hotel_preprocess.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "93391ab4",
   "metadata": {},
   "source": [
    "## Hotel data Preprocessing and Recommendation System"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7745d9f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f3a4913f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"D:\\Linearloop Internship\\Project - Travel\\Project_datasets\\HotelData.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "44efa66d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11885 entries, 0 to 11884\n",
      "Data columns (total 19 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Unnamed: 0    11885 non-null  int64  \n",
      " 1   _id           11885 non-null  object \n",
      " 2   Unnamed: 2    11885 non-null  int64  \n",
      " 3   Name          11885 non-null  object \n",
      " 4   URL           11885 non-null  object \n",
      " 5   Total Rating  11156 non-null  object \n",
      " 6   Review_Score  11156 non-null  object \n",
      " 7   New Total     8044 non-null   float64\n",
      " 8   Code          11885 non-null  int64  \n",
      " 9   Rating        8044 non-null   float64\n",
      " 10  City_y        11885 non-null  object \n",
      " 11  Amenities     11885 non-null  object \n",
      " 12  Latitude      11885 non-null  int64  \n",
      " 13  Longitude     11885 non-null  int64  \n",
      " 14  Price         11813 non-null  float64\n",
      " 15  Address       11885 non-null  object \n",
      " 16  City          11885 non-null  object \n",
      " 17  CityLabel     11885 non-null  int64  \n",
      " 18  cluster       11885 non-null  int64  \n",
      "dtypes: float64(3), int64(7), object(9)\n",
      "memory usage: 1.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "733b3b02",
   "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>Unnamed: 0</th>\n",
       "      <th>_id</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Name</th>\n",
       "      <th>URL</th>\n",
       "      <th>Total Rating</th>\n",
       "      <th>Review_Score</th>\n",
       "      <th>New Total</th>\n",
       "      <th>Code</th>\n",
       "      <th>Rating</th>\n",
       "      <th>City_y</th>\n",
       "      <th>Amenities</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Price</th>\n",
       "      <th>Address</th>\n",
       "      <th>City</th>\n",
       "      <th>CityLabel</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>628752bdd04899399ca36d45</td>\n",
       "      <td>0</td>\n",
       "      <td>Cloud Green Resort &amp; Spa</td>\n",
       "      <td>/hotels/cloud-green-resort-spa-hotel-in-mahaba...</td>\n",
       "      <td>465 Ratings</td>\n",
       "      <td>3.9 / 5</td>\n",
       "      <td>465.0</td>\n",
       "      <td>4018</td>\n",
       "      <td>3.9</td>\n",
       "      <td>Mahabaleshwar</td>\n",
       "      <td>['Sanitizers', 'Parking (Free)', 'Wifi (Free)'...</td>\n",
       "      <td>17582</td>\n",
       "      <td>73746</td>\n",
       "      <td>2119.0</td>\n",
       "      <td>311,   Mahabaleshwar panchgani  road, near lin...</td>\n",
       "      <td>Mahabaleshwar</td>\n",
       "      <td>38</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>628752bdd04899399ca36d46</td>\n",
       "      <td>1</td>\n",
       "      <td>Forest County Resort</td>\n",
       "      <td>/hotels/forest-county-resort-hotel-in-mahabale...</td>\n",
       "      <td>225 Ratings</td>\n",
       "      <td>3.8 / 5</td>\n",
       "      <td>225.0</td>\n",
       "      <td>6018</td>\n",
       "      <td>3.8</td>\n",
       "      <td>Mahabaleshwar</td>\n",
       "      <td>['Masks (Paid)', 'Gloves (Paid)', 'Thermal scr...</td>\n",
       "      <td>17857</td>\n",
       "      <td>73812</td>\n",
       "      <td>4650.0</td>\n",
       "      <td>VARCHI MALUSARWADI, POST-TALDEV, HOUSE NO.82,,...</td>\n",
       "      <td>Mahabaleshwar</td>\n",
       "      <td>38</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>628752bdd04899399ca36d47</td>\n",
       "      <td>2</td>\n",
       "      <td>Regenta MPG Club</td>\n",
       "      <td>/hotels/regenta-mpg-club-hotel-in-mahabaleshwa...</td>\n",
       "      <td>391 Ratings</td>\n",
       "      <td>4.3 / 5</td>\n",
       "      <td>391.0</td>\n",
       "      <td>5018</td>\n",
       "      <td>4.3</td>\n",
       "      <td>Mahabaleshwar</td>\n",
       "      <td>['Hair nets (for hotel staff)', 'Thermal scree...</td>\n",
       "      <td>17795</td>\n",
       "      <td>73689</td>\n",
       "      <td>6388.0</td>\n",
       "      <td>MPG Club, 233 Old Tapola Road, Dist.Satara,</td>\n",
       "      <td>Mahabaleshwar</td>\n",
       "      <td>38</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>628752bdd04899399ca36d48</td>\n",
       "      <td>3</td>\n",
       "      <td>Bellevue Resort, Panchgani</td>\n",
       "      <td>/hotels/bellevue-resort-panchgani-hotel-in-mah...</td>\n",
       "      <td>248 Ratings</td>\n",
       "      <td>3.9 / 5</td>\n",
       "      <td>248.0</td>\n",
       "      <td>4018</td>\n",
       "      <td>3.9</td>\n",
       "      <td>Mahabaleshwar</td>\n",
       "      <td>['Sanitizers', 'Parking (Free)', 'Wifi (Free)'...</td>\n",
       "      <td>17</td>\n",
       "      <td>73</td>\n",
       "      <td>2392.0</td>\n",
       "      <td>Bhilar Kaswand Road, Bhilar Hill station betwe...</td>\n",
       "      <td>Mahabaleshwar</td>\n",
       "      <td>38</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>628752bdd04899399ca36d49</td>\n",
       "      <td>4</td>\n",
       "      <td>NANDANVAN TENT AND RESORT</td>\n",
       "      <td>/hotels/nandanvan-tent-and-resort-hotel-in-mah...</td>\n",
       "      <td>27 Ratings</td>\n",
       "      <td>4.4 / 5</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Mahabaleshwar</td>\n",
       "      <td>['Hospital in the vicinity (Between 5 and 10 k...</td>\n",
       "      <td>17</td>\n",
       "      <td>73</td>\n",
       "      <td>2637.0</td>\n",
       "      <td>SR NO 5/1 , NEAR BSNL OFFICE , MAHABALESHWAR T...</td>\n",
       "      <td>Mahabaleshwar</td>\n",
       "      <td>38</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                       _id  Unnamed: 2  \\\n",
       "0           0  628752bdd04899399ca36d45           0   \n",
       "1           1  628752bdd04899399ca36d46           1   \n",
       "2           2  628752bdd04899399ca36d47           2   \n",
       "3           3  628752bdd04899399ca36d48           3   \n",
       "4           4  628752bdd04899399ca36d49           4   \n",
       "\n",
       "                         Name  \\\n",
       "0    Cloud Green Resort & Spa   \n",
       "1        Forest County Resort   \n",
       "2            Regenta MPG Club   \n",
       "3  Bellevue Resort, Panchgani   \n",
       "4   NANDANVAN TENT AND RESORT   \n",
       "\n",
       "                                                 URL Total Rating  \\\n",
       "0  /hotels/cloud-green-resort-spa-hotel-in-mahaba...  465 Ratings   \n",
       "1  /hotels/forest-county-resort-hotel-in-mahabale...  225 Ratings   \n",
       "2  /hotels/regenta-mpg-club-hotel-in-mahabaleshwa...  391 Ratings   \n",
       "3  /hotels/bellevue-resort-panchgani-hotel-in-mah...  248 Ratings   \n",
       "4  /hotels/nandanvan-tent-and-resort-hotel-in-mah...   27 Ratings   \n",
       "\n",
       "  Review_Score  New Total  Code  Rating         City_y  \\\n",
       "0      3.9 / 5      465.0  4018     3.9  Mahabaleshwar   \n",
       "1      3.8 / 5      225.0  6018     3.8  Mahabaleshwar   \n",
       "2      4.3 / 5      391.0  5018     4.3  Mahabaleshwar   \n",
       "3      3.9 / 5      248.0  4018     3.9  Mahabaleshwar   \n",
       "4      4.4 / 5       27.0  2017     4.4  Mahabaleshwar   \n",
       "\n",
       "                                           Amenities  Latitude  Longitude  \\\n",
       "0  ['Sanitizers', 'Parking (Free)', 'Wifi (Free)'...     17582      73746   \n",
       "1  ['Masks (Paid)', 'Gloves (Paid)', 'Thermal scr...     17857      73812   \n",
       "2  ['Hair nets (for hotel staff)', 'Thermal scree...     17795      73689   \n",
       "3  ['Sanitizers', 'Parking (Free)', 'Wifi (Free)'...        17         73   \n",
       "4  ['Hospital in the vicinity (Between 5 and 10 k...        17         73   \n",
       "\n",
       "    Price                                            Address           City  \\\n",
       "0  2119.0  311,   Mahabaleshwar panchgani  road, near lin...  Mahabaleshwar   \n",
       "1  4650.0  VARCHI MALUSARWADI, POST-TALDEV, HOUSE NO.82,,...  Mahabaleshwar   \n",
       "2  6388.0        MPG Club, 233 Old Tapola Road, Dist.Satara,  Mahabaleshwar   \n",
       "3  2392.0  Bhilar Kaswand Road, Bhilar Hill station betwe...  Mahabaleshwar   \n",
       "4  2637.0  SR NO 5/1 , NEAR BSNL OFFICE , MAHABALESHWAR T...  Mahabaleshwar   \n",
       "\n",
       "   CityLabel  cluster  \n",
       "0         38        4  \n",
       "1         38        4  \n",
       "2         38        4  \n",
       "3         38        4  \n",
       "4         38        4  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "810a71d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Unnamed: 0','Unnamed: 2'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "14a691a2",
   "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>_id</th>\n",
       "      <th>Name</th>\n",
       "      <th>URL</th>\n",
       "      <th>Total Rating</th>\n",
       "      <th>Review_Score</th>\n",
       "      <th>New Total</th>\n",
       "      <th>Code</th>\n",
       "      <th>Rating</th>\n",
       "      <th>City_y</th>\n",
       "      <th>Amenities</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Price</th>\n",
       "      <th>Address</th>\n",
       "      <th>City</th>\n",
       "      <th>CityLabel</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>628752bdd04899399ca36d45</td>\n",
       "      <td>Cloud Green Resort &amp; Spa</td>\n",
       "      <td>/hotels/cloud-green-resort-spa-hotel-in-mahaba...</td>\n",
       "      <td>465 Ratings</td>\n",
       "      <td>3.9 / 5</td>\n",
       "      <td>465.0</td>\n",
       "      <td>4018</td>\n",
       "      <td>3.9</td>\n",
       "      <td>Mahabaleshwar</td>\n",
       "      <td>['Sanitizers', 'Parking (Free)', 'Wifi (Free)'...</td>\n",
       "      <td>17582</td>\n",
       "      <td>73746</td>\n",
       "      <td>2119.0</td>\n",
       "      <td>311,   Mahabaleshwar panchgani  road, near lin...</td>\n",
       "      <td>Mahabaleshwar</td>\n",
       "      <td>38</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>628752bdd04899399ca36d46</td>\n",
       "      <td>Forest County Resort</td>\n",
       "      <td>/hotels/forest-county-resort-hotel-in-mahabale...</td>\n",
       "      <td>225 Ratings</td>\n",
       "      <td>3.8 / 5</td>\n",
       "      <td>225.0</td>\n",
       "      <td>6018</td>\n",
       "      <td>3.8</td>\n",
       "      <td>Mahabaleshwar</td>\n",
       "      <td>['Masks (Paid)', 'Gloves (Paid)', 'Thermal scr...</td>\n",
       "      <td>17857</td>\n",
       "      <td>73812</td>\n",
       "      <td>4650.0</td>\n",
       "      <td>VARCHI MALUSARWADI, POST-TALDEV, HOUSE NO.82,,...</td>\n",
       "      <td>Mahabaleshwar</td>\n",
       "      <td>38</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>628752bdd04899399ca36d47</td>\n",
       "      <td>Regenta MPG Club</td>\n",
       "      <td>/hotels/regenta-mpg-club-hotel-in-mahabaleshwa...</td>\n",
       "      <td>391 Ratings</td>\n",
       "      <td>4.3 / 5</td>\n",
       "      <td>391.0</td>\n",
       "      <td>5018</td>\n",
       "      <td>4.3</td>\n",
       "      <td>Mahabaleshwar</td>\n",
       "      <td>['Hair nets (for hotel staff)', 'Thermal scree...</td>\n",
       "      <td>17795</td>\n",
       "      <td>73689</td>\n",
       "      <td>6388.0</td>\n",
       "      <td>MPG Club, 233 Old Tapola Road, Dist.Satara,</td>\n",
       "      <td>Mahabaleshwar</td>\n",
       "      <td>38</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>628752bdd04899399ca36d48</td>\n",
       "      <td>Bellevue Resort, Panchgani</td>\n",
       "      <td>/hotels/bellevue-resort-panchgani-hotel-in-mah...</td>\n",
       "      <td>248 Ratings</td>\n",
       "      <td>3.9 / 5</td>\n",
       "      <td>248.0</td>\n",
       "      <td>4018</td>\n",
       "      <td>3.9</td>\n",
       "      <td>Mahabaleshwar</td>\n",
       "      <td>['Sanitizers', 'Parking (Free)', 'Wifi (Free)'...</td>\n",
       "      <td>17</td>\n",
       "      <td>73</td>\n",
       "      <td>2392.0</td>\n",
       "      <td>Bhilar Kaswand Road, Bhilar Hill station betwe...</td>\n",
       "      <td>Mahabaleshwar</td>\n",
       "      <td>38</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>628752bdd04899399ca36d49</td>\n",
       "      <td>NANDANVAN TENT AND RESORT</td>\n",
       "      <td>/hotels/nandanvan-tent-and-resort-hotel-in-mah...</td>\n",
       "      <td>27 Ratings</td>\n",
       "      <td>4.4 / 5</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Mahabaleshwar</td>\n",
       "      <td>['Hospital in the vicinity (Between 5 and 10 k...</td>\n",
       "      <td>17</td>\n",
       "      <td>73</td>\n",
       "      <td>2637.0</td>\n",
       "      <td>SR NO 5/1 , NEAR BSNL OFFICE , MAHABALESHWAR T...</td>\n",
       "      <td>Mahabaleshwar</td>\n",
       "      <td>38</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        _id                        Name  \\\n",
       "0  628752bdd04899399ca36d45    Cloud Green Resort & Spa   \n",
       "1  628752bdd04899399ca36d46        Forest County Resort   \n",
       "2  628752bdd04899399ca36d47            Regenta MPG Club   \n",
       "3  628752bdd04899399ca36d48  Bellevue Resort, Panchgani   \n",
       "4  628752bdd04899399ca36d49   NANDANVAN TENT AND RESORT   \n",
       "\n",
       "                                                 URL Total Rating  \\\n",
       "0  /hotels/cloud-green-resort-spa-hotel-in-mahaba...  465 Ratings   \n",
       "1  /hotels/forest-county-resort-hotel-in-mahabale...  225 Ratings   \n",
       "2  /hotels/regenta-mpg-club-hotel-in-mahabaleshwa...  391 Ratings   \n",
       "3  /hotels/bellevue-resort-panchgani-hotel-in-mah...  248 Ratings   \n",
       "4  /hotels/nandanvan-tent-and-resort-hotel-in-mah...   27 Ratings   \n",
       "\n",
       "  Review_Score  New Total  Code  Rating         City_y  \\\n",
       "0      3.9 / 5      465.0  4018     3.9  Mahabaleshwar   \n",
       "1      3.8 / 5      225.0  6018     3.8  Mahabaleshwar   \n",
       "2      4.3 / 5      391.0  5018     4.3  Mahabaleshwar   \n",
       "3      3.9 / 5      248.0  4018     3.9  Mahabaleshwar   \n",
       "4      4.4 / 5       27.0  2017     4.4  Mahabaleshwar   \n",
       "\n",
       "                                           Amenities  Latitude  Longitude  \\\n",
       "0  ['Sanitizers', 'Parking (Free)', 'Wifi (Free)'...     17582      73746   \n",
       "1  ['Masks (Paid)', 'Gloves (Paid)', 'Thermal scr...     17857      73812   \n",
       "2  ['Hair nets (for hotel staff)', 'Thermal scree...     17795      73689   \n",
       "3  ['Sanitizers', 'Parking (Free)', 'Wifi (Free)'...        17         73   \n",
       "4  ['Hospital in the vicinity (Between 5 and 10 k...        17         73   \n",
       "\n",
       "    Price                                            Address           City  \\\n",
       "0  2119.0  311,   Mahabaleshwar panchgani  road, near lin...  Mahabaleshwar   \n",
       "1  4650.0  VARCHI MALUSARWADI, POST-TALDEV, HOUSE NO.82,,...  Mahabaleshwar   \n",
       "2  6388.0        MPG Club, 233 Old Tapola Road, Dist.Satara,  Mahabaleshwar   \n",
       "3  2392.0  Bhilar Kaswand Road, Bhilar Hill station betwe...  Mahabaleshwar   \n",
       "4  2637.0  SR NO 5/1 , NEAR BSNL OFFICE , MAHABALESHWAR T...  Mahabaleshwar   \n",
       "\n",
       "   CityLabel  cluster  \n",
       "0         38        4  \n",
       "1         38        4  \n",
       "2         38        4  \n",
       "3         38        4  \n",
       "4         38        4  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3ecbfdf7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Mahabaleshwar', 'Mahabalipuram', 'Manali', 'Mysore', 'Mussoorie',\n",
       "       'Mathura', 'Mcleodganj', 'Nainital', 'Munnar', 'Mumbai',\n",
       "       'Mukteshwar', 'Park', 'Ooty', 'Pushkar', 'Pondicherry', 'Orchha',\n",
       "       'Ranchi', 'Rishikesh', 'Ranikhet', 'Shimla', 'Sonamarg', 'Shirdi',\n",
       "       'Srinagar', 'Spiti', 'Shimoga', 'Tirupati', 'Tawang', 'Valley',\n",
       "       'Varanasi', 'Varkala', 'Chopta', 'Coonoor', 'Dalhousie',\n",
       "       'Madikeri', 'Coorg', 'Gaya', 'Dhanaulti', 'Darjeeling', 'Gangtok',\n",
       "       'Gokarna', 'Deoghar', 'Hazaribagh', 'Gulmarg', 'Jabalpur', 'Hampi',\n",
       "       'Kanatal', 'Joshimath', 'Auli', 'Kasol', 'Jodhpur', 'Kanyakumari',\n",
       "       'Jaisalmer', 'Kausani', 'Khajjiar', 'Khajuraho', 'Kodaikanal',\n",
       "       'Visakhapatnam', 'Cherrapunji', 'Madurai', 'Lake', 'Lonavala',\n",
       "       'Wayanad', 'Ziro', 'udaipur', 'Abu', 'Ajmer', 'Agra', 'Alleppey',\n",
       "       'Binsar', 'Almora', 'Bankura', 'Chandigarh'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.City.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "12d81abf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "72"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.City.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ef13a8cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "_id                0\n",
       "Name               0\n",
       "URL                0\n",
       "Total Rating     729\n",
       "Review_Score     729\n",
       "New Total       3841\n",
       "Code               0\n",
       "Rating          3841\n",
       "City_y             0\n",
       "Amenities          0\n",
       "Latitude           0\n",
       "Longitude          0\n",
       "Price             72\n",
       "Address            0\n",
       "City               0\n",
       "CityLabel          0\n",
       "cluster            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a70ba75a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Code','New Total'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b7bb4b0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['City_y'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e75ab1a5",
   "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>_id</th>\n",
       "      <th>Name</th>\n",
       "      <th>URL</th>\n",
       "      <th>Total Rating</th>\n",
       "      <th>Review_Score</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Amenities</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Price</th>\n",
       "      <th>Address</th>\n",
       "      <th>City</th>\n",
       "      <th>CityLabel</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>628752bdd04899399ca36d45</td>\n",
       "      <td>Cloud Green Resort &amp; Spa</td>\n",
       "      <td>/hotels/cloud-green-resort-spa-hotel-in-mahaba...</td>\n",
       "      <td>465 Ratings</td>\n",
       "      <td>3.9 / 5</td>\n",
       "      <td>3.9</td>\n",
       "      <td>['Sanitizers', 'Parking (Free)', 'Wifi (Free)'...</td>\n",
       "      <td>17582</td>\n",
       "      <td>73746</td>\n",
       "      <td>2119.0</td>\n",
       "      <td>311,   Mahabaleshwar panchgani  road, near lin...</td>\n",
       "      <td>Mahabaleshwar</td>\n",
       "      <td>38</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>628752bdd04899399ca36d46</td>\n",
       "      <td>Forest County Resort</td>\n",
       "      <td>/hotels/forest-county-resort-hotel-in-mahabale...</td>\n",
       "      <td>225 Ratings</td>\n",
       "      <td>3.8 / 5</td>\n",
       "      <td>3.8</td>\n",
       "      <td>['Masks (Paid)', 'Gloves (Paid)', 'Thermal scr...</td>\n",
       "      <td>17857</td>\n",
       "      <td>73812</td>\n",
       "      <td>4650.0</td>\n",
       "      <td>VARCHI MALUSARWADI, POST-TALDEV, HOUSE NO.82,,...</td>\n",
       "      <td>Mahabaleshwar</td>\n",
       "      <td>38</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>628752bdd04899399ca36d47</td>\n",
       "      <td>Regenta MPG Club</td>\n",
       "      <td>/hotels/regenta-mpg-club-hotel-in-mahabaleshwa...</td>\n",
       "      <td>391 Ratings</td>\n",
       "      <td>4.3 / 5</td>\n",
       "      <td>4.3</td>\n",
       "      <td>['Hair nets (for hotel staff)', 'Thermal scree...</td>\n",
       "      <td>17795</td>\n",
       "      <td>73689</td>\n",
       "      <td>6388.0</td>\n",
       "      <td>MPG Club, 233 Old Tapola Road, Dist.Satara,</td>\n",
       "      <td>Mahabaleshwar</td>\n",
       "      <td>38</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>628752bdd04899399ca36d48</td>\n",
       "      <td>Bellevue Resort, Panchgani</td>\n",
       "      <td>/hotels/bellevue-resort-panchgani-hotel-in-mah...</td>\n",
       "      <td>248 Ratings</td>\n",
       "      <td>3.9 / 5</td>\n",
       "      <td>3.9</td>\n",
       "      <td>['Sanitizers', 'Parking (Free)', 'Wifi (Free)'...</td>\n",
       "      <td>17</td>\n",
       "      <td>73</td>\n",
       "      <td>2392.0</td>\n",
       "      <td>Bhilar Kaswand Road, Bhilar Hill station betwe...</td>\n",
       "      <td>Mahabaleshwar</td>\n",
       "      <td>38</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>628752bdd04899399ca36d49</td>\n",
       "      <td>NANDANVAN TENT AND RESORT</td>\n",
       "      <td>/hotels/nandanvan-tent-and-resort-hotel-in-mah...</td>\n",
       "      <td>27 Ratings</td>\n",
       "      <td>4.4 / 5</td>\n",
       "      <td>4.4</td>\n",
       "      <td>['Hospital in the vicinity (Between 5 and 10 k...</td>\n",
       "      <td>17</td>\n",
       "      <td>73</td>\n",
       "      <td>2637.0</td>\n",
       "      <td>SR NO 5/1 , NEAR BSNL OFFICE , MAHABALESHWAR T...</td>\n",
       "      <td>Mahabaleshwar</td>\n",
       "      <td>38</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        _id                        Name  \\\n",
       "0  628752bdd04899399ca36d45    Cloud Green Resort & Spa   \n",
       "1  628752bdd04899399ca36d46        Forest County Resort   \n",
       "2  628752bdd04899399ca36d47            Regenta MPG Club   \n",
       "3  628752bdd04899399ca36d48  Bellevue Resort, Panchgani   \n",
       "4  628752bdd04899399ca36d49   NANDANVAN TENT AND RESORT   \n",
       "\n",
       "                                                 URL Total Rating  \\\n",
       "0  /hotels/cloud-green-resort-spa-hotel-in-mahaba...  465 Ratings   \n",
       "1  /hotels/forest-county-resort-hotel-in-mahabale...  225 Ratings   \n",
       "2  /hotels/regenta-mpg-club-hotel-in-mahabaleshwa...  391 Ratings   \n",
       "3  /hotels/bellevue-resort-panchgani-hotel-in-mah...  248 Ratings   \n",
       "4  /hotels/nandanvan-tent-and-resort-hotel-in-mah...   27 Ratings   \n",
       "\n",
       "  Review_Score  Rating                                          Amenities  \\\n",
       "0      3.9 / 5     3.9  ['Sanitizers', 'Parking (Free)', 'Wifi (Free)'...   \n",
       "1      3.8 / 5     3.8  ['Masks (Paid)', 'Gloves (Paid)', 'Thermal scr...   \n",
       "2      4.3 / 5     4.3  ['Hair nets (for hotel staff)', 'Thermal scree...   \n",
       "3      3.9 / 5     3.9  ['Sanitizers', 'Parking (Free)', 'Wifi (Free)'...   \n",
       "4      4.4 / 5     4.4  ['Hospital in the vicinity (Between 5 and 10 k...   \n",
       "\n",
       "   Latitude  Longitude   Price  \\\n",
       "0     17582      73746  2119.0   \n",
       "1     17857      73812  4650.0   \n",
       "2     17795      73689  6388.0   \n",
       "3        17         73  2392.0   \n",
       "4        17         73  2637.0   \n",
       "\n",
       "                                             Address           City  \\\n",
       "0  311,   Mahabaleshwar panchgani  road, near lin...  Mahabaleshwar   \n",
       "1  VARCHI MALUSARWADI, POST-TALDEV, HOUSE NO.82,,...  Mahabaleshwar   \n",
       "2        MPG Club, 233 Old Tapola Road, Dist.Satara,  Mahabaleshwar   \n",
       "3  Bhilar Kaswand Road, Bhilar Hill station betwe...  Mahabaleshwar   \n",
       "4  SR NO 5/1 , NEAR BSNL OFFICE , MAHABALESHWAR T...  Mahabaleshwar   \n",
       "\n",
       "   CityLabel  cluster  \n",
       "0         38        4  \n",
       "1         38        4  \n",
       "2         38        4  \n",
       "3         38        4  \n",
       "4         38        4  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0d268659",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_price = round(df['Price'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "51f3f752",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_19712\\154942636.py:1: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df['Price'].fillna(mean_price, inplace=True)\n"
     ]
    }
   ],
   "source": [
    "df['Price'].fillna(mean_price, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4e9e5ad8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "_id                0\n",
       "Name               0\n",
       "URL                0\n",
       "Total Rating     729\n",
       "Review_Score     729\n",
       "Rating          3841\n",
       "Amenities          0\n",
       "Latitude           0\n",
       "Longitude          0\n",
       "Price              0\n",
       "Address            0\n",
       "City               0\n",
       "CityLabel          0\n",
       "cluster            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0d955881",
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_rating(rating_str):\n",
    "    if not isinstance(rating_str, str):\n",
    "        return None\n",
    "    \n",
    "    parts = rating_str.split('/')\n",
    "    \n",
    "    first_part = parts[0].strip()\n",
    "    \n",
    "    return first_part"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b0a1aa01",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Review_Score\"] = df[\"Review_Score\"].apply(extract_rating)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "abfb010d",
   "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>_id</th>\n",
       "      <th>Name</th>\n",
       "      <th>URL</th>\n",
       "      <th>Total Rating</th>\n",
       "      <th>Review_Score</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Amenities</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Price</th>\n",
       "      <th>Address</th>\n",
       "      <th>City</th>\n",
       "      <th>CityLabel</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>628752bdd04899399ca36d45</td>\n",
       "      <td>Cloud Green Resort &amp; Spa</td>\n",
       "      <td>/hotels/cloud-green-resort-spa-hotel-in-mahaba...</td>\n",
       "      <td>465 Ratings</td>\n",
       "      <td>3.9</td>\n",
       "      <td>3.9</td>\n",
       "      <td>['Sanitizers', 'Parking (Free)', 'Wifi (Free)'...</td>\n",
       "      <td>17582</td>\n",
       "      <td>73746</td>\n",
       "      <td>2119.0</td>\n",
       "      <td>311,   Mahabaleshwar panchgani  road, near lin...</td>\n",
       "      <td>Mahabaleshwar</td>\n",
       "      <td>38</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>628752bdd04899399ca36d46</td>\n",
       "      <td>Forest County Resort</td>\n",
       "      <td>/hotels/forest-county-resort-hotel-in-mahabale...</td>\n",
       "      <td>225 Ratings</td>\n",
       "      <td>3.8</td>\n",
       "      <td>3.8</td>\n",
       "      <td>['Masks (Paid)', 'Gloves (Paid)', 'Thermal scr...</td>\n",
       "      <td>17857</td>\n",
       "      <td>73812</td>\n",
       "      <td>4650.0</td>\n",
       "      <td>VARCHI MALUSARWADI, POST-TALDEV, HOUSE NO.82,,...</td>\n",
       "      <td>Mahabaleshwar</td>\n",
       "      <td>38</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>628752bdd04899399ca36d47</td>\n",
       "      <td>Regenta MPG Club</td>\n",
       "      <td>/hotels/regenta-mpg-club-hotel-in-mahabaleshwa...</td>\n",
       "      <td>391 Ratings</td>\n",
       "      <td>4.3</td>\n",
       "      <td>4.3</td>\n",
       "      <td>['Hair nets (for hotel staff)', 'Thermal scree...</td>\n",
       "      <td>17795</td>\n",
       "      <td>73689</td>\n",
       "      <td>6388.0</td>\n",
       "      <td>MPG Club, 233 Old Tapola Road, Dist.Satara,</td>\n",
       "      <td>Mahabaleshwar</td>\n",
       "      <td>38</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>628752bdd04899399ca36d48</td>\n",
       "      <td>Bellevue Resort, Panchgani</td>\n",
       "      <td>/hotels/bellevue-resort-panchgani-hotel-in-mah...</td>\n",
       "      <td>248 Ratings</td>\n",
       "      <td>3.9</td>\n",
       "      <td>3.9</td>\n",
       "      <td>['Sanitizers', 'Parking (Free)', 'Wifi (Free)'...</td>\n",
       "      <td>17</td>\n",
       "      <td>73</td>\n",
       "      <td>2392.0</td>\n",
       "      <td>Bhilar Kaswand Road, Bhilar Hill station betwe...</td>\n",
       "      <td>Mahabaleshwar</td>\n",
       "      <td>38</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>628752bdd04899399ca36d49</td>\n",
       "      <td>NANDANVAN TENT AND RESORT</td>\n",
       "      <td>/hotels/nandanvan-tent-and-resort-hotel-in-mah...</td>\n",
       "      <td>27 Ratings</td>\n",
       "      <td>4.4</td>\n",
       "      <td>4.4</td>\n",
       "      <td>['Hospital in the vicinity (Between 5 and 10 k...</td>\n",
       "      <td>17</td>\n",
       "      <td>73</td>\n",
       "      <td>2637.0</td>\n",
       "      <td>SR NO 5/1 , NEAR BSNL OFFICE , MAHABALESHWAR T...</td>\n",
       "      <td>Mahabaleshwar</td>\n",
       "      <td>38</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        _id                        Name  \\\n",
       "0  628752bdd04899399ca36d45    Cloud Green Resort & Spa   \n",
       "1  628752bdd04899399ca36d46        Forest County Resort   \n",
       "2  628752bdd04899399ca36d47            Regenta MPG Club   \n",
       "3  628752bdd04899399ca36d48  Bellevue Resort, Panchgani   \n",
       "4  628752bdd04899399ca36d49   NANDANVAN TENT AND RESORT   \n",
       "\n",
       "                                                 URL Total Rating  \\\n",
       "0  /hotels/cloud-green-resort-spa-hotel-in-mahaba...  465 Ratings   \n",
       "1  /hotels/forest-county-resort-hotel-in-mahabale...  225 Ratings   \n",
       "2  /hotels/regenta-mpg-club-hotel-in-mahabaleshwa...  391 Ratings   \n",
       "3  /hotels/bellevue-resort-panchgani-hotel-in-mah...  248 Ratings   \n",
       "4  /hotels/nandanvan-tent-and-resort-hotel-in-mah...   27 Ratings   \n",
       "\n",
       "  Review_Score  Rating                                          Amenities  \\\n",
       "0          3.9     3.9  ['Sanitizers', 'Parking (Free)', 'Wifi (Free)'...   \n",
       "1          3.8     3.8  ['Masks (Paid)', 'Gloves (Paid)', 'Thermal scr...   \n",
       "2          4.3     4.3  ['Hair nets (for hotel staff)', 'Thermal scree...   \n",
       "3          3.9     3.9  ['Sanitizers', 'Parking (Free)', 'Wifi (Free)'...   \n",
       "4          4.4     4.4  ['Hospital in the vicinity (Between 5 and 10 k...   \n",
       "\n",
       "   Latitude  Longitude   Price  \\\n",
       "0     17582      73746  2119.0   \n",
       "1     17857      73812  4650.0   \n",
       "2     17795      73689  6388.0   \n",
       "3        17         73  2392.0   \n",
       "4        17         73  2637.0   \n",
       "\n",
       "                                             Address           City  \\\n",
       "0  311,   Mahabaleshwar panchgani  road, near lin...  Mahabaleshwar   \n",
       "1  VARCHI MALUSARWADI, POST-TALDEV, HOUSE NO.82,,...  Mahabaleshwar   \n",
       "2        MPG Club, 233 Old Tapola Road, Dist.Satara,  Mahabaleshwar   \n",
       "3  Bhilar Kaswand Road, Bhilar Hill station betwe...  Mahabaleshwar   \n",
       "4  SR NO 5/1 , NEAR BSNL OFFICE , MAHABALESHWAR T...  Mahabaleshwar   \n",
       "\n",
       "   CityLabel  cluster  \n",
       "0         38        4  \n",
       "1         38        4  \n",
       "2         38        4  \n",
       "3         38        4  \n",
       "4         38        4  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "243e6205",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_19712\\2768001096.py:3: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df['Review_Score'].fillna(mean_review_score, inplace=True)\n"
     ]
    }
   ],
   "source": [
    "df['Review_Score'] = pd.to_numeric(df['Review_Score'], errors='coerce')\n",
    "mean_review_score = round(df['Review_Score'].mean(), 1)\n",
    "df['Review_Score'].fillna(mean_review_score, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "dcd39e51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "_id                0\n",
       "Name               0\n",
       "URL                0\n",
       "Total Rating     729\n",
       "Review_Score       0\n",
       "Rating          3841\n",
       "Amenities          0\n",
       "Latitude           0\n",
       "Longitude          0\n",
       "Price              0\n",
       "Address            0\n",
       "City               0\n",
       "CityLabel          0\n",
       "cluster            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "187d730d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Total Rating','Rating'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1f086e13",
   "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>_id</th>\n",
       "      <th>Name</th>\n",
       "      <th>URL</th>\n",
       "      <th>Review_Score</th>\n",
       "      <th>Amenities</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Price</th>\n",
       "      <th>Address</th>\n",
       "      <th>City</th>\n",
       "      <th>CityLabel</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>628752bdd04899399ca36d45</td>\n",
       "      <td>Cloud Green Resort &amp; Spa</td>\n",
       "      <td>/hotels/cloud-green-resort-spa-hotel-in-mahaba...</td>\n",
       "      <td>3.9</td>\n",
       "      <td>['Sanitizers', 'Parking (Free)', 'Wifi (Free)'...</td>\n",
       "      <td>17582</td>\n",
       "      <td>73746</td>\n",
       "      <td>2119.0</td>\n",
       "      <td>311,   Mahabaleshwar panchgani  road, near lin...</td>\n",
       "      <td>Mahabaleshwar</td>\n",
       "      <td>38</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>628752bdd04899399ca36d46</td>\n",
       "      <td>Forest County Resort</td>\n",
       "      <td>/hotels/forest-county-resort-hotel-in-mahabale...</td>\n",
       "      <td>3.8</td>\n",
       "      <td>['Masks (Paid)', 'Gloves (Paid)', 'Thermal scr...</td>\n",
       "      <td>17857</td>\n",
       "      <td>73812</td>\n",
       "      <td>4650.0</td>\n",
       "      <td>VARCHI MALUSARWADI, POST-TALDEV, HOUSE NO.82,,...</td>\n",
       "      <td>Mahabaleshwar</td>\n",
       "      <td>38</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>628752bdd04899399ca36d47</td>\n",
       "      <td>Regenta MPG Club</td>\n",
       "      <td>/hotels/regenta-mpg-club-hotel-in-mahabaleshwa...</td>\n",
       "      <td>4.3</td>\n",
       "      <td>['Hair nets (for hotel staff)', 'Thermal scree...</td>\n",
       "      <td>17795</td>\n",
       "      <td>73689</td>\n",
       "      <td>6388.0</td>\n",
       "      <td>MPG Club, 233 Old Tapola Road, Dist.Satara,</td>\n",
       "      <td>Mahabaleshwar</td>\n",
       "      <td>38</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>628752bdd04899399ca36d48</td>\n",
       "      <td>Bellevue Resort, Panchgani</td>\n",
       "      <td>/hotels/bellevue-resort-panchgani-hotel-in-mah...</td>\n",
       "      <td>3.9</td>\n",
       "      <td>['Sanitizers', 'Parking (Free)', 'Wifi (Free)'...</td>\n",
       "      <td>17</td>\n",
       "      <td>73</td>\n",
       "      <td>2392.0</td>\n",
       "      <td>Bhilar Kaswand Road, Bhilar Hill station betwe...</td>\n",
       "      <td>Mahabaleshwar</td>\n",
       "      <td>38</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>628752bdd04899399ca36d49</td>\n",
       "      <td>NANDANVAN TENT AND RESORT</td>\n",
       "      <td>/hotels/nandanvan-tent-and-resort-hotel-in-mah...</td>\n",
       "      <td>4.4</td>\n",
       "      <td>['Hospital in the vicinity (Between 5 and 10 k...</td>\n",
       "      <td>17</td>\n",
       "      <td>73</td>\n",
       "      <td>2637.0</td>\n",
       "      <td>SR NO 5/1 , NEAR BSNL OFFICE , MAHABALESHWAR T...</td>\n",
       "      <td>Mahabaleshwar</td>\n",
       "      <td>38</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        _id                        Name  \\\n",
       "0  628752bdd04899399ca36d45    Cloud Green Resort & Spa   \n",
       "1  628752bdd04899399ca36d46        Forest County Resort   \n",
       "2  628752bdd04899399ca36d47            Regenta MPG Club   \n",
       "3  628752bdd04899399ca36d48  Bellevue Resort, Panchgani   \n",
       "4  628752bdd04899399ca36d49   NANDANVAN TENT AND RESORT   \n",
       "\n",
       "                                                 URL  Review_Score  \\\n",
       "0  /hotels/cloud-green-resort-spa-hotel-in-mahaba...           3.9   \n",
       "1  /hotels/forest-county-resort-hotel-in-mahabale...           3.8   \n",
       "2  /hotels/regenta-mpg-club-hotel-in-mahabaleshwa...           4.3   \n",
       "3  /hotels/bellevue-resort-panchgani-hotel-in-mah...           3.9   \n",
       "4  /hotels/nandanvan-tent-and-resort-hotel-in-mah...           4.4   \n",
       "\n",
       "                                           Amenities  Latitude  Longitude  \\\n",
       "0  ['Sanitizers', 'Parking (Free)', 'Wifi (Free)'...     17582      73746   \n",
       "1  ['Masks (Paid)', 'Gloves (Paid)', 'Thermal scr...     17857      73812   \n",
       "2  ['Hair nets (for hotel staff)', 'Thermal scree...     17795      73689   \n",
       "3  ['Sanitizers', 'Parking (Free)', 'Wifi (Free)'...        17         73   \n",
       "4  ['Hospital in the vicinity (Between 5 and 10 k...        17         73   \n",
       "\n",
       "    Price                                            Address           City  \\\n",
       "0  2119.0  311,   Mahabaleshwar panchgani  road, near lin...  Mahabaleshwar   \n",
       "1  4650.0  VARCHI MALUSARWADI, POST-TALDEV, HOUSE NO.82,,...  Mahabaleshwar   \n",
       "2  6388.0        MPG Club, 233 Old Tapola Road, Dist.Satara,  Mahabaleshwar   \n",
       "3  2392.0  Bhilar Kaswand Road, Bhilar Hill station betwe...  Mahabaleshwar   \n",
       "4  2637.0  SR NO 5/1 , NEAR BSNL OFFICE , MAHABALESHWAR T...  Mahabaleshwar   \n",
       "\n",
       "   CityLabel  cluster  \n",
       "0         38        4  \n",
       "1         38        4  \n",
       "2         38        4  \n",
       "3         38        4  \n",
       "4         38        4  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "45b43b46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "72"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.CityLabel.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "80351515",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "67c70ce4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11885 entries, 0 to 11884\n",
      "Data columns (total 12 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   _id           11885 non-null  object \n",
      " 1   Name          11885 non-null  object \n",
      " 2   URL           11885 non-null  object \n",
      " 3   Review_Score  11885 non-null  float64\n",
      " 4   Amenities     11885 non-null  object \n",
      " 5   Latitude      11885 non-null  int64  \n",
      " 6   Longitude     11885 non-null  int64  \n",
      " 7   Price         11885 non-null  float64\n",
      " 8   Address       11885 non-null  object \n",
      " 9   City          11885 non-null  object \n",
      " 10  CityLabel     11885 non-null  int64  \n",
      " 11  cluster       11885 non-null  int64  \n",
      "dtypes: float64(2), int64(4), object(6)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bcf7ce1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Price'] = df['Price'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "089fad85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "_id             0\n",
       "Name            0\n",
       "URL             0\n",
       "Review_Score    0\n",
       "Amenities       0\n",
       "Latitude        0\n",
       "Longitude       0\n",
       "Price           0\n",
       "Address         0\n",
       "City            0\n",
       "CityLabel       0\n",
       "cluster         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d3c70c17",
   "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>_id</th>\n",
       "      <th>Name</th>\n",
       "      <th>URL</th>\n",
       "      <th>Review_Score</th>\n",
       "      <th>Amenities</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Price</th>\n",
       "      <th>Address</th>\n",
       "      <th>City</th>\n",
       "      <th>CityLabel</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>628752bdd04899399ca36d45</td>\n",
       "      <td>Cloud Green Resort &amp; Spa</td>\n",
       "      <td>/hotels/cloud-green-resort-spa-hotel-in-mahaba...</td>\n",
       "      <td>3.9</td>\n",
       "      <td>['Sanitizers', 'Parking (Free)', 'Wifi (Free)'...</td>\n",
       "      <td>17582</td>\n",
       "      <td>73746</td>\n",
       "      <td>2119</td>\n",
       "      <td>311,   Mahabaleshwar panchgani  road, near lin...</td>\n",
       "      <td>Mahabaleshwar</td>\n",
       "      <td>38</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>628752bdd04899399ca36d46</td>\n",
       "      <td>Forest County Resort</td>\n",
       "      <td>/hotels/forest-county-resort-hotel-in-mahabale...</td>\n",
       "      <td>3.8</td>\n",
       "      <td>['Masks (Paid)', 'Gloves (Paid)', 'Thermal scr...</td>\n",
       "      <td>17857</td>\n",
       "      <td>73812</td>\n",
       "      <td>4650</td>\n",
       "      <td>VARCHI MALUSARWADI, POST-TALDEV, HOUSE NO.82,,...</td>\n",
       "      <td>Mahabaleshwar</td>\n",
       "      <td>38</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>628752bdd04899399ca36d47</td>\n",
       "      <td>Regenta MPG Club</td>\n",
       "      <td>/hotels/regenta-mpg-club-hotel-in-mahabaleshwa...</td>\n",
       "      <td>4.3</td>\n",
       "      <td>['Hair nets (for hotel staff)', 'Thermal scree...</td>\n",
       "      <td>17795</td>\n",
       "      <td>73689</td>\n",
       "      <td>6388</td>\n",
       "      <td>MPG Club, 233 Old Tapola Road, Dist.Satara,</td>\n",
       "      <td>Mahabaleshwar</td>\n",
       "      <td>38</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>628752bdd04899399ca36d48</td>\n",
       "      <td>Bellevue Resort, Panchgani</td>\n",
       "      <td>/hotels/bellevue-resort-panchgani-hotel-in-mah...</td>\n",
       "      <td>3.9</td>\n",
       "      <td>['Sanitizers', 'Parking (Free)', 'Wifi (Free)'...</td>\n",
       "      <td>17</td>\n",
       "      <td>73</td>\n",
       "      <td>2392</td>\n",
       "      <td>Bhilar Kaswand Road, Bhilar Hill station betwe...</td>\n",
       "      <td>Mahabaleshwar</td>\n",
       "      <td>38</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>628752bdd04899399ca36d49</td>\n",
       "      <td>NANDANVAN TENT AND RESORT</td>\n",
       "      <td>/hotels/nandanvan-tent-and-resort-hotel-in-mah...</td>\n",
       "      <td>4.4</td>\n",
       "      <td>['Hospital in the vicinity (Between 5 and 10 k...</td>\n",
       "      <td>17</td>\n",
       "      <td>73</td>\n",
       "      <td>2637</td>\n",
       "      <td>SR NO 5/1 , NEAR BSNL OFFICE , MAHABALESHWAR T...</td>\n",
       "      <td>Mahabaleshwar</td>\n",
       "      <td>38</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        _id                        Name  \\\n",
       "0  628752bdd04899399ca36d45    Cloud Green Resort & Spa   \n",
       "1  628752bdd04899399ca36d46        Forest County Resort   \n",
       "2  628752bdd04899399ca36d47            Regenta MPG Club   \n",
       "3  628752bdd04899399ca36d48  Bellevue Resort, Panchgani   \n",
       "4  628752bdd04899399ca36d49   NANDANVAN TENT AND RESORT   \n",
       "\n",
       "                                                 URL  Review_Score  \\\n",
       "0  /hotels/cloud-green-resort-spa-hotel-in-mahaba...           3.9   \n",
       "1  /hotels/forest-county-resort-hotel-in-mahabale...           3.8   \n",
       "2  /hotels/regenta-mpg-club-hotel-in-mahabaleshwa...           4.3   \n",
       "3  /hotels/bellevue-resort-panchgani-hotel-in-mah...           3.9   \n",
       "4  /hotels/nandanvan-tent-and-resort-hotel-in-mah...           4.4   \n",
       "\n",
       "                                           Amenities  Latitude  Longitude  \\\n",
       "0  ['Sanitizers', 'Parking (Free)', 'Wifi (Free)'...     17582      73746   \n",
       "1  ['Masks (Paid)', 'Gloves (Paid)', 'Thermal scr...     17857      73812   \n",
       "2  ['Hair nets (for hotel staff)', 'Thermal scree...     17795      73689   \n",
       "3  ['Sanitizers', 'Parking (Free)', 'Wifi (Free)'...        17         73   \n",
       "4  ['Hospital in the vicinity (Between 5 and 10 k...        17         73   \n",
       "\n",
       "   Price                                            Address           City  \\\n",
       "0   2119  311,   Mahabaleshwar panchgani  road, near lin...  Mahabaleshwar   \n",
       "1   4650  VARCHI MALUSARWADI, POST-TALDEV, HOUSE NO.82,,...  Mahabaleshwar   \n",
       "2   6388        MPG Club, 233 Old Tapola Road, Dist.Satara,  Mahabaleshwar   \n",
       "3   2392  Bhilar Kaswand Road, Bhilar Hill station betwe...  Mahabaleshwar   \n",
       "4   2637  SR NO 5/1 , NEAR BSNL OFFICE , MAHABALESHWAR T...  Mahabaleshwar   \n",
       "\n",
       "   CityLabel  cluster  \n",
       "0         38        4  \n",
       "1         38        4  \n",
       "2         38        4  \n",
       "3         38        4  \n",
       "4         38        4  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b6717124",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[\\'Sanitizers\\', \\'Parking (Free)\\', \\'Wifi (Free)\\', \\'Air Conditioning\\', \\'Room service (Limited duration)\\', \\'Power backup\\', \\'Intercom\\', \\'Telephone\\', \\'Housekeeping\\', \\'Public restrooms\\', \\'Laundry (Paid)\\', \\'Newspaper (English)\\', \\'Bathroom (Attached)\\', \\'Swimming Pool (Common Pool, Kids Pool)\\', \"Children\\'s play area\", \\'Restaurant\\', \\'Dining Area\\', \\'Barbeque\\', \\'Kids meals\\', \\'CCTV\\', \\'Fire extinguishers\\', \\'Security\\', \\'Luggage storage\\', \\'Mail services\\', \\'Luggage assistance\\', \\'Electrical Sockets\\', \\'Doctor on call\\', \\'Ticket/ Tour Assistance\\', \\'Bellboy service\\', \\'Wake-up Call / Service\\', \\'Facial treatments\\', \\'Spa (Paid)\\', \\'Massage (Couple Massage, Foot massage)\\', \\'Steam and Sauna (Paid)\\', \\'Bonfire (On Request)\\', \\'Vehicle Rentals\\', \\'Indoor games\\', \\'Lawn\\', \\'Reception\\', \\'Seating Area\\', \\'Conference room\\', \\'Printer\\']'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Amenities'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9bbacfe6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_text(text):\n",
    "    cleaned_text = text.replace('[', '').replace(']', '').replace('\\'', '').replace('/', '')\n",
    "    return cleaned_text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "17b7b3f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Amenities'] = df['Amenities'].apply(clean_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0e69af55",
   "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>_id</th>\n",
       "      <th>Name</th>\n",
       "      <th>URL</th>\n",
       "      <th>Review_Score</th>\n",
       "      <th>Amenities</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Price</th>\n",
       "      <th>Address</th>\n",
       "      <th>City</th>\n",
       "      <th>CityLabel</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>628752bdd04899399ca36d45</td>\n",
       "      <td>Cloud Green Resort &amp; Spa</td>\n",
       "      <td>/hotels/cloud-green-resort-spa-hotel-in-mahaba...</td>\n",
       "      <td>3.9</td>\n",
       "      <td>Sanitizers, Parking (Free), Wifi (Free), Air C...</td>\n",
       "      <td>17582</td>\n",
       "      <td>73746</td>\n",
       "      <td>2119</td>\n",
       "      <td>311,   Mahabaleshwar panchgani  road, near lin...</td>\n",
       "      <td>Mahabaleshwar</td>\n",
       "      <td>38</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>628752bdd04899399ca36d46</td>\n",
       "      <td>Forest County Resort</td>\n",
       "      <td>/hotels/forest-county-resort-hotel-in-mahabale...</td>\n",
       "      <td>3.8</td>\n",
       "      <td>Masks (Paid), Gloves (Paid), Thermal screening...</td>\n",
       "      <td>17857</td>\n",
       "      <td>73812</td>\n",
       "      <td>4650</td>\n",
       "      <td>VARCHI MALUSARWADI, POST-TALDEV, HOUSE NO.82,,...</td>\n",
       "      <td>Mahabaleshwar</td>\n",
       "      <td>38</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>628752bdd04899399ca36d47</td>\n",
       "      <td>Regenta MPG Club</td>\n",
       "      <td>/hotels/regenta-mpg-club-hotel-in-mahabaleshwa...</td>\n",
       "      <td>4.3</td>\n",
       "      <td>Hair nets (for hotel staff), Thermal screening...</td>\n",
       "      <td>17795</td>\n",
       "      <td>73689</td>\n",
       "      <td>6388</td>\n",
       "      <td>MPG Club, 233 Old Tapola Road, Dist.Satara,</td>\n",
       "      <td>Mahabaleshwar</td>\n",
       "      <td>38</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>628752bdd04899399ca36d48</td>\n",
       "      <td>Bellevue Resort, Panchgani</td>\n",
       "      <td>/hotels/bellevue-resort-panchgani-hotel-in-mah...</td>\n",
       "      <td>3.9</td>\n",
       "      <td>Sanitizers, Parking (Free), Wifi (Free), Air C...</td>\n",
       "      <td>17</td>\n",
       "      <td>73</td>\n",
       "      <td>2392</td>\n",
       "      <td>Bhilar Kaswand Road, Bhilar Hill station betwe...</td>\n",
       "      <td>Mahabaleshwar</td>\n",
       "      <td>38</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>628752bdd04899399ca36d49</td>\n",
       "      <td>NANDANVAN TENT AND RESORT</td>\n",
       "      <td>/hotels/nandanvan-tent-and-resort-hotel-in-mah...</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Hospital in the vicinity (Between 5 and 10 km)...</td>\n",
       "      <td>17</td>\n",
       "      <td>73</td>\n",
       "      <td>2637</td>\n",
       "      <td>SR NO 5/1 , NEAR BSNL OFFICE , MAHABALESHWAR T...</td>\n",
       "      <td>Mahabaleshwar</td>\n",
       "      <td>38</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        _id                        Name  \\\n",
       "0  628752bdd04899399ca36d45    Cloud Green Resort & Spa   \n",
       "1  628752bdd04899399ca36d46        Forest County Resort   \n",
       "2  628752bdd04899399ca36d47            Regenta MPG Club   \n",
       "3  628752bdd04899399ca36d48  Bellevue Resort, Panchgani   \n",
       "4  628752bdd04899399ca36d49   NANDANVAN TENT AND RESORT   \n",
       "\n",
       "                                                 URL  Review_Score  \\\n",
       "0  /hotels/cloud-green-resort-spa-hotel-in-mahaba...           3.9   \n",
       "1  /hotels/forest-county-resort-hotel-in-mahabale...           3.8   \n",
       "2  /hotels/regenta-mpg-club-hotel-in-mahabaleshwa...           4.3   \n",
       "3  /hotels/bellevue-resort-panchgani-hotel-in-mah...           3.9   \n",
       "4  /hotels/nandanvan-tent-and-resort-hotel-in-mah...           4.4   \n",
       "\n",
       "                                           Amenities  Latitude  Longitude  \\\n",
       "0  Sanitizers, Parking (Free), Wifi (Free), Air C...     17582      73746   \n",
       "1  Masks (Paid), Gloves (Paid), Thermal screening...     17857      73812   \n",
       "2  Hair nets (for hotel staff), Thermal screening...     17795      73689   \n",
       "3  Sanitizers, Parking (Free), Wifi (Free), Air C...        17         73   \n",
       "4  Hospital in the vicinity (Between 5 and 10 km)...        17         73   \n",
       "\n",
       "   Price                                            Address           City  \\\n",
       "0   2119  311,   Mahabaleshwar panchgani  road, near lin...  Mahabaleshwar   \n",
       "1   4650  VARCHI MALUSARWADI, POST-TALDEV, HOUSE NO.82,,...  Mahabaleshwar   \n",
       "2   6388        MPG Club, 233 Old Tapola Road, Dist.Satara,  Mahabaleshwar   \n",
       "3   2392  Bhilar Kaswand Road, Bhilar Hill station betwe...  Mahabaleshwar   \n",
       "4   2637  SR NO 5/1 , NEAR BSNL OFFICE , MAHABALESHWAR T...  Mahabaleshwar   \n",
       "\n",
       "   CityLabel  cluster  \n",
       "0         38        4  \n",
       "1         38        4  \n",
       "2         38        4  \n",
       "3         38        4  \n",
       "4         38        4  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d590a308",
   "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>_id</th>\n",
       "      <th>Name</th>\n",
       "      <th>URL</th>\n",
       "      <th>Review_Score</th>\n",
       "      <th>Amenities</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Price</th>\n",
       "      <th>Address</th>\n",
       "      <th>City</th>\n",
       "      <th>CityLabel</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>11885</td>\n",
       "      <td>11885</td>\n",
       "      <td>11885</td>\n",
       "      <td>11885.000000</td>\n",
       "      <td>11885</td>\n",
       "      <td>11885.000000</td>\n",
       "      <td>11885.000000</td>\n",
       "      <td>11885.000000</td>\n",
       "      <td>11885</td>\n",
       "      <td>11885</td>\n",
       "      <td>11885.000000</td>\n",
       "      <td>11885.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>11885</td>\n",
       "      <td>11425</td>\n",
       "      <td>11629</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11523</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11592</td>\n",
       "      <td>72</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>628752bdd04899399ca36d45</td>\n",
       "      <td>Hotel President</td>\n",
       "      <td>/hotels/goldstone-farms-hotel-in-pushkar-47836...</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Behind Powerhouse, Bhagawanpura , Pushkar</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>840</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.780606</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2024.098443</td>\n",
       "      <td>7295.982667</td>\n",
       "      <td>3071.485486</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.602524</td>\n",
       "      <td>20.300210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.492292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6696.811823</td>\n",
       "      <td>22486.937019</td>\n",
       "      <td>3768.290476</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.102769</td>\n",
       "      <td>14.091328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>-109.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>1323.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>2072.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.900000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>3468.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34933.000000</td>\n",
       "      <td>88947.000000</td>\n",
       "      <td>86925.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>51.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             _id             Name  \\\n",
       "count                      11885            11885   \n",
       "unique                     11885            11425   \n",
       "top     628752bdd04899399ca36d45  Hotel President   \n",
       "freq                           1                6   \n",
       "mean                         NaN              NaN   \n",
       "std                          NaN              NaN   \n",
       "min                          NaN              NaN   \n",
       "25%                          NaN              NaN   \n",
       "50%                          NaN              NaN   \n",
       "75%                          NaN              NaN   \n",
       "max                          NaN              NaN   \n",
       "\n",
       "                                                      URL  Review_Score  \\\n",
       "count                                               11885  11885.000000   \n",
       "unique                                              11629           NaN   \n",
       "top     /hotels/goldstone-farms-hotel-in-pushkar-47836...           NaN   \n",
       "freq                                                    6           NaN   \n",
       "mean                                                  NaN      3.780606   \n",
       "std                                                   NaN      0.492292   \n",
       "min                                                   NaN      1.100000   \n",
       "25%                                                   NaN      3.800000   \n",
       "50%                                                   NaN      3.800000   \n",
       "75%                                                   NaN      3.900000   \n",
       "max                                                   NaN      4.900000   \n",
       "\n",
       "       Amenities      Latitude     Longitude         Price  \\\n",
       "count      11885  11885.000000  11885.000000  11885.000000   \n",
       "unique     11523           NaN           NaN           NaN   \n",
       "top                        NaN           NaN           NaN   \n",
       "freq          39           NaN           NaN           NaN   \n",
       "mean         NaN   2024.098443   7295.982667   3071.485486   \n",
       "std          NaN   6696.811823  22486.937019   3768.290476   \n",
       "min          NaN      8.000000   -109.000000    199.000000   \n",
       "25%          NaN     17.000000     74.000000   1323.000000   \n",
       "50%          NaN     25.000000     77.000000   2072.000000   \n",
       "75%          NaN     30.000000     79.000000   3468.000000   \n",
       "max          NaN  34933.000000  88947.000000  86925.000000   \n",
       "\n",
       "                                          Address    City     CityLabel  \\\n",
       "count                                       11885   11885  11885.000000   \n",
       "unique                                      11592      72           NaN   \n",
       "top     Behind Powerhouse, Bhagawanpura , Pushkar  Mumbai           NaN   \n",
       "freq                                            6     840           NaN   \n",
       "mean                                          NaN     NaN     39.602524   \n",
       "std                                           NaN     NaN     21.102769   \n",
       "min                                           NaN     NaN      0.000000   \n",
       "25%                                           NaN     NaN     24.000000   \n",
       "50%                                           NaN     NaN     43.000000   \n",
       "75%                                           NaN     NaN     56.000000   \n",
       "max                                           NaN     NaN     71.000000   \n",
       "\n",
       "             cluster  \n",
       "count   11885.000000  \n",
       "unique           NaN  \n",
       "top              NaN  \n",
       "freq             NaN  \n",
       "mean       20.300210  \n",
       "std        14.091328  \n",
       "min         0.000000  \n",
       "25%         8.000000  \n",
       "50%        18.000000  \n",
       "75%        31.000000  \n",
       "max        51.000000  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2f0d17e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Price vs Review Score')]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.figure(figsize=(15,7))\n",
    "sns.regplot(data=df, y='Price', x='Review_Score', color='r').set(title='Price vs Review Score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ec56dbba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "filtered_df = df[df['Review_Score'] >= 3]\n",
    "\n",
    "plot = filtered_df[['Name', 'Review_Score']].drop_duplicates()\n",
    "\n",
    "sns.set(font_scale=1.5)\n",
    "\n",
    "a4_dims = (15, 10)\n",
    "fig, ax = plt.subplots(figsize=a4_dims)\n",
    "\n",
    "sns.countplot(ax=ax, x='Review_Score', data=plot)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "315446c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Number of hotels in each city')]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = df[['City']]\n",
    "plot.groupby('City').count()\n",
    "sns.set(font_scale = 1.5)\n",
    "a4_dims = (15,7)\n",
    "fig, ax = plt.subplots(figsize=a4_dims)\n",
    "plt.xticks(rotation=90)\n",
    "sns.countplot(ax = ax, x = 'City', data=plot).set(title='Number of hotels in each city')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ad391e98",
   "metadata": {},
   "outputs": [],
   "source": [
    "def citybased(city):\n",
    "    df['City'] = df['City'].str.lower()\n",
    "    citybase = df[df['City'] == city.lower()]\n",
    "    citybase = citybase.sort_values(by='Review_Score', ascending=False)\n",
    "    citybase.drop_duplicates(subset = '_id', keep = 'first', inplace=True)\n",
    "    if(citybase.empty==0):\n",
    "        hname = citybase[['Name','Price','Review_Score','Address','Amenities','URL']]\n",
    "        return hname.head(5)\n",
    "    else:\n",
    "        print('No Hotels Available!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "8c549f9a",
   "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>Name</th>\n",
       "      <th>Price</th>\n",
       "      <th>Review_Score</th>\n",
       "      <th>Address</th>\n",
       "      <th>Amenities</th>\n",
       "      <th>URL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9745</th>\n",
       "      <td>HISTORIA ROYAL</td>\n",
       "      <td>2565</td>\n",
       "      <td>3.8</td>\n",
       "      <td>Historia Royal 100 Feet Road New Bhupalpura Ud...</td>\n",
       "      <td>Masks (Free), Disinfectant wipes (Free), Sanit...</td>\n",
       "      <td>/hotels/historia-royal-hotel-in-udaipur-439721...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10224</th>\n",
       "      <td>Hotel Krishn Palace</td>\n",
       "      <td>974</td>\n",
       "      <td>3.8</td>\n",
       "      <td>Krishna Plaza,Opp. Ashoka Place Garden, Plot N...</td>\n",
       "      <td>Masks (Paid), Sanitizers (Free), Parking (Free...</td>\n",
       "      <td>/hotels/krishn-palace-hotel-in-udaipur-8116599...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10226</th>\n",
       "      <td>StayVista at Mewar Mansion</td>\n",
       "      <td>8100</td>\n",
       "      <td>3.8</td>\n",
       "      <td>Mewar Mansion, Nohra village, behind Araliyas ...</td>\n",
       "      <td>KitchenKitchenette (Microwave), Power backup, ...</td>\n",
       "      <td>/hotels/stayvista-at-mewar-mansion-hotel-in-ud...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10227</th>\n",
       "      <td>Hotel Rion</td>\n",
       "      <td>1824</td>\n",
       "      <td>3.8</td>\n",
       "      <td>TORAN BAWRI, 7, MEWAR MOTORS LINK ROAD, CITY\\r...</td>\n",
       "      <td>Masks (Paid), Disinfectant wipes, Gloves, Sani...</td>\n",
       "      <td>/hotels/rion-hotel-in-udaipur-3392505165256393...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10228</th>\n",
       "      <td>Nandan Van Farm House NO 8</td>\n",
       "      <td>5445</td>\n",
       "      <td>3.8</td>\n",
       "      <td>8, Parswanath Farm Villa  opposite kesariya b...</td>\n",
       "      <td>KitchenKitchenette (Cooking appliances, Cutler...</td>\n",
       "      <td>/hotels/nandan-van-farm-house-no-8-hotel-in-ud...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Name  Price  Review_Score  \\\n",
       "9745               HISTORIA ROYAL   2565           3.8   \n",
       "10224         Hotel Krishn Palace    974           3.8   \n",
       "10226  StayVista at Mewar Mansion   8100           3.8   \n",
       "10227                  Hotel Rion   1824           3.8   \n",
       "10228  Nandan Van Farm House NO 8   5445           3.8   \n",
       "\n",
       "                                                 Address  \\\n",
       "9745   Historia Royal 100 Feet Road New Bhupalpura Ud...   \n",
       "10224  Krishna Plaza,Opp. Ashoka Place Garden, Plot N...   \n",
       "10226  Mewar Mansion, Nohra village, behind Araliyas ...   \n",
       "10227  TORAN BAWRI, 7, MEWAR MOTORS LINK ROAD, CITY\\r...   \n",
       "10228   8, Parswanath Farm Villa  opposite kesariya b...   \n",
       "\n",
       "                                               Amenities  \\\n",
       "9745   Masks (Free), Disinfectant wipes (Free), Sanit...   \n",
       "10224  Masks (Paid), Sanitizers (Free), Parking (Free...   \n",
       "10226  KitchenKitchenette (Microwave), Power backup, ...   \n",
       "10227  Masks (Paid), Disinfectant wipes, Gloves, Sani...   \n",
       "10228  KitchenKitchenette (Cooking appliances, Cutler...   \n",
       "\n",
       "                                                     URL  \n",
       "9745   /hotels/historia-royal-hotel-in-udaipur-439721...  \n",
       "10224  /hotels/krishn-palace-hotel-in-udaipur-8116599...  \n",
       "10226  /hotels/stayvista-at-mewar-mansion-hotel-in-ud...  \n",
       "10227  /hotels/rion-hotel-in-udaipur-3392505165256393...  \n",
       "10228  /hotels/nandan-van-farm-house-no-8-hotel-in-ud...  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "citybased('udaipur')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "815f19a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Sanitizers, Parking (Free), Wifi (Free), Air Conditioning, Room service (Limited duration), Power backup, Intercom, Telephone, Housekeeping, Public restrooms, Laundry (Paid), Newspaper (English), Bathroom (Attached), Swimming Pool (Common Pool, Kids Pool), \"Childrens play area\", Restaurant, Dining Area, Barbeque, Kids meals, CCTV, Fire extinguishers, Security, Luggage storage, Mail services, Luggage assistance, Electrical Sockets, Doctor on call, Ticket Tour Assistance, Bellboy service, Wake-up Call  Service, Facial treatments, Spa (Paid), Massage (Couple Massage, Foot massage), Steam and Sauna (Paid), Bonfire (On Request), Vehicle Rentals, Indoor games, Lawn, Reception, Seating Area, Conference room, Printer'"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Amenities'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "22cd73ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from nltk.corpus import stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "48143a10",
   "metadata": {},
   "outputs": [],
   "source": [
    "def requirementbased(city, features):\n",
    "    df['Name'] = df['Name'].str.lower()\n",
    "    df['Amenities'] = df['Amenities'].str.lower()\n",
    "    features = features.lower()\n",
    "    features_token = word_tokenize(features)\n",
    "    sw = stopwords.words('english')\n",
    "    lemm = WordNetLemmatizer()\n",
    "    f1_set = {w for w in features_token if not w in sw}\n",
    "    f_set = set()\n",
    "    for se in f1_set:\n",
    "        f_set.add(lemm.lemmatize(se))\n",
    "    reqbased = df[df['City'] == city.lower()]\n",
    "    if reqbased.empty:\n",
    "            print(\"No hotels data found for this city.\")\n",
    "            return None\n",
    "    reqbased = reqbased.sort_values(by='Review_Score', ascending=False)\n",
    "    reqbased = reqbased.set_index(np.arange(reqbased.shape[0]))\n",
    "    cos = [];\n",
    "    \n",
    "    for i in range(reqbased.shape[0]):\n",
    "        temp_tokens = word_tokenize(reqbased['Amenities'][i])\n",
    "        temp1_set = {w for w in temp_tokens if not w in sw}\n",
    "        temp_set = set()\n",
    "        for se in temp1_set:\n",
    "            temp_set.add(lemm.lemmatize(se))\n",
    "        revector = temp_set.intersection(f_set)\n",
    "        cos.append(len(revector))\n",
    "    reqbased['Similarity'] = cos\n",
    "    reqbased = reqbased.sort_values(by='Similarity', ascending=False)\n",
    "    reqbased.drop_duplicates(subset='_id',keep='first',inplace=True)\n",
    "    return reqbased[['Name','Price','Review_Score','Address','Amenities','URL','Similarity']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "bd0dee32",
   "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>Name</th>\n",
       "      <th>Price</th>\n",
       "      <th>Review_Score</th>\n",
       "      <th>Address</th>\n",
       "      <th>Amenities</th>\n",
       "      <th>URL</th>\n",
       "      <th>Similarity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>silver oaks- 2bhk apartment</td>\n",
       "      <td>4500</td>\n",
       "      <td>3.8</td>\n",
       "      <td>Highland Park Teracces, Flat No: 100 C, Third ...</td>\n",
       "      <td>sanitizers (free), sanitizers installed, kitch...</td>\n",
       "      <td>/hotels/silver-oaks-2bhk-apartment-hotel-in-ch...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>hotel gulmeher by ocean hospitality</td>\n",
       "      <td>1286</td>\n",
       "      <td>3.8</td>\n",
       "      <td>Hotel NIDO- Shimla kalka highway Zirakpur, Pun...</td>\n",
       "      <td>masks (free), disinfectant wipes (free), glove...</td>\n",
       "      <td>/hotels/gulmeher-by-ocean-hospitality-hotel-in...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>hotel glass inn</td>\n",
       "      <td>1740</td>\n",
       "      <td>3.5</td>\n",
       "      <td>Sco No.1 wadhawa Nagar SHIMLA KALKA HIGHWAY , ...</td>\n",
       "      <td>disinfectant wipes (free), hair nets, thermal ...</td>\n",
       "      <td>/hotels/glass-inn-hotel-in-chandigarh-96417825...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>dreamz inn &amp; suites</td>\n",
       "      <td>3003</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Plot No 7,8,9 and 10, Shri Bala Ji Complex, Dh...</td>\n",
       "      <td>masks, disinfectant wipes, sanitizers, face sh...</td>\n",
       "      <td>/hotels/dreamz-inn-suites-hotel-in-chandigarh-...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>hotel classic</td>\n",
       "      <td>2814</td>\n",
       "      <td>3.8</td>\n",
       "      <td>Sec 403-403, Sec 35-C, Chandigarh</td>\n",
       "      <td>masks, disinfectant wipes, gloves, sanitizers,...</td>\n",
       "      <td>/hotels/classic-hotel-in-chandigarh-6372139558...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Name  Price  Review_Score  \\\n",
       "159          silver oaks- 2bhk apartment   4500           3.8   \n",
       "141  hotel gulmeher by ocean hospitality   1286           3.8   \n",
       "291                      hotel glass inn   1740           3.5   \n",
       "11                   dreamz inn & suites   3003           4.4   \n",
       "217                        hotel classic   2814           3.8   \n",
       "\n",
       "                                               Address  \\\n",
       "159  Highland Park Teracces, Flat No: 100 C, Third ...   \n",
       "141  Hotel NIDO- Shimla kalka highway Zirakpur, Pun...   \n",
       "291  Sco No.1 wadhawa Nagar SHIMLA KALKA HIGHWAY , ...   \n",
       "11   Plot No 7,8,9 and 10, Shri Bala Ji Complex, Dh...   \n",
       "217                  Sec 403-403, Sec 35-C, Chandigarh   \n",
       "\n",
       "                                             Amenities  \\\n",
       "159  sanitizers (free), sanitizers installed, kitch...   \n",
       "141  masks (free), disinfectant wipes (free), glove...   \n",
       "291  disinfectant wipes (free), hair nets, thermal ...   \n",
       "11   masks, disinfectant wipes, sanitizers, face sh...   \n",
       "217  masks, disinfectant wipes, gloves, sanitizers,...   \n",
       "\n",
       "                                                   URL  Similarity  \n",
       "159  /hotels/silver-oaks-2bhk-apartment-hotel-in-ch...           2  \n",
       "141  /hotels/gulmeher-by-ocean-hospitality-hotel-in...           1  \n",
       "291  /hotels/glass-inn-hotel-in-chandigarh-96417825...           1  \n",
       "11   /hotels/dreamz-inn-suites-hotel-in-chandigarh-...           1  \n",
       "217  /hotels/classic-hotel-in-chandigarh-6372139558...           1  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "requirementbased('chandigarh', 'i want hot water')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "8e218fb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def recommedor(city, features, price):\n",
    "    df['Name'] = df['Name'].str.lower()\n",
    "    df['Amenities'] = df['Amenities'].str.lower()\n",
    "    features = features.lower()\n",
    "    features_token = word_tokenize(features)\n",
    "    sw = stopwords.words('english')\n",
    "    lemm = WordNetLemmatizer()\n",
    "    f1_set = {w for w in features_token if not w in sw}\n",
    "    f_set = set()\n",
    "    for se in f1_set:\n",
    "        f_set.add(lemm.lemmatize(se))\n",
    "    reqbased = df[df['City'] == city.lower()]\n",
    "    if reqbased.empty:\n",
    "            print(\"No hotels data found for this city.\")\n",
    "            return None\n",
    "    reqbased = reqbased[reqbased['Price'] <= price].sort_values(by='Review_Score', ascending=False)\n",
    "    reqbased = reqbased.set_index(np.arange(reqbased.shape[0]))\n",
    "    cos = [];\n",
    "    \n",
    "    for i in range(reqbased.shape[0]):\n",
    "        temp_tokens = word_tokenize(reqbased['Amenities'][i])\n",
    "        temp1_set = {w for w in temp_tokens if not w in sw}\n",
    "        temp_set = set()\n",
    "        for se in temp1_set:\n",
    "            temp_set.add(lemm.lemmatize(se))\n",
    "        revector = temp_set.intersection(f_set)\n",
    "        cos.append(len(revector))\n",
    "    reqbased['Similarity'] = cos\n",
    "    reqbased = reqbased.sort_values(by='Similarity', ascending=False)\n",
    "    reqbased.drop_duplicates(subset='_id',keep='first',inplace=True)\n",
    "    return reqbased[['Name','Price','Review_Score','Address','Amenities','URL','Similarity']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "0555808b",
   "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>Name</th>\n",
       "      <th>Price</th>\n",
       "      <th>Review_Score</th>\n",
       "      <th>Address</th>\n",
       "      <th>Amenities</th>\n",
       "      <th>URL</th>\n",
       "      <th>Similarity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>neenava dormitory</td>\n",
       "      <td>491</td>\n",
       "      <td>4.5</td>\n",
       "      <td>GALA NO 4, SHREE GANESH COMPOUND, DSILVA BAUG ...</td>\n",
       "      <td>masks (paid), gloves (paid), thermal screening...</td>\n",
       "      <td>/hotels/neenava-dormitory-hotel-in-mumbai-2455...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sleep well dormitory</td>\n",
       "      <td>450</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1,2nd Floor, Shree Dhara Dham Society, Ramraj ...</td>\n",
       "      <td>parking (free), wifi (free), air conditioning ...</td>\n",
       "      <td>/hotels/sleep-well-dormitory-hotel-in-mumbai-6...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>hotel shadab palace</td>\n",
       "      <td>761</td>\n",
       "      <td>3.7</td>\n",
       "      <td>Tandel Street (N),Siddhi Mohalla,Near Baba Gha...</td>\n",
       "      <td>masks (paid), gloves (paid), sanitizers (paid)...</td>\n",
       "      <td>/hotels/shadab-palace-hotel-in-mumbai-21737546...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>hotel new vasantashram</td>\n",
       "      <td>685</td>\n",
       "      <td>3.4</td>\n",
       "      <td>Hostel New Vashantashram,  commissioner of Pol...</td>\n",
       "      <td>wifi (free), housekeeping, laundry (paid), tel...</td>\n",
       "      <td>/hotels/new-vasantashram-hotel-in-mumbai-65283...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>oyo 9957 hotel terminus square</td>\n",
       "      <td>930</td>\n",
       "      <td>3.5</td>\n",
       "      <td>104, 1st Floor, Radhika Residency, Mandakini P...</td>\n",
       "      <td>sanitizers, face shields (for hotel staff), sa...</td>\n",
       "      <td>/hotels/oyo-9957-terminus-square-hotel-in-mumb...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Name  Price  Review_Score  \\\n",
       "4                neenava dormitory    491           4.5   \n",
       "0             sleep well dormitory    450           4.5   \n",
       "51             hotel shadab palace    761           3.7   \n",
       "59          hotel new vasantashram    685           3.4   \n",
       "58  oyo 9957 hotel terminus square    930           3.5   \n",
       "\n",
       "                                              Address  \\\n",
       "4   GALA NO 4, SHREE GANESH COMPOUND, DSILVA BAUG ...   \n",
       "0   1,2nd Floor, Shree Dhara Dham Society, Ramraj ...   \n",
       "51  Tandel Street (N),Siddhi Mohalla,Near Baba Gha...   \n",
       "59  Hostel New Vashantashram,  commissioner of Pol...   \n",
       "58  104, 1st Floor, Radhika Residency, Mandakini P...   \n",
       "\n",
       "                                            Amenities  \\\n",
       "4   masks (paid), gloves (paid), thermal screening...   \n",
       "0   parking (free), wifi (free), air conditioning ...   \n",
       "51  masks (paid), gloves (paid), sanitizers (paid)...   \n",
       "59  wifi (free), housekeeping, laundry (paid), tel...   \n",
       "58  sanitizers, face shields (for hotel staff), sa...   \n",
       "\n",
       "                                                  URL  Similarity  \n",
       "4   /hotels/neenava-dormitory-hotel-in-mumbai-2455...           1  \n",
       "0   /hotels/sleep-well-dormitory-hotel-in-mumbai-6...           0  \n",
       "51  /hotels/shadab-palace-hotel-in-mumbai-21737546...           0  \n",
       "59  /hotels/new-vasantashram-hotel-in-mumbai-65283...           0  \n",
       "58  /hotels/oyo-9957-terminus-square-hotel-in-mumb...           0  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recommedor('Mumbai', 'i want hot water', 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "f85c5308",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "# Pickle the function\n",
    "with open('recommedor.pkl', 'wb') as f:\n",
    "    pickle.dump(recommedor, f)\n",
    "\n",
    "# Pickle the DataFrame\n",
    "with open('hotel_dataframe.pkl', 'wb') as f:\n",
    "    pickle.dump(df, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4306317f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}