House_Price_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style='color:purple' align='center'>Data Science Regression Project: Predicting Home Prices in Banglore <br> 1)Ajeet Yadav <br> 2) Vishal Yadav <br> 3) Surendra Sahu <br> Professor:- Yogesh Gite</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataset is downloaded from here: https://www.kaggle.com/amitabhajoy/bengaluru-house-price-data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": "markdown",
   "metadata": {},
   "source": [
    "<h2 style='color:blue'>Data Load: Load banglore home prices into a dataframe</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>area_type</th>\n",
       "      <th>availability</th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>society</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>balcony</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>19-Dec</td>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>Coomee</td>\n",
       "      <td>1056</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Plot  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>Theanmp</td>\n",
       "      <td>2600</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>120.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1440</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>62.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>Soiewre</td>\n",
       "      <td>1521</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>95.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1200</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>51.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              area_type   availability                  location       size  \\\n",
       "0  Super built-up  Area         19-Dec  Electronic City Phase II      2 BHK   \n",
       "1            Plot  Area  Ready To Move          Chikka Tirupathi  4 Bedroom   \n",
       "2        Built-up  Area  Ready To Move               Uttarahalli      3 BHK   \n",
       "3  Super built-up  Area  Ready To Move        Lingadheeranahalli      3 BHK   \n",
       "4  Super built-up  Area  Ready To Move                  Kothanur      2 BHK   \n",
       "\n",
       "   society total_sqft  bath  balcony   price  \n",
       "0  Coomee        1056   2.0      1.0   39.07  \n",
       "1  Theanmp       2600   5.0      3.0  120.00  \n",
       "2      NaN       1440   2.0      3.0   62.00  \n",
       "3  Soiewre       1521   3.0      1.0   95.00  \n",
       "4      NaN       1200   2.0      1.0   51.00  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.read_csv(\"bengaluru_house_prices.csv\")\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13320, 9)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['area_type', 'availability', 'location', 'size', 'society',\n",
       "       'total_sqft', 'bath', 'balcony', 'price'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Super built-up  Area', 'Plot  Area', 'Built-up  Area',\n",
       "       'Carpet  Area'], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['area_type'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": [
    "df1['area_type'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Drop features that are not required to build our model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13320, 5)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df1.drop(['area_type','society','balcony','availability'],axis='columns')\n",
    "df2.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 style='color:blue'>Data Cleaning: Handle NA values</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location       1\n",
       "size          16\n",
       "total_sqft     0\n",
       "bath          73\n",
       "price          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13320, 5)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = df2.dropna()\n",
    "df3.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13246, 5)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 style='color:blue'>Feature Engineering</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Add new feature(integer) for bhk (Bedrooms Hall Kitchen)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-12-681cf3aca53d>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df3['bhk'] = df3['size'].apply(lambda x: int(x.split(' ')[0]))\n"
     ]
    },
    {
     "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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3['bhk'] = df3['size'].apply(lambda x: int(x.split(' ')[0]))\n",
    "df3.bhk.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Explore total_sqft feature**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2+3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "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>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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3[~df3['total_sqft'].apply(is_float)].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Above shows that total_sqft can be a range (e.g. 2100-2850). For such case we can just take average of min and max value in the range. There are other cases such as 34.46Sq. Meter which one can convert to square ft using unit conversion. I am going to just drop such corner cases to keep things simple**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>bhk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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",
       "  </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"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = df3.copy()\n",
    "df4.total_sqft = df4.total_sqft.apply(convert_sqft_to_num)\n",
    "df4 = df4[df4.total_sqft.notnull()]\n",
    "df4.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**For below row, it shows total_sqft as 2475 which is an average of the range 2100-2850**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location      Yelahanka\n",
       "size              4 BHK\n",
       "total_sqft       2475.0\n",
       "bath                4.0\n",
       "price             186.0\n",
       "bhk                   4\n",
       "Name: 30, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.loc[30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2475.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(2100+2850)/2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 style=\"color:blue\">Feature Engineering</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Add new feature called price per square feet**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5 = df4.copy()\n",
    "df5['price_per_sqft'] = df5['price']*100000/df5['total_sqft']\n",
    "df5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1.320000e+04\n",
       "mean     7.920759e+03\n",
       "std      1.067272e+05\n",
       "min      2.678298e+02\n",
       "25%      4.267701e+03\n",
       "50%      5.438331e+03\n",
       "75%      7.317073e+03\n",
       "max      1.200000e+07\n",
       "Name: price_per_sqft, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5_stats = df5['price_per_sqft'].describe()\n",
    "df5_stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Examine locations which is a categorical variable. We need to apply dimensionality reduction technique here to reduce number of locations**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Whitefield                  533\n",
       "Sarjapur  Road              392\n",
       "Electronic City             304\n",
       "Kanakpura Road              264\n",
       "Thanisandra                 235\n",
       "                           ... \n",
       "T C Palya main Road           1\n",
       "Sindhi Colony                 1\n",
       "HAL Layout                    1\n",
       "Electronic city phase 1,      1\n",
       "Bettadasanapura               1\n",
       "Name: location, Length: 1287, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.location = df5.location.apply(lambda x: x.strip())\n",
    "location_stats = df5['location'].value_counts(ascending=False)\n",
    "location_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13200"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "location_stats.values.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "240"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(location_stats[location_stats>10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1287"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(location_stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1047"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(location_stats[location_stats<=10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 style=\"color:blue\">Dimensionality Reduction</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Any location having less than 10 data points should be tagged as \"other\" location. This way number of categories can be reduced by huge amount. Later on when we do one hot encoding, it will help us with having fewer dummy columns**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ganga Nagar                 10\n",
       "Thyagaraja Nagar            10\n",
       "Sector 1 HSR Layout         10\n",
       "Dairy Circle                10\n",
       "Kalkere                     10\n",
       "                            ..\n",
       "T C Palya main Road          1\n",
       "Sindhi Colony                1\n",
       "HAL Layout                   1\n",
       "Electronic city phase 1,     1\n",
       "Bettadasanapura              1\n",
       "Name: location, Length: 1047, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "location_stats_less_than_10 = location_stats[location_stats<=10]\n",
    "location_stats_less_than_10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1287"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df5.location.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "241"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.location = df5.location.apply(lambda x: 'other' if x in location_stats_less_than_10 else x)\n",
    "len(df5.location.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 style=\"color:blue\">Outlier Removal Using Business Logic</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**As a data scientist when you have a conversation with your business manager (who has expertise in real estate), he will tell you that normally square ft per bedroom is 300 (i.e. 2 bhk apartment is minimum 600 sqft. If you have for example 400 sqft apartment with 2 bhk than that seems suspicious and can be removed as an outlier. We will remove such outliers by keeping our minimum thresold per bhk to be 300 sqft**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>bhk</th>\n",
       "      <th>price_per_sqft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>other</td>\n",
       "      <td>6 Bedroom</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>6</td>\n",
       "      <td>36274.509804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>HSR Layout</td>\n",
       "      <td>8 Bedroom</td>\n",
       "      <td>600.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>8</td>\n",
       "      <td>33333.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Murugeshpalya</td>\n",
       "      <td>6 Bedroom</td>\n",
       "      <td>1407.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>6</td>\n",
       "      <td>10660.980810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Devarachikkanahalli</td>\n",
       "      <td>8 Bedroom</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>8</td>\n",
       "      <td>6296.296296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>other</td>\n",
       "      <td>3 Bedroom</td>\n",
       "      <td>500.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>3</td>\n",
       "      <td>20000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               location       size  total_sqft  bath  price  bhk  \\\n",
       "9                 other  6 Bedroom      1020.0   6.0  370.0    6   \n",
       "45           HSR Layout  8 Bedroom       600.0   9.0  200.0    8   \n",
       "58        Murugeshpalya  6 Bedroom      1407.0   4.0  150.0    6   \n",
       "68  Devarachikkanahalli  8 Bedroom      1350.0   7.0   85.0    8   \n",
       "70                other  3 Bedroom       500.0   3.0  100.0    3   \n",
       "\n",
       "    price_per_sqft  \n",
       "9     36274.509804  \n",
       "45    33333.333333  \n",
       "58    10660.980810  \n",
       "68     6296.296296  \n",
       "70    20000.000000  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5[df5.total_sqft/df5.bhk<300].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Check above data points. We have 6 bhk apartment with 1020 sqft. Another one is 8 bhk and total sqft is 600. These are clear data errors that can be removed safely**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13200, 7)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12456, 7)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6 = df5[~(df5.total_sqft/df5.bhk<300)]\n",
    "df6.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 style='color:blue'>Outlier Removal Using Standard Deviation and Mean</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6.price_per_sqft.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Here we find that min price per sqft is 267 rs/sqft whereas max is 12000000, this shows a wide variation in property prices. We should remove outliers per location using mean and one standard deviation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10242, 7)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def remove_pps_outliers(df):\n",
    "    df_out = pd.DataFrame()\n",
    "    for key, subdf in df.groupby('location'):\n",
    "        m = np.mean(subdf.price_per_sqft)\n",
    "        st = np.std(subdf.price_per_sqft)\n",
    "        reduced_df = subdf[(subdf.price_per_sqft>(m-st)) & (subdf.price_per_sqft<=(m+st))]\n",
    "        df_out = pd.concat([df_out,reduced_df],ignore_index=True)\n",
    "    return df_out\n",
    "df7 = remove_pps_outliers(df6)\n",
    "df7.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Let's check if for a given location how does the 2 BHK and 3 BHK property prices look like**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_scatter_chart(df,location):\n",
    "    bhk2 = df[(df.location==location) & (df.bhk==2)]\n",
    "    bhk3 = df[(df.location==location) & (df.bhk==3)]\n",
    "    matplotlib.rcParams['figure.figsize'] = (15,10)\n",
    "    plt.scatter(bhk2.total_sqft,bhk2.price,color='blue',label='2 BHK', s=50)\n",
    "    plt.scatter(bhk3.total_sqft,bhk3.price,marker='+', color='green',label='3 BHK', s=50)\n",
    "    plt.xlabel(\"Total Square Feet Area\")\n",
    "    plt.ylabel(\"Price (Lakh Indian Rupees)\")\n",
    "    plt.title(location)\n",
    "    plt.legend()\n",
    "    \n",
    "plot_scatter_chart(df7,\"Rajaji Nagar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scatter_chart(df7,\"Hebbal\")"
   ]
  },
  {
   "cell_type": "markdown",
   "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",
    "```\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": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7317, 7)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def remove_bhk_outliers(df):\n",
    "    exclude_indices = np.array([])\n",
    "    for location, location_df in df.groupby('location'):\n",
    "        bhk_stats = {}\n",
    "        for bhk, bhk_df in location_df.groupby('bhk'):\n",
    "            bhk_stats[bhk] = {\n",
    "                'mean': np.mean(bhk_df.price_per_sqft),\n",
    "                'std': np.std(bhk_df.price_per_sqft),\n",
    "                'count': bhk_df.shape[0]\n",
    "            }\n",
    "        for bhk, bhk_df in location_df.groupby('bhk'):\n",
    "            stats = bhk_stats.get(bhk-1)\n",
    "            if stats and stats['count']>5:\n",
    "                exclude_indices = np.append(exclude_indices, bhk_df[bhk_df.price_per_sqft<(stats['mean'])].index.values)\n",
    "    return df.drop(exclude_indices,axis='index')\n",
    "df8 = remove_bhk_outliers(df7)\n",
    "# df8 = df7.copy()\n",
    "df8.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Plot same scatter chart again to visualize price_per_sqft for 2 BHK and 3 BHK properties**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scatter_chart(df8,\"Rajaji Nagar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scatter_chart(df8,\"Hebbal\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Based on above charts we can see that data points highlighted in red below are outliers and they are being removed due to remove_bhk_outliers function**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 style='color:green'>Before and after outlier removal: Rajaji Nagar</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"rajaji_nagar_outliers.png\"></img>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 style='color:green'>Before and after outlier removal: Hebbal</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"hebbal_outliers.png\"></img>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 41,
     "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(df8.price_per_sqft,rwidth=0.8)\n",
    "plt.xlabel(\"Price Per Square Feet\")\n",
    "plt.ylabel(\"Count\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 style='color:blue'>Outlier Removal Using Bathrooms Feature</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4.,  3.,  2.,  5.,  8.,  1.,  6.,  7.,  9., 12., 16., 13.])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df8.bath.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df8.bath,rwidth=0.8)\n",
    "plt.xlabel(\"Number of bathrooms\")\n",
    "plt.ylabel(\"Count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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",
       "      <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>8483</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>8572</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>9306</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>9637</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",
       "8483           other  10 BHK     12000.0  12.0  525.0   10     4375.000000\n",
       "8572           other  16 BHK     10000.0  16.0  550.0   16     5500.000000\n",
       "9306           other  11 BHK      6000.0  12.0  150.0   11     2500.000000\n",
       "9637           other  13 BHK      5425.0  13.0  275.0   13     5069.124424"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df8[df8.bath>10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**It is unusual to have 2 more bathrooms than number of bedrooms in a home**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>bhk</th>\n",
       "      <th>price_per_sqft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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>8408</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",
       "8408          other      6 BHK     11338.0   9.0  1000.0    6     8819.897689"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df8[df8.bath>df8.bhk+2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Again the business manager has a conversation with you (i.e. a data scientist) that if you have 4 bedroom home and even if you have bathroom in all 4 rooms plus one guest bathroom, you will have total bath = total bed + 1 max. Anything above that is an outlier or a data error and can be removed**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7239, 7)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df9 = df8[df8.bath<df8.bhk+2]\n",
    "df9.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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",
       "      <th>price_per_sqft</th>\n",
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       "      <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",
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       "</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"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df9.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <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",
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       "    <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",
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       "    <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",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              location  total_sqft  bath  price  bhk\n",
       "0  1st Block Jayanagar      2850.0   4.0  428.0    4\n",
       "1  1st Block Jayanagar      1630.0   3.0  194.0    3\n",
       "2  1st Block Jayanagar      1875.0   2.0  235.0    3"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df10 = df9.drop(['size','price_per_sqft'],axis='columns')\n",
    "df10.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 style='color:blue'>Use One Hot Encoding For Location</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <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",
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       "      <th>7th Phase JP Nagar</th>\n",
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       "      <th>9th Phase JP Nagar</th>\n",
       "      <th>...</th>\n",
       "      <th>Vishveshwarya Layout</th>\n",
       "      <th>Vishwapriya Layout</th>\n",
       "      <th>Vittasandra</th>\n",
       "      <th>Whitefield</th>\n",
       "      <th>Yelachenahalli</th>\n",
       "      <th>Yelahanka</th>\n",
       "      <th>Yelahanka New Town</th>\n",
       "      <th>Yelenahalli</th>\n",
       "      <th>Yeshwanthpur</th>\n",
       "      <th>other</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 241 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",
       "\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",
       "\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",
       "\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",
       "\n",
       "   Vittasandra  Whitefield  Yelachenahalli  Yelahanka  Yelahanka New Town  \\\n",
       "0            0           0               0          0                   0   \n",
       "1            0           0               0          0                   0   \n",
       "2            0           0               0          0                   0   \n",
       "\n",
       "   Yelenahalli  Yeshwanthpur  other  \n",
       "0            0             0      0  \n",
       "1            0             0      0  \n",
       "2            0             0      0  \n",
       "\n",
       "[3 rows x 241 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummies = pd.get_dummies(df10.location)\n",
    "dummies.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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>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",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3</td>\n",
       "      <td>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>4</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>1235.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>2</td>\n",
       "      <td>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>5 rows × 245 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",
       "3  1st Block Jayanagar      1200.0   2.0  130.0    3                    1   \n",
       "4  1st Block Jayanagar      1235.0   2.0  148.0    2                    1   \n",
       "\n",
       "   1st Phase JP Nagar  2nd Phase Judicial Layout  2nd Stage Nagarbhavi  \\\n",
       "0                   0                          0                     0   \n",
       "1                   0                          0                     0   \n",
       "2                   0                          0                     0   \n",
       "3                   0                          0                     0   \n",
       "4                   0                          0                     0   \n",
       "\n",
       "   5th Block Hbr Layout  ...  Vijayanagar  Vishveshwarya Layout  \\\n",
       "0                     0  ...            0                     0   \n",
       "1                     0  ...            0                     0   \n",
       "2                     0  ...            0                     0   \n",
       "3                     0  ...            0                     0   \n",
       "4                     0  ...            0                     0   \n",
       "\n",
       "   Vishwapriya Layout  Vittasandra  Whitefield  Yelachenahalli  Yelahanka  \\\n",
       "0                   0            0           0               0          0   \n",
       "1                   0            0           0               0          0   \n",
       "2                   0            0           0               0          0   \n",
       "3                   0            0           0               0          0   \n",
       "4                   0            0           0               0          0   \n",
       "\n",
       "   Yelahanka New Town  Yelenahalli  Yeshwanthpur  \n",
       "0                   0            0             0  \n",
       "1                   0            0             0  \n",
       "2                   0            0             0  \n",
       "3                   0            0             0  \n",
       "4                   0            0             0  \n",
       "\n",
       "[5 rows x 245 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df11 = pd.concat([df10,dummies.drop('other',axis='columns')],axis='columns')\n",
    "df11.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>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",
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       "      <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",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 244 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",
       "\n",
       "   2nd Phase Judicial Layout  2nd Stage Nagarbhavi  5th Block Hbr Layout  \\\n",
       "0                          0                     0                     0   \n",
       "1                          0                     0                     0   \n",
       "\n",
       "   5th Phase JP Nagar  ...  Vijayanagar  Vishveshwarya Layout  \\\n",
       "0                   0  ...            0                     0   \n",
       "1                   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",
       "\n",
       "   Yelahanka New Town  Yelenahalli  Yeshwanthpur  \n",
       "0                   0            0             0  \n",
       "1                   0            0             0  \n",
       "\n",
       "[2 rows x 244 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df12 = df11.drop('location',axis='columns')\n",
    "df12.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 style='color:blue'>Build a Model Now...</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7239, 244)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df12.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "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>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",
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       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>2850.0</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",
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       "    <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",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 243 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",
       "\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  6th Phase JP Nagar  ...  Vijayanagar  \\\n",
       "0                   0                   0  ...            0   \n",
       "1                   0                   0  ...            0   \n",
       "2                   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",
       "\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",
       "\n",
       "[3 rows x 243 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df12.drop(['price'],axis='columns')\n",
    "X.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7239, 243)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    428.0\n",
       "1    194.0\n",
       "2    235.0\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df12.price\n",
    "y.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7239"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8629132245229443"
      ]
     },
     "execution_count": 58,
     "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": [
    "<h2 style='color:blue'>Use K Fold cross validation to measure accuracy of our LinearRegression model</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.82702546, 0.86027005, 0.85322178, 0.8436466 , 0.85481502])"
      ]
     },
     "execution_count": 59,
     "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",
   "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": [
    "<h2 style='color:blue'>Find best model using GridSearchCV</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>0.847796</td>\n",
       "      <td>{'normalize': False}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>lasso</td>\n",
       "      <td>0.726753</td>\n",
       "      <td>{'alpha': 2, 'selection': 'random'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>decision_tree</td>\n",
       "      <td>0.719153</td>\n",
       "      <td>{'criterion': 'friedman_mse', 'splitter': 'best'}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               model  best_score  \\\n",
       "0  linear_regression    0.847796   \n",
       "1              lasso    0.726753   \n",
       "2      decision_tree    0.719153   \n",
       "\n",
       "                                         best_params  \n",
       "0                               {'normalize': False}  \n",
       "1                {'alpha': 2, 'selection': 'random'}  \n",
       "2  {'criterion': 'friedman_mse', 'splitter': 'best'}  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "def find_best_model_using_gridsearchcv(X,y):\n",
    "    algos = {\n",
    "        'linear_regression' : {\n",
    "            'model': LinearRegression(),\n",
    "            'params': {\n",
    "                '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": "markdown",
   "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": [
    "<h2 style='color:blue'>Test the model for few properties</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "83.86570258312135"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_price('1st Phase JP Nagar',1000, 2, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86.08062284986896"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_price('1st Phase JP Nagar',1000, 3, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "193.3119773317992"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_price('Indira Nagar',1000, 2, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "195.52689759854684"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_price('Indira Nagar',1000, 3, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "194.02487115852682"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_price('Indira Nagar',1000, 3, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "197.0289240385669"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_price('Indira Nagar',1000, 3, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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