Price_prediction_Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d6bd92ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "import matplotlib \n",
    "matplotlib.rcParams[\"figure.figsize\"] = (20,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2d3a304f",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Area</th>\n",
       "      <th>Location</th>\n",
       "      <th>No. of Bedrooms</th>\n",
       "      <th>Resale</th>\n",
       "      <th>MaintenanceStaff</th>\n",
       "      <th>Gymnasium</th>\n",
       "      <th>SwimmingPool</th>\n",
       "      <th>LandscapedGardens</th>\n",
       "      <th>JoggingTrack</th>\n",
       "      <th>...</th>\n",
       "      <th>LiftAvailable</th>\n",
       "      <th>BED</th>\n",
       "      <th>VaastuCompliant</th>\n",
       "      <th>Microwave</th>\n",
       "      <th>GolfCourse</th>\n",
       "      <th>TV</th>\n",
       "      <th>DiningTable</th>\n",
       "      <th>Sofa</th>\n",
       "      <th>Wardrobe</th>\n",
       "      <th>Refrigerator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>4850000</td>\n",
       "      <td>720</td>\n",
       "      <td>Kharghar</td>\n",
       "      <td>1</td>\n",
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       "      <th>1</th>\n",
       "      <td>4500000</td>\n",
       "      <td>600</td>\n",
       "      <td>Kharghar</td>\n",
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       "      <td>6700000</td>\n",
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       "      <td>0</td>\n",
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       "      <th>3</th>\n",
       "      <td>4500000</td>\n",
       "      <td>650</td>\n",
       "      <td>Kharghar</td>\n",
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       "      <th>4</th>\n",
       "      <td>5000000</td>\n",
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       "<p>5 rows × 40 columns</p>\n",
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      "text/plain": [
       "     Price  Area  Location  No. of Bedrooms  Resale  MaintenanceStaff  \\\n",
       "0  4850000   720  Kharghar                1       1                 1   \n",
       "1  4500000   600  Kharghar                1       1                 1   \n",
       "2  6700000   650  Kharghar                1       1                 1   \n",
       "3  4500000   650  Kharghar                1       1                 1   \n",
       "4  5000000   665  Kharghar                1       1                 1   \n",
       "\n",
       "   Gymnasium  SwimmingPool  LandscapedGardens  JoggingTrack  ...  \\\n",
       "0          0             0                  0             0  ...   \n",
       "1          1             1                  0             1  ...   \n",
       "2          1             1                  0             1  ...   \n",
       "3          0             0                  1             0  ...   \n",
       "4          0             0                  1             0  ...   \n",
       "\n",
       "   LiftAvailable  BED  VaastuCompliant  Microwave  GolfCourse  TV  \\\n",
       "0              1    0                1          0           0   0   \n",
       "1              1    0                1          0           0   0   \n",
       "2              1    0                1          0           0   0   \n",
       "3              1    1                1          0           0   0   \n",
       "4              1    0                1          0           0   0   \n",
       "\n",
       "   DiningTable  Sofa  Wardrobe  Refrigerator  \n",
       "0            0     0         0             0  \n",
       "1            0     0         0             0  \n",
       "2            0     0         0             0  \n",
       "3            0     0         1             0  \n",
       "4            0     0         0             0  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.read_csv(r\"C:\\Users\\ASUS\\Downloads\\archive (5)\\Mumbai.csv\")\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "04ec8969",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7719, 40)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "49f6a0d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Price', 'Area', 'Location', 'No. of Bedrooms', 'Resale',\n",
       "       'MaintenanceStaff', 'Gymnasium', 'SwimmingPool', 'LandscapedGardens',\n",
       "       'JoggingTrack', 'RainWaterHarvesting', 'IndoorGames', 'ShoppingMall',\n",
       "       'Intercom', 'SportsFacility', 'ATM', 'ClubHouse', 'School',\n",
       "       '24X7Security', 'PowerBackup', 'CarParking', 'StaffQuarter',\n",
       "       'Cafeteria', 'MultipurposeRoom', 'Hospital', 'WashingMachine',\n",
       "       'Gasconnection', 'AC', 'Wifi', 'Children'splayarea', 'LiftAvailable',\n",
       "       'BED', 'VaastuCompliant', 'Microwave', 'GolfCourse', 'TV',\n",
       "       'DiningTable', 'Sofa', 'Wardrobe', 'Refrigerator'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6de306ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Price</th>\n",
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       "      <th>No. of Bedrooms</th>\n",
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      "text/plain": [
       "     Price  Area  Location  No. of Bedrooms\n",
       "0  4850000   720  Kharghar                1\n",
       "1  4500000   600  Kharghar                1\n",
       "2  6700000   650  Kharghar                1\n",
       "3  4500000   650  Kharghar                1\n",
       "4  5000000   665  Kharghar                1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2=df1.drop(['Resale',\n",
    "       'MaintenanceStaff', 'Gymnasium', 'SwimmingPool', 'LandscapedGardens',\n",
    "       'JoggingTrack', 'RainWaterHarvesting', 'IndoorGames', 'ShoppingMall',\n",
    "       'Intercom', 'SportsFacility', 'ATM', 'ClubHouse', 'School',\n",
    "       '24X7Security', 'PowerBackup', 'CarParking', 'StaffQuarter',\n",
    "       'Cafeteria', 'MultipurposeRoom', 'Hospital', 'WashingMachine',\n",
    "       'Gasconnection', 'AC', 'Wifi', \"Children'splayarea\", 'LiftAvailable',\n",
    "       'BED', 'VaastuCompliant', 'Microwave', 'GolfCourse', 'TV',\n",
    "       'DiningTable', 'Sofa', 'Wardrobe', 'Refrigerator'], axis ='columns')\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "56879d6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Price              0\n",
       "Area               0\n",
       "Location           0\n",
       "No. of Bedrooms    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e2188f22",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "     Price  Area  Location  No. of Bedrooms  BHK\n",
       "0  4850000   720  Kharghar                1    1\n",
       "1  4500000   600  Kharghar                1    1\n",
       "2  6700000   650  Kharghar                1    1\n",
       "3  4500000   650  Kharghar                1    1\n",
       "4  5000000   665  Kharghar                1    1"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['BHK'] = df2['No. of Bedrooms'].apply(lambda x: int (x))\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "abe0febe",
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   "outputs": [
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       "      <td>45.0</td>\n",
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       "      <td>50.0</td>\n",
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      ],
      "text/plain": [
       "     Price  Area  Location  No. of Bedrooms  BHK  Total_price\n",
       "0  4850000   720  Kharghar                1    1         48.5\n",
       "1  4500000   600  Kharghar                1    1         45.0\n",
       "2  6700000   650  Kharghar                1    1         67.0\n",
       "3  4500000   650  Kharghar                1    1         45.0\n",
       "4  5000000   665  Kharghar                1    1         50.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['Total_price'] = df2.Price/100000\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e8fe13f8",
   "metadata": {},
   "outputs": [
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       "      <td>720</td>\n",
       "      <td>Kharghar</td>\n",
       "      <td>1</td>\n",
       "      <td>48.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>600</td>\n",
       "      <td>Kharghar</td>\n",
       "      <td>1</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>650</td>\n",
       "      <td>Kharghar</td>\n",
       "      <td>1</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>650</td>\n",
       "      <td>Kharghar</td>\n",
       "      <td>1</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>665</td>\n",
       "      <td>Kharghar</td>\n",
       "      <td>1</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Area  Location  BHK  Total_price\n",
       "0   720  Kharghar    1         48.5\n",
       "1   600  Kharghar    1         45.0\n",
       "2   650  Kharghar    1         67.0\n",
       "3   650  Kharghar    1         45.0\n",
       "4   665  Kharghar    1         50.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3=df2.drop(['Price','No. of Bedrooms'], axis='columns')\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "124ec796",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Kharghar               681\n",
       "Thane West             577\n",
       "Mira Road East         481\n",
       "Ulwe                   391\n",
       "Nala Sopara            225\n",
       "                      ... \n",
       "Sea Face                 1\n",
       "Shiv Sagar Estate        1\n",
       "Kulupwadi                1\n",
       "Off Nepean Sea Road      1\n",
       "Padle Gaon               1\n",
       "Name: Location, Length: 413, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.Location = df3.Location.apply(lambda x: x.strip())\n",
    "Location_stats = df3['Location'].value_counts(ascending=False)\n",
    "Location_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9a029b1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7719"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Location_stats.values.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6c68bfcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "205"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(Location_stats[Location_stats>2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c1f35bdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "413"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(Location_stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "565d1b31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sector-35 Kamothe      2\n",
       "Saki Naka              2\n",
       "MHADA Colony 20        2\n",
       "Vikhroli West          2\n",
       "Tolaram Colony         2\n",
       "                      ..\n",
       "Sea Face               1\n",
       "Shiv Sagar Estate      1\n",
       "Kulupwadi              1\n",
       "Off Nepean Sea Road    1\n",
       "Padle Gaon             1\n",
       "Name: Location, Length: 208, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Location_stats_less_than_2 = Location_stats[Location_stats<=2]\n",
    "Location_stats_less_than_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1b62e139",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "413"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df3.Location.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "800d2966",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "206"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.Location = df3.Location.apply(lambda x: 'other' if x in Location_stats_less_than_2 else x)\n",
    "len(df3.Location.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2364b444",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Area</th>\n",
       "      <th>Location</th>\n",
       "      <th>BHK</th>\n",
       "      <th>Total_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>720</td>\n",
       "      <td>Kharghar</td>\n",
       "      <td>1</td>\n",
       "      <td>48.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>600</td>\n",
       "      <td>Kharghar</td>\n",
       "      <td>1</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>650</td>\n",
       "      <td>Kharghar</td>\n",
       "      <td>1</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>650</td>\n",
       "      <td>Kharghar</td>\n",
       "      <td>1</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>665</td>\n",
       "      <td>Kharghar</td>\n",
       "      <td>1</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Area  Location  BHK  Total_price\n",
       "0   720  Kharghar    1         48.5\n",
       "1   600  Kharghar    1         45.0\n",
       "2   650  Kharghar    1         67.0\n",
       "3   650  Kharghar    1         45.0\n",
       "4   665  Kharghar    1         50.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "96cd9154",
   "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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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Area</th>\n",
       "      <th>Location</th>\n",
       "      <th>BHK</th>\n",
       "      <th>Total_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1271</th>\n",
       "      <td>245</td>\n",
       "      <td>Mira Road East</td>\n",
       "      <td>1</td>\n",
       "      <td>21.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1400</th>\n",
       "      <td>400</td>\n",
       "      <td>mumbai</td>\n",
       "      <td>2</td>\n",
       "      <td>35.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1661</th>\n",
       "      <td>441</td>\n",
       "      <td>Jawahar Nagar</td>\n",
       "      <td>2</td>\n",
       "      <td>88.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2450</th>\n",
       "      <td>245</td>\n",
       "      <td>Mira Road East</td>\n",
       "      <td>1</td>\n",
       "      <td>79.59999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2512</th>\n",
       "      <td>220</td>\n",
       "      <td>Mira Road East</td>\n",
       "      <td>1</td>\n",
       "      <td>83.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Area        Location  BHK  Total_price\n",
       "1271   245  Mira Road East    1     21.00000\n",
       "1400   400          mumbai    2     35.00000\n",
       "1661   441   Jawahar Nagar    2     88.00000\n",
       "2450   245  Mira Road East    1     79.59999\n",
       "2512   220  Mira Road East    1     83.00000"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3[df3.Area/df3.BHK<250].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f4625c95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7663, 4)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4=df3[~(df3.Area/df3.BHK<250)]\n",
    "df4.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "349e531c",
   "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>Area</th>\n",
       "      <th>Location</th>\n",
       "      <th>BHK</th>\n",
       "      <th>Total_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>720</td>\n",
       "      <td>Kharghar</td>\n",
       "      <td>1</td>\n",
       "      <td>48.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>600</td>\n",
       "      <td>Kharghar</td>\n",
       "      <td>1</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>650</td>\n",
       "      <td>Kharghar</td>\n",
       "      <td>1</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>650</td>\n",
       "      <td>Kharghar</td>\n",
       "      <td>1</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>665</td>\n",
       "      <td>Kharghar</td>\n",
       "      <td>1</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Area  Location  BHK  Total_price\n",
       "0   720  Kharghar    1         48.5\n",
       "1   600  Kharghar    1         45.0\n",
       "2   650  Kharghar    1         67.0\n",
       "3   650  Kharghar    1         45.0\n",
       "4   665  Kharghar    1         50.0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5=df4.copy()\n",
    "df5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "1b9fd07d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Area</th>\n",
       "      <th>Location</th>\n",
       "      <th>BHK</th>\n",
       "      <th>Total_price</th>\n",
       "      <th>price_per_sqft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>720</td>\n",
       "      <td>Kharghar</td>\n",
       "      <td>1</td>\n",
       "      <td>48.5</td>\n",
       "      <td>6736.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>600</td>\n",
       "      <td>Kharghar</td>\n",
       "      <td>1</td>\n",
       "      <td>45.0</td>\n",
       "      <td>7500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>650</td>\n",
       "      <td>Kharghar</td>\n",
       "      <td>1</td>\n",
       "      <td>67.0</td>\n",
       "      <td>10307.692308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>650</td>\n",
       "      <td>Kharghar</td>\n",
       "      <td>1</td>\n",
       "      <td>45.0</td>\n",
       "      <td>6923.076923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>665</td>\n",
       "      <td>Kharghar</td>\n",
       "      <td>1</td>\n",
       "      <td>50.0</td>\n",
       "      <td>7518.796992</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Area  Location  BHK  Total_price  price_per_sqft\n",
       "0   720  Kharghar    1         48.5     6736.111111\n",
       "1   600  Kharghar    1         45.0     7500.000000\n",
       "2   650  Kharghar    1         67.0    10307.692308\n",
       "3   650  Kharghar    1         45.0     6923.076923\n",
       "4   665  Kharghar    1         50.0     7518.796992"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5['price_per_sqft']=df5['Total_price']*100000/df5['Area']\n",
    "df5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "5380d70b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      7663.000000\n",
       "mean      16144.092075\n",
       "std       19602.755817\n",
       "min         460.526316\n",
       "25%        6454.152908\n",
       "50%       10357.142857\n",
       "75%       18620.689655\n",
       "max      400000.000000\n",
       "Name: price_per_sqft, dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.price_per_sqft.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "b2a0b251",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6621, 5)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def remove_pps_outliers(df):\n",
    "    df_out = pd.DataFrame()\n",
    "    for key, subdf in df.groupby('Location'):\n",
    "        m = np.mean(subdf.price_per_sqft)\n",
    "        st = np.std(subdf.price_per_sqft)\n",
    "        reduced_df = subdf[(subdf.price_per_sqft>(m-st)) & (subdf.price_per_sqft<=(m+st))]\n",
    "        df_out = pd.concat([df_out,reduced_df],ignore_index=True)\n",
    "    return df_out\n",
    "df6 = remove_pps_outliers(df5)\n",
    "df6.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "f3293cdf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_scatter_chart(df,Location):\n",
    "    bhk2 = df[(df.Location==Location) & (df.BHK==2)]\n",
    "    bhk3 = df[(df.Location==Location) & (df.BHK==3)]\n",
    "    matplotlib.rcParams['figure.figsize'] = (15,10)\n",
    "    plt.scatter(bhk2.Area,bhk2.Total_price,color='blue',label='2 BHK', s=50)\n",
    "    plt.scatter(bhk3.Area,bhk3.Total_price,marker='+', color='green',label='3 BHK', s=50)\n",
    "    plt.xlabel(\"Total Square Feet Area\")\n",
    "    plt.ylabel(\"Price (Lakh Indian Rupees)\")\n",
    "    plt.title(Location)\n",
    "    plt.legend()\n",
    "    \n",
    "plot_scatter_chart(df6,\"Kharghar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "4270fdf4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scatter_chart(df6,\"Mira Road East\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "2b768cf3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4198, 5)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def remove_bhk_outliers(df):\n",
    "    exclude_indices = np.array([])\n",
    "    for Location, Location_df in df.groupby('Location'):\n",
    "        BHK_stats = {}\n",
    "        for BHK, BHK_df in Location_df.groupby('BHK'):\n",
    "            BHK_stats[BHK] = {\n",
    "                'mean': np.mean(BHK_df.price_per_sqft),\n",
    "                'std': np.std(BHK_df.price_per_sqft),\n",
    "                'count': BHK_df.shape[0]\n",
    "            }\n",
    "        for BHK, BHK_df in Location_df.groupby('BHK'):\n",
    "            stats = BHK_stats.get(BHK-1)\n",
    "            if stats and stats['count']>5:\n",
    "                exclude_indices = np.append(exclude_indices, BHK_df[BHK_df.price_per_sqft<(stats['mean'])].index.values)\n",
    "    return df.drop(exclude_indices,axis='index')\n",
    "df7 = remove_bhk_outliers(df6)\n",
    "df7.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "c9ad4483",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scatter_chart(df6,\"Kharghar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "eee675c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scatter_chart(df6,\"Mira Road East\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "3651b61b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "matplotlib.rcParams[\"figure.figsize\"] = (20,10)\n",
    "plt.hist(df7.price_per_sqft,rwidth=0.8)\n",
    "plt.xlabel(\"Price Per Square Feet\")\n",
    "plt.ylabel(\"Count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "6f030c3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AQBIhBgAAAAAAIIkQAwAAAAAAkESIAQAAAAAASCLEAAAAAAAAJBFiAAAAAAAAkggxAAAAAAAASYQYAAAAAACAJEIMAAAAAABAEiEGAAAAAAAgyf8FTSK6MH3SvJcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df7.BHK,rwidth=0.8)\n",
    "plt.xlabel(\"BHK\")\n",
    "plt.ylabel(\"Count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "84ea9495",
   "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>Area</th>\n",
       "      <th>Location</th>\n",
       "      <th>BHK</th>\n",
       "      <th>Total_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1350</td>\n",
       "      <td>4 Bunglows</td>\n",
       "      <td>2</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1350</td>\n",
       "      <td>4 Bunglows</td>\n",
       "      <td>2</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>400</td>\n",
       "      <td>Airoli</td>\n",
       "      <td>1</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1245</td>\n",
       "      <td>Airoli</td>\n",
       "      <td>2</td>\n",
       "      <td>149.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1183</td>\n",
       "      <td>Airoli</td>\n",
       "      <td>2</td>\n",
       "      <td>140.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Area    Location  BHK  Total_price\n",
       "0  1350  4 Bunglows    2        250.0\n",
       "1  1350  4 Bunglows    2        250.0\n",
       "2   400      Airoli    1         62.0\n",
       "4  1245      Airoli    2        149.0\n",
       "5  1183      Airoli    2        140.0"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df8=df7.drop(['price_per_sqft'],axis='columns')\n",
    "df8.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "1e86534c",
   "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>4 Bunglows</th>\n",
       "      <th>Airoli</th>\n",
       "      <th>Ambarnath</th>\n",
       "      <th>Ambernath East</th>\n",
       "      <th>Ambernath West</th>\n",
       "      <th>Andheri</th>\n",
       "      <th>Andheri East</th>\n",
       "      <th>Andheri West</th>\n",
       "      <th>Anjurdive</th>\n",
       "      <th>Antop Hill</th>\n",
       "      <th>...</th>\n",
       "      <th>kavesar</th>\n",
       "      <th>kolshet</th>\n",
       "      <th>matunga east</th>\n",
       "      <th>mumbai</th>\n",
       "      <th>other</th>\n",
       "      <th>roadpali navimumbai</th>\n",
       "      <th>taloja panchanand</th>\n",
       "      <th>thakur village kandivali east</th>\n",
       "      <th>vasant vihar thane west</th>\n",
       "      <th>vile parle west</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "<p>3 rows × 206 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   4 Bunglows  Airoli  Ambarnath  Ambernath East  Ambernath West  Andheri  \\\n",
       "0           1       0          0               0               0        0   \n",
       "1           1       0          0               0               0        0   \n",
       "2           0       1          0               0               0        0   \n",
       "\n",
       "   Andheri East  Andheri West  Anjurdive  Antop Hill  ...  kavesar  kolshet  \\\n",
       "0             0             0          0           0  ...        0        0   \n",
       "1             0             0          0           0  ...        0        0   \n",
       "2             0             0          0           0  ...        0        0   \n",
       "\n",
       "   matunga east  mumbai  other  roadpali navimumbai  taloja panchanand  \\\n",
       "0             0       0      0                    0                  0   \n",
       "1             0       0      0                    0                  0   \n",
       "2             0       0      0                    0                  0   \n",
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       "   thakur village kandivali east  vasant vihar thane west  vile parle west  \n",
       "0                              0                        0                0  \n",
       "1                              0                        0                0  \n",
       "2                              0                        0                0  \n",
       "\n",
       "[3 rows x 206 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummies=pd.get_dummies(df8.Location)\n",
    "dummies.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "5ca8e0c1",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   Area    Location  BHK  Total_price  4 Bunglows  Airoli  Ambarnath  \\\n",
       "0  1350  4 Bunglows    2        250.0           1       0          0   \n",
       "1  1350  4 Bunglows    2        250.0           1       0          0   \n",
       "2   400      Airoli    1         62.0           0       1          0   \n",
       "4  1245      Airoli    2        149.0           0       1          0   \n",
       "5  1183      Airoli    2        140.0           0       1          0   \n",
       "\n",
       "   Ambernath East  Ambernath West  Andheri  ...  kandivali  kavesar  kolshet  \\\n",
       "0               0               0        0  ...          0        0        0   \n",
       "1               0               0        0  ...          0        0        0   \n",
       "2               0               0        0  ...          0        0        0   \n",
       "4               0               0        0  ...          0        0        0   \n",
       "5               0               0        0  ...          0        0        0   \n",
       "\n",
       "   matunga east  mumbai  roadpali navimumbai  taloja panchanand  \\\n",
       "0             0       0                    0                  0   \n",
       "1             0       0                    0                  0   \n",
       "2             0       0                    0                  0   \n",
       "4             0       0                    0                  0   \n",
       "5             0       0                    0                  0   \n",
       "\n",
       "   thakur village kandivali east  vasant vihar thane west  vile parle west  \n",
       "0                              0                        0                0  \n",
       "1                              0                        0                0  \n",
       "2                              0                        0                0  \n",
       "4                              0                        0                0  \n",
       "5                              0                        0                0  \n",
       "\n",
       "[5 rows x 209 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df9=pd.concat([df8,dummies.drop('other',axis='columns')],axis='columns')\n",
    "df9.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "94944a7a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "   Area  BHK  Total_price  4 Bunglows  Airoli  Ambarnath  Ambernath East  \\\n",
       "0  1350    2        250.0           1       0          0               0   \n",
       "1  1350    2        250.0           1       0          0               0   \n",
       "2   400    1         62.0           0       1          0               0   \n",
       "4  1245    2        149.0           0       1          0               0   \n",
       "5  1183    2        140.0           0       1          0               0   \n",
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       "4               0        0             0  ...          0        0        0   \n",
       "5               0        0             0  ...          0        0        0   \n",
       "\n",
       "   matunga east  mumbai  roadpali navimumbai  taloja panchanand  \\\n",
       "0             0       0                    0                  0   \n",
       "1             0       0                    0                  0   \n",
       "2             0       0                    0                  0   \n",
       "4             0       0                    0                  0   \n",
       "5             0       0                    0                  0   \n",
       "\n",
       "   thakur village kandivali east  vasant vihar thane west  vile parle west  \n",
       "0                              0                        0                0  \n",
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       "2                              0                        0                0  \n",
       "4                              0                        0                0  \n",
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       "\n",
       "[5 rows x 208 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df10=df9.drop(['Location'],axis='columns')\n",
    "df10.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "3861a45c",
   "metadata": {
    "scrolled": true
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   "outputs": [
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       "      <th>Andheri West</th>\n",
       "      <th>...</th>\n",
       "      <th>kandivali</th>\n",
       "      <th>kavesar</th>\n",
       "      <th>kolshet</th>\n",
       "      <th>matunga east</th>\n",
       "      <th>mumbai</th>\n",
       "      <th>roadpali navimumbai</th>\n",
       "      <th>taloja panchanand</th>\n",
       "      <th>thakur village kandivali east</th>\n",
       "      <th>vasant vihar thane west</th>\n",
       "      <th>vile parle west</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1350</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>1350</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>400</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 207 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Area  BHK  4 Bunglows  Airoli  Ambarnath  Ambernath East  Ambernath West  \\\n",
       "0  1350    2           1       0          0               0               0   \n",
       "1  1350    2           1       0          0               0               0   \n",
       "2   400    1           0       1          0               0               0   \n",
       "\n",
       "   Andheri  Andheri East  Andheri West  ...  kandivali  kavesar  kolshet  \\\n",
       "0        0             0             0  ...          0        0        0   \n",
       "1        0             0             0  ...          0        0        0   \n",
       "2        0             0             0  ...          0        0        0   \n",
       "\n",
       "   matunga east  mumbai  roadpali navimumbai  taloja panchanand  \\\n",
       "0             0       0                    0                  0   \n",
       "1             0       0                    0                  0   \n",
       "2             0       0                    0                  0   \n",
       "\n",
       "   thakur village kandivali east  vasant vihar thane west  vile parle west  \n",
       "0                              0                        0                0  \n",
       "1                              0                        0                0  \n",
       "2                              0                        0                0  \n",
       "\n",
       "[3 rows x 207 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=df10.drop(['Total_price'],axis='columns')\n",
    "X.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "cda67dd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4198, 207)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "b594da29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    250.0\n",
       "1    250.0\n",
       "2     62.0\n",
       "Name: Total_price, dtype: float64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=df10.Total_price\n",
    "y.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "e910f082",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4198"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "a94a0b09",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2,random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "869e9567",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7048646670532179"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "lr_clf = LinearRegression()\n",
    "lr_clf.fit(X_train,y_train)\n",
    "lr_clf.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "0ba2d947",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6.26289614e-01,  7.47711662e-01, -6.47452889e+14,  6.18749091e-01,\n",
       "        4.41298970e-01])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from sklearn.model_selection import ShuffleSplit\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "cv = ShuffleSplit(n_splits=5, test_size=0.2, random_state=0)\n",
    "\n",
    "cross_val_score(LinearRegression(), X, y, cv=cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "429121d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_base.py:141: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2.\n",
      "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
      "\n",
      "from sklearn.pipeline import make_pipeline\n",
      "\n",
      "model = make_pipeline(StandardScaler(with_mean=False), LinearRegression())\n",
      "\n",
      "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
      "\n",
      "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
      "model.fit(X, y, **kwargs)\n",
      "\n",
      "\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_base.py:141: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2.\n",
      "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
      "\n",
      "from sklearn.pipeline import make_pipeline\n",
      "\n",
      "model = make_pipeline(StandardScaler(with_mean=False), LinearRegression())\n",
      "\n",
      "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
      "\n",
      "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
      "model.fit(X, y, **kwargs)\n",
      "\n",
      "\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_base.py:141: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2.\n",
      "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
      "\n",
      "from sklearn.pipeline import make_pipeline\n",
      "\n",
      "model = make_pipeline(StandardScaler(with_mean=False), LinearRegression())\n",
      "\n",
      "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
      "\n",
      "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
      "model.fit(X, y, **kwargs)\n",
      "\n",
      "\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_base.py:141: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2.\n",
      "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
      "\n",
      "from sklearn.pipeline import make_pipeline\n",
      "\n",
      "model = make_pipeline(StandardScaler(with_mean=False), LinearRegression())\n",
      "\n",
      "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
      "\n",
      "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
      "model.fit(X, y, **kwargs)\n",
      "\n",
      "\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_base.py:141: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2.\n",
      "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
      "\n",
      "from sklearn.pipeline import make_pipeline\n",
      "\n",
      "model = make_pipeline(StandardScaler(with_mean=False), LinearRegression())\n",
      "\n",
      "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
      "\n",
      "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
      "model.fit(X, y, **kwargs)\n",
      "\n",
      "\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_base.py:148: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\_classes.py:359: FutureWarning: Criterion 'mse' was deprecated in v1.0 and will be removed in version 1.2. Use `criterion='squared_error'` which is equivalent.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\_classes.py:359: FutureWarning: Criterion 'mse' was deprecated in v1.0 and will be removed in version 1.2. Use `criterion='squared_error'` which is equivalent.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\_classes.py:359: FutureWarning: Criterion 'mse' was deprecated in v1.0 and will be removed in version 1.2. Use `criterion='squared_error'` which is equivalent.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\_classes.py:359: FutureWarning: Criterion 'mse' was deprecated in v1.0 and will be removed in version 1.2. Use `criterion='squared_error'` which is equivalent.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\_classes.py:359: FutureWarning: Criterion 'mse' was deprecated in v1.0 and will be removed in version 1.2. Use `criterion='squared_error'` which is equivalent.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\_classes.py:359: FutureWarning: Criterion 'mse' was deprecated in v1.0 and will be removed in version 1.2. Use `criterion='squared_error'` which is equivalent.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\_classes.py:359: FutureWarning: Criterion 'mse' was deprecated in v1.0 and will be removed in version 1.2. Use `criterion='squared_error'` which is equivalent.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\_classes.py:359: FutureWarning: Criterion 'mse' was deprecated in v1.0 and will be removed in version 1.2. Use `criterion='squared_error'` which is equivalent.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\_classes.py:359: FutureWarning: Criterion 'mse' was deprecated in v1.0 and will be removed in version 1.2. Use `criterion='squared_error'` which is equivalent.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\_classes.py:359: FutureWarning: Criterion 'mse' was deprecated in v1.0 and will be removed in version 1.2. Use `criterion='squared_error'` which is equivalent.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\tree\\_classes.py:359: FutureWarning: Criterion 'mse' was deprecated in v1.0 and will be removed in version 1.2. Use `criterion='squared_error'` which is equivalent.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>best_score</th>\n",
       "      <th>best_params</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>linear_regression</td>\n",
       "      <td>-1.294906e+14</td>\n",
       "      <td>{'normalize': False}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>lasso</td>\n",
       "      <td>4.491233e-01</td>\n",
       "      <td>{'alpha': 1, 'selection': 'random'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>decision_tree</td>\n",
       "      <td>4.190155e-01</td>\n",
       "      <td>{'criterion': 'mse', 'splitter': 'random'}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               model    best_score                                 best_params\n",
       "0  linear_regression -1.294906e+14                        {'normalize': False}\n",
       "1              lasso  4.491233e-01         {'alpha': 1, 'selection': 'random'}\n",
       "2      decision_tree  4.190155e-01  {'criterion': 'mse', 'splitter': 'random'}"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "def find_best_model_using_gridsearchcv(X,y):\n",
    "    algos = {\n",
    "        'linear_regression' : {\n",
    "            'model': LinearRegression(),\n",
    "            'params': {\n",
    "                'normalize': [True, False]\n",
    "            }\n",
    "        },\n",
    "        'lasso': {\n",
    "            'model': Lasso(),\n",
    "            'params': {\n",
    "                'alpha': [1,2],\n",
    "                'selection': ['random', 'cyclic']\n",
    "            }\n",
    "        },\n",
    "        'decision_tree': {\n",
    "            'model': DecisionTreeRegressor(),\n",
    "            'params': {\n",
    "                'criterion' : ['mse','friedman_mse'],\n",
    "                'splitter': ['best','random']\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "    scores = []\n",
    "    cv = ShuffleSplit(n_splits=5, test_size=0.2, random_state=0)\n",
    "    for algo_name, config in algos.items():\n",
    "        gs =  GridSearchCV(config['model'], config['params'], cv=cv, return_train_score=False)\n",
    "        gs.fit(X,y)\n",
    "        scores.append({\n",
    "            'model': algo_name,\n",
    "            'best_score': gs.best_score_,\n",
    "            'best_params': gs.best_params_\n",
    "        })\n",
    "\n",
    "    return pd.DataFrame(scores,columns=['model','best_score','best_params'])\n",
    "\n",
    "find_best_model_using_gridsearchcv(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "4d92dcb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_price(Location,Area,BHK):    \n",
    "    loc_index = np.where(X.columns==Location)[0][0]\n",
    "\n",
    "    x = np.zeros(len(X.columns))\n",
    "    x[0] = Area\n",
    "    x[1] = BHK\n",
    "    if loc_index >= 0:\n",
    "        x[loc_index] = 1\n",
    "\n",
    "    return lr_clf.predict([x])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "63bcede6",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "250.00000000035115"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_price('4 Bunglows',1350,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "5e23183d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "97.61868022950027"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_price('Ambarnath',1050,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "3247d765",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "140.3451856496384"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_price('kavesar',1550,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "74826228",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open('mumbai_home_price.pickle','wb')as f:\n",
    "    pickle.dump(lr_clf,f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "31d5b24c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "columns = {\n",
    "    'data_columns':[col.lower() for col in X.columns]\n",
    "}\n",
    "with open(\"columns.json\",\"w\")as f:\n",
    "    f.write(json.dumps(columns))"
   ]
  }
 ],
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