Home-predict.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "33f10801-faec-4eb0-bd31-814dd241e7e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "matplotlib.rcParams[\"figure.figsize\"]=(20,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d2f945f9-f470-4704-b0a3-fee80bce84d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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_prices.csv\")\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "789123c6-9fa6-4510-befb-60fa5e71d8b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df1.drop([\"area_type\",\"availability\",\"society\",\"balcony\"], axis=\"columns\")\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "612eaddf-90b7-4cc2-a444-5b05725459cf",
   "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e8e52a7f-c233-4fb0-9e8f-44141c05ed2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = df2.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2300eee1-7e54-49ac-a637-88ef3e44ea97",
   "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1a816631-0045-4d9e-83ee-d13d110b150c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "    </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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b8ed2ecd-e863-481d-94e3-c7b7cd817fa2",
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3['size'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e4899471-39e0-46d8-84f3-ba289959de2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_7064\\2222900254.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": 9,
   "id": "c87da27e-3c77-4848-870b-58daaaa43d5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "78fedeb6-0913-40db-a89c-7dcc4a4858c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1056', '2600', '1440', ..., '1133 - 1384', '774', '4689'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3['total_sqft'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "61c689d5-3194-4a67-aa55-b65d33229843",
   "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": 12,
   "id": "5368a097-1a21-44ed-b4ea-09df94051fa0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "  </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"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3[~df3['total_sqft'].apply(is_float)].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9d782b73-d1fb-4999-b785-f8dcee21f1db",
   "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": 16,
   "id": "5dcf36c5-3e89-462c-a988-c715b3e4cdee",
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>bhk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1056.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>39.07</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>120.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>62.00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1521.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>95.00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>51.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   location       size  total_sqft  bath   price  bhk\n",
       "0  Electronic City Phase II      2 BHK      1056.0   2.0   39.07    2\n",
       "1          Chikka Tirupathi  4 Bedroom      2600.0   5.0  120.00    4\n",
       "2               Uttarahalli      3 BHK      1440.0   2.0   62.00    3\n",
       "3        Lingadheeranahalli      3 BHK      1521.0   3.0   95.00    3\n",
       "4                  Kothanur      2 BHK      1200.0   2.0   51.00    2"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = df3.copy()\n",
    "df4['total_sqft'] = df4['total_sqft'].apply(convert_sqft_to_num)\n",
    "df4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "43e975c7-ebba-4788-9815-8d72d208b91a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df5 = df4.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6657a6c7-97ec-4bf7-ac6e-3701ef8053a8",
   "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>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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5['price_per_sqft'] = df5['price']*100000/df5['total_sqft']\n",
    "df5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "82992915-baf3-4018-92d1-cdec7098ed5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1304"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df5.location.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3c134071-dd60-4973-b34d-7c93c28e48d3",
   "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": 21,
     "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",
    "location_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7f6eb949-cca6-45ea-a67c-94a1b3df3ad7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1052"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(location_stats[location_stats<=10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e4dbdff6-e841-4f57-a8fc-c571b10ecc39",
   "metadata": {},
   "outputs": [],
   "source": [
    "location_stats_less_than_10 = location_stats[location_stats<=10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f025e932-fcfe-40e6-9eb6-04434c987742",
   "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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "location_stats_less_than_10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "be8ff926-7eb6-4704-9807-f2eb2cf82960",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "242"
      ]
     },
     "execution_count": 25,
     "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": 27,
   "id": "e9325fb0-096f-408b-80b5-9abf91cbbc9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>size</th>\n",
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       "  </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": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "fad1fc1e-9f86-4a3e-9866-0750d7bf4963",
   "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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5[df5.total_sqft/df5.bhk<300].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "66755fec-e2f9-4ec8-aa06-055397996927",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12502, 7)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6 = df5[~(df5.total_sqft/df5.bhk<300)]\n",
    "df6.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4d291096-30f3-4139-8d94-ff24794b27c9",
   "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": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6.price_per_sqft.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "de58b72f-3e68-4be7-859c-9d47ad8b3cb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10241, 7)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def remove_pps_outliers(df):\n",
    "    df_out = pd.DataFrame()\n",
    "    for key, subdf in df.groupby('location'):\n",
    "        m = np.mean(subdf.price_per_sqft)\n",
    "        st = np.std(subdf.price_per_sqft)\n",
    "        reduced_df = subdf[(subdf.price_per_sqft>(m-st)) & (subdf.price_per_sqft<=(m+st))]\n",
    "        df_out = pd.concat([df_out,reduced_df],ignore_index = True)\n",
    "    return df_out\n",
    "df7 = remove_pps_outliers(df6)\n",
    "df7.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9d074f6e-6f52-40cc-98ad-0826ea832956",
   "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', label ='2 bhk', s =50)\n",
    "    plt.scatter(bhk3.total_sqft,bhk3.price, marker='+', color='green', label ='3 bhk', s =50)\n",
    "    plt.xlabel('Square Feet Area')\n",
    "    plt.ylabel('Price')\n",
    "    plt.title('Location')\n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "63f4016c-44d2-4eec-b87d-c8df3f7dcdd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scatter_chart(df7, \"Hebbal\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f16f3ba8-4f2a-4c2f-9fb1-6ab14061396e",
   "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",
    "df8 = remove_bhk_outliers(df7)\n",
    "\n",
    "df8.shape  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "1f3b525e-cf7a-4539-9235-d8a69010aaa8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scatter_chart(df8, \"Hebbal\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "46bb4387-41e1-4491-a1e5-d0b44ef6418c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'count')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams[\"figure.figsize\"] = (20,10)\n",
    "plt.hist(df8.price_per_sqft, rwidth = 0.8)\n",
    "plt.xlabel('Price per square feet')\n",
    "plt.ylabel('count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "35accf2e-a8de-4f70-b4ea-cd8a4c623998",
   "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,
   "id": "39dfb854-00cf-4506-8a24-334a12902f8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'count')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams[\"figure.figsize\"] = (20,10)\n",
    "plt.hist(df8.bath, rwidth = 0.8)\n",
    "plt.xlabel('Number of bathroom')\n",
    "plt.ylabel('count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "35a3a637-c2d1-4806-a548-9999fb934c8a",
   "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": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df8[df8.bath>df8.bhk+2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "5ccf81d8-6e76-4449-8de6-7f2a44c955bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7251, 7)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df9 =  df8[df8.bath<df8.bhk+2]\n",
    "df9.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "0929fbf1-d732-4ff0-8a46-f6f7aca3ad0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>total_sqft</th>\n",
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       "      <td>1630.0</td>\n",
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       "      <td>194.0</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
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       "              location  total_sqft  bath  price  bhk\n",
       "0  1st Block Jayanagar      2850.0   4.0  428.0    4\n",
       "1  1st Block Jayanagar      1630.0   3.0  194.0    3\n",
       "2  1st Block Jayanagar      1875.0   2.0  235.0    3"
      ]
     },
     "execution_count": 48,
     "metadata": {},
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   ],
   "source": [
    "df10 = df9.drop(['size','price_per_sqft'], axis ='columns')\n",
    "df10.head(3)"
   ]
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       "   1st Block Jayanagar  1st Phase JP Nagar  2nd Phase Judicial Layout  \\\n",
       "0                    1                   0                          0   \n",
       "1                    1                   0                          0   \n",
       "2                    1                   0                          0   \n",
       "3                    1                   0                          0   \n",
       "4                    1                   0                          0   \n",
       "\n",
       "   2nd Stage Nagarbhavi  5th Block Hbr Layout  5th Phase JP Nagar  \\\n",
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       "[5 rows x 242 columns]"
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     },
     "execution_count": 51,
     "metadata": {},
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   ],
   "source": [
    "dummies = pd.get_dummies(df10['location'], dtype=int)\n",
    "dummies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "ecd693c2-e44b-4ca1-b03e-bb5916aff445",
   "metadata": {},
   "outputs": [
    {
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      ],
      "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",
       "3  1st Block Jayanagar      1200.0   2.0  130.0    3                    1   \n",
       "4  1st Block Jayanagar      1235.0   2.0  148.0    2                    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",
       "3                   0                          0                     0   \n",
       "4                   0                          0                     0   \n",
       "\n",
       "   5th Block Hbr Layout  ...  Vijayanagar  Vishveshwarya Layout  \\\n",
       "0                     0  ...            0                     0   \n",
       "1                     0  ...            0                     0   \n",
       "2                     0  ...            0                     0   \n",
       "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 246 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df11 = pd.concat([df10,dummies.drop('other', axis='columns')], axis = 'columns')\n",
    "df11.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "f4a2eeee-6997-4832-9fd8-57277ecfe643",
   "metadata": {},
   "outputs": [
    {
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       "      <th>2nd Stage Nagarbhavi</th>\n",
       "      <th>5th Block Hbr Layout</th>\n",
       "      <th>5th Phase JP Nagar</th>\n",
       "      <th>...</th>\n",
       "      <th>Vijayanagar</th>\n",
       "      <th>Vishveshwarya Layout</th>\n",
       "      <th>Vishwapriya Layout</th>\n",
       "      <th>Vittasandra</th>\n",
       "      <th>Whitefield</th>\n",
       "      <th>Yelachenahalli</th>\n",
       "      <th>Yelahanka</th>\n",
       "      <th>Yelahanka New Town</th>\n",
       "      <th>Yelenahalli</th>\n",
       "      <th>Yeshwanthpur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2850.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>428.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1630.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1875.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <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>4</th>\n",
       "      <td>1235.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "    </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": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df12 = df11.drop('location', axis='columns')\n",
    "df12.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "8907baf6-ccf8-40e3-a5cb-25b27a9023c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>bhk</th>\n",
       "      <th>1st Block Jayanagar</th>\n",
       "      <th>1st Phase JP Nagar</th>\n",
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       "      <th>6th Phase JP Nagar</th>\n",
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       "      <th>Vittasandra</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1200.0</td>\n",
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       "      <td>3</td>\n",
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       "<p>5 rows × 244 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_sqft  bath  bhk  1st Block Jayanagar  1st Phase JP Nagar  \\\n",
       "0      2850.0   4.0    4                    1                   0   \n",
       "1      1630.0   3.0    3                    1                   0   \n",
       "2      1875.0   2.0    3                    1                   0   \n",
       "3      1200.0   2.0    3                    1                   0   \n",
       "4      1235.0   2.0    2                    1                   0   \n",
       "\n",
       "   2nd Phase Judicial Layout  2nd Stage Nagarbhavi  5th Block Hbr Layout  \\\n",
       "0                          0                     0                     0   \n",
       "1                          0                     0                     0   \n",
       "2                          0                     0                     0   \n",
       "3                          0                     0                     0   \n",
       "4                          0                     0                     0   \n",
       "\n",
       "   5th Phase JP Nagar  6th Phase JP Nagar  ...  Vijayanagar  \\\n",
       "0                   0                   0  ...            0   \n",
       "1                   0                   0  ...            0   \n",
       "2                   0                   0  ...            0   \n",
       "3                   0                   0  ...            0   \n",
       "4                   0                   0  ...            0   \n",
       "\n",
       "   Vishveshwarya Layout  Vishwapriya Layout  Vittasandra  Whitefield  \\\n",
       "0                     0                   0            0           0   \n",
       "1                     0                   0            0           0   \n",
       "2                     0                   0            0           0   \n",
       "3                     0                   0            0           0   \n",
       "4                     0                   0            0           0   \n",
       "\n",
       "   Yelachenahalli  Yelahanka  Yelahanka New Town  Yelenahalli  Yeshwanthpur  \n",
       "0               0          0                   0            0             0  \n",
       "1               0          0                   0            0             0  \n",
       "2               0          0                   0            0             0  \n",
       "3               0          0                   0            0             0  \n",
       "4               0          0                   0            0             0  \n",
       "\n",
       "[5 rows x 244 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df12.drop('price',axis='columns')\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "29a8ca9b-f795-42fe-a853-ae7689088b43",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df12.price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "43e8c19d-30e3-4889-ab9d-f487c344c0ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2, random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "eed9189e-3ccb-46eb-8a87-3c31c35d8e4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8452277697874349"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 60,
   "id": "8c6a40a5-19f3-4ec0-9587-f146028759fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.82430186, 0.77166234, 0.85089567, 0.80837764, 0.83653286])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import ShuffleSplit\n",
    "from sklearn.model_selection import cross_val_score\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": 64,
   "id": "4698d89a-1d3e-4f5c-937b-46bfd18537b1",
   "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": 65,
   "id": "f2204ff5-dd16-4d43-8107-3989caa4c59e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\base.py:464: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "184.58430202033293"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_price('Indira Nagar',1000,3,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "26ab6a6f-559d-4a16-9f94-64a411269859",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open('predict_model.pickle','wb') as f:\n",
    "    pickle.dump(lr_clf,f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "8127c18f-d635-47fb-989d-e31278d1d9c8",
   "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,
   "id": "d0713d88-e3ea-4e0c-b076-ef303d6794c8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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