real_state_project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5f8f38bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "matplotlib.rcParams[\"figure.figsize\"] = [20,10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b42207fb",
   "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,
   "id": "b348f83e",
   "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.groupby('area_type')['area_type'].agg('count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9bf669e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1056</td>\n",
       "      <td>2.0</td>\n",
       "      <td>39.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>2600</td>\n",
       "      <td>5.0</td>\n",
       "      <td>120.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1440</td>\n",
       "      <td>2.0</td>\n",
       "      <td>62.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1521</td>\n",
       "      <td>3.0</td>\n",
       "      <td>95.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1200</td>\n",
       "      <td>2.0</td>\n",
       "      <td>51.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   location       size total_sqft  bath   price\n",
       "0  Electronic City Phase II      2 BHK       1056   2.0   39.07\n",
       "1          Chikka Tirupathi  4 Bedroom       2600   5.0  120.00\n",
       "2               Uttarahalli      3 BHK       1440   2.0   62.00\n",
       "3        Lingadheeranahalli      3 BHK       1521   3.0   95.00\n",
       "4                  Kothanur      2 BHK       1200   2.0   51.00"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df1.drop(['availability','area_type','society','balcony'], axis = 'columns')\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8bbf3c30",
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "909edc7d",
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = df2.dropna()\n",
    "df3.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9ded1ab4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1056</td>\n",
       "      <td>2.0</td>\n",
       "      <td>39.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>2600</td>\n",
       "      <td>5.0</td>\n",
       "      <td>120.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1440</td>\n",
       "      <td>2.0</td>\n",
       "      <td>62.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1521</td>\n",
       "      <td>3.0</td>\n",
       "      <td>95.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1200</td>\n",
       "      <td>2.0</td>\n",
       "      <td>51.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   location       size total_sqft  bath   price\n",
       "0  Electronic City Phase II      2 BHK       1056   2.0   39.07\n",
       "1          Chikka Tirupathi  4 Bedroom       2600   5.0  120.00\n",
       "2               Uttarahalli      3 BHK       1440   2.0   62.00\n",
       "3        Lingadheeranahalli      3 BHK       1521   3.0   95.00\n",
       "4                  Kothanur      2 BHK       1200   2.0   51.00"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "970ac194",
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3['size'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "85d89171",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ashwa\\AppData\\Local\\Temp/ipykernel_10100/321011938.py: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": 10,
   "id": "19d78001",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f1bdb83d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ashwa\\ana\\lib\\site-packages\\pandas\\core\\frame.py:4906: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\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",
      "  return super().drop(\n"
     ]
    }
   ],
   "source": [
    "df3.drop('size', axis = 'columns', inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bc5fbe4a",
   "metadata": {},
   "outputs": [
    {
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       "  <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",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>1056</td>\n",
       "      <td>2.0</td>\n",
       "      <td>39.07</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>2600</td>\n",
       "      <td>5.0</td>\n",
       "      <td>120.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>1440</td>\n",
       "      <td>2.0</td>\n",
       "      <td>62.00</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>1521</td>\n",
       "      <td>3.0</td>\n",
       "      <td>95.00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kothanur</td>\n",
       "      <td>1200</td>\n",
       "      <td>2.0</td>\n",
       "      <td>51.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   location total_sqft  bath   price  bhk\n",
       "0  Electronic City Phase II       1056   2.0   39.07    2\n",
       "1          Chikka Tirupathi       2600   5.0  120.00    4\n",
       "2               Uttarahalli       1440   2.0   62.00    3\n",
       "3        Lingadheeranahalli       1521   3.0   95.00    3\n",
       "4                  Kothanur       1200   2.0   51.00    2"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "66a308e7",
   "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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.bhk.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5f08f623",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>bhk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1718</th>\n",
       "      <td>2Electronic City Phase II</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>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 total_sqft  bath  price  bhk\n",
       "1718  2Electronic City Phase II       8000  27.0  230.0   27\n",
       "4684                Munnekollal       2400  40.0  660.0   43"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3[df3.bhk>20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cca55c22",
   "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": 16,
   "id": "3932bc85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>bhk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Yelahanka</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>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>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>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>1015 - 1540</td>\n",
       "      <td>2.0</td>\n",
       "      <td>56.800</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12975</th>\n",
       "      <td>Whitefield</td>\n",
       "      <td>850 - 1060</td>\n",
       "      <td>2.0</td>\n",
       "      <td>38.190</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12990</th>\n",
       "      <td>Talaghattapura</td>\n",
       "      <td>1804 - 2273</td>\n",
       "      <td>3.0</td>\n",
       "      <td>122.000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13059</th>\n",
       "      <td>Harlur</td>\n",
       "      <td>1200 - 1470</td>\n",
       "      <td>2.0</td>\n",
       "      <td>72.760</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13265</th>\n",
       "      <td>Hoodi</td>\n",
       "      <td>1133 - 1384</td>\n",
       "      <td>2.0</td>\n",
       "      <td>59.135</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13299</th>\n",
       "      <td>Whitefield</td>\n",
       "      <td>2830 - 2882</td>\n",
       "      <td>5.0</td>\n",
       "      <td>154.500</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>190 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 location   total_sqft  bath    price  bhk\n",
       "30              Yelahanka  2100 - 2850   4.0  186.000    4\n",
       "122                Hebbal  3067 - 8156   4.0  477.000    4\n",
       "137    8th Phase JP Nagar  1042 - 1105   2.0   54.005    2\n",
       "165              Sarjapur  1145 - 1340   2.0   43.490    2\n",
       "188              KR Puram  1015 - 1540   2.0   56.800    2\n",
       "...                   ...          ...   ...      ...  ...\n",
       "12975          Whitefield   850 - 1060   2.0   38.190    2\n",
       "12990      Talaghattapura  1804 - 2273   3.0  122.000    3\n",
       "13059              Harlur  1200 - 1470   2.0   72.760    2\n",
       "13265               Hoodi  1133 - 1384   2.0   59.135    2\n",
       "13299          Whitefield  2830 - 2882   5.0  154.500    4\n",
       "\n",
       "[190 rows x 5 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3[~df3['total_sqft'].apply(is_float)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "be5c1bb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_sq_ft_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": 18,
   "id": "e1102851",
   "metadata": {},
   "outputs": [],
   "source": [
    "df4= df3.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "882e2e42",
   "metadata": {},
   "outputs": [],
   "source": [
    "df4['total_sqft']= df4.total_sqft.apply(convert_sq_ft_to_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "71bf47a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>bhk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>1056.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>39.07</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>120.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>62.00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>1521.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>95.00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kothanur</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>51.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   location  total_sqft  bath   price  bhk\n",
       "0  Electronic City Phase II      1056.0   2.0   39.07    2\n",
       "1          Chikka Tirupathi      2600.0   5.0  120.00    4\n",
       "2               Uttarahalli      1440.0   2.0   62.00    3\n",
       "3        Lingadheeranahalli      1521.0   3.0   95.00    3\n",
       "4                  Kothanur      1200.0   2.0   51.00    2"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.total_sqft.notnull()\n",
    "df4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "eedb429d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location      Yelahanka\n",
       "total_sqft       2475.0\n",
       "bath                4.0\n",
       "price             186.0\n",
       "bhk                   4\n",
       "Name: 30, dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.loc[30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e1d78702",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2475.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4['total_sqft'][30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7882a58e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df5 = df4.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ed623ff9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df5['price_per_sq_feet'] = df5['price']*100000/df5['total_sqft']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f5015ba2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>bhk</th>\n",
       "      <th>price_per_sq_feet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>1056.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>39.07</td>\n",
       "      <td>2</td>\n",
       "      <td>3699.810606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>120.00</td>\n",
       "      <td>4</td>\n",
       "      <td>4615.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>62.00</td>\n",
       "      <td>3</td>\n",
       "      <td>4305.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>1521.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>95.00</td>\n",
       "      <td>3</td>\n",
       "      <td>6245.890861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kothanur</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>51.00</td>\n",
       "      <td>2</td>\n",
       "      <td>4250.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   location  total_sqft  bath   price  bhk  price_per_sq_feet\n",
       "0  Electronic City Phase II      1056.0   2.0   39.07    2        3699.810606\n",
       "1          Chikka Tirupathi      2600.0   5.0  120.00    4        4615.384615\n",
       "2               Uttarahalli      1440.0   2.0   62.00    3        4305.555556\n",
       "3        Lingadheeranahalli      1521.0   3.0   95.00    3        6245.890861\n",
       "4                  Kothanur      1200.0   2.0   51.00    2        4250.000000"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "42a5dfc0",
   "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": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5['location'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c1d08248",
   "metadata": {},
   "outputs": [],
   "source": [
    "df5['location'] = df5['location'].apply(lambda x: x.strip())\n",
    "loc_stats = df5.groupby('location')['location'].agg('count').sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ccb11548",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location\n",
       "Whitefield               535\n",
       "Sarjapur  Road           392\n",
       "Electronic City          304\n",
       "Kanakpura Road           266\n",
       "Thanisandra              236\n",
       "                        ... \n",
       "1 Giri Nagar               1\n",
       "Kanakapura Road,           1\n",
       "Kanakapura main  Road      1\n",
       "Karnataka Shabarimala      1\n",
       "whitefiled                 1\n",
       "Name: location, Length: 1293, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loc_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4df42561",
   "metadata": {},
   "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": [
    "loc_less_than_10 = loc_stats[loc_stats<= 10]\n",
    "loc_less_than_10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "00c88b81",
   "metadata": {},
   "outputs": [],
   "source": [
    "df5['location'] = df5.location.apply(lambda x: 'other' if x in loc_less_than_10 else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5e727add",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>bhk</th>\n",
       "      <th>price_per_sq_feet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>1056.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>39.07</td>\n",
       "      <td>2</td>\n",
       "      <td>3699.810606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>120.00</td>\n",
       "      <td>4</td>\n",
       "      <td>4615.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>62.00</td>\n",
       "      <td>3</td>\n",
       "      <td>4305.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>1521.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>95.00</td>\n",
       "      <td>3</td>\n",
       "      <td>6245.890861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kothanur</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>51.00</td>\n",
       "      <td>2</td>\n",
       "      <td>4250.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Whitefield</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>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>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>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>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  total_sqft  bath   price  bhk  price_per_sq_feet\n",
       "0  Electronic City Phase II      1056.0   2.0   39.07    2        3699.810606\n",
       "1          Chikka Tirupathi      2600.0   5.0  120.00    4        4615.384615\n",
       "2               Uttarahalli      1440.0   2.0   62.00    3        4305.555556\n",
       "3        Lingadheeranahalli      1521.0   3.0   95.00    3        6245.890861\n",
       "4                  Kothanur      1200.0   2.0   51.00    2        4250.000000\n",
       "5                Whitefield      1170.0   2.0   38.00    2        3247.863248\n",
       "6          Old Airport Road      2732.0   4.0  204.00    4        7467.057101\n",
       "7              Rajaji Nagar      3300.0   4.0  600.00    4       18181.818182\n",
       "8              Marathahalli      1310.0   3.0   63.25    3        4828.244275\n",
       "9                     other      1020.0   6.0  370.00    6       36274.509804"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "719440ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(744, 6)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5[df5.total_sqft/df5.bhk<300]\n",
    "df5[df5.total_sqft/df5.bhk<300].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "cf3f65e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df6 = df5[~(df5.total_sqft/df5.bhk<300)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "91545363",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>bhk</th>\n",
       "      <th>price_per_sq_feet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>1056.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>39.07</td>\n",
       "      <td>2</td>\n",
       "      <td>3699.810606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>120.00</td>\n",
       "      <td>4</td>\n",
       "      <td>4615.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>62.00</td>\n",
       "      <td>3</td>\n",
       "      <td>4305.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>1521.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>95.00</td>\n",
       "      <td>3</td>\n",
       "      <td>6245.890861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kothanur</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>51.00</td>\n",
       "      <td>2</td>\n",
       "      <td>4250.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   location  total_sqft  bath   price  bhk  price_per_sq_feet\n",
       "0  Electronic City Phase II      1056.0   2.0   39.07    2        3699.810606\n",
       "1          Chikka Tirupathi      2600.0   5.0  120.00    4        4615.384615\n",
       "2               Uttarahalli      1440.0   2.0   62.00    3        4305.555556\n",
       "3        Lingadheeranahalli      1521.0   3.0   95.00    3        6245.890861\n",
       "4                  Kothanur      1200.0   2.0   51.00    2        4250.000000"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b1b3f71f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12502, 6)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f22d45d9",
   "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_sq_feet, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6.price_per_sq_feet.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3f7e0b77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10241, 6)"
      ]
     },
     "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_sq_feet)\n",
    "        st = np.std(subdf.price_per_sq_feet)\n",
    "        reduced_df = subdf[(subdf.price_per_sq_feet>(m-st)) & (subdf.price_per_sq_feet<=(m+st))]\n",
    "        df_out = pd.concat([df_out,reduced_df],ignore_index=True)\n",
    "    return df_out\n",
    "df7 = remove_pps_outliers(df6)\n",
    "df7.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "bf094b7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>bhk</th>\n",
       "      <th>price_per_sq_feet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>2850.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>428.0</td>\n",
       "      <td>4</td>\n",
       "      <td>15017.543860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>1630.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>3</td>\n",
       "      <td>11901.840491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>1875.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>3</td>\n",
       "      <td>12533.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10833.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>1235.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>2</td>\n",
       "      <td>11983.805668</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              location  total_sqft  bath  price  bhk  price_per_sq_feet\n",
       "0  1st Block Jayanagar      2850.0   4.0  428.0    4       15017.543860\n",
       "1  1st Block Jayanagar      1630.0   3.0  194.0    3       11901.840491\n",
       "2  1st Block Jayanagar      1875.0   2.0  235.0    3       12533.333333\n",
       "3  1st Block Jayanagar      1200.0   2.0  130.0    3       10833.333333\n",
       "4  1st Block Jayanagar      1235.0   2.0  148.0    2       11983.805668"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1c83e23c",
   "metadata": {},
   "outputs": [],
   "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', marker = '*',label = '2bhk', s= 50)\n",
    "    plt.scatter(bhk3.total_sqft,bhk3.price, color = 'red', marker = '+',label = '3bhk', s= 50)\n",
    "    plt.xlabel('total sqft area')\n",
    "    plt.ylabel('price per sqft')\n",
    "    plt.title(location)\n",
    "    plt.legend()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8ea73aba",
   "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": [
    "\n",
    "plot_scatter_chart(df7, \"Rajaji Nagar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f5cae90a",
   "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(df7, \"Hebbal\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "137e892c",
   "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(df7, \"Whitefield\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "1c1d3aa9",
   "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(df7, \"Sarjapur  Road\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b305164c",
   "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(df7,'Kanakpura Road')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "0812f119",
   "metadata": {},
   "outputs": [],
   "source": [
    "###some outliers in price of 2 bhk is greater than 3bhk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e7ec5a98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'1': {'mean': 4000, 'std': 2000, 'count': 34},\n",
       " '2': {'mean': 4300, 'std': 2300, 'count': 22}}"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{\n",
    "    '1' : {\n",
    "        'mean': 4000,\n",
    "        'std' : 2000,\n",
    "        'count': 34\n",
    "    },\n",
    "    '2' : {\n",
    "        'mean': 4300,\n",
    "        'std' : 2300,\n",
    "        'count': 22\n",
    "    },    \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "aaab68b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7329, 6)"
      ]
     },
     "execution_count": 47,
     "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_sq_feet),\n",
    "                'std': np.std(bhk_df.price_per_sq_feet),\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_sq_feet<(stats['mean'])].index.values)\n",
    "    return df.drop(exclude_indices,axis='index')\n",
    "df8 = remove_bhk_outliers(df7)\n",
    "# df8 = df7.copy()\n",
    "df8.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "fe68eba4",
   "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,\"Rajaji Nagar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "867834c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 49,
     "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_sq_feet,rwidth=0.8)\n",
    "plt.xlabel(\"Price Per Square Feet\")\n",
    "plt.ylabel(\"Count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "fca97f94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4.,  3.,  2.,  5.,  8.,  1.,  6.,  7.,  9., 12., 16., 13.])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df8.bath.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "e0d384ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 51,
     "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": 52,
   "id": "c6524c66",
   "metadata": {},
   "outputs": [],
   "source": [
    "#It is unusual to have 2 more bathrooms than number of bedrooms in a home"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "4ea97a33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>bhk</th>\n",
       "      <th>price_per_sq_feet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1626</th>\n",
       "      <td>Chikkabanavar</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>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>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>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  total_sqft  bath   price  bhk  price_per_sq_feet\n",
       "1626  Chikkabanavar      2460.0   7.0    80.0    4        3252.032520\n",
       "5238     Nagasandra      7000.0   8.0   450.0    4        6428.571429\n",
       "6711    Thanisandra      1806.0   6.0   116.0    3        6423.034330\n",
       "8411          other     11338.0   9.0  1000.0    6        8819.897689"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df8[df8.bath>df8.bhk+2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "62508fdc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7251, 6)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df9 = df8[df8.bath<df8.bhk+2]\n",
    "df9.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "bc78a7dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>bhk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>2850.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>428.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>1630.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>1875.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>1235.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              location  total_sqft  bath  price  bhk\n",
       "0  1st Block Jayanagar      2850.0   4.0  428.0    4\n",
       "1  1st Block Jayanagar      1630.0   3.0  194.0    3\n",
       "2  1st Block Jayanagar      1875.0   2.0  235.0    3\n",
       "3  1st Block Jayanagar      1200.0   2.0  130.0    3\n",
       "4  1st Block Jayanagar      1235.0   2.0  148.0    2"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df10 = df9.drop(\"price_per_sq_feet\", axis = 'columns')\n",
    "df10.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "95efe820",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "       1st Block Jayanagar  1st Phase JP Nagar  2nd Phase Judicial Layout  \\\n",
       "0                        1                   0                          0   \n",
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       "...                    ...                 ...                        ...   \n",
       "10232                    0                   0                          0   \n",
       "10233                    0                   0                          0   \n",
       "10236                    0                   0                          0   \n",
       "10237                    0                   0                          0   \n",
       "10240                    0                   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",
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       "\n",
       "       6th Phase JP Nagar  7th Phase JP Nagar  8th Phase JP Nagar  \\\n",
       "0                       0                   0                   0   \n",
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       "...                   ...                 ...                 ...   \n",
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       "10240                   0                   0                   0   \n",
       "\n",
       "       9th Phase JP Nagar  ...  Vishveshwarya Layout  Vishwapriya Layout  \\\n",
       "0                       0  ...                     0                   0   \n",
       "1                       0  ...                     0                   0   \n",
       "2                       0  ...                     0                   0   \n",
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       "10237                   0  ...                     0                   0   \n",
       "10240                   0  ...                     0                   0   \n",
       "\n",
       "       Vittasandra  Whitefield  Yelachenahalli  Yelahanka  Yelahanka New Town  \\\n",
       "0                0           0               0          0                   0   \n",
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       "\n",
       "       Yelenahalli  Yeshwanthpur  other  \n",
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       "\n",
       "[7251 rows x 242 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummies = pd.get_dummies(df10['location'])\n",
    "dummies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "25e852d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df11 = pd.concat([df10,dummies.drop('other',axis = 'columns')],axis = 'columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "a2e69b2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df12= df11.drop('location', axis = 'columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "284304cc",
   "metadata": {},
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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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 245 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_sqft  bath  price  bhk  1st Block Jayanagar  1st Phase JP Nagar  \\\n",
       "0      2850.0   4.0  428.0    4                    1                   0   \n",
       "1      1630.0   3.0  194.0    3                    1                   0   \n",
       "2      1875.0   2.0  235.0    3                    1                   0   \n",
       "3      1200.0   2.0  130.0    3                    1                   0   \n",
       "4      1235.0   2.0  148.0    2                    1                   0   \n",
       "\n",
       "   2nd Phase Judicial Layout  2nd Stage Nagarbhavi  5th Block Hbr Layout  \\\n",
       "0                          0                     0                     0   \n",
       "1                          0                     0                     0   \n",
       "2                          0                     0                     0   \n",
       "3                          0                     0                     0   \n",
       "4                          0                     0                     0   \n",
       "\n",
       "   5th Phase JP Nagar  ...  Vijayanagar  Vishveshwarya Layout  \\\n",
       "0                   0  ...            0                     0   \n",
       "1                   0  ...            0                     0   \n",
       "2                   0  ...            0                     0   \n",
       "3                   0  ...            0                     0   \n",
       "4                   0  ...            0                     0   \n",
       "\n",
       "   Vishwapriya Layout  Vittasandra  Whitefield  Yelachenahalli  Yelahanka  \\\n",
       "0                   0            0           0               0          0   \n",
       "1                   0            0           0               0          0   \n",
       "2                   0            0           0               0          0   \n",
       "3                   0            0           0               0          0   \n",
       "4                   0            0           0               0          0   \n",
       "\n",
       "   Yelahanka New Town  Yelenahalli  Yeshwanthpur  \n",
       "0                   0            0             0  \n",
       "1                   0            0             0  \n",
       "2                   0            0             0  \n",
       "3                   0            0             0  \n",
       "4                   0            0             0  \n",
       "\n",
       "[5 rows x 245 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df12.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "eead5105",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df12.drop('price', axis ='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "bad5179d",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df12.price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "7e735bde",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "520a5df9",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(x,y, test_size = 0.2,random_state = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "a90d8b6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "a549086b",
   "metadata": {},
   "outputs": [],
   "source": [
    "LR = LinearRegression(normalize =False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "d30b5723",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ashwa\\ana\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LinearRegression(normalize=False)"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LR.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "e3849660",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8673723050658283"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LR.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "d6190283",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "ecdcea57",
   "metadata": {},
   "outputs": [],
   "source": [
    "lasso = linear_model.Lasso(alpha = 1, selection = 'cyclic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "b5ca2b82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7025916682005547"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso.fit(x_train,y_train)\n",
    "lasso.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "7745a702",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "17bd4280",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = cross_val_score(linear_model.Lasso(alpha = 1, selection = 'cyclic'),x_train, y_train, cv = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "77883ebe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7096287969079489"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.average(clf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "142bec89",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "c2f1496e",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf2 = cross_val_score(RandomForestRegressor(n_estimators = 10),x_train,y_train,cv = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "b0c92eb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7404177678704797"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.average(clf2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ffc6d04",
   "metadata": {},
   "outputs": [],
   "source": [
    "#we can see that linear regression is giving best model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6624324",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ce1d2fa",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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