Housing Price Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1f860992",
   "metadata": {},
   "source": [
    "# Housing Price Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0a3f986f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "eb7744f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = pd.read_csv(\"housing.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "390ea379",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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,
   "id": "6c06fe05",
   "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       506 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,
   "id": "3bda9e0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>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",
       "      <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>6.284634</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>0.702617</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>5.885500</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>6.208500</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>3.677083</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.624000</td>\n",
       "      <td>6.623500</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  506.000000   \n",
       "mean     3.613524   11.363636   11.136779    0.069170    0.554695    6.284634   \n",
       "std      8.601545   23.322453    6.860353    0.253994    0.115878    0.702617   \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    5.885500   \n",
       "50%      0.256510    0.000000    9.690000    0.000000    0.538000    6.208500   \n",
       "75%      3.677083   12.500000   18.100000    0.000000    0.624000    6.623500   \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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "eafdadfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#housing[\"TAXRM\"] = housing[\"TAX\"] / housing[\"RM\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "411d72b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# #plotting histogram\n",
    "# %matplotlib inline\n",
    "# import matplotlib.pyplot as plt\n",
    "# housing.hist(bins=50, figsize=(20,15))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db8040ad",
   "metadata": {},
   "source": [
    "# Train Test Spliting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e2eb7515",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "74c70dcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# def split_train_test(data, test_ratio):\n",
    "#     np.random.seed(42)\n",
    "#     shuffled = np.random.permutation(len(data))\n",
    "#     test_set_size = int(len(data) * test_ratio)\n",
    "#     test_indices = shuffled[:test_set_size]\n",
    "#     train_indices = shuffled[test_set_size:]\n",
    "#     return data.iloc[train_indices], data.iloc[test_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c8548a06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows in train set: 404\n",
      "Rows in test set: 102\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(\"Rows in train set: \" + str(len(train_set)) + \"\\nRows in test set: \" + str(len(test_set)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4ca85c67",
   "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": 12,
   "id": "713eadc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = strat_train_set.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49ed1a89",
   "metadata": {},
   "source": [
    "## Looking for correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2818a7fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# correlation values\n",
    "# 1 : positive strong correlation\n",
    "# 0 : neutral \n",
    "# -ve : negative correlation\n",
    "    \n",
    "# if correlation value is positive then the entity is increasing with the given value\n",
    "# pearson coefficient value lies btw -1 to 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "710a2e4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_matrix = housing.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "78cda841",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MEDV       1.000000\n",
       "RM         0.679894\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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix['MEDV'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fdf2b9e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:xlabel='MEDV', ylabel='MEDV'>,\n",
       "        <AxesSubplot:xlabel='RM', ylabel='MEDV'>,\n",
       "        <AxesSubplot:xlabel='ZN', ylabel='MEDV'>,\n",
       "        <AxesSubplot:xlabel='LSTAT', ylabel='MEDV'>],\n",
       "       [<AxesSubplot:xlabel='MEDV', ylabel='RM'>,\n",
       "        <AxesSubplot:xlabel='RM', ylabel='RM'>,\n",
       "        <AxesSubplot:xlabel='ZN', ylabel='RM'>,\n",
       "        <AxesSubplot:xlabel='LSTAT', ylabel='RM'>],\n",
       "       [<AxesSubplot:xlabel='MEDV', ylabel='ZN'>,\n",
       "        <AxesSubplot:xlabel='RM', ylabel='ZN'>,\n",
       "        <AxesSubplot:xlabel='ZN', ylabel='ZN'>,\n",
       "        <AxesSubplot:xlabel='LSTAT', ylabel='ZN'>],\n",
       "       [<AxesSubplot:xlabel='MEDV', ylabel='LSTAT'>,\n",
       "        <AxesSubplot:xlabel='RM', ylabel='LSTAT'>,\n",
       "        <AxesSubplot:xlabel='ZN', ylabel='LSTAT'>,\n",
       "        <AxesSubplot:xlabel='LSTAT', ylabel='LSTAT'>]], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "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": 17,
   "id": "3c2f23a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='RM', ylabel='MEDV'>"
      ]
     },
     "execution_count": 17,
     "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 = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39277653",
   "metadata": {},
   "source": [
    "## Splitting labels and features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "128568d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = strat_train_set.drop(\"MEDV\", axis=1)\n",
    "housing_labels = strat_train_set[\"MEDV\"].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f16944f3",
   "metadata": {},
   "source": [
    "## Missing attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e706bd79",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Three options\n",
    "# 1. get rid of the data point\n",
    "# 2. Get rid of the column\n",
    "# 3. fill the value with mean, median or 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7181dc14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(404, 13)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1.\n",
    "housing.dropna(subset = ['RM']).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "46552bc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(404, 12)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#2. \n",
    "housing.drop(\"RM\", axis = 1).shape#whichever column has null values drop it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bbc0bd01",
   "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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#3. \n",
    "median = housing['RM'].median()\n",
    "housing['RM'].fillna(median)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c0687b8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SimpleImputer(strategy='median')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#automating the preprocessing\n",
    "from sklearn.impute import SimpleImputer\n",
    "imputer = SimpleImputer(strategy = \"median\")\n",
    "imputer.fit(housing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9ff20d4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.86735e-01, 0.00000e+00, 9.90000e+00, 0.00000e+00, 5.38000e-01,\n",
       "       6.21000e+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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.statistics_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4cf02586",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = imputer.transform(housing)#this is numpy array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b81e0d33",
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_tr = pd.DataFrame(X, columns=housing.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f7a4a2f",
   "metadata": {},
   "source": [
    "## Scikit-Learn Design"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff647747",
   "metadata": {},
   "source": [
    "1. Estimators: It istimates some parameter based on the dataset. Eg. Imputer. It has a fit() method and transform() method. fit() method: fits the dataset and calculates internal parameters.\n",
    "2. Transformers: Transform method takes input and returns output based on the learnings from fit(). It also has a convinience function called fit_transform() which fits and then transforms. \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 prediction."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27851f06",
   "metadata": {},
   "source": [
    "## Feature scaling\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "193dd94f",
   "metadata": {},
   "source": [
    "Primarily two types of scaling\n",
    "1. Min-Max scaling(Normalization)\n",
    "(valueMin)/(valueMax)\n",
    "sklearn provides a class called MinMaxScaler for this\n",
    "\n",
    "2. Standardization\n",
    "(value - min) / (std)\n",
    "sklearn provides a class called StandardScaler for this\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32f7fa70",
   "metadata": {},
   "source": [
    "# Creating a pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "cb9ff689",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f504c7dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "my_pipeline = Pipeline([\n",
    "    ('imputer', SimpleImputer(strategy=\"median\")), \n",
    "    ('std_scaler', StandardScaler())\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "cf90071d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.43942006,  3.12628155, -1.12165014, -0.27288841, -1.42262747,\n",
       "       -0.24141041, -1.31238772,  2.61111401, -1.0016859 , -0.5778192 ,\n",
       "       -0.97491834,  0.41164221, -0.86091034])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_num_tr = my_pipeline.fit_transform(housing) #returns numpy array\n",
    "housing_num_tr[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ab3e349",
   "metadata": {},
   "source": [
    "## Selecting desired model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5c875f8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "model = LinearRegression()\n",
    "model.fit(housing_num_tr, housing_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b515424",
   "metadata": {},
   "source": [
    "## Evaluating the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9344c4ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "import numpy as np\n",
    "housing_predictions = model.predict(housing_num_tr)\n",
    "lin_mse = mean_squared_error(housing_labels, housing_predictions)\n",
    "lin_rmse = np.sqrt(lin_mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "641907e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.829321492635759"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "00fbb492",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Dragon.joblib']"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from joblib import dump, load\n",
    "dump(model, \"Dragon.joblib\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1bb3679a",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = load(\"Dragon.joblib\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "57172bde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([23.94996896])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "model.predict(np.array([[-0.43942006,  3.12628155, -1.12165014, -0.27288841, -1.42262747,\n",
    "       -0.24141041, -1.31238772,  2.61111401, -1.0016859 , -0.5778192 ,\n",
    "       -0.97491834,  0.41164221, -0.86091034]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "425d04b0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}