4sCBAxg0aJDFciEiIiKyJSycEVGTpZ6hMXnyZPzf//2fznL1DI309HR06tQJS5cuRXR0NM6dO6e5mUl8fDy++OILZGRkoEWLFpgzZw6GDh2K/Px8ODs7WzolIiIdBQUFKCoqQkxMjKZNJpMhMjISubm5iIuLQ35+PlQqlVafgIAAhISEIDc3t9bCmVKphFKp1DwvLS0FAKhUKqhUKs3/7//XkThyboBj52dLucmchenX6XRvnfXlZwv5ExHZOhbOiKjJ4gwNImoKioqKAAB+fn5a7X5+frh06ZKmj6urK3x9fXX6qF+vT3JyMhITE3Xas7Ky4OHhodWWnZ3doPjtgSPnBjh2fraQW+oj5lt3ffmVl5ebb+NkEjXPHGrodaRr4nWliQzHwhkRkR7mnKFBRGQNEon2FywhhE5bTfX1WbhwIRISEjTPS0tLERgYiJiYGHh7ewO4N6MlOzsb0dHRkEqljcjA9jhyboBj52dLuYUo9pt8nTIngTfDquvNTz1LlIiIasfCGRGRHuacoWHIqU3mZkunqNiKhpwqoz4VRv2vpTjicbOn96Q9xHg/uVwO4N6Y5e/vr2kvLi7WjHFyuRyVlZUoKSnRGtOKi4sRERFR67plMhlkMplOu1Qq1fmyrq/NUThyboBj52cLuTVm1lB96svP2rkTEdkDFs6IiOpgjhkaxpzaZG62cIqKrWjMqTJvhlWbLhAD7Nmzx6LbsyR7eE/a26lNQUFBkMvlyM7ORu/evQEAlZWVyMnJQUpKCgAgNDQUUqkU2dnZGD16NACgsLAQZ86cQWpqqtViJyIiIrI2Fs6IiPQw5wwNQ05tMjdbOkXFVjTkVBn1qTCvnXCCstp8MwZqOqNwvNOA7ek9aYunNt2+fRs///yz5nlBQQFOnTqF5s2bo23btoiPj0dSUhKCg4MRHByMpKQkeHh4YPz48QAAHx8fTJ06FXPmzEGLFi3QvHlzzJ07Fz169NBcw5GIiIioKWLhjIhID3PO0DDm1CZzs4VTVGxFY06VUVZLzHqqTU2OfMzs4T1pi/GdOHECTzzxhOa5ujgfGxuL9PR0zJs3DxUVFZg+fTpKSkrQt29fZGVlae4QDAArVqyAi4sLRo8ejYqKCgwYMADp6em8QzARERE1aSycNULNO5wQkX3hDA0ichRRUVEQovZr7UkkEigUCigUilr7uLm5YdWqVVi1apUZIiQiIiKyTyycEVGTxRkaREREREREVBcWzoioyeIMDSIiIiIiIqqLk7UDICIiIiIiIiIiskUsnBEREREREREREenBwhkREREREREREZEeLJwRERERERERERHpwcIZERERERERERGRHiycERERERERERER6eFi7QCILKX9gi9Nvs5flw0x+TqJiIiIiIiIyDZwxhkREREREREREZEeLJwRERERERERERHpYfbCWXJyMiQSCeLj4zVtQggoFAoEBATA3d0dUVFROHv2rLlDISIiIiIiIiIiMphZC2d5eXlYv349evbsqdWempqK5cuXY/Xq1cjLy4NcLkd0dDTKysrMGQ4REREREREREZHBzFY4u337Np599ll89NFH8PX11bQLIbBy5UosXrwYI0eOREhICDZu3Ijy8nJs3brVXOEQEREREREREREZxWyFsxkzZmDIkCEYOHCgVntBQQGKiooQExOjaZPJZIiMjERubq65wiEiIiIiIiIiIjKKizlWmpGRge+++w55eXk6y4qKigAAfn5+Wu1+fn64dOmS3vUplUoolUrN89LSUgCASqWCSqWqNx6Zs9B+7iS0/iXzaOh+NuSYNkTN94EpmCtWY7dv7TgMYQ8xEhEREREREd3P5IWzy5cvY/bs2cjKyoKbm1ut/SQSidZzIYROm1pycjISExN12rOysuDh4VFvTKmP6G9/M6y63tdS4xm7n/fs2WOWOGp7HzSGuWI1VnZ2trVDqFd5ebm1QyAiIiIiIiIyiskLZ/n5+SguLkZoaKimraqqCkeOHMHq1atx7tw5APdmnvn7+2v6FBcX68xCU1u4cCESEhI0z0tLSxEYGIiYmBh4e3vXG1OIYr/Wc5mTwJth1XjthBOU1fqLddR4Dd3PZxSDzBJPzfeBKZgrVkOpVCpkZ2cjOjoaUqnUqrHURz1TlIiIiIiIrKv9gi9Nvs5flw0x+TqJbIHJC2cDBgzA6dOntdomT56MLl26YP78+ejQoQPkcjmys7PRu3dvAEBlZSVycnKQkpKid50ymQwymUynXSqVGlQsUFbpL9ooqyW1LiPTMXY/m6sAZI5jbSvFKkN/FqzJ1uMjIiIiIiIiqsnkhTMvLy+EhIRotXl6eqJFixaa9vj4eCQlJSE4OBjBwcFISkqCh4cHxo8fb+pwiIiIiIiIiIiIGsQsNweoz7x581BRUYHp06ejpKQEffv2RVZWFry8vKwRDhERERERERERkQ6LFM4OHz6s9VwikUChUEChUFhi80REREREREREREZzsnYAREREREREREREtoiFMyIiIiIiIiIiIj1YOCMiIiIiIiIiItKDhTMiIiIiIiIiIiI9WDgjIiIiIiIiu6FQKCCRSLQecrlcs1wIAYVCgYCAALi7uyMqKgpnz561YsREZM9YOCMiIiIiIiK70r17dxQWFmoep0+f1ixLTU3F8uXLsXr1auTl5UEulyM6OhplZWVWjJiI7BULZ0RERERERGRXXFxcIJfLNY9WrVoBuDfbbOXKlVi8eDFGjhyJkJAQbNy4EeXl5di6dauVoyYie8TCGREREREREdmVCxcuICAgAEFBQRg7diwuXrwIACgoKEBRURFiYmI0fWUyGSIjI5Gbm2utcInIjrlYOwAiIiIiIiIiQ/Xt2xebNm1Cp06dcP36dSxduhQRERE4e/YsioqKAAB+fn5ar/Hz88OlS5fqXK9SqYRSqdQ8Ly0tBQCoVCqoVKo6XytzFg1JxWgyJ6H1ry2pbx/V7Gdof1vEHGxDzRzMlQsLZ0RERERERGQ3Bg8erPl/jx49EB4ejo4dO2Ljxo3o168fAEAikWi9Rgih01ZTcnIyEhMTddqzsrLg4eFR52tTHzE0etN4M6zashs0wJ49e4zqn52dbaZILIc52AZ1DuXl5WZZPwtnREREREREZLc8PT3Ro0cPXLhwASNGjAAAFBUVwd/fX9OnuLhYZxZaTQsXLkRCQoLmeWlpKQIDAxETEwNvb+86Xxui2N/wBIwgcxJ4M6war51wgrK67kKgrTI2hzOKQRaIyjgqlQrZ2dmIjo6GVCq1djgN4og5qGeJmhoLZ0RERERNgEKh0JlJ4efnpzmtSQiBxMRErF+/HiUlJejbty/WrFmD7t27WyNcIiKDKZVK/PTTT3jssccQFBQEuVyO7Oxs9O7dGwBQWVmJnJwcpKSk1LkemUwGmUym0y6VSustLCirLFvEUlZLLL5NUzM0B1su6hjy3rB1jpSDufLgzQGIiIiImoju3bujsLBQ8zh9+rRmWWpqKpYvX47Vq1cjLy8Pcrkc0dHRKCsrs2LERES65s6di5ycHBQUFOCbb77BqFGjUFpaitjYWEgkEsTHxyMpKQmZmZk4c+YMJk2aBA8PD4wfP97aoRORHWLhjIioFgqFAhKJROshl8s1y4UQUCgUCAgIgLu7O6KionD27FkrRkxEVDcXFxfI5XLNo1WrVgDujWcrV67E4sWLMXLkSISEhGDjxo0oLy/H1q1brRw1EZG2K1euYNy4cejcuTNGjhwJV1dXHD9+HO3atQMAzJs3D/Hx8Zg+fTrCwsJw9epVZGVlwcvLy8qRE5E94qmaRER16N69Ow4cOKB57uzsrPm/enZGeno6OnXqhKVLlyI6Ohrnzp3jBzMiskkXLlxAQEAAZDIZ+vbti6SkJHTo0AEFBQUoKipCTEyMpq9MJkNkZCRyc3MRFxdnxaiJbEf7BV9aOwQCkJGRUedyiUQChUIBhUJhmYCIyKGxcEZEVAf17Iyaas7OAICNGzfCz88PW7du5ZdMMitzfXH7ddkQs6yXbEPfvn2xadMmdOrUCdevX8fSpUsRERGBs2fPaq5zVvPC2X5+frh06VKt61QqlVAqlZrn6ovyqlQqi90i3pocOTfAsfNraG4yZ2GOcExO5nQvzvryc8RjS0RkaiycERHVwRyzMwz5omlujvxlqKEa8mVI/cVE/a+9s+b7wZ7ek/YQoz6DBw/W/L9Hjx4IDw9Hx44dsXHjRvTr1w/AvVka9xNC6LTdLzk5WeeGAwCQlZUFDw8PrTZHuN19bRw5N8Cx8zM2t9RHzBSImdSXX3l5uYUiISKyXyycERHVwhyzMwDjvmiamyN/GTJWY74MvRlWbbpArGjPnj3WDsEu3pOO8kXT09MTPXr0wIULFzBixAgAQFFREfz9/TV9iouLdca5+y1cuBAJCQma56WlpQgMDERMTAy8vb0BOMbt7mvjyLkBjp1fQ3MLUew3Y1SmI3MSeDOsut781H+8IyKi2rFwRkRUC3PMzgAM+6Jpbo78ZaihGvJlSP3F5LUTTlBW2/ct4QHgjGKQ1bZtT+9JR/miqVQq8dNPP+Gxxx5DUFAQ5HI5srOz0bt3bwBAZWUlcnJykJKSUus6ZDIZZDKZTru+W8I7wu3ua+PIuQGOnZ+xuSmr7Gusry8/Rz2uRESmxMIZEZGBTDE7AzDui6a5OfKXIWM15suQslpid1+m9LGF94I9vCdtPb7azJ07F8OGDUPbtm1RXFyMpUuXorS0FLGxsZBIJIiPj0dSUhKCg4MRHByMpKQkeHh4YPz48dYOnYiIiMhqnKwdABGRvVDPzvD399eanaGmnp0RERFhxSiJiPS7cuUKxo0bh86dO2PkyJFwdXXF8ePH0a5dOwDAvHnzEB8fj+nTpyMsLAxXr15FVlYW7xJMRERETRpnnBER1YKzM4jIkWRkZNS5XCKRQKFQQKFQWCYgIiIiIjvAwhkRUS3UszN+//13tGrVCv369dOZnVFRUYHp06ejpKQEffv25ewMIiIiIiIiB2LyUzWTk5PRp08feHl5oXXr1hgxYgTOnTun1UcIAYVCgYCAALi7uyMqKgpnz541dShERI2SkZGBa9euobKyElevXsWOHTvQrVs3zXL17IzCwkL8+eefyMnJQUhIiBUjJiIiIiIiIlMyeeEsJycHM2bMwPHjx5GdnY27d+8iJiYGd+7c0fRJTU3F8uXLsXr1auTl5UEulyM6OhplZWWmDoeIiIiIiIiIiKhBTH6q5r59+7Seb9iwAa1bt0Z+fj4ef/xxCCGwcuVKLF68GCNHjgQAbNy4EX5+fti6dSvi4uJMHRIREREREREREZHRzH6Ns1u3bgEAmjdvDgAoKChAUVERYmJiNH1kMhkiIyORm5urt3CmVCqhVCo1z0tLSwEAKpUKKpWq3hhkzkL7uZPQ+pfMo6H72ZBj2hA13wemYK5Yjd2+teMwhD3ESERERERERHQ/sxbOhBBISEjAo48+qrnuT1FREQDAz89Pq6+fnx8uXbqkdz3JyclITEzUac/KyoKHh0e9caQ+or/9zbDqel9LjWfsft6zZ49Z4qjtfdAY5orVWNnZ2dYOoV7l5eXWDoEcRPsFX1o7BCIiIiKyEHN89vt12RCTr5Mcl1kLZzNnzsQPP/yAY8eO6SyTSCRaz4UQOm1qCxcuREJCguZ5aWkpAgMDERMTA29v73rjCFHs13oucxJ4M6war51wgrJa/zap8Rq6n88oBpklnprvA1MwV6yGUqlUyM7ORnR0NKRSqVVjqY96pigRERH9D78QEhER2TazFc5mzZqF3bt348iRI2jTpo2mXS6XA7g388zf31/TXlxcrDMLTU0mk0Emk+m0S6VSg4oFyir9RRtltaTWZWQ6xu7n4NeyzBSJ6Y+1rRSrDP1ZsCZbj4+IiIiIiIioJpMXzoQQmDVrFjIzM3H48GEEBQVpLQ8KCoJcLkd2djZ69+4NAKisrEROTg5SUlJMHQ4RERERETUx9c3kkzkLpD5y74wE/iGdiIjqYvLC2YwZM7B161Z8/vnn8PLy0lzTzMfHB+7u7pBIJIiPj0dSUhKCg4MRHByMpKQkeHh4YPz48aYOh4iIiIiIiIiIqEFMXjhbt24dACAqKkqrfcOGDZg0aRIAYN68eaioqMD06dNRUlKCvn37IisrC15eXqYOh8iszHWRcl6bhIiIiIiIiMj6zHKqZn0kEgkUCgUUCoWpN09ERERERERERGQSTtYOgIiIiIiIiIiIyBaZ7a6aRERERETkOMx1iQoiIiJbxsIZEREREZEDMVWBi3eeJCIi4qmaREREREREREREerFwRkREREREREREpAdP1SQiIiIiIiKiJqOxp7TXdir7r8uGNDY0skGccUZERERERERERKQHZ5wRERERkUMxx90fOYuAiIioaWLhjIiIiIioHvqKcbzrJBERkePjqZpERERERERERER6cMYZURNhjtNWAJ66QkREREREBPBSAY6KhTMiIiICwA97REREREQ18VRNIiIiIiIiIiIiPVg4IyIiIiIiIiIi0oOFMyIiIiIiIiIiIj1YOCMiIiIiIiIiItKDhTMiIiIiIiIiIiI9eFdNIiIiIrIKc9zJlYiIiMiUWDgjIiKz4ZdiMvQ9IHMWSH0ECFHsh7JKUm//X5cNaWxoRERERET14qmaREREREREREREenDGGREREdkdc8xm5Cw2IiIiIqqJhTMiIiIiIiIiIhtkrkufXHgzxizrdURWPVVz7dq1CAoKgpubG0JDQ3H06FFrhkNE1GAcz4jIUXA8IyJHwjGNiBrLajPOPv30U8THx2Pt2rXo378/PvzwQwwePBg//vgj2rZta62wiIiMxvGMiBwFxzMiciQc04hqF6LYb9SNmQzhqJe9sFrhbPny5Zg6dSqmTZsGAFi5ciX279+PdevWITk52VphEREZzRHGM979kogAxxjPiIjUOKYRkSlYpXBWWVmJ/Px8LFiwQKs9JiYGubm5Ov2VSiWUSqXm+a1btwAAf/zxB1QqVb3bc7l7R/t5tUB5eTVcVE6oqjZNZZV0cT833I0bNwzqp1KpUF5ejhs3bkAqldbZt+bPgakYGmtZWRkAQAhhljisxdjxDGj8mGYKNd875np/ODqOc6ZjC/uS45l5xrO6flfZ+9hjC+9bc3Lk/Bw5N+B/+dX3GdFRxzPA8p/RLDWeOcJ7lznYBnPk8Je5/zTJemr6ZuEAve01P2OYa0yzSuHs999/R1VVFfz8/LTa/fz8UFRUpNM/OTkZiYmJOu1BQUENjmF8g19JxuB+bpiW71o7AsMZG2tZWRl8fHzME4wVGDueAeYZ08h6OM6ZjrX3JcczjmcNYe33rbk5cn6OnBtgXH6ONp4Bjj2mOcJ7lznYBnvJwdqf0ax6V02JRLuqKYTQaQOAhQsXIiEhQfO8uroaf/zxB1q0aKG3f31KS0sRGBiIy5cvw9vb2/jAySDcz+ZnT/tYCIGysjIEBARYOxSzMHQ8A0w/pjWEPb13bBn3o+nY077kePY/hoxn9nRsjeXIuQGOnZ8j5wYYnp+jj2eA/X1Gq48jvHeZg21wxBzMNaZZpXDWsmVLODs761T6i4uLdf4iAAAymQwymUyr7YEHHmh0HN7e3nb7BrEn3M/mZy/72NH+kgkYP54B5hvTGsJe3ju2jvvRdOxlX3I8u8eY8cxejm1DOHJugGPn58i5AYbl54jjGWD/n9Hq4wjvXeZgGxwtB3OMaU4mX6MBXF1dERoaiuzsbK327OxsREREWCMkIqIG4XhGRI6C4xkRORKOaURkKlY7VTMhIQETJkxAWFgYwsPDsX79evz222948cUXrRUSEVGDcDwjIkfB8YyIHAnHNCIyBasVzsaMGYMbN27gjTfeQGFhIUJCQrBnzx60a9fO7NuWyWRYsmSJzjRcMi3uZ/PjPrYN1hzPGorvHdPgfjQd7kvbYI7xzJGPrSPnBjh2fo6cG+D4+RnKHj+j1ccRji1zsA3MwXAS4Yj3HiYiIiIiIiIiImokq1zjjIiIiIiIiIiIyNaxcEZERERERERERKQHC2dERERERERERER6sHBGRERERERERESkh8MWztauXYugoCC4ubkhNDQUR48eNeh1//73v+Hi4oKHHnrIvAE6AGP3sVKpxOLFi9GuXTvIZDJ07NgRH3/8sYWitV/G7udPPvkEvXr1goeHB/z9/TF58mTcuHHDQtGSPUlOToZEIkF8fLy1Q7FLV69exXPPPYcWLVrAw8MDDz30EPLz860dlt25e/cu/v73vyMoKAju7u7o0KED3njjDVRXV1s7NDKBhn4esyXJycno06cPvLy80Lp1a4wYMQLnzp3T6jNp0iRIJBKtR79+/awUsXEUCoVO7HK5XLNcCAGFQoGAgAC4u7sjKioKZ8+etWLEhmvfvr1ObhKJBDNmzABgf8ftyJEjGDZsGAICAiCRSLBr1y6t5YYcK6VSiVmzZqFly5bw9PTE8OHDceXKFQtmQXUxZLyp6fDhw3rf5//5z38sFLW2+sYUfXJychAaGgo3Nzd06NABH3zwgYWi1a++saMmWzgGphgf9NmxYwe6desGmUyGbt26ITMz00wZ1J2DSqXC/Pnz0aNHD3h6eiIgIAATJ07EtWvX6lxnenq63mPz559/GhWbQxbOPv30U8THx2Px4sU4efIkHnvsMQwePBi//fZbna+7desWJk6ciAEDBlgoUvvVkH08evRoHDx4EGlpaTh37hy2bduGLl26WDBq+2Psfj527BgmTpyIqVOn4uzZs/jss8+Ql5eHadOmWThysnV5eXlYv349evbsae1Q7FJJSQn69+8PqVSKvXv34scff8S7776LBx54wNqh2Z2UlBR88MEHWL16NX766Sekpqbi7bffxqpVq6wdGjVSQz+P2ZqcnBzMmDEDx48fR3Z2Nu7evYuYmBjcuXNHq99TTz2FwsJCzWPPnj1With43bt314r99OnTmmWpqalYvnw5Vq9ejby8PMjlckRHR6OsrMyKERsmLy9PK6/s7GwAwDPPPKPpY0/H7c6dO+jVqxdWr16td7khxyo+Ph6ZmZnIyMjAsWPHcPv2bQwdOhRVVVWWSoPqYOh4o8+5c+e03svBwcEWiFi/usaUmgoKCvDXv/4Vjz32GE6ePIlFixbh5Zdfxo4dOywYsTZDxg59rHkMTDE+1PT1119jzJgxmDBhAr7//ntMmDABo0ePxjfffGPxHMrLy/Hdd9/htddew3fffYedO3fi/PnzGD58eL3r9fb21jouhYWFcHNzMy444YAeeeQR8eKLL2q1denSRSxYsKDO140ZM0b8/e9/F0uWLBG9evUyY4T2z9h9vHfvXuHj4yNu3LhhifAchrH7+e233xYdOnTQanv//fdFmzZtzBYj2Z+ysjIRHBwssrOzRWRkpJg9e7a1Q7I78+fPF48++qi1w3AIQ4YMEVOmTNFqGzlypHjuueesFBGZSkM/j9m64uJiAUDk5ORo2mJjY8Xf/vY36wXVCHV97q2urhZyuVwsW7ZM0/bnn38KHx8f8cEHH1goQtOZPXu26Nixo6iurhZC2PdxAyAyMzM1zw05Vjdv3hRSqVRkZGRo+ly9elU4OTmJffv2WSx2Mpy+8aamQ4cOCQCipKTEcoHVwdjv0vPmzRNdunTRaouLixP9+vUzcWQNV3PsqMnWjkFDxgd9Ro8eLZ566imttkGDBomxY8eaPOaaauagz7fffisAiEuXLtXaZ8OGDcLHx6fR8TjcjLPKykrk5+cjJiZGqz0mJga5ubm1vm7Dhg345ZdfsGTJEnOHaPcaso93796NsLAwpKam4sEHH0SnTp0wd+5cVFRUWCJku9SQ/RwREYErV65gz549EELg+vXr2L59O4YMGWKJkMlOzJgxA0OGDMHAgQOtHYrdUo9pzzzzDFq3bo3evXvjo48+snZYdunRRx/FwYMHcf78eQDA999/j2PHjuGvf/2rlSOjxmjo5zF7cOvWLQBA8+bNtdoPHz6M1q1bo1OnTnj++edRXFxsjfAa5MKFCwgICEBQUBDGjh2LixcvArg3E6SoqEjrOMpkMkRGRtrdcaysrMSWLVswZcoUSCQSTbs9H7f7GXKs8vPzoVKptPoEBAQgJCTE7o5nU1HbeKNP79694e/vjwEDBuDQoUPmDq1OtY0p+nz99dc6vysGDRqEEydOQKVSmTvUetU2duhjS8fgfg0dy2s7NrYyXty6dQsSiaTeMz5u376Ndu3aoU2bNhg6dChOnjxp9LZcGhijzfr9999RVVUFPz8/rXY/Pz8UFRXpfc2FCxewYMECHD16FC4uDrdLTK4h+/jixYs4duwY3NzckJmZid9//x3Tp0/HH3/8weuc1aIh+zkiIgKffPIJxowZgz///BN3797F8OHDecoTaWRkZOC7775DXl6etUOxaxcvXsS6deuQkJCARYsW4dtvv8XLL78MmUyGiRMnWjs8uzJ//nzcunULXbp0gbOzM6qqqvDWW29h3Lhx1g6NGqEhv8PsgRACCQkJePTRRxESEqJpHzx4MJ555hm0a9cOBQUFeO211/Dkk08iPz8fMpnMihHXr2/fvti0aRM6deqE69evY+nSpYiIiMDZs2c1x0rfcbx06ZI1wm2wXbt24ebNm5g0aZKmzZ6PW02GHKuioiK4urrC19dXp489/1w6qtrGm5r8/f2xfv16hIaGQqlUYvPmzRgwYAAOHz6Mxx9/3IIR31PXmNKiRQud/kVFRXrft3fv3sXvv/8Of39/S4Wul76xoyZbOwY1NXQsr+3Y2MJ48eeff2LBggUYP348vL29a+3XpUsXpKeno0ePHigtLcV7772H/v374/vvvzfqVFqHrRLVrAYLIfRWiKuqqjB+/HgkJiaiU6dOlgrPIRi6jwGguroaEokEn3zyCXx8fAAAy5cvx6hRo7BmzRq4u7ubPV57Zcx+/vHHH/Hyyy/j9ddfx6BBg1BYWIhXX30VL774ItLS0iwRLtmwy5cvY/bs2cjKyjL+vH7SUl1djbCwMCQlJQG49xfGs2fPYt26dSycGenTTz/Fli1bsHXrVnTv3h2nTp1CfHw8AgICEBsba+3wqJGM+R1mD2bOnIkffvgBx44d02ofM2aM5v8hISEICwtDu3bt8OWXX2LkyJGWDtMogwcP1vy/R48eCA8PR8eOHbFx40bNhfId4TimpaVh8ODBCAgI0LTZ83GrTUOOlT0ez6agtvGmps6dO6Nz586a5+Hh4bh8+TLeeecdqxRt6hpTEhIS9L5G3/tWX7s16Bs7arK1Y1CbhowPtjj+q1QqjB07FtXV1Vi7dm2dffv166d105f+/fvj4YcfxqpVq/D+++8bvE2HO1WzZcuWcHZ21qmCFhcX61RLAaCsrAwnTpzAzJkz4eLiAhcXF7zxxhv4/vvv4eLigq+++spSodsNY/cxcK8K/+CDD2qKZgDQtWtXCCF4J59aNGQ/Jycno3///nj11VfRs2dPDBo0CGvXrsXHH3+MwsJCS4RNNiw/Px/FxcUIDQ3VjHc5OTl4//334eLiwgsDG8Hf3x/dunXTauvatavdXfTcFrz66qtYsGABxo4dix49emDChAl45ZVXkJycbO3QqBEa8jvM1s2aNQu7d+/GoUOH0KZNmzr7+vv7o127drhw4YKFojMdT09P9OjRAxcuXNDcCc/ej+OlS5dw4MCBem+WZM/HzZBjJZfLUVlZiZKSklr7kG0wZrzRp1+/fjbzPr5/TNFHLpfrfd+6uLjonaFmSYaOHfrY0jFo6Fhe27Gx5nihUqkwevRoFBQUIDs7u87ZZvo4OTmhT58+Rh8bhyucubq6IjQ0VHPnC7Xs7GxERETo9Pf29sbp06dx6tQpzePFF19E586dcerUKfTt29dSoduN+vZxzVu+uri4YNeuXfj1119x6tQpTf/z58/DyckJbdq0wfvvvw+JRFLnNOT71+ns7AxfX1/06tULcXFxOH78uLnStRpj38vAvbuNODlp/1g7OzsD+N9fbqjpGjBggM54FxYWhmeffRanTp3SvFdIm75bWP/2229IS0vTajt48CDatWsHAPWOaTk5OXBycsKiRYt0lv3yyy9o1qwZRo0aZda8bEVt41Z1dbWVIiJTaMjvMFslhMDMmTOxc+dOfPXVVwgKCqr3NTdu3MDly5dNeoqRvs9Xbdq0weTJk3H16lW0b99e73hV85Geng5Ad2zz9vZGREQENm3ahJ9++gn+/v4ICgqCXC7XHMfff/8dMpkMu3fvxoMPPqiJTaFQGLTtqKgoAMCkSZPQrFkznRxVKhXWrVuH8PBw+Pj4wN3dHV27dsWCBQtw48aNBu+7DRs2oHXr1vVe89Ucx81Sah4r4N61mXJycjQ/c6GhoZBKpVp9CgsLcebMGbv7uXRUDRlv9Dl58qTNvI+VSqVmTNEnPDxc53dFVlYWwsLCIJVKTRZHzTFUIpGgVatWiIqKwr/+9S+9rzF07NDHlo6BIeODPrUdG2uNF+qi2YULF3DgwIEGFVaFEDh16pTxx6bRtxewQRkZGUIqlYq0tDTx448/ivj4eOHp6Sl+/fVXIYQQCxYsEBMmTKj19byrZv3q2scbNmwQAERERIT4+uuvxaFDh8Trr78uJBKJkMlkIjc3V+Tk5Ijg4GAxbdo0IYQQvXr1EgAEAHH8+HG92wQgRo0aJb7++muRm5sr9u3bJ9555x3Rs2dPAUC8/PLLltwFFmHse3nDhg3CxcVFrF27Vvzyyy/i2LFjIiwsTDzyyCPWSoFsHO+qWb+vv/5a6/HXv/5VyGQy4ezsLOLi4sQ///lPoVAohLu7u9iyZYsQwrAxbfbs2cLZ2Vl88803mraqqirRv39/0apVK1FcXGyR/KwtNjZWPPjgg+Jf//qXKCgoEDt37hQtW7YU8+bNs3Zo1Ej1/Q6zFy+99JLw8fERhw8fFoWFhZpHeXm5EOLenYrnzJkjcnNzRUFBgTh06JAIDw8XDz74oCgtLTVZHOrPVxs2bBBff/21+Oqrr4RCoRAymUwEBQWJY8eOaY1VU6dOFQDEvn37tNrVY4v6c9W4cePEmjVrxIoVK0SHDh0EAOHm5qY5TsuWLRM+Pj5i586d4tVXX9WMbVOnTtXEdvnyZa1t7Ny5UwAQs2bN0mo/e/asEOLez72np6dWfnfu3BGRkZHC2dlZvPTSS+LLL78UX331lXjrrbeEr6+vCAwMFP/5z3+M3m9VVVWibdu2Yv78+VrtljpuplRWViZOnjwpTp48KQCI5cuXi5MnT2ruKHf/sTp9+rQYN26c8Pf318rnxRdfFG3atBEHDhwQ3333nXjyySdFr169xN27d62VFt2nvvFGCN3P/ytWrBCZmZni/Pnz4syZM2LBggUCgNixY4c1UhBz5swRhw8fFhcvXhTHjx8XQ4cOFV5eXrV+f7l48aLw8PAQr7zyivjxxx9FWlqakEqlYvv27SaNq+YYmpubK3bu3CmefPJJAUDs3r1bq39tY4e+HGzhGJhifJgwYYLWna///e9/C2dnZ7Fs2TLx008/iWXLlgkXF5daP9uaMweVSiWGDx8u2rRpI06dOqX186FUKmvNQaFQiH379olffvlFnDx5UkyePFm4uLhoff42hEMWzoQQYs2aNaJdu3bC1dVVPPzwwzq3DI+MjKz1tSycGaa2fawelB5++GGt/jNnzhQAhFQqFW3atBEJCQmivLxc5OXlCQBiyJAhAoB4/vnn9W4PgJgxY4ZO+927d8WUKVMEALF27VrTJ2plxr6X33//fdGtWzfh7u4u/P39xbPPPiuuXLli4ajJXrBwZjz1F74vvvhChISECJlMJrp06SLWr18vhBAGj2nl5eWiU6dOokuXLqKiokIIIURKSopVP+xaQ2lpqZg9e7Zo27atcHNzEx06dBCLFy/W+hBE9quu32H2Ql0oqvnYsGGDEOLez3JMTIxo1aqVkEqlom3btiI2Nlb89ttvJo1D/fkqLy9Pq/21114TADSFe7UlS5YIAOK///1vrXnNmDFDjBkzRvj7+wupVCpat24tAIiwsDBNv+rqarFkyRIhl8uFRCIRUqlUhISECB8fH60v8/crKCgQAMTbb7+td7m+wtkLL7wgAIiMjAyd/ufOnRM+Pj6ie/fuRhd49u/fLwCIc+fOabVb6riZ0qFDh/S+F2NjY4UQ2sdKJpOJxx9/XJw+fVprHRUVFWLmzJmiefPmwt3dXQwdOtSmc25q6htvhND9/J+SkiI6duwo3NzchK+vr3j00UfFl19+afng/7/7x5SAgAAxcuRITdFcCP3fXw4fPix69+4tXF1dRfv27cW6detMHldtY2h5ebmQyWRi3LhxWu21jR36crCFY2CK8SEyMlLTX+2zzz4TnTt3FlKpVHTp0sWsn1HrykH9e0Xf49ChQ7XmEB8fL9q2bStcXV1Fq1atRExMjMjNzTU6NoctnJH11DYoffnllwKASE5O1mp/8cUXBQBx+vRpERERIby8vMSdO3d01ltb4UyIewNey5YtRVBQkOkSISLSQ98XvvsZOqYJIURubq5wcnISr7zyijh9+rSQyWTi2WefNVfoRGTH6vt89dZbb2m1G1o4q6lVq1aic+fOOu3Hjx8XAMScOXPE+vXrBQCxefNmves2tnBWWFgoXFxcxKBBg/T2F0KIpKQkAcDks1CIqGmobQytrq4WXl5eYuLEiVaKjOyBw13jjGxXQUEBAGjdvbSiogLbtm1Dnz59EBISgilTpqCsrAyfffaZUet2d3fHwIEDUVBQwJsNEJHVGDumhYeHY+7cuXjvvfcwfPhwtGjRAqtWrbJw1ERkz37++WcAQKtWrRq9rlu3buGPP/7Qe6d59d25p0yZgrFjx8LDw8Nkd+w+dOgQ7t69ixEjRtTaR72s5vV2iIiMUVVVhbt370KlUuHKlSuIj4/HnTt3MH78eGuHRjaMhTMyG/WgdPv2bezfvx9Lly7F448/juHDh2v6bN++Hbdu3cLUqVMB3LsteLNmzRr0QUx9Ue5r166ZJgEiIiM1ZExLTEyEl5cXCgoK8P7778PX19dS4RKRHbr/89WXX36JpUuXwsvLS+vzlaGEEJovkBcuXMDEiRPh4eGBJUuWaPUrLy/Hp59+in79+qFbt27w8vLCM888g5ycHPzyyy+Nzkl9R+K6LoSuXsa7FxNRY/Tr1w9SqRSurq4IDAzEhx9+iNWrV2PQoEHWDo1sGAtnZDbqQcnLywtPPfUUfH198fnnn8PFxUXTJy0tDe7u7hg7diwAoFmzZnjmmWdw9OhRo28RK3jXSCKysoaMaRs2bMCtW7fg5OTEmRREVK/7P18NHToUcrkce/fuhZ+fn9HrWrt2reYLZKdOnbB3715s27YNoaGhWv3++c9/orS0FFOmTNG0TZkyBUIIbNiwodE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      "text/plain": [
       "<Figure size 1500x800 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(figsize = (15,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "kBDyQD7u-oTM"
   },
   "outputs": [],
   "source": [
    "df_cleaned = df.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UYqbQoHLlKa_"
   },
   "source": [
    "## **Transforming the Data**\n",
    "The data is not normally distributed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "PcHdFhEpkag5"
   },
   "outputs": [],
   "source": [
    "# Columns to transform\n",
    "columns_to_transform = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'LSTAT', 'B', 'MEDV']\n",
    "\n",
    "# Apply log transformation to the selected columns\n",
    "for column in columns_to_transform:\n",
    "    df[column] = np.log1p(df[column])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 890
    },
    "id": "Gfo_Smr-mkOC",
    "outputId": "5bfa0e8d-ec65-4993-918c-25905437fc36"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'CRIM'}>, <Axes: title={'center': 'ZN'}>,\n",
       "        <Axes: title={'center': 'INDUS'}>,\n",
       "        <Axes: title={'center': 'CHAS'}>],\n",
       "       [<Axes: title={'center': 'NOX'}>, <Axes: title={'center': 'RM'}>,\n",
       "        <Axes: title={'center': 'AGE'}>, <Axes: title={'center': 'DIS'}>],\n",
       "       [<Axes: title={'center': 'RAD'}>, <Axes: title={'center': 'TAX'}>,\n",
       "        <Axes: title={'center': 'PTRATIO'}>,\n",
       "        <Axes: title={'center': 'B'}>],\n",
       "       [<Axes: title={'center': 'LSTAT'}>,\n",
       "        <Axes: title={'center': 'MEDV'}>, <Axes: >, <Axes: >]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x800 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(figsize = (15,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "w6ABmpt1LA_k"
   },
   "source": [
    "## **Model Building**\n",
    "### **Linear Regression Model**\n",
    "**Import the necessary libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "ebnjtesZLyDL"
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Metrics for testing the model\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "JUXKy9skPHvb"
   },
   "outputs": [],
   "source": [
    "reg = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "ccTc04pUMadh"
   },
   "outputs": [],
   "source": [
    "df_x = df.drop(['MEDV'], axis = 1)\n",
    "df_y = df['MEDV']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "8O_CtiUZLt11"
   },
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(df_x, df_y, test_size = 0.20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 80
    },
    "id": "Pz3u5V9rN9YP",
    "outputId": "ef4d8cc1-3e9d-41f3-e8c0-6153956b1cf3"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "IWnJIQEjOs_m",
    "outputId": "52e30104-3175-4a8a-b72e-172e969757ae"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.6, 3.4, 3.5, 3.3, 3.3, 2.8, 2.9, 2.7, 2.7, 3.6, 2.9, 2.6, 2.9,\n",
       "       2.6, 3. , 3.9, 2.9, 3.1, 3.4, 2.8, 3. , 3. , 2.5, 3.5, 2.9, 2.9,\n",
       "       3.1, 3.3, 2.8, 2.5, 2.7, 2.5, 3.3, 3. , 3.3, 3.1, 3.1, 3.2, 3.7,\n",
       "       3.3, 2.7, 2.6, 3.1, 3.3, 3.9, 3. , 2.3, 2.8, 3. , 3.8, 3.1, 3.1,\n",
       "       2.7, 3.6, 2.7, 3. , 3.6, 3. , 2.5, 2.7, 3.3, 3.3, 2.6, 3.1, 2.9,\n",
       "       3.4, 3. , 3.8, 2.9, 2.8, 2.3, 3.1, 3.5, 3.4, 3. , 3.4, 3.6, 2.6,\n",
       "       3.3])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted = np.round(reg.predict(x_test), 1)\n",
    "y_predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nBWcEFOVPGL8",
    "outputId": "e1ecc95e-fc40-4170-9467-96d60d6d853f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.7, 3.5, 3.7, 3. , 3.2, 2.7, 3.2, 2.9, 2.7, 3.8, 2.7, 2.6, 3.1,\n",
       "       2.5, 3.1, 3.9, 2.9, 3. , 3.2, 2.9, 3.1, 3. , 2.7, 3.2, 3. , 3. ,\n",
       "       3. , 3.2, 3. , 2.1, 2.6, 2.5, 3.1, 3. , 3.2, 3. , 3. , 3.1, 3.6,\n",
       "       3.3, 2.7, 2.2, 3.1, 3.2, 3.9, 2.9, 2.3, 3. , 3.1, 3.9, 3.2, 3.1,\n",
       "       2.6, 3.8, 2.8, 3.1, 3.6, 3.1, 2.7, 2.1, 3.3, 3.3, 2.2, 3. , 3. ,\n",
       "       3.4, 3. , 3.9, 3. , 2.7, 2.3, 3.2, 3.6, 3.5, 3.1, 3.3, 3.4, 2.8,\n",
       "       3.2])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.round(np.array(y_test), 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "L6PLqmsYBT0t",
    "outputId": "d9c98ed6-a5c4-4810-ed6b-8ce769fde0f6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.1573609  -0.00505929 -0.00268174  0.12392862 -1.25269888  0.37834077\n",
      "  0.01868627 -0.28513895  0.1879008  -0.13015735 -0.63440647  0.04354214\n",
      " -0.40536369]\n"
     ]
    }
   ],
   "source": [
    "print(reg.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1QIyouPsBWyB",
    "outputId": "6cbd8dfd-a9ba-473a-bedd-dd4c0210605c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.379120363679073\n"
     ]
    }
   ],
   "source": [
    "print(reg.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DVC4Nc7_PbhC",
    "outputId": "f241c028-0e5a-421d-b9eb-6209807e4843"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.02565637352079923"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(y_test, y_predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "a1lyVWkRBGwe",
    "outputId": "f9af9407-d120-4ed2-deba-f05d16aa7ea2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.846094856826124"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test, y_predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rmC1gFHECQus",
    "outputId": "c18a6639-7c13-4aa2-d298-b0d94259095e"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\perpe\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([5.90703768])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict([[3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]) # Assuming all other 12 features are 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HGEXY9jj-1tt"
   },
   "source": [
    "## **Decision Trees**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "WoDPziPGDCx6"
   },
   "outputs": [],
   "source": [
    "df = df_cleaned.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "aH74bCvzS56b"
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Know the max_depth and min_samples_leaf needed for the decision tree\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# Checking for the perfomance metrics of the decision tree\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "Vf531bThEL-F"
   },
   "outputs": [],
   "source": [
    "df_x = df.drop(['MEDV'], axis = 1)\n",
    "df_y = df['MEDV']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "id": "6HHWotTTEW1q"
   },
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(df_x, df_y, test_size = 0.20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8bVI8TvTHgp0",
    "outputId": "a9c10da3-32cc-400f-af4a-378570611119"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters: {'max_depth': 10, 'min_samples_leaf': 8}\n"
     ]
    }
   ],
   "source": [
    "param_grid = {\n",
    "    'max_depth': [1, 5, 10, 15],\n",
    "    'min_samples_leaf': [1, 2, 4, 8]\n",
    "}\n",
    "\n",
    "grid_search = GridSearchCV(DecisionTreeRegressor(), param_grid, cv=5)\n",
    "grid_search.fit(x_train, y_train)\n",
    "\n",
    "print(\"Best Parameters:\", grid_search.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "id": "ivQv61m1DCWB"
   },
   "outputs": [],
   "source": [
    "reg = DecisionTreeRegressor(criterion = 'squared_error', max_depth = 10, min_samples_leaf = 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 80
    },
    "id": "kYNuBL_CEeEE",
    "outputId": "8dfc19ae-daff-4d6b-ab89-151f58d32892"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor(max_depth=10, min_samples_leaf=4)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor(max_depth=10, min_samples_leaf=4)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeRegressor(max_depth=10, min_samples_leaf=4)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "odUP80p6Ennr",
    "outputId": "de10eab7-ff09-4c2c-f76c-b96d0c76f9ea"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([13.8, 23.2, 15.2, 20.3, 19.3, 14.1, 37.1,  7.2, 21. , 15.9, 45.2,\n",
       "       20.5, 21. , 21. , 30.8, 11.7, 48.3, 24.1, 24. , 30.8, 12.7, 19.4,\n",
       "       17.8,  8.7, 16.2, 26.9, 19. , 13.8, 21.8, 35.9, 38.7, 20.3, 15.9,\n",
       "       15.9, 11.1, 16.9, 21. , 35.9, 37.1, 26.1, 19.4, 13.8, 12.2, 13.4,\n",
       "       45.2, 12.7, 21. , 30.8, 26.1, 20.3, 13.8, 30.8, 23.2, 16.2, 20.4,\n",
       "       11.1,  7.2, 21.3, 32.1, 21. , 26.4, 21. , 20.9, 15.2, 14.1, 24.1,\n",
       "       15.2, 24.3, 21. , 38.7, 50. ,  8.7, 20.4, 21. , 21. , 20.9, 19. ,\n",
       "       11.1, 23.2])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted = np.round(reg.predict(x_test), 1)\n",
    "y_predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "87W0-sWeEnjw",
    "outputId": "3bc4cd8f-12f1-4112-8906-e37e9a923b42"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([13.5, 19. , 20. , 18.7, 19.9, 13. , 38.7, 10.4, 18.5, 17. , 43.1,\n",
       "       24.5, 20.6, 22.6, 22.8,  8.1, 50. , 23.1, 25.3, 28.5, 17.9, 21.1,\n",
       "       19.5,  8.4, 12.6, 23.1, 19.8, 13.1, 22.8, 37.3, 10.4, 21. , 13.1,\n",
       "       19.4,  5. , 20. , 19.1, 37.9, 35.2, 23.9, 20.5, 16.6,  8.3, 15.6,\n",
       "       50. , 17.2, 22. , 32. , 28.6, 19.6, 19.1, 29.8, 21. , 18.1, 21.4,\n",
       "       13.4,  5. , 17.5, 33.2, 18.9, 29.6, 23.8, 22.4, 27.1, 10.9, 24.4,\n",
       "       15. , 24.1, 28.7, 34.9, 41.7,  8.8, 19.4, 25. , 22. , 24.4, 20.1,\n",
       "       10.8, 23.9])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.round(np.array(y_test), 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "p5LvZV2sEngA",
    "outputId": "1263c2b8-5fd1-4b7c-c5a6-ffb1f5597567"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20.534050632911395"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(y_test, y_predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PRPsMakwLMTZ",
    "outputId": "ce7fb214-aac7-41fe-e0b3-6d16697f4612"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7580231885945063"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test, y_predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 642
    },
    "id": "QOPdd9ULIlki",
    "outputId": "96295593-e093-450d-cc24-34ee1afa6133"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import plot_tree\n",
    "plt.figure(figsize = (20,10))\n",
    "plot_tree(reg, filled = True, max_depth = 3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3lHcZgjwLiuX"
   },
   "source": [
    "## **Random Forest**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "id": "jnwfhJCuMj9x"
   },
   "outputs": [],
   "source": [
    "df = df_cleaned.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "id": "cU8dT2PfMq-M"
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Library to use for the Hyperparameters for the RandomForestRegressor\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "# Checking for the perfomance metrics of the decision tree\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "id": "nWLwpVKcMrBr"
   },
   "outputs": [],
   "source": [
    "df_x = df.drop(['MEDV'], axis = 1)\n",
    "df_y = df['MEDV']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "id": "W2DF_4AWMrFo"
   },
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(df_x, df_y, test_size = 0.20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "v1r7Lk3OOcvs",
    "outputId": "fe5bd48c-1d71-429f-dfa2-2e31c775dbab"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': 100, 'min_samples_split': 2, 'min_samples_leaf': 1, 'max_depth': 20, 'bootstrap': True}\n"
     ]
    }
   ],
   "source": [
    "# Knowing the hyperparameters for the Random Forest\n",
    "hyper_parameters = RandomizedSearchCV(\n",
    "    estimator=RandomForestRegressor(), # Add the estimator here\n",
    "    param_distributions={ # Pass n_estimators within param_distributions\n",
    "        'n_estimators': [100],\n",
    "        'max_depth': [int(x) for x in np.linspace(10, 120, num = 12)],\n",
    "        'min_samples_split': [2, 6, 10],\n",
    "        'min_samples_leaf': [1, 3, 4],\n",
    "        'bootstrap': [True, False]\n",
    "    }\n",
    ")\n",
    "\n",
    "hyper_parameters.fit(x_train, y_train)\n",
    "print(hyper_parameters.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "id": "JXXNHYBzNav2"
   },
   "outputs": [],
   "source": [
    "forest = RandomForestRegressor(n_estimators = 100, max_depth = 10, min_samples_split = 2, \n",
    "                               min_samples_leaf = 1, bootstrap = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 80
    },
    "id": "xrb1p9uEOqq8",
    "outputId": "a3bdf8b1-6748-47d6-c1ab-60fa019fee5d"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(max_depth=10)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(max_depth=10)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(max_depth=10)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fWufWvJYOtmG",
    "outputId": "86311bef-32b9-457a-ca0e-9183a5838324"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12.2, 26.9, 13.8, 17.8, 32.1, 20.5, 19. , 20.7, 21. , 15.9, 24.1,\n",
       "       13.8, 19.3, 11.7, 21.3, 21.8, 14.1, 19.3, 21. , 48.3, 21.8, 12.4,\n",
       "       35.9, 28.9, 24. , 17.8, 24. , 19.4, 25.7, 19.3, 16.2, 24.1, 24. ,\n",
       "       28.9,  8.7, 12.7, 21. , 16.2, 20.3,  8.7, 13.4, 22. , 17.8, 12.2,\n",
       "       21.4, 24. , 48.3, 11.1,  7.2, 19. , 28.3, 45.2, 20.9, 13.8, 12.7,\n",
       "       26.1, 23.2, 25.7, 17.6, 21. , 20.4, 12.2, 45.2, 22. , 28.9, 15.9,\n",
       "       13.4, 15.2, 30.8, 23.2, 24.1, 24.1, 16.2, 16.2, 19.3, 15.2, 14.1,\n",
       "       17.8, 26.9])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted = np.round(reg.predict(x_test), 1)\n",
    "y_predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wVWySO1OP37L",
    "outputId": "ee43b70a-5cbe-4868-b679-8d818689145d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([16.3, 28.7, 13.9, 17.4, 33.2, 22. , 20.1, 20.8, 19.2, 19.4, 23.8,\n",
       "       13.1, 18.6, 12.7, 21.4, 20.9, 13.3, 17.5, 22.6, 50. , 22.8,  9.6,\n",
       "       37.3, 30.1, 24.1, 18. , 25.3, 21.5, 27.5, 19.6, 17.8, 23.3, 22.1,\n",
       "       28.2,  7.2, 13.1, 19.6, 15.6, 20.4,  8.4, 15.6, 23.2, 19.5, 11.7,\n",
       "       20.6, 25. , 50. , 13.4, 10.4, 18.6, 24.3, 45.4, 18.9, 14.8, 17.2,\n",
       "       25. , 19. , 22.2, 16.2, 22. , 19.4,  8.3, 43.1, 21.4, 28.4, 17.4,\n",
       "       14.4, 14.4, 32.4, 23.1, 23.7, 24.4, 15.4, 12.6, 23.2, 20. , 14.1,\n",
       "       19.5, 29.1])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.round(np.array(y_test), 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "brYshtQtP5rX",
    "outputId": "38b28611-e067-4650-a110-446a72874b84"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.5312658227848104"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(y_test, y_predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7j738jR1P5nn",
    "outputId": "9b58ab40-76b3-4e3c-937b-f0d4c8b93e39"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9491127026515177"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test, y_predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 642
    },
    "id": "1__c9YYwQdKn",
    "outputId": "07b1e56c-76d2-4322-8d32-6e2bd6e68d5b"
   },
   "outputs": [
    {
     "data": {
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AAAAAAAAAAACfoREBAAAAAAAAAAB8hkYEAAAAAAAAAADwGRoRAAAAAAAAAADAZ2hEAAAAAAAAAAAAn6ERAQAAAAAAAAAAfIZGBAAAAAAAAAAA8BkaEQAAAAAAAAAAwGdoRAAAAAAAAAAAAJ+hEQEAAAAAAAAAAHyGRgQAAAAAAAAAAPAZGhEAAAAAAAAAAMBnaEQAAAAAAAAAAACfoREBAAAAAAAAAAB8hkYEAAAAAAAAAADwGRoRAAAAAAAAAADAZ2hEAAAAAAAAAAAAn6ERAQAAAAAAAAAAfIZGBAAAAAAAAAAA8BkaEQAAAAAAAAAAwGdoRAAAAAAAAAAAAJ+hEQEAAAAAAAAAAHzm/wNqQgCMBUHmYQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Random Forest Plot\n",
    "plt.figure(figsize = (20,10))\n",
    "plot_tree(forest.estimators_[0], filled = True, max_depth = 3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rUNi4A-CTKSJ"
   },
   "source": [
    "## **Support Vector Machine**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "id": "ACcypg_UTJpC"
   },
   "outputs": [],
   "source": [
    "df = df_cleaned.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVR\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Library to use for the Hyperparameters for the Support Vector Regressor\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "# Checking for the perfomance metrics of the decision tree\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_x = df.drop(['MEDV'], axis = 1)\n",
    "df_y = df['MEDV']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(df_x, df_y, test_size = 0.20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Knowing the hyperparameters for the Random Forest\n",
    "hyper_parameters = RandomizedSearchCV(\n",
    "    estimator=SVR(), # Add the estimator here\n",
    "    param_distributions={ # Pass n_estimators within param_distributions\n",
    "        'kernel': ['linear', 'poly', 'rbf', 'sigmoid'],\n",
    "        'C': [0.1, 1, 10, 100],\n",
    "        'gamma': ['scale', 'auto']\n",
    "    }\n",
    ")\n",
    "\n",
    "#hyper_parameters.fit(x_train, y_train)\n",
    "#print(hyper_parameters.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_reg = SVR(kernel = 'linear', C = 10, gamma = 'scale')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "SVR(C=10, kernel='linear')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_reg.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 7.2, 20.9, 19. ,  8.7, 20.3, 16.2, 38.7, 21. , 24. , 26.1, 37.1,\n",
       "       12.2, 19.4, 22. , 16.2, 15.5, 24.3, 23.2, 26.1, 19.4, 24. , 28.3,\n",
       "       28.9, 14.1, 20.8, 20.5, 21. , 30.4, 21. , 19. , 19.4, 19.3, 28.3,\n",
       "       20.9, 19.4, 13.4, 12.2, 11.1, 15.4, 35.9, 13.8, 19.3, 15.2, 30.8,\n",
       "       13.4, 20.7, 30.8, 20.3, 15.2, 34.3, 37.1, 13.4, 24.1, 19. , 21. ,\n",
       "       34.3, 24. , 16.9, 17.6, 28.9, 26.4, 24.1, 26.9, 37.1, 21.3, 26.9,\n",
       "       13.8,  7.2, 45.2, 13.4, 11.1, 45.2, 35.9,  7.2, 12.4, 22.9, 24. ,\n",
       "       28.3, 14.1])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted = np.round(reg.predict(x_test), 1)\n",
    "y_predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10.4, 22.4, 20.1,  8.4, 21. , 15.6, 37. , 22. , 25.3, 26.6, 42.8,\n",
       "       13.8, 19.5, 23.2, 16.7, 16.7, 23.9, 21. , 26.2, 21.5, 25. , 28.1,\n",
       "       29. , 13. , 21.9, 18.5, 20.6, 29.9, 22. , 19.5, 21.1, 17.5, 24.5,\n",
       "       23.7, 19.3, 14.4, 11.5, 12.8, 15. , 37.9, 13.6, 18.2, 15.2, 29.8,\n",
       "       11.8, 18.3, 30.1, 20.3, 15. , 35.4, 43.8, 14. , 24.4, 20.2, 22.3,\n",
       "       34.9, 24.1, 20. , 16.2, 30.1, 29.6, 23.7, 25. , 39.8, 21.2, 23.1,\n",
       "       13.9,  7. , 43.5, 13.4, 11.3, 44. , 37.3,  8.5, 14.2, 23.3, 23.2,\n",
       "       24.3, 14.1])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.round(np.array(y_test), 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.192278481012658"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(y_test, y_predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9568593006303481"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test, y_predicted)"
   ]
  }
 ],
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