recommendation_system.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Restaurant Recommender System"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda\\envs\\Tensorflow\\python.exe\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "print(sys.executable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import string\n",
    "import numpy as np\n",
    "from collections import Counter\n",
    "import re\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "# plotting\n",
    "import seaborn as sns\n",
    "from wordcloud import WordCloud\n",
    "import matplotlib.pyplot as plt\n",
    "# nltk\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "# sklearn\n",
    "from sklearn.naive_bayes import BernoulliNB\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.metrics import confusion_matrix, classification_report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Restaurant</th>\n",
       "      <th>Reviewer</th>\n",
       "      <th>Review</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Metadata</th>\n",
       "      <th>Time</th>\n",
       "      <th>Pictures</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Beyond Flavours</td>\n",
       "      <td>Rusha Chakraborty</td>\n",
       "      <td>The ambience was good, food was quite good . h...</td>\n",
       "      <td>5</td>\n",
       "      <td>1 Review , 2 Followers</td>\n",
       "      <td>5/25/2019 15:54</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Beyond Flavours</td>\n",
       "      <td>Anusha Tirumalaneedi</td>\n",
       "      <td>Ambience is too good for a pleasant evening. S...</td>\n",
       "      <td>5</td>\n",
       "      <td>3 Reviews , 2 Followers</td>\n",
       "      <td>5/25/2019 14:20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Beyond Flavours</td>\n",
       "      <td>Ashok Shekhawat</td>\n",
       "      <td>A must try.. great food great ambience. Thnx f...</td>\n",
       "      <td>5</td>\n",
       "      <td>2 Reviews , 3 Followers</td>\n",
       "      <td>5/24/2019 22:54</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Beyond Flavours</td>\n",
       "      <td>Swapnil Sarkar</td>\n",
       "      <td>Soumen das and Arun was a great guy. Only beca...</td>\n",
       "      <td>5</td>\n",
       "      <td>1 Review , 1 Follower</td>\n",
       "      <td>5/24/2019 22:11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Beyond Flavours</td>\n",
       "      <td>Dileep</td>\n",
       "      <td>Food is good.we ordered Kodi drumsticks and ba...</td>\n",
       "      <td>5</td>\n",
       "      <td>3 Reviews , 2 Followers</td>\n",
       "      <td>5/24/2019 21:37</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Restaurant              Reviewer  \\\n",
       "0  Beyond Flavours     Rusha Chakraborty   \n",
       "1  Beyond Flavours  Anusha Tirumalaneedi   \n",
       "2  Beyond Flavours       Ashok Shekhawat   \n",
       "3  Beyond Flavours        Swapnil Sarkar   \n",
       "4  Beyond Flavours                Dileep   \n",
       "\n",
       "                                              Review Rating  \\\n",
       "0  The ambience was good, food was quite good . h...      5   \n",
       "1  Ambience is too good for a pleasant evening. S...      5   \n",
       "2  A must try.. great food great ambience. Thnx f...      5   \n",
       "3  Soumen das and Arun was a great guy. Only beca...      5   \n",
       "4  Food is good.we ordered Kodi drumsticks and ba...      5   \n",
       "\n",
       "                  Metadata             Time  Pictures  \n",
       "0   1 Review , 2 Followers  5/25/2019 15:54         0  \n",
       "1  3 Reviews , 2 Followers  5/25/2019 14:20         0  \n",
       "2  2 Reviews , 3 Followers  5/24/2019 22:54         0  \n",
       "3    1 Review , 1 Follower  5/24/2019 22:11         0  \n",
       "4  3 Reviews , 2 Followers  5/24/2019 21:37         0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rest_review = pd.read_csv(\"Restaurant_Review.csv\")\n",
    "\n",
    "rest_review.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 7 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   Restaurant  10000 non-null  object\n",
      " 1   Reviewer    9962 non-null   object\n",
      " 2   Review      9955 non-null   object\n",
      " 3   Rating      9962 non-null   object\n",
      " 4   Metadata    9962 non-null   object\n",
      " 5   Time        9962 non-null   object\n",
      " 6   Pictures    10000 non-null  int64 \n",
      "dtypes: int64(1), object(6)\n",
      "memory usage: 547.0+ KB\n"
     ]
    }
   ],
   "source": [
    "# Check Restaurants Review Data Information\n",
    "\n",
    "rest_review.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5      3832\n",
       "4      2373\n",
       "1      1735\n",
       "3      1193\n",
       "2       684\n",
       "4.5      69\n",
       "3.5      47\n",
       "2.5      19\n",
       "1.5       9\n",
       "Name: Rating, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove Row which has Rating = Like as it is has just one\n",
    "\n",
    "rest_review = rest_review[rest_review[\"Rating\"] != \"Like\"]\n",
    "rest_review[\"Rating\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert Rating Data Type to float\n",
    "\n",
    "rest_review[\"Rating\"] = rest_review[\"Rating\"].astype(\"float\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rating</th>\n",
       "      <th>Pictures</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9961.000000</td>\n",
       "      <td>9999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.601044</td>\n",
       "      <td>0.748575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.483461</td>\n",
       "      <td>2.570508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>64.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Rating     Pictures\n",
       "count  9961.000000  9999.000000\n",
       "mean      3.601044     0.748575\n",
       "std       1.483461     2.570508\n",
       "min       1.000000     0.000000\n",
       "25%       3.000000     0.000000\n",
       "50%       4.000000     0.000000\n",
       "75%       5.000000     0.000000\n",
       "max       5.000000    64.000000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Examine Statistics\n",
    "\n",
    "rest_review.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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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>Links</th>\n",
       "      <th>Cost</th>\n",
       "      <th>Collections</th>\n",
       "      <th>Cuisines</th>\n",
       "      <th>Timings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Beyond Flavours</td>\n",
       "      <td>https://www.zomato.com/hyderabad/beyond-flavou...</td>\n",
       "      <td>800</td>\n",
       "      <td>Food Hygiene Rated Restaurants in Hyderabad, C...</td>\n",
       "      <td>Chinese, Continental, Kebab, European, South I...</td>\n",
       "      <td>12noon to 3:30pm, 6:30pm to 11:30pm (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Paradise</td>\n",
       "      <td>https://www.zomato.com/hyderabad/paradise-gach...</td>\n",
       "      <td>800</td>\n",
       "      <td>Hyderabad's Hottest</td>\n",
       "      <td>Biryani, North Indian, Chinese</td>\n",
       "      <td>11 AM to 11 PM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Flechazo</td>\n",
       "      <td>https://www.zomato.com/hyderabad/flechazo-gach...</td>\n",
       "      <td>1,300</td>\n",
       "      <td>Great Buffets, Hyderabad's Hottest</td>\n",
       "      <td>Asian, Mediterranean, North Indian, Desserts</td>\n",
       "      <td>11:30 AM to 4:30 PM, 6:30 PM to 11 PM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Shah Ghouse Hotel &amp; Restaurant</td>\n",
       "      <td>https://www.zomato.com/hyderabad/shah-ghouse-h...</td>\n",
       "      <td>800</td>\n",
       "      <td>Late Night Restaurants</td>\n",
       "      <td>Biryani, North Indian, Chinese, Seafood, Bever...</td>\n",
       "      <td>12 Noon to 2 AM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Over The Moon Brew Company</td>\n",
       "      <td>https://www.zomato.com/hyderabad/over-the-moon...</td>\n",
       "      <td>1,200</td>\n",
       "      <td>Best Bars &amp; Pubs, Food Hygiene Rated Restauran...</td>\n",
       "      <td>Asian, Continental, North Indian, Chinese, Med...</td>\n",
       "      <td>12noon to 11pm (Mon, Tue, Wed, Thu, Sun), 12no...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Name  \\\n",
       "0                 Beyond Flavours   \n",
       "1                        Paradise   \n",
       "2                        Flechazo   \n",
       "3  Shah Ghouse Hotel & Restaurant   \n",
       "4      Over The Moon Brew Company   \n",
       "\n",
       "                                               Links   Cost  \\\n",
       "0  https://www.zomato.com/hyderabad/beyond-flavou...    800   \n",
       "1  https://www.zomato.com/hyderabad/paradise-gach...    800   \n",
       "2  https://www.zomato.com/hyderabad/flechazo-gach...  1,300   \n",
       "3  https://www.zomato.com/hyderabad/shah-ghouse-h...    800   \n",
       "4  https://www.zomato.com/hyderabad/over-the-moon...  1,200   \n",
       "\n",
       "                                         Collections  \\\n",
       "0  Food Hygiene Rated Restaurants in Hyderabad, C...   \n",
       "1                                Hyderabad's Hottest   \n",
       "2                 Great Buffets, Hyderabad's Hottest   \n",
       "3                             Late Night Restaurants   \n",
       "4  Best Bars & Pubs, Food Hygiene Rated Restauran...   \n",
       "\n",
       "                                            Cuisines  \\\n",
       "0  Chinese, Continental, Kebab, European, South I...   \n",
       "1                     Biryani, North Indian, Chinese   \n",
       "2       Asian, Mediterranean, North Indian, Desserts   \n",
       "3  Biryani, North Indian, Chinese, Seafood, Bever...   \n",
       "4  Asian, Continental, North Indian, Chinese, Med...   \n",
       "\n",
       "                                             Timings  \n",
       "0      12noon to 3:30pm, 6:30pm to 11:30pm (Mon-Sun)  \n",
       "1                                     11 AM to 11 PM  \n",
       "2              11:30 AM to 4:30 PM, 6:30 PM to 11 PM  \n",
       "3                                    12 Noon to 2 AM  \n",
       "4  12noon to 11pm (Mon, Tue, Wed, Thu, Sun), 12no...  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rest_name_info = pd.read_csv(\"Restaurant name and related info.csv\")\n",
    "\n",
    "rest_name_info.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 105 entries, 0 to 104\n",
      "Data columns (total 6 columns):\n",
      " #   Column       Non-Null Count  Dtype \n",
      "---  ------       --------------  ----- \n",
      " 0   Name         105 non-null    object\n",
      " 1   Links        105 non-null    object\n",
      " 2   Cost         105 non-null    object\n",
      " 3   Collections  51 non-null     object\n",
      " 4   Cuisines     105 non-null    object\n",
      " 5   Timings      104 non-null    object\n",
      "dtypes: object(6)\n",
      "memory usage: 5.0+ KB\n"
     ]
    }
   ],
   "source": [
    "# Check Restaurant Name and Information Dataset\n",
    "\n",
    "rest_name_info.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Links</th>\n",
       "      <th>Cost</th>\n",
       "      <th>Collections</th>\n",
       "      <th>Cuisines</th>\n",
       "      <th>Timings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>105</td>\n",
       "      <td>105</td>\n",
       "      <td>105</td>\n",
       "      <td>51</td>\n",
       "      <td>105</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>105</td>\n",
       "      <td>105</td>\n",
       "      <td>29</td>\n",
       "      <td>42</td>\n",
       "      <td>92</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Cafe Eclat</td>\n",
       "      <td>https://www.zomato.com/hyderabad/ks-bakers-gac...</td>\n",
       "      <td>500</td>\n",
       "      <td>Food Hygiene Rated Restaurants in Hyderabad</td>\n",
       "      <td>North Indian, Chinese</td>\n",
       "      <td>11 AM to 11 PM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Name                                              Links Cost  \\\n",
       "count          105                                                105  105   \n",
       "unique         105                                                105   29   \n",
       "top     Cafe Eclat  https://www.zomato.com/hyderabad/ks-bakers-gac...  500   \n",
       "freq             1                                                  1   13   \n",
       "\n",
       "                                        Collections               Cuisines  \\\n",
       "count                                            51                    105   \n",
       "unique                                           42                     92   \n",
       "top     Food Hygiene Rated Restaurants in Hyderabad  North Indian, Chinese   \n",
       "freq                                              4                      4   \n",
       "\n",
       "               Timings  \n",
       "count              104  \n",
       "unique              77  \n",
       "top     11 AM to 11 PM  \n",
       "freq                 6  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Examine Statistics\n",
    "\n",
    "rest_name_info.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 9954 entries, 0 to 9999\n",
      "Data columns (total 7 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   Restaurant  9954 non-null   object \n",
      " 1   Reviewer    9954 non-null   object \n",
      " 2   Review      9954 non-null   object \n",
      " 3   Rating      9954 non-null   float64\n",
      " 4   Metadata    9954 non-null   object \n",
      " 5   Time        9954 non-null   object \n",
      " 6   Pictures    9954 non-null   int64  \n",
      "dtypes: float64(1), int64(1), object(5)\n",
      "memory usage: 622.1+ KB\n"
     ]
    }
   ],
   "source": [
    "# Drop Rows which has null values\n",
    "\n",
    "rest_review.dropna(how='any',inplace=True)\n",
    "rest_review.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Top Rated Restaurants**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cnovert Metadata object type to str\n",
    "\n",
    "rest_review[\"Metadata\"] = rest_review[\"Metadata\"].astype(\"str\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Restaurant</th>\n",
       "      <th>Reviewer</th>\n",
       "      <th>Review</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Time</th>\n",
       "      <th>Pictures</th>\n",
       "      <th>No_of_Reviews</th>\n",
       "      <th>No_of_Followers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Beyond Flavours</td>\n",
       "      <td>Rusha Chakraborty</td>\n",
       "      <td>The ambience was good, food was quite good . h...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5/25/2019 15:54</td>\n",
       "      <td>0</td>\n",
       "      <td>1 Review</td>\n",
       "      <td>2 Followers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Beyond Flavours</td>\n",
       "      <td>Anusha Tirumalaneedi</td>\n",
       "      <td>Ambience is too good for a pleasant evening. S...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5/25/2019 14:20</td>\n",
       "      <td>0</td>\n",
       "      <td>3 Reviews</td>\n",
       "      <td>2 Followers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Beyond Flavours</td>\n",
       "      <td>Ashok Shekhawat</td>\n",
       "      <td>A must try.. great food great ambience. Thnx f...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5/24/2019 22:54</td>\n",
       "      <td>0</td>\n",
       "      <td>2 Reviews</td>\n",
       "      <td>3 Followers</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Restaurant              Reviewer  \\\n",
       "0  Beyond Flavours     Rusha Chakraborty   \n",
       "1  Beyond Flavours  Anusha Tirumalaneedi   \n",
       "2  Beyond Flavours       Ashok Shekhawat   \n",
       "\n",
       "                                              Review  Rating             Time  \\\n",
       "0  The ambience was good, food was quite good . h...     5.0  5/25/2019 15:54   \n",
       "1  Ambience is too good for a pleasant evening. S...     5.0  5/25/2019 14:20   \n",
       "2  A must try.. great food great ambience. Thnx f...     5.0  5/24/2019 22:54   \n",
       "\n",
       "   Pictures No_of_Reviews No_of_Followers  \n",
       "0         0     1 Review      2 Followers  \n",
       "1         0    3 Reviews      2 Followers  \n",
       "2         0    2 Reviews      3 Followers  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split Metadata Column in No_of_Reviews and No_of_Followers Column\n",
    "\n",
    "rest_review[['No_of_Reviews','No_of_Followers']] = rest_review[\"Metadata\"].str.split(\",\" , expand=True)\n",
    "\n",
    "# Delete Metadata Column\n",
    "del rest_review[\"Metadata\"]\n",
    "rest_review[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Restaurant</th>\n",
       "      <th>Reviewer</th>\n",
       "      <th>Review</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Time</th>\n",
       "      <th>Pictures</th>\n",
       "      <th>No_of_Reviews</th>\n",
       "      <th>No_of_Followers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Beyond Flavours</td>\n",
       "      <td>Rusha Chakraborty</td>\n",
       "      <td>The ambience was good, food was quite good . h...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5/25/2019 15:54</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Beyond Flavours</td>\n",
       "      <td>Anusha Tirumalaneedi</td>\n",
       "      <td>Ambience is too good for a pleasant evening. S...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5/25/2019 14:20</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Beyond Flavours</td>\n",
       "      <td>Ashok Shekhawat</td>\n",
       "      <td>A must try.. great food great ambience. Thnx f...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5/24/2019 22:54</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Restaurant              Reviewer  \\\n",
       "0  Beyond Flavours     Rusha Chakraborty   \n",
       "1  Beyond Flavours  Anusha Tirumalaneedi   \n",
       "2  Beyond Flavours       Ashok Shekhawat   \n",
       "\n",
       "                                              Review  Rating             Time  \\\n",
       "0  The ambience was good, food was quite good . h...     5.0  5/25/2019 15:54   \n",
       "1  Ambience is too good for a pleasant evening. S...     5.0  5/25/2019 14:20   \n",
       "2  A must try.. great food great ambience. Thnx f...     5.0  5/24/2019 22:54   \n",
       "\n",
       "   Pictures  No_of_Reviews  No_of_Followers  \n",
       "0         0              1              2.0  \n",
       "1         0              3              2.0  \n",
       "2         0              2              3.0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Delete characters from No_of_Reviews and No_of_Followers Column and Make it a numeric data type\n",
    "\n",
    "rest_review['No_of_Reviews'] = pd.to_numeric(rest_review['No_of_Reviews'].str.replace(r'\\D+', ''))\n",
    "rest_review['No_of_Followers'] = pd.to_numeric(rest_review['No_of_Followers'].str.replace(r'\\D+', '')) \n",
    "rest_review[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace null values with 0 and convert column to int data type\n",
    "rest_review[\"No_of_Followers\"] = rest_review[\"No_of_Followers\"].replace(np.nan, 0)\n",
    "rest_review[\"No_of_Followers\"] = rest_review[\"No_of_Followers\"].astype(\"int\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top Rated Restaurants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Restaurant</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AB's - Absolute Barbecues</td>\n",
       "      <td>4.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>B-Dubs</td>\n",
       "      <td>4.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3B's - Buddies, Bar &amp; Barbecue</td>\n",
       "      <td>4.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Paradise</td>\n",
       "      <td>4.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Flechazo</td>\n",
       "      <td>4.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>The Indi Grill</td>\n",
       "      <td>4.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Zega - Sheraton Hyderabad Hotel</td>\n",
       "      <td>4.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Over The Moon Brew Company</td>\n",
       "      <td>4.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Beyond Flavours</td>\n",
       "      <td>4.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Cascade - Radisson Hyderabad Hitec City</td>\n",
       "      <td>4.26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Restaurant  Rating\n",
       "3                 AB's - Absolute Barbecues    4.88\n",
       "11                                   B-Dubs    4.81\n",
       "2            3B's - Buddies, Bar & Barbecue    4.76\n",
       "67                                 Paradise    4.70\n",
       "35                                 Flechazo    4.66\n",
       "87                           The Indi Grill    4.60\n",
       "97          Zega - Sheraton Hyderabad Hotel    4.45\n",
       "64               Over The Moon Brew Company    4.34\n",
       "16                          Beyond Flavours    4.28\n",
       "19  Cascade - Radisson Hyderabad Hitec City    4.26"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 10 Rated Restaurants\n",
    "trr = rest_review.groupby(\"Restaurant\")[\"Rating\"].agg(\"mean\").to_frame().reset_index().sort_values(by = \"Rating\", ascending = False)\n",
    "trr_10 = trr[:10]\n",
    "trr_10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAAAAAAAAkxE0AgAAAAAAADAZQSMAAAAAAAAAkxE0AgAAAAAAADAZQSMAAAAAAAAAkxE0AgAAAAAAADDZ/wP4EegaZKeDRwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"white\")\n",
    "plt.figure(figsize = (18, 8))\n",
    "sns.barplot(x = \"Rating\", y = \"Restaurant\", data = trr_10, palette = \"gist_heat\")\n",
    "plt.title(\"10 Top Rated Restaurants\", size = 20)\n",
    "plt.xticks(size = 15)\n",
    "plt.yticks(size = 15)\n",
    "plt.xlabel(\"Rating\", size = 20)\n",
    "plt.ylabel(None)\n",
    "plt.savefig(\"10_Top_Rated_Restaurants.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Top Reviewed Restaurants**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Restaurant</th>\n",
       "      <th>No_of_Reviews</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Labonel</td>\n",
       "      <td>6628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Pista House</td>\n",
       "      <td>5429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Chinese Pavilion</td>\n",
       "      <td>5409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Dunkin' Donuts</td>\n",
       "      <td>5289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>The Old Madras Baking Company</td>\n",
       "      <td>5115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Collage - Hyatt Hyderabad Gachibowli</td>\n",
       "      <td>4764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Cascade - Radisson Hyderabad Hitec City</td>\n",
       "      <td>4444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Hyderabadi Daawat</td>\n",
       "      <td>4412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Faasos</td>\n",
       "      <td>4392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Driven Cafe</td>\n",
       "      <td>4378</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Restaurant  No_of_Reviews\n",
       "55                                  Labonel           6628\n",
       "68                              Pista House           5429\n",
       "20                         Chinese Pavilion           5409\n",
       "31                           Dunkin' Donuts           5289\n",
       "89            The Old Madras Baking Company           5115\n",
       "22     Collage - Hyatt Hyderabad Gachibowli           4764\n",
       "19  Cascade - Radisson Hyderabad Hitec City           4444\n",
       "44                        Hyderabadi Daawat           4412\n",
       "33                                   Faasos           4392\n",
       "30                              Driven Cafe           4378"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 10 Reviewed Restaurants\n",
    "\n",
    "trer = rest_review.groupby(\"Restaurant\")[\"No_of_Reviews\"].agg(\"sum\").reset_index().sort_values(by = \"No_of_Reviews\", ascending = False)\n",
    "trer_10 = trer[:10]\n",
    "trer_10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (18, 8))\n",
    "sns.barplot(x = \"No_of_Reviews\", y = \"Restaurant\", data = trer_10, palette = \"spring\")\n",
    "plt.title(\"10 Top Reviewed Restaurants\", size = 20)\n",
    "plt.xticks(size = 15)\n",
    "plt.yticks(size = 15)\n",
    "plt.xlabel(\"Number of Reviews\", size = 20)\n",
    "plt.ylabel(None)\n",
    "plt.savefig(\"10_Top_Reviewed_Restaurants.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Top Followed Restaurants**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>Restaurant</th>\n",
       "      <th>No_of_Followers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Pista House</td>\n",
       "      <td>51068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Komatose - Holiday Inn Express &amp; Suites</td>\n",
       "      <td>46229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Hyderabadi Daawat</td>\n",
       "      <td>45375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>The Tilt Bar Republic</td>\n",
       "      <td>42126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Dunkin' Donuts</td>\n",
       "      <td>37667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Chinese Pavilion</td>\n",
       "      <td>35426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Faasos</td>\n",
       "      <td>34640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>T Grill</td>\n",
       "      <td>32222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Cascade - Radisson Hyderabad Hitec City</td>\n",
       "      <td>31644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Barbeque Nation</td>\n",
       "      <td>30866</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Restaurant  No_of_Followers\n",
       "68                              Pista House            51068\n",
       "52  Komatose - Holiday Inn Express & Suites            46229\n",
       "44                        Hyderabadi Daawat            45375\n",
       "90                    The Tilt Bar Republic            42126\n",
       "31                           Dunkin' Donuts            37667\n",
       "20                         Chinese Pavilion            35426\n",
       "33                                   Faasos            34640\n",
       "80                                  T Grill            32222\n",
       "19  Cascade - Radisson Hyderabad Hitec City            31644\n",
       "13                          Barbeque Nation            30866"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfr = rest_review.groupby(\"Restaurant\")[\"No_of_Followers\"].agg(\"sum\").reset_index().sort_values(by = \"No_of_Followers\", ascending = False)\n",
    "tfr_10 = tfr[:10]\n",
    "tfr_10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (18, 8))\n",
    "sns.barplot(x = \"No_of_Followers\", y = \"Restaurant\", data = tfr_10, palette = \"summer\")\n",
    "plt.title(\"10 Top Followed Restaurants\", size = 20)\n",
    "plt.xticks(size = 15)\n",
    "plt.yticks(size = 15)\n",
    "plt.xlabel(\"Number of Followers\", size = 20)\n",
    "plt.ylabel(None)\n",
    "plt.savefig(\"10_Top_Followed_Restaurants.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Identify Top 20 word frequencies for Reviews"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Defining the set containing all the stopwords**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "stopwordlist = ['a', 'about', 'above', 'after', 'again', 'ain', 'all', 'am', 'an',\n",
    "             'and','any','are', 'as', 'at', 'be', 'because', 'been', 'before',\n",
    "             'being', 'below', 'between','both', 'by', 'can', 'd', 'did', 'do',\n",
    "             'does', 'doing', 'down', 'during', 'each','few', 'for', 'from',\n",
    "             'further', 'had', 'has', 'have', 'having', 'he', 'her', 'here',\n",
    "             'hers', 'herself', 'him', 'himself', 'his', 'how', 'i', 'if', 'in',\n",
    "             'into','is', 'it', 'its', 'itself', 'just', 'll', 'm', 'ma',\n",
    "             'me', 'more', 'most','my', 'myself', 'now', 'o', 'of', 'on', 'once',\n",
    "             'only', 'or', 'other', 'our', 'ours','ourselves', 'out', 'own', 're','s', 'same', 'she', \"shes\", 'should', \"shouldve\",'so', 'some', 'such',\n",
    "             't', 'than', 'that', \"thatll\", 'the', 'their', 'theirs', 'them',\n",
    "             'themselves', 'then', 'there', 'these', 'they', 'this', 'those',\n",
    "             'through', 'to', 'too','under', 'until', 'up', 've', 'very', 'was',\n",
    "             'we', 'were', 'what', 'when', 'where','which','while', 'who', 'whom',\n",
    "             'why', 'will', 'with', 'won', 'y', 'you', \"youd\",\"youll\", \"youre\",\n",
    "             \"youve\", 'your', 'yours', 'yourself', 'yourselves']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Cleaning and removing the above stop words list from the text/body**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    The ambience good, food quite good . Saturday ...\n",
       "1    Ambience good pleasant evening. Service prompt...\n",
       "2    A must try.. great food great ambience. Thnx s...\n",
       "3    Soumen das Arun great guy. Only behavior since...\n",
       "4    Food good.we ordered Kodi drumsticks basket mu...\n",
       "Name: Review, dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "STOPWORDS = set(stopwordlist)\n",
    "\n",
    "def cleaning_stopwords(text):\n",
    "    return \" \".join([word for word in str(text).split() if word not in STOPWORDS])\n",
    "\n",
    "rest_review['Review'] = rest_review['Review'].apply(lambda text: cleaning_stopwords(text))\n",
    "rest_review['Review'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Cleaning and removing punctuations**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9995    Madhumathi Mahajan Well start nice courteous s...\n",
       "9996    This place never disappointed us The food cour...\n",
       "9997    Bad rating mainly Chicken Bone found Veg food ...\n",
       "9998    I personally love prefer Chinese Food Had coup...\n",
       "9999    Checked try delicious chinese food here seen n...\n",
       "Name: Review, dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "punctuations_list = string.punctuation\n",
    "\n",
    "def cleaning_punctuations(text):\n",
    "    translator = str.maketrans('', '', punctuations_list)\n",
    "    return text.translate(translator)\n",
    "\n",
    "rest_review['Review'] = rest_review['Review'].apply(lambda x: cleaning_punctuations(x))\n",
    "rest_review['Review'].tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Cleaning and removing repeating characters**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9995    Madhumathi Mahajan Well start nice courteous s...\n",
       "9996    This place never disappointed us The food cour...\n",
       "9997    Bad rating mainly Chicken Bone found Veg food ...\n",
       "9998    I personally love prefer Chinese Food Had coup...\n",
       "9999    Checked try delicious chinese food here seen n...\n",
       "Name: Review, dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def cleaning_repeating_char(text):\n",
    "    return re.sub(r'(.)1+', r'1', text)\n",
    "\n",
    "rest_review['Review'] = rest_review['Review'].apply(lambda x: cleaning_repeating_char(x))\n",
    "rest_review['Review'].tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Cleaning and removing Numeric numbers**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9995    Madhumathi Mahajan Well start nice courteous s...\n",
       "9996    This place never disappointed us The food cour...\n",
       "9997    Bad rating mainly Chicken Bone found Veg food ...\n",
       "9998    I personally love prefer Chinese Food Had coup...\n",
       "9999    Checked try delicious chinese food here seen n...\n",
       "Name: Review, dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def cleaning_numbers(data):\n",
    "    return re.sub('[0-9]+', '', data)\n",
    "\n",
    "rest_review['Review'] = rest_review['Review'].apply(lambda x: cleaning_numbers(x))\n",
    "rest_review['Review'].tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Top 20 Two word Frequencies of Reviews** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "text = ' '.join(rest_review[\"Review\"])\n",
    "words = text.split()\n",
    "\n",
    "two_words = {' '.join(words):n for words,n in Counter(zip(words, words[1:])).items() if not  words[0][-1]==(',')}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>Reviews Words</th>\n",
       "      <th>Frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2874</th>\n",
       "      <td>I ordered</td>\n",
       "      <td>559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554</th>\n",
       "      <td>The food</td>\n",
       "      <td>471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>We ordered</td>\n",
       "      <td>391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1433</th>\n",
       "      <td>main course</td>\n",
       "      <td>387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>really good</td>\n",
       "      <td>376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>I would</td>\n",
       "      <td>371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1063</th>\n",
       "      <td>This place</td>\n",
       "      <td>353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2069</th>\n",
       "      <td>not good</td>\n",
       "      <td>349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>good but</td>\n",
       "      <td>318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>visit place</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>must try</td>\n",
       "      <td>306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>food good</td>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>529</th>\n",
       "      <td>The place</td>\n",
       "      <td>277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>good food</td>\n",
       "      <td>253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The ambience</td>\n",
       "      <td>237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>The service</td>\n",
       "      <td>231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>also good</td>\n",
       "      <td>229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1442</th>\n",
       "      <td>ice cream</td>\n",
       "      <td>224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2674</th>\n",
       "      <td>good place</td>\n",
       "      <td>223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1152</th>\n",
       "      <td>The staff</td>\n",
       "      <td>197</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Reviews Words  Frequency\n",
       "2874     I ordered        559\n",
       "554       The food        471\n",
       "294     We ordered        391\n",
       "1433   main course        387\n",
       "207    really good        376\n",
       "72         I would        371\n",
       "1063    This place        353\n",
       "2069      not good        349\n",
       "459       good but        318\n",
       "75     visit place        315\n",
       "40        must try        306\n",
       "242      food good        285\n",
       "529      The place        277\n",
       "2        good food        253\n",
       "0     The ambience        237\n",
       "224    The service        231\n",
       "97       also good        229\n",
       "1442     ice cream        224\n",
       "2674    good place        223\n",
       "1152     The staff        197"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "two_words_df = pd.DataFrame(two_words.items(), columns=['Reviews Words', 'Frequency'])\n",
    "two_words_df = two_words_df.sort_values(by = \"Frequency\", ascending = False)\n",
    "two_words_20 = two_words_df[:20]\n",
    "two_words_20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"whitegrid\")\n",
    "plt.figure(figsize = (18, 8))\n",
    "sns.barplot(y = \"Reviews Words\", x = \"Frequency\", data = two_words_20, palette = \"plasma\")\n",
    "plt.title(\"Top 20 Two-word Frequencies of Reviews\", size = 20)\n",
    "plt.xticks(size = 15)\n",
    "plt.yticks(size = 15)\n",
    "plt.xlabel(\"Reviews Words\", size = 20)\n",
    "plt.ylabel(None)\n",
    "plt.savefig(\"Top_20_Two-word_Frequencies_of_Reviews.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Identify Top 20 word frequencies for Cusines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Cleaning and removing the above stop words from the text/body**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Chinese, Continental, Kebab, European, South I...\n",
       "1                       Biryani, North Indian, Chinese\n",
       "2         Asian, Mediterranean, North Indian, Desserts\n",
       "3    Biryani, North Indian, Chinese, Seafood, Bever...\n",
       "4    Asian, Continental, North Indian, Chinese, Med...\n",
       "Name: Cuisines, dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rest_name_info['Cuisines'] = rest_name_info['Cuisines'].apply(lambda text: cleaning_stopwords(text))\n",
    "rest_name_info['Cuisines'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Cleaning and Removing Punctuations**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Chinese Continental Kebab European South India...\n",
       "1                         Biryani North Indian Chinese\n",
       "2            Asian Mediterranean North Indian Desserts\n",
       "3       Biryani North Indian Chinese Seafood Beverages\n",
       "4    Asian Continental North Indian Chinese Mediter...\n",
       "Name: Cuisines, dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rest_name_info['Cuisines'] = rest_name_info['Cuisines'].apply(lambda x: cleaning_punctuations(x))\n",
    "rest_name_info['Cuisines'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Cleaning and removing repeating characters**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Chinese Continental Kebab European South India...\n",
       "1                         Biryani North Indian Chinese\n",
       "2            Asian Mediterranean North Indian Desserts\n",
       "3       Biryani North Indian Chinese Seafood Beverages\n",
       "4    Asian Continental North Indian Chinese Mediter...\n",
       "Name: Cuisines, dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rest_name_info['Cuisines'] = rest_name_info['Cuisines'].apply(lambda x: cleaning_repeating_char(x))\n",
    "rest_name_info['Cuisines'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Cleaning and removing Numeric numbers**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Chinese Continental Kebab European South India...\n",
       "1                         Biryani North Indian Chinese\n",
       "2            Asian Mediterranean North Indian Desserts\n",
       "3       Biryani North Indian Chinese Seafood Beverages\n",
       "4    Asian Continental North Indian Chinese Mediter...\n",
       "Name: Cuisines, dtype: object"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rest_name_info['Cuisines'] = rest_name_info['Cuisines'].apply(lambda x: cleaning_numbers(x))\n",
    "rest_name_info['Cuisines'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Top 20 Two word Frequencies of Cuisines**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "text = ' '.join(rest_name_info['Cuisines'])\n",
    "words = text.split()\n",
    "\n",
    "two_words = {' '.join(words):n for words,n in Counter(zip(words, words[1:])).items() if not  words[0][-1]==(',')}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>Cuisine Words</th>\n",
       "      <th>Frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>North Indian</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Indian Chinese</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Fast Food</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>South Indian</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Indian North</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Chinese North</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Indian Continental</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Italian North</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Biryani North</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Food North</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Continental Italian</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chinese Continental</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Indian Kebab</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Indian Asian</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Indian Mughlai</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Continental North</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Chinese Biryani</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>Desserts Cafe</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Burger Fast</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Asian Continental</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Cuisine Words  Frequency\n",
       "6           North Indian         61\n",
       "9         Indian Chinese         27\n",
       "42             Fast Food         15\n",
       "4           South Indian          9\n",
       "5           Indian North          9\n",
       "33         Chinese North          8\n",
       "24    Indian Continental          6\n",
       "65         Italian North          6\n",
       "8          Biryani North          6\n",
       "28            Food North          6\n",
       "93   Continental Italian          6\n",
       "0    Chinese Continental          5\n",
       "34          Indian Kebab          3\n",
       "84          Indian Asian          3\n",
       "77        Indian Mughlai          3\n",
       "19     Continental North          3\n",
       "54       Chinese Biryani          3\n",
       "105        Desserts Cafe          3\n",
       "53           Burger Fast          3\n",
       "18     Asian Continental          3"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "two_words_dfc = pd.DataFrame(two_words.items(), columns=['Cuisine Words', 'Frequency'])\n",
    "two_words_dfc = two_words_dfc.sort_values(by = \"Frequency\", ascending = False)\n",
    "two_words_20c = two_words_dfc[:20]\n",
    "two_words_20c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"whitegrid\")\n",
    "plt.figure(figsize = (18, 8))\n",
    "sns.barplot(y = \"Cuisine Words\", x = \"Frequency\", data = two_words_20c, palette = \"hsv\")\n",
    "plt.title(\"Top 20 Two-word Frequencies of Cuisines\", size = 20)\n",
    "plt.xticks(size = 15)\n",
    "plt.yticks(size = 15)\n",
    "plt.xlabel(\"Cuisine Words\", size = 20)\n",
    "plt.ylabel(None)\n",
    "plt.savefig(\"Top_20_Two-word_Frequencies_of_Cuisines.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Recommendor System"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Preparing Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Restaurant Review Data\n",
    "\n",
    "df1 = pd.read_csv(\"Restaurant_Review.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Restaurant Name and Related Info Data\n",
    "\n",
    "df2 = pd.read_csv(\"Restaurant name and related info.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>Links</th>\n",
       "      <th>Cost</th>\n",
       "      <th>Collections</th>\n",
       "      <th>Cuisines</th>\n",
       "      <th>Timings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Beyond Flavours</td>\n",
       "      <td>https://www.zomato.com/hyderabad/beyond-flavou...</td>\n",
       "      <td>800</td>\n",
       "      <td>Food Hygiene Rated Restaurants in Hyderabad, C...</td>\n",
       "      <td>Chinese, Continental, Kebab, European, South I...</td>\n",
       "      <td>12noon to 3:30pm, 6:30pm to 11:30pm (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Paradise</td>\n",
       "      <td>https://www.zomato.com/hyderabad/paradise-gach...</td>\n",
       "      <td>800</td>\n",
       "      <td>Hyderabad's Hottest</td>\n",
       "      <td>Biryani, North Indian, Chinese</td>\n",
       "      <td>11 AM to 11 PM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Flechazo</td>\n",
       "      <td>https://www.zomato.com/hyderabad/flechazo-gach...</td>\n",
       "      <td>1,300</td>\n",
       "      <td>Great Buffets, Hyderabad's Hottest</td>\n",
       "      <td>Asian, Mediterranean, North Indian, Desserts</td>\n",
       "      <td>11:30 AM to 4:30 PM, 6:30 PM to 11 PM</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Name                                              Links   Cost  \\\n",
       "0  Beyond Flavours  https://www.zomato.com/hyderabad/beyond-flavou...    800   \n",
       "1         Paradise  https://www.zomato.com/hyderabad/paradise-gach...    800   \n",
       "2         Flechazo  https://www.zomato.com/hyderabad/flechazo-gach...  1,300   \n",
       "\n",
       "                                         Collections  \\\n",
       "0  Food Hygiene Rated Restaurants in Hyderabad, C...   \n",
       "1                                Hyderabad's Hottest   \n",
       "2                 Great Buffets, Hyderabad's Hottest   \n",
       "\n",
       "                                            Cuisines  \\\n",
       "0  Chinese, Continental, Kebab, European, South I...   \n",
       "1                     Biryani, North Indian, Chinese   \n",
       "2       Asian, Mediterranean, North Indian, Desserts   \n",
       "\n",
       "                                         Timings  \n",
       "0  12noon to 3:30pm, 6:30pm to 11:30pm (Mon-Sun)  \n",
       "1                                 11 AM to 11 PM  \n",
       "2          11:30 AM to 4:30 PM, 6:30 PM to 11 PM  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Examine Columns\n",
    "\n",
    "df2[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Links</th>\n",
       "      <th>Cost</th>\n",
       "      <th>Collections</th>\n",
       "      <th>Cuisines</th>\n",
       "      <th>Timings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Beyond Flavours</td>\n",
       "      <td>https://www.zomato.com/hyderabad/beyond-flavou...</td>\n",
       "      <td>800</td>\n",
       "      <td>Food Hygiene Rated Restaurants in Hyderabad, C...</td>\n",
       "      <td>[Chinese,  Continental,  Kebab,  European,  So...</td>\n",
       "      <td>12noon to 3:30pm, 6:30pm to 11:30pm (Mon-Sun)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Paradise</td>\n",
       "      <td>https://www.zomato.com/hyderabad/paradise-gach...</td>\n",
       "      <td>800</td>\n",
       "      <td>Hyderabad's Hottest</td>\n",
       "      <td>[Biryani,  North Indian,  Chinese]</td>\n",
       "      <td>11 AM to 11 PM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Flechazo</td>\n",
       "      <td>https://www.zomato.com/hyderabad/flechazo-gach...</td>\n",
       "      <td>1,300</td>\n",
       "      <td>Great Buffets, Hyderabad's Hottest</td>\n",
       "      <td>[Asian,  Mediterranean,  North Indian,  Desserts]</td>\n",
       "      <td>11:30 AM to 4:30 PM, 6:30 PM to 11 PM</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Name                                              Links   Cost  \\\n",
       "0  Beyond Flavours  https://www.zomato.com/hyderabad/beyond-flavou...    800   \n",
       "1         Paradise  https://www.zomato.com/hyderabad/paradise-gach...    800   \n",
       "2         Flechazo  https://www.zomato.com/hyderabad/flechazo-gach...  1,300   \n",
       "\n",
       "                                         Collections  \\\n",
       "0  Food Hygiene Rated Restaurants in Hyderabad, C...   \n",
       "1                                Hyderabad's Hottest   \n",
       "2                 Great Buffets, Hyderabad's Hottest   \n",
       "\n",
       "                                            Cuisines  \\\n",
       "0  [Chinese,  Continental,  Kebab,  European,  So...   \n",
       "1                 [Biryani,  North Indian,  Chinese]   \n",
       "2  [Asian,  Mediterranean,  North Indian,  Desserts]   \n",
       "\n",
       "                                         Timings  \n",
       "0  12noon to 3:30pm, 6:30pm to 11:30pm (Mon-Sun)  \n",
       "1                                 11 AM to 11 PM  \n",
       "2          11:30 AM to 4:30 PM, 6:30 PM to 11 PM  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split Cusines Column by Cuisine\n",
    "df2[\"Cuisines\"] = df2[\"Cuisines\"].str.split(\",\")\n",
    "df2[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**One Hot Encoding on Cuisines**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <td>Beyond Flavours</td>\n",
       "      <td>Paradise</td>\n",
       "      <td>Flechazo</td>\n",
       "      <td>Shah Ghouse Hotel &amp; Restaurant</td>\n",
       "      <td>Over The Moon Brew Company</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Links</th>\n",
       "      <td>https://www.zomato.com/hyderabad/beyond-flavou...</td>\n",
       "      <td>https://www.zomato.com/hyderabad/paradise-gach...</td>\n",
       "      <td>https://www.zomato.com/hyderabad/flechazo-gach...</td>\n",
       "      <td>https://www.zomato.com/hyderabad/shah-ghouse-h...</td>\n",
       "      <td>https://www.zomato.com/hyderabad/over-the-moon...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cost</th>\n",
       "      <td>800</td>\n",
       "      <td>800</td>\n",
       "      <td>1,300</td>\n",
       "      <td>800</td>\n",
       "      <td>1,200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Collections</th>\n",
       "      <td>Food Hygiene Rated Restaurants in Hyderabad, C...</td>\n",
       "      <td>Hyderabad's Hottest</td>\n",
       "      <td>Great Buffets, Hyderabad's Hottest</td>\n",
       "      <td>Late Night Restaurants</td>\n",
       "      <td>Best Bars &amp; Pubs, Food Hygiene Rated Restauran...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cuisines</th>\n",
       "      <td>[Chinese,  Continental,  Kebab,  European,  So...</td>\n",
       "      <td>[Biryani,  North Indian,  Chinese]</td>\n",
       "      <td>[Asian,  Mediterranean,  North Indian,  Desserts]</td>\n",
       "      <td>[Biryani,  North Indian,  Chinese,  Seafood,  ...</td>\n",
       "      <td>[Asian,  Continental,  North Indian,  Chinese,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cafe</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hyderabadi</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mithai</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thai</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malaysian</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>76 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                             0  \\\n",
       "Name                                           Beyond Flavours   \n",
       "Links        https://www.zomato.com/hyderabad/beyond-flavou...   \n",
       "Cost                                                       800   \n",
       "Collections  Food Hygiene Rated Restaurants in Hyderabad, C...   \n",
       "Cuisines     [Chinese,  Continental,  Kebab,  European,  So...   \n",
       "...                                                        ...   \n",
       " Cafe                                                      0.0   \n",
       "Hyderabadi                                                 0.0   \n",
       " Mithai                                                    0.0   \n",
       "Thai                                                       0.0   \n",
       " Malaysian                                                 0.0   \n",
       "\n",
       "                                                             1  \\\n",
       "Name                                                  Paradise   \n",
       "Links        https://www.zomato.com/hyderabad/paradise-gach...   \n",
       "Cost                                                       800   \n",
       "Collections                                Hyderabad's Hottest   \n",
       "Cuisines                    [Biryani,  North Indian,  Chinese]   \n",
       "...                                                        ...   \n",
       " Cafe                                                      0.0   \n",
       "Hyderabadi                                                 0.0   \n",
       " Mithai                                                    0.0   \n",
       "Thai                                                       0.0   \n",
       " Malaysian                                                 0.0   \n",
       "\n",
       "                                                             2  \\\n",
       "Name                                                  Flechazo   \n",
       "Links        https://www.zomato.com/hyderabad/flechazo-gach...   \n",
       "Cost                                                     1,300   \n",
       "Collections                 Great Buffets, Hyderabad's Hottest   \n",
       "Cuisines     [Asian,  Mediterranean,  North Indian,  Desserts]   \n",
       "...                                                        ...   \n",
       " Cafe                                                      0.0   \n",
       "Hyderabadi                                                 0.0   \n",
       " Mithai                                                    0.0   \n",
       "Thai                                                       0.0   \n",
       " Malaysian                                                 0.0   \n",
       "\n",
       "                                                             3  \\\n",
       "Name                            Shah Ghouse Hotel & Restaurant   \n",
       "Links        https://www.zomato.com/hyderabad/shah-ghouse-h...   \n",
       "Cost                                                       800   \n",
       "Collections                             Late Night Restaurants   \n",
       "Cuisines     [Biryani,  North Indian,  Chinese,  Seafood,  ...   \n",
       "...                                                        ...   \n",
       " Cafe                                                      0.0   \n",
       "Hyderabadi                                                 0.0   \n",
       " Mithai                                                    0.0   \n",
       "Thai                                                       0.0   \n",
       " Malaysian                                                 0.0   \n",
       "\n",
       "                                                             4  \n",
       "Name                                Over The Moon Brew Company  \n",
       "Links        https://www.zomato.com/hyderabad/over-the-moon...  \n",
       "Cost                                                     1,200  \n",
       "Collections  Best Bars & Pubs, Food Hygiene Rated Restauran...  \n",
       "Cuisines     [Asian,  Continental,  North Indian,  Chinese,...  \n",
       "...                                                        ...  \n",
       " Cafe                                                      0.0  \n",
       "Hyderabadi                                                 0.0  \n",
       " Mithai                                                    0.0  \n",
       "Thai                                                       0.0  \n",
       " Malaysian                                                 0.0  \n",
       "\n",
       "[76 rows x 5 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_c = df2.copy()\n",
    "\n",
    "# iterate in cuisine column\n",
    "for index, row in df2.iterrows(): \n",
    "    for genre in row[\"Cuisines\"]:\n",
    "        df_c.at[index, genre] = 1\n",
    "        \n",
    "#Filling in the NaN values with 0 to show that a movie doesn't have that column's \n",
    "df_c = df_c.fillna(0)\n",
    "df_c.head().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Content Based Recommendor System**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Restaurant</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Pakwaan Grand</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Club Rogue</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Flechazo</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Green Bawarchi Restaurant</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dunkin' Donuts</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Restaurant  Rating\n",
       "0              Pakwaan Grand     5.0\n",
       "1                 Club Rogue     3.5\n",
       "2                   Flechazo     2.0\n",
       "3  Green Bawarchi Restaurant     5.0\n",
       "4             Dunkin' Donuts     4.5"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "userInput = [\n",
    "            {'Restaurant':\"Pakwaan Grand\", 'Rating':5},\n",
    "            {'Restaurant':\"Club Rogue\", 'Rating':3.5},\n",
    "            {'Restaurant':\"Flechazo\", 'Rating':2},\n",
    "            {'Restaurant':\"Green Bawarchi Restaurant\", 'Rating':5},\n",
    "            {'Restaurant':\"Dunkin' Donuts\", 'Rating':4.5}\n",
    "         ] \n",
    "inputRestaurant = pd.DataFrame(userInput)\n",
    "inputRestaurant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Restaurant</th>\n",
       "      <th>Links</th>\n",
       "      <th>Cost</th>\n",
       "      <th>Collections</th>\n",
       "      <th>Cuisines</th>\n",
       "      <th>Timings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Flechazo</td>\n",
       "      <td>https://www.zomato.com/hyderabad/flechazo-gach...</td>\n",
       "      <td>1,300</td>\n",
       "      <td>Great Buffets, Hyderabad's Hottest</td>\n",
       "      <td>[Asian,  Mediterranean,  North Indian,  Desserts]</td>\n",
       "      <td>11:30 AM to 4:30 PM, 6:30 PM to 11 PM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Pakwaan Grand</td>\n",
       "      <td>https://www.zomato.com/hyderabad/pakwaan-grand...</td>\n",
       "      <td>400</td>\n",
       "      <td>Trending This Week</td>\n",
       "      <td>[North Indian,  Chinese,  Biryani]</td>\n",
       "      <td>12 Noon to 11 PM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Club Rogue</td>\n",
       "      <td>https://www.zomato.com/hyderabad/club-rogue-ga...</td>\n",
       "      <td>900</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[North Indian,  Fast Food]</td>\n",
       "      <td>11 AM to 12 Midnight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Green Bawarchi Restaurant</td>\n",
       "      <td>https://www.zomato.com/hyderabad/green-bawarch...</td>\n",
       "      <td>700</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[North Indian,  Chinese,  Biryani]</td>\n",
       "      <td>12 Noon to 11 PM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Dunkin' Donuts</td>\n",
       "      <td>https://www.zomato.com/hyderabad/dunkin-donuts...</td>\n",
       "      <td>550</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Desserts,  Cafe,  Beverages,  Burger,  Fast F...</td>\n",
       "      <td>10 AM to 11 PM</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Restaurant  \\\n",
       "2                    Flechazo   \n",
       "19              Pakwaan Grand   \n",
       "33                 Club Rogue   \n",
       "35  Green Bawarchi Restaurant   \n",
       "94             Dunkin' Donuts   \n",
       "\n",
       "                                                Links   Cost  \\\n",
       "2   https://www.zomato.com/hyderabad/flechazo-gach...  1,300   \n",
       "19  https://www.zomato.com/hyderabad/pakwaan-grand...    400   \n",
       "33  https://www.zomato.com/hyderabad/club-rogue-ga...    900   \n",
       "35  https://www.zomato.com/hyderabad/green-bawarch...    700   \n",
       "94  https://www.zomato.com/hyderabad/dunkin-donuts...    550   \n",
       "\n",
       "                           Collections  \\\n",
       "2   Great Buffets, Hyderabad's Hottest   \n",
       "19                  Trending This Week   \n",
       "33                                 NaN   \n",
       "35                                 NaN   \n",
       "94                                 NaN   \n",
       "\n",
       "                                             Cuisines  \\\n",
       "2   [Asian,  Mediterranean,  North Indian,  Desserts]   \n",
       "19                 [North Indian,  Chinese,  Biryani]   \n",
       "33                         [North Indian,  Fast Food]   \n",
       "35                 [North Indian,  Chinese,  Biryani]   \n",
       "94  [Desserts,  Cafe,  Beverages,  Burger,  Fast F...   \n",
       "\n",
       "                                  Timings  \n",
       "2   11:30 AM to 4:30 PM, 6:30 PM to 11 PM  \n",
       "19                       12 Noon to 11 PM  \n",
       "33                   11 AM to 12 Midnight  \n",
       "35                       12 Noon to 11 PM  \n",
       "94                         10 AM to 11 PM  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check Customer Input in Cuisine Data\n",
    "\n",
    "input_res = df2[df2[\"Name\"].isin(inputRestaurant[\"Restaurant\"].to_list())]\n",
    "input_res = input_res.rename(columns = {\"Name\": \"Restaurant\"})\n",
    "input_res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set Customer Input Restasurant as Index\n",
    "\n",
    "inputRestaurant = inputRestaurant.set_index(\"Restaurant\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set Restaurant as Index\n",
    "\n",
    "input_res = input_res.set_index(\"Restaurant\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rating</th>\n",
       "      <th>Links</th>\n",
       "      <th>Cost</th>\n",
       "      <th>Collections</th>\n",
       "      <th>Cuisines</th>\n",
       "      <th>Timings</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Restaurant</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pakwaan Grand</th>\n",
       "      <td>5.0</td>\n",
       "      <td>https://www.zomato.com/hyderabad/pakwaan-grand...</td>\n",
       "      <td>400</td>\n",
       "      <td>Trending This Week</td>\n",
       "      <td>[North Indian,  Chinese,  Biryani]</td>\n",
       "      <td>12 Noon to 11 PM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Club Rogue</th>\n",
       "      <td>3.5</td>\n",
       "      <td>https://www.zomato.com/hyderabad/club-rogue-ga...</td>\n",
       "      <td>900</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[North Indian,  Fast Food]</td>\n",
       "      <td>11 AM to 12 Midnight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flechazo</th>\n",
       "      <td>2.0</td>\n",
       "      <td>https://www.zomato.com/hyderabad/flechazo-gach...</td>\n",
       "      <td>1,300</td>\n",
       "      <td>Great Buffets, Hyderabad's Hottest</td>\n",
       "      <td>[Asian,  Mediterranean,  North Indian,  Desserts]</td>\n",
       "      <td>11:30 AM to 4:30 PM, 6:30 PM to 11 PM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Green Bawarchi Restaurant</th>\n",
       "      <td>5.0</td>\n",
       "      <td>https://www.zomato.com/hyderabad/green-bawarch...</td>\n",
       "      <td>700</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[North Indian,  Chinese,  Biryani]</td>\n",
       "      <td>12 Noon to 11 PM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dunkin' Donuts</th>\n",
       "      <td>4.5</td>\n",
       "      <td>https://www.zomato.com/hyderabad/dunkin-donuts...</td>\n",
       "      <td>550</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Desserts,  Cafe,  Beverages,  Burger,  Fast F...</td>\n",
       "      <td>10 AM to 11 PM</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Rating  \\\n",
       "Restaurant                          \n",
       "Pakwaan Grand                 5.0   \n",
       "Club Rogue                    3.5   \n",
       "Flechazo                      2.0   \n",
       "Green Bawarchi Restaurant     5.0   \n",
       "Dunkin' Donuts                4.5   \n",
       "\n",
       "                                                                       Links  \\\n",
       "Restaurant                                                                     \n",
       "Pakwaan Grand              https://www.zomato.com/hyderabad/pakwaan-grand...   \n",
       "Club Rogue                 https://www.zomato.com/hyderabad/club-rogue-ga...   \n",
       "Flechazo                   https://www.zomato.com/hyderabad/flechazo-gach...   \n",
       "Green Bawarchi Restaurant  https://www.zomato.com/hyderabad/green-bawarch...   \n",
       "Dunkin' Donuts             https://www.zomato.com/hyderabad/dunkin-donuts...   \n",
       "\n",
       "                            Cost                         Collections  \\\n",
       "Restaurant                                                             \n",
       "Pakwaan Grand                400                  Trending This Week   \n",
       "Club Rogue                   900                                 NaN   \n",
       "Flechazo                   1,300  Great Buffets, Hyderabad's Hottest   \n",
       "Green Bawarchi Restaurant    700                                 NaN   \n",
       "Dunkin' Donuts               550                                 NaN   \n",
       "\n",
       "                                                                    Cuisines  \\\n",
       "Restaurant                                                                     \n",
       "Pakwaan Grand                             [North Indian,  Chinese,  Biryani]   \n",
       "Club Rogue                                        [North Indian,  Fast Food]   \n",
       "Flechazo                   [Asian,  Mediterranean,  North Indian,  Desserts]   \n",
       "Green Bawarchi Restaurant                 [North Indian,  Chinese,  Biryani]   \n",
       "Dunkin' Donuts             [Desserts,  Cafe,  Beverages,  Burger,  Fast F...   \n",
       "\n",
       "                                                         Timings  \n",
       "Restaurant                                                        \n",
       "Pakwaan Grand                                   12 Noon to 11 PM  \n",
       "Club Rogue                                  11 AM to 12 Midnight  \n",
       "Flechazo                   11:30 AM to 4:30 PM, 6:30 PM to 11 PM  \n",
       "Green Bawarchi Restaurant                       12 Noon to 11 PM  \n",
       "Dunkin' Donuts                                    10 AM to 11 PM  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge Data on Restaurants\n",
    "\n",
    "inputRestaurant = pd.merge(inputRestaurant, input_res, left_index = True, right_index = True)\n",
    "inputRestaurant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Restaurant</th>\n",
       "      <th>Cuisines</th>\n",
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       "      <th>1</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Flechazo</td>\n",
       "      <td>[Asian,  Mediterranean,  North Indian,  Desserts]</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Green Bawarchi Restaurant</td>\n",
       "      <td>[North Indian,  Chinese,  Biryani]</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dunkin' Donuts</td>\n",
       "      <td>[Desserts,  Cafe,  Beverages,  Burger,  Fast F...</td>\n",
       "      <td>4.5</td>\n",
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      "text/plain": [
       "                  Restaurant  \\\n",
       "0              Pakwaan Grand   \n",
       "1                 Club Rogue   \n",
       "2                   Flechazo   \n",
       "3  Green Bawarchi Restaurant   \n",
       "4             Dunkin' Donuts   \n",
       "\n",
       "                                            Cuisines  Rating  \n",
       "0                 [North Indian,  Chinese,  Biryani]     5.0  \n",
       "1                         [North Indian,  Fast Food]     3.5  \n",
       "2  [Asian,  Mediterranean,  North Indian,  Desserts]     2.0  \n",
       "3                 [North Indian,  Chinese,  Biryani]     5.0  \n",
       "4  [Desserts,  Cafe,  Beverages,  Burger,  Fast F...     4.5  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show Necessary Columns\n",
    "inputRestaurant = inputRestaurant.reset_index()\n",
    "inputRestaurant = inputRestaurant[[\"Restaurant\", \"Cuisines\", \"Rating\"]] \n",
    "inputRestaurant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Cuisines</th>\n",
       "      <th>Timings</th>\n",
       "      <th>Chinese</th>\n",
       "      <th>Continental</th>\n",
       "      <th>Kebab</th>\n",
       "      <th>European</th>\n",
       "      <th>...</th>\n",
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       "      <th>Momos</th>\n",
       "      <th>Pizza</th>\n",
       "      <th>Arabian</th>\n",
       "      <th>North Eastern</th>\n",
       "      <th>Cafe</th>\n",
       "      <th>Hyderabadi</th>\n",
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       "      <th>Malaysian</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Flechazo</td>\n",
       "      <td>https://www.zomato.com/hyderabad/flechazo-gach...</td>\n",
       "      <td>1,300</td>\n",
       "      <td>Great Buffets, Hyderabad's Hottest</td>\n",
       "      <td>[Asian,  Mediterranean,  North Indian,  Desserts]</td>\n",
       "      <td>11:30 AM to 4:30 PM, 6:30 PM to 11 PM</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>19</th>\n",
       "      <td>Pakwaan Grand</td>\n",
       "      <td>https://www.zomato.com/hyderabad/pakwaan-grand...</td>\n",
       "      <td>400</td>\n",
       "      <td>Trending This Week</td>\n",
       "      <td>[North Indian,  Chinese,  Biryani]</td>\n",
       "      <td>12 Noon to 11 PM</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Club Rogue</td>\n",
       "      <td>https://www.zomato.com/hyderabad/club-rogue-ga...</td>\n",
       "      <td>900</td>\n",
       "      <td>0</td>\n",
       "      <td>[North Indian,  Fast Food]</td>\n",
       "      <td>11 AM to 12 Midnight</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Green Bawarchi Restaurant</td>\n",
       "      <td>https://www.zomato.com/hyderabad/green-bawarch...</td>\n",
       "      <td>700</td>\n",
       "      <td>0</td>\n",
       "      <td>[North Indian,  Chinese,  Biryani]</td>\n",
       "      <td>12 Noon to 11 PM</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Dunkin' Donuts</td>\n",
       "      <td>https://www.zomato.com/hyderabad/dunkin-donuts...</td>\n",
       "      <td>550</td>\n",
       "      <td>0</td>\n",
       "      <td>[Desserts,  Cafe,  Beverages,  Burger,  Fast F...</td>\n",
       "      <td>10 AM to 11 PM</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "<p>5 rows × 76 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Name  \\\n",
       "2                    Flechazo   \n",
       "19              Pakwaan Grand   \n",
       "33                 Club Rogue   \n",
       "35  Green Bawarchi Restaurant   \n",
       "94             Dunkin' Donuts   \n",
       "\n",
       "                                                Links   Cost  \\\n",
       "2   https://www.zomato.com/hyderabad/flechazo-gach...  1,300   \n",
       "19  https://www.zomato.com/hyderabad/pakwaan-grand...    400   \n",
       "33  https://www.zomato.com/hyderabad/club-rogue-ga...    900   \n",
       "35  https://www.zomato.com/hyderabad/green-bawarch...    700   \n",
       "94  https://www.zomato.com/hyderabad/dunkin-donuts...    550   \n",
       "\n",
       "                           Collections  \\\n",
       "2   Great Buffets, Hyderabad's Hottest   \n",
       "19                  Trending This Week   \n",
       "33                                   0   \n",
       "35                                   0   \n",
       "94                                   0   \n",
       "\n",
       "                                             Cuisines  \\\n",
       "2   [Asian,  Mediterranean,  North Indian,  Desserts]   \n",
       "19                 [North Indian,  Chinese,  Biryani]   \n",
       "33                         [North Indian,  Fast Food]   \n",
       "35                 [North Indian,  Chinese,  Biryani]   \n",
       "94  [Desserts,  Cafe,  Beverages,  Burger,  Fast F...   \n",
       "\n",
       "                                  Timings  Chinese   Continental   Kebab  \\\n",
       "2   11:30 AM to 4:30 PM, 6:30 PM to 11 PM      0.0           0.0     0.0   \n",
       "19                       12 Noon to 11 PM      0.0           0.0     0.0   \n",
       "33                   11 AM to 12 Midnight      0.0           0.0     0.0   \n",
       "35                       12 Noon to 11 PM      0.0           0.0     0.0   \n",
       "94                         10 AM to 11 PM      0.0           0.0     0.0   \n",
       "\n",
       "     European  ...   Burger  Momos   Pizza   Arabian  North Eastern   Cafe  \\\n",
       "2         0.0  ...      0.0    0.0     0.0       0.0            0.0    0.0   \n",
       "19        0.0  ...      0.0    0.0     0.0       0.0            0.0    0.0   \n",
       "33        0.0  ...      0.0    0.0     0.0       0.0            0.0    0.0   \n",
       "35        0.0  ...      0.0    0.0     0.0       0.0            0.0    0.0   \n",
       "94        0.0  ...      1.0    0.0     0.0       0.0            0.0    1.0   \n",
       "\n",
       "    Hyderabadi   Mithai  Thai   Malaysian  \n",
       "2          0.0      0.0   0.0         0.0  \n",
       "19         0.0      0.0   0.0         0.0  \n",
       "33         0.0      0.0   0.0         0.0  \n",
       "35         0.0      0.0   0.0         0.0  \n",
       "94         0.0      0.0   0.0         0.0  \n",
       "\n",
       "[5 rows x 76 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check Restaurants\n",
    "user_Restaurants = df_c[df_c[\"Name\"].isin(inputRestaurant[\"Restaurant\"].to_list())]\n",
    "user_Restaurants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Chinese</th>\n",
       "      <th>Continental</th>\n",
       "      <th>Kebab</th>\n",
       "      <th>European</th>\n",
       "      <th>South Indian</th>\n",
       "      <th>North Indian</th>\n",
       "      <th>Biryani</th>\n",
       "      <th>Chinese</th>\n",
       "      <th>Asian</th>\n",
       "      <th>Mediterranean</th>\n",
       "      <th>...</th>\n",
       "      <th>Burger</th>\n",
       "      <th>Momos</th>\n",
       "      <th>Pizza</th>\n",
       "      <th>Arabian</th>\n",
       "      <th>North Eastern</th>\n",
       "      <th>Cafe</th>\n",
       "      <th>Hyderabadi</th>\n",
       "      <th>Mithai</th>\n",
       "      <th>Thai</th>\n",
       "      <th>Malaysian</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 70 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Chinese   Continental   Kebab   European   South Indian   North Indian  \\\n",
       "0      0.0           0.0     0.0        0.0            0.0            1.0   \n",
       "1      0.0           0.0     0.0        0.0            0.0            0.0   \n",
       "2      0.0           0.0     0.0        0.0            0.0            0.0   \n",
       "3      0.0           0.0     0.0        0.0            0.0            0.0   \n",
       "4      0.0           0.0     0.0        0.0            0.0            0.0   \n",
       "\n",
       "   Biryani   Chinese  Asian   Mediterranean  ...   Burger  Momos   Pizza  \\\n",
       "0      0.0       0.0    1.0             1.0  ...      0.0    0.0     0.0   \n",
       "1      0.0       1.0    0.0             0.0  ...      0.0    0.0     0.0   \n",
       "2      0.0       0.0    0.0             0.0  ...      0.0    0.0     0.0   \n",
       "3      0.0       1.0    0.0             0.0  ...      0.0    0.0     0.0   \n",
       "4      0.0       0.0    0.0             0.0  ...      1.0    0.0     0.0   \n",
       "\n",
       "    Arabian  North Eastern   Cafe  Hyderabadi   Mithai  Thai   Malaysian  \n",
       "0       0.0            0.0    0.0         0.0      0.0   0.0         0.0  \n",
       "1       0.0            0.0    0.0         0.0      0.0   0.0         0.0  \n",
       "2       0.0            0.0    0.0         0.0      0.0   0.0         0.0  \n",
       "3       0.0            0.0    0.0         0.0      0.0   0.0         0.0  \n",
       "4       0.0            0.0    1.0         0.0      0.0   0.0         0.0  \n",
       "\n",
       "[5 rows x 70 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cuisine Table\n",
    "\n",
    "user_Restaurants = user_Restaurants.reset_index(drop = True)\n",
    "user_Restaurants  = user_Restaurants.drop(columns = [\"Name\", \"Links\", \"Cost\", \"Collections\", \"Cuisines\", \"Timings\"]) \n",
    "user_Restaurants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Chinese          0.0\n",
       " Continental     0.0\n",
       " Kebab           0.0\n",
       " European        0.0\n",
       " South Indian    0.0\n",
       "                ... \n",
       " Cafe            4.5\n",
       "Hyderabadi       0.0\n",
       " Mithai          0.0\n",
       "Thai             0.0\n",
       " Malaysian       0.0\n",
       "Length: 70, dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add weights to Cuisine\n",
    "userProfile = user_Restaurants.transpose().dot(inputRestaurant[\"Rating\"])\n",
    "userProfile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>Cuisines</th>\n",
       "      <th>Timings</th>\n",
       "      <th>Chinese</th>\n",
       "      <th>Continental</th>\n",
       "      <th>Kebab</th>\n",
       "      <th>European</th>\n",
       "      <th>South Indian</th>\n",
       "      <th>North Indian</th>\n",
       "      <th>Biryani</th>\n",
       "      <th>Chinese</th>\n",
       "      <th>...</th>\n",
       "      <th>Burger</th>\n",
       "      <th>Momos</th>\n",
       "      <th>Pizza</th>\n",
       "      <th>Arabian</th>\n",
       "      <th>North Eastern</th>\n",
       "      <th>Cafe</th>\n",
       "      <th>Hyderabadi</th>\n",
       "      <th>Mithai</th>\n",
       "      <th>Thai</th>\n",
       "      <th>Malaysian</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Beyond Flavours</th>\n",
       "      <td>[Chinese,  Continental,  Kebab,  European,  So...</td>\n",
       "      <td>12noon to 3:30pm, 6:30pm to 11:30pm (Mon-Sun)</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paradise</th>\n",
       "      <td>[Biryani,  North Indian,  Chinese]</td>\n",
       "      <td>11 AM to 11 PM</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flechazo</th>\n",
       "      <td>[Asian,  Mediterranean,  North Indian,  Desserts]</td>\n",
       "      <td>11:30 AM to 4:30 PM, 6:30 PM to 11 PM</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shah Ghouse Hotel &amp; Restaurant</th>\n",
       "      <td>[Biryani,  North Indian,  Chinese,  Seafood,  ...</td>\n",
       "      <td>12 Noon to 2 AM</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Over The Moon Brew Company</th>\n",
       "      <td>[Asian,  Continental,  North Indian,  Chinese,...</td>\n",
       "      <td>12noon to 11pm (Mon, Tue, Wed, Thu, Sun), 12no...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IndiBlaze</th>\n",
       "      <td>[Fast Food,  Salad]</td>\n",
       "      <td>11 AM to 11 PM</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweet Basket</th>\n",
       "      <td>[Bakery,  Mithai]</td>\n",
       "      <td>10 AM to 10 PM (Mon-Thu), 8 AM to 10:30 PM (Fr...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angaara Counts 3</th>\n",
       "      <td>[North Indian,  Biryani,  Chinese]</td>\n",
       "      <td>12 Noon to 11 PM</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wich Please</th>\n",
       "      <td>[Fast Food]</td>\n",
       "      <td>8am to 12:30AM (Mon-Sun)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Republic Of Noodles - Lemon Tree Hotel</th>\n",
       "      <td>[Thai,  Asian,  Chinese,  Malaysian]</td>\n",
       "      <td>11:30 AM to 3 PM, 7 PM to 12 Midnight</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>105 rows × 72 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                 Cuisines  \\\n",
       "Name                                                                                        \n",
       "Beyond Flavours                         [Chinese,  Continental,  Kebab,  European,  So...   \n",
       "Paradise                                               [Biryani,  North Indian,  Chinese]   \n",
       "Flechazo                                [Asian,  Mediterranean,  North Indian,  Desserts]   \n",
       "Shah Ghouse Hotel & Restaurant          [Biryani,  North Indian,  Chinese,  Seafood,  ...   \n",
       "Over The Moon Brew Company              [Asian,  Continental,  North Indian,  Chinese,...   \n",
       "...                                                                                   ...   \n",
       "IndiBlaze                                                             [Fast Food,  Salad]   \n",
       "Sweet Basket                                                            [Bakery,  Mithai]   \n",
       "Angaara Counts 3                                       [North Indian,  Biryani,  Chinese]   \n",
       "Wich Please                                                                   [Fast Food]   \n",
       "Republic Of Noodles - Lemon Tree Hotel               [Thai,  Asian,  Chinese,  Malaysian]   \n",
       "\n",
       "                                                                                  Timings  \\\n",
       "Name                                                                                        \n",
       "Beyond Flavours                             12noon to 3:30pm, 6:30pm to 11:30pm (Mon-Sun)   \n",
       "Paradise                                                                   11 AM to 11 PM   \n",
       "Flechazo                                            11:30 AM to 4:30 PM, 6:30 PM to 11 PM   \n",
       "Shah Ghouse Hotel & Restaurant                                            12 Noon to 2 AM   \n",
       "Over The Moon Brew Company              12noon to 11pm (Mon, Tue, Wed, Thu, Sun), 12no...   \n",
       "...                                                                                   ...   \n",
       "IndiBlaze                                                                  11 AM to 11 PM   \n",
       "Sweet Basket                            10 AM to 10 PM (Mon-Thu), 8 AM to 10:30 PM (Fr...   \n",
       "Angaara Counts 3                                                         12 Noon to 11 PM   \n",
       "Wich Please                                                      8am to 12:30AM (Mon-Sun)   \n",
       "Republic Of Noodles - Lemon Tree Hotel              11:30 AM to 3 PM, 7 PM to 12 Midnight   \n",
       "\n",
       "                                        Chinese   Continental   Kebab  \\\n",
       "Name                                                                    \n",
       "Beyond Flavours                             1.0           1.0     1.0   \n",
       "Paradise                                    0.0           0.0     0.0   \n",
       "Flechazo                                    0.0           0.0     0.0   \n",
       "Shah Ghouse Hotel & Restaurant              0.0           0.0     0.0   \n",
       "Over The Moon Brew Company                  0.0           1.0     0.0   \n",
       "...                                         ...           ...     ...   \n",
       "IndiBlaze                                   0.0           0.0     0.0   \n",
       "Sweet Basket                                0.0           0.0     0.0   \n",
       "Angaara Counts 3                            0.0           0.0     0.0   \n",
       "Wich Please                                 0.0           0.0     0.0   \n",
       "Republic Of Noodles - Lemon Tree Hotel      0.0           0.0     0.0   \n",
       "\n",
       "                                         European   South Indian  \\\n",
       "Name                                                               \n",
       "Beyond Flavours                               1.0            1.0   \n",
       "Paradise                                      0.0            0.0   \n",
       "Flechazo                                      0.0            0.0   \n",
       "Shah Ghouse Hotel & Restaurant                0.0            0.0   \n",
       "Over The Moon Brew Company                    0.0            0.0   \n",
       "...                                           ...            ...   \n",
       "IndiBlaze                                     0.0            0.0   \n",
       "Sweet Basket                                  0.0            0.0   \n",
       "Angaara Counts 3                              0.0            0.0   \n",
       "Wich Please                                   0.0            0.0   \n",
       "Republic Of Noodles - Lemon Tree Hotel        0.0            0.0   \n",
       "\n",
       "                                         North Indian  Biryani   Chinese  ...  \\\n",
       "Name                                                                      ...   \n",
       "Beyond Flavours                                   1.0      0.0       0.0  ...   \n",
       "Paradise                                          1.0      1.0       1.0  ...   \n",
       "Flechazo                                          1.0      0.0       0.0  ...   \n",
       "Shah Ghouse Hotel & Restaurant                    1.0      1.0       1.0  ...   \n",
       "Over The Moon Brew Company                        1.0      0.0       1.0  ...   \n",
       "...                                               ...      ...       ...  ...   \n",
       "IndiBlaze                                         0.0      0.0       0.0  ...   \n",
       "Sweet Basket                                      0.0      0.0       0.0  ...   \n",
       "Angaara Counts 3                                  0.0      0.0       1.0  ...   \n",
       "Wich Please                                       0.0      0.0       0.0  ...   \n",
       "Republic Of Noodles - Lemon Tree Hotel            0.0      0.0       1.0  ...   \n",
       "\n",
       "                                         Burger  Momos   Pizza   Arabian  \\\n",
       "Name                                                                       \n",
       "Beyond Flavours                             0.0    0.0     0.0       0.0   \n",
       "Paradise                                    0.0    0.0     0.0       0.0   \n",
       "Flechazo                                    0.0    0.0     0.0       0.0   \n",
       "Shah Ghouse Hotel & Restaurant              0.0    0.0     0.0       0.0   \n",
       "Over The Moon Brew Company                  0.0    0.0     0.0       0.0   \n",
       "...                                         ...    ...     ...       ...   \n",
       "IndiBlaze                                   0.0    0.0     0.0       0.0   \n",
       "Sweet Basket                                0.0    0.0     0.0       0.0   \n",
       "Angaara Counts 3                            0.0    0.0     0.0       0.0   \n",
       "Wich Please                                 0.0    0.0     0.0       0.0   \n",
       "Republic Of Noodles - Lemon Tree Hotel      0.0    0.0     0.0       0.0   \n",
       "\n",
       "                                        North Eastern   Cafe  Hyderabadi  \\\n",
       "Name                                                                       \n",
       "Beyond Flavours                                   0.0    0.0         0.0   \n",
       "Paradise                                          0.0    0.0         0.0   \n",
       "Flechazo                                          0.0    0.0         0.0   \n",
       "Shah Ghouse Hotel & Restaurant                    0.0    0.0         0.0   \n",
       "Over The Moon Brew Company                        0.0    0.0         0.0   \n",
       "...                                               ...    ...         ...   \n",
       "IndiBlaze                                         0.0    0.0         0.0   \n",
       "Sweet Basket                                      0.0    0.0         0.0   \n",
       "Angaara Counts 3                                  0.0    0.0         0.0   \n",
       "Wich Please                                       0.0    0.0         0.0   \n",
       "Republic Of Noodles - Lemon Tree Hotel            0.0    0.0         0.0   \n",
       "\n",
       "                                         Mithai  Thai   Malaysian  \n",
       "Name                                                               \n",
       "Beyond Flavours                             0.0   0.0         0.0  \n",
       "Paradise                                    0.0   0.0         0.0  \n",
       "Flechazo                                    0.0   0.0         0.0  \n",
       "Shah Ghouse Hotel & Restaurant              0.0   0.0         0.0  \n",
       "Over The Moon Brew Company                  0.0   0.0         0.0  \n",
       "...                                         ...   ...         ...  \n",
       "IndiBlaze                                   0.0   0.0         0.0  \n",
       "Sweet Basket                                1.0   0.0         0.0  \n",
       "Angaara Counts 3                            0.0   0.0         0.0  \n",
       "Wich Please                                 0.0   0.0         0.0  \n",
       "Republic Of Noodles - Lemon Tree Hotel      0.0   1.0         1.0  \n",
       "\n",
       "[105 rows x 72 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cuisine Table by Restaurants\n",
    "\n",
    "CuisineTable = df_c.set_index(df_c[\"Name\"])\n",
    "CuisineTable = CuisineTable.drop(columns = [\"Name\", \"Links\", \"Cost\", \"Collections\"])\n",
    "CuisineTable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name\n",
       "Beyond Flavours                   0.069444\n",
       "Paradise                          0.187500\n",
       "Flechazo                          0.277778\n",
       "Shah Ghouse Hotel & Restaurant    0.250000\n",
       "Over The Moon Brew Company        0.326389\n",
       "dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Multiply the genres by the weights and then take the weighted average\n",
    "\n",
    "recommendationTable_df = ((CuisineTable*userProfile).sum(axis=1))/(userProfile.sum())\n",
    "recommendationTable_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name\n",
       "Biryanis And More            0.381944\n",
       "Pakwaan Grand                0.381944\n",
       "Aromas@11SIX                 0.381944\n",
       "Green Bawarchi Restaurant    0.381944\n",
       "Olive Garden                 0.381944\n",
       "dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Sort our recommendations in descending order\n",
    "recommendationTable_df = recommendationTable_df.sort_values(ascending=False)\n",
    "\n",
    "#Just a peek at the values\n",
    "recommendationTable_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The final recommendation Table**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>Restaurant</th>\n",
       "      <th>Cuisines</th>\n",
       "      <th>Cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Flechazo</td>\n",
       "      <td>[Asian,  Mediterranean,  North Indian,  Desserts]</td>\n",
       "      <td>1,300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Over The Moon Brew Company</td>\n",
       "      <td>[Asian,  Continental,  North Indian,  Chinese,...</td>\n",
       "      <td>1,200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>10 Downing Street</td>\n",
       "      <td>[North Indian,  Chinese,  Continental]</td>\n",
       "      <td>1,900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Pakwaan Grand</td>\n",
       "      <td>[North Indian,  Chinese,  Biryani]</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>SKYHY</td>\n",
       "      <td>[North Indian,  Chinese,  Continental]</td>\n",
       "      <td>1,400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Green Bawarchi Restaurant</td>\n",
       "      <td>[North Indian,  Chinese,  Biryani]</td>\n",
       "      <td>700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Biryanis And More</td>\n",
       "      <td>[North Indian,  Biryani,  Chinese]</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Being Hungry</td>\n",
       "      <td>[North Indian,  Chinese]</td>\n",
       "      <td>450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>PourHouse7</td>\n",
       "      <td>[North Indian,  Continental,  Chinese,  Italian]</td>\n",
       "      <td>1,200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Shree Santosh Dhaba Family Restaurant</td>\n",
       "      <td>[North Indian,  Chinese,  Fast Food]</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Hyderabad Chefs</td>\n",
       "      <td>[North Indian,  Chinese]</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Komatose - Holiday Inn Express &amp; Suites</td>\n",
       "      <td>[North Indian,  Chinese,  Japanese,  Sushi]</td>\n",
       "      <td>1,500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Aromas@11SIX</td>\n",
       "      <td>[North Indian,  Chinese,  Mughlai,  Biryani]</td>\n",
       "      <td>750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Olive Garden</td>\n",
       "      <td>[North Indian,  Chinese,  Continental,  Biryani]</td>\n",
       "      <td>700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Delhi-39</td>\n",
       "      <td>[North Indian,  Chinese]</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Al Saba Restaurant</td>\n",
       "      <td>[North Indian,  Chinese,  Seafood,  Biryani,  ...</td>\n",
       "      <td>750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>T Grill</td>\n",
       "      <td>[North Indian,  Andhra,  Biryani,  Chinese]</td>\n",
       "      <td>700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Dunkin' Donuts</td>\n",
       "      <td>[Desserts,  Cafe,  Beverages,  Burger,  Fast F...</td>\n",
       "      <td>550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Desi Bytes</td>\n",
       "      <td>[North Indian,  Chinese]</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>Angaara Counts 3</td>\n",
       "      <td>[North Indian,  Biryani,  Chinese]</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Restaurant  \\\n",
       "2                                   Flechazo   \n",
       "4                 Over The Moon Brew Company   \n",
       "18                         10 Downing Street   \n",
       "19                             Pakwaan Grand   \n",
       "24                                     SKYHY   \n",
       "35                 Green Bawarchi Restaurant   \n",
       "52                         Biryanis And More   \n",
       "57                              Being Hungry   \n",
       "60                                PourHouse7   \n",
       "62     Shree Santosh Dhaba Family Restaurant   \n",
       "64                           Hyderabad Chefs   \n",
       "65   Komatose - Holiday Inn Express & Suites   \n",
       "66                              Aromas@11SIX   \n",
       "79                              Olive Garden   \n",
       "81                                  Delhi-39   \n",
       "82                        Al Saba Restaurant   \n",
       "84                                   T Grill   \n",
       "94                            Dunkin' Donuts   \n",
       "95                                Desi Bytes   \n",
       "102                         Angaara Counts 3   \n",
       "\n",
       "                                              Cuisines   Cost  \n",
       "2    [Asian,  Mediterranean,  North Indian,  Desserts]  1,300  \n",
       "4    [Asian,  Continental,  North Indian,  Chinese,...  1,200  \n",
       "18              [North Indian,  Chinese,  Continental]  1,900  \n",
       "19                  [North Indian,  Chinese,  Biryani]    400  \n",
       "24              [North Indian,  Chinese,  Continental]  1,400  \n",
       "35                  [North Indian,  Chinese,  Biryani]    700  \n",
       "52                  [North Indian,  Biryani,  Chinese]    500  \n",
       "57                            [North Indian,  Chinese]    450  \n",
       "60    [North Indian,  Continental,  Chinese,  Italian]  1,200  \n",
       "62                [North Indian,  Chinese,  Fast Food]    500  \n",
       "64                            [North Indian,  Chinese]    600  \n",
       "65         [North Indian,  Chinese,  Japanese,  Sushi]  1,500  \n",
       "66        [North Indian,  Chinese,  Mughlai,  Biryani]    750  \n",
       "79    [North Indian,  Chinese,  Continental,  Biryani]    700  \n",
       "81                            [North Indian,  Chinese]    600  \n",
       "82   [North Indian,  Chinese,  Seafood,  Biryani,  ...    750  \n",
       "84         [North Indian,  Andhra,  Biryani,  Chinese]    700  \n",
       "94   [Desserts,  Cafe,  Beverages,  Burger,  Fast F...    550  \n",
       "95                            [North Indian,  Chinese]    400  \n",
       "102                 [North Indian,  Biryani,  Chinese]    500  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recommendations = df2.loc[df2['Name'].isin(recommendationTable_df.head(20).keys())]\n",
    "recommendations = recommendations[[\"Name\", \"Cuisines\", \"Cost\"]]\n",
    "recommendations.columns = [\"Restaurant\", \"Cuisines\", \"Cost\"]\n",
    "recommendations[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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