Real State.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f2238b55",
   "metadata": {},
   "source": [
    "#  Real Estate Price Predictor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c2456a6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "330646c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = pd.read_csv(\"data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a4ede734",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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",
       "      <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": "bb8d88fd",
   "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": "bf1dcc77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CHAS\n",
       "0    471\n",
       "1     35\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['CHAS'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "816d6420",
   "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>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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "787cf40b",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "974411f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#import matplotlib.pyplot as plt\n",
    "#housing.hist(bins=50, figsize=(20, 15))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ba55f9a",
   "metadata": {},
   "source": [
    "# Train Test splitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "337fe0df",
   "metadata": {},
   "outputs": [],
   "source": [
    "#without using sklearn\n",
    "\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]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ce446393",
   "metadata": {},
   "outputs": [],
   "source": [
    "#train_set, test_set = split_train_test(housing, 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6823179b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(f\"Rows in train set :{len(train_set)}\\nRows in test set :{len(test_set)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "82195e3e",
   "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(f\"Rows in train set :{len(train_set)}\\nRows in test set :{len(test_set)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e5ac88fa",
   "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,
   "id": "71033f0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CHAS\n",
       "0    95\n",
       "1     7\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strat_test_set['CHAS'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d134d1be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CHAS\n",
       "0    376\n",
       "1     28\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strat_train_set['CHAS'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "056632b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = strat_train_set.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c50e1e6",
   "metadata": {},
   "source": [
    "# Looking for Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "22ce4c03",
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_matrix = housing.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ee856390",
   "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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix['MEDV'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "44864ea5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: xlabel='MEDV', ylabel='MEDV'>,\n",
       "        <Axes: xlabel='RM', ylabel='MEDV'>,\n",
       "        <Axes: xlabel='ZN', ylabel='MEDV'>,\n",
       "        <Axes: xlabel='LSTAT', ylabel='MEDV'>],\n",
       "       [<Axes: xlabel='MEDV', ylabel='RM'>,\n",
       "        <Axes: xlabel='RM', ylabel='RM'>,\n",
       "        <Axes: xlabel='ZN', ylabel='RM'>,\n",
       "        <Axes: xlabel='LSTAT', ylabel='RM'>],\n",
       "       [<Axes: xlabel='MEDV', ylabel='ZN'>,\n",
       "        <Axes: xlabel='RM', ylabel='ZN'>,\n",
       "        <Axes: xlabel='ZN', ylabel='ZN'>,\n",
       "        <Axes: xlabel='LSTAT', ylabel='ZN'>],\n",
       "       [<Axes: xlabel='MEDV', ylabel='LSTAT'>,\n",
       "        <Axes: xlabel='RM', ylabel='LSTAT'>,\n",
       "        <Axes: xlabel='ZN', ylabel='LSTAT'>,\n",
       "        <Axes: xlabel='LSTAT', ylabel='LSTAT'>]], dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 16 Axes>"
      ]
     },
     "metadata": {},
     "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": 20,
   "id": "6d130501",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='RM', ylabel='MEDV'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind=\"scatter\", x=\"RM\", y=\"MEDV\", alpha=0.8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91fb750e",
   "metadata": {},
   "source": [
    "# Trying Out Attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "125d2608",
   "metadata": {},
   "outputs": [],
   "source": [
    "housing['TAXRM'] = housing['TAX']/housing['RM']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e00a8ebc",
   "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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c8140e6f",
   "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",
       "TAXRM     -0.525160\n",
       "LSTAT     -0.740494\n",
       "Name: MEDV, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = housing.corr()\n",
    "corr_matrix['MEDV'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "000836e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='TAXRM', ylabel='MEDV'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind=\"scatter\", x=\"TAXRM\", y=\"MEDV\", alpha=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c9e04c35",
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = strat_train_set.drop(\"MEDV\", axis=1)\n",
    "housing_lables = strat_train_set[\"MEDV\"].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fccff87",
   "metadata": {},
   "source": [
    "# MISSING ATTRIBUTES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4424ddd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SimpleImputer(strategy='median')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.impute import SimpleImputer    #FOR MISSING ATTRIBUTES IT FILL MEDIAN\n",
    "imputer = SimpleImputer(strategy = \"median\")\n",
    "imputer.fit(housing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7e58a723",
   "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": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.statistics_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c34121d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = imputer.transform(housing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "deea2d1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_tr = pd.DataFrame(X, columns=housing.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "492bd3a0",
   "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",
       "    </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>6.279908</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>0.712983</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>0.086962</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>6.210000</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>6.630250</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    6.279908   \n",
       "std      8.099383   22.150636    6.877817    0.254290    0.116875    0.712983   \n",
       "min      0.006320    0.000000    0.740000    0.000000    0.389000    3.561000   \n",
       "25%      0.086962    0.000000    5.190000    0.000000    0.453000    5.878750   \n",
       "50%      0.286735    0.000000    9.900000    0.000000    0.538000    6.210000   \n",
       "75%      3.731923   12.500000   18.100000    0.000000    0.631000    6.630250   \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": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_tr.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee2393f8",
   "metadata": {},
   "source": [
    "# Scikit-learn Design\n",
    "Primarily, three types of objects\n",
    "\n",
    "\tEstimators - 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",
    "\tTransformers - 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",
    "\tPredictors - LinearRegression model is an eg. of predictor. fit() and predict() are two common functions. It also  gives score() function which will evaluate and predictions.\n",
    "\n",
    "# Feature Scaling\n",
    "\tPrimarily, two types of feature scaling methods:\n",
    "\n",
    "\tMin-max scaling (Normalization) (value - min)/(max - min) Sklearn provides a class called MinMaxScaler for this\n",
    "\n",
    "\tStandardization (value - mean)/std Sklearn provides a class called Standard Scaler for this"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9da99f93",
   "metadata": {},
   "source": [
    "# Creating a Pipeline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "150584ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler   # wahi feature scaling\n",
    "my_pipeline = Pipeline([\n",
    "    ('imputer', SimpleImputer(strategy=\"median\")),\n",
    "    #      ....... add as many as you want in your pipeline\n",
    "    ('std_scaler', StandardScaler()),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a17930c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_num_tr = my_pipeline.fit_transform(housing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "13c37ae0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.43942006,  3.12628155, -1.12165014, ..., -0.97491834,\n",
       "         0.41164221, -0.86091034],\n",
       "       [-0.44352175,  3.12628155, -1.35893781, ..., -0.69277865,\n",
       "         0.39131918, -0.94116739],\n",
       "       [ 0.15682292, -0.4898311 ,  0.98336806, ...,  0.81196637,\n",
       "         0.44624347,  0.81480158],\n",
       "       ...,\n",
       "       [-0.43525657, -0.4898311 , -1.23083158, ..., -0.22254583,\n",
       "         0.41831233, -1.27603303],\n",
       "       [ 0.14210728, -0.4898311 ,  0.98336806, ...,  0.81196637,\n",
       "        -3.15239177,  0.73869575],\n",
       "       [-0.43974024, -0.4898311 ,  0.37049623, ..., -0.97491834,\n",
       "         0.41070422,  0.09940681]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_num_tr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e63526c6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(404, 13)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_num_tr.shape\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "038ed7c8",
   "metadata": {},
   "source": [
    "# SELECTING DESIRED MODEL\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "24e62ad2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 35,
     "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_lables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1a32b858",
   "metadata": {},
   "outputs": [],
   "source": [
    "some_data = housing.iloc[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "69244a7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "some_lables = housing_lables.iloc[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d244765c",
   "metadata": {},
   "outputs": [],
   "source": [
    "prepared_data = my_pipeline.transform(some_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "fdaebe9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([22.274, 25.829, 16.317, 23.419, 23.533])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(prepared_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c4039b15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[21.9, 24.5, 16.7, 23.1, 23.0]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(some_lables)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c761498e",
   "metadata": {},
   "source": [
    "# Evaluating the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "80b3f2ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "housing_prediction = model.predict(housing_num_tr)\n",
    "mse = mean_squared_error(housing_lables, housing_prediction)\n",
    "rmse = np.sqrt(mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a7f2de9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.2409050384120595"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "508168fc",
   "metadata": {},
   "source": [
    "# Using Better Evaluation Technique - Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e1fc5013",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "scores = cross_val_score(model, housing_num_tr, housing_lables, scoring=\"neg_mean_squared_error\", cv=10)\n",
    "rmse_scores = np.sqrt(-scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "33e2b566",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.77312513, 2.66693368, 4.4206371 , 2.72285943, 3.35439127,\n",
       "       2.47601968, 4.44270231, 3.39429026, 3.25295866, 3.18339198])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse_scores\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b1251b95",
   "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": 46,
   "id": "602efa72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [2.77312513 2.66693368 4.4206371  2.72285943 3.35439127 2.47601968\n",
      " 4.44270231 3.39429026 3.25295866 3.18339198]\n",
      "Mean:  3.268730951110089\n",
      "Standard Deviation:  0.653443405408612\n"
     ]
    }
   ],
   "source": [
    "print_scores(rmse_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "066b256f",
   "metadata": {},
   "source": [
    "# Saving Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "1704e95d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Real-Estate.joblib']"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from joblib import dump, load\n",
    "dump(model, 'Real-Estate.joblib')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e59f9c13",
   "metadata": {},
   "source": [
    "## Testing model on test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "a378db71",
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "752678eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0571342822335805"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_rmse\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "523aec2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[25.081 11.684 25.766 22.193 19.199 14.964 19.74  14.295 32.429 42.573\n",
      " 19.149 12.037 24.543 26.244 19.379 11.094 31.947 14.397 23.508 19.245\n",
      " 19.93  17.363 18.549 21.869 18.2   30.795 16.363 32.933  8.902 33.88\n",
      " 24.125 21.504 22.726 11.374 21.029 11.394 44.435 24.472 23.525 42.162\n",
      " 24.111 29.757 20.469 20.791 18.854 33.382 43.902 20.353 20.04  21.324\n",
      " 21.168 14.277 20.643 14.981 25.363 33.189 42.278 29.607 19.268 21.028\n",
      " 46.65   9.732 19.076 24.722 15.485 33.347 20.083 17.775 19.283 33.005\n",
      " 25.815 23.013 21.801 22.303 34.713 13.719 15.839 19.833 20.956 21.206\n",
      " 22.415 20.96  14.395 23.009 20.873 21.397 14.427 20.931 21.84  23.22\n",
      " 18.883 27.122  7.396 27.07  19.044 29.664 19.652 31.672 14.221 26.842\n",
      " 21.807 20.502] [16.5, 10.2, 30.1, 23.0, 14.4, 15.6, 19.4, 14.1, 30.3, 35.2, 23.1, 13.8, 25.0, 27.9, 19.5, 12.3, 32.2, 13.5, 23.8, 21.7, 19.2, 19.5, 10.4, 23.2, 18.6, 28.5, 15.2, 32.0, 7.2, 34.6, 20.1, 20.6, 23.6, 13.1, 23.8, 12.7, 43.1, 24.7, 22.2, 44.0, 28.1, 31.0, 21.7, 23.4, 19.5, 33.1, 41.7, 18.7, 19.9, 20.6, 21.2, 13.6, 20.3, 17.8, 27.1, 31.5, 50.0, 29.1, 18.9, 20.4, 50.0, 7.2, 17.2, 36.2, 14.6, 33.2, 23.8, 19.9, 21.5, 37.3, 27.0, 22.0, 24.3, 19.8, 33.3, 7.0, 19.4, 20.9, 21.1, 20.4, 22.2, 11.9, 11.7, 21.6, 19.7, 23.0, 16.7, 21.7, 20.6, 23.3, 19.6, 28.0, 5.0, 24.4, 20.8, 24.8, 21.8, 23.6, 19.0, 25.0, 20.3, 21.5]\n"
     ]
    }
   ],
   "source": [
    "print(final_predictions, list(Y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af1db2a7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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