Bengaluru-House-Price-Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('Bengaluru_House_Data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13315</th>\n",
       "      <td>Built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Whitefield</td>\n",
       "      <td>5 Bedroom</td>\n",
       "      <td>ArsiaEx</td>\n",
       "      <td>3453</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>231.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13316</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Richards Town</td>\n",
       "      <td>4 BHK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3600</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>400.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13317</th>\n",
       "      <td>Built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Raja Rajeshwari Nagar</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>Mahla T</td>\n",
       "      <td>1141</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>60.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13318</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>18-Jun</td>\n",
       "      <td>Padmanabhanagar</td>\n",
       "      <td>4 BHK</td>\n",
       "      <td>SollyCl</td>\n",
       "      <td>4689</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>488.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13319</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Doddathoguru</td>\n",
       "      <td>1 BHK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>550</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13320 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  area_type   availability                  location  \\\n",
       "0      Super built-up  Area         19-Dec  Electronic City Phase II   \n",
       "1                Plot  Area  Ready To Move          Chikka Tirupathi   \n",
       "2            Built-up  Area  Ready To Move               Uttarahalli   \n",
       "3      Super built-up  Area  Ready To Move        Lingadheeranahalli   \n",
       "4      Super built-up  Area  Ready To Move                  Kothanur   \n",
       "...                     ...            ...                       ...   \n",
       "13315        Built-up  Area  Ready To Move                Whitefield   \n",
       "13316  Super built-up  Area  Ready To Move             Richards Town   \n",
       "13317        Built-up  Area  Ready To Move     Raja Rajeshwari Nagar   \n",
       "13318  Super built-up  Area         18-Jun           Padmanabhanagar   \n",
       "13319  Super built-up  Area  Ready To Move              Doddathoguru   \n",
       "\n",
       "            size  society total_sqft  bath  balcony   price  \n",
       "0          2 BHK  Coomee        1056   2.0      1.0   39.07  \n",
       "1      4 Bedroom  Theanmp       2600   5.0      3.0  120.00  \n",
       "2          3 BHK      NaN       1440   2.0      3.0   62.00  \n",
       "3          3 BHK  Soiewre       1521   3.0      1.0   95.00  \n",
       "4          2 BHK      NaN       1200   2.0      1.0   51.00  \n",
       "...          ...      ...        ...   ...      ...     ...  \n",
       "13315  5 Bedroom  ArsiaEx       3453   4.0      0.0  231.00  \n",
       "13316      4 BHK      NaN       3600   5.0      NaN  400.00  \n",
       "13317      2 BHK  Mahla T       1141   2.0      1.0   60.00  \n",
       "13318      4 BHK  SollyCl       4689   4.0      1.0  488.00  \n",
       "13319      1 BHK      NaN        550   1.0      1.0   17.00  \n",
       "\n",
       "[13320 rows x 9 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "area_type          0\n",
       "availability       0\n",
       "location           1\n",
       "size              16\n",
       "society         5502\n",
       "total_sqft         0\n",
       "bath              73\n",
       "balcony          609\n",
       "price              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(['area_type', 'availability', 'society', 'balcony'], axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Whitefield                      540\n",
       "Sarjapur  Road                  399\n",
       "Electronic City                 302\n",
       "Kanakpura Road                  273\n",
       "Thanisandra                     234\n",
       "                               ... \n",
       "Chikbasavanapura                  1\n",
       "2nd phase jp nagar, jp nagar      1\n",
       "Chikkaballapur                    1\n",
       "Bhattarahalli                     1\n",
       "M C Layout                        1\n",
       "Name: location, Length: 1305, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['location'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['location'].fillna(value = 'Sarjapur Road', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['bath'].fillna(np.median(data['bath']), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['size'].fillna(value = '2 BHK', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['bhk'] = data['size'].str.split().str.get(0).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert(x):\n",
    "    temp = x.split('-')\n",
    "    if len(temp) == 2:\n",
    "        return (float(temp[0]) + float(temp[1]))/2\n",
    "    try :\n",
    "        return float(x)\n",
    "    except :\n",
    "        return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['total_sqft'] = data['total_sqft'].apply(convert)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['location'] = data['location'].apply(lambda x : x.strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "location_value_counts = data['location'].value_counts()\n",
    "location_value_counts_less_10 = location_value_counts[location_value_counts < 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['location'] = data['location'].apply(lambda x : 'other' if x in location_value_counts_less_10 else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    " data = data[data['total_sqft']/data['bhk'] >= 300]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-17-6ba6291b9ac4>: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",
      "  data['price_per_sqft'] = data['price'] * 100000/data['total_sqft']\n"
     ]
    }
   ],
   "source": [
    "data['price_per_sqft'] = data['price'] * 100000/data['total_sqft']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def outliers_sqft(df):\n",
    "    df_output = pd.DataFrame()\n",
    "    for key, sub_df in df.groupby('location'):\n",
    "        mean = np.mean(sub_df.price_per_sqft)\n",
    "        std = np.std(sub_df.price_per_sqft)\n",
    "        gen_df = sub_df[(sub_df.price_per_sqft > (mean - std)) & (sub_df.price_per_sqft <= (mean + std))]\n",
    "        #print(df_output)\n",
    "        df_output = pd.concat([df_output, gen_df], ignore_index=True)\n",
    "    return df_output    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = outliers_sqft(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def outliers_bhk(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')      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = outliers_bhk(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(['size', 'price_per_sqft'], axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['bath'] = data['bath'].fillna(np.mean(data['bath']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.drop('price', axis = 1)\n",
    "y = data['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression, Lasso, Ridge\n",
    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
    "from sklearn.compose import make_column_transformer\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>bhk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7207</th>\n",
       "      <td>Vidyaranyapura</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1141</th>\n",
       "      <td>Bellandur</td>\n",
       "      <td>982.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9558</th>\n",
       "      <td>other</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3492</th>\n",
       "      <td>Hulimavu</td>\n",
       "      <td>1818.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9528</th>\n",
       "      <td>other</td>\n",
       "      <td>420.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5451</th>\n",
       "      <td>Old Madras Road</td>\n",
       "      <td>1065.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4455</th>\n",
       "      <td>Kengeri</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1689</th>\n",
       "      <td>Choodasandra</td>\n",
       "      <td>1530.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4234</th>\n",
       "      <td>Kanakpura Road</td>\n",
       "      <td>1240.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6708</th>\n",
       "      <td>Thanisandra</td>\n",
       "      <td>1265.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4959 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             location  total_sqft  bath  bhk\n",
       "7207   Vidyaranyapura      1200.0   2.0    3\n",
       "1141        Bellandur       982.0   2.0    2\n",
       "9558            other      1600.0   3.0    3\n",
       "3492         Hulimavu      1818.0   3.0    3\n",
       "9528            other       420.0   1.0    1\n",
       "...               ...         ...   ...  ...\n",
       "5451  Old Madras Road      1065.0   2.0    2\n",
       "4455          Kengeri      1200.0   2.0    2\n",
       "1689     Choodasandra      1530.0   3.0    3\n",
       "4234   Kanakpura Road      1240.0   2.0    2\n",
       "6708      Thanisandra      1265.0   2.0    3\n",
       "\n",
       "[4959 rows x 4 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>bhk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1807</th>\n",
       "      <td>Devarachikkanahalli</td>\n",
       "      <td>947.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3750</th>\n",
       "      <td>Jigani</td>\n",
       "      <td>927.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3927</th>\n",
       "      <td>Kaggadasapura</td>\n",
       "      <td>1140.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4994</th>\n",
       "      <td>Malleshwaram</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4413</th>\n",
       "      <td>Kathriguppe</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9784</th>\n",
       "      <td>other</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1841</th>\n",
       "      <td>Doddaballapur</td>\n",
       "      <td>1690.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4562</th>\n",
       "      <td>Kodigehaali</td>\n",
       "      <td>1166.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4081</th>\n",
       "      <td>Kanakapura</td>\n",
       "      <td>711.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8054</th>\n",
       "      <td>Yeshwanthpur</td>\n",
       "      <td>1855.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2443 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 location  total_sqft  bath  bhk\n",
       "1807  Devarachikkanahalli       947.0   2.0    2\n",
       "3750               Jigani       927.0   2.0    2\n",
       "3927        Kaggadasapura      1140.0   2.0    2\n",
       "4994         Malleshwaram      2000.0   3.0    3\n",
       "4413          Kathriguppe      1350.0   3.0    3\n",
       "...                   ...         ...   ...  ...\n",
       "9784                other      1020.0   2.0    2\n",
       "1841        Doddaballapur      1690.0   3.0    4\n",
       "4562          Kodigehaali      1166.0   2.0    2\n",
       "4081           Kanakapura       711.0   1.0    1\n",
       "8054         Yeshwanthpur      1855.0   3.0    3\n",
       "\n",
       "[2443 rows x 4 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:4163: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\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",
      "  return super().drop(\n"
     ]
    }
   ],
   "source": [
    "X_train.drop('location', axis =1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:4163: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\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",
      "  return super().drop(\n"
     ]
    }
   ],
   "source": [
    "X_test.drop('location', axis =1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "column_trans = make_column_transformer((OneHotEncoder(sparse = False), ['location']), remainder = 'passthrough')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LinearRegression(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe = make_pipeline(scaler, lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                ('linearregression', LinearRegression(normalize=True))])"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict = pipe.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7520391570884885"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test, predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "lasso = Lasso()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe_lasso = make_pipeline(scaler, lasso)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('standardscaler', StandardScaler()), ('lasso', Lasso())])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe_lasso.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict_lasso = pipe_lasso.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7532136005282466"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test, predict_lasso)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "ridge = Ridge()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe_ridge = make_pipeline(scaler, ridge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('standardscaler', StandardScaler()), ('ridge', Ridge())])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe_ridge.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test =pd.DataFrame(X_test, columns=X_train.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict_ridge = pipe_ridge.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7520723613164065"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test, predict_ridge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle_out = open('classifier.pkl', 'wb')\n",
    "pickle.dump(pipe_ridge, pickle_out)\n",
    "pickle_out.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test.to_csv(r'Test file.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train.to_csv(r'Train file.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>bhk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>999.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1300.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1211.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>980.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1260.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2438</th>\n",
       "      <td>1584.01</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2439</th>\n",
       "      <td>1672.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2440</th>\n",
       "      <td>1200.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2441</th>\n",
       "      <td>2775.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2442</th>\n",
       "      <td>3205.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2443 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      total_sqft  bath  bhk\n",
       "0         999.00   2.0    2\n",
       "1        1300.00   2.0    3\n",
       "2        1211.00   2.0    2\n",
       "3         980.00   2.0    2\n",
       "4        1260.00   5.0    3\n",
       "...          ...   ...  ...\n",
       "2438     1584.01   3.0    3\n",
       "2439     1672.00   3.0    3\n",
       "2440     1200.00   4.0    4\n",
       "2441     2775.00   3.0    4\n",
       "2442     3205.00   4.0    4\n",
       "\n",
       "[2443 rows x 3 columns]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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