Real_Estate_Price_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d497ee8a",
   "metadata": {},
   "source": [
    "Data Set used from: https://www.kaggle.com/amitabhajoy/bengaluru-house-price-data?select=Bengaluru_House_Data.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "77845856",
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "plt.rcParams[\"figure.figsize\"] = (20,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "94b0a571",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area_type</th>\n",
       "      <th>availability</th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>society</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>balcony</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>19-Dec</td>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>Coomee</td>\n",
       "      <td>1056</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Plot  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>Theanmp</td>\n",
       "      <td>2600</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>120.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1440</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>62.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>Soiewre</td>\n",
       "      <td>1521</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>95.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1200</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>51.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              area_type   availability                  location       size  \\\n",
       "0  Super built-up  Area         19-Dec  Electronic City Phase II      2 BHK   \n",
       "1            Plot  Area  Ready To Move          Chikka Tirupathi  4 Bedroom   \n",
       "2        Built-up  Area  Ready To Move               Uttarahalli      3 BHK   \n",
       "3  Super built-up  Area  Ready To Move        Lingadheeranahalli      3 BHK   \n",
       "4  Super built-up  Area  Ready To Move                  Kothanur      2 BHK   \n",
       "\n",
       "   society total_sqft  bath  balcony   price  \n",
       "0  Coomee        1056   2.0      1.0   39.07  \n",
       "1  Theanmp       2600   5.0      3.0  120.00  \n",
       "2      NaN       1440   2.0      3.0   62.00  \n",
       "3  Soiewre       1521   3.0      1.0   95.00  \n",
       "4      NaN       1200   2.0      1.0   51.00  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reading csv file\n",
    "df = pd.read_csv(\"Bengaluru_House_Data.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "088c217b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13320, 9)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# size of the dataframe\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "46abcfd9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 13320 entries, 0 to 13319\n",
      "Data columns (total 9 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   area_type     13320 non-null  object \n",
      " 1   availability  13320 non-null  object \n",
      " 2   location      13319 non-null  object \n",
      " 3   size          13304 non-null  object \n",
      " 4   society       7818 non-null   object \n",
      " 5   total_sqft    13320 non-null  object \n",
      " 6   bath          13247 non-null  float64\n",
      " 7   balcony       12711 non-null  float64\n",
      " 8   price         13320 non-null  float64\n",
      "dtypes: float64(3), object(6)\n",
      "memory usage: 936.7+ KB\n"
     ]
    }
   ],
   "source": [
    "# dataframe information\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d51e6690",
   "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": [
    "# Checking the unique area_type in our data set:\n",
    "df['area_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a597428b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1056</td>\n",
       "      <td>2.0</td>\n",
       "      <td>39.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>2600</td>\n",
       "      <td>5.0</td>\n",
       "      <td>120.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1440</td>\n",
       "      <td>2.0</td>\n",
       "      <td>62.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1521</td>\n",
       "      <td>3.0</td>\n",
       "      <td>95.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1200</td>\n",
       "      <td>2.0</td>\n",
       "      <td>51.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                   location       size total_sqft  bath   price\n",
       "0  Electronic City Phase II      2 BHK       1056   2.0   39.07\n",
       "1          Chikka Tirupathi  4 Bedroom       2600   5.0  120.00\n",
       "2               Uttarahalli      3 BHK       1440   2.0   62.00\n",
       "3        Lingadheeranahalli      3 BHK       1521   3.0   95.00\n",
       "4                  Kothanur      2 BHK       1200   2.0   51.00"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here we're assuming that these variables are not required for price prediction.\n",
    "# Hence, dropping some of the variables.\n",
    "df1 = df.drop(['area_type','society','availability','balcony'],axis='columns')\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "295ff376",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13320, 5)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Shape of remaining dataFrame\n",
    "df1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "37dff3a4",
   "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets check for the NULL values:\n",
    "df1.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e572e9d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# So for null values we can either drop the rows or impute. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4664ea35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location      0\n",
       "size          0\n",
       "total_sqft    0\n",
       "bath          0\n",
       "price         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove the NULL values\n",
    "banglore_df = df1.dropna()\n",
    "# Validate the final df\n",
    "banglore_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "819e0b0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13246, 5)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "banglore_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a8dfd8a",
   "metadata": {},
   "source": [
    "## Feature Engineering\n",
    "\n",
    "###  Adding New feature: BHK derived from size column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b47048ef",
   "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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now, first explore size column\n",
    "banglore_df['size'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8bea776a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-14-df44f38a8ad4>:2: 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",
      "  banglore_df['BHK'] = banglore_df['size'].apply(lambda x: int(x.split(' ')[0]))\n"
     ]
    }
   ],
   "source": [
    "# lets extract only the number from this column & create separate column\n",
    "banglore_df['BHK'] = banglore_df['size'].apply(lambda x: int(x.split(' ')[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "06ff238a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>BHK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1056</td>\n",
       "      <td>2.0</td>\n",
       "      <td>39.07</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>2600</td>\n",
       "      <td>5.0</td>\n",
       "      <td>120.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1440</td>\n",
       "      <td>2.0</td>\n",
       "      <td>62.00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1521</td>\n",
       "      <td>3.0</td>\n",
       "      <td>95.00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1200</td>\n",
       "      <td>2.0</td>\n",
       "      <td>51.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   location       size total_sqft  bath   price  BHK\n",
       "0  Electronic City Phase II      2 BHK       1056   2.0   39.07    2\n",
       "1          Chikka Tirupathi  4 Bedroom       2600   5.0  120.00    4\n",
       "2               Uttarahalli      3 BHK       1440   2.0   62.00    3\n",
       "3        Lingadheeranahalli      3 BHK       1521   3.0   95.00    3\n",
       "4                  Kothanur      2 BHK       1200   2.0   51.00    2"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "banglore_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8c2f7caf",
   "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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the unique values of BHK\n",
    "banglore_df['BHK'].unique() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d478a389",
   "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>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",
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      ],
      "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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "banglore_df[banglore_df.BHK > 20]\n",
    "# Probably outliers, will handle later"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a130ca77",
   "metadata": {},
   "source": [
    "### Correcting total_sqft column values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bc8ae838",
   "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": [
    "# total_sqft field has records as range\n",
    "banglore_df['total_sqft'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "dc0b30ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to check if there total_sqft contains range or other values.\n",
    "def  is_float(x):\n",
    "    try:\n",
    "        float(x)\n",
    "    except:\n",
    "        return False\n",
    "    return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "937f7f99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>BHK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1056</td>\n",
       "      <td>2.0</td>\n",
       "      <td>39.07</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>2600</td>\n",
       "      <td>5.0</td>\n",
       "      <td>120.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1440</td>\n",
       "      <td>2.0</td>\n",
       "      <td>62.00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1521</td>\n",
       "      <td>3.0</td>\n",
       "      <td>95.00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1200</td>\n",
       "      <td>2.0</td>\n",
       "      <td>51.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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</td>\n",
       "      <td>4.0</td>\n",
       "      <td>231.00</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13316</th>\n",
       "      <td>Richards Town</td>\n",
       "      <td>4 BHK</td>\n",
       "      <td>3600</td>\n",
       "      <td>5.0</td>\n",
       "      <td>400.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13317</th>\n",
       "      <td>Raja Rajeshwari Nagar</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1141</td>\n",
       "      <td>2.0</td>\n",
       "      <td>60.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13318</th>\n",
       "      <td>Padmanabhanagar</td>\n",
       "      <td>4 BHK</td>\n",
       "      <td>4689</td>\n",
       "      <td>4.0</td>\n",
       "      <td>488.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13319</th>\n",
       "      <td>Doddathoguru</td>\n",
       "      <td>1 BHK</td>\n",
       "      <td>550</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13056 rows × 6 columns</p>\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\n",
       "...                         ...        ...        ...   ...     ...  ...\n",
       "13315                Whitefield  5 Bedroom       3453   4.0  231.00    5\n",
       "13316             Richards Town      4 BHK       3600   5.0  400.00    4\n",
       "13317     Raja Rajeshwari Nagar      2 BHK       1141   2.0   60.00    2\n",
       "13318           Padmanabhanagar      4 BHK       4689   4.0  488.00    4\n",
       "13319              Doddathoguru      1 BHK        550   1.0   17.00    1\n",
       "\n",
       "[13056 rows x 6 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this will return the True values.\n",
    "banglore_df[banglore_df['total_sqft'].apply(is_float)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "21c7c6c7",
   "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>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12975</th>\n",
       "      <td>Whitefield</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>850 - 1060</td>\n",
       "      <td>2.0</td>\n",
       "      <td>38.190</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12990</th>\n",
       "      <td>Talaghattapura</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1804 - 2273</td>\n",
       "      <td>3.0</td>\n",
       "      <td>122.000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13059</th>\n",
       "      <td>Harlur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1200 - 1470</td>\n",
       "      <td>2.0</td>\n",
       "      <td>72.760</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13265</th>\n",
       "      <td>Hoodi</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1133 - 1384</td>\n",
       "      <td>2.0</td>\n",
       "      <td>59.135</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13299</th>\n",
       "      <td>Whitefield</td>\n",
       "      <td>4 BHK</td>\n",
       "      <td>2830 - 2882</td>\n",
       "      <td>5.0</td>\n",
       "      <td>154.500</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>190 rows × 6 columns</p>\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",
       "...                   ...    ...          ...   ...      ...  ...\n",
       "12975          Whitefield  2 BHK   850 - 1060   2.0   38.190    2\n",
       "12990      Talaghattapura  3 BHK  1804 - 2273   3.0  122.000    3\n",
       "13059              Harlur  2 BHK  1200 - 1470   2.0   72.760    2\n",
       "13265               Hoodi  2 BHK  1133 - 1384   2.0   59.135    2\n",
       "13299          Whitefield  4 BHK  2830 - 2882   5.0  154.500    4\n",
       "\n",
       "[190 rows x 6 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# but we need the False values.\n",
    "banglore_df[~banglore_df['total_sqft'].apply(is_float)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3ddef0b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to convert range values to average of the range\n",
    "def convert_sqft_range_num(x):\n",
    "    nums = x.split('-')\n",
    "    if len(nums)==2:\n",
    "        return np.mean([float(n) for n in nums])\n",
    "    try:\n",
    "        return float(x)\n",
    "    except:\n",
    "        return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4d5c3a16",
   "metadata": {},
   "outputs": [],
   "source": [
    "convert_sqft_range_num('1600sq')\n",
    "# doesn't return anything"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4a766249",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1100.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "convert_sqft_range_num('1000-1200')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e29e26ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now lest apply this function on the new copy of the banglore_df dataframe.\n",
    "banglore_df1 = banglore_df.copy()\n",
    "\n",
    "banglore_df1['total_sqft'] = banglore_df1['total_sqft'].apply(convert_sqft_range_num)\n",
    "banglore_df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bc4003ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>BHK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Yelahanka</td>\n",
       "      <td>4 BHK</td>\n",
       "      <td>2475.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>186.0</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      2475.0   4.0  186.0    4"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking value at previous location\n",
    "banglore_df1.iloc[[30]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e2be94bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [location, size, total_sqft, bath, price, BHK]\n",
       "Index: []"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# all the values are corrected\n",
    "banglore_df1[~banglore_df1['total_sqft'].apply(is_float)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e424c2c0",
   "metadata": {},
   "source": [
    "### Adding price_per_sqft column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "42b28e1e",
   "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",
       "  </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",
       "\n",
       "   price_per_sqft  \n",
       "0     3699.810606  \n",
       "1     4615.384615  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# adding new column to the same dataFrame:\n",
    "banglore_df1['price_per_sqft'] = banglore_df1['price']*100000/banglore_df1['total_sqft']\n",
    "banglore_df1.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43ce7163",
   "metadata": {},
   "source": [
    "### Exploring location column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ce13e253",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Since, in this dataframe we have one categorical variable (location). We can first check the number of unique location\n",
    "# and then transform it based on number of properties in the location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "29e3c3c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# First remove extra spaces from location:\n",
    "banglore_df1['location'] = banglore_df1['location'].apply(lambda x: x.strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ece648cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1293"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print unique counts\n",
    "banglore_df1['location'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f3031a1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Whitefield                535\n",
       "Sarjapur  Road            392\n",
       "Electronic City           304\n",
       "Kanakpura Road            266\n",
       "Thanisandra               236\n",
       "                         ... \n",
       "Byrasandra Extension        1\n",
       "Banashankari 6th Stage      1\n",
       "Hanumagiri                  1\n",
       "Vasantapura main road       1\n",
       "Jeevanhalli                 1\n",
       "Name: location, Length: 1293, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking count per location:\n",
    "banglore_df1.location.value_counts() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "fb7112e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Whitefield                535\n",
       "Sarjapur  Road            392\n",
       "Electronic City           304\n",
       "Kanakpura Road            266\n",
       "Thanisandra               236\n",
       "                         ... \n",
       "Byrasandra Extension        1\n",
       "Banashankari 6th Stage      1\n",
       "Hanumagiri                  1\n",
       "Vasantapura main road       1\n",
       "Jeevanhalli                 1\n",
       "Name: location, Length: 1293, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# since there are lots of less properties, we can change the location to 'Other'\n",
    "location_stats = banglore_df1['location'].value_counts()\n",
    "location_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "8ffb6b7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sector 1 HSR Layout       10\n",
       "1st Block Koramangala     10\n",
       "Ganga Nagar               10\n",
       "Dodsworth Layout          10\n",
       "Dairy Circle              10\n",
       "                          ..\n",
       "Byrasandra Extension       1\n",
       "Banashankari 6th Stage     1\n",
       "Hanumagiri                 1\n",
       "Vasantapura main road      1\n",
       "Jeevanhalli                1\n",
       "Name: location, Length: 1052, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#location where count is <= 10.\n",
    "\n",
    "location_less_than_10 = location_stats[location_stats <= 10]\n",
    "location_less_than_10"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c748287",
   "metadata": {},
   "source": [
    "### Dimensionality reduction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "664c948f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "242"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here we're going to update the location as 'Other', if the number of count is less than 10.\n",
    "# this will help us in hot encoding, when we'll have fewer dummy columns.\n",
    "\n",
    "banglore_df1['location'] = banglore_df1['location'].apply(lambda x: 'Other' if x in location_less_than_10 else x)\n",
    "# checking the number of unique location:\n",
    "banglore_df1.location.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e1d8c04b",
   "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": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "banglore_df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3451b45",
   "metadata": {},
   "source": [
    "### Removing outliers using Business Logic "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4e2858bc",
   "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",
       "    <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>15571.428571</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>10833.333333</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>9090.909091</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>9043.927649</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>16666.666667</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        36274.509804  \n",
       "45       33333.333333  \n",
       "58       10660.980810  \n",
       "68        6296.296296  \n",
       "70       20000.000000  \n",
       "...               ...  \n",
       "13277    15571.428571  \n",
       "13279    10833.333333  \n",
       "13281     9090.909091  \n",
       "13303     9043.927649  \n",
       "13311    16666.666667  \n",
       "\n",
       "[744 rows x 7 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Baed on domain knowledge, we can set some rules or threshold. Which can help in removing outliers.\n",
    "# Here, we're assuming that if total_sqft per room is less than 300, then it is outlier or an err record.\n",
    "\n",
    "banglore_df1[ banglore_df1['total_sqft']/banglore_df1['BHK'] < 300]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "1924f42c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12502, 7)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets remove these values and store the rest of the records for further processing:\n",
    "# or we could've just written greater than 300 area records only.\n",
    "banglore_df2 = banglore_df1[~(banglore_df1['total_sqft']/banglore_df1['BHK'] < 300)]\n",
    "banglore_df2.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e87b7c9b",
   "metadata": {},
   "source": [
    "### Outlier Removal using Standard Deviation and Mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "13fd7e32",
   "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": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now lets look at the price_per_sqft column:\n",
    "banglore_df2['price_per_sqft'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f3f99fd",
   "metadata": {},
   "source": [
    "#### There is a lot of variation among min & max value, we can remove outliers based on price per location."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6f2df6d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#banglore_df2.groupby(by='location').agg({'price_per_sqft':[np.mean, np.std]})\n",
    "\n",
    "def remove_outliers(df):\n",
    "    # initialize the output datafram\n",
    "    df_out = pd.DataFrame()\n",
    "    \n",
    "    for loc, groups in df.groupby('location'):\n",
    "        mean_per_loc = np.mean(groups.price_per_sqft)\n",
    "        std_per_loc  = np.std(groups.price_per_sqft)\n",
    "        # only including the records which are within 1 standard deviation (per location)\n",
    "        reduced_df =  groups[(groups.price_per_sqft > (mean_per_loc - std_per_loc)) & \n",
    "                             (groups.price_per_sqft <= (mean_per_loc + std_per_loc))]\n",
    "        df_out = pd.concat([df_out,reduced_df],ignore_index = True)\n",
    "        \n",
    "    return df_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "508f1564",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10241, 7)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calling the new function:\n",
    "banglore_df3 = remove_outliers(banglore_df2)\n",
    "banglore_df3.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be47a024",
   "metadata": {},
   "source": [
    "#### Lets check the prices for 2 BHK and 3 BHK houses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f150012a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generinc function which take dataframe & location. And then prints scatter plot for prices between 2BHK & 3BHK homes\n",
    "def plot_scatter_graph(df,location):\n",
    "    bhk2 = df[(df.location==location) & (df.BHK==2)]\n",
    "    bhk3 = df[(df.location==location) & (df.BHK==3)]\n",
    "    plt.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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7da112a9",
   "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_graph(banglore_df3,'Rajaji Nagar')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d53d69a",
   "metadata": {},
   "source": [
    "##### Here for same location and same square feet area, prices for 2 bhk houses are greater than 3 bhk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "105dedb2",
   "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_graph(banglore_df3,'Hebbal')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "980a62d1",
   "metadata": {},
   "source": [
    "We will remove properties where for same location price of 3 BHK apartment is less than 2 BHK apartment.For this I'll create dictionary for each BHK and then I'll compare the mean with previous BHK values.\n",
    "\n",
    "{\n",
    "    '1' : {\n",
    "        'mean': 3500,\n",
    "        'std: 1800,\n",
    "        'count': 24\n",
    "    },\n",
    "    '2' : {\n",
    "        'mean': 4100,\n",
    "        'std: 2100,\n",
    "        'count': 27\n",
    "    },    \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "aacd7377",
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to detect & remove BHK outliers:\n",
    "\n",
    "def remove_BHK_outliers(df):\n",
    "    exclude_indices = np.array([])\n",
    "    for loc, location_df in df.groupby('location'):\n",
    "        bhk_statistics = {}\n",
    "        for bhk, bhk_df in location_df.groupby('BHK'):\n",
    "            bhk_statistics[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_statistics.get(bhk-1)\n",
    "            if stats and stats['count']>5:\n",
    "                # storing indices in numpy array\n",
    "                exclude_indices = np.append(exclude_indices, bhk_df[bhk_df.price_per_sqft<(stats['mean'])].index.values)\n",
    "                \n",
    "    return df.drop(exclude_indices,axis='index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "02297763",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7329, 7)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "banglore_df4 = remove_BHK_outliers(banglore_df3)\n",
    "banglore_df4.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26e66c7e",
   "metadata": {},
   "source": [
    "#### Plotting again for the same 2 previous locations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "511a6fe0",
   "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_graph(banglore_df4,'Rajaji Nagar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "a479d7a1",
   "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_graph(banglore_df4,'Hebbal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "38371419",
   "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": [
    "# Checking properties by plotting historgram of price_per_square_feet  \n",
    "\n",
    "plt.hist(banglore_df4.price_per_sqft,rwidth=0.8)\n",
    "plt.xlabel(\"Price Per Square Feet\")\n",
    "plt.ylabel(\"Number of properties\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb9ea839",
   "metadata": {},
   "source": [
    "#### Outliers for bathroom feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "1b32f44c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4.,  3.,  2.,  5.,  8.,  1.,  6.,  7.,  9., 12., 16., 13.])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the unique bathroom values\n",
    "banglore_df4.bath.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "670e4ed6",
   "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": [
    "# bathrooms could be outliers, we'll have to consult business user to set the rules for finding outliers!\n",
    "plt.hist(banglore_df4.bath,rwidth=0.8)\n",
    "plt.xlabel(\"Number of bathrooms\")\n",
    "plt.ylabel(\"Count of properties\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58f7573f",
   "metadata": {},
   "source": [
    "#### Business has suggested that its unusal to have properties where number of bathrooms > (Number of bedrooms) +2.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "cc4cf311",
   "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>5850</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",
       "    <tr>\n",
       "      <th>9012</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",
       "  </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",
       "5850          Other      6 BHK     11338.0   9.0  1000.0    6     8819.897689\n",
       "9012    Thanisandra      3 BHK      1806.0   6.0   116.0    3     6423.034330"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets find such properties.\n",
    "banglore_df4[banglore_df4.bath > banglore_df4.BHK +2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "ea0b0448",
   "metadata": {},
   "outputs": [],
   "source": [
    "#banglore_df4.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4090a82c",
   "metadata": {},
   "source": [
    "#### Business has again suggested to only keep the maximum limit of bathroom to be (total bedrooms +1 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "7efafdf6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7251, 7)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Changing the selection criteria:\n",
    "banglore_df5 = banglore_df4[banglore_df4.bath < banglore_df4.BHK +2]\n",
    "banglore_df5.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "079763c3",
   "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>1st Block Jayanagar</td>\n",
       "      <td>4 BHK</td>\n",
       "      <td>2850.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>428.0</td>\n",
       "      <td>4</td>\n",
       "      <td>15017.543860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1630.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>3</td>\n",
       "      <td>11901.840491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1875.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>3</td>\n",
       "      <td>12533.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              location   size  total_sqft  bath  price  BHK  price_per_sqft\n",
       "0  1st Block Jayanagar  4 BHK      2850.0   4.0  428.0    4    15017.543860\n",
       "1  1st Block Jayanagar  3 BHK      1630.0   3.0  194.0    3    11901.840491\n",
       "2  1st Block Jayanagar  3 BHK      1875.0   2.0  235.0    3    12533.333333"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "banglore_df5.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c3e87b8",
   "metadata": {},
   "source": [
    "#### Finally drop the columns which are not required for modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "e1389c99",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping size & price_per_sqft columns:\n",
    "df_final = banglore_df5.drop(['size','price_per_sqft'],axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "6e3faae1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>BHK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>2850.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>428.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>1630.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>1875.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>1235.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              location  total_sqft  bath  price  BHK\n",
       "0  1st Block Jayanagar      2850.0   4.0  428.0    4\n",
       "1  1st Block Jayanagar      1630.0   3.0  194.0    3\n",
       "2  1st Block Jayanagar      1875.0   2.0  235.0    3\n",
       "3  1st Block Jayanagar      1200.0   2.0  130.0    3\n",
       "4  1st Block Jayanagar      1235.0   2.0  148.0    2"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "4ff555ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7251, 5)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc5658e2",
   "metadata": {},
   "source": [
    "### One hot encoding for Location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "30333826",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>1st Block Jayanagar</th>\n",
       "      <th>1st Phase JP Nagar</th>\n",
       "      <th>2nd Phase Judicial Layout</th>\n",
       "      <th>2nd Stage Nagarbhavi</th>\n",
       "      <th>5th Block Hbr Layout</th>\n",
       "      <th>5th Phase JP Nagar</th>\n",
       "      <th>6th Phase JP Nagar</th>\n",
       "      <th>7th Phase JP Nagar</th>\n",
       "      <th>8th Phase JP Nagar</th>\n",
       "      <th>9th Phase JP Nagar</th>\n",
       "      <th>...</th>\n",
       "      <th>Vijayanagar</th>\n",
       "      <th>Vishveshwarya Layout</th>\n",
       "      <th>Vishwapriya Layout</th>\n",
       "      <th>Vittasandra</th>\n",
       "      <th>Whitefield</th>\n",
       "      <th>Yelachenahalli</th>\n",
       "      <th>Yelahanka</th>\n",
       "      <th>Yelahanka New Town</th>\n",
       "      <th>Yelenahalli</th>\n",
       "      <th>Yeshwanthpur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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>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  ...  Vijayanagar  Vishveshwarya Layout  \\\n",
       "0                   0  ...            0                     0   \n",
       "1                   0  ...            0                     0   \n",
       "2                   0  ...            0                     0   \n",
       "3                   0  ...            0                     0   \n",
       "4                   0  ...            0                     0   \n",
       "\n",
       "   Vishwapriya Layout  Vittasandra  Whitefield  Yelachenahalli  Yelahanka  \\\n",
       "0                   0            0           0               0          0   \n",
       "1                   0            0           0               0          0   \n",
       "2                   0            0           0               0          0   \n",
       "3                   0            0           0               0          0   \n",
       "4                   0            0           0               0          0   \n",
       "\n",
       "   Yelahanka New Town  Yelenahalli  Yeshwanthpur  \n",
       "0                   0            0             0  \n",
       "1                   0            0             0  \n",
       "2                   0            0             0  \n",
       "3                   0            0             0  \n",
       "4                   0            0             0  \n",
       "\n",
       "[5 rows x 242 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use pands get_dummies method\n",
    "# pd.get_dummies(df_final)\n",
    "\n",
    "dummies = pd.get_dummies(df_final.location)\n",
    "# It converts categorical values to 1 or 0.\n",
    "dummies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "0c12ee24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>Vijayanagar</th>\n",
       "      <th>Vishveshwarya Layout</th>\n",
       "      <th>Vishwapriya Layout</th>\n",
       "      <th>Vittasandra</th>\n",
       "      <th>Whitefield</th>\n",
       "      <th>Yelachenahalli</th>\n",
       "      <th>Yelahanka</th>\n",
       "      <th>Yelahanka New Town</th>\n",
       "      <th>Yelenahalli</th>\n",
       "      <th>Yeshwanthpur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>2850.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>428.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>1630.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>1875.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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>3 rows × 246 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              location  total_sqft  bath  price  BHK  1st Block Jayanagar  \\\n",
       "0  1st Block Jayanagar      2850.0   4.0  428.0    4                    1   \n",
       "1  1st Block Jayanagar      1630.0   3.0  194.0    3                    1   \n",
       "2  1st Block Jayanagar      1875.0   2.0  235.0    3                    1   \n",
       "\n",
       "   1st Phase JP Nagar  2nd Phase Judicial Layout  2nd Stage Nagarbhavi  \\\n",
       "0                   0                          0                     0   \n",
       "1                   0                          0                     0   \n",
       "2                   0                          0                     0   \n",
       "\n",
       "   5th Block Hbr Layout  ...  Vijayanagar  Vishveshwarya Layout  \\\n",
       "0                     0  ...            0                     0   \n",
       "1                     0  ...            0                     0   \n",
       "2                     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",
       "\n",
       "   Yelahanka New Town  Yelenahalli  Yeshwanthpur  \n",
       "0                   0            0             0  \n",
       "1                   0            0             0  \n",
       "2                   0            0             0  \n",
       "\n",
       "[3 rows x 246 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now append this dummies dataframe with df_final:\n",
    "df_final1 = pd.concat([df_final,dummies.drop('Other',axis='columns')],axis='columns') # dropping Other from dummies.\n",
    "df_final1.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "2be88b49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7251, 245)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can drop location column as well, since it is already converted.\n",
    "df_final1.drop('location',axis='columns',inplace=True)\n",
    "# Dropping using inplace True\n",
    "\n",
    "df_final1.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dad24b13",
   "metadata": {},
   "source": [
    "### Building Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "5aa614ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>BHK</th>\n",
       "      <th>1st Block Jayanagar</th>\n",
       "      <th>1st Phase JP Nagar</th>\n",
       "      <th>2nd Phase Judicial Layout</th>\n",
       "      <th>2nd Stage Nagarbhavi</th>\n",
       "      <th>5th Block Hbr Layout</th>\n",
       "      <th>5th Phase JP Nagar</th>\n",
       "      <th>...</th>\n",
       "      <th>Vijayanagar</th>\n",
       "      <th>Vishveshwarya Layout</th>\n",
       "      <th>Vishwapriya Layout</th>\n",
       "      <th>Vittasandra</th>\n",
       "      <th>Whitefield</th>\n",
       "      <th>Yelachenahalli</th>\n",
       "      <th>Yelahanka</th>\n",
       "      <th>Yelahanka New Town</th>\n",
       "      <th>Yelenahalli</th>\n",
       "      <th>Yeshwanthpur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2850.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>428.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1630.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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>2</th>\n",
       "      <td>1875.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 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",
       "\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",
       "\n",
       "   5th Phase JP Nagar  ...  Vijayanagar  Vishveshwarya Layout  \\\n",
       "0                   0  ...            0                     0   \n",
       "1                   0  ...            0                     0   \n",
       "2                   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",
       "\n",
       "   Yelahanka New Town  Yelenahalli  Yeshwanthpur  \n",
       "0                   0            0             0  \n",
       "1                   0            0             0  \n",
       "2                   0            0             0  \n",
       "\n",
       "[3 rows x 245 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final1.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "e508db7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>BHK</th>\n",
       "      <th>1st Block Jayanagar</th>\n",
       "      <th>1st Phase JP Nagar</th>\n",
       "      <th>2nd Phase Judicial Layout</th>\n",
       "      <th>2nd Stage Nagarbhavi</th>\n",
       "      <th>5th Block Hbr Layout</th>\n",
       "      <th>5th Phase JP Nagar</th>\n",
       "      <th>6th Phase JP Nagar</th>\n",
       "      <th>...</th>\n",
       "      <th>Vijayanagar</th>\n",
       "      <th>Vishveshwarya Layout</th>\n",
       "      <th>Vishwapriya Layout</th>\n",
       "      <th>Vittasandra</th>\n",
       "      <th>Whitefield</th>\n",
       "      <th>Yelachenahalli</th>\n",
       "      <th>Yelahanka</th>\n",
       "      <th>Yelahanka New Town</th>\n",
       "      <th>Yelenahalli</th>\n",
       "      <th>Yeshwanthpur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2850.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1630.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>2</th>\n",
       "      <td>1875.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1235.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>5 rows × 244 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_sqft  bath  BHK  1st Block Jayanagar  1st Phase JP Nagar  \\\n",
       "0      2850.0   4.0    4                    1                   0   \n",
       "1      1630.0   3.0    3                    1                   0   \n",
       "2      1875.0   2.0    3                    1                   0   \n",
       "3      1200.0   2.0    3                    1                   0   \n",
       "4      1235.0   2.0    2                    1                   0   \n",
       "\n",
       "   2nd Phase Judicial Layout  2nd Stage Nagarbhavi  5th Block Hbr Layout  \\\n",
       "0                          0                     0                     0   \n",
       "1                          0                     0                     0   \n",
       "2                          0                     0                     0   \n",
       "3                          0                     0                     0   \n",
       "4                          0                     0                     0   \n",
       "\n",
       "   5th Phase JP Nagar  6th Phase JP Nagar  ...  Vijayanagar  \\\n",
       "0                   0                   0  ...            0   \n",
       "1                   0                   0  ...            0   \n",
       "2                   0                   0  ...            0   \n",
       "3                   0                   0  ...            0   \n",
       "4                   0                   0  ...            0   \n",
       "\n",
       "   Vishveshwarya Layout  Vishwapriya Layout  Vittasandra  Whitefield  \\\n",
       "0                     0                   0            0           0   \n",
       "1                     0                   0            0           0   \n",
       "2                     0                   0            0           0   \n",
       "3                     0                   0            0           0   \n",
       "4                     0                   0            0           0   \n",
       "\n",
       "   Yelachenahalli  Yelahanka  Yelahanka New Town  Yelenahalli  Yeshwanthpur  \n",
       "0               0          0                   0            0             0  \n",
       "1               0          0                   0            0             0  \n",
       "2               0          0                   0            0             0  \n",
       "3               0          0                   0            0             0  \n",
       "4               0          0                   0            0             0  \n",
       "\n",
       "[5 rows x 244 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets break the data into X (Independent variables) & y (dependent variable)\n",
    "\n",
    "X = df_final1.drop('price',axis='columns')\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "13e9c46f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7251, 244)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "b3f5df8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    428.0\n",
       "1    194.0\n",
       "2    235.0\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# and y can be assigned as:\n",
    "y = df_final1['price']\n",
    "y.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "5dc36afb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7251,)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "47247ec3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import linear regression models from sklearn\n",
    "\n",
    "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": 68,
   "id": "deba2a75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8691914452174444"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lr_clf = LinearRegression()\n",
    "lr_clf.fit(X_train,y_train)\n",
    "lr_clf.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d5971bb",
   "metadata": {},
   "source": [
    "### Using K Fold cross validation to measure accuracy of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "e42d5ade",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.85430675, 0.84187647, 0.84728412, 0.85171729, 0.87168018])"
      ]
     },
     "execution_count": 69,
     "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=0)\n",
    "\n",
    "cross_val_score(LinearRegression(), X, y, cv=cv)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03e656d4",
   "metadata": {},
   "source": [
    "#### we can see that even in 5 iterations we are getting score which is above 80% everytime. Now lets try some other model to find out which one fits best."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0daa3d52",
   "metadata": {},
   "source": [
    "## Find best model using GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "dac53c4b",
   "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>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>0.853373</td>\n",
       "      <td>{'normalize': True}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>lasso</td>\n",
       "      <td>0.727661</td>\n",
       "      <td>{'alpha': 1, 'selection': 'random'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>decision_tree</td>\n",
       "      <td>0.731251</td>\n",
       "      <td>{'criterion': 'friedman_mse', 'splitter': 'ran...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               model  best_score  \\\n",
       "0  linear_regression    0.853373   \n",
       "1              lasso    0.727661   \n",
       "2      decision_tree    0.731251   \n",
       "\n",
       "                                         best_params  \n",
       "0                                {'normalize': True}  \n",
       "1                {'alpha': 1, 'selection': 'random'}  \n",
       "2  {'criterion': 'friedman_mse', 'splitter': 'ran...  "
      ]
     },
     "execution_count": 70,
     "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_gridSearchcv(X,y):\n",
    "    algos = {\n",
    "             'linear_regression' : {\n",
    "             'model': LinearRegression(),\n",
    "             'params': {\n",
    "                'normalize': [True, False]\n",
    "            }\n",
    "        },\n",
    "        'lasso': {\n",
    "            'model': Lasso(),\n",
    "            'params': {\n",
    "                'alpha': [1,2],\n",
    "                'selection': ['random', 'cyclic']\n",
    "            }\n",
    "        },\n",
    "        'decision_tree': {\n",
    "            'model': DecisionTreeRegressor(),\n",
    "            'params': {\n",
    "                'criterion' : ['mse','friedman_mse'],\n",
    "                'splitter': ['best','random']\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "    scores = []\n",
    "    cv = ShuffleSplit(n_splits=5, test_size=0.2, random_state=0)\n",
    "    for algo_name, config in algos.items():\n",
    "        gs =  GridSearchCV(config['model'], config['params'], cv=cv, return_train_score=False)\n",
    "        gs.fit(X,y)\n",
    "        scores.append({\n",
    "            'model': algo_name,\n",
    "            'best_score': gs.best_score_,\n",
    "            'best_params': gs.best_params_\n",
    "        })\n",
    "\n",
    "    return pd.DataFrame(scores,columns=['model','best_score','best_params'])\n",
    "\n",
    "find_best_model_gridSearchcv(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ccfb3fa",
   "metadata": {},
   "source": [
    "#### From above model comparison we can say that Linear Regression performs the best score. So, we'll use it for prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85b8a7c7",
   "metadata": {},
   "source": [
    "### Test the model :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "fd1d995e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using it to access column location.\n",
    "np.where(X.columns=='5th Block Hbr Layout')[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "6cf55203",
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_price(location,sqft,bath,bhk):    \n",
    "    loc_index = np.where(X.columns==location)[0][0]\n",
    "\n",
    "    x = np.zeros(len(X.columns))\n",
    "    x[0] = sqft\n",
    "    x[1] = bath\n",
    "    x[2] = bhk\n",
    "    if loc_index >= 0:\n",
    "        x[loc_index] = 1\n",
    "\n",
    "    return lr_clf.predict([x])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "762015b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "82.81981031368403"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test1\n",
    "predict_price('1st Phase JP Nagar',1000, 2,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "e1e4099c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "179.3706688280748"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test 2\n",
    "predict_price('Indira Nagar',1000, 2, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1b3ab4e",
   "metadata": {},
   "source": [
    "### Export the model to a pickle file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "19ef688a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open('banglore_home_prices_model.pickle','wb') as f:\n",
    "    pickle.dump(lr_clf,f)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7801e5cd",
   "metadata": {},
   "source": [
    "### Export location & column information in json file:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "d3fddb94",
   "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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