Bangalore House Price Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "19a5aa29",
   "metadata": {},
   "source": [
    "# Predict the Bangalore House Price (Regression Problem)\n",
    "\n",
    "#### Problem Definition:\n",
    "The goal is to predict the Banglore House price based on Location, Number of BHK, Number of bathroom and Total sq.ft area.\n",
    "\n",
    "#### Data: [From Kaggle](https://www.kaggle.com/datasets/amitabhajoy/bengaluru-house-price-data?resource=download)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "90db9b1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing basic libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1576f0de",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area_type</th>\n",
       "      <th>availability</th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>society</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>balcony</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>19-Dec</td>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>Coomee</td>\n",
       "      <td>1056</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Plot  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>Theanmp</td>\n",
       "      <td>2600</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>120.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1440</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>62.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>Soiewre</td>\n",
       "      <td>1521</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>95.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1200</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>51.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              area_type   availability                  location       size  \\\n",
       "0  Super built-up  Area         19-Dec  Electronic City Phase II      2 BHK   \n",
       "1            Plot  Area  Ready To Move          Chikka Tirupathi  4 Bedroom   \n",
       "2        Built-up  Area  Ready To Move               Uttarahalli      3 BHK   \n",
       "3  Super built-up  Area  Ready To Move        Lingadheeranahalli      3 BHK   \n",
       "4  Super built-up  Area  Ready To Move                  Kothanur      2 BHK   \n",
       "\n",
       "   society total_sqft  bath  balcony   price  \n",
       "0  Coomee        1056   2.0      1.0   39.07  \n",
       "1  Theanmp       2600   5.0      3.0  120.00  \n",
       "2      NaN       1440   2.0      3.0   62.00  \n",
       "3  Soiewre       1521   3.0      1.0   95.00  \n",
       "4      NaN       1200   2.0      1.0   51.00  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"Data/Bengaluru_House_Data.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "844b7c7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bath</th>\n",
       "      <th>balcony</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>13247.000000</td>\n",
       "      <td>12711.000000</td>\n",
       "      <td>13320.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.692610</td>\n",
       "      <td>1.584376</td>\n",
       "      <td>112.565627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.341458</td>\n",
       "      <td>0.817263</td>\n",
       "      <td>148.971674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>72.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>120.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>40.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3600.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               bath       balcony         price\n",
       "count  13247.000000  12711.000000  13320.000000\n",
       "mean       2.692610      1.584376    112.565627\n",
       "std        1.341458      0.817263    148.971674\n",
       "min        1.000000      0.000000      8.000000\n",
       "25%        2.000000      1.000000     50.000000\n",
       "50%        2.000000      2.000000     72.000000\n",
       "75%        3.000000      2.000000    120.000000\n",
       "max       40.000000      3.000000   3600.000000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b791bbdc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 13320 entries, 0 to 13319\n",
      "Data columns (total 9 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   area_type     13320 non-null  object \n",
      " 1   availability  13320 non-null  object \n",
      " 2   location      13319 non-null  object \n",
      " 3   size          13304 non-null  object \n",
      " 4   society       7818 non-null   object \n",
      " 5   total_sqft    13320 non-null  object \n",
      " 6   bath          13247 non-null  float64\n",
      " 7   balcony       12711 non-null  float64\n",
      " 8   price         13320 non-null  float64\n",
      "dtypes: float64(3), object(6)\n",
      "memory usage: 936.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ff5cfabb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13320, 9)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abe1e2ef",
   "metadata": {},
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6b6ee9a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping 'area_type','availability','society' and 'balcony' features \n",
    "df.drop(['area_type','availability','society','balcony'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5f7cb5c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1056</td>\n",
       "      <td>2.0</td>\n",
       "      <td>39.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>2600</td>\n",
       "      <td>5.0</td>\n",
       "      <td>120.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1440</td>\n",
       "      <td>2.0</td>\n",
       "      <td>62.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1521</td>\n",
       "      <td>3.0</td>\n",
       "      <td>95.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1200</td>\n",
       "      <td>2.0</td>\n",
       "      <td>51.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   location       size total_sqft  bath   price\n",
       "0  Electronic City Phase II      2 BHK       1056   2.0   39.07\n",
       "1          Chikka Tirupathi  4 Bedroom       2600   5.0  120.00\n",
       "2               Uttarahalli      3 BHK       1440   2.0   62.00\n",
       "3        Lingadheeranahalli      3 BHK       1521   3.0   95.00\n",
       "4                  Kothanur      2 BHK       1200   2.0   51.00"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "552e0ce3",
   "metadata": {},
   "source": [
    "### Dealing with missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ec5bda48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location       1\n",
       "size          16\n",
       "total_sqft     0\n",
       "bath          73\n",
       "price          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "326453af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Whitefield                        540\n",
       "Sarjapur  Road                    399\n",
       "Electronic City                   302\n",
       "Kanakpura Road                    273\n",
       "Thanisandra                       234\n",
       "                                 ... \n",
       "Bapuji Layout                       1\n",
       "1st Stage Radha Krishna Layout      1\n",
       "BEML Layout 5th stage               1\n",
       "singapura paradise                  1\n",
       "Abshot Layout                       1\n",
       "Name: location, Length: 1305, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.location.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ef30c698",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.location.fillna(\"Electronic City\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5cb9430f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location       0\n",
       "size          16\n",
       "total_sqft     0\n",
       "bath          73\n",
       "price          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "38612956",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2 BHK         5199\n",
       "3 BHK         4310\n",
       "4 Bedroom      826\n",
       "4 BHK          591\n",
       "3 Bedroom      547\n",
       "1 BHK          538\n",
       "2 Bedroom      329\n",
       "5 Bedroom      297\n",
       "6 Bedroom      191\n",
       "1 Bedroom      105\n",
       "8 Bedroom       84\n",
       "7 Bedroom       83\n",
       "5 BHK           59\n",
       "9 Bedroom       46\n",
       "6 BHK           30\n",
       "7 BHK           17\n",
       "1 RK            13\n",
       "10 Bedroom      12\n",
       "9 BHK            8\n",
       "8 BHK            5\n",
       "11 BHK           2\n",
       "11 Bedroom       2\n",
       "10 BHK           2\n",
       "14 BHK           1\n",
       "13 BHK           1\n",
       "12 Bedroom       1\n",
       "27 BHK           1\n",
       "43 Bedroom       1\n",
       "16 BHK           1\n",
       "19 BHK           1\n",
       "18 Bedroom       1\n",
       "Name: size, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['size'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d9864e38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0            2 BHK\n",
       "1        4 Bedroom\n",
       "2            3 BHK\n",
       "3            3 BHK\n",
       "4            2 BHK\n",
       "           ...    \n",
       "13315    5 Bedroom\n",
       "13316        4 BHK\n",
       "13317        2 BHK\n",
       "13318        4 BHK\n",
       "13319        1 BHK\n",
       "Name: size, Length: 13320, dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"size\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f412e4d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"size\"].fillna(\"2 BHK\",inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "95a90018",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location       0\n",
       "size           0\n",
       "total_sqft     0\n",
       "bath          73\n",
       "price          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "825c766e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Bhk\"]=df[\"size\"].str.split().str.get(0).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "79585017",
   "metadata": {},
   "outputs": [],
   "source": [
    "Data=df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f1fc68fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "Data.drop(\"size\",axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f0b45ae2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4684     43\n",
       "1718     27\n",
       "3379     19\n",
       "11559    18\n",
       "3609     16\n",
       "         ..\n",
       "11498     1\n",
       "3744      1\n",
       "3741      1\n",
       "11509     1\n",
       "13319     1\n",
       "Name: Bhk, Length: 13320, dtype: int32"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# There are outliers in Bhk features. So we will be keeping only 16Bhk and less entries.\n",
    "df['Bhk'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b300e996",
   "metadata": {},
   "outputs": [],
   "source": [
    "Data=Data[Data['Bhk']<16]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "806b7dc4",
   "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>location</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>Bhk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>1056</td>\n",
       "      <td>2.0</td>\n",
       "      <td>39.07</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>2600</td>\n",
       "      <td>5.0</td>\n",
       "      <td>120.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>1440</td>\n",
       "      <td>2.0</td>\n",
       "      <td>62.00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>1521</td>\n",
       "      <td>3.0</td>\n",
       "      <td>95.00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kothanur</td>\n",
       "      <td>1200</td>\n",
       "      <td>2.0</td>\n",
       "      <td>51.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   location total_sqft  bath   price  Bhk\n",
       "0  Electronic City Phase II       1056   2.0   39.07    2\n",
       "1          Chikka Tirupathi       2600   5.0  120.00    4\n",
       "2               Uttarahalli       1440   2.0   62.00    3\n",
       "3        Lingadheeranahalli       1521   3.0   95.00    3\n",
       "4                  Kothanur       1200   2.0   51.00    2"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5b4e5348",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 13315 entries, 0 to 13319\n",
      "Data columns (total 5 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   location    13315 non-null  object \n",
      " 1   total_sqft  13315 non-null  object \n",
      " 2   bath        13242 non-null  float64\n",
      " 3   price       13315 non-null  float64\n",
      " 4   Bhk         13315 non-null  int32  \n",
      "dtypes: float64(2), int32(1), object(2)\n",
      "memory usage: 572.1+ KB\n"
     ]
    }
   ],
   "source": [
    "Data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "54fd8f6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data[\"bath\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f3297f82",
   "metadata": {},
   "outputs": [],
   "source": [
    "Data['bath'].fillna(Data[\"bath\"].median(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "70e4221d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location      0\n",
       "total_sqft    0\n",
       "bath          0\n",
       "price         0\n",
       "Bhk           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "59b2accc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1056', '2600', '1440', ..., '1133 - 1384', '774', '4689'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We are having range of area in 'total_sqft' features, so we will create a function to deal with it.\n",
    "Data['total_sqft'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d8794448",
   "metadata": {},
   "outputs": [],
   "source": [
    "def range_remover(x):\n",
    "    data=x.split(\"-\")\n",
    "    if len(data)==2:\n",
    "        return (float(data[0])+float(data[1]))/2\n",
    "    try:\n",
    "        return float(x)\n",
    "    except:\n",
    "        return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4d1bafde",
   "metadata": {},
   "outputs": [],
   "source": [
    "Data['total_sqft']=Data['total_sqft'].apply(range_remover)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "aad55622",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1056. , 2600. , 1440. , ..., 1258.5,  774. , 4689. ])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data['total_sqft'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b9f8dbed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>Bhk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>1056.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>39.07</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>120.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>62.00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>1521.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>95.00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kothanur</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>51.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   location  total_sqft  bath   price  Bhk\n",
       "0  Electronic City Phase II      1056.0   2.0   39.07    2\n",
       "1          Chikka Tirupathi      2600.0   5.0  120.00    4\n",
       "2               Uttarahalli      1440.0   2.0   62.00    3\n",
       "3        Lingadheeranahalli      1521.0   3.0   95.00    3\n",
       "4                  Kothanur      1200.0   2.0   51.00    2"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "20076753",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    13315.000000\n",
       "mean       112.447927\n",
       "std        148.834436\n",
       "min          8.000000\n",
       "25%         50.000000\n",
       "50%         72.000000\n",
       "75%        120.000000\n",
       "max       3600.000000\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data['price'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c90d6fa",
   "metadata": {},
   "source": [
    "#### Adding Price Per Square feet column because it will help in removing outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2c9962f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "Data[\"price_per_sqft\"]=(Data[\"price\"]/Data[\"total_sqft\"])*100000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a4be9e79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>location</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>Bhk</th>\n",
       "      <th>price_per_sqft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>1056.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>39.07</td>\n",
       "      <td>2</td>\n",
       "      <td>3699.810606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>120.00</td>\n",
       "      <td>4</td>\n",
       "      <td>4615.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>62.00</td>\n",
       "      <td>3</td>\n",
       "      <td>4305.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>1521.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>95.00</td>\n",
       "      <td>3</td>\n",
       "      <td>6245.890861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kothanur</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>51.00</td>\n",
       "      <td>2</td>\n",
       "      <td>4250.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   location  total_sqft  bath   price  Bhk  price_per_sqft\n",
       "0  Electronic City Phase II      1056.0   2.0   39.07    2     3699.810606\n",
       "1          Chikka Tirupathi      2600.0   5.0  120.00    4     4615.384615\n",
       "2               Uttarahalli      1440.0   2.0   62.00    3     4305.555556\n",
       "3        Lingadheeranahalli      1521.0   3.0   95.00    3     6245.890861\n",
       "4                  Kothanur      1200.0   2.0   51.00    2     4250.000000"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "37f82259",
   "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>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>Bhk</th>\n",
       "      <th>price_per_sqft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>13269.000000</td>\n",
       "      <td>13315.000000</td>\n",
       "      <td>13315.000000</td>\n",
       "      <td>13315.000000</td>\n",
       "      <td>1.326900e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1558.435808</td>\n",
       "      <td>2.681036</td>\n",
       "      <td>112.447927</td>\n",
       "      <td>2.794593</td>\n",
       "      <td>7.904675e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1235.172466</td>\n",
       "      <td>1.264791</td>\n",
       "      <td>148.834436</td>\n",
       "      <td>1.208639</td>\n",
       "      <td>1.064494e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.678298e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1100.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.266667e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1276.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.433830e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1680.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.310871e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>52272.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>3600.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>1.200000e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         total_sqft          bath         price           Bhk  price_per_sqft\n",
       "count  13269.000000  13315.000000  13315.000000  13315.000000    1.326900e+04\n",
       "mean    1558.435808      2.681036    112.447927      2.794593    7.904675e+03\n",
       "std     1235.172466      1.264791    148.834436      1.208639    1.064494e+05\n",
       "min        1.000000      1.000000      8.000000      1.000000    2.678298e+02\n",
       "25%     1100.000000      2.000000     50.000000      2.000000    4.266667e+03\n",
       "50%     1276.000000      2.000000     72.000000      3.000000    5.433830e+03\n",
       "75%     1680.000000      3.000000    120.000000      3.000000    7.310871e+03\n",
       "max    52272.000000     15.000000   3600.000000     14.000000    1.200000e+07"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "5d9536c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Whitefield                        540\n",
       "Sarjapur  Road                    399\n",
       "Electronic City                   303\n",
       "Kanakpura Road                    273\n",
       "Thanisandra                       234\n",
       "                                 ... \n",
       "Vasantapura main road               1\n",
       "Bapuji Layout                       1\n",
       "1st Stage Radha Krishna Layout      1\n",
       "BEML Layout 5th stage               1\n",
       "Abshot Layout                       1\n",
       "Name: location, Length: 1302, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Locations feature is having many unique locations(1302), so we will try to reduce it as much as possible.\n",
    "Data[\"location\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0ace04ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# There are white spaces in locations, so we will remove them using strip().\n",
    "Data[\"location\"]=Data[\"location\"].apply(lambda x: x.strip())\n",
    "location_counts=Data[\"location\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "84d49547",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Whitefield           541\n",
       "Sarjapur  Road       399\n",
       "Electronic City      305\n",
       "Kanakpura Road       273\n",
       "Thanisandra          237\n",
       "                    ... \n",
       "Marathalli bridge      1\n",
       "Papareddipalya         1\n",
       "K R C kothanur         1\n",
       "1Channasandra          1\n",
       "Abshot Layout          1\n",
       "Name: location, Length: 1291, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Leading and the trailing white spaces are removed.\n",
    "location_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "0f8d8107",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Basapura                 10\n",
       "1st Block Koramangala    10\n",
       "Sadashiva Nagar          10\n",
       "Sector 1 HSR Layout      10\n",
       "Naganathapura            10\n",
       "                         ..\n",
       "Marathalli bridge         1\n",
       "Papareddipalya            1\n",
       "K R C kothanur            1\n",
       "1Channasandra             1\n",
       "Abshot Layout             1\n",
       "Name: location, Length: 1050, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding the locations having location count less then or equal to 10.\n",
    "location_10_counts=location_counts[location_counts<=10]\n",
    "location_10_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "bb707042",
   "metadata": {},
   "outputs": [],
   "source": [
    "Data[\"location\"]=Data[\"location\"].apply(lambda x: \"other\" if x in location_10_counts else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9c5d4b32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "other                 2881\n",
       "Whitefield             541\n",
       "Sarjapur  Road         399\n",
       "Electronic City        305\n",
       "Kanakpura Road         273\n",
       "                      ... \n",
       "Nehru Nagar             11\n",
       "Banjara Layout          11\n",
       "LB Shastri Nagar        11\n",
       "Pattandur Agrahara      11\n",
       "Narayanapura            11\n",
       "Name: location, Length: 242, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We are able to reduce the no of unique locations from 1302 to 242 \n",
    "Data[\"location\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e96b6e63",
   "metadata": {},
   "source": [
    "### Outlier detection and removal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "bd137fa0",
   "metadata": {
    "scrolled": true
   },
   "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>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>Bhk</th>\n",
       "      <th>price_per_sqft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>13269.000000</td>\n",
       "      <td>13315.000000</td>\n",
       "      <td>13315.000000</td>\n",
       "      <td>13315.000000</td>\n",
       "      <td>1.326900e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1558.435808</td>\n",
       "      <td>2.681036</td>\n",
       "      <td>112.447927</td>\n",
       "      <td>2.794593</td>\n",
       "      <td>7.904675e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1235.172466</td>\n",
       "      <td>1.264791</td>\n",
       "      <td>148.834436</td>\n",
       "      <td>1.208639</td>\n",
       "      <td>1.064494e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.678298e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1100.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.266667e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1276.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.433830e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1680.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.310871e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>52272.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>3600.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>1.200000e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         total_sqft          bath         price           Bhk  price_per_sqft\n",
       "count  13269.000000  13315.000000  13315.000000  13315.000000    1.326900e+04\n",
       "mean    1558.435808      2.681036    112.447927      2.794593    7.904675e+03\n",
       "std     1235.172466      1.264791    148.834436      1.208639    1.064494e+05\n",
       "min        1.000000      1.000000      8.000000      1.000000    2.678298e+02\n",
       "25%     1100.000000      2.000000     50.000000      2.000000    4.266667e+03\n",
       "50%     1276.000000      2.000000     72.000000      3.000000    5.433830e+03\n",
       "75%     1680.000000      3.000000    120.000000      3.000000    7.310871e+03\n",
       "max    52272.000000     15.000000   3600.000000     14.000000    1.200000e+07"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As you can see there are outliers in \"total_sqft\". (min=1 sqft & max=52272 sqft)\n",
    "Data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a6ad94e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    13269.000000\n",
       "mean       575.204981\n",
       "std        388.197821\n",
       "min          0.250000\n",
       "25%        473.333333\n",
       "50%        552.500000\n",
       "75%        625.000000\n",
       "max      26136.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(Data[\"total_sqft\"]/Data[\"Bhk\"]).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ec0036ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "Data=Data[(Data[\"total_sqft\"]/Data[\"Bhk\"])>=300]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b6b69a2b",
   "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>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>Bhk</th>\n",
       "      <th>price_per_sqft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>12529.000000</td>\n",
       "      <td>12529.000000</td>\n",
       "      <td>12529.000000</td>\n",
       "      <td>12529.000000</td>\n",
       "      <td>12529.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1593.893665</td>\n",
       "      <td>2.558464</td>\n",
       "      <td>111.347393</td>\n",
       "      <td>2.649773</td>\n",
       "      <td>6304.043527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1259.083927</td>\n",
       "      <td>1.071272</td>\n",
       "      <td>152.032899</td>\n",
       "      <td>0.969408</td>\n",
       "      <td>4162.397896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>300.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.440000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>267.829813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1116.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4210.526316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1300.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5294.117647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1700.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6916.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>52272.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>3600.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>176470.588235</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         total_sqft          bath         price           Bhk  price_per_sqft\n",
       "count  12529.000000  12529.000000  12529.000000  12529.000000    12529.000000\n",
       "mean    1593.893665      2.558464    111.347393      2.649773     6304.043527\n",
       "std     1259.083927      1.071272    152.032899      0.969408     4162.397896\n",
       "min      300.000000      1.000000      8.440000      1.000000      267.829813\n",
       "25%     1116.000000      2.000000     49.000000      2.000000     4210.526316\n",
       "50%     1300.000000      2.000000     70.000000      3.000000     5294.117647\n",
       "75%     1700.000000      3.000000    115.000000      3.000000     6916.666667\n",
       "max    52272.000000     14.000000   3600.000000     13.000000   176470.588235"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f0e13b20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     12529.000000\n",
       "mean       6304.043527\n",
       "std        4162.397896\n",
       "min         267.829813\n",
       "25%        4210.526316\n",
       "50%        5294.117647\n",
       "75%        6916.666667\n",
       "max      176470.588235\n",
       "Name: price_per_sqft, dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# price_per_sqft max value=176470, outlier.\n",
    "Data[\"price_per_sqft\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b79e64e7",
   "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>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>Bhk</th>\n",
       "      <th>price_per_sqft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10300.000000</td>\n",
       "      <td>10300.000000</td>\n",
       "      <td>10300.000000</td>\n",
       "      <td>10300.000000</td>\n",
       "      <td>10300.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1507.616185</td>\n",
       "      <td>2.470388</td>\n",
       "      <td>91.241837</td>\n",
       "      <td>2.573592</td>\n",
       "      <td>5659.078319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>876.752879</td>\n",
       "      <td>0.970382</td>\n",
       "      <td>86.228577</td>\n",
       "      <td>0.887891</td>\n",
       "      <td>2265.884204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>300.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1250.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1110.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4244.864208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1286.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5175.519668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1650.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6428.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>30400.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>2200.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>24509.803922</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         total_sqft          bath         price           Bhk  price_per_sqft\n",
       "count  10300.000000  10300.000000  10300.000000  10300.000000    10300.000000\n",
       "mean    1507.616185      2.470388     91.241837      2.573592     5659.078319\n",
       "std      876.752879      0.970382     86.228577      0.887891     2265.884204\n",
       "min      300.000000      1.000000     10.000000      1.000000     1250.000000\n",
       "25%     1110.000000      2.000000     49.000000      2.000000     4244.864208\n",
       "50%     1286.000000      2.000000     67.000000      2.000000     5175.519668\n",
       "75%     1650.000000      3.000000    100.000000      3.000000     6428.571429\n",
       "max    30400.000000     13.000000   2200.000000     13.000000    24509.803922"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def sqft_outlier_removal(df):\n",
    "    op_df=pd.DataFrame()\n",
    "    for location,subdf in df.groupby(\"location\"):\n",
    "        m=np.mean(subdf.price_per_sqft)\n",
    "        sd=np.std(subdf.price_per_sqft)\n",
    "        \n",
    "        gen_df=subdf[(subdf.price_per_sqft>(m-sd)) & (subdf.price_per_sqft<=(m+sd))]\n",
    "        op_df=pd.concat([op_df,gen_df], ignore_index=True)\n",
    "    return op_df\n",
    "Data=sqft_outlier_removal(Data)\n",
    "Data.describe()   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "06eeb7de",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Bhk_outlier_removal(df):\n",
    "    exclude_indices=np.array([])\n",
    "    for location,location_df in df.groupby(\"location\"):\n",
    "        bhk_stats={}\n",
    "        for bhk,bhk_df in location_df.groupby(\"Bhk\"):\n",
    "            bhk_stats[bhk]={\n",
    "                'mean':np.mean(bhk_df.price_per_sqft),\n",
    "                'std':np.std(bhk_df.price_per_sqft),\n",
    "                'count':bhk_df.shape[0]  \n",
    "            }\n",
    "        for bhk,bhk_df in location_df.groupby('Bhk'):\n",
    "            stats=bhk_stats.get(bhk-1) # get() function is used,bcoz if it didn't get the value it will not throw error and return none.\n",
    "            if stats and stats['count']>5:\n",
    "                exclude_indices=np.append(exclude_indices,bhk_df[bhk_df.price_per_sqft<(stats['mean'])].index.values)\n",
    "    return df.drop(exclude_indices,axis='index')   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "6b0f0852",
   "metadata": {},
   "outputs": [],
   "source": [
    "Data=Bhk_outlier_removal(Data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "2ab83c76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7360, 6)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "afe63962",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>Bhk</th>\n",
       "      <th>price_per_sqft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>2850.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>428.0</td>\n",
       "      <td>4</td>\n",
       "      <td>15017.543860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>1630.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>3</td>\n",
       "      <td>11901.840491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>1875.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>3</td>\n",
       "      <td>12533.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10833.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>1235.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>2</td>\n",
       "      <td>11983.805668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10291</th>\n",
       "      <td>other</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5833.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10292</th>\n",
       "      <td>other</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1</td>\n",
       "      <td>11111.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10295</th>\n",
       "      <td>other</td>\n",
       "      <td>1353.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>2</td>\n",
       "      <td>8130.081301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10296</th>\n",
       "      <td>other</td>\n",
       "      <td>812.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3201.970443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10299</th>\n",
       "      <td>other</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>4</td>\n",
       "      <td>11111.111111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7360 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  location  total_sqft  bath  price  Bhk  price_per_sqft\n",
       "0      1st Block Jayanagar      2850.0   4.0  428.0    4    15017.543860\n",
       "1      1st Block Jayanagar      1630.0   3.0  194.0    3    11901.840491\n",
       "2      1st Block Jayanagar      1875.0   2.0  235.0    3    12533.333333\n",
       "3      1st Block Jayanagar      1200.0   2.0  130.0    3    10833.333333\n",
       "4      1st Block Jayanagar      1235.0   2.0  148.0    2    11983.805668\n",
       "...                    ...         ...   ...    ...  ...             ...\n",
       "10291                other      1200.0   2.0   70.0    2     5833.333333\n",
       "10292                other      1800.0   1.0  200.0    1    11111.111111\n",
       "10295                other      1353.0   2.0  110.0    2     8130.081301\n",
       "10296                other       812.0   1.0   26.0    1     3201.970443\n",
       "10299                other      3600.0   5.0  400.0    4    11111.111111\n",
       "\n",
       "[7360 rows x 6 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "d05c7323",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Since price_per_sqft was the features responsible for handling outliers, now we can drop it.\n",
    "Data.drop(\"price_per_sqft\",axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f32fd62c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving clean data to csv.\n",
    "Data.to_csv(\"Data/Clean_data.csv\",index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae2a9419",
   "metadata": {},
   "source": [
    "### Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "4949a772",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting data into into independent and dependent features.\n",
    "x=Data.drop(\"price\",axis=1) # features variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "f9d88a26",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=Data[\"price\"] # target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "e1592400",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression,Lasso,Ridge\n",
    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
    "from sklearn.compose import make_column_transformer\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "8a489142",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "668f5e73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((5888, 4), (5888,), (1472, 4), (1472,))"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape,y_train.shape,x_test.shape,y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d74adea3",
   "metadata": {},
   "source": [
    "##### Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "93850c8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "column_transformer=make_column_transformer((OneHotEncoder(sparse=False),['location']), remainder='passthrough')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "e9a82650",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler=StandardScaler()\n",
    "linear_rg=LinearRegression(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "60176e9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe=make_pipeline(column_transformer,scaler,linear_rg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "c167b1f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\r.saroj\\Desktop\\PD\\Data_Science\\env\\lib\\site-packages\\sklearn\\linear_model\\_base.py:141: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2.\n",
      "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
      "\n",
      "from sklearn.pipeline import make_pipeline\n",
      "\n",
      "model = make_pipeline(StandardScaler(with_mean=False), LinearRegression())\n",
      "\n",
      "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
      "\n",
      "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
      "model.fit(X, y, **kwargs)\n",
      "\n",
      "\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('columntransformer',\n",
       "                 ColumnTransformer(remainder='passthrough',\n",
       "                                   transformers=[('onehotencoder',\n",
       "                                                  OneHotEncoder(sparse=False),\n",
       "                                                  ['location'])])),\n",
       "                ('standardscaler', StandardScaler()),\n",
       "                ('linearregression', LinearRegression(normalize=True))])"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "2858e0b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_lr=pipe.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "85f8b556",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8381860339652341"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test,y_pred_lr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f9a6129",
   "metadata": {},
   "source": [
    "#### Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "6fb0376c",
   "metadata": {},
   "outputs": [],
   "source": [
    "lasso=Lasso()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "21c6d67b",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe_lasso=make_pipeline(column_transformer,scaler,lasso)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "bda34988",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('columntransformer',\n",
       "                 ColumnTransformer(remainder='passthrough',\n",
       "                                   transformers=[('onehotencoder',\n",
       "                                                  OneHotEncoder(sparse=False),\n",
       "                                                  ['location'])])),\n",
       "                ('standardscaler', StandardScaler()), ('lasso', Lasso())])"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe_lasso.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "d66bdf22",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict_lasso=pipe_lasso.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "ddca5552",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8263029869374969"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test,y_predict_lasso)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24d58133",
   "metadata": {},
   "source": [
    "#### Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "70047c20",
   "metadata": {},
   "outputs": [],
   "source": [
    "ridge=Ridge()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "a0ecee24",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe_ridg=make_pipeline(column_transformer,scaler,ridge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "bdd1ebb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('columntransformer',\n",
       "                 ColumnTransformer(remainder='passthrough',\n",
       "                                   transformers=[('onehotencoder',\n",
       "                                                  OneHotEncoder(sparse=False),\n",
       "                                                  ['location'])])),\n",
       "                ('standardscaler', StandardScaler()), ('ridge', Ridge())])"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe_ridg.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "eebc7328",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_ridge=pipe_ridg.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "662e3fa7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8383227066936583"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test,y_pred_ridge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "38f5191b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\"Linerregression\":0.8381860339652341\n",
      "\"Lasso\":0.8263029869374969\n",
      "\"Ridge\":0.8383227066936583\n"
     ]
    }
   ],
   "source": [
    "print(f'''\"Linerregression\":{r2_score(y_test,y_pred_lr)}\n",
    "\"Lasso\":{r2_score(y_test,y_predict_lasso)}\n",
    "\"Ridge\":{r2_score(y_test,y_pred_ridge)}''')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ef81dde",
   "metadata": {},
   "source": [
    "#### Since Ridge regession model giving good R2 score, we will dump it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "e1bc7cff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(pipe_ridg,open('Ridge_Model.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32fa492b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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