Real Estates.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simple Real Estate - Price Predictor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = pd.read_csv(\"data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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": {
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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": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # For plotting histogram\n",
    "# import matplotlib.pyplot as plt\n",
    "# housing.hist(bins=50, figsize=(20, 15))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train-Test Splitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For learning purpose\n",
    "import numpy as np\n",
    "def split_train_test(data, test_ratio):\n",
    "    np.random.seed(42)\n",
    "    shuffled = np.random.permutation(len(data))\n",
    "    print(shuffled)\n",
    "    test_set_size = int(len(data) * test_ratio)\n",
    "    test_indices = shuffled[:test_set_size]\n",
    "    train_indices = shuffled[test_set_size:] \n",
    "    return data.iloc[train_indices], data.iloc[test_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_set, test_set = split_train_test(housing, 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(f\"Rows in train set: {len(train_set)}\\nRows in test set: {len(test_set)}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows in train set: 404\n",
      "Rows in test set: 102\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train_set, test_set  = train_test_split(housing, test_size=0.2, random_state=42)\n",
    "print(f\"Rows in train set: {len(train_set)}\\nRows in test set: {len(test_set)}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)\n",
    "for train_index, test_index in split.split(housing, housing['CHAS']):\n",
    "    strat_train_set = housing.loc[train_index]\n",
    "    strat_test_set = housing.loc[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    95\n",
       "1     7\n",
       "Name: CHAS, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strat_test_set['CHAS'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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": [],
   "source": [
    "# 95/7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 376/28"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = strat_train_set.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Looking for Correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MEDV       1.000000\n",
       "RM         0.680857\n",
       "B          0.361761\n",
       "ZN         0.339741\n",
       "DIS        0.240451\n",
       "CHAS       0.205066\n",
       "AGE       -0.364596\n",
       "RAD       -0.374693\n",
       "CRIM      -0.393715\n",
       "NOX       -0.422873\n",
       "TAX       -0.456657\n",
       "INDUS     -0.473516\n",
       "PTRATIO   -0.493534\n",
       "LSTAT     -0.740494\n",
       "Name: MEDV, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = housing.corr()\n",
    "corr_matrix['MEDV'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from pandas.plotting import scatter_matrix\n",
    "# attributes = [\"MEDV\", \"RM\", \"ZN\", \"LSTAT\"]\n",
    "# scatter_matrix(housing[attributes], figsize = (12,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2366bda1820>"
      ]
     },
     "execution_count": 21,
     "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": "markdown",
   "metadata": {},
   "source": [
    "## Trying out Attribute combinations\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing[\"TAXRM\"] = housing['TAX']/housing['RM']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "      <th>TAXRM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>0.04819</td>\n",
       "      <td>80.0</td>\n",
       "      <td>3.64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.392</td>\n",
       "      <td>6.108</td>\n",
       "      <td>32.0</td>\n",
       "      <td>9.2203</td>\n",
       "      <td>1</td>\n",
       "      <td>315</td>\n",
       "      <td>16.4</td>\n",
       "      <td>392.89</td>\n",
       "      <td>6.57</td>\n",
       "      <td>21.9</td>\n",
       "      <td>51.571709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>0.01501</td>\n",
       "      <td>80.0</td>\n",
       "      <td>2.01</td>\n",
       "      <td>0</td>\n",
       "      <td>0.435</td>\n",
       "      <td>6.635</td>\n",
       "      <td>29.7</td>\n",
       "      <td>8.3440</td>\n",
       "      <td>4</td>\n",
       "      <td>280</td>\n",
       "      <td>17.0</td>\n",
       "      <td>390.94</td>\n",
       "      <td>5.99</td>\n",
       "      <td>24.5</td>\n",
       "      <td>42.200452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>4.87141</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.10</td>\n",
       "      <td>0</td>\n",
       "      <td>0.614</td>\n",
       "      <td>6.484</td>\n",
       "      <td>93.6</td>\n",
       "      <td>2.3053</td>\n",
       "      <td>24</td>\n",
       "      <td>666</td>\n",
       "      <td>20.2</td>\n",
       "      <td>396.21</td>\n",
       "      <td>18.68</td>\n",
       "      <td>16.7</td>\n",
       "      <td>102.714374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>0.18159</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.38</td>\n",
       "      <td>0</td>\n",
       "      <td>0.493</td>\n",
       "      <td>6.376</td>\n",
       "      <td>54.3</td>\n",
       "      <td>4.5404</td>\n",
       "      <td>5</td>\n",
       "      <td>287</td>\n",
       "      <td>19.6</td>\n",
       "      <td>396.90</td>\n",
       "      <td>6.87</td>\n",
       "      <td>23.1</td>\n",
       "      <td>45.012547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>0.30347</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.38</td>\n",
       "      <td>0</td>\n",
       "      <td>0.493</td>\n",
       "      <td>6.312</td>\n",
       "      <td>28.9</td>\n",
       "      <td>5.4159</td>\n",
       "      <td>5</td>\n",
       "      <td>287</td>\n",
       "      <td>19.6</td>\n",
       "      <td>396.90</td>\n",
       "      <td>6.15</td>\n",
       "      <td>23.0</td>\n",
       "      <td>45.468948</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD  TAX  \\\n",
       "254  0.04819  80.0   3.64     0  0.392  6.108  32.0  9.2203    1  315   \n",
       "348  0.01501  80.0   2.01     0  0.435  6.635  29.7  8.3440    4  280   \n",
       "476  4.87141   0.0  18.10     0  0.614  6.484  93.6  2.3053   24  666   \n",
       "321  0.18159   0.0   7.38     0  0.493  6.376  54.3  4.5404    5  287   \n",
       "326  0.30347   0.0   7.38     0  0.493  6.312  28.9  5.4159    5  287   \n",
       "\n",
       "     PTRATIO       B  LSTAT  MEDV       TAXRM  \n",
       "254     16.4  392.89   6.57  21.9   51.571709  \n",
       "348     17.0  390.94   5.99  24.5   42.200452  \n",
       "476     20.2  396.21  18.68  16.7  102.714374  \n",
       "321     19.6  396.90   6.87  23.1   45.012547  \n",
       "326     19.6  396.90   6.15  23.0   45.468948  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MEDV       1.000000\n",
       "RM         0.680857\n",
       "B          0.361761\n",
       "ZN         0.339741\n",
       "DIS        0.240451\n",
       "CHAS       0.205066\n",
       "AGE       -0.364596\n",
       "RAD       -0.374693\n",
       "CRIM      -0.393715\n",
       "NOX       -0.422873\n",
       "TAX       -0.456657\n",
       "INDUS     -0.473516\n",
       "PTRATIO   -0.493534\n",
       "TAXRM     -0.528626\n",
       "LSTAT     -0.740494\n",
       "Name: MEDV, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = housing.corr()\n",
    "corr_matrix['MEDV'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2366be30790>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind=\"scatter\", x=\"TAXRM\", y=\"MEDV\", alpha=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = strat_train_set.drop(\"MEDV\", axis=1)\n",
    "housing_labels = strat_train_set[\"MEDV\"].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Missing Attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To take care of missing attributes, you have three options:\n",
    "#     1. Get rid of the missing data points\n",
    "#     2. Get rid of the whole attribute\n",
    "#     3. Set the value to some value(0, mean or median)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(399, 13)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = housing.dropna(subset=[\"RM\"]) #Option 1\n",
    "a.shape\n",
    "# Note that the original housing dataframe will remain unchanged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(404, 12)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.drop(\"RM\", axis=1).shape # Option 2\n",
    "# Note that there is no RM column and also note that the original housing dataframe will remain unchanged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "median = housing[\"RM\"].median() # Compute median for Option 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "254    6.108\n",
       "348    6.635\n",
       "476    6.484\n",
       "321    6.376\n",
       "326    6.312\n",
       "       ...  \n",
       "155    6.152\n",
       "423    6.103\n",
       "98     7.820\n",
       "455    6.525\n",
       "216    5.888\n",
       "Name: RM, Length: 404, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing[\"RM\"].fillna(median) # Option 3\n",
    "# Note that the original housing dataframe will remain unchanged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(404, 13)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>404.000000</td>\n",
       "      <td>404.000000</td>\n",
       "      <td>404.000000</td>\n",
       "      <td>404.000000</td>\n",
       "      <td>404.000000</td>\n",
       "      <td>399.000000</td>\n",
       "      <td>404.000000</td>\n",
       "      <td>404.000000</td>\n",
       "      <td>404.000000</td>\n",
       "      <td>404.000000</td>\n",
       "      <td>404.000000</td>\n",
       "      <td>404.000000</td>\n",
       "      <td>404.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.602814</td>\n",
       "      <td>10.836634</td>\n",
       "      <td>11.344950</td>\n",
       "      <td>0.069307</td>\n",
       "      <td>0.558064</td>\n",
       "      <td>6.279481</td>\n",
       "      <td>69.039851</td>\n",
       "      <td>3.746210</td>\n",
       "      <td>9.735149</td>\n",
       "      <td>412.341584</td>\n",
       "      <td>18.473267</td>\n",
       "      <td>353.392822</td>\n",
       "      <td>12.791609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.099383</td>\n",
       "      <td>22.150636</td>\n",
       "      <td>6.877817</td>\n",
       "      <td>0.254290</td>\n",
       "      <td>0.116875</td>\n",
       "      <td>0.716784</td>\n",
       "      <td>28.258248</td>\n",
       "      <td>2.099057</td>\n",
       "      <td>8.731259</td>\n",
       "      <td>168.672623</td>\n",
       "      <td>2.129243</td>\n",
       "      <td>96.069235</td>\n",
       "      <td>7.235740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.006320</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.740000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.389000</td>\n",
       "      <td>3.561000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>1.129600</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>1.730000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.086963</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": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.describe() # before we started filling missing attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SimpleImputer(strategy='median')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "imputer = SimpleImputer(strategy=\"median\")\n",
    "imputer.fit(housing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.86735e-01, 0.00000e+00, 9.90000e+00, 0.00000e+00, 5.38000e-01,\n",
       "       6.20900e+00, 7.82000e+01, 3.12220e+00, 5.00000e+00, 3.37000e+02,\n",
       "       1.90000e+01, 3.90955e+02, 1.15700e+01])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.statistics_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = imputer.transform(housing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_tr = pd.DataFrame(X, columns=housing.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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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": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_tr.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scikit-learn Design"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Primarily, three types of objects\n",
    "1. Estimators - It estimates some parameter based on a dataset. Eg. imputer. It has a fit method and transform method. Fit method - Fits the dataset and calculates internal parameters\n",
    "\n",
    "2. Transformers - transform method takes input and returns output based on the learnings from fit(). It also has a convenience function called fit_transform() which fits and then transforms.\n",
    "\n",
    "3. Predictors - LinearRegression model is an example of predictor. fit() and predict() are two common functions. It also gives score() function which will evaluate the predictions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Scaling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Primarily, two types of feature scaling methods:\n",
    "1. Min-max scaling (Normalization)\n",
    "    (value - min)/(max - min)\n",
    "    Sklearn provides a class called MinMaxScaler for this\n",
    "    \n",
    "2. Standardization\n",
    "    (value - mean)/std\n",
    "    Sklearn provides a class called StandardScaler for this\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "my_pipeline = Pipeline([\n",
    "    ('imputer', SimpleImputer(strategy=\"median\")),\n",
    "    #     ..... add as many as you want in your pipeline\n",
    "    ('std_scaler', StandardScaler()),\n",
    "])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_num_tr = my_pipeline.fit_transform(housing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(404, 13)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_num_tr.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selecting a desired model for Real Estates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "# model = LinearRegression()\n",
    "# model = DecisionTreeRegressor()\n",
    "model = RandomForestRegressor()\n",
    "model.fit(housing_num_tr, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "some_data = housing.iloc[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "some_labels = housing_labels.iloc[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "prepared_data = my_pipeline.transform(some_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([22.453, 25.803, 16.523, 23.357, 23.59 ])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(prepared_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[21.9, 24.5, 16.7, 23.1, 23.0]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(some_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "housing_predictions = model.predict(housing_num_tr)\n",
    "mse = mean_squared_error(housing_labels, housing_predictions)\n",
    "rmse = np.sqrt(mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.2115435607129235"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using better evaluation technique - Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1 2 3 4 5 6 7 8 9 10\n",
    "from sklearn.model_selection import cross_val_score\n",
    "scores = cross_val_score(model, housing_num_tr, housing_labels, scoring=\"neg_mean_squared_error\", cv=10)\n",
    "rmse_scores = np.sqrt(-scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.77361665, 2.75963359, 4.42231362, 2.67028886, 3.63435485,\n",
       "       2.7201576 , 4.56994948, 3.39378358, 3.36070204, 3.13357894])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_scores(scores):\n",
    "    print(\"Scores:\", scores)\n",
    "    print(\"Mean: \", scores.mean())\n",
    "    print(\"Standard deviation: \", scores.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [2.77361665 2.75963359 4.42231362 2.67028886 3.63435485 2.7201576\n",
      " 4.56994948 3.39378358 3.36070204 3.13357894]\n",
      "Mean:  3.343837921803478\n",
      "Standard deviation:  0.6562727897751384\n"
     ]
    }
   ],
   "source": [
    "print_scores(rmse_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quiz: Convert this notebook into a python file and run the pipeline using Visual Studio Code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Real_estate.joblib']"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from joblib import dump, load\n",
    "dump(model, 'Real_estate.joblib') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing the model on test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = strat_test_set.drop(\"MEDV\", axis=1)\n",
    "Y_test = strat_test_set[\"MEDV\"].copy()\n",
    "X_test_prepared = my_pipeline.transform(X_test)\n",
    "final_predictions = model.predict(X_test_prepared)\n",
    "final_mse = mean_squared_error(Y_test, final_predictions)\n",
    "final_rmse = np.sqrt(final_mse)\n",
    "# print(final_predictions, list(Y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.914429847042698"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prepared_data[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([24.243])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from joblib import dump, load\n",
    "import numpy as np\n",
    "model = load('Real_estate.joblib') \n",
    "features = np.array([[-5.43942006, 4.12628155, -1.6165014, -0.67288841, -1.42262747,\n",
    "       -11.44443979304, -49.31238772,  7.61111401, -26.0016879 , -0.5778192 ,\n",
    "       -0.97491834,  0.41164221, -66.86091034]])\n",
    "model.predict(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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