restaurant_preprocess.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "32efe5c6",
   "metadata": {},
   "source": [
    "## Restaurant data Preprocessing and Recommendation System"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "33409ece",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bdfee3f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel(\"D:\\Linearloop Internship\\Project - Travel\\Project_datasets\\indian restaurants Dataset.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f452f278",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>res_id</th>\n",
       "      <th>name</th>\n",
       "      <th>establishment</th>\n",
       "      <th>city</th>\n",
       "      <th>city_id</th>\n",
       "      <th>locality</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>country_id</th>\n",
       "      <th>locality_verbose</th>\n",
       "      <th>...</th>\n",
       "      <th>timings</th>\n",
       "      <th>average_cost_for_two</th>\n",
       "      <th>price_range</th>\n",
       "      <th>currency</th>\n",
       "      <th>highlights</th>\n",
       "      <th>aggregate_rating</th>\n",
       "      <th>rating_text</th>\n",
       "      <th>votes</th>\n",
       "      <th>photo_count</th>\n",
       "      <th>opentable_support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3400299</td>\n",
       "      <td>Bikanervala</td>\n",
       "      <td>['Quick Bites']</td>\n",
       "      <td>Agra</td>\n",
       "      <td>34</td>\n",
       "      <td>Khandari</td>\n",
       "      <td>27.211450</td>\n",
       "      <td>78.002381</td>\n",
       "      <td>1</td>\n",
       "      <td>Khandari, Agra</td>\n",
       "      <td>...</td>\n",
       "      <td>8:30am â€“ 10:30pm (Mon-Sun)</td>\n",
       "      <td>700</td>\n",
       "      <td>2</td>\n",
       "      <td>Rs.</td>\n",
       "      <td>['Lunch', 'Takeaway Available', 'Credit Card',...</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>814</td>\n",
       "      <td>154</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3400005</td>\n",
       "      <td>Mama Chicken Mama Franky House</td>\n",
       "      <td>['Quick Bites']</td>\n",
       "      <td>Agra</td>\n",
       "      <td>34</td>\n",
       "      <td>Agra Cantt</td>\n",
       "      <td>27.160569</td>\n",
       "      <td>78.011583</td>\n",
       "      <td>1</td>\n",
       "      <td>Agra Cantt, Agra</td>\n",
       "      <td>...</td>\n",
       "      <td>12:30PM to 12Midnight (Mon, Wed, Thu, Fri, Sat...</td>\n",
       "      <td>600</td>\n",
       "      <td>2</td>\n",
       "      <td>Rs.</td>\n",
       "      <td>['Delivery', 'No Alcohol Available', 'Dinner',...</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>1203</td>\n",
       "      <td>161</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3401013</td>\n",
       "      <td>Bhagat Halwai</td>\n",
       "      <td>['Quick Bites']</td>\n",
       "      <td>Agra</td>\n",
       "      <td>34</td>\n",
       "      <td>Shahganj</td>\n",
       "      <td>27.182938</td>\n",
       "      <td>77.979684</td>\n",
       "      <td>1</td>\n",
       "      <td>Shahganj, Agra</td>\n",
       "      <td>...</td>\n",
       "      <td>9:30 AM to 11 PM</td>\n",
       "      <td>300</td>\n",
       "      <td>1</td>\n",
       "      <td>Rs.</td>\n",
       "      <td>['No Alcohol Available', 'Dinner', 'Takeaway A...</td>\n",
       "      <td>4.2</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>801</td>\n",
       "      <td>107</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3400290</td>\n",
       "      <td>Bhagat Halwai</td>\n",
       "      <td>['Quick Bites']</td>\n",
       "      <td>Agra</td>\n",
       "      <td>34</td>\n",
       "      <td>Civil Lines</td>\n",
       "      <td>27.205668</td>\n",
       "      <td>78.004799</td>\n",
       "      <td>1</td>\n",
       "      <td>Civil Lines, Agra</td>\n",
       "      <td>...</td>\n",
       "      <td>8am â€“ 11pm (Mon-Sun)</td>\n",
       "      <td>300</td>\n",
       "      <td>1</td>\n",
       "      <td>Rs.</td>\n",
       "      <td>['Takeaway Available', 'Credit Card', 'Lunch',...</td>\n",
       "      <td>4.3</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>693</td>\n",
       "      <td>157</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3401744</td>\n",
       "      <td>The Salt Cafe Kitchen &amp; Bar</td>\n",
       "      <td>['Casual Dining']</td>\n",
       "      <td>Agra</td>\n",
       "      <td>34</td>\n",
       "      <td>Tajganj</td>\n",
       "      <td>27.157709</td>\n",
       "      <td>78.052421</td>\n",
       "      <td>1</td>\n",
       "      <td>Tajganj, Agra</td>\n",
       "      <td>...</td>\n",
       "      <td>11:30 AM to 11:30 PM</td>\n",
       "      <td>1000</td>\n",
       "      <td>3</td>\n",
       "      <td>Rs.</td>\n",
       "      <td>['Lunch', 'Serves Alcohol', 'Cash', 'Credit Ca...</td>\n",
       "      <td>4.9</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>470</td>\n",
       "      <td>291</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    res_id                            name      establishment  city  city_id  \\\n",
       "0  3400299                     Bikanervala    ['Quick Bites']  Agra       34   \n",
       "1  3400005  Mama Chicken Mama Franky House    ['Quick Bites']  Agra       34   \n",
       "2  3401013                   Bhagat Halwai    ['Quick Bites']  Agra       34   \n",
       "3  3400290                   Bhagat Halwai    ['Quick Bites']  Agra       34   \n",
       "4  3401744     The Salt Cafe Kitchen & Bar  ['Casual Dining']  Agra       34   \n",
       "\n",
       "      locality   latitude  longitude  country_id   locality_verbose  ...  \\\n",
       "0     Khandari  27.211450  78.002381           1     Khandari, Agra  ...   \n",
       "1   Agra Cantt  27.160569  78.011583           1   Agra Cantt, Agra  ...   \n",
       "2     Shahganj  27.182938  77.979684           1     Shahganj, Agra  ...   \n",
       "3  Civil Lines  27.205668  78.004799           1  Civil Lines, Agra  ...   \n",
       "4      Tajganj  27.157709  78.052421           1      Tajganj, Agra  ...   \n",
       "\n",
       "                                             timings average_cost_for_two  \\\n",
       "0                       8:30am â€“ 10:30pm (Mon-Sun)                  700   \n",
       "1  12:30PM to 12Midnight (Mon, Wed, Thu, Fri, Sat...                  600   \n",
       "2                                   9:30 AM to 11 PM                  300   \n",
       "3                             8am â€“ 11pm (Mon-Sun)                  300   \n",
       "4                               11:30 AM to 11:30 PM                 1000   \n",
       "\n",
       "   price_range  currency                                         highlights  \\\n",
       "0            2       Rs.  ['Lunch', 'Takeaway Available', 'Credit Card',...   \n",
       "1            2       Rs.  ['Delivery', 'No Alcohol Available', 'Dinner',...   \n",
       "2            1       Rs.  ['No Alcohol Available', 'Dinner', 'Takeaway A...   \n",
       "3            1       Rs.  ['Takeaway Available', 'Credit Card', 'Lunch',...   \n",
       "4            3       Rs.  ['Lunch', 'Serves Alcohol', 'Cash', 'Credit Ca...   \n",
       "\n",
       "  aggregate_rating  rating_text votes  photo_count  opentable_support  \n",
       "0              4.4    Very Good   814          154                  0  \n",
       "1              4.4    Very Good  1203          161                  0  \n",
       "2              4.2    Very Good   801          107                  0  \n",
       "3              4.3    Very Good   693          157                  0  \n",
       "4              4.9    Excellent   470          291                  0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fa192add",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60371, 21)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3b07e364",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.city.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d06b2e72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([\"['Quick Bites']\", \"['Casual Dining']\", \"['Bakery']\", \"['CafÃ©']\",\n",
       "       \"['Dhaba']\", \"['Bhojanalya']\", \"['Bar']\", \"['Sweet Shop']\",\n",
       "       \"['Fine Dining']\", \"['Food Truck']\", \"['Dessert Parlour']\",\n",
       "       \"['Lounge']\", \"['Pub']\", \"['Beverage Shop']\", \"['Kiosk']\",\n",
       "       \"['Paan Shop']\", \"['Confectionery']\", '[]', \"['Club']\",\n",
       "       \"['Food Court']\", \"['Shack']\", \"['Mess']\", \"['Butcher Shop']\",\n",
       "       \"['Microbrewery']\", \"['Cocktail Bar']\", \"['Pop up']\",\n",
       "       \"['Irani Cafe']\"], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.establishment.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c2b98cca",
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_special_chars(text):\n",
    "    special_chars = [\"[\", \"]\", \"'\", '\"']\n",
    "\n",
    "    # Remove special characters from the text\n",
    "    cleaned_text = text\n",
    "    for char in special_chars:\n",
    "        cleaned_text = cleaned_text.replace(char, \"\")\n",
    "\n",
    "    return cleaned_text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "03ab2ae2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['establishment'] = df.establishment.apply(remove_special_chars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2894ea10",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['highlights'] = df.highlights.apply(remove_special_chars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e73dbf55",
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_string(input_string):\n",
    "    unwanted_chars = ['â€“','â€™']  # List of unwanted characters\n",
    "\n",
    "    # Remove unwanted characters from the string\n",
    "    if isinstance(input_string, str):\n",
    "        unwanted_chars = ['â€“']  # List of unwanted characters\n",
    "\n",
    "        # Remove unwanted characters from the string\n",
    "        cleaned_string = input_string\n",
    "        for char in unwanted_chars:\n",
    "            cleaned_string = cleaned_string.replace(char, \"\")\n",
    "\n",
    "        return cleaned_string\n",
    "    else:\n",
    "        return input_string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "415c7566",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.apply(clean_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cb5bea4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 60371 entries, 0 to 60370\n",
      "Data columns (total 21 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   res_id                60371 non-null  int64  \n",
      " 1   name                  60371 non-null  object \n",
      " 2   establishment         60371 non-null  object \n",
      " 3   city                  60371 non-null  object \n",
      " 4   city_id               60371 non-null  int64  \n",
      " 5   locality              60371 non-null  object \n",
      " 6   latitude              60371 non-null  float64\n",
      " 7   longitude             60371 non-null  float64\n",
      " 8   country_id            60371 non-null  int64  \n",
      " 9   locality_verbose      60371 non-null  object \n",
      " 10  cuisines              60371 non-null  object \n",
      " 11  timings               60371 non-null  object \n",
      " 12  average_cost_for_two  60371 non-null  int64  \n",
      " 13  price_range           60371 non-null  int64  \n",
      " 14  currency              60371 non-null  object \n",
      " 15  highlights            60371 non-null  object \n",
      " 16  aggregate_rating      60371 non-null  float64\n",
      " 17  rating_text           60371 non-null  object \n",
      " 18  votes                 60371 non-null  int64  \n",
      " 19  photo_count           60371 non-null  int64  \n",
      " 20  opentable_support     60371 non-null  int64  \n",
      "dtypes: float64(3), int64(8), object(10)\n",
      "memory usage: 9.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a5370101",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "res_id                  0\n",
       "name                    0\n",
       "establishment           0\n",
       "city                    0\n",
       "city_id                 0\n",
       "locality                0\n",
       "latitude                0\n",
       "longitude               0\n",
       "country_id              0\n",
       "locality_verbose        0\n",
       "cuisines                0\n",
       "timings                 0\n",
       "average_cost_for_two    0\n",
       "price_range             0\n",
       "currency                0\n",
       "highlights              0\n",
       "aggregate_rating        0\n",
       "rating_text             0\n",
       "votes                   0\n",
       "photo_count             0\n",
       "opentable_support       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ce8dcd33",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['res_id','city_id','locality','latitude','longitude','price_range','currency','votes','photo_count','opentable_support'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "14ebd39a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['country_id','rating_text'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "23a70e14",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>establishment</th>\n",
       "      <th>city</th>\n",
       "      <th>locality_verbose</th>\n",
       "      <th>cuisines</th>\n",
       "      <th>timings</th>\n",
       "      <th>average_cost_for_two</th>\n",
       "      <th>highlights</th>\n",
       "      <th>aggregate_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bikanervala</td>\n",
       "      <td>Quick Bites</td>\n",
       "      <td>Agra</td>\n",
       "      <td>Khandari, Agra</td>\n",
       "      <td>North Indian, South Indian, Mithai, Street Foo...</td>\n",
       "      <td>8:30am â€“ 10:30pm (Mon-Sun)</td>\n",
       "      <td>700</td>\n",
       "      <td>Lunch, Takeaway Available, Credit Card, Dinner...</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mama Chicken Mama Franky House</td>\n",
       "      <td>Quick Bites</td>\n",
       "      <td>Agra</td>\n",
       "      <td>Agra Cantt, Agra</td>\n",
       "      <td>North Indian, Mughlai, Rolls, Chinese, Fast Fo...</td>\n",
       "      <td>12:30PM to 12Midnight (Mon, Wed, Thu, Fri, Sat...</td>\n",
       "      <td>600</td>\n",
       "      <td>Delivery, No Alcohol Available, Dinner, Takeaw...</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bhagat Halwai</td>\n",
       "      <td>Quick Bites</td>\n",
       "      <td>Agra</td>\n",
       "      <td>Shahganj, Agra</td>\n",
       "      <td>Fast Food, Mithai</td>\n",
       "      <td>9:30 AM to 11 PM</td>\n",
       "      <td>300</td>\n",
       "      <td>No Alcohol Available, Dinner, Takeaway Availab...</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bhagat Halwai</td>\n",
       "      <td>Quick Bites</td>\n",
       "      <td>Agra</td>\n",
       "      <td>Civil Lines, Agra</td>\n",
       "      <td>Desserts, Bakery, Fast Food, South Indian</td>\n",
       "      <td>8am â€“ 11pm (Mon-Sun)</td>\n",
       "      <td>300</td>\n",
       "      <td>Takeaway Available, Credit Card, Lunch, Delive...</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>The Salt Cafe Kitchen &amp; Bar</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Agra</td>\n",
       "      <td>Tajganj, Agra</td>\n",
       "      <td>North Indian, Continental, Italian</td>\n",
       "      <td>11:30 AM to 11:30 PM</td>\n",
       "      <td>1000</td>\n",
       "      <td>Lunch, Serves Alcohol, Cash, Credit Card, Dinn...</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             name  establishment  city   locality_verbose  \\\n",
       "0                     Bikanervala    Quick Bites  Agra     Khandari, Agra   \n",
       "1  Mama Chicken Mama Franky House    Quick Bites  Agra   Agra Cantt, Agra   \n",
       "2                   Bhagat Halwai    Quick Bites  Agra     Shahganj, Agra   \n",
       "3                   Bhagat Halwai    Quick Bites  Agra  Civil Lines, Agra   \n",
       "4     The Salt Cafe Kitchen & Bar  Casual Dining  Agra      Tajganj, Agra   \n",
       "\n",
       "                                            cuisines  \\\n",
       "0  North Indian, South Indian, Mithai, Street Foo...   \n",
       "1  North Indian, Mughlai, Rolls, Chinese, Fast Fo...   \n",
       "2                                  Fast Food, Mithai   \n",
       "3          Desserts, Bakery, Fast Food, South Indian   \n",
       "4                 North Indian, Continental, Italian   \n",
       "\n",
       "                                             timings  average_cost_for_two  \\\n",
       "0                       8:30am â€“ 10:30pm (Mon-Sun)                   700   \n",
       "1  12:30PM to 12Midnight (Mon, Wed, Thu, Fri, Sat...                   600   \n",
       "2                                   9:30 AM to 11 PM                   300   \n",
       "3                             8am â€“ 11pm (Mon-Sun)                   300   \n",
       "4                               11:30 AM to 11:30 PM                  1000   \n",
       "\n",
       "                                          highlights  aggregate_rating  \n",
       "0  Lunch, Takeaway Available, Credit Card, Dinner...               4.4  \n",
       "1  Delivery, No Alcohol Available, Dinner, Takeaw...               4.4  \n",
       "2  No Alcohol Available, Dinner, Takeaway Availab...               4.2  \n",
       "3  Takeaway Available, Credit Card, Lunch, Delive...               4.3  \n",
       "4  Lunch, Serves Alcohol, Cash, Credit Card, Dinn...               4.9  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d301dd60",
   "metadata": {},
   "outputs": [],
   "source": [
    "change_c = {\n",
    "    'name' : 'Name',\n",
    "    'establishment' : 'Type',\n",
    "    'city' : 'City',\n",
    "    'locality_verbose' : 'Address',\n",
    "    'cuisines' : 'Cuisines',\n",
    "    'timings' : 'Timings',\n",
    "    'average_cost_for_two' : 'Cost for Two',\n",
    "    'highlights' : 'Highlights',\n",
    "    'aggregate_rating' : 'Rating'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5d9aa597",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns=change_c, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f8a5579e",
   "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>Type</th>\n",
       "      <th>City</th>\n",
       "      <th>Address</th>\n",
       "      <th>Cuisines</th>\n",
       "      <th>Timings</th>\n",
       "      <th>Cost for Two</th>\n",
       "      <th>Highlights</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bikanervala</td>\n",
       "      <td>Quick Bites</td>\n",
       "      <td>Agra</td>\n",
       "      <td>Khandari, Agra</td>\n",
       "      <td>North Indian, South Indian, Mithai, Street Foo...</td>\n",
       "      <td>8:30am â€“ 10:30pm (Mon-Sun)</td>\n",
       "      <td>700</td>\n",
       "      <td>Lunch, Takeaway Available, Credit Card, Dinner...</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mama Chicken Mama Franky House</td>\n",
       "      <td>Quick Bites</td>\n",
       "      <td>Agra</td>\n",
       "      <td>Agra Cantt, Agra</td>\n",
       "      <td>North Indian, Mughlai, Rolls, Chinese, Fast Fo...</td>\n",
       "      <td>12:30PM to 12Midnight (Mon, Wed, Thu, Fri, Sat...</td>\n",
       "      <td>600</td>\n",
       "      <td>Delivery, No Alcohol Available, Dinner, Takeaw...</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bhagat Halwai</td>\n",
       "      <td>Quick Bites</td>\n",
       "      <td>Agra</td>\n",
       "      <td>Shahganj, Agra</td>\n",
       "      <td>Fast Food, Mithai</td>\n",
       "      <td>9:30 AM to 11 PM</td>\n",
       "      <td>300</td>\n",
       "      <td>No Alcohol Available, Dinner, Takeaway Availab...</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bhagat Halwai</td>\n",
       "      <td>Quick Bites</td>\n",
       "      <td>Agra</td>\n",
       "      <td>Civil Lines, Agra</td>\n",
       "      <td>Desserts, Bakery, Fast Food, South Indian</td>\n",
       "      <td>8am â€“ 11pm (Mon-Sun)</td>\n",
       "      <td>300</td>\n",
       "      <td>Takeaway Available, Credit Card, Lunch, Delive...</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>The Salt Cafe Kitchen &amp; Bar</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Agra</td>\n",
       "      <td>Tajganj, Agra</td>\n",
       "      <td>North Indian, Continental, Italian</td>\n",
       "      <td>11:30 AM to 11:30 PM</td>\n",
       "      <td>1000</td>\n",
       "      <td>Lunch, Serves Alcohol, Cash, Credit Card, Dinn...</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Name           Type  City            Address  \\\n",
       "0                     Bikanervala    Quick Bites  Agra     Khandari, Agra   \n",
       "1  Mama Chicken Mama Franky House    Quick Bites  Agra   Agra Cantt, Agra   \n",
       "2                   Bhagat Halwai    Quick Bites  Agra     Shahganj, Agra   \n",
       "3                   Bhagat Halwai    Quick Bites  Agra  Civil Lines, Agra   \n",
       "4     The Salt Cafe Kitchen & Bar  Casual Dining  Agra      Tajganj, Agra   \n",
       "\n",
       "                                            Cuisines  \\\n",
       "0  North Indian, South Indian, Mithai, Street Foo...   \n",
       "1  North Indian, Mughlai, Rolls, Chinese, Fast Fo...   \n",
       "2                                  Fast Food, Mithai   \n",
       "3          Desserts, Bakery, Fast Food, South Indian   \n",
       "4                 North Indian, Continental, Italian   \n",
       "\n",
       "                                             Timings  Cost for Two  \\\n",
       "0                       8:30am â€“ 10:30pm (Mon-Sun)           700   \n",
       "1  12:30PM to 12Midnight (Mon, Wed, Thu, Fri, Sat...           600   \n",
       "2                                   9:30 AM to 11 PM           300   \n",
       "3                             8am â€“ 11pm (Mon-Sun)           300   \n",
       "4                               11:30 AM to 11:30 PM          1000   \n",
       "\n",
       "                                          Highlights  Rating  \n",
       "0  Lunch, Takeaway Available, Credit Card, Dinner...     4.4  \n",
       "1  Delivery, No Alcohol Available, Dinner, Takeaw...     4.4  \n",
       "2  No Alcohol Available, Dinner, Takeaway Availab...     4.2  \n",
       "3  Takeaway Available, Credit Card, Lunch, Delive...     4.3  \n",
       "4  Lunch, Serves Alcohol, Cash, Credit Card, Dinn...     4.9  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1a3c5de2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Name'] = df['Name'].apply(clean_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d2b13510",
   "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='Rating', ascending=False)\n",
    "    if(citybase.empty==0):\n",
    "        hname = citybase[['Name','Type','Cuisines','Highlights','Address','Rating','Cost for Two','Timings']]\n",
    "        return hname.head(10)\n",
    "    else:\n",
    "        print('No Restaurants data!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9ae7b74e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Type</th>\n",
       "      <th>Cuisines</th>\n",
       "      <th>Highlights</th>\n",
       "      <th>Address</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Cost for Two</th>\n",
       "      <th>Timings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>933</th>\n",
       "      <td>Buttercupp</td>\n",
       "      <td>Dessert Parlour</td>\n",
       "      <td>Desserts, Bakery</td>\n",
       "      <td>Cash, Credit Card, Takeaway Available, Debit C...</td>\n",
       "      <td>Vastrapur, Ahmedabad</td>\n",
       "      <td>4.9</td>\n",
       "      <td>300</td>\n",
       "      <td>2PM to 8PM (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>950</th>\n",
       "      <td>Yanki Sizzlers</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Continental, Italian, Chinese</td>\n",
       "      <td>Lunch, Takeaway Available, Debit Card, No Alco...</td>\n",
       "      <td>Thaltej, Ahmedabad</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1200</td>\n",
       "      <td>12 Noon to 3 PM, 7 PM to 11 PM (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>994</th>\n",
       "      <td>TGâ€™s - The Oriental Grill - Hyatt</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Chinese, Japanese</td>\n",
       "      <td>Dinner, Credit Card, Debit Card, Cash, Valet P...</td>\n",
       "      <td>Hyatt, Vastrapur, Ahmedabad</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1500</td>\n",
       "      <td>12:30 PM to 3 PM, 6:45 PM to 11:30 PM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>963</th>\n",
       "      <td>Yanki Sizzlers</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Continental, Italian, Chinese, Mexican</td>\n",
       "      <td>Dinner, Takeaway Available, Debit Card, Lunch,...</td>\n",
       "      <td>C G Road, Ahmedabad</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1200</td>\n",
       "      <td>Mon-Sun (12 Noon to 3 PM, 7 PM to 11 PM)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>873</th>\n",
       "      <td>Huber &amp; Holly</td>\n",
       "      <td>Dessert Parlour</td>\n",
       "      <td>Desserts, Italian</td>\n",
       "      <td>Cash, Takeaway Available, Credit Card, Deliver...</td>\n",
       "      <td>Bodakdev, Ahmedabad</td>\n",
       "      <td>4.8</td>\n",
       "      <td>500</td>\n",
       "      <td>10am â€“ 3am (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1149</th>\n",
       "      <td>Hoglatto</td>\n",
       "      <td>Dessert Parlour</td>\n",
       "      <td>Desserts, Bakery</td>\n",
       "      <td>Takeaway Available, Credit Card, Debit Card, C...</td>\n",
       "      <td>Vastrapur, Ahmedabad</td>\n",
       "      <td>4.8</td>\n",
       "      <td>400</td>\n",
       "      <td>11 AM to 11 PM (Mon-Sat), 1 PM to 11 PM (Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>894</th>\n",
       "      <td>Kadak Bhagat</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>North Indian, Chinese</td>\n",
       "      <td>Dinner, Cash, Credit Card, Lunch, Takeaway Ava...</td>\n",
       "      <td>Mani Nagar, Ahmedabad</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1200</td>\n",
       "      <td>11:30 AM to 3:30 PM, 7 PM to 11 PM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2067</th>\n",
       "      <td>Kadak Bhagat</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>North Indian, Chinese</td>\n",
       "      <td>Dinner, Cash, Credit Card, Lunch, Takeaway Ava...</td>\n",
       "      <td>Mani Nagar, Ahmedabad</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1200</td>\n",
       "      <td>11:30 AM to 3:30 PM, 7 PM to 11 PM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>969</th>\n",
       "      <td>China House - Hyatt Regency</td>\n",
       "      <td>Fine Dining</td>\n",
       "      <td>Chinese, Asian</td>\n",
       "      <td>Cash, Debit Card, No Alcohol Available, Dinner...</td>\n",
       "      <td>Hyatt Regency, Ashram Road, Ahmedabad</td>\n",
       "      <td>4.7</td>\n",
       "      <td>2400</td>\n",
       "      <td>12:30 PM to 3 PM, 7 PM to 11:30 PM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2133</th>\n",
       "      <td>Swati Snacks</td>\n",
       "      <td>Quick Bites</td>\n",
       "      <td>Fast Food, Street Food, South Indian</td>\n",
       "      <td>Credit Card, Cash, Delivery, Dinner, Takeaway ...</td>\n",
       "      <td>Ellis Bridge, Ahmedabad</td>\n",
       "      <td>4.7</td>\n",
       "      <td>600</td>\n",
       "      <td>12 Noon to 10:45 PM</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     Name             Type  \\\n",
       "933                            Buttercupp  Dessert Parlour   \n",
       "950                        Yanki Sizzlers    Casual Dining   \n",
       "994   TGâ€™s - The Oriental Grill - Hyatt    Casual Dining   \n",
       "963                        Yanki Sizzlers    Casual Dining   \n",
       "873                         Huber & Holly  Dessert Parlour   \n",
       "1149                             Hoglatto  Dessert Parlour   \n",
       "894                          Kadak Bhagat    Casual Dining   \n",
       "2067                         Kadak Bhagat    Casual Dining   \n",
       "969           China House - Hyatt Regency      Fine Dining   \n",
       "2133                         Swati Snacks      Quick Bites   \n",
       "\n",
       "                                    Cuisines  \\\n",
       "933                         Desserts, Bakery   \n",
       "950            Continental, Italian, Chinese   \n",
       "994                        Chinese, Japanese   \n",
       "963   Continental, Italian, Chinese, Mexican   \n",
       "873                        Desserts, Italian   \n",
       "1149                        Desserts, Bakery   \n",
       "894                    North Indian, Chinese   \n",
       "2067                   North Indian, Chinese   \n",
       "969                           Chinese, Asian   \n",
       "2133    Fast Food, Street Food, South Indian   \n",
       "\n",
       "                                             Highlights  \\\n",
       "933   Cash, Credit Card, Takeaway Available, Debit C...   \n",
       "950   Lunch, Takeaway Available, Debit Card, No Alco...   \n",
       "994   Dinner, Credit Card, Debit Card, Cash, Valet P...   \n",
       "963   Dinner, Takeaway Available, Debit Card, Lunch,...   \n",
       "873   Cash, Takeaway Available, Credit Card, Deliver...   \n",
       "1149  Takeaway Available, Credit Card, Debit Card, C...   \n",
       "894   Dinner, Cash, Credit Card, Lunch, Takeaway Ava...   \n",
       "2067  Dinner, Cash, Credit Card, Lunch, Takeaway Ava...   \n",
       "969   Cash, Debit Card, No Alcohol Available, Dinner...   \n",
       "2133  Credit Card, Cash, Delivery, Dinner, Takeaway ...   \n",
       "\n",
       "                                    Address  Rating  Cost for Two  \\\n",
       "933                    Vastrapur, Ahmedabad     4.9           300   \n",
       "950                      Thaltej, Ahmedabad     4.9          1200   \n",
       "994             Hyatt, Vastrapur, Ahmedabad     4.9          1500   \n",
       "963                     C G Road, Ahmedabad     4.9          1200   \n",
       "873                     Bodakdev, Ahmedabad     4.8           500   \n",
       "1149                   Vastrapur, Ahmedabad     4.8           400   \n",
       "894                   Mani Nagar, Ahmedabad     4.8          1200   \n",
       "2067                  Mani Nagar, Ahmedabad     4.8          1200   \n",
       "969   Hyatt Regency, Ashram Road, Ahmedabad     4.7          2400   \n",
       "2133                Ellis Bridge, Ahmedabad     4.7           600   \n",
       "\n",
       "                                            Timings  \n",
       "933                            2PM to 8PM (Mon-Sun)  \n",
       "950        12 Noon to 3 PM, 7 PM to 11 PM (Mon-Sun)  \n",
       "994           12:30 PM to 3 PM, 6:45 PM to 11:30 PM  \n",
       "963        Mon-Sun (12 Noon to 3 PM, 7 PM to 11 PM)  \n",
       "873                          10am â€“ 3am (Mon-Sun)  \n",
       "1149  11 AM to 11 PM (Mon-Sat), 1 PM to 11 PM (Sun)  \n",
       "894              11:30 AM to 3:30 PM, 7 PM to 11 PM  \n",
       "2067             11:30 AM to 3:30 PM, 7 PM to 11 PM  \n",
       "969              12:30 PM to 3 PM, 7 PM to 11:30 PM  \n",
       "2133                            12 Noon to 10:45 PM  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "citybased('ahmedabad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5db07cbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Description'] = df['Type'] + ' ' + df['Cuisines'] + ' ' + df['Highlights']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4218511f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Type</th>\n",
       "      <th>City</th>\n",
       "      <th>Address</th>\n",
       "      <th>Cuisines</th>\n",
       "      <th>Timings</th>\n",
       "      <th>Cost for Two</th>\n",
       "      <th>Highlights</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bikanervala</td>\n",
       "      <td>Quick Bites</td>\n",
       "      <td>agra</td>\n",
       "      <td>Khandari, Agra</td>\n",
       "      <td>North Indian, South Indian, Mithai, Street Foo...</td>\n",
       "      <td>8:30am â€“ 10:30pm (Mon-Sun)</td>\n",
       "      <td>700</td>\n",
       "      <td>Lunch, Takeaway Available, Credit Card, Dinner...</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Quick Bites North Indian, South Indian, Mithai...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mama Chicken Mama Franky House</td>\n",
       "      <td>Quick Bites</td>\n",
       "      <td>agra</td>\n",
       "      <td>Agra Cantt, Agra</td>\n",
       "      <td>North Indian, Mughlai, Rolls, Chinese, Fast Fo...</td>\n",
       "      <td>12:30PM to 12Midnight (Mon, Wed, Thu, Fri, Sat...</td>\n",
       "      <td>600</td>\n",
       "      <td>Delivery, No Alcohol Available, Dinner, Takeaw...</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Quick Bites North Indian, Mughlai, Rolls, Chin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bhagat Halwai</td>\n",
       "      <td>Quick Bites</td>\n",
       "      <td>agra</td>\n",
       "      <td>Shahganj, Agra</td>\n",
       "      <td>Fast Food, Mithai</td>\n",
       "      <td>9:30 AM to 11 PM</td>\n",
       "      <td>300</td>\n",
       "      <td>No Alcohol Available, Dinner, Takeaway Availab...</td>\n",
       "      <td>4.2</td>\n",
       "      <td>Quick Bites Fast Food, Mithai No Alcohol Avail...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bhagat Halwai</td>\n",
       "      <td>Quick Bites</td>\n",
       "      <td>agra</td>\n",
       "      <td>Civil Lines, Agra</td>\n",
       "      <td>Desserts, Bakery, Fast Food, South Indian</td>\n",
       "      <td>8am â€“ 11pm (Mon-Sun)</td>\n",
       "      <td>300</td>\n",
       "      <td>Takeaway Available, Credit Card, Lunch, Delive...</td>\n",
       "      <td>4.3</td>\n",
       "      <td>Quick Bites Desserts, Bakery, Fast Food, South...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>The Salt Cafe Kitchen &amp; Bar</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>agra</td>\n",
       "      <td>Tajganj, Agra</td>\n",
       "      <td>North Indian, Continental, Italian</td>\n",
       "      <td>11:30 AM to 11:30 PM</td>\n",
       "      <td>1000</td>\n",
       "      <td>Lunch, Serves Alcohol, Cash, Credit Card, Dinn...</td>\n",
       "      <td>4.9</td>\n",
       "      <td>Casual Dining North Indian, Continental, Itali...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Name           Type  City            Address  \\\n",
       "0                     Bikanervala    Quick Bites  agra     Khandari, Agra   \n",
       "1  Mama Chicken Mama Franky House    Quick Bites  agra   Agra Cantt, Agra   \n",
       "2                   Bhagat Halwai    Quick Bites  agra     Shahganj, Agra   \n",
       "3                   Bhagat Halwai    Quick Bites  agra  Civil Lines, Agra   \n",
       "4     The Salt Cafe Kitchen & Bar  Casual Dining  agra      Tajganj, Agra   \n",
       "\n",
       "                                            Cuisines  \\\n",
       "0  North Indian, South Indian, Mithai, Street Foo...   \n",
       "1  North Indian, Mughlai, Rolls, Chinese, Fast Fo...   \n",
       "2                                  Fast Food, Mithai   \n",
       "3          Desserts, Bakery, Fast Food, South Indian   \n",
       "4                 North Indian, Continental, Italian   \n",
       "\n",
       "                                             Timings  Cost for Two  \\\n",
       "0                       8:30am â€“ 10:30pm (Mon-Sun)           700   \n",
       "1  12:30PM to 12Midnight (Mon, Wed, Thu, Fri, Sat...           600   \n",
       "2                                   9:30 AM to 11 PM           300   \n",
       "3                             8am â€“ 11pm (Mon-Sun)           300   \n",
       "4                               11:30 AM to 11:30 PM          1000   \n",
       "\n",
       "                                          Highlights  Rating  \\\n",
       "0  Lunch, Takeaway Available, Credit Card, Dinner...     4.4   \n",
       "1  Delivery, No Alcohol Available, Dinner, Takeaw...     4.4   \n",
       "2  No Alcohol Available, Dinner, Takeaway Availab...     4.2   \n",
       "3  Takeaway Available, Credit Card, Lunch, Delive...     4.3   \n",
       "4  Lunch, Serves Alcohol, Cash, Credit Card, Dinn...     4.9   \n",
       "\n",
       "                                         Description  \n",
       "0  Quick Bites North Indian, South Indian, Mithai...  \n",
       "1  Quick Bites North Indian, Mughlai, Rolls, Chin...  \n",
       "2  Quick Bites Fast Food, Mithai No Alcohol Avail...  \n",
       "3  Quick Bites Desserts, Bakery, Fast Food, South...  \n",
       "4  Casual Dining North Indian, Continental, Itali...  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6e433a6d",
   "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": 26,
   "id": "4f2c7912",
   "metadata": {},
   "outputs": [],
   "source": [
    "def requirement_based(city, description=None, locality=None):\n",
    "    df['City'] = df['City'].str.lower()\n",
    "    df['Description'] = df['Description'].str.lower()\n",
    "    df['Address'] = df['Address'].str.lower()\n",
    "    \n",
    "    if description:\n",
    "        description = description.lower()\n",
    "        description_tokens = word_tokenize(description)\n",
    "        sw = stopwords.words('english')\n",
    "        lemm = WordNetLemmatizer()\n",
    "        f1_set = {w for w in description_tokens if not w in sw}\n",
    "        f_set = set()\n",
    "        for se in f1_set:\n",
    "            f_set.add(lemm.lemmatize(se))\n",
    "    \n",
    "    if locality:\n",
    "        locality = locality.lower()\n",
    "        sw = stopwords.words('english')\n",
    "        lemm = WordNetLemmatizer()\n",
    "        locality_tokens = word_tokenize(locality)\n",
    "        f1_set = {w for w in locality_tokens if not w in sw}\n",
    "        locality_set = set()\n",
    "        for se in f1_set:\n",
    "            locality_set.add(lemm.lemmatize(se))\n",
    "    \n",
    "    req_based = df[df['City'] == city.lower()]\n",
    "    \n",
    "    if locality:\n",
    "        req_based = req_based[req_based['Address'].str.contains(locality.lower())]\n",
    "    \n",
    "    if req_based.empty:\n",
    "        print(\"No attractions data found for this city and locality.\")\n",
    "        return None\n",
    "    \n",
    "    cos_desc = []\n",
    "    cos_locality = []\n",
    "\n",
    "    if description:\n",
    "        for i in range(req_based.shape[0]):\n",
    "            temp_tokens = word_tokenize(req_based['Description'].iloc[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_desc.append(len(revector))\n",
    "    \n",
    "    if locality:\n",
    "        for i in range(req_based.shape[0]):\n",
    "            temp_tokens = word_tokenize(req_based['Address'].iloc[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(locality_set)\n",
    "            cos_locality.append(len(revector))\n",
    "            \n",
    "    req_based['Desc_Similarity'] = cos_desc\n",
    "    req_based['Locality_Similarity'] = cos_locality\n",
    "    \n",
    "    req_based['Total_Similarity'] = req_based['Desc_Similarity'] + req_based['Locality_Similarity']\n",
    "    \n",
    "    req_based = req_based.sort_values(by='Total_Similarity', ascending=False).reset_index(drop=True)\n",
    "    \n",
    "    return req_based[['Name', 'Type', 'Cuisines', 'Highlights', 'Address', 'Rating', 'Cost for Two', 'Timings', 'Total_Similarity']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e005ccde",
   "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>Type</th>\n",
       "      <th>Cuisines</th>\n",
       "      <th>Highlights</th>\n",
       "      <th>Address</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Cost for Two</th>\n",
       "      <th>Timings</th>\n",
       "      <th>Total_Similarity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hotel Moti Mahal</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>North Indian, Chinese</td>\n",
       "      <td>Dinner, Cash, Takeaway Available, Breakfast, D...</td>\n",
       "      <td>kalupur, ahmedabad</td>\n",
       "      <td>3.9</td>\n",
       "      <td>600</td>\n",
       "      <td>6 AM to 11 PM</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Shudh Vaishno Bhojnalay</td>\n",
       "      <td>Bhojanalya</td>\n",
       "      <td>North Indian</td>\n",
       "      <td>Dinner, Takeaway Available, Breakfast, Lunch, ...</td>\n",
       "      <td>kalupur, ahmedabad</td>\n",
       "      <td>0.0</td>\n",
       "      <td>200</td>\n",
       "      <td>5 AM to 11 PM</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shankar Bhojnalay</td>\n",
       "      <td>Bhojanalya</td>\n",
       "      <td>North Indian</td>\n",
       "      <td>Dinner, Takeaway Available, Breakfast, Lunch, ...</td>\n",
       "      <td>kalupur, ahmedabad</td>\n",
       "      <td>0.0</td>\n",
       "      <td>200</td>\n",
       "      <td>7:30 AM to 11 PM</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Revabhai Bhojnalay</td>\n",
       "      <td>Bhojanalya</td>\n",
       "      <td>North Indian</td>\n",
       "      <td>Dinner, Takeaway Available, Breakfast, Lunch, ...</td>\n",
       "      <td>kalupur, ahmedabad</td>\n",
       "      <td>0.0</td>\n",
       "      <td>200</td>\n",
       "      <td>8 AM to 11 PM</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Moti Bakery</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Bakery, Fast Food</td>\n",
       "      <td>Takeaway Available, Cash, Breakfast, Indoor Se...</td>\n",
       "      <td>kalupur, ahmedabad</td>\n",
       "      <td>3.7</td>\n",
       "      <td>100</td>\n",
       "      <td>7 AM to 11 PM</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Kantilal Manilal's Gopi</td>\n",
       "      <td>Sweet Shop</td>\n",
       "      <td>Mithai</td>\n",
       "      <td>Delivery, Cash, No Seating Available, Takeaway...</td>\n",
       "      <td>kalupur, ahmedabad</td>\n",
       "      <td>3.6</td>\n",
       "      <td>250</td>\n",
       "      <td>8am â€“ 10pm (Mon-Sun)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Mohan Cholafali Centre</td>\n",
       "      <td>Kiosk</td>\n",
       "      <td>Gujarati</td>\n",
       "      <td>No Seating Available, Dinner, Takeaway Availab...</td>\n",
       "      <td>kalupur, ahmedabad</td>\n",
       "      <td>0.0</td>\n",
       "      <td>200</td>\n",
       "      <td>2 PM to 8:30 PM</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Name           Type               Cuisines  \\\n",
       "0         Hotel Moti Mahal  Casual Dining  North Indian, Chinese   \n",
       "1  Shudh Vaishno Bhojnalay     Bhojanalya           North Indian   \n",
       "2        Shankar Bhojnalay     Bhojanalya           North Indian   \n",
       "3       Revabhai Bhojnalay     Bhojanalya           North Indian   \n",
       "4              Moti Bakery         Bakery      Bakery, Fast Food   \n",
       "5  Kantilal Manilal's Gopi     Sweet Shop                 Mithai   \n",
       "6   Mohan Cholafali Centre          Kiosk               Gujarati   \n",
       "\n",
       "                                          Highlights             Address  \\\n",
       "0  Dinner, Cash, Takeaway Available, Breakfast, D...  kalupur, ahmedabad   \n",
       "1  Dinner, Takeaway Available, Breakfast, Lunch, ...  kalupur, ahmedabad   \n",
       "2  Dinner, Takeaway Available, Breakfast, Lunch, ...  kalupur, ahmedabad   \n",
       "3  Dinner, Takeaway Available, Breakfast, Lunch, ...  kalupur, ahmedabad   \n",
       "4  Takeaway Available, Cash, Breakfast, Indoor Se...  kalupur, ahmedabad   \n",
       "5  Delivery, Cash, No Seating Available, Takeaway...  kalupur, ahmedabad   \n",
       "6  No Seating Available, Dinner, Takeaway Availab...  kalupur, ahmedabad   \n",
       "\n",
       "   Rating  Cost for Two                 Timings  Total_Similarity  \n",
       "0     3.9           600           6 AM to 11 PM                 2  \n",
       "1     0.0           200           5 AM to 11 PM                 2  \n",
       "2     0.0           200        7:30 AM to 11 PM                 2  \n",
       "3     0.0           200           8 AM to 11 PM                 2  \n",
       "4     3.7           100           7 AM to 11 PM                 1  \n",
       "5     3.6           250  8am â€“ 10pm (Mon-Sun)                 1  \n",
       "6     0.0           200         2 PM to 8:30 PM                 1  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "requirement_based('ahmedabad', 'north', 'kalupur')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "62e0e836",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop_duplicates(keep=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1fda4a5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def rest_recc(city, description=None, locality=None):\n",
    "    df['City'] = df['City'].str.lower()\n",
    "    df['Description'] = df['Description'].str.lower()\n",
    "    df['Address'] = df['Address'].str.lower()\n",
    "    \n",
    "    if description:\n",
    "        description = description.lower()\n",
    "        description_tokens = word_tokenize(description)\n",
    "        sw = stopwords.words('english')\n",
    "        lemm = WordNetLemmatizer()\n",
    "        f1_set = {w for w in description_tokens if not w in sw}\n",
    "        f_set = set()\n",
    "        for se in f1_set:\n",
    "            f_set.add(lemm.lemmatize(se))\n",
    "    \n",
    "    if locality:\n",
    "        locality = locality.lower()\n",
    "        sw = stopwords.words('english')\n",
    "        lemm = WordNetLemmatizer()\n",
    "        locality_tokens = word_tokenize(locality)\n",
    "        f1_set = {w for w in locality_tokens if not w in sw}\n",
    "        locality_set = set()\n",
    "        for se in f1_set:\n",
    "            locality_set.add(lemm.lemmatize(se))\n",
    "    \n",
    "    req_based = df[df['City'] == city.lower()]\n",
    "    \n",
    "    if locality:\n",
    "        req_based = req_based[req_based['Address'].str.contains(locality.lower())]\n",
    "    \n",
    "    if req_based.empty:\n",
    "        print(\"No attractions data found for this city and locality.\")\n",
    "        return None\n",
    "    \n",
    "    cos_desc = []\n",
    "    cos_locality = []\n",
    "\n",
    "    if description:\n",
    "        for i in range(req_based.shape[0]):\n",
    "            temp_tokens = word_tokenize(req_based['Description'].iloc[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_desc.append(len(revector))\n",
    "    \n",
    "    if locality:\n",
    "        for i in range(req_based.shape[0]):\n",
    "            temp_tokens = word_tokenize(req_based['Address'].iloc[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(locality_set)\n",
    "            cos_locality.append(len(revector))\n",
    "            \n",
    "    req_based['Desc_Similarity'] = cos_desc\n",
    "    req_based['Locality_Similarity'] = cos_locality\n",
    "    \n",
    "    req_based['Total_Similarity'] = req_based['Desc_Similarity'] + req_based['Locality_Similarity']\n",
    "    \n",
    "    # Weight for balancing rating and similarity\n",
    "    rating_weight = 0.5\n",
    "    similarity_weight = 0.5\n",
    "    \n",
    "    # Calculate weighted score\n",
    "    req_based['Weighted_Score'] = (req_based['Rating'] * rating_weight) + (req_based['Total_Similarity'] * similarity_weight)\n",
    "    \n",
    "    # Sort by weighted score and reset index\n",
    "    req_based = req_based.sort_values(by='Weighted_Score', ascending=False).reset_index(drop=True)\n",
    "    \n",
    "    return req_based[['Name', 'Type', 'Cuisines', 'Highlights', 'Address', 'Rating', 'Cost for Two', 'Timings', 'Weighted_Score']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "cecf7173",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No attractions data found for this city and locality.\n"
     ]
    }
   ],
   "source": [
    "rest_recc('jaisalmer', 'north indian punjabi', 'jaisalmer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e63208d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "with open('rest_dataframe.pkl', 'wb') as f:\n",
    "    pickle.dump(df, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c341b863",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(48839, 10)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "fa66bf89",
   "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>Type</th>\n",
       "      <th>City</th>\n",
       "      <th>Address</th>\n",
       "      <th>Cuisines</th>\n",
       "      <th>Timings</th>\n",
       "      <th>Cost for Two</th>\n",
       "      <th>Highlights</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Name, Type, City, Address, Cuisines, Timings, Cost for Two, Highlights, Rating, Description]\n",
       "Index: []"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['City'] == 'jaisalmer']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8aa470b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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