House Price Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ed4f3016",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "matplotlib.rcParams[\"figure.figsize\"]=(20,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ffd9f605",
   "metadata": {},
   "outputs": [],
   "source": [
    "#DATA CLEANING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7bda27fd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "        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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df1=pd.read_csv(\"banglore.csv\")\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9db51e0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13320, 9)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d3a10b93",
   "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>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",
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      ],
      "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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2=df1.drop(['area_type', 'balcony', 'availability', 'society'], axis='columns')\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f8b9dafc",
   "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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8a91b974",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location      0\n",
       "size          0\n",
       "total_sqft    0\n",
       "bath          0\n",
       "price         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3=df2.dropna()\n",
    "df3.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c1cf94e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2 BHK', '4 Bedroom', '3 BHK', '4 BHK', '6 Bedroom', '3 Bedroom',\n",
       "       '1 BHK', '1 RK', '1 Bedroom', '8 Bedroom', '2 Bedroom',\n",
       "       '7 Bedroom', '5 BHK', '7 BHK', '6 BHK', '5 Bedroom', '11 BHK',\n",
       "       '9 BHK', '9 Bedroom', '27 BHK', '10 Bedroom', '11 Bedroom',\n",
       "       '10 BHK', '19 BHK', '16 BHK', '43 Bedroom', '14 BHK', '8 BHK',\n",
       "       '12 Bedroom', '13 BHK', '18 Bedroom'], dtype=object)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3['size'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "44178c04",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df3.loc[:, 'bhk'] = df3['size'].apply(lambda x: int(x.split(' ')[0]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "296d545a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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</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</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</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</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</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   2.0   39.07    2\n",
       "1          Chikka Tirupathi  4 Bedroom       2600   5.0  120.00    4\n",
       "2               Uttarahalli      3 BHK       1440   2.0   62.00    3\n",
       "3        Lingadheeranahalli      3 BHK       1521   3.0   95.00    3\n",
       "4                  Kothanur      2 BHK       1200   2.0   51.00    2"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c339f782",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  4,  3,  6,  1,  8,  7,  5, 11,  9, 27, 10, 19, 16, 43, 14, 12,\n",
       "       13, 18], dtype=int64)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3['bhk'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "51132eeb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<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",
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       "  <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",
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      ],
      "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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3[df3.bhk>20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f1d179a8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1056', '2600', '1440', ..., '1133 - 1384', '774', '4689'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.total_sqft.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d6d2a23d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def is_float(x):\n",
    "    try:\n",
    "        float(x)\n",
    "    except:\n",
    "        return False\n",
    "    return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "72323704",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<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>30</th>\n",
       "      <td>Yelahanka</td>\n",
       "      <td>4 BHK</td>\n",
       "      <td>2100 - 2850</td>\n",
       "      <td>4.0</td>\n",
       "      <td>186.000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>Hebbal</td>\n",
       "      <td>4 BHK</td>\n",
       "      <td>3067 - 8156</td>\n",
       "      <td>4.0</td>\n",
       "      <td>477.000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>8th Phase JP Nagar</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1042 - 1105</td>\n",
       "      <td>2.0</td>\n",
       "      <td>54.005</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>Sarjapur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1145 - 1340</td>\n",
       "      <td>2.0</td>\n",
       "      <td>43.490</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>KR Puram</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1015 - 1540</td>\n",
       "      <td>2.0</td>\n",
       "      <td>56.800</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>Kengeri</td>\n",
       "      <td>1 BHK</td>\n",
       "      <td>34.46Sq. Meter</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549</th>\n",
       "      <td>Hennur Road</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1195 - 1440</td>\n",
       "      <td>2.0</td>\n",
       "      <td>63.770</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>648</th>\n",
       "      <td>Arekere</td>\n",
       "      <td>9 Bedroom</td>\n",
       "      <td>4125Perch</td>\n",
       "      <td>9.0</td>\n",
       "      <td>265.000</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>661</th>\n",
       "      <td>Yelahanka</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1120 - 1145</td>\n",
       "      <td>2.0</td>\n",
       "      <td>48.130</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>672</th>\n",
       "      <td>Bettahalsoor</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>3090 - 5002</td>\n",
       "      <td>4.0</td>\n",
       "      <td>445.000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>772</th>\n",
       "      <td>Banashankari Stage VI</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1160 - 1195</td>\n",
       "      <td>2.0</td>\n",
       "      <td>59.935</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>775</th>\n",
       "      <td>Basavanagara</td>\n",
       "      <td>1 BHK</td>\n",
       "      <td>1000Sq. Meter</td>\n",
       "      <td>2.0</td>\n",
       "      <td>93.000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>850</th>\n",
       "      <td>Bannerghatta Road</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1115 - 1130</td>\n",
       "      <td>2.0</td>\n",
       "      <td>58.935</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872</th>\n",
       "      <td>Singapura Village</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1100Sq. Yards</td>\n",
       "      <td>2.0</td>\n",
       "      <td>45.000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>Chandapura</td>\n",
       "      <td>1 BHK</td>\n",
       "      <td>520 - 645</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.135</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>927</th>\n",
       "      <td>Thanisandra</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1000 - 1285</td>\n",
       "      <td>2.0</td>\n",
       "      <td>43.415</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>959</th>\n",
       "      <td>Kammasandra</td>\n",
       "      <td>1 BHK</td>\n",
       "      <td>650 - 665</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.410</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>990</th>\n",
       "      <td>Sarjapur</td>\n",
       "      <td>1 BHK</td>\n",
       "      <td>633 - 666</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.535</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1019</th>\n",
       "      <td>Marathi Layout</td>\n",
       "      <td>1 Bedroom</td>\n",
       "      <td>5.31Acres</td>\n",
       "      <td>1.0</td>\n",
       "      <td>110.000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1086</th>\n",
       "      <td>Narasapura</td>\n",
       "      <td>2 Bedroom</td>\n",
       "      <td>30Acres</td>\n",
       "      <td>2.0</td>\n",
       "      <td>29.500</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   location       size      total_sqft  bath    price  bhk\n",
       "30                Yelahanka      4 BHK     2100 - 2850   4.0  186.000    4\n",
       "122                  Hebbal      4 BHK     3067 - 8156   4.0  477.000    4\n",
       "137      8th Phase JP Nagar      2 BHK     1042 - 1105   2.0   54.005    2\n",
       "165                Sarjapur      2 BHK     1145 - 1340   2.0   43.490    2\n",
       "188                KR Puram      2 BHK     1015 - 1540   2.0   56.800    2\n",
       "410                 Kengeri      1 BHK  34.46Sq. Meter   1.0   18.500    1\n",
       "549             Hennur Road      2 BHK     1195 - 1440   2.0   63.770    2\n",
       "648                 Arekere  9 Bedroom       4125Perch   9.0  265.000    9\n",
       "661               Yelahanka      2 BHK     1120 - 1145   2.0   48.130    2\n",
       "672            Bettahalsoor  4 Bedroom     3090 - 5002   4.0  445.000    4\n",
       "772   Banashankari Stage VI      2 BHK     1160 - 1195   2.0   59.935    2\n",
       "775            Basavanagara      1 BHK   1000Sq. Meter   2.0   93.000    1\n",
       "850       Bannerghatta Road      2 BHK     1115 - 1130   2.0   58.935    2\n",
       "872       Singapura Village      2 BHK   1100Sq. Yards   2.0   45.000    2\n",
       "886              Chandapura      1 BHK       520 - 645   1.0   15.135    1\n",
       "927             Thanisandra      2 BHK     1000 - 1285   2.0   43.415    2\n",
       "959             Kammasandra      1 BHK       650 - 665   1.0   18.410    1\n",
       "990                Sarjapur      1 BHK       633 - 666   1.0   17.535    1\n",
       "1019         Marathi Layout  1 Bedroom       5.31Acres   1.0  110.000    1\n",
       "1086             Narasapura  2 Bedroom         30Acres   2.0   29.500    2"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3[~df3['total_sqft'].apply(is_float)].head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4690065b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_sqft_to_num(x):\n",
    "    tokens=x.split('-')\n",
    "    if len(tokens) == 2:\n",
    "        return (float(tokens[0])+float(tokens[1]))/2\n",
    "    try:\n",
    "        return float(x)\n",
    "    except:\n",
    "        return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "23d104eb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2483.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "convert_sqft_to_num('2166- 2800')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "04367e7a",
   "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",
       "  </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"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = df3.copy()\n",
    "df4['total_sqft'] = df4['total_sqft'].apply(convert_sqft_to_num)\n",
    "df4.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f14d556a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location      Yelahanka\n",
       "size              4 BHK\n",
       "total_sqft       2475.0\n",
       "bath                4.0\n",
       "price             186.0\n",
       "bhk                   4\n",
       "Name: 30, dtype: object"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.loc[30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb989e0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#FEATURE ENGINEERING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a81e8ca1",
   "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>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>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",
       "      <td>3699.810606</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",
       "      <td>4615.384615</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",
       "      <td>4305.555556</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",
       "      <td>6245.890861</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",
       "      <td>4250.000000</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   \n",
       "\n",
       "   price_per_sqft  \n",
       "0     3699.810606  \n",
       "1     4615.384615  \n",
       "2     4305.555556  \n",
       "3     6245.890861  \n",
       "4     4250.000000  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5 = df4.copy()\n",
    "df5['price_per_sqft'] = df5['price']*100000/df5['total_sqft']\n",
    "df5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "128cbb42",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1304"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df5.location.unique()) #dimensionality curse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b8a7af99",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location\n",
       "Whitefield               535\n",
       "Sarjapur  Road           392\n",
       "Electronic City          304\n",
       "Kanakpura Road           266\n",
       "Thanisandra              236\n",
       "                        ... \n",
       "1 Giri Nagar               1\n",
       "Kanakapura Road,           1\n",
       "Kanakapura main  Road      1\n",
       "Karnataka Shabarimala      1\n",
       "whitefiled                 1\n",
       "Name: location, Length: 1293, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#strip extra or leading space in location\n",
    "df5.location = df5.location.apply(lambda x: x.strip())\n",
    "location_stats = df5.groupby('location')['location'].agg('count').sort_values(ascending=False)\n",
    "location_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ea90aed4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1052"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(location_stats[location_stats<=10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a90580d3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location\n",
       "Basapura                 10\n",
       "1st Block Koramangala    10\n",
       "Gunjur Palya             10\n",
       "Kalkere                  10\n",
       "Sector 1 HSR Layout      10\n",
       "                         ..\n",
       "1 Giri Nagar              1\n",
       "Kanakapura Road,          1\n",
       "Kanakapura main  Road     1\n",
       "Karnataka Shabarimala     1\n",
       "whitefiled                1\n",
       "Name: location, Length: 1052, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "location_stats_less_than_10=location_stats[location_stats<=10]\n",
    "location_stats_less_than_10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "bd8f2735",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1293"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df5.location.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "bd87c4d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "242"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.location = df5.location.apply(lambda x: 'other' if x in location_stats_less_than_10 else x)\n",
    "len(df5.location.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c7811c0d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "      <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>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",
       "      <td>3699.810606</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",
       "      <td>4615.384615</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",
       "      <td>4305.555556</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",
       "      <td>6245.890861</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",
       "      <td>4250.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Whitefield</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1170.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>38.00</td>\n",
       "      <td>2</td>\n",
       "      <td>3247.863248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Old Airport Road</td>\n",
       "      <td>4 BHK</td>\n",
       "      <td>2732.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>204.00</td>\n",
       "      <td>4</td>\n",
       "      <td>7467.057101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Rajaji Nagar</td>\n",
       "      <td>4 BHK</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>600.00</td>\n",
       "      <td>4</td>\n",
       "      <td>18181.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Marathahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1310.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>63.25</td>\n",
       "      <td>3</td>\n",
       "      <td>4828.244275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>other</td>\n",
       "      <td>6 Bedroom</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>370.00</td>\n",
       "      <td>6</td>\n",
       "      <td>36274.509804</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   \n",
       "5                Whitefield      2 BHK      1170.0   2.0   38.00    2   \n",
       "6          Old Airport Road      4 BHK      2732.0   4.0  204.00    4   \n",
       "7              Rajaji Nagar      4 BHK      3300.0   4.0  600.00    4   \n",
       "8              Marathahalli      3 BHK      1310.0   3.0   63.25    3   \n",
       "9                     other  6 Bedroom      1020.0   6.0  370.00    6   \n",
       "\n",
       "   price_per_sqft  \n",
       "0     3699.810606  \n",
       "1     4615.384615  \n",
       "2     4305.555556  \n",
       "3     6245.890861  \n",
       "4     4250.000000  \n",
       "5     3247.863248  \n",
       "6     7467.057101  \n",
       "7    18181.818182  \n",
       "8     4828.244275  \n",
       "9    36274.509804  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75ce3843",
   "metadata": {},
   "outputs": [],
   "source": [
    "#OUTLIER DETECTION AND REMOVAL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "98526462",
   "metadata": {
    "scrolled": false
   },
   "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>9</th>\n",
       "      <td>other</td>\n",
       "      <td>6 Bedroom</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>6</td>\n",
       "      <td>36274.509804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>HSR Layout</td>\n",
       "      <td>8 Bedroom</td>\n",
       "      <td>600.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>8</td>\n",
       "      <td>33333.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Murugeshpalya</td>\n",
       "      <td>6 Bedroom</td>\n",
       "      <td>1407.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>6</td>\n",
       "      <td>10660.980810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Devarachikkanahalli</td>\n",
       "      <td>8 Bedroom</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>8</td>\n",
       "      <td>6296.296296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>other</td>\n",
       "      <td>3 Bedroom</td>\n",
       "      <td>500.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>3</td>\n",
       "      <td>20000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               location       size  total_sqft  bath  price  bhk  \\\n",
       "9                 other  6 Bedroom      1020.0   6.0  370.0    6   \n",
       "45           HSR Layout  8 Bedroom       600.0   9.0  200.0    8   \n",
       "58        Murugeshpalya  6 Bedroom      1407.0   4.0  150.0    6   \n",
       "68  Devarachikkanahalli  8 Bedroom      1350.0   7.0   85.0    8   \n",
       "70                other  3 Bedroom       500.0   3.0  100.0    3   \n",
       "\n",
       "    price_per_sqft  \n",
       "9     36274.509804  \n",
       "45    33333.333333  \n",
       "58    10660.980810  \n",
       "68     6296.296296  \n",
       "70    20000.000000  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5[df5.total_sqft/df5.bhk<300].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "f9eabf72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13246, 7)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "0ecb43db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12502, 7)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6 = df5[~(df5.total_sqft/df5.bhk<300)]\n",
    "df6.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "2d5afb44",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     12456.000000\n",
       "mean       6308.502826\n",
       "std        4168.127339\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": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6.price_per_sqft.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "5d7fb33c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10241, 7)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def remove_pps_outliers(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",
    "        reduced_df = subdf[(subdf.price_per_sqft>(m-st)) & (subdf.price_per_sqft<=(m+st))]\n",
    "        df_out = pd.concat([df_out,reduced_df],ignore_index=True)\n",
    "    return df_out\n",
    "df7=remove_pps_outliers(df6)\n",
    "df7.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "ec0bf5c1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_scatter_chart(df,location):\n",
    "    bhk2 = df[(df.location==location) & (df.bhk==2)]\n",
    "    bhk3 = df[(df.location==location) & (df.bhk==3)]\n",
    "    matplotlib.rcParams['figure.figsize'] = (15,10)\n",
    "    plt.scatter(bhk2.total_sqft,bhk2.price,color='blue', label='2 BHK', s=50)\n",
    "    plt.scatter(bhk3.total_sqft,bhk3.price,marker='+',color='green', label='3 BHK', s=50)\n",
    "    plt.xlabel(\"total square feet area\")\n",
    "    plt.ylabel(\"price\")\n",
    "    plt.title(location)\n",
    "    plt.legend()\n",
    "plot_scatter_chart(df7,\"Rajaji Nagar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "916b5859",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7329, 7)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#2 bhk price > 3 bhk price\n",
    "def remove_bhk_outliers(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",
    "\n",
    "df8 = remove_bhk_outliers(df7)\n",
    "df8.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "78f26718",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scatter_chart(df7,\"Hebbal\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "15715448",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'count')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df8.price_per_sqft, rwidth=0.8)\n",
    "plt.xlabel(\"price per square feet\")\n",
    "plt.ylabel(\"count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "1e67fa0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4.,  3.,  2.,  5.,  8.,  1.,  6.,  7.,  9., 12., 16., 13.])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df8.bath.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "2d8793b5",
   "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>5277</th>\n",
       "      <td>Neeladri Nagar</td>\n",
       "      <td>10 BHK</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>10</td>\n",
       "      <td>4000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8486</th>\n",
       "      <td>other</td>\n",
       "      <td>10 BHK</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>525.0</td>\n",
       "      <td>10</td>\n",
       "      <td>4375.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8575</th>\n",
       "      <td>other</td>\n",
       "      <td>16 BHK</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>16</td>\n",
       "      <td>5500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9308</th>\n",
       "      <td>other</td>\n",
       "      <td>11 BHK</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>11</td>\n",
       "      <td>2500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9639</th>\n",
       "      <td>other</td>\n",
       "      <td>13 BHK</td>\n",
       "      <td>5425.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>13</td>\n",
       "      <td>5069.124424</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            location    size  total_sqft  bath  price  bhk  price_per_sqft\n",
       "5277  Neeladri Nagar  10 BHK      4000.0  12.0  160.0   10     4000.000000\n",
       "8486           other  10 BHK     12000.0  12.0  525.0   10     4375.000000\n",
       "8575           other  16 BHK     10000.0  16.0  550.0   16     5500.000000\n",
       "9308           other  11 BHK      6000.0  12.0  150.0   11     2500.000000\n",
       "9639           other  13 BHK      5425.0  13.0  275.0   13     5069.124424"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df8[df8.bath>10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "78d89939",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<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>1626</th>\n",
       "      <td>Chikkabanavar</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>2460.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3252.032520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5238</th>\n",
       "      <td>Nagasandra</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>4</td>\n",
       "      <td>6428.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6711</th>\n",
       "      <td>Thanisandra</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1806.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>3</td>\n",
       "      <td>6423.034330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8411</th>\n",
       "      <td>other</td>\n",
       "      <td>6 BHK</td>\n",
       "      <td>11338.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>6</td>\n",
       "      <td>8819.897689</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           location       size  total_sqft  bath   price  bhk  price_per_sqft\n",
       "1626  Chikkabanavar  4 Bedroom      2460.0   7.0    80.0    4     3252.032520\n",
       "5238     Nagasandra  4 Bedroom      7000.0   8.0   450.0    4     6428.571429\n",
       "6711    Thanisandra      3 BHK      1806.0   6.0   116.0    3     6423.034330\n",
       "8411          other      6 BHK     11338.0   9.0  1000.0    6     8819.897689"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df8[df8.bath>df8.bhk+2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "cd038307",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7251, 7)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df9 = df8[df8.bath<df8.bhk+2]\n",
    "df9.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "9ce7872c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <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",
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       "  </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",
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       "    <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",
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       "      <td>235.0</td>\n",
       "      <td>3</td>\n",
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       "</table>\n",
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      ],
      "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"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df10 = df9.drop(['size', 'price_per_sqft'], axis = 'columns')\n",
    "df10.shape\n",
    "df10.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "e03e75d6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1st Block Jayanagar</th>\n",
       "      <th>1st Phase JP Nagar</th>\n",
       "      <th>2nd Phase Judicial Layout</th>\n",
       "      <th>2nd Stage Nagarbhavi</th>\n",
       "      <th>5th Block Hbr Layout</th>\n",
       "      <th>5th Phase JP Nagar</th>\n",
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       "      <th>3</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 242 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   1st Block Jayanagar  1st Phase JP Nagar  2nd Phase Judicial Layout  \\\n",
       "0                    1                   0                          0   \n",
       "1                    1                   0                          0   \n",
       "2                    1                   0                          0   \n",
       "3                    1                   0                          0   \n",
       "\n",
       "   2nd Stage Nagarbhavi  5th Block Hbr Layout  5th Phase JP Nagar  \\\n",
       "0                     0                     0                   0   \n",
       "1                     0                     0                   0   \n",
       "2                     0                     0                   0   \n",
       "3                     0                     0                   0   \n",
       "\n",
       "   6th Phase JP Nagar  7th Phase JP Nagar  8th Phase JP Nagar  \\\n",
       "0                   0                   0                   0   \n",
       "1                   0                   0                   0   \n",
       "2                   0                   0                   0   \n",
       "3                   0                   0                   0   \n",
       "\n",
       "   9th Phase JP Nagar  ...  Vishveshwarya Layout  Vishwapriya Layout  \\\n",
       "0                   0  ...                     0                   0   \n",
       "1                   0  ...                     0                   0   \n",
       "2                   0  ...                     0                   0   \n",
       "3                   0  ...                     0                   0   \n",
       "\n",
       "   Vittasandra  Whitefield  Yelachenahalli  Yelahanka  Yelahanka New Town  \\\n",
       "0            0           0               0          0                   0   \n",
       "1            0           0               0          0                   0   \n",
       "2            0           0               0          0                   0   \n",
       "3            0           0               0          0                   0   \n",
       "\n",
       "   Yelenahalli  Yeshwanthpur  other  \n",
       "0            0             0      0  \n",
       "1            0             0      0  \n",
       "2            0             0      0  \n",
       "3            0             0      0  \n",
       "\n",
       "[4 rows x 242 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#one hot encoding for converting location string data to numeric\n",
    "dummies = pd.get_dummies(df10.location, dtype='int')\n",
    "dummies.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "aeeb9b43",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "<p>3 rows × 246 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              location  total_sqft  bath  price  bhk  1st Block Jayanagar  \\\n",
       "0  1st Block Jayanagar      2850.0   4.0  428.0    4                    1   \n",
       "1  1st Block Jayanagar      1630.0   3.0  194.0    3                    1   \n",
       "2  1st Block Jayanagar      1875.0   2.0  235.0    3                    1   \n",
       "\n",
       "   1st Phase JP Nagar  2nd Phase Judicial Layout  2nd Stage Nagarbhavi  \\\n",
       "0                   0                          0                     0   \n",
       "1                   0                          0                     0   \n",
       "2                   0                          0                     0   \n",
       "\n",
       "   5th Block Hbr Layout  ...  Vijayanagar  Vishveshwarya Layout  \\\n",
       "0                     0  ...            0                     0   \n",
       "1                     0  ...            0                     0   \n",
       "2                     0  ...            0                     0   \n",
       "\n",
       "   Vishwapriya Layout  Vittasandra  Whitefield  Yelachenahalli  Yelahanka  \\\n",
       "0                   0            0           0               0          0   \n",
       "1                   0            0           0               0          0   \n",
       "2                   0            0           0               0          0   \n",
       "\n",
       "   Yelahanka New Town  Yelenahalli  Yeshwanthpur  \n",
       "0                   0            0             0  \n",
       "1                   0            0             0  \n",
       "2                   0            0             0  \n",
       "\n",
       "[3 rows x 246 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df11 = pd.concat([df10,dummies.drop('other', axis='columns')],axis='columns')\n",
    "df11.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "87122981",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>bhk</th>\n",
       "      <th>1st Block Jayanagar</th>\n",
       "      <th>1st Phase JP Nagar</th>\n",
       "      <th>2nd Phase Judicial Layout</th>\n",
       "      <th>2nd Stage Nagarbhavi</th>\n",
       "      <th>5th Block Hbr Layout</th>\n",
       "      <th>5th Phase JP Nagar</th>\n",
       "      <th>...</th>\n",
       "      <th>Vijayanagar</th>\n",
       "      <th>Vishveshwarya Layout</th>\n",
       "      <th>Vishwapriya Layout</th>\n",
       "      <th>Vittasandra</th>\n",
       "      <th>Whitefield</th>\n",
       "      <th>Yelachenahalli</th>\n",
       "      <th>Yelahanka</th>\n",
       "      <th>Yelahanka New Town</th>\n",
       "      <th>Yelenahalli</th>\n",
       "      <th>Yeshwanthpur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>2850.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>428.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1630.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1875.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 245 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_sqft  bath  price  bhk  1st Block Jayanagar  1st Phase JP Nagar  \\\n",
       "0      2850.0   4.0  428.0    4                    1                   0   \n",
       "1      1630.0   3.0  194.0    3                    1                   0   \n",
       "2      1875.0   2.0  235.0    3                    1                   0   \n",
       "\n",
       "   2nd Phase Judicial Layout  2nd Stage Nagarbhavi  5th Block Hbr Layout  \\\n",
       "0                          0                     0                     0   \n",
       "1                          0                     0                     0   \n",
       "2                          0                     0                     0   \n",
       "\n",
       "   5th Phase JP Nagar  ...  Vijayanagar  Vishveshwarya Layout  \\\n",
       "0                   0  ...            0                     0   \n",
       "1                   0  ...            0                     0   \n",
       "2                   0  ...            0                     0   \n",
       "\n",
       "   Vishwapriya Layout  Vittasandra  Whitefield  Yelachenahalli  Yelahanka  \\\n",
       "0                   0            0           0               0          0   \n",
       "1                   0            0           0               0          0   \n",
       "2                   0            0           0               0          0   \n",
       "\n",
       "   Yelahanka New Town  Yelenahalli  Yeshwanthpur  \n",
       "0                   0            0             0  \n",
       "1                   0            0             0  \n",
       "2                   0            0             0  \n",
       "\n",
       "[3 rows x 245 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df12 = df11.drop('location', axis='columns')\n",
    "df12.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "1f54eec3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7251, 245)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df12.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "3cb9e10c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>bhk</th>\n",
       "      <th>1st Block Jayanagar</th>\n",
       "      <th>1st Phase JP Nagar</th>\n",
       "      <th>2nd Phase Judicial Layout</th>\n",
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       "      <th>Vijayanagar</th>\n",
       "      <th>Vishveshwarya Layout</th>\n",
       "      <th>Vishwapriya Layout</th>\n",
       "      <th>Vittasandra</th>\n",
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       "      <th>Yelachenahalli</th>\n",
       "      <th>Yelahanka</th>\n",
       "      <th>Yelahanka New Town</th>\n",
       "      <th>Yelenahalli</th>\n",
       "      <th>Yeshwanthpur</th>\n",
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       "      <td>1630.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1875.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1235.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 244 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_sqft  bath  bhk  1st Block Jayanagar  1st Phase JP Nagar  \\\n",
       "0      2850.0   4.0    4                    1                   0   \n",
       "1      1630.0   3.0    3                    1                   0   \n",
       "2      1875.0   2.0    3                    1                   0   \n",
       "3      1200.0   2.0    3                    1                   0   \n",
       "4      1235.0   2.0    2                    1                   0   \n",
       "\n",
       "   2nd Phase Judicial Layout  2nd Stage Nagarbhavi  5th Block Hbr Layout  \\\n",
       "0                          0                     0                     0   \n",
       "1                          0                     0                     0   \n",
       "2                          0                     0                     0   \n",
       "3                          0                     0                     0   \n",
       "4                          0                     0                     0   \n",
       "\n",
       "   5th Phase JP Nagar  6th Phase JP Nagar  ...  Vijayanagar  \\\n",
       "0                   0                   0  ...            0   \n",
       "1                   0                   0  ...            0   \n",
       "2                   0                   0  ...            0   \n",
       "3                   0                   0  ...            0   \n",
       "4                   0                   0  ...            0   \n",
       "\n",
       "   Vishveshwarya Layout  Vishwapriya Layout  Vittasandra  Whitefield  \\\n",
       "0                     0                   0            0           0   \n",
       "1                     0                   0            0           0   \n",
       "2                     0                   0            0           0   \n",
       "3                     0                   0            0           0   \n",
       "4                     0                   0            0           0   \n",
       "\n",
       "   Yelachenahalli  Yelahanka  Yelahanka New Town  Yelenahalli  Yeshwanthpur  \n",
       "0               0          0                   0            0             0  \n",
       "1               0          0                   0            0             0  \n",
       "2               0          0                   0            0             0  \n",
       "3               0          0                   0            0             0  \n",
       "4               0          0                   0            0             0  \n",
       "\n",
       "[5 rows x 244 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#independent features\n",
    "X = df12.drop('price', axis='columns')\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "34702ccf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    428.0\n",
       "1    194.0\n",
       "2    235.0\n",
       "3    130.0\n",
       "4    148.0\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dependent features\n",
    "y = df12.price\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "d579cb65",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2, random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "e10c2b70",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.845227769787429"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lr = LinearRegression()\n",
    "lr.fit(X_train,y_train)\n",
    "lr.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "49937124",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.82430186, 0.77166234, 0.85089567, 0.80837764, 0.83653286])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#k fold cross validation\n",
    "from sklearn.model_selection import ShuffleSplit\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "cv = ShuffleSplit(n_splits = 5, test_size = 0.2, random_state=0)\n",
    "cross_val_score(LinearRegression(), X, y, cv=cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "47ac1130",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['total_sqft', 'bath', 'bhk', '1st Block Jayanagar',\n",
       "       '1st Phase JP Nagar', '2nd Phase Judicial Layout',\n",
       "       '2nd Stage Nagarbhavi', '5th Block Hbr Layout', '5th Phase JP Nagar',\n",
       "       '6th Phase JP Nagar',\n",
       "       ...\n",
       "       'Vijayanagar', 'Vishveshwarya Layout', 'Vishwapriya Layout',\n",
       "       'Vittasandra', 'Whitefield', 'Yelachenahalli', 'Yelahanka',\n",
       "       'Yelahanka New Town', 'Yelenahalli', 'Yeshwanthpur'],\n",
       "      dtype='object', length=244)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "f5a74e6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_price(location,sqft,bath,bhk):    \n",
    "    loc_index = np.where(X.columns==location)[0][0]\n",
    "\n",
    "    x = np.zeros(len(X.columns))\n",
    "    x[0] = sqft\n",
    "    x[1] = bath\n",
    "    x[2] = bhk\n",
    "    if loc_index >= 0:\n",
    "        x[loc_index] = 1\n",
    "\n",
    "    return lr.predict([x])[0]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "ab390282",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\MARIAM\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:464: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "81.72616900747835"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " predict_price('1st Phase JP Nagar', 1000, 2, 3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "1bc717aa",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\MARIAM\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:464: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "184.58430202033549"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " predict_price('Indira Nagar', 1000, 3, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "e3233090",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open('banglore_home_prices_model.pickle','wb') as f:\n",
    "    pickle.dump(lr,f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "5a826879",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "columns = {\n",
    "    'data_columns' : [col.lower() for col in X.columns]\n",
    "}\n",
    "with open(\"columns.json\",\"w\") as f:\n",
    "    f.write(json.dumps(columns))"
   ]
  }
 ],
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