Kanpur Real Estate.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# #Kanpur Real estate price predictor\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing=pd.read_csv('data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1</td>\n",
       "      <td>296</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD  TAX  PTRATIO  \\\n",
       "0  0.00632  18.0   2.31     0  0.538  6.575  65.2  4.0900    1  296     15.3   \n",
       "1  0.02731   0.0   7.07     0  0.469  6.421  78.9  4.9671    2  242     17.8   \n",
       "2  0.02729   0.0   7.07     0  0.469  7.185  61.1  4.9671    2  242     17.8   \n",
       "3  0.03237   0.0   2.18     0  0.458  6.998  45.8  6.0622    3  222     18.7   \n",
       "4  0.06905   0.0   2.18     0  0.458  7.147  54.2  6.0622    3  222     18.7   \n",
       "\n",
       "        B  LSTAT  MEDV  \n",
       "0  396.90   4.98  24.0  \n",
       "1  396.90   9.14  21.6  \n",
       "2  392.83   4.03  34.7  \n",
       "3  394.63   2.94  33.4  \n",
       "4  396.90   5.33  36.2  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 506 entries, 0 to 505\n",
      "Data columns (total 14 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   CRIM     506 non-null    float64\n",
      " 1   ZN       506 non-null    float64\n",
      " 2   INDUS    506 non-null    float64\n",
      " 3   CHAS     506 non-null    int64  \n",
      " 4   NOX      506 non-null    float64\n",
      " 5   RM       501 non-null    float64\n",
      " 6   AGE      506 non-null    float64\n",
      " 7   DIS      506 non-null    float64\n",
      " 8   RAD      506 non-null    int64  \n",
      " 9   TAX      506 non-null    int64  \n",
      " 10  PTRATIO  506 non-null    float64\n",
      " 11  B        506 non-null    float64\n",
      " 12  LSTAT    506 non-null    float64\n",
      " 13  MEDV     506 non-null    float64\n",
      "dtypes: float64(11), int64(3)\n",
      "memory usage: 55.5 KB\n"
     ]
    }
   ],
   "source": [
    "housing.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'AGE'}>,\n",
       "        <AxesSubplot:title={'center':'B'}>,\n",
       "        <AxesSubplot:title={'center':'CHAS'}>,\n",
       "        <AxesSubplot:title={'center':'CRIM'}>],\n",
       "       [<AxesSubplot:title={'center':'DIS'}>,\n",
       "        <AxesSubplot:title={'center':'INDUS'}>,\n",
       "        <AxesSubplot:title={'center':'LSTAT'}>,\n",
       "        <AxesSubplot:title={'center':'MEDV'}>],\n",
       "       [<AxesSubplot:title={'center':'NOX'}>,\n",
       "        <AxesSubplot:title={'center':'PTRATIO'}>,\n",
       "        <AxesSubplot:title={'center':'RAD'}>,\n",
       "        <AxesSubplot:title={'center':'RM'}>],\n",
       "       [<AxesSubplot:title={'center':'TAX'}>,\n",
       "        <AxesSubplot:title={'center':'ZN'}>, <AxesSubplot:>,\n",
       "        <AxesSubplot:>]], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "housing.hist(bins=50, figsize=(20,15))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train test splitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#for learniny\n",
    "# import numpy as np\n",
    "\n",
    "# def split_train_test(data, test_ratio):\n",
    "#     np.random.seed(42)\n",
    "#     shuffled=np.random.permutation(len(data))\n",
    "#     print(shuffled)\n",
    "#     test_set_size=int(len(data) * test_ratio)\n",
    "#     test_indices=shuffled[:test_set_size]\n",
    "#     train_indices=shuffled[test_set_size:]\n",
    "    \n",
    "#     return data.iloc[train_indices], data.iloc[test_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_set,test_set =split_train_test(housing, 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(f\"train {len(train_set)} \\n test {len(test_set)} \")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train 404 \n",
      " test 102 \n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train_set,test_set = train_test_split(housing, test_size=0.2, random_state= 42)\n",
    "print(f\"train {len(train_set)} \\n test {len(test_set)} \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)\n",
    "for train_index, test_index in split.split(housing,housing['CHAS']):\n",
    "    \n",
    "    strat_train_set=housing.loc[train_index]\n",
    "    strat_test_set=housing.loc[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "strat_test_set[\"CHAS\"].value_counts()\n",
    "housing=strat_train_set.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Looking for correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_matrix = housing.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix[\"MEDV\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'attributes' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-17-f1e4052adb11>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplotting\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mscatter_matrix\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mattr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"MEDV\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"RM\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"ZN\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"LSTAT\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mscatter_matrix\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhousing\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mattributes\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfigsize\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m12\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m8\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'attributes' is not defined"
     ]
    }
   ],
   "source": [
    "from pandas.plotting import scatter_matrix\n",
    "attr=[\"MEDV\", \"RM\", \"ZN\", \"LSTAT\"]\n",
    "scatter_matrix(housing[attributes], figsize = (12, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='RM', ylabel='MEDV'>"
      ]
     },
     "execution_count": 45,
     "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=\"RM\", y=\"MEDV\", alpha=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing= strat_train_set.drop(\"MEDV\", axis=1)\n",
    "housing_label=strat_train_set[\"MEDV\"].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MIssing Attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#1.remove missing attri, 2. get rid pf whole attri, \n",
    "#3. set the value to(0, mean, mediAN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(399, 14)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1\n",
    "a = housing.dropna(subset=[\"RM\"])\n",
    "a.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndentationError",
     "evalue": "unexpected indent (<ipython-input-21-27b0d493dfc1>, line 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-21-27b0d493dfc1>\"\u001b[1;36m, line \u001b[1;32m2\u001b[0m\n\u001b[1;33m    housing.drop(\"RM\", axis=1).shape\u001b[0m\n\u001b[1;37m    ^\u001b[0m\n\u001b[1;31mIndentationError\u001b[0m\u001b[1;31m:\u001b[0m unexpected indent\n"
     ]
    }
   ],
   "source": [
    "#  2\n",
    "    housing.drop(\"RM\", axis=1).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3\n",
    "median = housing[\"RM\"].median()"
   ]
  },
  {
   "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": [
    "housing[\"RM\"].fillna(median)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(404, 14)"
      ]
     },
     "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": [
    "# fits missing values as strategy in any missing column\n",
    "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": [
       "(14,)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.statistics_.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=imputer.transform(housing)"
   ]
  },
  {
   "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",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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",
       "      <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",
       "      <td>22.509406</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",
       "      <td>9.385531</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",
       "    </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",
       "      <td>16.600000</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",
       "    </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",
       "      <td>25.000000</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",
       "    </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        MEDV  \n",
       "count  404.000000  404.000000  \n",
       "mean    12.791609   22.509406  \n",
       "std      7.235740    9.385531  \n",
       "min      1.730000    5.000000  \n",
       "25%      6.847500   16.600000  \n",
       "50%     11.570000   21.150000  \n",
       "75%     17.102500   25.000000  \n",
       "max     36.980000   50.000000  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_tr.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scikit-learn Design"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# primarlily 3 type objects\n",
    "# estimators[eg. imputers]\n",
    "# transformer\n",
    "# predictor\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating PipeLine \n",
    "it is also created by sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_num_tr = my_pipeline.fit_transform(housing_tr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(404, 14)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_num_tr\n",
    "housing_num_tr.shape\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selecting a desired Model\n",
    "and then training and testing  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(404, 14)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_num_tr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "model=LinearRegression()\n",
    "model.fit(housing_num_tr,housing_label)"
   ]
  }
 ],
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