Dragon Real Estates.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dragon 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": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1</td>\n",
       "      <td>296</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD  TAX  PTRATIO  \\\n",
       "0  0.00632  18.0   2.31     0  0.538  6.575  65.2  4.0900    1  296     15.3   \n",
       "1  0.02731   0.0   7.07     0  0.469  6.421  78.9  4.9671    2  242     17.8   \n",
       "2  0.02729   0.0   7.07     0  0.469  7.185  61.1  4.9671    2  242     17.8   \n",
       "3  0.03237   0.0   2.18     0  0.458  6.998  45.8  6.0622    3  222     18.7   \n",
       "4  0.06905   0.0   2.18     0  0.458  7.147  54.2  6.0622    3  222     18.7   \n",
       "\n",
       "        B  LSTAT  MEDV  \n",
       "0  396.90   4.98  24.0  \n",
       "1  396.90   9.14  21.6  \n",
       "2  392.83   4.03  34.7  \n",
       "3  394.63   2.94  33.4  \n",
       "4  396.90   5.33  36.2  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 506 entries, 0 to 505\n",
      "Data columns (total 14 columns):\n",
      "CRIM       506 non-null float64\n",
      "ZN         506 non-null float64\n",
      "INDUS      506 non-null float64\n",
      "CHAS       506 non-null int64\n",
      "NOX        506 non-null float64\n",
      "RM         501 non-null float64\n",
      "AGE        506 non-null float64\n",
      "DIS        506 non-null float64\n",
      "RAD        506 non-null int64\n",
      "TAX        506 non-null int64\n",
      "PTRATIO    506 non-null float64\n",
      "B          506 non-null float64\n",
      "LSTAT      506 non-null float64\n",
      "MEDV       506 non-null float64\n",
      "dtypes: float64(11), int64(3)\n",
      "memory usage: 55.4 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": [
    "%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 0xcbdc8f0>"
      ]
     },
     "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 0xb9239b0>"
      ]
     },
     "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",
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       "    .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(add_indicator=False, copy=True, fill_value=None,\n",
       "              missing_values=nan, strategy='median', verbose=0)"
      ]
     },
     "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",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "    </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 Dragon Real Estates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\haris\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=10,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "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([23.09, 25.95, 16.63, 23.  , 23.32])"
      ]
     },
     "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": [
       "1.274726722800531"
      ]
     },
     "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([3.04485171, 2.48131898, 4.63312016, 2.8778676 , 3.41281409,\n",
       "       3.03586684, 4.85712775, 3.52571837, 2.89743852, 4.18037857])"
      ]
     },
     "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: [3.04485171 2.48131898 4.63312016 2.8778676  3.41281409 3.03586684\n",
      " 4.85712775 3.52571837 2.89743852 4.18037857]\n",
      "Mean:  3.494650261111624\n",
      "Standard deviation:  0.762041223886678\n"
     ]
    }
   ],
   "source": [
    "print_scores(rmse_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Dragon.joblib']"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from joblib import dump, load\n",
    "dump(model, 'Dragon.joblib') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing the model on test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = strat_test_set.drop(\"MEDV\", axis=1)\n",
    "Y_test = strat_test_set[\"MEDV\"].copy()\n",
    "X_test_prepared = my_pipeline.transform(X_test)\n",
    "final_predictions = model.predict(X_test_prepared)\n",
    "final_mse = mean_squared_error(Y_test, final_predictions)\n",
    "final_rmse = np.sqrt(final_mse)\n",
    "# print(final_predictions, list(Y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.2156768262654114"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prepared_data[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([23.02])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from joblib import dump, load\n",
    "import numpy as np\n",
    "model = load('Dragon.joblib') \n",
    "features = np.array([[-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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