Data Science Project on Housing Price Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Science Project on Prediction of Bengaluru Housing Price"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Project Description"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "This data science project series walks through step by step process of how to build a real estate price prediction website. \n",
    "I will first build a model using sklearn and linear regression using bangaluru housing prices dataset from kaggle.com. Second step would be to write a python flask server that uses the saved model to serve http requests. \n",
    "Third component is the website built in html, css, bootstrap and javascript that allows user to enter home square ft area, bedrooms etc and it will call python flask server to retrieve the predicted price.\n",
    "During model building I will cover data science concepts such as data loading and cleaning, outlier detection and removal, feature engineering, dimensionality reduction, gridsearchcv for hyperparameter tunning, k fold cross validation etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Technology and tools wise this project covers."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "1) Python\n",
    "2) Numpy and Pandas for data cleaning\n",
    "3) Matplotlib for data visualization\n",
    "4) Sklearn for model building\n",
    "5) Jupyter notebook, visual studio code and pycharm as IDE\n",
    "6) Python flask for http server\n",
    "7) HTML/CSS/Bootstrap/Javascript for UI\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "matplotlib.rcParams[\"figure.figsize\"]=(20,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1= pd.read_csv(\"C:/Users/ARPITA/Downloads/Bengaluru_House_Data.csv\")\n",
    "#data set is taken from keggle.com\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13320, 9)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examine area_type feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "area_type\n",
       "Built-up  Area          2418\n",
       "Carpet  Area              87\n",
       "Plot  Area              2025\n",
       "Super built-up  Area    8790\n",
       "Name: area_type, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.groupby('area_type')['area_type'].agg('count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Drop not important columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1056</td>\n",
       "      <td>2.0</td>\n",
       "      <td>39.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>2600</td>\n",
       "      <td>5.0</td>\n",
       "      <td>120.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1440</td>\n",
       "      <td>2.0</td>\n",
       "      <td>62.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1521</td>\n",
       "      <td>3.0</td>\n",
       "      <td>95.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1200</td>\n",
       "      <td>2.0</td>\n",
       "      <td>51.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   location       size total_sqft  bath   price\n",
       "0  Electronic City Phase II      2 BHK       1056   2.0   39.07\n",
       "1          Chikka Tirupathi  4 Bedroom       2600   5.0  120.00\n",
       "2               Uttarahalli      3 BHK       1440   2.0   62.00\n",
       "3        Lingadheeranahalli      3 BHK       1521   3.0   95.00\n",
       "4                  Kothanur      2 BHK       1200   2.0   51.00"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2= df1.drop(['area_type','society','balcony','availability'],axis='columns')\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data cleaning process starts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### It starts with handling the NAN values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "two methods can be used : one is to drop the rows and another is fill the nan value with the mediun of the total column \n",
    "as in this example we have 13320 rows what we can do is to drop those nan rows which are 73 only in number in case of bath."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.isnull().sum() #return count of number of rows of a particular column having null value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3=df2.dropna() #dropna drop the nul valued rows\n",
    "df3.isnull().sum() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now lets move towards the size feature of the dataset where it can be seen that some values are having odd name like 4 bedroom etc. so to handle it following code will be written."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3['size'].unique() #all the unique values are displayed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ARPITA\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df3['bhk']=df3['size'].apply(lambda x:int(x.split(' ')[0]))\n",
    "# here we split the string into two tokens using python lambda function and seperate the first string(0) as integer "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>bhk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1056</td>\n",
       "      <td>2.0</td>\n",
       "      <td>39.07</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>2600</td>\n",
       "      <td>5.0</td>\n",
       "      <td>120.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1440</td>\n",
       "      <td>2.0</td>\n",
       "      <td>62.00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1521</td>\n",
       "      <td>3.0</td>\n",
       "      <td>95.00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1200</td>\n",
       "      <td>2.0</td>\n",
       "      <td>51.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   location       size total_sqft  bath   price  bhk\n",
       "0  Electronic City Phase II      2 BHK       1056   2.0   39.07    2\n",
       "1          Chikka Tirupathi  4 Bedroom       2600   5.0  120.00    4\n",
       "2               Uttarahalli      3 BHK       1440   2.0   62.00    3\n",
       "3        Lingadheeranahalli      3 BHK       1521   3.0   95.00    3\n",
       "4                  Kothanur      2 BHK       1200   2.0   51.00    2"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head() #printing the new dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3['bhk'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>bhk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1718</th>\n",
       "      <td>2Electronic City Phase II</td>\n",
       "      <td>27 BHK</td>\n",
       "      <td>8000</td>\n",
       "      <td>27.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4684</th>\n",
       "      <td>Munnekollal</td>\n",
       "      <td>43 Bedroom</td>\n",
       "      <td>2400</td>\n",
       "      <td>40.0</td>\n",
       "      <td>660.0</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       location        size total_sqft  bath  price  bhk\n",
       "1718  2Electronic City Phase II      27 BHK       8000  27.0  230.0   27\n",
       "4684                Munnekollal  43 Bedroom       2400  40.0  660.0   43"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3[df3.bhk>20] #looks like it have some error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1056', '2600', '1440', ..., '1133 - 1384', '774', '4689'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.total_sqft.unique() #range can be seen but we want a single number not range hence we have to convert it and one of the way it to take average of this kind-of range\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What kind-of variations are available in this total_sqft available. So when it will catch a range it will throw the same\n",
    "in except block. If it spot an integer then convert the same into float value\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "def is_float(x):\n",
    "    try:\n",
    "        float(x)\n",
    "    except:\n",
    "        return False\n",
    "    return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>bhk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Yelahanka</td>\n",
       "      <td>4 BHK</td>\n",
       "      <td>2100 - 2850</td>\n",
       "      <td>4.0</td>\n",
       "      <td>186.000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>Hebbal</td>\n",
       "      <td>4 BHK</td>\n",
       "      <td>3067 - 8156</td>\n",
       "      <td>4.0</td>\n",
       "      <td>477.000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>8th Phase JP Nagar</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1042 - 1105</td>\n",
       "      <td>2.0</td>\n",
       "      <td>54.005</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>Sarjapur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1145 - 1340</td>\n",
       "      <td>2.0</td>\n",
       "      <td>43.490</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>KR Puram</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1015 - 1540</td>\n",
       "      <td>2.0</td>\n",
       "      <td>56.800</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>Kengeri</td>\n",
       "      <td>1 BHK</td>\n",
       "      <td>34.46Sq. Meter</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549</th>\n",
       "      <td>Hennur Road</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1195 - 1440</td>\n",
       "      <td>2.0</td>\n",
       "      <td>63.770</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>648</th>\n",
       "      <td>Arekere</td>\n",
       "      <td>9 Bedroom</td>\n",
       "      <td>4125Perch</td>\n",
       "      <td>9.0</td>\n",
       "      <td>265.000</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>661</th>\n",
       "      <td>Yelahanka</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1120 - 1145</td>\n",
       "      <td>2.0</td>\n",
       "      <td>48.130</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>672</th>\n",
       "      <td>Bettahalsoor</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>3090 - 5002</td>\n",
       "      <td>4.0</td>\n",
       "      <td>445.000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               location       size      total_sqft  bath    price  bhk\n",
       "30            Yelahanka      4 BHK     2100 - 2850   4.0  186.000    4\n",
       "122              Hebbal      4 BHK     3067 - 8156   4.0  477.000    4\n",
       "137  8th Phase JP Nagar      2 BHK     1042 - 1105   2.0   54.005    2\n",
       "165            Sarjapur      2 BHK     1145 - 1340   2.0   43.490    2\n",
       "188            KR Puram      2 BHK     1015 - 1540   2.0   56.800    2\n",
       "410             Kengeri      1 BHK  34.46Sq. Meter   1.0   18.500    1\n",
       "549         Hennur Road      2 BHK     1195 - 1440   2.0   63.770    2\n",
       "648             Arekere  9 Bedroom       4125Perch   9.0  265.000    9\n",
       "661           Yelahanka      2 BHK     1120 - 1145   2.0   48.130    2\n",
       "672        Bettahalsoor  4 Bedroom     3090 - 5002   4.0  445.000    4"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3[~df3['total_sqft'].apply(is_float)].head(10) \n",
    "#negetion will return the range value once of total_sqft because it has been thrown in the exception block\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_sqft_to_num(x):\n",
    "    tokens=x.split('-')\n",
    "    if len(tokens)==2:\n",
    "        return (float(tokens[0])+float(tokens[1]))/2\n",
    "    try:\n",
    "        return float(x)\n",
    "    except:\n",
    "        return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4000.0"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "convert_sqft_to_num('2000 - 6000')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "convert_sqft_to_num('34.46Sq. Meter') #do not return anything"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>bhk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1056.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>39.07</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>120.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>62.00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1521.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>95.00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>51.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Whitefield</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1170.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>38.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Old Airport Road</td>\n",
       "      <td>4 BHK</td>\n",
       "      <td>2732.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>204.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Rajaji Nagar</td>\n",
       "      <td>4 BHK</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>600.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Marathahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1310.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>63.25</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Gandhi Bazar</td>\n",
       "      <td>6 Bedroom</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>370.00</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   location       size  total_sqft  bath   price  bhk\n",
       "0  Electronic City Phase II      2 BHK      1056.0   2.0   39.07    2\n",
       "1          Chikka Tirupathi  4 Bedroom      2600.0   5.0  120.00    4\n",
       "2               Uttarahalli      3 BHK      1440.0   2.0   62.00    3\n",
       "3        Lingadheeranahalli      3 BHK      1521.0   3.0   95.00    3\n",
       "4                  Kothanur      2 BHK      1200.0   2.0   51.00    2\n",
       "5                Whitefield      2 BHK      1170.0   2.0   38.00    2\n",
       "6          Old Airport Road      4 BHK      2732.0   4.0  204.00    4\n",
       "7              Rajaji Nagar      4 BHK      3300.0   4.0  600.00    4\n",
       "8              Marathahalli      3 BHK      1310.0   3.0   63.25    3\n",
       "9              Gandhi Bazar  6 Bedroom      1020.0   6.0  370.00    6"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4= df3.copy()\n",
    "df4['total_sqft']=df4['total_sqft'].apply(convert_sqft_to_num)\n",
    "df4.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location      Yelahanka\n",
       "size              4 BHK\n",
       "total_sqft         2475\n",
       "bath                  4\n",
       "price               186\n",
       "bhk                   4\n",
       "Name: 30, dtype: object"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.loc[30] #checking index of 30"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering and Dimensionality Reduction Technique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Feature Engineering\n",
    "df5= df4.copy()\n",
    "# create price per square feet column\n",
    "\n",
    "df5['price_per_sqft']= df5['price']*100000/df5['total_sqft']\n",
    "df5.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Location is a categorical entity. If too same many locations are there then it create a problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1304"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df5.location.unique()) #so many different unique locations available"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dimentional Reduction:\n",
    "One of the way to reduce dimention is to introduce 'other' categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location\n",
       "Whitefield           535\n",
       "Sarjapur  Road       392\n",
       "Electronic City      304\n",
       "Kanakpura Road       266\n",
       "Thanisandra          236\n",
       "                    ... \n",
       "LIC Colony             1\n",
       "Kuvempu Layout         1\n",
       "Kumbhena Agrahara      1\n",
       "Kudlu Village,         1\n",
       "1 Annasandrapalya      1\n",
       "Name: location, Length: 1293, dtype: int64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.location= df5.location.apply(lambda x:x.strip()) #remove extra space\n",
    "#here location_stats is series\n",
    "location_stats= df5.groupby('location')['location'].agg('count').sort_values(ascending= False)\n",
    "location_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1052"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(location_stats[location_stats<=10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location\n",
       "BTM 1st Stage          10\n",
       "Basapura               10\n",
       "Sector 1 HSR Layout    10\n",
       "Naganathapura          10\n",
       "Kalkere                10\n",
       "                       ..\n",
       "LIC Colony              1\n",
       "Kuvempu Layout          1\n",
       "Kumbhena Agrahara       1\n",
       "Kudlu Village,          1\n",
       "1 Annasandrapalya       1\n",
       "Name: location, Length: 1052, dtype: int64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "location_stats_lessthan_10=location_stats[location_stats<=10]\n",
    "location_stats_lessthan_10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1293"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df5.location.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "242"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.location= df5.location.apply(lambda x: 'other' if x in location_stats_lessthan_10 else x)\n",
    "#transfering all location which have less number of rows to other\n",
    "len(df5.location.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>bhk</th>\n",
       "      <th>price_per_sqft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1056.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>39.07</td>\n",
       "      <td>2</td>\n",
       "      <td>3699.810606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>120.00</td>\n",
       "      <td>4</td>\n",
       "      <td>4615.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>62.00</td>\n",
       "      <td>3</td>\n",
       "      <td>4305.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1521.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>95.00</td>\n",
       "      <td>3</td>\n",
       "      <td>6245.890861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>51.00</td>\n",
       "      <td>2</td>\n",
       "      <td>4250.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Whitefield</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1170.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>38.00</td>\n",
       "      <td>2</td>\n",
       "      <td>3247.863248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Old Airport Road</td>\n",
       "      <td>4 BHK</td>\n",
       "      <td>2732.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>204.00</td>\n",
       "      <td>4</td>\n",
       "      <td>7467.057101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Rajaji Nagar</td>\n",
       "      <td>4 BHK</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>600.00</td>\n",
       "      <td>4</td>\n",
       "      <td>18181.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Marathahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1310.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>63.25</td>\n",
       "      <td>3</td>\n",
       "      <td>4828.244275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>other</td>\n",
       "      <td>6 Bedroom</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>370.00</td>\n",
       "      <td>6</td>\n",
       "      <td>36274.509804</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   location       size  total_sqft  bath   price  bhk  \\\n",
       "0  Electronic City Phase II      2 BHK      1056.0   2.0   39.07    2   \n",
       "1          Chikka Tirupathi  4 Bedroom      2600.0   5.0  120.00    4   \n",
       "2               Uttarahalli      3 BHK      1440.0   2.0   62.00    3   \n",
       "3        Lingadheeranahalli      3 BHK      1521.0   3.0   95.00    3   \n",
       "4                  Kothanur      2 BHK      1200.0   2.0   51.00    2   \n",
       "5                Whitefield      2 BHK      1170.0   2.0   38.00    2   \n",
       "6          Old Airport Road      4 BHK      2732.0   4.0  204.00    4   \n",
       "7              Rajaji Nagar      4 BHK      3300.0   4.0  600.00    4   \n",
       "8              Marathahalli      3 BHK      1310.0   3.0   63.25    3   \n",
       "9                     other  6 Bedroom      1020.0   6.0  370.00    6   \n",
       "\n",
       "   price_per_sqft  \n",
       "0     3699.810606  \n",
       "1     4615.384615  \n",
       "2     4305.555556  \n",
       "3     6245.890861  \n",
       "4     4250.000000  \n",
       "5     3247.863248  \n",
       "6     7467.057101  \n",
       "7    18181.818182  \n",
       "8     4828.244275  \n",
       "9    36274.509804  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outlier Detection and Removal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13246, 7)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12502, 7)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6=df5[~(df5.total_sqft/df5.bhk < 300)] #removing those rows whose sqft per bhk is less than 300 which helps in cutting out errors in rows\n",
    "df6.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6.price_per_sqft.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So in the last code we can see that minimum price per square feet is 267 which is too low price and maximum price is 176470 which is also an unusual figure. Hence we need to remove all these rows from the data set as we need a generic model for predicting the price. Therefore we will use the method of Standard deviation to do the same"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10241, 7)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def remove_pps_outliers(df):\n",
    "    df_out=pd.DataFrame()\n",
    "    for key,subdf in df.groupby('location'):\n",
    "        m= np.mean(subdf.price_per_sqft) #mean\n",
    "        st=np.std(subdf.price_per_sqft) #standard deviation\n",
    "        reduced_df= subdf[(subdf.price_per_sqft >(m-st))& (subdf.price_per_sqft<=(m+st))]\n",
    "        df_out=pd.concat([df_out,reduced_df],ignore_index=True)\n",
    "    return df_out\n",
    "\n",
    "df7=remove_pps_outliers(df6)\n",
    "df7.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ploting a scatter plot according to location to judge or to see 3bhk having lesser price but having the same total sqr. ft.  as 2bhk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_scatter_chart(df,location):\n",
    "    bh2= df[(df.location==location) & (df.bhk==2)]\n",
    "    bh3= df[(df.location==location) & (df.bhk==3)]\n",
    "    matplotlib.rcParams['figure.figsize']=(15,20)\n",
    "    plt.scatter(bh2.total_sqft,bh2.price,color='red',label='2-BHK',s=50)\n",
    "    plt.scatter(bh3.total_sqft,bh3.price,marker='+',color='green',label='3-BHK',s=50)\n",
    "    plt.xlabel(\"Total square feet area\")\n",
    "    plt.ylabel(\"Price\")\n",
    "    plt.title(location)\n",
    "    plt.legend()\n",
    "    \n",
    "plot_scatter_chart(df7,\"Rajaji Nagar\")"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "We should also remove properties where for same location, the price of (for example) 3 bedroom apartment is less than 2 bedroom apartment (with same square ft area). What we will do is for a given location, we will build a dictionary of stats per bhk, i.e.\n",
    "\n",
    "{\n",
    "    '1' : {\n",
    "        'mean': 4000,\n",
    "        'std: 2000,\n",
    "        'count': 34\n",
    "    },\n",
    "    '2' : {\n",
    "        'mean': 4300,\n",
    "        'std: 2300,\n",
    "        'count': 22\n",
    "    },    \n",
    "}\n",
    "Now we can remove those 2 BHK apartments whose price_per_sqft is less than mean price_per_sqft of 1 BHK apartment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "matplotlib.rcParams[\"figure.figsize\"]=(20,10)\n",
    "plt.hist(df7.price_per_sqft, rwidth= 0.8)\n",
    "plt.xlabel(\"Price per square feet\")\n",
    "plt.ylabel(\"Count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4.,  3.,  2.,  5.,  8.,  1.,  6.,  7.,  9., 12., 16., 13.])"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7.bath.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>bhk</th>\n",
       "      <th>price_per_sqft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5277</th>\n",
       "      <td>Neeladri Nagar</td>\n",
       "      <td>10 BHK</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>10</td>\n",
       "      <td>4000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8486</th>\n",
       "      <td>other</td>\n",
       "      <td>10 BHK</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>525.0</td>\n",
       "      <td>10</td>\n",
       "      <td>4375.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8575</th>\n",
       "      <td>other</td>\n",
       "      <td>16 BHK</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>16</td>\n",
       "      <td>5500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9308</th>\n",
       "      <td>other</td>\n",
       "      <td>11 BHK</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>11</td>\n",
       "      <td>2500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9639</th>\n",
       "      <td>other</td>\n",
       "      <td>13 BHK</td>\n",
       "      <td>5425.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>13</td>\n",
       "      <td>5069.124424</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            location    size  total_sqft  bath  price  bhk  price_per_sqft\n",
       "5277  Neeladri Nagar  10 BHK      4000.0  12.0  160.0   10     4000.000000\n",
       "8486           other  10 BHK     12000.0  12.0  525.0   10     4375.000000\n",
       "8575           other  16 BHK     10000.0  16.0  550.0   16     5500.000000\n",
       "9308           other  11 BHK      6000.0  12.0  150.0   11     2500.000000\n",
       "9639           other  13 BHK      5425.0  13.0  275.0   13     5069.124424"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7[df7.bath>10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df7.bath,rwidth=0.8)\n",
    "plt.xlabel(\"Number of bethrooms\")\n",
    "plt.ylabel(\"Count\")"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "We can see 13 no. of bathrooms are present in some bhk. So that is unusual. Hence all bathrooms which are greater than bedroom pus 2 will be droped\n",
    "# Here we can see the dataset having more number of bathrooms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>bhk</th>\n",
       "      <th>price_per_sqft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1626</th>\n",
       "      <td>Chikkabanavar</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>2460.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3252.032520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5238</th>\n",
       "      <td>Nagasandra</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>4</td>\n",
       "      <td>6428.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6711</th>\n",
       "      <td>Thanisandra</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1806.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>3</td>\n",
       "      <td>6423.034330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8411</th>\n",
       "      <td>other</td>\n",
       "      <td>6 BHK</td>\n",
       "      <td>11338.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>6</td>\n",
       "      <td>8819.897689</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           location       size  total_sqft  bath   price  bhk  price_per_sqft\n",
       "1626  Chikkabanavar  4 Bedroom      2460.0   7.0    80.0    4     3252.032520\n",
       "5238     Nagasandra  4 Bedroom      7000.0   8.0   450.0    4     6428.571429\n",
       "6711    Thanisandra      3 BHK      1806.0   6.0   116.0    3     6423.034330\n",
       "8411          other      6 BHK     11338.0   9.0  1000.0    6     8819.897689"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7[df7.bath> df7.bhk+2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10147, 7)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df8 =df7[df7.bath<df7.bhk+2]\n",
    "df8.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "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": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df9 =df8.drop([\"price_per_sqft\",\"size\"],axis=\"columns\")\n",
    "df9.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
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       "   1st Block Jayanagar  1st Phase JP Nagar  2nd Phase Judicial Layout  \\\n",
       "0                    1                   0                          0   \n",
       "1                    1                   0                          0   \n",
       "2                    1                   0                          0   \n",
       "3                    1                   0                          0   \n",
       "4                    1                   0                          0   \n",
       "\n",
       "   2nd Stage Nagarbhavi  5th Block Hbr Layout  5th Phase JP Nagar  \\\n",
       "0                     0                     0                   0   \n",
       "1                     0                     0                   0   \n",
       "2                     0                     0                   0   \n",
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       "\n",
       "   6th Phase JP Nagar  7th Phase JP Nagar  8th Phase JP Nagar  \\\n",
       "0                   0                   0                   0   \n",
       "1                   0                   0                   0   \n",
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       "4                   0                   0                   0   \n",
       "\n",
       "   9th Phase JP Nagar  ...  Vishveshwarya Layout  Vishwapriya Layout  \\\n",
       "0                   0  ...                     0                   0   \n",
       "1                   0  ...                     0                   0   \n",
       "2                   0  ...                     0                   0   \n",
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       "\n",
       "   Vittasandra  Whitefield  Yelachenahalli  Yelahanka  Yelahanka New Town  \\\n",
       "0            0           0               0          0                   0   \n",
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       "4            0           0               0          0                   0   \n",
       "\n",
       "   Yelenahalli  Yeshwanthpur  other  \n",
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       "\n",
       "[5 rows x 242 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummies= pd.get_dummies(df9.location) #one-hot-encoding\n",
    "dummies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
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       "              location  total_sqft  bath  price  bhk  1st Block Jayanagar  \\\n",
       "0  1st Block Jayanagar      2850.0   4.0  428.0    4                    1   \n",
       "1  1st Block Jayanagar      1630.0   3.0  194.0    3                    1   \n",
       "2  1st Block Jayanagar      1875.0   2.0  235.0    3                    1   \n",
       "3  1st Block Jayanagar      1200.0   2.0  130.0    3                    1   \n",
       "4  1st Block Jayanagar      1235.0   2.0  148.0    2                    1   \n",
       "\n",
       "   1st Phase JP Nagar  2nd Phase Judicial Layout  2nd Stage Nagarbhavi  \\\n",
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       "\n",
       "   5th Block Hbr Layout  ...  Vijayanagar  Vishveshwarya Layout  \\\n",
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       "2                     0  ...            0                     0   \n",
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       "\n",
       "   Vishwapriya Layout  Vittasandra  Whitefield  Yelachenahalli  Yelahanka  \\\n",
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       "\n",
       "   Yelahanka New Town  Yelenahalli  Yeshwanthpur  \n",
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     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df10= pd.concat([df9,dummies.drop('other',axis='columns')],axis='columns')\n",
    "# as in dummies we must drop one column, hence other column is dropped\n",
    "df10.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
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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>2</th>\n",
       "      <td>1875.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1235.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <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 × 245 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_sqft  bath  price  bhk  1st Block Jayanagar  1st Phase JP Nagar  \\\n",
       "0      2850.0   4.0  428.0    4                    1                   0   \n",
       "1      1630.0   3.0  194.0    3                    1                   0   \n",
       "2      1875.0   2.0  235.0    3                    1                   0   \n",
       "3      1200.0   2.0  130.0    3                    1                   0   \n",
       "4      1235.0   2.0  148.0    2                    1                   0   \n",
       "\n",
       "   2nd Phase Judicial Layout  2nd Stage Nagarbhavi  5th Block Hbr Layout  \\\n",
       "0                          0                     0                     0   \n",
       "1                          0                     0                     0   \n",
       "2                          0                     0                     0   \n",
       "3                          0                     0                     0   \n",
       "4                          0                     0                     0   \n",
       "\n",
       "   5th Phase JP Nagar  ...  Vijayanagar  Vishveshwarya Layout  \\\n",
       "0                   0  ...            0                     0   \n",
       "1                   0  ...            0                     0   \n",
       "2                   0  ...            0                     0   \n",
       "3                   0  ...            0                     0   \n",
       "4                   0  ...            0                     0   \n",
       "\n",
       "   Vishwapriya Layout  Vittasandra  Whitefield  Yelachenahalli  Yelahanka  \\\n",
       "0                   0            0           0               0          0   \n",
       "1                   0            0           0               0          0   \n",
       "2                   0            0           0               0          0   \n",
       "3                   0            0           0               0          0   \n",
       "4                   0            0           0               0          0   \n",
       "\n",
       "   Yelahanka New Town  Yelenahalli  Yeshwanthpur  \n",
       "0                   0            0             0  \n",
       "1                   0            0             0  \n",
       "2                   0            0             0  \n",
       "3                   0            0             0  \n",
       "4                   0            0             0  \n",
       "\n",
       "[5 rows x 245 columns]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df11= df10.drop('location',axis='columns')\n",
    "df11.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10147, 245)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df11.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model building process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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>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",
       "      <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>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": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x= df11.drop('price',axis='columns')\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    428.0\n",
       "1    194.0\n",
       "2    235.0\n",
       "3    130.0\n",
       "4    148.0\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y= df11.price\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training and Testing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train, y_test = train_test_split(x,y,test_size=0.2,random_state= 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7919477337586842"
      ]
     },
     "execution_count": 101,
     "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",
   "metadata": {},
   "source": [
    "### Use K Fold cross validation to measure accuracy of our LinearRegression model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.81155684, 0.77261421, 0.80214981, 0.80426801, 0.79827292])"
      ]
     },
     "execution_count": 104,
     "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",
    "cross_val_score(LinearRegression(),x,y,cv=cv)\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "We can see that in 5 iterations we get a score above 80% all the time. This is pretty good but we want to test few other algorithms for regression to see if we can get even better score. We will use GridSearchCV for this purpose"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find best model using GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "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>0.797772</td>\n",
       "      <td>{'normalize': True}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>lasso</td>\n",
       "      <td>0.665198</td>\n",
       "      <td>{'alpha': 1, 'selection': 'random'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>decision_tree</td>\n",
       "      <td>0.697071</td>\n",
       "      <td>{'criterion': 'mse', 'splitter': 'best'}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               model  best_score                               best_params\n",
       "0  linear_regression    0.797772                       {'normalize': True}\n",
       "1              lasso    0.665198       {'alpha': 1, 'selection': 'random'}\n",
       "2      decision_tree    0.697071  {'criterion': 'mse', 'splitter': 'best'}"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "def find_best_model_using_gridsearchcv(x,y):\n",
    "    algos = {\n",
    "        'linear_regression' : {\n",
    "            'model': LinearRegression(),\n",
    "            'params': {\n",
    "                '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_using_gridsearchcv(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['total_sqft', 'bath', 'bhk', '1st Block Jayanagar',\n",
       "       '1st Phase JP Nagar', '2nd Phase Judicial Layout',\n",
       "       '2nd Stage Nagarbhavi', '5th Block Hbr Layout', '5th Phase JP Nagar',\n",
       "       '6th Phase JP Nagar',\n",
       "       ...\n",
       "       'Vijayanagar', 'Vishveshwarya Layout', 'Vishwapriya Layout',\n",
       "       'Vittasandra', 'Whitefield', 'Yelachenahalli', 'Yelahanka',\n",
       "       'Yelahanka New Town', 'Yelenahalli', 'Yeshwanthpur'],\n",
       "      dtype='object', length=244)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.columns"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Based on above results we can say that LinearRegression gives the best score. Hence we will use that."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test the model for few properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "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": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86.33120168613027"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_price('1st Phase JP Nagar',1000, 2, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81.037487508134"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_price('1st Phase JP Nagar',1000, 2, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "168.61759521870724"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_price('Indira Nagar',1000, 2, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "170.17148677340523"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_price('Indira Nagar',1000, 3, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Export the tested model to a pickle file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "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",
   "metadata": {},
   "source": [
    "### Export location and column information to a file that will be useful later on in our prediction application"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "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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