model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Bad key \"text.kerning_factor\" on line 4 in\n",
      "C:\\Users\\brigu\\Anaconda3\\envs\\tf\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test_patch.mplstyle.\n",
      "You probably need to get an updated matplotlibrc file from\n",
      "https://github.com/matplotlib/matplotlib/blob/v3.1.3/matplotlibrc.template\n",
      "or from the matplotlib source distribution\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "matplotlib.rcParams[\"figure.figsize\"] = (20,10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "importing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              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",
      "5  Super built-up  Area  Ready To Move                Whitefield      2 BHK   \n",
      "6  Super built-up  Area         18-May          Old Airport Road      4 BHK   \n",
      "7  Super built-up  Area  Ready To Move              Rajaji Nagar      4 BHK   \n",
      "8  Super built-up  Area  Ready To Move              Marathahalli      3 BHK   \n",
      "9            Plot  Area  Ready To Move              Gandhi Bazar  6 Bedroom   \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  \n",
      "5  DuenaTa       1170   2.0      1.0   38.00  \n",
      "6  Jaades        2732   4.0      NaN  204.00  \n",
      "7  Brway G       3300   4.0      NaN  600.00  \n",
      "8      NaN       1310   3.0      1.0   63.25  \n",
      "9      NaN       1020   6.0      NaN  370.00  \n",
      "(13320, 9)\n"
     ]
    }
   ],
   "source": [
    "dataset = pd.read_csv('.\\dataset\\Bengaluru_House_Data.csv')\n",
    "print(dataset.head(10))\n",
    "print(dataset.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data preprocessing<br>\n",
    " getting the count of area type in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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\n"
     ]
    }
   ],
   "source": [
    "print(dataset.groupby('area_type')['area_type'].agg('count'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " droping unnecessary columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13320, 5)\n"
     ]
    }
   ],
   "source": [
    "dataset.drop(['area_type','society','availability','balcony'], axis='columns', inplace=True)\n",
    "print(dataset.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " data cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "location       1\n",
      "size          16\n",
      "total_sqft     0\n",
      "bath          73\n",
      "price          0\n",
      "dtype: int64\n",
      "(13246, 5)\n"
     ]
    }
   ],
   "source": [
    "print(dataset.isnull().sum())\n",
    "dataset.dropna(inplace=True)\n",
    "print(dataset.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# data engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['2 BHK' '4 Bedroom' '3 BHK' '4 BHK' '6 Bedroom' '3 Bedroom' '1 BHK'\n",
      " '1 RK' '1 Bedroom' '8 Bedroom' '2 Bedroom' '7 Bedroom' '5 BHK' '7 BHK'\n",
      " '6 BHK' '5 Bedroom' '11 BHK' '9 BHK' '9 Bedroom' '27 BHK' '10 Bedroom'\n",
      " '11 Bedroom' '10 BHK' '19 BHK' '16 BHK' '43 Bedroom' '14 BHK' '8 BHK'\n",
      " '12 Bedroom' '13 BHK' '18 Bedroom']\n"
     ]
    }
   ],
   "source": [
    "print(dataset['size'].unique())\n",
    "dataset['bhk'] = dataset['size'].apply(lambda x: float(x.split(' ')[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# exploring 'total_sqft' column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['1056' '2600' '1440' ... '1133 - 1384' '774' '4689']\n"
     ]
    }
   ],
   "source": [
    "print(dataset['total_sqft'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## defining a function to check whether the value is float or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               location       size      total_sqft  bath    price  bhk\n",
      "30            Yelahanka      4 BHK     2100 - 2850   4.0  186.000  4.0\n",
      "122              Hebbal      4 BHK     3067 - 8156   4.0  477.000  4.0\n",
      "137  8th Phase JP Nagar      2 BHK     1042 - 1105   2.0   54.005  2.0\n",
      "165            Sarjapur      2 BHK     1145 - 1340   2.0   43.490  2.0\n",
      "188            KR Puram      2 BHK     1015 - 1540   2.0   56.800  2.0\n",
      "410             Kengeri      1 BHK  34.46Sq. Meter   1.0   18.500  1.0\n",
      "549         Hennur Road      2 BHK     1195 - 1440   2.0   63.770  2.0\n",
      "648             Arekere  9 Bedroom       4125Perch   9.0  265.000  9.0\n",
      "661           Yelahanka      2 BHK     1120 - 1145   2.0   48.130  2.0\n",
      "672        Bettahalsoor  4 Bedroom     3090 - 5002   4.0  445.000  4.0\n"
     ]
    }
   ],
   "source": [
    "print(dataset[~dataset['total_sqft'].apply(is_float)].head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## defining a function to convert the range of column values to a single value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "290.0\n",
      "2475.0\n",
      "None\n"
     ]
    }
   ],
   "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\n",
    "#### testing the function\n",
    "print(convert_sqft_to_num('290'))\n",
    "print(convert_sqft_to_num('2100 - 2850'))\n",
    "print(convert_sqft_to_num('4.46Sq. Meter'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## applying this function to the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    1056.0\n",
      "1    2600.0\n",
      "2    1440.0\n",
      "3    1521.0\n",
      "4    1200.0\n",
      "5    1170.0\n",
      "6    2732.0\n",
      "7    3300.0\n",
      "8    1310.0\n",
      "9    1020.0\n",
      "Name: total_sqft, dtype: float64\n",
      "location      Yelahanka\n",
      "size              4 BHK\n",
      "total_sqft         2475\n",
      "bath                  4\n",
      "price               186\n",
      "bhk                   4\n",
      "Name: 30, dtype: object\n"
     ]
    }
   ],
   "source": [
    "dataset['total_sqft'] = dataset['total_sqft'].apply(convert_sqft_to_num)\n",
    "print(dataset['total_sqft'].head(10))\n",
    "print(dataset.loc[30])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " feature engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   location       size  total_sqft  bath   price  bhk\n",
      "0  Electronic City Phase II      2 BHK      1056.0   2.0   39.07  2.0\n",
      "1          Chikka Tirupathi  4 Bedroom      2600.0   5.0  120.00  4.0\n",
      "2               Uttarahalli      3 BHK      1440.0   2.0   62.00  3.0\n",
      "3        Lingadheeranahalli      3 BHK      1521.0   3.0   95.00  3.0\n",
      "4                  Kothanur      2 BHK      1200.0   2.0   51.00  2.0\n",
      "5                Whitefield      2 BHK      1170.0   2.0   38.00  2.0\n",
      "6          Old Airport Road      4 BHK      2732.0   4.0  204.00  4.0\n",
      "7              Rajaji Nagar      4 BHK      3300.0   4.0  600.00  4.0\n",
      "8              Marathahalli      3 BHK      1310.0   3.0   63.25  3.0\n",
      "9              Gandhi Bazar  6 Bedroom      1020.0   6.0  370.00  6.0\n"
     ]
    }
   ],
   "source": [
    "print(dataset.head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# creating new colomn 'price_per_sqft' as we know<br>\n",
    "# in real estate market, price per sqft matters alot. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0         3699.810606\n",
      "1         4615.384615\n",
      "2         4305.555556\n",
      "3         6245.890861\n",
      "4         4250.000000\n",
      "             ...     \n",
      "13315     6689.834926\n",
      "13316    11111.111111\n",
      "13317     5258.545136\n",
      "13318    10407.336319\n",
      "13319     3090.909091\n",
      "Name: price_per_sqft, Length: 13246, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "dataset['price_per_sqft'] = dataset['price']*100000/dataset['total_sqft']\n",
    "print(dataset['price_per_sqft'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# exploring 'location' column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1304\n"
     ]
    }
   ],
   "source": [
    "print(len(dataset['location'].unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['location'] = dataset['location'].apply(lambda x: x.strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "location\n",
      "Whitefield               535\n",
      "Sarjapur  Road           392\n",
      "Electronic City          304\n",
      "Kanakpura Road           266\n",
      "Thanisandra              236\n",
      "Yelahanka                210\n",
      "Uttarahalli              186\n",
      "Hebbal                   176\n",
      "Marathahalli             175\n",
      "Raja Rajeshwari Nagar    171\n",
      "Name: location, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "location_stats = dataset.groupby('location')['location'].agg('count').sort_values(ascending=False)\n",
    "print(location_stats[0:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## creating 'location_stats' to get the location with total count or occurance <br>\n",
    "## occurance, and 'location_stats_less_than_10' to get the location with <= 10 <br>\n",
    "## occurance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1052\n",
      "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\n"
     ]
    }
   ],
   "source": [
    "print(len(location_stats[location_stats <= 10]))\n",
    "location_stats_less_than_10 = location_stats[location_stats <= 10]\n",
    "print(location_stats_less_than_10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## redefining the 'location' column as 'other' value where location count<br>\n",
    "## is <= 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    Electronic City Phase II\n",
      "1            Chikka Tirupathi\n",
      "2                 Uttarahalli\n",
      "3          Lingadheeranahalli\n",
      "4                    Kothanur\n",
      "5                  Whitefield\n",
      "6            Old Airport Road\n",
      "7                Rajaji Nagar\n",
      "8                Marathahalli\n",
      "9                       other\n",
      "Name: location, dtype: object\n",
      "242\n"
     ]
    }
   ],
   "source": [
    "dataset['location'] = dataset['location'].apply(lambda x: 'other' if x in location_stats_less_than_10 else x)\n",
    "print(dataset['location'].head(10))\n",
    "print(len(dataset['location'].unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Outlier detection and removal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# checking that 'total_sqft'/'bhk', if it's very less than there is some<br>\n",
    "# anomaly and we have to remove these outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 location       size  total_sqft  bath  price  bhk  \\\n",
      "4086       Sarjapur  Road  4 Bedroom         1.0   4.0  120.0  4.0   \n",
      "4972                other      7 BHK         5.0   7.0  115.0  7.0   \n",
      "349                 other  3 Bedroom        11.0   3.0   74.0  3.0   \n",
      "1020                other      1 BHK        15.0   1.0   30.0  1.0   \n",
      "1122                other  5 Bedroom        24.0   2.0  150.0  5.0   \n",
      "5970          Mysore Road  1 Bedroom        45.0   1.0   23.0  1.0   \n",
      "11558          Whitefield  4 Bedroom        60.0   4.0  218.0  4.0   \n",
      "13216               other      1 BHK       250.0   2.0   40.0  1.0   \n",
      "119           Hennur Road  2 Bedroom       276.0   3.0   23.0  2.0   \n",
      "10526  Yelahanka New Town      1 BHK       284.0   1.0    8.0  1.0   \n",
      "\n",
      "       price_per_sqft  \n",
      "4086     1.200000e+07  \n",
      "4972     2.300000e+06  \n",
      "349      6.727273e+05  \n",
      "1020     2.000000e+05  \n",
      "1122     6.250000e+05  \n",
      "5970     5.111111e+04  \n",
      "11558    3.633333e+05  \n",
      "13216    1.600000e+04  \n",
      "119      8.333333e+03  \n",
      "10526    2.816901e+03  \n",
      "(13246, 7)\n"
     ]
    }
   ],
   "source": [
    "print(dataset[dataset['total_sqft'] / dataset['bhk'] < 300].sort_values(by='total_sqft').head(10))\n",
    "print(dataset.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12502, 7)\n"
     ]
    }
   ],
   "source": [
    "dataset = dataset[~(dataset['total_sqft'] / dataset['bhk'] < 300)]\n",
    "print(dataset.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# checking columns where 'price_per_sqft' is very low<br>\n",
    "# where it should not be that low, so it's an anomaly and <br>\n",
    "# we have to remove those rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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\n"
     ]
    }
   ],
   "source": [
    "print(dataset['price_per_sqft'].describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# function to remove these extreme cases of very high or low values<br>\n",
    "# of 'price_per_sqft' based on std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_pps_outliers(df):\n",
    "    df_out = pd.DataFrame()\n",
    "    for key, subdf in df.groupby('location'):\n",
    "        mean = np.mean(subdf['price_per_sqft'])\n",
    "        std = np.std(subdf['price_per_sqft'])\n",
    "        reduced_df = subdf[(subdf['price_per_sqft'] > (mean - std)) & (subdf['price_per_sqft'] <= (mean + std))]\n",
    "        df_out = pd.concat([df_out, reduced_df], ignore_index=True)\n",
    "    return df_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10241, 7)\n"
     ]
    }
   ],
   "source": [
    "dataset = remove_pps_outliers(dataset)\n",
    "print(dataset.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plotting graoh where we can visualize that properties with same location<br>\n",
    "# and the price of 3 bhk properties with higher 'total_sqft' is less than <br>\n",
    "# 2 bhk properties with lower 'total_sqft'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "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'], \n",
    "                bhk2['price'], \n",
    "                color='blue', \n",
    "                label='2 BHK', \n",
    "                s=50\n",
    "                )\n",
    "    plt.scatter(bhk3['total_sqft'], \n",
    "                bhk3['price'], \n",
    "                marker='+',\n",
    "                color='green', \n",
    "                label='3 BHK', \n",
    "                s=50\n",
    "                )\n",
    "    plt.xlabel('Total Square Feet Area')\n",
    "    plt.ylabel('Price')\n",
    "    plt.title(location)\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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"
    },
    {
     "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(dataset,\"Hebbal\")\n",
    "plot_scatter_chart(dataset,\"Rajaji Nagar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# defining a funcion where we can get the rows where 'bhk' & 'location'<br>\n",
    "# is same but the property with less 'bhk' have more price than the property<br>\n",
    "# which have more 'bhk'. So, it's also an anomalu and we have to remove these <br>\n",
    "# properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7329, 7)\n"
     ]
    }
   ],
   "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",
    "        \n",
    "dataset = remove_bhk_outliers(dataset)\n",
    "print(dataset.shape)\n",
    "            \n",
    "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'], \n",
    "                bhk2['price'], \n",
    "                color='blue', \n",
    "                label='2 BHK', \n",
    "                s=50\n",
    "                )\n",
    "    plt.scatter(bhk3['total_sqft'], \n",
    "                bhk3['price'], \n",
    "                marker='+',\n",
    "                color='green', \n",
    "                label='3 BHK', \n",
    "                s=50\n",
    "                )\n",
    "    plt.xlabel('Total Square Feet Area')\n",
    "    plt.ylabel('Price')\n",
    "    plt.title(location)\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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"
    },
    {
     "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(dataset,\"Hebbal\")\n",
    "plot_scatter_chart(dataset,\"Rajaji Nagar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# histogram for properties per sqaure feet area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "matplotlib.rcParams['figure.figsize'] = (20,10)\n",
    "plt.hist(dataset['price_per_sqft'], rwidth=0.8)\n",
    "plt.xlabel('Price Per Square Feet')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Histogram of Properties by Price Per Square Feet')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# exploring bathroom feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 4.  3.  2.  5.  8.  1.  6.  7.  9. 12. 16. 13.]\n"
     ]
    }
   ],
   "source": [
    "print(dataset['bath'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## having 10 bedrooms and bathroom > 10 is unusual<br>\n",
    "## so, we will remove these anomalies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            location    size  total_sqft  bath  price   bhk  price_per_sqft\n",
      "5277  Neeladri Nagar  10 BHK      4000.0  12.0  160.0  10.0     4000.000000\n",
      "8486           other  10 BHK     12000.0  12.0  525.0  10.0     4375.000000\n",
      "8575           other  16 BHK     10000.0  16.0  550.0  16.0     5500.000000\n",
      "9308           other  11 BHK      6000.0  12.0  150.0  11.0     2500.000000\n",
      "9639           other  13 BHK      5425.0  13.0  275.0  13.0     5069.124424\n"
     ]
    }
   ],
   "source": [
    "print(dataset[dataset['bath'] > 10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## plotting histogram of bathroom "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "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(dataset['bath'], rwidth=0.8, color='red')\n",
    "plt.xlabel('Number of Bathrooms')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Histogram of Bathroom per Property')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           location       size  total_sqft  bath   price  bhk  price_per_sqft\n",
      "1626  Chikkabanavar  4 Bedroom      2460.0   7.0    80.0  4.0     3252.032520\n",
      "5238     Nagasandra  4 Bedroom      7000.0   8.0   450.0  4.0     6428.571429\n",
      "6711    Thanisandra      3 BHK      1806.0   6.0   116.0  3.0     6423.034330\n",
      "8411          other      6 BHK     11338.0   9.0  1000.0  6.0     8819.897689\n",
      "(7251, 7)\n"
     ]
    }
   ],
   "source": [
    "print(dataset[dataset['bath'] > dataset['bhk'] + 2])\n",
    "dataset = dataset[dataset['bath'] < dataset['bhk'] + 2]\n",
    "print(dataset.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# after removing outliers, dropping unwanted features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              location  total_sqft  bath  price  bhk\n",
      "0  1st Block Jayanagar      2850.0   4.0  428.0  4.0\n",
      "1  1st Block Jayanagar      1630.0   3.0  194.0  3.0\n",
      "2  1st Block Jayanagar      1875.0   2.0  235.0  3.0\n",
      "3  1st Block Jayanagar      1200.0   2.0  130.0  3.0\n",
      "4  1st Block Jayanagar      1235.0   2.0  148.0  2.0\n"
     ]
    }
   ],
   "source": [
    "dataset.drop(['size','price_per_sqft'], axis='columns', inplace=True)\n",
    "print(dataset.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " one hot encoding the 'location' column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   1st Block Jayanagar  1st Phase JP Nagar  2nd Phase Judicial Layout  \\\n",
      "0                    1                   0                          0   \n",
      "1                    1                   0                          0   \n",
      "2                    1                   0                          0   \n",
      "3                    1                   0                          0   \n",
      "4                    1                   0                          0   \n",
      "\n",
      "   2nd Stage Nagarbhavi  5th Block Hbr Layout  5th Phase JP Nagar  \\\n",
      "0                     0                     0                   0   \n",
      "1                     0                     0                   0   \n",
      "2                     0                     0                   0   \n",
      "3                     0                     0                   0   \n",
      "4                     0                     0                   0   \n",
      "\n",
      "   6th Phase JP Nagar  7th Phase JP Nagar  8th Phase JP Nagar  \\\n",
      "0                   0                   0                   0   \n",
      "1                   0                   0                   0   \n",
      "2                   0                   0                   0   \n",
      "3                   0                   0                   0   \n",
      "4                   0                   0                   0   \n",
      "\n",
      "   9th Phase JP Nagar  ...  Vishveshwarya Layout  Vishwapriya Layout  \\\n",
      "0                   0  ...                     0                   0   \n",
      "1                   0  ...                     0                   0   \n",
      "2                   0  ...                     0                   0   \n",
      "3                   0  ...                     0                   0   \n",
      "4                   0  ...                     0                   0   \n",
      "\n",
      "   Vittasandra  Whitefield  Yelachenahalli  Yelahanka  Yelahanka New Town  \\\n",
      "0            0           0               0          0                   0   \n",
      "1            0           0               0          0                   0   \n",
      "2            0           0               0          0                   0   \n",
      "3            0           0               0          0                   0   \n",
      "4            0           0               0          0                   0   \n",
      "\n",
      "   Yelenahalli  Yeshwanthpur  other  \n",
      "0            0             0      0  \n",
      "1            0             0      0  \n",
      "2            0             0      0  \n",
      "3            0             0      0  \n",
      "4            0             0      0  \n",
      "\n",
      "[5 rows x 242 columns]\n"
     ]
    }
   ],
   "source": [
    "dummies = pd.get_dummies(dataset['location'])\n",
    "print(dummies.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   total_sqft  bath  price  bhk  1st Block Jayanagar  1st Phase JP Nagar  \\\n",
      "0      2850.0   4.0  428.0  4.0                    1                   0   \n",
      "1      1630.0   3.0  194.0  3.0                    1                   0   \n",
      "2      1875.0   2.0  235.0  3.0                    1                   0   \n",
      "3      1200.0   2.0  130.0  3.0                    1                   0   \n",
      "4      1235.0   2.0  148.0  2.0                    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]\n",
      "(7251, 245)\n"
     ]
    }
   ],
   "source": [
    "dataset = pd.concat([dataset,dummies.drop('other', axis='columns')], axis='columns')\n",
    "dataset.drop('location', axis=1, inplace=True)\n",
    "print(dataset.head())\n",
    "print(dataset.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " distributing independent features in 'X' and dependent feature in 'y'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7251, 244)\n",
      "(7251,)\n"
     ]
    }
   ],
   "source": [
    "X = dataset.drop(['price'],axis= 'columns')\n",
    "y = dataset['price']\n",
    "print(X.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " splitting the dataset into training set and test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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": "markdown",
   "metadata": {},
   "source": [
    " training  the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8452277697874312\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train,y_train)\n",
    "print(regressor.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " k-fold cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.82430186, 0.77166234, 0.85089567, 0.80837764, 0.83653286])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import ShuffleSplit, cross_val_score\n",
    "cv = ShuffleSplit(n_splits=5, test_size = 0.2, random_state=0)\n",
    "cross_val_score(regressor,X,y,cv=cv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " grid search, hyper parameter tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.tree import DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               model  best_score  \\\n",
      "0  linear_regression    0.818354   \n",
      "1              lasso    0.687453   \n",
      "2      decision_tree    0.722519   \n",
      "\n",
      "                                         best_params  \n",
      "0                                {'normalize': True}  \n",
      "1                {'alpha': 1, 'selection': 'random'}  \n",
      "2  {'criterion': 'friedman_mse', 'splitter': 'best'}  \n"
     ]
    }
   ],
   "source": [
    "def find_best_model_using_gridsearch(X,y):\n",
    "    algos = {\n",
    "        'linear_regression': {\n",
    "            'model': LinearRegression(),\n",
    "            'params': { 'normalize': [True, False]}\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'],\n",
    "                          config['params'],\n",
    "                          cv=cv,\n",
    "                          n_jobs=-1,\n",
    "                          return_train_score=False\n",
    "                          )\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",
    "    return pd.DataFrame(scores,columns=['model','best_score','best_params'])\n",
    "model_scores = find_best_model_using_gridsearch(X,y)\n",
    "print(model_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# so after running grid search, linear regression model have the best score<br>\n",
    "# so i will use linear regression model on the whole dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " evaluating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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",
    "    return regressor.predict([x])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "85.83901256841601\n",
      "87.76501220608625\n",
      "181.59488997129654\n"
     ]
    }
   ],
   "source": [
    "print(predict_price('1st Phase JP Nagar',1000,2,2))\n",
    "print(predict_price('1st Phase JP Nagar',1000,3,3))\n",
    "print(predict_price('Indira Nagar',1000,3,3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "saving the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open('bangalore_home_prices_model.pickle','wb') as f:\n",
    "    pickle.dump(regressor,f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "exporting columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "columns = {'data_columns': [col.lower() for col in X.columns]}\n",
    "with open(\"columns.json\",\"w\") as f:\n",
    "    f.write(json.dumps(columns))"
   ]
  }
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