samar_real_estates.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Samar Real Estate - House Price Predictor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = pd.read_csv(\"data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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</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</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</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</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</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>5.33</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  0.538  6.575  65.2  4.0900    1  296     15.3   \n",
       "1  0.02731   0.0   7.07     0  0.469  6.421  78.9  4.9671    2  242     17.8   \n",
       "2  0.02729   0.0   7.07     0  0.469  7.185  61.1  4.9671    2  242     17.8   \n",
       "3  0.03237   0.0   2.18     0  0.458  6.998  45.8  6.0622    3  222     18.7   \n",
       "4  0.06905   0.0   2.18     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   5.33  36.2  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "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     506 non-null    float64\n",
      " 1   ZN       506 non-null    float64\n",
      " 2   INDUS    506 non-null    float64\n",
      " 3   CHAS     506 non-null    int64  \n",
      " 4   NOX      506 non-null    float64\n",
      " 5   RM       501 non-null    float64\n",
      " 6   AGE      506 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    506 non-null    float64\n",
      " 13  MEDV     506 non-null    float64\n",
      "dtypes: float64(11), int64(3)\n",
      "memory usage: 55.5 KB\n"
     ]
    }
   ],
   "source": [
    "housing.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CHAS\n",
       "0    471\n",
       "1     35\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['CHAS'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>501.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.613524</td>\n",
       "      <td>11.363636</td>\n",
       "      <td>11.136779</td>\n",
       "      <td>0.069170</td>\n",
       "      <td>0.554695</td>\n",
       "      <td>6.284341</td>\n",
       "      <td>68.574901</td>\n",
       "      <td>3.795043</td>\n",
       "      <td>9.549407</td>\n",
       "      <td>408.237154</td>\n",
       "      <td>18.455534</td>\n",
       "      <td>356.674032</td>\n",
       "      <td>12.653063</td>\n",
       "      <td>22.532806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.601545</td>\n",
       "      <td>23.322453</td>\n",
       "      <td>6.860353</td>\n",
       "      <td>0.253994</td>\n",
       "      <td>0.115878</td>\n",
       "      <td>0.705587</td>\n",
       "      <td>28.148861</td>\n",
       "      <td>2.105710</td>\n",
       "      <td>8.707259</td>\n",
       "      <td>168.537116</td>\n",
       "      <td>2.164946</td>\n",
       "      <td>91.294864</td>\n",
       "      <td>7.141062</td>\n",
       "      <td>9.197104</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>0.385000</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>0.320000</td>\n",
       "      <td>1.730000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.082045</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.190000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.449000</td>\n",
       "      <td>5.884000</td>\n",
       "      <td>45.025000</td>\n",
       "      <td>2.100175</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>279.000000</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>375.377500</td>\n",
       "      <td>6.950000</td>\n",
       "      <td>17.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.256510</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.690000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.538000</td>\n",
       "      <td>6.208000</td>\n",
       "      <td>77.500000</td>\n",
       "      <td>3.207450</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>19.050000</td>\n",
       "      <td>391.440000</td>\n",
       "      <td>11.360000</td>\n",
       "      <td>21.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.677083</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.624000</td>\n",
       "      <td>6.625000</td>\n",
       "      <td>94.075000</td>\n",
       "      <td>5.188425</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>666.000000</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>396.225000</td>\n",
       "      <td>16.955000</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  506.000000  506.000000  506.000000  506.000000  506.000000  501.000000   \n",
       "mean     3.613524   11.363636   11.136779    0.069170    0.554695    6.284341   \n",
       "std      8.601545   23.322453    6.860353    0.253994    0.115878    0.705587   \n",
       "min      0.006320    0.000000    0.460000    0.000000    0.385000    3.561000   \n",
       "25%      0.082045    0.000000    5.190000    0.000000    0.449000    5.884000   \n",
       "50%      0.256510    0.000000    9.690000    0.000000    0.538000    6.208000   \n",
       "75%      3.677083   12.500000   18.100000    0.000000    0.624000    6.625000   \n",
       "max     88.976200  100.000000   27.740000    1.000000    0.871000    8.780000   \n",
       "\n",
       "              AGE         DIS         RAD         TAX     PTRATIO           B  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean    68.574901    3.795043    9.549407  408.237154   18.455534  356.674032   \n",
       "std     28.148861    2.105710    8.707259  168.537116    2.164946   91.294864   \n",
       "min      2.900000    1.129600    1.000000  187.000000   12.600000    0.320000   \n",
       "25%     45.025000    2.100175    4.000000  279.000000   17.400000  375.377500   \n",
       "50%     77.500000    3.207450    5.000000  330.000000   19.050000  391.440000   \n",
       "75%     94.075000    5.188425   24.000000  666.000000   20.200000  396.225000   \n",
       "max    100.000000   12.126500   24.000000  711.000000   22.000000  396.900000   \n",
       "\n",
       "            LSTAT        MEDV  \n",
       "count  506.000000  506.000000  \n",
       "mean    12.653063   22.532806  \n",
       "std      7.141062    9.197104  \n",
       "min      1.730000    5.000000  \n",
       "25%      6.950000   17.025000  \n",
       "50%     11.360000   21.200000  \n",
       "75%     16.955000   25.000000  \n",
       "max     37.970000   50.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # For plotting histogram\n",
    "# import matplotlib.pyplot as plt\n",
    "# housing.hist(bins=50, figsize=(20, 15))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train-Test Splitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For learning purpose\n",
    "import numpy as np\n",
    "def split_train_test(data, test_ratio):\n",
    "    np.random.seed(42)\n",
    "    shuffled = np.random.permutation(len(data))\n",
    "    print(shuffled)\n",
    "    test_set_size = int(len(data) * test_ratio)\n",
    "    test_indices = shuffled[:test_set_size]\n",
    "    train_indices = shuffled[test_set_size:] \n",
    "    return data.iloc[train_indices], data.iloc[test_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_set, test_set = split_train_test(housing, 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(f\"Rows in train set: {len(train_set)}\\nRows in test set: {len(test_set)}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows in train set: 404\n",
      "Rows in test set: 102\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train_set, test_set  = train_test_split(housing, test_size=0.2, random_state=42)\n",
    "print(f\"Rows in train set: {len(train_set)}\\nRows in test set: {len(test_set)}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)\n",
    "for train_index, test_index in split.split(housing, housing['CHAS']):\n",
    "    strat_train_set = housing.loc[train_index]\n",
    "    strat_test_set = housing.loc[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CHAS\n",
       "0    95\n",
       "1     7\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strat_test_set['CHAS'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CHAS\n",
       "0    376\n",
       "1     28\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strat_train_set['CHAS'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 95/7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 376/28"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = strat_train_set.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Looking for Correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MEDV       1.000000\n",
       "RM         0.680857\n",
       "B          0.361761\n",
       "ZN         0.339741\n",
       "DIS        0.240451\n",
       "CHAS       0.205066\n",
       "AGE       -0.364596\n",
       "RAD       -0.374693\n",
       "CRIM      -0.393715\n",
       "NOX       -0.422873\n",
       "TAX       -0.456657\n",
       "INDUS     -0.473516\n",
       "PTRATIO   -0.493534\n",
       "LSTAT     -0.740494\n",
       "Name: MEDV, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = housing.corr()\n",
    "corr_matrix['MEDV'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from pandas.plotting import scatter_matrix\n",
    "# attributes = [\"MEDV\", \"RM\", \"ZN\", \"LSTAT\"]\n",
    "# scatter_matrix(housing[attributes], figsize = (12,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='RM', ylabel='MEDV'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind=\"scatter\", x=\"RM\", y=\"MEDV\", alpha=0.8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Trying out Attribute combinations\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing[\"TAXRM\"] = housing['TAX']/housing['RM']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <th>TAXRM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>0.04819</td>\n",
       "      <td>80.0</td>\n",
       "      <td>3.64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.392</td>\n",
       "      <td>6.108</td>\n",
       "      <td>32.0</td>\n",
       "      <td>9.2203</td>\n",
       "      <td>1</td>\n",
       "      <td>315</td>\n",
       "      <td>16.4</td>\n",
       "      <td>392.89</td>\n",
       "      <td>6.57</td>\n",
       "      <td>21.9</td>\n",
       "      <td>51.571709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>0.01501</td>\n",
       "      <td>80.0</td>\n",
       "      <td>2.01</td>\n",
       "      <td>0</td>\n",
       "      <td>0.435</td>\n",
       "      <td>6.635</td>\n",
       "      <td>29.7</td>\n",
       "      <td>8.3440</td>\n",
       "      <td>4</td>\n",
       "      <td>280</td>\n",
       "      <td>17.0</td>\n",
       "      <td>390.94</td>\n",
       "      <td>5.99</td>\n",
       "      <td>24.5</td>\n",
       "      <td>42.200452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>4.87141</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.10</td>\n",
       "      <td>0</td>\n",
       "      <td>0.614</td>\n",
       "      <td>6.484</td>\n",
       "      <td>93.6</td>\n",
       "      <td>2.3053</td>\n",
       "      <td>24</td>\n",
       "      <td>666</td>\n",
       "      <td>20.2</td>\n",
       "      <td>396.21</td>\n",
       "      <td>18.68</td>\n",
       "      <td>16.7</td>\n",
       "      <td>102.714374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>0.18159</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.38</td>\n",
       "      <td>0</td>\n",
       "      <td>0.493</td>\n",
       "      <td>6.376</td>\n",
       "      <td>54.3</td>\n",
       "      <td>4.5404</td>\n",
       "      <td>5</td>\n",
       "      <td>287</td>\n",
       "      <td>19.6</td>\n",
       "      <td>396.90</td>\n",
       "      <td>6.87</td>\n",
       "      <td>23.1</td>\n",
       "      <td>45.012547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>0.30347</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.38</td>\n",
       "      <td>0</td>\n",
       "      <td>0.493</td>\n",
       "      <td>6.312</td>\n",
       "      <td>28.9</td>\n",
       "      <td>5.4159</td>\n",
       "      <td>5</td>\n",
       "      <td>287</td>\n",
       "      <td>19.6</td>\n",
       "      <td>396.90</td>\n",
       "      <td>6.15</td>\n",
       "      <td>23.0</td>\n",
       "      <td>45.468948</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD  TAX  \\\n",
       "254  0.04819  80.0   3.64     0  0.392  6.108  32.0  9.2203    1  315   \n",
       "348  0.01501  80.0   2.01     0  0.435  6.635  29.7  8.3440    4  280   \n",
       "476  4.87141   0.0  18.10     0  0.614  6.484  93.6  2.3053   24  666   \n",
       "321  0.18159   0.0   7.38     0  0.493  6.376  54.3  4.5404    5  287   \n",
       "326  0.30347   0.0   7.38     0  0.493  6.312  28.9  5.4159    5  287   \n",
       "\n",
       "     PTRATIO       B  LSTAT  MEDV       TAXRM  \n",
       "254     16.4  392.89   6.57  21.9   51.571709  \n",
       "348     17.0  390.94   5.99  24.5   42.200452  \n",
       "476     20.2  396.21  18.68  16.7  102.714374  \n",
       "321     19.6  396.90   6.87  23.1   45.012547  \n",
       "326     19.6  396.90   6.15  23.0   45.468948  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MEDV       1.000000\n",
       "RM         0.680857\n",
       "B          0.361761\n",
       "ZN         0.339741\n",
       "DIS        0.240451\n",
       "CHAS       0.205066\n",
       "AGE       -0.364596\n",
       "RAD       -0.374693\n",
       "CRIM      -0.393715\n",
       "NOX       -0.422873\n",
       "TAX       -0.456657\n",
       "INDUS     -0.473516\n",
       "PTRATIO   -0.493534\n",
       "TAXRM     -0.528626\n",
       "LSTAT     -0.740494\n",
       "Name: MEDV, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = housing.corr()\n",
    "corr_matrix['MEDV'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='TAXRM', ylabel='MEDV'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3bsSiRYvKdcpUMp8QQgipEGUfkVmxYgUuuugiXHDBBSm3d3V1wTCMlNtnz56NqVOn4vXXX894PE3TEAgEUn6KjUrmE0IIIZWhrCMyjz32GN566y387W9/G3Jfb28vFEVBc3Nzyu1tbW3o7e3NeMx77rkHd911V7FPNWFwyXwAkEUBHEiUzKeEVEIIIWR0lG1EZv/+/fjSl76Ehx9+GG63u2jH/cY3vgG/35/42b9/f9GODVDJfEIIIaSSlC2Q6erqwuHDh3HGGWdAkiRIkoT169fjF7/4BSRJQltbG3Rdh8/nS/m9vr4+tLe3Zzyuy+VCY2Njyk8xUcl8QgghpHKULZA5//zzsXXrVmzZsiXxc+aZZ+Lqq69O/Lcsy1i3bl3id7q7u7Fv3z4sXry4XKdNJfMJIYSQClK2HJmGhgbMmzcv5ba6ujqMGzcucfv111+P22+/Ha2trWhsbMQXv/hFLF68uKwrlgAqmU8IIYRUirIvv87mpz/9KQRBwJVXXglN03DhhRfi3nvvLfdpjXrJfCrNTwghhKTHOOe83CdRSoFAAE1NTfD7/UXPlyk1qldDCCFkrMq1/y57HRmSGdWrIYQQQrKjQKZCDa5XI4sCGtwy6lxSol4NIYQQMtZRIFOhqF4NIYQQMjwKZCoU1ashhBBChkeBTAkUY1dsqldDCCGEDK+il19Xm2KvMqJ6NYQQQkh2FMgUUXyVUb1LQotXgWpYeH7bIQDA3ZfNG+a3hxrtejWEEEJItaFApkhKuSt2Z3PuAQwFPYQQQsYSCmSKJL7KqMWrpNzukUUMRHT0+gsPZHJBxfMIIYSMRZTsWyTpVhnppo2BiA6RsZKvMqLieYQQQsYiCmSKJHmVkS+iY39/BO8fCeFIUENANXH/y+8jqBoleW4qnkcIIWSsokCmiO5YOgvL5k3CQERHIBa0NHpktHjlko6O1HrxvGIsZyeEEFKbKEemiBrcMm46bwbWvdOHepeEJq8CRXRiRUE1Rpz0m0nytJYsHo9Nq714HuX9EEIIGQ6NyBRZrz8Ky+ZoTgpigNKOjtRq8TzK+yGEEDIcCmSKrFxbC8SntTgHBiI6OEdVF8+jvB9CCCG5oKmlIouPjjy/7RA4nJGYqGEhrJlYNm9S0UdHkuvG1ELxvPj5HwnqZV3OTgghpDpQIFMCo7G1QLb8kc7m1qI9z2gZ3B6RMYQ1C4poojkpmKn2vB9CCCHFRYFMCeSztUChIyjF3g6h3NK1x7RsHAlpEARW8pEtQggh1YkCmRLKtrVAUDVw51Pb8ep7R2FxDq8i5bwip8cXxbqdfZAEBpckQhaFom2HUA6ZtnewbI6BiA7DsqHGRmKqOe+HEEJI8VEgUwZB1cCV972G9w6HwBiDwADVsPDc1oMAso+oBFUDdz+zHX0BFYwx9IcN1LsltDe6qzZ/JNP2DnUuCbpl4zsXn4IJDa6qzfshhBBSOhTIlMGdT2/He4dDEBiDJDDYHFANG8DwIyqr1nbjjT39YIyBxW4LRJ3iew1uqSrzR4argzN/cjMFMIQQQtKi5dejrMcXxavvHQUASAIDYwyiwCAwBs20EdHNjLVm4lMwTW4ZjR4ZPHY7gxPMBKJGVdaNqdU6OIQQQkqPRmRGWbxgnsickRgxNqwiMMCwkXWDyeQpGK/ivHUh1YTNOQCGD09vrdr8kdFY6UUIIaT2UCAzytqbPPAqElTDSWDltjOuYsX+/+yTxmccgUiegmlwy+hs9kC3bPgjOgSB4TuXzK3a0v35rPQihBBC4mhqaZTFp1EUyRmKMSwOw+KwOcA5wBjPuEt2uikY1bBg2hznz26riY6/s9mDhdNaa6IthBBCSo8CmTK4Y+ksjK93JUZh4jiAZ9/OvpdQrW1FQAghhIwETS2VQUA1EVJNCLH8GA5nNIYD0EyO/3nrAJYvnoaZbQ1DfpemYAghhJDjaESmDHr9UUQNC4w5kYwdC2LiorqFn7wwNnd47vFF0bW3f8imkJluJ4QQMrbRiEwZtDd54JFFBKIGrFgEwxiAWEAjCMDbPf5Ep5088pJtj6VqTfQFMu8ddfN5M3Dvy+/XXHsJIYQUBwUyZdDZ7MH5c9rwx7/tgxWPZGJBDGPO9JFpcXz3me3YeSiY0oHrpo117/TVzB5LcZn2jnpj9zH0h/Waay8hhJDioECmTO5YOgsDYR3PbT3k5MjAqSXT5JFR75IwEDHw5u5+NHnkRAf+7NaDMEwb4+tdKXsSVeseS3GZ9loybI4PjoTR3lhb7SWEEFI8lCNTJg1uGb+6+gxcfkYnGtwSJtS7MH18HRo9MkK6CYCjySOjwS1DFgU0uGW4RAFR3YIYzxKO8cgiNNPKWBG40sUL/bllMeV2Z/sGDkFI/ZhWe3sJIYQUDwUyZXbXpXNx2emd8CgiQpoJzoGzTmiFRxGHdOx1LgkAQ1gzU26P70lUbXssxSUX+ktm2hwCY7BtO+X2am8vIYSQ4qGppTJLt5waAK76zcYhmyiaNodHEaGZzl5EHllE1LAQ1kwsmzepaqdZ4oX+nt/mTLPF26UZFk6cUIf+sF5T7SWEEFI8NCJTIZIr2mbbRPHiU9tx8akdNVcQL1Ohv4c+/2EqAEgIISQjxjnnwz+segUCATQ1NcHv96OxsbHcp5Oz4ZZZx0dwnL2vec0UxstU6I8KABJCyNiSa/9NgUyFy9SB12o9GUIIIQTIvf+mHJkKF59qGixT3RWA6qsQQggZOyiQKYN0oyz5TJ1kqrtC9VUIIYSMNRTIjKJ000EfmdEKzhle/+DYsFNE8WDnSFCHZlpo8Sop93tkEQMRHb1+CmQIIYSMDRTIjKJ000FrthwEONDe5M44RTQ4ABIZQ1izoIgmmr0KdNOGYdmJQIjqqxBCCBkrKJAZJYOng3TLhs0Bw7LBwOCSRMiikHaKKF0AZFo2DgdVDER0aKZzLM45TppYj0Y3va2EEELGBqojM0riZfgVUUCPL4rdR8Lo8UVh2YBlc2jm8aq2ySX4BwdA8e0KJjS4YNkcEd2CZXMIALyKhGMhHavWdpevoUXS44uia29/YgdwQgghJB366j5K4mX4D/pVqIYFgTFIAoNlc3AAAxEjkROTXII/HgANzodRJAGcA+PqXGj0SJBEAYooIKAaidEcAFVXe4WWlRNCCMkHBTKjpLPZgzNPaMGazT0QGIPAAJvHy9kBYc1ERDdh2nxICf74PkTJ2xWENBMAQ6NHglc5/jZ6ZBH9YR3ffWY7dh4KVl0wQMvKCSGE5IOmlkbR5Qs6IIsMHByG5WyE2OyV0eSRwDnQHx5agj/TdgW6acOjiLBsp56hbtmI6CaCmomIbuHN3f0QGNDiVSAw4Plthyp+yinTNFqdS0oZZSKEEELiaERmlARVA09uPgjLBjgHBMbgVURMavIgrJuoc9n4zsWnYP7k5iHTQPGgZv2uIxiI6HBJIi4+tQOGZeHFHYdxNKRDMyxYsSLNAgNavZ6qqzGTaRqNlpUTQgjJhAKZUbJqbTc27DoCtywiqpsAgKBmYl9/GG5ZxLJ5k7B03qS0v5tuh+zOZg+CqoG39vnw3uEQGGMQBSfvRjVsBFQTTUkBQTUEA/E8osHTaMk5Q4QQQkgyCmRGQfKUycQGCb0BFSHVhGnbUA0bS05pz2k358HbFQRUE6pho6PJDSW2fBsMeP9wCCHNhG7aUCQnIKiGYCA+jfb8tkPgcIKvqGENyRmiDSQJIYTEUSAzCpKnTESBobPZA92yoRoWIpqJqxdNLSgJN/m4ySMY9W4JgagBX1RHi1dJGwxUqnTTaPGcIVrRRAghZDAKZEZBuikTRRSgGhY8ilTwKEmmqZgGlwTT4hAZGxIMVLpM02gAsHLNNlrRRAghJAUFMqMg1ymTkRzXsDkkgcG0OTTDwuULOnHTeTPSTsEMDhIqcapm8DQabZRJCCEkHQpkRkm2KZORuPm8GXhj9zF8cCQMm3MIjOHECXW4+bwZmDQoGBg8NSOLAryKiIhuwbDsnKZqyhX00IomQggh6VAgM0qyTZmMxL0vv4/+sI72RhcEQYBt2+gP67j35feHTLcMLjZ3YCCCHp+FOkXC5BZP1qmacuen0IomQggh6VBBvFHW2ezBwmmtRQlikqdbWupcaPLIaKlzpS0gN3hqhnNAN508Gt20wYGsxefiQVAxi+zls59SpsKAYc3EuTMn0GgMIYSMUTQiU8WyTbccDWl4ufswzps1EZ3NQ/dsMiwbNucQGWBxDtOyoYhC2qmaYuenFDq6U6rpOUIIIdWLApkqlm66xbI5DgxEoBo2fvriLvzH+g9w7swJWL54WspjZVGAwBisWF6NJGauN1Ps/JRC91Mq1fQcIYSQ6kWBTBVLtxpqX38YEd2GVxExvt6VEiQMfqwiMYR1G25ZBAMSUzWDV1IVMz+lGKM7g1c0EUIIGbsoR6bK3bF0FpbNmwTL5th9JISI7mxGaVg2Dgc1eBUpkfeyfNFULJs3CZwDAxEdzV4FJ0+sR7NXxkBk6IaVccXMT4mP7rhlMeV2jyxCMy30+mljSEIIIbmjEZkq1+CWccfSWXj1vaPQLZ643bI4/BEdADCxwYWBiI6gZqadmsllqqZY+Sm0+ogQQkgxUSBTA+58ejv2HAtDEADLGZCBDUDgQEg14ZGFlCBh8NRMLlM1xcpPKVZxQMqTIYQQAlAgU/V6fFG8+t5RAIAsCAA4LNsZmbEBmLaNkGbi0tM6i9LhFyM/ZSSjO+WuZ0MIIaSyUCBT5Xr9UVi2Uw/G5oAsMgBIBDMMwPmz2ypqifJIRncKXfFECCGkNlEgU0KjMf3R3uSBV5GgGs5u2rAZJIGBcw4O4ONz2/CTT59ekuceqXxHd2i/JUIIIYOVddXSfffdh1NPPRWNjY1obGzE4sWL8fzzzyfuV1UVK1aswLhx41BfX48rr7wSfX19ZTzj3ARVAyvXbMNVv9mImx9+C1f9ZiNWrtmGoGoU/bniOScuSYBbFmBYNjTThs0BkQFNbiWn582nym650IonQgghg5V1RGby5Mn4wQ9+gJNPPhmcczz00EP4xCc+gc2bN2Pu3Lm47bbb8Nxzz+Hxxx9HU1MTbrnlFlxxxRV49dVXy3nawxrt6Y/4tNGTm3sABogA6lwSmj0y1r3TB0USMj5vNeWcDLfiCWDo2ttPCcBlQgnYhJByYJxzPvzDRk9rayt+9KMf4ZOf/CQmTJiARx55BJ/85CcBAO+88w7mzJmD119/HYsWLUr7+5qmQdO0xL8DgQCmTJkCv9+PxsbGkp9/jy+Kq36zEQJDSiAQUA1wDjx646IhF/lidAA9vig+dd9rsDlHk0eBIgnDPi8ArFyzLRF0uWUnSAjFVhBlC7rK1WnFz7fOJSVWPAVVI1b8z674YKwWVVMwTAipHoFAAE1NTcP23xWTI2NZFh5//HGEw2EsXrwYXV1dMAwDF1xwQeIxs2fPxtSpU7MGMvfccw/uuuuu0TrtIfIp51/MDqDXH4XFOVq8SspoRbZtBArJOSl3p5VuxdP4eheOhXQ0uCkBuBwoAZsQUk5lD2S2bt2KxYsXQ1VV1NfX48knn8Qpp5yCLVu2QFEUNDc3pzy+ra0Nvb29GY/3jW98A7fffnvi3/ERmdGST8G3YnYAhRSayyXoij8uPvJS7k5r8IongOG2P25Bg5sSgMuBErAJIeWWVyDzq1/9Ctdcc82Q4GIkZs2ahS1btsDv9+P//b//h2uvvRbr168v+Hgulwsul6to55evXAu+FbsDKKTQXLbgRxYFPPzGPmzaM5AYeTnzhBa8ubu/Ijqt+Iqnrr39Rd3QkuSn2BuKEkJIvvJatfStb30LHR0d+Od//mf85S9/KcoJKIqCk046CQsXLsQ999yD0047DT//+c/R3t4OXdfh8/lSHt/X14f29vaiPHepxPc/iu9plG4Po1KswBn8vIZl48xpLVi+eFrax2fbQ8mriNiw6wgEBrR4FQgMWLfzMI6FtIpaNZQcjCWjLQ9GB73+hJByyyuQ6e3txf33349Dhw7h4x//OKZPn467774b+/fvL9oJ2bYNTdOwcOFCyLKMdevWJe7r7u7Gvn37sHjx4qI9XynEpz8evXER7r36DDx64yLcfdm8lBySUnQA8ef9z88uxOlTmgEObN7vww0Pbcq4/Dtd0HXOyRMQ0a3EyAsHIAoMHlmAYXGEBh0n0zmPxpLuYm5oSfJHrz8hpNzymlryeDz47Gc/i89+9rP44IMPsHr1ajzwwAO46667cMEFF+D666/HZZddBlnOLenzG9/4BpYtW4apU6ciGAzikUcewcsvv4w//elPaGpqwvXXX4/bb78dra2taGxsxBe/+EUsXrw4Y6JvpclW8K1Yew6l84eN+9C1dyBlJVKmPJZ0VXZ7/VG8+v5ReGUZPb4oQqoJm3Ow2O8EVBOiKGQ859FOCC7WhpakMPT6E0LKacTLrznn+POf/4zVq1djzZo1qKurw+HDh3P63euvvx7r1q3DoUOH0NTUhFNPPRVf+9rX8PGPfxyAUxDvy1/+Mh599FFomoYLL7wQ9957b15TS7ku3yqHfDr8XJc7d+0dwM0Pd0ESBbQm5S0kL8MGkPVY8SXk/WEdqmFBYAwCA0ybw+Yc08Z5YdnIeM6FLukeKapjUl70+hNCiinX/rsodWReeuklPPDAA3jiiSfgcrkwMDAw0kMWTSUHMnHZOoBcg534417Y3osjIQ0iY2jwyGhvdEMUGAzLxrGwhoVTW7DjUHDIsQKqmXIOt//3FqzZ3AOBOVse2BywOYdbFtBa58JPP306AD7knAupo0MIIYQMVvI6Mvv378eDDz6I1atXY9++fTjnnHPw29/+FldeeWWhhxyzMk1B9fii+O4z2/Hm7n40eeSsy53jy6LdkgBRYOAcCESNxPGjhoWobuGNPf1ocsuod0kIayaeefsg3th9bEgxuaVz2/D81kMwbQ7T5hAYQ6NHxrg6BQHVAMCxcFrrkHOmVSyEEEJGU16BjK7reOKJJ/C73/0Of/nLXzBp0iRce+21uO6663DiiSeW6hzHnPjoyrp3+tDn18AYwBiDVzmefJu83HnwUu6IYSeCmGDUQL8iIqKZABjqFQkB1UzkvVg2hz9ioL3JlRIohXUTExrcsG0biiRCFgUokoCAamRNRi6kng0hhBBSqLwCmfb2dkQiEVx88cV45plncOGFF0IQyrrvZM1JHoVxSQIADgaWMroyeHRj8ChIe6MbgBMQWTaHadn48PRWbN7nQ1BzghiBMYgCg2k7M4shzcL4eiFRF2bTngGcOa0FG949AlEUoDAkVqNkS0YuZRIzIYQQMlhegcy3v/1tLF++HBMmTCjV+YxZ6UZhuEuCwBgYY2AAQqoJ3bShmqmjG4NHQUSBobPZg/6wANPiuPfqhWhvcuNT97+G/vDxIMZKSo9SdQu6ZUOJrUYaiOi4fEEn6lxS3qtRaBULIYSQ0ZJXIBMv/f/uu+/iqaeewp49e8AYw/Tp03HZZZfR9NIIxHNcJIEhPgoT0kwn4LCdpc8cgC+qw7L5kNGNOZMa8Obu/pRREM20sWzeJCyc1gIAOLWzCS/4+5xjccCOjcbEj23GApn4NNCJE+tx98wJea9GSbekm0ZiCCGElELeyb733HMPVq5cCc45Jk6cCM45jhw5gq9//ev493//d3zlK18pxXnWtOQcF5csoj/sTCMxOCuF6mKJuZxziIxhybx23LF0VsqKJtWwYFgcx0IaPIoIjywNGQW5fcksvPLeUaiGnUjgdUkMumknHpNu+ihbPZxsCv09QgghJFd5BTIvvfQSvv3tb2PlypX40pe+hJYW55t+f38/fvazn+HrX/86PvzhD+Occ84pycnWquQcF1kUUO+WEIgaiZEStyxAECScdUIrVl4yNxEcJNdraa1T4FUs+FUDC6e2pDwubmZbAy5fMBnPvn0QiiSg3iVBN20cCWqQRAEhzaRpIEIIIVUlr0Dm/vvvxw033IB/+7d/S7m9tbUV3/3ud9Hb24v77ruPApk8Dc5xiSfrBqJGYhTm4vkdKbVjsm06ueNQMONzJeevxAOXT505BcsXTUVQMxPTQD2+KHb1BStiWoimqKoXvXeE1LZK+BvPK5B588038Yc//CHj/cuXL8dnP/vZEZ/UWJNupU+DWwIHHzIKE1dovZbh8leCqoGVa7aN2vYC2Yz2VgekeOi9I6S2VdLfeF5rp/v6+nDCCSdkvH/69Ono7e0d6TmNSek2b7x4fgd+9KnT0gYkI910srPZg4XTWoccO550nLzr9fPbDmHV2u6RNzJPlXQuJD/03hFS2yrpbzyvERlVVaEoSsb7ZVmGrusjPqmxKN+VPqWo15Jtuiq5AF+uxxrJcGMxz4WMLnrvCKltlfY3nveqpf/8z/9EfX192vuCwcy5GSQ3+az0KXa9lmJsL1Cs4Uba6qB60XtHSG2rtL/xvAKZqVOn4re//e2wjyGjo9j1WoqxvUB8uLHeJWXdG2o0zoWUB713hNS2SvsbzyuQ2bNnT4lOg4xEseq1xKernn37IKKGhXqXBNPmOU9XFXO4kbY6qF703hFS2yrtb7zg3a/J6Mo26lKsEZmgasCwnMJ6gaiOo0GnuN5FsaXfw51XsYcbly+aisNBFVsP+KHGIn2qcVMdaJsKQmpbJf2N5xXI/NM//RMeffRRNDU1AQB+8IMf4KabbkJzczMA4NixY/iHf/gH7Nixo+gnOlYl78GkGjbcsoDzZ7clPizFXP62am03/rzzMMbXKxAFhrBmQrNsKJIw5HjpcmHOnNYCWRQSw426acOw7MT9uQ43Dj62KDAsmNyM25fMwsy2hrzbRUYfbVNBSG2rpL9xxnnSzoHDEEURhw4dwsSJEwEAjY2N2LJlS2KPpb6+PnR0dMCyrGyHGVWBQABNTU3w+/1obGws9+nk7ev/83es2XIQpsXBOQdjDJLIcNnpHZBFMZGP4pad+cpQbGgvn3wUwBlZueo3GyEwpAQtAdUA58CjNy5K+ZAmVxVOfu7WOgVHQxp004Zm2rA5wDnHSRPr8T9f+EhOAVamYxfSLkIIIdUp1/47rzoyg2OePGIgUoAeXxTPvt0L3bQhMAZZFCAwZ2+kZ/5+COt29iXyUWTRGTWJ71bd44vm9VzxaSG3LKbc7pFFaKaFXv/x4w3OhZFFAS5ZhCQwBFQDzR4FEd2CZXMIALyKhGMhPaf6AumOPZJ2EUIIqW2UI1PBth7wI6pbEBmDKDAAgMgAzhmihgVJZGj0pA7lFZqPkk8WenIujGVz9AZUhFQTlm2Dc8Alm2hvdMOjiJBEAYooIKAaOSX8VtqyPkIIIZUtrxEZxhgYY0NuI6XCYz/pyYJQcGXfweJZ6CHNREA1YFh2Yifsc2dOSAkekoOe3oCKQNTZrVtgDIwBqmkhYljwKhKUWFCUbmQnnZFWLCaEEDK25DUiwznH5z73ObhcLgBOpd+bbroJdXV1AABN04p/hmPY/MnN8CgiIroFZnMIDLA5YHEOryLinJkTsOHdI0Vb/pZrFnpimfbWgwhGTcRDWQ6g3i0jrJkIqSZ0y04EMvlsnVBJy/oIIYRUtrwCmc9+9rMpIzDXXHNN2seQ4uhs9uCi+R14aksPTJvDjg3OKKKAi+Z34NsXz0ms7inG8rd8stDvWDoLR4IqXtzRBzAGBqDRI6O90Y2DvigCqgF/REezV8k7EKmkZX2EEEIqW16rlqpRta9aSiy/3tmHqGHBI4s4f05byhLrci1/6/FF8an7XoPNOZo8ChTJGX3xRXQMRHQ0emRYNi94WXglLOsjhBBSHrn233kFMtddd92wj2GM4YEHHsj1kCVX7YFMXC6dejk6/vhS6TqXNGQa6KbzZlAgQgghpCC59t95TS2tXr0a06ZNw4IFC2jp9ShL3oZgcMBSrI0aC5FtGqjBLVMAQwghpKTyCmS+8IUv4NFHH8Xu3bvx+c9/Htdccw1aW1tLdW5kkEwBi27aWPdO34g3ahxOuhGfSqruSAghZOzJO0dG0zQ88cQT+N3vfofXXnsNF110Ea6//nosWbKkIpdi18rUEpC+4q1fNWCYNsbXu3KqyJtN6r5JKt7pDWB2eyNmttWXbcRnNFAQVjh67QghpVKSqSUAcLlcuOqqq3DVVVdh7969WL16NW6++WaYpont27ejvr5+RCdO0su0s7RqWAhEjETBvLh8Csglj/SEdQP+iAHTdu5ztiyQIAoMjW457xGfSu7oyjklV+3otSOEVIoRVfYVBAGMMXDOK2p/pVqUqeJtnUvCkaCOsGbCqxx/O/MpILdqbXdipCeQFMQATt0af9SEyIDOZm9iuwSXJKRU6q2kvJ1cJbe7lFNytYheO0JIpcg7kEmeWnrllVdw8cUX41e/+hWWLl0KQcirUDDJQ6YtBEybw6OI0EynEm8+BeR6fFFsPeBL5NcIjMGw0z/W4sDeY2FEDQs2BxgAWWTYfsCP+19+P2PejlsS4JJE2LZdUR1d8giXSxZhWDZcsggO5LSVwliWaXSQXjtCSDnkFcjcfPPNeOyxxzBlyhRcd911ePTRRzF+/PhSnRtJkq3i7cWntkMWxZwLyCWPlgRVA4GoiQaPBGGYFKeAaqb827I5vv+/OxHWzZRv5s9uPQg9FhH5Ise3L1Akhpe6D1dER9frj0I1LOiWjYhPhc05BMbgdYlQRIH2dMqC9sMihFSSvAKZ+++/H1OnTsWJJ56I9evXY/369Wkf98QTTxTl5Eiq4ZY655qPkjwt0FqnIKia8EcM5JurzQHs64+gs9md8s08qlsY0I2UxwqMw9IZLFtL6ejKlUPT3uRBRLcQ0U1IggCROVs/+CMG6lwS7emURT4bjBJCSKmNaIsCMrqGW+qcXGsmk3TTAo0eGf1hHfmsXxNjuVE2hm5rGdaPj9zEPy3OdBSHsxckyymHZqRBTvLvA0hzLA7OAdOyU7bnVA0TYc1Md0gC2g+LEFJZ8i6IR8ovl4Alk3TTAuPqFPgiemIvp1zYnCc6/qMhDU0eBaLAnKka/XjiNwdSNpWURAaAZ00WvWPprBElCicHSaphxc7HySXyyBLOnTkBS+ZOhEcRoZs2dOt4wwU4+UA/eaEb9y8/M/cXZIyh/bAIIZViRKuWSGWKj0Q4IQRPGYUYPC2gm3Zi9EFkTrCRS0CT/BDd5NjXH8bkFi8GwtqQ30/+Z6NbBsDSJosaNscLO3pxJKhh097+glfEJAdJTmDltE8SBNQpwPPbDiGsm5AFATYHJIFBEJyNLzl3grS3e/wVkctTqagQIiGkUlAgU0PiIxEvdR/GsZAOw7Ihiwzj6l342KyJuGPprMS0wHNbD+JoSINm2rBiO2tLAkOjW4IvauQ8zaSIDIokQDVsHA1piOhWyghMMoEBHz15PACeMipk2Ry9ARXBqAHT5nhxRy88ioSJDU79mnxWxAxejRTxqZBiq+kiuoW2Rjc4gE17BnDyxHr0BlSIjEGAE8DZnKPeLTnnREmrw8p3dJACH0JIsVEgU0PiIxGqYUM3LTDGoFscvoieMqJxx9JZeGP3Mbx3OATAyXcRmBNQaKY9JAIRAGRYlQ2LA5IooEUW8bmzT8DDb+yDwBgiujkkGKpzifjqklmwgZRRod6AikA0trpJcEZForqJ3oCa6OxyXRGTPHVmWDZsziHFlmOZNodh2YljXbagE137fFANC6btrFpq9MioU0QwxihptYiqoa4QIaQ6UeGXGhEfiXDJTt6HKAiQRQEiY9BNnlLALqCaUA0bk5o9mD6+DidOrMfMtgbUuURohgXGnNGZepeIJreUMYgBAM45QqoJWRIwY0I9LJujo8mdspoljoHh3pffT4wK+VUDvX4VgaieeEy9S4YUK7QYUk3olvPsua6ISZ46k0QBAmOxkRZnCbgsColjffjEcbh8QScaPBLG1yuY0upBg1tC1LBw7swJNGJQRPEgW2BAi1eBwJwpvlVru8t9aoSQKkeBTI2Ij0RIAovVRHFuF5gzXSIKAjTTQq8/mnhsg0uCV5GgiAJEgWFyixeNHhlNHhntjS5MH1+PJm/2b8s2d5YtnzyxAQAgCsdHYyTBqQAsiwyKKKDBI2P9riPY1ReEbtowTBvHwhpM2znHBreEzmYP6t0SOADTtp0tGFQDYc3MKbhIDpL8ER1uRYBp2zBtG15FhGpaKce6Y+ksXDy/A4okIqQ5501Jq8U1eKWcLApocMuoc0mJ4JoQQgpFU0s1Ij4SEZ8isbmTvBsfibBsO2VEI1MdkAa3jDNPaMHL3YfhOxpCVB9+6wkBQHdvAHc+vR1hzYQey7uRBAbOnamiBo+EBpeEgYiOn7zQjU17BzC+3gWbc/QMRJ3l2YxBFBjaG93QTQuqYSOimfAoUs7BRVA1EkFSIGKAwwmuFFGALLEhgQolrZbeSAvoFfO9ofeZkNpDgUyNSK7toUgCoroJm7NYnQ8Bmmlj2by2xMU7Wx2QO5bOwpX3+fHe4VBOSb8CA9yyCLcswiUK6AuosDlgxIKZRo+McXUKBiJOrZq3e/xwS860j1NNV0JINRGIGmjxyjBtDrcsYskp7bh60dS8Op1Va7ux7p0+jK93QRQYwpoJzbTxsVkTsx5rJEvaSXaFFtArZl4N5eiQakXB9/AokKkh8VGGl7oPw4oltioiQ7NXSaxailu+aCoOB1VsPeCHGutQ4kHMIV8UB30qGHMWcFvDBDM2B7yKs8KoyauACQxHghpEgaHeLSEcNfH+kRBsDrgkBtPiYIzB4k5BuvgqJ9PmOBbS0OhRUioW5ypdsT+vIiGgGti0dwBfvnBW3hcCuoiMXKEF9Iq5MSVtckmqDQXfuaNApoYMniZJV0dm8B+HKDAsmNyM25fMwsw2J8/lKy/+3anOy4cuoU7H4k6HP7XVC8DpqNyygOnj67Fl/wCspGxhzYwdMWmoJ140T2DAaVOa8e9XnFpQ0JDrFEYuwQldRIor3wJ6xdyYkja5JNWIgu/cUSBTg7JNk6T749i0dwB/eH1vIgjaesCfcxATF4rlpogCw4GBCFTDxtYDvpQgJh0n1ELim/ruo5E8njXVcFMYDW4ZK9dsyyk4SX6d6l0SwpqJZ7ceBEAXkULkm4tUrI0pe3xRvNx9GBHdxPh614iORchooeA7PxTIjCE9vijW7eyLrSYSwWOJwMlLs3v90cSS53xY3AkY+sMawroFl+RUDU4WD1qSxf/NmLNVQnxlVa4jJ8mGm8L4w+t7c/qGE7+IyCLDsbAOVbcS5/nk5h4sXzQVM9sb836NSO65SCPdmHLoDu8GooaFaa11EGNL+miTS1KpaIf5/FAgM0YEVQN3P7MdfQEVHEBfQANjTiATrxvzwZEgTpzQ4GwIWcBz+MIaoroFkbFYMbrsjxcFBklgsBIrrbgzcuKSch45GSzTFMbyRVNxw++7cvqG88GRII4EVWjm8TaIsXyhiG7ixy/uwn/QPkwlNdKNKVet7cb/bj0EzbShGRYsDoQ1C7v6gjhxfB00y6ZNLknFoh3m80OBzBixam03Nu7uh500ZRTfV0gUnNyVJzcfxJeXzMLs9gb0BbW8n2NyqxcfHAnDsDkEMFjDhEOcc1ixQsKKxBIrq/6wcV/Bc8PppjAA5DW98OTmgylBDOCMOAkABIFh6wHah2k0FLoxZXxETTOdOkQCY3CJgG5xmDbHvoEIJja4qV4QqVi0w3x+KJAZA+IXdtPiaUML23a2D3hhex82vn8Mps0hCYCZ5wzT4aCWmJYSRAEi5ykrnpKfu04RnSXag1ZWLV88DTc8tGnEc8OdzR40uqXE9EJEN+GLGFANC1OzTC/0+KLYtGcALklA1LBT9o2yAdTLIixO+zCNhkJr/PT6o4joJrRYEOO81wwuxqHHtqj46adPx8JpLaVvBCEFoh3mc0eBTAUp1VLfXn8UQdVAVDchsjTLqRkSWwIoIkNLnQKXJDiF6nJ8DgbAHzUSoxia6QQByXkxDIBHEXHJqR24/qMnIKiZGLyyqmtv/7Bzw/E2Dfc6JSfsjq93QTVshHUzsVN3um848bnp9iY39h6LpIzKMAa4FYGGdkdZvjV+2ps8EAVneb8SL3ENp0yAKMQDm0ImTwkZPVSsM3cUyFSAUi71DaoGHn5jHwJRI2M9GAYgqJoAgIBqIqRZqHdLmNTsxrGwDt0cPt+FAzAsnhK4xP/fLQtQRAE3nTsDn1jQmfWPMdvcsCwKePiNfdi0Z2DY1yld1v/UVi/29UegGhaOhjR401QMjj8/50CzV4E/aoAlHdewOC6cO7J9mOjCVFqdzR6cfdJ4rNnckxhdjO9s7pYFeBWpoEC01t63WmtPraJincOjQKYClLJewKq13diw6wjcioiwNnS7AQYkghQBiO3VBASiBmxbgkcWYdvIaSVTfEuEwdTYFM1jf9uPQ34VyxdNRVAz015As80Nt9Yp2BALToZ7nbYe8COkmWhJ2ivK2U/Kg2MhDbd+fCbOmzUx6/M701/Oppg25/AqIi4+taPgoV2qTTN67rp0Lrb1ONWpDTtefVqAIgl5bwhaa+9brbWHEApkyqyU9QKSjz2xwY33j4SgDUp8iRejAwBJZM5+RwyADYQ0E00eGaad2wSTxQFJiCcRpw7etze6Ydkcj2/ajyc396DOJWa8gKabGz7n5Al4c0//sK9T/CK9bmcf/BEdgaiBRo+M9kY3RIEhaljwKFLaICbd87skAd5GF07tbEopGlgIKnA1ehrcMv7nCx/BnU9tx6vvHYXFObyKlPi85aPW3rdaaw8hFMiUWTwxsc4lQbdsKLHplGLUC0iuRSAKDDMm1OOgz8mX4QBavQpOnFiH9w+HYcVGHmA7O2dzzmFzjhkT69C1R8/5OU0bcEsCbM6hx+ayxHgOjm5Ct2yYNkdbowuWzdNeQNPNDff6o3j1/aOod2Wvq5B8kW5wywioBnwRHabl7H6tWTYunt+R9TUtxdw0FbgqvUQxR3DMn9yMzmYPfvLp00f0Ptba+1Zr7SEEoECmrIKqgYc37oMvYqA/rEMSBNS7JbQ3uotSL2BwvokoMExp9aI/1rHfe/VCtDe5cdVvNoJzp5ZLSDVhxuaHvIqEf/7QFHTt9YHx9CueAGcUxraRSAzWTRtCLMmSARAFJzgLqWZKjZoGt5z1Ajp4bni4ugqDL9JeRQLzA76IEcv9MeFVROimjaBqDDuMXsy5aSpwVTpB1cD3n9uBZ9/uje3WzuFRRFw0vwPfvnjOiN7HWnvfaq09hABOWgQpk1Vru7Hh3SNwywJYrCCcP6JjX38EYc3Mey4/rscXRdfefgDOLtchzURANWBYNgKqAc2wsOSUdiyc1pLICYkaFhrcEqa0ejC+XkGDR8LlCzoxZVwdFFGAwABZZIn/jpMEBlkUochCYorKhjOiE/93vduJl+3Y/koCY5CSRp7i1XyziZ/n4LYkv07xi7RbFgE4OTEsVvCPAZjQ4EK9S8ILO3uxam133q9r8mvb48t+voMlB5XJqMBV4eLvxZ1Pb8eaLQcR0U0IsSKPEd3CU1t6Cn6f42rtfau19hAC0IhM2QzOX+kNqAipJiwAqmFhydy2vOfyg6oxJCdg8YnjcMGciXjt/f6MtQiSc0JCmgmXJOLjp7TjjqWzEFBNjKtXMBDWoZk2ODhEgcGOTRvFx1cYGCSRJVYueRURHAymZaNOEcFiUY3FOZo9cmIKLZ8L6HB1FQaPQOmmjZBqJp67P6wn9qrMd6uBkSZIUoGr4kl+LyK6iYGIAdvmkEWWGP1jtlP8bt3OPtx03oyCX99ae99qrT2EABTIlM3g/JXOZg9000bUsBDRTVx91tS8VhAEVQNX3vca3jscAmMMAnMCohd39uKi+R149MZFGfMEhssJ8SoiDvqcb3As6X8BwLABm9sQBMCMBTGMAS5ZxOITx8GrSHj9g2MIaSbcsgDT5qhTRBiWnfcFNH6eXXsH8E5vALPbG1OKmg2+SHPuBE6WjdioDIMoAJbN895qoBgJklTgqjiS3wuvIjkBKgDLBuKzjkJsBV3UsEY8XVJr71uttYcQCmTKJF29FEUSoJpWQXUu7nx6O947HHKmbWJLqFXDyVpZv+sIbjpvBhZOa816jHS5BKvWdsdqrojQTNupDsyd2hwMTiBjcQ4rNlItMKDRLaPe5QQwy+ZNSgRRDW4Zf3h9b8EX0FxGRZIv0hHdqY3D4EyBxav5MgYwztG1ZwBdeweGrfBarARJKnA1coPfi/iO67bFYcUS1J19u5zHe+SRT5eM1vs2Wp8L+hySWkOBTJmMZIh38AWoa+8AXn7nsLObteAsexYFBthOhd2Ibg77rTTdRS3eaTS6ZTS4Zacq7rFILDFYwLRxXhwJagioRqJqalPSUud4R58cRI3kAprLqMjgi/Qv1r2Hv757BODxvZ2cKQfGgP6Ijpsf7sKS2DRaphGwYidIUoGrwg1+LxRJQINbxkBsVMa0bAgsVtVXFHD+nLaCViql+3yW6n0rV10X+hySWkGBTBkljx4cC2kQBIZzstS5GHzBk0UBXkXEsbCG/ogBwNkYj8FZQi0JDKbtdN5IqU+b+ZjJF9HBnUai3kys/Ltlc0xp9eJYSMMhv4r2Rhda645vyljMlRD5jorEL9LfumgOrri3H6rhLPu2Y1/VGQMEOEnHw00R0U60lSPde9He6IZuWAjrzjal8eKFF83Pr3hhuQIKqutCyMhQIFNGDW4ZdyydhbBmOgm6NsemPQNYtbY77cVz8AXvwEAUPb5oyioiwBmRsThgxRJyI7qF2/64Je1FOdtF9KbzZqR0GpIoJL7tCowlOhLGYiNALPVEBnf0I+koCh0VqXNJ+OhJ4/H6B8cgiwJ8sYAPABo8Mlq9CgKqkXWKqJDRMxq2L41M74VbEbFkbjuWzG1Hch2ZfJQjoKC6LoSMHAUyZRZfgl3vkuCWxYwXzyG5AZbt1GuBk+QoAGk3eBQZMK3VC92yhxx3uIvoTefNGNJpKJKAsG7GlozDWc5t2jhxQh36wzoCqpHoXAKqgbNOOJ6Xk09HMTgQyHdUJDloihomLJtDNy3YnENkDA2xKTAgfTA0+PlzTZCk8u/FkS0QzPZeFPoalyugoLouhIwcBTJllM/Fc/AFz7TsWGKjM/oiCszZN8k+XriOAZjS6oVLFuGSxSHHzeUiOrjTaPbK6Gh2I6JbKZ3IzefNwI9e6Mar7x1FSDVjWyFwdO0bwFW/2YgzT2jBm7tz32IgXSCQz6jIqrXdePbtg3BJAhrdMuoUCQNhHYCI1noFrUltTg6Gsj1/Lvk9NE0wMrkEgqVIVs03oMj03PmeE01bEjJyZQ1k7rnnHjzxxBN455134PF48JGPfAQ//OEPMWvW8W+5qqriy1/+Mh577DFomoYLL7wQ9957L9ra2sp45sWRz8Vz8AUvMc0Ty/ngABRRgMmcnao5d+q9uCSnOJxu2uAcKYm/uVxEM3UaTjl4HwCGE8d7ce/L72PTnoHE0mabc0xscKPOJTnLwLf3QTMtTGn1Zm1rtkAg11GRXX1BPLn5AFTDRhBAf9iARxHhkgUYtomIbkISWNpgaOWabVkDkWwJkjRNMHL5BIL5JKsOF2DkGlBkCrRuPm8G7n35/bxH4qiuCyEjV9ZAZv369VixYgU+9KEPwTRNfPOb38SSJUuwY8cO1NXVAQBuu+02PPfcc3j88cfR1NSEW265BVdccQVeffXVcp56UeTzbSzdBc+Z5rGdjRoBGJYNzjncsYuhS3K2JejxRZ1ie7Fquw+/sQ8z2xryuogmdxpB1cD9SRftsGbBtGxMbHChySPDFzFgc46wbsX2OzIRMSxYNsfuo2G4ZRET611o8MhZtxgAhk51JQdVzpgTR0A1UzqLn7zQjYhuQYzV0zEsG/6onahxM67O2edpcDA00kCEpglGZiSvf6ZAJdepvlz/FjIFWm/sPob+sF7QSBzVdSFkZMoayKxduzbl36tXr8bEiRPR1dWFc845B36/Hw888AAeeeQR/OM//iMA4MEHH8ScOXOwceNGLFq0qBynXTT5fhvLNM0T0kwMhHUYFociiWipU9CpiDga0rCvP4yo4XTi4BxuRcKGXUewam037r5sXkEX0eSLeb1LwrGQDptzhHQLzbGATIzt23TQjiKkmce3L+BO8vGe/ggkAWhwS7j41M7YMvL+nAKBRreE+18+mLZzCqgm3u7xQ2DO9gSmbSdyhzicOjch3cTHZk3E1WdNTen4dvUFRxiIMIADIdVAS9LqLZomyE0hgeBwgUo+IzzD/S1kCrQMm+ODI2G0N7oKCoCprgshI1NROTJ+vx8A0NrqJIh2dXXBMAxccMEFicfMnj0bU6dOxeuvv542kNE0DZqmJf4dCARKfNYjk08gkW2aJ3mEor3J6ezvfGo7nv77wdg0k4D6WIJrWDdTLrD5XESTL+YuWURQdVYBxQOXZo8MgbFYzRYbIc1Z4aRbQ1ORTdsJbDJtMRA3OBDI1jldtqADls1R75YQjBqILdyKvTJAnSKh0S1j054BfHnJrJS2Znt+UWA4EtTSdkrJnWlANaGZFnxRA5OaPNBje0IlJz2T9ArJFxkcVIc1E8++fRCAs+ounxGe4QKKTIGWU4CSQxBSt67LdySO6roQUpiKCWRs28att96Ks88+G/PmOd+Uent7oSgKmpubUx7b1taG3t7etMe55557cNddd5X6dIumkG9jgy94mS6AVy+aig3vHkFdbEVUfH+jdBfYXC+ivf4oVMOCbtmI+FTYPFZgDsfLw9e7nbLxDE4FXc6O75vNGBL7HQkMCGsWdvWFUjawzDZC1bV3AC/s6IVLFtN2Tpct6ITIGBSRwa2ICGtOyeH4aExbrFhfug4m3fOHNRNHQhokgeHOp7ennZpI7kyntHhw0K8iops4MBCJ7f1zPOmZVjBllu8IZfyzIEuCs7u5aiY2Jn1y8wGcMa25oBG2TH8LmQIt045VE7ZTg3UaiSNkdFRMILNixQps27YNr7zyyoiO841vfAO333574t+BQABTpkwZ6emVXCm+jbU3eeBVJDAgEcTolg1/RIcosIIusO1NHkR0K5YwKyS+jdrc2cPItGwEojoAJ3jg8f9Aakk+BkBigMk53ukNJLYJyDRCdfN5M7ByzTa8sL0XR0IaRMYQ1a1EFWGP7BQG/O2G952cHN10dr6OPZ/AGJq8MtyyiIBqZOxgBj9/WLMADrR6FXhjicvJUxPpphs6WzzoD+kIagZEBrTUueCWRYRUA0//vQdhzcRPPn163q/9WJDLCGV8BCz+WYgHxpLobM/hJJxbWPNWT1FXBGUKtDTDSlt+gBJ2CRkdFRHI3HLLLXj22WexYcMGTJ48OXF7e3s7dF2Hz+dLGZXp6+tDe3t72mO5XC64XK609401yRdey+YIase/tXoVEfe//H6BowM85V+iwMBjczgHfFHY/HjQwgf/VtJojA0nGXd20g7UmUao4quJ3LEEZs6BQNRItDNqWIjqFt7Y048WrwxRAAJRM/H8dmyqyxfRETWsjB1M8vNvPeDHd5/ZniiDDwydmuj1RxHRzcTqrGNh3Umstm1YsdfBJYk4HNQSCddP//0gwIC7Lp1LIzOD5DJCGR8Bc0sCBAaYsTfZ5s40j1O1GXj3SAiLThyHDbuOFG1FULZAO75qiRJ2CRldZQ1kOOf44he/iCeffBIvv/wypk+fnnL/woULIcsy1q1bhyuvvBIA0N3djX379mHx4sXlOOWKlenCH7+QPrm5BxHdhMAYGt0yGt1SQfVNev1ReBQRkiAgoluJYfUmjwTOnWDJ+fLrfDOO56Yki383tjjHyRPr027amDxCNXjUI2LYiSAmGDXQr4iIaCYAhqbEvlAWBMHJ1eGx5wyqJiwbuHxB57AdTLzOjm7ZkEQBumlDkVKn5j44HMKTm3vgixjoDx8fhXI6U2cOTTNt7DkWhhV7nSQGGDbHup2HUadIVFsmg0wjlIM/C37VRFB1Nge1bQ6TOWUG6t0SLJvjH06agIjmJICrsZGYZfMmYfniaeja2z8kz2y4qd1sgRYl7BJSHmUNZFasWIFHHnkETz31FBoaGhJ5L01NTfB4PGhqasL111+P22+/Ha2trWhsbMQXv/hFLF68uOpXLBXLcKs2GtwybjpvBtbt7EO9S0STR0l0yGyY0vzptDd54JEl1ClOvolh2ZBFZ9fueHAhMgbDcoKYtJEMAxhjOHlCHR76/IeHfc7BSZbxirxB1UhMZ314eis27/PBLYvQLRsh1XSWX8f2m+po8kCznCXZN503Y9iRkKBq4OE39sEXMTAQMSAyhnq3hPZGd2Jq4snNPdjw7hG4ZQFR3UokFls2B2MMApycIM20Icd237ZsJ9Cpd0tUW6YA6T4LYS3k1E6Ck3/V6JHhVUT4IgZ+9Kd3YMWqOZ8+pRk3nTMDT2zuwQ0PbUrZryyiWzAsO6/6L/nk1xBCSkcY/iGlc99998Hv9+O8887DpEmTEj9//OMfE4/56U9/iosvvhhXXnklzjnnHLS3t+OJJ54o41mXTo8viq69/ejxRXP+nfgwu8CAFq8CgQHPbzuEVWu7E4/p9UdhcY5m7/EgBnBGFjTTiq14yk18uiqkmVBNC4rkBDFhzcRZJ7ZCYE7gkIhdBgcxAJo8Mu69+gy8cNu5mJRn9VPAmcrqbPZgYoMLE+pduPfqhfjOJXMTWzwkVz22uZMf41FEtHgVWJzn1N5Va7uxYdcRuGUR4Bw25/BHDezrDyOsmTjzhBZs2juAepeEqa118LqOfyewOeCRBTS4JfBE/qezyabNnRVVDS4p79eeDP0suGURzbHPvcCc/KQGt4SjIQ2m5YyitcQ+9117B/C1J95O+XvxRQy8ezgEX0TP+PdDCKlsZZ9aGo7b7cavf/1r/PrXvx6FMyqPQvfnybWAWLHLoGfKE1i+eBq29wRwYMDpnDO9u76Igf/7Qndsg7/hZUyyNG0smzcpMTUVf4xLFsHgrCYBnG/oiiRkTfJNlvy6TmyQ0BtQEVJNmLaNqGHjjKkt+IeTndyLOpcEizsjPu/rQVi20+6oYTudq8CcKQ8bkEXnXOJL4GlFS/7SfRbqFBERzal2rcaWyksCQ6tXyVrvJb5fmcgYdJODc2fqiCoxE1JdKiLZd6wrdH+edHUt0m1FUOwy6IPzBBpcEv6wcR9ueGgTIoaTI2Om28ESx/eE+uBIGF17B9Lmx6QzOHgSGcPCaS1Yvmhq2sdIogDNtOBVRIyrc3a4zrW9ya9rfPQnpBno86tQTRtbe/zo2juAiG7hWEiHLAqod0vOaFQsfBNjo0EW52iI3RcfiQnrJq1oGYF0gfSnzpyC5YumIqiZOBLUcOfT21NGyYCh9V7iI3cic94nIzaCQ5WYCakuFMiU2UjKsiePtAiMJUYOBm9F0OCWi1oGPTmhceG01pT9icZ5XdAMjpBmpvwOgzM6IYsM3OYw7NRl18OJB0+7+oL4yQvdeLvHjy37fbjh910po1fxACueiLtp70BiJCbX9qZ7XX0RHbEBHoRUIxGocTgronxhPVFBOHlKyx2bYjq1swmb9/lw1NDgVSRa0TIC2QpDBjUTExrcOdV7SexXxp3b44+l+i+EVBcKZMpsJPvzJI+0HA1pWbciKEYZ9HRTYIN3te7xOQXzxFjhu+OdO0vUsjFjHce4OlfKypFc/OH1vdi0dwBuSYAkibBte8joVXwU6h9mTii4vadMasAbe/pxNKTHNsF0bmcADNtJLhPF4yuz4knN9S4RLV4ZjDnf7G3OcWAggs37fU7SqcBw5gktVBSvCOLvc1A1sHLNtpTPpVsWcDSkDVvvxdmvzIRbFsAY8hq5I4RUBgpkymyk+St3LJ2FsGbmtBUBMLJVFemmwNbtPAzdtDBtXB10y0YwasCOJfsm58hYsTouNne+GTd5JPz7/+7MOyfope7DUA0LgagZS+hlUCSGl7oPpx29yqe9yYFa1DChGTaiugUWK4ojMmdqTLeOL+nmjGFSkweGbeGQX0NEt6AadmIqSY/l8kiigBavU2smOcAkI5fuc3kspGN8vQuqYWet9xLfryyiW1T/hZAqRYFMmY00f6XBLee1FUGhsm2Y1+c3EVINuGQxdcUSnLyEeNKtYTvLYJs8EkSBJVaO5JMTdCykQbec4zg5D05irRXSRtzO5A5xXJ0L4E5g4pYEmJazpNoJapw2Wkkrovp9euI4DE4i+0CstkydS0JrbMQtn80EyfCyTc2qho2ffvp0xPcfy1bvheq/EFK9KJCpACPNX0m3FQFQ3Ln+TFNgDS4J/aKAoGbC4KlBjCiwWKdigXPgMx+ainNmTsC//+9OCAwF7BR8vD6NKDjDJCIDbIvDsDhSN0HIT3KH6FWclUrBWF2cqOEkgVo2hxirRhzfksHtEuBTnemnOkWAS5YS1ZPjj5vQMPy0YaV0pJVyHrkabmoW4Fg4beiGnbnuV0aKo9o+V6S6UCBTAUaav1LsVUnpZJsCG1ev4MMntOLV944mbhdi0zCaaSXyS9bt7MOxsAbVsNBal39OEMAhiwJ004pVynUCBc45FElE5gXfw0vuEHsDKgJRAwJjEMBhAzBMG7LorHpJDpnCmrM1AgPQ1uhBnUuCbtowLBumbaNnIIqobqHedXzKLDnALHTpfbFVynkMZ/DfSLFLC5DiqpbPFaluFMhUkJF8KyzmqqRM55YtWLr7snno2juAL/xXF8KaCcNylrPy2H5DUmzF0pu7+2FYHF4l/46nvcmDcfVOETPNtJykWwZ4FAnNXnlEnVa8QwypBkKqs5WDs6eTEy2xWFXe8Q0KFFFAUDVQ55YhCQyaYeFwUMPhoIrprnookpCoW+NRRGiWnXEzweQVX/lMsxVboSUARku2DrHUQTwpXKV/rkhtoECmRhRjVdJwhguWFk5rwYVz2/H8tkNoEBiOBDVwAWDcyRNpqXNBFA0cC2nwq0beHU9nswcfPWkcntx8EFasJr0tAKZt46MnjRvRNE08UHv67z2wOIfEAMt2llbXuSW0eGUEVRNfOn8mfvWXd+GWRdQrklMp2S0jpDk7gveHNTS4ZQRVAyHNxMdmTUSzV0n7mo1k6X0xVcp5ZJOtQyx1EE8KUw2fK1IbKJCpIMUIQko5159LsBTvPP60vdfZeyg2IhPWTPT4ohhXp8CjiFg4tQU7DgXz6niCqoG/7RmAllRtj9kAFzg4ZyMexk5eAaZbHCx28lHdmT5ySQKe+3sP+gIqGGPoDxuJ/Zc6mtzYPxCFbtrYeywMw3KmwTbv9+FjsybiPz+7EEHNTHnNdvUFC156X0wjKQEwGobrEG86bwZt2FiBKv1zRWoHBTIVoNrmkbMFS/Fg50hIxZ+29UFgTqdjcyAQNaCbFlrrXFh5yVwAyKvjufPp7dh9NAxJYLFqrE5+jCwKeP2DY7jzqe3Y8O6RgoexG9wyfvLp0wEGPPv3gzBjq6wAZ/m4atr4274BZ/VS7HfiG2U2uCVMaHDhlEmNeP2DY2itd6r4ZjuHSsnvKOd55BJ85NohljthlwKpVJXy+Sa1jwKZClBr88g9vih2HAyiLtaRO1VuncTciG5hySlONd98Lvo9vmgimVgSWGJ3acvm0EwbIdXAq+8dLWgYe3AHdNM5J+JP23ph2pZTL4YxNLolBFUDts1Q55IQ1kwwOKNNgagBzjk+NmsiNu0dQItXzukcRiNJOxflOI98gvd4hxjUTKcQoihAEYWK6RCr7YvIaKmUzzepfRTIlFktziPHv0F3NLlxNKzHNlx0lk27JAH+qIGrfrMxr4t+rz/qLH9mTu0YMTYkIjCn0q5T14U7u1UnyTaMna4DWnziOBwLqVDjVZIBeBWnWm8wagAMaPHKEAWWWGYNMHx4eisuX9CJV98/mtdQeqXkd4z2eeQTvDe6JbhlAe8dDgEARMbgkkW4JAH/NL/8HWKtfREppkr5fJPaRoFMmdXiPHL8G7Ru2ehs9kC3bJiWDdW04Y8YeGvfAJo8cl4X/XitHNWwoRoWEFuxZNocnHOcdWIrdhwM5jyM3eOL4u5ntuONPf1och8/l6e29ICDgzGAwRn5CWkmwAAWm2ZySSI6m53dk/0RHYLA8J3YVFm+Q+mjkaSdi9E8j3yD91Vru3E0pMGriNBMOzayZ6KjuX5UOsRsr0ktfhEppkr5fJPaRoFMmZViHrncF410Q8qmzRHRTQAcdYqzGzTnzoUul4t+/Jj/u9UJejTDgh4rUHPSxHr86JOnJb4ZZxvGjo/CrNvZl0jaZWDwKhJckpioQlzvkhBKmj4KqWZi527VdLYtUA0Lps2x7JT2xPELHUovd37HaJ5HrsF7jy+KrQd8WPdOHxrdznRdvEaPZjpbQQRUs2TTN7lMGdXiF5FSqJTPN6lNFMiUWTHnkStprj7dkPLCKc145f1j6Atq4LHy/vVuCePqFARUA1sP+LIGYMnHjOomBIHh7JPG465L5+a8w3c82Inn2cRzXACg2eO8RpxztHhlCLHpIx677aMnTcD4ehde/+BYxuPTUPrwhgveG1xSYhPIoGogEDXR4HEqLsdr9CiSUPJAIZcpI0poJaT8KJCpAMXq/Cpprj7dkPKPX+iGbjobKsb3SQpEDeiGBTDgu8/ugGXzjAHYcMPUw92fPA3gkkT0h50AJj7i0ux1nosxBkUW0RkbAfBFdYiM4buXzRt2Xx4aSh9eZ7MHZ05rwbp3+mBYNhrcckrw/oeN+xKf49Y6BUHVTFRajr+WpQ4Ucp0yooRWQsqPApkKUIzOr1Ln6uMX+x5fFJv2DMTyXCxYnIOBgYMjrFtQJAZFFOB2i8MGYMMNU2e6P3kaQBYF1LslBKIGGJzCdxHNhCQwIDZtFJ9GsmyOJfOOTx/lMkyey2PGYrATHzV8c08/dJOjT9PQH9Yxrt6FZfMmYfniabjhoU0pn+NGjwxfREcgaqDFK8O0eckDhXymjMo9CjcWP0eEJKNApoKMZB650ufq4+fX3ujC/oForKjd8b2RGty5LVkeTraL+uBpgPZGN4D41BKDIDB84vROMMbx2vv9JeuUKmkKcLQljxpOG+dFUDMRUk18eHprbJuL/iGf4/ZGN0zTRkgzcTSko8kjp1RHLkUnns+UUblG4cby54iQZBTI1IjRnqvP96IdP7/egAbL5pBFJ8nWtJ1VKLphpzw+3wAsl4t6ummABrcEzjk+PL0V37lk7qjsRl1JU4CjKd2oYatXgSQwbNozgB5fdMjn2LI5egMqIrHpR1lkOPOEFtx83oySduKFTBmNdkLrWP0cETIYBTI1YrTm6gv9FtjZ7MGZJ7RgzeYeZ0PGWD0YxhgY54gYFnTTdvYuQv4BWK4X9XTTABef2jHk/EvVKVXqFOBoyGXUcOG01pTPsT9iIKA6uUyNHhn1Lgkbdh3Bth4/+sN6STvxck8ZZTOWP0eEDEaBTAXLd1RgNC68I/kWePmCDjy/9RBMm8O0OQTG0OSRYdk2gqoJX1RHi1fJOwDL56Je7mTcSp8CLKVcRw3jn9d1O/sQVJ0k30aPjPZGN0SBwbA5PjgSRnujq6SdeLk/K9mM5c8RIYNRIFOBCh31KPWFdyTfAnt8UYQ1Cy11CgQAiiRCFp1ltL6IDst2KrYWEoAVclFPTkLu2ts/otcq2yqpwbeP5eW6uY4axj/HZ580Ht98citavDK8yvFLlbPijUMQhJTjl6oTr8QaKGP5c0TIYBTIVKCRzn2X6sJbSMAwOCgLa852BRPqXVAkAQHVQNSwcPmCTtx03oyCArBCLurFSJRMPkZEdwrmnX3SeHx1ySzc+/L7aY891pfr5jNqOKHBBVlg0AwrJZCJj+bZdmpe1VjqxMf654iQZBTIVJhKnvsuJGAYHJS5RAGHgxoGIgZ0y07pyBrcckFtK+SiXoxEyVVru/G/Ww9BM21osSXlazb3YN3OPkiCgAZ3+mPnOwVYiVMbhcpl1DA5QAyoJjTTgi9qYFKTs92FZlg4cUId+sM6AqoxZjvxSs7hIWQ0USBTYSp57jvXgCHeSQFsSFDW5FXABAbDtPGdS+Zi/uSmorQnn4t6MYLF+DE009n7SWAMisBg2Db8URNeRURH7Bjpjp3LFGAtL6/NNmqYHGROafHgoF9FRDdxYCCCCQ1uLJs3CTefNyMx6jVWO/F0QSEA7OoL1kTQS0iuKJCpMJU+950tYBjc8QJOjZbJLd6UY3hkp30TGpSiXWzzyQ8qRrDY648iopvQYkGMKDgbSgqMwQJ39oKybCix9zDdsYebAiz38tpyjASlCzKnj69Df0SHadn46adPx8JpLQAw6om4gwOGShkl62z2oNEt1WzQS8hwKJCpMJU+950tYFi5ZltKxxvUTGimjUP+KKaPr08co5RBWS75QcUIFtubPBAFBotzKLEgBgB4rMafzQEzKZDJt83lnGIs9UhQtuAjU5DZ4JIwENERL6KYPOpXasmvh2pYiOgWAA6PIsIjSxURMJQ76CWknCiQqUDVMPc9OGBI1/E6exoJiOgW+sPakD11Kn2KbLhjnH3SeKzZ3APT5pAEJ3gBkPhv1bQhi3ZBbS7nFGOpOsVcAqRhN5R0y1i5Zhte6j6MYyEdhmVDFhnG1bvwsVkTSxJQJL8eumXHdnEHJEFAnYKyBwyVnFdHyGigQKYCVXL9ikySO954NdaQasKybXAO9IcNGBaHW66MoKwYweJdl87Fth4/3jscgmEDAgPcsgBZZJjQ4IZq2AUfO1uHLgoMR4JaSTqoUnaKuQRIwwWZf3h9L57fdgiqYUM3LTDGoFscvohekoAiZaNRWUTEp0KKLfuO6BbaGt1lDxgqOa+OkNFAgUwFq8T6FZkkd7wB1YQ/ojtVewGIAoMiMiyY2ozvXDIXQPkTEosRLDa4ZfzPFz6CO5/ajlffOwqLc3iV41MNAdUs+NjpOvSwZuJISIMkMNz59PaS5EGUqlPMJ0DKFGQuXzQVN/y+C5LAnARrgUESnG0MdJOjySMUPaBIfj0My4bNubOxKJxl4IZllz1gqPS8OkJKjQIZUhTxjveZt3vgj5hOJkMsYcQlOpVZtx8M4McvdGPTnoGKSUgcabDY4Jbxk0+fnjYgynU5eaZganCHHtYsgDv7E3ldUkFTPsMFbqXqFPMJkAYHmU4eDMf2QwEcCWrQTQsWR2zXchuSAKegoiBAM62iBhTJr4dLFp36NbEpRIExyKJQ9oCh0vPqCCk1CmRI0dyxdBZe7j4MX8RM3CYw55trMFYPZN3Ow2jxyjWXkFhIQDRczkhyh771gB/ffWY7FEkoaMon1wTeUnWKhQRIjW4J9798MHHOQdWAatgQBIBxJ+3Xsjk4R2xkxs45oMh1JG7w6+F1ibHd0oFGtwzVrIyAoRry6ggpFQpkSNEEVBMW5xAFgMFZkiwwBsvmCGomwDla60u7P041yTWptrPZg15/FBZ3coyS5TqtkU8Cb7E7xXjQcOYJLdiw68iwAVL88Q9v3IcN7zrTUfUuCcdCOjgA2wYYS10hJosMmmlj2by2ogR0mV4PRRRiVYY5ZImBc1REwFCNeXWEFAsFMqRoev1RWDZHg1tGSDXBOcDBwTmHbXPIooAGV+pHLt4Rbz3gq7oL8Eg6jXyTakcy5ZPvcxWrUxwcNMiigNY6BRHdSgRI58ycgCVzJ6LHF02phRLRTfgiBtyygIkNbqiGU5dIYk7gIjIW+2w5z+VVRCzNIaAoZEVWpsJzlfh5raa8OkKKhQIZUjTxzlYRBQiMIaQ6+yoBTkfT4JGGdMRhzURYM/HdZ3fAsnlZ8mby7bCLUWcl36TakUz5FJrAO9JOMV3Q0B/Wcc7JE3D5gk48ubkHm/YM4NX3jsIliXDLAo6FdDS4JdS5JPSHdUQNG70BFRPqXRAYA4czXTmp2Q1JEBA2TIAD9169MFEoL9smniNZkTX49RhpwFBpoyeVdj6E5IoCGVI0yZ2ts8+QjLBmQrNsXDy/AwCGdMRHghrAAEUU4HaLo5o3U2hAUow6K4WMsBQ65VOOVS3ZgoZNewcAIDFt1OJVEFINvHc4Aq8iocMtQ7dsSIIAm3OEVBMT6l2od0vwRfREkq1pc1gWx7J5k7BwWsuw72elLFOutK0nKu18CMkXBTKkqJI725BmwiWJ+Pgp7SmdbbwjFhmDJApo8cplyZspJCApVp2VQkZYCp3yKceqlmxBw7GQhlffO5ryGiqSCMZYytYO9W4J/ogOC07QVaeIiGgCJFFIfLaSA7nh3s/RqM2Ty3tTaVV4K+18CMkXBTKkqIbrbJPvOxLUcefT21CXIW+mlN+QCw1ICvlWn+vy6lxHWAqZ8hntVS2ZgoagasSmGzlaZOc11E0bpmWDAbA4T2zt0N7ohh7blDOim/AqEj515hQsXzQVQc1MeT2zvZ/rdvbh7JPGY/7kppLV5sl1VKPSqvBW2vkQUggKZEhJZOts4/f1+KJlK+RV6DRDPtM08c5t3c4+RA0LHlnE+XPa0i6vLnVuwmivahk8CiQA6A2o0EwbiiTAtDh0MwxFEhHWLNicw4rlU0V1K1GfxS0LWDK3DVefNTWvjUB1y4ZuWBgI6whrJr755FbUuyR8ZEYrzp/dhtc/OFa02jxA7qMalTK9VannQ0ghKJAhJZep8+xs9uDMaS1Y904fDMse1b2YCs0byWea5nvP7sRTW3oSCc9B1cTjm/ZDN2386wUnp7wmo9VZjOZz3bF0FgzLwrNv9yKkObWFGACXJEASOEKahYhuQxJYYutHgR1fxh9f1XT5go5hA6/4+xnWTIR1CyHVhGHZ4LHnbPJI4Bz4887DWDZvEh69cdGIa/PE5TOqUWlVeCvtfAgpBAUypGSyDbcDzrfYN/f0Qzc5+jQN/WEd4+pdo1KXYyR5I7lM0/T4onhu68FE0qoQWzasWzb+5639+Ot7R8q2Smu0NLhlyKIIkQEiAxhz6gqFNSffxanXi9hKJIaWOhl1igjL5vjq0ln4666jiVVNImOYP7kJX/74TMxsbxzyXPH38/EuJ1AUEN8n2+GPmuhs9iSCi5vOm4EJDcqIavPE5TOqUWlVeCvtfAgpBAUypGSyDbcDSNw3bZwXQc1ESDXx4emto5ZgWGjeSC7TNFsP+BDVLYjMKQwIOJ25aQEmBwzTxrh6V00nVsZHKjyKiLBuOSMvjAE2ENadvZLAOSY2OkUSFVGAYTkbbf5pWy827R2AVxahGTZCmokXd/Th1feO4fIFnWkDv+WLp+HJzQdgMgYeKzAjMGevr5BqQh+0L1KxRiPyPU6lVeGttPMhJF8UyJCSyJp8+U4fwAG35NSb4bEcBUlg2LRnYNQSDEeaN5J9mobFfo6zOYcd+2+PIkEWhZpOrIyPVNS7JPSHjVghOye4iOfDMMYSQQwQW0HEGN7u8aPeJSGgmghpJoTYBqSqYeHZrQcBDA38gqqBOpeEtkYJhmnjUEAFA0tsk2FaNszYKFg8uDhlUgPe2NM/otGI+KjGs28fRNRw2mvaPONxKq0Kb6WdDyH5okCGlES24fZA1EBYczaW5NyZVqh3SxhXpyCgGqOeYFiKvJH5k5vgUUREdBPM5imdt8Cc1yGuVhMr4yMVls1R75acPYpsgHPuTDMJznuvGhYYkAgiFk5rwZb9PoiyM5IixEa1OHcCEpc4dJfrHp+zCk6MbYnR5FUQ0i0Eok4AxQCopg3NsHDBnIm4/+X3sX7XEUQNE4Zp41hIh1cR4ZbzH40IqgYMy4JhcQSiOo4GNXgUERfN78h6nEqrwltp50NIriiQISWRbbhdM51vxgJjkARnN+FA1IBuWmitc9VEgmFnswcXn9qONVsOwrRiK3KY06F6ZRGKVPuJlcn5F3WKCNvmCGkmbM7hVSRcNH8SGON47f3+lCmN5Yum4obfdyEce6wUm5qz4rupy87nqtefuq2BZloIaxZMy4Zlc4yrU6CbFiK68/qKjGHZvEnQTTsxrTmuzgVVseCPGlgwtRnfuWRu3p35qrXd+PPOwxhfr0AUWKIIZHISMSGkdCiQISWRKYkwEDUgMAav4ix1tTkSibAR3cKSU1pq5lvhty46BbIoYt07fVANG25ZQL1LwtGQhoBqjInEyuT8C5cswKu4hiTtppvSiE/VAM4ojM1tZ2SFAT0DUbhlEQ0uaUgeliI6dWEGIjrqXBJa61xYckoLLl/QiRMn1gMArvrNxrRTnjsPBfNuX7opVK8iIaAaNTldSEglokCGlEy6JMIPT2/F5n0+NHtkHA3rif2YnGW5Ii5f0Fneky6ieO7BTb4ZiY46eQRhLCRW5pJ/kW5KI/56PLn5AEKas2Ekg7NZpB0rmnf/hg+wac9AShDR7FUgCAyGZeM7F5+C+ZObU47dtbe/qHVTqA4LIeVHgQwpmUy7Bl/1m43QLRudzR7ollPVVTVtiIwlvjXXksEd9VhMrMw3/yL+2Vkytw03/n4TdIsnUqcb3TIa3BJefe8oLJtjfL0r5Xc9samnCQ2uIc9Z7LopVIeFkPIThn8IISPT2ezBwmmtic7s3JkTENJMBFQDDM7UgWZYOHfmhDHRqQOprwnJzKuIaHA79WUAJ1E4olsIaiZMi0OMJQsnyxZEDP78GZaNgOoknxfy+Sv28ZL1+KLo2tuPHl+04GOQkaP3ofLRiAwZdVS3YuTGzogOQ1A1oZmWU1gwKTncq0g495Q2bHj3SF7Lp4v9+Sv28Wg36spA70P1YDxeOapGBQIBNDU1we/3o7FxaEVQUj5jpzMunrFycU3ep+qgXwXgJIXLIgPnDKZtw6uIeOLms/GH1/cmXg9RYDi1swm3L5mFmW0NWZ+j2J+/Yh1v5ZptiQRmd2yaLBQLzmqtaGIlo/eh/HLtv2lEhpQN1a3IX66bE1a7eDslgUGMrWpztnjgkAWGRrcMWWIIqgbuvmwedvUG8OMXd2HrAT827/fhhoc2DRvgFfvzV+jxBueQ0W7U5Ue7glcXCmQIqRJj5eKa3E6XJKI/bECEkx/DAUxu9cCyOThHovP/w8Z96No7kPLtudIDvHSja3MmNUA1LLTW0SqocqLVaNWFkn0JqRLxi2u6TQ410ykQVwuS26lIAurdEuzYDDjnHBHNTEmmHRzgyaJTiK7OJSUCvHLJligaH3USGNDiVSAw4M3d/YjoVl4JzKT4klejJaP3oTLRiAwhVWKsLPUd3M72RjcAJ8EXYBAEhmWntCeSaSvx2/NwuUzZRteOhTT4VYN2oy4j2hW8utCIDCFVopRLfSvJ4HbanKPBLaHBLeGCUybi8Zs+grsvm5cIAAZ/e9ZNG2HNRFA1yhbgpRtteX7bIaxa2w0g++iaRxFx1gmt4BwYiOjgHLSqrwzuWDoLy+ZNovehCtCIDCFVZCwsXe/xRbHklDaENROb9g4k2nnxqR1pk3fjgc9zWw/iSFCDZtqJqaiTJtaj0T26l7lccpmyja55ZAkrL5kLALSqr4xoV/DqQYEMIVWkli+u6aZjzjyhBZcv6MCJExqytvPm82bg6b/3IKwfz2mQBIajIQ2r1nbnnfA7ktc3l6muhdNac5q6qJX3tprR6srKR4EMIVWoFi+u6ZaWb9h1BHWKhLsvm5j1d3/0p24EVROiAIhMAIezqkk3eV4ruopRpyfXXKaxMLpGyGigQIYQUnYjWVre44vi1feOgjEGWWBgjAFgsGJbX0R1M+eE38HBVFAz8fTfDyKsm/jJ/zk9p7bkmihay6NrcbXcNlI5KJAhhJTdSFYe9fqjsDiHECucJ8Z2lxQYoNscgsBySvhNDqa8ioTegBrbnd3G01sOAhy46xNzcxqZyWe0JdPoWjUHAWOlAjWpDBTIEELKbiRLy9ubPPAqElTDgmrYgO0EMabtJPyefdL4nAKB5GCqN6DCH9HBGIPEAAvAunf6UOeScsq3GcloS7mDgGIEUGOlAjWpDBTIEELKbiR1O5JXLQGAZtowbKd43kkT63HXpXNzOod4MOVXDfjCOmwAiK1+EsDhVcS8KygXkstUriCgWAHUWKlATSpHWevIbNiwAZdccgk6OjrAGMOaNWtS7uec4zvf+Q4mTZoEj8eDCy64AO+++255TpYQUlIjqdtxx9JZuGh+B1rrXGjyyBhfp+Cy0zvxP1/4SM6dcDwgOhbUnCAmCQcQ1e2SV1AuZ5Xi4Wrf5GqsVKAmlaOsIzLhcBinnXYarrvuOlxxxRVD7l+1ahV+8Ytf4KGHHsL06dOxcuVKXHjhhdixYwfcbncZzpgQUiojmY4pVuLs8kVT8cRbB6BbzjJuBkAQGAQGhDQTXsVV0gJ75apSXMxRlLFSgZpUjrIGMsuWLcOyZcvS3sc5x89+9jN8+9vfxic+8QkAwO9//3u0tbVhzZo1+MxnPjOap0oIGSUjWVo+0mXpQc1EvVuCKDCENROCwCAyZwWUzTnmT24q6bRIKYKAXIK7YgZQVN6fjLaKzZHZvXs3ent7ccEFFyRua2pqwllnnYXXX389YyCjaRo0TUv8OxAIlPxcCSGlMbgTLvVKnnggoYgCJFGIrVpy8mS8ioQvf3xm0Z8zWTGDgHxyXoodQFGNHDKaKjaQ6e3tBQC0tbWl3N7W1pa4L5177rkHd911V0nPjRBSWoM7YVkU4FVERHQLhmWXbCVPciDR4JbQ4pUR1kxolo2L53dgZntj0Z4rk2IFAfkkDRd7FGUs1MghlaNiA5lCfeMb38Dtt9+e+HcgEMCUKVPKeEaEkHwN7oQPDETR44uiThExucVb0pU8yYFESDPhkkR8PGm37VIrRhBQSM5LKUZRarECNak8FRvItLe3AwD6+vowadKkxO19fX04/fTTM/6ey+WCy+Uq9ekRQkpkcCesWzZ004bIGHTT2XqgwS2XbDlvpYwmjCQIKCTnpVLaTUi+yrr8Opvp06ejvb0d69atS9wWCATwxhtvYPHixWU8M0JIKQ1evmtazm7WIgNszmFYzuLoUi/n7Wz2YOG01qrszJNzXpLlkvNSze0mY1NZR2RCoRDee++9xL93796NLVu2oLW1FVOnTsWtt96K733vezj55JMTy687Ojpw2WWXle+kCSElNTjxVBIFCIzFtiFgiWRUWs6bGa0cImNJWQOZTZs24WMf+1ji3/HclmuvvRarV6/GHXfcgXA4jBtvvBE+nw8f/ehHsXbtWqohQ0gNS9cJK5KAsG7CLQtgDAioxrCdci5TJLU8jTJWVg7V8ntIcsM4j9XgrlGBQABNTU3w+/1obCz9igNCyMiNZNVSLsuOy72f0Wiq1Y5+LL2HY1Wu/XfFJvsSQsauTImnuXTKuSw7LnQ/o3yDgkoIImp15RBtTEniKJAhhJTUSDrzwZ3wcJ1yLsuOEfvv5MdwAKph4YUdvTj7pHGYP7k55Xny/fZf6GhBJQQ+1YA2piTJKJAhhJREOYb+c1l2DCDxGMvm6A2oCKoGDMuZZf/XRzdjYqMbH5s1MXGu+X77z/fxNE2Sn3LtSUUqEwUyhJCSKMfQf66l9uOPCagmAlEDdlKqoGlz+CJ64lxvOm9GXt/+CxktqLZpknKPHNHGlCRZxdaRIYRUr8GduSwKaHDLqHNJKVM8xRZf8RTSTARUA4ZlJ1Y4nTtzQmJq6tyZE+BXDQSiBgCO2HZKEBkgCQJ0k8MlCVi/6wi2HvCn1LWJy1THZnAdnOEeX67XqhBB1cDKNdtw1W824uaH38JVv9mIlWu2Iagao3oeubzPZOygQIYQUnT5dubFdMfSWVg2bxI4BwYiOjjHkGXHdyydhbNOaAXnHBwMgBPEyKIAIVZ4TxQEaKYFgOdVXC7fYnTlfK3yFR85EhjQ4lUgMOD5bYewam33qJ9LLu8zGRtoaokQUnTlHPrPpdR+g1vGykvm4u0Dfui2DX/YAGMMjDFYtlN4z7KdZd7zJzfnVVwu32J01TJNUmkJtrSlAomjERlCSNGlG/rvj+jwRQyceULLqHQ4w5Xa72z24Pw5bQAHXLIIy7ZhWDYszqFIDJppJ6Yp8v32n8/jq2WapFJHjmhLBUIjMoSQkoh32i91H8beYxEYlg1ZZHhzdz9WrtlWEStyks/Rsp19nBSRodmrJFYtAfl/+8/38aNVhXckoxfVMnJExh6q7EsIKanb/7gF697pQ4NLQr1bhmpYCMWmWXJZkTMaUwfx5wAYAF62aYpStbVYy7tXrtmG57cdQp1LGjJlVomrq0h1o8q+hJCy6/FFsWnvAFq8St55FaNZW6VSqt+W6jyKtbx7rOzfRKoLBTKEkJIZSeGyaqutUqmKmaRbygRbStolhaJAhhBSMoXmVVTaCplqVooquMUcOaKqxmSkaNUSIaRkCl2RU6krZKpRvnVtRlsl1aYh1YkCGUJISRVSuKzSO99qUsnLu6upqjGpXDS1RAgpqULyKvItKkeyq9QkXdr8kRQDBTKEkFGRb15FOTvfWks8rdQquFSbhhQDBTKEkIpUjs631hNPK2WZeRyNvJFioECGEFLRRrPzpSXfo69Sp71I9aBAhhBCQEu+y6VSp71I9aBAhhBCQImn5VZp016ketDya0LImNTji6Jrb39iiS8t+SakOtGIDCFkTMmW0EuJp4RUHwpkCCFjSraEXko8JaT6UCBDCBkzhkvovem8GZR4SkiVoUCGEDJm5JrQS4mnhFQPSvYlhIwZxUzoHZwsTAgpDxqRIYSMGcWoJFvr1X8JqTY0IkMIGVMK2Y07WTxZWGBAi1eBwIDntx3CqrXdJT5zQkg6NCJDCBlTRlJJlqr/ElJ5KJAhhIxJhST0UvVfQioPTS0RQkiOqPovIZWHAhlCCMlRPFk4pJkIqAYMy0ZANRDWTJw7cwKNxhBSBjS1RAgheaDqv4RUFgpkCCEkDyNJFiaEFB8FMoQQUgCq/ktIZaAcGUIIIYRULQpkCCGEEFK1KJAhhBBCSNWiQIYQQgghVYsCGUIIIYRULQpkCCGEEFK1KJAhhBBCSNWiQIYQQgghVYsCGUIIIYRULQpkCCGEEFK1an6LAs45ACAQCJT5TAghhBCSq3i/He/HM6n5QCYYDAIApkyZUuYzIYQQQki+gsEgmpqaMt7P+HChTpWzbRsHDx5EQ0MDGGMZHxcIBDBlyhTs378fjY2No3iGpVfLbQNqu3213DagtttXy20Dart9tdw2oHraxzlHMBhER0cHBCFzJkzNj8gIgoDJkyfn/PjGxsaKfmNHopbbBtR2+2q5bUBtt6+W2wbUdvtquW1AdbQv20hMHCX7EkIIIaRqUSBDCCGEkKpFgUyMy+XCnXfeCZfLVe5TKbpabhtQ2+2r5bYBtd2+Wm4bUNvtq+W2AbXXvppP9iWEEEJI7aIRGUIIIYRULQpkCCGEEFK1KJAhhBBCSNWiQIYQQgghVWtMBTL33HMPPvShD6GhoQETJ07EZZddhu7u7pTHqKqKFStWYNy4caivr8eVV16Jvr6+Mp1x4X7wgx+AMYZbb701cVu1t62npwfXXHMNxo0bB4/Hg/nz52PTpk2J+znn+M53voNJkybB4/HgggsuwLvvvlvGM86NZVlYuXIlpk+fDo/HgxkzZuDuu+9O2V+kmtq2YcMGXHLJJejo6ABjDGvWrEm5P5e29Pf34+qrr0ZjYyOam5tx/fXXIxQKjWIrMsvWPsMw8LWvfQ3z589HXV0dOjo68NnPfhYHDx5MOUaltm+49y7ZTTfdBMYYfvazn6XcXqltA3Jr386dO3HppZeiqakJdXV1+NCHPoR9+/Yl7q/U6+hwbQuFQrjlllswefJkeDwenHLKKbj//vtTHlOpbRvOmApk1q9fjxUrVmDjxo148cUXYRgGlixZgnA4nHjMbbfdhmeeeQaPP/441q9fj4MHD+KKK64o41nn729/+xv+4z/+A6eeemrK7dXctoGBAZx99tmQZRnPP/88duzYgR//+MdoaWlJPGbVqlX4xS9+gfvvvx9vvPEG6urqcOGFF0JV1TKe+fB++MMf4r777sOvfvUr7Ny5Ez/84Q+xatUq/PKXv0w8ppraFg6Hcdppp+HXv/512vtzacvVV1+N7du348UXX8Szzz6LDRs24MYbbxytJmSVrX2RSARvvfUWVq5cibfeegtPPPEEuru7cemll6Y8rlLbN9x7F/fkk09i48aN6OjoGHJfpbYNGL5977//Pj760Y9i9uzZePnll/H2229j5cqVcLvdicdU6nV0uLbdfvvtWLt2Lf7rv/4LO3fuxK233opbbrkFTz/9dOIxldq2YfEx7PDhwxwAX79+Peecc5/Px2VZ5o8//njiMTt37uQA+Ouvv16u08xLMBjkJ598Mn/xxRf5ueeey7/0pS9xzqu/bV/72tf4Rz/60Yz327bN29vb+Y9+9KPEbT6fj7tcLv7oo4+OxikW7KKLLuLXXXddym1XXHEFv/rqqznn1d02APzJJ59M/DuXtuzYsYMD4H/7298Sj3n++ec5Y4z39PSM2rnnYnD70nnzzTc5AL53717OefW0L1PbDhw4wDs7O/m2bdv4tGnT+E9/+tPEfdXSNs7Tt+/Tn/40v+aaazL+TrVcR9O1be7cufy73/1uym1nnHEG/9a3vsU5r562pTOmRmQG8/v9AIDW1lYAQFdXFwzDwAUXXJB4zOzZszF16lS8/vrrZTnHfK1YsQIXXXRRShuA6m/b008/jTPPPBOf+tSnMHHiRCxYsAC//e1vE/fv3r0bvb29Ke1ramrCWWedVfHt+8hHPoJ169Zh165dAIC///3veOWVV7Bs2TIA1d22wXJpy+uvv47m5maceeaZicdccMEFEAQBb7zxxqif80j5/X4wxtDc3Ayguttn2zaWL1+Or371q5g7d+6Q+6u9bc899xxmzpyJCy+8EBMnTsRZZ52VMkVTzdfRj3zkI3j66afR09MDzjleeukl7Nq1C0uWLAFQ3W0bs4GMbdu49dZbcfbZZ2PevHkAgN7eXiiKkrjgxLW1taG3t7cMZ5mfxx57DG+99RbuueeeIfdVe9s++OAD3HfffTj55JPxpz/9CV/4whfwr//6r3jooYcAINGGtra2lN+rhvZ9/etfx2c+8xnMnj0bsixjwYIFuPXWW3H11VcDqO62DZZLW3p7ezFx4sSU+yVJQmtra9W1V1VVfO1rX8NVV12V2Jyvmtv3wx/+EJIk4V//9V/T3l/NbTt8+DBCoRB+8IMfYOnSpXjhhRdw+eWX44orrsD69esBVPd19Je//CVOOeUUTJ48GYqiYOnSpfj1r3+Nc845B0B1t63md7/OZMWKFdi2bRteeeWVcp9KUezfvx9f+tKX8OKLL6bM59YK27Zx5pln4t///d8BAAsWLMC2bdtw//3349prry3z2Y3Mf//3f+Phhx/GI488grlz52LLli249dZb0dHRUfVtG8sMw8D/+T//B5xz3HfffeU+nRHr6urCz3/+c7z11ltgjJX7dIrOtm0AwCc+8QncdtttAIDTTz8dr732Gu6//36ce+655Ty9EfvlL3+JjRs34umnn8a0adOwYcMGrFixAh0dHUNG8KvNmByRueWWW/Dss8/ipZdewuTJkxO3t7e3Q9d1+Hy+lMf39fWhvb19lM8yP11dXTh8+DDOOOMMSJIESZKwfv16/OIXv4AkSWhra6vatgHApEmTcMopp6TcNmfOnMRqgngbBmfYV0P7vvrVryZGZebPn4/ly5fjtttuS4ysVXPbBsulLe3t7Th8+HDK/aZpor+/v2raGw9i9u7dixdffDExGgNUb/v++te/4vDhw5g6dWriGrN37158+ctfxgknnACgetsGAOPHj4ckScNeZ6rxOhqNRvHNb34TP/nJT3DJJZfg1FNPxS233IJPf/rT+L//9/8CqN62AWMskOGc45ZbbsGTTz6Jv/zlL5g+fXrK/QsXLoQsy1i3bl3itu7ubuzbtw+LFy8e7dPNy/nnn4+tW7diy5YtiZ8zzzwTV199deK/q7VtAHD22WcPWSq/a9cuTJs2DQAwffp0tLe3p7QvEAjgjTfeqPj2RSIRCELqn6IoiolviNXctsFyacvixYvh8/nQ1dWVeMxf/vIX2LaNs846a9TPOV/xIObdd9/Fn//8Z4wbNy7l/mpt3/Lly/H222+nXGM6Ojrw1a9+FX/6058AVG/bAEBRFHzoQx/Kep2p1j7CMAwYhpH1OlOtbQMwtlYtfeELX+BNTU385Zdf5ocOHUr8RCKRxGNuuukmPnXqVP6Xv/yFb9q0iS9evJgvXry4jGdduORVS5xXd9vefPNNLkkS//73v8/fffdd/vDDD3Ov18v/67/+K/GYH/zgB7y5uZk/9dRT/O233+af+MQn+PTp03k0Gi3jmQ/v2muv5Z2dnfzZZ5/lu3fv5k888QQfP348v+OOOxKPqaa2BYNBvnnzZr5582YOgP/kJz/hmzdvTqzayaUtS5cu5QsWLOBvvPEGf+WVV/jJJ5/Mr7rqqnI1KUW29um6zi+99FI+efJkvmXLlpTrjKZpiWNUavuGe+8GG7xqifPKbRvnw7fviSee4LIs89/85jf83Xff5b/85S+5KIr8r3/9a+IYlXodHa5t5557Lp87dy5/6aWX+AcffMAffPBB7na7+b333ps4RqW2bThjKpABkPbnwQcfTDwmGo3ym2++mbe0tHCv18svv/xyfujQofKd9AgMDmSqvW3PPPMMnzdvHne5XHz27Nn8N7/5Tcr9tm3zlStX8ra2Nu5yufj555/Pu7u7y3S2uQsEAvxLX/oSnzp1Kne73fzEE0/k3/rWt1I6vmpq20svvZT27+zaa6/lnOfWlmPHjvGrrrqK19fX88bGRv75z3+eB4PBMrRmqGzt2717d8brzEsvvZQ4RqW2b7j3brB0gUylto3z3Nr3wAMP8JNOOom73W5+2mmn8TVr1qQco1Kvo8O17dChQ/xzn/sc7+jo4G63m8+aNYv/+Mc/5rZtJ45RqW0bDuM8qXwoIYQQQkgVGVM5MoQQQgipLRTIEEIIIaRqUSBDCCGEkKpFgQwhhBBCqhYFMoQQQgipWhTIEEIIIaRqUSBDCCGEkKpFgQwhhBBCqhYFMoQQQgipWhTIEEJKijGW9eff/u3fEo+dPXs2XC4Xent7U44RDocxY8YM3H777Sm379mzB42Njfjtb38LAHj55ZdTjj1hwgT80z/9E7Zu3Zrye5/73OfAGMNNN9005HxXrFgBxhg+97nPFecFIISUFAUyhJCSOnToUOLnZz/7GRobG1Nu+8pXvgIAeOWVVxCNRvHJT34SDz30UMox6urq8OCDD+KXv/wl/vrXvwJwdrP//Oc/j7PPPhv/8i//kvL47u5uHDp0CH/605+gaRouuugi6Lqe8pgpU6bgscceQzQaTdymqioeeeQRTJ06tRQvBSGkBCiQIYSUVHt7e+KnqakJjLGU2+rr6wEADzzwAP75n/8Zy5cvx+9+97shxznnnHPwxS9+EZ///OcRDofx85//HFu2bMF//ud/DnnsxIkT0d7ejjPOOAO33nor9u/fj3feeSflMWeccQamTJmCJ554InHbE088galTp2LBggVFfhUIIaVCgQwhpOyCwSAef/xxXHPNNfj4xz8Ov9+fGHlJ9v3vfx+SJOGaa67BN7/5Tfzyl79EZ2dnxuP6/X489thjAABFUYbcf9111+HBBx9M/Pt3v/sdPv/5zxehRYSQ0UKBDCGk7B577DGcfPLJmDt3LkRRxGc+8xk88MADQx7n8Xjw85//HGvWrMF5552Ha665Ju3xJk+ejPr6ejQ3N+ORRx7BpZdeitmzZw953DXXXINXXnkFe/fuxd69e/Hqq69mPCYhpDJRIEMIKbvf/e53KQHENddcg8cffxzBYHDIYx944AF4vV5s3boVfr8/7fH++te/oqurC6tXr8bMmTNx//33p33chAkTcNFFF2H16tV48MEHcdFFF2H8+PHFaRQhZFRQIEMIKasdO3Zg48aNuOOOOyBJEiRJwqJFixCJRBLTQnF//OMf8eyzz+K1115DQ0MDbrvttrTHnD59OmbNmoVrr70WN9xwAz796U9nfP7rrrsOq1evxkMPPYTrrruuqG0jhJQeBTKEkLJ64IEHcM455+Dvf/87tmzZkvi5/fbbU6aX+vr6sGLFCnzve9/DaaedhtWrV+P3v/89nn/++azHX7FiBbZt24Ynn3wy7f1Lly6FruswDAMXXnhhUdtGCCk9CmQIIWVjGAb+8Ic/4KqrrsK8efNSfm644Qa88cYb2L59OwDgxhtvxJw5c3DrrbcCAD784Q/jq1/9Km688caMU0wA4PV68S//8i+48847wTkfcr8oiti5cyd27NgBURRL0k5CSOlQIEMIKZunn34ax44dw+WXXz7kvjlz5mDOnDl44IEH8Pvf/x5//vOf8eCDD0IQjl+27rrrLjQ3N2ecYoq75ZZbsHPnTjz++ONp729sbERjY+PIGkMIKQvG031FIYQQQgipAjQiQwghhJCqRYEMIYQQQqoWBTKEEEIIqVoUyBBCCCGkalEgQwghhJCqRYEMIYQQQqoWBTKEEEIIqVoUyBBCCCGkalEgQwghhJCqRYEMIYQQQqoWBTKEEEIIqVr/HxfWVfbNfJSzAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind=\"scatter\", x=\"TAXRM\", y=\"MEDV\", alpha=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = strat_train_set.drop(\"MEDV\", axis=1)\n",
    "housing_labels = strat_train_set[\"MEDV\"].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Missing Attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To take care of missing attributes, you have three options:\n",
    "#     1. Get rid of the missing data points\n",
    "#     2. Get rid of the whole attribute\n",
    "#     3. Set the value to some value(0, mean or median)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(399, 13)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = housing.dropna(subset=[\"RM\"]) #Option 1\n",
    "a.shape\n",
    "# Note that the original housing dataframe will remain unchanged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(404, 12)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.drop(\"RM\", axis=1).shape # Option 2\n",
    "# Note that there is no RM column and also note that the original housing dataframe will remain unchanged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "median = housing[\"RM\"].median() # Compute median for Option 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "254    6.108\n",
       "348    6.635\n",
       "476    6.484\n",
       "321    6.376\n",
       "326    6.312\n",
       "       ...  \n",
       "155    6.152\n",
       "423    6.103\n",
       "98     7.820\n",
       "455    6.525\n",
       "216    5.888\n",
       "Name: RM, Length: 404, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing[\"RM\"].fillna(median) # Option 3\n",
    "# Note that the original housing dataframe will remain unchanged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(404, 13)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>404.000000</td>\n",
       "      <td>404.000000</td>\n",
       "      <td>404.000000</td>\n",
       "      <td>404.000000</td>\n",
       "      <td>404.000000</td>\n",
       "      <td>399.000000</td>\n",
       "      <td>404.000000</td>\n",
       "      <td>404.000000</td>\n",
       "      <td>404.000000</td>\n",
       "      <td>404.000000</td>\n",
       "      <td>404.000000</td>\n",
       "      <td>404.000000</td>\n",
       "      <td>404.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.602814</td>\n",
       "      <td>10.836634</td>\n",
       "      <td>11.344950</td>\n",
       "      <td>0.069307</td>\n",
       "      <td>0.558064</td>\n",
       "      <td>6.279481</td>\n",
       "      <td>69.039851</td>\n",
       "      <td>3.746210</td>\n",
       "      <td>9.735149</td>\n",
       "      <td>412.341584</td>\n",
       "      <td>18.473267</td>\n",
       "      <td>353.392822</td>\n",
       "      <td>12.791609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.099383</td>\n",
       "      <td>22.150636</td>\n",
       "      <td>6.877817</td>\n",
       "      <td>0.254290</td>\n",
       "      <td>0.116875</td>\n",
       "      <td>0.716784</td>\n",
       "      <td>28.258248</td>\n",
       "      <td>2.099057</td>\n",
       "      <td>8.731259</td>\n",
       "      <td>168.672623</td>\n",
       "      <td>2.129243</td>\n",
       "      <td>96.069235</td>\n",
       "      <td>7.235740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.006320</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.740000</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>13.000000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>1.730000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.086962</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.190000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.453000</td>\n",
       "      <td>5.876500</td>\n",
       "      <td>44.850000</td>\n",
       "      <td>2.035975</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>284.000000</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>374.617500</td>\n",
       "      <td>6.847500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.286735</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.900000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.538000</td>\n",
       "      <td>6.209000</td>\n",
       "      <td>78.200000</td>\n",
       "      <td>3.122200</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>337.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>390.955000</td>\n",
       "      <td>11.570000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.731923</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.631000</td>\n",
       "      <td>6.630500</td>\n",
       "      <td>94.100000</td>\n",
       "      <td>5.100400</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>666.000000</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>395.630000</td>\n",
       "      <td>17.102500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>73.534100</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>36.980000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CRIM          ZN       INDUS        CHAS         NOX          RM  \\\n",
       "count  404.000000  404.000000  404.000000  404.000000  404.000000  399.000000   \n",
       "mean     3.602814   10.836634   11.344950    0.069307    0.558064    6.279481   \n",
       "std      8.099383   22.150636    6.877817    0.254290    0.116875    0.716784   \n",
       "min      0.006320    0.000000    0.740000    0.000000    0.389000    3.561000   \n",
       "25%      0.086962    0.000000    5.190000    0.000000    0.453000    5.876500   \n",
       "50%      0.286735    0.000000    9.900000    0.000000    0.538000    6.209000   \n",
       "75%      3.731923   12.500000   18.100000    0.000000    0.631000    6.630500   \n",
       "max     73.534100  100.000000   27.740000    1.000000    0.871000    8.780000   \n",
       "\n",
       "              AGE         DIS         RAD         TAX     PTRATIO           B  \\\n",
       "count  404.000000  404.000000  404.000000  404.000000  404.000000  404.000000   \n",
       "mean    69.039851    3.746210    9.735149  412.341584   18.473267  353.392822   \n",
       "std     28.258248    2.099057    8.731259  168.672623    2.129243   96.069235   \n",
       "min      2.900000    1.129600    1.000000  187.000000   13.000000    0.320000   \n",
       "25%     44.850000    2.035975    4.000000  284.000000   17.400000  374.617500   \n",
       "50%     78.200000    3.122200    5.000000  337.000000   19.000000  390.955000   \n",
       "75%     94.100000    5.100400   24.000000  666.000000   20.200000  395.630000   \n",
       "max    100.000000   12.126500   24.000000  711.000000   22.000000  396.900000   \n",
       "\n",
       "            LSTAT  \n",
       "count  404.000000  \n",
       "mean    12.791609  \n",
       "std      7.235740  \n",
       "min      1.730000  \n",
       "25%      6.847500  \n",
       "50%     11.570000  \n",
       "75%     17.102500  \n",
       "max     36.980000  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.describe() # before we started filling missing attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
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       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
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       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
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       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
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       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
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       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
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       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
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       "  background-position: center center;\n",
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       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
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       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
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       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</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 fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;SimpleImputer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "SimpleImputer(strategy='median')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "imputer = SimpleImputer(strategy=\"median\")\n",
    "imputer.fit(housing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.86735e-01, 0.00000e+00, 9.90000e+00, 0.00000e+00, 5.38000e-01,\n",
       "       6.20900e+00, 7.82000e+01, 3.12220e+00, 5.00000e+00, 3.37000e+02,\n",
       "       1.90000e+01, 3.90955e+02, 1.15700e+01])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.statistics_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = imputer.transform(housing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_tr = pd.DataFrame(X, columns=housing.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>404.000000</td>\n",
       "      <td>404.000000</td>\n",
       "      <td>404.000000</td>\n",
       "      <td>404.000000</td>\n",
       "      <td>404.000000</td>\n",
       "      <td>404.000000</td>\n",
       "      <td>404.000000</td>\n",
       "      <td>404.000000</td>\n",
       "      <td>404.000000</td>\n",
       "      <td>404.000000</td>\n",
       "      <td>404.000000</td>\n",
       "      <td>404.000000</td>\n",
       "      <td>404.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.602814</td>\n",
       "      <td>10.836634</td>\n",
       "      <td>11.344950</td>\n",
       "      <td>0.069307</td>\n",
       "      <td>0.558064</td>\n",
       "      <td>6.278609</td>\n",
       "      <td>69.039851</td>\n",
       "      <td>3.746210</td>\n",
       "      <td>9.735149</td>\n",
       "      <td>412.341584</td>\n",
       "      <td>18.473267</td>\n",
       "      <td>353.392822</td>\n",
       "      <td>12.791609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.099383</td>\n",
       "      <td>22.150636</td>\n",
       "      <td>6.877817</td>\n",
       "      <td>0.254290</td>\n",
       "      <td>0.116875</td>\n",
       "      <td>0.712366</td>\n",
       "      <td>28.258248</td>\n",
       "      <td>2.099057</td>\n",
       "      <td>8.731259</td>\n",
       "      <td>168.672623</td>\n",
       "      <td>2.129243</td>\n",
       "      <td>96.069235</td>\n",
       "      <td>7.235740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.006320</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.740000</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>13.000000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>1.730000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.086962</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.190000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.453000</td>\n",
       "      <td>5.878750</td>\n",
       "      <td>44.850000</td>\n",
       "      <td>2.035975</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>284.000000</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>374.617500</td>\n",
       "      <td>6.847500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.286735</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.900000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.538000</td>\n",
       "      <td>6.209000</td>\n",
       "      <td>78.200000</td>\n",
       "      <td>3.122200</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>337.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>390.955000</td>\n",
       "      <td>11.570000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.731923</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.631000</td>\n",
       "      <td>6.630000</td>\n",
       "      <td>94.100000</td>\n",
       "      <td>5.100400</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>666.000000</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>395.630000</td>\n",
       "      <td>17.102500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>73.534100</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>36.980000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CRIM          ZN       INDUS        CHAS         NOX          RM  \\\n",
       "count  404.000000  404.000000  404.000000  404.000000  404.000000  404.000000   \n",
       "mean     3.602814   10.836634   11.344950    0.069307    0.558064    6.278609   \n",
       "std      8.099383   22.150636    6.877817    0.254290    0.116875    0.712366   \n",
       "min      0.006320    0.000000    0.740000    0.000000    0.389000    3.561000   \n",
       "25%      0.086962    0.000000    5.190000    0.000000    0.453000    5.878750   \n",
       "50%      0.286735    0.000000    9.900000    0.000000    0.538000    6.209000   \n",
       "75%      3.731923   12.500000   18.100000    0.000000    0.631000    6.630000   \n",
       "max     73.534100  100.000000   27.740000    1.000000    0.871000    8.780000   \n",
       "\n",
       "              AGE         DIS         RAD         TAX     PTRATIO           B  \\\n",
       "count  404.000000  404.000000  404.000000  404.000000  404.000000  404.000000   \n",
       "mean    69.039851    3.746210    9.735149  412.341584   18.473267  353.392822   \n",
       "std     28.258248    2.099057    8.731259  168.672623    2.129243   96.069235   \n",
       "min      2.900000    1.129600    1.000000  187.000000   13.000000    0.320000   \n",
       "25%     44.850000    2.035975    4.000000  284.000000   17.400000  374.617500   \n",
       "50%     78.200000    3.122200    5.000000  337.000000   19.000000  390.955000   \n",
       "75%     94.100000    5.100400   24.000000  666.000000   20.200000  395.630000   \n",
       "max    100.000000   12.126500   24.000000  711.000000   22.000000  396.900000   \n",
       "\n",
       "            LSTAT  \n",
       "count  404.000000  \n",
       "mean    12.791609  \n",
       "std      7.235740  \n",
       "min      1.730000  \n",
       "25%      6.847500  \n",
       "50%     11.570000  \n",
       "75%     17.102500  \n",
       "max     36.980000  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_tr.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scikit-learn Design"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Primarily, three types of objects\n",
    "1. Estimators - It estimates some parameter based on a dataset. Eg. imputer. It has a fit method and transform method. Fit method - Fits the dataset and calculates internal parameters\n",
    "\n",
    "2. Transformers - transform method takes input and returns output based on the learnings from fit(). It also has a convenience function called fit_transform() which fits and then transforms.\n",
    "\n",
    "3. Predictors - LinearRegression model is an example of predictor. fit() and predict() are two common functions. It also gives score() function which will evaluate the predictions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Scaling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Primarily, two types of feature scaling methods:\n",
    "1. Min-max scaling (Normalization)\n",
    "    (value - min)/(max - min)\n",
    "    Sklearn provides a class called MinMaxScaler for this\n",
    "    \n",
    "2. Standardization\n",
    "    (value - mean)/std\n",
    "    Sklearn provides a class called StandardScaler for this\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "my_pipeline = Pipeline([\n",
    "    ('imputer', SimpleImputer(strategy=\"median\")),\n",
    "    #     ..... add as many as you want in your pipeline\n",
    "    ('std_scaler', StandardScaler()),\n",
    "])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_num_tr = my_pipeline.fit_transform(housing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(404, 13)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_num_tr.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selecting a desired model for Samar Real Estates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor()</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 fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "# model = LinearRegression()\n",
    "# model = DecisionTreeRegressor()\n",
    "model = RandomForestRegressor()\n",
    "model.fit(housing_num_tr, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "some_data = housing.iloc[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "some_labels = housing_labels.iloc[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "prepared_data = my_pipeline.transform(some_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([22.453, 25.607, 16.372, 23.31 , 23.352])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(prepared_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[21.9, 24.5, 16.7, 23.1, 23.0]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(some_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "housing_predictions = model.predict(housing_num_tr)\n",
    "mse = mean_squared_error(housing_labels, housing_predictions)\n",
    "rmse = np.sqrt(mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.218714271372713"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using better evaluation technique - Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1 2 3 4 5 6 7 8 9 10\n",
    "from sklearn.model_selection import cross_val_score\n",
    "scores = cross_val_score(model, housing_num_tr, housing_labels, scoring=\"neg_mean_squared_error\", cv=10)\n",
    "rmse_scores = np.sqrt(-scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.87717666, 2.81397413, 4.32996149, 2.52720309, 3.51672223,\n",
       "       2.68653064, 4.87330924, 3.47697533, 3.26795647, 2.99953332])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_scores(scores):\n",
    "    print(\"Scores:\", scores)\n",
    "    print(\"Mean: \", scores.mean())\n",
    "    print(\"Standard deviation: \", scores.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [2.87717666 2.81397413 4.32996149 2.52720309 3.51672223 2.68653064\n",
      " 4.87330924 3.47697533 3.26795647 2.99953332]\n",
      "Mean:  3.3369342592086886\n",
      "Standard deviation:  0.7127859058975486\n"
     ]
    }
   ],
   "source": [
    "print_scores(rmse_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Samar.joblib']"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from joblib import dump, load\n",
    "dump(model, 'Samar.joblib') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing the model on test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = strat_test_set.drop(\"MEDV\", axis=1)\n",
    "Y_test = strat_test_set[\"MEDV\"].copy()\n",
    "X_test_prepared = my_pipeline.transform(X_test)\n",
    "final_predictions = model.predict(X_test_prepared)\n",
    "final_mse = mean_squared_error(Y_test, final_predictions)\n",
    "final_rmse = np.sqrt(final_mse)\n",
    "# print(final_predictions, list(Y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.95387063286229"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.43942006,  3.12628155, -1.12165014, -0.27288841, -1.42262747,\n",
       "       -0.23979304, -1.31238772,  2.61111401, -1.0016859 , -0.5778192 ,\n",
       "       -0.97491834,  0.41164221, -0.86091034])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prepared_data[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([25.068])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from joblib import dump, load\n",
    "import numpy as np\n",
    "model = load('Samar.joblib') \n",
    "features = np.array([[-5.43942006, 4.12628155, -1.6165014, -0.67288841, -1.42262747,\n",
    "       -11.44443979304, -49.31238772,  7.61111401, -26.0016879 , -0.5778192 ,\n",
    "       -0.97491834,  0.41164221, -66.86091034]])\n",
    "model.predict(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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