{
 "cells": [
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   },
   "outputs": [],
   "source": [
    "# read and manipulate the date\n",
    "# real world raw data (csv file)\n",
    "# cleaning of raw data and data analysis\n",
    "# feature engineering\n",
    "# prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2ad6acd5",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd   # pandas is used for data manipulation, extraction etc...\n",
    "import numpy as np    # for mathematical calculations, mean, median, variance, standard deviation etc... \n",
    "import matplotlib.pyplot as plt  # data visualization library for plotting graphs etc...\n",
    "import seaborn as sns  # advanced data visualization library for exploring and understanding the data..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "80c68191",
   "metadata": {
    "execution": {
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     "iopub.status.busy": "2022-04-18T16:17:33.640238Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df=pd.read_csv('../input/zomato-bangalore-restaurants/zomato.csv')  # to load and read the data of csv file..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1851e00d",
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   "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>url</th>\n",
       "      <th>address</th>\n",
       "      <th>name</th>\n",
       "      <th>online_order</th>\n",
       "      <th>book_table</th>\n",
       "      <th>rate</th>\n",
       "      <th>votes</th>\n",
       "      <th>phone</th>\n",
       "      <th>location</th>\n",
       "      <th>rest_type</th>\n",
       "      <th>dish_liked</th>\n",
       "      <th>cuisines</th>\n",
       "      <th>approx_cost(for two people)</th>\n",
       "      <th>reviews_list</th>\n",
       "      <th>menu_item</th>\n",
       "      <th>listed_in(type)</th>\n",
       "      <th>listed_in(city)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>https://www.zomato.com/bangalore/jalsa-banasha...</td>\n",
       "      <td>942, 21st Main Road, 2nd Stage, Banashankari, ...</td>\n",
       "      <td>Jalsa</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4.1/5</td>\n",
       "      <td>775</td>\n",
       "      <td>080 42297555\\r\\n+91 9743772233</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Pasta, Lunch Buffet, Masala Papad, Paneer Laja...</td>\n",
       "      <td>North Indian, Mughlai, Chinese</td>\n",
       "      <td>800</td>\n",
       "      <td>[('Rated 4.0', 'RATED\\n  A beautiful place to ...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>https://www.zomato.com/bangalore/spice-elephan...</td>\n",
       "      <td>2nd Floor, 80 Feet Road, Near Big Bazaar, 6th ...</td>\n",
       "      <td>Spice Elephant</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>4.1/5</td>\n",
       "      <td>787</td>\n",
       "      <td>080 41714161</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Momos, Lunch Buffet, Chocolate Nirvana, Thai G...</td>\n",
       "      <td>Chinese, North Indian, Thai</td>\n",
       "      <td>800</td>\n",
       "      <td>[('Rated 4.0', 'RATED\\n  Had been here for din...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>https://www.zomato.com/SanchurroBangalore?cont...</td>\n",
       "      <td>1112, Next to KIMS Medical College, 17th Cross...</td>\n",
       "      <td>San Churro Cafe</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>3.8/5</td>\n",
       "      <td>918</td>\n",
       "      <td>+91 9663487993</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Cafe, Casual Dining</td>\n",
       "      <td>Churros, Cannelloni, Minestrone Soup, Hot Choc...</td>\n",
       "      <td>Cafe, Mexican, Italian</td>\n",
       "      <td>800</td>\n",
       "      <td>[('Rated 3.0', \"RATED\\n  Ambience is not that ...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>https://www.zomato.com/bangalore/addhuri-udupi...</td>\n",
       "      <td>1st Floor, Annakuteera, 3rd Stage, Banashankar...</td>\n",
       "      <td>Addhuri Udupi Bhojana</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.7/5</td>\n",
       "      <td>88</td>\n",
       "      <td>+91 9620009302</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Quick Bites</td>\n",
       "      <td>Masala Dosa</td>\n",
       "      <td>South Indian, North Indian</td>\n",
       "      <td>300</td>\n",
       "      <td>[('Rated 4.0', \"RATED\\n  Great food and proper...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>https://www.zomato.com/bangalore/grand-village...</td>\n",
       "      <td>10, 3rd Floor, Lakshmi Associates, Gandhi Baza...</td>\n",
       "      <td>Grand Village</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.8/5</td>\n",
       "      <td>166</td>\n",
       "      <td>+91 8026612447\\r\\n+91 9901210005</td>\n",
       "      <td>Basavanagudi</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Panipuri, Gol Gappe</td>\n",
       "      <td>North Indian, Rajasthani</td>\n",
       "      <td>600</td>\n",
       "      <td>[('Rated 4.0', 'RATED\\n  Very good restaurant ...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 url  \\\n",
       "0  https://www.zomato.com/bangalore/jalsa-banasha...   \n",
       "1  https://www.zomato.com/bangalore/spice-elephan...   \n",
       "2  https://www.zomato.com/SanchurroBangalore?cont...   \n",
       "3  https://www.zomato.com/bangalore/addhuri-udupi...   \n",
       "4  https://www.zomato.com/bangalore/grand-village...   \n",
       "\n",
       "                                             address                   name  \\\n",
       "0  942, 21st Main Road, 2nd Stage, Banashankari, ...                  Jalsa   \n",
       "1  2nd Floor, 80 Feet Road, Near Big Bazaar, 6th ...         Spice Elephant   \n",
       "2  1112, Next to KIMS Medical College, 17th Cross...        San Churro Cafe   \n",
       "3  1st Floor, Annakuteera, 3rd Stage, Banashankar...  Addhuri Udupi Bhojana   \n",
       "4  10, 3rd Floor, Lakshmi Associates, Gandhi Baza...          Grand Village   \n",
       "\n",
       "  online_order book_table   rate  votes                             phone  \\\n",
       "0          Yes        Yes  4.1/5    775    080 42297555\\r\\n+91 9743772233   \n",
       "1          Yes         No  4.1/5    787                      080 41714161   \n",
       "2          Yes         No  3.8/5    918                    +91 9663487993   \n",
       "3           No         No  3.7/5     88                    +91 9620009302   \n",
       "4           No         No  3.8/5    166  +91 8026612447\\r\\n+91 9901210005   \n",
       "\n",
       "       location            rest_type  \\\n",
       "0  Banashankari        Casual Dining   \n",
       "1  Banashankari        Casual Dining   \n",
       "2  Banashankari  Cafe, Casual Dining   \n",
       "3  Banashankari          Quick Bites   \n",
       "4  Basavanagudi        Casual Dining   \n",
       "\n",
       "                                          dish_liked  \\\n",
       "0  Pasta, Lunch Buffet, Masala Papad, Paneer Laja...   \n",
       "1  Momos, Lunch Buffet, Chocolate Nirvana, Thai G...   \n",
       "2  Churros, Cannelloni, Minestrone Soup, Hot Choc...   \n",
       "3                                        Masala Dosa   \n",
       "4                                Panipuri, Gol Gappe   \n",
       "\n",
       "                         cuisines approx_cost(for two people)  \\\n",
       "0  North Indian, Mughlai, Chinese                         800   \n",
       "1     Chinese, North Indian, Thai                         800   \n",
       "2          Cafe, Mexican, Italian                         800   \n",
       "3      South Indian, North Indian                         300   \n",
       "4        North Indian, Rajasthani                         600   \n",
       "\n",
       "                                        reviews_list menu_item  \\\n",
       "0  [('Rated 4.0', 'RATED\\n  A beautiful place to ...        []   \n",
       "1  [('Rated 4.0', 'RATED\\n  Had been here for din...        []   \n",
       "2  [('Rated 3.0', \"RATED\\n  Ambience is not that ...        []   \n",
       "3  [('Rated 4.0', \"RATED\\n  Great food and proper...        []   \n",
       "4  [('Rated 4.0', 'RATED\\n  Very good restaurant ...        []   \n",
       "\n",
       "  listed_in(type) listed_in(city)  \n",
       "0          Buffet    Banashankari  \n",
       "1          Buffet    Banashankari  \n",
       "2          Buffet    Banashankari  \n",
       "3          Buffet    Banashankari  \n",
       "4          Buffet    Banashankari  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()  # it returns how the data looks like-- first 5 rows of the data frame..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f9d51f0b",
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    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(51717, 17)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape  # for total no. of rows and columns..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c2b523ef",
   "metadata": {
    "execution": {
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    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['url', 'address', 'name', 'online_order', 'book_table', 'rate', 'votes', 'phone', 'location', 'rest_type', 'dish_liked', 'cuisines', 'approx_cost(for two people)', 'reviews_list', 'menu_item', 'listed_in(type)', 'listed_in(city)']\n"
     ]
    }
   ],
   "source": [
    "print(list(df.columns))  # list of all the columns of dataset..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ecc97b03",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "url                                0\n",
       "address                            0\n",
       "name                               0\n",
       "online_order                       0\n",
       "book_table                         0\n",
       "rate                            7775\n",
       "votes                              0\n",
       "phone                           1208\n",
       "location                          21\n",
       "rest_type                        227\n",
       "dish_liked                     28078\n",
       "cuisines                          45\n",
       "approx_cost(for two people)      346\n",
       "reviews_list                       0\n",
       "menu_item                          0\n",
       "listed_in(type)                    0\n",
       "listed_in(city)                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()  # sum of all the missing values in each and every feature(column)..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b7439988",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "## for all the features that have NAN values..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2ff1df76",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['rate',\n",
       " 'phone',\n",
       " 'location',\n",
       " 'rest_type',\n",
       " 'dish_liked',\n",
       " 'cuisines',\n",
       " 'approx_cost(for two people)']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_na=[]  # defining the list\n",
    "for feature in df.columns: \n",
    "    if df[feature].isnull().sum()>1: \n",
    "        feature_na.append(feature)  # consider the feature and append each and every feature...\n",
    "feature_na"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b05458c3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:17:47.818529Z",
     "iopub.status.busy": "2022-04-18T16:17:47.818237Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# for each and every feature, what is the percentage of missing values...\n",
    "# inside for loop, created a placeholder, which recieves values from format function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ac0255b2",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rate has 15.0337 % missing values \n",
      "phone has 2.3358 % missing values \n",
      "location has 0.0406 % missing values \n",
      "rest_type has 0.4389 % missing values \n",
      "dish_liked has 54.2916 % missing values \n",
      "cuisines has 0.087 % missing values \n",
      "approx_cost(for two people) has 0.669 % missing values \n"
     ]
    }
   ],
   "source": [
    "for feature in feature_na:\n",
    "    print('{} has {} % missing values '.format(feature,np.round(df[feature].isnull().sum()/len(df)*100,4)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "03803d83",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:17:48.422889Z",
     "iopub.status.busy": "2022-04-18T16:17:48.422584Z",
     "iopub.status.idle": "2022-04-18T16:17:48.426258Z",
     "shell.execute_reply": "2022-04-18T16:17:48.425221Z"
    },
    "papermill": {
     "duration": 0.140393,
     "end_time": "2022-04-18T16:17:48.428402",
     "exception": false,
     "start_time": "2022-04-18T16:17:48.288009",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "## Getting datatypes of features and its overview..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "247a120e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:17:48.702158Z",
     "iopub.status.busy": "2022-04-18T16:17:48.701784Z",
     "iopub.status.idle": "2022-04-18T16:17:48.816268Z",
     "shell.execute_reply": "2022-04-18T16:17:48.814968Z"
    },
    "papermill": {
     "duration": 0.256565,
     "end_time": "2022-04-18T16:17:48.819055",
     "exception": false,
     "start_time": "2022-04-18T16:17:48.562490",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 51717 entries, 0 to 51716\n",
      "Data columns (total 17 columns):\n",
      " #   Column                       Non-Null Count  Dtype \n",
      "---  ------                       --------------  ----- \n",
      " 0   url                          51717 non-null  object\n",
      " 1   address                      51717 non-null  object\n",
      " 2   name                         51717 non-null  object\n",
      " 3   online_order                 51717 non-null  object\n",
      " 4   book_table                   51717 non-null  object\n",
      " 5   rate                         43942 non-null  object\n",
      " 6   votes                        51717 non-null  int64 \n",
      " 7   phone                        50509 non-null  object\n",
      " 8   location                     51696 non-null  object\n",
      " 9   rest_type                    51490 non-null  object\n",
      " 10  dish_liked                   23639 non-null  object\n",
      " 11  cuisines                     51672 non-null  object\n",
      " 12  approx_cost(for two people)  51371 non-null  object\n",
      " 13  reviews_list                 51717 non-null  object\n",
      " 14  menu_item                    51717 non-null  object\n",
      " 15  listed_in(type)              51717 non-null  object\n",
      " 16  listed_in(city)              51717 non-null  object\n",
      "dtypes: int64(1), object(16)\n",
      "memory usage: 6.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "621ed9d8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:17:49.097560Z",
     "iopub.status.busy": "2022-04-18T16:17:49.097287Z",
     "iopub.status.idle": "2022-04-18T16:17:49.101106Z",
     "shell.execute_reply": "2022-04-18T16:17:49.100029Z"
    },
    "papermill": {
     "duration": 0.145249,
     "end_time": "2022-04-18T16:17:49.103090",
     "exception": false,
     "start_time": "2022-04-18T16:17:48.957841",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "## Performing data cleaning on approx_cost feature......\n",
    "## cost must be in int or float, so will change its datatype from object to float..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "abd78462",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:17:49.371907Z",
     "iopub.status.busy": "2022-04-18T16:17:49.371126Z",
     "iopub.status.idle": "2022-04-18T16:17:49.384959Z",
     "shell.execute_reply": "2022-04-18T16:17:49.384342Z"
    },
    "papermill": {
     "duration": 0.149867,
     "end_time": "2022-04-18T16:17:49.387043",
     "exception": false,
     "start_time": "2022-04-18T16:17:49.237176",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        False\n",
       "1        False\n",
       "2        False\n",
       "3        False\n",
       "4        False\n",
       "         ...  \n",
       "51712    False\n",
       "51713    False\n",
       "51714    False\n",
       "51715    False\n",
       "51716    False\n",
       "Name: approx_cost(for two people), Length: 51717, dtype: bool"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['approx_cost(for two people)'].isnull()  # to check where it exactly contains null value..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b441ed03",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:17:49.659800Z",
     "iopub.status.busy": "2022-04-18T16:17:49.659211Z",
     "iopub.status.idle": "2022-04-18T16:17:49.695300Z",
     "shell.execute_reply": "2022-04-18T16:17:49.694476Z"
    },
    "papermill": {
     "duration": 0.176349,
     "end_time": "2022-04-18T16:17:49.697912",
     "exception": false,
     "start_time": "2022-04-18T16:17:49.521563",
     "status": "completed"
    },
    "tags": []
   },
   "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>url</th>\n",
       "      <th>address</th>\n",
       "      <th>name</th>\n",
       "      <th>online_order</th>\n",
       "      <th>book_table</th>\n",
       "      <th>rate</th>\n",
       "      <th>votes</th>\n",
       "      <th>phone</th>\n",
       "      <th>location</th>\n",
       "      <th>rest_type</th>\n",
       "      <th>dish_liked</th>\n",
       "      <th>cuisines</th>\n",
       "      <th>approx_cost(for two people)</th>\n",
       "      <th>reviews_list</th>\n",
       "      <th>menu_item</th>\n",
       "      <th>listed_in(type)</th>\n",
       "      <th>listed_in(city)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1662</th>\n",
       "      <td>https://www.zomato.com/bangalore/papa-khan-ras...</td>\n",
       "      <td>6, Abbiah Layout KC Halli Main Road, Bommanaha...</td>\n",
       "      <td>Papa Khan Rasoi</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>Delivery</td>\n",
       "      <td>Bannerghatta Road</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1768</th>\n",
       "      <td>https://www.zomato.com/bangalore/innate-jp-nag...</td>\n",
       "      <td>45, 14th Main, 7th phase, Puttehahalli Palya, ...</td>\n",
       "      <td>Innate</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>JP Nagar</td>\n",
       "      <td>Delivery</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Beverages</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>Delivery</td>\n",
       "      <td>Bannerghatta Road</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1795</th>\n",
       "      <td>https://www.zomato.com/bangalore/super-chefs-b...</td>\n",
       "      <td>8/3, Opposite Suryadhoya Convention hall, Bann...</td>\n",
       "      <td>Super Chef's (New Royal treat)</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bannerghatta Road</td>\n",
       "      <td>Quick Bites</td>\n",
       "      <td>NaN</td>\n",
       "      <td>North Indian, Chinese, South Indian</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[('Rated 1.0', 'RATED\\n  Many items were missi...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Delivery</td>\n",
       "      <td>Bannerghatta Road</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3069</th>\n",
       "      <td>https://www.zomato.com/bangalore/super-chefs-b...</td>\n",
       "      <td>8/3, Opposite Suryadhoya Convention hall, Bann...</td>\n",
       "      <td>Super Chef's (New Royal treat)</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>-</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bannerghatta Road</td>\n",
       "      <td>Quick Bites</td>\n",
       "      <td>NaN</td>\n",
       "      <td>North Indian, Chinese, South Indian</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[('Rated 1.0', 'RATED\\n  Many items were missi...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Delivery</td>\n",
       "      <td>Basavanagudi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3774</th>\n",
       "      <td>https://www.zomato.com/bangalore/the-hub-ibis-...</td>\n",
       "      <td>Ibis Bengaluru, Opposite RMZ Ecospace Business...</td>\n",
       "      <td>The Hub - Ibis Bengaluru</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.9/5</td>\n",
       "      <td>237</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Marathahalli</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Dal Makhani, Cocktails, Lunch Buffet, Breakfas...</td>\n",
       "      <td>Finger Food</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[('Rated 4.0', 'RATED\\n  The Hub is a small ba...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Bellandur</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50940</th>\n",
       "      <td>https://www.zomato.com/bangalore/cinnabon-whit...</td>\n",
       "      <td>Ground Floor, The Forum Neighborhood Mall, Pre...</td>\n",
       "      <td>Cinnabon</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4.0 /5</td>\n",
       "      <td>80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Whitefield</td>\n",
       "      <td>Quick Bites</td>\n",
       "      <td>Rolls, Cinnamon Roll, Cinnamon Bun</td>\n",
       "      <td>Beverages, American, Bakery</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[('Rated 4.0', \"RATED\\n  Who else is a Cinnabo...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Desserts</td>\n",
       "      <td>Whitefield</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51233</th>\n",
       "      <td>https://www.zomato.com/bangalore/mayas-kitchen...</td>\n",
       "      <td>43/9, Borewell Rd, Palm Meadows, Nallurhalli, ...</td>\n",
       "      <td>Mayas kitchen</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Whitefield</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>NaN</td>\n",
       "      <td>South Indian, Chinese, Chettinad</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[('Rated 4.0', \"RATED\\n  Food was pretty good,...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Dine-out</td>\n",
       "      <td>Whitefield</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51247</th>\n",
       "      <td>https://www.zomato.com/bangalore/mast-kalandar...</td>\n",
       "      <td>D64, 33, 1st Floor, Sundari Armadale, Next To ...</td>\n",
       "      <td>Mast Kalandar</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2.6 /5</td>\n",
       "      <td>76</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Whitefield</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Gulab Jamun, Lassi, Dal Makhani, Buttermilk, T...</td>\n",
       "      <td>North Indian</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[('Rated 1.0', 'RATED\\n  Terrible food in this...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Dine-out</td>\n",
       "      <td>Whitefield</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51271</th>\n",
       "      <td>https://www.zomato.com/bangalore/karavalli-foo...</td>\n",
       "      <td>N254-107, 1st Main Road, Near ITPL Gate 3, KIA...</td>\n",
       "      <td>Karavalli Food Court</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.2 /5</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Whitefield</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>NaN</td>\n",
       "      <td>South Indian</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[('Rated 1.0', 'RATED\\n  i was a regular custo...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Dine-out</td>\n",
       "      <td>Whitefield</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51641</th>\n",
       "      <td>https://www.zomato.com/bangalore/andhra-spices...</td>\n",
       "      <td>188, ITPL Main Road, Brookefields, Bangalore, ...</td>\n",
       "      <td>Andhra Spices</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.2 /5</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brookefield</td>\n",
       "      <td>Quick Bites</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Andhra, Chinese, South Indian</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[('Rated 2.0', \"RATED\\n  Food is not upto the ...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Dine-out</td>\n",
       "      <td>Whitefield</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>346 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     url  \\\n",
       "1662   https://www.zomato.com/bangalore/papa-khan-ras...   \n",
       "1768   https://www.zomato.com/bangalore/innate-jp-nag...   \n",
       "1795   https://www.zomato.com/bangalore/super-chefs-b...   \n",
       "3069   https://www.zomato.com/bangalore/super-chefs-b...   \n",
       "3774   https://www.zomato.com/bangalore/the-hub-ibis-...   \n",
       "...                                                  ...   \n",
       "50940  https://www.zomato.com/bangalore/cinnabon-whit...   \n",
       "51233  https://www.zomato.com/bangalore/mayas-kitchen...   \n",
       "51247  https://www.zomato.com/bangalore/mast-kalandar...   \n",
       "51271  https://www.zomato.com/bangalore/karavalli-foo...   \n",
       "51641  https://www.zomato.com/bangalore/andhra-spices...   \n",
       "\n",
       "                                                 address  \\\n",
       "1662   6, Abbiah Layout KC Halli Main Road, Bommanaha...   \n",
       "1768   45, 14th Main, 7th phase, Puttehahalli Palya, ...   \n",
       "1795   8/3, Opposite Suryadhoya Convention hall, Bann...   \n",
       "3069   8/3, Opposite Suryadhoya Convention hall, Bann...   \n",
       "3774   Ibis Bengaluru, Opposite RMZ Ecospace Business...   \n",
       "...                                                  ...   \n",
       "50940  Ground Floor, The Forum Neighborhood Mall, Pre...   \n",
       "51233  43/9, Borewell Rd, Palm Meadows, Nallurhalli, ...   \n",
       "51247  D64, 33, 1st Floor, Sundari Armadale, Next To ...   \n",
       "51271  N254-107, 1st Main Road, Near ITPL Gate 3, KIA...   \n",
       "51641  188, ITPL Main Road, Brookefields, Bangalore, ...   \n",
       "\n",
       "                                 name online_order book_table    rate  votes  \\\n",
       "1662                  Papa Khan Rasoi           No         No     NaN      0   \n",
       "1768                           Innate           No         No     NaN      0   \n",
       "1795   Super Chef's (New Royal treat)           No         No     NaN      0   \n",
       "3069   Super Chef's (New Royal treat)           No         No       -      0   \n",
       "3774         The Hub - Ibis Bengaluru           No         No   3.9/5    237   \n",
       "...                               ...          ...        ...     ...    ...   \n",
       "50940                        Cinnabon           No         No  4.0 /5     80   \n",
       "51233                   Mayas kitchen           No         No     NaN      0   \n",
       "51247                   Mast Kalandar           No         No  2.6 /5     76   \n",
       "51271            Karavalli Food Court           No         No  3.2 /5      4   \n",
       "51641                   Andhra Spices           No         No  3.2 /5      4   \n",
       "\n",
       "      phone           location      rest_type  \\\n",
       "1662    NaN                NaN            NaN   \n",
       "1768    NaN           JP Nagar       Delivery   \n",
       "1795    NaN  Bannerghatta Road    Quick Bites   \n",
       "3069    NaN  Bannerghatta Road    Quick Bites   \n",
       "3774    NaN       Marathahalli            Bar   \n",
       "...     ...                ...            ...   \n",
       "50940   NaN         Whitefield    Quick Bites   \n",
       "51233   NaN         Whitefield  Casual Dining   \n",
       "51247   NaN         Whitefield  Casual Dining   \n",
       "51271   NaN         Whitefield  Casual Dining   \n",
       "51641   NaN        Brookefield    Quick Bites   \n",
       "\n",
       "                                              dish_liked  \\\n",
       "1662                                                 NaN   \n",
       "1768                                                 NaN   \n",
       "1795                                                 NaN   \n",
       "3069                                                 NaN   \n",
       "3774   Dal Makhani, Cocktails, Lunch Buffet, Breakfas...   \n",
       "...                                                  ...   \n",
       "50940                 Rolls, Cinnamon Roll, Cinnamon Bun   \n",
       "51233                                                NaN   \n",
       "51247  Gulab Jamun, Lassi, Dal Makhani, Buttermilk, T...   \n",
       "51271                                                NaN   \n",
       "51641                                                NaN   \n",
       "\n",
       "                                  cuisines approx_cost(for two people)  \\\n",
       "1662                                   NaN                         NaN   \n",
       "1768                             Beverages                         NaN   \n",
       "1795   North Indian, Chinese, South Indian                         NaN   \n",
       "3069   North Indian, Chinese, South Indian                         NaN   \n",
       "3774                           Finger Food                         NaN   \n",
       "...                                    ...                         ...   \n",
       "50940          Beverages, American, Bakery                         NaN   \n",
       "51233     South Indian, Chinese, Chettinad                         NaN   \n",
       "51247                         North Indian                         NaN   \n",
       "51271                         South Indian                         NaN   \n",
       "51641        Andhra, Chinese, South Indian                         NaN   \n",
       "\n",
       "                                            reviews_list menu_item  \\\n",
       "1662                                                  []        []   \n",
       "1768                                                  []        []   \n",
       "1795   [('Rated 1.0', 'RATED\\n  Many items were missi...        []   \n",
       "3069   [('Rated 1.0', 'RATED\\n  Many items were missi...        []   \n",
       "3774   [('Rated 4.0', 'RATED\\n  The Hub is a small ba...        []   \n",
       "...                                                  ...       ...   \n",
       "50940  [('Rated 4.0', \"RATED\\n  Who else is a Cinnabo...        []   \n",
       "51233  [('Rated 4.0', \"RATED\\n  Food was pretty good,...        []   \n",
       "51247  [('Rated 1.0', 'RATED\\n  Terrible food in this...        []   \n",
       "51271  [('Rated 1.0', 'RATED\\n  i was a regular custo...        []   \n",
       "51641  [('Rated 2.0', \"RATED\\n  Food is not upto the ...        []   \n",
       "\n",
       "      listed_in(type)    listed_in(city)  \n",
       "1662         Delivery  Bannerghatta Road  \n",
       "1768         Delivery  Bannerghatta Road  \n",
       "1795         Delivery  Bannerghatta Road  \n",
       "3069         Delivery       Basavanagudi  \n",
       "3774           Buffet          Bellandur  \n",
       "...               ...                ...  \n",
       "50940        Desserts         Whitefield  \n",
       "51233        Dine-out         Whitefield  \n",
       "51247        Dine-out         Whitefield  \n",
       "51271        Dine-out         Whitefield  \n",
       "51641        Dine-out         Whitefield  \n",
       "\n",
       "[346 rows x 17 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['approx_cost(for two people)'].isnull()]  # give all the null values in approx_cost....."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c2d11aac",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:17:49.973844Z",
     "iopub.status.busy": "2022-04-18T16:17:49.973262Z",
     "iopub.status.idle": "2022-04-18T16:17:49.977090Z",
     "shell.execute_reply": "2022-04-18T16:17:49.976333Z"
    },
    "papermill": {
     "duration": 0.145257,
     "end_time": "2022-04-18T16:17:49.979158",
     "exception": false,
     "start_time": "2022-04-18T16:17:49.833901",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# to check if there is any special character...\n",
    "# comma(,)-- becoz of (,), it is an object datatype..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "afdc3a19",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:17:50.253274Z",
     "iopub.status.busy": "2022-04-18T16:17:50.252696Z",
     "iopub.status.idle": "2022-04-18T16:17:50.263745Z",
     "shell.execute_reply": "2022-04-18T16:17:50.262943Z"
    },
    "papermill": {
     "duration": 0.150392,
     "end_time": "2022-04-18T16:17:50.265777",
     "exception": false,
     "start_time": "2022-04-18T16:17:50.115385",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['800', '300', '600', '700', '550', '500', '450', '650', '400',\n",
       "       '900', '200', '750', '150', '850', '100', '1,200', '350', '250',\n",
       "       '950', '1,000', '1,500', '1,300', '199', '80', '1,100', '160',\n",
       "       '1,600', '230', '130', '50', '190', '1,700', nan, '1,400', '180',\n",
       "       '1,350', '2,200', '2,000', '1,800', '1,900', '330', '2,500',\n",
       "       '2,100', '3,000', '2,800', '3,400', '40', '1,250', '3,500',\n",
       "       '4,000', '2,400', '2,600', '120', '1,450', '469', '70', '3,200',\n",
       "       '60', '560', '240', '360', '6,000', '1,050', '2,300', '4,100',\n",
       "       '5,000', '3,700', '1,650', '2,700', '4,500', '140'], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['approx_cost(for two people)'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c79685a7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:17:50.541379Z",
     "iopub.status.busy": "2022-04-18T16:17:50.540792Z",
     "iopub.status.idle": "2022-04-18T16:17:50.544594Z",
     "shell.execute_reply": "2022-04-18T16:17:50.543947Z"
    },
    "papermill": {
     "duration": 0.143368,
     "end_time": "2022-04-18T16:17:50.546570",
     "exception": false,
     "start_time": "2022-04-18T16:17:50.403202",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# using lambda function, replacing (,) with ('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ae45335b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:17:50.819761Z",
     "iopub.status.busy": "2022-04-18T16:17:50.819474Z",
     "iopub.status.idle": "2022-04-18T16:17:50.845274Z",
     "shell.execute_reply": "2022-04-18T16:17:50.844292Z"
    },
    "papermill": {
     "duration": 0.165065,
     "end_time": "2022-04-18T16:17:50.847737",
     "exception": false,
     "start_time": "2022-04-18T16:17:50.682672",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         800\n",
       "1         800\n",
       "2         800\n",
       "3         300\n",
       "4         600\n",
       "         ... \n",
       "51712    1500\n",
       "51713     600\n",
       "51714    2000\n",
       "51715    2500\n",
       "51716    1500\n",
       "Name: approx_cost(for two people), Length: 51717, dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['approx_cost(for two people)'].astype(str).apply(lambda x:x.replace(',',''))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "61392759",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:17:51.135057Z",
     "iopub.status.busy": "2022-04-18T16:17:51.134458Z",
     "iopub.status.idle": "2022-04-18T16:17:51.157880Z",
     "shell.execute_reply": "2022-04-18T16:17:51.157133Z"
    },
    "papermill": {
     "duration": 0.171204,
     "end_time": "2022-04-18T16:17:51.160263",
     "exception": false,
     "start_time": "2022-04-18T16:17:50.989059",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df['approx_cost(for two people)']=df['approx_cost(for two people)'].astype(str).apply(lambda x:x.replace(',',''))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e2043813",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:17:51.445650Z",
     "iopub.status.busy": "2022-04-18T16:17:51.445047Z",
     "iopub.status.idle": "2022-04-18T16:17:51.472971Z",
     "shell.execute_reply": "2022-04-18T16:17:51.472111Z"
    },
    "papermill": {
     "duration": 0.174977,
     "end_time": "2022-04-18T16:17:51.475442",
     "exception": false,
     "start_time": "2022-04-18T16:17:51.300465",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df['approx_cost(for two people)']=df['approx_cost(for two people)'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c9044c69",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:17:51.754370Z",
     "iopub.status.busy": "2022-04-18T16:17:51.753683Z",
     "iopub.status.idle": "2022-04-18T16:17:51.760156Z",
     "shell.execute_reply": "2022-04-18T16:17:51.759294Z"
    },
    "papermill": {
     "duration": 0.148813,
     "end_time": "2022-04-18T16:17:51.762335",
     "exception": false,
     "start_time": "2022-04-18T16:17:51.613522",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['approx_cost(for two people)'].dtype  # converted into float..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c77e9278",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:17:52.042555Z",
     "iopub.status.busy": "2022-04-18T16:17:52.041711Z",
     "iopub.status.idle": "2022-04-18T16:17:52.045559Z",
     "shell.execute_reply": "2022-04-18T16:17:52.044590Z"
    },
    "papermill": {
     "duration": 0.147033,
     "end_time": "2022-04-18T16:17:52.047561",
     "exception": false,
     "start_time": "2022-04-18T16:17:51.900528",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Clean the rate column...\n",
    "# rating is out of 5 star, so we need to remove the '/5' and clean the feature...\n",
    "# due to NAN and special characters, it is an object data type...(convert in float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f26b75df",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:17:52.328383Z",
     "iopub.status.busy": "2022-04-18T16:17:52.327627Z",
     "iopub.status.idle": "2022-04-18T16:17:52.338916Z",
     "shell.execute_reply": "2022-04-18T16:17:52.338037Z"
    },
    "papermill": {
     "duration": 0.15321,
     "end_time": "2022-04-18T16:17:52.341067",
     "exception": false,
     "start_time": "2022-04-18T16:17:52.187857",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['4.1/5', '3.8/5', '3.7/5', '3.6/5', '4.6/5', '4.0/5', '4.2/5',\n",
       "       '3.9/5', '3.1/5', '3.0/5', '3.2/5', '3.3/5', '2.8/5', '4.4/5',\n",
       "       '4.3/5', 'NEW', '2.9/5', '3.5/5', nan, '2.6/5', '3.8 /5', '3.4/5',\n",
       "       '4.5/5', '2.5/5', '2.7/5', '4.7/5', '2.4/5', '2.2/5', '2.3/5',\n",
       "       '3.4 /5', '-', '3.6 /5', '4.8/5', '3.9 /5', '4.2 /5', '4.0 /5',\n",
       "       '4.1 /5', '3.7 /5', '3.1 /5', '2.9 /5', '3.3 /5', '2.8 /5',\n",
       "       '3.5 /5', '2.7 /5', '2.5 /5', '3.2 /5', '2.6 /5', '4.5 /5',\n",
       "       '4.3 /5', '4.4 /5', '4.9/5', '2.1/5', '2.0/5', '1.8/5', '4.6 /5',\n",
       "       '4.9 /5', '3.0 /5', '4.8 /5', '2.3 /5', '4.7 /5', '2.4 /5',\n",
       "       '2.1 /5', '2.2 /5', '2.0 /5', '1.8 /5'], dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['rate'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "141925f6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:17:52.641761Z",
     "iopub.status.busy": "2022-04-18T16:17:52.640954Z",
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     "shell.execute_reply": "2022-04-18T16:17:52.656070Z"
    },
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     "duration": 0.176997,
     "end_time": "2022-04-18T16:17:52.659204",
     "exception": false,
     "start_time": "2022-04-18T16:17:52.482207",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7775"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['rate'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9eec0d25",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:17:52.951443Z",
     "iopub.status.busy": "2022-04-18T16:17:52.950801Z",
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     "shell.execute_reply": "2022-04-18T16:17:52.953483Z"
    },
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     "duration": 0.147571,
     "end_time": "2022-04-18T16:17:52.956861",
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     "start_time": "2022-04-18T16:17:52.809290",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# split function to split the value in two parts from '/' and only have the first part..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "acae9ea4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:17:53.242332Z",
     "iopub.status.busy": "2022-04-18T16:17:53.242009Z",
     "iopub.status.idle": "2022-04-18T16:17:53.246473Z",
     "shell.execute_reply": "2022-04-18T16:17:53.245528Z"
    },
    "papermill": {
     "duration": 0.147412,
     "end_time": "2022-04-18T16:17:53.248486",
     "exception": false,
     "start_time": "2022-04-18T16:17:53.101074",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def split(x):\n",
    "    return x.split('/')[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7756a18d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:17:53.528056Z",
     "iopub.status.busy": "2022-04-18T16:17:53.527569Z",
     "iopub.status.idle": "2022-04-18T16:17:53.559147Z",
     "shell.execute_reply": "2022-04-18T16:17:53.558132Z"
    },
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     "duration": 0.174198,
     "end_time": "2022-04-18T16:17:53.561570",
     "exception": false,
     "start_time": "2022-04-18T16:17:53.387372",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df['rate']=df['rate'].astype(str).apply(split)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3d277769",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:17:53.850438Z",
     "iopub.status.busy": "2022-04-18T16:17:53.849816Z",
     "iopub.status.idle": "2022-04-18T16:17:53.859724Z",
     "shell.execute_reply": "2022-04-18T16:17:53.859074Z"
    },
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     "exception": false,
     "start_time": "2022-04-18T16:17:53.707852",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['4.1', '3.8', '3.7', '3.6', '4.6', '4.0', '4.2', '3.9', '3.1',\n",
       "       '3.0', '3.2', '3.3', '2.8', '4.4', '4.3', 'NEW', '2.9', '3.5',\n",
       "       'nan', '2.6', '3.8 ', '3.4', '4.5', '2.5', '2.7', '4.7', '2.4',\n",
       "       '2.2', '2.3', '3.4 ', '-', '3.6 ', '4.8', '3.9 ', '4.2 ', '4.0 ',\n",
       "       '4.1 ', '3.7 ', '3.1 ', '2.9 ', '3.3 ', '2.8 ', '3.5 ', '2.7 ',\n",
       "       '2.5 ', '3.2 ', '2.6 ', '4.5 ', '4.3 ', '4.4 ', '4.9', '2.1',\n",
       "       '2.0', '1.8', '4.6 ', '4.9 ', '3.0 ', '4.8 ', '2.3 ', '4.7 ',\n",
       "       '2.4 ', '2.1 ', '2.2 ', '2.0 ', '1.8 '], dtype=object)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['rate'].unique()  # got all the values after splitting..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "88cafa61",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:17:54.159497Z",
     "iopub.status.busy": "2022-04-18T16:17:54.159186Z",
     "iopub.status.idle": "2022-04-18T16:17:54.163258Z",
     "shell.execute_reply": "2022-04-18T16:17:54.162264Z"
    },
    "papermill": {
     "duration": 0.156057,
     "end_time": "2022-04-18T16:17:54.165198",
     "exception": false,
     "start_time": "2022-04-18T16:17:54.009141",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# remove NEW and - also..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "cbea9217",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:17:54.447382Z",
     "iopub.status.busy": "2022-04-18T16:17:54.447073Z",
     "iopub.status.idle": "2022-04-18T16:17:54.458848Z",
     "shell.execute_reply": "2022-04-18T16:17:54.457850Z"
    },
    "papermill": {
     "duration": 0.15421,
     "end_time": "2022-04-18T16:17:54.460856",
     "exception": false,
     "start_time": "2022-04-18T16:17:54.306646",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df['rate'].replace('-',0,inplace=True)\n",
    "df['rate'].replace('NEW',0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "57a82e9e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:17:54.740087Z",
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     "start_time": "2022-04-18T16:17:54.599775",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df['rate']=df['rate'].astype(float)  # converting into float..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "027b1a16",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:17:55.059835Z",
     "iopub.status.busy": "2022-04-18T16:17:55.059247Z",
     "iopub.status.idle": "2022-04-18T16:17:55.065795Z",
     "shell.execute_reply": "2022-04-18T16:17:55.064952Z"
    },
    "papermill": {
     "duration": 0.153617,
     "end_time": "2022-04-18T16:17:55.067853",
     "exception": false,
     "start_time": "2022-04-18T16:17:54.914236",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['rate'].dtype  # rate feature converted into float data type..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e621c282",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:17:55.352927Z",
     "iopub.status.busy": "2022-04-18T16:17:55.352300Z",
     "iopub.status.idle": "2022-04-18T16:17:55.356368Z",
     "shell.execute_reply": "2022-04-18T16:17:55.355656Z"
    },
    "papermill": {
     "duration": 0.151905,
     "end_time": "2022-04-18T16:17:55.358452",
     "exception": false,
     "start_time": "2022-04-18T16:17:55.206547",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "## Analyze the categories of restaurants..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "57a125bb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:17:55.651514Z",
     "iopub.status.busy": "2022-04-18T16:17:55.650965Z",
     "iopub.status.idle": "2022-04-18T16:17:55.667371Z",
     "shell.execute_reply": "2022-04-18T16:17:55.666587Z"
    },
    "papermill": {
     "duration": 0.163935,
     "end_time": "2022-04-18T16:17:55.669405",
     "exception": false,
     "start_time": "2022-04-18T16:17:55.505470",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Quick Bites                   19132\n",
       "Casual Dining                 10330\n",
       "Cafe                           3732\n",
       "Delivery                       2604\n",
       "Dessert Parlor                 2263\n",
       "                              ...  \n",
       "Dessert Parlor, Kiosk             2\n",
       "Food Court, Beverage Shop         2\n",
       "Dessert Parlor, Food Court        2\n",
       "Sweet Shop, Dessert Parlor        1\n",
       "Quick Bites, Kiosk                1\n",
       "Name: rest_type, Length: 93, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['rest_type'].value_counts()  # accessing the types and quantity of restaurants..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4f117879",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:17:55.955046Z",
     "iopub.status.busy": "2022-04-18T16:17:55.954499Z",
     "iopub.status.idle": "2022-04-18T16:17:55.958345Z",
     "shell.execute_reply": "2022-04-18T16:17:55.957738Z"
    },
    "papermill": {
     "duration": 0.149401,
     "end_time": "2022-04-18T16:17:55.960378",
     "exception": false,
     "start_time": "2022-04-18T16:17:55.810977",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# to plot these rest_type features..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ab67a4b8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:17:56.246042Z",
     "iopub.status.busy": "2022-04-18T16:17:56.245723Z",
     "iopub.status.idle": "2022-04-18T16:17:56.613780Z",
     "shell.execute_reply": "2022-04-18T16:17:56.612765Z"
    },
    "papermill": {
     "duration": 0.51252,
     "end_time": "2022-04-18T16:17:56.616380",
     "exception": false,
     "start_time": "2022-04-18T16:17:56.103860",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))  # to set width and height...\n",
    "df['rest_type'].value_counts().nlargest(20).plot.bar(color='red')  # for top 20 entries..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5fb6eab3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:17:56.904411Z",
     "iopub.status.busy": "2022-04-18T16:17:56.904004Z",
     "iopub.status.idle": "2022-04-18T16:17:56.908402Z",
     "shell.execute_reply": "2022-04-18T16:17:56.907237Z"
    },
    "papermill": {
     "duration": 0.150868,
     "end_time": "2022-04-18T16:17:56.910657",
     "exception": false,
     "start_time": "2022-04-18T16:17:56.759789",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# for summarizing these insights, created a function....\n",
    "# Quick Bites and Casual Dining are dominating over other restaurants..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "71aaef24",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:17:57.199703Z",
     "iopub.status.busy": "2022-04-18T16:17:57.198699Z",
     "iopub.status.idle": "2022-04-18T16:17:57.203431Z",
     "shell.execute_reply": "2022-04-18T16:17:57.202790Z"
    },
    "papermill": {
     "duration": 0.152114,
     "end_time": "2022-04-18T16:17:57.205451",
     "exception": false,
     "start_time": "2022-04-18T16:17:57.053337",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def mark(x):\n",
    "    if x in ('Quick Bites','Casual Dining'):\n",
    "        return 'Quick Bites + Casual Dining'\n",
    "    else:\n",
    "        return 'others'    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "5f5cbb44",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:17:57.493505Z",
     "iopub.status.busy": "2022-04-18T16:17:57.492582Z",
     "iopub.status.idle": "2022-04-18T16:17:57.513052Z",
     "shell.execute_reply": "2022-04-18T16:17:57.512070Z"
    },
    "papermill": {
     "duration": 0.168395,
     "end_time": "2022-04-18T16:17:57.515490",
     "exception": false,
     "start_time": "2022-04-18T16:17:57.347095",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df['Top_types']=df['rest_type'].apply(mark)  # applied mark func. at top of rest_type feature and stored the values in Top_types..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b0b05aab",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:17:57.807041Z",
     "iopub.status.busy": "2022-04-18T16:17:57.806348Z",
     "iopub.status.idle": "2022-04-18T16:17:57.827315Z",
     "shell.execute_reply": "2022-04-18T16:17:57.826611Z"
    },
    "papermill": {
     "duration": 0.172002,
     "end_time": "2022-04-18T16:17:57.829240",
     "exception": false,
     "start_time": "2022-04-18T16:17:57.657238",
     "status": "completed"
    },
    "tags": []
   },
   "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>url</th>\n",
       "      <th>address</th>\n",
       "      <th>name</th>\n",
       "      <th>online_order</th>\n",
       "      <th>book_table</th>\n",
       "      <th>rate</th>\n",
       "      <th>votes</th>\n",
       "      <th>phone</th>\n",
       "      <th>location</th>\n",
       "      <th>rest_type</th>\n",
       "      <th>dish_liked</th>\n",
       "      <th>cuisines</th>\n",
       "      <th>approx_cost(for two people)</th>\n",
       "      <th>reviews_list</th>\n",
       "      <th>menu_item</th>\n",
       "      <th>listed_in(type)</th>\n",
       "      <th>listed_in(city)</th>\n",
       "      <th>Top_types</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>https://www.zomato.com/bangalore/jalsa-banasha...</td>\n",
       "      <td>942, 21st Main Road, 2nd Stage, Banashankari, ...</td>\n",
       "      <td>Jalsa</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4.1</td>\n",
       "      <td>775</td>\n",
       "      <td>080 42297555\\r\\n+91 9743772233</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Pasta, Lunch Buffet, Masala Papad, Paneer Laja...</td>\n",
       "      <td>North Indian, Mughlai, Chinese</td>\n",
       "      <td>800.0</td>\n",
       "      <td>[('Rated 4.0', 'RATED\\n  A beautiful place to ...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Quick Bites + Casual Dining</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>https://www.zomato.com/bangalore/spice-elephan...</td>\n",
       "      <td>2nd Floor, 80 Feet Road, Near Big Bazaar, 6th ...</td>\n",
       "      <td>Spice Elephant</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>4.1</td>\n",
       "      <td>787</td>\n",
       "      <td>080 41714161</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Momos, Lunch Buffet, Chocolate Nirvana, Thai G...</td>\n",
       "      <td>Chinese, North Indian, Thai</td>\n",
       "      <td>800.0</td>\n",
       "      <td>[('Rated 4.0', 'RATED\\n  Had been here for din...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Quick Bites + Casual Dining</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>https://www.zomato.com/SanchurroBangalore?cont...</td>\n",
       "      <td>1112, Next to KIMS Medical College, 17th Cross...</td>\n",
       "      <td>San Churro Cafe</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>3.8</td>\n",
       "      <td>918</td>\n",
       "      <td>+91 9663487993</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Cafe, Casual Dining</td>\n",
       "      <td>Churros, Cannelloni, Minestrone Soup, Hot Choc...</td>\n",
       "      <td>Cafe, Mexican, Italian</td>\n",
       "      <td>800.0</td>\n",
       "      <td>[('Rated 3.0', \"RATED\\n  Ambience is not that ...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>others</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>https://www.zomato.com/bangalore/addhuri-udupi...</td>\n",
       "      <td>1st Floor, Annakuteera, 3rd Stage, Banashankar...</td>\n",
       "      <td>Addhuri Udupi Bhojana</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.7</td>\n",
       "      <td>88</td>\n",
       "      <td>+91 9620009302</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Quick Bites</td>\n",
       "      <td>Masala Dosa</td>\n",
       "      <td>South Indian, North Indian</td>\n",
       "      <td>300.0</td>\n",
       "      <td>[('Rated 4.0', \"RATED\\n  Great food and proper...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Quick Bites + Casual Dining</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>https://www.zomato.com/bangalore/grand-village...</td>\n",
       "      <td>10, 3rd Floor, Lakshmi Associates, Gandhi Baza...</td>\n",
       "      <td>Grand Village</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.8</td>\n",
       "      <td>166</td>\n",
       "      <td>+91 8026612447\\r\\n+91 9901210005</td>\n",
       "      <td>Basavanagudi</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Panipuri, Gol Gappe</td>\n",
       "      <td>North Indian, Rajasthani</td>\n",
       "      <td>600.0</td>\n",
       "      <td>[('Rated 4.0', 'RATED\\n  Very good restaurant ...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Quick Bites + Casual Dining</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 url  \\\n",
       "0  https://www.zomato.com/bangalore/jalsa-banasha...   \n",
       "1  https://www.zomato.com/bangalore/spice-elephan...   \n",
       "2  https://www.zomato.com/SanchurroBangalore?cont...   \n",
       "3  https://www.zomato.com/bangalore/addhuri-udupi...   \n",
       "4  https://www.zomato.com/bangalore/grand-village...   \n",
       "\n",
       "                                             address                   name  \\\n",
       "0  942, 21st Main Road, 2nd Stage, Banashankari, ...                  Jalsa   \n",
       "1  2nd Floor, 80 Feet Road, Near Big Bazaar, 6th ...         Spice Elephant   \n",
       "2  1112, Next to KIMS Medical College, 17th Cross...        San Churro Cafe   \n",
       "3  1st Floor, Annakuteera, 3rd Stage, Banashankar...  Addhuri Udupi Bhojana   \n",
       "4  10, 3rd Floor, Lakshmi Associates, Gandhi Baza...          Grand Village   \n",
       "\n",
       "  online_order book_table  rate  votes                             phone  \\\n",
       "0          Yes        Yes   4.1    775    080 42297555\\r\\n+91 9743772233   \n",
       "1          Yes         No   4.1    787                      080 41714161   \n",
       "2          Yes         No   3.8    918                    +91 9663487993   \n",
       "3           No         No   3.7     88                    +91 9620009302   \n",
       "4           No         No   3.8    166  +91 8026612447\\r\\n+91 9901210005   \n",
       "\n",
       "       location            rest_type  \\\n",
       "0  Banashankari        Casual Dining   \n",
       "1  Banashankari        Casual Dining   \n",
       "2  Banashankari  Cafe, Casual Dining   \n",
       "3  Banashankari          Quick Bites   \n",
       "4  Basavanagudi        Casual Dining   \n",
       "\n",
       "                                          dish_liked  \\\n",
       "0  Pasta, Lunch Buffet, Masala Papad, Paneer Laja...   \n",
       "1  Momos, Lunch Buffet, Chocolate Nirvana, Thai G...   \n",
       "2  Churros, Cannelloni, Minestrone Soup, Hot Choc...   \n",
       "3                                        Masala Dosa   \n",
       "4                                Panipuri, Gol Gappe   \n",
       "\n",
       "                         cuisines  approx_cost(for two people)  \\\n",
       "0  North Indian, Mughlai, Chinese                        800.0   \n",
       "1     Chinese, North Indian, Thai                        800.0   \n",
       "2          Cafe, Mexican, Italian                        800.0   \n",
       "3      South Indian, North Indian                        300.0   \n",
       "4        North Indian, Rajasthani                        600.0   \n",
       "\n",
       "                                        reviews_list menu_item  \\\n",
       "0  [('Rated 4.0', 'RATED\\n  A beautiful place to ...        []   \n",
       "1  [('Rated 4.0', 'RATED\\n  Had been here for din...        []   \n",
       "2  [('Rated 3.0', \"RATED\\n  Ambience is not that ...        []   \n",
       "3  [('Rated 4.0', \"RATED\\n  Great food and proper...        []   \n",
       "4  [('Rated 4.0', 'RATED\\n  Very good restaurant ...        []   \n",
       "\n",
       "  listed_in(type) listed_in(city)                    Top_types  \n",
       "0          Buffet    Banashankari  Quick Bites + Casual Dining  \n",
       "1          Buffet    Banashankari  Quick Bites + Casual Dining  \n",
       "2          Buffet    Banashankari                       others  \n",
       "3          Buffet    Banashankari  Quick Bites + Casual Dining  \n",
       "4          Buffet    Banashankari  Quick Bites + Casual Dining  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "445cf3a3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:17:58.118164Z",
     "iopub.status.busy": "2022-04-18T16:17:58.117838Z",
     "iopub.status.idle": "2022-04-18T16:17:59.642394Z",
     "shell.execute_reply": "2022-04-18T16:17:59.641369Z"
    },
    "papermill": {
     "duration": 1.671817,
     "end_time": "2022-04-18T16:17:59.644832",
     "exception": false,
     "start_time": "2022-04-18T16:17:57.973015",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import plotly.express as px  # advance data visualization library used for generating deployment level visuals..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "942105b3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:17:59.959697Z",
     "iopub.status.busy": "2022-04-18T16:17:59.959361Z",
     "iopub.status.idle": "2022-04-18T16:17:59.971169Z",
     "shell.execute_reply": "2022-04-18T16:17:59.970390Z"
    },
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     "end_time": "2022-04-18T16:17:59.973196",
     "exception": false,
     "start_time": "2022-04-18T16:17:59.802671",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "values=df['Top_types'].value_counts().values  # values of pie chart..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "762743a6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:18:00.273413Z",
     "iopub.status.busy": "2022-04-18T16:18:00.273099Z",
     "iopub.status.idle": "2022-04-18T16:18:00.284184Z",
     "shell.execute_reply": "2022-04-18T16:18:00.283464Z"
    },
    "papermill": {
     "duration": 0.160349,
     "end_time": "2022-04-18T16:18:00.286264",
     "exception": false,
     "start_time": "2022-04-18T16:18:00.125915",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "labels=df['Top_types'].value_counts().index  # labels of pie chart..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d968f71d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:18:00.577804Z",
     "iopub.status.busy": "2022-04-18T16:18:00.576803Z",
     "iopub.status.idle": "2022-04-18T16:18:00.580673Z",
     "shell.execute_reply": "2022-04-18T16:18:00.580063Z"
    },
    "papermill": {
     "duration": 0.152034,
     "end_time": "2022-04-18T16:18:00.582604",
     "exception": false,
     "start_time": "2022-04-18T16:18:00.430570",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# for pie chart, need data frame, names(labels) and values..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "0beb083f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:18:00.883091Z",
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     "shell.execute_reply": "2022-04-18T16:18:02.071381Z"
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     "duration": 1.341494,
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     "exception": false,
     "start_time": "2022-04-18T16:18:00.733053",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "fig=px.pie(df,names=labels,values=values, title='Pie Chart')\n",
    "fig.show()  # to show the pie chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "325e4efc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:18:02.365173Z",
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     "shell.execute_reply": "2022-04-18T16:18:02.367689Z"
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# from the above pie chart it is clear that 57% of the resturants belong to Q.B and C.D and rest 43% belong to other categories..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "bd3aaf78",
   "metadata": {
    "execution": {
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     "status": "completed"
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   "source": [
    "## Create a new data frame in which we have votes, cost and rating of each restaurant...\n",
    "# Grouped the data by name and then aggregate, for each rest. count the no. of votes and also count no. of restaurants using url...\n",
    "#converted into data frame by using reset_index()"
   ]
  },
  {
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>8787</th>\n",
       "      <td>late100</td>\n",
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       "      <td>5</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>8788</th>\n",
       "      <td>nu.tree</td>\n",
       "      <td>1443</td>\n",
       "      <td>8</td>\n",
       "      <td>400.0</td>\n",
       "      <td>4.314286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8789</th>\n",
       "      <td>re:cess - Hilton Bangalore Embassy GolfLinks</td>\n",
       "      <td>438</td>\n",
       "      <td>3</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>4.100000</td>\n",
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       "    <tr>\n",
       "      <th>8790</th>\n",
       "      <td>repEAT Hub</td>\n",
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       "      <td>sCoolMeal</td>\n",
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       "      <td>5</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>8792 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              name  votes  url  \\\n",
       "0                                     #FeelTheROLL     14    2   \n",
       "1                                       #L-81 Cafe    432    9   \n",
       "2                                    #Vibes Restro      0    3   \n",
       "3                                          #refuel    111    3   \n",
       "4                           'Brahmins' Thatte Idli      0    1   \n",
       "...                                            ...    ...  ...   \n",
       "8787                                       late100      0    5   \n",
       "8788                                       nu.tree   1443    8   \n",
       "8789  re:cess - Hilton Bangalore Embassy GolfLinks    438    3   \n",
       "8790                                    repEAT Hub      0    2   \n",
       "8791                                     sCoolMeal      0    5   \n",
       "\n",
       "      approx_cost(for two people)      rate  \n",
       "0                           200.0  3.400000  \n",
       "1                           400.0  3.900000  \n",
       "2                           700.0       NaN  \n",
       "3                           400.0  3.700000  \n",
       "4                           100.0       NaN  \n",
       "...                           ...       ...  \n",
       "8787                        200.0       NaN  \n",
       "8788                        400.0  4.314286  \n",
       "8789                       1200.0  4.100000  \n",
       "8790                        200.0       NaN  \n",
       "8791                        300.0       NaN  \n",
       "\n",
       "[8792 rows x 5 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rest=df.groupby('name').agg({'votes':'sum','url':'count','approx_cost(for two people)':'mean','rate':'mean'}).reset_index()\n",
    "rest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "38ddfb55",
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   "source": [
    "# manipulated the data frame according to our convinience...changing the names of columns..."
   ]
  },
  {
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    {
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      "text/plain": [
       "                     Name  Total_votes  Total_unities  Avg_approx_cost  \\\n",
       "0            #FeelTheROLL           14              2            200.0   \n",
       "1              #L-81 Cafe          432              9            400.0   \n",
       "2           #Vibes Restro            0              3            700.0   \n",
       "3                 #refuel          111              3            400.0   \n",
       "4  'Brahmins' Thatte Idli            0              1            100.0   \n",
       "\n",
       "   Mean_rating  \n",
       "0          3.4  \n",
       "1          3.9  \n",
       "2          NaN  \n",
       "3          3.7  \n",
       "4          NaN  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
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   ],
   "source": [
    "rest.columns=['Name','Total_votes','Total_unities','Avg_approx_cost','Mean_rating']\n",
    "rest.head()"
   ]
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    {
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      "text/plain": [
       "                     Name  Total_votes  Total_unities  Avg_approx_cost  \\\n",
       "0            #FeelTheROLL           14              2            200.0   \n",
       "1              #L-81 Cafe          432              9            400.0   \n",
       "2           #Vibes Restro            0              3            700.0   \n",
       "3                 #refuel          111              3            400.0   \n",
       "4  'Brahmins' Thatte Idli            0              1            100.0   \n",
       "\n",
       "   Mean_rating  Votes_per_unity  \n",
       "0          3.4              7.0  \n",
       "1          3.9             48.0  \n",
       "2          NaN              0.0  \n",
       "3          3.7             37.0  \n",
       "4          NaN              0.0  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
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    }
   ],
   "source": [
    "rest['Votes_per_unity']=rest['Total_votes']/rest['Total_unities']  # added a new column\n",
    "rest.head()"
   ]
  },
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       "      <th>7426</th>\n",
       "      <td>Swadisht North Indian Restaurant</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>23.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5375</th>\n",
       "      <td>Night Punjabi Folk</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8792 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Name  Total_votes  Total_unities  \\\n",
       "1320                   Cafe Coffee Day         3089             96   \n",
       "5549                            Onesta       347520             85   \n",
       "3788                         Just Bake         2898             73   \n",
       "2446                 Empire Restaurant       229808             71   \n",
       "2577                 Five Star Chicken         3134             70   \n",
       "...                                ...          ...            ...   \n",
       "1900                        Cool Break           11              1   \n",
       "8076       The Shake Factory Originals            8              1   \n",
       "5216        Nethravathi Military Hotel            0              1   \n",
       "7426  Swadisht North Indian Restaurant           23              1   \n",
       "5375                Night Punjabi Folk            0              1   \n",
       "\n",
       "      Avg_approx_cost  Mean_rating  Votes_per_unity  \n",
       "1320       844.791667     3.147191        32.177083  \n",
       "5549       600.000000     4.410588      4088.470588  \n",
       "3788       400.000000     3.355882        39.698630  \n",
       "2446       685.211268     3.916901      3236.732394  \n",
       "2577       257.857143     3.425000        44.771429  \n",
       "...               ...          ...              ...  \n",
       "1900       150.000000     3.400000        11.000000  \n",
       "8076       200.000000     3.300000         8.000000  \n",
       "5216       200.000000          NaN         0.000000  \n",
       "7426       200.000000     3.200000        23.000000  \n",
       "5375       200.000000          NaN         0.000000  \n",
       "\n",
       "[8792 rows x 6 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "popular=rest.sort_values(by='Total_unities',ascending=False)  #sorting the data frame according to the Total_unities...\n",
    "popular"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "175684cf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:18:04.590990Z",
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    "tags": []
   },
   "outputs": [],
   "source": [
    "## restuarant overview analysis...\n",
    "# avg votes recieved by restaurant.\n",
    "# Top 5 most voted restaurants.\n",
    "# Top 5 least voted restaurants."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "c50ee31c",
   "metadata": {
    "execution": {
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    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<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>Name</th>\n",
       "      <th>Total_votes</th>\n",
       "      <th>Total_unities</th>\n",
       "      <th>Avg_approx_cost</th>\n",
       "      <th>Mean_rating</th>\n",
       "      <th>Votes_per_unity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5549</th>\n",
       "      <td>Onesta</td>\n",
       "      <td>347520</td>\n",
       "      <td>85</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>4.410588</td>\n",
       "      <td>4088.470588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8269</th>\n",
       "      <td>Truffles</td>\n",
       "      <td>301059</td>\n",
       "      <td>43</td>\n",
       "      <td>900.000000</td>\n",
       "      <td>4.595349</td>\n",
       "      <td>7001.372093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2446</th>\n",
       "      <td>Empire Restaurant</td>\n",
       "      <td>229808</td>\n",
       "      <td>71</td>\n",
       "      <td>685.211268</td>\n",
       "      <td>3.916901</td>\n",
       "      <td>3236.732394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3054</th>\n",
       "      <td>Hammered</td>\n",
       "      <td>180602</td>\n",
       "      <td>47</td>\n",
       "      <td>1272.340426</td>\n",
       "      <td>4.329787</td>\n",
       "      <td>3842.595745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7751</th>\n",
       "      <td>The Black Pearl</td>\n",
       "      <td>172122</td>\n",
       "      <td>18</td>\n",
       "      <td>1427.777778</td>\n",
       "      <td>4.727778</td>\n",
       "      <td>9562.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Name  Total_votes  Total_unities  Avg_approx_cost  \\\n",
       "5549             Onesta       347520             85       600.000000   \n",
       "8269           Truffles       301059             43       900.000000   \n",
       "2446  Empire Restaurant       229808             71       685.211268   \n",
       "3054           Hammered       180602             47      1272.340426   \n",
       "7751    The Black Pearl       172122             18      1427.777778   \n",
       "\n",
       "      Mean_rating  Votes_per_unity  \n",
       "5549     4.410588      4088.470588  \n",
       "8269     4.595349      7001.372093  \n",
       "2446     3.916901      3236.732394  \n",
       "3054     4.329787      3842.595745  \n",
       "7751     4.727778      9562.333333  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 5 most voted restaurants.\n",
    "popular.sort_values(by='Total_votes', ascending=False).query('Total_votes > 0').head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "f1b537e2",
   "metadata": {
    "execution": {
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    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>Total_votes</th>\n",
       "      <th>Total_unities</th>\n",
       "      <th>Avg_approx_cost</th>\n",
       "      <th>Mean_rating</th>\n",
       "      <th>Votes_per_unity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6840</th>\n",
       "      <td>Silver Spoon</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>800.0</td>\n",
       "      <td>3.30</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3420</th>\n",
       "      <td>House of Mandarin</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>300.0</td>\n",
       "      <td>3.30</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>858</th>\n",
       "      <td>Begum's Biriyani</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>500.0</td>\n",
       "      <td>3.20</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4580</th>\n",
       "      <td>Mahadwani Restaurant</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>300.0</td>\n",
       "      <td>3.30</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8632</th>\n",
       "      <td>Wood Fire Pizza</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>350.0</td>\n",
       "      <td>1.55</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Name  Total_votes  Total_unities  Avg_approx_cost  \\\n",
       "6840          Silver Spoon            4              1            800.0   \n",
       "3420     House of Mandarin            4              1            300.0   \n",
       "858       Begum's Biriyani            4              1            500.0   \n",
       "4580  Mahadwani Restaurant            4              1            300.0   \n",
       "8632       Wood Fire Pizza            4              4            350.0   \n",
       "\n",
       "      Mean_rating  Votes_per_unity  \n",
       "6840         3.30              4.0  \n",
       "3420         3.30              4.0  \n",
       "858          3.20              4.0  \n",
       "4580         3.30              4.0  \n",
       "8632         1.55              1.0  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 5 least voted restaurants (using tail)...\n",
    "popular.sort_values(by='Total_votes', ascending=False).query('Total_votes > 0').tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "ea73f2f8",
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    "execution": {
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    "tags": []
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Top 5 Least Voted Restaurants\\n(with at least 1 vote)')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1800 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#creating a subplot...(Plotting all the 3 statistics)\n",
    "fig, (ax1,ax2,ax3)= plt.subplots(3,1,figsize=(20,25))  # 3 axis for these 3 analysis...\n",
    "# ax1-- for avg votes recieved by restaurant\n",
    "ax1.text(0.50, 0.50, int(popular['Total_votes'].mean()), fontsize=54, ha='center') # ha for text to be center...\n",
    "ax1.text(0.50, 0.35, 'is the average of votes', fontsize=18, ha='center')\n",
    "ax1.text(0.50, 0.30, 'recieved by the restaurants', fontsize=18, ha='center')\n",
    "\n",
    "# ax2-- for top 5 most voted restaurants\n",
    "sns.barplot(x='Total_votes', y='Name', data=popular.sort_values(by='Total_votes', ascending=False).query('Total_votes > 0').head(5),ax=ax2, palette='plasma')\n",
    "ax2.set_title('Top 5 Most Voted Restaurants', size=16)\n",
    "\n",
    "# ax3-- for top 5 least voted restaurants\n",
    "sns.barplot(x='Total_votes', y='Name', data=popular.sort_values(by='Total_votes', ascending=False).query('Total_votes > 0').tail(5),ax=ax3, palette='plasma_r')\n",
    "ax3.set_title('Top 5 Least Voted Restaurants\\n(with at least 1 vote)', size=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "76b3519e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:18:06.464679Z",
     "iopub.status.busy": "2022-04-18T16:18:06.464361Z",
     "iopub.status.idle": "2022-04-18T16:18:06.468277Z",
     "shell.execute_reply": "2022-04-18T16:18:06.467417Z"
    },
    "papermill": {
     "duration": 0.164252,
     "end_time": "2022-04-18T16:18:06.470156",
     "exception": false,
     "start_time": "2022-04-18T16:18:06.305904",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "## PERFORMING IN DEPTH ANALYSIS OF RESTAURANT...\n",
    "# Most expensive and cheapest restaurants\n",
    "# How many restaurants offer book table services and online order services"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "1b79f8cd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:18:06.781703Z",
     "iopub.status.busy": "2022-04-18T16:18:06.781430Z",
     "iopub.status.idle": "2022-04-18T16:18:07.348614Z",
     "shell.execute_reply": "2022-04-18T16:18:07.347692Z"
    },
    "papermill": {
     "duration": 0.727279,
     "end_time": "2022-04-18T16:18:07.351732",
     "exception": false,
     "start_time": "2022-04-18T16:18:06.624453",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Top 5 Cheapest Restaurants')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1800 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Most expensive and cheapest restaurants\n",
    "fig, (ax1,ax2,ax3)= plt.subplots(3,1,figsize=(20,25))  # 3 axis for these 3 analysis...\n",
    "ax1.text(0.50, 0.50, int(popular['Avg_approx_cost'].mean()), fontsize=54, ha='center') # ha for text to be center...\n",
    "ax1.text(0.50, 0.35, 'is the mean approx cost', fontsize=18, ha='center')\n",
    "ax1.text(0.50, 0.30, 'of Bengaluru restaurants', fontsize=18, ha='center')\n",
    "\n",
    "sns.barplot(x='Avg_approx_cost', y='Name', data=popular.sort_values(by='Avg_approx_cost', ascending=False).query('Avg_approx_cost > 0').head(5),ax=ax2, palette='plasma')\n",
    "ax2.set_title('Top 5 Most Expensive Restaurants', size=16)\n",
    "\n",
    "sns.barplot(x='Avg_approx_cost', y='Name', data=popular.sort_values(by='Avg_approx_cost', ascending=False).query('Avg_approx_cost > 0').tail(5),ax=ax3, palette='plasma_r')\n",
    "ax3.set_title('Top 5 Cheapest Restaurants', size=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "89e42df2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:18:07.664344Z",
     "iopub.status.busy": "2022-04-18T16:18:07.663737Z",
     "iopub.status.idle": "2022-04-18T16:18:07.668321Z",
     "shell.execute_reply": "2022-04-18T16:18:07.667307Z"
    },
    "papermill": {
     "duration": 0.162719,
     "end_time": "2022-04-18T16:18:07.670344",
     "exception": false,
     "start_time": "2022-04-18T16:18:07.507625",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# How many restaurants offer book table services and online order services"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "acb78829",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:18:07.984230Z",
     "iopub.status.busy": "2022-04-18T16:18:07.983910Z",
     "iopub.status.idle": "2022-04-18T16:18:07.997758Z",
     "shell.execute_reply": "2022-04-18T16:18:07.996825Z"
    },
    "papermill": {
     "duration": 0.175671,
     "end_time": "2022-04-18T16:18:07.999851",
     "exception": false,
     "start_time": "2022-04-18T16:18:07.824180",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "x=df['book_table'].value_counts()  # checking the number of restaurants providing this service... \n",
    "labels=['No Book Table', 'Book Table']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "2b694947",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:18:08.314229Z",
     "iopub.status.busy": "2022-04-18T16:18:08.313919Z",
     "iopub.status.idle": "2022-04-18T16:18:08.356387Z",
     "shell.execute_reply": "2022-04-18T16:18:08.355563Z"
    },
    "papermill": {
     "duration": 0.199084,
     "end_time": "2022-04-18T16:18:08.358489",
     "exception": false,
     "start_time": "2022-04-18T16:18:08.159405",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>                            <div id=\"4ab15762-b516-4192-9ece-07e0c1630388\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"4ab15762-b516-4192-9ece-07e0c1630388\")) {                    Plotly.newPlot(                        \"4ab15762-b516-4192-9ece-07e0c1630388\",                        [{\"hoverinfo\":\"label+percent\",\"labels\":[\"No Book Table\",\"Book Table\"],\"pull\":[0,0,0,0.2,0],\"textfont\":{\"size\":25},\"textinfo\":\"percent\",\"values\":[45268,6449],\"type\":\"pie\"}],                        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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.graph_objs as go\n",
    "from plotly.offline import iplot  #ipython plot-- to display the plot, passed it to iplot...\n",
    "\n",
    "trace=go.Pie(labels=labels,values=x,hoverinfo='label+percent',textinfo='percent',textfont=dict(size=25),\n",
    "              pull=[0, 0, 0,0.2, 0])\n",
    "iplot([trace])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "c08b2c4c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:18:08.670833Z",
     "iopub.status.busy": "2022-04-18T16:18:08.670548Z",
     "iopub.status.idle": "2022-04-18T16:18:08.673934Z",
     "shell.execute_reply": "2022-04-18T16:18:08.673307Z"
    },
    "papermill": {
     "duration": 0.163391,
     "end_time": "2022-04-18T16:18:08.675683",
     "exception": false,
     "start_time": "2022-04-18T16:18:08.512292",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# from the above pie chart, it is clear that most of the restaurants do not provide book table services... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "d2d956b0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:18:08.989281Z",
     "iopub.status.busy": "2022-04-18T16:18:08.988717Z",
     "iopub.status.idle": "2022-04-18T16:18:09.047239Z",
     "shell.execute_reply": "2022-04-18T16:18:09.046182Z"
    },
    "papermill": {
     "duration": 0.219015,
     "end_time": "2022-04-18T16:18:09.049951",
     "exception": false,
     "start_time": "2022-04-18T16:18:08.830936",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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     "metadata": {},
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   ],
   "source": [
    "x=df['online_order'].value_counts()\n",
    "labels=['Accepted','Not accepted']\n",
    "\n",
    "fig = px.pie(df, values=x, names=labels,title='Pie chart')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "a8f3a354",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:18:09.376419Z",
     "iopub.status.busy": "2022-04-18T16:18:09.376082Z",
     "iopub.status.idle": "2022-04-18T16:18:09.380897Z",
     "shell.execute_reply": "2022-04-18T16:18:09.379961Z"
    },
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    "tags": []
   },
   "outputs": [],
   "source": [
    "# from the above pie chart, it is clear that most of the restaurants accept online order services... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "e6ab96f9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:18:09.719615Z",
     "iopub.status.busy": "2022-04-18T16:18:09.719294Z",
     "iopub.status.idle": "2022-04-18T16:18:09.723143Z",
     "shell.execute_reply": "2022-04-18T16:18:09.722210Z"
    },
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     "start_time": "2022-04-18T16:18:09.552323",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "## ANALYSING BEST BUDGET RESTAURANTS...\n",
    "#1 Best budget restaurants in any location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "c0db2f19",
   "metadata": {
    "execution": {
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     "status": "completed"
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    "tags": []
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   "outputs": [],
   "source": [
    "#1 Best budget restaurants in any location\n",
    "# created a function that accepts the location and type of rest. and gives the best budget restaurant accordingly..\n",
    "# with the condition of rating greater than 4.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "98991013",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:18:10.365780Z",
     "iopub.status.busy": "2022-04-18T16:18:10.365277Z",
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     "shell.execute_reply": "2022-04-18T16:18:10.369637Z"
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     "status": "completed"
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    "tags": []
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   "outputs": [],
   "source": [
    "def return_budget(location,restaurant_type):\n",
    "    filter=(df['approx_cost(for two people)']<=400) & (df['location']==location) & (df['rate']>=4) & (df['rest_type']==restaurant_type)\n",
    "    budget=df[filter]\n",
    "    return (budget['name'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "37b3946e",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2022-04-18T16:18:10.726223Z"
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    {
     "data": {
      "text/plain": [
       "array(['Hiyar Majhe Kolkata', 'Swadista Aahar', 'Swad Punjab Da',\n",
       "       'Roti Wala', 'Litti Twist', 'The Shawarma Shop', 'Gorbandh',\n",
       "       'Yum In My Tum', 'Nanna Munna Paratha House', 'Chaatimes',\n",
       "       'Andhra Kitchen', \"Muthashy's\", 'Ashirvaad Grand', 'Petoo',\n",
       "       'Ilyazsab The House Of Chicken', 'Kannur Food Point',\n",
       "       \"Domino's Pizza\", 'KFC', 'Hari Super Sandwich',\n",
       "       'The Biriyani Pedia', 'World Of Asia'], dtype=object)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "return_budget('BTM','Quick Bites')  # provide any location and restaurant type according to our convinience"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "bf6ff352",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:18:11.050076Z",
     "iopub.status.busy": "2022-04-18T16:18:11.049664Z",
     "iopub.status.idle": "2022-04-18T16:18:11.053379Z",
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   "outputs": [],
   "source": [
    "#2 Extract latitude and longitude for any place (for geographical analysis)\n",
    "#-- means, to find highest rated restaurant at a particular location (we need latitude and longitude)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "b4ad841a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:18:11.379560Z",
     "iopub.status.busy": "2022-04-18T16:18:11.379234Z",
     "iopub.status.idle": "2022-04-18T16:18:11.382652Z",
     "shell.execute_reply": "2022-04-18T16:18:11.382029Z"
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    },
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   },
   "outputs": [],
   "source": [
    "## GEOGRAPHICAL ANALYSIS...\n",
    "#1 Extract latitude nad longitudes of each and every restaurant\n",
    "#-- for finding highest rated restaurant at a particular location (we need latitude and longitude)\n",
    "#2 Spatial analysis (geo. analysis) --generating the map\n",
    "#-- where exactly most of the restaurants are situated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "44f9f160",
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    "execution": {
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     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Basavanagudi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mysore Road</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jayanagar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kumaraswamy Layout</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>West Bangalore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>Magadi Road</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Yelahanka</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Sahakara Nagar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Peenya</td>\n",
       "    </tr>\n",
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       "<p>94 rows × 1 columns</p>\n",
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       "                  Name\n",
       "0         Banashankari\n",
       "1         Basavanagudi\n",
       "2          Mysore Road\n",
       "3            Jayanagar\n",
       "4   Kumaraswamy Layout\n",
       "..                 ...\n",
       "89      West Bangalore\n",
       "90         Magadi Road\n",
       "91           Yelahanka\n",
       "92      Sahakara Nagar\n",
       "93              Peenya\n",
       "\n",
       "[94 rows x 1 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# there are total 93 unique locations\n",
    "locations=pd.DataFrame({'Name':df['location'].unique()})  # converting it into data frame specifically for location\n",
    "locations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "756c67c6",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#2 where exactly most of the restaurants are situated\n",
    "# for each location, there is count of the no. of restaurants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "76b7ee8a",
   "metadata": {
    "execution": {
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   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\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>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BTM</td>\n",
       "      <td>5124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HSR</td>\n",
       "      <td>2523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Koramangala 5th Block</td>\n",
       "      <td>2504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>JP Nagar</td>\n",
       "      <td>2235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Whitefield</td>\n",
       "      <td>2144</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Name  count\n",
       "0                    BTM   5124\n",
       "1                    HSR   2523\n",
       "2  Koramangala 5th Block   2504\n",
       "3               JP Nagar   2235\n",
       "4             Whitefield   2144"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Rest_locations=pd.DataFrame(df['location'].value_counts().reset_index())  #Rest_type data frame \n",
    "Rest_locations.columns=['Name','count']  # changing the column/feature name\n",
    "Rest_locations.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "60b4d3e0",
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th {\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>Name</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BTM</td>\n",
       "      <td>5124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HSR</td>\n",
       "      <td>2523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Koramangala 5th Block</td>\n",
       "      <td>2504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>JP Nagar</td>\n",
       "      <td>2235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Whitefield</td>\n",
       "      <td>2144</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Name  count\n",
       "0                    BTM   5124\n",
       "1                    HSR   2523\n",
       "2  Koramangala 5th Block   2504\n",
       "3               JP Nagar   2235\n",
       "4             Whitefield   2144"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merging both the data frames on the basis of common (feature i.e; name) to get the total no. of restaurants at each location\n",
    "Restaurant_locations=Rest_locations.merge(locations,on='Name',how=\"left\").dropna()  # used for spatial analysis\n",
    "Restaurant_locations.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "f3b18b15",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:18:13.180751Z",
     "iopub.status.busy": "2022-04-18T16:18:13.180190Z",
     "iopub.status.idle": "2022-04-18T16:18:13.183574Z",
     "shell.execute_reply": "2022-04-18T16:18:13.182983Z"
    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#-- Spatial analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "31ee261d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:18:13.512967Z",
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    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "## ANALYSING CUSTOMER BEHAVIOUR USING WORDCLOUD\n",
    "#1 Analyze customer preference (likes/dislikes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "cb2f0542",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# wordcloud-- data visualization technique used for representing text data in which the size of each word indicates its importance\n",
    "#-- bigger the size of dish, more it is liked \n",
    "from wordcloud import WordCloud, STOPWORDS  # STOPWORDS-- used to deal with unneccesary words like is, am are etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "df289bba",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:18:14.234487Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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       "\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>url</th>\n",
       "      <th>address</th>\n",
       "      <th>name</th>\n",
       "      <th>online_order</th>\n",
       "      <th>book_table</th>\n",
       "      <th>rate</th>\n",
       "      <th>votes</th>\n",
       "      <th>phone</th>\n",
       "      <th>location</th>\n",
       "      <th>rest_type</th>\n",
       "      <th>dish_liked</th>\n",
       "      <th>cuisines</th>\n",
       "      <th>approx_cost(for two people)</th>\n",
       "      <th>reviews_list</th>\n",
       "      <th>menu_item</th>\n",
       "      <th>listed_in(type)</th>\n",
       "      <th>listed_in(city)</th>\n",
       "      <th>Top_types</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>https://www.zomato.com/bangalore/addhuri-udupi...</td>\n",
       "      <td>1st Floor, Annakuteera, 3rd Stage, Banashankar...</td>\n",
       "      <td>Addhuri Udupi Bhojana</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.7</td>\n",
       "      <td>88</td>\n",
       "      <td>+91 9620009302</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Quick Bites</td>\n",
       "      <td>Masala Dosa</td>\n",
       "      <td>South Indian, North Indian</td>\n",
       "      <td>300.0</td>\n",
       "      <td>[('Rated 4.0', \"RATED\\n  Great food and proper...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Quick Bites + Casual Dining</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>https://www.zomato.com/bangalore/foodiction-1-...</td>\n",
       "      <td>2/1, 7th Main, Dwarakangar, Hosakeregalli, Ban...</td>\n",
       "      <td>Foodiction</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>2.8</td>\n",
       "      <td>506</td>\n",
       "      <td>+91 9916107070</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Quick Bites</td>\n",
       "      <td>Burgers, Lassi, Chicken Grill, Naan, Momos, Ch...</td>\n",
       "      <td>North Indian, Fast Food, Chinese, Burger</td>\n",
       "      <td>500.0</td>\n",
       "      <td>[('Rated 1.0', \"RATED\\n  Worst restaurant ever...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Delivery</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Quick Bites + Casual Dining</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>https://www.zomato.com/bangalore/faasos-banash...</td>\n",
       "      <td>80, BDA Complex, 2nd Stage, Banashankari, Bang...</td>\n",
       "      <td>Faasos</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>4.2</td>\n",
       "      <td>415</td>\n",
       "      <td>+91 7700020020</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Quick Bites</td>\n",
       "      <td>Rolls, Veggie Wrap, Chocolate Fantasy, Rice Bo...</td>\n",
       "      <td>North Indian, Biryani, Fast Food</td>\n",
       "      <td>500.0</td>\n",
       "      <td>[('Rated 3.0', 'RATED\\n  Not worth for the mon...</td>\n",
       "      <td>['Chole Kulcha Meal', 'Upvas Aloo Paratha With...</td>\n",
       "      <td>Delivery</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Quick Bites + Casual Dining</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>https://www.zomato.com/bangalore/fast-and-fres...</td>\n",
       "      <td>32, 7th Main Road, Dwaraka Nagar,Near PES Coll...</td>\n",
       "      <td>Fast And Fresh</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>2.8</td>\n",
       "      <td>91</td>\n",
       "      <td>+91 9742974232</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Quick Bites</td>\n",
       "      <td>NaN</td>\n",
       "      <td>North Indian, Chinese, Fast Food</td>\n",
       "      <td>400.0</td>\n",
       "      <td>[('Rated 1.0', 'RATED\\n  Half baked rice in a ...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Delivery</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Quick Bites + Casual Dining</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>https://www.zomato.com/bangalore/maruthi-davan...</td>\n",
       "      <td>556/A, Water Tank Road, 3rd Stage, Banashankar...</td>\n",
       "      <td>Maruthi Davangere Benne Dosa</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>4.0</td>\n",
       "      <td>17</td>\n",
       "      <td>+91 9019563485</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Quick Bites</td>\n",
       "      <td>NaN</td>\n",
       "      <td>South Indian</td>\n",
       "      <td>150.0</td>\n",
       "      <td>[('Rated 5.0', \"RATED\\n  This restaurant serve...</td>\n",
       "      <td>['Benne Dosa', 'Benne Masala Dosa', 'Benne Kal...</td>\n",
       "      <td>Delivery</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Quick Bites + Casual Dining</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  url  \\\n",
       "3   https://www.zomato.com/bangalore/addhuri-udupi...   \n",
       "31  https://www.zomato.com/bangalore/foodiction-1-...   \n",
       "34  https://www.zomato.com/bangalore/faasos-banash...   \n",
       "36  https://www.zomato.com/bangalore/fast-and-fres...   \n",
       "39  https://www.zomato.com/bangalore/maruthi-davan...   \n",
       "\n",
       "                                              address  \\\n",
       "3   1st Floor, Annakuteera, 3rd Stage, Banashankar...   \n",
       "31  2/1, 7th Main, Dwarakangar, Hosakeregalli, Ban...   \n",
       "34  80, BDA Complex, 2nd Stage, Banashankari, Bang...   \n",
       "36  32, 7th Main Road, Dwaraka Nagar,Near PES Coll...   \n",
       "39  556/A, Water Tank Road, 3rd Stage, Banashankar...   \n",
       "\n",
       "                            name online_order book_table  rate  votes  \\\n",
       "3          Addhuri Udupi Bhojana           No         No   3.7     88   \n",
       "31                    Foodiction          Yes         No   2.8    506   \n",
       "34                        Faasos          Yes         No   4.2    415   \n",
       "36                Fast And Fresh          Yes         No   2.8     91   \n",
       "39  Maruthi Davangere Benne Dosa          Yes         No   4.0     17   \n",
       "\n",
       "             phone      location    rest_type  \\\n",
       "3   +91 9620009302  Banashankari  Quick Bites   \n",
       "31  +91 9916107070  Banashankari  Quick Bites   \n",
       "34  +91 7700020020  Banashankari  Quick Bites   \n",
       "36  +91 9742974232  Banashankari  Quick Bites   \n",
       "39  +91 9019563485  Banashankari  Quick Bites   \n",
       "\n",
       "                                           dish_liked  \\\n",
       "3                                         Masala Dosa   \n",
       "31  Burgers, Lassi, Chicken Grill, Naan, Momos, Ch...   \n",
       "34  Rolls, Veggie Wrap, Chocolate Fantasy, Rice Bo...   \n",
       "36                                                NaN   \n",
       "39                                                NaN   \n",
       "\n",
       "                                    cuisines  approx_cost(for two people)  \\\n",
       "3                 South Indian, North Indian                        300.0   \n",
       "31  North Indian, Fast Food, Chinese, Burger                        500.0   \n",
       "34          North Indian, Biryani, Fast Food                        500.0   \n",
       "36          North Indian, Chinese, Fast Food                        400.0   \n",
       "39                              South Indian                        150.0   \n",
       "\n",
       "                                         reviews_list  \\\n",
       "3   [('Rated 4.0', \"RATED\\n  Great food and proper...   \n",
       "31  [('Rated 1.0', \"RATED\\n  Worst restaurant ever...   \n",
       "34  [('Rated 3.0', 'RATED\\n  Not worth for the mon...   \n",
       "36  [('Rated 1.0', 'RATED\\n  Half baked rice in a ...   \n",
       "39  [('Rated 5.0', \"RATED\\n  This restaurant serve...   \n",
       "\n",
       "                                            menu_item listed_in(type)  \\\n",
       "3                                                  []          Buffet   \n",
       "31                                                 []        Delivery   \n",
       "34  ['Chole Kulcha Meal', 'Upvas Aloo Paratha With...        Delivery   \n",
       "36                                                 []        Delivery   \n",
       "39  ['Benne Dosa', 'Benne Masala Dosa', 'Benne Kal...        Delivery   \n",
       "\n",
       "   listed_in(city)                    Top_types  \n",
       "3     Banashankari  Quick Bites + Casual Dining  \n",
       "31    Banashankari  Quick Bites + Casual Dining  \n",
       "34    Banashankari  Quick Bites + Casual Dining  \n",
       "36    Banashankari  Quick Bites + Casual Dining  \n",
       "39    Banashankari  Quick Bites + Casual Dining  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=df[df['rest_type']=='Quick Bites']  # data w.r.t Quick Bites restaurants\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "86791b0b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:18:14.603651Z",
     "iopub.status.busy": "2022-04-18T16:18:14.602808Z",
     "iopub.status.idle": "2022-04-18T16:18:19.557780Z",
     "shell.execute_reply": "2022-04-18T16:18:19.556922Z"
    },
    "papermill": {
     "duration": 5.124823,
     "end_time": "2022-04-18T16:18:19.561754",
     "exception": false,
     "start_time": "2022-04-18T16:18:14.436931",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 1499.5, 1499.5, -0.5)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# gather the data of dish_liked feature and then creat a wordcloud\n",
    "# created an empty list to store the entire data of popular dishes\n",
    "stopwords=set(STOPWORDS)\n",
    "dishes=''\n",
    "for word in data['dish_liked'].dropna():\n",
    "    words=word.split()\n",
    "    # Converts each token into lowercase \n",
    "    for i in range(len(words)): \n",
    "        words[i] = words[i].lower() \n",
    "    dishes=dishes+ \" \".join(words)+\" \"\n",
    "wordcloud = WordCloud(max_font_size=None, background_color='white', collocations=False,stopwords = stopwords,width=1500, height=1500).generate(dishes)\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.imshow(wordcloud)\n",
    "plt.axis(\"off\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "7fc92389",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:18:19.905621Z",
     "iopub.status.busy": "2022-04-18T16:18:19.904601Z",
     "iopub.status.idle": "2022-04-18T16:18:19.908938Z",
     "shell.execute_reply": "2022-04-18T16:18:19.908025Z"
    },
    "papermill": {
     "duration": 0.176817,
     "end_time": "2022-04-18T16:18:19.911084",
     "exception": false,
     "start_time": "2022-04-18T16:18:19.734267",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "## ANALYSING REVIEW OF RESTAURANT\n",
    "#1 Analyze review of particular restaurant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "908de5e1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:18:20.248841Z",
     "iopub.status.busy": "2022-04-18T16:18:20.248533Z",
     "iopub.status.idle": "2022-04-18T16:18:20.252457Z",
     "shell.execute_reply": "2022-04-18T16:18:20.251546Z"
    },
    "papermill": {
     "duration": 0.174538,
     "end_time": "2022-04-18T16:18:20.254573",
     "exception": false,
     "start_time": "2022-04-18T16:18:20.080035",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# fetch the data of review_list column\n",
    "# cleaning the data (removing special characters, extra spaces, repeated words)\n",
    "# store the data in a word cloud\n",
    "# display the wordcloud of reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "fd96e056",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:18:20.588844Z",
     "iopub.status.busy": "2022-04-18T16:18:20.588268Z",
     "iopub.status.idle": "2022-04-18T16:18:20.594974Z",
     "shell.execute_reply": "2022-04-18T16:18:20.594107Z"
    },
    "papermill": {
     "duration": 0.175895,
     "end_time": "2022-04-18T16:18:20.597279",
     "exception": false,
     "start_time": "2022-04-18T16:18:20.421384",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[(\\'rated 4.0\\', \\'rated\\\\n  a beautiful place to dine in.the interiors take you back to the mughal era. the lightings are just perfect.we went there on the occasion of christmas and so they had only limited items available. but the taste and service was not compromised at all.the only complaint is that the breads could have been better.would surely like to come here again.\\'), (\\'rated 4.0\\', \\'rated\\\\n  i was here for dinner with my family on a weekday. the restaurant was completely empty. ambience is good with some good old hindi music. seating arrangement are good too. we ordered masala papad, panner and baby corn starters, lemon and corrionder soup, butter roti, olive and chilli paratha. food was fresh and good, service is good too. good for family hangout.\\\\ncheers\\'), (\\'rated 2.0\\', \\'rated\\\\n  its a restaurant near to banashankari bda. me along with few of my office friends visited to have buffet but unfortunately they only provide veg buffet. on inquiring they said this place is mostly visited by vegetarians. anyways we ordered ala carte items which took ages to come. food was ok ok. definitely not visiting anymore.\\'), (\\'rated 4.0\\', \\'rated\\\\n  we went here on a weekend and one of us had the buffet while two of us took ala carte. firstly the ambience and service of this place is great! the buffet had a lot of items and the good was good. we had a pumpkin halwa intm the dessert which was amazing. must try! the kulchas are great here. cheers!\\'), (\\'rated 5.0\\', \\'rated\\\\n  the best thing about the place is itã\\x83\\\\x83ã\\x82\\\\x83ã\\x83\\\\x82ã\\x82\\\\x82ã\\x83\\\\x83ã\\x82\\\\x82ã\\x83\\\\x82ã\\x82\\\\x92s ambiance. second best thing was yummy ? food. we try buffet and buffet food was not disappointed us.\\\\ntest ?. ?? ?? ?? ?? ??\\\\nquality ?. ??????????.\\\\nservice: staff was very professional and friendly.\\\\n\\\\noverall experience was excellent.\\\\n\\\\nsubirmajumder85.wixsite.com\\'), (\\'rated 5.0\\', \\'rated\\\\n  great food and pleasant ambience. expensive but coll place to chill and relax......\\\\n\\\\nservice is really very very good and friendly staff...\\\\n\\\\nfood : 5/5\\\\nservice : 5/5\\\\nambience :5/5\\\\noverall :5/5\\'), (\\'rated 4.0\\', \\'rated\\\\n  good ambience with tasty food.\\\\ncheese chilli paratha with bhutta palak methi curry is a good combo.\\\\nlemon chicken in the starters is a must try item.\\\\negg fried rice was also quite tasty.\\\\nin the mocktails, recommend \"alice in junoon\". do not miss it.\\'), (\\'rated 4.0\\', \\'rated\\\\n  you canã\\x83\\\\x83ã\\x82\\\\x83ã\\x83\\\\x82ã\\x82\\\\x82ã\\x83\\\\x83ã\\x82\\\\x82ã\\x83\\\\x82ã\\x82\\\\x92t go wrong with jalsa. never been a fan of their buffet and thus always order alacarteã\\x83\\\\x83ã\\x82\\\\x83ã\\x83\\\\x82ã\\x82\\\\x82ã\\x83\\\\x83ã\\x82\\\\x82ã\\x83\\\\x82ã\\x82\\\\x92. service at times can be on the slower side but food is worth the wait.\\'), (\\'rated 5.0\\', \\'rated\\\\n  overdelighted by the service and food provided at this place. a royal and ethnic atmosphere builds a strong essence of being in india and also the quality and taste of food is truly authentic. i would totally recommend to visit this place once.\\'), (\\'rated 4.0\\', \\'rated\\\\n  the place is nice and comfortable. food wise all jalea outlets maintain a good standard. the soya chaap was a standout dish. clearly one of trademark dish as per me and a must try.\\\\n\\\\nthe only concern is the parking. it very congested and limited to just 5cars. the basement parking is very steep and makes it cumbersome\\'), (\\'rated 4.0\\', \\'rated\\\\n  the place is nice and comfortable. food wise all jalea outlets maintain a good standard. the soya chaap was a standout dish. clearly one of trademark dish as per me and a must try.\\\\n\\\\nthe only concern is the parking. it very congested and limited to just 5cars. the basement parking is very steep and makes it cumbersome\\'), (\\'rated 4.0\\', \\'rated\\\\n  the place is nice and comfortable. food wise all jalea outlets maintain a good standard. the soya chaap was a standout dish. clearly one of trademark dish as per me and a must try.\\\\n\\\\nthe only concern is the parking. it very congested and limited to just 5cars. the basement parking is very steep and makes it cumbersome\\')]'"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fetch the data of review_list column\n",
    "df['reviews_list'][0]\n",
    "data=df['reviews_list'][0].lower()\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "dc1bcead",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:18:20.934308Z",
     "iopub.status.busy": "2022-04-18T16:18:20.934015Z",
     "iopub.status.idle": "2022-04-18T16:18:20.944994Z",
     "shell.execute_reply": "2022-04-18T16:18:20.944171Z"
    },
    "papermill": {
     "duration": 0.181745,
     "end_time": "2022-04-18T16:18:20.946929",
     "exception": false,
     "start_time": "2022-04-18T16:18:20.765184",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' rated rated n a beautiful place to dine in the interiors take you back to the mughal era the lightings are just perfect we went there on the occasion of christmas and so they had only limited items available but the taste and service was not compromised at all the only complaint is that the breads could have been better would surely like to come here again rated rated n i was here for dinner with my family on a weekday the restaurant was completely empty ambience is good with some good old hindi music seating arrangement are good too we ordered masala papad panner and baby corn starters lemon and corrionder soup butter roti olive and chilli paratha food was fresh and good service is good too good for family hangout ncheers rated rated n its a restaurant near to banashankari bda me along with few of my office friends visited to have buffet but unfortunately they only provide veg buffet on inquiring they said this place is mostly visited by vegetarians anyways we ordered ala carte items which took ages to come food was ok ok definitely not visiting anymore rated rated n we went here on a weekend and one of us had the buffet while two of us took ala carte firstly the ambience and service of this place is great the buffet had a lot of items and the good was good we had a pumpkin halwa intm the dessert which was amazing must try the kulchas are great here cheers rated rated n the best thing about the place is it s ambiance second best thing was yummy food we try buffet and buffet food was not disappointed us ntest nquality nservice staff was very professional and friendly n noverall e perience was e cellent n nsubirmajumder wi site com rated rated n great food and pleasant ambience e pensive but coll place to chill and rela n nservice is really very very good and friendly staff n nfood nservice nambience noverall rated rated n good ambience with tasty food ncheese chilli paratha with bhutta palak methi curry is a good combo nlemon chicken in the starters is a must try item negg fried rice was also quite tasty nin the mocktails recommend alice in junoon do not miss it rated rated n you can t go wrong with jalsa never been a fan of their buffet and thus always order alacarte service at times can be on the slower side but food is worth the wait rated rated n overdelighted by the service and food provided at this place a royal and ethnic atmosphere builds a strong essence of being in india and also the quality and taste of food is truly authentic i would totally recommend to visit this place once rated rated n the place is nice and comfortable food wise all jalea outlets maintain a good standard the soya chaap was a standout dish clearly one of trademark dish as per me and a must try n nthe only concern is the parking it very congested and limited to just cars the basement parking is very steep and makes it cumbersome rated rated n the place is nice and comfortable food wise all jalea outlets maintain a good standard the soya chaap was a standout dish clearly one of trademark dish as per me and a must try n nthe only concern is the parking it very congested and limited to just cars the basement parking is very steep and makes it cumbersome rated rated n the place is nice and comfortable food wise all jalea outlets maintain a good standard the soya chaap was a standout dish clearly one of trademark dish as per me and a must try n nthe only concern is the parking it very congested and limited to just cars the basement parking is very steep and makes it cumbersome '"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cleaning the data (removing special characters) using substitute function of regular expression module\n",
    "import re  \n",
    "data2=re.sub('[^a-zA-Z]', ' ',data)  # except words...replace other things with white space\n",
    "data2\n",
    "\n",
    "# cleaning the data (removing repeated words)\n",
    "data3=re.sub('repeat', ' ',data2)\n",
    "data3\n",
    "\n",
    "# cleaning the data (removing repeated characters)\n",
    "data4=re.sub('x', ' ', data3)\n",
    "data4\n",
    "\n",
    "# cleaning the data (removing extra white spaces)\n",
    "data5=re.sub(' +',' ',data4)\n",
    "data5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "1c4a8438",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:18:21.286675Z",
     "iopub.status.busy": "2022-04-18T16:18:21.285785Z",
     "iopub.status.idle": "2022-04-18T16:18:21.304005Z",
     "shell.execute_reply": "2022-04-18T16:18:21.303116Z"
    },
    "papermill": {
     "duration": 0.189724,
     "end_time": "2022-04-18T16:18:21.306449",
     "exception": false,
     "start_time": "2022-04-18T16:18:21.116725",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "dataset=df[df['rest_type']=='Quick Bites']  # data w.r.t some Quick Bite restaurants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "00169a01",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:18:21.651604Z",
     "iopub.status.busy": "2022-04-18T16:18:21.651285Z",
     "iopub.status.idle": "2022-04-18T16:18:35.792020Z",
     "shell.execute_reply": "2022-04-18T16:18:35.791153Z"
    },
    "papermill": {
     "duration": 14.314063,
     "end_time": "2022-04-18T16:18:35.794486",
     "exception": false,
     "start_time": "2022-04-18T16:18:21.480423",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# store the data in a word cloud\n",
    "total_review=' '  # empty list to store all the reviews for word cloud\n",
    "for review in dataset['reviews_list']:  # accessing the review list column\n",
    "    review=review.lower()\n",
    "    review=re.sub('[^a-zA-Z]', ' ',review)\n",
    "    review=re.sub('rated', ' ',review)\n",
    "    review=re.sub('x',' ',review)\n",
    "    review=re.sub(' +',' ',review)\n",
    "    total_review=total_review + str(review)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "11ae5130",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:18:36.142776Z",
     "iopub.status.busy": "2022-04-18T16:18:36.142081Z",
     "iopub.status.idle": "2022-04-18T16:19:10.828598Z",
     "shell.execute_reply": "2022-04-18T16:19:10.827922Z"
    },
    "papermill": {
     "duration": 34.869081,
     "end_time": "2022-04-18T16:19:10.833284",
     "exception": false,
     "start_time": "2022-04-18T16:18:35.964203",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 799.5, 799.5, -0.5)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# display the wordcloud of reviews\n",
    "wordcloud = WordCloud(width = 800, height = 800, \n",
    "                background_color ='white', \n",
    "                stopwords = stopwords, \n",
    "                min_font_size = 10).generate(total_review) \n",
    "# plot the WordCloud image                        \n",
    "plt.figure(figsize = (12, 8)) \n",
    "plt.imshow(wordcloud) \n",
    "plt.axis(\"off\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "855bee03",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:19:11.191949Z",
     "iopub.status.busy": "2022-04-18T16:19:11.191454Z",
     "iopub.status.idle": "2022-04-18T16:19:11.194501Z",
     "shell.execute_reply": "2022-04-18T16:19:11.193949Z"
    },
    "papermill": {
     "duration": 0.185839,
     "end_time": "2022-04-18T16:19:11.196406",
     "exception": false,
     "start_time": "2022-04-18T16:19:11.010567",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "## PREPARING THE DATA FOR MODELLING PURPOSE\n",
    "#1 preparing the data for ML modelling\n",
    "#2 create target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "b0a3e295",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:19:11.551999Z",
     "iopub.status.busy": "2022-04-18T16:19:11.551498Z",
     "iopub.status.idle": "2022-04-18T16:19:11.554669Z",
     "shell.execute_reply": "2022-04-18T16:19:11.554094Z"
    },
    "papermill": {
     "duration": 0.182422,
     "end_time": "2022-04-18T16:19:11.556686",
     "exception": false,
     "start_time": "2022-04-18T16:19:11.374264",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Imbalance data --outliers cause imbalance data\n",
    "# outliers -- large deviations in the values of training data which causes less accurate models and poor results\n",
    "# --if a data is trained w.r.t any ML algo and consist of outliers; \n",
    "# --then if new data is to be predicted on the basis of trained data, it will give poor and less accurate results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "6ece2ca9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:19:11.912071Z",
     "iopub.status.busy": "2022-04-18T16:19:11.911618Z",
     "iopub.status.idle": "2022-04-18T16:19:11.933153Z",
     "shell.execute_reply": "2022-04-18T16:19:11.932310Z"
    },
    "papermill": {
     "duration": 0.202394,
     "end_time": "2022-04-18T16:19:11.935236",
     "exception": false,
     "start_time": "2022-04-18T16:19:11.732842",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>url</th>\n",
       "      <th>address</th>\n",
       "      <th>name</th>\n",
       "      <th>online_order</th>\n",
       "      <th>book_table</th>\n",
       "      <th>rate</th>\n",
       "      <th>votes</th>\n",
       "      <th>phone</th>\n",
       "      <th>location</th>\n",
       "      <th>rest_type</th>\n",
       "      <th>dish_liked</th>\n",
       "      <th>cuisines</th>\n",
       "      <th>approx_cost(for two people)</th>\n",
       "      <th>reviews_list</th>\n",
       "      <th>menu_item</th>\n",
       "      <th>listed_in(type)</th>\n",
       "      <th>listed_in(city)</th>\n",
       "      <th>Top_types</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>https://www.zomato.com/bangalore/jalsa-banasha...</td>\n",
       "      <td>942, 21st Main Road, 2nd Stage, Banashankari, ...</td>\n",
       "      <td>Jalsa</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4.1</td>\n",
       "      <td>775</td>\n",
       "      <td>080 42297555\\r\\n+91 9743772233</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Pasta, Lunch Buffet, Masala Papad, Paneer Laja...</td>\n",
       "      <td>North Indian, Mughlai, Chinese</td>\n",
       "      <td>800.0</td>\n",
       "      <td>[('Rated 4.0', 'RATED\\n  A beautiful place to ...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Quick Bites + Casual Dining</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>https://www.zomato.com/bangalore/spice-elephan...</td>\n",
       "      <td>2nd Floor, 80 Feet Road, Near Big Bazaar, 6th ...</td>\n",
       "      <td>Spice Elephant</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>4.1</td>\n",
       "      <td>787</td>\n",
       "      <td>080 41714161</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Momos, Lunch Buffet, Chocolate Nirvana, Thai G...</td>\n",
       "      <td>Chinese, North Indian, Thai</td>\n",
       "      <td>800.0</td>\n",
       "      <td>[('Rated 4.0', 'RATED\\n  Had been here for din...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Quick Bites + Casual Dining</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>https://www.zomato.com/SanchurroBangalore?cont...</td>\n",
       "      <td>1112, Next to KIMS Medical College, 17th Cross...</td>\n",
       "      <td>San Churro Cafe</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>3.8</td>\n",
       "      <td>918</td>\n",
       "      <td>+91 9663487993</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Cafe, Casual Dining</td>\n",
       "      <td>Churros, Cannelloni, Minestrone Soup, Hot Choc...</td>\n",
       "      <td>Cafe, Mexican, Italian</td>\n",
       "      <td>800.0</td>\n",
       "      <td>[('Rated 3.0', \"RATED\\n  Ambience is not that ...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>others</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>https://www.zomato.com/bangalore/addhuri-udupi...</td>\n",
       "      <td>1st Floor, Annakuteera, 3rd Stage, Banashankar...</td>\n",
       "      <td>Addhuri Udupi Bhojana</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.7</td>\n",
       "      <td>88</td>\n",
       "      <td>+91 9620009302</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Quick Bites</td>\n",
       "      <td>Masala Dosa</td>\n",
       "      <td>South Indian, North Indian</td>\n",
       "      <td>300.0</td>\n",
       "      <td>[('Rated 4.0', \"RATED\\n  Great food and proper...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Quick Bites + Casual Dining</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>https://www.zomato.com/bangalore/grand-village...</td>\n",
       "      <td>10, 3rd Floor, Lakshmi Associates, Gandhi Baza...</td>\n",
       "      <td>Grand Village</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.8</td>\n",
       "      <td>166</td>\n",
       "      <td>+91 8026612447\\r\\n+91 9901210005</td>\n",
       "      <td>Basavanagudi</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Panipuri, Gol Gappe</td>\n",
       "      <td>North Indian, Rajasthani</td>\n",
       "      <td>600.0</td>\n",
       "      <td>[('Rated 4.0', 'RATED\\n  Very good restaurant ...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Quick Bites + Casual Dining</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 url  \\\n",
       "0  https://www.zomato.com/bangalore/jalsa-banasha...   \n",
       "1  https://www.zomato.com/bangalore/spice-elephan...   \n",
       "2  https://www.zomato.com/SanchurroBangalore?cont...   \n",
       "3  https://www.zomato.com/bangalore/addhuri-udupi...   \n",
       "4  https://www.zomato.com/bangalore/grand-village...   \n",
       "\n",
       "                                             address                   name  \\\n",
       "0  942, 21st Main Road, 2nd Stage, Banashankari, ...                  Jalsa   \n",
       "1  2nd Floor, 80 Feet Road, Near Big Bazaar, 6th ...         Spice Elephant   \n",
       "2  1112, Next to KIMS Medical College, 17th Cross...        San Churro Cafe   \n",
       "3  1st Floor, Annakuteera, 3rd Stage, Banashankar...  Addhuri Udupi Bhojana   \n",
       "4  10, 3rd Floor, Lakshmi Associates, Gandhi Baza...          Grand Village   \n",
       "\n",
       "  online_order book_table  rate  votes                             phone  \\\n",
       "0          Yes        Yes   4.1    775    080 42297555\\r\\n+91 9743772233   \n",
       "1          Yes         No   4.1    787                      080 41714161   \n",
       "2          Yes         No   3.8    918                    +91 9663487993   \n",
       "3           No         No   3.7     88                    +91 9620009302   \n",
       "4           No         No   3.8    166  +91 8026612447\\r\\n+91 9901210005   \n",
       "\n",
       "       location            rest_type  \\\n",
       "0  Banashankari        Casual Dining   \n",
       "1  Banashankari        Casual Dining   \n",
       "2  Banashankari  Cafe, Casual Dining   \n",
       "3  Banashankari          Quick Bites   \n",
       "4  Basavanagudi        Casual Dining   \n",
       "\n",
       "                                          dish_liked  \\\n",
       "0  Pasta, Lunch Buffet, Masala Papad, Paneer Laja...   \n",
       "1  Momos, Lunch Buffet, Chocolate Nirvana, Thai G...   \n",
       "2  Churros, Cannelloni, Minestrone Soup, Hot Choc...   \n",
       "3                                        Masala Dosa   \n",
       "4                                Panipuri, Gol Gappe   \n",
       "\n",
       "                         cuisines  approx_cost(for two people)  \\\n",
       "0  North Indian, Mughlai, Chinese                        800.0   \n",
       "1     Chinese, North Indian, Thai                        800.0   \n",
       "2          Cafe, Mexican, Italian                        800.0   \n",
       "3      South Indian, North Indian                        300.0   \n",
       "4        North Indian, Rajasthani                        600.0   \n",
       "\n",
       "                                        reviews_list menu_item  \\\n",
       "0  [('Rated 4.0', 'RATED\\n  A beautiful place to ...        []   \n",
       "1  [('Rated 4.0', 'RATED\\n  Had been here for din...        []   \n",
       "2  [('Rated 3.0', \"RATED\\n  Ambience is not that ...        []   \n",
       "3  [('Rated 4.0', \"RATED\\n  Great food and proper...        []   \n",
       "4  [('Rated 4.0', 'RATED\\n  Very good restaurant ...        []   \n",
       "\n",
       "  listed_in(type) listed_in(city)                    Top_types  \n",
       "0          Buffet    Banashankari  Quick Bites + Casual Dining  \n",
       "1          Buffet    Banashankari  Quick Bites + Casual Dining  \n",
       "2          Buffet    Banashankari                       others  \n",
       "3          Buffet    Banashankari  Quick Bites + Casual Dining  \n",
       "4          Buffet    Banashankari  Quick Bites + Casual Dining  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "1670cfed",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:19:12.304214Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# splitting the data in two parts;\n",
    "# 1st for the new restaurants-- rating--0\n",
    "# 2nd for training purpose-- rating>0 for ML model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "d9b8848b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:19:12.669856Z",
     "iopub.status.busy": "2022-04-18T16:19:12.669227Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# created a function which distinguish between new restaurants and the restaurants that are already rated(for ML --training)\n",
    "def assign(x):\n",
    "    if x>0:\n",
    "        return 1  # for training\n",
    "    else:\n",
    "        return 0  # for new restaurants\n",
    "df['rated']=df['rate'].apply(assign)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "ca8da1a6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:19:13.076240Z",
     "iopub.status.busy": "2022-04-18T16:19:13.075963Z",
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     "shell.execute_reply": "2022-04-18T16:19:13.096603Z"
    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# splitting the data frames\n",
    "new_restaurants = df[df['rated'] == 0]\n",
    "train_val_restaurants = df.query('rated == 1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "01c94ae0",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "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>url</th>\n",
       "      <th>address</th>\n",
       "      <th>name</th>\n",
       "      <th>online_order</th>\n",
       "      <th>book_table</th>\n",
       "      <th>rate</th>\n",
       "      <th>votes</th>\n",
       "      <th>phone</th>\n",
       "      <th>location</th>\n",
       "      <th>rest_type</th>\n",
       "      <th>dish_liked</th>\n",
       "      <th>cuisines</th>\n",
       "      <th>approx_cost(for two people)</th>\n",
       "      <th>reviews_list</th>\n",
       "      <th>menu_item</th>\n",
       "      <th>listed_in(type)</th>\n",
       "      <th>listed_in(city)</th>\n",
       "      <th>Top_types</th>\n",
       "      <th>rated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>https://www.zomato.com/bangalore/jalsa-banasha...</td>\n",
       "      <td>942, 21st Main Road, 2nd Stage, Banashankari, ...</td>\n",
       "      <td>Jalsa</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4.1</td>\n",
       "      <td>775</td>\n",
       "      <td>080 42297555\\r\\n+91 9743772233</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Pasta, Lunch Buffet, Masala Papad, Paneer Laja...</td>\n",
       "      <td>North Indian, Mughlai, Chinese</td>\n",
       "      <td>800.0</td>\n",
       "      <td>[('Rated 4.0', 'RATED\\n  A beautiful place to ...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Quick Bites + Casual Dining</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>https://www.zomato.com/bangalore/spice-elephan...</td>\n",
       "      <td>2nd Floor, 80 Feet Road, Near Big Bazaar, 6th ...</td>\n",
       "      <td>Spice Elephant</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>4.1</td>\n",
       "      <td>787</td>\n",
       "      <td>080 41714161</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Momos, Lunch Buffet, Chocolate Nirvana, Thai G...</td>\n",
       "      <td>Chinese, North Indian, Thai</td>\n",
       "      <td>800.0</td>\n",
       "      <td>[('Rated 4.0', 'RATED\\n  Had been here for din...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Quick Bites + Casual Dining</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>https://www.zomato.com/SanchurroBangalore?cont...</td>\n",
       "      <td>1112, Next to KIMS Medical College, 17th Cross...</td>\n",
       "      <td>San Churro Cafe</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>3.8</td>\n",
       "      <td>918</td>\n",
       "      <td>+91 9663487993</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Cafe, Casual Dining</td>\n",
       "      <td>Churros, Cannelloni, Minestrone Soup, Hot Choc...</td>\n",
       "      <td>Cafe, Mexican, Italian</td>\n",
       "      <td>800.0</td>\n",
       "      <td>[('Rated 3.0', \"RATED\\n  Ambience is not that ...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>others</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>https://www.zomato.com/bangalore/addhuri-udupi...</td>\n",
       "      <td>1st Floor, Annakuteera, 3rd Stage, Banashankar...</td>\n",
       "      <td>Addhuri Udupi Bhojana</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.7</td>\n",
       "      <td>88</td>\n",
       "      <td>+91 9620009302</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Quick Bites</td>\n",
       "      <td>Masala Dosa</td>\n",
       "      <td>South Indian, North Indian</td>\n",
       "      <td>300.0</td>\n",
       "      <td>[('Rated 4.0', \"RATED\\n  Great food and proper...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Quick Bites + Casual Dining</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>https://www.zomato.com/bangalore/grand-village...</td>\n",
       "      <td>10, 3rd Floor, Lakshmi Associates, Gandhi Baza...</td>\n",
       "      <td>Grand Village</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.8</td>\n",
       "      <td>166</td>\n",
       "      <td>+91 8026612447\\r\\n+91 9901210005</td>\n",
       "      <td>Basavanagudi</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Panipuri, Gol Gappe</td>\n",
       "      <td>North Indian, Rajasthani</td>\n",
       "      <td>600.0</td>\n",
       "      <td>[('Rated 4.0', 'RATED\\n  Very good restaurant ...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Quick Bites + Casual Dining</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 url  \\\n",
       "0  https://www.zomato.com/bangalore/jalsa-banasha...   \n",
       "1  https://www.zomato.com/bangalore/spice-elephan...   \n",
       "2  https://www.zomato.com/SanchurroBangalore?cont...   \n",
       "3  https://www.zomato.com/bangalore/addhuri-udupi...   \n",
       "4  https://www.zomato.com/bangalore/grand-village...   \n",
       "\n",
       "                                             address                   name  \\\n",
       "0  942, 21st Main Road, 2nd Stage, Banashankari, ...                  Jalsa   \n",
       "1  2nd Floor, 80 Feet Road, Near Big Bazaar, 6th ...         Spice Elephant   \n",
       "2  1112, Next to KIMS Medical College, 17th Cross...        San Churro Cafe   \n",
       "3  1st Floor, Annakuteera, 3rd Stage, Banashankar...  Addhuri Udupi Bhojana   \n",
       "4  10, 3rd Floor, Lakshmi Associates, Gandhi Baza...          Grand Village   \n",
       "\n",
       "  online_order book_table  rate  votes                             phone  \\\n",
       "0          Yes        Yes   4.1    775    080 42297555\\r\\n+91 9743772233   \n",
       "1          Yes         No   4.1    787                      080 41714161   \n",
       "2          Yes         No   3.8    918                    +91 9663487993   \n",
       "3           No         No   3.7     88                    +91 9620009302   \n",
       "4           No         No   3.8    166  +91 8026612447\\r\\n+91 9901210005   \n",
       "\n",
       "       location            rest_type  \\\n",
       "0  Banashankari        Casual Dining   \n",
       "1  Banashankari        Casual Dining   \n",
       "2  Banashankari  Cafe, Casual Dining   \n",
       "3  Banashankari          Quick Bites   \n",
       "4  Basavanagudi        Casual Dining   \n",
       "\n",
       "                                          dish_liked  \\\n",
       "0  Pasta, Lunch Buffet, Masala Papad, Paneer Laja...   \n",
       "1  Momos, Lunch Buffet, Chocolate Nirvana, Thai G...   \n",
       "2  Churros, Cannelloni, Minestrone Soup, Hot Choc...   \n",
       "3                                        Masala Dosa   \n",
       "4                                Panipuri, Gol Gappe   \n",
       "\n",
       "                         cuisines  approx_cost(for two people)  \\\n",
       "0  North Indian, Mughlai, Chinese                        800.0   \n",
       "1     Chinese, North Indian, Thai                        800.0   \n",
       "2          Cafe, Mexican, Italian                        800.0   \n",
       "3      South Indian, North Indian                        300.0   \n",
       "4        North Indian, Rajasthani                        600.0   \n",
       "\n",
       "                                        reviews_list menu_item  \\\n",
       "0  [('Rated 4.0', 'RATED\\n  A beautiful place to ...        []   \n",
       "1  [('Rated 4.0', 'RATED\\n  Had been here for din...        []   \n",
       "2  [('Rated 3.0', \"RATED\\n  Ambience is not that ...        []   \n",
       "3  [('Rated 4.0', \"RATED\\n  Great food and proper...        []   \n",
       "4  [('Rated 4.0', 'RATED\\n  Very good restaurant ...        []   \n",
       "\n",
       "  listed_in(type) listed_in(city)                    Top_types  rated  \n",
       "0          Buffet    Banashankari  Quick Bites + Casual Dining      1  \n",
       "1          Buffet    Banashankari  Quick Bites + Casual Dining      1  \n",
       "2          Buffet    Banashankari                       others      1  \n",
       "3          Buffet    Banashankari  Quick Bites + Casual Dining      1  \n",
       "4          Buffet    Banashankari  Quick Bites + Casual Dining      1  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_val_restaurants.head()  # this is the entire data frame required for the training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "1622d1e2",
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   },
   "outputs": [],
   "source": [
    "#2 Create a target variable --on the basis of which prediction is done..."
   ]
  },
  {
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   "execution_count": 97,
   "id": "dd9c5295",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.1, 3.8, 3.7, 3.6, 4.6, 4. , 4.2, 3.9, 3.1, 3. , 3.2, 3.3, 2.8,\n",
       "       4.4, 4.3, 2.9, 3.5, 2.6, 3.4, 4.5, 2.5, 2.7, 4.7, 2.4, 2.2, 2.3,\n",
       "       4.8, 4.9, 2.1, 2. , 1.8])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_val_restaurants['rate'].unique()  # on the basis of which target varible is created"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "d3cfebbf",
   "metadata": {
    "execution": {
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   },
   "outputs": [],
   "source": [
    "# By now we've already splitted our original data into new_restaurants and train_val_restaurants using pandas DataFrames.\n",
    "# The main point here is to define a fair threshold(rating) for splitting the restaurants into good and bad ones.\n",
    "# Taking 3.75 as the threshold value... Good restaurants>=3.75 rating ; Bad restaurants<3.75 rating"
   ]
  },
  {
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   "id": "65b2b239",
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   "source": [
    "import warnings\n",
    "from warnings import filterwarnings\n",
    "filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "1d7358ad",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:19:15.313438Z",
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   "outputs": [],
   "source": [
    "# Defining a custom threshold for splitting restaurants into good and bad ; storing in new column 'target'\n",
    "# Good restaurants-1\n",
    "# Bad restaurants-0\n",
    "threshold = 3.75\n",
    "train_val_restaurants['target'] = train_val_restaurants['rate'].apply(lambda x: 1 if x >= threshold else 0)"
   ]
  },
  {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>url</th>\n",
       "      <th>address</th>\n",
       "      <th>name</th>\n",
       "      <th>online_order</th>\n",
       "      <th>book_table</th>\n",
       "      <th>rate</th>\n",
       "      <th>votes</th>\n",
       "      <th>phone</th>\n",
       "      <th>location</th>\n",
       "      <th>rest_type</th>\n",
       "      <th>dish_liked</th>\n",
       "      <th>cuisines</th>\n",
       "      <th>approx_cost(for two people)</th>\n",
       "      <th>reviews_list</th>\n",
       "      <th>menu_item</th>\n",
       "      <th>listed_in(type)</th>\n",
       "      <th>listed_in(city)</th>\n",
       "      <th>Top_types</th>\n",
       "      <th>rated</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>https://www.zomato.com/bangalore/jalsa-banasha...</td>\n",
       "      <td>942, 21st Main Road, 2nd Stage, Banashankari, ...</td>\n",
       "      <td>Jalsa</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4.1</td>\n",
       "      <td>775</td>\n",
       "      <td>080 42297555\\r\\n+91 9743772233</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Pasta, Lunch Buffet, Masala Papad, Paneer Laja...</td>\n",
       "      <td>North Indian, Mughlai, Chinese</td>\n",
       "      <td>800.0</td>\n",
       "      <td>[('Rated 4.0', 'RATED\\n  A beautiful place to ...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Quick Bites + Casual Dining</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>https://www.zomato.com/bangalore/spice-elephan...</td>\n",
       "      <td>2nd Floor, 80 Feet Road, Near Big Bazaar, 6th ...</td>\n",
       "      <td>Spice Elephant</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>4.1</td>\n",
       "      <td>787</td>\n",
       "      <td>080 41714161</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Momos, Lunch Buffet, Chocolate Nirvana, Thai G...</td>\n",
       "      <td>Chinese, North Indian, Thai</td>\n",
       "      <td>800.0</td>\n",
       "      <td>[('Rated 4.0', 'RATED\\n  Had been here for din...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Quick Bites + Casual Dining</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>https://www.zomato.com/SanchurroBangalore?cont...</td>\n",
       "      <td>1112, Next to KIMS Medical College, 17th Cross...</td>\n",
       "      <td>San Churro Cafe</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>3.8</td>\n",
       "      <td>918</td>\n",
       "      <td>+91 9663487993</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Cafe, Casual Dining</td>\n",
       "      <td>Churros, Cannelloni, Minestrone Soup, Hot Choc...</td>\n",
       "      <td>Cafe, Mexican, Italian</td>\n",
       "      <td>800.0</td>\n",
       "      <td>[('Rated 3.0', \"RATED\\n  Ambience is not that ...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>others</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>https://www.zomato.com/bangalore/addhuri-udupi...</td>\n",
       "      <td>1st Floor, Annakuteera, 3rd Stage, Banashankar...</td>\n",
       "      <td>Addhuri Udupi Bhojana</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.7</td>\n",
       "      <td>88</td>\n",
       "      <td>+91 9620009302</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Quick Bites</td>\n",
       "      <td>Masala Dosa</td>\n",
       "      <td>South Indian, North Indian</td>\n",
       "      <td>300.0</td>\n",
       "      <td>[('Rated 4.0', \"RATED\\n  Great food and proper...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Quick Bites + Casual Dining</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>https://www.zomato.com/bangalore/grand-village...</td>\n",
       "      <td>10, 3rd Floor, Lakshmi Associates, Gandhi Baza...</td>\n",
       "      <td>Grand Village</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.8</td>\n",
       "      <td>166</td>\n",
       "      <td>+91 8026612447\\r\\n+91 9901210005</td>\n",
       "      <td>Basavanagudi</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Panipuri, Gol Gappe</td>\n",
       "      <td>North Indian, Rajasthani</td>\n",
       "      <td>600.0</td>\n",
       "      <td>[('Rated 4.0', 'RATED\\n  Very good restaurant ...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Quick Bites + Casual Dining</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 url  \\\n",
       "0  https://www.zomato.com/bangalore/jalsa-banasha...   \n",
       "1  https://www.zomato.com/bangalore/spice-elephan...   \n",
       "2  https://www.zomato.com/SanchurroBangalore?cont...   \n",
       "3  https://www.zomato.com/bangalore/addhuri-udupi...   \n",
       "4  https://www.zomato.com/bangalore/grand-village...   \n",
       "\n",
       "                                             address                   name  \\\n",
       "0  942, 21st Main Road, 2nd Stage, Banashankari, ...                  Jalsa   \n",
       "1  2nd Floor, 80 Feet Road, Near Big Bazaar, 6th ...         Spice Elephant   \n",
       "2  1112, Next to KIMS Medical College, 17th Cross...        San Churro Cafe   \n",
       "3  1st Floor, Annakuteera, 3rd Stage, Banashankar...  Addhuri Udupi Bhojana   \n",
       "4  10, 3rd Floor, Lakshmi Associates, Gandhi Baza...          Grand Village   \n",
       "\n",
       "  online_order book_table  rate  votes                             phone  \\\n",
       "0          Yes        Yes   4.1    775    080 42297555\\r\\n+91 9743772233   \n",
       "1          Yes         No   4.1    787                      080 41714161   \n",
       "2          Yes         No   3.8    918                    +91 9663487993   \n",
       "3           No         No   3.7     88                    +91 9620009302   \n",
       "4           No         No   3.8    166  +91 8026612447\\r\\n+91 9901210005   \n",
       "\n",
       "       location            rest_type  \\\n",
       "0  Banashankari        Casual Dining   \n",
       "1  Banashankari        Casual Dining   \n",
       "2  Banashankari  Cafe, Casual Dining   \n",
       "3  Banashankari          Quick Bites   \n",
       "4  Basavanagudi        Casual Dining   \n",
       "\n",
       "                                          dish_liked  \\\n",
       "0  Pasta, Lunch Buffet, Masala Papad, Paneer Laja...   \n",
       "1  Momos, Lunch Buffet, Chocolate Nirvana, Thai G...   \n",
       "2  Churros, Cannelloni, Minestrone Soup, Hot Choc...   \n",
       "3                                        Masala Dosa   \n",
       "4                                Panipuri, Gol Gappe   \n",
       "\n",
       "                         cuisines  approx_cost(for two people)  \\\n",
       "0  North Indian, Mughlai, Chinese                        800.0   \n",
       "1     Chinese, North Indian, Thai                        800.0   \n",
       "2          Cafe, Mexican, Italian                        800.0   \n",
       "3      South Indian, North Indian                        300.0   \n",
       "4        North Indian, Rajasthani                        600.0   \n",
       "\n",
       "                                        reviews_list menu_item  \\\n",
       "0  [('Rated 4.0', 'RATED\\n  A beautiful place to ...        []   \n",
       "1  [('Rated 4.0', 'RATED\\n  Had been here for din...        []   \n",
       "2  [('Rated 3.0', \"RATED\\n  Ambience is not that ...        []   \n",
       "3  [('Rated 4.0', \"RATED\\n  Great food and proper...        []   \n",
       "4  [('Rated 4.0', 'RATED\\n  Very good restaurant ...        []   \n",
       "\n",
       "  listed_in(type) listed_in(city)                    Top_types  rated  target  \n",
       "0          Buffet    Banashankari  Quick Bites + Casual Dining      1       1  \n",
       "1          Buffet    Banashankari  Quick Bites + Casual Dining      1       1  \n",
       "2          Buffet    Banashankari                       others      1       1  \n",
       "3          Buffet    Banashankari  Quick Bites + Casual Dining      1       0  \n",
       "4          Buffet    Banashankari  Quick Bites + Casual Dining      1       1  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "train_val_restaurants.head()"
   ]
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.graph_objs as go\n",
    "from plotly.offline import iplot\n",
    "x=train_val_restaurants['target'].value_counts()  # to check if there is imbalance data or not (outliers)\n",
    "labels=['Good Restaurants(1)','Bad Restaurants(0)']\n",
    "print(x)\n",
    "trace=go.Pie(labels=labels,values=x,hoverinfo='label+percent',textinfo='percent',textfont=dict(size=25),\n",
    "              pull=[0, 0, 0,0.2, 0])\n",
    "iplot([trace])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "6a5691e3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:19:16.516668Z",
     "iopub.status.busy": "2022-04-18T16:19:16.516016Z",
     "iopub.status.idle": "2022-04-18T16:19:16.519669Z",
     "shell.execute_reply": "2022-04-18T16:19:16.519079Z"
    },
    "papermill": {
     "duration": 0.189201,
     "end_time": "2022-04-18T16:19:16.521626",
     "exception": false,
     "start_time": "2022-04-18T16:19:16.332425",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# from the above pie chart, it is clear that approx both the values are equal; so there is no imbalance data... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "0ce1fc6c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:19:16.892413Z",
     "iopub.status.busy": "2022-04-18T16:19:16.892089Z",
     "iopub.status.idle": "2022-04-18T16:19:16.895978Z",
     "shell.execute_reply": "2022-04-18T16:19:16.895104Z"
    },
    "papermill": {
     "duration": 0.190924,
     "end_time": "2022-04-18T16:19:16.898235",
     "exception": false,
     "start_time": "2022-04-18T16:19:16.707311",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "## PERFORM FEATURE IMPORTANCE/EXTRACTION ON DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "46b1ff2b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:19:17.266078Z",
     "iopub.status.busy": "2022-04-18T16:19:17.265760Z",
     "iopub.status.idle": "2022-04-18T16:19:17.269798Z",
     "shell.execute_reply": "2022-04-18T16:19:17.268831Z"
    },
    "papermill": {
     "duration": 0.188266,
     "end_time": "2022-04-18T16:19:17.271901",
     "exception": false,
     "start_time": "2022-04-18T16:19:17.083635",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Define the features to be used on training. (important features)\n",
    "# Here we will take a look at the raw data to select valuable features and apply some steps to create another ones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "3d340d50",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:19:17.637061Z",
     "iopub.status.busy": "2022-04-18T16:19:17.636400Z",
     "iopub.status.idle": "2022-04-18T16:19:17.640481Z",
     "shell.execute_reply": "2022-04-18T16:19:17.639827Z"
    },
    "papermill": {
     "duration": 0.188006,
     "end_time": "2022-04-18T16:19:17.642421",
     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# the main idea is to get the length of the list of cuisine and rest_type feature so that we can create a new column.\n",
    "def count(x):\n",
    "    return len(x.split(','))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "b8f8c5ee",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:19:18.014507Z",
     "iopub.status.busy": "2022-04-18T16:19:18.014193Z",
     "iopub.status.idle": "2022-04-18T16:19:18.089824Z",
     "shell.execute_reply": "2022-04-18T16:19:18.088804Z"
    },
    "papermill": {
     "duration": 0.263278,
     "end_time": "2022-04-18T16:19:18.092357",
     "exception": false,
     "start_time": "2022-04-18T16:19:17.829079",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# as it have some NAN values, that's why very firstly; have to convert into str  &  then apply count function\n",
    "train_val_restaurants['total_cuisines']=train_val_restaurants['cuisines'].astype(str).apply(count)\n",
    "train_val_restaurants['multiple_types']=train_val_restaurants['rest_type'].astype(str).apply(count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "66a5b971",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:19:18.462217Z",
     "iopub.status.busy": "2022-04-18T16:19:18.461897Z",
     "iopub.status.idle": "2022-04-18T16:19:18.468889Z",
     "shell.execute_reply": "2022-04-18T16:19:18.467910Z"
    },
    "papermill": {
     "duration": 0.196808,
     "end_time": "2022-04-18T16:19:18.471192",
     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['url', 'address', 'name', 'online_order', 'book_table', 'rate', 'votes',\n",
       "       'phone', 'location', 'rest_type', 'dish_liked', 'cuisines',\n",
       "       'approx_cost(for two people)', 'reviews_list', 'menu_item',\n",
       "       'listed_in(type)', 'listed_in(city)', 'Top_types', 'rated', 'target',\n",
       "       'total_cuisines', 'multiple_types'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# from these features, will choose the important features which will play important role in the prediction\n",
    "train_val_restaurants.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "335d1664",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:19:18.858549Z",
     "iopub.status.busy": "2022-04-18T16:19:18.858178Z",
     "iopub.status.idle": "2022-04-18T16:19:18.863507Z",
     "shell.execute_reply": "2022-04-18T16:19:18.862720Z"
    },
    "papermill": {
     "duration": 0.210121,
     "end_time": "2022-04-18T16:19:18.865462",
     "exception": false,
     "start_time": "2022-04-18T16:19:18.655341",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# important features...\n",
    "imp_features=['online_order','book_table','location','rest_type',\n",
    "              'multiple_types','total_cuisines','listed_in(type)', \n",
    "              'listed_in(city)','approx_cost(for two people)','target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "f1195fed",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:19:19.244607Z",
     "iopub.status.busy": "2022-04-18T16:19:19.244270Z",
     "iopub.status.idle": "2022-04-18T16:19:19.270166Z",
     "shell.execute_reply": "2022-04-18T16:19:19.269290Z"
    },
    "papermill": {
     "duration": 0.220633,
     "end_time": "2022-04-18T16:19:19.272378",
     "exception": false,
     "start_time": "2022-04-18T16:19:19.051745",
     "status": "completed"
    },
    "tags": []
   },
   "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>online_order</th>\n",
       "      <th>book_table</th>\n",
       "      <th>location</th>\n",
       "      <th>rest_type</th>\n",
       "      <th>multiple_types</th>\n",
       "      <th>total_cuisines</th>\n",
       "      <th>listed_in(type)</th>\n",
       "      <th>listed_in(city)</th>\n",
       "      <th>approx_cost(for two people)</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>800.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>800.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Cafe, Casual Dining</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>800.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Quick Bites</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>300.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Basavanagudi</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>600.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  online_order book_table      location            rest_type  multiple_types  \\\n",
       "0          Yes        Yes  Banashankari        Casual Dining               1   \n",
       "1          Yes         No  Banashankari        Casual Dining               1   \n",
       "2          Yes         No  Banashankari  Cafe, Casual Dining               2   \n",
       "3           No         No  Banashankari          Quick Bites               1   \n",
       "4           No         No  Basavanagudi        Casual Dining               1   \n",
       "\n",
       "   total_cuisines listed_in(type) listed_in(city)  \\\n",
       "0               3          Buffet    Banashankari   \n",
       "1               3          Buffet    Banashankari   \n",
       "2               3          Buffet    Banashankari   \n",
       "3               2          Buffet    Banashankari   \n",
       "4               2          Buffet    Banashankari   \n",
       "\n",
       "   approx_cost(for two people)  target  \n",
       "0                        800.0       1  \n",
       "1                        800.0       1  \n",
       "2                        800.0       1  \n",
       "3                        300.0       0  \n",
       "4                        600.0       1  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = train_val_restaurants[imp_features]\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "d386b630",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:19:19.702577Z",
     "iopub.status.busy": "2022-04-18T16:19:19.701474Z",
     "iopub.status.idle": "2022-04-18T16:19:19.705892Z",
     "shell.execute_reply": "2022-04-18T16:19:19.705303Z"
    },
    "papermill": {
     "duration": 0.224665,
     "end_time": "2022-04-18T16:19:19.708110",
     "exception": false,
     "start_time": "2022-04-18T16:19:19.483445",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "## COMING ACCROSS WITH THE MISSING VALUES IN THE DATA...\n",
    "#1 removing all the missing values\n",
    "#2 separate catagorical and numerical values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "b4353f7f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:19:20.152517Z",
     "iopub.status.busy": "2022-04-18T16:19:20.151903Z",
     "iopub.status.idle": "2022-04-18T16:19:20.188535Z",
     "shell.execute_reply": "2022-04-18T16:19:20.187428Z"
    },
    "papermill": {
     "duration": 0.269985,
     "end_time": "2022-04-18T16:19:20.191229",
     "exception": false,
     "start_time": "2022-04-18T16:19:19.921244",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "online_order                     0\n",
       "book_table                       0\n",
       "location                         0\n",
       "rest_type                      149\n",
       "multiple_types                   0\n",
       "total_cuisines                   0\n",
       "listed_in(type)                  0\n",
       "listed_in(city)                  0\n",
       "approx_cost(for two people)    247\n",
       "target                           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#1 removing all the missing values\n",
    "# checking how many missing values are there\n",
    "data.isnull().sum()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "910cfb69",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:19:20.648386Z",
     "iopub.status.busy": "2022-04-18T16:19:20.646859Z",
     "iopub.status.idle": "2022-04-18T16:19:20.713952Z",
     "shell.execute_reply": "2022-04-18T16:19:20.712961Z"
    },
    "papermill": {
     "duration": 0.298238,
     "end_time": "2022-04-18T16:19:20.717000",
     "exception": false,
     "start_time": "2022-04-18T16:19:20.418762",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "data.dropna(how='any', inplace=True)  # --any is used to remove all missing values where ever present in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "d991d39a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:19:21.197445Z",
     "iopub.status.busy": "2022-04-18T16:19:21.196857Z",
     "iopub.status.idle": "2022-04-18T16:19:21.205810Z",
     "shell.execute_reply": "2022-04-18T16:19:21.204916Z"
    },
    "papermill": {
     "duration": 0.237579,
     "end_time": "2022-04-18T16:19:21.208506",
     "exception": false,
     "start_time": "2022-04-18T16:19:20.970927",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "online_order                    object\n",
       "book_table                      object\n",
       "location                        object\n",
       "rest_type                       object\n",
       "multiple_types                   int64\n",
       "total_cuisines                   int64\n",
       "listed_in(type)                 object\n",
       "listed_in(city)                 object\n",
       "approx_cost(for two people)    float64\n",
       "target                           int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#2 separate catagorical and numerical values (-- separate objects from int and float)\n",
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "cbdbe8e7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:19:21.682307Z",
     "iopub.status.busy": "2022-04-18T16:19:21.681927Z",
     "iopub.status.idle": "2022-04-18T16:19:21.688523Z",
     "shell.execute_reply": "2022-04-18T16:19:21.687927Z"
    },
    "papermill": {
     "duration": 0.244441,
     "end_time": "2022-04-18T16:19:21.690597",
     "exception": false,
     "start_time": "2022-04-18T16:19:21.446156",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "cat_features=[col for col in data.columns if data[col].dtype=='O']  # if dtype is object, store it in a cat_features list\n",
    "num_features=[col for col in data.columns if data[col].dtype!='O']  # if dtype is int/float, store it in a num_features list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "60b02d6d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:19:22.138092Z",
     "iopub.status.busy": "2022-04-18T16:19:22.137478Z",
     "iopub.status.idle": "2022-04-18T16:19:22.165327Z",
     "shell.execute_reply": "2022-04-18T16:19:22.164615Z"
    },
    "papermill": {
     "duration": 0.255268,
     "end_time": "2022-04-18T16:19:22.167753",
     "exception": false,
     "start_time": "2022-04-18T16:19:21.912485",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "online_order has total 2 unique features\n",
      "book_table has total 2 unique features\n",
      "location has total 92 unique features\n",
      "rest_type has total 87 unique features\n",
      "listed_in(type) has total 7 unique features\n",
      "listed_in(city) has total 30 unique features\n"
     ]
    }
   ],
   "source": [
    "# for each cat_feature we found the total no. of unique values \n",
    "for feature in cat_features:\n",
    "    print('{} has total {} unique features'.format(feature, data[feature].nunique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "401c9c31",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:19:22.547266Z",
     "iopub.status.busy": "2022-04-18T16:19:22.546949Z",
     "iopub.status.idle": "2022-04-18T16:19:22.550533Z",
     "shell.execute_reply": "2022-04-18T16:19:22.549934Z"
    },
    "papermill": {
     "duration": 0.196784,
     "end_time": "2022-04-18T16:19:22.552646",
     "exception": false,
     "start_time": "2022-04-18T16:19:22.355862",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "## PERFORM FEATURE ENCODING ON THE DATA\n",
    "#1 reducing the unique no. of features\n",
    "#2 feature encoding --converting catagorical values into numerical values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "775c57c4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:19:23.011256Z",
     "iopub.status.busy": "2022-04-18T16:19:23.010677Z",
     "iopub.status.idle": "2022-04-18T16:19:23.013887Z",
     "shell.execute_reply": "2022-04-18T16:19:23.013280Z"
    },
    "papermill": {
     "duration": 0.230926,
     "end_time": "2022-04-18T16:19:23.016171",
     "exception": false,
     "start_time": "2022-04-18T16:19:22.785245",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#1 reducing the unique no. of features\n",
    "# checking the % contibution of each location --if contribution is greater than threshold, keeping them(imp) and rest as others.\n",
    "# location and rest_type has max features; so reducing thre less important ones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "915e7348",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:19:23.473843Z",
     "iopub.status.busy": "2022-04-18T16:19:23.473313Z",
     "iopub.status.idle": "2022-04-18T16:19:23.486767Z",
     "shell.execute_reply": "2022-04-18T16:19:23.486039Z"
    },
    "papermill": {
     "duration": 0.243027,
     "end_time": "2022-04-18T16:19:23.489764",
     "exception": false,
     "start_time": "2022-04-18T16:19:23.246737",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total feature in location are 92\n",
      "Total feature in rest_type are 87\n"
     ]
    }
   ],
   "source": [
    "cols=['location','rest_type']\n",
    "for col in cols:\n",
    "    print('Total feature in {} are {}'.format(col,data[col].nunique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "b7c9d4a4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:19:23.950444Z",
     "iopub.status.busy": "2022-04-18T16:19:23.950155Z",
     "iopub.status.idle": "2022-04-18T16:19:23.967076Z",
     "shell.execute_reply": "2022-04-18T16:19:23.966384Z"
    },
    "papermill": {
     "duration": 0.252297,
     "end_time": "2022-04-18T16:19:23.969440",
     "exception": false,
     "start_time": "2022-04-18T16:19:23.717143",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BTM                      9.398851\n",
       "Koramangala 5th Block    5.565651\n",
       "HSR                      4.829057\n",
       "Indiranagar              4.361416\n",
       "JP Nagar                 4.143345\n",
       "Jayanagar                3.959197\n",
       "Whitefield               3.808970\n",
       "Marathahalli             3.416443\n",
       "Bannerghatta Road        2.970609\n",
       "Koramangala 7th Block    2.556274\n",
       "Koramangala 6th Block    2.553851\n",
       "Brigade Road             2.549005\n",
       "Bellandur                2.415740\n",
       "Sarjapur Road            2.069250\n",
       "Koramangala 1st Block    2.064404\n",
       "Ulsoor                   2.057135\n",
       "Koramangala 4th Block    2.037750\n",
       "Electronic City          2.020789\n",
       "MG Road                  1.921446\n",
       "Banashankari             1.802719\n",
       "Kalyan Nagar             1.681568\n",
       "Malleshwaram             1.553149\n",
       "Residency Road           1.463497\n",
       "Richmond Road            1.463497\n",
       "Basavanagudi             1.441690\n",
       "Frazer Town              1.371423\n",
       "Church Street            1.322963\n",
       "Brookefield              1.315694\n",
       "New BEL Road             1.226042\n",
       "Kammanahalli             1.194543\n",
       "Lavelle Road             1.165467\n",
       "Cunningham Road          1.150929\n",
       "Banaswadi                1.133968\n",
       "Rajajinagar              1.117007\n",
       "Domlur                   0.983742\n",
       "Shanti Nagar             0.891667\n",
       "Old Airport Road         0.855322\n",
       "St. Marks Road           0.831092\n",
       "Shivajinagar             0.671173\n",
       "Commercial Street        0.654212\n",
       "Ejipura                  0.620290\n",
       "Jeevan Bhima Nagar       0.598483\n",
       "Vasanth Nagar            0.593637\n",
       "Koramangala 8th Block    0.513678\n",
       "Koramangala 3rd Block    0.462795\n",
       "Wilson Garden            0.453103\n",
       "Name: location, dtype: float64"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for location, setting threshold=0.4 for selecting important, and less important to others.\n",
    "values=data['location'].value_counts()/len(data)*100\n",
    "values\n",
    "\n",
    "threshold=0.4\n",
    "imp=values[values>threshold]\n",
    "imp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "32388625",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:19:24.429414Z",
     "iopub.status.busy": "2022-04-18T16:19:24.429089Z",
     "iopub.status.idle": "2022-04-18T16:19:24.443489Z",
     "shell.execute_reply": "2022-04-18T16:19:24.442187Z"
    },
    "papermill": {
     "duration": 0.246488,
     "end_time": "2022-04-18T16:19:24.446108",
     "exception": false,
     "start_time": "2022-04-18T16:19:24.199620",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Banashankari\n",
       "1        Banashankari\n",
       "2        Banashankari\n",
       "3        Banashankari\n",
       "4        Basavanagudi\n",
       "             ...     \n",
       "51709      Whitefield\n",
       "51711      Whitefield\n",
       "51712      Whitefield\n",
       "51715           other\n",
       "51716           other\n",
       "Name: location, Length: 41271, dtype: object"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# if value<threshold, assign it to others else keep the important ones\n",
    "data['location']=np.where(data['location'].isin(imp.index),data['location'],'other')\n",
    "data['location']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "f7c251c6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:19:24.899385Z",
     "iopub.status.busy": "2022-04-18T16:19:24.899099Z",
     "iopub.status.idle": "2022-04-18T16:19:24.913508Z",
     "shell.execute_reply": "2022-04-18T16:19:24.912855Z"
    },
    "papermill": {
     "duration": 0.243483,
     "end_time": "2022-04-18T16:19:24.915602",
     "exception": false,
     "start_time": "2022-04-18T16:19:24.672119",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Quick Bites           33.643478\n",
       "Casual Dining         23.299654\n",
       "Cafe                   8.163117\n",
       "Dessert Parlor         4.482566\n",
       "Delivery               4.048848\n",
       "Takeaway, Delivery     3.099028\n",
       "Casual Dining, Bar     2.645926\n",
       "Bakery                 1.705798\n",
       "Beverage Shop          1.555572\n",
       "Bar                    1.550726\n",
       "Name: rest_type, dtype: float64"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for rest_type, setting threshold=1.5 for selecting important, and less important to others.\n",
    "values2=data['rest_type'].value_counts()/len(data)*100\n",
    "values2\n",
    "\n",
    "threshold=1.5\n",
    "imp2=values2[values2>threshold]\n",
    "imp2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "8796bc52",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:19:25.381944Z",
     "iopub.status.busy": "2022-04-18T16:19:25.381264Z",
     "iopub.status.idle": "2022-04-18T16:19:25.392662Z",
     "shell.execute_reply": "2022-04-18T16:19:25.391967Z"
    },
    "papermill": {
     "duration": 0.249959,
     "end_time": "2022-04-18T16:19:25.395179",
     "exception": false,
     "start_time": "2022-04-18T16:19:25.145220",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0             Casual Dining\n",
       "1             Casual Dining\n",
       "2                     other\n",
       "3               Quick Bites\n",
       "4             Casual Dining\n",
       "                ...        \n",
       "51709    Casual Dining, Bar\n",
       "51711    Casual Dining, Bar\n",
       "51712                   Bar\n",
       "51715                   Bar\n",
       "51716                 other\n",
       "Name: rest_type, Length: 41271, dtype: object"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# if value<threshold, assign it to others else keep the important ones\n",
    "data['rest_type']=np.where(data['rest_type'].isin(imp2.index),data['rest_type'],'other')\n",
    "data['rest_type']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "202ddae4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:19:25.862682Z",
     "iopub.status.busy": "2022-04-18T16:19:25.862133Z",
     "iopub.status.idle": "2022-04-18T16:19:25.893163Z",
     "shell.execute_reply": "2022-04-18T16:19:25.891940Z"
    },
    "papermill": {
     "duration": 0.269403,
     "end_time": "2022-04-18T16:19:25.895715",
     "exception": false,
     "start_time": "2022-04-18T16:19:25.626312",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "online_order has total 2 unique features\n",
      "book_table has total 2 unique features\n",
      "location has total 47 unique features\n",
      "rest_type has total 11 unique features\n",
      "listed_in(type) has total 7 unique features\n",
      "listed_in(city) has total 30 unique features\n"
     ]
    }
   ],
   "source": [
    "# after applying feature reduction, we will observe less number of features\n",
    "for feature in cat_features:\n",
    "    print('{} has total {} unique features'.format(feature, data[feature].nunique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "6aea22bb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:19:26.291379Z",
     "iopub.status.busy": "2022-04-18T16:19:26.291016Z",
     "iopub.status.idle": "2022-04-18T16:19:26.294962Z",
     "shell.execute_reply": "2022-04-18T16:19:26.294210Z"
    },
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     "end_time": "2022-04-18T16:19:26.296943",
     "exception": false,
     "start_time": "2022-04-18T16:19:26.092786",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#2 feature encoding --converting catagorical values into numerical values (ML works on numeric data)\n",
    "# one-hot encoding "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "d746eecd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:19:26.701502Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['online_order',\n",
       " 'book_table',\n",
       " 'location',\n",
       " 'rest_type',\n",
       " 'listed_in(type)',\n",
       " 'listed_in(city)']"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "3b495cc5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:19:27.094545Z",
     "iopub.status.busy": "2022-04-18T16:19:27.093914Z",
     "iopub.status.idle": "2022-04-18T16:19:27.253228Z",
     "shell.execute_reply": "2022-04-18T16:19:27.252376Z"
    },
    "papermill": {
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     "end_time": "2022-04-18T16:19:27.255460",
     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "data_cat = data[cat_features]\n",
    "for col in cat_features:\n",
    "    col_encoded = pd.get_dummies(data_cat[col],prefix=col,drop_first=True)\n",
    "    data_cat=pd.concat([data_cat,col_encoded],axis=1)\n",
    "    data_cat.drop(col, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "7e1482fd",
   "metadata": {
    "execution": {
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     "status": "completed"
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   "outputs": [
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       "      <th></th>\n",
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       "      <th>location_Basavanagudi</th>\n",
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      "text/plain": [
       "   online_order_Yes  book_table_Yes  location_Banashankari  \\\n",
       "0                 1               1                      1   \n",
       "1                 1               0                      1   \n",
       "2                 1               0                      1   \n",
       "3                 0               0                      1   \n",
       "4                 0               0                      0   \n",
       "\n",
       "   location_Banaswadi  location_Bannerghatta Road  location_Basavanagudi  \\\n",
       "0                   0                           0                      0   \n",
       "1                   0                           0                      0   \n",
       "2                   0                           0                      0   \n",
       "3                   0                           0                      0   \n",
       "4                   0                           0                      1   \n",
       "\n",
       "   location_Bellandur  location_Brigade Road  location_Brookefield  \\\n",
       "0                   0                      0                     0   \n",
       "1                   0                      0                     0   \n",
       "2                   0                      0                     0   \n",
       "3                   0                      0                     0   \n",
       "4                   0                      0                     0   \n",
       "\n",
       "   location_Church Street  ...  listed_in(city)_Lavelle Road  \\\n",
       "0                       0  ...                             0   \n",
       "1                       0  ...                             0   \n",
       "2                       0  ...                             0   \n",
       "3                       0  ...                             0   \n",
       "4                       0  ...                             0   \n",
       "\n",
       "   listed_in(city)_MG Road  listed_in(city)_Malleshwaram  \\\n",
       "0                        0                             0   \n",
       "1                        0                             0   \n",
       "2                        0                             0   \n",
       "3                        0                             0   \n",
       "4                        0                             0   \n",
       "\n",
       "   listed_in(city)_Marathahalli  listed_in(city)_New BEL Road  \\\n",
       "0                             0                             0   \n",
       "1                             0                             0   \n",
       "2                             0                             0   \n",
       "3                             0                             0   \n",
       "4                             0                             0   \n",
       "\n",
       "   listed_in(city)_Old Airport Road  listed_in(city)_Rajajinagar  \\\n",
       "0                                 0                            0   \n",
       "1                                 0                            0   \n",
       "2                                 0                            0   \n",
       "3                                 0                            0   \n",
       "4                                 0                            0   \n",
       "\n",
       "   listed_in(city)_Residency Road  listed_in(city)_Sarjapur Road  \\\n",
       "0                               0                              0   \n",
       "1                               0                              0   \n",
       "2                               0                              0   \n",
       "3                               0                              0   \n",
       "4                               0                              0   \n",
       "\n",
       "   listed_in(city)_Whitefield  \n",
       "0                           0  \n",
       "1                           0  \n",
       "2                           0  \n",
       "3                           0  \n",
       "4                           0  \n",
       "\n",
       "[5 rows x 93 columns]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_cat.head()  # after applying feature encoding, all the data is converted into numerical values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "e1a46c5f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:19:28.112588Z",
     "iopub.status.busy": "2022-04-18T16:19:28.110612Z",
     "iopub.status.idle": "2022-04-18T16:19:28.117154Z",
     "shell.execute_reply": "2022-04-18T16:19:28.116194Z"
    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "### PREPARE ENTIRE DATA FOR MODELLING\n",
    "#1 concatenate both the datas numerical-catagorical data and numerical data\n",
    "#2 Apply Random Forest on the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "5d9731c2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:19:28.507802Z",
     "iopub.status.busy": "2022-04-18T16:19:28.507151Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "data_final=pd.concat([data.loc[:,['multiple_types','total_cuisines','approx_cost(for two people)','target']],data_cat],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
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    "execution": {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 97 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   multiple_types  total_cuisines  approx_cost(for two people)  target  \\\n",
       "0               1               3                        800.0       1   \n",
       "1               1               3                        800.0       1   \n",
       "2               2               3                        800.0       1   \n",
       "3               1               2                        300.0       0   \n",
       "4               1               2                        600.0       1   \n",
       "\n",
       "   online_order_Yes  book_table_Yes  location_Banashankari  \\\n",
       "0                 1               1                      1   \n",
       "1                 1               0                      1   \n",
       "2                 1               0                      1   \n",
       "3                 0               0                      1   \n",
       "4                 0               0                      0   \n",
       "\n",
       "   location_Banaswadi  location_Bannerghatta Road  location_Basavanagudi  ...  \\\n",
       "0                   0                           0                      0  ...   \n",
       "1                   0                           0                      0  ...   \n",
       "2                   0                           0                      0  ...   \n",
       "3                   0                           0                      0  ...   \n",
       "4                   0                           0                      1  ...   \n",
       "\n",
       "   listed_in(city)_Lavelle Road  listed_in(city)_MG Road  \\\n",
       "0                             0                        0   \n",
       "1                             0                        0   \n",
       "2                             0                        0   \n",
       "3                             0                        0   \n",
       "4                             0                        0   \n",
       "\n",
       "   listed_in(city)_Malleshwaram  listed_in(city)_Marathahalli  \\\n",
       "0                             0                             0   \n",
       "1                             0                             0   \n",
       "2                             0                             0   \n",
       "3                             0                             0   \n",
       "4                             0                             0   \n",
       "\n",
       "   listed_in(city)_New BEL Road  listed_in(city)_Old Airport Road  \\\n",
       "0                             0                                 0   \n",
       "1                             0                                 0   \n",
       "2                             0                                 0   \n",
       "3                             0                                 0   \n",
       "4                             0                                 0   \n",
       "\n",
       "   listed_in(city)_Rajajinagar  listed_in(city)_Residency Road  \\\n",
       "0                            0                               0   \n",
       "1                            0                               0   \n",
       "2                            0                               0   \n",
       "3                            0                               0   \n",
       "4                            0                               0   \n",
       "\n",
       "   listed_in(city)_Sarjapur Road  listed_in(city)_Whitefield  \n",
       "0                              0                           0  \n",
       "1                              0                           0  \n",
       "2                              0                           0  \n",
       "3                              0                           0  \n",
       "4                              0                           0  \n",
       "\n",
       "[5 rows x 97 columns]"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "b8b91d65",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:19:29.719979Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X = data_final.drop('target', axis=1)  # independent data\n",
    "y = data_final['target'].values  # dependent data (prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "d2818268",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:19:30.333289Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(41271, 97)"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_final.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "4c41c346",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:19:30.717776Z",
     "iopub.status.busy": "2022-04-18T16:19:30.717187Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 20% data is used for testing and 80% for training\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.20, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "9f841b00",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:19:31.362460Z",
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     "exception": false,
     "start_time": "2022-04-18T16:19:31.172247",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(33016, 96)"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape  # 80% data for training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "c9e593e7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:19:31.782666Z",
     "iopub.status.busy": "2022-04-18T16:19:31.782255Z",
     "iopub.status.idle": "2022-04-18T16:19:36.255796Z",
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     "end_time": "2022-04-18T16:19:36.258332",
     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#2 Apply RANDOM FOREST (ensemble ML algo) on data\n",
    "# Import the random forest model.\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Initialize the model with some parameters.\n",
    "model = RandomForestClassifier(n_estimators=100, min_samples_leaf=10, random_state=1)\n",
    "\n",
    "# Fit the training data into the model.\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions.\n",
    "predictions = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "0657b2a8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:19:36.642608Z",
     "iopub.status.busy": "2022-04-18T16:19:36.641675Z",
     "iopub.status.idle": "2022-04-18T16:19:36.653571Z",
     "shell.execute_reply": "2022-04-18T16:19:36.652664Z"
    },
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     "duration": 0.20599,
     "end_time": "2022-04-18T16:19:36.655967",
     "exception": false,
     "start_time": "2022-04-18T16:19:36.449977",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3556, 1283],\n",
       "       [ 696, 2720]])"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confusion matrix.\n",
    "from sklearn.metrics import confusion_matrix,accuracy_score\n",
    "confusion_matrix(predictions, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "6e637356",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:19:37.041838Z",
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7602665051483949"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#accuracy_score.\n",
    "accuracy_score(predictions,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "31ad8750",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:19:37.434067Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#fit naive bayes\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "c1da92f7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:19:38.231775Z",
     "iopub.status.busy": "2022-04-18T16:19:38.230829Z",
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    },
    "tags": []
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   "outputs": [],
   "source": [
    "### classifier models\n",
    "models = []\n",
    "models.append(('LogisticRegression', LogisticRegression()))\n",
    "models.append(('Naive Bayes',GaussianNB()))\n",
    "models.append(('RandomForest', RandomForestClassifier()))\n",
    "models.append(('Decision Tree', DecisionTreeClassifier()))\n",
    "models.append(('KNN', KNeighborsClassifier(n_neighbors = 5)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "87b520fc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T16:19:38.669793Z",
     "iopub.status.busy": "2022-04-18T16:19:38.669534Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression\n",
      "[('LogisticRegression', LogisticRegression()), ('Naive Bayes', GaussianNB()), ('RandomForest', RandomForestClassifier()), ('Decision Tree', DecisionTreeClassifier()), ('KNN', KNeighborsClassifier())]\n",
      "Naive Bayes\n",
      "[('LogisticRegression', LogisticRegression()), ('Naive Bayes', GaussianNB()), ('RandomForest', RandomForestClassifier()), ('Decision Tree', DecisionTreeClassifier()), ('KNN', KNeighborsClassifier())]\n",
      "RandomForest\n",
      "[('LogisticRegression', LogisticRegression()), ('Naive Bayes', GaussianNB()), ('RandomForest', RandomForestClassifier()), ('Decision Tree', DecisionTreeClassifier()), ('KNN', KNeighborsClassifier())]\n",
      "Decision Tree\n",
      "[('LogisticRegression', LogisticRegression()), ('Naive Bayes', GaussianNB()), ('RandomForest', RandomForestClassifier()), ('Decision Tree', DecisionTreeClassifier()), ('KNN', KNeighborsClassifier())]\n",
      "KNN\n",
      "[('LogisticRegression', LogisticRegression()), ('Naive Bayes', GaussianNB()), ('RandomForest', RandomForestClassifier()), ('Decision Tree', DecisionTreeClassifier()), ('KNN', KNeighborsClassifier())]\n"
     ]
    }
   ],
   "source": [
    "for name,model in models:\n",
    "    print(name)\n",
    "    print(models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "cc1a53a6",
   "metadata": {
    "execution": {
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    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression\n",
      "[[3448 1507]\n",
      " [ 804 2496]]\n",
      "0.7200484554815263\n",
      "\n",
      "\n",
      "Naive Bayes\n",
      "[[3040 1460]\n",
      " [1212 2543]]\n",
      "0.6763173834039976\n",
      "\n",
      "\n",
      "RandomForest\n",
      "[[3511  948]\n",
      " [ 741 3055]]\n",
      "0.7953967292549969\n",
      "\n",
      "\n",
      "Decision Tree\n",
      "[[3639  817]\n",
      " [ 613 3186]]\n",
      "0.8267716535433071\n",
      "\n",
      "\n",
      "KNN\n",
      "[[3623 1006]\n",
      " [ 629 2997]]\n",
      "0.8019382192610539\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Make predictions on validation dataset\n",
    "\n",
    "for name, model in models:\n",
    "    print(name)\n",
    "    model.fit(X_train, y_train)\n",
    "    \n",
    "    # Make predictions.\n",
    "    predictions = model.predict(X_test)\n",
    "\n",
    "    # Compute the error.\n",
    "    from sklearn.metrics import confusion_matrix\n",
    "    print(confusion_matrix(predictions, y_test))\n",
    "\n",
    "    from sklearn.metrics import accuracy_score\n",
    "    print(accuracy_score(predictions,y_test))\n",
    "    print('\\n')"
   ]
  },
  {
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