House_Price_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dragen Real State Price Predictor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = pd.read_csv(\"C:\\\\Users\\\\Binit Yadav\\\\Desktop\\\\binit\\\\PDF\\\\data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0</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": [
       "0    471\n",
       "1     35\n",
       "Name: CHAS, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['CHAS'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>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>3.677082</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%      3.677082   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": "markdown",
   "metadata": {},
   "source": [
    "# Train - Test  Splitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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)} \\n Rows in test set : {len(test_set)} \\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    376\n",
       "1     28\n",
       "Name: CHAS, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "strat_train_set['CHAS'].value_counts() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    95\n",
       "1     7\n",
       "Name: CHAS, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strat_test_set['CHAS'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = strat_train_set.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Looking For Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = housing.corr()\n",
    "corr_matrix['MEDV'].sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x00000174955B62C8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000174958BAB08>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000174958F5948>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001749592E808>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000017495969688>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000174959A2488>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000174959DA388>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000017495A140C8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000017495A142C8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000017495A4F248>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000017495ABACC8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000017495AF4B88>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000017495B2DA08>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000017495B668C8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000017495B9D788>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000017495BD7548>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x504 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas.plotting import scatter_matrix\n",
    "attributes = ['MEDV','RM','ZN','LSTAT']\n",
    "scatter_matrix(housing [attributes],figsize = (5,7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17495e07cc8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nojGFHpgT9CVfBpP/MYArATyWfP8xAJ8s9ByCUCkKbbzmhnWdPZj38EugpEu2IahkWe69fRGs2NSdtW9c19E2YTjueWZX6okgpjH+7VdvYNWWv6X2fXDjbsx96EXHcwi1Ta4iry4ieg3AEwDWMvOxfA5ORCqAbQBaAKxg5q1ENIaZDwIAMx8kotNt9l0MYDEATJxoXTouCJXAy+Ilw5WTbtHrOuOXt37MNDJx/5EBhFQFkbjZcl8yZwr6o5pl1e/dT+8EAHxjw66s/azOIdQ2udw+4wE8AOBjAN4koqeI6NNE5Oq5kJk1Zm4D0AzgfCKa7lYwZn6EmduZuX306NFudxMET/GqeMl4knj0xbeyXDnhgJoVzLXKCgoHFFw3Y6Jt1W9AJdz99M4sxQ8AQVVBp0dPMoI/cVT+SeX9a2b+RwATAPwYCTfNX4lolduTMPNRAJsBXAbgEBGNBYDkv4cLlF0Qyo4XxUtGuuV1P9iC//jdW1nbo5pm2ZJ5YXuz6b1Pf6Q51ZbBquo3rieUvBX9UQ1ff3pnQcVgQnXiOuDLzFEAuwC8DuA9ANY15UmIaDQRDU/+3AjgUgBvAFgP4Mbkx24EsC5/sQWhMpSqeMmw9LsPHU89Sdilai6ZMyXVbtnY54U3D+PJV8199Vd37E9Z7kbVb3q2z13zp0JzSO3uiySKwb68Zju6Dx3P63qE6iNnYzcimgjg0wA+A6AJwM8AXMnMr+fYdSyAx5J+fwXAambeQESvAFhNRJ8DsBfANcVcgCCUk3y7WlqRHjOIaHoq6GpFOKDg8uln4N5fvY5HX/orFCJE4jqCKiGW4drJbP28aOYkXDb9DFOe/ZBwICV7VNNx9XnNWNfVg77IqYUnGteLagonVAeORV5E9Hsk/P5rADzBzBWptJIiL8FvFFq8ZFVYZUVTWIWmMxZ+uBlPvLovS9FbYVXEZSVj+vsAbOWRgevVTzED3L8C4AWuhjJgQSgjhQ4psRrW0hBUoOuMcCAxgvGOuVMxffwwNIVUzH3opZyKf1BQhQ5z62enjKRM2e+76lx8ec12RDMCwTJwvbZxVP7M/DsiupGIbgVwTvLt1wE8yMwrPZdOEGoMq5gBAPzy1o+hP6qZrPSufUehWqRsphMOKPjPz34Y08YNNVn8+YyCXNA2HlPHDsUVD75oyhKSgq/aJleR1w0AvgTgywDGIeECWgbgi8ltvsbLQhxBKAS7hmctY4ZktUFoHtEITbe2+oMqoSGo4M75UzGsMWjaZtkZlAg7D9iX6bSMGYIHrmmVRmx1RC6f/xYA1zLznoz3JwP4GTPP9FI4g0J8/jJFSPAzbmMGD23cje8896bpvZAK/NunzkUkruOeZ3Zl/Y339kUw81sbs9xF4QDh/qudg7jSiK22KKax29BMxQ8AyfeGFi+aN8gUIaEcFPNk6bbh2XUzJiIcMLt+FEUxtXCw+hu3MurcDIaRRmz1Qy7l71S54tuRPDJFSPAapz74pWTk4DDuvzrbHdMf1Wz/xvcfGUBj0DqcJ98DwSBXts8HiGi7xfsE4EwP5CkJMkVI8JJ8A6rFYgRkO/cdRduE4WgZMwS9fZGsv/GTsTj2vXsC55wxxDKoDCSqhfe9ewLHBmKmILFQf+RU/mWRosSUohBH8DeV9E1bpWsWmxbpdD128Svjb5x1RkRjxHRgyRN/QlAlfOb8CVjdsT+1rSGoIK7piCc/AwABBfjuwjaJhdUpuZR/IzO/AQBEFGbmlLMw2Zv/b14KVwwL2sZjdssoCV7VIJUO5pf6ydLpepyeMma3jMIDV7fiS0/+ySyLxnjy1f145pYL0B/V0BRSceDYSdy0ssPU1C2uA0vXdHn2xCL4m1w+/5+m/fxKxrZ/L7EsJUeCV7WHH4L5pZxPm+t67OJXq7buxex7n8eytdth0aQTqpIY69iadBMNawxa1gyoJDGAeiWX5U82P1u9FgTP8cLlUgilerLMdT1WTxlRTcOKTd2WrZkNNJ1NTyJ2NQMaSyysXsll+bPNz1avBcFz/BTML8WTZa7rsXrKWDJnCkI2rZmBRAHY/Vebn0QSWUPnIqiestkCCnD/1a3yZFyn5LL8m4noQSSsfONnJF9LlEgoO34K5mcGaQsJQru5nsynDABYsdk8wjEcUPCda1oxtDGAaeOG2bZxmN0yKlnpS5LtU+fkqvC90XYjAGZ+zGl7qZCunkImlcj2ST/nS93vYPna7VCJENN0XP7BM/CrHYcQUglxnXMGofNdODK3r+/syVowig16S3Vv7eFU4euo/P2CKH+hHLhNt4xqOjRdtwy0GjQEFWxYckFWs7bevghWbd2LFZu6EVQJUY1x1/ypWDRjkq0sxkJj1cahVMq60hlUgjcUrPyJaL3TgZl5QZGyuUKUv+Al6Qo5pFqnW7rpwZ9OSCWACKE0BT84HMgazm7wzU9Ox6KZiQXAvNBo0BmmPj2l7rNvdX3Sy782KKaf/ywA+wA8AWArJMNHqDHWdfaYFLKRQZNesfvKX3rB7vU+ACRbIzOi8cTrr/1iB1QC7Frzf+2pHZhw2iCMG9aQlddvRSmzm/ySQSWUl1zK/wwAH0dihON1AJ5BYqLXTq8FEwSvMXLsrSxxQ/l9/7dvYuWWvXkf22rMYq5hXIsfexU6ACVHD/+TMR1NITVvmezwUwaVUD4cUz2ZWWPmZ5n5RgAzAXQD2ExEt5RFOkHwEKsCKoOYriMW1/JS/CEFUCnxn5uxi5mc1BhRjXO6l8Iq4cCxk0XNqkjvSFrKojWhenAzwD0MYC4S1v9kAA8C+B9vxRLqkXJnm9hN1QoHEspvT+8J18cyUi1v+3mXY/GVGwjORTRRjXHTyo5UfMIY++j2vtkFd6UdSn3hqPyJ6DEA0wH8CsDdzLyjLFIJdUclsk0yc+yjmoYlc6bguhkTMXJwGN2Hjrs+VkhVcDKmIagSIvHi5Mr1zMBIxCaMReZrT+3A4LDqOsXUqSOpKP36IZfl/1kA/QDOAnArUcoXSQCYmX070KWUSP6zt+TTIrmUv4vevggmjWzChiUX4MCxkwDYVCDVMmYIrmwbi3WdB3MeK6rp2NFzDH0RLWtbSCUw3LuCVADZR3HGOG+u1tIS3BUMcg1wz9X+oeaR/GfvcauQSvm7SD/WybgGZkZjMJA67uyWUVi1dS+eTRZuRTVGQIFlbn9AIcQ1HT95xbrJrQ5g3vQz8FRX7kUEyF/xp5NLkUtwVzCoe+XuhB86SNYDbhRSKX8X3YeOY+maU8eKaYy4jtRxb/t5F2Z967f47nNvIhLXk2mbiSHoX738HIQDifx9AAgQENfZMZMnrrFrxZ9O2ObbubB9PBqCCgYFsz+QS5FLcFcwyBnwrWfkEbk8uOlvY9d22M3vorcvgp0H3gPA2PfuAL7+9A7EHMxrO/dMVGP8Yc+7pqIriyzRkvHJ85qxYftB9EfNwk4Y0YQ75k7F3Rt2IRxQEInrCKsEUsiVIpfgrgCI8ndEHpHLRy6F1BRSs1Ig3eS7r+vswW2rOx1bMeTDb18/XJoDueCMoQ1Zih8AHnp+N4iAaNrKw0R4ZskFaBkzxNWxJbgriNvHAXlELi9OLZL7oxrCqrn4KaySpXI06O2LYNmaLteKvymsIhwo/Cth7H/bx8/Cmi/MLOrLtaB1LP7rxbcstwVUBSqZjx5WFfRHNVP+fjp27wv1i1j+OZBHZH9kOzWPaAQp5v4IpJDjU9j+IwNQKFfWfIKmkIq750/D6UMbcNNjryKSR5FWOEC4c940U659b18EpAAOHRosCSiEZZ84GzPOHIlf7/hfy88khrKY5YvpiWyjTz/ySlZAXJIWqhcvv3ui/F1Qz4/IflEchfTx39FzDAMum7HFNB1tE4bjwLGBvDpYqQSs+twMBAOqSfFveuMwGgIK+qP5af/vfboN81rHofvQccsFKBxQcP/V5wKA6V7cMXcq7nlmV1a67NSxQ12n0Qr+wuvvnih/wZZ88u/LQT5PYb19EdzzzK6s9+2eAyafNgjzHn4JQUVBPMOTpBJBs+l+G1AVXPfoVgRVBdG4hr87azRe7H4HAYXyVvwAMLQx8ZXsj2poCCqmOEdIJfzghnZceNZoADDdC7vkhM59RyVpoQopx3dPfP6CLXbDwys58Nvt6EQr2RsCCv7tU9MtP//m2/2p1M9sRc8I2DRbM1JB+6MaYjrw2zfeRiTOJsVv7BlSCUGVEFCAQRaB6qBKmDZuGABYurMUJTF9yyD9XtglJ7RNGC5JC1VIOb57ovwFW6o528mybw8BOuffl1zjxLzbQmEkFP8tF0/Blq9cgq1fvRRP3DQT3/zkdIQDCgaF1FRvIGNRc0o2sAre2n2+ZcwQSVqoQsrx3ZNJXoIjXowLTMfLgFam7HfMm4qvr99ZUMfNUmA1ICXf8Y25/MB2x/ND0F7Ij1J892SMo1AUXimOcgST02XfeeAYbvjRqyU9fj4MCQfw+OdnoHXC8IL2l4lb9Uex371iJnkJgifZTrkCWqVacMyyu3f4hAMKdOaSPiVENefH9lzXLBXn9YeXmYaeKX8imgBgJRLTwHQAjzDz94noNABPIjEbYA+Ahcx8xCs5BH+y/8hAVhDVUGR2A8vdkqlEjdfjhjW4PkYkruMfZk3CE3/Ym1fOvxNL5rTYfpHdPAVVcwxG8B9eWv5xALcx8x+JaAiAbUT0HIB/ALCRmb9NRLcDuB3Acg/lEHyIVfvjiJaYnlVMilumEl344WY82bEfqkKI5DGAHQBWbvkbgqoCN0ViQQJiDh8LBwjXzZhouc1oNBeNO19zIbUOgmCHZ8qfmQ8COJj8+TgRvQ5gPIArAVyU/NhjADZDlH9dYZeDD2Zc++hWqGT9ROCmgVvmwlHI/F0DnYHPfGSCbavmdJwUPwD889+931L+dZ09WPrzrlTnUAO7a5aKc6FUlMXnT0STAXwIwFYAY5ILA5j5IBGdbrPPYgCLAWDiRGuLSahOrHzXAFIKMG7RusCNa2PngWPJdg75o5L1gPWjAzGEA2Q55D2dgEKI6/af+f7Gbhw5EcU3rvxg6j1jscpU/IDzNddzxblQOjzP8yeiwQDWAvgSM7/ndj9mfoSZ25m5ffTo0d4JKJQdu9m5mTSFVdd56es6e3DTyg6ccGj05oSdW/+pzgM5FT8AR8VvsPKVvabRkHYD5EMBycUXvMdT5U9EQSQU/ypmNoa+HyKiscntYwGUr0eu4AvSC5KcWjIv/fuz8fLyi3MGew0L2o2S9oKgTfWvFZ37jqZ+tloEQyrhl7dcII3XBM/xTPlTYuDvDwG8zszfTdu0HsCNyZ9vBLDOKxkE/7KgbTxeXn4xfnrTTPzDR63demeObnJl/dpZ0FYU8wdvpeIbggq+dOkU18doS8vxt6rKfeCaVtc9+QWhGLz0+c9GYgD8a0TUmXzvqwC+DWA1EX0OwF4A13gog+BjDN9184hGPL5lr6nvfnqfGyvS0zndupEAwGWHZ0usdtMZ+MS0M/C9jbtNNQFBlfCpD43D6o6e1Hs3zJqYpdiNAK4xaczpmgWhlHiZ7fMS7KtqLvHqvEL1MXJwGN9d2Iala7ZDVQiazrj/anuft1VO/B1zp+LO9TuglWhil1vumj8VOw++h8xKeWbGBS2jsfhj70fnvqNomzDc1qIvtq5BEApB2jvUINXax8WN3FYtDgIKoCqJWbZeYTWX5V/+7kx8/mNnZsljkKv1gjFb+KaVHSbZpWWDUCqkvUMd4ZfhK4UsQG5SGK3SROM6EHfp9snFkHAAM943Ar994+3Ue5dNPx3P7sjOS5g2bih2HjhmmbYKZOfqp98Tw9pXQFmLVq20bKhWI6ReEOVfQ/hl+IqXC1BTSEXEQ99OVNNw++UfwCUfGIO33unHJ6aOQTCgYvOf38my7peteQ0a67DL8kzP1U+/J1FNh6brtrOFa6Flg1+MEMEeUf41hB8af3m5ABkKJZ6h/JVkn34rgipBSQZ5IxqjIahA0xlxjS0DuHEN+Pj/eyG17Se/34Ovz59meewTsURNQUBJNIIDc+ocAEz99zPviRWDQip0Zscc/2qwpv1ihAjOiPKvIfzQ+MurBShdoWSic6LCtjGo4kQ0DhClunLed9W5qXYITSEVa/+4Hw4w6RMAABT6SURBVP/xu7dsz5M5xSumMb6xYRfunD8V92zYBYUoq5CsMRjAikXnYVhjEE0hFf1RzaSc7Sqa0wmphP+8/jxMGzesqOZvfsAPRoiQG5nkVUM4TX8qF14tQLly+ZkZ18+ciIBKaAgoiGuJoeYL2san0kkPHDuJR1/6a97nVgiYPm4YXl5+MW6cNSlre0zXMW3cULQmM3oyx0y6SUXVmXH0RMzR4jcWv+OROE7GdCxbu900zcsv+MEIEXIjyr/GMIqnHv/8DFfVsaXGqwUolwINKsAPX96TnJ+rIaox7nlmF3r7IljX2YPZ9z6PL6zsKKg//0BMx46eYwCAH/9+T9b2O+ZOdbw+456EHWZBxnXgy2u2m9o/pOPHecp2+MEIEXIjbp8apJKNv3r7Ipg0sgkbllyQ5f4ohvR2xnGNs3rpnNSAsJrdGXPngfds3UUGl54zGi929yKkKjgZ1yzjAV9/eicmnNaY5c5oCquYPj53YdaCtvEYPiiEf/7vbalYQSbRuI4rHnwRD1zT6qqXfySuObbHqCTSfdT/iPIXXOEm0Gjlky50ZKEV6QplQ9cB/CDDhZM5dCWhLDmrRbRBUAW+Pn86Fs2cZLq+V/7yDpY80Wk+lsZ4byCWpYA1nW3dGZn3bNq4odBzlBdHNc7Zy5/1RGBZUQjzHn7Jt75/6T7qb8TtI+TEcJtc/+hWzL73eazv7Mn6TLl80iMHh9E6YTjmtY7D4LDZ6s3sr/aRSSMwbdwwxCxSQxuDCn5440ewaOak1HGbRzQm3SjWi8XQxpBrd4bVPTMUuIP3B4C9O2dB23hsWHIBOLmYnYzpvvb9C/5GLH/BEbdpe6XO8Mj1pNE8ojFLqWeme77Y3Ysj/VHcNX8avvbUDtM2BlJ9dHr7Ili1dS9WbNqNkKoiqunIbAEUUBJFXSMHh3P24nG6Z7NbRkFVFMeiNKfgaH9UQ1hVEI1LJo1QHKL8BUfcKvVSZni4SWl8dsf/ugredu47mrDuCbj76V0IqoneQYbFvq6zB8vWbE9V2Ubi8cQ1qgQCI6Co0FjH/Ve3pq43Vy8ep3sGACFVyWrnoGk6gmriXE7BUcmkEUqFKH/BEbeBxlLNl3XzpLFqy9+yLHk7jBbKi2ZMwmXTzsga7J6YA2DRlyegYsWiD2FYY8j09OFGvlwKOnNbXNOhKkrC28TOswFkjq9QKkT5C47kE2gsRYaHXeqi8aTR2xfB3U/vtPyMqsDU1fPK1rHoj2ro7Yukgo+5XFUGidz97IKrXPIBp+7Z0jVdUElBXNdw80Utpm2G8o5qGnSGaQHKVQ2bfp+NojLjGitFNVQeC2ZE+Qs5WdA2HlPHDsUVD70EgFNpk3ZZKcV8+ZtCalZa5smYnnrS2H9kAEE1oTQzCSiEJRe9Hys2d4MYWNd1EL/edQgALF1HdrUD4QCZWjOkK7Vc8hkkHFKEmK4jpgErNndjxebulByG8j42EMXNq/6EmBZP7evGhz9ycNg3raCrpfJYMCPKX7AlXfGVK9CYOA+Z0jbDKqE/2VKheURjVgsGIDGA/c7503DPhl2mhmnpC9XUsUNNtQfZVriOJXNacN2Mial4wPI0C33JnClonTDcUT7jvmW6kww5lq45tWAai0shPny/9M/xixxC/ojyFyzJtObumDu1LIHG5hGNIIVME9VJodR50hW2SoRIXMOVrePxLxe9H/1RzbGHzhUPvohwQDVZp3auKiul9p3n3kQ4oGQtPunyAc7upEhcx0+37sUtl0xJLa53zJ2Ke57ZlZcP3y/9c/wih5A/ovyFLKwU3z3P7MKC1nFY3bE/9bmF7c0l/4K7CWgaCttIz/zNrkN4ZsdBywXKwLC8o0n3Srp1auWq2nngPSgW+f6RuI6gSggHkEoLNfz5BrlaUTy8aTdOawqZFP4d86Zi+rhhrn34uYLK5fLBS/ZR9SKTvIQsuvYdxaJHt6AvcsqV0RRSEdPZ5PbxYuKUobSsumOm033oOK548EVE054QGoIK7piX6L5pBKcbggp0BijZbtlgSDiAxz8/w7ICOTP9M5OmsIqlf382Dr13Ej96+a8IKApimo675k9LFY2t7+zBsrXboWmMWEYBQlNYRSyuZ8tu8QSQ7jvPVOjGOTI/X24fvJ0cQuWRSV5CXuzoOWZS/AAQ03SEAgqip+KSJX+8d9seYl1nD5au2W5SnoY8RvfN9AWkKaRi3sMvmVxJdtZp96HjiWM7jITsj2i4/9dvoD+arA1A4l597akdACXSStMHs3/+sVdNssY0zgpaq0S4e8MuROPWvnM7hZ7psqqED176+FQn0t5BMNHbF8E9z+zKev/Lnzg7q5laKR/ve/siWLYmd3sIQ7lZKWdDHqMFhNFeuWXMEFNbhnCAslw1vX0RPLhxNy5/8IWsY4dUBUGVMCh06utiKP5M7n56V0rmkYPDuPCs0XjgmlZTS4i75k+1mBugI6Sa3UzG4pq4N12W9ya9LUViJvAxKGR9HC8x7rko/upBLH/BhFUArymkYsb7RhZdXOTkh161da+rWbZ2wdSQmkjPBBJuq8xzmOME3XjkhbdSqZcMOLp5FAX410vPxgO/+XPOawyqlCWzlWU8JBwwZRn90+zJ+NHLe0zHMhazxL3JfsrZf2TAlO55Mq5B1xmZhc/igxesEOUvmLAK4Gmc6FzZOmF4wY/3Tn7o3r4IVmzanbVPVMtWWlbyhQIKfnnLBdh58D3Mvvd5R9/zv2/uRiSupxT90jXbHefphgKnfPFu2knYdfnMDCrPbhmFRz77Yfz+L7348ct/xeNb9kLTE8HkhrSMJABYsanb4t4k3Fm5xkOGA9JLX7BGlL9gIle2TSFFXLn80PuPDCCkqqm+OgZL5rRkncuqQnbJnIQLJ/McS9d0mXzdVk8NCgERO8WvEn55ywU5U0hTn3epaI2FMKBQKrZiXHs4AKxY9KFUdXHXvqNZvYAS92ZKTrkGBVX852c/jAvPGu0oj1CfiPIXsih1AC9XLriVNR8OEK6bMdFRvnQXzkObukEZfvRInFM59YDNU42DPr/2/AloGTPEshArE2OhGNEUsnQ7GRixDTsXU0hVMawx5NgnKBxQUvfGSS4djGnjhjrKLdQvEvAVLCllAC9XLrjV2L/0Lpp2GC6c45E4onE9a5gLADy8qdsUgM08z10Lptr211/dsT8VVM3c74ZZE02vH7imNeV2cpp7YBXbsLsv9vfmXFOFsrEtqBICCmR0ouAKsfwFz7Fy1WRm2+T7tGH1NBFSKSv9M6Sag8Z2wdfbft6V5dNPfzqx2u+Ll5yVeg0As+993jHF0i62ASSC6hqzpcJ2ujeZ24x7IymXQi5E+ZeBaut46IW8Ttk2RlA2n3iC1dMEUcIlkm5ZW2W6ZJ4n1bguo2jMygq3a2L3wpuHbVMs02MORJljYoCrzhuPK9vGWXYRtTu307Zq+BsTKo+4fTzGzQhEP+G1vOmumvR89d6+CLr2HXU9jtDOVXT/1e7GLGbSMmZIVj6+233XdfbgppUdOBHNKIzLWDysOoICwK9eO4jF/70NL3e/4+LKBaE0iOXvIdXW8dBree0Cv6u27sW/b+52TNG0ehqxc4cUGqwuJNB9qoOn2Zq3SrG06lgKACccWmQLgleI8veQaut46LW8Vq6aqKZjxabdiMTZdsFxqhGwcocUM1Mg332t7pldiqVVx9J0/Py3IdQe4vbxkGrreOi1vFaumiVzWhBSzYNQ0tsRpD+NOLV9yId8XUxONI9ozKpPsEuxTL/+prCatd2416WUTxDsEMvfQ9y0J/YT5ZDXKjtlxWZzBWv6glPqp5FSd7z8/m/fRLqrnwDHe5Z+/Tt6jmV18fTLdC6h9hHl7zGV7HhYSNZOueXNteCU8mkkvWNnKWIa3YeOY+WWvab3GMDUsc6FVYZrqXXCcFw2/Yy80kUFoVSI8i8DxfigC6VQC9frtFS3rYkNSvU0sq6zB0t/3mXZBrrQp4jOfUdt328ZM8TVMdL/Nrr2Ha2qGJFQ3Xim/InoRwDmATjMzNOT750G4EkAkwHsAbCQmY94JUO9UmjWjtdDQHLJ5cZVUsiilGoDbRFoLSam0WYxa8Dp/VxUW4xIqG68DPj+BMBlGe/dDmAjM08BsDH5Wigxhp88nVw93fMJrBYakCxELoNi2k3sPzIAlbJHMgLAHXOnFmxVt4wZghtmmfsP3TBromurPxOrgLifY0RCdeOZ5c/MLxDR5Iy3rwRwUfLnxwBsBrDcKxnqlUIsSLeB1WKeDipl2TaPaETMooPboJCK6eOHFXXsb1z5QdwwczI69x1FW3JwjBOZbrXM1zIVSygX5fb5j2HmgwDAzAeJ6HS7DxLRYgCLAWDiROvujoI1hfjJ3SjmYovAKpX9NHJwGHfNn5YYs5iGzta99/OlZcwQV9Z+5sK58MPNWL1tf9ZCWokYkVB/+Dbgy8yPAHgESAxwr7A4VUe+FqQbxVyKtMtKWbaLZk4CKDFmMagSNN26iZpXWC2cRqaQZPYIlaDcyv8QEY1NWv1jARwu8/mrhlJk3eRrQeZSzKVy21TKsl00YxIum3aG7fV5melkN34yHcnsEcpJuZX/egA3Avh28t91ZT5/VeB11o0TubpHVlPRmhV215fvPc93obBaODORzB6hnBCzNx4VInoCieDuKACHANwF4CkAqwFMBLAXwDXM/G6uY7W3t3NHR4cncvqN3r5IqtDHoCGo4OXlF/tGyZbKQs7nOIWcM9c+xvamkIq5D71kagXtdM8LXZzXd/aYFs6F7c1Y3ZHt8y+WamshLngHEW1j5narbV5m+3zGZtMlXp2zFqiGZnCZ1nMhyiYfBVqIss21T/r2E9F4Vq81u3teTNA710CYUvx+K/nUKFQXvg341ivVVuhTiLLJR4EWomxz7WO1PZOoZn3Pi12cnQbCFEu1tRAXKot09fQZ1VToU2jHzXyKvQopDMu1j9X2TJbMabG8535enIspohPqD7H8fYhfC30y3TuFWsH5KNBClG2ufXIFX8MBwnUzrGtL/Bz0bgqpiGj+XJgE/yHK36f4rdDHyr0zu2VUQVZwPgq0EGWba5/M7QOxOIgIDQHV1fFLtTiXMjBr/H4omcDREEw8AfhlYRL8h2fZPqWknrJ98qFcWR1OGUgvd7+TpWTdBhj9ku2TPjugXE9apQzMWv1+Qirhl7d+rOA+Q0JtUJFsH8FbypnV4eTeKcYKzufpppAnoVz7WAVfy0GpA7NWv59wQEV/xkB5QUhHAr5ViBejDZ3I5UMvpuNmPVLqwKyfg9CCfxHlX4WUO6ujmjKQ3FDpGbmlVta19vsRyoO4faqQSlh6fs1Ayhc/FEF5kTFUK78foXxIwLdKyWwVIJWcufFb6wxpwyB4jQR8axCx9PLHbV1CuZSy39J5hfpClH8VI8ojP9y4y/zgFhKEciABX6FuyBUYLXcWlSBUErH8hZrBjbvGyV1WDR1VBaFUiPIXaoJ83DV27jLJlxfqCXH7CFVPqdw1ki8v1BNi+QtVTyndNZJFJdQLovyFqseLillR+kKtI24foeoRd40g5I9Y/kJNIO4aQcgPUf5CzSDuGkFwj7h9BEEQ6hBR/oIgCHWIKH9BEIQ6RJS/IAhCHSLKXxAEoQ6pimEuRPQ2gL9VWo4cjALwTqWFKANynbVHvVxrPV7nJGYebfWhqlD+1QARddhNzKkl5Dprj3q5VrlOM+L2EQRBqENE+QuCINQhovxLxyOVFqBMyHXWHvVyrXKdaYjPXxAEoQ4Ry18QBKEOEeUvCIJQh4jyLwFEpBLRn4hoQ6Vl8RIi2kNErxFRJxF1VFoeryCi4US0hojeIKLXiWhWpWUqNUR0dvL3aPz3HhF9qdJyeQER/SsR7SSiHUT0BBE1VFomryCiLyavc2eu36e0dC4NXwTwOoChlRakDMxh5lovlPk+gGeZ+WoiCgEYVGmBSg0z/xlAG5AwXgD0APhFRYXyACIaD+BWAFOZeYCIVgO4FsBPKiqYBxDRdAA3ATgfQBTAs0T0DDPvtvq8WP5FQkTNAOYCeLTSsgjFQ0RDAVwI4IcAwMxRZj5aWak85xIAf2Fmv1fRF0oAQCMRBZBYyA9UWB6v+ACALcx8gpnjAH4H4FN2HxblXzzfA7AMgJ7rgzUAA/gNEW0josWVFsYjzgTwNoAfJ115jxJRU6WF8phrATxRaSG8gJl7ADwAYC+AgwCOMfNvKiuVZ+wAcCERjSSiQQCuADDB7sOi/IuAiOYBOMzM2yotS5mYzcznAbgcwM1EdGGlBfKAAIDzAPwHM38IQD+A2ysrknck3VoLAPy80rJ4ARGNAHAlgPcBGAegiYiur6xU3sDMrwO4F8BzAJ4F0AUgbvd5Uf7FMRvAAiLaA+BnAC4moscrK5J3MPOB5L+HkfAPn19ZiTxhP4D9zLw1+XoNEotBrXI5gD8y86FKC+IRlwL4KzO/zcwxAP8D4KMVlskzmPmHzHweM18I4F0Alv5+QJR/UTDzV5i5mZknI/Ho/Dwz16RVQURNRDTE+BnA3yPxmFlTMPP/AthHRGcn37oEwK4KiuQ1n0GNunyS7AUwk4gGEREh8ft8vcIyeQYRnZ78dyKA/wOH361k+whuGQPgF4nvDwIAfsrMz1ZWJM+4BcCqpEvkLQD/WGF5PCHpF/44gC9UWhavYOatRLQGwB+RcIH8CbXd5mEtEY0EEANwMzMfsfugtHcQBEGoQ8TtIwiCUIeI8hcEQahDRPkLgiDUIaL8BUEQ6hBR/oIgCHWIKH9BcAERacnulzuI6GkiGp58fzIRMRHdk/bZUUQUI6KHKyexIDgjyl8Q3DHAzG3MPB2Jysmb07a9BWBe2utrAOwsp3CCkC+i/AUhf14BMD7t9QCA14moPfn60wBWl10qQcgDUf6CkAfJ3veXAFifselnAK5NtvjWULttg4UaQZS/ILijkYg6AfQCOA2JzonpPItEq4TPAHiyzLIJQt6I8hcEdwwwcxuASQBCMPv8wcxRANsA3AZgbfnFE4T8EOUvCHnAzMeQGAv4ZSIKZmz+DoDlzNxbfskEIT9E+QtCnjDzn5AYlHFtxvs7mfmxykglCPkhXT0FQRDqELH8BUEQ6hBR/oIgCHWIKH9BEIQ6RJS/IAhCHSLKXxAEoQ4R5S8IglCHiPIXBEGoQ/4/X2gwrZRjWqIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind = \"scatter\",x = 'RM',y  = 'MEDV')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Trying OUt Attributes Combinations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "254     51.571709\n",
       "348     42.200452\n",
       "476    102.714374\n",
       "321     45.012547\n",
       "326     45.468948\n",
       "          ...    \n",
       "155     65.507152\n",
       "423    109.126659\n",
       "98      35.294118\n",
       "455    102.068966\n",
       "216     46.875000\n",
       "Name: TAXRM, Length: 404, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['TAXRM'] = housing['TAX']/housing['RM']\n",
    "housing['TAXRM']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>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",
       "      <td>404.000000</td>\n",
       "      <td>399.000000</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",
       "      <td>22.509406</td>\n",
       "      <td>67.696004</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",
       "      <td>9.385531</td>\n",
       "      <td>31.577122</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",
       "      <td>5.000000</td>\n",
       "      <td>24.645639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.086963</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",
       "      <td>16.600000</td>\n",
       "      <td>44.910026</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",
       "      <td>21.150000</td>\n",
       "      <td>54.831220</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",
       "      <td>25.000000</td>\n",
       "      <td>98.673976</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",
       "      <td>50.000000</td>\n",
       "      <td>187.026116</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%      0.086963    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        MEDV       TAXRM  \n",
       "count  404.000000  404.000000  399.000000  \n",
       "mean    12.791609   22.509406   67.696004  \n",
       "std      7.235740    9.385531   31.577122  \n",
       "min      1.730000    5.000000   24.645639  \n",
       "25%      6.847500   16.600000   44.910026  \n",
       "50%     11.570000   21.150000   54.831220  \n",
       "75%     17.102500   25.000000   98.673976  \n",
       "max     36.980000   50.000000  187.026116  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = housing.corr()\n",
    "corr_matrix['MEDV'].sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17495e442c8>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind = \"scatter\",x = 'TAXRM',y  = 'MEDV')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "254    21.9\n",
       "348    24.5\n",
       "476    16.7\n",
       "321    23.1\n",
       "326    23.0\n",
       "       ... \n",
       "155    15.6\n",
       "423    13.4\n",
       "98     43.8\n",
       "455    14.1\n",
       "216    23.3\n",
       "Name: MEDV, Length: 404, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing =   strat_train_set.drop('MEDV',axis = 1) \n",
    "housing_labels =  strat_train_set['MEDV'].copy()\n",
    "housing_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Missing Attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(399, 13)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = housing.dropna(subset =['RM'])      #option1\n",
    "a.shape\n",
    "#original data fram remain unchanged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</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>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>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",
       "    </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>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",
       "    </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>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",
       "    </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>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",
       "    </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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>3.53501</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.58</td>\n",
       "      <td>1</td>\n",
       "      <td>0.871</td>\n",
       "      <td>82.6</td>\n",
       "      <td>1.7455</td>\n",
       "      <td>5</td>\n",
       "      <td>403</td>\n",
       "      <td>14.7</td>\n",
       "      <td>88.01</td>\n",
       "      <td>15.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>7.05042</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.10</td>\n",
       "      <td>0</td>\n",
       "      <td>0.614</td>\n",
       "      <td>85.1</td>\n",
       "      <td>2.0218</td>\n",
       "      <td>24</td>\n",
       "      <td>666</td>\n",
       "      <td>20.2</td>\n",
       "      <td>2.52</td>\n",
       "      <td>23.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>0.08187</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.89</td>\n",
       "      <td>0</td>\n",
       "      <td>0.445</td>\n",
       "      <td>36.9</td>\n",
       "      <td>3.4952</td>\n",
       "      <td>2</td>\n",
       "      <td>276</td>\n",
       "      <td>18.0</td>\n",
       "      <td>393.53</td>\n",
       "      <td>3.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>4.75237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.10</td>\n",
       "      <td>0</td>\n",
       "      <td>0.713</td>\n",
       "      <td>86.5</td>\n",
       "      <td>2.4358</td>\n",
       "      <td>24</td>\n",
       "      <td>666</td>\n",
       "      <td>20.2</td>\n",
       "      <td>50.92</td>\n",
       "      <td>18.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>0.04560</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.89</td>\n",
       "      <td>1</td>\n",
       "      <td>0.550</td>\n",
       "      <td>56.0</td>\n",
       "      <td>3.1121</td>\n",
       "      <td>5</td>\n",
       "      <td>276</td>\n",
       "      <td>16.4</td>\n",
       "      <td>392.80</td>\n",
       "      <td>13.51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>404 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        CRIM    ZN  INDUS  CHAS    NOX   AGE     DIS  RAD  TAX  PTRATIO  \\\n",
       "254  0.04819  80.0   3.64     0  0.392  32.0  9.2203    1  315     16.4   \n",
       "348  0.01501  80.0   2.01     0  0.435  29.7  8.3440    4  280     17.0   \n",
       "476  4.87141   0.0  18.10     0  0.614  93.6  2.3053   24  666     20.2   \n",
       "321  0.18159   0.0   7.38     0  0.493  54.3  4.5404    5  287     19.6   \n",
       "326  0.30347   0.0   7.38     0  0.493  28.9  5.4159    5  287     19.6   \n",
       "..       ...   ...    ...   ...    ...   ...     ...  ...  ...      ...   \n",
       "155  3.53501   0.0  19.58     1  0.871  82.6  1.7455    5  403     14.7   \n",
       "423  7.05042   0.0  18.10     0  0.614  85.1  2.0218   24  666     20.2   \n",
       "98   0.08187   0.0   2.89     0  0.445  36.9  3.4952    2  276     18.0   \n",
       "455  4.75237   0.0  18.10     0  0.713  86.5  2.4358   24  666     20.2   \n",
       "216  0.04560   0.0  13.89     1  0.550  56.0  3.1121    5  276     16.4   \n",
       "\n",
       "          B  LSTAT  \n",
       "254  392.89   6.57  \n",
       "348  390.94   5.99  \n",
       "476  396.21  18.68  \n",
       "321  396.90   6.87  \n",
       "326  396.90   6.15  \n",
       "..      ...    ...  \n",
       "155   88.01  15.02  \n",
       "423    2.52  23.29  \n",
       "98   393.53   3.57  \n",
       "455   50.92  18.13  \n",
       "216  392.80  13.51  \n",
       "\n",
       "[404 rows x 12 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.drop('RM',axis = 1)               #option2\n",
    "#original data fram remain unchanged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median = housing['RM'].median()     #compute for option 3\n",
    "housing['RM'].fillna(median)       #option3\n",
    "#original data fram remain unchanged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(404, 13)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SimpleImputer(strategy='median')"
      ]
     },
     "execution_count": 25,
     "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": 26,
   "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": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.statistics_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = imputer.transform(housing)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_tr = pd.DataFrame(X,columns = housing.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>0.086963</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%      0.086963    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": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_tr.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scikit Learn Design"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creat Pipeline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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 pipeline as u want\n",
    "                       ('std_scaler', StandardScaler()),\n",
    "                       ])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_num_tr = my_pipeline.fit_transform(housing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.43942006,  3.12628155, -1.12165014, ..., -0.97491834,\n",
       "         0.41164221, -0.86091034],\n",
       "       [-0.44352175,  3.12628155, -1.35893781, ..., -0.69277865,\n",
       "         0.39131918, -0.94116739],\n",
       "       [ 0.15682292, -0.4898311 ,  0.98336806, ...,  0.81196637,\n",
       "         0.44624347,  0.81480158],\n",
       "       ...,\n",
       "       [-0.43525657, -0.4898311 , -1.23083158, ..., -0.22254583,\n",
       "         0.41831233, -1.27603303],\n",
       "       [ 0.14210728, -0.4898311 ,  0.98336806, ...,  0.81196637,\n",
       "        -3.15239177,  0.73869575],\n",
       "       [-0.43974024, -0.4898311 ,  0.37049623, ..., -0.97491834,\n",
       "         0.41070422,  0.09940681]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_num_tr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Selecting a Desired Dragonmodel for Real Estates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 37,
     "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 = RandomForestRegressor()\n",
    "#model = DecisionTreeRegressor()\n",
    "#model = LinearRegression()\n",
    "model.fit(housing_num_tr,housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "some_data = housing.iloc[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "some_labels = housing_labels.iloc[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "prepared_data = my_pipeline.transform(some_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([22.259, 25.581, 16.443, 23.33 , 23.617])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(prepared_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[21.9, 24.5, 16.7, 23.1, 23.0]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list (some_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " # Evaluating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.4575625123762352"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using better evaluation technique - Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.87550075, 2.77982201, 4.54687659, 2.54078878, 3.47244542,\n",
       "       2.64369504, 4.68627431, 3.28243822, 3.41800749, 3.32562501])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_scores(scores):\n",
    "    print(\"Scores\",scores)\n",
    "    print(\"Mean\",scores.mean())\n",
    "    print(\"Standard seviation\",scores.std())\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores [ -8.26850456  -7.72741039 -20.67408676  -6.45560763 -12.0578772\n",
      "  -6.98912347 -21.9611669  -10.77440065 -11.68277522 -11.05978172]\n",
      "Mean -11.765073451646334\n",
      "Standard seviation 5.142286049710338\n"
     ]
    }
   ],
   "source": [
    "print_scores(scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Saving the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Dragon.joblib']"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from joblib import dump ,load \n",
    "dump (model , 'Dragon.joblib')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing the Model on test data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.9530923639134476"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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