house_price_predict.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1=pd.read_csv('Bengaluru_House_Data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>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": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13320, 9)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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": [
    "data1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Super built-up  Area    8790\n",
      "Built-up  Area          2418\n",
      "Plot  Area              2025\n",
      "Carpet  Area              87\n",
      "Name: area_type, dtype: int64\n",
      "********************\n",
      "Ready To Move    10581\n",
      "18-Dec             307\n",
      "18-May             295\n",
      "18-Apr             271\n",
      "18-Aug             200\n",
      "                 ...  \n",
      "15-Aug               1\n",
      "17-Jan               1\n",
      "16-Nov               1\n",
      "16-Jan               1\n",
      "14-Jul               1\n",
      "Name: availability, Length: 81, dtype: int64\n",
      "********************\n",
      "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\n",
      "********************\n",
      "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\n",
      "********************\n",
      "GrrvaGr    80\n",
      "PrarePa    76\n",
      "Sryalan    59\n",
      "Prtates    59\n",
      "GMown E    56\n",
      "           ..\n",
      "Amionce     1\n",
      "JaghtDe     1\n",
      "Jauraht     1\n",
      "Brity U     1\n",
      "RSntsAp     1\n",
      "Name: society, Length: 2688, dtype: int64\n",
      "********************\n",
      "1200    843\n",
      "1100    221\n",
      "1500    205\n",
      "2400    196\n",
      "600     180\n",
      "       ... \n",
      "3580      1\n",
      "2461      1\n",
      "1437      1\n",
      "2155      1\n",
      "4689      1\n",
      "Name: total_sqft, Length: 2117, dtype: int64\n",
      "********************\n",
      "2.0     6908\n",
      "3.0     3286\n",
      "4.0     1226\n",
      "1.0      788\n",
      "5.0      524\n",
      "6.0      273\n",
      "7.0      102\n",
      "8.0       64\n",
      "9.0       43\n",
      "10.0      13\n",
      "12.0       7\n",
      "13.0       3\n",
      "11.0       3\n",
      "16.0       2\n",
      "27.0       1\n",
      "40.0       1\n",
      "15.0       1\n",
      "14.0       1\n",
      "18.0       1\n",
      "Name: bath, dtype: int64\n",
      "********************\n",
      "2.0    5113\n",
      "1.0    4897\n",
      "3.0    1672\n",
      "0.0    1029\n",
      "Name: balcony, dtype: int64\n",
      "********************\n",
      "75.00     310\n",
      "65.00     302\n",
      "55.00     275\n",
      "60.00     270\n",
      "45.00     240\n",
      "         ... \n",
      "351.00      1\n",
      "54.10       1\n",
      "80.64       1\n",
      "32.73       1\n",
      "488.00      1\n",
      "Name: price, Length: 1994, dtype: int64\n",
      "********************\n"
     ]
    }
   ],
   "source": [
    "#print(data1.columns)\n",
    "for col in data1.columns:\n",
    "    print(data1[col].value_counts())\n",
    "    print(\"*\"*20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "area_type          0\n",
       "availability       0\n",
       "location           1\n",
       "size              16\n",
       "society         5502\n",
       "total_sqft         0\n",
       "bath              73\n",
       "balcony          609\n",
       "price              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1.drop(columns=['area_type','availability','society','balcony'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>bath</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>13247.000000</td>\n",
       "      <td>13320.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.692610</td>\n",
       "      <td>112.565627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.341458</td>\n",
       "      <td>148.971674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\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>120.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>40.000000</td>\n",
       "      <td>3600.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               bath         price\n",
       "count  13247.000000  13320.000000\n",
       "mean       2.692610    112.565627\n",
       "std        1.341458    148.971674\n",
       "min        1.000000      8.000000\n",
       "25%        2.000000     50.000000\n",
       "50%        2.000000     72.000000\n",
       "75%        3.000000    120.000000\n",
       "max       40.000000   3600.000000"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 13320 entries, 0 to 13319\n",
      "Data columns (total 5 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   location    13319 non-null  object \n",
      " 1   size        13304 non-null  object \n",
      " 2   total_sqft  13320 non-null  object \n",
      " 3   bath        13247 non-null  float64\n",
      " 4   price       13320 non-null  float64\n",
      "dtypes: float64(2), object(3)\n",
      "memory usage: 520.4+ KB\n"
     ]
    }
   ],
   "source": [
    "data1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1['location'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1['location']=data1['location'].fillna('Sarjapur Road')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1['size'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1['size']=data1['size'].fillna('2 BHK')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0     6908\n",
       "3.0     3286\n",
       "4.0     1226\n",
       "1.0      788\n",
       "5.0      524\n",
       "6.0      273\n",
       "7.0      102\n",
       "8.0       64\n",
       "9.0       43\n",
       "10.0      13\n",
       "12.0       7\n",
       "13.0       3\n",
       "11.0       3\n",
       "16.0       2\n",
       "27.0       1\n",
       "40.0       1\n",
       "15.0       1\n",
       "14.0       1\n",
       "18.0       1\n",
       "Name: bath, dtype: int64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1['bath'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1['bath']=data1['bath'].fillna(data1['bath'].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 13320 entries, 0 to 13319\n",
      "Data columns (total 5 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   location    13320 non-null  object \n",
      " 1   size        13320 non-null  object \n",
      " 2   total_sqft  13320 non-null  object \n",
      " 3   bath        13320 non-null  float64\n",
      " 4   price       13320 non-null  float64\n",
      "dtypes: float64(2), object(3)\n",
      "memory usage: 520.4+ KB\n"
     ]
    }
   ],
   "source": [
    "data1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1['bhk']=data1['size'].str.split().str.get(0).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\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>location</th>\n",
       "      <th>size</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>1718</th>\n",
       "      <td>2Electronic City Phase II</td>\n",
       "      <td>27 BHK</td>\n",
       "      <td>8000</td>\n",
       "      <td>27.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4684</th>\n",
       "      <td>Munnekollal</td>\n",
       "      <td>43 Bedroom</td>\n",
       "      <td>2400</td>\n",
       "      <td>40.0</td>\n",
       "      <td>660.0</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       location        size total_sqft  bath  price  bhk\n",
       "1718  2Electronic City Phase II      27 BHK       8000  27.0  230.0   27\n",
       "4684                Munnekollal  43 Bedroom       2400  40.0  660.0   43"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1[data1.bhk>20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1056', '2600', '1440', ..., '1133 - 1384', '774', '4689'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1['total_sqft'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convertRange(x):\n",
    "    temp=x.split('-')\n",
    "    if len(temp) == 2:\n",
    "        return (float(temp[0])) + float(temp[1])/2\n",
    "    try:\n",
    "        return float(x);\n",
    "    except:\n",
    "        return None;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1['total_sqft']=data1['total_sqft'].apply(convertRange)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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>size</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>2 BHK</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>4 Bedroom</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>3 BHK</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>3 BHK</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>2 BHK</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       size  total_sqft  bath   price  bhk\n",
       "0  Electronic City Phase II      2 BHK      1056.0   2.0   39.07    2\n",
       "1          Chikka Tirupathi  4 Bedroom      2600.0   5.0  120.00    4\n",
       "2               Uttarahalli      3 BHK      1440.0   2.0   62.00    3\n",
       "3        Lingadheeranahalli      3 BHK      1521.0   3.0   95.00    3\n",
       "4                  Kothanur      2 BHK      1200.0   2.0   51.00    2"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1['price_per_sqft']=data1['price']*100000/data1['total_sqft']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         3699.810606\n",
       "1         4615.384615\n",
       "2         4305.555556\n",
       "3         6245.890861\n",
       "4         4250.000000\n",
       "             ...     \n",
       "13315     6689.834926\n",
       "13316    11111.111111\n",
       "13317     5258.545136\n",
       "13318    10407.336319\n",
       "13319     3090.909091\n",
       "Name: price_per_sqft, Length: 13320, dtype: float64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1['price_per_sqft']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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>13274.000000</td>\n",
       "      <td>13320.000000</td>\n",
       "      <td>13320.000000</td>\n",
       "      <td>13320.000000</td>\n",
       "      <td>1.327400e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1572.225941</td>\n",
       "      <td>2.688814</td>\n",
       "      <td>112.565627</td>\n",
       "      <td>2.802778</td>\n",
       "      <td>7.883254e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1254.823072</td>\n",
       "      <td>1.338754</td>\n",
       "      <td>148.971674</td>\n",
       "      <td>1.294496</td>\n",
       "      <td>1.064303e+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>4.227642e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1280.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.416667e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1690.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.293505e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>52272.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>3600.000000</td>\n",
       "      <td>43.000000</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  13274.000000  13320.000000  13320.000000  13320.000000    1.327400e+04\n",
       "mean    1572.225941      2.688814    112.565627      2.802778    7.883254e+03\n",
       "std     1254.823072      1.338754    148.971674      1.294496    1.064303e+05\n",
       "min        1.000000      1.000000      8.000000      1.000000    2.678298e+02\n",
       "25%     1100.000000      2.000000     50.000000      2.000000    4.227642e+03\n",
       "50%     1280.000000      2.000000     72.000000      3.000000    5.416667e+03\n",
       "75%     1690.000000      3.000000    120.000000      3.000000    7.293505e+03\n",
       "max    52272.000000     40.000000   3600.000000     43.000000    1.200000e+07"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Whitefield                        540\n",
       "Sarjapur  Road                    399\n",
       "Electronic City                   302\n",
       "Kanakpura Road                    273\n",
       "Thanisandra                       234\n",
       "                                 ... \n",
       "1st Stage Radha Krishna Layout      1\n",
       "BEML Layout 5th stage               1\n",
       "singapura paradise                  1\n",
       "Uvce Layout                         1\n",
       "Abshot Layout                       1\n",
       "Name: location, Length: 1306, dtype: int64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1['location'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1['location']=data1['location'].apply(lambda x:x.strip())\n",
    "location_count=data1['location'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BTM 1st Stage                         10\n",
       "Nagadevanahalli                       10\n",
       "Basapura                              10\n",
       "Sector 1 HSR Layout                   10\n",
       "Dairy Circle                          10\n",
       "                                      ..\n",
       "1Channasandra                          1\n",
       "Hosahalli                              1\n",
       "Vijayabank bank layout                 1\n",
       "near Ramanashree California resort     1\n",
       "Abshot Layout                          1\n",
       "Name: location, Length: 1054, dtype: int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "location_count_less10=location_count[location_count<=10]\n",
    "location_count_less10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1['location']=data1['location'].apply(lambda x: 'other' if x in location_count_less10 else x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Outlier detection and removal**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<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>13274.000000</td>\n",
       "      <td>13320.000000</td>\n",
       "      <td>13320.000000</td>\n",
       "      <td>13320.000000</td>\n",
       "      <td>1.327400e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1572.225941</td>\n",
       "      <td>2.688814</td>\n",
       "      <td>112.565627</td>\n",
       "      <td>2.802778</td>\n",
       "      <td>7.883254e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1254.823072</td>\n",
       "      <td>1.338754</td>\n",
       "      <td>148.971674</td>\n",
       "      <td>1.294496</td>\n",
       "      <td>1.064303e+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>4.227642e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1280.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.416667e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1690.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.293505e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>52272.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>3600.000000</td>\n",
       "      <td>43.000000</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  13274.000000  13320.000000  13320.000000  13320.000000    1.327400e+04\n",
       "mean    1572.225941      2.688814    112.565627      2.802778    7.883254e+03\n",
       "std     1254.823072      1.338754    148.971674      1.294496    1.064303e+05\n",
       "min        1.000000      1.000000      8.000000      1.000000    2.678298e+02\n",
       "25%     1100.000000      2.000000     50.000000      2.000000    4.227642e+03\n",
       "50%     1280.000000      2.000000     72.000000      3.000000    5.416667e+03\n",
       "75%     1690.000000      3.000000    120.000000      3.000000    7.293505e+03\n",
       "max    52272.000000     40.000000   3600.000000     43.000000    1.200000e+07"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    13274.000000\n",
       "mean       579.732987\n",
       "std        392.247238\n",
       "min          0.250000\n",
       "25%        475.000000\n",
       "50%        554.000000\n",
       "75%        627.500000\n",
       "max      26136.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(data1['total_sqft']/data1['bhk']).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<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>12530.000000</td>\n",
       "      <td>12530.000000</td>\n",
       "      <td>12530.000000</td>\n",
       "      <td>12530.000000</td>\n",
       "      <td>12530.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1607.911902</td>\n",
       "      <td>2.559537</td>\n",
       "      <td>111.382401</td>\n",
       "      <td>2.650838</td>\n",
       "      <td>6278.292738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1277.977557</td>\n",
       "      <td>1.077938</td>\n",
       "      <td>152.077329</td>\n",
       "      <td>0.976678</td>\n",
       "      <td>4171.053803</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>1120.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4177.777778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1302.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5273.281068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1710.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6896.551724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>52272.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>3600.000000</td>\n",
       "      <td>16.000000</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  12530.000000  12530.000000  12530.000000  12530.000000    12530.000000\n",
       "mean    1607.911902      2.559537    111.382401      2.650838     6278.292738\n",
       "std     1277.977557      1.077938    152.077329      0.976678     4171.053803\n",
       "min      300.000000      1.000000      8.440000      1.000000      267.829813\n",
       "25%     1120.000000      2.000000     49.000000      2.000000     4177.777778\n",
       "50%     1302.500000      2.000000     70.000000      3.000000     5273.281068\n",
       "75%     1710.000000      3.000000    115.000000      3.000000     6896.551724\n",
       "max    52272.000000     16.000000   3600.000000     16.000000   176470.588235"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1=data1[((data1['total_sqft']/data1['bhk'])>=300)]\n",
    "data1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12530, 7)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     12530.000000\n",
       "mean       6278.292738\n",
       "std        4171.053803\n",
       "min         267.829813\n",
       "25%        4177.777778\n",
       "50%        5273.281068\n",
       "75%        6896.551724\n",
       "max      176470.588235\n",
       "Name: price_per_sqft, dtype: float64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.price_per_sqft.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(data1.groupby('location'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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>10287.000000</td>\n",
       "      <td>10287.00000</td>\n",
       "      <td>10287.000000</td>\n",
       "      <td>10287.000000</td>\n",
       "      <td>10287.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1517.904648</td>\n",
       "      <td>2.47176</td>\n",
       "      <td>91.305304</td>\n",
       "      <td>2.575872</td>\n",
       "      <td>5641.654913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>903.645032</td>\n",
       "      <td>0.97517</td>\n",
       "      <td>86.528904</td>\n",
       "      <td>0.895390</td>\n",
       "      <td>2270.194350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>300.000000</td>\n",
       "      <td>1.00000</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>2.00000</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>1290.000000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5159.705160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1650.000000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6412.588374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>30400.000000</td>\n",
       "      <td>16.00000</td>\n",
       "      <td>2200.000000</td>\n",
       "      <td>16.000000</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  10287.000000  10287.00000  10287.000000  10287.000000    10287.000000\n",
       "mean    1517.904648      2.47176     91.305304      2.575872     5641.654913\n",
       "std      903.645032      0.97517     86.528904      0.895390     2270.194350\n",
       "min      300.000000      1.00000     10.000000      1.000000     1250.000000\n",
       "25%     1110.000000      2.00000     49.000000      2.000000     4210.526316\n",
       "50%     1290.000000      2.00000     67.000000      2.000000     5159.705160\n",
       "75%     1650.000000      3.00000    100.000000      3.000000     6412.588374\n",
       "max    30400.000000     16.00000   2200.000000     16.000000    24509.803922"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def remove_out_sqft(df):\n",
    "    df_out=pd.DataFrame()\n",
    "    for key,subdf in df.groupby('location'):\n",
    "        m=np.mean(subdf.price_per_sqft)\n",
    "        st=np.std(subdf.price_per_sqft)\n",
    "        gen_df=subdf[(subdf.price_per_sqft>(m-st))&(subdf.price_per_sqft<=(m+st))]\n",
    "        df_out=pd.concat([df_out,gen_df],ignore_index=True)\n",
    "    return df_out\n",
    "data1=remove_out_sqft(data1)\n",
    "data1.describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bhk_outlier_remover(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)\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')\n",
    "data1=bhk_outlier_remover(data1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7358, 7)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "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>size</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>4 BHK</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>3 BHK</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>3 BHK</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>3 BHK</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>2 BHK</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10278</th>\n",
       "      <td>other</td>\n",
       "      <td>2 BHK</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>10279</th>\n",
       "      <td>other</td>\n",
       "      <td>1 BHK</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>10282</th>\n",
       "      <td>other</td>\n",
       "      <td>2 BHK</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>10283</th>\n",
       "      <td>other</td>\n",
       "      <td>1 Bedroom</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>10286</th>\n",
       "      <td>other</td>\n",
       "      <td>4 BHK</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>7358 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  location       size  total_sqft  bath  price  bhk  \\\n",
       "0      1st Block Jayanagar      4 BHK      2850.0   4.0  428.0    4   \n",
       "1      1st Block Jayanagar      3 BHK      1630.0   3.0  194.0    3   \n",
       "2      1st Block Jayanagar      3 BHK      1875.0   2.0  235.0    3   \n",
       "3      1st Block Jayanagar      3 BHK      1200.0   2.0  130.0    3   \n",
       "4      1st Block Jayanagar      2 BHK      1235.0   2.0  148.0    2   \n",
       "...                    ...        ...         ...   ...    ...  ...   \n",
       "10278                other      2 BHK      1200.0   2.0   70.0    2   \n",
       "10279                other      1 BHK      1800.0   1.0  200.0    1   \n",
       "10282                other      2 BHK      1353.0   2.0  110.0    2   \n",
       "10283                other  1 Bedroom       812.0   1.0   26.0    1   \n",
       "10286                other      4 BHK      3600.0   5.0  400.0    4   \n",
       "\n",
       "       price_per_sqft  \n",
       "0        15017.543860  \n",
       "1        11901.840491  \n",
       "2        12533.333333  \n",
       "3        10833.333333  \n",
       "4        11983.805668  \n",
       "...               ...  \n",
       "10278     5833.333333  \n",
       "10279    11111.111111  \n",
       "10282     8130.081301  \n",
       "10283     3201.970443  \n",
       "10286    11111.111111  \n",
       "\n",
       "[7358 rows x 7 columns]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1.drop(columns=['size','price_per_sqft'],inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Cleaned Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "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>1st Block Jayanagar</td>\n",
       "      <td>2850.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>428.0</td>\n",
       "      <td>4</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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              location  total_sqft  bath  price  bhk\n",
       "0  1st Block Jayanagar      2850.0   4.0  428.0    4\n",
       "1  1st Block Jayanagar      1630.0   3.0  194.0    3\n",
       "2  1st Block Jayanagar      1875.0   2.0  235.0    3\n",
       "3  1st Block Jayanagar      1200.0   2.0  130.0    3\n",
       "4  1st Block Jayanagar      1235.0   2.0  148.0    2"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1.to_csv(\"Cleaned_data.csv\")\n",
    "X=data1.drop(columns=['price'])\n",
    "Y=data1['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "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.pipeline import make_pipeline\n",
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.2,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5886, 4)\n",
      "(1472, 4)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Applying Linear Regression**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import make_column_transformer\n",
    "\n",
    "column_trans=make_column_transformer((OneHotEncoder(sparse=False),['location']),remainder='passthrough')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler=StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr=LinearRegression(normalize=True)\n",
    "pipe=make_pipeline(column_trans,scaler,lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Dell\\AppData\\Roaming\\Python\\Python310\\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": [
       "0.8392045362078312"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.fit(X_train,Y_train)\n",
    "Y_pred_lr=pipe.predict(X_test)\n",
    "r2_score(Y_test,Y_pred_lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "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": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''Lasso Regression'''\n",
    "lasso=Lasso()\n",
    "pipe=make_pipeline(column_trans,scaler,lasso)\n",
    "pipe.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8315444728453546"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pred_lasso=pipe.predict(X_test)\n",
    "#print(Y_pred_lasso)\n",
    "r2_score(Y_test,Y_pred_lasso)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(pipe,open('bangalore_house_price2.pk1','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'data_columns': ['1st block jayanagar',\n",
       "  '1st phase jp nagar',\n",
       "  '2nd phase judicial layout',\n",
       "  '2nd stage nagarbhavi',\n",
       "  '5th block hbr layout',\n",
       "  '5th phase jp nagar',\n",
       "  '6th phase jp nagar',\n",
       "  '7th phase jp nagar',\n",
       "  '8th phase jp nagar',\n",
       "  '9th phase jp nagar',\n",
       "  'aecs layout',\n",
       "  'abbigere',\n",
       "  'akshaya nagar',\n",
       "  'ambalipura',\n",
       "  'ambedkar nagar',\n",
       "  'amruthahalli',\n",
       "  'anandapura',\n",
       "  'ananth nagar',\n",
       "  'anekal',\n",
       "  'anjanapura',\n",
       "  'ardendale',\n",
       "  'arekere',\n",
       "  'attibele',\n",
       "  'beml layout',\n",
       "  'btm 2nd stage',\n",
       "  'btm layout',\n",
       "  'babusapalaya',\n",
       "  'badavala nagar',\n",
       "  'balagere',\n",
       "  'banashankari',\n",
       "  'banashankari stage ii',\n",
       "  'banashankari stage iii',\n",
       "  'banashankari stage v',\n",
       "  'banashankari stage vi',\n",
       "  'banaswadi',\n",
       "  'banjara layout',\n",
       "  'bannerghatta',\n",
       "  'bannerghatta road',\n",
       "  'basavangudi',\n",
       "  'basaveshwara nagar',\n",
       "  'battarahalli',\n",
       "  'begur',\n",
       "  'begur road',\n",
       "  'bellandur',\n",
       "  'benson town',\n",
       "  'bharathi nagar',\n",
       "  'bhoganhalli',\n",
       "  'billekahalli',\n",
       "  'binny pete',\n",
       "  'bisuvanahalli',\n",
       "  'bommanahalli',\n",
       "  'bommasandra',\n",
       "  'bommasandra industrial area',\n",
       "  'bommenahalli',\n",
       "  'brookefield',\n",
       "  'budigere',\n",
       "  'cv raman nagar',\n",
       "  'chamrajpet',\n",
       "  'chandapura',\n",
       "  'channasandra',\n",
       "  'chikka tirupathi',\n",
       "  'chikkabanavar',\n",
       "  'chikkalasandra',\n",
       "  'choodasandra',\n",
       "  'cooke town',\n",
       "  'cox town',\n",
       "  'cunningham road',\n",
       "  'dasanapura',\n",
       "  'dasarahalli',\n",
       "  'devanahalli',\n",
       "  'devarachikkanahalli',\n",
       "  'dodda nekkundi',\n",
       "  'doddaballapur',\n",
       "  'doddakallasandra',\n",
       "  'doddathoguru',\n",
       "  'domlur',\n",
       "  'dommasandra',\n",
       "  'epip zone',\n",
       "  'electronic city',\n",
       "  'electronic city phase ii',\n",
       "  'electronics city phase 1',\n",
       "  'frazer town',\n",
       "  'gm palaya',\n",
       "  'garudachar palya',\n",
       "  'giri nagar',\n",
       "  'gollarapalya hosahalli',\n",
       "  'gottigere',\n",
       "  'green glen layout',\n",
       "  'gubbalala',\n",
       "  'gunjur',\n",
       "  'hal 2nd stage',\n",
       "  'hbr layout',\n",
       "  'hrbr layout',\n",
       "  'hsr layout',\n",
       "  'haralur road',\n",
       "  'harlur',\n",
       "  'hebbal',\n",
       "  'hebbal kempapura',\n",
       "  'hegde nagar',\n",
       "  'hennur',\n",
       "  'hennur road',\n",
       "  'hoodi',\n",
       "  'horamavu agara',\n",
       "  'horamavu banaswadi',\n",
       "  'hormavu',\n",
       "  'hosa road',\n",
       "  'hosakerehalli',\n",
       "  'hoskote',\n",
       "  'hosur road',\n",
       "  'hulimavu',\n",
       "  'isro layout',\n",
       "  'itpl',\n",
       "  'iblur village',\n",
       "  'indira nagar',\n",
       "  'jp nagar',\n",
       "  'jakkur',\n",
       "  'jalahalli',\n",
       "  'jalahalli east',\n",
       "  'jigani',\n",
       "  'judicial layout',\n",
       "  'kr puram',\n",
       "  'kadubeesanahalli',\n",
       "  'kadugodi',\n",
       "  'kaggadasapura',\n",
       "  'kaggalipura',\n",
       "  'kaikondrahalli',\n",
       "  'kalena agrahara',\n",
       "  'kalyan nagar',\n",
       "  'kambipura',\n",
       "  'kammanahalli',\n",
       "  'kammasandra',\n",
       "  'kanakapura',\n",
       "  'kanakpura road',\n",
       "  'kannamangala',\n",
       "  'karuna nagar',\n",
       "  'kasavanhalli',\n",
       "  'kasturi nagar',\n",
       "  'kathriguppe',\n",
       "  'kaval byrasandra',\n",
       "  'kenchenahalli',\n",
       "  'kengeri',\n",
       "  'kengeri satellite town',\n",
       "  'kereguddadahalli',\n",
       "  'kodichikkanahalli',\n",
       "  'kodigehaali',\n",
       "  'kodigehalli',\n",
       "  'kodihalli',\n",
       "  'kogilu',\n",
       "  'konanakunte',\n",
       "  'koramangala',\n",
       "  'kothannur',\n",
       "  'kothanur',\n",
       "  'kudlu',\n",
       "  'kudlu gate',\n",
       "  'kumaraswami layout',\n",
       "  'kundalahalli',\n",
       "  'lb shastri nagar',\n",
       "  'laggere',\n",
       "  'lakshminarayana pura',\n",
       "  'lingadheeranahalli',\n",
       "  'magadi road',\n",
       "  'mahadevpura',\n",
       "  'mahalakshmi layout',\n",
       "  'mallasandra',\n",
       "  'malleshpalya',\n",
       "  'malleshwaram',\n",
       "  'marathahalli',\n",
       "  'margondanahalli',\n",
       "  'marsur',\n",
       "  'mico layout',\n",
       "  'munnekollal',\n",
       "  'murugeshpalya',\n",
       "  'mysore road',\n",
       "  'ngr layout',\n",
       "  'nri layout',\n",
       "  'nagarbhavi',\n",
       "  'nagasandra',\n",
       "  'nagavara',\n",
       "  'nagavarapalya',\n",
       "  'narayanapura',\n",
       "  'neeladri nagar',\n",
       "  'nehru nagar',\n",
       "  'ombr layout',\n",
       "  'old airport road',\n",
       "  'old madras road',\n",
       "  'padmanabhanagar',\n",
       "  'pai layout',\n",
       "  'panathur',\n",
       "  'parappana agrahara',\n",
       "  'pattandur agrahara',\n",
       "  'poorna pragna layout',\n",
       "  'prithvi layout',\n",
       "  'r.t. nagar',\n",
       "  'rachenahalli',\n",
       "  'raja rajeshwari nagar',\n",
       "  'rajaji nagar',\n",
       "  'rajiv nagar',\n",
       "  'ramagondanahalli',\n",
       "  'ramamurthy nagar',\n",
       "  'rayasandra',\n",
       "  'sahakara nagar',\n",
       "  'sanjay nagar',\n",
       "  'sarakki nagar',\n",
       "  'sarjapur',\n",
       "  'sarjapur  road',\n",
       "  'sarjapura - attibele road',\n",
       "  'sector 2 hsr layout',\n",
       "  'sector 7 hsr layout',\n",
       "  'seegehalli',\n",
       "  'shampura',\n",
       "  'shivaji nagar',\n",
       "  'singasandra',\n",
       "  'somasundara palya',\n",
       "  'sompura',\n",
       "  'sonnenahalli',\n",
       "  'subramanyapura',\n",
       "  'sultan palaya',\n",
       "  'tc palaya',\n",
       "  'talaghattapura',\n",
       "  'thanisandra',\n",
       "  'thigalarapalya',\n",
       "  'thubarahalli',\n",
       "  'thyagaraja nagar',\n",
       "  'tindlu',\n",
       "  'tumkur road',\n",
       "  'ulsoor',\n",
       "  'uttarahalli',\n",
       "  'varthur',\n",
       "  'varthur road',\n",
       "  'vasanthapura',\n",
       "  'vidyaranyapura',\n",
       "  'vijayanagar',\n",
       "  'vishveshwarya layout',\n",
       "  'vishwapriya layout',\n",
       "  'vittasandra',\n",
       "  'whitefield',\n",
       "  'yelachenahalli',\n",
       "  'yelahanka',\n",
       "  'yelahanka new town',\n",
       "  'yelenahalli',\n",
       "  'yeshwanthpur',\n",
       "  'other']}"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import json\n",
    "columns = {'data_columns': [col.lower() for col in X['location'].unique().tolist()]}\n",
    "with open(\"columns.json\",\"w\") as f:\n",
    "    f.write(json.dumps(columns))\n",
    "columns"
   ]
  }
 ],
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