Real Estate Price.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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",
    "import seaborn as sns\n",
    "matplotlib.rcParams[\"figure.figsize\"] = (20,10)\n",
    "\n",
    "pd.set_option('display.max_columns',244)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area_type</th>\n",
       "      <th>availability</th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>society</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>balcony</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>19-Dec</td>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>Coomee</td>\n",
       "      <td>1056</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Plot  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>Theanmp</td>\n",
       "      <td>2600</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>120.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1440</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>62.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>Soiewre</td>\n",
       "      <td>1521</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>95.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1200</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>51.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              area_type   availability                  location       size  \\\n",
       "0  Super built-up  Area         19-Dec  Electronic City Phase II      2 BHK   \n",
       "1            Plot  Area  Ready To Move          Chikka Tirupathi  4 Bedroom   \n",
       "2        Built-up  Area  Ready To Move               Uttarahalli      3 BHK   \n",
       "3  Super built-up  Area  Ready To Move        Lingadheeranahalli      3 BHK   \n",
       "4  Super built-up  Area  Ready To Move                  Kothanur      2 BHK   \n",
       "\n",
       "   society total_sqft  bath  balcony   price  \n",
       "0  Coomee        1056   2.0      1.0   39.07  \n",
       "1  Theanmp       2600   5.0      3.0  120.00  \n",
       "2      NaN       1440   2.0      3.0   62.00  \n",
       "3  Soiewre       1521   3.0      1.0   95.00  \n",
       "4      NaN       1200   2.0      1.0   51.00  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.read_csv(\"Bengaluru_House_Data.csv\")\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13320, 9)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "area_type\n",
       "Built-up  Area          2418\n",
       "Carpet  Area              87\n",
       "Plot  Area              2025\n",
       "Super built-up  Area    8790\n",
       "Name: area_type, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.groupby('area_type')['area_type'].agg('count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1056</td>\n",
       "      <td>2.0</td>\n",
       "      <td>39.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>2600</td>\n",
       "      <td>5.0</td>\n",
       "      <td>120.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1440</td>\n",
       "      <td>2.0</td>\n",
       "      <td>62.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1521</td>\n",
       "      <td>3.0</td>\n",
       "      <td>95.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1200</td>\n",
       "      <td>2.0</td>\n",
       "      <td>51.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2  = df1.drop(['area_type','society','balcony','availability'], axis='columns')\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Yegon\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='price', ylabel='Density'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df2.price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location       1\n",
       "size          16\n",
       "total_sqft     0\n",
       "bath          73\n",
       "price          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location      0\n",
       "size          0\n",
       "total_sqft    0\n",
       "bath          0\n",
       "price         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = df2.dropna()\n",
    "df3.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13246, 5)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3['size'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-12-4c4c73fbe7f4>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df3['bhk'] = df3['size'].apply(lambda x: int(x.split(' ')[0]))\n"
     ]
    }
   ],
   "source": [
    "df3['bhk'] = df3['size'].apply(lambda x: int(x.split(' ')[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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</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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Whitefield                                  534\n",
       "Sarjapur  Road                              392\n",
       "Electronic City                             302\n",
       "Kanakpura Road                              266\n",
       "Thanisandra                                 233\n",
       "                                           ... \n",
       "Ramaswamy Palya - Kammanahalli Main Road      1\n",
       "kanakapura road                               1\n",
       "Prakruthi Nagar                               1\n",
       "manyata                                       1\n",
       "Whitefield ECC Road                           1\n",
       "Name: location, Length: 1304, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "locations = df3.location.value_counts()\n",
    "locations.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3['bhk'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3[df3.bhk>20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1056', '2600', '1440', ..., '1133 - 1384', '774', '4689'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.total_sqft.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": 19,
   "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>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",
       "  </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"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3[~df3['total_sqft'].apply(is_float)].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2166.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "convert_sqft_to_num('2166')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2475.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "convert_sqft_to_num('2100 - 2850')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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",
       "  </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"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = df3.copy()\n",
    "df4['total_sqft'] = df4['total_sqft'].apply(convert_sqft_to_num)\n",
    "df4.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>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": 24,
     "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": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Electronic City Phase II', 'Chikka Tirupathi', 'Uttarahalli', ...,\n",
       "       '12th cross srinivas nagar banshankari 3rd stage',\n",
       "       'Havanur extension', 'Abshot Layout'], dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.location.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1304"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df5.location.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.location = df5.location.apply(lambda x: x.strip())\n",
    "                                  \n",
    "location_stats = df5.groupby('location')['location'].agg('count').sort_values(ascending=False)\n",
    "len(location_stats.head())                                                                   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1052"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(location_stats[location_stats <=10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# datapoints with less than one location\n",
    "location_stats_less_than_10 = location_stats[location_stats<=10]\n",
    "location_stats_less_than_10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1293"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total number of unique datapoints(location)\n",
    "len(df5.location.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "242"
      ]
     },
     "execution_count": 31,
     "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": 32,
   "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>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": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>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": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5[df5.total_sqft/df5.bhk<300].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13246, 7)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12502, 7)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6 = df5[~(df5.total_sqft/df5.bhk<300)]\n",
    "df6.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "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": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6.price_per_sqft.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10241, 7)"
      ]
     },
     "execution_count": 37,
     "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",
    "\n",
    "df7 = remove_pps_outliers(df6)\n",
    "df7.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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 Per Square Feet\")\n",
    "    plt.title(location)\n",
    "    plt.legend()\n",
    "    \n",
    "plot_scatter_chart(df7,\"Hebbal\")    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7329, 7)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scatter_chart(df8,\"Hebbal\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "matplotlib.rcParams[\"figure.figsize\"] = (20,10)\n",
    "plt.hist(df8.price_per_sqft, rwidth=0.8)\n",
    "plt.xlabel(\"Price Per Square Feet\", fontsize=20)\n",
    "plt.ylabel(\"Count\", fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4.,  3.,  2.,  5.,  8.,  1.,  6.,  7.,  9., 12., 16., 13.])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df8.bath.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df8[df8.bath>10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df8.bath, rwidth=0.8)\n",
    "plt.xlabel(\"Number of bathrooms\")\n",
    "plt.ylabel(\"Count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>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": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df8[df8.bath>df8.bhk+2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7251, 7)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df9 = df8[df8.bath<df8.bhk+2]\n",
    "df9.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>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": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df10 = df9.drop(['size','price_per_sqft'], axis='columns')\n",
    "df10.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "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>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>6th Phase JP Nagar</th>\n",
       "      <th>7th Phase JP Nagar</th>\n",
       "      <th>8th Phase JP Nagar</th>\n",
       "      <th>9th Phase JP Nagar</th>\n",
       "      <th>AECS Layout</th>\n",
       "      <th>Abbigere</th>\n",
       "      <th>Akshaya Nagar</th>\n",
       "      <th>Ambalipura</th>\n",
       "      <th>Ambedkar Nagar</th>\n",
       "      <th>Amruthahalli</th>\n",
       "      <th>Anandapura</th>\n",
       "      <th>Ananth Nagar</th>\n",
       "      <th>Anekal</th>\n",
       "      <th>Anjanapura</th>\n",
       "      <th>Ardendale</th>\n",
       "      <th>Arekere</th>\n",
       "      <th>Attibele</th>\n",
       "      <th>BEML Layout</th>\n",
       "      <th>BTM 2nd Stage</th>\n",
       "      <th>BTM Layout</th>\n",
       "      <th>Babusapalaya</th>\n",
       "      <th>Badavala Nagar</th>\n",
       "      <th>Balagere</th>\n",
       "      <th>Banashankari</th>\n",
       "      <th>Banashankari Stage II</th>\n",
       "      <th>Banashankari Stage III</th>\n",
       "      <th>Banashankari Stage V</th>\n",
       "      <th>Banashankari Stage VI</th>\n",
       "      <th>Banaswadi</th>\n",
       "      <th>Banjara Layout</th>\n",
       "      <th>Bannerghatta</th>\n",
       "      <th>Bannerghatta Road</th>\n",
       "      <th>Basavangudi</th>\n",
       "      <th>Basaveshwara Nagar</th>\n",
       "      <th>Battarahalli</th>\n",
       "      <th>Begur</th>\n",
       "      <th>Begur Road</th>\n",
       "      <th>Bellandur</th>\n",
       "      <th>Benson Town</th>\n",
       "      <th>Bharathi Nagar</th>\n",
       "      <th>Bhoganhalli</th>\n",
       "      <th>Billekahalli</th>\n",
       "      <th>Binny Pete</th>\n",
       "      <th>Bisuvanahalli</th>\n",
       "      <th>Bommanahalli</th>\n",
       "      <th>Bommasandra</th>\n",
       "      <th>Bommasandra Industrial Area</th>\n",
       "      <th>Bommenahalli</th>\n",
       "      <th>Brookefield</th>\n",
       "      <th>Budigere</th>\n",
       "      <th>CV Raman Nagar</th>\n",
       "      <th>Chamrajpet</th>\n",
       "      <th>Chandapura</th>\n",
       "      <th>Channasandra</th>\n",
       "      <th>Chikka Tirupathi</th>\n",
       "      <th>Chikkabanavar</th>\n",
       "      <th>Chikkalasandra</th>\n",
       "      <th>Choodasandra</th>\n",
       "      <th>Cooke Town</th>\n",
       "      <th>Cox Town</th>\n",
       "      <th>Cunningham Road</th>\n",
       "      <th>Dasanapura</th>\n",
       "      <th>Dasarahalli</th>\n",
       "      <th>Devanahalli</th>\n",
       "      <th>Devarachikkanahalli</th>\n",
       "      <th>Dodda Nekkundi</th>\n",
       "      <th>Doddaballapur</th>\n",
       "      <th>Doddakallasandra</th>\n",
       "      <th>Doddathoguru</th>\n",
       "      <th>Domlur</th>\n",
       "      <th>Dommasandra</th>\n",
       "      <th>EPIP Zone</th>\n",
       "      <th>Electronic City</th>\n",
       "      <th>Electronic City Phase II</th>\n",
       "      <th>Electronics City Phase 1</th>\n",
       "      <th>Frazer Town</th>\n",
       "      <th>GM Palaya</th>\n",
       "      <th>Garudachar Palya</th>\n",
       "      <th>Giri Nagar</th>\n",
       "      <th>Gollarapalya Hosahalli</th>\n",
       "      <th>Gottigere</th>\n",
       "      <th>Green Glen Layout</th>\n",
       "      <th>Gubbalala</th>\n",
       "      <th>Gunjur</th>\n",
       "      <th>HAL 2nd Stage</th>\n",
       "      <th>HBR Layout</th>\n",
       "      <th>HRBR Layout</th>\n",
       "      <th>HSR Layout</th>\n",
       "      <th>Haralur Road</th>\n",
       "      <th>Harlur</th>\n",
       "      <th>Hebbal</th>\n",
       "      <th>Hebbal Kempapura</th>\n",
       "      <th>Hegde Nagar</th>\n",
       "      <th>Hennur</th>\n",
       "      <th>Hennur Road</th>\n",
       "      <th>Hoodi</th>\n",
       "      <th>Horamavu Agara</th>\n",
       "      <th>Horamavu Banaswadi</th>\n",
       "      <th>Hormavu</th>\n",
       "      <th>Hosa Road</th>\n",
       "      <th>Hosakerehalli</th>\n",
       "      <th>Hoskote</th>\n",
       "      <th>Hosur Road</th>\n",
       "      <th>Hulimavu</th>\n",
       "      <th>ISRO Layout</th>\n",
       "      <th>ITPL</th>\n",
       "      <th>Iblur Village</th>\n",
       "      <th>Indira Nagar</th>\n",
       "      <th>JP Nagar</th>\n",
       "      <th>Jakkur</th>\n",
       "      <th>Jalahalli</th>\n",
       "      <th>Jalahalli East</th>\n",
       "      <th>Jigani</th>\n",
       "      <th>Judicial Layout</th>\n",
       "      <th>KR Puram</th>\n",
       "      <th>Kadubeesanahalli</th>\n",
       "      <th>Kadugodi</th>\n",
       "      <th>Kaggadasapura</th>\n",
       "      <th>Kaggalipura</th>\n",
       "      <th>Kaikondrahalli</th>\n",
       "      <th>Kalena Agrahara</th>\n",
       "      <th>Kalyan nagar</th>\n",
       "      <th>Kambipura</th>\n",
       "      <th>Kammanahalli</th>\n",
       "      <th>Kammasandra</th>\n",
       "      <th>Kanakapura</th>\n",
       "      <th>Kanakpura Road</th>\n",
       "      <th>Kannamangala</th>\n",
       "      <th>Karuna Nagar</th>\n",
       "      <th>Kasavanhalli</th>\n",
       "      <th>Kasturi Nagar</th>\n",
       "      <th>Kathriguppe</th>\n",
       "      <th>Kaval Byrasandra</th>\n",
       "      <th>Kenchenahalli</th>\n",
       "      <th>Kengeri</th>\n",
       "      <th>Kengeri Satellite Town</th>\n",
       "      <th>Kereguddadahalli</th>\n",
       "      <th>Kodichikkanahalli</th>\n",
       "      <th>Kodigehaali</th>\n",
       "      <th>Kodigehalli</th>\n",
       "      <th>Kodihalli</th>\n",
       "      <th>Kogilu</th>\n",
       "      <th>Konanakunte</th>\n",
       "      <th>Koramangala</th>\n",
       "      <th>Kothannur</th>\n",
       "      <th>Kothanur</th>\n",
       "      <th>Kudlu</th>\n",
       "      <th>Kudlu Gate</th>\n",
       "      <th>Kumaraswami Layout</th>\n",
       "      <th>Kundalahalli</th>\n",
       "      <th>LB Shastri Nagar</th>\n",
       "      <th>Laggere</th>\n",
       "      <th>Lakshminarayana Pura</th>\n",
       "      <th>Lingadheeranahalli</th>\n",
       "      <th>Magadi Road</th>\n",
       "      <th>Mahadevpura</th>\n",
       "      <th>Mahalakshmi Layout</th>\n",
       "      <th>Mallasandra</th>\n",
       "      <th>Malleshpalya</th>\n",
       "      <th>Malleshwaram</th>\n",
       "      <th>Marathahalli</th>\n",
       "      <th>Margondanahalli</th>\n",
       "      <th>Marsur</th>\n",
       "      <th>Mico Layout</th>\n",
       "      <th>Munnekollal</th>\n",
       "      <th>Murugeshpalya</th>\n",
       "      <th>Mysore Road</th>\n",
       "      <th>NGR Layout</th>\n",
       "      <th>NRI Layout</th>\n",
       "      <th>Nagarbhavi</th>\n",
       "      <th>Nagasandra</th>\n",
       "      <th>Nagavara</th>\n",
       "      <th>Nagavarapalya</th>\n",
       "      <th>Narayanapura</th>\n",
       "      <th>Neeladri Nagar</th>\n",
       "      <th>Nehru Nagar</th>\n",
       "      <th>OMBR Layout</th>\n",
       "      <th>Old Airport Road</th>\n",
       "      <th>Old Madras Road</th>\n",
       "      <th>Padmanabhanagar</th>\n",
       "      <th>Pai Layout</th>\n",
       "      <th>Panathur</th>\n",
       "      <th>Parappana Agrahara</th>\n",
       "      <th>Pattandur Agrahara</th>\n",
       "      <th>Poorna Pragna Layout</th>\n",
       "      <th>Prithvi Layout</th>\n",
       "      <th>R.T. Nagar</th>\n",
       "      <th>Rachenahalli</th>\n",
       "      <th>Raja Rajeshwari Nagar</th>\n",
       "      <th>Rajaji Nagar</th>\n",
       "      <th>Rajiv Nagar</th>\n",
       "      <th>Ramagondanahalli</th>\n",
       "      <th>Ramamurthy Nagar</th>\n",
       "      <th>Rayasandra</th>\n",
       "      <th>Sahakara Nagar</th>\n",
       "      <th>Sanjay nagar</th>\n",
       "      <th>Sarakki Nagar</th>\n",
       "      <th>Sarjapur</th>\n",
       "      <th>Sarjapur  Road</th>\n",
       "      <th>Sarjapura - Attibele Road</th>\n",
       "      <th>Sector 2 HSR Layout</th>\n",
       "      <th>Sector 7 HSR Layout</th>\n",
       "      <th>Seegehalli</th>\n",
       "      <th>Shampura</th>\n",
       "      <th>Shivaji Nagar</th>\n",
       "      <th>Singasandra</th>\n",
       "      <th>Somasundara Palya</th>\n",
       "      <th>Sompura</th>\n",
       "      <th>Sonnenahalli</th>\n",
       "      <th>Subramanyapura</th>\n",
       "      <th>Sultan Palaya</th>\n",
       "      <th>TC Palaya</th>\n",
       "      <th>Talaghattapura</th>\n",
       "      <th>Thanisandra</th>\n",
       "      <th>Thigalarapalya</th>\n",
       "      <th>Thubarahalli</th>\n",
       "      <th>Thyagaraja Nagar</th>\n",
       "      <th>Tindlu</th>\n",
       "      <th>Tumkur Road</th>\n",
       "      <th>Ulsoor</th>\n",
       "      <th>Uttarahalli</th>\n",
       "      <th>Varthur</th>\n",
       "      <th>Varthur Road</th>\n",
       "      <th>Vasanthapura</th>\n",
       "      <th>Vidyaranyapura</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",
       "      <th>other</th>\n",
       "    </tr>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\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",
       "\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",
       "\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",
       "\n",
       "   9th Phase JP Nagar  AECS Layout  Abbigere  Akshaya Nagar  Ambalipura  \\\n",
       "0                   0            0         0              0           0   \n",
       "1                   0            0         0              0           0   \n",
       "2                   0            0         0              0           0   \n",
       "\n",
       "   Ambedkar Nagar  Amruthahalli  Anandapura  Ananth Nagar  Anekal  Anjanapura  \\\n",
       "0               0             0           0             0       0           0   \n",
       "1               0             0           0             0       0           0   \n",
       "2               0             0           0             0       0           0   \n",
       "\n",
       "   Ardendale  Arekere  Attibele  BEML Layout  BTM 2nd Stage  BTM Layout  \\\n",
       "0          0        0         0            0              0           0   \n",
       "1          0        0         0            0              0           0   \n",
       "2          0        0         0            0              0           0   \n",
       "\n",
       "   Babusapalaya  Badavala Nagar  Balagere  Banashankari  \\\n",
       "0             0               0         0             0   \n",
       "1             0               0         0             0   \n",
       "2             0               0         0             0   \n",
       "\n",
       "   Banashankari Stage II  Banashankari Stage III  Banashankari Stage V  \\\n",
       "0                      0                       0                     0   \n",
       "1                      0                       0                     0   \n",
       "2                      0                       0                     0   \n",
       "\n",
       "   Banashankari Stage VI  Banaswadi  Banjara Layout  Bannerghatta  \\\n",
       "0                      0          0               0             0   \n",
       "1                      0          0               0             0   \n",
       "2                      0          0               0             0   \n",
       "\n",
       "   Bannerghatta Road  Basavangudi  Basaveshwara Nagar  Battarahalli  Begur  \\\n",
       "0                  0            0                   0             0      0   \n",
       "1                  0            0                   0             0      0   \n",
       "2                  0            0                   0             0      0   \n",
       "\n",
       "   Begur Road  Bellandur  Benson Town  Bharathi Nagar  Bhoganhalli  \\\n",
       "0           0          0            0               0            0   \n",
       "1           0          0            0               0            0   \n",
       "2           0          0            0               0            0   \n",
       "\n",
       "   Billekahalli  Binny Pete  Bisuvanahalli  Bommanahalli  Bommasandra  \\\n",
       "0             0           0              0             0            0   \n",
       "1             0           0              0             0            0   \n",
       "2             0           0              0             0            0   \n",
       "\n",
       "   Bommasandra Industrial Area  Bommenahalli  Brookefield  Budigere  \\\n",
       "0                            0             0            0         0   \n",
       "1                            0             0            0         0   \n",
       "2                            0             0            0         0   \n",
       "\n",
       "   CV Raman Nagar  Chamrajpet  Chandapura  Channasandra  Chikka Tirupathi  \\\n",
       "0               0           0           0             0                 0   \n",
       "1               0           0           0             0                 0   \n",
       "2               0           0           0             0                 0   \n",
       "\n",
       "   Chikkabanavar  Chikkalasandra  Choodasandra  Cooke Town  Cox Town  \\\n",
       "0              0               0             0           0         0   \n",
       "1              0               0             0           0         0   \n",
       "2              0               0             0           0         0   \n",
       "\n",
       "   Cunningham Road  Dasanapura  Dasarahalli  Devanahalli  Devarachikkanahalli  \\\n",
       "0                0           0            0            0                    0   \n",
       "1                0           0            0            0                    0   \n",
       "2                0           0            0            0                    0   \n",
       "\n",
       "   Dodda Nekkundi  Doddaballapur  Doddakallasandra  Doddathoguru  Domlur  \\\n",
       "0               0              0                 0             0       0   \n",
       "1               0              0                 0             0       0   \n",
       "2               0              0                 0             0       0   \n",
       "\n",
       "   Dommasandra  EPIP Zone  Electronic City  Electronic City Phase II  \\\n",
       "0            0          0                0                         0   \n",
       "1            0          0                0                         0   \n",
       "2            0          0                0                         0   \n",
       "\n",
       "   Electronics City Phase 1  Frazer Town  GM Palaya  Garudachar Palya  \\\n",
       "0                         0            0          0                 0   \n",
       "1                         0            0          0                 0   \n",
       "2                         0            0          0                 0   \n",
       "\n",
       "   Giri Nagar  Gollarapalya Hosahalli  Gottigere  Green Glen Layout  \\\n",
       "0           0                       0          0                  0   \n",
       "1           0                       0          0                  0   \n",
       "2           0                       0          0                  0   \n",
       "\n",
       "   Gubbalala  Gunjur  HAL 2nd Stage  HBR Layout  HRBR Layout  HSR Layout  \\\n",
       "0          0       0              0           0            0           0   \n",
       "1          0       0              0           0            0           0   \n",
       "2          0       0              0           0            0           0   \n",
       "\n",
       "   Haralur Road  Harlur  Hebbal  Hebbal Kempapura  Hegde Nagar  Hennur  \\\n",
       "0             0       0       0                 0            0       0   \n",
       "1             0       0       0                 0            0       0   \n",
       "2             0       0       0                 0            0       0   \n",
       "\n",
       "   Hennur Road  Hoodi  Horamavu Agara  Horamavu Banaswadi  Hormavu  Hosa Road  \\\n",
       "0            0      0               0                   0        0          0   \n",
       "1            0      0               0                   0        0          0   \n",
       "2            0      0               0                   0        0          0   \n",
       "\n",
       "   Hosakerehalli  Hoskote  Hosur Road  Hulimavu  ISRO Layout  ITPL  \\\n",
       "0              0        0           0         0            0     0   \n",
       "1              0        0           0         0            0     0   \n",
       "2              0        0           0         0            0     0   \n",
       "\n",
       "   Iblur Village  Indira Nagar  JP Nagar  Jakkur  Jalahalli  Jalahalli East  \\\n",
       "0              0             0         0       0          0               0   \n",
       "1              0             0         0       0          0               0   \n",
       "2              0             0         0       0          0               0   \n",
       "\n",
       "   Jigani  Judicial Layout  KR Puram  Kadubeesanahalli  Kadugodi  \\\n",
       "0       0                0         0                 0         0   \n",
       "1       0                0         0                 0         0   \n",
       "2       0                0         0                 0         0   \n",
       "\n",
       "   Kaggadasapura  Kaggalipura  Kaikondrahalli  Kalena Agrahara  Kalyan nagar  \\\n",
       "0              0            0               0                0             0   \n",
       "1              0            0               0                0             0   \n",
       "2              0            0               0                0             0   \n",
       "\n",
       "   Kambipura  Kammanahalli  Kammasandra  Kanakapura  Kanakpura Road  \\\n",
       "0          0             0            0           0               0   \n",
       "1          0             0            0           0               0   \n",
       "2          0             0            0           0               0   \n",
       "\n",
       "   Kannamangala  Karuna Nagar  Kasavanhalli  Kasturi Nagar  Kathriguppe  \\\n",
       "0             0             0             0              0            0   \n",
       "1             0             0             0              0            0   \n",
       "2             0             0             0              0            0   \n",
       "\n",
       "   Kaval Byrasandra  Kenchenahalli  Kengeri  Kengeri Satellite Town  \\\n",
       "0                 0              0        0                       0   \n",
       "1                 0              0        0                       0   \n",
       "2                 0              0        0                       0   \n",
       "\n",
       "   Kereguddadahalli  Kodichikkanahalli  Kodigehaali  Kodigehalli  Kodihalli  \\\n",
       "0                 0                  0            0            0          0   \n",
       "1                 0                  0            0            0          0   \n",
       "2                 0                  0            0            0          0   \n",
       "\n",
       "   Kogilu  Konanakunte  Koramangala  Kothannur  Kothanur  Kudlu  Kudlu Gate  \\\n",
       "0       0            0            0          0         0      0           0   \n",
       "1       0            0            0          0         0      0           0   \n",
       "2       0            0            0          0         0      0           0   \n",
       "\n",
       "   Kumaraswami Layout  Kundalahalli  LB Shastri Nagar  Laggere  \\\n",
       "0                   0             0                 0        0   \n",
       "1                   0             0                 0        0   \n",
       "2                   0             0                 0        0   \n",
       "\n",
       "   Lakshminarayana Pura  Lingadheeranahalli  Magadi Road  Mahadevpura  \\\n",
       "0                     0                   0            0            0   \n",
       "1                     0                   0            0            0   \n",
       "2                     0                   0            0            0   \n",
       "\n",
       "   Mahalakshmi Layout  Mallasandra  Malleshpalya  Malleshwaram  Marathahalli  \\\n",
       "0                   0            0             0             0             0   \n",
       "1                   0            0             0             0             0   \n",
       "2                   0            0             0             0             0   \n",
       "\n",
       "   Margondanahalli  Marsur  Mico Layout  Munnekollal  Murugeshpalya  \\\n",
       "0                0       0            0            0              0   \n",
       "1                0       0            0            0              0   \n",
       "2                0       0            0            0              0   \n",
       "\n",
       "   Mysore Road  NGR Layout  NRI Layout  Nagarbhavi  Nagasandra  Nagavara  \\\n",
       "0            0           0           0           0           0         0   \n",
       "1            0           0           0           0           0         0   \n",
       "2            0           0           0           0           0         0   \n",
       "\n",
       "   Nagavarapalya  Narayanapura  Neeladri Nagar  Nehru Nagar  OMBR Layout  \\\n",
       "0              0             0               0            0            0   \n",
       "1              0             0               0            0            0   \n",
       "2              0             0               0            0            0   \n",
       "\n",
       "   Old Airport Road  Old Madras Road  Padmanabhanagar  Pai Layout  Panathur  \\\n",
       "0                 0                0                0           0         0   \n",
       "1                 0                0                0           0         0   \n",
       "2                 0                0                0           0         0   \n",
       "\n",
       "   Parappana Agrahara  Pattandur Agrahara  Poorna Pragna Layout  \\\n",
       "0                   0                   0                     0   \n",
       "1                   0                   0                     0   \n",
       "2                   0                   0                     0   \n",
       "\n",
       "   Prithvi Layout  R.T. Nagar  Rachenahalli  Raja Rajeshwari Nagar  \\\n",
       "0               0           0             0                      0   \n",
       "1               0           0             0                      0   \n",
       "2               0           0             0                      0   \n",
       "\n",
       "   Rajaji Nagar  Rajiv Nagar  Ramagondanahalli  Ramamurthy Nagar  Rayasandra  \\\n",
       "0             0            0                 0                 0           0   \n",
       "1             0            0                 0                 0           0   \n",
       "2             0            0                 0                 0           0   \n",
       "\n",
       "   Sahakara Nagar  Sanjay nagar  Sarakki Nagar  Sarjapur  Sarjapur  Road  \\\n",
       "0               0             0              0         0               0   \n",
       "1               0             0              0         0               0   \n",
       "2               0             0              0         0               0   \n",
       "\n",
       "   Sarjapura - Attibele Road  Sector 2 HSR Layout  Sector 7 HSR Layout  \\\n",
       "0                          0                    0                    0   \n",
       "1                          0                    0                    0   \n",
       "2                          0                    0                    0   \n",
       "\n",
       "   Seegehalli  Shampura  Shivaji Nagar  Singasandra  Somasundara Palya  \\\n",
       "0           0         0              0            0                  0   \n",
       "1           0         0              0            0                  0   \n",
       "2           0         0              0            0                  0   \n",
       "\n",
       "   Sompura  Sonnenahalli  Subramanyapura  Sultan Palaya  TC Palaya  \\\n",
       "0        0             0               0              0          0   \n",
       "1        0             0               0              0          0   \n",
       "2        0             0               0              0          0   \n",
       "\n",
       "   Talaghattapura  Thanisandra  Thigalarapalya  Thubarahalli  \\\n",
       "0               0            0               0             0   \n",
       "1               0            0               0             0   \n",
       "2               0            0               0             0   \n",
       "\n",
       "   Thyagaraja Nagar  Tindlu  Tumkur Road  Ulsoor  Uttarahalli  Varthur  \\\n",
       "0                 0       0            0       0            0        0   \n",
       "1                 0       0            0       0            0        0   \n",
       "2                 0       0            0       0            0        0   \n",
       "\n",
       "   Varthur Road  Vasanthapura  Vidyaranyapura  Vijayanagar  \\\n",
       "0             0             0               0            0   \n",
       "1             0             0               0            0   \n",
       "2             0             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",
       "\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",
       "\n",
       "   other  \n",
       "0      0  \n",
       "1      0  \n",
       "2      0  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummies = pd.get_dummies(df10.location)\n",
    "dummies.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>bhk</th>\n",
       "      <th>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>6th Phase JP Nagar</th>\n",
       "      <th>7th Phase JP Nagar</th>\n",
       "      <th>8th Phase JP Nagar</th>\n",
       "      <th>9th Phase JP Nagar</th>\n",
       "      <th>AECS Layout</th>\n",
       "      <th>Abbigere</th>\n",
       "      <th>Akshaya Nagar</th>\n",
       "      <th>Ambalipura</th>\n",
       "      <th>Ambedkar Nagar</th>\n",
       "      <th>Amruthahalli</th>\n",
       "      <th>Anandapura</th>\n",
       "      <th>Ananth Nagar</th>\n",
       "      <th>Anekal</th>\n",
       "      <th>Anjanapura</th>\n",
       "      <th>Ardendale</th>\n",
       "      <th>Arekere</th>\n",
       "      <th>Attibele</th>\n",
       "      <th>BEML Layout</th>\n",
       "      <th>BTM 2nd Stage</th>\n",
       "      <th>BTM Layout</th>\n",
       "      <th>Babusapalaya</th>\n",
       "      <th>Badavala Nagar</th>\n",
       "      <th>Balagere</th>\n",
       "      <th>Banashankari</th>\n",
       "      <th>Banashankari Stage II</th>\n",
       "      <th>Banashankari Stage III</th>\n",
       "      <th>Banashankari Stage V</th>\n",
       "      <th>Banashankari Stage VI</th>\n",
       "      <th>Banaswadi</th>\n",
       "      <th>Banjara Layout</th>\n",
       "      <th>Bannerghatta</th>\n",
       "      <th>Bannerghatta Road</th>\n",
       "      <th>Basavangudi</th>\n",
       "      <th>Basaveshwara Nagar</th>\n",
       "      <th>Battarahalli</th>\n",
       "      <th>Begur</th>\n",
       "      <th>Begur Road</th>\n",
       "      <th>Bellandur</th>\n",
       "      <th>Benson Town</th>\n",
       "      <th>Bharathi Nagar</th>\n",
       "      <th>Bhoganhalli</th>\n",
       "      <th>Billekahalli</th>\n",
       "      <th>Binny Pete</th>\n",
       "      <th>Bisuvanahalli</th>\n",
       "      <th>Bommanahalli</th>\n",
       "      <th>Bommasandra</th>\n",
       "      <th>Bommasandra Industrial Area</th>\n",
       "      <th>Bommenahalli</th>\n",
       "      <th>Brookefield</th>\n",
       "      <th>Budigere</th>\n",
       "      <th>CV Raman Nagar</th>\n",
       "      <th>Chamrajpet</th>\n",
       "      <th>Chandapura</th>\n",
       "      <th>Channasandra</th>\n",
       "      <th>Chikka Tirupathi</th>\n",
       "      <th>Chikkabanavar</th>\n",
       "      <th>Chikkalasandra</th>\n",
       "      <th>Choodasandra</th>\n",
       "      <th>Cooke Town</th>\n",
       "      <th>Cox Town</th>\n",
       "      <th>Cunningham Road</th>\n",
       "      <th>Dasanapura</th>\n",
       "      <th>Dasarahalli</th>\n",
       "      <th>Devanahalli</th>\n",
       "      <th>Devarachikkanahalli</th>\n",
       "      <th>Dodda Nekkundi</th>\n",
       "      <th>Doddaballapur</th>\n",
       "      <th>Doddakallasandra</th>\n",
       "      <th>Doddathoguru</th>\n",
       "      <th>Domlur</th>\n",
       "      <th>Dommasandra</th>\n",
       "      <th>EPIP Zone</th>\n",
       "      <th>Electronic City</th>\n",
       "      <th>Electronic City Phase II</th>\n",
       "      <th>Electronics City Phase 1</th>\n",
       "      <th>Frazer Town</th>\n",
       "      <th>GM Palaya</th>\n",
       "      <th>Garudachar Palya</th>\n",
       "      <th>Giri Nagar</th>\n",
       "      <th>Gollarapalya Hosahalli</th>\n",
       "      <th>Gottigere</th>\n",
       "      <th>Green Glen Layout</th>\n",
       "      <th>Gubbalala</th>\n",
       "      <th>Gunjur</th>\n",
       "      <th>HAL 2nd Stage</th>\n",
       "      <th>HBR Layout</th>\n",
       "      <th>HRBR Layout</th>\n",
       "      <th>HSR Layout</th>\n",
       "      <th>Haralur Road</th>\n",
       "      <th>Harlur</th>\n",
       "      <th>Hebbal</th>\n",
       "      <th>Hebbal Kempapura</th>\n",
       "      <th>Hegde Nagar</th>\n",
       "      <th>Hennur</th>\n",
       "      <th>Hennur Road</th>\n",
       "      <th>Hoodi</th>\n",
       "      <th>Horamavu Agara</th>\n",
       "      <th>Horamavu Banaswadi</th>\n",
       "      <th>Hormavu</th>\n",
       "      <th>Hosa Road</th>\n",
       "      <th>Hosakerehalli</th>\n",
       "      <th>Hoskote</th>\n",
       "      <th>Hosur Road</th>\n",
       "      <th>Hulimavu</th>\n",
       "      <th>ISRO Layout</th>\n",
       "      <th>ITPL</th>\n",
       "      <th>Iblur Village</th>\n",
       "      <th>Indira Nagar</th>\n",
       "      <th>JP Nagar</th>\n",
       "      <th>Jakkur</th>\n",
       "      <th>Jalahalli</th>\n",
       "      <th>...</th>\n",
       "      <th>Judicial Layout</th>\n",
       "      <th>KR Puram</th>\n",
       "      <th>Kadubeesanahalli</th>\n",
       "      <th>Kadugodi</th>\n",
       "      <th>Kaggadasapura</th>\n",
       "      <th>Kaggalipura</th>\n",
       "      <th>Kaikondrahalli</th>\n",
       "      <th>Kalena Agrahara</th>\n",
       "      <th>Kalyan nagar</th>\n",
       "      <th>Kambipura</th>\n",
       "      <th>Kammanahalli</th>\n",
       "      <th>Kammasandra</th>\n",
       "      <th>Kanakapura</th>\n",
       "      <th>Kanakpura Road</th>\n",
       "      <th>Kannamangala</th>\n",
       "      <th>Karuna Nagar</th>\n",
       "      <th>Kasavanhalli</th>\n",
       "      <th>Kasturi Nagar</th>\n",
       "      <th>Kathriguppe</th>\n",
       "      <th>Kaval Byrasandra</th>\n",
       "      <th>Kenchenahalli</th>\n",
       "      <th>Kengeri</th>\n",
       "      <th>Kengeri Satellite Town</th>\n",
       "      <th>Kereguddadahalli</th>\n",
       "      <th>Kodichikkanahalli</th>\n",
       "      <th>Kodigehaali</th>\n",
       "      <th>Kodigehalli</th>\n",
       "      <th>Kodihalli</th>\n",
       "      <th>Kogilu</th>\n",
       "      <th>Konanakunte</th>\n",
       "      <th>Koramangala</th>\n",
       "      <th>Kothannur</th>\n",
       "      <th>Kothanur</th>\n",
       "      <th>Kudlu</th>\n",
       "      <th>Kudlu Gate</th>\n",
       "      <th>Kumaraswami Layout</th>\n",
       "      <th>Kundalahalli</th>\n",
       "      <th>LB Shastri Nagar</th>\n",
       "      <th>Laggere</th>\n",
       "      <th>Lakshminarayana Pura</th>\n",
       "      <th>Lingadheeranahalli</th>\n",
       "      <th>Magadi Road</th>\n",
       "      <th>Mahadevpura</th>\n",
       "      <th>Mahalakshmi Layout</th>\n",
       "      <th>Mallasandra</th>\n",
       "      <th>Malleshpalya</th>\n",
       "      <th>Malleshwaram</th>\n",
       "      <th>Marathahalli</th>\n",
       "      <th>Margondanahalli</th>\n",
       "      <th>Marsur</th>\n",
       "      <th>Mico Layout</th>\n",
       "      <th>Munnekollal</th>\n",
       "      <th>Murugeshpalya</th>\n",
       "      <th>Mysore Road</th>\n",
       "      <th>NGR Layout</th>\n",
       "      <th>NRI Layout</th>\n",
       "      <th>Nagarbhavi</th>\n",
       "      <th>Nagasandra</th>\n",
       "      <th>Nagavara</th>\n",
       "      <th>Nagavarapalya</th>\n",
       "      <th>Narayanapura</th>\n",
       "      <th>Neeladri Nagar</th>\n",
       "      <th>Nehru Nagar</th>\n",
       "      <th>OMBR Layout</th>\n",
       "      <th>Old Airport Road</th>\n",
       "      <th>Old Madras Road</th>\n",
       "      <th>Padmanabhanagar</th>\n",
       "      <th>Pai Layout</th>\n",
       "      <th>Panathur</th>\n",
       "      <th>Parappana Agrahara</th>\n",
       "      <th>Pattandur Agrahara</th>\n",
       "      <th>Poorna Pragna Layout</th>\n",
       "      <th>Prithvi Layout</th>\n",
       "      <th>R.T. Nagar</th>\n",
       "      <th>Rachenahalli</th>\n",
       "      <th>Raja Rajeshwari Nagar</th>\n",
       "      <th>Rajaji Nagar</th>\n",
       "      <th>Rajiv Nagar</th>\n",
       "      <th>Ramagondanahalli</th>\n",
       "      <th>Ramamurthy Nagar</th>\n",
       "      <th>Rayasandra</th>\n",
       "      <th>Sahakara Nagar</th>\n",
       "      <th>Sanjay nagar</th>\n",
       "      <th>Sarakki Nagar</th>\n",
       "      <th>Sarjapur</th>\n",
       "      <th>Sarjapur  Road</th>\n",
       "      <th>Sarjapura - Attibele Road</th>\n",
       "      <th>Sector 2 HSR Layout</th>\n",
       "      <th>Sector 7 HSR Layout</th>\n",
       "      <th>Seegehalli</th>\n",
       "      <th>Shampura</th>\n",
       "      <th>Shivaji Nagar</th>\n",
       "      <th>Singasandra</th>\n",
       "      <th>Somasundara Palya</th>\n",
       "      <th>Sompura</th>\n",
       "      <th>Sonnenahalli</th>\n",
       "      <th>Subramanyapura</th>\n",
       "      <th>Sultan Palaya</th>\n",
       "      <th>TC Palaya</th>\n",
       "      <th>Talaghattapura</th>\n",
       "      <th>Thanisandra</th>\n",
       "      <th>Thigalarapalya</th>\n",
       "      <th>Thubarahalli</th>\n",
       "      <th>Thyagaraja Nagar</th>\n",
       "      <th>Tindlu</th>\n",
       "      <th>Tumkur Road</th>\n",
       "      <th>Ulsoor</th>\n",
       "      <th>Uttarahalli</th>\n",
       "      <th>Varthur</th>\n",
       "      <th>Varthur Road</th>\n",
       "      <th>Vasanthapura</th>\n",
       "      <th>Vidyaranyapura</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",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>2850.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>428.0</td>\n",
       "      <td>4</td>\n",
       "      <td>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>0</td>\n",
       "      <td>0</td>\n",
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       "      <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>0</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>0</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>0</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>0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <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>0</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>0</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>0</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>0</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>0</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>0</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>0</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>0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>0</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",
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       "      <td>0</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",
       "      <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>0</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>0</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",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <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>0</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>0</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>0</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>0</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>0</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>0</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>0</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>0</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>0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>1630.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <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>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</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",
       "      <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>0</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>0</td>\n",
       "      <td>0</td>\n",
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       "      <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>0</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>0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>1875.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<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  5th Phase JP Nagar  6th Phase JP Nagar  \\\n",
       "0                     0                   0                   0   \n",
       "1                     0                   0                   0   \n",
       "2                     0                   0                   0   \n",
       "\n",
       "   7th Phase JP Nagar  8th Phase JP Nagar  9th Phase JP Nagar  AECS Layout  \\\n",
       "0                   0                   0                   0            0   \n",
       "1                   0                   0                   0            0   \n",
       "2                   0                   0                   0            0   \n",
       "\n",
       "   Abbigere  Akshaya Nagar  Ambalipura  Ambedkar Nagar  Amruthahalli  \\\n",
       "0         0              0           0               0             0   \n",
       "1         0              0           0               0             0   \n",
       "2         0              0           0               0             0   \n",
       "\n",
       "   Anandapura  Ananth Nagar  Anekal  Anjanapura  Ardendale  Arekere  Attibele  \\\n",
       "0           0             0       0           0          0        0         0   \n",
       "1           0             0       0           0          0        0         0   \n",
       "2           0             0       0           0          0        0         0   \n",
       "\n",
       "   BEML Layout  BTM 2nd Stage  BTM Layout  Babusapalaya  Badavala Nagar  \\\n",
       "0            0              0           0             0               0   \n",
       "1            0              0           0             0               0   \n",
       "2            0              0           0             0               0   \n",
       "\n",
       "   Balagere  Banashankari  Banashankari Stage II  Banashankari Stage III  \\\n",
       "0         0             0                      0                       0   \n",
       "1         0             0                      0                       0   \n",
       "2         0             0                      0                       0   \n",
       "\n",
       "   Banashankari Stage V  Banashankari Stage VI  Banaswadi  Banjara Layout  \\\n",
       "0                     0                      0          0               0   \n",
       "1                     0                      0          0               0   \n",
       "2                     0                      0          0               0   \n",
       "\n",
       "   Bannerghatta  Bannerghatta Road  Basavangudi  Basaveshwara Nagar  \\\n",
       "0             0                  0            0                   0   \n",
       "1             0                  0            0                   0   \n",
       "2             0                  0            0                   0   \n",
       "\n",
       "   Battarahalli  Begur  Begur Road  Bellandur  Benson Town  Bharathi Nagar  \\\n",
       "0             0      0           0          0            0               0   \n",
       "1             0      0           0          0            0               0   \n",
       "2             0      0           0          0            0               0   \n",
       "\n",
       "   Bhoganhalli  Billekahalli  Binny Pete  Bisuvanahalli  Bommanahalli  \\\n",
       "0            0             0           0              0             0   \n",
       "1            0             0           0              0             0   \n",
       "2            0             0           0              0             0   \n",
       "\n",
       "   Bommasandra  Bommasandra Industrial Area  Bommenahalli  Brookefield  \\\n",
       "0            0                            0             0            0   \n",
       "1            0                            0             0            0   \n",
       "2            0                            0             0            0   \n",
       "\n",
       "   Budigere  CV Raman Nagar  Chamrajpet  Chandapura  Channasandra  \\\n",
       "0         0               0           0           0             0   \n",
       "1         0               0           0           0             0   \n",
       "2         0               0           0           0             0   \n",
       "\n",
       "   Chikka Tirupathi  Chikkabanavar  Chikkalasandra  Choodasandra  Cooke Town  \\\n",
       "0                 0              0               0             0           0   \n",
       "1                 0              0               0             0           0   \n",
       "2                 0              0               0             0           0   \n",
       "\n",
       "   Cox Town  Cunningham Road  Dasanapura  Dasarahalli  Devanahalli  \\\n",
       "0         0                0           0            0            0   \n",
       "1         0                0           0            0            0   \n",
       "2         0                0           0            0            0   \n",
       "\n",
       "   Devarachikkanahalli  Dodda Nekkundi  Doddaballapur  Doddakallasandra  \\\n",
       "0                    0               0              0                 0   \n",
       "1                    0               0              0                 0   \n",
       "2                    0               0              0                 0   \n",
       "\n",
       "   Doddathoguru  Domlur  Dommasandra  EPIP Zone  Electronic City  \\\n",
       "0             0       0            0          0                0   \n",
       "1             0       0            0          0                0   \n",
       "2             0       0            0          0                0   \n",
       "\n",
       "   Electronic City Phase II  Electronics City Phase 1  Frazer Town  GM Palaya  \\\n",
       "0                         0                         0            0          0   \n",
       "1                         0                         0            0          0   \n",
       "2                         0                         0            0          0   \n",
       "\n",
       "   Garudachar Palya  Giri Nagar  Gollarapalya Hosahalli  Gottigere  \\\n",
       "0                 0           0                       0          0   \n",
       "1                 0           0                       0          0   \n",
       "2                 0           0                       0          0   \n",
       "\n",
       "   Green Glen Layout  Gubbalala  Gunjur  HAL 2nd Stage  HBR Layout  \\\n",
       "0                  0          0       0              0           0   \n",
       "1                  0          0       0              0           0   \n",
       "2                  0          0       0              0           0   \n",
       "\n",
       "   HRBR Layout  HSR Layout  Haralur Road  Harlur  Hebbal  Hebbal Kempapura  \\\n",
       "0            0           0             0       0       0                 0   \n",
       "1            0           0             0       0       0                 0   \n",
       "2            0           0             0       0       0                 0   \n",
       "\n",
       "   Hegde Nagar  Hennur  Hennur Road  Hoodi  Horamavu Agara  \\\n",
       "0            0       0            0      0               0   \n",
       "1            0       0            0      0               0   \n",
       "2            0       0            0      0               0   \n",
       "\n",
       "   Horamavu Banaswadi  Hormavu  Hosa Road  Hosakerehalli  Hoskote  Hosur Road  \\\n",
       "0                   0        0          0              0        0           0   \n",
       "1                   0        0          0              0        0           0   \n",
       "2                   0        0          0              0        0           0   \n",
       "\n",
       "   Hulimavu  ISRO Layout  ITPL  Iblur Village  Indira Nagar  JP Nagar  Jakkur  \\\n",
       "0         0            0     0              0             0         0       0   \n",
       "1         0            0     0              0             0         0       0   \n",
       "2         0            0     0              0             0         0       0   \n",
       "\n",
       "   Jalahalli  ...  Judicial Layout  KR Puram  Kadubeesanahalli  Kadugodi  \\\n",
       "0          0  ...                0         0                 0         0   \n",
       "1          0  ...                0         0                 0         0   \n",
       "2          0  ...                0         0                 0         0   \n",
       "\n",
       "   Kaggadasapura  Kaggalipura  Kaikondrahalli  Kalena Agrahara  Kalyan nagar  \\\n",
       "0              0            0               0                0             0   \n",
       "1              0            0               0                0             0   \n",
       "2              0            0               0                0             0   \n",
       "\n",
       "   Kambipura  Kammanahalli  Kammasandra  Kanakapura  Kanakpura Road  \\\n",
       "0          0             0            0           0               0   \n",
       "1          0             0            0           0               0   \n",
       "2          0             0            0           0               0   \n",
       "\n",
       "   Kannamangala  Karuna Nagar  Kasavanhalli  Kasturi Nagar  Kathriguppe  \\\n",
       "0             0             0             0              0            0   \n",
       "1             0             0             0              0            0   \n",
       "2             0             0             0              0            0   \n",
       "\n",
       "   Kaval Byrasandra  Kenchenahalli  Kengeri  Kengeri Satellite Town  \\\n",
       "0                 0              0        0                       0   \n",
       "1                 0              0        0                       0   \n",
       "2                 0              0        0                       0   \n",
       "\n",
       "   Kereguddadahalli  Kodichikkanahalli  Kodigehaali  Kodigehalli  Kodihalli  \\\n",
       "0                 0                  0            0            0          0   \n",
       "1                 0                  0            0            0          0   \n",
       "2                 0                  0            0            0          0   \n",
       "\n",
       "   Kogilu  Konanakunte  Koramangala  Kothannur  Kothanur  Kudlu  Kudlu Gate  \\\n",
       "0       0            0            0          0         0      0           0   \n",
       "1       0            0            0          0         0      0           0   \n",
       "2       0            0            0          0         0      0           0   \n",
       "\n",
       "   Kumaraswami Layout  Kundalahalli  LB Shastri Nagar  Laggere  \\\n",
       "0                   0             0                 0        0   \n",
       "1                   0             0                 0        0   \n",
       "2                   0             0                 0        0   \n",
       "\n",
       "   Lakshminarayana Pura  Lingadheeranahalli  Magadi Road  Mahadevpura  \\\n",
       "0                     0                   0            0            0   \n",
       "1                     0                   0            0            0   \n",
       "2                     0                   0            0            0   \n",
       "\n",
       "   Mahalakshmi Layout  Mallasandra  Malleshpalya  Malleshwaram  Marathahalli  \\\n",
       "0                   0            0             0             0             0   \n",
       "1                   0            0             0             0             0   \n",
       "2                   0            0             0             0             0   \n",
       "\n",
       "   Margondanahalli  Marsur  Mico Layout  Munnekollal  Murugeshpalya  \\\n",
       "0                0       0            0            0              0   \n",
       "1                0       0            0            0              0   \n",
       "2                0       0            0            0              0   \n",
       "\n",
       "   Mysore Road  NGR Layout  NRI Layout  Nagarbhavi  Nagasandra  Nagavara  \\\n",
       "0            0           0           0           0           0         0   \n",
       "1            0           0           0           0           0         0   \n",
       "2            0           0           0           0           0         0   \n",
       "\n",
       "   Nagavarapalya  Narayanapura  Neeladri Nagar  Nehru Nagar  OMBR Layout  \\\n",
       "0              0             0               0            0            0   \n",
       "1              0             0               0            0            0   \n",
       "2              0             0               0            0            0   \n",
       "\n",
       "   Old Airport Road  Old Madras Road  Padmanabhanagar  Pai Layout  Panathur  \\\n",
       "0                 0                0                0           0         0   \n",
       "1                 0                0                0           0         0   \n",
       "2                 0                0                0           0         0   \n",
       "\n",
       "   Parappana Agrahara  Pattandur Agrahara  Poorna Pragna Layout  \\\n",
       "0                   0                   0                     0   \n",
       "1                   0                   0                     0   \n",
       "2                   0                   0                     0   \n",
       "\n",
       "   Prithvi Layout  R.T. Nagar  Rachenahalli  Raja Rajeshwari Nagar  \\\n",
       "0               0           0             0                      0   \n",
       "1               0           0             0                      0   \n",
       "2               0           0             0                      0   \n",
       "\n",
       "   Rajaji Nagar  Rajiv Nagar  Ramagondanahalli  Ramamurthy Nagar  Rayasandra  \\\n",
       "0             0            0                 0                 0           0   \n",
       "1             0            0                 0                 0           0   \n",
       "2             0            0                 0                 0           0   \n",
       "\n",
       "   Sahakara Nagar  Sanjay nagar  Sarakki Nagar  Sarjapur  Sarjapur  Road  \\\n",
       "0               0             0              0         0               0   \n",
       "1               0             0              0         0               0   \n",
       "2               0             0              0         0               0   \n",
       "\n",
       "   Sarjapura - Attibele Road  Sector 2 HSR Layout  Sector 7 HSR Layout  \\\n",
       "0                          0                    0                    0   \n",
       "1                          0                    0                    0   \n",
       "2                          0                    0                    0   \n",
       "\n",
       "   Seegehalli  Shampura  Shivaji Nagar  Singasandra  Somasundara Palya  \\\n",
       "0           0         0              0            0                  0   \n",
       "1           0         0              0            0                  0   \n",
       "2           0         0              0            0                  0   \n",
       "\n",
       "   Sompura  Sonnenahalli  Subramanyapura  Sultan Palaya  TC Palaya  \\\n",
       "0        0             0               0              0          0   \n",
       "1        0             0               0              0          0   \n",
       "2        0             0               0              0          0   \n",
       "\n",
       "   Talaghattapura  Thanisandra  Thigalarapalya  Thubarahalli  \\\n",
       "0               0            0               0             0   \n",
       "1               0            0               0             0   \n",
       "2               0            0               0             0   \n",
       "\n",
       "   Thyagaraja Nagar  Tindlu  Tumkur Road  Ulsoor  Uttarahalli  Varthur  \\\n",
       "0                 0       0            0       0            0        0   \n",
       "1                 0       0            0       0            0        0   \n",
       "2                 0       0            0       0            0        0   \n",
       "\n",
       "   Varthur Road  Vasanthapura  Vidyaranyapura  Vijayanagar  \\\n",
       "0             0             0               0            0   \n",
       "1             0             0               0            0   \n",
       "2             0             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",
       "\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",
       "\n",
       "[3 rows x 246 columns]"
      ]
     },
     "execution_count": 49,
     "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": 50,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'X' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-50-b0971ce5c355>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'data.csv'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'X' is not defined"
     ]
    }
   ],
   "source": [
    " X.to_csv('data.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df12 = df11.drop('location', axis='columns')\n",
    "# df12.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df12 = df10.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df12.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df11.drop(['price','location'], axis='columns')\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    " X.to_csv('data.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'X' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-cc0eb14647fc>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'X' is not defined"
     ]
    }
   ],
   "source": [
    "X.columns[4:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.iloc[:,3:].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df10.price\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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=0.35,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(df10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lr_clf = LinearRegression()\n",
    "lr_clf.fit(X_train, y_train)\n",
    "lr_clf.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_clf.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "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",
    "\n",
    "cross_val_score(LinearRegression(), X, y, cv=cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "def find_best_model_using_gridsearchcv(X,y):\n",
    "    algos = {\n",
    "        'linear_regression':{\n",
    "            'model': LinearRegression(),\n",
    "            'params':{\n",
    "                'normalize':[True, False]\n",
    "            }\n",
    "        },\n",
    "        'lasso':{\n",
    "            'model': Lasso(),\n",
    "            'params':{\n",
    "                'alpha':[1,2],\n",
    "                'selection':['random','cyclic']\n",
    "            }\n",
    "        },\n",
    "        'decision_tree':{\n",
    "             'model': DecisionTreeRegressor(),\n",
    "             'params':{\n",
    "                 'criterion':['mse','friedman_mse'],\n",
    "                 'splitter':['best','random']\n",
    "             }         \n",
    "        }\n",
    "    }\n",
    "    scores = []\n",
    "    cv = ShuffleSplit(n_splits=5, test_size=0.2, random_state=0)\n",
    "    for algo_name, config in algos.items():\n",
    "        gs = GridSearchCV(config['model'], config['params'], cv=cv, return_train_score=False)\n",
    "        gs.fit(X,y)\n",
    "        scores.append({\n",
    "            'model': algo_name,\n",
    "            'best_score': gs.best_score_,\n",
    "            'best_params': gs.best_params_\n",
    "        })\n",
    "        \n",
    "    return pd.DataFrame(scores, columns=['model','best_score','best_params'])\n",
    "\n",
    "find_best_model_using_gridsearchcv(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.columns[4:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    " np.where(X.columns=='2nd Phase Judicial Layout')[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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",
    "    return lr_clf.predict([x])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict_price('1st Phase JP Nagar', 1000,2,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict_price('Indira Nagar', 1000,2,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict_price('Indira Nagar', 1000,3,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "locations = df11.iloc[:,5:]\n",
    "locations.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df11.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pickling the model\n",
    "import pickle\n",
    "pickle_out = open(\"model.pkl\", \"wb\")\n",
    "pickle.dump(lr_clf, pickle_out)\n",
    "pickle_out.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import pickle\n",
    "# data = {\"model\":lr_clf,\"le_location\":location_n}\n",
    "# with open('banglore_home_prices_model.pickle','wb') as f:\n",
    "#     pickle.dump(data,f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# with open('banglore_home_prices_model.pickle','rb') as f:\n",
    "#     pickle.load(f)\n",
    "    \n",
    "# regressor_loaded = data[\"model\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# y_pred = regressor_loaded.predict(X)\n",
    "# y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df12.columns[4:0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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