Real Estate House Price Predictor Project .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 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": [
    "df=pd.read_csv('data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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": [
    "df.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": [
    "df.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": [
    "df['CHAS'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "</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"
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   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000013BB9F03130>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000013BBBF49610>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000013BBBF7AA60>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000013BBBFA5EE0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000013BBBFDE370>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000013BBC00A700>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000013BBC00A7F0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000013BBC035CA0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000013BBC09B4F0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000013BBC0C6940>,\n",
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       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000013BBC1596A0>,\n",
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       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000013BBC1B0040>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000013BBC1DD520>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(bins=50,figsize=(20,15))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# if you are in any python file you need to write plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train test spliting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#learning purpose\n",
    "def split_train_test(data,test_ratio):\n",
    "    import numpy as np\n",
    "    np.random.seed(42)\n",
    "    shuffled=np.random.permutation(len(data))\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": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_set,test_set=split_train_test(df,0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# (f'printRows in train set {len(train_set)}\\nRows in test set {len(test_set)}\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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(df,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": 14,
   "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(df,df['CHAS']):\n",
    "    strat_train_set=df.loc[train_index]\n",
    "    strat_test_set=df.loc[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    376\n",
       "1     28\n",
       "Name: CHAS, 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": [
    {
     "data": {
      "text/plain": [
       "0    95\n",
       "1     7\n",
       "Name: CHAS, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strat_test_set['CHAS'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 376/28"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 95/7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=strat_train_set.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Looking for Correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix=df.corr()\n",
    "corr_matrix['MEDV'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas.plotting import scatter_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000013BCCFCDDC0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000013BCCFF7220>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000013BCD023670>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000013BCD04EAC0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000013BCD07FF40>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000013BCD0B6310>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000013BCD0B6400>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000013BCD0E28B0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000013BCD13C100>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000013BCD1754F0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000013BCD1A1940>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000013BCD1CCD90>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000013BCD205220>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000013BCD232670>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000013BCD25EAC0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000013BCD28EF10>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "attributes=['MEDV','RM','ZN','LSTAT']\n",
    "scatter_matrix(df[attributes],figsize=(12,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x13bcd2df070>"
      ]
     },
     "execution_count": 23,
     "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": [
    "df.plot(kind='scatter',x='RM',y='MEDV',alpha=0.8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Trying out Attribute combinations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['TAXRM']=df['TAX']/df['RM']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix=df.corr()\n",
    "corr_matrix['MEDV'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x13bcd4734c0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tdMvN/k1EbO3ZoyjKNFrWx1/YZnlld4Ijw2lcz+B6ZlrzteIOmAC3P7yXNb1JMPDqiXFOTeTo60zgeN68F02VGim5deNKrr/3J/nnqM9fUZSQllX8tbZZLkyRDLt1xiN+le/K7iRHhicYmcgRj1pT5vgOjqZJRGzSjlvXbJvCi1MiYnP9vT+ZEpAOM5Q020dRlJZV/KWs5GrbLPd3JkhlXV47kfIHshhDb3uMr/zuxnxWT5hlM55xOD7uZwklY3ZJy3uu0jDDi1PhhSm8bdR4SEVRmo+WVfxwekNMwvz9sPDZv+9nAIXxA4ATgX/9xHiWZKxtmuU92zTMSheLwtVMaPFrzx5FUUJaWvHD7KpcB0fTtMcj9LbHybkeE1mXgdE0N93/U2IRiz/4tbOAoEumgYhl4RqDJYLrTVb5FqZh1uKSmeliUWo1oz17FEUJaXnFPxtCy9kJhrQfGfbTOZOxCAbDPU+8DBg/Z0omn+cZM8XyLpWGOZNLptqLhY5kVBSlHC2bznk6hBb1yESO/YOjuMaf3vXi0CiZnIclcMOlZwPQ0+Yr3J5kDJhqeZdLw6zkkqklZ78rGaW/M8HgaFpTOhVFyaMW/yzZuLqHWMTGEt/PbwDPg0OnUpzZk+TKi1Zy5UUrK2b1zMYlU4v/Xts4KIpSiropfhFJAE8A8eA43zHGbBORXuDbwFrgFeA6Y8zJeslRirnIohkcTWOMwbYsLDHkXJNv+XDDpWfn9zvT/mt1yVR7sZht/EBRlMVPPS3+DPAuY8yYiESBJ0Xk+8A1wKPGmDtE5BbgFuCzdZRjCnNlBfd3JrAtC9fzguZuPj3tMa68aOW051e62NQaYK7mYlHKJTQykeN/v3KcX1m7tGWVv3YwVZQZFL+ILDfGDMxmx8bPbxwL7kaDPwNcDbwz2P4N4DHmSfFXYwVXqxi6klE+85vr+cP7f0po61sCdjDvtpBqLja1KqSZLhbFLqHjY2mGxrJ8/nsvYFvSkm4fdX0pis9Mwd2ficgjIvL7ItJV685FxBaR3cAg8Igx5sfAcmPMEYDgtuQvT0RuEJFdIrJraGio1kOXZKbA6OP7Brnm7qf46H3PVNVlc93yDlb3tHHOsnbOXtbO+WcsIRmLTAm0luucefB4Kt9Hp9bjVkNhj6GRiRxDY1n6O+MNayTXaLSDqaJMMpOrZxXwG8D7gS+IyH8A3wJ2GmMmZtq5McYFNopIN/BdEXlTtYIZY+4B7gF/9GK1r6tEpcDobHzivrtHsC0hYlmBz12mBFpLXWwGR9J84GtPY1tCzvXIuYbuZLTkcU/HNRG6hP73K8f5/PdeoDMRzcvQapW8s0mdVZTFSkWL3xjjGmN+aIz5P4HVwN8BW4GXReQfqz2IMeYUvkvnt4ABEVkBENzOW/P6Up02w8DobFobh/sbTTvsHxzl8PAE6ZzD7oOTserilM1U1uFEKovjeRwZTjMwkmZwNMNE1p123LlYCXQlo/zK2qXYltSUNrrYmE3qrKIsVqrO4zfGZIG9wAvACHBBpeeLSF9g6SMibfgrh18AO4EPB0/7MPBQ7WLPnsvW97Pjxs38zYfeyo4bN+d9vDMphnDmbrFr4OxlHVjAGUsSrOvvZElbNO9CCK31my8/j6xjOJXKMpH16E3GODHu7ydi+x/BkeGJYKzjZEvluXJNVLrgtQp6DhRlkhmzekRkDfA7wO8C7cD9wNXGmBdmeOkK4BsiYuNfYB4wxjwcuIseEJE/AF4D3nc6b2A2lAqMlkuTBPjmj1/lnidewrb8wG0YFHx83yCfe/DnDI5lsMR3+XQmooxnHL73/GHueeJlXM8j63oYY4hHbGwLcq7BM4aIZQW3/vyv0XSOWMTv7pl2JlcA4e3puCa0klfPgaKEzJTV8+/4fv7vADcYY3ZVu2NjzHPAW0psPw68u0Y554Xi1sb/68UhPvfgHo6O+OGMlV1txKMWtz+8l3uXdXD7w3uJRSwkaMdwZDiNbfn/f/nR/ZwYzyIIWdcjYgnnLff79WccD2MIevcL/Z0xYhGbu67byDl9HXnfPsxto7XZ9CVabOg5UJSZXT23AmuNMTfXovQXMl3JKIdPTfChv/0J23bu4cjwBCZosHZkJE3EsjAGnn3tBFnHIx6xWbEkgQQN2LKOx4cueQMnxrNYIoj4PTtdz5DJ+Uq8Ix7h1veczxlLEqxYkqAtFmH7lgu5+A2TgWR1TSiKUi8qWvzGmMdF5MMi8nHg/GDzC8BXjDF/X3fpGkCY3RMqeyOGnAOW+G2Xx7MOWcfjL/51P0dH0hwdSbOyq43VPW35QS5DY5MB4aBBJ+Dfhpb7e9+6mve+dXVFt4O6JhRFqQcVLX4R+T3gE8CngJX4bp/PADcFjy06wiye9rh/TRQk774xGDzP773fFrNY1d0GwOEgMBsOcjmnr5Ol7TE84z8/YgmWgGf80Y43X35e/jjrlnfOWCg203MKKReEVqaj50ppdur1HZ0puPt/Ab9tjHmlYNuPRORa/CDvorP6C1sur+hKcOjUBCJwxpIkN77zbM4/o5NPf+c54hGbeMRmXX+E0XSOu67byMVv6AF8Zf3FazewbeceHNcQsYXP/OZ61i3vZP/AGHc+si9/vLmsHtXK1OrRc6U0O/X8jko4SarkgyJ7jTEl0zYrPTbXbNq0yezaNX8hhvCEGwOegRsuPYsrL1qZD7pec/dTUwq9XM+w48bNJRulFbppanltqddXotZ9tzJ6rpRmZ66+oyLyjDFmU/H2mSz+StW5M1buLlQq+dZraaVcnEFSS/VorVd7rUytHj1XSrNT7+/oTIr/jSLyXIntApx92kdvYgqVdrHlHV4YDgyNAYZz+jqr2md/Z8LvnZPO0R6LBOmcU4vEwlTS2bSPqLRvZRKdSaw0O/X+js6o+OfkKAuY0PJ2PT8we+t73sgVG1aw++DJmv1vuw+eJON4HBvLALC0PcYXr91AVzI6xcLPOh6OZ1jWEcf1jJ/z75qKV/tK+1amojOJlWan3t/RmRR/mzHmFwAiEjfGZMIHROQS4NU5kaJJCVM70zmXY2NZPGO46ds/JZV1uPvxAzVZ5OG+OhMRepKxfEO3jat7pjWIc70cR4bTeJ7h5ESOMA6zf2CMdcunry4q7VspjabKKs1OPb+jMxVwfbPg//8oeux/zJkUTcrgaBrXMxwbywIQsfzT9Rf/ug/XMzU1dCv02dmWsKQtiiX+9sLHRtO+0nc9w8BoBtf1B7X3d8a585F9JdO6Ku1bKU+tqbKKMt/U6zs6k+KXMv+Xur/oCP3mXlDM5RmDiBC1rXxDNaiu02OlJnDhY6ms41v6xk8BjVqCZQlrl7bT2x4ve3HRzpOKotTCTIrflPm/1P1FR1cyyq3v8cMcjudhgGXtUWIRi1vfc35N7RQqtWAIH8s6Xv7isrwzgQRN4TxjKipzbe+gKEotzJTHP4hfqCX4HTrvDx/CH5K+vO4SMv95/MX8y3OH+cL3f5HvvXPDpWfn5+oeGBoFJN9cbSYq5eYfPJ7iA197mljEIhmL5Mclrupuq2pcos6TVRSlkHJ5/DMp/g+XfRAwxnxjDmSbkUYrfvCVathqOWihz9aNK3lw9+H8cyop5kKlPDKRY9/ACOuXL2FJW3SKsi4sHhOBmy8/j3XLO+dVmS/GC8hifE+KMhOzKuCaL8W+UPj6ky8Ti/iZN6msw1d+9CJrepMkY5GKmT2FqZrHxzKMpB0Evyq4Oxmlqy0apIqezxUbVnLvso78hWH10mReaQF1V1qLsZXBYnxPinI6zNSPf2elx40xW+ZWnOaluJLOEplyW66y7uDxFJ978OfEIhYRy+LUhDNlvydTOcYyDsbAx+/fzZ7Dw/xgz0D+8a0bV/LPzx7C9Qy2JWzfcmHdlFaYFhq+L8+YGdNUm53ZzFJWlMXOTHn8vwocxB+w/mNaIJOnHMWVdF7gIgtvSwVfwwldR0fSWCK0RUufvpxr8GdwwV8//hJrl7WTjEVIZR3++6MvgjHYloXB8Nl/fo4ffuIygDl3XQyOpkllXY6PZfCbUENve3xBtzLQ9gyKMp2ZFP8ZwOX4Yxc/AHwP+JYxZk+9BWs2SlXSffxd5/Lg7sMlK+tCSzOc0JVzPTJO+f2HkRbXTN5JZf3GTACu6xG1hOPjWf7pmdf41k8O5l8bui5qbeoWtodIOy79nQkSEZuBkTSeIX8hCp+zUNH2DIoynZl8/C7wA+AHIhLHvwA8JiK3G2P+aj4EbCZKVdL93q+exYGhUcbSLh0Jm+FUjq5kNG9pJmMR+tpjHBquvpjqVDpLPGozUPSanGewBf7uqVdoj0fyimzbzr188JJR7nv6YD7wXMmPHfq8U1mXY2MZlrbHaI9H+MDb1iAiUBDwFxGGxtKsXpqs8Ww1B9qeQVGmU82w9ThwJb7SXwt8BdhRX7Gal+KOm7sPnuSWHc9P65ETtksIUzJrYTjlELMyeCUe60hEiNpW3nWRdTxeP5niju/vQwRWdbcRi1hl/diFfvwT475cJ1I52uMRvv7kK1hAJDJZ3uG7sha2h6+a0nfN+lFaiZkmcH0D+HfgYmC7MeZXjDF/aow5NC/SNTnDqRzbdu7l2FgGSwRLfFfMtp2+J+zmy89jaCyLMb4P365Cf9oCZ3QluGrDyvzkrkJ+4/zlgDAykSPreBw65XfHDo9/ZHhyLnCpKt9wWxiUjlgWGP9+1Ba6k9Fg2piv9Jd1xDmnr2O2p6hpqFT6/vi+Qa65+yk+et8zXHP3Uzy+b7ABEirK/DFT5e6HgPOAm4B/F5GR4G9UREbqL15z4/fy8fJK1xJBkHwnzXXLO1nV3cZZyzo4s6cNO+j1E7GEtkjpq4Br4Ohwmh/sOeqPbiwos7CAx/cPMZbJ8frJFPsHR/GM4YwlbXmj3EDepVHKj+23oYB0zvW7fnoeBGMhI7awfcubWNWdZHlnnFXdSe645qJFbQEXZv0kYxFsS7j94b0leyLVOgZPRzsuPhbLZzqTj3+mC0NL09+ZwLasfGYPgMFXoIVKN+t6dCb8Vg9DoxlG0w5OKT9OQFtU6E7GSOcchsZywbxeQODYWHbyvn9Af0zkkgSHh33rX0TK+rF3HzxJOudwfDyb38ey4HlhXGDzucumuT0Wqyuk2qyfWmsBtHZg8bGYPtMZffxKebqSUbZvuWCaj3/7lgvzVbiFPfJ7kjESUYuxzJT46TRSWQ/XM/S2JzgxnvMzfZhU9oVZNwBDoxnO7Emyqrst306inB/79of3sqQtSm97nPGsg+cZvvz+t9DXESftuPngdLHS27ZzL67nYVsW27cs3C98MdVk/dRaC1BYuzFTcZ+yMFhs9SCq+E+Ty9b384ObLp0yjasrGeXg8RS37vg5YvyAq+MaDIacM9V9UwrXwMlUlmNjmbzSL6Yw5Nq/JM7/c9UbWb98CemgQ2cpiq3bJYko4xmHXxwd4dNPvpx/XqElc/B4ipv/6WecSmXzq5tbdjzPD266dEF+4YupJuun1pGZt+74OYOjfu3Gyu4EncF51tqBhctiqwdRxT8HdCWjXPyGyaEnj+8b5FP/9LMp2TwRS+hJRshU8vEUcHQ4jQhT3TpFmGC/8YhN1vG4/t6f5B8rtQwttm5TWYd0zuXux16iLWZNs2R2HzzJH3/3eY6NZRFAMFiWcGwsw4GhsSnveSEzU9ZPtbUAw6kct+x4nqHRNK4HDoZDpyZY0ytaO7DAWWz1IOrDL8HpBHBCS/9EUQqn4xmGxmrbnyVS9gOyxFf6yzpifOY3z+fOR345LUB58HhqyvsobN88OJLmtRMpcq7H0ZEJMjn/ghQOlTkwNMrtD+8NAtb+RcbxDJNN/RZXV+5KWT/Vtr0+MDTGsbEMtmURsfzzlnMN6ZyrtQMLnMXW+lwt/iJOJ4ATtmgYHE1TzuEykfMqWvEhoaItxraEj156Fu9+4xmErqVSy9DBkXS+xXPh+7hsfT/3LuvgA197mjW9SeIRm1MTOQ4PT9AenxzSHjqSOhNRbMufCDY1xXPmAfOLKSBc3Ri8yc8rYluI+PMVvnDNRVx6XuXvUDOdq2aSpZlYTOM6VfEXcDoBnMIWDX6OfGnNblt+2mfO9WZU/tNeK9DfEWNVT7Jk//9CFysQECMAACAASURBVM6JVLZs59ChINgcjmpc1d3GoVMTjEzkiEctbrvqgnzuvuN5nNnTxqGTExhgRVeCz29904znYzFlQIQUB72LOaevk6XtMY6PZzHG/wYs64jz5jMru8Sa6Vw1kyzNyEzfgYWCunoKCGfsGkN+pu5Ms3QLXwt+i4aV3QmiBdVa4UmO2n6eP/gplzNhMbVmdklblGNBgdjVX50sNCpehmYdj2UdcZIx/7pe+D4e3zfIJx/YzdGRNPsHRxme8JXUiq42vvK7G9lx42YuW98/ZZ+WCKt6ktx+9YU8/IfvmFER1JIbvxgYTuV49tWTHBga5barLmBVdxv9nXFWdbfxxWs3VFQUzXSumkkWpb6oxV/A/oHRKZWwyzpiJKJ2xQDOcCrHgaExxjI5XM+3ujsTUdb0Cumcy+eufCPt8Sif/s7PODGexTW+n7y3PerfrxDrNfi+fNdARGAs42CJYABT1DK5cBmaiNhcf+9PpgWiEhE7vypZ2eVb+a+dmCBiCd1tEY4OZ7j4Db3545da2obxj0pL3cWWAVGOcDjPlx/dn29/sbQ9xm1XXci65R1VuQOa6Vw1kyxKfVHFHzCcynHnI7+kryPGsfEcnjEMjmb48u+8peyX/vF9g1Ny+NtjNm2xCE7UIAL/7bcvylvHX7p2w5Rc+BsuPYsv/9t+TqSyeJ7Jp23GIgJGyLpe3llkCfR0xBhO+e09BWiPR0jn3Ck/ysJl6G1XXcC2nXtIZdygIvfCfKpnPGITsSw/awgAw3Da4aZv/xQwXLFhZf49Fu6zWjfAYsuAKIVf27CH109O4HiGqD3ZsuNLP/wFD33s16pSls10rppJFqW+qKsnILR2lnYkOLevg7VL21nZ1ca65VP71IQW795Dw/zxd59naMR/nQDjWZeIJfzF+96cd5mEbFzdw13XbeDL738LD31sM91tUU6kskFC/qRDJ+sYXOMvA2xL6O+Mc+NlZ9Mei2AweMawoiuRD8JW/lHKlEBt4Q87Fyw1DGBbFrYIxhj+7HsvlG1XUK0bYLFlQBQTnovwoxPAcU3w/2TLjpn2sX9gFGBOztVctBJY7J+bMola/AGhUkxlnfz0qeLWC4XtjIdG05NtF4JBKpb43TLBVCz3v/ny8yZXF2M53EAJhyFh1/OV/pndbcSjFj/YM8A//P7b+V8vDvHXj7+E6xmyjmH7ltI/yslAsxCPxCaDuzduzhcrZXJ+cNkS8Dzjp2oCA6MZvvf8YT7w9jdM2Wc1boDCbJDFlAFRTHgu2mMRBMHgf/7h7ITi700xpVZOO27cPOtzNZcB2cX8uSmTqMUf0JWMsnXjSl47keKV4+O8diLF1o0rpyi1wnbGxS0XDL4v/thYhk8+8Fw+8FrKUv7C93+B6xmWdiQ4s6eNqC1ELeHsZe2c2d1GROANvUmWtEWJWBZZx2NoLMOq7raCbp1TBSi0+Eop6TC4e9n6fm6+fD0i0NMWwTV+n39DEHwWuOeJl/OWY7jfcBhLJnAXFbsBSnW4rJQbv5AotqbD9+x4Hiu6EkSsMO4ytWVHuX2VWjkBszpX9QjILpbPTSlP3Sx+EVkN/D3+FC8PuMcY82UR6QW+jd/b/xXgOmPMyXrJUS3DqRwP7j7Mmt5k3uJ/cPdhfu9Xz5oyWCXfzti2cEtU4fZ1xjEYtu3cw0Mf6ymphDM5vxdPxnFJRIPpVuLvM/Tt51yP4YkcR4LGa3/4zZ+ScRw6ElESUXvKPNzdB08WrSjWA6V9tX4sYx+xiEUiGsfxPE5NuEQs/72t6EpgiX+RKN7v1o0rS04cW2x9TAopZ02HKyfbElZ0tXHFRcvZfG4fbz6zO39OSlnNlVZO4eO1NMfTgKwyG+rp6nGAm40xz4pIJ/CMiDwCXA88aoy5Q0RuAW4BPltHOaqiMB0zpPAHFFp5YSdO1/OVfjSw9gh62J8YzwE5DIbvPX+YKy/yA6WFSjhiCzdffj53PrIPx3VZ1hFnIuvw0rExvCD/+9DJCTzCVg9RBkbTOJ7h2HgOC7BtoTcZy1fYFirdOx/Zl3cnjaZzuJ7h1ve8ka5klP0Do4xnHE6kfBfTZC8gP54Qi1i4nslnABXu98Hdh7n3+rflRzWGiuXA0ChZx+9ACotH+VS6oIUuke89f5h7nniJf3thkH97YZDbrroAoKzrpVwAdf/AGB+975kpr6m0nxANyCqzoW6uHmPMEWPMs8H/o8ALwCrgauAbwdO+AWytlwy1UPgDCm8Lf0Bh4Av8LpsiQlciAuIv7xE/N19kMlZ7zxN+47NSAbMrNqxgx42b+ZsPvZUHbvhVupNx+jvjWOL7921bsMXvjzM84RTGf/HwWwEMjWUYS09m6oS3xvhug5svPw/XM9iWcOcj+3h83yCJiO23ZPamF5AdHUmTdTxuu+qCKRlAwBSXU6EbwK8LeC5fFzAykSOVdci53oKe1QulrenCuo6RiRx3P3YAmFwJbtu5h207y7teSgVQ/Yv0vimv2bZzL9t27pnRhaMBWWU2zEtwV0TWAm8BfgwsN8YcAf/iICIlo1AicgNwA8CaNWvqLmM1XRqLc+XTjpu/feKXg9zxg335rpkrutpwPY8DQ2NlA2ZhquT+gVEyjj//1glMcNvyLyKuByJeyYIvEQHxn19s8eUcjy98/xfTWgP/+XvfTG97jOOjmbystiW8YWmSrONx13UbufgNPXkFk3HcKZO+PvnAbrZvuTA/3D0MIq/oSnD41ASvn5pAMPS2x/jA157m1vecPyU9dCFRyZoO23McOjWBMf7KTCyhM2aTjEeIR2JA6dVP8feh1AVmIpudtq3cKkoDskqt1D24KyIdwD8DnzDGVD21yxhzjzFmkzFmU19fX/0ELOCy9f15K7w4HTMkDHytXpqkvzORd3u8961rWNWdZMWSBMuXxDkyPMHRkTSffGD3jIHORMTO1wLAZJ8ez/jWvWum9u0RIGb7Yxk74tMtvq0bV/Jf73uGoyNpXj85wWg6l7dWn33tBMfGsjhm8ljhNSUWsfKtGsILYdYxvH4yhTGG5Z2J/DzfsHAt63ikcx4DIxks8d1EbVGbUxMOR0fS3PTt3fzLc0fm6BOaX8pZ00DeBRS65sIGdsPpHAZ/NRDGcTwDo+kcw6nclDTO8PtQarVpWxa2JWVXoKVk1YCsUi11tfhFJIqv9P/RGBMOaB8QkRWBtb8CaKoBp+EPJ7TCKhVvFftft2+5gG0793I4sI5XdlUefB6SdlyWtsc4kcphW5NKvlwvH0sm+8CEPXuKq3ZjEQsJgtRHhtOBkjL8/X+8BsEM4HD3rmdwPaalh162vp8PXjLKHd/fhyXC4FiGFVYC25K8b/vIcDpfwBTucDTjErOFiGXheB5f+P4LbD532YJUSqWs6VBxR22/C2fYwA78pnbprMNw2i+264hHiEcsPv2d50hlXYwxtMf9n13osy+12ty+ZfICU24FqiizpZ5ZPQJ8HXjBGHNXwUM7gQ8DdwS3D9VLhtlQTU502aDfjZu567oN3HT/7qCrpW9KzxTo7O9M0B6P0B6PYIkwnnEYGMkErh5Dsf5vi9ks60hMUdSFbiPwg9QrliQ4MpIO8v49bnznOdz92IGgc6SffmiMoScZ467rNkxp1xC+z/uePjglbnF4eIKl7XG++qMDRGxhWXuMo6MZckUTY8KLjiWCbcmCDvQWN+YqDPRbluTPzYquNo4MT7CmN0n/kjZG035WVu/SduIRm9dOpADobfezqcq13Ch016gLR6kH9XT1bMYf1v4uEdkd/F2Br/AvF5H9wOXB/aag2pzoQp9s2NQtrNY8p6+TWMS3dGHmJTr4iuXmy88j63hkHQ/XGJDJoqpiJrIu/+MDb5mxXUJ7PMLyzjjLO+N88yOXcOVFK/NTtEyBq6ctZpdss3xgaAzX81jR1RYMZjfkXMNoOseh4QkOnUpxfHzSRRW1/IA0+DEGzxh6k9GgAnnxZJlMCfS3+cq4Nxkj67j0JKP5704iaiMy2Y3Vn23g/1+qAWApd426cJR6UM+snieNMWKM2WCM2Rj8/Ysx5rgx5t3GmHXB7Yl6yVArM2VxhIRK7PhYmheHxnjl+DiHhyfYPzA2qyyLx/cN5gepOJ6HMYauROnnx2z/I9v16omK7RJGJnLsHxzlyPAEGdfj+UOn6Er6M4KXdcTxjN/+oVzBUWEXz0MnU3jG5CtTJ7K+39n1ppaR5YK4RMwCCfxRx8azUwrhFgthPOi+j7ydL/z2RcQiFlHb4mQqx/Ex//sSpv56xhC1gwsu/v+adqk0Em3ZUEC1OdGhhX7Tt3cDk50873xkH5vPXVZTlkXxKmNkIsfASKZg0tUk8YiV7+P/tSdf4Rv/8WpJV9TG1T0kohG62/xU0OGJXCCrcMWGFSVnBJeSKRax6O+Ic2g4nZ8ObwUuoqgl5MLIJr4FEY1YOK5H1oM1vW3EI9a0QrjFRPh+7n78AG0xm3jEzjf3i9o2EVv4+LvO5cHdh0nn/HoNY/yJXK3gs9eBLs2LKv4CqknpDFm3vJNV3W1Ebd/Ssy2Z4suvdmBD8SqjPR7Jt38ApgRhs45f1bu0PUpXW7Rshay/T7/jpgjYMj3IWmlebihTJucxWJBtZIfVxY4XWK5Cd1uU42NZIraFMWBZAp5/kQqrkitVps4V86lkCo9V/Pn1tseJ2hafu/KN/MrapXQlo/zer56Vfz7U7xw0EzrQpblRxV9EtdZ6f2cin2sfpt3NZulevMpwPM9XpuPZKUof/ItCW9TijK42oHxud39nIog9GER895EwGWSFysonfP3h4YlgyHpBBpDrdwV1PLAwnEzlWNIWYWlHHGMgk3MZGM3k3RyTlamj0ypT50oRzKeSmd5wb3p7DNuSvNKH6cHhxazw4fQm2SnzgzZpmyVzVTFZaj9/evWbWL4kjl306Uxk/VYLqayfKljJFbXlzSvIuYaM45FzDY7rkXE89g+MTmumVkqmGy49278j4cwwfxXi4aeZRiy/bcSyjhhtsQij6RwHT6YYHMvQmYiQyroFlanrSw6Dn4vJTnPdpKxSe+NSxwrbY9SjcnYuWi03gmpjZUrjUIu/iFqsx7mqmCy1n/a4zS07nufIsP9jiVqCZfkZIamsX01rW1bJ1sz+ZKgBLHxFTXCbczy+9EO/QVs5Syx0Y7zj3D5WdSfJOS6DYxnsoMzXeCboIWT5vYlSOfo74li2P9UrHNiedTz+4n1v5py+jro2EpvLfc/02YejOS2R/GjO8YzDuuWdp9VWeTayNDPaP6j5UYu/gNlYj3OVble8n8vW9/PFazcQsfwq3Uhg/kswMMWndIWXr6A8ohGLRMQiHrGIWkLEnlRYMN0S+5fnjnDVX/0v/svf7+L6e3/CxWu6GBhN+ysGz7AkEcWyLL/3fJCjb4zB8TyitsWStmh+eW+J0JmIlK1MnStFMFf7ruazD0dzvnJ8nANDY5wYz+SPNdvvQSmrfqHPvtX+Qc2PKv4CZrtErdeS/M1ndtPXmQiCvQYv8NtHbYvORJRYxGLbzj08++rJKcf24w9h+mCAQNS2y7YB+JfnDnPTt3/K0ZE0R4bTpDIO//O5I6zo8ucFRGxhNO1gjN/0TSBfq/Cp/3RexfYC9VQEc7XvSp+9P0z9BF/6oT88JyxOGxzNcPPl62f9PkrNMJhJloVCNe1PlMahrp4CZrNEreeSvCsZ5Y5rLsrP6h3P+K0ABkYyDIxm6EpEODWR46b7f0osYk1pAbB9ywVT5gEvbY/x+a0XAtPbAAB84fu/AHwXjmcMx8b9sZCJqM3KrjaOjKTxPENPUJCViNr5ds9XbFhBX2ei6gZ3c53RMhf7DgPaI+kc7bFIfrRlGJTOOh5HR9Ks6m7j3L44Odd3ZxWP5qyWSgHQerlK5ju9strMNmX+UcVfQC3pnDA/2QuXre/noY/1cGBolE98+2eMBnJ5xjA0liVi+f1hSrUAKJWvP5zK8efvffOUbfsHRoMMpck2C/68Af/+krYoEVvIOh7f/MglLGmLTlMglZRvocJZt3x6hfBccLpKZvfBk2Qcb8qF8rarLsy3S+5MRDkyPMHrJ1Oc09cZtLGQfPO1uVhhhLGJdcs7a/oeVsNCjhkoc48q/iJqsR7nMrBYyRrrSkbpTESJBsNXBoOWygBLgp5AtjX92MX5+uV+/GFq6rL2KMfGc4G1K9zwjrP5wZ6jeeXz+a1vYvXSZH7fxZRSvgtB4YQX8M5EhJ5kLHi/whldccD/XEcmcoDgeIYDQ2N0Jiabr0Ht72smq34uV0iaXqkUo4q/BIUKrJJCnqsleTXKMRGxSedcToxniQUVsTnXd02c4Zm8a6LcsWf68YcWZn9Q7PWp/3Qe7920hhsuPafs+y8+N6XuLwSFU3wBX9IWZTzjQJDImso6HBlJI+K3zOjrjDEwkqF3aXLKrINa3lc1q8u5cpXoeEalGFX8FZhJIdfqGipFNcoxlCOTc8l5hgh+hWx/Z5STqRwjEzniUavisWf68V+2vp+bMy5f+P4LJKI2dz/+En2diXzMoNy58Vs6G7a8eSU/2HN0yrla2d1W8ZjNQrkL+Dl9Hdx21QX88Y7n8VwPyxJWdieI2hYimfzUrdm+r/kaoKLplUoxqvjLUK21ero/3pmGbx8YGmPbzj1+35wlbZyayAGGs5Z2YDAkY1Huum5DyZ47hcz04w+HsIc9ZypZseG5Sedcjo1l8TzDVx87wIolcZZ1JvKvvff6t1U8ZrivRrcwmOkCbtsWiD9b2Zipzdfg9NJT5yMAWvz+PGMmC/SUlkTTOctQS0rd6eTyl8tD3z8wxjV3P8VN9/+UQ6cmyOQ8bEtY1Z1EREhl/dTF7Vsu4OI39M547JnSHmt5v2Eh07ExfzygFcwdODaezdcJGOMPmKl0zHLpjKfDbFNrL1vfz73Xv40/vuJ87r3+bVNGSyaiFmf2+KuXw8MTuJ7hhnecxUTW5fhoJj+n+HT98M++epJny3RdPV3C9MqPvOMsQPj6ky/P2TlXFh5q8ZdhvpbHpazN4uHbxvh9c9rjEeJRi1Xdbdx13cb89K1qqbQ6qeX9hqmPnjFELAvX+Pn8xhhyrofjkX/tuuWdJY9ZD///6QSSS7220FUVj9is6/dbU3zwkjfw9Sdf5lgQZO9tj81K3sJjF6fefvHaDXUJgn/9yZeJRZo75qLUH7X4yzCf1YfFxS5hymPW8Xj52BjGQM41DI6kyTr+Mr1WpR9SbnVSy/vtSka59T1vBMIiLqE3GUGCgSPFry11zOIVRsTyu3766ae1czrVruVemwhkC1djjuevuv7+P17l2GjGH7JiwclUlm0798zKUh9O5di2cy/HxvyYgSXC8fHZ768Si6EwTJkb1OKvwHwF32C6r9f14LUTqfykLIB0zqEtFuHrT77M1598ec5TI2t5v1dsWAEYvvD9X2CJX9n7+a3nsW55Z1UZQIUrjKzjcSiYU/zJB3azfcuFZd9XuZjA6WSulHtt6Koq9I1fvXEl9z39WjAX2f9wbEvyE9hmE+NxPS+v9IEpE93m8junQV4lRBX/DDSi+rArGeVDl6zhT7/3AuAnFUYsGMt6dCf9gG69lunVvt/hVI51yzv55kcuIe24FS8U5Vwwt13lD6c/VOVw+kqunNkqteFUjtF0Dtcr/dr+zgR//t4389PXTvIPT7/GjmcPcXzcj22E7aodz8yYSlspJThsrxFiMETsuR9XORdZaMriQBV/k/KWNT1Eg2paywK/LY5HPOJ750Kr9MDQKJ2J6JytSKrJsimlgMtV5Fby5V+2vp+7rotUNZx+ppjAbJRa4ftI5xwyjuBE/fGRN1x6Fk+9eIw7H9mH6xkOnZqgryNGezyKJX5r6lBVWwI3vvMcwG/xUHjuqkkJLtVeo9Q4zFLnttbVaK3T4RqdcaXUB1X8Tco5fR0s64xzbCyDZyTfHC1sjp9xXFJZh08+8Fy+b//pun6qCY7WGpSdyQVTOJzetspb6tW4cmY78jIe8ZvXZR2P//z21fzD069xzxMvcejUBP2dcZIx/2dybDxHZyJGxLbwCobQR2yL7rYo19z91JRzt3F1T9UpweXGYZZTvqcTyK5mVbcQKq6V2aPB3SalK+k3aFvVnWR5Z5xVPUk+8e5zAd8izjp+W4VYZO4GkFQTHK0mQHjweIp/23uUg8dTM7ZNrjaoXG375WpTawvfh+sZHNeQcVz+9slXsC0hmCvPsbFs3v9uggH1y9qjiPgTzeJRi8/85uSgmXjExnENn3twD4/tG5zSBrtSADtsr1GYmlsu3bXebZsXeltoZWbU4m9iwgZtg6Npv2WD47J145mkHZcjw2n+5LvPE7Gmun5mGxCsNjg6ky/9rx79JV/50Yv559/wjrP4g187i3ueeAnHNbPu3DnX/ulQ3hPjGQZG0jjBxBqBfNvpnGewxVf2ve0xhkYzpHMubbEIX/6dC/KB7PDcZR2PV4dTuJ6f0nr7w3sZSefo63CJReyqA9hQeWVV7xYM2uJh8aOKv8npSkbZffDklGX31o0r+ednD3N0JM3RkTQru9qIR63TytCoNjhaSQEfPJ7KK/2IZZFzXb762Eus7mnDtiw+8o6zuPKilVU3eCtmLrOsupJRbr58PR+//9m80rfFHy/peIZE1MI1BtfAqVSOUxNZetujeJ7hA29bzeZz+6ZlYR065c8odoP9jaYdlnXEGRzNAP7845kC2CGVlG+9s3M0+2fxo66eJqd42Q0EytXQ1xHPF3edbvVoLXn85YZs7BsYAcKxjCavAEOX1NeffHlWshXLORcTzwDWLe+gvzNB1BYSUSs/5QzA9fyBN8s6YtgWrOlN0haNMDiW4Y4f7OPqr066XrqSUW649CzAT8U0+M3cEOiIR+hfEqcnGWN1T9Ivwqsif75wPoDrmSnKt941JvNZw6I0BrX4m5xiyy+c9+pbl4IlFksSNnddt3FKC+bZUItFXcpCX798CeAXOllBbxsB2qI2sYjVdO6C/s4EsSBLyhjyIy2jlrCm128/7c829t0tB0+OIeI/1y2YfwBw/hlLOGOJPy1tcCSDwWAF8wxcz3AyleXURA5L/AH1iahd0YIuNR/gi9dumFUgezbMZw2LMv+oxd/kFAc1c66XDzzalmAwDKcd+jric3K807GoVy9N8vF3+QFoNxCyJ+mPiGxGd4GfSnkhS9tj+TGVPckoyzrj+cK5W99zPrYljGcdjGfIOX4geGAkw3jG5XvPH+aau5/i09/5GY5ncFyPnuDc9SRjuJ5/8evvjFc9srFwPsC6/k5WdrWRiEbYuHrqhX0uVz/lzk899680DrX4m5xin7objD9MZd38wPOeZIx0cGFoNH/47vPYuvFM9g2McCqV4+7HDzR1sdBl6/v54Scu48DQKCCc0+ePUiy0dNvjET734B5ywcUsavsX3GNjGe5+7EC+o6mfEmq467oN9HUkSDsuo+kcn/7OcyRjEbqT1Y1sLDcfoJrVkubeK9Wgin8BULjsTkRsrr/3J/S2+26fsOKzmSzp1UuT+Uldl19wRtWKqFFKy0+l7J22LeSy9f382W8LN963i/Gsh+P657wjPhl4dT0TrBIMnYlo/v2HKZBhoNTxmLEqd7bBVc29n4peBMujrp4FQrjsXr00mR+QHrpTmtGSDqnWXVCPFs1zxeP7BvmT7z7PeNaPVkcswbZgIuc3qDsxnuHA0BivHB/n0KkJ9g+M5l87m0Bpra/xWzqfYNtOzb0PaebvUzMgprALWJOyadMms2vXrkaL0VQsJmtmOJXjmrufmpKz7nqGHTdubvh7C2VzPMPrJ1L5+ErUtuhJRvkvl57NF3/wC8DPXlrWHqUtFpkm+2w+r8LXACVfH1r5Wcfj6EiaVd1tdCb8x8czDn/zobfWbcB9s9LM36f5RkSeMcZsKt6urp4FSiOax9WLZi4YCmVrj0WwbQs7CAL3d8aJRy0uXtPNqu42orZF1Lb8QHAJ2WfzeYWvKefCKUz17UxEOTqS5tCpCdb1R2acwbyYaebvU7Ogrh6l4VTbjqERJCI2Wccj47isCNI1we/Pc9tVF3BOX2fQ4sGQzrmcSmXxjKmqK2g1k8IqtU8oVHC25ReHAUG30dbNvW/m71OzoBa/0nCatV1waGk7nuHoiRS9yRirutu44dKzp1Qgb924kr/8t/15N1BPUG1dLrBaSxC2lgre05nOtpho1u9TM6GKX2kKmq1gqNDSXtYRJxnzLf9/+P235zN2wuf987OHsC3Bxs+0Gss4bNu5l4c+Vn5Qfeh/TmUdPvfgz/nmRy6Zst+QShk+pRTcZ37zfDoTjf1ZN0P8qdm+T82GKn6laWimuEWxpe3PPnam1UuEg+cFyc8TMPiVvaV8yoX7PZXKcnQkjTGGD3ztaT6/9U3TLP+ZrNdCBbd/YJQ7H9mXf+1cpXPWosibKaW0mb5PzUbdFL+I/C1wFTBojHlTsK0X+DawFngFuM4Yc7JeMijKbKk2l96foOUXdIWuHn92glV2UD3A0GiagZFMPmaQc72yjdtmsl7D+x+975k5HV4PtSnyWmc1KI2jnsHde4HfKtp2C/CoMWYd8GhwX1Gajmpz6YvbPnjGsKwjzvYt5QfV33z5eRwby+Z7GUUt4UQql5+zW06eSvUQpzNIvVyguda+/DrMfeFQN4vfGPOEiKwt2nw18M7g/28AjwGfrZcMinI6FFdMpx03r/QKre9SbR8qWbjrlneysruNI8MT+SEvjudVlQ1UjnpU+9aaFqntnBcO8+3jX26MOQJgjDkiImWdfyJyA3ADwJo1a+ZJPEWZSvE8hFTWxRhDe9z/6YSKslTbh3KEraD7O+McG8vi+AOVufU95592W+1aMllmcs3Uqsg1m2bh0LTBXWPMPcA94FfuNlgcpUUpVI4Ry+K1EykAetvjOF55v3wlChXkii6/7/6t73kjV2xYcVqyRIS19wAACttJREFU1prJMpNFPxtFrtk0C4P5VvwDIrIisPZXANpAQ2lqCpVjOucGs3f9YGwiWltFaGF2TL0UZC2ZLNVY9LORc66yaZohLXSxMt+KfyfwYeCO4PaheT6+otREoXKM2la+G2rULj1joJyyKudLb6RCq9aib0RaZDOlhS5G6takTUS+hR/IXQYMANuAB4EHgDXAa8D7jDEnZtqXNmlT6kU1VmWohIyBiZzv40/GInlFGSqkSj11rrn7KcAv8HJcg+N5U4q2qpGj0nNOxzpuNstam6zNHfPepM0Y87tlHnp3vY6pKLVQrVVZ7O6A6Z0yKwVKB0fTpLIuJ8b9Pj6Oa7AsyRdtATPKUUnW07WOZ2vR1+uCoU3W6k/TBncVpZ7UWmxUrBwrVeSGt6GySkTs/OxcxzVBZa/BtoRtO/cA/jD6cnJUkhVoSNFUPV0xmhZaf7Q7p9KSzHWxUaWOkGnHZWl7LP9cAWzx4wSOa3A9r6IclWRtRNFUrYVdtTKb4TVKbajFr7QkhYo6YllBcLPySMRKzBQobY9HSERtDp2awBiDZfmB4ogtgFS0bquxgOfCOq7WdTMfrhhNC60vavErLUmoqEfTDvsHRzk8PEE657D7YPnWUTP10L9sfT87btzM33zorey4cXPe9REey7aEpe1xRISeNl+Rbd9yIdu3VLZuK1nAc2Ud1zKqcL763Vc7tlOpHR29qLQsw6kcV3/1SQz+hC3H88pmj8yFTzu0qMP2D8XB4UZm9dSaRVOY6VSc3VTN8dSSnx909KKiFDE4ms77qQFsq7TLYq66TlbKnqkms+Z0X1+O2bhuZuuK0fz85kBdPUrLUq3LYrF3nZyt66ZWV0y9g8JK9ajiV1qWav3j9fBpVztzdz6YryyaxX4BXUioq0dpaapxWcx118lmdHfMRxaN5uc3DxrcVZQqmYugZKu3IzidoLBSOxrcVZTTZC5aG7R6OwLNz28OVPErSh0pduvcfPl5QGu7O3QIeuPR4K6i1IlSWSx3PvJLbr58vbYjUBqKWvyKUifKuXXWLe9gx42b61KMpSjVoIpfUepEpSyWcu6OZsz4URYf6upRlDpRa368Fjgp84Va/IpSR2rJYmn1jB9l/lDFryh1ptosFi1wUuYLdfUoSpOgA0iU+UItfkVpIrTASZkPVPErSpOhBU5KvVFXj6IoSouhil9RFKXFUMWvKIrSYqjiVxRFaTFU8SuKorQYC2IQi4gMAa+WeGgZcGyexamGZpSrGWWC5pSrGWWC5pSrGWWC5pSrETK9wRjTV7xxQSj+cojIrlLTZRpNM8rVjDJBc8rVjDJBc8rVjDJBc8rVTDKpq0dRFKXFUMWvKIrSYix0xX9PowUoQzPK1YwyQXPK1YwyQXPK1YwyQXPK1TQyLWgfv6IoilI7C93iVxRFUWpEFb+iKEqLsWAUv4isFpH/T0ReEJE9InJTsL1XRB4Rkf3BbU8DZLNF5Kci8nATydQtIt8RkV8E5+xXGy2XiPxR8Nn9XES+JSKJRsgkIn8rIoMi8vOCbWXlEJFbReRFEdknIr85jzL9efD5PSci3xWR7vmUqZxcBY99SkSMiCybT7nKySQifxgcd4+IfGk+ZSonl4hsFJGnRWS3iOwSkbfNt1wlMcYsiD9gBXBx8H8n8EvgAuBLwC3B9luALzZAtk8C3wQeDu43g0zfAD4S/B8DuhspF7AKeBloC+4/AFzfCJmAS4GLgZ8XbCspR/Ad+xkQB84CDgD2PMn0n4BI8P8X51umcnIF21cDP8QvrFzWBOfq14F/A+LB/f5mOFfAvwLvCf6/AnhsvuUq9bdgLH5jzBFjzLPB/6PAC/jK5Gp8JUdwu3U+5RKRM4Erga8VbG60TEvwv4RfBzDGZI0xpxotF/78hzYRiQBJ4HAjZDLGPAGcKNpcTo6rgfuNMRljzMvAi8DbmGNKyWSM+VdjjBPcfRo4cz5lKidXwF8CnwEKs0Madq6AG4E7jDGZ4DmD8ylTBbkMsCT4vwv/Oz+vcpViwSj+QkRkLfAW4MfAcmPMEfAvDkD/PIvz3/F/AF7BtkbLdDYwBPxd4IL6moi0N1IuY8wh4C+A14AjwLAx5l8bKVMR5eRYBRwseN7rwbb55veB7wf/N1QmEdkCHDLG/KzooUbKdR7wDhH5sYg8LiK/0gQyAXwC+HMROYj//b+1GeRacIpfRDqAfwY+YYwZabAsVwGDxphnGilHCSL4S867jTFvAcbx3RcNI/CZX42/rF0JtIvIBxspU5VIiW3zmgMtIn8COMA/hptKPG1eZBKRJPAnwG2lHi6xbb7OVQToAS4BPg08ICLSYJnAX4n8kTFmNfBHBKtwGizXglL8IhLFV/r/aIzZEWweEJEVweMrgMFyr68Dm4EtIvIKcD/wLhG5r8EygW89vG6M+XFw/zv4F4JGyvUbwMvGmCFjTA7YAfwfDZapkHJyvI7vzw45k8nlet0RkQ8DVwH/2QTO4QbLdA7+xftnwff+TOBZETmjwXK9DuwwPj/BX4Eva7BMAB/G/64D/BOT7pyGyrVgFH9w9f468IIx5q6Ch3bin1yC24fmSyZjzK3GmDONMWuB9wM/MsZ8sJEyBXIdBQ6KyPpg07uBvQ2W6zXgEhFJBp/lu/HjNA09VwWUk2Mn8H4RiYvIWcA64CfzIZCI/BbwWWCLMSZVJGtDZDLGPG+M6TfGrA2+96/jJ10cbaRcwIPAuwBE5Dz8hIZjDZYJfGV+WfD/u4D9wf+NlWu+osin+wf8Gv5S6Dlgd/B3BbAUeDQ4oY8CvQ2S751MZvU0XCZgI7ArOF8P4i+DGyoXsB34BfBz4B/wMxrmXSbgW/hxhhy+4vqDSnLguzYOAPsIMjTmSaYX8f3A4ff9r+dTpnJyFT3+CkFWT4PPVQy4L/huPQu8qxnOVaC3nsHP4Pkx8Nb5lqvUn7ZsUBRFaTEWjKtHURRFmRtU8SuKorQYqvgVRVFaDFX8iqIoLYYqfkVRlBZDFb/SsojI0qBr4m4ROSoihwruLxeRnIj814Lnd4rIARFZF9yPisjzIvL24L4bvPbnIvI/w26aIrI26GL5pwX7Whbs//+d7/etKKr4lZbFGHPcGLPRGLMR+GvgLwvuX4vfGO13C54/it9r5avBpk8B/24mK6Qngte/Cb9Z18cKDvcSfgVuyPuAPfV4X4oyE6r4FaU0vwvcDJwpIvnmWcaYBwBPRD4DfJTJplvF/AdTm25NAC+IyKbg/u/gt6ZWlHlHFb+iFCEiq4EzjN/z5QF8JV3IJ/D743/eGDOtZbGI2PgtKXYWPXQ/fpn+mYDL/PaMUZQ8qvgVZTrvZ9Iav58Cd0/Ab+GX5v//7d07SsVAAIXhf0qxs1TQDYiu4jauwNuIK7BRcAdaWboASwsRtBFchDbuwPYiVoLNsZjgjSMIgo9i/q+cJEPSnLzgzHozvlBKuQdmwBJw22y/ASbDfOc/ecLSdxj80mdTYHdon7wCNkc/dJeBPWrL4lYpZWN03Mvwf2CN2h0z/sZPkldqb8s+tWVW+hcGvzQyNJouJlnJvIHymPoWAHXlqaMkj9QlN0+HttF3SZ6pN4eDoUp87AQ4TDL7zeuQvmLwSx9Ngctm7AKYllImwCrzJS2vgSdgp50kyR21kXG7GX9IctbuL/0l2zklqTM+8UtSZwx+SeqMwS9JnTH4JakzBr8kdcbgl6TOGPyS1Jk3hgY1R1Vx+QkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='scatter',x='TAXRM',y='MEDV',alpha=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=strat_train_set.drop('MEDV',axis=1)\n",
    "df_labels=strat_train_set['MEDV'].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Missing Attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To take care of missing attributes,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 or median)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(399, 13)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=df.dropna(subset=[\"RM\"])#op 1\n",
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(404, 12)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop('RM',axis=1).shape #op 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "median=df['RM'].median()#op 3-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['RM'].fillna(median)#OP 3-2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>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>0.086963</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",
       "    </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%      0.086963    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  \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": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe() #before started imputing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SimpleImputer(strategy='median')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "imputer=SimpleImputer(strategy='median')\n",
    "imputer.fit(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.statistics_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=imputer.transform(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_tr=pd.DataFrame(X,columns=df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_tr.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# scikit learn design"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "three types of objects\n",
    "1.Estimators-It estimate some parameters based on a  dataset. Eg.Imputer\n",
    "it has fit method and transform method. fit method fits the dataseta and calculates internal\n",
    "parameters.\n",
    "2.Transformers -transform method takes input and returns output based on the learning from fit().\n",
    "it also has a convenience function called fit_transform() which fits and then predict.\n",
    "3.Predictors-Linearregression model is ann simple example of predictor.fit() and predict() are common\n",
    "functions. It also gives score() function which will evalute the predictions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Featute Scaling "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Two types of feature scaling method\n",
    "#  1.min-max scaling(normalization)\n",
    "#     (value-min)/(max-min)---> MinMaxScaler() class provided by sklearn\n",
    "#  2. standardization\n",
    "#      (value-min)/std ----->StandardScaler() class provided\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# creating pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "my_pipeline=Pipeline([\n",
    "    ('imputer',SimpleImputer(strategy='median')),\n",
    "    ('std_scaler',StandardScaler()),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_num_tr=my_pipeline.fit_transform(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(404, 13)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_num_tr.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# selecting A desired model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 45,
     "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,df_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([22.418, 25.866, 16.614, 23.324, 23.635])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "some_data=df.iloc[:5]\n",
    "some_labels=df_labels.iloc[:5]\n",
    "\n",
    "prepared_data=my_pipeline.transform(some_data)\n",
    "model.predict(prepared_data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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",
       "Name: MEDV, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "some_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "import numpy as np\n",
    "housing_predictions= model.predict(housing_num_tr)\n",
    "mse=mean_squared_error(df_labels,housing_predictions)\n",
    "rmse=np.sqrt(mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.3012989188643864"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using better Evalution technique -Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "scores=cross_val_score(model,housing_num_tr,df_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([2.86413133, 2.98862768, 4.53374157, 2.61617578, 3.40984742,\n",
       "       2.70387348, 4.60259408, 3.31048658, 3.12642528, 3.1950717 ])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scores: [2.86413133 2.98862768 4.53374157 2.61617578 3.40984742 2.70387348\n",
      " 4.60259408 3.31048658 3.12642528 3.1950717 ]\n",
      "Mean: 3.3350974900027595\n",
      "Standard Deviation: 0.6610433001069689\n"
     ]
    }
   ],
   "source": [
    "def print_scores(scores):\n",
    "    print('scores:',scores)\n",
    "    print('Mean:',scores.mean())\n",
    "    print('Standard Deviation:',scores.std())\n",
    "\n",
    "print_scores(rmse_scores)    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# saving the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Dragon.joblib']"
      ]
     },
     "execution_count": 53,
     "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": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[25.265 11.433 25.209 21.886 18.12  14.916 20.042 14.571 31.729 42.688\n",
      " 19.51  12.12  25.834 30.573 19.598 11.09  31.749 14.277 23.575 18.593\n",
      " 20.091 18.151 18.225 21.973 18.271 31.381 15.799 32.779  8.619 33.477\n",
      " 23.984 21.126 22.925 11.047 20.875 11.144 44.94  24.535 23.311 42.053\n",
      " 24.215 30.961 20.932 20.728 19.346 33.829 44.757 20.303 20.176 21.677\n",
      " 21.314 14.676 21.26  14.812 25.347 32.905 42.92  28.683 19.815 20.996\n",
      " 46.675 10.037 19.237 25.459 14.444 33.488 20.243 17.749 19.163 34.104\n",
      " 25.699 22.788 21.863 22.438 34.062 13.028 15.773 20.316 20.792 21.47\n",
      " 22.372 21.026 14.064 22.93  20.411 21.161 13.842 21.559 21.394 23.522\n",
      " 18.441 26.805  7.248 26.777 19.19  29.162 19.807 31.547 14.652 27.121\n",
      " 21.23  20.384] [16.5, 10.2, 30.1, 23.0, 14.4, 15.6, 19.4, 14.1, 30.3, 35.2, 23.1, 13.8, 25.0, 27.9, 19.5, 12.3, 32.2, 13.5, 23.8, 21.7, 19.2, 19.5, 10.4, 23.2, 18.6, 28.5, 15.2, 32.0, 7.2, 34.6, 20.1, 20.6, 23.6, 13.1, 23.8, 12.7, 43.1, 24.7, 22.2, 44.0, 28.1, 31.0, 21.7, 23.4, 19.5, 33.1, 41.7, 18.7, 19.9, 20.6, 21.2, 13.6, 20.3, 17.8, 27.1, 31.5, 50.0, 29.1, 18.9, 20.4, 50.0, 7.2, 17.2, 36.2, 14.6, 33.2, 23.8, 19.9, 21.5, 37.3, 27.0, 22.0, 24.3, 19.8, 33.3, 7.0, 19.4, 20.9, 21.1, 20.4, 22.2, 11.9, 11.7, 21.6, 19.7, 23.0, 16.7, 21.7, 20.6, 23.3, 19.6, 28.0, 5.0, 24.4, 20.8, 24.8, 21.8, 23.6, 19.0, 25.0, 20.3, 21.5]\n"
     ]
    }
   ],
   "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": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.975956157106418"
      ]
     },
     "execution_count": 55,
     "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": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([22.418])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from joblib import dump,load\n",
    "import numpy as np\n",
    "model=load('dragon.joblib')\n",
    "features=np.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]])\n",
    "model.predict(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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