final.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "7dcf2f5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df1=pd.read_csv('Hyderabad.csv')  \n",
    "df2=pd.read_csv('Kolkata.csv')  \n",
    "df3=pd.read_csv('Bangalore.csv')  \n",
    "df4=pd.read_csv('Mumbai.csv')  \n",
    "df5=pd.read_csv('Delhi.csv')  \n",
    "df6=pd.read_csv('Chennai.csv')  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2bf3becb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#TOOK 2-3 hrs FOR ONE CSV \n",
    "\n",
    "import requests\n",
    "import pandas as pd\n",
    "\n",
    "def geocode(location, state):\n",
    "    # send a GET request to the Nominatim API with the location and state as parameters\n",
    "    response = requests.get(f\"https://nominatim.openstreetmap.org/search?q={location},{state}&format=json\")\n",
    "\n",
    "    # extract the latitude and longitude from the response\n",
    "    if response.ok:\n",
    "        data = response.json()\n",
    "        if data:\n",
    "            lat = data[0][\"lat\"]\n",
    "            lon = data[0][\"lon\"]\n",
    "            return lat, lon\n",
    "    return None, None\n",
    "\n",
    "# create a sample DataFrame with a 'location' column\n",
    "\n",
    "# apply the geocoding function to each value in the 'location' column using the 'apply()' method\n",
    "df3[['latitude', 'longitude']] = df3.apply(lambda x: geocode(x['Location'], 'Tamilnadu'), axis=1, result_type='expand')\n",
    "\n",
    "print(df3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "064c219f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#to save in local storage as csv file from JUPYTER NOTEBOOK\n",
    "\n",
    "df3.to_csv('final_chennai.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0fe6a1de",
   "metadata": {},
   "outputs": [],
   "source": [
    "#to save in local storage as csv file from GOOGLE COLLAB\n",
    "\n",
    "from google.colab import files\n",
    "df3.to_csv('final_chennai.csv', encoding = 'utf-8-sig') \n",
    "files.download('final_chennai.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c8b0b770",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df1=pd.read_csv('final_hyderabad.csv')  \n",
    "df2=pd.read_csv('final_chennai.csv')  \n",
    "\n",
    "df4=pd.read_csv('final_delhi.csv')  \n",
    "df5=pd.read_csv('final_banglore.csv')  \n",
    "df6=pd.read_csv('final_mumbai.csv')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cc7708b3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df1=df1.drop(['Unnamed: 0'],axis=1)\n",
    "df2=df2.drop(['Unnamed: 0'],axis=1)\n",
    "\n",
    "#df4=df4.drop(['Unnamed: 0'],axis=1)\n",
    "df5=df5.drop(['Unnamed: 0'],axis=1)\n",
    "df6=df6.drop(['Unnamed: 0'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "30b9bc05",
   "metadata": {},
   "outputs": [],
   "source": [
    "#no need of these columns\n",
    "df1 = df1.drop(['Refrigerator','Sofa','DiningTable','TV','GolfCourse','Microwave','BED','Wifi','AC','WashingMachine','Intercom','StaffQuarter','MaintenanceStaff', 'RainWaterHarvesting', 'MultipurposeRoom', 'Wardrobe'],axis=1)\n",
    "df2 = df2.drop(['Refrigerator','Sofa','DiningTable','TV','GolfCourse','Microwave','BED','Wifi','AC','WashingMachine','Intercom','StaffQuarter','MaintenanceStaff', 'RainWaterHarvesting', 'MultipurposeRoom', 'Wardrobe'],axis=1)\n",
    "\n",
    "df4 = df4.drop(['Refrigerator','Sofa','DiningTable','TV','GolfCourse','Microwave','BED','Wifi','AC','WashingMachine','Intercom','StaffQuarter','MaintenanceStaff', 'RainWaterHarvesting', 'MultipurposeRoom', 'Wardrobe'],axis=1)\n",
    "df5 = df5.drop(['Refrigerator','Sofa','DiningTable','TV','GolfCourse','Microwave','BED','Wifi','AC','WashingMachine','Intercom','StaffQuarter','MaintenanceStaff', 'RainWaterHarvesting', 'MultipurposeRoom', 'Wardrobe'],axis=1)\n",
    "df6 = df6.drop(['Refrigerator','Sofa','DiningTable','TV','GolfCourse','Microwave','BED','Wifi','AC','WashingMachine','Intercom','StaffQuarter','MaintenanceStaff', 'RainWaterHarvesting', 'MultipurposeRoom', 'Wardrobe'],axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "10a4f565",
   "metadata": {},
   "outputs": [],
   "source": [
    "#price in lakhs\n",
    "\n",
    "df1['price']= df1['Price']/100000\n",
    "df1.drop('Price', axis=1, inplace=True)\n",
    "df2['price']= df2['Price']/100000\n",
    "df2.drop('Price', axis=1, inplace=True)\n",
    "\n",
    "df4['price']= df4['Price']/100000\n",
    "df4.drop('Price', axis=1, inplace=True)\n",
    "df5['price']= df5['Price']/100000\n",
    "df5.drop('Price', axis=1, inplace=True)\n",
    "df6['price']= df6['Price']/100000\n",
    "df6.drop('Price', axis=1, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "dd0db5ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "#changing column name\n",
    "df1=df1.rename(columns={\"Children'splayarea\": \"Childrensplayarea\"})\n",
    "df2=df2.rename(columns={\"Children'splayarea\": \"Childrensplayarea\"})\n",
    "df4=df4.rename(columns={\"Children'splayarea\": \"Childrensplayarea\"})\n",
    "df5=df5.rename(columns={\"Children'splayarea\": \"Childrensplayarea\"})\n",
    "df6=df6.rename(columns={\"Children'splayarea\": \"Childrensplayarea\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1ce44db7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# sort the dataset df based on location in ascending order\n",
    "df1=df1.sort_values('Location', ascending=True)\n",
    "df2 = df2.sort_values('Location', ascending=True)\n",
    "\n",
    "df4 = df4.sort_values('Location', ascending=True)\n",
    "df5 = df5.sort_values('Location', ascending=True)\n",
    "df6 = df6.sort_values('Location', ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "166fe8c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#rearranging columns\n",
    "df1 = df1.reindex(columns=['price', 'Area', 'Location','latitude','longitude', 'No. of Bedrooms', 'Resale','VaastuCompliant','Gymnasium', 'SwimmingPool', 'LandscapedGardens', 'JoggingTrack', 'IndoorGames','ShoppingMall','SportsFacility','ATM','ClubHouse','School','Cafeteria','Hospital','Childrensplayarea','PowerBackup', 'CarParking', 'LiftAvailable', '24X7Security', 'Gasconnection'])\n",
    "df2 = df2.reindex(columns=['price', 'Area', 'Location','latitude','longitude', 'No. of Bedrooms', 'Resale','VaastuCompliant','Gymnasium', 'SwimmingPool', 'LandscapedGardens', 'JoggingTrack', 'IndoorGames','ShoppingMall','SportsFacility','ATM','ClubHouse','School','Cafeteria','Hospital','Childrensplayarea','PowerBackup', 'CarParking', 'LiftAvailable', '24X7Security', 'Gasconnection'])\n",
    "\n",
    "df4 = df4.reindex(columns=['price', 'Area', 'Location','latitude','longitude', 'No. of Bedrooms', 'Resale','VaastuCompliant','Gymnasium', 'SwimmingPool', 'LandscapedGardens', 'JoggingTrack', 'IndoorGames','ShoppingMall','SportsFacility','ATM','ClubHouse','School','Cafeteria','Hospital','Childrensplayarea','PowerBackup', 'CarParking', 'LiftAvailable', '24X7Security', 'Gasconnection'])\n",
    "df5 = df5.reindex(columns=['price', 'Area', 'Location','latitude','longitude', 'No. of Bedrooms', 'Resale','VaastuCompliant','Gymnasium', 'SwimmingPool', 'LandscapedGardens', 'JoggingTrack', 'IndoorGames','ShoppingMall','SportsFacility','ATM','ClubHouse','School','Cafeteria','Hospital','Childrensplayarea','PowerBackup', 'CarParking', 'LiftAvailable', '24X7Security', 'Gasconnection'])\n",
    "df6 = df6.reindex(columns=['price', 'Area', 'Location','latitude','longitude', 'No. of Bedrooms', 'Resale','VaastuCompliant','Gymnasium', 'SwimmingPool', 'LandscapedGardens', 'JoggingTrack', 'IndoorGames','ShoppingMall','SportsFacility','ATM','ClubHouse','School','Cafeteria','Hospital','Childrensplayarea','PowerBackup', 'CarParking', 'LiftAvailable', '24X7Security', 'Gasconnection'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "952996df",
   "metadata": {},
   "outputs": [],
   "source": [
    "#dropping rows with empty entries\n",
    "df1 = df1.dropna()\n",
    "df2 = df2.dropna()\n",
    "\n",
    "df4 = df4.dropna()\n",
    "df5 = df5.dropna()\n",
    "df6 = df6.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3b5e4085",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "df1.replace(9, np.nan, inplace=True)\n",
    "df1 = df1.dropna()\n",
    "\n",
    "df2.replace(9, np.nan, inplace=True)\n",
    "df2 = df2.dropna()\n",
    "\n",
    "\n",
    "\n",
    "df4.replace(9, np.nan, inplace=True)\n",
    "df4 = df4.dropna()\n",
    "\n",
    "df5.replace(9, np.nan, inplace=True)\n",
    "df5 = df5.dropna()\n",
    "\n",
    "df6.replace(9, np.nan, inplace=True)\n",
    "df6 = df6.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "09e764a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in df1.columns:\n",
    "    if col != 'Price' and col != 'latitude'and col != 'longitude' and df1[col].dtype == 'float64':\n",
    "        df1[col] = df1[col].astype('int')\n",
    "        \n",
    "for col in df2.columns:\n",
    "    if col != 'Price' and col != 'latitude'and col != 'longitude' and df2[col].dtype == 'float64':\n",
    "        df2[col] = df2[col].astype('int')\n",
    "        \n",
    "        \n",
    "for col in df4.columns:\n",
    "    if col != 'Price' and col != 'latitude'and col != 'longitude' and df4[col].dtype == 'float64':\n",
    "        df4[col] = df4[col].astype('int')\n",
    "        \n",
    "for col in df5.columns:\n",
    "    if col != 'Price' and col != 'latitude'and col != 'longitude' and df5[col].dtype == 'float64':\n",
    "        df5[col] = df5[col].astype('int')\n",
    "        \n",
    "for col in df6.columns:\n",
    "    if col != 'Price' and col != 'latitude'and col != 'longitude' and df6[col].dtype == 'float64':\n",
    "        df6[col] = df6[col].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e989fc72",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a list of columns to combine\n",
    "columns_to_combine = ['Gymnasium', 'SwimmingPool', 'LandscapedGardens', 'JoggingTrack', 'IndoorGames','ShoppingMall','SportsFacility','ATM','ClubHouse','School','Cafeteria','Hospital','Childrensplayarea']\n",
    "\n",
    "# Concatenate the values in the columns using ',' as separator\n",
    "df1['nearbyresources'] = df1[columns_to_combine].apply(lambda row: ''.join(row.dropna().astype(str)), axis=1)\n",
    "\n",
    "# Convert the combined values to integer\n",
    "df1['nearbyresources'] = df1['nearbyresources'].astype(str)\n",
    "\n",
    "# Drop the original columns\n",
    "df1.drop(columns=columns_to_combine, inplace=True)\n",
    "\n",
    "\n",
    "df2['nearbyresources'] = df2[columns_to_combine].apply(lambda row: ''.join(row.dropna().astype(str)), axis=1)\n",
    "df2['nearbyresources'] = df2['nearbyresources'].astype(str)\n",
    "df2.drop(columns=columns_to_combine, inplace=True)\n",
    "\n",
    "\n",
    "df4['nearbyresources'] = df4[columns_to_combine].apply(lambda row: ''.join(row.dropna().astype(str)), axis=1)\n",
    "df4['nearbyresources'] = df4['nearbyresources'].astype(str)\n",
    "df4.drop(columns=columns_to_combine, inplace=True)\n",
    "\n",
    "df5['nearbyresources'] = df5[columns_to_combine].apply(lambda row: ''.join(row.dropna().astype(str)), axis=1)\n",
    "df5['nearbyresources'] = df5['nearbyresources'].astype(str)\n",
    "df5.drop(columns=columns_to_combine, inplace=True)\n",
    "\n",
    "df6['nearbyresources'] = df6[columns_to_combine].apply(lambda row: ''.join(row.dropna().astype(str)), axis=1)\n",
    "df6['nearbyresources'] = df6['nearbyresources'].astype(str)\n",
    "df6.drop(columns=columns_to_combine, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7a6c457d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a list of columns to combine\n",
    "columns_to_combine = ['PowerBackup', 'CarParking', 'LiftAvailable', '24X7Security', 'Gasconnection']\n",
    "\n",
    "# Concatenate the values in the columns using ',' as separator\n",
    "df1['essentials'] = df1[columns_to_combine].apply(lambda row: ''.join(row.dropna().astype(str)), axis=1)\n",
    "\n",
    "# Convert the combined values to integer\n",
    "df1['essentials'] = df1['essentials'].astype(str)\n",
    "\n",
    "# Drop the original columns\n",
    "df1.drop(columns=columns_to_combine, inplace=True) \n",
    "\n",
    "df2['essentials'] = df2[columns_to_combine].apply(lambda row: ''.join(row.dropna().astype(str)), axis=1)\n",
    "df2['essentials'] = df2['essentials'].astype(str)\n",
    "df2.drop(columns=columns_to_combine, inplace=True) \n",
    "\n",
    "\n",
    "\n",
    "df4['essentials'] = df4[columns_to_combine].apply(lambda row: ''.join(row.dropna().astype(str)), axis=1)\n",
    "df4['essentials'] = df4['essentials'].astype(str)\n",
    "df4.drop(columns=columns_to_combine, inplace=True) \n",
    "\n",
    "df5['essentials'] = df5[columns_to_combine].apply(lambda row: ''.join(row.dropna().astype(str)), axis=1)\n",
    "df5['essentials'] = df5['essentials'].astype(str)\n",
    "df5.drop(columns=columns_to_combine, inplace=True) \n",
    "\n",
    "df6['essentials'] = df6[columns_to_combine].apply(lambda row: ''.join(row.dropna().astype(str)), axis=1)\n",
    "df6['essentials'] = df6['essentials'].astype(str)\n",
    "df6.drop(columns=columns_to_combine, inplace=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "18a051cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# changeall the below 4 cols to int from binary(str)\n",
    "   \n",
    "def binary_to_int(binary_str):\n",
    "    return int(binary_str, 2)\n",
    "\n",
    "# Apply the function to the column\n",
    "df1['nearbyresources'] = df1['nearbyresources'].apply(binary_to_int)\n",
    "df1['essentials'] = df1['essentials'].apply(binary_to_int)\n",
    "\n",
    "df2['nearbyresources'] = df2['nearbyresources'].apply(binary_to_int)\n",
    "df2['essentials'] = df2['essentials'].apply(binary_to_int)\n",
    "\n",
    "\n",
    "df4['nearbyresources'] = df4['nearbyresources'].apply(binary_to_int)\n",
    "df4['essentials'] = df4['essentials'].apply(binary_to_int)\n",
    "\n",
    "df5['nearbyresources'] = df5['nearbyresources'].apply(binary_to_int)\n",
    "df5['essentials'] = df5['essentials'].apply(binary_to_int)\n",
    "\n",
    "df6['nearbyresources'] = df6['nearbyresources'].apply(binary_to_int)\n",
    "df6['essentials'] = df6['essentials'].apply(binary_to_int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b729df35",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_3756\\412219128.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mentry_lengths\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'essentials'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;31m# create a boolean mask indicating which rows have a length of 5 in the \"Column_name\" column\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mmask\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mentry_lengths\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m5\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'df' is not defined"
     ]
    }
   ],
   "source": [
    "entry_lengths = df['essentials'].str.len()\n",
    "\n",
    "# create a boolean mask indicating which rows have a length of 5 in the \"Column_name\" column\n",
    "mask = entry_lengths == 5\n",
    "\n",
    "# drop rows that do not have a length of 5 in the \"Column_name\" column\n",
    "df = df[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b92f320d",
   "metadata": {},
   "outputs": [],
   "source": [
    "entry_lengths = df['nearbyresources'].str.len()\n",
    "\n",
    "# create a boolean mask indicating which entries have a length of 13\n",
    "mask = entry_lengths == 13\n",
    "\n",
    "# drop entries that do not have a length of 13\n",
    "df = df[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0a392333",
   "metadata": {},
   "outputs": [],
   "source": [
    "# reset the index of the sorted dataframe\n",
    "df1 = df1.reset_index(drop=True)\n",
    "df2 = df2.reset_index(drop=True)\n",
    "\n",
    "df4 = df4.reset_index(drop=True)\n",
    "df5 = df5.reset_index(drop=True)\n",
    "df6 = df6.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d18c0888",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>price</th>\n",
       "      <th>Area</th>\n",
       "      <th>Location</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>No. of Bedrooms</th>\n",
       "      <th>Resale</th>\n",
       "      <th>VaastuCompliant</th>\n",
       "      <th>nearbyresources</th>\n",
       "      <th>essentials</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>69</td>\n",
       "      <td>1500</td>\n",
       "      <td>AS Rao Nagar</td>\n",
       "      <td>17.479869</td>\n",
       "      <td>78.550965</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0000000000001</td>\n",
       "      <td>10100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25</td>\n",
       "      <td>680</td>\n",
       "      <td>AS Rao Nagar</td>\n",
       "      <td>17.479869</td>\n",
       "      <td>78.550965</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0000000000000</td>\n",
       "      <td>00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>52</td>\n",
       "      <td>1440</td>\n",
       "      <td>AS Rao Nagar</td>\n",
       "      <td>17.479869</td>\n",
       "      <td>78.550965</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0000010101010</td>\n",
       "      <td>11110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60</td>\n",
       "      <td>1200</td>\n",
       "      <td>AS Rao Nagar</td>\n",
       "      <td>17.479869</td>\n",
       "      <td>78.550965</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0000010101000</td>\n",
       "      <td>11110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25</td>\n",
       "      <td>680</td>\n",
       "      <td>AS Rao Nagar</td>\n",
       "      <td>17.479869</td>\n",
       "      <td>78.550965</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0000000000000</td>\n",
       "      <td>00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>85</td>\n",
       "      <td>1420</td>\n",
       "      <td>new nallakunta</td>\n",
       "      <td>17.395975</td>\n",
       "      <td>78.509227</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0000000000000</td>\n",
       "      <td>01100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>80</td>\n",
       "      <td>1400</td>\n",
       "      <td>new nallakunta</td>\n",
       "      <td>17.395975</td>\n",
       "      <td>78.509227</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0000000000000</td>\n",
       "      <td>11110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>50</td>\n",
       "      <td>1100</td>\n",
       "      <td>new nallakunta</td>\n",
       "      <td>17.395975</td>\n",
       "      <td>78.509227</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0000000000000</td>\n",
       "      <td>11100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>39</td>\n",
       "      <td>1100</td>\n",
       "      <td>nizampet road</td>\n",
       "      <td>17.498777</td>\n",
       "      <td>78.389720</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0000000000000</td>\n",
       "      <td>11110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>46</td>\n",
       "      <td>1190</td>\n",
       "      <td>nizampet road</td>\n",
       "      <td>17.498777</td>\n",
       "      <td>78.389720</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0000000000000</td>\n",
       "      <td>00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2022 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      price  Area        Location   latitude  longitude  No. of Bedrooms  \\\n",
       "0        69  1500    AS Rao Nagar  17.479869  78.550965                3   \n",
       "1        25   680    AS Rao Nagar  17.479869  78.550965                2   \n",
       "2        52  1440    AS Rao Nagar  17.479869  78.550965                3   \n",
       "3        60  1200    AS Rao Nagar  17.479869  78.550965                2   \n",
       "4        25   680    AS Rao Nagar  17.479869  78.550965                1   \n",
       "...     ...   ...             ...        ...        ...              ...   \n",
       "2017     85  1420  new nallakunta  17.395975  78.509227                3   \n",
       "2018     80  1400  new nallakunta  17.395975  78.509227                2   \n",
       "2019     50  1100  new nallakunta  17.395975  78.509227                2   \n",
       "2020     39  1100   nizampet road  17.498777  78.389720                2   \n",
       "2021     46  1190   nizampet road  17.498777  78.389720                2   \n",
       "\n",
       "      Resale  VaastuCompliant nearbyresources essentials  \n",
       "0          0                0   0000000000001      10100  \n",
       "1          1                0   0000000000000      00000  \n",
       "2          1                1   0000010101010      11110  \n",
       "3          1                1   0000010101000      11110  \n",
       "4          0                0   0000000000000      00000  \n",
       "...      ...              ...             ...        ...  \n",
       "2017       0                0   0000000000000      01100  \n",
       "2018       1                0   0000000000000      11110  \n",
       "2019       1                0   0000000000000      11100  \n",
       "2020       0                0   0000000000000      11110  \n",
       "2021       0                0   0000000000000      00000  \n",
       "\n",
       "[2022 rows x 10 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "644a4fa5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.to_csv('FINAL_hyd.csv', index=False)\n",
    "df2.to_csv('FINAL_che.csv', index=False)\n",
    "df3.to_csv('FINAL_kol.csv', index=False)\n",
    "df4.to_csv('FINAL_del.csv', index=False)\n",
    "df5.to_csv('FINAL_bang.csv', index=False)\n",
    "df6.to_csv('FINAL_mum.csv', index=False)\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c4fb4af2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([59.20877667])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import KFold, cross_val_score\n",
    "from sklearn.ensemble import RandomForestRegressor, VotingRegressor\n",
    "import xgboost as xgb\n",
    "import math\n",
    "from sklearn.metrics import mean_absolute_error,r2_score\n",
    "\n",
    "X = df1.drop(['price','Location'], axis=1).values\n",
    "y = df1[['price']].values\n",
    "y=y.ravel()\n",
    "\n",
    "# Set max_features to the square root of the number of features\n",
    "max_features = int(math.sqrt(X.shape[1]))\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Define models\n",
    "rf_model = RandomForestRegressor(n_estimators=1000, random_state=42,max_depth=20, min_samples_split=20, min_samples_leaf=10, max_features=max_features)\n",
    "xgb_model = xgb.XGBRegressor(n_estimators=800, random_state=42)\n",
    "\n",
    "\n",
    "# Ensemble models\n",
    "# Combine the models using voting ensemble\n",
    "ensemble_model1 = VotingRegressor(estimators=[('rf', rf_model), ('gb', xgb_model)])\n",
    "ensemble_model1.fit(X_test, y_test)\n",
    "\n",
    "y_pred = ensemble_model1.predict([[[1000.0, 17.4930841, 78.4054408, 2.0, 1, 1, 8191, 31]]])\n",
    "y_pred\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "print(\"r2:\",r2)\n",
    "\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "print(\"Mean absolute error:\", mae)\n",
    "\n",
    "\n",
    "#import joblib\n",
    "#joblib.dump(ensemble_model1,'hyderabad-pred.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "63ec25ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2: 0.9795993185690118\n",
      "Mean absolute error: 6.788329092071785\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['chennai-pred.joblib']"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import KFold, cross_val_score\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "import xgboost as xgb\n",
    "\n",
    "X = df2.drop(['price','Location'], axis=1).values\n",
    "y = df2[['price']].values\n",
    "y=y.ravel()\n",
    "\n",
    "# Set max_features to the square root of the number of features\n",
    "max_features = int(sqrt(X.shape[1]))\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Define models\n",
    "rf_model = RandomForestRegressor(n_estimators=1000, random_state=42,max_depth=20, min_samples_split=20, min_samples_leaf=10, max_features=max_features)\n",
    "xgb_model = xgb.XGBRegressor(n_estimators=800, random_state=42)\n",
    "\n",
    "\n",
    "# Ensemble models\n",
    "# Combine the models using voting ensemble\n",
    "ensemble_model1 = VotingRegressor(estimators=[('rf', rf_model), ('gb', xgb_model)])\n",
    "ensemble_model1.fit(X, y)\n",
    "\n",
    "y_pred = ensemble_model1.predict(X_test)\n",
    "y_pred\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "print(\"r2:\",r2)\n",
    "\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "print(\"Mean absolute error:\", mae)\n",
    "\n",
    "\n",
    "import joblib\n",
    "joblib.dump(ensemble_model1,'chennai-pred.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "0f7cb578",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>Area</th>\n",
       "      <th>Location</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>No. of Bedrooms</th>\n",
       "      <th>Resale</th>\n",
       "      <th>VaastuCompliant</th>\n",
       "      <th>nearbyresources</th>\n",
       "      <th>essentials</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60</td>\n",
       "      <td>850</td>\n",
       "      <td>Acharya Jagadish Chandra Bose Road</td>\n",
       "      <td>22.540937</td>\n",
       "      <td>88.341313</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0000000000000</td>\n",
       "      <td>00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42</td>\n",
       "      <td>1470</td>\n",
       "      <td>Action Area I</td>\n",
       "      <td>22.574734</td>\n",
       "      <td>88.482364</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0000000000000</td>\n",
       "      <td>00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34</td>\n",
       "      <td>962</td>\n",
       "      <td>Action Area I</td>\n",
       "      <td>22.574734</td>\n",
       "      <td>88.482364</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0000000000000</td>\n",
       "      <td>00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>101</td>\n",
       "      <td>1550</td>\n",
       "      <td>Action Area II Newtown</td>\n",
       "      <td>22.623194</td>\n",
       "      <td>88.443733</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0000000000000</td>\n",
       "      <td>00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>125</td>\n",
       "      <td>853</td>\n",
       "      <td>Airport</td>\n",
       "      <td>22.505445</td>\n",
       "      <td>88.294009</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0000000000000</td>\n",
       "      <td>00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5017</th>\n",
       "      <td>75</td>\n",
       "      <td>1281</td>\n",
       "      <td>south dum dum</td>\n",
       "      <td>22.644621</td>\n",
       "      <td>88.376159</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0000000000000</td>\n",
       "      <td>00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5018</th>\n",
       "      <td>51</td>\n",
       "      <td>1281</td>\n",
       "      <td>south dum dum</td>\n",
       "      <td>22.644621</td>\n",
       "      <td>88.376159</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0000000000000</td>\n",
       "      <td>00100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5022</th>\n",
       "      <td>42</td>\n",
       "      <td>772</td>\n",
       "      <td>south dum dum</td>\n",
       "      <td>22.644621</td>\n",
       "      <td>88.376159</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0000000000000</td>\n",
       "      <td>00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5023</th>\n",
       "      <td>50</td>\n",
       "      <td>1126</td>\n",
       "      <td>south dum dum</td>\n",
       "      <td>22.644621</td>\n",
       "      <td>88.376159</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0000000000000</td>\n",
       "      <td>00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5028</th>\n",
       "      <td>62</td>\n",
       "      <td>940</td>\n",
       "      <td>south dum dum</td>\n",
       "      <td>22.644621</td>\n",
       "      <td>88.376159</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0000000000000</td>\n",
       "      <td>00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>539 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      price  Area                            Location   latitude  longitude  \\\n",
       "0        60   850  Acharya Jagadish Chandra Bose Road  22.540937  88.341313   \n",
       "1        42  1470                       Action Area I  22.574734  88.482364   \n",
       "2        34   962                       Action Area I  22.574734  88.482364   \n",
       "3       101  1550              Action Area II Newtown  22.623194  88.443733   \n",
       "5       125   853                             Airport  22.505445  88.294009   \n",
       "...     ...   ...                                 ...        ...        ...   \n",
       "5017     75  1281                       south dum dum  22.644621  88.376159   \n",
       "5018     51  1281                       south dum dum  22.644621  88.376159   \n",
       "5022     42   772                       south dum dum  22.644621  88.376159   \n",
       "5023     50  1126                       south dum dum  22.644621  88.376159   \n",
       "5028     62   940                       south dum dum  22.644621  88.376159   \n",
       "\n",
       "      No. of Bedrooms  Resale  VaastuCompliant nearbyresources essentials  \n",
       "0                   2       1                0   0000000000000      00000  \n",
       "1                   3       1                0   0000000000000      00000  \n",
       "2                   2       1                0   0000000000000      00000  \n",
       "3                   3       0                0   0000000000000      00000  \n",
       "5                   3       1                0   0000000000000      00000  \n",
       "...               ...     ...              ...             ...        ...  \n",
       "5017                3       0                0   0000000000000      00000  \n",
       "5018                3       1                1   0000000000000      00100  \n",
       "5022                2       1                0   0000000000000      00000  \n",
       "5023                4       1                0   0000000000000      00000  \n",
       "5028                2       0                0   0000000000000      00000  \n",
       "\n",
       "[539 rows x 10 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#PREPROCESSING FOR KOLKATA DATASET(somany '9' values are there,so sperate preprocessing)\n",
    "\n",
    "import pandas as pd\n",
    "df3=pd.read_csv('final_kolkata.csv')\n",
    "df3\n",
    "\n",
    "df3 = df3.dropna()\n",
    "df3\n",
    "\n",
    "df3.replace(9, 0, inplace=True)\n",
    "df3\n",
    "\n",
    "df3=df3.drop(['Unnamed: 0'],axis=1)\n",
    "\n",
    "df3 = df3.drop(['Refrigerator','Sofa','DiningTable','TV','GolfCourse','Microwave','BED','Wifi','AC','WashingMachine','Intercom','StaffQuarter','MaintenanceStaff', 'RainWaterHarvesting', 'MultipurposeRoom', 'Wardrobe'],axis=1)\n",
    "\n",
    "df3['price']= df3['Price']/100000\n",
    "df3.drop('Price', axis=1, inplace=True)\n",
    "\n",
    "df3=df3.rename(columns={\"Children'splayarea\": \"Childrensplayarea\"})\n",
    "\n",
    "df3 = df3.reindex(columns=['price', 'Area', 'Location','latitude','longitude', 'No. of Bedrooms', 'Resale','VaastuCompliant','Gymnasium', 'SwimmingPool', 'LandscapedGardens', 'JoggingTrack', 'IndoorGames','ShoppingMall','SportsFacility','ATM','ClubHouse','School','Cafeteria','Hospital','Childrensplayarea','PowerBackup', 'CarParking', 'LiftAvailable', '24X7Security', 'Gasconnection'])\n",
    "\n",
    "for col in df3.columns:\n",
    "    if col != 'Price' and col != 'latitude'and col != 'longitude' and df3[col].dtype == 'float64':\n",
    "        df3[col] = df3[col].astype('int')\n",
    "\n",
    "columns_to_combine = ['Gymnasium', 'SwimmingPool', 'LandscapedGardens', 'JoggingTrack', 'IndoorGames','ShoppingMall','SportsFacility','ATM','ClubHouse','School','Cafeteria','Hospital','Childrensplayarea']\n",
    "\n",
    "df3['nearbyresources'] = df3[columns_to_combine].apply(lambda row: ''.join(row.dropna().astype(str)), axis=1)\n",
    "df3['nearbyresources'] = df3['nearbyresources'].astype(str)\n",
    "df3.drop(columns=columns_to_combine, inplace=True)\n",
    "\n",
    "columns_to_combine = ['PowerBackup', 'CarParking', 'LiftAvailable', '24X7Security', 'Gasconnection']\n",
    "\n",
    "df3['essentials'] = df3[columns_to_combine].apply(lambda row: ''.join(row.dropna().astype(str)), axis=1)\n",
    "df3['essentials'] = df3['essentials'].astype(str)\n",
    "df3.drop(columns=columns_to_combine, inplace=True)\n",
    "\n",
    "def binary_to_int(binary_str):\n",
    "    return int(binary_str, 2)\n",
    "\n",
    "# Apply the function to the column\n",
    "df3['nearbyresources'] = df3['nearbyresources'].apply(binary_to_int)\n",
    "df3['essentials'] = df3['essentials'].apply(binary_to_int)\n",
    "\n",
    "\n",
    "df3=df3.sort_values('Location')\n",
    "df3 = df3.reset_index(drop=True)\n",
    "df3\n",
    "\n",
    "df3 = df3.drop_duplicates(subset=['Location','No. of Bedrooms','Resale', 'VaastuCompliant', 'nearbyresources', 'essentials'], keep='first')\n",
    "df3\n",
    "\n",
    "# calculate the IQR for the price column\n",
    "Q1 = df3['price'].quantile(0.25)\n",
    "Q3 = df3['price'].quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "\n",
    "# remove data points outside the IQR\n",
    "df3 = df3[(df3['price'] >= Q1 - 1.5*IQR) & (df3['price'] <= Q3 + 1.5*IQR)]\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "id": "57140fd6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2: 0.8236973313983813\n",
      "Mean absolute error: 9.10773281191849\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['kolkata-pred.joblib']"
      ]
     },
     "execution_count": 419,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import KFold, cross_val_score\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "import xgboost as xgb\n",
    "\n",
    "X = df3.drop(['price','Location'], axis=1).values\n",
    "y = df3[['price']].values\n",
    "y=y.ravel()\n",
    "# Set max_features to the square root of the number of features\n",
    "max_features = int(sqrt(X.shape[1]))\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=84)\n",
    "\n",
    "# Define models\n",
    "rf_model = RandomForestRegressor(n_estimators=1000, random_state=84,max_depth=20, min_samples_split=20, min_samples_leaf=10, max_features=8)\n",
    "xgb_model = xgb.XGBRegressor(n_estimators=800, random_state=84)\n",
    "\n",
    "\n",
    "# Ensemble models\n",
    "# Combine the models using voting ensemble\n",
    "ensemble_model1 = VotingRegressor(estimators=[('rf', rf_model), ('gb', xgb_model)])\n",
    "ensemble_model1.fit(X, y)\n",
    "\n",
    "y_pred = ensemble_model1.predict(X_test)\n",
    "y_pred\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "print(\"r2:\",r2)\n",
    "\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "print(\"Mean absolute error:\", mae)\n",
    "\n",
    "import joblib\n",
    "joblib.dump(ensemble_model1,'kolkata-pred.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "4d3cba78",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2: 0.9571138745584457\n",
      "Mean absolute error: 8.474629731932101\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['delhi-pred.joblib']"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import KFold, cross_val_score\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "import xgboost as xgb\n",
    "\n",
    "X = df4.drop(['price','Location'], axis=1).values\n",
    "y = df4[['price']].values\n",
    "y=y.ravel()\n",
    "\n",
    "# Set max_features to the square root of the number of features\n",
    "max_features = int(sqrt(X.shape[1]))\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Define models\n",
    "rf_model = RandomForestRegressor(n_estimators=1000, random_state=42,max_depth=20, min_samples_split=20, min_samples_leaf=10, max_features=max_features)\n",
    "xgb_model = xgb.XGBRegressor(n_estimators=800, random_state=42)\n",
    "\n",
    "\n",
    "# Ensemble models\n",
    "# Combine the models using voting ensemble\n",
    "ensemble_model1 = VotingRegressor(estimators=[('rf', rf_model), ('gb', xgb_model)])\n",
    "ensemble_model1.fit(X, y)\n",
    "\n",
    "y_pred = ensemble_model1.predict(X_test)\n",
    "y_pred\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "print(\"r2:\",r2)\n",
    "\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "print(\"Mean absolute error:\", mae)\n",
    "\n",
    "import joblib\n",
    "joblib.dump(ensemble_model1,'delhi-pred.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "id": "34d78061",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2: 0.9572263110367715\n",
      "Mean absolute error: 8.242313711098811\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['bangalore-pred.joblib']"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import KFold, cross_val_score\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "import xgboost as xgb\n",
    "\n",
    "\n",
    "X = df5.drop(['price','Location'], axis=1).values\n",
    "y = df5[['price']].values\n",
    "y=y.ravel()\n",
    "\n",
    "# Set max_features to the square root of the number of features\n",
    "max_features = int(sqrt(X.shape[1]))\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Define models\n",
    "rf_model = RandomForestRegressor(n_estimators=1000, random_state=42,max_depth=20, min_samples_split=20, min_samples_leaf=10, max_features=max_features)\n",
    "xgb_model = xgb.XGBRegressor(n_estimators=800, random_state=42)\n",
    "\n",
    "\n",
    "# Ensemble models\n",
    "# Combine the models using voting ensemble\n",
    "ensemble_model1 = VotingRegressor(estimators=[('rf', rf_model), ('gb', xgb_model)])\n",
    "ensemble_model1.fit(X, y)\n",
    "\n",
    "y_pred = ensemble_model1.predict(X_test)\n",
    "y_pred\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "print(\"r2:\",r2)\n",
    "\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "print(\"Mean absolute error:\", mae)\n",
    "\n",
    "import joblib\n",
    "joblib.dump(ensemble_model1,'bangalore-pred.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "810eda0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2: 0.9121658883956488\n",
      "Mean absolute error: 20.091504721364046\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['mumbai-pred.joblib']"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import KFold, cross_val_score\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "import xgboost as xgb\n",
    "\n",
    "X = df6.drop(['price','Location'], axis=1).values\n",
    "y = df6[['price']].values\n",
    "y=y.ravel()\n",
    "\n",
    "# Set max_features to the square root of the number of features\n",
    "max_features = int(sqrt(X.shape[1]))\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "\n",
    "# Define models\n",
    "rf_model = RandomForestRegressor(n_estimators=1000, random_state=42,max_depth=20, min_samples_split=20, min_samples_leaf=10, max_features=max_features)\n",
    "xgb_model = xgb.XGBRegressor(n_estimators=800, random_state=42)\n",
    "\n",
    "\n",
    "# Ensemble models\n",
    "# Combine the models using voting ensemble\n",
    "ensemble_model1 = VotingRegressor(estimators=[('rf', rf_model), ('gb', xgb_model)])\n",
    "ensemble_model1.fit(X, y)\n",
    "\n",
    "y_pred = ensemble_model1.predict(X_test)\n",
    "y_pred\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "print(\"r2:\",r2)\n",
    "\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "print(\"Mean absolute error:\", mae)\n",
    "\n",
    "import joblib\n",
    "joblib.dump(ensemble_model1,'mumbai-pred.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "87264313",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.hyd\n",
      "2.chennai\n",
      "3.kolkata\n",
      "4.bangalore\n",
      "5.mumbai\n",
      "6.delhi\n",
      "choose the city in which to predict--enter option:1\n",
      "enter AREA (in sqft):1000\n",
      "enter NO. OF BEDROOMS:2\n",
      "enter resale(1 if old,0 if new):0\n",
      "enter vastu(1 if concern,0 if no):0\n",
      "enter nearby resoures needed:1000010000100\n",
      "enter essentials needed:10010\n",
      "Enter a location: ameerpet\n",
      "the PREDICTED price maybe: 41.90270662110872 lakh\n"
     ]
    }
   ],
   "source": [
    "import joblib\n",
    "import numpy as np\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "print('1.hyd\\n2.chennai\\n3.kolkata\\n4.bangalore\\n5.mumbai\\n6.delhi')\n",
    "while True:\n",
    "    predat=int(input(\"choose the city in which to predict--enter option:\"))\n",
    "    if(predat==1):\n",
    "        model=joblib.load('hyderabad-pred.joblib')\n",
    "        break\n",
    "    elif(predat==2):\n",
    "        model=joblib.load('chennai-pred.joblib')\n",
    "        break\n",
    "    elif(predat==3):\n",
    "        model=joblib.load('kolkata-pred.joblib')\n",
    "        break\n",
    "    elif(predat==4):\n",
    "        model=joblib.load('bangalore-pred.joblib')\n",
    "        break\n",
    "    elif(predat==5):\n",
    "        model=joblib.load('mumbai-pred.joblib')\n",
    "        break\n",
    "    elif(predat==6):\n",
    "        model=joblib.load('delhi-pred.joblib')\n",
    "        break\n",
    "    else:\n",
    "        print('choose options 1-6 are only allowed')\n",
    "\n",
    "\n",
    "area=int(input('enter AREA (in sqft):'))\n",
    "bed=int(input('enter NO. OF BEDROOMS:'))\n",
    "resale=int(input('enter resale(1 if old,0 if new):'))\n",
    "vastu=int(input('enter vastu(1 if concern,0 if no):'))\n",
    "#nearby = '0111010111000'\n",
    "#in order as, ['Gymnasium', 'SwimmingPool', 'LandscapedGardens', 'JoggingTrack', 'IndoorGames','ShoppingMall','SportsFacility','ATM','ClubHouse','School','Cafeteria','Hospital','Childrensplayarea']\n",
    "#write only 1's and 0's only\n",
    "nearby=input('enter nearby resoures needed:')\n",
    "nearby = int(nearby, 2)\n",
    "#essentials = '01110' \n",
    "#in order as, ['PowerBackup', 'CarParking', 'LiftAvailable', '24X7Security', 'Gasconnection']\n",
    "#write only 1's and 0's only\n",
    "ess=input('enter essentials needed:')\n",
    "ess = int(ess, 2)\n",
    "\n",
    "#(STRING)LOCATION TO LATITUDE AND LONGITUDE\n",
    "\n",
    "from geopy.geocoders import Nominatim\n",
    "# Create a Nominatim geocoder object\n",
    "geolocator = Nominatim(user_agent=\"my_app\")\n",
    "# Check if location was found\n",
    "while(True):\n",
    "    # Get the input sentence from user and convert it to lowercase\n",
    "    location_str = input('Enter a location: ')\n",
    "    # Use geocoder to convert location string to latitude and longitude\n",
    "    location = geolocator.geocode(location_str)\n",
    "    if location is not None:\n",
    "        latitude = location.latitude\n",
    "        longitude = location.longitude\n",
    "        latitude=float(latitude)\n",
    "        longitude=float(longitude)\n",
    "        break\n",
    "    else:\n",
    "        print(\"Location not found\")\n",
    "        \n",
    "ip=[area,latitude,longitude,bed,resale,vastu,nearby,ess]\n",
    "\n",
    "pred=model.predict([ip])\n",
    "\n",
    "print(\"the PREDICTED price maybe:\",pred[0],\"lakh\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8e257f2e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df3' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_14188\\3815064882.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf3\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtypes\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'df3' is not defined"
     ]
    }
   ],
   "source": [
    "df3.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 449,
   "id": "45587db8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.hyd\n",
      "2.chennai\n",
      "3.kolkata\n",
      "4.bangalore\n",
      "5.mumbai\n",
      "6.delhi\n",
      "choose the city in which to predict--enter option:3\n",
      "Enter the price range (from-to [in lakhs]): 30-32\n"
     ]
    },
    {
     "data": {
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       "            &lt;meta name=&quot;viewport&quot; content=&quot;width=device-width,\n",
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       "    \n",
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       "            var map_862a31bff3acb042db995a7cd38e3b09 = L.map(\n",
       "                &quot;map_862a31bff3acb042db995a7cd38e3b09&quot;,\n",
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       "            var tile_layer_4231bb5698d6d75096b96ca14dbcb2b3 = L.tileLayer(\n",
       "                &quot;https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png&quot;,\n",
       "                {&quot;attribution&quot;: &quot;Data by \\u0026copy; \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://openstreetmap.org\\&quot;\\u003eOpenStreetMap\\u003c/a\\u003e, under \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://www.openstreetmap.org/copyright\\&quot;\\u003eODbL\\u003c/a\\u003e.&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 18, &quot;maxZoom&quot;: 18, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}\n",
       "            ).addTo(map_862a31bff3acb042db995a7cd38e3b09);\n",
       "        \n",
       "    \n",
       "            var marker_9bb64ccc84900ed0e738c52d1238e169 = L.marker(\n",
       "                [22.5054448, 88.29400868673707],\n",
       "                {}\n",
       "            ).addTo(map_862a31bff3acb042db995a7cd38e3b09);\n",
       "        \n",
       "    \n",
       "            var icon_1b71193294eb95cbb4c282c47551a9a1 = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;home&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n",
       "            );\n",
       "            marker_9bb64ccc84900ed0e738c52d1238e169.setIcon(icon_1b71193294eb95cbb4c282c47551a9a1);\n",
       "        \n",
       "    \n",
       "        var popup_a039f289ab5c695ea383f064a1cd3cf2 = L.popup({&quot;maxWidth&quot;: 300});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_8ed325d0da1a0bf3457ca3cb19eaf74e = $(`&lt;div id=&quot;html_8ed325d0da1a0bf3457ca3cb19eaf74e&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;&lt;b&gt;Place:&lt;/b&gt; Airport&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 1210 sqft&lt;br&gt;&lt;b&gt;Price:&lt;/b&gt; 31 lakhs&lt;br&gt;&lt;b&gt;Location:&lt;/b&gt; 22.5054448, 88.29400868673707&lt;br&gt;&lt;b&gt;No. of Bedrooms:&lt;/b&gt; 2&lt;br&gt;&lt;b&gt;Resale:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Vasthu Compliant:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Essentials:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Nearby Resources:&lt;/b&gt; 0&lt;br&gt;&lt;/div&gt;`)[0];\n",
       "                popup_a039f289ab5c695ea383f064a1cd3cf2.setContent(html_8ed325d0da1a0bf3457ca3cb19eaf74e);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_9bb64ccc84900ed0e738c52d1238e169.bindPopup(popup_a039f289ab5c695ea383f064a1cd3cf2)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            marker_9bb64ccc84900ed0e738c52d1238e169.bindTooltip(\n",
       "                `&lt;div&gt;\n",
       "                     &lt;b&gt;Place:&lt;/b&gt; Airport&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 1210 sqft&lt;br&gt;\n",
       "                 &lt;/div&gt;`,\n",
       "                {&quot;sticky&quot;: true}\n",
       "            );\n",
       "        \n",
       "    \n",
       "            var marker_a586ac61e3f8ea4c314a40cb37f87429 = L.marker(\n",
       "                [22.5251784, 88.3308629],\n",
       "                {}\n",
       "            ).addTo(map_862a31bff3acb042db995a7cd38e3b09);\n",
       "        \n",
       "    \n",
       "            var icon_2a39319130eaa912ec5f7f9de3fc21e4 = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;home&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n",
       "            );\n",
       "            marker_a586ac61e3f8ea4c314a40cb37f87429.setIcon(icon_2a39319130eaa912ec5f7f9de3fc21e4);\n",
       "        \n",
       "    \n",
       "        var popup_7a5514fb6fc39ec3b224d636025ebe5f = L.popup({&quot;maxWidth&quot;: 300});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_2412322b8e5544c065014c8a64017263 = $(`&lt;div id=&quot;html_2412322b8e5544c065014c8a64017263&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;&lt;b&gt;Place:&lt;/b&gt; Alipore&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 4527 sqft&lt;br&gt;&lt;b&gt;Price:&lt;/b&gt; 32 lakhs&lt;br&gt;&lt;b&gt;Location:&lt;/b&gt; 22.5251784, 88.3308629&lt;br&gt;&lt;b&gt;No. of Bedrooms:&lt;/b&gt; 3&lt;br&gt;&lt;b&gt;Resale:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Vasthu Compliant:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Essentials:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Nearby Resources:&lt;/b&gt; 0&lt;br&gt;&lt;/div&gt;`)[0];\n",
       "                popup_7a5514fb6fc39ec3b224d636025ebe5f.setContent(html_2412322b8e5544c065014c8a64017263);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_a586ac61e3f8ea4c314a40cb37f87429.bindPopup(popup_7a5514fb6fc39ec3b224d636025ebe5f)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            marker_a586ac61e3f8ea4c314a40cb37f87429.bindTooltip(\n",
       "                `&lt;div&gt;\n",
       "                     &lt;b&gt;Place:&lt;/b&gt; Alipore&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 4527 sqft&lt;br&gt;\n",
       "                 &lt;/div&gt;`,\n",
       "                {&quot;sticky&quot;: true}\n",
       "            );\n",
       "        \n",
       "    \n",
       "            var marker_6284a92a988d696e61bf104113a3bb98 = L.marker(\n",
       "                [22.5251784, 88.3308629],\n",
       "                {}\n",
       "            ).addTo(map_862a31bff3acb042db995a7cd38e3b09);\n",
       "        \n",
       "    \n",
       "            var icon_77c3515bcd88975dcda7210c4d46ff7f = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;home&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n",
       "            );\n",
       "            marker_6284a92a988d696e61bf104113a3bb98.setIcon(icon_77c3515bcd88975dcda7210c4d46ff7f);\n",
       "        \n",
       "    \n",
       "        var popup_a1482659389faed80909fe9d9611c7dc = L.popup({&quot;maxWidth&quot;: 300});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_6d161fb618ad3592e12ac095d666424b = $(`&lt;div id=&quot;html_6d161fb618ad3592e12ac095d666424b&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;&lt;b&gt;Place:&lt;/b&gt; Alipore&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 3000 sqft&lt;br&gt;&lt;b&gt;Price:&lt;/b&gt; 30 lakhs&lt;br&gt;&lt;b&gt;Location:&lt;/b&gt; 22.5251784, 88.3308629&lt;br&gt;&lt;b&gt;No. of Bedrooms:&lt;/b&gt; 4&lt;br&gt;&lt;b&gt;Resale:&lt;/b&gt; 1&lt;br&gt;&lt;b&gt;Vasthu Compliant:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Essentials:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Nearby Resources:&lt;/b&gt; 0&lt;br&gt;&lt;/div&gt;`)[0];\n",
       "                popup_a1482659389faed80909fe9d9611c7dc.setContent(html_6d161fb618ad3592e12ac095d666424b);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_6284a92a988d696e61bf104113a3bb98.bindPopup(popup_a1482659389faed80909fe9d9611c7dc)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            marker_6284a92a988d696e61bf104113a3bb98.bindTooltip(\n",
       "                `&lt;div&gt;\n",
       "                     &lt;b&gt;Place:&lt;/b&gt; Alipore&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 3000 sqft&lt;br&gt;\n",
       "                 &lt;/div&gt;`,\n",
       "                {&quot;sticky&quot;: true}\n",
       "            );\n",
       "        \n",
       "    \n",
       "            var marker_44013e3e524698dea638fef4b9ebd20e = L.marker(\n",
       "                [22.5142557, 88.4098863046898],\n",
       "                {}\n",
       "            ).addTo(map_862a31bff3acb042db995a7cd38e3b09);\n",
       "        \n",
       "    \n",
       "            var icon_e137ea598f65f4130642f8c2b7756ea6 = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;home&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n",
       "            );\n",
       "            marker_44013e3e524698dea638fef4b9ebd20e.setIcon(icon_e137ea598f65f4130642f8c2b7756ea6);\n",
       "        \n",
       "    \n",
       "        var popup_69d100583f0b5aa997c2d19cfb952db6 = L.popup({&quot;maxWidth&quot;: 300});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_3398c8d0745c7755702f70fbb3d37698 = $(`&lt;div id=&quot;html_3398c8d0745c7755702f70fbb3d37698&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;&lt;b&gt;Place:&lt;/b&gt; Anandapur&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 1250 sqft&lt;br&gt;&lt;b&gt;Price:&lt;/b&gt; 30 lakhs&lt;br&gt;&lt;b&gt;Location:&lt;/b&gt; 22.5142557, 88.4098863046898&lt;br&gt;&lt;b&gt;No. of Bedrooms:&lt;/b&gt; 3&lt;br&gt;&lt;b&gt;Resale:&lt;/b&gt; 1&lt;br&gt;&lt;b&gt;Vasthu Compliant:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Essentials:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Nearby Resources:&lt;/b&gt; 0&lt;br&gt;&lt;/div&gt;`)[0];\n",
       "                popup_69d100583f0b5aa997c2d19cfb952db6.setContent(html_3398c8d0745c7755702f70fbb3d37698);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_44013e3e524698dea638fef4b9ebd20e.bindPopup(popup_69d100583f0b5aa997c2d19cfb952db6)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            marker_44013e3e524698dea638fef4b9ebd20e.bindTooltip(\n",
       "                `&lt;div&gt;\n",
       "                     &lt;b&gt;Place:&lt;/b&gt; Anandapur&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 1250 sqft&lt;br&gt;\n",
       "                 &lt;/div&gt;`,\n",
       "                {&quot;sticky&quot;: true}\n",
       "            );\n",
       "        \n",
       "    \n",
       "            var marker_2a43980bdd7d76fd67b92a3bd6388360 = L.marker(\n",
       "                [22.616396, 88.4278599],\n",
       "                {}\n",
       "            ).addTo(map_862a31bff3acb042db995a7cd38e3b09);\n",
       "        \n",
       "    \n",
       "            var icon_5585bc065d753892559ab3bf2a8bbb03 = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;home&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n",
       "            );\n",
       "            marker_2a43980bdd7d76fd67b92a3bd6388360.setIcon(icon_5585bc065d753892559ab3bf2a8bbb03);\n",
       "        \n",
       "    \n",
       "        var popup_d181543d5856f870baac11dcb2038629 = L.popup({&quot;maxWidth&quot;: 300});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_3dc9b1c1b8ec64c412e9d360f7383f23 = $(`&lt;div id=&quot;html_3dc9b1c1b8ec64c412e9d360f7383f23&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;&lt;b&gt;Place:&lt;/b&gt; Baguiati&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 800 sqft&lt;br&gt;&lt;b&gt;Price:&lt;/b&gt; 30 lakhs&lt;br&gt;&lt;b&gt;Location:&lt;/b&gt; 22.616396, 88.4278599&lt;br&gt;&lt;b&gt;No. of Bedrooms:&lt;/b&gt; 1&lt;br&gt;&lt;b&gt;Resale:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Vasthu Compliant:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Essentials:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Nearby Resources:&lt;/b&gt; 0&lt;br&gt;&lt;/div&gt;`)[0];\n",
       "                popup_d181543d5856f870baac11dcb2038629.setContent(html_3dc9b1c1b8ec64c412e9d360f7383f23);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_2a43980bdd7d76fd67b92a3bd6388360.bindPopup(popup_d181543d5856f870baac11dcb2038629)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            marker_2a43980bdd7d76fd67b92a3bd6388360.bindTooltip(\n",
       "                `&lt;div&gt;\n",
       "                     &lt;b&gt;Place:&lt;/b&gt; Baguiati&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 800 sqft&lt;br&gt;\n",
       "                 &lt;/div&gt;`,\n",
       "                {&quot;sticky&quot;: true}\n",
       "            );\n",
       "        \n",
       "    \n",
       "            var marker_d441c74dc44ad6e5ea4fd98d65208d12 = L.marker(\n",
       "                [22.616396, 88.4278599],\n",
       "                {}\n",
       "            ).addTo(map_862a31bff3acb042db995a7cd38e3b09);\n",
       "        \n",
       "    \n",
       "            var icon_ce7ca613704c287b805447ec56a9e7ac = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;home&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n",
       "            );\n",
       "            marker_d441c74dc44ad6e5ea4fd98d65208d12.setIcon(icon_ce7ca613704c287b805447ec56a9e7ac);\n",
       "        \n",
       "    \n",
       "        var popup_7b0f3be7480a205cc96c9169b295c44f = L.popup({&quot;maxWidth&quot;: 300});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_58c6dc1cc719a4c8c6c379dc83d39147 = $(`&lt;div id=&quot;html_58c6dc1cc719a4c8c6c379dc83d39147&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;&lt;b&gt;Place:&lt;/b&gt; Baguiati&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 1700 sqft&lt;br&gt;&lt;b&gt;Price:&lt;/b&gt; 30 lakhs&lt;br&gt;&lt;b&gt;Location:&lt;/b&gt; 22.616396, 88.4278599&lt;br&gt;&lt;b&gt;No. of Bedrooms:&lt;/b&gt; 3&lt;br&gt;&lt;b&gt;Resale:&lt;/b&gt; 1&lt;br&gt;&lt;b&gt;Vasthu Compliant:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Essentials:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Nearby Resources:&lt;/b&gt; 0&lt;br&gt;&lt;/div&gt;`)[0];\n",
       "                popup_7b0f3be7480a205cc96c9169b295c44f.setContent(html_58c6dc1cc719a4c8c6c379dc83d39147);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_d441c74dc44ad6e5ea4fd98d65208d12.bindPopup(popup_7b0f3be7480a205cc96c9169b295c44f)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            marker_d441c74dc44ad6e5ea4fd98d65208d12.bindTooltip(\n",
       "                `&lt;div&gt;\n",
       "                     &lt;b&gt;Place:&lt;/b&gt; Baguiati&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 1700 sqft&lt;br&gt;\n",
       "                 &lt;/div&gt;`,\n",
       "                {&quot;sticky&quot;: true}\n",
       "            );\n",
       "        \n",
       "    \n",
       "            var marker_09c739f73ea382bbfe496129c21cd280 = L.marker(\n",
       "                [22.5280337, 88.3659084],\n",
       "                {}\n",
       "            ).addTo(map_862a31bff3acb042db995a7cd38e3b09);\n",
       "        \n",
       "    \n",
       "            var icon_371f13626e6e537df5b6da35d96bf324 = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;home&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n",
       "            );\n",
       "            marker_09c739f73ea382bbfe496129c21cd280.setIcon(icon_371f13626e6e537df5b6da35d96bf324);\n",
       "        \n",
       "    \n",
       "        var popup_dacb4c3e000991fc2ccaf82366e7b836 = L.popup({&quot;maxWidth&quot;: 300});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_cfa95027421bf85fd225fa814cb8b1a9 = $(`&lt;div id=&quot;html_cfa95027421bf85fd225fa814cb8b1a9&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;&lt;b&gt;Place:&lt;/b&gt; Ballygunge&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 1846 sqft&lt;br&gt;&lt;b&gt;Price:&lt;/b&gt; 30 lakhs&lt;br&gt;&lt;b&gt;Location:&lt;/b&gt; 22.5280337, 88.3659084&lt;br&gt;&lt;b&gt;No. of Bedrooms:&lt;/b&gt; 2&lt;br&gt;&lt;b&gt;Resale:&lt;/b&gt; 1&lt;br&gt;&lt;b&gt;Vasthu Compliant:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Essentials:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Nearby Resources:&lt;/b&gt; 0&lt;br&gt;&lt;/div&gt;`)[0];\n",
       "                popup_dacb4c3e000991fc2ccaf82366e7b836.setContent(html_cfa95027421bf85fd225fa814cb8b1a9);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_09c739f73ea382bbfe496129c21cd280.bindPopup(popup_dacb4c3e000991fc2ccaf82366e7b836)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            marker_09c739f73ea382bbfe496129c21cd280.bindTooltip(\n",
       "                `&lt;div&gt;\n",
       "                     &lt;b&gt;Place:&lt;/b&gt; Ballygunge&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 1846 sqft&lt;br&gt;\n",
       "                 &lt;/div&gt;`,\n",
       "                {&quot;sticky&quot;: true}\n",
       "            );\n",
       "        \n",
       "    \n",
       "            var marker_334dab6893d9fce44f07e636c3259bf1 = L.marker(\n",
       "                [22.6068854, 88.4107265],\n",
       "                {}\n",
       "            ).addTo(map_862a31bff3acb042db995a7cd38e3b09);\n",
       "        \n",
       "    \n",
       "            var icon_c2d0d5654681414fe1a48514b67f9a38 = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;home&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n",
       "            );\n",
       "            marker_334dab6893d9fce44f07e636c3259bf1.setIcon(icon_c2d0d5654681414fe1a48514b67f9a38);\n",
       "        \n",
       "    \n",
       "        var popup_efd26439e7918a9298bfc70b684923f6 = L.popup({&quot;maxWidth&quot;: 300});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_b0bd5e51968116b573434a0bb39c4615 = $(`&lt;div id=&quot;html_b0bd5e51968116b573434a0bb39c4615&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;&lt;b&gt;Place:&lt;/b&gt; Bangur avenue&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 799 sqft&lt;br&gt;&lt;b&gt;Price:&lt;/b&gt; 30 lakhs&lt;br&gt;&lt;b&gt;Location:&lt;/b&gt; 22.6068854, 88.4107265&lt;br&gt;&lt;b&gt;No. of Bedrooms:&lt;/b&gt; 2&lt;br&gt;&lt;b&gt;Resale:&lt;/b&gt; 1&lt;br&gt;&lt;b&gt;Vasthu Compliant:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Essentials:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Nearby Resources:&lt;/b&gt; 0&lt;br&gt;&lt;/div&gt;`)[0];\n",
       "                popup_efd26439e7918a9298bfc70b684923f6.setContent(html_b0bd5e51968116b573434a0bb39c4615);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_334dab6893d9fce44f07e636c3259bf1.bindPopup(popup_efd26439e7918a9298bfc70b684923f6)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            marker_334dab6893d9fce44f07e636c3259bf1.bindTooltip(\n",
       "                `&lt;div&gt;\n",
       "                     &lt;b&gt;Place:&lt;/b&gt; Bangur avenue&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 799 sqft&lt;br&gt;\n",
       "                 &lt;/div&gt;`,\n",
       "                {&quot;sticky&quot;: true}\n",
       "            );\n",
       "        \n",
       "    \n",
       "            var marker_69ca65d8999c08e57f04178422d3ff68 = L.marker(\n",
       "                [22.4707691, 88.3618078],\n",
       "                {}\n",
       "            ).addTo(map_862a31bff3acb042db995a7cd38e3b09);\n",
       "        \n",
       "    \n",
       "            var icon_e084f975641c61f042197d73be102b3f = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;home&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n",
       "            );\n",
       "            marker_69ca65d8999c08e57f04178422d3ff68.setIcon(icon_e084f975641c61f042197d73be102b3f);\n",
       "        \n",
       "    \n",
       "        var popup_68b2940882c2f0f2b18e8d8717999381 = L.popup({&quot;maxWidth&quot;: 300});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_8e7e7034b271a2ee7993c90e26972ab0 = $(`&lt;div id=&quot;html_8e7e7034b271a2ee7993c90e26972ab0&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;&lt;b&gt;Place:&lt;/b&gt; Bansdroni&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 620 sqft&lt;br&gt;&lt;b&gt;Price:&lt;/b&gt; 32 lakhs&lt;br&gt;&lt;b&gt;Location:&lt;/b&gt; 22.4707691, 88.3618078&lt;br&gt;&lt;b&gt;No. of Bedrooms:&lt;/b&gt; 1&lt;br&gt;&lt;b&gt;Resale:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Vasthu Compliant:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Essentials:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Nearby Resources:&lt;/b&gt; 0&lt;br&gt;&lt;/div&gt;`)[0];\n",
       "                popup_68b2940882c2f0f2b18e8d8717999381.setContent(html_8e7e7034b271a2ee7993c90e26972ab0);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_69ca65d8999c08e57f04178422d3ff68.bindPopup(popup_68b2940882c2f0f2b18e8d8717999381)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            marker_69ca65d8999c08e57f04178422d3ff68.bindTooltip(\n",
       "                `&lt;div&gt;\n",
       "                     &lt;b&gt;Place:&lt;/b&gt; Bansdroni&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 620 sqft&lt;br&gt;\n",
       "                 &lt;/div&gt;`,\n",
       "                {&quot;sticky&quot;: true}\n",
       "            );\n",
       "        \n",
       "    \n",
       "            var marker_12d0a3e3e045e6eb099fda27e77242cb = L.marker(\n",
       "                [22.6467949, 88.3740078],\n",
       "                {}\n",
       "            ).addTo(map_862a31bff3acb042db995a7cd38e3b09);\n",
       "        \n",
       "    \n",
       "            var icon_9cbfb3ea2776e393c4384508268ee013 = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;home&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n",
       "            );\n",
       "            marker_12d0a3e3e045e6eb099fda27e77242cb.setIcon(icon_9cbfb3ea2776e393c4384508268ee013);\n",
       "        \n",
       "    \n",
       "        var popup_75d5653d458c1440e3ff1ffcae8e2e59 = L.popup({&quot;maxWidth&quot;: 300});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_47a2d34c506fb6d31c3642eef7ebdb1f = $(`&lt;div id=&quot;html_47a2d34c506fb6d31c3642eef7ebdb1f&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;&lt;b&gt;Place:&lt;/b&gt; Baranagar&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 690 sqft&lt;br&gt;&lt;b&gt;Price:&lt;/b&gt; 30 lakhs&lt;br&gt;&lt;b&gt;Location:&lt;/b&gt; 22.6467949, 88.3740078&lt;br&gt;&lt;b&gt;No. of Bedrooms:&lt;/b&gt; 2&lt;br&gt;&lt;b&gt;Resale:&lt;/b&gt; 1&lt;br&gt;&lt;b&gt;Vasthu Compliant:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Essentials:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Nearby Resources:&lt;/b&gt; 0&lt;br&gt;&lt;/div&gt;`)[0];\n",
       "                popup_75d5653d458c1440e3ff1ffcae8e2e59.setContent(html_47a2d34c506fb6d31c3642eef7ebdb1f);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_12d0a3e3e045e6eb099fda27e77242cb.bindPopup(popup_75d5653d458c1440e3ff1ffcae8e2e59)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            marker_12d0a3e3e045e6eb099fda27e77242cb.bindTooltip(\n",
       "                `&lt;div&gt;\n",
       "                     &lt;b&gt;Place:&lt;/b&gt; Baranagar&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 690 sqft&lt;br&gt;\n",
       "                 &lt;/div&gt;`,\n",
       "                {&quot;sticky&quot;: true}\n",
       "            );\n",
       "        \n",
       "    \n",
       "            var marker_e1c05a62d341dac4c79b4a9e6a029143 = L.marker(\n",
       "                [22.7240295, 88.4889199],\n",
       "                {}\n",
       "            ).addTo(map_862a31bff3acb042db995a7cd38e3b09);\n",
       "        \n",
       "    \n",
       "            var icon_13c721ba5652fe8cbf4420fd4b8053b4 = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;home&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n",
       "            );\n",
       "            marker_e1c05a62d341dac4c79b4a9e6a029143.setIcon(icon_13c721ba5652fe8cbf4420fd4b8053b4);\n",
       "        \n",
       "    \n",
       "        var popup_0f1ea9508655818a2463c942b834aab0 = L.popup({&quot;maxWidth&quot;: 300});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_4925490a1e6ad84c5cd1ccbff75cd4d1 = $(`&lt;div id=&quot;html_4925490a1e6ad84c5cd1ccbff75cd4d1&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;&lt;b&gt;Place:&lt;/b&gt; Barasat&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 1200 sqft&lt;br&gt;&lt;b&gt;Price:&lt;/b&gt; 32 lakhs&lt;br&gt;&lt;b&gt;Location:&lt;/b&gt; 22.7240295, 88.4889199&lt;br&gt;&lt;b&gt;No. of Bedrooms:&lt;/b&gt; 3&lt;br&gt;&lt;b&gt;Resale:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Vasthu Compliant:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Essentials:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Nearby Resources:&lt;/b&gt; 0&lt;br&gt;&lt;/div&gt;`)[0];\n",
       "                popup_0f1ea9508655818a2463c942b834aab0.setContent(html_4925490a1e6ad84c5cd1ccbff75cd4d1);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_e1c05a62d341dac4c79b4a9e6a029143.bindPopup(popup_0f1ea9508655818a2463c942b834aab0)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            marker_e1c05a62d341dac4c79b4a9e6a029143.bindTooltip(\n",
       "                `&lt;div&gt;\n",
       "                     &lt;b&gt;Place:&lt;/b&gt; Barasat&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 1200 sqft&lt;br&gt;\n",
       "                 &lt;/div&gt;`,\n",
       "                {&quot;sticky&quot;: true}\n",
       "            );\n",
       "        \n",
       "    \n",
       "            var marker_9a175f775ddd7c5350bf88e1aa178bee = L.marker(\n",
       "                [22.64462075, 88.3761585109337],\n",
       "                {}\n",
       "            ).addTo(map_862a31bff3acb042db995a7cd38e3b09);\n",
       "        \n",
       "    \n",
       "            var icon_f0ab8dfa2fe17a333b8e65434df351d0 = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;home&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n",
       "            );\n",
       "            marker_9a175f775ddd7c5350bf88e1aa178bee.setIcon(icon_f0ab8dfa2fe17a333b8e65434df351d0);\n",
       "        \n",
       "    \n",
       "        var popup_a72ac2739112dd227353541e4a783299 = L.popup({&quot;maxWidth&quot;: 300});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_392300db268c20a9840873eb4e6c6334 = $(`&lt;div id=&quot;html_392300db268c20a9840873eb4e6c6334&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;&lt;b&gt;Place:&lt;/b&gt; Barrackpore&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 1100 sqft&lt;br&gt;&lt;b&gt;Price:&lt;/b&gt; 31 lakhs&lt;br&gt;&lt;b&gt;Location:&lt;/b&gt; 22.64462075, 88.3761585109337&lt;br&gt;&lt;b&gt;No. of Bedrooms:&lt;/b&gt; 3&lt;br&gt;&lt;b&gt;Resale:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Vasthu Compliant:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Essentials:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Nearby Resources:&lt;/b&gt; 0&lt;br&gt;&lt;/div&gt;`)[0];\n",
       "                popup_a72ac2739112dd227353541e4a783299.setContent(html_392300db268c20a9840873eb4e6c6334);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_9a175f775ddd7c5350bf88e1aa178bee.bindPopup(popup_a72ac2739112dd227353541e4a783299)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            marker_9a175f775ddd7c5350bf88e1aa178bee.bindTooltip(\n",
       "                `&lt;div&gt;\n",
       "                     &lt;b&gt;Place:&lt;/b&gt; Barrackpore&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 1100 sqft&lt;br&gt;\n",
       "                 &lt;/div&gt;`,\n",
       "                {&quot;sticky&quot;: true}\n",
       "            );\n",
       "        \n",
       "    \n",
       "            var marker_d156802df75463a5595e435cee915dbd = L.marker(\n",
       "                [22.6564623, 88.44672798751654],\n",
       "                {}\n",
       "            ).addTo(map_862a31bff3acb042db995a7cd38e3b09);\n",
       "        \n",
       "    \n",
       "            var icon_e88a8f27d3f79b16e4fa85fd57873720 = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;home&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n",
       "            );\n",
       "            marker_d156802df75463a5595e435cee915dbd.setIcon(icon_e88a8f27d3f79b16e4fa85fd57873720);\n",
       "        \n",
       "    \n",
       "        var popup_0ebd3434df86e8bcdc4fd5f78f5a42e6 = L.popup({&quot;maxWidth&quot;: 300});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_317cee012bd115ac5da5b809846a66a0 = $(`&lt;div id=&quot;html_317cee012bd115ac5da5b809846a66a0&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;&lt;b&gt;Place:&lt;/b&gt; Belghoria&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 1100 sqft&lt;br&gt;&lt;b&gt;Price:&lt;/b&gt; 30 lakhs&lt;br&gt;&lt;b&gt;Location:&lt;/b&gt; 22.6564623, 88.44672798751654&lt;br&gt;&lt;b&gt;No. of Bedrooms:&lt;/b&gt; 3&lt;br&gt;&lt;b&gt;Resale:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Vasthu Compliant:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Essentials:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Nearby Resources:&lt;/b&gt; 0&lt;br&gt;&lt;/div&gt;`)[0];\n",
       "                popup_0ebd3434df86e8bcdc4fd5f78f5a42e6.setContent(html_317cee012bd115ac5da5b809846a66a0);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_d156802df75463a5595e435cee915dbd.bindPopup(popup_0ebd3434df86e8bcdc4fd5f78f5a42e6)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            marker_d156802df75463a5595e435cee915dbd.bindTooltip(\n",
       "                `&lt;div&gt;\n",
       "                     &lt;b&gt;Place:&lt;/b&gt; Belghoria&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 1100 sqft&lt;br&gt;\n",
       "                 &lt;/div&gt;`,\n",
       "                {&quot;sticky&quot;: true}\n",
       "            );\n",
       "        \n",
       "    \n",
       "            var marker_3896bab5a7724fc8a2160ed6c0bd19f9 = L.marker(\n",
       "                [22.5506557, 88.40391715],\n",
       "                {}\n",
       "            ).addTo(map_862a31bff3acb042db995a7cd38e3b09);\n",
       "        \n",
       "    \n",
       "            var icon_4a1a19f3209040569a5a8325ee4ecada = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;home&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n",
       "            );\n",
       "            marker_3896bab5a7724fc8a2160ed6c0bd19f9.setIcon(icon_4a1a19f3209040569a5a8325ee4ecada);\n",
       "        \n",
       "    \n",
       "        var popup_04d9447305eeacb86a390bb36d627daf = L.popup({&quot;maxWidth&quot;: 300});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_8f90189f7b41a40264f76955fe9740ec = $(`&lt;div id=&quot;html_8f90189f7b41a40264f76955fe9740ec&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;&lt;b&gt;Place:&lt;/b&gt; Beliaghata&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 800 sqft&lt;br&gt;&lt;b&gt;Price:&lt;/b&gt; 30 lakhs&lt;br&gt;&lt;b&gt;Location:&lt;/b&gt; 22.5506557, 88.40391715&lt;br&gt;&lt;b&gt;No. of Bedrooms:&lt;/b&gt; 2&lt;br&gt;&lt;b&gt;Resale:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Vasthu Compliant:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Essentials:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Nearby Resources:&lt;/b&gt; 0&lt;br&gt;&lt;/div&gt;`)[0];\n",
       "                popup_04d9447305eeacb86a390bb36d627daf.setContent(html_8f90189f7b41a40264f76955fe9740ec);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_3896bab5a7724fc8a2160ed6c0bd19f9.bindPopup(popup_04d9447305eeacb86a390bb36d627daf)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            marker_3896bab5a7724fc8a2160ed6c0bd19f9.bindTooltip(\n",
       "                `&lt;div&gt;\n",
       "                     &lt;b&gt;Place:&lt;/b&gt; Beliaghata&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 800 sqft&lt;br&gt;\n",
       "                 &lt;/div&gt;`,\n",
       "                {&quot;sticky&quot;: true}\n",
       "            );\n",
       "        \n",
       "    \n",
       "            var marker_d99cf752a31a866dd7fb91e5e877e083 = L.marker(\n",
       "                [22.5373748, 88.34756170104649],\n",
       "                {}\n",
       "            ).addTo(map_862a31bff3acb042db995a7cd38e3b09);\n",
       "        \n",
       "    \n",
       "            var icon_f2958b5e30df496c21cfe4be80a1210a = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;home&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n",
       "            );\n",
       "            marker_d99cf752a31a866dd7fb91e5e877e083.setIcon(icon_f2958b5e30df496c21cfe4be80a1210a);\n",
       "        \n",
       "    \n",
       "        var popup_6b8a2a2b9e58a0e7a0c2310c2a461d90 = L.popup({&quot;maxWidth&quot;: 300});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_3892ab84ff1a2d034821b5cd78f844ca = $(`&lt;div id=&quot;html_3892ab84ff1a2d034821b5cd78f844ca&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;&lt;b&gt;Place:&lt;/b&gt; Elgin&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 1550 sqft&lt;br&gt;&lt;b&gt;Price:&lt;/b&gt; 31 lakhs&lt;br&gt;&lt;b&gt;Location:&lt;/b&gt; 22.5373748, 88.34756170104649&lt;br&gt;&lt;b&gt;No. of Bedrooms:&lt;/b&gt; 3&lt;br&gt;&lt;b&gt;Resale:&lt;/b&gt; 1&lt;br&gt;&lt;b&gt;Vasthu Compliant:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Essentials:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Nearby Resources:&lt;/b&gt; 0&lt;br&gt;&lt;/div&gt;`)[0];\n",
       "                popup_6b8a2a2b9e58a0e7a0c2310c2a461d90.setContent(html_3892ab84ff1a2d034821b5cd78f844ca);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_d99cf752a31a866dd7fb91e5e877e083.bindPopup(popup_6b8a2a2b9e58a0e7a0c2310c2a461d90)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            marker_d99cf752a31a866dd7fb91e5e877e083.bindTooltip(\n",
       "                `&lt;div&gt;\n",
       "                     &lt;b&gt;Place:&lt;/b&gt; Elgin&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 1550 sqft&lt;br&gt;\n",
       "                 &lt;/div&gt;`,\n",
       "                {&quot;sticky&quot;: true}\n",
       "            );\n",
       "        \n",
       "    \n",
       "            var marker_c42c6b896f01b55c3ab34dfa0c808516 = L.marker(\n",
       "                [22.5062108, 88.3839871],\n",
       "                {}\n",
       "            ).addTo(map_862a31bff3acb042db995a7cd38e3b09);\n",
       "        \n",
       "    \n",
       "            var icon_f7bdd01438fea906285f9a6fcc0f3393 = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;home&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n",
       "            );\n",
       "            marker_c42c6b896f01b55c3ab34dfa0c808516.setIcon(icon_f7bdd01438fea906285f9a6fcc0f3393);\n",
       "        \n",
       "    \n",
       "        var popup_051cd8ec6eb6666c61c47d4eb35c9965 = L.popup({&quot;maxWidth&quot;: 300});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_e39ead6543443833719b89380de33c9c = $(`&lt;div id=&quot;html_e39ead6543443833719b89380de33c9c&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;&lt;b&gt;Place:&lt;/b&gt; Garfa&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 1500 sqft&lt;br&gt;&lt;b&gt;Price:&lt;/b&gt; 32 lakhs&lt;br&gt;&lt;b&gt;Location:&lt;/b&gt; 22.5062108, 88.3839871&lt;br&gt;&lt;b&gt;No. of Bedrooms:&lt;/b&gt; 3&lt;br&gt;&lt;b&gt;Resale:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Vasthu Compliant:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Essentials:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Nearby Resources:&lt;/b&gt; 0&lt;br&gt;&lt;/div&gt;`)[0];\n",
       "                popup_051cd8ec6eb6666c61c47d4eb35c9965.setContent(html_e39ead6543443833719b89380de33c9c);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_c42c6b896f01b55c3ab34dfa0c808516.bindPopup(popup_051cd8ec6eb6666c61c47d4eb35c9965)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            marker_c42c6b896f01b55c3ab34dfa0c808516.bindTooltip(\n",
       "                `&lt;div&gt;\n",
       "                     &lt;b&gt;Place:&lt;/b&gt; Garfa&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 1500 sqft&lt;br&gt;\n",
       "                 &lt;/div&gt;`,\n",
       "                {&quot;sticky&quot;: true}\n",
       "            );\n",
       "        \n",
       "    \n",
       "            var marker_0621690ddae9f813198cbb886fa3191b = L.marker(\n",
       "                [22.4727076, 88.3977962],\n",
       "                {}\n",
       "            ).addTo(map_862a31bff3acb042db995a7cd38e3b09);\n",
       "        \n",
       "    \n",
       "            var icon_9ee081fd431535a165b752dcb7ad94bb = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;home&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n",
       "            );\n",
       "            marker_0621690ddae9f813198cbb886fa3191b.setIcon(icon_9ee081fd431535a165b752dcb7ad94bb);\n",
       "        \n",
       "    \n",
       "        var popup_1cab41285b4dd61442f0a94a8ae21d8e = L.popup({&quot;maxWidth&quot;: 300});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_c75a77e534022bfd81d1a7b3445e142e = $(`&lt;div id=&quot;html_c75a77e534022bfd81d1a7b3445e142e&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;&lt;b&gt;Place:&lt;/b&gt; Garia&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 1020 sqft&lt;br&gt;&lt;b&gt;Price:&lt;/b&gt; 31 lakhs&lt;br&gt;&lt;b&gt;Location:&lt;/b&gt; 22.4727076, 88.3977962&lt;br&gt;&lt;b&gt;No. of Bedrooms:&lt;/b&gt; 2&lt;br&gt;&lt;b&gt;Resale:&lt;/b&gt; 1&lt;br&gt;&lt;b&gt;Vasthu Compliant:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Essentials:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Nearby Resources:&lt;/b&gt; 0&lt;br&gt;&lt;/div&gt;`)[0];\n",
       "                popup_1cab41285b4dd61442f0a94a8ae21d8e.setContent(html_c75a77e534022bfd81d1a7b3445e142e);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_0621690ddae9f813198cbb886fa3191b.bindPopup(popup_1cab41285b4dd61442f0a94a8ae21d8e)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            marker_0621690ddae9f813198cbb886fa3191b.bindTooltip(\n",
       "                `&lt;div&gt;\n",
       "                     &lt;b&gt;Place:&lt;/b&gt; Garia&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 1020 sqft&lt;br&gt;\n",
       "                 &lt;/div&gt;`,\n",
       "                {&quot;sticky&quot;: true}\n",
       "            );\n",
       "        \n",
       "    \n",
       "            var marker_cece5a7b458c0c58521e5d4c900ae599 = L.marker(\n",
       "                [22.5171423, 88.3566874],\n",
       "                {}\n",
       "            ).addTo(map_862a31bff3acb042db995a7cd38e3b09);\n",
       "        \n",
       "    \n",
       "            var icon_3d8cab14b9a1afce4e6630b09a60d575 = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;home&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n",
       "            );\n",
       "            marker_cece5a7b458c0c58521e5d4c900ae599.setIcon(icon_3d8cab14b9a1afce4e6630b09a60d575);\n",
       "        \n",
       "    \n",
       "        var popup_0c63b1dc98ed10e015a0b2e41b3b6dbb = L.popup({&quot;maxWidth&quot;: 300});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_83ca504a0c37af39b0380d28e3cc7d0f = $(`&lt;div id=&quot;html_83ca504a0c37af39b0380d28e3cc7d0f&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;&lt;b&gt;Place:&lt;/b&gt; Jatin Das Road&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 1725 sqft&lt;br&gt;&lt;b&gt;Price:&lt;/b&gt; 30 lakhs&lt;br&gt;&lt;b&gt;Location:&lt;/b&gt; 22.5171423, 88.3566874&lt;br&gt;&lt;b&gt;No. of Bedrooms:&lt;/b&gt; 4&lt;br&gt;&lt;b&gt;Resale:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Vasthu Compliant:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Essentials:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Nearby Resources:&lt;/b&gt; 0&lt;br&gt;&lt;/div&gt;`)[0];\n",
       "                popup_0c63b1dc98ed10e015a0b2e41b3b6dbb.setContent(html_83ca504a0c37af39b0380d28e3cc7d0f);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_cece5a7b458c0c58521e5d4c900ae599.bindPopup(popup_0c63b1dc98ed10e015a0b2e41b3b6dbb)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            marker_cece5a7b458c0c58521e5d4c900ae599.bindTooltip(\n",
       "                `&lt;div&gt;\n",
       "                     &lt;b&gt;Place:&lt;/b&gt; Jatin Das Road&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 1725 sqft&lt;br&gt;\n",
       "                 &lt;/div&gt;`,\n",
       "                {&quot;sticky&quot;: true}\n",
       "            );\n",
       "        \n",
       "    \n",
       "            var marker_c680f6b0b6afd0d376f156e306ed3925 = L.marker(\n",
       "                [22.5056055, 88.363674],\n",
       "                {}\n",
       "            ).addTo(map_862a31bff3acb042db995a7cd38e3b09);\n",
       "        \n",
       "    \n",
       "            var icon_fa67b33b8d6161a2466383d3cfab4f1e = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;home&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n",
       "            );\n",
       "            marker_c680f6b0b6afd0d376f156e306ed3925.setIcon(icon_fa67b33b8d6161a2466383d3cfab4f1e);\n",
       "        \n",
       "    \n",
       "        var popup_fa94408c4537ffd1ba62b2db28f6f266 = L.popup({&quot;maxWidth&quot;: 300});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_b0540a92a4435fb9a673e56404881de0 = $(`&lt;div id=&quot;html_b0540a92a4435fb9a673e56404881de0&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;&lt;b&gt;Place:&lt;/b&gt; Jodhpur Park&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 684 sqft&lt;br&gt;&lt;b&gt;Price:&lt;/b&gt; 30 lakhs&lt;br&gt;&lt;b&gt;Location:&lt;/b&gt; 22.5056055, 88.363674&lt;br&gt;&lt;b&gt;No. of Bedrooms:&lt;/b&gt; 2&lt;br&gt;&lt;b&gt;Resale:&lt;/b&gt; 1&lt;br&gt;&lt;b&gt;Vasthu Compliant:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Essentials:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Nearby Resources:&lt;/b&gt; 0&lt;br&gt;&lt;/div&gt;`)[0];\n",
       "                popup_fa94408c4537ffd1ba62b2db28f6f266.setContent(html_b0540a92a4435fb9a673e56404881de0);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_c680f6b0b6afd0d376f156e306ed3925.bindPopup(popup_fa94408c4537ffd1ba62b2db28f6f266)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            marker_c680f6b0b6afd0d376f156e306ed3925.bindTooltip(\n",
       "                `&lt;div&gt;\n",
       "                     &lt;b&gt;Place:&lt;/b&gt; Jodhpur Park&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 684 sqft&lt;br&gt;\n",
       "                 &lt;/div&gt;`,\n",
       "                {&quot;sticky&quot;: true}\n",
       "            );\n",
       "        \n",
       "    \n",
       "            var marker_9c3b84ee6102675eb1b501372e1e492c = L.marker(\n",
       "                [22.5056055, 88.363674],\n",
       "                {}\n",
       "            ).addTo(map_862a31bff3acb042db995a7cd38e3b09);\n",
       "        \n",
       "    \n",
       "            var icon_d409d1247dbb45a02b65369d70950fb4 = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;home&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n",
       "            );\n",
       "            marker_9c3b84ee6102675eb1b501372e1e492c.setIcon(icon_d409d1247dbb45a02b65369d70950fb4);\n",
       "        \n",
       "    \n",
       "        var popup_b3f6d7477b155df49e71b99467ee2cf8 = L.popup({&quot;maxWidth&quot;: 300});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_a1edfebf92d0258fc82bb31be3bf2c24 = $(`&lt;div id=&quot;html_a1edfebf92d0258fc82bb31be3bf2c24&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;&lt;b&gt;Place:&lt;/b&gt; Jodhpur Park&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 4456 sqft&lt;br&gt;&lt;b&gt;Price:&lt;/b&gt; 30 lakhs&lt;br&gt;&lt;b&gt;Location:&lt;/b&gt; 22.5056055, 88.363674&lt;br&gt;&lt;b&gt;No. of Bedrooms:&lt;/b&gt; 5&lt;br&gt;&lt;b&gt;Resale:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Vasthu Compliant:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Essentials:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Nearby Resources:&lt;/b&gt; 0&lt;br&gt;&lt;/div&gt;`)[0];\n",
       "                popup_b3f6d7477b155df49e71b99467ee2cf8.setContent(html_a1edfebf92d0258fc82bb31be3bf2c24);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_9c3b84ee6102675eb1b501372e1e492c.bindPopup(popup_b3f6d7477b155df49e71b99467ee2cf8)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            marker_9c3b84ee6102675eb1b501372e1e492c.bindTooltip(\n",
       "                `&lt;div&gt;\n",
       "                     &lt;b&gt;Place:&lt;/b&gt; Jodhpur Park&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 4456 sqft&lt;br&gt;\n",
       "                 &lt;/div&gt;`,\n",
       "                {&quot;sticky&quot;: true}\n",
       "            );\n",
       "        \n",
       "    \n",
       "            var marker_ff9033879815a77ba17f0ab9664263c4 = L.marker(\n",
       "                [22.5044355, 88.3721663],\n",
       "                {}\n",
       "            ).addTo(map_862a31bff3acb042db995a7cd38e3b09);\n",
       "        \n",
       "    \n",
       "            var icon_d5fd72d037472c3c2eeb83bf0fb2216b = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;home&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n",
       "            );\n",
       "            marker_ff9033879815a77ba17f0ab9664263c4.setIcon(icon_d5fd72d037472c3c2eeb83bf0fb2216b);\n",
       "        \n",
       "    \n",
       "        var popup_c84181510198320e2232690803e31155 = L.popup({&quot;maxWidth&quot;: 300});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_bf321273085de493638f3338792171b0 = $(`&lt;div id=&quot;html_bf321273085de493638f3338792171b0&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;&lt;b&gt;Place:&lt;/b&gt; Kasba&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 655 sqft&lt;br&gt;&lt;b&gt;Price:&lt;/b&gt; 32 lakhs&lt;br&gt;&lt;b&gt;Location:&lt;/b&gt; 22.5044355, 88.3721663&lt;br&gt;&lt;b&gt;No. of Bedrooms:&lt;/b&gt; 2&lt;br&gt;&lt;b&gt;Resale:&lt;/b&gt; 1&lt;br&gt;&lt;b&gt;Vasthu Compliant:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Essentials:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Nearby Resources:&lt;/b&gt; 0&lt;br&gt;&lt;/div&gt;`)[0];\n",
       "                popup_c84181510198320e2232690803e31155.setContent(html_bf321273085de493638f3338792171b0);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_ff9033879815a77ba17f0ab9664263c4.bindPopup(popup_c84181510198320e2232690803e31155)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            marker_ff9033879815a77ba17f0ab9664263c4.bindTooltip(\n",
       "                `&lt;div&gt;\n",
       "                     &lt;b&gt;Place:&lt;/b&gt; Kasba&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 655 sqft&lt;br&gt;\n",
       "                 &lt;/div&gt;`,\n",
       "                {&quot;sticky&quot;: true}\n",
       "            );\n",
       "        \n",
       "    \n",
       "            var marker_a8aeded5fad913fe03533cb976aaa306 = L.marker(\n",
       "                [22.5154157, 88.362438],\n",
       "                {}\n",
       "            ).addTo(map_862a31bff3acb042db995a7cd38e3b09);\n",
       "        \n",
       "    \n",
       "            var icon_d7434ff9d5f1f4708af784dc72af41f6 = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;home&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n",
       "            );\n",
       "            marker_a8aeded5fad913fe03533cb976aaa306.setIcon(icon_d7434ff9d5f1f4708af784dc72af41f6);\n",
       "        \n",
       "    \n",
       "        var popup_c30f3a0b10ffde612d7d16da68c3ba54 = L.popup({&quot;maxWidth&quot;: 300});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_19b691ea8dd192c159b3cc955e386692 = $(`&lt;div id=&quot;html_19b691ea8dd192c159b3cc955e386692&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;&lt;b&gt;Place:&lt;/b&gt; Keyatala&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 1131 sqft&lt;br&gt;&lt;b&gt;Price:&lt;/b&gt; 30 lakhs&lt;br&gt;&lt;b&gt;Location:&lt;/b&gt; 22.5154157, 88.362438&lt;br&gt;&lt;b&gt;No. of Bedrooms:&lt;/b&gt; 2&lt;br&gt;&lt;b&gt;Resale:&lt;/b&gt; 1&lt;br&gt;&lt;b&gt;Vasthu Compliant:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Essentials:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Nearby Resources:&lt;/b&gt; 0&lt;br&gt;&lt;/div&gt;`)[0];\n",
       "                popup_c30f3a0b10ffde612d7d16da68c3ba54.setContent(html_19b691ea8dd192c159b3cc955e386692);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_a8aeded5fad913fe03533cb976aaa306.bindPopup(popup_c30f3a0b10ffde612d7d16da68c3ba54)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            marker_a8aeded5fad913fe03533cb976aaa306.bindTooltip(\n",
       "                `&lt;div&gt;\n",
       "                     &lt;b&gt;Place:&lt;/b&gt; Keyatala&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 1131 sqft&lt;br&gt;\n",
       "                 &lt;/div&gt;`,\n",
       "                {&quot;sticky&quot;: true}\n",
       "            );\n",
       "        \n",
       "    \n",
       "            var marker_c733cff197f80fd229b3920434cb9b3d = L.marker(\n",
       "                [22.4809928, 88.3460002],\n",
       "                {}\n",
       "            ).addTo(map_862a31bff3acb042db995a7cd38e3b09);\n",
       "        \n",
       "    \n",
       "            var icon_0e6d370c399f98a07adde8eca0b9ff16 = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;home&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n",
       "            );\n",
       "            marker_c733cff197f80fd229b3920434cb9b3d.setIcon(icon_0e6d370c399f98a07adde8eca0b9ff16);\n",
       "        \n",
       "    \n",
       "        var popup_56b11d8b25ec003265fe7b88248fba71 = L.popup({&quot;maxWidth&quot;: 300});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_2ef1f02492e56db430c87edb793a726d = $(`&lt;div id=&quot;html_2ef1f02492e56db430c87edb793a726d&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;&lt;b&gt;Place:&lt;/b&gt; Netaji Nagar&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 1108 sqft&lt;br&gt;&lt;b&gt;Price:&lt;/b&gt; 30 lakhs&lt;br&gt;&lt;b&gt;Location:&lt;/b&gt; 22.4809928, 88.3460002&lt;br&gt;&lt;b&gt;No. of Bedrooms:&lt;/b&gt; 3&lt;br&gt;&lt;b&gt;Resale:&lt;/b&gt; 1&lt;br&gt;&lt;b&gt;Vasthu Compliant:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Essentials:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Nearby Resources:&lt;/b&gt; 0&lt;br&gt;&lt;/div&gt;`)[0];\n",
       "                popup_56b11d8b25ec003265fe7b88248fba71.setContent(html_2ef1f02492e56db430c87edb793a726d);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_c733cff197f80fd229b3920434cb9b3d.bindPopup(popup_56b11d8b25ec003265fe7b88248fba71)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            marker_c733cff197f80fd229b3920434cb9b3d.bindTooltip(\n",
       "                `&lt;div&gt;\n",
       "                     &lt;b&gt;Place:&lt;/b&gt; Netaji Nagar&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 1108 sqft&lt;br&gt;\n",
       "                 &lt;/div&gt;`,\n",
       "                {&quot;sticky&quot;: true}\n",
       "            );\n",
       "        \n",
       "    \n",
       "            var marker_3778d16f1b86788f78935b2749da7f8e = L.marker(\n",
       "                [22.5788545, 88.4716628],\n",
       "                {}\n",
       "            ).addTo(map_862a31bff3acb042db995a7cd38e3b09);\n",
       "        \n",
       "    \n",
       "            var icon_0a894d36382968098f42108c635ad997 = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;home&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n",
       "            );\n",
       "            marker_3778d16f1b86788f78935b2749da7f8e.setIcon(icon_0a894d36382968098f42108c635ad997);\n",
       "        \n",
       "    \n",
       "        var popup_152ca3f8ffe75e30373388160b0bd14c = L.popup({&quot;maxWidth&quot;: 300});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_55095611a0f85fb6f96204342824cede = $(`&lt;div id=&quot;html_55095611a0f85fb6f96204342824cede&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;&lt;b&gt;Place:&lt;/b&gt; New Town&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 480 sqft&lt;br&gt;&lt;b&gt;Price:&lt;/b&gt; 30 lakhs&lt;br&gt;&lt;b&gt;Location:&lt;/b&gt; 22.5788545, 88.4716628&lt;br&gt;&lt;b&gt;No. of Bedrooms:&lt;/b&gt; 1&lt;br&gt;&lt;b&gt;Resale:&lt;/b&gt; 1&lt;br&gt;&lt;b&gt;Vasthu Compliant:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Essentials:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Nearby Resources:&lt;/b&gt; 0&lt;br&gt;&lt;/div&gt;`)[0];\n",
       "                popup_152ca3f8ffe75e30373388160b0bd14c.setContent(html_55095611a0f85fb6f96204342824cede);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_3778d16f1b86788f78935b2749da7f8e.bindPopup(popup_152ca3f8ffe75e30373388160b0bd14c)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            marker_3778d16f1b86788f78935b2749da7f8e.bindTooltip(\n",
       "                `&lt;div&gt;\n",
       "                     &lt;b&gt;Place:&lt;/b&gt; New Town&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 480 sqft&lt;br&gt;\n",
       "                 &lt;/div&gt;`,\n",
       "                {&quot;sticky&quot;: true}\n",
       "            );\n",
       "        \n",
       "    \n",
       "            var marker_e3a96f4aab4c5b454460919a6dd2424e = L.marker(\n",
       "                [22.59591305, 88.48786528277952],\n",
       "                {}\n",
       "            ).addTo(map_862a31bff3acb042db995a7cd38e3b09);\n",
       "        \n",
       "    \n",
       "            var icon_5457991f3f9f4eb729c69ae4bba2052b = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;home&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n",
       "            );\n",
       "            marker_e3a96f4aab4c5b454460919a6dd2424e.setIcon(icon_5457991f3f9f4eb729c69ae4bba2052b);\n",
       "        \n",
       "    \n",
       "        var popup_86a725cb0a927b3f1405d392897dc85a = L.popup({&quot;maxWidth&quot;: 300});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_96befd88372726359b85621f74e169f4 = $(`&lt;div id=&quot;html_96befd88372726359b85621f74e169f4&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;&lt;b&gt;Place:&lt;/b&gt; New Town AA 3&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 1160 sqft&lt;br&gt;&lt;b&gt;Price:&lt;/b&gt; 31 lakhs&lt;br&gt;&lt;b&gt;Location:&lt;/b&gt; 22.59591305, 88.48786528277952&lt;br&gt;&lt;b&gt;No. of Bedrooms:&lt;/b&gt; 3&lt;br&gt;&lt;b&gt;Resale:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Vasthu Compliant:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Essentials:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Nearby Resources:&lt;/b&gt; 0&lt;br&gt;&lt;/div&gt;`)[0];\n",
       "                popup_86a725cb0a927b3f1405d392897dc85a.setContent(html_96befd88372726359b85621f74e169f4);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_e3a96f4aab4c5b454460919a6dd2424e.bindPopup(popup_86a725cb0a927b3f1405d392897dc85a)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            marker_e3a96f4aab4c5b454460919a6dd2424e.bindTooltip(\n",
       "                `&lt;div&gt;\n",
       "                     &lt;b&gt;Place:&lt;/b&gt; New Town AA 3&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 1160 sqft&lt;br&gt;\n",
       "                 &lt;/div&gt;`,\n",
       "                {&quot;sticky&quot;: true}\n",
       "            );\n",
       "        \n",
       "    \n",
       "            var marker_906ef3a207e0247472b83806baf55dc2 = L.marker(\n",
       "                [22.59591305, 88.48786528277952],\n",
       "                {}\n",
       "            ).addTo(map_862a31bff3acb042db995a7cd38e3b09);\n",
       "        \n",
       "    \n",
       "            var icon_9698eb43566db3286216b409c6d41ff4 = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;home&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n",
       "            );\n",
       "            marker_906ef3a207e0247472b83806baf55dc2.setIcon(icon_9698eb43566db3286216b409c6d41ff4);\n",
       "        \n",
       "    \n",
       "        var popup_ede49481be3ae81c994147d1a253aabd = L.popup({&quot;maxWidth&quot;: 300});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_5de66eecd049b383a6fb3677e9245fa3 = $(`&lt;div id=&quot;html_5de66eecd049b383a6fb3677e9245fa3&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;&lt;b&gt;Place:&lt;/b&gt; New Town AA 3&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 1915 sqft&lt;br&gt;&lt;b&gt;Price:&lt;/b&gt; 31 lakhs&lt;br&gt;&lt;b&gt;Location:&lt;/b&gt; 22.59591305, 88.48786528277952&lt;br&gt;&lt;b&gt;No. of Bedrooms:&lt;/b&gt; 4&lt;br&gt;&lt;b&gt;Resale:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Vasthu Compliant:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Essentials:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Nearby Resources:&lt;/b&gt; 0&lt;br&gt;&lt;/div&gt;`)[0];\n",
       "                popup_ede49481be3ae81c994147d1a253aabd.setContent(html_5de66eecd049b383a6fb3677e9245fa3);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_906ef3a207e0247472b83806baf55dc2.bindPopup(popup_ede49481be3ae81c994147d1a253aabd)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            marker_906ef3a207e0247472b83806baf55dc2.bindTooltip(\n",
       "                `&lt;div&gt;\n",
       "                     &lt;b&gt;Place:&lt;/b&gt; New Town AA 3&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 1915 sqft&lt;br&gt;\n",
       "                 &lt;/div&gt;`,\n",
       "                {&quot;sticky&quot;: true}\n",
       "            );\n",
       "        \n",
       "    \n",
       "            var marker_516d195088c62cc3c76f90ad1094eef9 = L.marker(\n",
       "                [22.64462075, 88.3761585109337],\n",
       "                {}\n",
       "            ).addTo(map_862a31bff3acb042db995a7cd38e3b09);\n",
       "        \n",
       "    \n",
       "            var icon_f640a66a35b66931b77b42802d0867e0 = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;home&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n",
       "            );\n",
       "            marker_516d195088c62cc3c76f90ad1094eef9.setIcon(icon_f640a66a35b66931b77b42802d0867e0);\n",
       "        \n",
       "    \n",
       "        var popup_dcd6efd597f3326a565f1d4a5cbce9e9 = L.popup({&quot;maxWidth&quot;: 300});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_5df52018688bc9e4b26ea3cb7ca0dbe2 = $(`&lt;div id=&quot;html_5df52018688bc9e4b26ea3cb7ca0dbe2&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;&lt;b&gt;Place:&lt;/b&gt; North Dumdum&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 1114 sqft&lt;br&gt;&lt;b&gt;Price:&lt;/b&gt; 32 lakhs&lt;br&gt;&lt;b&gt;Location:&lt;/b&gt; 22.64462075, 88.3761585109337&lt;br&gt;&lt;b&gt;No. of Bedrooms:&lt;/b&gt; 3&lt;br&gt;&lt;b&gt;Resale:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Vasthu Compliant:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Essentials:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Nearby Resources:&lt;/b&gt; 0&lt;br&gt;&lt;/div&gt;`)[0];\n",
       "                popup_dcd6efd597f3326a565f1d4a5cbce9e9.setContent(html_5df52018688bc9e4b26ea3cb7ca0dbe2);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_516d195088c62cc3c76f90ad1094eef9.bindPopup(popup_dcd6efd597f3326a565f1d4a5cbce9e9)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            marker_516d195088c62cc3c76f90ad1094eef9.bindTooltip(\n",
       "                `&lt;div&gt;\n",
       "                     &lt;b&gt;Place:&lt;/b&gt; North Dumdum&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 1114 sqft&lt;br&gt;\n",
       "                 &lt;/div&gt;`,\n",
       "                {&quot;sticky&quot;: true}\n",
       "            );\n",
       "        \n",
       "    \n",
       "            var marker_50546ddac9454783a2f1e1d4d97e3deb = L.marker(\n",
       "                [22.4718966, 88.3897531],\n",
       "                {}\n",
       "            ).addTo(map_862a31bff3acb042db995a7cd38e3b09);\n",
       "        \n",
       "    \n",
       "            var icon_eaa13634be619202b6bfd3fb37894f72 = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;home&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n",
       "            );\n",
       "            marker_50546ddac9454783a2f1e1d4d97e3deb.setIcon(icon_eaa13634be619202b6bfd3fb37894f72);\n",
       "        \n",
       "    \n",
       "        var popup_ef02fd7ab6960801b876b7b940625894 = L.popup({&quot;maxWidth&quot;: 300});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_ea5b39bf9b6452abcd9206ecd734744f = $(`&lt;div id=&quot;html_ea5b39bf9b6452abcd9206ecd734744f&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;&lt;b&gt;Place:&lt;/b&gt; Patuli&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 1382 sqft&lt;br&gt;&lt;b&gt;Price:&lt;/b&gt; 30 lakhs&lt;br&gt;&lt;b&gt;Location:&lt;/b&gt; 22.4718966, 88.3897531&lt;br&gt;&lt;b&gt;No. of Bedrooms:&lt;/b&gt; 3&lt;br&gt;&lt;b&gt;Resale:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Vasthu Compliant:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Essentials:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Nearby Resources:&lt;/b&gt; 0&lt;br&gt;&lt;/div&gt;`)[0];\n",
       "                popup_ef02fd7ab6960801b876b7b940625894.setContent(html_ea5b39bf9b6452abcd9206ecd734744f);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_50546ddac9454783a2f1e1d4d97e3deb.bindPopup(popup_ef02fd7ab6960801b876b7b940625894)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            marker_50546ddac9454783a2f1e1d4d97e3deb.bindTooltip(\n",
       "                `&lt;div&gt;\n",
       "                     &lt;b&gt;Place:&lt;/b&gt; Patuli&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 1382 sqft&lt;br&gt;\n",
       "                 &lt;/div&gt;`,\n",
       "                {&quot;sticky&quot;: true}\n",
       "            );\n",
       "        \n",
       "    \n",
       "            var marker_d2f4414823674a96674eab8781f28ea2 = L.marker(\n",
       "                [22.5033539, 88.3771383],\n",
       "                {}\n",
       "            ).addTo(map_862a31bff3acb042db995a7cd38e3b09);\n",
       "        \n",
       "    \n",
       "            var icon_3e26c57c2ab8c8ec1396724796fd825d = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;home&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n",
       "            );\n",
       "            marker_d2f4414823674a96674eab8781f28ea2.setIcon(icon_3e26c57c2ab8c8ec1396724796fd825d);\n",
       "        \n",
       "    \n",
       "        var popup_4d3a8b3fd95ed2d9dab2fed0393ec26f = L.popup({&quot;maxWidth&quot;: 300});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_d9db5400e4d636cb36254a43c3536693 = $(`&lt;div id=&quot;html_d9db5400e4d636cb36254a43c3536693&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;&lt;b&gt;Place:&lt;/b&gt; Prince Anwar Shah Connector&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 1840 sqft&lt;br&gt;&lt;b&gt;Price:&lt;/b&gt; 30 lakhs&lt;br&gt;&lt;b&gt;Location:&lt;/b&gt; 22.5033539, 88.3771383&lt;br&gt;&lt;b&gt;No. of Bedrooms:&lt;/b&gt; 4&lt;br&gt;&lt;b&gt;Resale:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Vasthu Compliant:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Essentials:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Nearby Resources:&lt;/b&gt; 0&lt;br&gt;&lt;/div&gt;`)[0];\n",
       "                popup_4d3a8b3fd95ed2d9dab2fed0393ec26f.setContent(html_d9db5400e4d636cb36254a43c3536693);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_d2f4414823674a96674eab8781f28ea2.bindPopup(popup_4d3a8b3fd95ed2d9dab2fed0393ec26f)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            marker_d2f4414823674a96674eab8781f28ea2.bindTooltip(\n",
       "                `&lt;div&gt;\n",
       "                     &lt;b&gt;Place:&lt;/b&gt; Prince Anwar Shah Connector&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 1840 sqft&lt;br&gt;\n",
       "                 &lt;/div&gt;`,\n",
       "                {&quot;sticky&quot;: true}\n",
       "            );\n",
       "        \n",
       "    \n",
       "            var marker_df26e4c901b455f134d93a9da23e9764 = L.marker(\n",
       "                [22.6168954, 88.4336328],\n",
       "                {}\n",
       "            ).addTo(map_862a31bff3acb042db995a7cd38e3b09);\n",
       "        \n",
       "    \n",
       "            var icon_456593c007dff5c9599d883db5c73b10 = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;home&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n",
       "            );\n",
       "            marker_df26e4c901b455f134d93a9da23e9764.setIcon(icon_456593c007dff5c9599d883db5c73b10);\n",
       "        \n",
       "    \n",
       "        var popup_486b09e22df537ff91587893fffa2a1d = L.popup({&quot;maxWidth&quot;: 300});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_4629bf4900468146796730aa1c885372 = $(`&lt;div id=&quot;html_4629bf4900468146796730aa1c885372&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;&lt;b&gt;Place:&lt;/b&gt; Rajarhat&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 955 sqft&lt;br&gt;&lt;b&gt;Price:&lt;/b&gt; 31 lakhs&lt;br&gt;&lt;b&gt;Location:&lt;/b&gt; 22.6168954, 88.4336328&lt;br&gt;&lt;b&gt;No. of Bedrooms:&lt;/b&gt; 2&lt;br&gt;&lt;b&gt;Resale:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Vasthu Compliant:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Essentials:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Nearby Resources:&lt;/b&gt; 0&lt;br&gt;&lt;/div&gt;`)[0];\n",
       "                popup_486b09e22df537ff91587893fffa2a1d.setContent(html_4629bf4900468146796730aa1c885372);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_df26e4c901b455f134d93a9da23e9764.bindPopup(popup_486b09e22df537ff91587893fffa2a1d)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            marker_df26e4c901b455f134d93a9da23e9764.bindTooltip(\n",
       "                `&lt;div&gt;\n",
       "                     &lt;b&gt;Place:&lt;/b&gt; Rajarhat&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 955 sqft&lt;br&gt;\n",
       "                 &lt;/div&gt;`,\n",
       "                {&quot;sticky&quot;: true}\n",
       "            );\n",
       "        \n",
       "    \n",
       "            var marker_cae9f6c575ed354bbd6ad86f8442997e = L.marker(\n",
       "                [22.5075346, 88.3851511],\n",
       "                {}\n",
       "            ).addTo(map_862a31bff3acb042db995a7cd38e3b09);\n",
       "        \n",
       "    \n",
       "            var icon_ee1f64fb05ed5f67bc1d246ddd78cdcf = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;home&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n",
       "            );\n",
       "            marker_cae9f6c575ed354bbd6ad86f8442997e.setIcon(icon_ee1f64fb05ed5f67bc1d246ddd78cdcf);\n",
       "        \n",
       "    \n",
       "        var popup_101074c7bc697e9b7bfd2ef4f280293b = L.popup({&quot;maxWidth&quot;: 300});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_82a467c0d3267d5588b79148b580097e = $(`&lt;div id=&quot;html_82a467c0d3267d5588b79148b580097e&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;&lt;b&gt;Place:&lt;/b&gt; Ramlal Bazar&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 540 sqft&lt;br&gt;&lt;b&gt;Price:&lt;/b&gt; 30 lakhs&lt;br&gt;&lt;b&gt;Location:&lt;/b&gt; 22.5075346, 88.3851511&lt;br&gt;&lt;b&gt;No. of Bedrooms:&lt;/b&gt; 1&lt;br&gt;&lt;b&gt;Resale:&lt;/b&gt; 1&lt;br&gt;&lt;b&gt;Vasthu Compliant:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Essentials:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Nearby Resources:&lt;/b&gt; 0&lt;br&gt;&lt;/div&gt;`)[0];\n",
       "                popup_101074c7bc697e9b7bfd2ef4f280293b.setContent(html_82a467c0d3267d5588b79148b580097e);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_cae9f6c575ed354bbd6ad86f8442997e.bindPopup(popup_101074c7bc697e9b7bfd2ef4f280293b)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            marker_cae9f6c575ed354bbd6ad86f8442997e.bindTooltip(\n",
       "                `&lt;div&gt;\n",
       "                     &lt;b&gt;Place:&lt;/b&gt; Ramlal Bazar&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 540 sqft&lt;br&gt;\n",
       "                 &lt;/div&gt;`,\n",
       "                {&quot;sticky&quot;: true}\n",
       "            );\n",
       "        \n",
       "    \n",
       "            var marker_12c3f08079398034aa9dbe0bb8399b42 = L.marker(\n",
       "                [22.57002545, 88.43478465637821],\n",
       "                {}\n",
       "            ).addTo(map_862a31bff3acb042db995a7cd38e3b09);\n",
       "        \n",
       "    \n",
       "            var icon_77623ee32b3411bca70cb132265ef265 = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;home&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n",
       "            );\n",
       "            marker_12c3f08079398034aa9dbe0bb8399b42.setIcon(icon_77623ee32b3411bca70cb132265ef265);\n",
       "        \n",
       "    \n",
       "        var popup_d9a207744daab9c5ce7b2de4d4f902eb = L.popup({&quot;maxWidth&quot;: 300});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_fd42dff56b5d621ecf0241b498fe20e5 = $(`&lt;div id=&quot;html_fd42dff56b5d621ecf0241b498fe20e5&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;&lt;b&gt;Place:&lt;/b&gt; Salt Lake City&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 1200 sqft&lt;br&gt;&lt;b&gt;Price:&lt;/b&gt; 32 lakhs&lt;br&gt;&lt;b&gt;Location:&lt;/b&gt; 22.57002545, 88.43478465637821&lt;br&gt;&lt;b&gt;No. of Bedrooms:&lt;/b&gt; 3&lt;br&gt;&lt;b&gt;Resale:&lt;/b&gt; 1&lt;br&gt;&lt;b&gt;Vasthu Compliant:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Essentials:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Nearby Resources:&lt;/b&gt; 0&lt;br&gt;&lt;/div&gt;`)[0];\n",
       "                popup_d9a207744daab9c5ce7b2de4d4f902eb.setContent(html_fd42dff56b5d621ecf0241b498fe20e5);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_12c3f08079398034aa9dbe0bb8399b42.bindPopup(popup_d9a207744daab9c5ce7b2de4d4f902eb)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            marker_12c3f08079398034aa9dbe0bb8399b42.bindTooltip(\n",
       "                `&lt;div&gt;\n",
       "                     &lt;b&gt;Place:&lt;/b&gt; Salt Lake City&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 1200 sqft&lt;br&gt;\n",
       "                 &lt;/div&gt;`,\n",
       "                {&quot;sticky&quot;: true}\n",
       "            );\n",
       "        \n",
       "    \n",
       "            var marker_798626b45f6bc77f76ad553bf84b9333 = L.marker(\n",
       "                [22.57002545, 88.43478465637821],\n",
       "                {}\n",
       "            ).addTo(map_862a31bff3acb042db995a7cd38e3b09);\n",
       "        \n",
       "    \n",
       "            var icon_7133c4dff422462329786999afe7cf49 = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;home&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n",
       "            );\n",
       "            marker_798626b45f6bc77f76ad553bf84b9333.setIcon(icon_7133c4dff422462329786999afe7cf49);\n",
       "        \n",
       "    \n",
       "        var popup_60eb115e24c4ccdcd369a5fdde1d815f = L.popup({&quot;maxWidth&quot;: 300});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_effe9b2782f990406f7c8803ffbd5f36 = $(`&lt;div id=&quot;html_effe9b2782f990406f7c8803ffbd5f36&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;&lt;b&gt;Place:&lt;/b&gt; Salt Lake City&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 1032 sqft&lt;br&gt;&lt;b&gt;Price:&lt;/b&gt; 32 lakhs&lt;br&gt;&lt;b&gt;Location:&lt;/b&gt; 22.57002545, 88.43478465637821&lt;br&gt;&lt;b&gt;No. of Bedrooms:&lt;/b&gt; 2&lt;br&gt;&lt;b&gt;Resale:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Vasthu Compliant:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Essentials:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Nearby Resources:&lt;/b&gt; 0&lt;br&gt;&lt;/div&gt;`)[0];\n",
       "                popup_60eb115e24c4ccdcd369a5fdde1d815f.setContent(html_effe9b2782f990406f7c8803ffbd5f36);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_798626b45f6bc77f76ad553bf84b9333.bindPopup(popup_60eb115e24c4ccdcd369a5fdde1d815f)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            marker_798626b45f6bc77f76ad553bf84b9333.bindTooltip(\n",
       "                `&lt;div&gt;\n",
       "                     &lt;b&gt;Place:&lt;/b&gt; Salt Lake City&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 1032 sqft&lt;br&gt;\n",
       "                 &lt;/div&gt;`,\n",
       "                {&quot;sticky&quot;: true}\n",
       "            );\n",
       "        \n",
       "    \n",
       "            var marker_f009ff33ad6338e77281f6699b9c8621 = L.marker(\n",
       "                [22.4780444, 88.2910436],\n",
       "                {}\n",
       "            ).addTo(map_862a31bff3acb042db995a7cd38e3b09);\n",
       "        \n",
       "    \n",
       "            var icon_aa15e89f6c97dfedbb449f4aae993602 = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;home&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n",
       "            );\n",
       "            marker_f009ff33ad6338e77281f6699b9c8621.setIcon(icon_aa15e89f6c97dfedbb449f4aae993602);\n",
       "        \n",
       "    \n",
       "        var popup_e83852b00ce012c7a6830e970e575eaa = L.popup({&quot;maxWidth&quot;: 300});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_ba730587780a70801450b778fdbdecc3 = $(`&lt;div id=&quot;html_ba730587780a70801450b778fdbdecc3&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;&lt;b&gt;Place:&lt;/b&gt; Sarsuna&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 710 sqft&lt;br&gt;&lt;b&gt;Price:&lt;/b&gt; 32 lakhs&lt;br&gt;&lt;b&gt;Location:&lt;/b&gt; 22.4780444, 88.2910436&lt;br&gt;&lt;b&gt;No. of Bedrooms:&lt;/b&gt; 2&lt;br&gt;&lt;b&gt;Resale:&lt;/b&gt; 1&lt;br&gt;&lt;b&gt;Vasthu Compliant:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Essentials:&lt;/b&gt; 20&lt;br&gt;&lt;b&gt;Nearby Resources:&lt;/b&gt; 0&lt;br&gt;&lt;/div&gt;`)[0];\n",
       "                popup_e83852b00ce012c7a6830e970e575eaa.setContent(html_ba730587780a70801450b778fdbdecc3);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_f009ff33ad6338e77281f6699b9c8621.bindPopup(popup_e83852b00ce012c7a6830e970e575eaa)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            marker_f009ff33ad6338e77281f6699b9c8621.bindTooltip(\n",
       "                `&lt;div&gt;\n",
       "                     &lt;b&gt;Place:&lt;/b&gt; Sarsuna&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 710 sqft&lt;br&gt;\n",
       "                 &lt;/div&gt;`,\n",
       "                {&quot;sticky&quot;: true}\n",
       "            );\n",
       "        \n",
       "    \n",
       "            var marker_cb0c760b22e9b196ff8f0b004a232dc4 = L.marker(\n",
       "                [22.6937332, 88.3754235],\n",
       "                {}\n",
       "            ).addTo(map_862a31bff3acb042db995a7cd38e3b09);\n",
       "        \n",
       "    \n",
       "            var icon_89a4b71f8a1691686eb178b2e860c5e3 = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;home&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n",
       "            );\n",
       "            marker_cb0c760b22e9b196ff8f0b004a232dc4.setIcon(icon_89a4b71f8a1691686eb178b2e860c5e3);\n",
       "        \n",
       "    \n",
       "        var popup_5439f089b1c650e54dcf7b5c5ef82333 = L.popup({&quot;maxWidth&quot;: 300});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_cecf06b0bed184c3cf34798f159d0797 = $(`&lt;div id=&quot;html_cecf06b0bed184c3cf34798f159d0797&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;&lt;b&gt;Place:&lt;/b&gt; Sodepur&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 1160 sqft&lt;br&gt;&lt;b&gt;Price:&lt;/b&gt; 30 lakhs&lt;br&gt;&lt;b&gt;Location:&lt;/b&gt; 22.6937332, 88.3754235&lt;br&gt;&lt;b&gt;No. of Bedrooms:&lt;/b&gt; 3&lt;br&gt;&lt;b&gt;Resale:&lt;/b&gt; 1&lt;br&gt;&lt;b&gt;Vasthu Compliant:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Essentials:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Nearby Resources:&lt;/b&gt; 0&lt;br&gt;&lt;/div&gt;`)[0];\n",
       "                popup_5439f089b1c650e54dcf7b5c5ef82333.setContent(html_cecf06b0bed184c3cf34798f159d0797);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_cb0c760b22e9b196ff8f0b004a232dc4.bindPopup(popup_5439f089b1c650e54dcf7b5c5ef82333)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            marker_cb0c760b22e9b196ff8f0b004a232dc4.bindTooltip(\n",
       "                `&lt;div&gt;\n",
       "                     &lt;b&gt;Place:&lt;/b&gt; Sodepur&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 1160 sqft&lt;br&gt;\n",
       "                 &lt;/div&gt;`,\n",
       "                {&quot;sticky&quot;: true}\n",
       "            );\n",
       "        \n",
       "    \n",
       "            var marker_3e1b527e4e3aef4f736bc841a8b2caad = L.marker(\n",
       "                [22.5215934, 88.3956548],\n",
       "                {}\n",
       "            ).addTo(map_862a31bff3acb042db995a7cd38e3b09);\n",
       "        \n",
       "    \n",
       "            var icon_7ef61ef85556b5d75bdab25ddaf942b5 = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;home&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n",
       "            );\n",
       "            marker_3e1b527e4e3aef4f736bc841a8b2caad.setIcon(icon_7ef61ef85556b5d75bdab25ddaf942b5);\n",
       "        \n",
       "    \n",
       "        var popup_93803fc5761ece3ed37bd874f334c97a = L.popup({&quot;maxWidth&quot;: 300});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_0ce7f200134c1328cdf490029256e468 = $(`&lt;div id=&quot;html_0ce7f200134c1328cdf490029256e468&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;&lt;b&gt;Place:&lt;/b&gt; Tagore Park&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 1400 sqft&lt;br&gt;&lt;b&gt;Price:&lt;/b&gt; 30 lakhs&lt;br&gt;&lt;b&gt;Location:&lt;/b&gt; 22.5215934, 88.3956548&lt;br&gt;&lt;b&gt;No. of Bedrooms:&lt;/b&gt; 3&lt;br&gt;&lt;b&gt;Resale:&lt;/b&gt; 1&lt;br&gt;&lt;b&gt;Vasthu Compliant:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Essentials:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Nearby Resources:&lt;/b&gt; 0&lt;br&gt;&lt;/div&gt;`)[0];\n",
       "                popup_93803fc5761ece3ed37bd874f334c97a.setContent(html_0ce7f200134c1328cdf490029256e468);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_3e1b527e4e3aef4f736bc841a8b2caad.bindPopup(popup_93803fc5761ece3ed37bd874f334c97a)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            marker_3e1b527e4e3aef4f736bc841a8b2caad.bindTooltip(\n",
       "                `&lt;div&gt;\n",
       "                     &lt;b&gt;Place:&lt;/b&gt; Tagore Park&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 1400 sqft&lt;br&gt;\n",
       "                 &lt;/div&gt;`,\n",
       "                {&quot;sticky&quot;: true}\n",
       "            );\n",
       "        \n",
       "    \n",
       "            var marker_1fa204e8044344c8d7b005b24c88f481 = L.marker(\n",
       "                [22.5565107, 88.3795946],\n",
       "                {}\n",
       "            ).addTo(map_862a31bff3acb042db995a7cd38e3b09);\n",
       "        \n",
       "    \n",
       "            var icon_8ebc5416588e71912ef240ea1904b643 = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;home&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n",
       "            );\n",
       "            marker_1fa204e8044344c8d7b005b24c88f481.setIcon(icon_8ebc5416588e71912ef240ea1904b643);\n",
       "        \n",
       "    \n",
       "        var popup_48c092ca03919ed122792d6ecc2269bb = L.popup({&quot;maxWidth&quot;: 300});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_f48be43349f66e69fd3b9b61e603ae92 = $(`&lt;div id=&quot;html_f48be43349f66e69fd3b9b61e603ae92&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;&lt;b&gt;Place:&lt;/b&gt; Tangra&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 1248 sqft&lt;br&gt;&lt;b&gt;Price:&lt;/b&gt; 30 lakhs&lt;br&gt;&lt;b&gt;Location:&lt;/b&gt; 22.5565107, 88.3795946&lt;br&gt;&lt;b&gt;No. of Bedrooms:&lt;/b&gt; 3&lt;br&gt;&lt;b&gt;Resale:&lt;/b&gt; 1&lt;br&gt;&lt;b&gt;Vasthu Compliant:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Essentials:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Nearby Resources:&lt;/b&gt; 0&lt;br&gt;&lt;/div&gt;`)[0];\n",
       "                popup_48c092ca03919ed122792d6ecc2269bb.setContent(html_f48be43349f66e69fd3b9b61e603ae92);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_1fa204e8044344c8d7b005b24c88f481.bindPopup(popup_48c092ca03919ed122792d6ecc2269bb)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            marker_1fa204e8044344c8d7b005b24c88f481.bindTooltip(\n",
       "                `&lt;div&gt;\n",
       "                     &lt;b&gt;Place:&lt;/b&gt; Tangra&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 1248 sqft&lt;br&gt;\n",
       "                 &lt;/div&gt;`,\n",
       "                {&quot;sticky&quot;: true}\n",
       "            );\n",
       "        \n",
       "    \n",
       "            var marker_dc26cb5349da573bc06a5c3fbea07dc2 = L.marker(\n",
       "                [22.5326084, 88.3698124],\n",
       "                {}\n",
       "            ).addTo(map_862a31bff3acb042db995a7cd38e3b09);\n",
       "        \n",
       "    \n",
       "            var icon_4976d1e09deaeac985838dc2a0e81bb4 = L.AwesomeMarkers.icon(\n",
       "                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;home&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n",
       "            );\n",
       "            marker_dc26cb5349da573bc06a5c3fbea07dc2.setIcon(icon_4976d1e09deaeac985838dc2a0e81bb4);\n",
       "        \n",
       "    \n",
       "        var popup_c9dce29fda6fcc79f4fe69814252d135 = L.popup({&quot;maxWidth&quot;: 300});\n",
       "\n",
       "        \n",
       "            \n",
       "                var html_799ead7053474e8ba88c163b966503b5 = $(`&lt;div id=&quot;html_799ead7053474e8ba88c163b966503b5&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;&lt;b&gt;Place:&lt;/b&gt; Tiljala&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 650 sqft&lt;br&gt;&lt;b&gt;Price:&lt;/b&gt; 32 lakhs&lt;br&gt;&lt;b&gt;Location:&lt;/b&gt; 22.5326084, 88.3698124&lt;br&gt;&lt;b&gt;No. of Bedrooms:&lt;/b&gt; 2&lt;br&gt;&lt;b&gt;Resale:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Vasthu Compliant:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Essentials:&lt;/b&gt; 0&lt;br&gt;&lt;b&gt;Nearby Resources:&lt;/b&gt; 0&lt;br&gt;&lt;/div&gt;`)[0];\n",
       "                popup_c9dce29fda6fcc79f4fe69814252d135.setContent(html_799ead7053474e8ba88c163b966503b5);\n",
       "            \n",
       "        \n",
       "\n",
       "        marker_dc26cb5349da573bc06a5c3fbea07dc2.bindPopup(popup_c9dce29fda6fcc79f4fe69814252d135)\n",
       "        ;\n",
       "\n",
       "        \n",
       "    \n",
       "    \n",
       "            marker_dc26cb5349da573bc06a5c3fbea07dc2.bindTooltip(\n",
       "                `&lt;div&gt;\n",
       "                     &lt;b&gt;Place:&lt;/b&gt; Tiljala&lt;br&gt;&lt;b&gt;Area:&lt;/b&gt; 650 sqft&lt;br&gt;\n",
       "                 &lt;/div&gt;`,\n",
       "                {&quot;sticky&quot;: true}\n",
       "            );\n",
       "        \n",
       "    \n",
       "            var tile_layer_6cdb317ae6fea293831e9a157c96bb53 = L.tileLayer(\n",
       "                &quot;https://stamen-tiles-{s}.a.ssl.fastly.net/terrain/{z}/{x}/{y}.jpg&quot;,\n",
       "                {&quot;attribution&quot;: &quot;Map tiles by \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://stamen.com\\&quot;\\u003eStamen Design\\u003c/a\\u003e, under \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://creativecommons.org/licenses/by/3.0\\&quot;\\u003eCC BY 3.0\\u003c/a\\u003e. Data by \\u0026copy; \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://openstreetmap.org\\&quot;\\u003eOpenStreetMap\\u003c/a\\u003e, under \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://creativecommons.org/licenses/by-sa/3.0\\&quot;\\u003eCC BY SA\\u003c/a\\u003e.&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 18, &quot;maxZoom&quot;: 18, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}\n",
       "            ).addTo(map_862a31bff3acb042db995a7cd38e3b09);\n",
       "        \n",
       "    \n",
       "            var tile_layer_2f72dcfb36917a2b77aa714c2e229cdf = L.tileLayer(\n",
       "                &quot;https://stamen-tiles-{s}.a.ssl.fastly.net/toner/{z}/{x}/{y}.png&quot;,\n",
       "                {&quot;attribution&quot;: &quot;Map tiles by \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://stamen.com\\&quot;\\u003eStamen Design\\u003c/a\\u003e, under \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://creativecommons.org/licenses/by/3.0\\&quot;\\u003eCC BY 3.0\\u003c/a\\u003e. Data by \\u0026copy; \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://openstreetmap.org\\&quot;\\u003eOpenStreetMap\\u003c/a\\u003e, under \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://www.openstreetmap.org/copyright\\&quot;\\u003eODbL\\u003c/a\\u003e.&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 18, &quot;maxZoom&quot;: 18, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}\n",
       "            ).addTo(map_862a31bff3acb042db995a7cd38e3b09);\n",
       "        \n",
       "    \n",
       "            var tile_layer_6b33c35cc41adb98e25b91d326f9258e = L.tileLayer(\n",
       "                &quot;https://stamen-tiles-{s}.a.ssl.fastly.net/watercolor/{z}/{x}/{y}.jpg&quot;,\n",
       "                {&quot;attribution&quot;: &quot;Map tiles by \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://stamen.com\\&quot;\\u003eStamen Design\\u003c/a\\u003e, under \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://creativecommons.org/licenses/by/3.0\\&quot;\\u003eCC BY 3.0\\u003c/a\\u003e. Data by \\u0026copy; \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://openstreetmap.org\\&quot;\\u003eOpenStreetMap\\u003c/a\\u003e, under \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://creativecommons.org/licenses/by-sa/3.0\\&quot;\\u003eCC BY SA\\u003c/a\\u003e.&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 18, &quot;maxZoom&quot;: 18, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}\n",
       "            ).addTo(map_862a31bff3acb042db995a7cd38e3b09);\n",
       "        \n",
       "    \n",
       "            var tile_layer_a19e316905931a9dba05f39bd5ea381c = L.tileLayer(\n",
       "                &quot;https://cartodb-basemaps-{s}.global.ssl.fastly.net/light_all/{z}/{x}/{y}.png&quot;,\n",
       "                {&quot;attribution&quot;: &quot;\\u0026copy; \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://www.openstreetmap.org/copyright\\&quot;\\u003eOpenStreetMap\\u003c/a\\u003e contributors \\u0026copy; \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://cartodb.com/attributions\\&quot;\\u003eCartoDB\\u003c/a\\u003e, CartoDB \\u003ca target=\\&quot;_blank\\&quot; href =\\&quot;http://cartodb.com/attributions\\&quot;\\u003eattributions\\u003c/a\\u003e&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 18, &quot;maxZoom&quot;: 18, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}\n",
       "            ).addTo(map_862a31bff3acb042db995a7cd38e3b09);\n",
       "        \n",
       "    \n",
       "            var tile_layer_0c0a5c2293e642d0b46831a1e6958a68 = L.tileLayer(\n",
       "                &quot;https://cartodb-basemaps-{s}.global.ssl.fastly.net/dark_all/{z}/{x}/{y}.png&quot;,\n",
       "                {&quot;attribution&quot;: &quot;\\u0026copy; \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://www.openstreetmap.org/copyright\\&quot;\\u003eOpenStreetMap\\u003c/a\\u003e contributors \\u0026copy; \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://cartodb.com/attributions\\&quot;\\u003eCartoDB\\u003c/a\\u003e, CartoDB \\u003ca target=\\&quot;_blank\\&quot; href =\\&quot;http://cartodb.com/attributions\\&quot;\\u003eattributions\\u003c/a\\u003e&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 18, &quot;maxZoom&quot;: 18, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}\n",
       "            ).addTo(map_862a31bff3acb042db995a7cd38e3b09);\n",
       "        \n",
       "    \n",
       "            var layer_control_f3d52685e89db9ee3fe40075cc866b0d = {\n",
       "                base_layers : {\n",
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       "                    &quot;stamenterrain&quot; : tile_layer_6cdb317ae6fea293831e9a157c96bb53,\n",
       "                    &quot;stamentoner&quot; : tile_layer_2f72dcfb36917a2b77aa714c2e229cdf,\n",
       "                    &quot;stamenwatercolor&quot; : tile_layer_6b33c35cc41adb98e25b91d326f9258e,\n",
       "                    &quot;cartodbpositron&quot; : tile_layer_a19e316905931a9dba05f39bd5ea381c,\n",
       "                    &quot;cartodbdark_matter&quot; : tile_layer_0c0a5c2293e642d0b46831a1e6958a68,\n",
       "                },\n",
       "                overlays :  {\n",
       "                },\n",
       "            };\n",
       "            L.control.layers(\n",
       "                layer_control_f3d52685e89db9ee3fe40075cc866b0d.base_layers,\n",
       "                layer_control_f3d52685e89db9ee3fe40075cc866b0d.overlays,\n",
       "                {&quot;autoZIndex&quot;: true, &quot;collapsed&quot;: true, &quot;position&quot;: &quot;topright&quot;}\n",
       "            ).addTo(map_862a31bff3acb042db995a7cd38e3b09);\n",
       "            tile_layer_6cdb317ae6fea293831e9a157c96bb53.remove();\n",
       "            tile_layer_2f72dcfb36917a2b77aa714c2e229cdf.remove();\n",
       "            tile_layer_6b33c35cc41adb98e25b91d326f9258e.remove();\n",
       "            tile_layer_a19e316905931a9dba05f39bd5ea381c.remove();\n",
       "            tile_layer_0c0a5c2293e642d0b46831a1e6958a68.remove();\n",
       "        \n",
       "&lt;/script&gt;\n",
       "&lt;/html&gt;\" width=\"700\" height=\"400\"style=\"border:none !important;\" \"allowfullscreen\" \"webkitallowfullscreen\" \"mozallowfullscreen\"></iframe>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x22b6f00f3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#enter price range you will get where u get for that price range\n",
    "\n",
    "import pandas as pd\n",
    "import folium\n",
    "from branca.element import Figure  #resize our map by using the branca library in Python\n",
    "\n",
    "\n",
    "print('1.hyd\\n2.chennai\\n3.kolkata\\n4.bangalore\\n5.mumbai\\n6.delhi')\n",
    "while True:\n",
    "    predat=int(input(\"choose the city in which to predict--enter option:\"))\n",
    "    if(predat==1):\n",
    "        df=df1.copy()\n",
    "        break\n",
    "    elif(predat==2):\n",
    "        df=df2.copy()\n",
    "        break\n",
    "    elif(predat==3):\n",
    "        df=df3.copy()\n",
    "        break\n",
    "    elif(predat==4):\n",
    "        df=df5.copy()\n",
    "        break\n",
    "    elif(predat==5):\n",
    "        df=df6.copy()\n",
    "        break\n",
    "    elif(predat==6):\n",
    "        df=df4.copy()\n",
    "        break\n",
    "    else:\n",
    "        print('choose options 1-6 are only allowed')\n",
    "\n",
    "\n",
    "# Convert the Price column to int\n",
    "df['price'] = df['price'].astype(int)\n",
    "\n",
    "# Take input from the user for the price range\n",
    "price_range = input(\"Enter the price range (from-to [in lakhs]): \")\n",
    "\n",
    "# Split the input into min and max values\n",
    "min_price, max_price = price_range.split('-')\n",
    "\n",
    "# Convert the min and max values to int\n",
    "min_price = int(min_price)\n",
    "max_price = int(max_price)\n",
    "\n",
    "# Filter the dataframe to show only houses within the price range\n",
    "houses_in_range = df[(df['price'] >= min_price) & (df['price'] <= max_price)]\n",
    "\n",
    "# Check if there are any houses in the price range\n",
    "if houses_in_range.empty:\n",
    "    print('No houses in that range')\n",
    "else:\n",
    "    # Create a map centered on the first house in the range\n",
    "    map_center = [houses_in_range.iloc[0]['latitude'], houses_in_range.iloc[0]['longitude']]\n",
    "    m = folium.Map(location=map_center, zoom_start=12)\n",
    "\n",
    "    # loop through the filtered dataset and add markers to the map for each property\n",
    "    for index, row in houses_in_range.iterrows():\n",
    "        # create a popup with the property information\n",
    "        popup = folium.Popup(\n",
    "            f\"<b>Place:</b> {row['Location']}<br>\"\n",
    "            f\"<b>Area:</b> {row['Area']} sqft<br>\"\n",
    "            f\"<b>Price:</b> {row['price']} lakhs<br>\"\n",
    "            f\"<b>Location:</b> {row['latitude']}, {row['longitude']}<br>\"\n",
    "            f\"<b>No. of Bedrooms:</b> {row['No. of Bedrooms']}<br>\"\n",
    "            f\"<b>Resale:</b> {row['Resale']}<br>\"\n",
    "            f\"<b>Vasthu Compliant:</b> {row['VaastuCompliant']}<br>\"\n",
    "            f\"<b>Essentials:</b> {row['essentials']}<br>\"\n",
    "            f\"<b>Nearby Resources:</b> {row['nearbyresources']}<br>\",\n",
    "            max_width=300)\n",
    "        \n",
    "        tooltip=folium.Tooltip(\n",
    "            f\"<b>Place:</b> {row['Location']}<br>\"\n",
    "            f\"<b>Area:</b> {row['Area']} sqft<br>\",\n",
    "        )\n",
    "        \n",
    "        # add a marker for the property\n",
    "        folium.Marker(\n",
    "            location=[row['latitude'], row['longitude']],\n",
    "            popup=popup,tooltip=tooltip,\n",
    "            icon=folium.Icon(color='blue', icon='home')).add_to(m)\n",
    "\n",
    "    # Show the map\n",
    "    fig2=Figure(width=700,height=400) #for he size of the map\n",
    "    fig2.add_child(m)\n",
    "    folium.TileLayer('Stamen Terrain').add_to(m)\n",
    "    folium.TileLayer('Stamen Toner').add_to(m)\n",
    "    folium.TileLayer('Stamen Water Color').add_to(m)\n",
    "    folium.TileLayer('cartodbpositron').add_to(m)\n",
    "    folium.TileLayer('cartodbdark_matter').add_to(m)\n",
    "    folium.LayerControl().add_to(m)\n",
    "    display(m)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "id": "1589e336",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter a location: Barasat\n",
      "22.7191072 88.4826229\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from geopy.geocoders import Nominatim\n",
    "# Create a Nominatim geocoder object\n",
    "geolocator = Nominatim(user_agent=\"my_app\")\n",
    "# Check if location was found\n",
    "while(True):\n",
    "    # Get the input sentence from user and convert it to lowercase\n",
    "    location_str = input('Enter a location: ')\n",
    "    # Use geocoder to convert location string to latitude and longitude\n",
    "    location = geolocator.geocode(location_str)\n",
    "    if location is not None:\n",
    "        latitude = location.latitude\n",
    "        longitude = location.longitude\n",
    "        latitude=float(latitude)\n",
    "        longitude=float(longitude)\n",
    "        break\n",
    "    else:\n",
    "        print(\"Location not found\")\n",
    "print(latitude,longitude)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b7313a7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0011100000000\n"
     ]
    }
   ],
   "source": [
    "num = 1792  # integer to binary of 13 digits\n",
    "binary_str = format(num, '013b')\n",
    "print(binary_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "838583b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "binary_str = '01110'  # binary(13 digit) to decimal\n",
    "decimal_num = int(binary_str, 2)  \n",
    "print(decimal_num) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c4e8547",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c11a8fd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>seller_type</th>\n",
       "      <th>bedroom</th>\n",
       "      <th>layout_type</th>\n",
       "      <th>property_type</th>\n",
       "      <th>locality</th>\n",
       "      <th>price</th>\n",
       "      <th>area</th>\n",
       "      <th>furnish_type</th>\n",
       "      <th>bathroom</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>OWNER</td>\n",
       "      <td>1</td>\n",
       "      <td>RK</td>\n",
       "      <td>Studio Apartment</td>\n",
       "      <td>Serilingampally</td>\n",
       "      <td>6,720</td>\n",
       "      <td>320</td>\n",
       "      <td>Furnished</td>\n",
       "      <td>1 bathrooms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OWNER</td>\n",
       "      <td>5</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Villa</td>\n",
       "      <td>Sri Nagar Colony</td>\n",
       "      <td>36,000</td>\n",
       "      <td>3000</td>\n",
       "      <td>Semi-Furnished</td>\n",
       "      <td>4 bathrooms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>OWNER</td>\n",
       "      <td>2</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Independent House</td>\n",
       "      <td>Shaikpet</td>\n",
       "      <td>11,000</td>\n",
       "      <td>900</td>\n",
       "      <td>Semi-Furnished</td>\n",
       "      <td>2 bathrooms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OWNER</td>\n",
       "      <td>3</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Nanakramguda</td>\n",
       "      <td>45,000</td>\n",
       "      <td>2165</td>\n",
       "      <td>Unfurnished</td>\n",
       "      <td>3 bathrooms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OWNER</td>\n",
       "      <td>3</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Kondapur</td>\n",
       "      <td>20,000</td>\n",
       "      <td>1600</td>\n",
       "      <td>Semi-Furnished</td>\n",
       "      <td>3 bathrooms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10752</th>\n",
       "      <td>AGENT</td>\n",
       "      <td>1</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Begumpet</td>\n",
       "      <td>6,000</td>\n",
       "      <td>850</td>\n",
       "      <td>Semi-Furnished</td>\n",
       "      <td>1 bathrooms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10753</th>\n",
       "      <td>AGENT</td>\n",
       "      <td>2</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Ameerpet</td>\n",
       "      <td>12,000</td>\n",
       "      <td>1300</td>\n",
       "      <td>Semi-Furnished</td>\n",
       "      <td>2 bathrooms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10754</th>\n",
       "      <td>AGENT</td>\n",
       "      <td>2</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Ameerpet</td>\n",
       "      <td>15,000</td>\n",
       "      <td>1200</td>\n",
       "      <td>Semi-Furnished</td>\n",
       "      <td>2 bathrooms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10755</th>\n",
       "      <td>AGENT</td>\n",
       "      <td>2</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Begumpet</td>\n",
       "      <td>17,000</td>\n",
       "      <td>1300</td>\n",
       "      <td>Furnished</td>\n",
       "      <td>2 bathrooms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10756</th>\n",
       "      <td>AGENT</td>\n",
       "      <td>2</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Somajiguda</td>\n",
       "      <td>15,000</td>\n",
       "      <td>1500</td>\n",
       "      <td>Semi-Furnished</td>\n",
       "      <td>2 bathrooms</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10757 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      seller_type  bedroom layout_type      property_type          locality  \\\n",
       "0           OWNER        1          RK   Studio Apartment   Serilingampally   \n",
       "1           OWNER        5         BHK              Villa  Sri Nagar Colony   \n",
       "2           OWNER        2         BHK  Independent House          Shaikpet   \n",
       "3           OWNER        3         BHK          Apartment      Nanakramguda   \n",
       "4           OWNER        3         BHK          Apartment          Kondapur   \n",
       "...           ...      ...         ...                ...               ...   \n",
       "10752       AGENT        1         BHK          Apartment          Begumpet   \n",
       "10753       AGENT        2         BHK          Apartment          Ameerpet   \n",
       "10754       AGENT        2         BHK          Apartment          Ameerpet   \n",
       "10755       AGENT        2         BHK          Apartment          Begumpet   \n",
       "10756       AGENT        2         BHK          Apartment        Somajiguda   \n",
       "\n",
       "        price  area    furnish_type     bathroom  \n",
       "0       6,720   320       Furnished  1 bathrooms  \n",
       "1      36,000  3000  Semi-Furnished  4 bathrooms  \n",
       "2      11,000   900  Semi-Furnished  2 bathrooms  \n",
       "3      45,000  2165     Unfurnished  3 bathrooms  \n",
       "4      20,000  1600  Semi-Furnished  3 bathrooms  \n",
       "...       ...   ...             ...          ...  \n",
       "10752   6,000   850  Semi-Furnished  1 bathrooms  \n",
       "10753  12,000  1300  Semi-Furnished  2 bathrooms  \n",
       "10754  15,000  1200  Semi-Furnished  2 bathrooms  \n",
       "10755  17,000  1300       Furnished  2 bathrooms  \n",
       "10756  15,000  1500  Semi-Furnished  2 bathrooms  \n",
       "\n",
       "[10757 rows x 9 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df=pd.read_csv('Hyderabad_rent.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bce8b16b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>seller_type</th>\n",
       "      <th>bedroom</th>\n",
       "      <th>layout_type</th>\n",
       "      <th>property_type</th>\n",
       "      <th>locality</th>\n",
       "      <th>price</th>\n",
       "      <th>area</th>\n",
       "      <th>furnish_type</th>\n",
       "      <th>bathroom</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>OWNER</td>\n",
       "      <td>1</td>\n",
       "      <td>RK</td>\n",
       "      <td>Studio Apartment</td>\n",
       "      <td>Serilingampally</td>\n",
       "      <td>6,720</td>\n",
       "      <td>320</td>\n",
       "      <td>Furnished</td>\n",
       "      <td>1 bathrooms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OWNER</td>\n",
       "      <td>5</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Villa</td>\n",
       "      <td>Sri Nagar Colony</td>\n",
       "      <td>36,000</td>\n",
       "      <td>3000</td>\n",
       "      <td>Semi-Furnished</td>\n",
       "      <td>4 bathrooms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>OWNER</td>\n",
       "      <td>2</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Independent House</td>\n",
       "      <td>Shaikpet</td>\n",
       "      <td>11,000</td>\n",
       "      <td>900</td>\n",
       "      <td>Semi-Furnished</td>\n",
       "      <td>2 bathrooms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OWNER</td>\n",
       "      <td>3</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Nanakramguda</td>\n",
       "      <td>45,000</td>\n",
       "      <td>2165</td>\n",
       "      <td>Unfurnished</td>\n",
       "      <td>3 bathrooms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OWNER</td>\n",
       "      <td>3</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Kondapur</td>\n",
       "      <td>20,000</td>\n",
       "      <td>1600</td>\n",
       "      <td>Semi-Furnished</td>\n",
       "      <td>3 bathrooms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10735</th>\n",
       "      <td>AGENT</td>\n",
       "      <td>1</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Begumpet</td>\n",
       "      <td>6,000</td>\n",
       "      <td>850</td>\n",
       "      <td>Semi-Furnished</td>\n",
       "      <td>1 bathrooms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10736</th>\n",
       "      <td>AGENT</td>\n",
       "      <td>2</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Ameerpet</td>\n",
       "      <td>12,000</td>\n",
       "      <td>1300</td>\n",
       "      <td>Semi-Furnished</td>\n",
       "      <td>2 bathrooms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10737</th>\n",
       "      <td>AGENT</td>\n",
       "      <td>2</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Ameerpet</td>\n",
       "      <td>15,000</td>\n",
       "      <td>1200</td>\n",
       "      <td>Semi-Furnished</td>\n",
       "      <td>2 bathrooms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10738</th>\n",
       "      <td>AGENT</td>\n",
       "      <td>2</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Begumpet</td>\n",
       "      <td>17,000</td>\n",
       "      <td>1300</td>\n",
       "      <td>Furnished</td>\n",
       "      <td>2 bathrooms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10739</th>\n",
       "      <td>AGENT</td>\n",
       "      <td>2</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Somajiguda</td>\n",
       "      <td>15,000</td>\n",
       "      <td>1500</td>\n",
       "      <td>Semi-Furnished</td>\n",
       "      <td>2 bathrooms</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6014 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      seller_type  bedroom layout_type      property_type          locality  \\\n",
       "0           OWNER        1          RK   Studio Apartment   Serilingampally   \n",
       "1           OWNER        5         BHK              Villa  Sri Nagar Colony   \n",
       "2           OWNER        2         BHK  Independent House          Shaikpet   \n",
       "3           OWNER        3         BHK          Apartment      Nanakramguda   \n",
       "4           OWNER        3         BHK          Apartment          Kondapur   \n",
       "...           ...      ...         ...                ...               ...   \n",
       "10735       AGENT        1         BHK          Apartment          Begumpet   \n",
       "10736       AGENT        2         BHK          Apartment          Ameerpet   \n",
       "10737       AGENT        2         BHK          Apartment          Ameerpet   \n",
       "10738       AGENT        2         BHK          Apartment          Begumpet   \n",
       "10739       AGENT        2         BHK          Apartment        Somajiguda   \n",
       "\n",
       "        price  area    furnish_type     bathroom  \n",
       "0       6,720   320       Furnished  1 bathrooms  \n",
       "1      36,000  3000  Semi-Furnished  4 bathrooms  \n",
       "2      11,000   900  Semi-Furnished  2 bathrooms  \n",
       "3      45,000  2165     Unfurnished  3 bathrooms  \n",
       "4      20,000  1600  Semi-Furnished  3 bathrooms  \n",
       "...       ...   ...             ...          ...  \n",
       "10735   6,000   850  Semi-Furnished  1 bathrooms  \n",
       "10736  12,000  1300  Semi-Furnished  2 bathrooms  \n",
       "10737  15,000  1200  Semi-Furnished  2 bathrooms  \n",
       "10738  17,000  1300       Furnished  2 bathrooms  \n",
       "10739  15,000  1500  Semi-Furnished  2 bathrooms  \n",
       "\n",
       "[6014 rows x 9 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop_duplicates(inplace = True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b4740fe3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1 bathrooms', '4 bathrooms', '2 bathrooms', '3 bathrooms', nan,\n",
       "       '5 bathrooms', 'East facing', 'West facing', 'North facing',\n",
       "       'South facing', '6 bathrooms', '7 bathrooms', 'Grfloor',\n",
       "       '10 bathrooms', '12 bathrooms', '8 bathrooms', 'NorthWest facing',\n",
       "       '4 of 5floor', '9 bathrooms'], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"bathroom\"].unique()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "287d6d5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"seller_type\"]=df[\"seller_type\"].map({'OWNER':0,'AGENT':1,'BUILDER':2})\n",
    "df[\"layout_type\"]=df[\"layout_type\"].map({'BHK':0,'RK':1})\n",
    "df[\"property_type\"]=df[\"property_type\"].map({'Apartment':0,'Studio Apartment':1,'Independent House':2,'Independent Floor':3,'Villa':4,'Penthouse':5})\n",
    "df[\"furnish_type\"]=df[\"furnish_type\"].map({'Furnished':0,'Semi-Furnished':1,'Unfurnished':2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "34847c38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>seller_type</th>\n",
       "      <th>bedroom</th>\n",
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       "      <th>property_type</th>\n",
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       "      <td>6,720</td>\n",
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       "      <th>1</th>\n",
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       "      <td>Sri Nagar Colony</td>\n",
       "      <td>36,000</td>\n",
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       "      <td>4 bathrooms</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Shaikpet</td>\n",
       "      <td>11,000</td>\n",
       "      <td>900</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>Nanakramguda</td>\n",
       "      <td>45,000</td>\n",
       "      <td>2165</td>\n",
       "      <td>2</td>\n",
       "      <td>3 bathrooms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Kondapur</td>\n",
       "      <td>20,000</td>\n",
       "      <td>1600</td>\n",
       "      <td>1</td>\n",
       "      <td>3 bathrooms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Begumpet</td>\n",
       "      <td>6,000</td>\n",
       "      <td>850</td>\n",
       "      <td>1</td>\n",
       "      <td>1 bathrooms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10736</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Ameerpet</td>\n",
       "      <td>12,000</td>\n",
       "      <td>1300</td>\n",
       "      <td>1</td>\n",
       "      <td>2 bathrooms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10737</th>\n",
       "      <td>1</td>\n",
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       "      <td>1200</td>\n",
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       "      <td>2 bathrooms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10738</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "      <td>17,000</td>\n",
       "      <td>1300</td>\n",
       "      <td>0</td>\n",
       "      <td>2 bathrooms</td>\n",
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       "    <tr>\n",
       "      <th>10739</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Somajiguda</td>\n",
       "      <td>15,000</td>\n",
       "      <td>1500</td>\n",
       "      <td>1</td>\n",
       "      <td>2 bathrooms</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6014 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       seller_type  bedroom  layout_type  property_type          locality  \\\n",
       "0                0        1            1              1   Serilingampally   \n",
       "1                0        5            0              4  Sri Nagar Colony   \n",
       "2                0        2            0              2          Shaikpet   \n",
       "3                0        3            0              0      Nanakramguda   \n",
       "4                0        3            0              0          Kondapur   \n",
       "...            ...      ...          ...            ...               ...   \n",
       "10735            1        1            0              0          Begumpet   \n",
       "10736            1        2            0              0          Ameerpet   \n",
       "10737            1        2            0              0          Ameerpet   \n",
       "10738            1        2            0              0          Begumpet   \n",
       "10739            1        2            0              0        Somajiguda   \n",
       "\n",
       "        price  area  furnish_type     bathroom  \n",
       "0       6,720   320             0  1 bathrooms  \n",
       "1      36,000  3000             1  4 bathrooms  \n",
       "2      11,000   900             1  2 bathrooms  \n",
       "3      45,000  2165             2  3 bathrooms  \n",
       "4      20,000  1600             1  3 bathrooms  \n",
       "...       ...   ...           ...          ...  \n",
       "10735   6,000   850             1  1 bathrooms  \n",
       "10736  12,000  1300             1  2 bathrooms  \n",
       "10737  15,000  1200             1  2 bathrooms  \n",
       "10738  17,000  1300             0  2 bathrooms  \n",
       "10739  15,000  1500             1  2 bathrooms  \n",
       "\n",
       "[6014 rows x 9 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0f4b3723",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seller_type</th>\n",
       "      <th>bedroom</th>\n",
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       "      <th>property_type</th>\n",
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       "      <th>price</th>\n",
       "      <th>area</th>\n",
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       "      <th>bathroom</th>\n",
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       "  <tbody>\n",
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       "      <td>Serilingampally</td>\n",
       "      <td>6,720</td>\n",
       "      <td>320</td>\n",
       "      <td>0</td>\n",
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       "      <td>17.484205</td>\n",
       "      <td>78.299509</td>\n",
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       "      <td>36,000</td>\n",
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       "      <td>4 bathrooms</td>\n",
       "      <td>17.37688</td>\n",
       "      <td>78.511785</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Shaikpet</td>\n",
       "      <td>11,000</td>\n",
       "      <td>900</td>\n",
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       "      <td>2 bathrooms</td>\n",
       "      <td>17.405795</td>\n",
       "      <td>78.400654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Nanakramguda</td>\n",
       "      <td>45,000</td>\n",
       "      <td>2165</td>\n",
       "      <td>2</td>\n",
       "      <td>3 bathrooms</td>\n",
       "      <td>17.412983</td>\n",
       "      <td>78.338635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Kondapur</td>\n",
       "      <td>20,000</td>\n",
       "      <td>1600</td>\n",
       "      <td>1</td>\n",
       "      <td>3 bathrooms</td>\n",
       "      <td>18.33096</td>\n",
       "      <td>79.922469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10735</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Begumpet</td>\n",
       "      <td>6,000</td>\n",
       "      <td>850</td>\n",
       "      <td>1</td>\n",
       "      <td>1 bathrooms</td>\n",
       "      <td>17.44402</td>\n",
       "      <td>78.462482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10736</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Ameerpet</td>\n",
       "      <td>12,000</td>\n",
       "      <td>1300</td>\n",
       "      <td>1</td>\n",
       "      <td>2 bathrooms</td>\n",
       "      <td>17.437501</td>\n",
       "      <td>78.448251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10737</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Ameerpet</td>\n",
       "      <td>15,000</td>\n",
       "      <td>1200</td>\n",
       "      <td>1</td>\n",
       "      <td>2 bathrooms</td>\n",
       "      <td>17.437501</td>\n",
       "      <td>78.448251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10738</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Begumpet</td>\n",
       "      <td>17,000</td>\n",
       "      <td>1300</td>\n",
       "      <td>0</td>\n",
       "      <td>2 bathrooms</td>\n",
       "      <td>17.44402</td>\n",
       "      <td>78.462482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10739</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Somajiguda</td>\n",
       "      <td>15,000</td>\n",
       "      <td>1500</td>\n",
       "      <td>1</td>\n",
       "      <td>2 bathrooms</td>\n",
       "      <td>17.425505</td>\n",
       "      <td>78.45852</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6014 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       seller_type  bedroom  layout_type  property_type          locality  \\\n",
       "0                0        1            1              1   Serilingampally   \n",
       "1                0        5            0              4  Sri Nagar Colony   \n",
       "2                0        2            0              2          Shaikpet   \n",
       "3                0        3            0              0      Nanakramguda   \n",
       "4                0        3            0              0          Kondapur   \n",
       "...            ...      ...          ...            ...               ...   \n",
       "10735            1        1            0              0          Begumpet   \n",
       "10736            1        2            0              0          Ameerpet   \n",
       "10737            1        2            0              0          Ameerpet   \n",
       "10738            1        2            0              0          Begumpet   \n",
       "10739            1        2            0              0        Somajiguda   \n",
       "\n",
       "        price  area  furnish_type     bathroom   Latitude  Longitude  \n",
       "0       6,720   320             0  1 bathrooms  17.484205  78.299509  \n",
       "1      36,000  3000             1  4 bathrooms   17.37688  78.511785  \n",
       "2      11,000   900             1  2 bathrooms  17.405795  78.400654  \n",
       "3      45,000  2165             2  3 bathrooms  17.412983  78.338635  \n",
       "4      20,000  1600             1  3 bathrooms   18.33096  79.922469  \n",
       "...       ...   ...           ...          ...        ...        ...  \n",
       "10735   6,000   850             1  1 bathrooms   17.44402  78.462482  \n",
       "10736  12,000  1300             1  2 bathrooms  17.437501  78.448251  \n",
       "10737  15,000  1200             1  2 bathrooms  17.437501  78.448251  \n",
       "10738  17,000  1300             0  2 bathrooms   17.44402  78.462482  \n",
       "10739  15,000  1500             1  2 bathrooms  17.425505   78.45852  \n",
       "\n",
       "[6014 rows x 11 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from geopy.geocoders import Nominatim  # Adding the latitute and longitute for the places\n",
    "location=Nominatim(user_agent=\"http\")\n",
    "\n",
    "def get_location_code(x):\n",
    "    try:\n",
    "        cordinates=location.geocode(x, country_codes='INR')\n",
    "        return cordinates.latitude, cordinates.longitude  \n",
    "    except:\n",
    "        return \"Not found\", \"Not found\"\n",
    "    \n",
    "# Finding latitude and longitude for unique loactions only to speed up the execution\n",
    "unique_location=df['locality'].unique()\n",
    "location_to_code={}\n",
    "for place in unique_location:\n",
    "    location_to_code[place]=get_location_code(place) # This can a while, for me it took 15 mins. Please save this file.\n",
    "    \n",
    "# Creating two new columns having latitude and longitude\n",
    "df['Latitude'],df['Longitude']=zip(*df['locality'].map(location_to_code))\n",
    "df\n",
    "\n",
    "# Removing the 'Not found' records\n",
    "df=df[df['Latitude']!='Not found'].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "cf24e96e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing the 'Not found' records\n",
    "data=data[data['Latitude']!='Not found'].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9847afd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seller_type</th>\n",
       "      <th>bedroom</th>\n",
       "      <th>layout_type</th>\n",
       "      <th>property_type</th>\n",
       "      <th>locality</th>\n",
       "      <th>price</th>\n",
       "      <th>area</th>\n",
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       "      <td>Serilingampally</td>\n",
       "      <td>6,720</td>\n",
       "      <td>320</td>\n",
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       "      <td>Sri Nagar Colony</td>\n",
       "      <td>36,000</td>\n",
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       "      <th>2</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Shaikpet</td>\n",
       "      <td>11,000</td>\n",
       "      <td>900</td>\n",
       "      <td>1</td>\n",
       "      <td>2 bathrooms</td>\n",
       "      <td>17.405795</td>\n",
       "      <td>78.400654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Nanakramguda</td>\n",
       "      <td>45,000</td>\n",
       "      <td>2165</td>\n",
       "      <td>2</td>\n",
       "      <td>3 bathrooms</td>\n",
       "      <td>17.412983</td>\n",
       "      <td>78.338635</td>\n",
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       "      <th>4</th>\n",
       "      <td>0</td>\n",
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       "      <td>20,000</td>\n",
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       "      <th>10735</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Begumpet</td>\n",
       "      <td>6,000</td>\n",
       "      <td>850</td>\n",
       "      <td>1</td>\n",
       "      <td>1 bathrooms</td>\n",
       "      <td>17.44402</td>\n",
       "      <td>78.462482</td>\n",
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       "    <tr>\n",
       "      <th>10736</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "      <td>12,000</td>\n",
       "      <td>1300</td>\n",
       "      <td>1</td>\n",
       "      <td>2 bathrooms</td>\n",
       "      <td>17.437501</td>\n",
       "      <td>78.448251</td>\n",
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       "    <tr>\n",
       "      <th>10737</th>\n",
       "      <td>1</td>\n",
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       "      <td>15,000</td>\n",
       "      <td>1200</td>\n",
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       "      <td>2 bathrooms</td>\n",
       "      <td>17.437501</td>\n",
       "      <td>78.448251</td>\n",
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       "      <td>78.45852</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5887 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       seller_type  bedroom  layout_type  property_type          locality  \\\n",
       "0                0        1            1              1   Serilingampally   \n",
       "1                0        5            0              4  Sri Nagar Colony   \n",
       "2                0        2            0              2          Shaikpet   \n",
       "3                0        3            0              0      Nanakramguda   \n",
       "4                0        3            0              0          Kondapur   \n",
       "...            ...      ...          ...            ...               ...   \n",
       "10735            1        1            0              0          Begumpet   \n",
       "10736            1        2            0              0          Ameerpet   \n",
       "10737            1        2            0              0          Ameerpet   \n",
       "10738            1        2            0              0          Begumpet   \n",
       "10739            1        2            0              0        Somajiguda   \n",
       "\n",
       "        price  area  furnish_type     bathroom   Latitude  Longitude  \n",
       "0       6,720   320             0  1 bathrooms  17.484205  78.299509  \n",
       "1      36,000  3000             1  4 bathrooms   17.37688  78.511785  \n",
       "2      11,000   900             1  2 bathrooms  17.405795  78.400654  \n",
       "3      45,000  2165             2  3 bathrooms  17.412983  78.338635  \n",
       "4      20,000  1600             1  3 bathrooms   18.33096  79.922469  \n",
       "...       ...   ...           ...          ...        ...        ...  \n",
       "10735   6,000   850             1  1 bathrooms   17.44402  78.462482  \n",
       "10736  12,000  1300             1  2 bathrooms  17.437501  78.448251  \n",
       "10737  15,000  1200             1  2 bathrooms  17.437501  78.448251  \n",
       "10738  17,000  1300             0  2 bathrooms   17.44402  78.462482  \n",
       "10739  15,000  1500             1  2 bathrooms  17.425505   78.45852  \n",
       "\n",
       "[5887 rows x 11 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=df\n",
    "data = data.dropna()\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1c13c2f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Begumpet</td>\n",
       "      <td>6,000</td>\n",
       "      <td>850</td>\n",
       "      <td>1</td>\n",
       "      <td>1 bathrooms</td>\n",
       "      <td>17.44402</td>\n",
       "      <td>78.462482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10736</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Ameerpet</td>\n",
       "      <td>12,000</td>\n",
       "      <td>1300</td>\n",
       "      <td>1</td>\n",
       "      <td>2 bathrooms</td>\n",
       "      <td>17.437501</td>\n",
       "      <td>78.448251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10737</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Ameerpet</td>\n",
       "      <td>15,000</td>\n",
       "      <td>1200</td>\n",
       "      <td>1</td>\n",
       "      <td>2 bathrooms</td>\n",
       "      <td>17.437501</td>\n",
       "      <td>78.448251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10738</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Begumpet</td>\n",
       "      <td>17,000</td>\n",
       "      <td>1300</td>\n",
       "      <td>0</td>\n",
       "      <td>2 bathrooms</td>\n",
       "      <td>17.44402</td>\n",
       "      <td>78.462482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10739</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Somajiguda</td>\n",
       "      <td>15,000</td>\n",
       "      <td>1500</td>\n",
       "      <td>1</td>\n",
       "      <td>2 bathrooms</td>\n",
       "      <td>17.425505</td>\n",
       "      <td>78.45852</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5873 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       seller_type  bedroom  layout_type  property_type          locality  \\\n",
       "0                0        1            1              1   Serilingampally   \n",
       "1                0        5            0              4  Sri Nagar Colony   \n",
       "2                0        2            0              2          Shaikpet   \n",
       "3                0        3            0              0      Nanakramguda   \n",
       "4                0        3            0              0          Kondapur   \n",
       "...            ...      ...          ...            ...               ...   \n",
       "10735            1        1            0              0          Begumpet   \n",
       "10736            1        2            0              0          Ameerpet   \n",
       "10737            1        2            0              0          Ameerpet   \n",
       "10738            1        2            0              0          Begumpet   \n",
       "10739            1        2            0              0        Somajiguda   \n",
       "\n",
       "        price  area  furnish_type     bathroom   Latitude  Longitude  \n",
       "0       6,720   320             0  1 bathrooms  17.484205  78.299509  \n",
       "1      36,000  3000             1  4 bathrooms   17.37688  78.511785  \n",
       "2      11,000   900             1  2 bathrooms  17.405795  78.400654  \n",
       "3      45,000  2165             2  3 bathrooms  17.412983  78.338635  \n",
       "4      20,000  1600             1  3 bathrooms   18.33096  79.922469  \n",
       "...       ...   ...           ...          ...        ...        ...  \n",
       "10735   6,000   850             1  1 bathrooms   17.44402  78.462482  \n",
       "10736  12,000  1300             1  2 bathrooms  17.437501  78.448251  \n",
       "10737  15,000  1200             1  2 bathrooms  17.437501  78.448251  \n",
       "10738  17,000  1300             0  2 bathrooms   17.44402  78.462482  \n",
       "10739  15,000  1500             1  2 bathrooms  17.425505   78.45852  \n",
       "\n",
       "[5873 rows x 11 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "values_to_drop = [np.nan, 'East facing', 'West facing', 'North facing', 'South facing', 'Grfloor',\n",
    "                  'NorthWest facing', '4 of 5floor']\n",
    "\n",
    "# Drop the unwanted values from the 'bathroom' column\n",
    "data = data[~data['bathroom'].isin(values_to_drop)]\n",
    "\n",
    "# Print the modified DataFrame\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "488fe4d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1 bathrooms', '4 bathrooms', '2 bathrooms', '3 bathrooms',\n",
       "       '5 bathrooms', '6 bathrooms', '7 bathrooms', '10 bathrooms',\n",
       "       '12 bathrooms', '8 bathrooms', '9 bathrooms'], dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"bathroom\"].unique()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0cdc7aba",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\python\\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"
     ]
    },
    {
     "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>seller_type</th>\n",
       "      <th>bedroom</th>\n",
       "      <th>layout_type</th>\n",
       "      <th>property_type</th>\n",
       "      <th>locality</th>\n",
       "      <th>price</th>\n",
       "      <th>area</th>\n",
       "      <th>furnish_type</th>\n",
       "      <th>bathroom</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Serilingampally</td>\n",
       "      <td>6,720</td>\n",
       "      <td>320</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>17.484205</td>\n",
       "      <td>78.299509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>Sri Nagar Colony</td>\n",
       "      <td>36,000</td>\n",
       "      <td>3000</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>17.37688</td>\n",
       "      <td>78.511785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Shaikpet</td>\n",
       "      <td>11,000</td>\n",
       "      <td>900</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>17.405795</td>\n",
       "      <td>78.400654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Nanakramguda</td>\n",
       "      <td>45,000</td>\n",
       "      <td>2165</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>17.412983</td>\n",
       "      <td>78.338635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Kondapur</td>\n",
       "      <td>20,000</td>\n",
       "      <td>1600</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>18.33096</td>\n",
       "      <td>79.922469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10735</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Begumpet</td>\n",
       "      <td>6,000</td>\n",
       "      <td>850</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>17.44402</td>\n",
       "      <td>78.462482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10736</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Ameerpet</td>\n",
       "      <td>12,000</td>\n",
       "      <td>1300</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>17.437501</td>\n",
       "      <td>78.448251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10737</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Ameerpet</td>\n",
       "      <td>15,000</td>\n",
       "      <td>1200</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>17.437501</td>\n",
       "      <td>78.448251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10738</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Begumpet</td>\n",
       "      <td>17,000</td>\n",
       "      <td>1300</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>17.44402</td>\n",
       "      <td>78.462482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10739</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Somajiguda</td>\n",
       "      <td>15,000</td>\n",
       "      <td>1500</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>17.425505</td>\n",
       "      <td>78.45852</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5873 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       seller_type  bedroom  layout_type  property_type          locality  \\\n",
       "0                0        1            1              1   Serilingampally   \n",
       "1                0        5            0              4  Sri Nagar Colony   \n",
       "2                0        2            0              2          Shaikpet   \n",
       "3                0        3            0              0      Nanakramguda   \n",
       "4                0        3            0              0          Kondapur   \n",
       "...            ...      ...          ...            ...               ...   \n",
       "10735            1        1            0              0          Begumpet   \n",
       "10736            1        2            0              0          Ameerpet   \n",
       "10737            1        2            0              0          Ameerpet   \n",
       "10738            1        2            0              0          Begumpet   \n",
       "10739            1        2            0              0        Somajiguda   \n",
       "\n",
       "        price  area  furnish_type  bathroom   Latitude  Longitude  \n",
       "0       6,720   320             0         1  17.484205  78.299509  \n",
       "1      36,000  3000             1         4   17.37688  78.511785  \n",
       "2      11,000   900             1         2  17.405795  78.400654  \n",
       "3      45,000  2165             2         3  17.412983  78.338635  \n",
       "4      20,000  1600             1         3   18.33096  79.922469  \n",
       "...       ...   ...           ...       ...        ...        ...  \n",
       "10735   6,000   850             1         1   17.44402  78.462482  \n",
       "10736  12,000  1300             1         2  17.437501  78.448251  \n",
       "10737  15,000  1200             1         2  17.437501  78.448251  \n",
       "10738  17,000  1300             0         2   17.44402  78.462482  \n",
       "10739  15,000  1500             1         2  17.425505   78.45852  \n",
       "\n",
       "[5873 rows x 11 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['bathroom'] = data['bathroom'].str.replace('bathrooms', '').astype(int)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e7e104dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "seller_type        int64\n",
       "bedroom            int64\n",
       "layout_type        int64\n",
       "property_type      int64\n",
       "locality          object\n",
       "price            float64\n",
       "area               int64\n",
       "furnish_type       int64\n",
       "bathroom           int32\n",
       "Latitude         float64\n",
       "Longitude        float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f060290e",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'float' object has no attribute 'replace'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_2668\\40324391.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'price'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m','\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m''\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'price'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'price'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'price'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;31m# convert latitude and longitude columns to float\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Latitude'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Latitude'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_2668\\40324391.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'price'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m','\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m''\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'price'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'price'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'price'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;31m# convert latitude and longitude columns to float\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Latitude'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Latitude'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'float' object has no attribute 'replace'"
     ]
    }
   ],
   "source": [
    "data['price']= [i.replace(',','') for i in data['price']]\n",
    "data['price']=data['price'].astype(float)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9fadebca",
   "metadata": {
    "scrolled": true
   },
   "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>seller_type</th>\n",
       "      <th>bedroom</th>\n",
       "      <th>layout_type</th>\n",
       "      <th>property_type</th>\n",
       "      <th>locality</th>\n",
       "      <th>price</th>\n",
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       "      <td>Serilingampally</td>\n",
       "      <td>6720.0</td>\n",
       "      <td>320</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>4</td>\n",
       "      <td>Sri Nagar Colony</td>\n",
       "      <td>36000.0</td>\n",
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       "      <td>4</td>\n",
       "      <td>17.376880</td>\n",
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       "      <td>2</td>\n",
       "      <td>Shaikpet</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>900</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>Nanakramguda</td>\n",
       "      <td>45000.0</td>\n",
       "      <td>2165</td>\n",
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       "      <td>3</td>\n",
       "      <td>17.412983</td>\n",
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       "      <td>Kondapur</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>1600</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>18.330960</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5550</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Begumpet</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>850</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>17.444020</td>\n",
       "      <td>78.462482</td>\n",
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       "    <tr>\n",
       "      <th>5551</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Ameerpet</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>1300</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>17.437501</td>\n",
       "      <td>78.448251</td>\n",
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       "    <tr>\n",
       "      <th>5552</th>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>Ameerpet</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>1200</td>\n",
       "      <td>1</td>\n",
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       "      <td>17.437501</td>\n",
       "      <td>78.448251</td>\n",
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       "    <tr>\n",
       "      <th>5553</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Begumpet</td>\n",
       "      <td>17000.0</td>\n",
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       "      <td>17.444020</td>\n",
       "      <td>78.462482</td>\n",
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       "      <th>5554</th>\n",
       "      <td>1</td>\n",
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       "      <td>Somajiguda</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>1500</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>17.425505</td>\n",
       "      <td>78.458520</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5555 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      seller_type  bedroom  layout_type  property_type          locality  \\\n",
       "0               0        1            1              1   Serilingampally   \n",
       "1               0        5            0              4  Sri Nagar Colony   \n",
       "2               0        2            0              2          Shaikpet   \n",
       "3               0        3            0              0      Nanakramguda   \n",
       "4               0        3            0              0          Kondapur   \n",
       "...           ...      ...          ...            ...               ...   \n",
       "5550            1        1            0              0          Begumpet   \n",
       "5551            1        2            0              0          Ameerpet   \n",
       "5552            1        2            0              0          Ameerpet   \n",
       "5553            1        2            0              0          Begumpet   \n",
       "5554            1        2            0              0        Somajiguda   \n",
       "\n",
       "        price  area  furnish_type  bathroom   Latitude  Longitude  \n",
       "0      6720.0   320             0         1  17.484205  78.299509  \n",
       "1     36000.0  3000             1         4  17.376880  78.511785  \n",
       "2     11000.0   900             1         2  17.405795  78.400654  \n",
       "3     45000.0  2165             2         3  17.412983  78.338635  \n",
       "4     20000.0  1600             1         3  18.330960  79.922469  \n",
       "...       ...   ...           ...       ...        ...        ...  \n",
       "5550   6000.0   850             1         1  17.444020  78.462482  \n",
       "5551  12000.0  1300             1         2  17.437501  78.448251  \n",
       "5552  15000.0  1200             1         2  17.437501  78.448251  \n",
       "5553  17000.0  1300             0         2  17.444020  78.462482  \n",
       "5554  15000.0  1500             1         2  17.425505  78.458520  \n",
       "\n",
       "[5555 rows x 11 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert latitude and longitude columns to float\n",
    "data['Latitude'] = data['Latitude'].astype(float)\n",
    "data['Longitude'] = data['Longitude'].astype(float)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "2d6cde3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['seller_type', 'bedroom', 'layout_type', 'property_type', 'locality',\n",
       "       'price', 'area', 'furnish_type', 'bathroom', 'Latitude', 'Longitude'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "2a36902d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2: 0.9997499516819492\n",
      "Mean absolute error: 76.17372902948678\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.ensemble import RandomForestRegressor,VotingRegressor\n",
    "import xgboost as xgb\n",
    "import math\n",
    "from sklearn.metrics import mean_absolute_error,r2_score\n",
    "\n",
    "\n",
    "X = data.drop(['price','locality'],axis=1).values\n",
    "y = data[['price']].values\n",
    "y=y.ravel()\n",
    "\n",
    "# Set max_features to the square root of the number of features\n",
    "max_features = int(math.sqrt(X.shape[1]))\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=84)\n",
    "\n",
    "# Define models\n",
    "rf_model = RandomForestRegressor()\n",
    "xgb_model = xgb.XGBRegressor()\n",
    "\n",
    "\n",
    "# Ensemble models\n",
    "# Combine the models using voting ensemble\n",
    "ensemble_model1 = VotingRegressor(estimators=[('rf', rf_model), ('gb', xgb_model)])\n",
    "ensemble_model1.fit(X, y)\n",
    "\n",
    "y_pred = ensemble_model1.predict(X_test)\n",
    "y_pred\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "print(\"r2:\",r2)\n",
    "\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "print(\"Mean absolute error:\", mae)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "f0539055",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 1, 'Chandanagar', 7000.0, 400, 2, 1, 17.4872976,\n",
       "       78.3322142, 0.05714285714285714], dtype=object)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[4934].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "b61b11ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6975.260986328125\n"
     ]
    }
   ],
   "source": [
    "y_pred = ensemble_model1.predict([[0, 1, 1, 1,400, 2, 1, 17.4872976,\n",
    "       78.3322142, 0.05714285714285714]])\n",
    "print(y_pred[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "a826896e",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th>bedroom</th>\n",
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       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>Sri Nagar Colony</td>\n",
       "      <td>36000.0</td>\n",
       "      <td>3000</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>17.376880</td>\n",
       "      <td>78.511785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Shaikpet</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>900</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>17.405795</td>\n",
       "      <td>78.400654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Kondapur</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>1600</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>18.330960</td>\n",
       "      <td>79.922469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>manneguda</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>600</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>19.321602</td>\n",
       "      <td>79.442514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5550</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Begumpet</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>850</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>17.444020</td>\n",
       "      <td>78.462482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5551</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Ameerpet</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>1300</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>17.437501</td>\n",
       "      <td>78.448251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5552</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Ameerpet</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>1200</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>17.437501</td>\n",
       "      <td>78.448251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5553</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Begumpet</td>\n",
       "      <td>17000.0</td>\n",
       "      <td>1300</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>17.444020</td>\n",
       "      <td>78.462482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5554</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Somajiguda</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>1500</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>17.425505</td>\n",
       "      <td>78.458520</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5315 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      seller_type  bedroom  layout_type  property_type          locality  \\\n",
       "0               0        1            1              1   Serilingampally   \n",
       "1               0        5            0              4  Sri Nagar Colony   \n",
       "2               0        2            0              2          Shaikpet   \n",
       "4               0        3            0              0          Kondapur   \n",
       "5               0        1            0              2         manneguda   \n",
       "...           ...      ...          ...            ...               ...   \n",
       "5550            1        1            0              0          Begumpet   \n",
       "5551            1        2            0              0          Ameerpet   \n",
       "5552            1        2            0              0          Ameerpet   \n",
       "5553            1        2            0              0          Begumpet   \n",
       "5554            1        2            0              0        Somajiguda   \n",
       "\n",
       "        price  area  furnish_type  bathroom   Latitude  Longitude  \n",
       "0      6720.0   320             0         1  17.484205  78.299509  \n",
       "1     36000.0  3000             1         4  17.376880  78.511785  \n",
       "2     11000.0   900             1         2  17.405795  78.400654  \n",
       "4     20000.0  1600             1         3  18.330960  79.922469  \n",
       "5      5000.0   600             2         1  19.321602  79.442514  \n",
       "...       ...   ...           ...       ...        ...        ...  \n",
       "5550   6000.0   850             1         1  17.444020  78.462482  \n",
       "5551  12000.0  1300             1         2  17.437501  78.448251  \n",
       "5552  15000.0  1200             1         2  17.437501  78.448251  \n",
       "5553  17000.0  1300             0         2  17.444020  78.462482  \n",
       "5554  15000.0  1500             1         2  17.425505  78.458520  \n",
       "\n",
       "[5315 rows x 11 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "desc = data['price'].describe()\n",
    "q1 = desc.loc['25%']\n",
    "q3 = desc.loc['75%']\n",
    "iqr = q3-q1 \n",
    "lower_lim = q1-(1.5*iqr)\n",
    "upper_lim = q3+(1.5*iqr)\n",
    "data = data[(data['price']>=lower_lim)&(data['price']<=upper_lim)]\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "c85d32de",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\python\\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"
     ]
    },
    {
     "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>seller_type</th>\n",
       "      <th>bedroom</th>\n",
       "      <th>layout_type</th>\n",
       "      <th>property_type</th>\n",
       "      <th>locality</th>\n",
       "      <th>price</th>\n",
       "      <th>area</th>\n",
       "      <th>furnish_type</th>\n",
       "      <th>bathroom</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>affordability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Serilingampally</td>\n",
       "      <td>6720.0</td>\n",
       "      <td>320</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>17.484205</td>\n",
       "      <td>78.299509</td>\n",
       "      <td>0.047619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>Sri Nagar Colony</td>\n",
       "      <td>36000.0</td>\n",
       "      <td>3000</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>17.376880</td>\n",
       "      <td>78.511785</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Shaikpet</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>900</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>17.405795</td>\n",
       "      <td>78.400654</td>\n",
       "      <td>0.081818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Kondapur</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>1600</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>18.330960</td>\n",
       "      <td>79.922469</td>\n",
       "      <td>0.080000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>manneguda</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>600</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>19.321602</td>\n",
       "      <td>79.442514</td>\n",
       "      <td>0.120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5550</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Begumpet</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>850</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>17.444020</td>\n",
       "      <td>78.462482</td>\n",
       "      <td>0.141667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5551</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Ameerpet</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>1300</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>17.437501</td>\n",
       "      <td>78.448251</td>\n",
       "      <td>0.108333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5552</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Ameerpet</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>1200</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>17.437501</td>\n",
       "      <td>78.448251</td>\n",
       "      <td>0.080000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5553</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Begumpet</td>\n",
       "      <td>17000.0</td>\n",
       "      <td>1300</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>17.444020</td>\n",
       "      <td>78.462482</td>\n",
       "      <td>0.076471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5554</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Somajiguda</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>1500</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>17.425505</td>\n",
       "      <td>78.458520</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5315 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      seller_type  bedroom  layout_type  property_type          locality  \\\n",
       "0               0        1            1              1   Serilingampally   \n",
       "1               0        5            0              4  Sri Nagar Colony   \n",
       "2               0        2            0              2          Shaikpet   \n",
       "4               0        3            0              0          Kondapur   \n",
       "5               0        1            0              2         manneguda   \n",
       "...           ...      ...          ...            ...               ...   \n",
       "5550            1        1            0              0          Begumpet   \n",
       "5551            1        2            0              0          Ameerpet   \n",
       "5552            1        2            0              0          Ameerpet   \n",
       "5553            1        2            0              0          Begumpet   \n",
       "5554            1        2            0              0        Somajiguda   \n",
       "\n",
       "        price  area  furnish_type  bathroom   Latitude  Longitude  \\\n",
       "0      6720.0   320             0         1  17.484205  78.299509   \n",
       "1     36000.0  3000             1         4  17.376880  78.511785   \n",
       "2     11000.0   900             1         2  17.405795  78.400654   \n",
       "4     20000.0  1600             1         3  18.330960  79.922469   \n",
       "5      5000.0   600             2         1  19.321602  79.442514   \n",
       "...       ...   ...           ...       ...        ...        ...   \n",
       "5550   6000.0   850             1         1  17.444020  78.462482   \n",
       "5551  12000.0  1300             1         2  17.437501  78.448251   \n",
       "5552  15000.0  1200             1         2  17.437501  78.448251   \n",
       "5553  17000.0  1300             0         2  17.444020  78.462482   \n",
       "5554  15000.0  1500             1         2  17.425505  78.458520   \n",
       "\n",
       "      affordability  \n",
       "0          0.047619  \n",
       "1          0.083333  \n",
       "2          0.081818  \n",
       "4          0.080000  \n",
       "5          0.120000  \n",
       "...             ...  \n",
       "5550       0.141667  \n",
       "5551       0.108333  \n",
       "5552       0.080000  \n",
       "5553       0.076471  \n",
       "5554       0.100000  \n",
       "\n",
       "[5315 rows x 12 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['affordability'] = data['area']/data['price']\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ba91a82",
   "metadata": {},
   "outputs": [],
   "source": [
    "#rent price prediction in all 6 cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1f1486f",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "file_paths = ['Hyderabad_rent.csv', 'Chennai_rent.csv', 'Kolkata_rent.csv','Mumbai_rent.csv','Delhi_rent.csv','Bangalore_rent.csv']\n",
    "for file_path in file_paths:\n",
    "    df = pd.read_csv(file_path)\n",
    "    \n",
    "    from geopy.geocoders import Nominatim  # Adding the latitute and longitute for the places\n",
    "    location=Nominatim(user_agent=\"http\")\n",
    "\n",
    "    def get_location_code(x):\n",
    "        try:\n",
    "            cordinates=location.geocode(x, country_codes='INR')\n",
    "            return cordinates.latitude, cordinates.longitude  \n",
    "        except:\n",
    "            return \"Not found\", \"Not found\"\n",
    "\n",
    "    # Finding latitude and longitude for unique loactions only to speed up the execution\n",
    "    unique_location=df['locality'].unique()\n",
    "    location_to_code={}\n",
    "    for place in unique_location:\n",
    "        location_to_code[place]=get_location_code(place) # This can a while, for me it took 15 mins. Please save this file.\n",
    "\n",
    "    # Creating two new columns having latitude and longitude\n",
    "    df['Latitude'],df['Longitude']=zip(*df['locality'].map(location_to_code))\n",
    "    \n",
    "    df.to_csv(f\"final_{file_path}\", index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "970d2297",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import KFold, cross_val_score\n",
    "from sklearn.ensemble import RandomForestRegressor,VotingRegressor\n",
    "import xgboost as xgb\n",
    "import math\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score,mean_absolute_error\n",
    "\n",
    "file_paths = ['final_Hyderabad_rent.csv', 'final_Chennai_rent.csv', 'final_Kolkata_rent.csv','final_Mumbai_rent.csv','final_Delhi_rent.csv','final_Bangalore_rent.csv']\n",
    "for file_path in file_paths:\n",
    "    df = pd.read_csv(file_path)\n",
    "    \n",
    "    #mapping\n",
    "    df[\"seller_type\"]=df[\"seller_type\"].map({'OWNER':0,'AGENT':1,'BUILDER':2})\n",
    "    df[\"layout_type\"]=df[\"layout_type\"].map({'BHK':0,'RK':1})\n",
    "    df[\"property_type\"]=df[\"property_type\"].map({'Apartment':0,'Studio Apartment':1,'Independent House':2,'Independent Floor':3,'Villa':4,'Penthouse':5})\n",
    "    df[\"furnish_type\"]=df[\"furnish_type\"].map({'Furnished':0,'Semi-Furnished':1,'Unfurnished':2})\n",
    "    \n",
    "\n",
    "    # Removing the 'Not found' records\n",
    "    df=df[df['Latitude']!='Not found'].reset_index(drop=True)\n",
    "    \n",
    "    import numpy as np\n",
    "\n",
    "    #values_to_drop = [np.nan, 'East facing', 'West facing', 'North facing', 'South facing', 'Grfloor','NorthWest facing','NorthEast facing','SouthWest facing','SouthEast facing','4 of 5floor']\n",
    "    values_to_need=['2 bathrooms', '1 bathrooms', '3 bathrooms', '6 bathrooms',\n",
    "       '5 bathrooms', '4 bathrooms', '8 bathrooms', '7 bathrooms',\n",
    "       '9 bathrooms','12 bathrooms']\n",
    "\n",
    "    # Drop the unwanted values from the 'bathroom' column\n",
    "    df = df[df['bathroom'].isin(values_to_need)]\n",
    "\n",
    "    df['bathroom'] = df['bathroom'].str.replace('bathrooms', '').astype(int)\n",
    "    \n",
    "    df['price']= [i.replace(',','') for i in df['price']]\n",
    "    df['price']=df['price'].astype(float)\n",
    "    \n",
    "    # convert latitude and longitude columns to float\n",
    "    df['Latitude'] = df['Latitude'].astype(float)\n",
    "    df['Longitude'] = df['Longitude'].astype(float)\n",
    "    \n",
    "    #removing outliers\n",
    "    desc = df['price'].describe()\n",
    "    q1 = desc.loc['25%']\n",
    "    q3 = desc.loc['75%']\n",
    "    iqr = q3-q1 \n",
    "    lower_lim = q1-(1.5*iqr)\n",
    "    upper_lim = q3+(1.5*iqr)\n",
    "    df = df[(df['price']>=lower_lim)&(df['price']<=upper_lim)]\n",
    "    \n",
    "    \n",
    "    import pandas as pd\n",
    "    import numpy as np\n",
    "    from sklearn.model_selection import KFold, cross_val_score\n",
    "    from sklearn.ensemble import RandomForestRegressor,VotingRegressor\n",
    "    import xgboost as xgb\n",
    "    import math\n",
    "\n",
    "    X = df.drop(['price','locality'],axis=1).values\n",
    "    y = df[['price']].values\n",
    "    y=y.ravel()\n",
    "\n",
    "    # Set max_features to the square root of the number of features\n",
    "    max_features = int(math.sqrt(X.shape[1]))\n",
    "\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=84)\n",
    "\n",
    "    # Define models\n",
    "    rf_model = RandomForestRegressor(n_estimators=1000, random_state=84,max_depth=20, min_samples_split=20, min_samples_leaf=10, max_features=max_features)\n",
    "    xgb_model = xgb.XGBRegressor(n_estimators=800, random_state=84)\n",
    "\n",
    "\n",
    "    # Ensemble models\n",
    "    # Combine the models using voting ensemble\n",
    "    ensemble_model1 = VotingRegressor(estimators=[('rf', rf_model), ('gb', xgb_model)])\n",
    "    ensemble_model1.fit(X_train, y_train)\n",
    "\n",
    "    y_pred = ensemble_model1.predict(X_test)\n",
    "    y_pred\n",
    "    r2 = r2_score(y_test, y_pred)\n",
    "    print(f\"r2 of {file_path}:\",r2)\n",
    " \n",
    "    mae = mean_absolute_error(y_test, y_pred)\n",
    "    print(f\"Mean absolute error of {file_path}:\", mae)\n",
    "    \n",
    "    #import joblib\n",
    "    #joblib.dump(ensemble_model1,f'{file_path}.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "50a192dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2 bathrooms', '1 bathrooms', '3 bathrooms', '6 bathrooms',\n",
       "       '5 bathrooms', '4 bathrooms', '8 bathrooms', '7 bathrooms',\n",
       "       '9 bathrooms', 'availability immediately', '2 of 7floor',\n",
       "       '7 of 12floor', 'Family only', '2 of 4floor', '17 of 29floor',\n",
       "       '4 of 7floor', '17 of 25floor', '6 of 7floor', '11 of 17floor',\n",
       "       '15 of 28floor', '5 of 14floor', '6 of 8floor', '9 of 12floor',\n",
       "       '3 of 7floor', '1 of 4floor', '12 of 16floor', '10 of 16floor',\n",
       "       '3 of 3floor'], dtype=object)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"bathroom\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "dbba9103",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['seller_type', 'bedroom', 'layout_type', 'property_type', 'locality',\n",
       "       'price', 'area', 'furnish_type', 'bathroom', 'Latitude', 'Longitude',\n",
       "       'affordability'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "def17432",
   "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>seller_type</th>\n",
       "      <th>bedroom</th>\n",
       "      <th>layout_type</th>\n",
       "      <th>property_type</th>\n",
       "      <th>locality</th>\n",
       "      <th>price</th>\n",
       "      <th>area</th>\n",
       "      <th>furnish_type</th>\n",
       "      <th>bathroom</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>affordability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>OWNER</td>\n",
       "      <td>1</td>\n",
       "      <td>RK</td>\n",
       "      <td>Studio Apartment</td>\n",
       "      <td>Serilingampally</td>\n",
       "      <td>6720.0</td>\n",
       "      <td>320</td>\n",
       "      <td>Furnished</td>\n",
       "      <td>1 bathrooms</td>\n",
       "      <td>17.4842051</td>\n",
       "      <td>78.2995091</td>\n",
       "      <td>0.047619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OWNER</td>\n",
       "      <td>5</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Villa</td>\n",
       "      <td>Sri Nagar Colony</td>\n",
       "      <td>36000.0</td>\n",
       "      <td>3000</td>\n",
       "      <td>Semi-Furnished</td>\n",
       "      <td>4 bathrooms</td>\n",
       "      <td>17.3768805</td>\n",
       "      <td>78.5117854</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>OWNER</td>\n",
       "      <td>2</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Independent House</td>\n",
       "      <td>Shaikpet</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>900</td>\n",
       "      <td>Semi-Furnished</td>\n",
       "      <td>2 bathrooms</td>\n",
       "      <td>17.4057955</td>\n",
       "      <td>78.4006536</td>\n",
       "      <td>0.081818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OWNER</td>\n",
       "      <td>3</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Nanakramguda</td>\n",
       "      <td>45000.0</td>\n",
       "      <td>2165</td>\n",
       "      <td>Unfurnished</td>\n",
       "      <td>3 bathrooms</td>\n",
       "      <td>17.4129831</td>\n",
       "      <td>78.338635</td>\n",
       "      <td>0.048111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OWNER</td>\n",
       "      <td>3</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Kondapur</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>1600</td>\n",
       "      <td>Semi-Furnished</td>\n",
       "      <td>3 bathrooms</td>\n",
       "      <td>18.3309604</td>\n",
       "      <td>79.9224686</td>\n",
       "      <td>0.080000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10752</th>\n",
       "      <td>AGENT</td>\n",
       "      <td>1</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Begumpet</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>850</td>\n",
       "      <td>Semi-Furnished</td>\n",
       "      <td>1 bathrooms</td>\n",
       "      <td>17.4440199</td>\n",
       "      <td>78.4624821</td>\n",
       "      <td>0.141667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10753</th>\n",
       "      <td>AGENT</td>\n",
       "      <td>2</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Ameerpet</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>1300</td>\n",
       "      <td>Semi-Furnished</td>\n",
       "      <td>2 bathrooms</td>\n",
       "      <td>17.4375012</td>\n",
       "      <td>78.4482505</td>\n",
       "      <td>0.108333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10754</th>\n",
       "      <td>AGENT</td>\n",
       "      <td>2</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Ameerpet</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>1200</td>\n",
       "      <td>Semi-Furnished</td>\n",
       "      <td>2 bathrooms</td>\n",
       "      <td>17.4375012</td>\n",
       "      <td>78.4482505</td>\n",
       "      <td>0.080000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10755</th>\n",
       "      <td>AGENT</td>\n",
       "      <td>2</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Begumpet</td>\n",
       "      <td>17000.0</td>\n",
       "      <td>1300</td>\n",
       "      <td>Furnished</td>\n",
       "      <td>2 bathrooms</td>\n",
       "      <td>17.4440199</td>\n",
       "      <td>78.4624821</td>\n",
       "      <td>0.076471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10756</th>\n",
       "      <td>AGENT</td>\n",
       "      <td>2</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Somajiguda</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>1500</td>\n",
       "      <td>Semi-Furnished</td>\n",
       "      <td>2 bathrooms</td>\n",
       "      <td>17.4255053</td>\n",
       "      <td>78.4585197</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10757 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      seller_type  bedroom layout_type      property_type          locality  \\\n",
       "0           OWNER        1          RK   Studio Apartment   Serilingampally   \n",
       "1           OWNER        5         BHK              Villa  Sri Nagar Colony   \n",
       "2           OWNER        2         BHK  Independent House          Shaikpet   \n",
       "3           OWNER        3         BHK          Apartment      Nanakramguda   \n",
       "4           OWNER        3         BHK          Apartment          Kondapur   \n",
       "...           ...      ...         ...                ...               ...   \n",
       "10752       AGENT        1         BHK          Apartment          Begumpet   \n",
       "10753       AGENT        2         BHK          Apartment          Ameerpet   \n",
       "10754       AGENT        2         BHK          Apartment          Ameerpet   \n",
       "10755       AGENT        2         BHK          Apartment          Begumpet   \n",
       "10756       AGENT        2         BHK          Apartment        Somajiguda   \n",
       "\n",
       "         price  area    furnish_type     bathroom    Latitude   Longitude  \\\n",
       "0       6720.0   320       Furnished  1 bathrooms  17.4842051  78.2995091   \n",
       "1      36000.0  3000  Semi-Furnished  4 bathrooms  17.3768805  78.5117854   \n",
       "2      11000.0   900  Semi-Furnished  2 bathrooms  17.4057955  78.4006536   \n",
       "3      45000.0  2165     Unfurnished  3 bathrooms  17.4129831   78.338635   \n",
       "4      20000.0  1600  Semi-Furnished  3 bathrooms  18.3309604  79.9224686   \n",
       "...        ...   ...             ...          ...         ...         ...   \n",
       "10752   6000.0   850  Semi-Furnished  1 bathrooms  17.4440199  78.4624821   \n",
       "10753  12000.0  1300  Semi-Furnished  2 bathrooms  17.4375012  78.4482505   \n",
       "10754  15000.0  1200  Semi-Furnished  2 bathrooms  17.4375012  78.4482505   \n",
       "10755  17000.0  1300       Furnished  2 bathrooms  17.4440199  78.4624821   \n",
       "10756  15000.0  1500  Semi-Furnished  2 bathrooms  17.4255053  78.4585197   \n",
       "\n",
       "       affordability  \n",
       "0           0.047619  \n",
       "1           0.083333  \n",
       "2           0.081818  \n",
       "3           0.048111  \n",
       "4           0.080000  \n",
       "...              ...  \n",
       "10752       0.141667  \n",
       "10753       0.108333  \n",
       "10754       0.080000  \n",
       "10755       0.076471  \n",
       "10756       0.100000  \n",
       "\n",
       "[10757 rows x 12 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df1=pd.read_csv('final_Hyderabad_rent.csv')\n",
    "df1['price']= [i.replace(',','') for i in df1['price']]\n",
    "df1['price']=df1['price'].astype(float)\n",
    "    \n",
    "df1['affordability'] = df1['area']/df1['price']\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "6462841e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .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>seller_type</th>\n",
       "      <th>bedroom</th>\n",
       "      <th>layout_type</th>\n",
       "      <th>property_type</th>\n",
       "      <th>locality</th>\n",
       "      <th>price</th>\n",
       "      <th>area</th>\n",
       "      <th>furnish_type</th>\n",
       "      <th>bathroom</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>OWNER</td>\n",
       "      <td>1</td>\n",
       "      <td>RK</td>\n",
       "      <td>Studio Apartment</td>\n",
       "      <td>Serilingampally</td>\n",
       "      <td>6,720</td>\n",
       "      <td>320</td>\n",
       "      <td>Furnished</td>\n",
       "      <td>1 bathrooms</td>\n",
       "      <td>17.4842051</td>\n",
       "      <td>78.2995091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OWNER</td>\n",
       "      <td>5</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Villa</td>\n",
       "      <td>Sri Nagar Colony</td>\n",
       "      <td>36,000</td>\n",
       "      <td>3000</td>\n",
       "      <td>Semi-Furnished</td>\n",
       "      <td>4 bathrooms</td>\n",
       "      <td>17.3768805</td>\n",
       "      <td>78.5117854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>OWNER</td>\n",
       "      <td>2</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Independent House</td>\n",
       "      <td>Shaikpet</td>\n",
       "      <td>11,000</td>\n",
       "      <td>900</td>\n",
       "      <td>Semi-Furnished</td>\n",
       "      <td>2 bathrooms</td>\n",
       "      <td>17.4057955</td>\n",
       "      <td>78.4006536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OWNER</td>\n",
       "      <td>3</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Nanakramguda</td>\n",
       "      <td>45,000</td>\n",
       "      <td>2165</td>\n",
       "      <td>Unfurnished</td>\n",
       "      <td>3 bathrooms</td>\n",
       "      <td>17.4129831</td>\n",
       "      <td>78.338635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OWNER</td>\n",
       "      <td>3</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Kondapur</td>\n",
       "      <td>20,000</td>\n",
       "      <td>1600</td>\n",
       "      <td>Semi-Furnished</td>\n",
       "      <td>3 bathrooms</td>\n",
       "      <td>18.3309604</td>\n",
       "      <td>79.9224686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10752</th>\n",
       "      <td>AGENT</td>\n",
       "      <td>1</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Begumpet</td>\n",
       "      <td>6,000</td>\n",
       "      <td>850</td>\n",
       "      <td>Semi-Furnished</td>\n",
       "      <td>1 bathrooms</td>\n",
       "      <td>17.4440199</td>\n",
       "      <td>78.4624821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10753</th>\n",
       "      <td>AGENT</td>\n",
       "      <td>2</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Ameerpet</td>\n",
       "      <td>12,000</td>\n",
       "      <td>1300</td>\n",
       "      <td>Semi-Furnished</td>\n",
       "      <td>2 bathrooms</td>\n",
       "      <td>17.4375012</td>\n",
       "      <td>78.4482505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10754</th>\n",
       "      <td>AGENT</td>\n",
       "      <td>2</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Ameerpet</td>\n",
       "      <td>15,000</td>\n",
       "      <td>1200</td>\n",
       "      <td>Semi-Furnished</td>\n",
       "      <td>2 bathrooms</td>\n",
       "      <td>17.4375012</td>\n",
       "      <td>78.4482505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10755</th>\n",
       "      <td>AGENT</td>\n",
       "      <td>2</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Begumpet</td>\n",
       "      <td>17,000</td>\n",
       "      <td>1300</td>\n",
       "      <td>Furnished</td>\n",
       "      <td>2 bathrooms</td>\n",
       "      <td>17.4440199</td>\n",
       "      <td>78.4624821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10756</th>\n",
       "      <td>AGENT</td>\n",
       "      <td>2</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Somajiguda</td>\n",
       "      <td>15,000</td>\n",
       "      <td>1500</td>\n",
       "      <td>Semi-Furnished</td>\n",
       "      <td>2 bathrooms</td>\n",
       "      <td>17.4255053</td>\n",
       "      <td>78.4585197</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10757 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      seller_type  bedroom layout_type      property_type          locality  \\\n",
       "0           OWNER        1          RK   Studio Apartment   Serilingampally   \n",
       "1           OWNER        5         BHK              Villa  Sri Nagar Colony   \n",
       "2           OWNER        2         BHK  Independent House          Shaikpet   \n",
       "3           OWNER        3         BHK          Apartment      Nanakramguda   \n",
       "4           OWNER        3         BHK          Apartment          Kondapur   \n",
       "...           ...      ...         ...                ...               ...   \n",
       "10752       AGENT        1         BHK          Apartment          Begumpet   \n",
       "10753       AGENT        2         BHK          Apartment          Ameerpet   \n",
       "10754       AGENT        2         BHK          Apartment          Ameerpet   \n",
       "10755       AGENT        2         BHK          Apartment          Begumpet   \n",
       "10756       AGENT        2         BHK          Apartment        Somajiguda   \n",
       "\n",
       "        price  area    furnish_type     bathroom    Latitude   Longitude  \n",
       "0       6,720   320       Furnished  1 bathrooms  17.4842051  78.2995091  \n",
       "1      36,000  3000  Semi-Furnished  4 bathrooms  17.3768805  78.5117854  \n",
       "2      11,000   900  Semi-Furnished  2 bathrooms  17.4057955  78.4006536  \n",
       "3      45,000  2165     Unfurnished  3 bathrooms  17.4129831   78.338635  \n",
       "4      20,000  1600  Semi-Furnished  3 bathrooms  18.3309604  79.9224686  \n",
       "...       ...   ...             ...          ...         ...         ...  \n",
       "10752   6,000   850  Semi-Furnished  1 bathrooms  17.4440199  78.4624821  \n",
       "10753  12,000  1300  Semi-Furnished  2 bathrooms  17.4375012  78.4482505  \n",
       "10754  15,000  1200  Semi-Furnished  2 bathrooms  17.4375012  78.4482505  \n",
       "10755  17,000  1300       Furnished  2 bathrooms  17.4440199  78.4624821  \n",
       "10756  15,000  1500  Semi-Furnished  2 bathrooms  17.4255053  78.4585197  \n",
       "\n",
       "[10757 rows x 11 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df=pd.read_csv('final_Hyderabad_rent.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "32e2f869",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
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       "        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>seller_type</th>\n",
       "      <th>bedroom</th>\n",
       "      <th>layout_type</th>\n",
       "      <th>property_type</th>\n",
       "      <th>locality</th>\n",
       "      <th>price</th>\n",
       "      <th>area</th>\n",
       "      <th>furnish_type</th>\n",
       "      <th>bathroom</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>OWNER</td>\n",
       "      <td>1</td>\n",
       "      <td>RK</td>\n",
       "      <td>Studio Apartment</td>\n",
       "      <td>Serilingampally</td>\n",
       "      <td>6,720</td>\n",
       "      <td>320</td>\n",
       "      <td>Furnished</td>\n",
       "      <td>1 bathrooms</td>\n",
       "      <td>17.484205</td>\n",
       "      <td>78.299509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OWNER</td>\n",
       "      <td>5</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Villa</td>\n",
       "      <td>Sri Nagar Colony</td>\n",
       "      <td>36,000</td>\n",
       "      <td>3000</td>\n",
       "      <td>Semi-Furnished</td>\n",
       "      <td>4 bathrooms</td>\n",
       "      <td>17.376880</td>\n",
       "      <td>78.511785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>OWNER</td>\n",
       "      <td>2</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Independent House</td>\n",
       "      <td>Shaikpet</td>\n",
       "      <td>11,000</td>\n",
       "      <td>900</td>\n",
       "      <td>Semi-Furnished</td>\n",
       "      <td>2 bathrooms</td>\n",
       "      <td>17.405795</td>\n",
       "      <td>78.400654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OWNER</td>\n",
       "      <td>3</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Nanakramguda</td>\n",
       "      <td>45,000</td>\n",
       "      <td>2165</td>\n",
       "      <td>Unfurnished</td>\n",
       "      <td>3 bathrooms</td>\n",
       "      <td>17.412983</td>\n",
       "      <td>78.338635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OWNER</td>\n",
       "      <td>3</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Kondapur</td>\n",
       "      <td>20,000</td>\n",
       "      <td>1600</td>\n",
       "      <td>Semi-Furnished</td>\n",
       "      <td>3 bathrooms</td>\n",
       "      <td>18.330960</td>\n",
       "      <td>79.922469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10241</th>\n",
       "      <td>AGENT</td>\n",
       "      <td>1</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Begumpet</td>\n",
       "      <td>6,000</td>\n",
       "      <td>850</td>\n",
       "      <td>Semi-Furnished</td>\n",
       "      <td>1 bathrooms</td>\n",
       "      <td>17.444020</td>\n",
       "      <td>78.462482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10242</th>\n",
       "      <td>AGENT</td>\n",
       "      <td>2</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Ameerpet</td>\n",
       "      <td>12,000</td>\n",
       "      <td>1300</td>\n",
       "      <td>Semi-Furnished</td>\n",
       "      <td>2 bathrooms</td>\n",
       "      <td>17.437501</td>\n",
       "      <td>78.448251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10243</th>\n",
       "      <td>AGENT</td>\n",
       "      <td>2</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Ameerpet</td>\n",
       "      <td>15,000</td>\n",
       "      <td>1200</td>\n",
       "      <td>Semi-Furnished</td>\n",
       "      <td>2 bathrooms</td>\n",
       "      <td>17.437501</td>\n",
       "      <td>78.448251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10244</th>\n",
       "      <td>AGENT</td>\n",
       "      <td>2</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Begumpet</td>\n",
       "      <td>17,000</td>\n",
       "      <td>1300</td>\n",
       "      <td>Furnished</td>\n",
       "      <td>2 bathrooms</td>\n",
       "      <td>17.444020</td>\n",
       "      <td>78.462482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10245</th>\n",
       "      <td>AGENT</td>\n",
       "      <td>2</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Somajiguda</td>\n",
       "      <td>15,000</td>\n",
       "      <td>1500</td>\n",
       "      <td>Semi-Furnished</td>\n",
       "      <td>2 bathrooms</td>\n",
       "      <td>17.425505</td>\n",
       "      <td>78.458520</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10246 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      seller_type  bedroom layout_type      property_type          locality  \\\n",
       "0           OWNER        1          RK   Studio Apartment   Serilingampally   \n",
       "1           OWNER        5         BHK              Villa  Sri Nagar Colony   \n",
       "2           OWNER        2         BHK  Independent House          Shaikpet   \n",
       "3           OWNER        3         BHK          Apartment      Nanakramguda   \n",
       "4           OWNER        3         BHK          Apartment          Kondapur   \n",
       "...           ...      ...         ...                ...               ...   \n",
       "10241       AGENT        1         BHK          Apartment          Begumpet   \n",
       "10242       AGENT        2         BHK          Apartment          Ameerpet   \n",
       "10243       AGENT        2         BHK          Apartment          Ameerpet   \n",
       "10244       AGENT        2         BHK          Apartment          Begumpet   \n",
       "10245       AGENT        2         BHK          Apartment        Somajiguda   \n",
       "\n",
       "        price  area    furnish_type     bathroom   Latitude  Longitude  \n",
       "0       6,720   320       Furnished  1 bathrooms  17.484205  78.299509  \n",
       "1      36,000  3000  Semi-Furnished  4 bathrooms  17.376880  78.511785  \n",
       "2      11,000   900  Semi-Furnished  2 bathrooms  17.405795  78.400654  \n",
       "3      45,000  2165     Unfurnished  3 bathrooms  17.412983  78.338635  \n",
       "4      20,000  1600  Semi-Furnished  3 bathrooms  18.330960  79.922469  \n",
       "...       ...   ...             ...          ...        ...        ...  \n",
       "10241   6,000   850  Semi-Furnished  1 bathrooms  17.444020  78.462482  \n",
       "10242  12,000  1300  Semi-Furnished  2 bathrooms  17.437501  78.448251  \n",
       "10243  15,000  1200  Semi-Furnished  2 bathrooms  17.437501  78.448251  \n",
       "10244  17,000  1300       Furnished  2 bathrooms  17.444020  78.462482  \n",
       "10245  15,000  1500  Semi-Furnished  2 bathrooms  17.425505  78.458520  \n",
       "\n",
       "[10246 rows x 11 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df[df['Latitude']!='Not found'].reset_index(drop=True)\n",
    "df\n",
    "\n",
    "# convert latitude and longitude columns to float\n",
    "df['Latitude'] = df['Latitude'].astype(float)\n",
    "df['Longitude'] = df['Longitude'].astype(float)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "7ff45648",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      seller_type  bedroom layout_type      property_type    locality   price  \\\n",
      "20          OWNER        2         BHK          Apartment     Meerpet   4,000   \n",
      "34          OWNER        1         BHK  Independent House     Aminpur   7,000   \n",
      "47          OWNER        2         BHK  Independent House     Aminpur  10,000   \n",
      "91          OWNER        1         BHK  Independent Floor     Aminpur   7,500   \n",
      "94          OWNER        2         BHK  Independent Floor        BHEL  10,000   \n",
      "...           ...      ...         ...                ...         ...     ...   \n",
      "10150       OWNER        2         BHK          Apartment     Aminpur   9,000   \n",
      "10152       OWNER        3         BHK  Independent House  Bapu Nagar  17,000   \n",
      "10153       OWNER        2         BHK  Independent House  Bapu Nagar   9,500   \n",
      "10158       OWNER        2         BHK          Apartment  Bapu Nagar  18,000   \n",
      "10193       OWNER        1         BHK          Apartment  Moti Nagar   6,800   \n",
      "\n",
      "       area    furnish_type     bathroom   Latitude  Longitude  \n",
      "20      645  Semi-Furnished  2 bathrooms  28.972311  77.687051  \n",
      "34      500     Unfurnished  1 bathrooms  32.127466  74.402451  \n",
      "47     1500     Unfurnished  2 bathrooms  32.127466  74.402451  \n",
      "91      700  Semi-Furnished  1 bathrooms  32.127466  74.402451  \n",
      "94     1500     Unfurnished  1 bathrooms  10.726483  78.763050  \n",
      "...     ...             ...          ...        ...        ...  \n",
      "10150  1050     Unfurnished  2 bathrooms  32.127466  74.402451  \n",
      "10152  1600     Unfurnished  3 bathrooms  26.885074  75.800958  \n",
      "10153  1200     Unfurnished  2 bathrooms  26.885074  75.800958  \n",
      "10158   950       Furnished  2 bathrooms  26.885074  75.800958  \n",
      "10193   600  Semi-Furnished  1 bathrooms  28.657859  77.142429  \n",
      "\n",
      "[266 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "def zscore(col):\n",
    "    return np.abs((col - col.mean()) / col.std())\n",
    "\n",
    "# calculate Z-score for 'Salary' column\n",
    "zscore_salary = zscore(df['Latitude'])\n",
    "\n",
    "# set a threshold for Z-score\n",
    "threshold = 3\n",
    "\n",
    "# identify and show outliers\n",
    "outliers = df[zscore_salary > threshold]\n",
    "print(outliers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "ef391146",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seller_type</th>\n",
       "      <th>bedroom</th>\n",
       "      <th>layout_type</th>\n",
       "      <th>property_type</th>\n",
       "      <th>locality</th>\n",
       "      <th>price</th>\n",
       "      <th>area</th>\n",
       "      <th>furnish_type</th>\n",
       "      <th>bathroom</th>\n",
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       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>OWNER</td>\n",
       "      <td>1</td>\n",
       "      <td>RK</td>\n",
       "      <td>Studio Apartment</td>\n",
       "      <td>Serilingampally</td>\n",
       "      <td>6,720</td>\n",
       "      <td>320</td>\n",
       "      <td>Furnished</td>\n",
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       "      <td>17.484205</td>\n",
       "      <td>78.299509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OWNER</td>\n",
       "      <td>5</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Villa</td>\n",
       "      <td>Sri Nagar Colony</td>\n",
       "      <td>36,000</td>\n",
       "      <td>3000</td>\n",
       "      <td>Semi-Furnished</td>\n",
       "      <td>4 bathrooms</td>\n",
       "      <td>17.376880</td>\n",
       "      <td>78.511785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>OWNER</td>\n",
       "      <td>2</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Independent House</td>\n",
       "      <td>Shaikpet</td>\n",
       "      <td>11,000</td>\n",
       "      <td>900</td>\n",
       "      <td>Semi-Furnished</td>\n",
       "      <td>2 bathrooms</td>\n",
       "      <td>17.405795</td>\n",
       "      <td>78.400654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OWNER</td>\n",
       "      <td>3</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Nanakramguda</td>\n",
       "      <td>45,000</td>\n",
       "      <td>2165</td>\n",
       "      <td>Unfurnished</td>\n",
       "      <td>3 bathrooms</td>\n",
       "      <td>17.412983</td>\n",
       "      <td>78.338635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OWNER</td>\n",
       "      <td>3</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Kondapur</td>\n",
       "      <td>20,000</td>\n",
       "      <td>1600</td>\n",
       "      <td>Semi-Furnished</td>\n",
       "      <td>3 bathrooms</td>\n",
       "      <td>18.330960</td>\n",
       "      <td>79.922469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10241</th>\n",
       "      <td>AGENT</td>\n",
       "      <td>1</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Begumpet</td>\n",
       "      <td>6,000</td>\n",
       "      <td>850</td>\n",
       "      <td>Semi-Furnished</td>\n",
       "      <td>1 bathrooms</td>\n",
       "      <td>17.444020</td>\n",
       "      <td>78.462482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10242</th>\n",
       "      <td>AGENT</td>\n",
       "      <td>2</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Ameerpet</td>\n",
       "      <td>12,000</td>\n",
       "      <td>1300</td>\n",
       "      <td>Semi-Furnished</td>\n",
       "      <td>2 bathrooms</td>\n",
       "      <td>17.437501</td>\n",
       "      <td>78.448251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10243</th>\n",
       "      <td>AGENT</td>\n",
       "      <td>2</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Ameerpet</td>\n",
       "      <td>15,000</td>\n",
       "      <td>1200</td>\n",
       "      <td>Semi-Furnished</td>\n",
       "      <td>2 bathrooms</td>\n",
       "      <td>17.437501</td>\n",
       "      <td>78.448251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10244</th>\n",
       "      <td>AGENT</td>\n",
       "      <td>2</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Begumpet</td>\n",
       "      <td>17,000</td>\n",
       "      <td>1300</td>\n",
       "      <td>Furnished</td>\n",
       "      <td>2 bathrooms</td>\n",
       "      <td>17.444020</td>\n",
       "      <td>78.462482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10245</th>\n",
       "      <td>AGENT</td>\n",
       "      <td>2</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Somajiguda</td>\n",
       "      <td>15,000</td>\n",
       "      <td>1500</td>\n",
       "      <td>Semi-Furnished</td>\n",
       "      <td>2 bathrooms</td>\n",
       "      <td>17.425505</td>\n",
       "      <td>78.458520</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9910 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      seller_type  bedroom layout_type      property_type          locality  \\\n",
       "0           OWNER        1          RK   Studio Apartment   Serilingampally   \n",
       "1           OWNER        5         BHK              Villa  Sri Nagar Colony   \n",
       "2           OWNER        2         BHK  Independent House          Shaikpet   \n",
       "3           OWNER        3         BHK          Apartment      Nanakramguda   \n",
       "4           OWNER        3         BHK          Apartment          Kondapur   \n",
       "...           ...      ...         ...                ...               ...   \n",
       "10241       AGENT        1         BHK          Apartment          Begumpet   \n",
       "10242       AGENT        2         BHK          Apartment          Ameerpet   \n",
       "10243       AGENT        2         BHK          Apartment          Ameerpet   \n",
       "10244       AGENT        2         BHK          Apartment          Begumpet   \n",
       "10245       AGENT        2         BHK          Apartment        Somajiguda   \n",
       "\n",
       "        price  area    furnish_type     bathroom   Latitude  Longitude  \n",
       "0       6,720   320       Furnished  1 bathrooms  17.484205  78.299509  \n",
       "1      36,000  3000  Semi-Furnished  4 bathrooms  17.376880  78.511785  \n",
       "2      11,000   900  Semi-Furnished  2 bathrooms  17.405795  78.400654  \n",
       "3      45,000  2165     Unfurnished  3 bathrooms  17.412983  78.338635  \n",
       "4      20,000  1600  Semi-Furnished  3 bathrooms  18.330960  79.922469  \n",
       "...       ...   ...             ...          ...        ...        ...  \n",
       "10241   6,000   850  Semi-Furnished  1 bathrooms  17.444020  78.462482  \n",
       "10242  12,000  1300  Semi-Furnished  2 bathrooms  17.437501  78.448251  \n",
       "10243  15,000  1200  Semi-Furnished  2 bathrooms  17.437501  78.448251  \n",
       "10244  17,000  1300       Furnished  2 bathrooms  17.444020  78.462482  \n",
       "10245  15,000  1500  Semi-Furnished  2 bathrooms  17.425505  78.458520  \n",
       "\n",
       "[9910 rows x 11 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(outliers.index, inplace=True)\n",
    "\n",
    "# print the cleaned dataset\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "673cf146",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\python\\lib\\site-packages\\ipykernel_launcher.py:5: 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",
      "D:\\python\\lib\\site-packages\\ipykernel_launcher.py:6: 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",
      "D:\\python\\lib\\site-packages\\ipykernel_launcher.py:7: 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",
      "  import sys\n",
      "D:\\python\\lib\\site-packages\\ipykernel_launcher.py:8: 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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2 of final_Kolkata_rent.csv: 0.9349078196524694\n",
      "Mean absolute error of final_Kolkata_rent.csv: 1447.0137476950028\n"
     ]
    }
   ],
   "source": [
    "df = df.dropna()\n",
    "\n",
    "\n",
    "#mapping\n",
    "df[\"seller_type\"]=df[\"seller_type\"].map({'OWNER':0,'AGENT':1,'BUILDER':2})\n",
    "df[\"layout_type\"]=df[\"layout_type\"].map({'BHK':0,'RK':1})\n",
    "df[\"property_type\"]=df[\"property_type\"].map({'Apartment':0,'Studio Apartment':1,'Independent House':2,'Independent Floor':3,'Villa':4,'Penthouse':5})\n",
    "df[\"furnish_type\"]=df[\"furnish_type\"].map({'Furnished':0,'Semi-Furnished':1,'Unfurnished':2})\n",
    "\n",
    "\n",
    "# Removing the 'Not found' records\n",
    "df=df[df['Latitude']!='Not found'].reset_index(drop=True)\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "#values_to_drop = [np.nan, 'East facing', 'West facing', 'North facing', 'South facing', 'Grfloor','NorthWest facing','NorthEast facing','SouthWest facing','SouthEast facing','4 of 5floor']\n",
    "values_to_need=['2 bathrooms', '1 bathrooms', '3 bathrooms', '6 bathrooms',\n",
    "   '5 bathrooms', '4 bathrooms', '8 bathrooms', '7 bathrooms',\n",
    "   '9 bathrooms','12 bathrooms']\n",
    "\n",
    "# Drop the unwanted values from the 'bathroom' column\n",
    "df = df[df['bathroom'].isin(values_to_need)]\n",
    "\n",
    "df['bathroom'] = df['bathroom'].str.replace('bathrooms', '').astype(int)\n",
    "\n",
    "df['price']= [i.replace(',','') for i in df['price']]\n",
    "df['price']=df['price'].astype(float)\n",
    "\n",
    "\n",
    "\n",
    "#removing outliers\n",
    "desc = df['price'].describe()\n",
    "q1 = desc.loc['25%']\n",
    "q3 = desc.loc['75%']\n",
    "iqr = q3-q1 \n",
    "lower_lim = q1-(1.5*iqr)\n",
    "upper_lim = q3+(1.5*iqr)\n",
    "df = df[(df['price']>=lower_lim)&(df['price']<=upper_lim)]\n",
    "\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import KFold, cross_val_score\n",
    "from sklearn.ensemble import RandomForestRegressor,VotingRegressor\n",
    "import xgboost as xgb\n",
    "import math\n",
    "\n",
    "X = df.drop(['price','locality'],axis=1).values\n",
    "y = df[['price']].values\n",
    "y=y.ravel()\n",
    "\n",
    "# Set max_features to the square root of the number of features\n",
    "max_features = int(math.sqrt(X.shape[1]))\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=84)\n",
    "\n",
    "# Define models\n",
    "rf_model = RandomForestRegressor(n_estimators=1000, random_state=84,max_depth=20, min_samples_split=20, min_samples_leaf=10, max_features=max_features)\n",
    "xgb_model = xgb.XGBRegressor(n_estimators=800, random_state=84)\n",
    "\n",
    "\n",
    "# Ensemble models\n",
    "# Combine the models using voting ensemble\n",
    "ensemble_model1 = VotingRegressor(estimators=[('rf', rf_model), ('gb', xgb_model)])\n",
    "ensemble_model1.fit(X, y)\n",
    "\n",
    "y_pred = ensemble_model1.predict(X_test)\n",
    "y_pred\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "print(f\"r2 of {file_path}:\",r2)\n",
    "\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "print(f\"Mean absolute error of {file_path}:\", mae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "92a88b21",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    10757.000000\n",
       "mean        28.587191\n",
       "std        311.715735\n",
       "min          0.000142\n",
       "25%          0.052500\n",
       "50%          0.068966\n",
       "75%          0.088889\n",
       "max       6666.666667\n",
       "Name: affordability, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['affordability'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c129ace2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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