Machine Learning Project 1 - House Price Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning Project 1 - House Price Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.read_csv('bengaluru_house_prices.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>2.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>39.070</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>5.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>120.000</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>2.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>62.000</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>3.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>95.000</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>2.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>51.000</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 2.000    1.000  39.070  \n",
       "1  Theanmp       2600 5.000    3.000 120.000  \n",
       "2      NaN       1440 2.000    3.000  62.000  \n",
       "3  Soiewre       1521 3.000    1.000  95.000  \n",
       "4      NaN       1200 2.000    1.000  51.000  "
      ]
     },
     "execution_count": 406,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 13320 entries, 0 to 13319\n",
      "Data columns (total 9 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   area_type     13320 non-null  object \n",
      " 1   availability  13320 non-null  object \n",
      " 2   location      13319 non-null  object \n",
      " 3   size          13304 non-null  object \n",
      " 4   society       7818 non-null   object \n",
      " 5   total_sqft    13320 non-null  object \n",
      " 6   bath          13247 non-null  float64\n",
      " 7   balcony       12711 non-null  float64\n",
      " 8   price         13320 non-null  float64\n",
      "dtypes: float64(3), object(6)\n",
      "memory usage: 936.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13320, 9)"
      ]
     },
     "execution_count": 338,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df1.drop(['area_type','society','balcony'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "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>availability</th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19-Dec</td>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1056</td>\n",
       "      <td>2.000</td>\n",
       "      <td>39.070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>2600</td>\n",
       "      <td>5.000</td>\n",
       "      <td>120.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1440</td>\n",
       "      <td>2.000</td>\n",
       "      <td>62.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1521</td>\n",
       "      <td>3.000</td>\n",
       "      <td>95.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1200</td>\n",
       "      <td>2.000</td>\n",
       "      <td>51.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    availability                  location       size total_sqft  bath   price\n",
       "0         19-Dec  Electronic City Phase II      2 BHK       1056 2.000  39.070\n",
       "1  Ready To Move          Chikka Tirupathi  4 Bedroom       2600 5.000 120.000\n",
       "2  Ready To Move               Uttarahalli      3 BHK       1440 2.000  62.000\n",
       "3  Ready To Move        Lingadheeranahalli      3 BHK       1521 3.000  95.000\n",
       "4  Ready To Move                  Kothanur      2 BHK       1200 2.000  51.000"
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "availability     0\n",
       "location         1\n",
       "size            16\n",
       "total_sqft       0\n",
       "bath            73\n",
       "price            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 341,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = df2.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "availability    0\n",
       "location        0\n",
       "size            0\n",
       "total_sqft      0\n",
       "bath            0\n",
       "price           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 402,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>availability</th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19-Dec</td>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1056</td>\n",
       "      <td>2.000</td>\n",
       "      <td>39.070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>2600</td>\n",
       "      <td>5.000</td>\n",
       "      <td>120.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1440</td>\n",
       "      <td>2.000</td>\n",
       "      <td>62.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1521</td>\n",
       "      <td>3.000</td>\n",
       "      <td>95.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1200</td>\n",
       "      <td>2.000</td>\n",
       "      <td>51.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    availability                  location       size total_sqft  bath   price\n",
       "0         19-Dec  Electronic City Phase II      2 BHK       1056 2.000  39.070\n",
       "1  Ready To Move          Chikka Tirupathi  4 Bedroom       2600 5.000 120.000\n",
       "2  Ready To Move               Uttarahalli      3 BHK       1440 2.000  62.000\n",
       "3  Ready To Move        Lingadheeranahalli      3 BHK       1521 3.000  95.000\n",
       "4  Ready To Move                  Kothanur      2 BHK       1200 2.000  51.000"
      ]
     },
     "execution_count": 403,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['19-Dec', 'Ready To Move', '18-May', '18-Feb', '18-Nov', '20-Dec',\n",
       "       '17-Oct', '21-Dec', '19-Sep', '20-Sep', '18-Mar', '18-Apr',\n",
       "       '20-Aug', '19-Mar', '17-Sep', '18-Dec', '17-Aug', '19-Apr',\n",
       "       '18-Jun', '22-Dec', '22-Jan', '18-Aug', '19-Jan', '17-Jul',\n",
       "       '18-Jul', '21-Jun', '20-May', '19-Aug', '18-Sep', '17-May',\n",
       "       '17-Jun', '18-Oct', '21-May', '18-Jan', '20-Mar', '17-Dec',\n",
       "       '16-Mar', '19-Jun', '22-Jun', '19-Jul', '21-Feb', '19-May',\n",
       "       '17-Nov', '20-Oct', '20-Jun', '19-Feb', '21-Oct', '21-Jan',\n",
       "       '17-Mar', '17-Apr', '22-May', '19-Oct', '21-Jul', '21-Nov',\n",
       "       '21-Mar', '16-Dec', '22-Mar', '20-Jan', '21-Sep', '21-Aug',\n",
       "       '14-Nov', '19-Nov', '15-Nov', '16-Jul', '15-Jun', '17-Feb',\n",
       "       '20-Nov', '20-Jul', '16-Sep', '15-Oct', '20-Feb', '15-Dec',\n",
       "       '16-Oct', '22-Nov', '15-Aug', '17-Jan', '16-Nov', '20-Apr',\n",
       "       '16-Jan', '14-Jul'], dtype=object)"
      ]
     },
     "execution_count": 404,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3['availability'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "availability\n",
       "14-Jul               1\n",
       "14-Nov               1\n",
       "15-Aug               1\n",
       "15-Dec               1\n",
       "15-Jun               1\n",
       "                 ...  \n",
       "22-Jun              19\n",
       "22-Mar               3\n",
       "22-May               8\n",
       "22-Nov               2\n",
       "Ready To Move    10564\n",
       "Name: availability, Length: 80, dtype: int64"
      ]
     },
     "execution_count": 346,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.groupby(\"availability\")[\"availability\"].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df3['availability']=df3['availability'].apply(lambda x: x if x in ('Ready To Move') else 'Future Possession')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "availability\n",
       "Future Possession     2682\n",
       "Ready To Move        10564\n",
       "Name: availability, dtype: int64"
      ]
     },
     "execution_count": 411,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.groupby(\"availability\")[\"availability\"].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Electronic City Phase II', 'Chikka Tirupathi', 'Uttarahalli', ...,\n",
       "       '12th cross srinivas nagar banshankari 3rd stage',\n",
       "       'Havanur extension', 'Abshot Layout'], dtype=object)"
      ]
     },
     "execution_count": 412,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3['location'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location\n",
       "Whitefield                            534\n",
       "Sarjapur  Road                        392\n",
       "Electronic City                       302\n",
       "Kanakpura Road                        266\n",
       "Thanisandra                           233\n",
       "                                     ... \n",
       "Konappana Agrahara                      1\n",
       "Kodanda Reddy Layout                    1\n",
       "Kirloskar layout, Basaveshwarnagar      1\n",
       "Kengeri Satellite Town Stage II         1\n",
       " Anekal                                 1\n",
       "Name: location, Length: 1304, dtype: int64"
      ]
     },
     "execution_count": 413,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.groupby(\"location\")[\"location\"].count().sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "metadata": {},
   "outputs": [],
   "source": [
    "locations = df3.groupby('location')['location'].count().sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location\n",
       " Anekal                                 1\n",
       "Kengeri Satellite Town Stage II         1\n",
       "Kirloskar layout, Basaveshwarnagar      1\n",
       "Kodanda Reddy Layout                    1\n",
       "Konappana Agrahara                      1\n",
       "                                     ... \n",
       "Thanisandra                           233\n",
       "Kanakpura Road                        266\n",
       "Electronic City                       302\n",
       "Sarjapur  Road                        392\n",
       "Whitefield                            534\n",
       "Name: location, Length: 1304, dtype: int64"
      ]
     },
     "execution_count": 415,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "locations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "metadata": {},
   "outputs": [],
   "source": [
    "locations_20cnt = locations[locations <= 20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location\n",
       " Anekal                                1\n",
       "Kengeri Satellite Town Stage II        1\n",
       "Kirloskar layout, Basaveshwarnagar     1\n",
       "Kodanda Reddy Layout                   1\n",
       "Konappana Agrahara                     1\n",
       "                                      ..\n",
       "HBR Layout                            20\n",
       "Sanjay nagar                          20\n",
       "Yelachenahalli                        20\n",
       "Binny Pete                            20\n",
       "Poorna Pragna Layout                  20\n",
       "Name: location, Length: 1161, dtype: int64"
      ]
     },
     "execution_count": 417,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "locations_20cnt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df3['location'] = df3['location'].apply(lambda x: 'Others' if x in locations_20cnt else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location\n",
       "Others                4314\n",
       "Whitefield             534\n",
       "Sarjapur  Road         392\n",
       "Electronic City        302\n",
       "Kanakpura Road         266\n",
       "                      ... \n",
       "Kathriguppe             22\n",
       "Rayasandra              22\n",
       "Ulsoor                  21\n",
       "Basaveshwara Nagar      21\n",
       "Hoskote                 21\n",
       "Name: location, Length: 144, dtype: int64"
      ]
     },
     "execution_count": 419,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.groupby(\"location\")[\"location\"].count().sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "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>availability</th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Future Possession</td>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1056</td>\n",
       "      <td>2.000</td>\n",
       "      <td>39.070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Others</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>2600</td>\n",
       "      <td>5.000</td>\n",
       "      <td>120.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1440</td>\n",
       "      <td>2.000</td>\n",
       "      <td>62.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1521</td>\n",
       "      <td>3.000</td>\n",
       "      <td>95.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1200</td>\n",
       "      <td>2.000</td>\n",
       "      <td>51.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        availability                  location       size total_sqft  bath  \\\n",
       "0  Future Possession  Electronic City Phase II      2 BHK       1056 2.000   \n",
       "1      Ready To Move                    Others  4 Bedroom       2600 5.000   \n",
       "2      Ready To Move               Uttarahalli      3 BHK       1440 2.000   \n",
       "3      Ready To Move        Lingadheeranahalli      3 BHK       1521 3.000   \n",
       "4      Ready To Move                  Kothanur      2 BHK       1200 2.000   \n",
       "\n",
       "    price  \n",
       "0  39.070  \n",
       "1 120.000  \n",
       "2  62.000  \n",
       "3  95.000  \n",
       "4  51.000  "
      ]
     },
     "execution_count": 420,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2 BHK', '4 Bedroom', '3 BHK', '4 BHK', '6 Bedroom', '3 Bedroom',\n",
       "       '1 BHK', '1 RK', '1 Bedroom', '8 Bedroom', '2 Bedroom',\n",
       "       '7 Bedroom', '5 BHK', '7 BHK', '6 BHK', '5 Bedroom', '11 BHK',\n",
       "       '9 BHK', '9 Bedroom', '27 BHK', '10 Bedroom', '11 Bedroom',\n",
       "       '10 BHK', '19 BHK', '16 BHK', '43 Bedroom', '14 BHK', '8 BHK',\n",
       "       '12 Bedroom', '13 BHK', '18 Bedroom'], dtype=object)"
      ]
     },
     "execution_count": 358,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3['size'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "import re \n",
    "df3['bhks'] =df3['size'].apply(lambda x:  int(re.findall('\\d+',x)[0].strip()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 422,
   "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>availability</th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>bhks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Future Possession</td>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1056</td>\n",
       "      <td>2.000</td>\n",
       "      <td>39.070</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Others</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>2600</td>\n",
       "      <td>5.000</td>\n",
       "      <td>120.000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1440</td>\n",
       "      <td>2.000</td>\n",
       "      <td>62.000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1521</td>\n",
       "      <td>3.000</td>\n",
       "      <td>95.000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1200</td>\n",
       "      <td>2.000</td>\n",
       "      <td>51.000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        availability                  location       size total_sqft  bath  \\\n",
       "0  Future Possession  Electronic City Phase II      2 BHK       1056 2.000   \n",
       "1      Ready To Move                    Others  4 Bedroom       2600 5.000   \n",
       "2      Ready To Move               Uttarahalli      3 BHK       1440 2.000   \n",
       "3      Ready To Move        Lingadheeranahalli      3 BHK       1521 3.000   \n",
       "4      Ready To Move                  Kothanur      2 BHK       1200 2.000   \n",
       "\n",
       "    price  bhks  \n",
       "0  39.070     2  \n",
       "1 120.000     4  \n",
       "2  62.000     3  \n",
       "3  95.000     3  \n",
       "4  51.000     2  "
      ]
     },
     "execution_count": 422,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1056', '2600', '1440', ..., '1133 - 1384', '774', '4689'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 423,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3['total_sqft'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 424,
   "metadata": {},
   "outputs": [],
   "source": [
    " def get_mean(x):\n",
    "    if re.findall('-',x):\n",
    "        ss = x.strip().split('-')\n",
    "        return ((float(ss[0])+float(ss[0]))/2)\n",
    "    try:\n",
    "        return float(x.strip())\n",
    "    except:\n",
    "        return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 425,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df3['total_sqft_new']=df3['total_sqft'].apply(get_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 426,
   "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>availability</th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>bhks</th>\n",
       "      <th>total_sqft_new</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Future Possession</td>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1056</td>\n",
       "      <td>2.000</td>\n",
       "      <td>39.070</td>\n",
       "      <td>2</td>\n",
       "      <td>1056.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Others</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>2600</td>\n",
       "      <td>5.000</td>\n",
       "      <td>120.000</td>\n",
       "      <td>4</td>\n",
       "      <td>2600.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1440</td>\n",
       "      <td>2.000</td>\n",
       "      <td>62.000</td>\n",
       "      <td>3</td>\n",
       "      <td>1440.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1521</td>\n",
       "      <td>3.000</td>\n",
       "      <td>95.000</td>\n",
       "      <td>3</td>\n",
       "      <td>1521.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1200</td>\n",
       "      <td>2.000</td>\n",
       "      <td>51.000</td>\n",
       "      <td>2</td>\n",
       "      <td>1200.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        availability                  location       size total_sqft  bath  \\\n",
       "0  Future Possession  Electronic City Phase II      2 BHK       1056 2.000   \n",
       "1      Ready To Move                    Others  4 Bedroom       2600 5.000   \n",
       "2      Ready To Move               Uttarahalli      3 BHK       1440 2.000   \n",
       "3      Ready To Move        Lingadheeranahalli      3 BHK       1521 3.000   \n",
       "4      Ready To Move                  Kothanur      2 BHK       1200 2.000   \n",
       "\n",
       "    price  bhks  total_sqft_new  \n",
       "0  39.070     2        1056.000  \n",
       "1 120.000     4        2600.000  \n",
       "2  62.000     3        1440.000  \n",
       "3  95.000     3        1521.000  \n",
       "4  51.000     2        1200.000  "
      ]
     },
     "execution_count": 426,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "availability       0\n",
       "location           0\n",
       "size               0\n",
       "total_sqft         0\n",
       "bath               0\n",
       "price              0\n",
       "bhks               0\n",
       "total_sqft_new    46\n",
       "dtype: int64"
      ]
     },
     "execution_count": 430,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "metadata": {},
   "outputs": [],
   "source": [
    "df4 = df3.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 432,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "availability      0\n",
       "location          0\n",
       "size              0\n",
       "total_sqft        0\n",
       "bath              0\n",
       "price             0\n",
       "bhks              0\n",
       "total_sqft_new    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 432,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.,  5.,  3.,  4.,  6.,  1.,  9.,  8.,  7., 11., 10., 14., 27.,\n",
       "       12., 16., 40., 15., 13., 18.])"
      ]
     },
     "execution_count": 433,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4['bath'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bath\n",
       "40.000       1\n",
       "18.000       1\n",
       "15.000       1\n",
       "14.000       1\n",
       "27.000       1\n",
       "16.000       2\n",
       "13.000       3\n",
       "11.000       3\n",
       "12.000       7\n",
       "10.000      13\n",
       "9.000       41\n",
       "8.000       64\n",
       "7.000      102\n",
       "6.000      269\n",
       "5.000      521\n",
       "1.000      781\n",
       "4.000     1222\n",
       "3.000     3274\n",
       "2.000     6893\n",
       "Name: bath, dtype: int64"
      ]
     },
     "execution_count": 434,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.groupby('bath')['bath'].count().sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 435,
   "metadata": {},
   "outputs": [],
   "source": [
    "df5 = df4[df4['bath'] <= 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "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>availability</th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>bhks</th>\n",
       "      <th>total_sqft_new</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Future Possession</td>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1056</td>\n",
       "      <td>2.000</td>\n",
       "      <td>39.070</td>\n",
       "      <td>2</td>\n",
       "      <td>1056.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Others</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>2600</td>\n",
       "      <td>5.000</td>\n",
       "      <td>120.000</td>\n",
       "      <td>4</td>\n",
       "      <td>2600.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1440</td>\n",
       "      <td>2.000</td>\n",
       "      <td>62.000</td>\n",
       "      <td>3</td>\n",
       "      <td>1440.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1521</td>\n",
       "      <td>3.000</td>\n",
       "      <td>95.000</td>\n",
       "      <td>3</td>\n",
       "      <td>1521.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1200</td>\n",
       "      <td>2.000</td>\n",
       "      <td>51.000</td>\n",
       "      <td>2</td>\n",
       "      <td>1200.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        availability                  location       size total_sqft  bath  \\\n",
       "0  Future Possession  Electronic City Phase II      2 BHK       1056 2.000   \n",
       "1      Ready To Move                    Others  4 Bedroom       2600 5.000   \n",
       "2      Ready To Move               Uttarahalli      3 BHK       1440 2.000   \n",
       "3      Ready To Move        Lingadheeranahalli      3 BHK       1521 3.000   \n",
       "4      Ready To Move                  Kothanur      2 BHK       1200 2.000   \n",
       "\n",
       "    price  bhks  total_sqft_new  \n",
       "0  39.070     2        1056.000  \n",
       "1 120.000     4        2600.000  \n",
       "2  62.000     3        1440.000  \n",
       "3  95.000     3        1521.000  \n",
       "4  51.000     2        1200.000  "
      ]
     },
     "execution_count": 436,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "metadata": {},
   "outputs": [],
   "source": [
    "df6  = df5.drop(['size','total_sqft'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "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>availability</th>\n",
       "      <th>location</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>bhks</th>\n",
       "      <th>total_sqft_new</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Future Possession</td>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2.000</td>\n",
       "      <td>39.070</td>\n",
       "      <td>2</td>\n",
       "      <td>1056.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Others</td>\n",
       "      <td>5.000</td>\n",
       "      <td>120.000</td>\n",
       "      <td>4</td>\n",
       "      <td>2600.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>2.000</td>\n",
       "      <td>62.000</td>\n",
       "      <td>3</td>\n",
       "      <td>1440.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3.000</td>\n",
       "      <td>95.000</td>\n",
       "      <td>3</td>\n",
       "      <td>1521.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2.000</td>\n",
       "      <td>51.000</td>\n",
       "      <td>2</td>\n",
       "      <td>1200.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        availability                  location  bath   price  bhks  \\\n",
       "0  Future Possession  Electronic City Phase II 2.000  39.070     2   \n",
       "1      Ready To Move                    Others 5.000 120.000     4   \n",
       "2      Ready To Move               Uttarahalli 2.000  62.000     3   \n",
       "3      Ready To Move        Lingadheeranahalli 3.000  95.000     3   \n",
       "4      Ready To Move                  Kothanur 2.000  51.000     2   \n",
       "\n",
       "   total_sqft_new  \n",
       "0        1056.000  \n",
       "1        2600.000  \n",
       "2        1440.000  \n",
       "3        1521.000  \n",
       "4        1200.000  "
      ]
     },
     "execution_count": 438,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 439,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>availability</th>\n",
       "      <th>location</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>bhks</th>\n",
       "      <th>total_sqft_new</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Others</td>\n",
       "      <td>6.000</td>\n",
       "      <td>370.000</td>\n",
       "      <td>6</td>\n",
       "      <td>1020.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Bisuvanahalli</td>\n",
       "      <td>3.000</td>\n",
       "      <td>48.000</td>\n",
       "      <td>3</td>\n",
       "      <td>1180.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Electronic City</td>\n",
       "      <td>1.000</td>\n",
       "      <td>23.100</td>\n",
       "      <td>2</td>\n",
       "      <td>660.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Electronic City</td>\n",
       "      <td>2.000</td>\n",
       "      <td>47.000</td>\n",
       "      <td>3</td>\n",
       "      <td>1025.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Bisuvanahalli</td>\n",
       "      <td>2.000</td>\n",
       "      <td>35.000</td>\n",
       "      <td>3</td>\n",
       "      <td>1075.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13281</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Margondanahalli</td>\n",
       "      <td>5.000</td>\n",
       "      <td>125.000</td>\n",
       "      <td>5</td>\n",
       "      <td>1375.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13300</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Hosakerehalli</td>\n",
       "      <td>6.000</td>\n",
       "      <td>145.000</td>\n",
       "      <td>5</td>\n",
       "      <td>1500.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13303</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Vidyaranyapura</td>\n",
       "      <td>5.000</td>\n",
       "      <td>70.000</td>\n",
       "      <td>5</td>\n",
       "      <td>774.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13306</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Others</td>\n",
       "      <td>5.000</td>\n",
       "      <td>325.000</td>\n",
       "      <td>4</td>\n",
       "      <td>1200.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13311</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Ramamurthy Nagar</td>\n",
       "      <td>9.000</td>\n",
       "      <td>250.000</td>\n",
       "      <td>7</td>\n",
       "      <td>1500.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1480 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        availability          location  bath   price  bhks  total_sqft_new\n",
       "9      Ready To Move            Others 6.000 370.000     6        1020.000\n",
       "16     Ready To Move     Bisuvanahalli 3.000  48.000     3        1180.000\n",
       "26     Ready To Move   Electronic City 1.000  23.100     2         660.000\n",
       "29     Ready To Move   Electronic City 2.000  47.000     3        1025.000\n",
       "31     Ready To Move     Bisuvanahalli 2.000  35.000     3        1075.000\n",
       "...              ...               ...   ...     ...   ...             ...\n",
       "13281  Ready To Move   Margondanahalli 5.000 125.000     5        1375.000\n",
       "13300  Ready To Move     Hosakerehalli 6.000 145.000     5        1500.000\n",
       "13303  Ready To Move    Vidyaranyapura 5.000  70.000     5         774.000\n",
       "13306  Ready To Move            Others 5.000 325.000     4        1200.000\n",
       "13311  Ready To Move  Ramamurthy Nagar 9.000 250.000     7        1500.000\n",
       "\n",
       "[1480 rows x 6 columns]"
      ]
     },
     "execution_count": 439,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6[df6['total_sqft_new']/df6['bhks'] < 400]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {},
   "outputs": [],
   "source": [
    "df7 = df6[df6['total_sqft_new']/df6['bhks'] > 400]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .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>availability</th>\n",
       "      <th>location</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>bhks</th>\n",
       "      <th>total_sqft_new</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Future Possession</td>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2.000</td>\n",
       "      <td>39.070</td>\n",
       "      <td>2</td>\n",
       "      <td>1056.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Others</td>\n",
       "      <td>5.000</td>\n",
       "      <td>120.000</td>\n",
       "      <td>4</td>\n",
       "      <td>2600.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>2.000</td>\n",
       "      <td>62.000</td>\n",
       "      <td>3</td>\n",
       "      <td>1440.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3.000</td>\n",
       "      <td>95.000</td>\n",
       "      <td>3</td>\n",
       "      <td>1521.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2.000</td>\n",
       "      <td>51.000</td>\n",
       "      <td>2</td>\n",
       "      <td>1200.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        availability                  location  bath   price  bhks  \\\n",
       "0  Future Possession  Electronic City Phase II 2.000  39.070     2   \n",
       "1      Ready To Move                    Others 5.000 120.000     4   \n",
       "2      Ready To Move               Uttarahalli 2.000  62.000     3   \n",
       "3      Ready To Move        Lingadheeranahalli 3.000  95.000     3   \n",
       "4      Ready To Move                  Kothanur 2.000  51.000     2   \n",
       "\n",
       "   total_sqft_new  \n",
       "0        1056.000  \n",
       "1        2600.000  \n",
       "2        1440.000  \n",
       "3        1521.000  \n",
       "4        1200.000  "
      ]
     },
     "execution_count": 441,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df7['price_per_sqft']=df7['price']*100000/df7['total_sqft_new']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>availability</th>\n",
       "      <th>location</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
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       "      <th>total_sqft_new</th>\n",
       "      <th>price_per_sqft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Future Possession</td>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2.000</td>\n",
       "      <td>39.070</td>\n",
       "      <td>2</td>\n",
       "      <td>1056.000</td>\n",
       "      <td>3699.811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Others</td>\n",
       "      <td>5.000</td>\n",
       "      <td>120.000</td>\n",
       "      <td>4</td>\n",
       "      <td>2600.000</td>\n",
       "      <td>4615.385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>2.000</td>\n",
       "      <td>62.000</td>\n",
       "      <td>3</td>\n",
       "      <td>1440.000</td>\n",
       "      <td>4305.556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3.000</td>\n",
       "      <td>95.000</td>\n",
       "      <td>3</td>\n",
       "      <td>1521.000</td>\n",
       "      <td>6245.891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2.000</td>\n",
       "      <td>51.000</td>\n",
       "      <td>2</td>\n",
       "      <td>1200.000</td>\n",
       "      <td>4250.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13315</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Whitefield</td>\n",
       "      <td>4.000</td>\n",
       "      <td>231.000</td>\n",
       "      <td>5</td>\n",
       "      <td>3453.000</td>\n",
       "      <td>6689.835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13316</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Others</td>\n",
       "      <td>5.000</td>\n",
       "      <td>400.000</td>\n",
       "      <td>4</td>\n",
       "      <td>3600.000</td>\n",
       "      <td>11111.111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13317</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Raja Rajeshwari Nagar</td>\n",
       "      <td>2.000</td>\n",
       "      <td>60.000</td>\n",
       "      <td>2</td>\n",
       "      <td>1141.000</td>\n",
       "      <td>5258.545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13318</th>\n",
       "      <td>Future Possession</td>\n",
       "      <td>Padmanabhanagar</td>\n",
       "      <td>4.000</td>\n",
       "      <td>488.000</td>\n",
       "      <td>4</td>\n",
       "      <td>4689.000</td>\n",
       "      <td>10407.336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13319</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Doddathoguru</td>\n",
       "      <td>1.000</td>\n",
       "      <td>17.000</td>\n",
       "      <td>1</td>\n",
       "      <td>550.000</td>\n",
       "      <td>3090.909</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11446 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            availability                  location  bath   price  bhks  \\\n",
       "0      Future Possession  Electronic City Phase II 2.000  39.070     2   \n",
       "1          Ready To Move                    Others 5.000 120.000     4   \n",
       "2          Ready To Move               Uttarahalli 2.000  62.000     3   \n",
       "3          Ready To Move        Lingadheeranahalli 3.000  95.000     3   \n",
       "4          Ready To Move                  Kothanur 2.000  51.000     2   \n",
       "...                  ...                       ...   ...     ...   ...   \n",
       "13315      Ready To Move                Whitefield 4.000 231.000     5   \n",
       "13316      Ready To Move                    Others 5.000 400.000     4   \n",
       "13317      Ready To Move     Raja Rajeshwari Nagar 2.000  60.000     2   \n",
       "13318  Future Possession           Padmanabhanagar 4.000 488.000     4   \n",
       "13319      Ready To Move              Doddathoguru 1.000  17.000     1   \n",
       "\n",
       "       total_sqft_new  price_per_sqft  \n",
       "0            1056.000        3699.811  \n",
       "1            2600.000        4615.385  \n",
       "2            1440.000        4305.556  \n",
       "3            1521.000        6245.891  \n",
       "4            1200.000        4250.000  \n",
       "...               ...             ...  \n",
       "13315        3453.000        6689.835  \n",
       "13316        3600.000       11111.111  \n",
       "13317        1141.000        5258.545  \n",
       "13318        4689.000       10407.336  \n",
       "13319         550.000        3090.909  \n",
       "\n",
       "[11446 rows x 7 columns]"
      ]
     },
     "execution_count": 443,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    11446.000\n",
       "mean      6131.272\n",
       "std       3932.983\n",
       "min        267.830\n",
       "25%       4202.939\n",
       "50%       5263.158\n",
       "75%       6768.856\n",
       "max     176470.588\n",
       "Name: price_per_sqft, dtype: float64"
      ]
     },
     "execution_count": 444,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7['price_per_sqft'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 445,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rmv_price_outlier(df):\n",
    "    df_new=pd.DataFrame()\n",
    "    for key,sdf in df.groupby('location'):\n",
    "        m = sdf['price_per_sqft'].mean()\n",
    "        s = sdf['price_per_sqft'].std()\n",
    "        #print (sdf['location'])\n",
    "        rdf = sdf[(sdf['price_per_sqft'] <= m+s) & (sdf['price_per_sqft'] > m-s)]\n",
    "        #print(rdf)\n",
    "        df_new = pd.concat([df_new,rdf],ignore_index=True)\n",
    "    return df_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "metadata": {},
   "outputs": [],
   "source": [
    "df8 = rmv_price_outlier(df7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 447,
   "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",
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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>availability</th>\n",
       "      <th>location</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>bhks</th>\n",
       "      <th>total_sqft_new</th>\n",
       "      <th>price_per_sqft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>1st Phase JP Nagar</td>\n",
       "      <td>4.000</td>\n",
       "      <td>250.000</td>\n",
       "      <td>4</td>\n",
       "      <td>2825.000</td>\n",
       "      <td>8849.558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>1st Phase JP Nagar</td>\n",
       "      <td>3.000</td>\n",
       "      <td>167.000</td>\n",
       "      <td>3</td>\n",
       "      <td>1875.000</td>\n",
       "      <td>8906.667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>1st Phase JP Nagar</td>\n",
       "      <td>4.000</td>\n",
       "      <td>210.000</td>\n",
       "      <td>3</td>\n",
       "      <td>2065.000</td>\n",
       "      <td>10169.492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>1st Phase JP Nagar</td>\n",
       "      <td>3.000</td>\n",
       "      <td>157.000</td>\n",
       "      <td>3</td>\n",
       "      <td>2024.000</td>\n",
       "      <td>7756.917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>1st Phase JP Nagar</td>\n",
       "      <td>3.000</td>\n",
       "      <td>225.000</td>\n",
       "      <td>3</td>\n",
       "      <td>2059.000</td>\n",
       "      <td>10927.635</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    availability            location  bath   price  bhks  total_sqft_new  \\\n",
       "0  Ready To Move  1st Phase JP Nagar 4.000 250.000     4        2825.000   \n",
       "1  Ready To Move  1st Phase JP Nagar 3.000 167.000     3        1875.000   \n",
       "2  Ready To Move  1st Phase JP Nagar 4.000 210.000     3        2065.000   \n",
       "3  Ready To Move  1st Phase JP Nagar 3.000 157.000     3        2024.000   \n",
       "4  Ready To Move  1st Phase JP Nagar 3.000 225.000     3        2059.000   \n",
       "\n",
       "   price_per_sqft  \n",
       "0        8849.558  \n",
       "1        8906.667  \n",
       "2       10169.492  \n",
       "3        7756.917  \n",
       "4       10927.635  "
      ]
     },
     "execution_count": 447,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df8.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9512, 7)"
      ]
     },
     "execution_count": 448,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df8.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 449,
   "metadata": {},
   "outputs": [],
   "source": [
    "availability_dummy = pd.get_dummies(df8['availability'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 450,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ready To Move</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9507</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9508</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9509</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9510</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9511</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9512 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Ready To Move\n",
       "0                 1\n",
       "1                 1\n",
       "2                 1\n",
       "3                 1\n",
       "4                 1\n",
       "...             ...\n",
       "9507              1\n",
       "9508              1\n",
       "9509              1\n",
       "9510              1\n",
       "9511              1\n",
       "\n",
       "[9512 rows x 1 columns]"
      ]
     },
     "execution_count": 450,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "availability_dummy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 451,
   "metadata": {},
   "outputs": [],
   "source": [
    "location_dummy = pd.get_dummies(df8['location'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 452,
   "metadata": {},
   "outputs": [],
   "source": [
    "df9=pd.concat([df8, availability_dummy,location_dummy],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 453,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>availability</th>\n",
       "      <th>location</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>bhks</th>\n",
       "      <th>total_sqft_new</th>\n",
       "      <th>price_per_sqft</th>\n",
       "      <th>Ready To Move</th>\n",
       "      <th>2nd Stage Nagarbhavi</th>\n",
       "      <th>5th Phase JP Nagar</th>\n",
       "      <th>...</th>\n",
       "      <th>Ulsoor</th>\n",
       "      <th>Uttarahalli</th>\n",
       "      <th>Varthur</th>\n",
       "      <th>Vidyaranyapura</th>\n",
       "      <th>Vijayanagar</th>\n",
       "      <th>Vittasandra</th>\n",
       "      <th>Whitefield</th>\n",
       "      <th>Yelahanka</th>\n",
       "      <th>Yelahanka New Town</th>\n",
       "      <th>Yeshwanthpur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>1st Phase JP Nagar</td>\n",
       "      <td>4.000</td>\n",
       "      <td>250.000</td>\n",
       "      <td>4</td>\n",
       "      <td>2825.000</td>\n",
       "      <td>8849.558</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>1st Phase JP Nagar</td>\n",
       "      <td>3.000</td>\n",
       "      <td>167.000</td>\n",
       "      <td>3</td>\n",
       "      <td>1875.000</td>\n",
       "      <td>8906.667</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>1st Phase JP Nagar</td>\n",
       "      <td>4.000</td>\n",
       "      <td>210.000</td>\n",
       "      <td>3</td>\n",
       "      <td>2065.000</td>\n",
       "      <td>10169.492</td>\n",
       "      <td>1</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>Ready To Move</td>\n",
       "      <td>1st Phase JP Nagar</td>\n",
       "      <td>3.000</td>\n",
       "      <td>157.000</td>\n",
       "      <td>3</td>\n",
       "      <td>2024.000</td>\n",
       "      <td>7756.917</td>\n",
       "      <td>1</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>Ready To Move</td>\n",
       "      <td>1st Phase JP Nagar</td>\n",
       "      <td>3.000</td>\n",
       "      <td>225.000</td>\n",
       "      <td>3</td>\n",
       "      <td>2059.000</td>\n",
       "      <td>10927.635</td>\n",
       "      <td>1</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 × 151 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    availability            location  bath   price  bhks  total_sqft_new  \\\n",
       "0  Ready To Move  1st Phase JP Nagar 4.000 250.000     4        2825.000   \n",
       "1  Ready To Move  1st Phase JP Nagar 3.000 167.000     3        1875.000   \n",
       "2  Ready To Move  1st Phase JP Nagar 4.000 210.000     3        2065.000   \n",
       "3  Ready To Move  1st Phase JP Nagar 3.000 157.000     3        2024.000   \n",
       "4  Ready To Move  1st Phase JP Nagar 3.000 225.000     3        2059.000   \n",
       "\n",
       "   price_per_sqft  Ready To Move  2nd Stage Nagarbhavi  5th Phase JP Nagar  \\\n",
       "0        8849.558              1                     0                   0   \n",
       "1        8906.667              1                     0                   0   \n",
       "2       10169.492              1                     0                   0   \n",
       "3        7756.917              1                     0                   0   \n",
       "4       10927.635              1                     0                   0   \n",
       "\n",
       "   ...  Ulsoor  Uttarahalli  Varthur  Vidyaranyapura  Vijayanagar  \\\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",
       "   Vittasandra  Whitefield  Yelahanka  Yelahanka New Town  Yeshwanthpur  \n",
       "0            0           0          0                   0             0  \n",
       "1            0           0          0                   0             0  \n",
       "2            0           0          0                   0             0  \n",
       "3            0           0          0                   0             0  \n",
       "4            0           0          0                   0             0  \n",
       "\n",
       "[5 rows x 151 columns]"
      ]
     },
     "execution_count": 453,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df9.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "metadata": {},
   "outputs": [],
   "source": [
    "df10=df9.drop(['availability','location','price_per_sqft'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 455,
   "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>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>bhks</th>\n",
       "      <th>total_sqft_new</th>\n",
       "      <th>Ready To Move</th>\n",
       "      <th>2nd Stage Nagarbhavi</th>\n",
       "      <th>5th Phase JP Nagar</th>\n",
       "      <th>6th Phase JP Nagar</th>\n",
       "      <th>7th Phase JP Nagar</th>\n",
       "      <th>8th Phase JP Nagar</th>\n",
       "      <th>...</th>\n",
       "      <th>Ulsoor</th>\n",
       "      <th>Uttarahalli</th>\n",
       "      <th>Varthur</th>\n",
       "      <th>Vidyaranyapura</th>\n",
       "      <th>Vijayanagar</th>\n",
       "      <th>Vittasandra</th>\n",
       "      <th>Whitefield</th>\n",
       "      <th>Yelahanka</th>\n",
       "      <th>Yelahanka New Town</th>\n",
       "      <th>Yeshwanthpur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.000</td>\n",
       "      <td>250.000</td>\n",
       "      <td>4</td>\n",
       "      <td>2825.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.000</td>\n",
       "      <td>167.000</td>\n",
       "      <td>3</td>\n",
       "      <td>1875.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.000</td>\n",
       "      <td>210.000</td>\n",
       "      <td>3</td>\n",
       "      <td>2065.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.000</td>\n",
       "      <td>157.000</td>\n",
       "      <td>3</td>\n",
       "      <td>2024.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.000</td>\n",
       "      <td>225.000</td>\n",
       "      <td>3</td>\n",
       "      <td>2059.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 148 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   bath   price  bhks  total_sqft_new  Ready To Move  2nd Stage Nagarbhavi  \\\n",
       "0 4.000 250.000     4        2825.000              1                     0   \n",
       "1 3.000 167.000     3        1875.000              1                     0   \n",
       "2 4.000 210.000     3        2065.000              1                     0   \n",
       "3 3.000 157.000     3        2024.000              1                     0   \n",
       "4 3.000 225.000     3        2059.000              1                     0   \n",
       "\n",
       "   5th Phase JP Nagar  6th Phase JP Nagar  7th 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",
       "   8th Phase JP Nagar  ...  Ulsoor  Uttarahalli  Varthur  Vidyaranyapura  \\\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",
       "   Vijayanagar  Vittasandra  Whitefield  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",
       "   Yeshwanthpur  \n",
       "0             0  \n",
       "1             0  \n",
       "2             0  \n",
       "3             0  \n",
       "4             0  \n",
       "\n",
       "[5 rows x 148 columns]"
      ]
     },
     "execution_count": 455,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df10.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 456,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df10.drop(['price'], axis=1)\n",
    "y = df10['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 457,
   "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=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 458,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7609"
      ]
     },
     "execution_count": 458,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 459,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1903"
      ]
     },
     "execution_count": 459,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 460,
   "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>bath</th>\n",
       "      <th>bhks</th>\n",
       "      <th>total_sqft_new</th>\n",
       "      <th>Ready To Move</th>\n",
       "      <th>2nd Stage Nagarbhavi</th>\n",
       "      <th>5th Phase JP Nagar</th>\n",
       "      <th>6th Phase JP Nagar</th>\n",
       "      <th>7th Phase JP Nagar</th>\n",
       "      <th>8th Phase JP Nagar</th>\n",
       "      <th>9th Phase JP Nagar</th>\n",
       "      <th>...</th>\n",
       "      <th>Ulsoor</th>\n",
       "      <th>Uttarahalli</th>\n",
       "      <th>Varthur</th>\n",
       "      <th>Vidyaranyapura</th>\n",
       "      <th>Vijayanagar</th>\n",
       "      <th>Vittasandra</th>\n",
       "      <th>Whitefield</th>\n",
       "      <th>Yelahanka</th>\n",
       "      <th>Yelahanka New Town</th>\n",
       "      <th>Yeshwanthpur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7609.000</td>\n",
       "      <td>7609.000</td>\n",
       "      <td>7609.000</td>\n",
       "      <td>7609.000</td>\n",
       "      <td>7609.000</td>\n",
       "      <td>7609.000</td>\n",
       "      <td>7609.000</td>\n",
       "      <td>7609.000</td>\n",
       "      <td>7609.000</td>\n",
       "      <td>7609.000</td>\n",
       "      <td>...</td>\n",
       "      <td>7609.000</td>\n",
       "      <td>7609.000</td>\n",
       "      <td>7609.000</td>\n",
       "      <td>7609.000</td>\n",
       "      <td>7609.000</td>\n",
       "      <td>7609.000</td>\n",
       "      <td>7609.000</td>\n",
       "      <td>7609.000</td>\n",
       "      <td>7609.000</td>\n",
       "      <td>7609.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.451</td>\n",
       "      <td>2.523</td>\n",
       "      <td>1518.513</td>\n",
       "      <td>0.782</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.003</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.048</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.918</td>\n",
       "      <td>0.825</td>\n",
       "      <td>898.791</td>\n",
       "      <td>0.413</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.047</td>\n",
       "      <td>0.040</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.052</td>\n",
       "      <td>...</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0.044</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.213</td>\n",
       "      <td>0.131</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>410.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>1120.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>1300.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>1650.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.000</td>\n",
       "      <td>11.000</td>\n",
       "      <td>30400.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 147 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          bath     bhks  total_sqft_new  Ready To Move  2nd Stage Nagarbhavi  \\\n",
       "count 7609.000 7609.000        7609.000       7609.000              7609.000   \n",
       "mean     2.451    2.523        1518.513          0.782                 0.000   \n",
       "std      0.918    0.825         898.791          0.413                 0.011   \n",
       "min      1.000    1.000         410.000          0.000                 0.000   \n",
       "25%      2.000    2.000        1120.000          1.000                 0.000   \n",
       "50%      2.000    2.000        1300.000          1.000                 0.000   \n",
       "75%      3.000    3.000        1650.000          1.000                 0.000   \n",
       "max      9.000   11.000       30400.000          1.000                 1.000   \n",
       "\n",
       "       5th Phase JP Nagar  6th Phase JP Nagar  7th Phase JP Nagar  \\\n",
       "count            7609.000            7609.000            7609.000   \n",
       "mean                0.002               0.002               0.010   \n",
       "std                 0.047               0.040               0.100   \n",
       "min                 0.000               0.000               0.000   \n",
       "25%                 0.000               0.000               0.000   \n",
       "50%                 0.000               0.000               0.000   \n",
       "75%                 0.000               0.000               0.000   \n",
       "max                 1.000               1.000               1.000   \n",
       "\n",
       "       8th Phase JP Nagar  9th Phase JP Nagar  ...   Ulsoor  Uttarahalli  \\\n",
       "count            7609.000            7609.000  ... 7609.000     7609.000   \n",
       "mean                0.004               0.003  ...    0.001        0.014   \n",
       "std                 0.059               0.052  ...    0.030        0.118   \n",
       "min                 0.000               0.000  ...    0.000        0.000   \n",
       "25%                 0.000               0.000  ...    0.000        0.000   \n",
       "50%                 0.000               0.000  ...    0.000        0.000   \n",
       "75%                 0.000               0.000  ...    0.000        0.000   \n",
       "max                 1.000               1.000  ...    1.000        1.000   \n",
       "\n",
       "       Varthur  Vidyaranyapura  Vijayanagar  Vittasandra  Whitefield  \\\n",
       "count 7609.000        7609.000     7609.000     7609.000    7609.000   \n",
       "mean     0.005           0.002        0.002        0.003       0.048   \n",
       "std      0.071           0.044        0.050        0.056       0.213   \n",
       "min      0.000           0.000        0.000        0.000       0.000   \n",
       "25%      0.000           0.000        0.000        0.000       0.000   \n",
       "50%      0.000           0.000        0.000        0.000       0.000   \n",
       "75%      0.000           0.000        0.000        0.000       0.000   \n",
       "max      1.000           1.000        1.000        1.000       1.000   \n",
       "\n",
       "       Yelahanka  Yelahanka New Town  Yeshwanthpur  \n",
       "count   7609.000            7609.000      7609.000  \n",
       "mean       0.017               0.004         0.007  \n",
       "std        0.131               0.059         0.082  \n",
       "min        0.000               0.000         0.000  \n",
       "25%        0.000               0.000         0.000  \n",
       "50%        0.000               0.000         0.000  \n",
       "75%        0.000               0.000         0.000  \n",
       "max        1.000               1.000         1.000  \n",
       "\n",
       "[8 rows x 147 columns]"
      ]
     },
     "execution_count": 460,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 461,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 465,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.88749248, 0.83974159, 0.77816298, 0.78621149, 0.80342302,\n",
       "       0.75351985])"
      ]
     },
     "execution_count": 465,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_score =cross_val_score(estimator=LinearRegression(),X=X_train, y=y_train, cv=6)\n",
    "model_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 467,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8080919019704589"
      ]
     },
     "execution_count": 467,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_score.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 469,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.04412209423154256"
      ]
     },
     "execution_count": 469,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_score.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 471,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.85158695, 0.68749202, 0.54764214, 0.7192667 , 0.45725236])"
      ]
     },
     "execution_count": 471,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_score =cross_val_score(estimator=DecisionTreeRegressor(),X=X_train, y=y_train, cv=5)\n",
    "model_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 473,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6526480329027052"
      ]
     },
     "execution_count": 473,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_score.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 475,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.13742965399712503"
      ]
     },
     "execution_count": 475,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_score.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 476,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 476,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_lr = LinearRegression()\n",
    "model_lr.fit(X_train,y_train)"
   ]
  }
 ],
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