house predictor project 1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3dfec680",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\GIRIRAJ ROY\\anaconda3\\Lib\\site-packages\\pandas\\core\\arrays\\masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n",
      "  from pandas.core import (\n"
     ]
    }
   ],
   "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": 3,
   "id": "fa09027e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(r\"C:\\Users\\GIRIRAJ ROY\\Downloads\\bengaluru_house_prices.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3c9bff95",
   "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>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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "42d68ab4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13320, 9)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ab097326",
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('area_type')['area_type'].agg('count')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "65bc1dba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1=df.drop(['area_type','society','balcony','availability'],axis='columns')\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d154f3cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location       1\n",
       "size          16\n",
       "total_sqft     0\n",
       "bath          73\n",
       "price          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#data cleaning process\n",
    "df1.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f5f2451f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location      0\n",
       "size          0\n",
       "total_sqft    0\n",
       "bath          0\n",
       "price         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2=df1.dropna()\n",
    "df2.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a911f809",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13246, 5)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "75ea040d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#as we can see that in the size column sometimes its written that 4 bdrooms and smetimes tis written 3\n",
    "#4 bhk hence to solve this we will apply  function that will take into account\n",
    "#only the first string as the numeric value and store it asa a avalue for the size column "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "565eadd9",
   "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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['size'].unique()\n",
    "#gives all the unique values present in the size column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3cb677bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\GIRIRAJ ROY\\AppData\\Local\\Temp\\ipykernel_9684\\1681632236.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",
      "  df2['bhk']=df2['size'].apply(lambda x: int(x.split(' ')[0]))\n"
     ]
    }
   ],
   "source": [
    "df2['bhk']=df2['size'].apply(lambda x: int(x.split(' ')[0]))\n",
    "#splits the values in the size column on (\" \") and the first element id to be taken into consideration for the \n",
    "#vlae of the bhk column created"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d46b6952",
   "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",
       "    </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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7b9e672a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  4,  3,  6,  1,  8,  7,  5, 11,  9, 27, 10, 19, 16, 43, 14, 12,\n",
       "       13, 18], dtype=int64)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['bhk'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b823a5d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>bhk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1718</th>\n",
       "      <td>2Electronic City Phase II</td>\n",
       "      <td>27 BHK</td>\n",
       "      <td>8000</td>\n",
       "      <td>27.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4684</th>\n",
       "      <td>Munnekollal</td>\n",
       "      <td>43 Bedroom</td>\n",
       "      <td>2400</td>\n",
       "      <td>40.0</td>\n",
       "      <td>660.0</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       location        size total_sqft  bath  price  bhk\n",
       "1718  2Electronic City Phase II      27 BHK       8000  27.0  230.0   27\n",
       "4684                Munnekollal  43 Bedroom       2400  40.0  660.0   43"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[df2.bhk>20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "12c2c3af",
   "metadata": {},
   "outputs": [],
   "source": [
    "#this looks like a glitch because a 2400 sqft room cannot have 43 bedrooms "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "39c7f9a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1056', '2600', '1440', ..., '1133 - 1384', '774', '4689'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.total_sqft.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "eb81e152",
   "metadata": {},
   "outputs": [],
   "source": [
    "#converting values which are given in form of a range into single nmbers \n",
    "#like 1133-1384\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2545c2c9",
   "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": 21,
   "id": "badada60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>bhk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Yelahanka</td>\n",
       "      <td>4 BHK</td>\n",
       "      <td>2100 - 2850</td>\n",
       "      <td>4.0</td>\n",
       "      <td>186.000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>Hebbal</td>\n",
       "      <td>4 BHK</td>\n",
       "      <td>3067 - 8156</td>\n",
       "      <td>4.0</td>\n",
       "      <td>477.000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>8th Phase JP Nagar</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1042 - 1105</td>\n",
       "      <td>2.0</td>\n",
       "      <td>54.005</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>Sarjapur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1145 - 1340</td>\n",
       "      <td>2.0</td>\n",
       "      <td>43.490</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>KR Puram</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1015 - 1540</td>\n",
       "      <td>2.0</td>\n",
       "      <td>56.800</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>Kengeri</td>\n",
       "      <td>1 BHK</td>\n",
       "      <td>34.46Sq. Meter</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549</th>\n",
       "      <td>Hennur Road</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1195 - 1440</td>\n",
       "      <td>2.0</td>\n",
       "      <td>63.770</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>648</th>\n",
       "      <td>Arekere</td>\n",
       "      <td>9 Bedroom</td>\n",
       "      <td>4125Perch</td>\n",
       "      <td>9.0</td>\n",
       "      <td>265.000</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>661</th>\n",
       "      <td>Yelahanka</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1120 - 1145</td>\n",
       "      <td>2.0</td>\n",
       "      <td>48.130</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>672</th>\n",
       "      <td>Bettahalsoor</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>3090 - 5002</td>\n",
       "      <td>4.0</td>\n",
       "      <td>445.000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               location       size      total_sqft  bath    price  bhk\n",
       "30            Yelahanka      4 BHK     2100 - 2850   4.0  186.000    4\n",
       "122              Hebbal      4 BHK     3067 - 8156   4.0  477.000    4\n",
       "137  8th Phase JP Nagar      2 BHK     1042 - 1105   2.0   54.005    2\n",
       "165            Sarjapur      2 BHK     1145 - 1340   2.0   43.490    2\n",
       "188            KR Puram      2 BHK     1015 - 1540   2.0   56.800    2\n",
       "410             Kengeri      1 BHK  34.46Sq. Meter   1.0   18.500    1\n",
       "549         Hennur Road      2 BHK     1195 - 1440   2.0   63.770    2\n",
       "648             Arekere  9 Bedroom       4125Perch   9.0  265.000    9\n",
       "661           Yelahanka      2 BHK     1120 - 1145   2.0   48.130    2\n",
       "672        Bettahalsoor  4 Bedroom     3090 - 5002   4.0  445.000    4"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[-df2['total_sqft'].apply(is_float)].head(10)\n",
    "#'-' sign return false values of the function implemented inside  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1c63618d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#now we encounter anothere problem that is the total_sqft also has several datapoints where \n",
    "#34.46 sq meter this kinda values aint uniform "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "676d49eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#first we will take the datapoints on the total_sqft which is like a range \n",
    "#then we will take the average value "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "540a213c",
   "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": 25,
   "id": "45bfbcca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2400.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "convert_sqft_to_num('2400')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "66f36da4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2500.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "convert_sqft_to_num('2300-2700')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2332705a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>bhk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1056.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>39.07</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>120.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>62.00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1521.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>95.00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>51.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   location       size  total_sqft  bath   price  bhk\n",
       "0  Electronic City Phase II      2 BHK      1056.0   2.0   39.07    2\n",
       "1          Chikka Tirupathi  4 Bedroom      2600.0   5.0  120.00    4\n",
       "2               Uttarahalli      3 BHK      1440.0   2.0   62.00    3\n",
       "3        Lingadheeranahalli      3 BHK      1521.0   3.0   95.00    3\n",
       "4                  Kothanur      2 BHK      1200.0   2.0   51.00    2"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3=df2.copy()\n",
    "df3['total_sqft']=df3['total_sqft'].apply(convert_sqft_to_num)\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "49891bdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location      Kengeri\n",
       "size            1 BHK\n",
       "total_sqft        NaN\n",
       "bath              1.0\n",
       "price            18.5\n",
       "bhk                 1\n",
       "Name: 410, dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.loc[410]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4262bea0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#we are creating a new dataframe in each step of our data procesing pipeline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "65aaf4a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#our data s cleaned now , now we will go through some feature engineering techniques "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "247785cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df4=df3.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "67fc3e7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#we will create a price per sqft column "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "cfe73101",
   "metadata": {},
   "outputs": [],
   "source": [
    "df4['price_per_sqft']=df3['price']*100000/df4['total_sqft']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "8980ac54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>bhk</th>\n",
       "      <th>price_per_sqft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1056.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>39.07</td>\n",
       "      <td>2</td>\n",
       "      <td>3699.810606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>120.00</td>\n",
       "      <td>4</td>\n",
       "      <td>4615.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>62.00</td>\n",
       "      <td>3</td>\n",
       "      <td>4305.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1521.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>95.00</td>\n",
       "      <td>3</td>\n",
       "      <td>6245.890861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>51.00</td>\n",
       "      <td>2</td>\n",
       "      <td>4250.000000</td>\n",
       "    </tr>\n",
       "  </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": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "79b24b3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#locaitons is a ctegorical feature "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "25c6ca2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1304"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df4.location.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e8387305",
   "metadata": {},
   "outputs": [],
   "source": [
    "#as the types of locaions are very large hence \n",
    "#we cant use one hot encoding \n",
    "#this is cause dimensionality curse and we need to solve it "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "fd7df99a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#to find same locations \n",
    "#first we have removed the extra space in the starting and the end of the locaiotn \n",
    "\n",
    "df4.location=df4.location.apply(lambda x: x.strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "dfb347c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "location_stats=df4.groupby('location')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8bc0e1bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "location_stats=df4.groupby('location')['location'].agg('count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "cf07b54c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location\n",
       "1 Annasandrapalya                                   1\n",
       "1 Giri Nagar                                        1\n",
       "1 Immadihalli                                       1\n",
       "1 Ramamurthy Nagar                                  1\n",
       "12th cross srinivas nagar banshankari 3rd stage     1\n",
       "1A Block Koramangala                                1\n",
       "1Channasandra                                       1\n",
       "1Hanuman Nagar                                      1\n",
       "1Hoysalanagar                                       1\n",
       "1Kasavanhalli                                       1\n",
       "1st Block BEL Layout                                2\n",
       "1st Block HBR Layout                                3\n",
       "1st Block HRBR Layout                               8\n",
       "1st Block Jayanagar                                14\n",
       "1st Block Koramangala                              10\n",
       "1st Phase JP Nagar                                 25\n",
       "1st Stage Domlur                                    1\n",
       "1st Stage Indira Nagar                              5\n",
       "1st Stage Radha Krishna Layout                      1\n",
       "2 Bedroom Furnished Farm House in Kolar Road        1\n",
       "2Electronic City Phase II                           1\n",
       "2nd Block Bel Layout                                2\n",
       "2nd Block Hbr Layout                                1\n",
       "2nd Block Hrbr Layout                               7\n",
       "2nd Block Jayanagar                                 4\n",
       "2nd Block Koramangala                               2\n",
       "2nd Phase JP Nagar                                  9\n",
       "2nd Phase Judicial Layout                          11\n",
       "2nd Stage Arekere Mico Layout                       3\n",
       "2nd Stage Nagarbhavi                               24\n",
       "2nd phase jp nagar, jp nagar                        1\n",
       "3rd Block Banashankari                              5\n",
       "3rd Block HBR Layout                                1\n",
       "3rd Block Hrbr Layout                               7\n",
       "3rd Block Jayanagar                                 3\n",
       "3rd Block Koramangala                               2\n",
       "3rd Phase Iti Layout                                1\n",
       "3rd Phase JP Nagar                                  3\n",
       "3rd Stage Raja Rajeshwari Nagar                     1\n",
       "4 Bedroom Farm House in Bagalur                     1\n",
       "4th Block HBR Layout                                1\n",
       "4th Block Jayanagar                                 4\n",
       "4th Block Koramangala                               9\n",
       "4th Phase JP Nagar                                  4\n",
       "4th T block Jayanagar                               7\n",
       "5 Bedroom Farm House in Lakshmipura                 1\n",
       "5th Block Hbr Layout                               12\n",
       "5th Block Jayanagar                                 3\n",
       "5th Phase JP Nagar                                 39\n",
       "5th Stage BEML Layout                               3\n",
       "Name: location, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "location_stats[:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b7ef82d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "location_stats=df4.groupby('location')['location'].agg('count').sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f81cdbdd",
   "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": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "location_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "e2278a03",
   "metadata": {},
   "outputs": [],
   "source": [
    "#now we wll segregate locaitons like that , any locaitons havng less than 10 datapoints\n",
    "#will be treated as other location "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "88f34e7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1039"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(location_stats[location_stats<10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "f01fea5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "location_stats_less_than_10=location_stats[location_stats<=10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "1d0e48cc",
   "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": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "location_stats_less_than_10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "098aed3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1293"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df4.location.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "a525836b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "242"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.location=df4.location.apply(lambda x:'other' if x in location_stats_less_than_10 else x)\n",
    "len(df4.location.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "0f491297",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>bhk</th>\n",
       "      <th>price_per_sqft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1056.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>39.07</td>\n",
       "      <td>2</td>\n",
       "      <td>3699.810606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>120.00</td>\n",
       "      <td>4</td>\n",
       "      <td>4615.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>62.00</td>\n",
       "      <td>3</td>\n",
       "      <td>4305.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1521.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>95.00</td>\n",
       "      <td>3</td>\n",
       "      <td>6245.890861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>51.00</td>\n",
       "      <td>2</td>\n",
       "      <td>4250.000000</td>\n",
       "    </tr>\n",
       "  </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": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "d2971b35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>bhk</th>\n",
       "      <th>price_per_sqft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1056.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>39.07</td>\n",
       "      <td>2</td>\n",
       "      <td>3699.810606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>120.00</td>\n",
       "      <td>4</td>\n",
       "      <td>4615.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>62.00</td>\n",
       "      <td>3</td>\n",
       "      <td>4305.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1521.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>95.00</td>\n",
       "      <td>3</td>\n",
       "      <td>6245.890861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>51.00</td>\n",
       "      <td>2</td>\n",
       "      <td>4250.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13315</th>\n",
       "      <td>Whitefield</td>\n",
       "      <td>5 Bedroom</td>\n",
       "      <td>3453.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>231.00</td>\n",
       "      <td>5</td>\n",
       "      <td>6689.834926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13316</th>\n",
       "      <td>other</td>\n",
       "      <td>4 BHK</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>400.00</td>\n",
       "      <td>4</td>\n",
       "      <td>11111.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13317</th>\n",
       "      <td>Raja Rajeshwari Nagar</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1141.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>60.00</td>\n",
       "      <td>2</td>\n",
       "      <td>5258.545136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13318</th>\n",
       "      <td>Padmanabhanagar</td>\n",
       "      <td>4 BHK</td>\n",
       "      <td>4689.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>488.00</td>\n",
       "      <td>4</td>\n",
       "      <td>10407.336319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13319</th>\n",
       "      <td>Doddathoguru</td>\n",
       "      <td>1 BHK</td>\n",
       "      <td>550.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.00</td>\n",
       "      <td>1</td>\n",
       "      <td>3090.909091</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13246 rows × 7 columns</p>\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",
       "13315                Whitefield  5 Bedroom      3453.0   4.0  231.00    5   \n",
       "13316                     other      4 BHK      3600.0   5.0  400.00    4   \n",
       "13317     Raja Rajeshwari Nagar      2 BHK      1141.0   2.0   60.00    2   \n",
       "13318           Padmanabhanagar      4 BHK      4689.0   4.0  488.00    4   \n",
       "13319              Doddathoguru      1 BHK       550.0   1.0   17.00    1   \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",
       "...               ...  \n",
       "13315     6689.834926  \n",
       "13316    11111.111111  \n",
       "13317     5258.545136  \n",
       "13318    10407.336319  \n",
       "13319     3090.909091  \n",
       "\n",
       "[13246 rows x 7 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "fc429b81",
   "metadata": {},
   "outputs": [],
   "source": [
    "#we will ow do outliers detection and removal \n",
    "#outliers are datapoints which are data errors we need to modify or remove them "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "eee050b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#what is the typical sqft per bedroom?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "004c14a5",
   "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": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4[df4.total_sqft/df4.bhk<300].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "ac6ef4f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#a 600 sqft home cannot have a 8 bedrooms hence we have to remonve this datapioints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "9fba067e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13246, 7)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "b6240f0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12502, 7)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5=df4[~(df4.total_sqft/df4.bhk<300)]\n",
    "df5.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "24c90cc5",
   "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": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.price_per_sqft.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "12ece969",
   "metadata": {},
   "outputs": [],
   "source": [
    "#we see that min is 267 per sqft , lathough we know that getting a property in bangalore with 267 sqft is very unlikely "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "ec01e58d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#also the max is very high , hence we will remove the extreme cases "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "9197ef1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#we will remove some the price per sqft per location "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "3e9f34fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#we will calculate mean and standard deviation and then filter out which has more than one standard deviation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "f1c23d57",
   "metadata": {},
   "outputs": [],
   "source": [
    "#our datapoints should be lying between mean and one standard deviation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "c34a26b4",
   "metadata": {},
   "outputs": [],
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "9fb0f543",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10241, 7)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6=remove_pps_outliers(df5)\n",
    "df6.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "f425638a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#now there are some datapoints where features are all same but the price is wayy too different \n",
    "#we need to isualise and correct them using scatterplot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "7d6f25fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 3000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_scatter_chart(df,location):\n",
    "    bhk2=df[(df.location==location)&(df.bhk==2)]\n",
    "    bhk3=df[(df.location==location)&(df.bhk==3)]\n",
    "    matplotlib.rcParams['figure.figsize']=(30,15)\n",
    "    plt.scatter(bhk2.total_sqft,bhk2.price_per_sqft,color='blue',label='2 bhk' ,s=50)\n",
    "    plt.scatter(bhk3.total_sqft,bhk3.price_per_sqft,marker='D',color='green',label='3 bhk' ,s=50)\n",
    "    plt.xlabel(\"total square feet area\")\n",
    "    plt.ylabel(\"price er square feet area\")\n",
    "    plt.legend()\n",
    "plot_scatter_chart(df6,'Rajaji Nagar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "7059d5ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_scatter_chart(df,location):\n",
    "    bhk2=df[(df.location==location)&(df.bhk==2)]\n",
    "    bhk3=df[(df.location==location)&(df.bhk==3)]\n",
    "    matplotlib.rcParams['figure.figsize']=(20,15)\n",
    "    plt.scatter(bhk2.total_sqft,bhk2.price,color='blue',label='2 bhk' ,s=50)\n",
    "    plt.scatter(bhk3.total_sqft,bhk3.price,marker='D',color='green',label='3 bhk' ,s=50)\n",
    "    plt.xlabel(\"total square feet area\")\n",
    "    plt.ylabel(\"price er square feet area\")\n",
    "    plt.legend()\n",
    "plot_scatter_chart(df6,'Rajaji Nagar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "265daab9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#we see that for approx 1700 square feet area there are too many prices \n",
    "#also in tha same place price of to bedroom price is higher than threee"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "16d388b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_scatter_chart(df,location):\n",
    "    bhk2=df[(df.location==location)&(df.bhk==2)]\n",
    "    bhk3=df[(df.location==location)&(df.bhk==3)]\n",
    "    matplotlib.rcParams['figure.figsize']=(20,15)\n",
    "    plt.scatter(bhk2.total_sqft,bhk2.price,color='blue',label='2 bhk' ,s=50)\n",
    "    plt.scatter(bhk3.total_sqft,bhk3.price,marker='D',color='green',label='3 bhk' ,s=50)\n",
    "    plt.xlabel(\"total square feet area\")\n",
    "    plt.ylabel(\"price er square feet area\")\n",
    "    plt.legend()\n",
    "plot_scatter_chart(df6,'Hebbal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "8464307f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#here also there are places where price of to bedrooms is hegher htan three"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "230e8627",
   "metadata": {},
   "outputs": [],
   "source": [
    "#we should also remove properties where for same location , the price of(for example ) 3 bedroom apartment\n",
    "#is less than 2 bedroom apartment (with same sqft area) what we will do is for a given location we will build a dictionary \n",
    "#for stats per bhk ie\n",
    "#for ex if price of 2 bhk is more than a mean value it wont be considered "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "7360483b",
   "metadata": {},
   "outputs": [],
   "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",
    "#}\n",
    "#now we can reomve 2 bhk apartments whose preice_per_sqft is less than mean price_per_sqft of 1 bhk apartments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "ffa64f5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7329, 7)"
      ]
     },
     "execution_count": 74,
     "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",
    "df7 = remove_bhk_outliers(df6)\n",
    "# df8 = df7.copy()\n",
    "df7.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "9521dda5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_scatter_chart(df,location):\n",
    "    bhk2=df[(df.location==location)&(df.bhk==2)]\n",
    "    bhk3=df[(df.location==location)&(df.bhk==3)]\n",
    "    matplotlib.rcParams['figure.figsize']=(20,15)\n",
    "    plt.scatter(bhk2.total_sqft,bhk2.price,color='blue',label='2 bhk' ,s=50)\n",
    "    plt.scatter(bhk3.total_sqft,bhk3.price,marker='D',color='green',label='3 bhk' ,s=50)\n",
    "    plt.xlabel(\"total square feet area\")\n",
    "    plt.ylabel(\"price er square feet area\")\n",
    "    plt.legend()\n",
    "plot_scatter_chart(df7,'Hebbal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "95048c67",
   "metadata": {},
   "outputs": [],
   "source": [
    "#still there are some abnormalities but its fine "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "ea740177",
   "metadata": {},
   "outputs": [],
   "source": [
    "#how many properties in persqft area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "57e8e1ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bN25Mknzzm9/Mpz71qXzhC1/I0qVLc8kll+TTn/50rr/++iTJ/fffnyS58847s2LFitxyyy0vay0AALC78dVkAADAC7r//vtz00035YQTTqg9tnHjxtxwww3Zf//9n/M5v/3tb/ODH/wgc+bMydixY5Mkr3vd62rbr7766rzlLW/JJZdcUnvsO9/5Tpqbm/Pb3/42hxxyyIuu6+mnn87nPve59OvXL29729ty0UUXZcqUKbWrcg4++OB89atfzTHHHJOrr746r3rVq5Ikxx9/fC666KLn3W+vXr1y3XXXZerUqfnGN76Rt7zlLTnmmGPy3ve+N4cffniSv8WGpUuXZvny5TnggAOSJJdcckkmTpz4ouv+e6997Wu7rGXatGlpbW3ND3/4w4waNar2+Bve8IZcfvnltfuf+cxnXvbfb+t6t/rDH/6QH//4x9lrr73yrW99K3V1dUn+dvXLa17zmtxzzz0ZN25c/u3f/i1XXHFFTj311CR/uwLm17/+da655pqcccYZtffCfvvtl8bGxu36OwAAwO5MiAEAALbxn//5n9l3333zzDPPZNOmTTnllFPyta99rbZ96NChzxthkr99Bdfee++dY4455jm3L1q0KHffffdz/j7J//zP/7ykkLB+/focfPDB+eEPf5iGhoYsWrQov/vd73LjjTfWZqvVarZs2ZJly5bl0EMPTZIcddRRL3zw+dtvxJx00km59957s2DBgrS2tubyyy/Pt771rZx55plZunRpDjzwwC5RY8yYMS+632fbvHlzLrvsstx8883505/+lM7OznR2dqZv375d5p695lfy97v33nvTr1+/2v0BAwbU/nZ//3jytyt2/ud//idPPfVUHn/88Zx11lldvs7tmWeeeVm/zQMAAHsSIQYAANjGcccdl6uvvjq9e/dOU1NTevfu3WX7s0PBs239yrDns2XLlpx88sn54he/uM22IUOGvOBz77333vTv3z/7779/lx+d37JlS84555ycf/752zznwAMPfMlr3+pVr3pVTjzxxJx44on5zGc+kw9+8IP57Gc/mzPPPDPVanWb+a1Xkmy1115/+ybov5/9+693S5IrrrgiV111Vb785S9n5MiR6du3b6ZPn177OrDnW/Mr+fsNGzYsr3nNa7bZ35FHHtklYm21//77569//WuSv3092d9fqZMke++99wu+HgAA7OmEGAAAYBt9+/bNG97whpf9/JEjR2bLli2ZO3du7avJ/t5b3vKW/OhHP8pBBx2UXr227z9LniskbN3nI4888orW/UIOO+yw3HrrrbV///GPf8yTTz6ZpqamJMmCBQu6zG+9YmjFihUZMGBAkmzzezr33ntvTjnllPzTP/1Tkr8Fkccee6x29c7zeSV/v+fb380335yGhoYucWurSqWS1772tfn973+f97///c+5jz59+iT521U+AADA/2+vnl4AAACw+znooINyxhln5AMf+EBuvfXWLFu2LPfcc09+8IMfJEnOPffc/PnPf8773ve+3H///fn973+fO+64Ix/4wAde9v/I//jHP54FCxbk3HPPzeLFi/PYY4/lJz/5SaZNm7Zd+3n66adz/PHH53vf+14eeuihLFu2LD/84Q9z+eWX55RTTkmSjB07NsOHD88///M/51e/+lXuvffefOpTn+qynze84Q1pbm7OjBkz8tvf/ja33XZbrrjiim1m5syZk/nz52fp0qU555xz0tbW9qJr7O6/3/vf//4MGjQop5xySu69994sW7Ysc+fOzUc/+tE88cQTSZIZM2bk0ksvzVe+8pX89re/zcMPP5xrr702V155ZZKkoaEh++yzT1pbW7Ny5cq0t7dv9zoAAGB3JMQAAABFXH311fk//+f/5CMf+Uj+4R/+IVOnTs369euTJE1NTfn5z3+ezZs3Z/z48RkxYkQ++tGPplKp1L7Sa3sdfvjhmTt3bh577LG84x3vyJvf/OZ8+tOfftGv6nq2fffdN6NGjcpVV12Vd77znRkxYkQ+/elPZ+rUqZk5c2aSv33t2OzZs9PZ2Zm3ve1t+eAHP5gvfOELXfbTu3fvfP/7389vfvObHHHEEfniF7+Yz3/+811mPv3pT+ctb3lLxo8fn2OPPTaNjY1517ve9aJr7O6/36tf/er87Gc/y4EHHphTTz01hx56aD7wgQ9kw4YNtStkPvjBD+Zb3/pWrrvuuowcOTLHHHNMrrvuugwbNixJ0qtXr3z1q1/NNddck6amplq0AgCAPV1d9bm+3BgAAIDtVldXl9mzZ7+kmAIAAOwZXBEDAAAAAABQiBADAAAAAABQSK+eXgAAAMDuwjc/AwAAz+aKGAAAAAAAgEKEGAAAAAAAgEKEGAAAAAAAgEKEGAAAAAAAgEKEGAAAAAAAgEKEGAAAAAAAgEKEGAAAAAAAgEKEGAAAAAAAgEKEGAAAAAAAgEL+P8wPdtaKX/vXAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "matplotlib.rcParams[\"figure.figsize\"] = (20,10)\n",
    "plt.hist(df7.price_per_sqft,rwidth=0.8)\n",
    "plt.xlabel(\"Price Per Square Feet\")\n",
    "plt.ylabel(\"Count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "49bb7cc5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4.,  3.,  2.,  5.,  8.,  1.,  6.,  7.,  9., 12., 16., 13.])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7.bath.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "b2fdcf8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#some of this has >10 bathrooms\n",
    "#no. of bathrooms >> no of bedroom is a anomaly \n",
    "#so remove it "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "bdd97c27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df7.bath,rwidth=0.8)\n",
    "plt.xlabel(\"Number of bathrooms\")\n",
    "plt.ylabel(\"Count\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "e376a1ab",
   "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": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7[df7.bath>df7.bhk+2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "7e880ae0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7251, 7)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df8= df7[df7.bath<df7.bhk+2]\n",
    "df8.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "af47efb4",
   "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",
       "  </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"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#now we will go to machine larning training \n",
    "df9 = df8.drop(['size','price_per_sqft'],axis='columns')\n",
    "df9.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "e5f1c379",
   "metadata": {},
   "outputs": [],
   "source": [
    "#NOW MY DATA IS SET OF BEST MODEL BUILDING "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "da4df455",
   "metadata": {},
   "outputs": [],
   "source": [
    "#we will convert text column to one hot encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "2581c224",
   "metadata": {},
   "outputs": [],
   "source": [
    "dummies=pd.get_dummies(df9.location)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "46a89bb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df10=pd.concat([df9,dummies],axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "c00aff68",
   "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>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>bhk</th>\n",
       "      <th>1st Block Jayanagar</th>\n",
       "      <th>1st Phase JP Nagar</th>\n",
       "      <th>2nd Phase Judicial Layout</th>\n",
       "      <th>2nd Stage Nagarbhavi</th>\n",
       "      <th>5th Block Hbr Layout</th>\n",
       "      <th>...</th>\n",
       "      <th>Vishveshwarya Layout</th>\n",
       "      <th>Vishwapriya Layout</th>\n",
       "      <th>Vittasandra</th>\n",
       "      <th>Whitefield</th>\n",
       "      <th>Yelachenahalli</th>\n",
       "      <th>Yelahanka</th>\n",
       "      <th>Yelahanka New Town</th>\n",
       "      <th>Yelenahalli</th>\n",
       "      <th>Yeshwanthpur</th>\n",
       "      <th>other</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>2850.0</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>2750.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>413.0</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>2450.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>368.0</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1st Phase JP Nagar</td>\n",
       "      <td>1875.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>167.0</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1st Phase JP Nagar</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1st Phase JP Nagar</td>\n",
       "      <td>2065.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1st Phase JP Nagar</td>\n",
       "      <td>2059.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1st Phase JP Nagar</td>\n",
       "      <td>1394.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1st Phase JP Nagar</td>\n",
       "      <td>1077.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1st Phase JP Nagar</td>\n",
       "      <td>1566.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1st Phase JP Nagar</td>\n",
       "      <td>840.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1st Phase JP Nagar</td>\n",
       "      <td>1590.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1st Phase JP Nagar</td>\n",
       "      <td>2180.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1st Phase JP Nagar</td>\n",
       "      <td>1180.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>88.5</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1st Phase JP Nagar</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1st Phase JP Nagar</td>\n",
       "      <td>1394.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 247 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               location  total_sqft  bath  price  bhk  1st Block Jayanagar  \\\n",
       "0   1st Block Jayanagar      2850.0   4.0  428.0    4                 True   \n",
       "1   1st Block Jayanagar      1630.0   3.0  194.0    3                 True   \n",
       "2   1st Block Jayanagar      1875.0   2.0  235.0    3                 True   \n",
       "3   1st Block Jayanagar      1200.0   2.0  130.0    3                 True   \n",
       "4   1st Block Jayanagar      1235.0   2.0  148.0    2                 True   \n",
       "5   1st Block Jayanagar      2750.0   4.0  413.0    4                 True   \n",
       "6   1st Block Jayanagar      2450.0   4.0  368.0    4                 True   \n",
       "8    1st Phase JP Nagar      1875.0   3.0  167.0    3                False   \n",
       "9    1st Phase JP Nagar      1500.0   5.0   85.0    5                False   \n",
       "10   1st Phase JP Nagar      2065.0   4.0  210.0    3                False   \n",
       "12   1st Phase JP Nagar      2059.0   3.0  225.0    3                False   \n",
       "13   1st Phase JP Nagar      1394.0   2.0  100.0    2                False   \n",
       "14   1st Phase JP Nagar      1077.0   2.0   93.0    2                False   \n",
       "15   1st Phase JP Nagar      1566.0   2.0  180.0    2                False   \n",
       "16   1st Phase JP Nagar       840.0   2.0   50.0    1                False   \n",
       "17   1st Phase JP Nagar      1590.0   3.0  131.0    3                False   \n",
       "18   1st Phase JP Nagar      2180.0   3.0  210.0    3                False   \n",
       "19   1st Phase JP Nagar      1180.0   2.0   88.5    2                False   \n",
       "20   1st Phase JP Nagar      1200.0   2.0   86.0    2                False   \n",
       "21   1st Phase JP Nagar      1394.0   2.0   85.0    2                False   \n",
       "\n",
       "    1st Phase JP Nagar  2nd Phase Judicial Layout  2nd Stage Nagarbhavi  \\\n",
       "0                False                      False                 False   \n",
       "1                False                      False                 False   \n",
       "2                False                      False                 False   \n",
       "3                False                      False                 False   \n",
       "4                False                      False                 False   \n",
       "5                False                      False                 False   \n",
       "6                False                      False                 False   \n",
       "8                 True                      False                 False   \n",
       "9                 True                      False                 False   \n",
       "10                True                      False                 False   \n",
       "12                True                      False                 False   \n",
       "13                True                      False                 False   \n",
       "14                True                      False                 False   \n",
       "15                True                      False                 False   \n",
       "16                True                      False                 False   \n",
       "17                True                      False                 False   \n",
       "18                True                      False                 False   \n",
       "19                True                      False                 False   \n",
       "20                True                      False                 False   \n",
       "21                True                      False                 False   \n",
       "\n",
       "    5th Block Hbr Layout  ...  Vishveshwarya Layout  Vishwapriya Layout  \\\n",
       "0                  False  ...                 False               False   \n",
       "1                  False  ...                 False               False   \n",
       "2                  False  ...                 False               False   \n",
       "3                  False  ...                 False               False   \n",
       "4                  False  ...                 False               False   \n",
       "5                  False  ...                 False               False   \n",
       "6                  False  ...                 False               False   \n",
       "8                  False  ...                 False               False   \n",
       "9                  False  ...                 False               False   \n",
       "10                 False  ...                 False               False   \n",
       "12                 False  ...                 False               False   \n",
       "13                 False  ...                 False               False   \n",
       "14                 False  ...                 False               False   \n",
       "15                 False  ...                 False               False   \n",
       "16                 False  ...                 False               False   \n",
       "17                 False  ...                 False               False   \n",
       "18                 False  ...                 False               False   \n",
       "19                 False  ...                 False               False   \n",
       "20                 False  ...                 False               False   \n",
       "21                 False  ...                 False               False   \n",
       "\n",
       "    Vittasandra  Whitefield  Yelachenahalli  Yelahanka  Yelahanka New Town  \\\n",
       "0         False       False           False      False               False   \n",
       "1         False       False           False      False               False   \n",
       "2         False       False           False      False               False   \n",
       "3         False       False           False      False               False   \n",
       "4         False       False           False      False               False   \n",
       "5         False       False           False      False               False   \n",
       "6         False       False           False      False               False   \n",
       "8         False       False           False      False               False   \n",
       "9         False       False           False      False               False   \n",
       "10        False       False           False      False               False   \n",
       "12        False       False           False      False               False   \n",
       "13        False       False           False      False               False   \n",
       "14        False       False           False      False               False   \n",
       "15        False       False           False      False               False   \n",
       "16        False       False           False      False               False   \n",
       "17        False       False           False      False               False   \n",
       "18        False       False           False      False               False   \n",
       "19        False       False           False      False               False   \n",
       "20        False       False           False      False               False   \n",
       "21        False       False           False      False               False   \n",
       "\n",
       "    Yelenahalli  Yeshwanthpur  other  \n",
       "0         False         False  False  \n",
       "1         False         False  False  \n",
       "2         False         False  False  \n",
       "3         False         False  False  \n",
       "4         False         False  False  \n",
       "5         False         False  False  \n",
       "6         False         False  False  \n",
       "8         False         False  False  \n",
       "9         False         False  False  \n",
       "10        False         False  False  \n",
       "12        False         False  False  \n",
       "13        False         False  False  \n",
       "14        False         False  False  \n",
       "15        False         False  False  \n",
       "16        False         False  False  \n",
       "17        False         False  False  \n",
       "18        False         False  False  \n",
       "19        False         False  False  \n",
       "20        False         False  False  \n",
       "21        False         False  False  \n",
       "\n",
       "[20 rows x 247 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df10[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "2b1efd56",
   "metadata": {},
   "outputs": [
    {
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       "      <td>1st Block Jayanagar</td>\n",
       "      <td>1630.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>194.0</td>\n",
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       "      <td>1875.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>235.0</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <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>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>1235.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>148.0</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "<p>5 rows × 246 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              location  total_sqft  bath  price  bhk  1st Block Jayanagar  \\\n",
       "0  1st Block Jayanagar      2850.0   4.0  428.0    4                 True   \n",
       "1  1st Block Jayanagar      1630.0   3.0  194.0    3                 True   \n",
       "2  1st Block Jayanagar      1875.0   2.0  235.0    3                 True   \n",
       "3  1st Block Jayanagar      1200.0   2.0  130.0    3                 True   \n",
       "4  1st Block Jayanagar      1235.0   2.0  148.0    2                 True   \n",
       "\n",
       "   1st Phase JP Nagar  2nd Phase Judicial Layout  2nd Stage Nagarbhavi  \\\n",
       "0               False                      False                 False   \n",
       "1               False                      False                 False   \n",
       "2               False                      False                 False   \n",
       "3               False                      False                 False   \n",
       "4               False                      False                 False   \n",
       "\n",
       "   5th Block Hbr Layout  ...  Vijayanagar  Vishveshwarya Layout  \\\n",
       "0                 False  ...        False                 False   \n",
       "1                 False  ...        False                 False   \n",
       "2                 False  ...        False                 False   \n",
       "3                 False  ...        False                 False   \n",
       "4                 False  ...        False                 False   \n",
       "\n",
       "   Vishwapriya Layout  Vittasandra  Whitefield  Yelachenahalli  Yelahanka  \\\n",
       "0               False        False       False           False      False   \n",
       "1               False        False       False           False      False   \n",
       "2               False        False       False           False      False   \n",
       "3               False        False       False           False      False   \n",
       "4               False        False       False           False      False   \n",
       "\n",
       "   Yelahanka New Town  Yelenahalli  Yeshwanthpur  \n",
       "0               False        False         False  \n",
       "1               False        False         False  \n",
       "2               False        False         False  \n",
       "3               False        False         False  \n",
       "4               False        False         False  \n",
       "\n",
       "[5 rows x 246 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df10=pd.concat([df9,dummies.drop('other',axis='columns')],axis='columns')\n",
    "df10.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "f440554e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df11=df10.drop('location',axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "e199faf1",
   "metadata": {},
   "outputs": [
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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                 True               False   \n",
       "1      1630.0   3.0  194.0    3                 True               False   \n",
       "2      1875.0   2.0  235.0    3                 True               False   \n",
       "3      1200.0   2.0  130.0    3                 True               False   \n",
       "4      1235.0   2.0  148.0    2                 True               False   \n",
       "\n",
       "   2nd Phase Judicial Layout  2nd Stage Nagarbhavi  5th Block Hbr Layout  \\\n",
       "0                      False                 False                 False   \n",
       "1                      False                 False                 False   \n",
       "2                      False                 False                 False   \n",
       "3                      False                 False                 False   \n",
       "4                      False                 False                 False   \n",
       "\n",
       "   5th Phase JP Nagar  ...  Vijayanagar  Vishveshwarya Layout  \\\n",
       "0               False  ...        False                 False   \n",
       "1               False  ...        False                 False   \n",
       "2               False  ...        False                 False   \n",
       "3               False  ...        False                 False   \n",
       "4               False  ...        False                 False   \n",
       "\n",
       "   Vishwapriya Layout  Vittasandra  Whitefield  Yelachenahalli  Yelahanka  \\\n",
       "0               False        False       False           False      False   \n",
       "1               False        False       False           False      False   \n",
       "2               False        False       False           False      False   \n",
       "3               False        False       False           False      False   \n",
       "4               False        False       False           False      False   \n",
       "\n",
       "   Yelahanka New Town  Yelenahalli  Yeshwanthpur  \n",
       "0               False        False         False  \n",
       "1               False        False         False  \n",
       "2               False        False         False  \n",
       "3               False        False         False  \n",
       "4               False        False         False  \n",
       "\n",
       "[5 rows x 245 columns]"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df11.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "d8cd2090",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7251, 245)"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df11.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "75bee772",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df11.drop('price',axis='columns')\n",
    "x.head()\n",
    "z=x.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "ec53d0a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "print(type(z))\n",
    "print(type(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "bfbd4319",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df11.price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "c3553179",
   "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": 147,
   "id": "9a997410",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "print(type(z))\n",
    "print(type(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "6dde923f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "8ebd8cc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.845227769787429"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_clf=LinearRegression()\n",
    "lr_clf.fit(x_train,y_train)\n",
    "lr_clf.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "9ac7f130",
   "metadata": {},
   "outputs": [],
   "source": [
    "#we will onw use k fold cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "1005d122",
   "metadata": {},
   "outputs": [],
   "source": [
    "#we will import shuffle split so that each disribution will b euniform "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "c42925f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.84522777, 0.80004632, 0.88682995, 0.842265  , 0.86250633])"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import ShuffleSplit\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "cv=ShuffleSplit(n_splits=5,test_size=0.2,random_state=10)\n",
    "\n",
    "cross_val_score(LinearRegression(),x,y,cv=cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "a7e1449c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "print(type(z))\n",
    "print(type(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "93b31e8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#what will give me a best score \n",
    "#we will do grid search cv\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "07eaabd3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\GIRIRAJ ROY\\anaconda3\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:540: FitFailedWarning: \n",
      "10 fits failed out of a total of 20.\n",
      "The score on these train-test partitions for these parameters will be set to nan.\n",
      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "10 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"C:\\Users\\GIRIRAJ ROY\\anaconda3\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 888, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"C:\\Users\\GIRIRAJ ROY\\anaconda3\\Lib\\site-packages\\sklearn\\base.py\", line 1466, in wrapper\n",
      "    estimator._validate_params()\n",
      "  File \"C:\\Users\\GIRIRAJ ROY\\anaconda3\\Lib\\site-packages\\sklearn\\base.py\", line 666, in _validate_params\n",
      "    validate_parameter_constraints(\n",
      "  File \"C:\\Users\\GIRIRAJ ROY\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\_param_validation.py\", line 95, in validate_parameter_constraints\n",
      "    raise InvalidParameterError(\n",
      "sklearn.utils._param_validation.InvalidParameterError: The 'criterion' parameter of DecisionTreeRegressor must be a str among {'squared_error', 'absolute_error', 'friedman_mse', 'poisson'}. Got 'mse' instead.\n",
      "\n",
      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
      "C:\\Users\\GIRIRAJ ROY\\anaconda3\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:1102: UserWarning: One or more of the test scores are non-finite: [       nan        nan 0.72473061 0.69640971]\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "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>model</th>\n",
       "      <th>best_score</th>\n",
       "      <th>best_params</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>linear_regression</td>\n",
       "      <td>0.819001</td>\n",
       "      <td>{'fit_intercept': False, 'positive': False}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>lasso</td>\n",
       "      <td>0.687430</td>\n",
       "      <td>{'alpha': 2, 'selection': 'random'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>decision_tree</td>\n",
       "      <td>0.724731</td>\n",
       "      <td>{'criterion': 'friedman_mse', 'splitter': 'best'}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               model  best_score  \\\n",
       "0  linear_regression    0.819001   \n",
       "1              lasso    0.687430   \n",
       "2      decision_tree    0.724731   \n",
       "\n",
       "                                         best_params  \n",
       "0        {'fit_intercept': False, 'positive': False}  \n",
       "1                {'alpha': 2, 'selection': 'random'}  \n",
       "2  {'criterion': 'friedman_mse', 'splitter': 'best'}  "
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "def find_best_model_using_gridsearchcv(x,y):\n",
    "    algos = {\n",
    "        'linear_regression' : {\n",
    "            'model': LinearRegression(),\n",
    "            'params': {\n",
    "                'fit_intercept': [True, False],\n",
    "                'positive': [True, False]\n",
    "            }\n",
    "        },\n",
    "        'lasso': {\n",
    "            'model': Lasso(),\n",
    "            'params': {\n",
    "                'alpha': [1,2],\n",
    "                'selection': ['random', 'cyclic']\n",
    "            }\n",
    "        },\n",
    "        'decision_tree': {\n",
    "            'model': DecisionTreeRegressor(),\n",
    "            'params': {\n",
    "                'criterion' : ['mse','friedman_mse'],\n",
    "                'splitter': ['best','random']\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "    scores = []\n",
    "    cv = ShuffleSplit(n_splits=5, test_size=0.2, random_state=0)\n",
    "    for algo_name, config in algos.items():\n",
    "        gs =  GridSearchCV(config['model'], config['params'], cv=cv, return_train_score=False)\n",
    "        gs.fit(x,y)\n",
    "        scores.append({\n",
    "            'model': algo_name,\n",
    "            'best_score': gs.best_score_,\n",
    "            'best_params': gs.best_params_\n",
    "        })\n",
    "\n",
    "    return pd.DataFrame(scores,columns=['model','best_score','best_params'])\n",
    "\n",
    "find_best_model_using_gridsearchcv(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "c9366a68",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "print(type(z))\n",
    "print(type(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "ef5377d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['total_sqft', 'bath', 'bhk', '1st Block Jayanagar',\n",
       "       '1st Phase JP Nagar', '2nd Phase Judicial Layout',\n",
       "       '2nd Stage Nagarbhavi', '5th Block Hbr Layout', '5th Phase JP Nagar',\n",
       "       '6th Phase JP Nagar',\n",
       "       ...\n",
       "       'Vijayanagar', 'Vishveshwarya Layout', 'Vishwapriya Layout',\n",
       "       'Vittasandra', 'Whitefield', 'Yelachenahalli', 'Yelahanka',\n",
       "       'Yelahanka New Town', 'Yelenahalli', 'Yeshwanthpur'],\n",
       "      dtype='object', length=244)"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "739e2dfe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "print(type(z))\n",
    "print(type(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "83b5dc62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(x.columns=='2nd Phase Judicial Layout')[0][0]\n",
    "#shows the index of 2nd phase judicial layout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "67669633",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "print(type(z))\n",
    "print(type(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "2d7439cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "z=x.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "fa250e48",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def predict_price(location,sqft,bath,bhk):    \n",
    "    global z\n",
    "    global x\n",
    "    loc_index = np.where((x.columns)==location)[0][0]\n",
    "\n",
    "    t = np.zeros(len(x.columns))\n",
    "    t[0] = sqft\n",
    "    t[1] = bath\n",
    "    t[2] = bhk\n",
    "    if loc_index >= 0:\n",
    "        t[loc_index] = 1\n",
    "   \n",
    "    return lr_clf.predict([t])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "10b82e32",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\GIRIRAJ ROY\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "184.58430202033549"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_price('Indira Nagar',1000, 3, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "1b785820",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\GIRIRAJ ROY\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "184.58430202033549"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_price('Indira Nagar',1000, 3, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "0f85cd78",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\GIRIRAJ ROY\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "184.58430202033549"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_price('Indira Nagar',1000, 3, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "8b399e22",
   "metadata": {},
   "outputs": [],
   "source": [
    "#impoerting model to pickle file\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "f3d83355",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open('bangalore_house_prices_model.pickle','wb') as f:\n",
    "    pickle.dump(lr_clf,f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "d8f4eac4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json \n",
    "columns={\n",
    "    'data_columns':[col.lower() for col in x.columns]\n",
    "}\n",
    "with open(\"columns.json\",\"w\")as f:\n",
    "    f.write(json.dumps(columns))"
   ]
  },
  {
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