DataCleaning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4fe77048",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "import matplotlib \n",
    "matplotlib.rcParams[\"figure.figsize\"] = (20,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e5c85a64",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"Bengaluru_House_Data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "85d97d3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13320, 9) \n",
      "\n",
      "<bound method NDFrame.describe of                   area_type   availability                  location  \\\n",
      "0      Super built-up  Area         19-Dec  Electronic City Phase II   \n",
      "1                Plot  Area  Ready To Move          Chikka Tirupathi   \n",
      "2            Built-up  Area  Ready To Move               Uttarahalli   \n",
      "3      Super built-up  Area  Ready To Move        Lingadheeranahalli   \n",
      "4      Super built-up  Area  Ready To Move                  Kothanur   \n",
      "...                     ...            ...                       ...   \n",
      "13315        Built-up  Area  Ready To Move                Whitefield   \n",
      "13316  Super built-up  Area  Ready To Move             Richards Town   \n",
      "13317        Built-up  Area  Ready To Move     Raja Rajeshwari Nagar   \n",
      "13318  Super built-up  Area         18-Jun           Padmanabhanagar   \n",
      "13319  Super built-up  Area  Ready To Move              Doddathoguru   \n",
      "\n",
      "            size  society total_sqft  bath  balcony   price  \n",
      "0          2 BHK  Coomee        1056   2.0      1.0   39.07  \n",
      "1      4 Bedroom  Theanmp       2600   5.0      3.0  120.00  \n",
      "2          3 BHK      NaN       1440   2.0      3.0   62.00  \n",
      "3          3 BHK  Soiewre       1521   3.0      1.0   95.00  \n",
      "4          2 BHK      NaN       1200   2.0      1.0   51.00  \n",
      "...          ...      ...        ...   ...      ...     ...  \n",
      "13315  5 Bedroom  ArsiaEx       3453   4.0      0.0  231.00  \n",
      "13316      4 BHK      NaN       3600   5.0      NaN  400.00  \n",
      "13317      2 BHK  Mahla T       1141   2.0      1.0   60.00  \n",
      "13318      4 BHK  SollyCl       4689   4.0      1.0  488.00  \n",
      "13319      1 BHK      NaN        550   1.0      1.0   17.00  \n",
      "\n",
      "[13320 rows x 9 columns]> \n",
      "\n",
      "Index(['area_type', 'availability', 'location', 'size', 'society',\n",
      "       'total_sqft', 'bath', 'balcony', 'price'],\n",
      "      dtype='object') \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area_type</th>\n",
       "      <th>availability</th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>society</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>balcony</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>19-Dec</td>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>Coomee</td>\n",
       "      <td>1056</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Plot  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>Theanmp</td>\n",
       "      <td>2600</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>120.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1440</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>62.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>Soiewre</td>\n",
       "      <td>1521</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>95.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1200</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>51.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              area_type   availability                  location       size  \\\n",
       "0  Super built-up  Area         19-Dec  Electronic City Phase II      2 BHK   \n",
       "1            Plot  Area  Ready To Move          Chikka Tirupathi  4 Bedroom   \n",
       "2        Built-up  Area  Ready To Move               Uttarahalli      3 BHK   \n",
       "3  Super built-up  Area  Ready To Move        Lingadheeranahalli      3 BHK   \n",
       "4  Super built-up  Area  Ready To Move                  Kothanur      2 BHK   \n",
       "\n",
       "   society total_sqft  bath  balcony   price  \n",
       "0  Coomee        1056   2.0      1.0   39.07  \n",
       "1  Theanmp       2600   5.0      3.0  120.00  \n",
       "2      NaN       1440   2.0      3.0   62.00  \n",
       "3  Soiewre       1521   3.0      1.0   95.00  \n",
       "4      NaN       1200   2.0      1.0   51.00  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df.shape,'\\n')\n",
    "print(df.describe,'\\n')\n",
    "print(df.columns,'\\n') \n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6bbbcf75",
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('area_type')['area_type'].agg('count') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "278a0195",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Super built-up  Area    8790\n",
       "Built-up  Area          2418\n",
       "Plot  Area              2025\n",
       "Carpet  Area              87\n",
       "Name: area_type, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['area_type'].value_counts()  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c87b362",
   "metadata": {},
   "source": [
    "# "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "533031a4",
   "metadata": {},
   "source": [
    "# Data Preprocessing "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a044db0",
   "metadata": {},
   "source": [
    "# 1. Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f8279fcf",
   "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','availability','society','balcony'], axis=1)\n",
    "df1.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3d3f0f7e",
   "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": [
    "df1.isnull().sum() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9829b78a",
   "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": "cf9c30a1",
   "metadata": {
    "scrolled": true
   },
   "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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['size'].unique() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d8fc06c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\MOONCO~1\\AppData\\Local\\Temp/ipykernel_14024/1606862417.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])) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6f91580c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "be5f6532",
   "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>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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[df2.bhk>20] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0026d50a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1056', '2600', '1440', ..., '1133 - 1384', '774', '4689'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.total_sqft.unique() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "55f09b46",
   "metadata": {},
   "outputs": [],
   "source": [
    "#  we want to remove this range values from the total_sqft and at its place insert avg of this range value\n",
    "# we convert whole into float if it give valid value it means it is simple value and not valid means obvious range value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "138c58a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#  for this we made exception handeling function \n",
    "\n",
    "def isfloat(x):\n",
    "    try:\n",
    "        float(x)\n",
    "    except:\n",
    "        return False\n",
    "    return True\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c53b77dd",
   "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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[df2['total_sqft'].apply(isfloat)] # it return where value is true\n",
    "df2[-df2['total_sqft'].apply(isfloat)].head(10)  # it return where value is false -- ranfe value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b9fe9436",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def convert_sqrt_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": 19,
   "id": "c57b9a44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34556.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "convert_sqrt_to_num('34556') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6da2bae8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17300.5"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "convert_sqrt_to_num('34556-45') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "93729865",
   "metadata": {},
   "outputs": [],
   "source": [
    "convert_sqrt_to_num('345jn45') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e13b8ded",
   "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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Whitefield</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1170.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>38.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Old Airport Road</td>\n",
       "      <td>4 BHK</td>\n",
       "      <td>2732.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>204.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Rajaji Nagar</td>\n",
       "      <td>4 BHK</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>600.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Marathahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1310.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>63.25</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Gandhi Bazar</td>\n",
       "      <td>6 Bedroom</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>370.00</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   location       size  total_sqft  bath   price  bhk\n",
       "0  Electronic City Phase II      2 BHK      1056.0   2.0   39.07    2\n",
       "1          Chikka Tirupathi  4 Bedroom      2600.0   5.0  120.00    4\n",
       "2               Uttarahalli      3 BHK      1440.0   2.0   62.00    3\n",
       "3        Lingadheeranahalli      3 BHK      1521.0   3.0   95.00    3\n",
       "4                  Kothanur      2 BHK      1200.0   2.0   51.00    2\n",
       "5                Whitefield      2 BHK      1170.0   2.0   38.00    2\n",
       "6          Old Airport Road      4 BHK      2732.0   4.0  204.00    4\n",
       "7              Rajaji Nagar      4 BHK      3300.0   4.0  600.00    4\n",
       "8              Marathahalli      3 BHK      1310.0   3.0   63.25    3\n",
       "9              Gandhi Bazar  6 Bedroom      1020.0   6.0  370.00    6"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3= df2.copy() \n",
    "df3['total_sqft'] = df3['total_sqft'].apply(convert_sqrt_to_num)\n",
    "df3.head(10) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5e518556",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location      Yelahanka\n",
       "size              4 BHK\n",
       "total_sqft       2475.0\n",
       "bath                4.0\n",
       "price             186.0\n",
       "bhk                   4\n",
       "Name: 30, dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.loc[30]  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6279ff71",
   "metadata": {},
   "source": [
    "# 2. Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "89c5900c",
   "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>3699.81</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>4615.38</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>4305.56</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>6245.89</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>4250.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Whitefield</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1170.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>38.00</td>\n",
       "      <td>2</td>\n",
       "      <td>3247.86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   location       size  total_sqft  bath   price  bhk  \\\n",
       "0  Electronic City Phase II      2 BHK      1056.0   2.0   39.07    2   \n",
       "1          Chikka Tirupathi  4 Bedroom      2600.0   5.0  120.00    4   \n",
       "2               Uttarahalli      3 BHK      1440.0   2.0   62.00    3   \n",
       "3        Lingadheeranahalli      3 BHK      1521.0   3.0   95.00    3   \n",
       "4                  Kothanur      2 BHK      1200.0   2.0   51.00    2   \n",
       "5                Whitefield      2 BHK      1170.0   2.0   38.00    2   \n",
       "\n",
       "   Price_per_sqft  \n",
       "0         3699.81  \n",
       "1         4615.38  \n",
       "2         4305.56  \n",
       "3         6245.89  \n",
       "4         4250.00  \n",
       "5         3247.86  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 =df3.copy()\n",
    "\n",
    "df4['Price_per_sqft'] = round((df4['price']*100000)/df4['total_sqft'],ndigits=2)\n",
    "df4.head(6)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "52ac2f14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Whitefield           534\n",
       "Sarjapur  Road       392\n",
       "Electronic City      302\n",
       "Kanakpura Road       266\n",
       "Thanisandra          233\n",
       "                    ... \n",
       "Vidyapeeta             1\n",
       "Maruthi Extension      1\n",
       "Okalipura              1\n",
       "Old Town               1\n",
       "Abshot Layout          1\n",
       "Name: location, Length: 1304, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4['location'].value_counts()# df4.location.value_counts() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d39322ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We want to reduce  the size location cloumn, so we can apply one-hot encoding on it\n",
    "# 1 way = change to others "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3cfcf9b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Electronic City Phase II', 'Chikka Tirupathi', 'Uttarahalli', ...,\n",
       "       '12th cross srinivas nagar banshankari 3rd stage',\n",
       "       'Havanur extension', 'Abshot Layout'], dtype=object)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4['location'].unique() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d490b26f",
   "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",
       "Yelahanka                   210\n",
       "Uttarahalli                 186\n",
       "Hebbal                      176\n",
       "Marathahalli                175\n",
       "Raja Rajeshwari Nagar       171\n",
       "Bannerghatta Road           152\n",
       "Hennur Road                 150\n",
       "7th Phase JP Nagar          149\n",
       "Haralur Road                141\n",
       "Electronic City Phase II    131\n",
       "Rajaji Nagar                106\n",
       "Chandapura                   98\n",
       "Bellandur                    96\n",
       "KR Puram                     88\n",
       "Name: location, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4['location'] = df4['location'].apply(lambda x : x.strip())\n",
    "\n",
    "location_stats = df4.groupby('location')['location'].agg('count').sort_values(ascending=False)\n",
    "location_stats[:19]   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7763cece",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1052"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(location_stats[location_stats<=10]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "219bb0e0",
   "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": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "location_stats_less_then_10=location_stats[location_stats<=10] \n",
    "location_stats_less_then_10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0332caf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1293"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df4.location.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e90607c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "242"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4['location'] =df4['location'].apply(lambda x : 'other' if x in location_stats_less_then_10 else x ) \n",
    "\n",
    "len(df4.location.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ac0e1518",
   "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>3699.81</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>4615.38</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>4305.56</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>6245.89</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>4250.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Whitefield</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1170.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>38.00</td>\n",
       "      <td>2</td>\n",
       "      <td>3247.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Old Airport Road</td>\n",
       "      <td>4 BHK</td>\n",
       "      <td>2732.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>204.00</td>\n",
       "      <td>4</td>\n",
       "      <td>7467.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Rajaji Nagar</td>\n",
       "      <td>4 BHK</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>600.00</td>\n",
       "      <td>4</td>\n",
       "      <td>18181.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Marathahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1310.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>63.25</td>\n",
       "      <td>3</td>\n",
       "      <td>4828.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>other</td>\n",
       "      <td>6 Bedroom</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>370.00</td>\n",
       "      <td>6</td>\n",
       "      <td>36274.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Whitefield</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>70.00</td>\n",
       "      <td>3</td>\n",
       "      <td>3888.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Whitefield</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>2785.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>295.00</td>\n",
       "      <td>4</td>\n",
       "      <td>10592.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>7th Phase JP Nagar</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>38.00</td>\n",
       "      <td>2</td>\n",
       "      <td>3800.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Gottigere</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>40.00</td>\n",
       "      <td>2</td>\n",
       "      <td>3636.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Sarjapur</td>\n",
       "      <td>3 Bedroom</td>\n",
       "      <td>2250.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>148.00</td>\n",
       "      <td>3</td>\n",
       "      <td>6577.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Mysore Road</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1175.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>73.50</td>\n",
       "      <td>2</td>\n",
       "      <td>6255.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Bisuvanahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1180.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>48.00</td>\n",
       "      <td>3</td>\n",
       "      <td>4067.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Raja Rajeshwari Nagar</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1540.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>60.00</td>\n",
       "      <td>3</td>\n",
       "      <td>3896.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>other</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>2770.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>290.00</td>\n",
       "      <td>3</td>\n",
       "      <td>10469.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>other</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>48.00</td>\n",
       "      <td>2</td>\n",
       "      <td>4363.64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    location       size  total_sqft  bath   price  bhk  \\\n",
       "0   Electronic City Phase II      2 BHK      1056.0   2.0   39.07    2   \n",
       "1           Chikka Tirupathi  4 Bedroom      2600.0   5.0  120.00    4   \n",
       "2                Uttarahalli      3 BHK      1440.0   2.0   62.00    3   \n",
       "3         Lingadheeranahalli      3 BHK      1521.0   3.0   95.00    3   \n",
       "4                   Kothanur      2 BHK      1200.0   2.0   51.00    2   \n",
       "5                 Whitefield      2 BHK      1170.0   2.0   38.00    2   \n",
       "6           Old Airport Road      4 BHK      2732.0   4.0  204.00    4   \n",
       "7               Rajaji Nagar      4 BHK      3300.0   4.0  600.00    4   \n",
       "8               Marathahalli      3 BHK      1310.0   3.0   63.25    3   \n",
       "9                      other  6 Bedroom      1020.0   6.0  370.00    6   \n",
       "10                Whitefield      3 BHK      1800.0   2.0   70.00    3   \n",
       "11                Whitefield  4 Bedroom      2785.0   5.0  295.00    4   \n",
       "12        7th Phase JP Nagar      2 BHK      1000.0   2.0   38.00    2   \n",
       "13                 Gottigere      2 BHK      1100.0   2.0   40.00    2   \n",
       "14                  Sarjapur  3 Bedroom      2250.0   3.0  148.00    3   \n",
       "15               Mysore Road      2 BHK      1175.0   2.0   73.50    2   \n",
       "16             Bisuvanahalli      3 BHK      1180.0   3.0   48.00    3   \n",
       "17     Raja Rajeshwari Nagar      3 BHK      1540.0   3.0   60.00    3   \n",
       "18                     other      3 BHK      2770.0   4.0  290.00    3   \n",
       "19                     other      2 BHK      1100.0   2.0   48.00    2   \n",
       "\n",
       "    Price_per_sqft  \n",
       "0          3699.81  \n",
       "1          4615.38  \n",
       "2          4305.56  \n",
       "3          6245.89  \n",
       "4          4250.00  \n",
       "5          3247.86  \n",
       "6          7467.06  \n",
       "7         18181.82  \n",
       "8          4828.24  \n",
       "9         36274.51  \n",
       "10         3888.89  \n",
       "11        10592.46  \n",
       "12         3800.00  \n",
       "13         3636.36  \n",
       "14         6577.78  \n",
       "15         6255.32  \n",
       "16         4067.80  \n",
       "17         3896.10  \n",
       "18        10469.31  \n",
       "19         4363.64  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5892be4c",
   "metadata": {},
   "source": [
    "# Outlier Removal Using Business Logic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2627c6c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Detect outliner(name if the location word is very long, it might be right but it effect the result ) and remove them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e1036dfb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>bhk</th>\n",
       "      <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>36274.51</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>33333.33</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>10660.98</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>6296.30</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>20000.00</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>13277</th>\n",
       "      <td>other</td>\n",
       "      <td>7 Bedroom</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>218.0</td>\n",
       "      <td>7</td>\n",
       "      <td>15571.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13279</th>\n",
       "      <td>other</td>\n",
       "      <td>6 Bedroom</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>6</td>\n",
       "      <td>10833.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13281</th>\n",
       "      <td>Margondanahalli</td>\n",
       "      <td>5 Bedroom</td>\n",
       "      <td>1375.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>5</td>\n",
       "      <td>9090.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13303</th>\n",
       "      <td>Vidyaranyapura</td>\n",
       "      <td>5 Bedroom</td>\n",
       "      <td>774.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>5</td>\n",
       "      <td>9043.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13311</th>\n",
       "      <td>Ramamurthy Nagar</td>\n",
       "      <td>7 Bedroom</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>7</td>\n",
       "      <td>16666.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>744 rows × 7 columns</p>\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",
       "13277                other  7 Bedroom      1400.0   7.0  218.0    7   \n",
       "13279                other  6 Bedroom      1200.0   5.0  130.0    6   \n",
       "13281      Margondanahalli  5 Bedroom      1375.0   5.0  125.0    5   \n",
       "13303       Vidyaranyapura  5 Bedroom       774.0   5.0   70.0    5   \n",
       "13311     Ramamurthy Nagar  7 Bedroom      1500.0   9.0  250.0    7   \n",
       "\n",
       "       Price_per_sqft  \n",
       "9            36274.51  \n",
       "45           33333.33  \n",
       "58           10660.98  \n",
       "68            6296.30  \n",
       "70           20000.00  \n",
       "...               ...  \n",
       "13277        15571.43  \n",
       "13279        10833.33  \n",
       "13281         9090.91  \n",
       "13303         9043.93  \n",
       "13311        16666.67  \n",
       "\n",
       "[744 rows x 7 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sqrft per baedroom or size of 1 bedroom is 300 is typical but less then this unusual we don't need this(remove this anomalies)\n",
    "\n",
    "df4[df4['total_sqft']/df4['bhk']<300] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d0952348",
   "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>3699.81</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>4615.38</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>4305.56</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>6245.89</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>4250.00</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>6689.83</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>11111.11</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>5258.55</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>10407.34</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>3090.91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12502 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             3699.81  \n",
       "1             4615.38  \n",
       "2             4305.56  \n",
       "3             6245.89  \n",
       "4             4250.00  \n",
       "...               ...  \n",
       "13315         6689.83  \n",
       "13316        11111.11  \n",
       "13317         5258.55  \n",
       "13318        10407.34  \n",
       "13319         3090.91  \n",
       "\n",
       "[12502 rows x 7 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5=df4[-(df4['total_sqft']/df4['bhk']<300)]  \n",
    "df5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "95b1a5ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     12456.000000\n",
       "mean       6308.502836\n",
       "std        4168.127366\n",
       "min         267.830000\n",
       "25%        4210.530000\n",
       "50%        5294.120000\n",
       "75%        6916.670000\n",
       "max      176470.590000\n",
       "Name: Price_per_sqft, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.Price_per_sqft.describe() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "85f25943",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12502, 7)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.shape "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae138b20",
   "metadata": {},
   "source": [
    "Here we find that min price per sqft is 267 rs/sqft whereas max is 12000000, this shows a wide variation in property prices. We should remove outliers per location using mean and one standard deviation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "788e9018",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10241, 7)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def remove_pps_outliers(df):\n",
    "    df_out = pd.DataFrame()\n",
    "    for key, subdf in df.groupby('location'):\n",
    "        m = np.mean(subdf.Price_per_sqft)\n",
    "        st = np.std(subdf.Price_per_sqft)\n",
    "        reduced_df = subdf[(subdf.Price_per_sqft>(m-st)) & (subdf.Price_per_sqft<=(m+st))]\n",
    "        df_out = pd.concat([df_out,reduced_df],ignore_index=True)\n",
    "    return df_out\n",
    "df6 = remove_pps_outliers(df5) \n",
    "df6.shape "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bdf6f55",
   "metadata": {},
   "source": [
    "Let's check if for a given location how does the 2 BHK and 3 BHK property prices look like\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "346a39a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_scatter_chart(df,location):\n",
    "    bhk2 = df[(df.location==location) & (df.bhk==2)]\n",
    "    bhk3 = df[(df.location==location) & (df.bhk==3)]\n",
    "    matplotlib.rcParams['figure.figsize'] = (15,10)\n",
    "    plt.scatter(bhk2.total_sqft,bhk2.price,color='blue',label='2 BHK', s=50)\n",
    "    plt.scatter(bhk3.total_sqft,bhk3.price,marker='+', color='green',label='3 BHK', s=50)\n",
    "    plt.xlabel(\"Total Square Feet Area\")\n",
    "    plt.ylabel(\"Price (Lakh Indian Rupees)\")\n",
    "    plt.title(location)\n",
    "    plt.legend()\n",
    "    \n",
    "plot_scatter_chart(df6,\"Rajaji Nagar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fdb8109",
   "metadata": {},
   "source": [
    "We should also remove properties where for same location, the price of (for example) 3 bedroom apartment is less than 2 bedroom apartment (with same square ft area). What we will do is for a given location, we will build a dictionary of stats per bhk, i.e.\n",
    "\n",
    "{\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 remove those 2 BHK apartments whose price_per_sqft is less than mean price_per_sqft of 1 BHK apartment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2e564b3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7329, 7)"
      ]
     },
     "execution_count": 41,
     "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",
    "# df7 = df6.copy()\n",
    "df7.shape "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f94c431a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scatter_chart(df7,\"Rajaji Nagar\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "14c8e930",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5000.00    79\n",
       "6666.67    60\n",
       "4000.00    58\n",
       "6250.00    43\n",
       "6000.00    42\n",
       "           ..\n",
       "6271.44     1\n",
       "4899.62     1\n",
       "6693.23     1\n",
       "6737.01     1\n",
       "5541.13     1\n",
       "Name: Price_per_sqft, Length: 4703, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7.Price_per_sqft.value_counts() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "333090f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "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": 45,
   "id": "776e45fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0     4198\n",
       "3.0     1763\n",
       "1.0      568\n",
       "4.0      503\n",
       "5.0      175\n",
       "6.0       81\n",
       "7.0       16\n",
       "8.0       13\n",
       "9.0        7\n",
       "12.0       3\n",
       "16.0       1\n",
       "13.0       1\n",
       "Name: bath, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7.bath.value_counts() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "cdec768a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>bhk</th>\n",
       "      <th>Price_per_sqft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5277</th>\n",
       "      <td>Neeladri Nagar</td>\n",
       "      <td>10 BHK</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>10</td>\n",
       "      <td>4000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8486</th>\n",
       "      <td>other</td>\n",
       "      <td>10 BHK</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>525.0</td>\n",
       "      <td>10</td>\n",
       "      <td>4375.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8575</th>\n",
       "      <td>other</td>\n",
       "      <td>16 BHK</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>16</td>\n",
       "      <td>5500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9308</th>\n",
       "      <td>other</td>\n",
       "      <td>11 BHK</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>11</td>\n",
       "      <td>2500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9639</th>\n",
       "      <td>other</td>\n",
       "      <td>13 BHK</td>\n",
       "      <td>5425.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>13</td>\n",
       "      <td>5069.12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            location    size  total_sqft  bath  price  bhk  Price_per_sqft\n",
       "5277  Neeladri Nagar  10 BHK      4000.0  12.0  160.0   10         4000.00\n",
       "8486           other  10 BHK     12000.0  12.0  525.0   10         4375.00\n",
       "8575           other  16 BHK     10000.0  16.0  550.0   16         5500.00\n",
       "9308           other  11 BHK      6000.0  12.0  150.0   11         2500.00\n",
       "9639           other  13 BHK      5425.0  13.0  275.0   13         5069.12"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7[df7.bath>10] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "edbeb691",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df7.bath,rwidth=0.8) \n",
    "plt.xlabel('Number of Bathroom') \n",
    "plt.ylabel('Count')  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c501ec6",
   "metadata": {},
   "source": [
    "### Our business manager told us if 1 bhk have more then 2 or 2 bhk have more then 4  bathroom marked them outliner and remove (1bhk--> 3bath outliner ) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "887b8853",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df7[(df7.bhk<=df7.bath+2)] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "c0a5df9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7251, 7)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df8=df7[df7.bath<df7.bhk+2]\n",
    "df8.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "fcb34d15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>bhk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>2850.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>428.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>1630.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>1875.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>1235.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              location  total_sqft  bath  price  bhk\n",
       "0  1st Block Jayanagar      2850.0   4.0  428.0    4\n",
       "1  1st Block Jayanagar      1630.0   3.0  194.0    3\n",
       "2  1st Block Jayanagar      1875.0   2.0  235.0    3\n",
       "3  1st Block Jayanagar      1200.0   2.0  130.0    3\n",
       "4  1st Block Jayanagar      1235.0   2.0  148.0    2"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df9= df8.drop(['size','Price_per_sqft'],axis=1)\n",
    "df9.head(5) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "3e7b6df7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>1st Block Jayanagar</th>\n",
       "      <th>1st Phase JP Nagar</th>\n",
       "      <th>2nd Phase Judicial Layout</th>\n",
       "      <th>2nd Stage Nagarbhavi</th>\n",
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       "      <th>Vishveshwarya Layout</th>\n",
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       "      <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",
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       "<p>5 rows × 242 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   1st Block Jayanagar  1st Phase JP Nagar  2nd Phase Judicial Layout  \\\n",
       "0                    1                   0                          0   \n",
       "1                    1                   0                          0   \n",
       "2                    1                   0                          0   \n",
       "3                    1                   0                          0   \n",
       "4                    1                   0                          0   \n",
       "\n",
       "   2nd Stage Nagarbhavi  5th Block Hbr Layout  5th Phase JP Nagar  \\\n",
       "0                     0                     0                   0   \n",
       "1                     0                     0                   0   \n",
       "2                     0                     0                   0   \n",
       "3                     0                     0                   0   \n",
       "4                     0                     0                   0   \n",
       "\n",
       "   6th Phase JP Nagar  7th Phase JP Nagar  8th Phase JP Nagar  \\\n",
       "0                   0                   0                   0   \n",
       "1                   0                   0                   0   \n",
       "2                   0                   0                   0   \n",
       "3                   0                   0                   0   \n",
       "4                   0                   0                   0   \n",
       "\n",
       "   9th Phase JP Nagar  ...  Vishveshwarya Layout  Vishwapriya Layout  \\\n",
       "0                   0  ...                     0                   0   \n",
       "1                   0  ...                     0                   0   \n",
       "2                   0  ...                     0                   0   \n",
       "3                   0  ...                     0                   0   \n",
       "4                   0  ...                     0                   0   \n",
       "\n",
       "   Vittasandra  Whitefield  Yelachenahalli  Yelahanka  Yelahanka New Town  \\\n",
       "0            0           0               0          0                   0   \n",
       "1            0           0               0          0                   0   \n",
       "2            0           0               0          0                   0   \n",
       "3            0           0               0          0                   0   \n",
       "4            0           0               0          0                   0   \n",
       "\n",
       "   Yelenahalli  Yeshwanthpur  other  \n",
       "0            0             0      0  \n",
       "1            0             0      0  \n",
       "2            0             0      0  \n",
       "3            0             0      0  \n",
       "4            0             0      0  \n",
       "\n",
       "[5 rows x 242 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dumm=pd.get_dummies(df9.location)\n",
    "dumm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "459fb77f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1st Block Jayanagar</th>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10237</th>\n",
       "      <td>812.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10240</th>\n",
       "      <td>3600.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7251 rows × 245 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       total_sqft  bath  price  bhk  1st Block Jayanagar  1st Phase JP Nagar  \\\n",
       "0          2850.0   4.0  428.0    4                    1                   0   \n",
       "1          1630.0   3.0  194.0    3                    1                   0   \n",
       "2          1875.0   2.0  235.0    3                    1                   0   \n",
       "3          1200.0   2.0  130.0    3                    1                   0   \n",
       "4          1235.0   2.0  148.0    2                    1                   0   \n",
       "...           ...   ...    ...  ...                  ...                 ...   \n",
       "10232      1200.0   2.0   70.0    2                    0                   0   \n",
       "10233      1800.0   1.0  200.0    1                    0                   0   \n",
       "10236      1353.0   2.0  110.0    2                    0                   0   \n",
       "10237       812.0   1.0   26.0    1                    0                   0   \n",
       "10240      3600.0   5.0  400.0    4                    0                   0   \n",
       "\n",
       "       2nd Phase Judicial Layout  2nd Stage Nagarbhavi  5th Block Hbr Layout  \\\n",
       "0                              0                     0                     0   \n",
       "1                              0                     0                     0   \n",
       "2                              0                     0                     0   \n",
       "3                              0                     0                     0   \n",
       "4                              0                     0                     0   \n",
       "...                          ...                   ...                   ...   \n",
       "10232                          0                     0                     0   \n",
       "10233                          0                     0                     0   \n",
       "10236                          0                     0                     0   \n",
       "10237                          0                     0                     0   \n",
       "10240                          0                     0                     0   \n",
       "\n",
       "       5th Phase JP Nagar  ...  Vijayanagar  Vishveshwarya Layout  \\\n",
       "0                       0  ...            0                     0   \n",
       "1                       0  ...            0                     0   \n",
       "2                       0  ...            0                     0   \n",
       "3                       0  ...            0                     0   \n",
       "4                       0  ...            0                     0   \n",
       "...                   ...  ...          ...                   ...   \n",
       "10232                   0  ...            0                     0   \n",
       "10233                   0  ...            0                     0   \n",
       "10236                   0  ...            0                     0   \n",
       "10237                   0  ...            0                     0   \n",
       "10240                   0  ...            0                     0   \n",
       "\n",
       "       Vishwapriya Layout  Vittasandra  Whitefield  Yelachenahalli  Yelahanka  \\\n",
       "0                       0            0           0               0          0   \n",
       "1                       0            0           0               0          0   \n",
       "2                       0            0           0               0          0   \n",
       "3                       0            0           0               0          0   \n",
       "4                       0            0           0               0          0   \n",
       "...                   ...          ...         ...             ...        ...   \n",
       "10232                   0            0           0               0          0   \n",
       "10233                   0            0           0               0          0   \n",
       "10236                   0            0           0               0          0   \n",
       "10237                   0            0           0               0          0   \n",
       "10240                   0            0           0               0          0   \n",
       "\n",
       "       Yelahanka New Town  Yelenahalli  Yeshwanthpur  \n",
       "0                       0            0             0  \n",
       "1                       0            0             0  \n",
       "2                       0            0             0  \n",
       "3                       0            0             0  \n",
       "4                       0            0             0  \n",
       "...                   ...          ...           ...  \n",
       "10232                   0            0             0  \n",
       "10233                   0            0             0  \n",
       "10236                   0            0             0  \n",
       "10237                   0            0             0  \n",
       "10240                   0            0             0  \n",
       "\n",
       "[7251 rows x 245 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df10= pd.DataFrame.join(df9,dumm) \n",
    "df11=df10.drop(['location','other'],axis=1) \n",
    "df11 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d79cb6e0",
   "metadata": {},
   "source": [
    "### Now  cleaning is completed"
   ]
  }
 ],
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