Zomato.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c219c5bf",
   "metadata": {},
   "source": [
    "# Zomato Restaurants"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab905a12",
   "metadata": {},
   "source": [
    "The given dataset is about restaurants around the world. \n",
    "\n",
    "It contains information like Location,PriceRange,Cusines,Rating etc\n",
    "\n",
    "Using the information we need to predict:\n",
    "1. Avg Price for tow (Regression)\n",
    "2. Price Range (Classification)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "870f1029",
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LogisticRegression,Ridge,Lasso,RidgeCV,LassoCV\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import LabelEncoder, PowerTransformer\n",
    "from math import sqrt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "import xgboost as xgb\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, roc_curve, roc_auc_score, classification_report,auc\n",
    "from sklearn.metrics import r2_score, mean_squared_error\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ec165ecf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Restaurant ID</th>\n",
       "      <th>Restaurant Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>City</th>\n",
       "      <th>Address</th>\n",
       "      <th>Locality</th>\n",
       "      <th>Locality Verbose</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Cuisines</th>\n",
       "      <th>...</th>\n",
       "      <th>Currency</th>\n",
       "      <th>Has Table booking</th>\n",
       "      <th>Has Online delivery</th>\n",
       "      <th>Is delivering now</th>\n",
       "      <th>Switch to order menu</th>\n",
       "      <th>Price range</th>\n",
       "      <th>Aggregate rating</th>\n",
       "      <th>Rating color</th>\n",
       "      <th>Rating text</th>\n",
       "      <th>Votes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6317637</td>\n",
       "      <td>Le Petit Souffle</td>\n",
       "      <td>162</td>\n",
       "      <td>Makati City</td>\n",
       "      <td>Third Floor, Century City Mall, Kalayaan Avenu...</td>\n",
       "      <td>Century City Mall, Poblacion, Makati City</td>\n",
       "      <td>Century City Mall, Poblacion, Makati City, Mak...</td>\n",
       "      <td>121.027535</td>\n",
       "      <td>14.565443</td>\n",
       "      <td>French, Japanese, Desserts</td>\n",
       "      <td>...</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>4.8</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6304287</td>\n",
       "      <td>Izakaya Kikufuji</td>\n",
       "      <td>162</td>\n",
       "      <td>Makati City</td>\n",
       "      <td>Little Tokyo, 2277 Chino Roces Avenue, Legaspi...</td>\n",
       "      <td>Little Tokyo, Legaspi Village, Makati City</td>\n",
       "      <td>Little Tokyo, Legaspi Village, Makati City, Ma...</td>\n",
       "      <td>121.014101</td>\n",
       "      <td>14.553708</td>\n",
       "      <td>Japanese</td>\n",
       "      <td>...</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>4.5</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6300002</td>\n",
       "      <td>Heat - Edsa Shangri-La</td>\n",
       "      <td>162</td>\n",
       "      <td>Mandaluyong City</td>\n",
       "      <td>Edsa Shangri-La, 1 Garden Way, Ortigas, Mandal...</td>\n",
       "      <td>Edsa Shangri-La, Ortigas, Mandaluyong City</td>\n",
       "      <td>Edsa Shangri-La, Ortigas, Mandaluyong City, Ma...</td>\n",
       "      <td>121.056831</td>\n",
       "      <td>14.581404</td>\n",
       "      <td>Seafood, Asian, Filipino, Indian</td>\n",
       "      <td>...</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Green</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6318506</td>\n",
       "      <td>Ooma</td>\n",
       "      <td>162</td>\n",
       "      <td>Mandaluyong City</td>\n",
       "      <td>Third Floor, Mega Fashion Hall, SM Megamall, O...</td>\n",
       "      <td>SM Megamall, Ortigas, Mandaluyong City</td>\n",
       "      <td>SM Megamall, Ortigas, Mandaluyong City, Mandal...</td>\n",
       "      <td>121.056475</td>\n",
       "      <td>14.585318</td>\n",
       "      <td>Japanese, Sushi</td>\n",
       "      <td>...</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>4.9</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6314302</td>\n",
       "      <td>Sambo Kojin</td>\n",
       "      <td>162</td>\n",
       "      <td>Mandaluyong City</td>\n",
       "      <td>Third Floor, Mega Atrium, SM Megamall, Ortigas...</td>\n",
       "      <td>SM Megamall, Ortigas, Mandaluyong City</td>\n",
       "      <td>SM Megamall, Ortigas, Mandaluyong City, Mandal...</td>\n",
       "      <td>121.057508</td>\n",
       "      <td>14.584450</td>\n",
       "      <td>Japanese, Korean</td>\n",
       "      <td>...</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>4.8</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>229</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Restaurant ID         Restaurant Name  Country Code              City  \\\n",
       "0        6317637        Le Petit Souffle           162       Makati City   \n",
       "1        6304287        Izakaya Kikufuji           162       Makati City   \n",
       "2        6300002  Heat - Edsa Shangri-La           162  Mandaluyong City   \n",
       "3        6318506                    Ooma           162  Mandaluyong City   \n",
       "4        6314302             Sambo Kojin           162  Mandaluyong City   \n",
       "\n",
       "                                             Address  \\\n",
       "0  Third Floor, Century City Mall, Kalayaan Avenu...   \n",
       "1  Little Tokyo, 2277 Chino Roces Avenue, Legaspi...   \n",
       "2  Edsa Shangri-La, 1 Garden Way, Ortigas, Mandal...   \n",
       "3  Third Floor, Mega Fashion Hall, SM Megamall, O...   \n",
       "4  Third Floor, Mega Atrium, SM Megamall, Ortigas...   \n",
       "\n",
       "                                     Locality  \\\n",
       "0   Century City Mall, Poblacion, Makati City   \n",
       "1  Little Tokyo, Legaspi Village, Makati City   \n",
       "2  Edsa Shangri-La, Ortigas, Mandaluyong City   \n",
       "3      SM Megamall, Ortigas, Mandaluyong City   \n",
       "4      SM Megamall, Ortigas, Mandaluyong City   \n",
       "\n",
       "                                    Locality Verbose   Longitude   Latitude  \\\n",
       "0  Century City Mall, Poblacion, Makati City, Mak...  121.027535  14.565443   \n",
       "1  Little Tokyo, Legaspi Village, Makati City, Ma...  121.014101  14.553708   \n",
       "2  Edsa Shangri-La, Ortigas, Mandaluyong City, Ma...  121.056831  14.581404   \n",
       "3  SM Megamall, Ortigas, Mandaluyong City, Mandal...  121.056475  14.585318   \n",
       "4  SM Megamall, Ortigas, Mandaluyong City, Mandal...  121.057508  14.584450   \n",
       "\n",
       "                           Cuisines  ...          Currency Has Table booking  \\\n",
       "0        French, Japanese, Desserts  ...  Botswana Pula(P)               Yes   \n",
       "1                          Japanese  ...  Botswana Pula(P)               Yes   \n",
       "2  Seafood, Asian, Filipino, Indian  ...  Botswana Pula(P)               Yes   \n",
       "3                   Japanese, Sushi  ...  Botswana Pula(P)                No   \n",
       "4                  Japanese, Korean  ...  Botswana Pula(P)               Yes   \n",
       "\n",
       "  Has Online delivery Is delivering now Switch to order menu Price range  \\\n",
       "0                  No                No                   No           3   \n",
       "1                  No                No                   No           3   \n",
       "2                  No                No                   No           4   \n",
       "3                  No                No                   No           4   \n",
       "4                  No                No                   No           4   \n",
       "\n",
       "   Aggregate rating  Rating color Rating text Votes  \n",
       "0               4.8    Dark Green   Excellent   314  \n",
       "1               4.5    Dark Green   Excellent   591  \n",
       "2               4.4         Green   Very Good   270  \n",
       "3               4.9    Dark Green   Excellent   365  \n",
       "4               4.8    Dark Green   Excellent   229  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1= pd.read_csv(\"C:\\\\Users\\\\pooja\\\\OneDrive\\\\Desktop\\\\Internship\\\\zomato.csv\")\n",
    "df1.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5b658aa0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9551, 21)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e85fd90e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country Code</th>\n",
       "      <th>Country</th>\n",
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       "  </thead>\n",
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       "      <td>India</td>\n",
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       "      <th>1</th>\n",
       "      <td>14</td>\n",
       "      <td>Australia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>Brazil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37</td>\n",
       "      <td>Canada</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>94</td>\n",
       "      <td>Indonesia</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Country Code    Country\n",
       "0             1      India\n",
       "1            14  Australia\n",
       "2            30     Brazil\n",
       "3            37     Canada\n",
       "4            94  Indonesia"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2= pd.read_csv(\"C:\\\\Users\\\\pooja\\\\OneDrive\\\\Desktop\\\\Internship\\\\Country-Code.csv\")\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a2ed02aa",
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   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Restaurant ID</th>\n",
       "      <th>Restaurant Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>City</th>\n",
       "      <th>Address</th>\n",
       "      <th>Locality</th>\n",
       "      <th>Locality Verbose</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Cuisines</th>\n",
       "      <th>...</th>\n",
       "      <th>Has Table booking</th>\n",
       "      <th>Has Online delivery</th>\n",
       "      <th>Is delivering now</th>\n",
       "      <th>Switch to order menu</th>\n",
       "      <th>Price range</th>\n",
       "      <th>Aggregate rating</th>\n",
       "      <th>Rating color</th>\n",
       "      <th>Rating text</th>\n",
       "      <th>Votes</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6317637</td>\n",
       "      <td>Le Petit Souffle</td>\n",
       "      <td>162</td>\n",
       "      <td>Makati City</td>\n",
       "      <td>Third Floor, Century City Mall, Kalayaan Avenu...</td>\n",
       "      <td>Century City Mall, Poblacion, Makati City</td>\n",
       "      <td>Century City Mall, Poblacion, Makati City, Mak...</td>\n",
       "      <td>121.027535</td>\n",
       "      <td>14.565443</td>\n",
       "      <td>French, Japanese, Desserts</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>4.8</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>314</td>\n",
       "      <td>Phillipines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6304287</td>\n",
       "      <td>Izakaya Kikufuji</td>\n",
       "      <td>162</td>\n",
       "      <td>Makati City</td>\n",
       "      <td>Little Tokyo, 2277 Chino Roces Avenue, Legaspi...</td>\n",
       "      <td>Little Tokyo, Legaspi Village, Makati City</td>\n",
       "      <td>Little Tokyo, Legaspi Village, Makati City, Ma...</td>\n",
       "      <td>121.014101</td>\n",
       "      <td>14.553708</td>\n",
       "      <td>Japanese</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>4.5</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>591</td>\n",
       "      <td>Phillipines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6300002</td>\n",
       "      <td>Heat - Edsa Shangri-La</td>\n",
       "      <td>162</td>\n",
       "      <td>Mandaluyong City</td>\n",
       "      <td>Edsa Shangri-La, 1 Garden Way, Ortigas, Mandal...</td>\n",
       "      <td>Edsa Shangri-La, Ortigas, Mandaluyong City</td>\n",
       "      <td>Edsa Shangri-La, Ortigas, Mandaluyong City, Ma...</td>\n",
       "      <td>121.056831</td>\n",
       "      <td>14.581404</td>\n",
       "      <td>Seafood, Asian, Filipino, Indian</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Green</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>270</td>\n",
       "      <td>Phillipines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6318506</td>\n",
       "      <td>Ooma</td>\n",
       "      <td>162</td>\n",
       "      <td>Mandaluyong City</td>\n",
       "      <td>Third Floor, Mega Fashion Hall, SM Megamall, O...</td>\n",
       "      <td>SM Megamall, Ortigas, Mandaluyong City</td>\n",
       "      <td>SM Megamall, Ortigas, Mandaluyong City, Mandal...</td>\n",
       "      <td>121.056475</td>\n",
       "      <td>14.585318</td>\n",
       "      <td>Japanese, Sushi</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>4.9</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>365</td>\n",
       "      <td>Phillipines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6314302</td>\n",
       "      <td>Sambo Kojin</td>\n",
       "      <td>162</td>\n",
       "      <td>Mandaluyong City</td>\n",
       "      <td>Third Floor, Mega Atrium, SM Megamall, Ortigas...</td>\n",
       "      <td>SM Megamall, Ortigas, Mandaluyong City</td>\n",
       "      <td>SM Megamall, Ortigas, Mandaluyong City, Mandal...</td>\n",
       "      <td>121.057508</td>\n",
       "      <td>14.584450</td>\n",
       "      <td>Japanese, Korean</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>4.8</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>229</td>\n",
       "      <td>Phillipines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9546</th>\n",
       "      <td>5915730</td>\n",
       "      <td>NamlÛ± Gurme</td>\n",
       "      <td>208</td>\n",
       "      <td>ÛÁstanbul</td>\n",
       "      <td>Kemankeô Karamustafa Paôa Mahallesi, RÛ±htÛ±...</td>\n",
       "      <td>Karakí_y</td>\n",
       "      <td>Karakí_y, ÛÁstanbul</td>\n",
       "      <td>28.977392</td>\n",
       "      <td>41.022793</td>\n",
       "      <td>Turkish</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>4.1</td>\n",
       "      <td>Green</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>788</td>\n",
       "      <td>Turkey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9547</th>\n",
       "      <td>5908749</td>\n",
       "      <td>Ceviz AÛôacÛ±</td>\n",
       "      <td>208</td>\n",
       "      <td>ÛÁstanbul</td>\n",
       "      <td>Koôuyolu Mahallesi, Muhittin íìstí_ndaÛô Cadd...</td>\n",
       "      <td>Koôuyolu</td>\n",
       "      <td>Koôuyolu, ÛÁstanbul</td>\n",
       "      <td>29.041297</td>\n",
       "      <td>41.009847</td>\n",
       "      <td>World Cuisine, Patisserie, Cafe</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>4.2</td>\n",
       "      <td>Green</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>1034</td>\n",
       "      <td>Turkey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9548</th>\n",
       "      <td>5915807</td>\n",
       "      <td>Huqqa</td>\n",
       "      <td>208</td>\n",
       "      <td>ÛÁstanbul</td>\n",
       "      <td>Kuruí_eôme Mahallesi, Muallim Naci Caddesi, N...</td>\n",
       "      <td>Kuruí_eôme</td>\n",
       "      <td>Kuruí_eôme, ÛÁstanbul</td>\n",
       "      <td>29.034640</td>\n",
       "      <td>41.055817</td>\n",
       "      <td>Italian, World Cuisine</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>Yellow</td>\n",
       "      <td>Good</td>\n",
       "      <td>661</td>\n",
       "      <td>Turkey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9549</th>\n",
       "      <td>5916112</td>\n",
       "      <td>Aôôk Kahve</td>\n",
       "      <td>208</td>\n",
       "      <td>ÛÁstanbul</td>\n",
       "      <td>Kuruí_eôme Mahallesi, Muallim Naci Caddesi, N...</td>\n",
       "      <td>Kuruí_eôme</td>\n",
       "      <td>Kuruí_eôme, ÛÁstanbul</td>\n",
       "      <td>29.036019</td>\n",
       "      <td>41.057979</td>\n",
       "      <td>Restaurant Cafe</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Green</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>901</td>\n",
       "      <td>Turkey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9550</th>\n",
       "      <td>5927402</td>\n",
       "      <td>Walter's Coffee Roastery</td>\n",
       "      <td>208</td>\n",
       "      <td>ÛÁstanbul</td>\n",
       "      <td>CafeaÛôa Mahallesi, BademaltÛ± Sokak, No 21 B,...</td>\n",
       "      <td>Moda</td>\n",
       "      <td>Moda, ÛÁstanbul</td>\n",
       "      <td>29.026016</td>\n",
       "      <td>40.984776</td>\n",
       "      <td>Cafe</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Green</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>591</td>\n",
       "      <td>Turkey</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9551 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Restaurant ID           Restaurant Name  Country Code              City  \\\n",
       "0           6317637          Le Petit Souffle           162       Makati City   \n",
       "1           6304287          Izakaya Kikufuji           162       Makati City   \n",
       "2           6300002    Heat - Edsa Shangri-La           162  Mandaluyong City   \n",
       "3           6318506                      Ooma           162  Mandaluyong City   \n",
       "4           6314302               Sambo Kojin           162  Mandaluyong City   \n",
       "...             ...                       ...           ...               ...   \n",
       "9546        5915730              NamlÛ± Gurme           208         ÛÁstanbul   \n",
       "9547        5908749             Ceviz AÛôacÛ±           208         ÛÁstanbul   \n",
       "9548        5915807                     Huqqa           208         ÛÁstanbul   \n",
       "9549        5916112              Aôôk Kahve           208         ÛÁstanbul   \n",
       "9550        5927402  Walter's Coffee Roastery           208         ÛÁstanbul   \n",
       "\n",
       "                                                Address  \\\n",
       "0     Third Floor, Century City Mall, Kalayaan Avenu...   \n",
       "1     Little Tokyo, 2277 Chino Roces Avenue, Legaspi...   \n",
       "2     Edsa Shangri-La, 1 Garden Way, Ortigas, Mandal...   \n",
       "3     Third Floor, Mega Fashion Hall, SM Megamall, O...   \n",
       "4     Third Floor, Mega Atrium, SM Megamall, Ortigas...   \n",
       "...                                                 ...   \n",
       "9546  Kemankeô Karamustafa Paôa Mahallesi, RÛ±htÛ±...   \n",
       "9547  Koôuyolu Mahallesi, Muhittin íìstí_ndaÛô Cadd...   \n",
       "9548  Kuruí_eôme Mahallesi, Muallim Naci Caddesi, N...   \n",
       "9549  Kuruí_eôme Mahallesi, Muallim Naci Caddesi, N...   \n",
       "9550  CafeaÛôa Mahallesi, BademaltÛ± Sokak, No 21 B,...   \n",
       "\n",
       "                                        Locality  \\\n",
       "0      Century City Mall, Poblacion, Makati City   \n",
       "1     Little Tokyo, Legaspi Village, Makati City   \n",
       "2     Edsa Shangri-La, Ortigas, Mandaluyong City   \n",
       "3         SM Megamall, Ortigas, Mandaluyong City   \n",
       "4         SM Megamall, Ortigas, Mandaluyong City   \n",
       "...                                          ...   \n",
       "9546                                    Karakí_y   \n",
       "9547                                   Koôuyolu   \n",
       "9548                                 Kuruí_eôme   \n",
       "9549                                 Kuruí_eôme   \n",
       "9550                                        Moda   \n",
       "\n",
       "                                       Locality Verbose   Longitude  \\\n",
       "0     Century City Mall, Poblacion, Makati City, Mak...  121.027535   \n",
       "1     Little Tokyo, Legaspi Village, Makati City, Ma...  121.014101   \n",
       "2     Edsa Shangri-La, Ortigas, Mandaluyong City, Ma...  121.056831   \n",
       "3     SM Megamall, Ortigas, Mandaluyong City, Mandal...  121.056475   \n",
       "4     SM Megamall, Ortigas, Mandaluyong City, Mandal...  121.057508   \n",
       "...                                                 ...         ...   \n",
       "9546                                Karakí_y, ÛÁstanbul   28.977392   \n",
       "9547                               Koôuyolu, ÛÁstanbul   29.041297   \n",
       "9548                             Kuruí_eôme, ÛÁstanbul   29.034640   \n",
       "9549                             Kuruí_eôme, ÛÁstanbul   29.036019   \n",
       "9550                                    Moda, ÛÁstanbul   29.026016   \n",
       "\n",
       "       Latitude                          Cuisines  ...  Has Table booking  \\\n",
       "0     14.565443        French, Japanese, Desserts  ...                Yes   \n",
       "1     14.553708                          Japanese  ...                Yes   \n",
       "2     14.581404  Seafood, Asian, Filipino, Indian  ...                Yes   \n",
       "3     14.585318                   Japanese, Sushi  ...                 No   \n",
       "4     14.584450                  Japanese, Korean  ...                Yes   \n",
       "...         ...                               ...  ...                ...   \n",
       "9546  41.022793                           Turkish  ...                 No   \n",
       "9547  41.009847   World Cuisine, Patisserie, Cafe  ...                 No   \n",
       "9548  41.055817            Italian, World Cuisine  ...                 No   \n",
       "9549  41.057979                   Restaurant Cafe  ...                 No   \n",
       "9550  40.984776                              Cafe  ...                 No   \n",
       "\n",
       "     Has Online delivery Is delivering now Switch to order menu Price range  \\\n",
       "0                     No                No                   No           3   \n",
       "1                     No                No                   No           3   \n",
       "2                     No                No                   No           4   \n",
       "3                     No                No                   No           4   \n",
       "4                     No                No                   No           4   \n",
       "...                  ...               ...                  ...         ...   \n",
       "9546                  No                No                   No           3   \n",
       "9547                  No                No                   No           3   \n",
       "9548                  No                No                   No           4   \n",
       "9549                  No                No                   No           4   \n",
       "9550                  No                No                   No           2   \n",
       "\n",
       "     Aggregate rating  Rating color  Rating text Votes      Country  \n",
       "0                 4.8    Dark Green    Excellent   314  Phillipines  \n",
       "1                 4.5    Dark Green    Excellent   591  Phillipines  \n",
       "2                 4.4         Green    Very Good   270  Phillipines  \n",
       "3                 4.9    Dark Green    Excellent   365  Phillipines  \n",
       "4                 4.8    Dark Green    Excellent   229  Phillipines  \n",
       "...               ...           ...          ...   ...          ...  \n",
       "9546              4.1         Green    Very Good   788       Turkey  \n",
       "9547              4.2         Green    Very Good  1034       Turkey  \n",
       "9548              3.7        Yellow         Good   661       Turkey  \n",
       "9549              4.0         Green    Very Good   901       Turkey  \n",
       "9550              4.0         Green    Very Good   591       Turkey  \n",
       "\n",
       "[9551 rows x 22 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.merge (df1,df2)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fdc1ab52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9551, 22)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removing duplicates if any:\n",
    "data=data.drop_duplicates(keep='first')\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31dc0d4f",
   "metadata": {},
   "source": [
    "The dataset has no duplicate values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bac350a",
   "metadata": {},
   "source": [
    "The dataset has both numerical and categorical data\n",
    "\n",
    "The dataset also seems to have null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4fc20ad8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Restaurant ID           0\n",
       "Restaurant Name         0\n",
       "Country Code            0\n",
       "City                    0\n",
       "Address                 0\n",
       "Locality                0\n",
       "Locality Verbose        0\n",
       "Longitude               0\n",
       "Latitude                0\n",
       "Cuisines                9\n",
       "Average Cost for two    0\n",
       "Currency                0\n",
       "Has Table booking       0\n",
       "Has Online delivery     0\n",
       "Is delivering now       0\n",
       "Switch to order menu    0\n",
       "Price range             0\n",
       "Aggregate rating        0\n",
       "Rating color            0\n",
       "Rating text             0\n",
       "Votes                   0\n",
       "Country                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for missing values\n",
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fea1e53",
   "metadata": {},
   "source": [
    "Cuisines column has null values\n",
    "\n",
    "Dropping columns: \n",
    "\n",
    "Restaurant ID, Restaurant Name, Address as they are unique for every Restaurant and will not add value to our analysis \n",
    "\n",
    "Locality Verbose as Locality and Locality Verbose are same for most of the rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a1de1e29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9551, 18)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data= data.drop (columns=['Restaurant ID','Restaurant Name','Address','Locality Verbose'])\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7b476bc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country Code</th>\n",
       "      <th>City</th>\n",
       "      <th>Locality</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Cuisines</th>\n",
       "      <th>Average Cost for two</th>\n",
       "      <th>Currency</th>\n",
       "      <th>Has Table booking</th>\n",
       "      <th>Has Online delivery</th>\n",
       "      <th>Is delivering now</th>\n",
       "      <th>Switch to order menu</th>\n",
       "      <th>Price range</th>\n",
       "      <th>Aggregate rating</th>\n",
       "      <th>Rating color</th>\n",
       "      <th>Rating text</th>\n",
       "      <th>Votes</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>162</td>\n",
       "      <td>Makati City</td>\n",
       "      <td>Century City Mall, Poblacion, Makati City</td>\n",
       "      <td>121.027535</td>\n",
       "      <td>14.565443</td>\n",
       "      <td>French, Japanese, Desserts</td>\n",
       "      <td>1100</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>4.8</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>314</td>\n",
       "      <td>Phillipines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162</td>\n",
       "      <td>Makati City</td>\n",
       "      <td>Little Tokyo, Legaspi Village, Makati City</td>\n",
       "      <td>121.014101</td>\n",
       "      <td>14.553708</td>\n",
       "      <td>Japanese</td>\n",
       "      <td>1200</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>4.5</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>591</td>\n",
       "      <td>Phillipines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162</td>\n",
       "      <td>Mandaluyong City</td>\n",
       "      <td>Edsa Shangri-La, Ortigas, Mandaluyong City</td>\n",
       "      <td>121.056831</td>\n",
       "      <td>14.581404</td>\n",
       "      <td>Seafood, Asian, Filipino, Indian</td>\n",
       "      <td>4000</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Green</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>270</td>\n",
       "      <td>Phillipines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>162</td>\n",
       "      <td>Mandaluyong City</td>\n",
       "      <td>SM Megamall, Ortigas, Mandaluyong City</td>\n",
       "      <td>121.056475</td>\n",
       "      <td>14.585318</td>\n",
       "      <td>Japanese, Sushi</td>\n",
       "      <td>1500</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>4.9</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>365</td>\n",
       "      <td>Phillipines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162</td>\n",
       "      <td>Mandaluyong City</td>\n",
       "      <td>SM Megamall, Ortigas, Mandaluyong City</td>\n",
       "      <td>121.057508</td>\n",
       "      <td>14.584450</td>\n",
       "      <td>Japanese, Korean</td>\n",
       "      <td>1500</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>4.8</td>\n",
       "      <td>Dark Green</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>229</td>\n",
       "      <td>Phillipines</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Country Code              City                                    Locality  \\\n",
       "0           162       Makati City   Century City Mall, Poblacion, Makati City   \n",
       "1           162       Makati City  Little Tokyo, Legaspi Village, Makati City   \n",
       "2           162  Mandaluyong City  Edsa Shangri-La, Ortigas, Mandaluyong City   \n",
       "3           162  Mandaluyong City      SM Megamall, Ortigas, Mandaluyong City   \n",
       "4           162  Mandaluyong City      SM Megamall, Ortigas, Mandaluyong City   \n",
       "\n",
       "    Longitude   Latitude                          Cuisines  \\\n",
       "0  121.027535  14.565443        French, Japanese, Desserts   \n",
       "1  121.014101  14.553708                          Japanese   \n",
       "2  121.056831  14.581404  Seafood, Asian, Filipino, Indian   \n",
       "3  121.056475  14.585318                   Japanese, Sushi   \n",
       "4  121.057508  14.584450                  Japanese, Korean   \n",
       "\n",
       "   Average Cost for two          Currency Has Table booking  \\\n",
       "0                  1100  Botswana Pula(P)               Yes   \n",
       "1                  1200  Botswana Pula(P)               Yes   \n",
       "2                  4000  Botswana Pula(P)               Yes   \n",
       "3                  1500  Botswana Pula(P)                No   \n",
       "4                  1500  Botswana Pula(P)               Yes   \n",
       "\n",
       "  Has Online delivery Is delivering now Switch to order menu  Price range  \\\n",
       "0                  No                No                   No            3   \n",
       "1                  No                No                   No            3   \n",
       "2                  No                No                   No            4   \n",
       "3                  No                No                   No            4   \n",
       "4                  No                No                   No            4   \n",
       "\n",
       "   Aggregate rating Rating color Rating text  Votes      Country  \n",
       "0               4.8   Dark Green   Excellent    314  Phillipines  \n",
       "1               4.5   Dark Green   Excellent    591  Phillipines  \n",
       "2               4.4        Green   Very Good    270  Phillipines  \n",
       "3               4.9   Dark Green   Excellent    365  Phillipines  \n",
       "4               4.8   Dark Green   Excellent    229  Phillipines  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "98d19349",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country Code              15\n",
       "City                     141\n",
       "Locality                1208\n",
       "Longitude               8120\n",
       "Latitude                8677\n",
       "Cuisines                1825\n",
       "Average Cost for two     140\n",
       "Currency                  14\n",
       "Has Table booking          2\n",
       "Has Online delivery        2\n",
       "Is delivering now          2\n",
       "Switch to order menu       1\n",
       "Price range                4\n",
       "Aggregate rating          33\n",
       "Rating color               6\n",
       "Rating text                6\n",
       "Votes                   1012\n",
       "Country                   15\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9c11e38",
   "metadata": {},
   "source": [
    "Dropping Columns: \n",
    "\n",
    "Rating color as it is same as Rating text\n",
    "\n",
    "Switch to order menu as it has only one value 'No'\n",
    "\n",
    "Country code as it is same as Country Also the model will treat it as numerical data which could effect our anlysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5108c037",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9551, 15)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=data.drop(columns=['Rating color','Switch to order menu','Country Code'])\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b39776f",
   "metadata": {},
   "source": [
    "Now that we have deleted the columns which would not add value to our analysis\n",
    "Locality has too many categories making changes to column to reduce the number of categories "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9279d896",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        [Century City Mall,  Poblacion,  Makati City]\n",
       "1       [Little Tokyo,  Legaspi Village,  Makati City]\n",
       "2       [Edsa Shangri-La,  Ortigas,  Mandaluyong City]\n",
       "3           [SM Megamall,  Ortigas,  Mandaluyong City]\n",
       "4           [SM Megamall,  Ortigas,  Mandaluyong City]\n",
       "                             ...                      \n",
       "9546                                        [Karakí_y]\n",
       "9547                                       [Koôuyolu]\n",
       "9548                                     [Kuruí_eôme]\n",
       "9549                                     [Kuruí_eôme]\n",
       "9550                                            [Moda]\n",
       "Name: Locality, Length: 9551, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Locality'] = data['Locality'].str.split(',')\n",
    "data['Locality']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "08609805",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[' Poblacion',\n",
       " ' Legaspi Village',\n",
       " ' Ortigas',\n",
       " ' Ortigas',\n",
       " ' Ortigas',\n",
       " ' Ortigas',\n",
       " ' Mall of Asia Complex',\n",
       " ' Mall of Asia Complex',\n",
       " ' Pasay City',\n",
       " 'Kapitolyo',\n",
       " 'Kapitolyo',\n",
       " 'Kapitolyo',\n",
       " ' Diliman',\n",
       " 'Addition Hills',\n",
       " 'Little Baguio',\n",
       " ' Don Jose',\n",
       " ' Don Jose',\n",
       " 'Tagaytay City',\n",
       " ' Bonifacio Global City',\n",
       " 'Bonifacio Global City',\n",
       " ' Bonifacio Global City',\n",
       " ' Bonifacio Global City',\n",
       " 'Asa Norte',\n",
       " 'Asa Norte',\n",
       " 'Asa Norte',\n",
       " 'Asa Norte',\n",
       " 'Asa Sul',\n",
       " 'Asa Sul',\n",
       " 'Asa Sul',\n",
       " 'Asa Sul',\n",
       " 'Asa Sul',\n",
       " 'Asa Sul',\n",
       " 'í\\x81guas Claras',\n",
       " 'í\\x81guas Claras',\n",
       " 'í\\x81guas Claras',\n",
       " ' Asa Norte',\n",
       " 'Lago Sul',\n",
       " ' Guaríç I',\n",
       " ' Lago Sul',\n",
       " 'Setor De Clubes Esportivos Sul',\n",
       " ' Lago Norte',\n",
       " 'Sudoeste',\n",
       " 'Barra da Tijuca',\n",
       " 'Centro',\n",
       " 'Copacabana',\n",
       " 'Copacabana',\n",
       " 'Copacabana',\n",
       " ' Copacabana',\n",
       " 'Gíçvea',\n",
       " 'Ipanema',\n",
       " 'Ipanema',\n",
       " 'Ipanema',\n",
       " 'Lagoa',\n",
       " ' Barra da Tijuca',\n",
       " 'Leblon',\n",
       " 'Leblon',\n",
       " 'Leme',\n",
       " 'Leme',\n",
       " 'Madureira',\n",
       " 'Santa Teresa',\n",
       " 'Tijuca',\n",
       " 'Urca',\n",
       " ' Centro',\n",
       " ' Centro',\n",
       " 'Consolaí_í£o',\n",
       " 'Consolaí_í£o',\n",
       " ' Jardim Paulista',\n",
       " 'Itaim Bibi',\n",
       " 'Itaim Bibi',\n",
       " 'Itaim Bibi',\n",
       " 'Jardim Paulista',\n",
       " 'Jardim Paulista',\n",
       " 'Jardim Paulista',\n",
       " 'Moema',\n",
       " 'Pinheiros',\n",
       " 'Repí_blica',\n",
       " 'Repí_blica',\n",
       " ' Vila Mariana',\n",
       " ' Santo Amaro',\n",
       " 'Vila Maria',\n",
       " 'Vila Mariana',\n",
       " 'Vila SíÇnia',\n",
       " 'Albany',\n",
       " 'Albany',\n",
       " 'Albany',\n",
       " 'Albany',\n",
       " 'Albany',\n",
       " 'Albany',\n",
       " 'Albany',\n",
       " 'Albany',\n",
       " 'Albany',\n",
       " 'Albany',\n",
       " 'Albany',\n",
       " 'Albany',\n",
       " 'Albany',\n",
       " 'Albany',\n",
       " 'Albany',\n",
       " 'Albany',\n",
       " 'Albany',\n",
       " 'Albany',\n",
       " 'Albany',\n",
       " 'Sylvester',\n",
       " 'Athens',\n",
       " 'Athens',\n",
       " 'Athens',\n",
       " 'Athens',\n",
       " 'Athens',\n",
       " 'Athens',\n",
       " 'Athens',\n",
       " 'Athens',\n",
       " 'Athens',\n",
       " 'Athens',\n",
       " 'Athens',\n",
       " 'Athens',\n",
       " 'Athens',\n",
       " 'Athens',\n",
       " 'Athens',\n",
       " 'Athens',\n",
       " 'Athens',\n",
       " 'Athens',\n",
       " 'Athens',\n",
       " 'Athens',\n",
       " 'Augusta',\n",
       " 'Augusta',\n",
       " 'Augusta',\n",
       " 'Augusta',\n",
       " 'Augusta',\n",
       " 'Augusta',\n",
       " 'Augusta',\n",
       " 'Augusta',\n",
       " 'Augusta',\n",
       " 'Augusta',\n",
       " 'Augusta',\n",
       " 'Augusta',\n",
       " 'Augusta',\n",
       " 'Augusta',\n",
       " 'Augusta',\n",
       " 'Evans',\n",
       " 'Evans',\n",
       " 'Evans',\n",
       " 'Evans',\n",
       " 'Grovetown',\n",
       " 'Boise',\n",
       " 'Boise',\n",
       " 'Boise',\n",
       " 'Boise',\n",
       " 'Boise',\n",
       " 'Boise',\n",
       " 'Boise',\n",
       " 'Boise',\n",
       " 'Boise',\n",
       " 'Boise',\n",
       " 'Boise',\n",
       " 'Boise',\n",
       " 'Boise',\n",
       " 'Boise',\n",
       " 'Boise',\n",
       " 'Boise',\n",
       " 'Boise',\n",
       " 'Boise',\n",
       " 'Meridian',\n",
       " 'Nampa',\n",
       " 'Cedar Rapids',\n",
       " 'Cedar Rapids',\n",
       " 'Cedar Rapids',\n",
       " 'Cedar Rapids',\n",
       " 'Cedar Rapids',\n",
       " 'Cedar Rapids',\n",
       " 'Cedar Rapids',\n",
       " 'Cedar Rapids',\n",
       " 'Coralville',\n",
       " 'Coralville',\n",
       " 'Iowa City',\n",
       " 'Iowa City',\n",
       " 'Iowa City',\n",
       " 'Iowa City',\n",
       " 'Iowa City',\n",
       " 'Iowa City',\n",
       " 'Iowa City',\n",
       " 'Iowa City',\n",
       " 'Iowa City',\n",
       " 'Marion',\n",
       " 'Clatskanie',\n",
       " 'Cochrane',\n",
       " 'Columbus',\n",
       " 'Columbus',\n",
       " 'Columbus',\n",
       " 'Columbus',\n",
       " 'Columbus',\n",
       " 'Columbus',\n",
       " 'Columbus',\n",
       " 'Columbus',\n",
       " 'Columbus',\n",
       " 'Columbus',\n",
       " 'Columbus',\n",
       " 'Columbus',\n",
       " 'Columbus',\n",
       " 'Columbus',\n",
       " 'Columbus',\n",
       " 'Columbus',\n",
       " 'Columbus',\n",
       " 'Columbus',\n",
       " 'Columbus',\n",
       " 'Hamilton',\n",
       " 'Calhoun',\n",
       " 'Calhoun',\n",
       " 'Calhoun',\n",
       " 'Chatsworth',\n",
       " 'Dalton',\n",
       " 'Dalton',\n",
       " 'Dalton',\n",
       " 'Dalton',\n",
       " 'Dalton',\n",
       " 'Dalton',\n",
       " 'Dalton',\n",
       " 'Dalton',\n",
       " 'Fort Oglethorpe',\n",
       " 'Fort Oglethorpe',\n",
       " 'Fort Oglethorpe',\n",
       " 'LaFayette',\n",
       " 'Ringgold',\n",
       " 'Ringgold',\n",
       " 'Ringgold',\n",
       " 'Rock Spring',\n",
       " 'Bettendorf',\n",
       " 'Bettendorf',\n",
       " 'Bettendorf',\n",
       " 'Bettendorf',\n",
       " 'Bettendorf',\n",
       " 'Davenport',\n",
       " 'Davenport',\n",
       " 'Davenport',\n",
       " 'Davenport',\n",
       " 'Davenport',\n",
       " 'Davenport',\n",
       " 'Davenport',\n",
       " 'Davenport',\n",
       " 'Davenport',\n",
       " 'Davenport',\n",
       " 'Davenport',\n",
       " 'Davenport',\n",
       " 'Davenport',\n",
       " 'Davenport',\n",
       " 'Davenport',\n",
       " 'Ames',\n",
       " 'Ames',\n",
       " 'Beaverdale',\n",
       " 'Clive',\n",
       " 'Clive',\n",
       " 'Downtown',\n",
       " 'Downtown',\n",
       " 'Downtown',\n",
       " 'Downtown',\n",
       " 'Downtown',\n",
       " 'Downtown',\n",
       " 'East Village',\n",
       " 'East Village',\n",
       " 'Fairground',\n",
       " 'Greater South Side',\n",
       " 'Johnston',\n",
       " 'Kingman Place Historic District',\n",
       " 'Urbandale',\n",
       " 'West Des Moines',\n",
       " 'West Des Moines',\n",
       " 'Dubuque',\n",
       " 'Dubuque',\n",
       " 'Dubuque',\n",
       " 'Dubuque',\n",
       " 'Dubuque',\n",
       " 'Dubuque',\n",
       " 'Dubuque',\n",
       " 'Dubuque',\n",
       " 'Dubuque',\n",
       " 'Dubuque',\n",
       " 'Dubuque',\n",
       " 'Dubuque',\n",
       " 'Dubuque',\n",
       " 'Dubuque',\n",
       " 'Dubuque',\n",
       " 'Dubuque',\n",
       " 'Dubuque',\n",
       " 'Dubuque',\n",
       " 'Dubuque',\n",
       " 'Dubuque',\n",
       " 'Fernley',\n",
       " 'Braselton',\n",
       " 'Clarkesville',\n",
       " 'Dahlonega',\n",
       " 'Dahlonega',\n",
       " 'Dahlonega',\n",
       " 'Dahlonega',\n",
       " 'Dahlonega',\n",
       " 'Flowery Branch',\n",
       " 'Flowery Branch',\n",
       " 'Flowery Branch',\n",
       " 'Gainesville',\n",
       " 'Gainesville',\n",
       " 'Gainesville',\n",
       " 'Gainesville',\n",
       " 'Gainesville',\n",
       " 'Gainesville',\n",
       " 'Helen',\n",
       " 'Helen',\n",
       " 'Helen',\n",
       " 'Helen',\n",
       " 'Lakeview',\n",
       " 'Haymarket',\n",
       " 'Bonaire',\n",
       " 'Macon',\n",
       " 'Macon',\n",
       " 'Macon',\n",
       " 'Macon',\n",
       " 'Macon',\n",
       " 'Macon',\n",
       " 'Macon',\n",
       " 'Macon',\n",
       " 'Macon',\n",
       " 'Macon',\n",
       " 'Warner Robins',\n",
       " 'Warner Robins',\n",
       " 'Warner Robins',\n",
       " 'Warner Robins',\n",
       " 'Warner Robins',\n",
       " 'Warner Robins',\n",
       " 'Warner Robins',\n",
       " 'Warner Robins',\n",
       " 'Warner Robins',\n",
       " 'Mc Millan',\n",
       " 'Miller',\n",
       " 'Monroe',\n",
       " 'Ojo Caliente',\n",
       " 'Church Street District',\n",
       " 'Disney World Area',\n",
       " 'Disney: Downtown Disney',\n",
       " 'Disney: Downtown Disney',\n",
       " 'I-Drive Universal',\n",
       " 'I-Drive Universal',\n",
       " 'I-Drive Universal',\n",
       " 'I-Drive Universal',\n",
       " 'Mills 50',\n",
       " 'Mills 50',\n",
       " 'Restaurant Row',\n",
       " 'Sanford',\n",
       " 'The Milk District',\n",
       " 'Windermere',\n",
       " 'Winter Park',\n",
       " 'Winter Park',\n",
       " 'Winter Park',\n",
       " 'Winter Park',\n",
       " 'Winter Park',\n",
       " 'Winter Park',\n",
       " 'Navarre',\n",
       " 'Pensacola',\n",
       " 'Pensacola',\n",
       " 'Pensacola',\n",
       " 'Pensacola',\n",
       " 'Pensacola',\n",
       " 'Pensacola',\n",
       " 'Pensacola',\n",
       " 'Pensacola',\n",
       " 'Pensacola',\n",
       " 'Pensacola',\n",
       " 'Pensacola',\n",
       " 'Pensacola',\n",
       " 'Pensacola Beach',\n",
       " 'Pensacola Beach',\n",
       " 'Pensacola Beach',\n",
       " 'Pensacola Beach',\n",
       " 'Pensacola Beach',\n",
       " 'Perdido Key',\n",
       " 'Perdido Key',\n",
       " 'Blackfoot',\n",
       " 'Chubbuck',\n",
       " 'Lava Hot Springs',\n",
       " 'Lava Hot Springs',\n",
       " 'Pocatello',\n",
       " 'Pocatello',\n",
       " 'Pocatello',\n",
       " 'Pocatello',\n",
       " 'Pocatello',\n",
       " 'Pocatello',\n",
       " 'Pocatello',\n",
       " 'Pocatello',\n",
       " 'Pocatello',\n",
       " 'Pocatello',\n",
       " 'Pocatello',\n",
       " 'Pocatello',\n",
       " 'Pocatello',\n",
       " 'Pocatello',\n",
       " 'Pocatello',\n",
       " 'Pocatello',\n",
       " 'Potrero',\n",
       " 'Princeton',\n",
       " 'Kahuku',\n",
       " 'Kailua Kona',\n",
       " 'Kaimuki',\n",
       " 'Kihei',\n",
       " 'Kihei',\n",
       " 'Kihei',\n",
       " 'Kihei',\n",
       " 'Lahaina',\n",
       " 'Lahaina',\n",
       " 'Lahaina',\n",
       " 'Lahaina',\n",
       " 'Lahaina',\n",
       " 'Paia',\n",
       " 'Waikiki',\n",
       " 'Waikiki',\n",
       " 'Waikiki',\n",
       " 'Waikiki',\n",
       " 'Waikiki',\n",
       " 'Waikiki',\n",
       " 'Waikiki',\n",
       " 'Savannah',\n",
       " 'Savannah',\n",
       " 'Savannah',\n",
       " 'Savannah',\n",
       " 'Savannah',\n",
       " 'Savannah',\n",
       " 'Savannah',\n",
       " 'Savannah',\n",
       " 'Savannah',\n",
       " 'Savannah',\n",
       " 'Savannah',\n",
       " 'Savannah',\n",
       " 'Savannah',\n",
       " 'Savannah',\n",
       " 'Savannah',\n",
       " 'Savannah',\n",
       " 'Savannah',\n",
       " 'Savannah',\n",
       " 'Tybee Island',\n",
       " 'Tybee Island',\n",
       " 'Le Mars',\n",
       " 'Sioux City',\n",
       " 'Sioux City',\n",
       " 'Sioux City',\n",
       " 'Sioux City',\n",
       " 'Sioux City',\n",
       " 'Sioux City',\n",
       " 'Sioux City',\n",
       " 'Sioux City',\n",
       " 'Sioux City',\n",
       " 'Sioux City',\n",
       " 'Sioux City',\n",
       " 'Sioux City',\n",
       " 'Sioux City',\n",
       " 'Sioux City',\n",
       " 'Sioux City',\n",
       " 'Sioux City',\n",
       " 'Sioux City',\n",
       " 'Sioux City',\n",
       " 'South Sioux City',\n",
       " 'Clearwater',\n",
       " 'Downtown St Petersburg',\n",
       " 'Downtown St Petersburg',\n",
       " 'Downtown St Petersburg',\n",
       " 'Downtown St Petersburg',\n",
       " 'Hyde Park',\n",
       " 'Hyde Park',\n",
       " 'Hyde Park',\n",
       " 'Hyde Park',\n",
       " 'Hyde Park',\n",
       " 'Indian Rocks Indian Shores',\n",
       " 'Kenwood',\n",
       " 'Madeira Beach Redington Beach',\n",
       " 'New Tampa',\n",
       " 'Northeast St Petersburg',\n",
       " 'Palma Ceia',\n",
       " 'Seminole Heights',\n",
       " 'Seminole Heights',\n",
       " 'Seminole Heights',\n",
       " 'Ybor City',\n",
       " 'Valdosta',\n",
       " 'Valdosta',\n",
       " 'Valdosta',\n",
       " 'Valdosta',\n",
       " 'Valdosta',\n",
       " 'Valdosta',\n",
       " 'Valdosta',\n",
       " 'Valdosta',\n",
       " 'Valdosta',\n",
       " 'Valdosta',\n",
       " 'Valdosta',\n",
       " 'Valdosta',\n",
       " 'Valdosta',\n",
       " 'Valdosta',\n",
       " 'Valdosta',\n",
       " 'Valdosta',\n",
       " 'Valdosta',\n",
       " 'Valdosta',\n",
       " 'Valdosta',\n",
       " 'Valdosta',\n",
       " 'Vernonia',\n",
       " 'Cedar Falls',\n",
       " 'Cedar Falls',\n",
       " 'Cedar Falls',\n",
       " 'Cedar Falls',\n",
       " 'Cedar Falls',\n",
       " 'Cedar Falls',\n",
       " 'Cedar Falls',\n",
       " 'Cedar Falls',\n",
       " 'Cedar Falls',\n",
       " 'Cedar Falls',\n",
       " 'Cedar Falls',\n",
       " 'Cedar Falls',\n",
       " 'Cedar Falls',\n",
       " 'Waterloo',\n",
       " 'Waterloo',\n",
       " 'Waterloo',\n",
       " 'Waterloo',\n",
       " 'Waterloo',\n",
       " 'Waterloo',\n",
       " 'Waterloo',\n",
       " 'Weirton',\n",
       " 'Winchester Bay',\n",
       " 'Armidale',\n",
       " 'Balingup',\n",
       " 'Beechworth',\n",
       " 'Dicky Beach',\n",
       " 'East Ballina',\n",
       " 'Flaxton',\n",
       " 'Forrest',\n",
       " 'Hepburn Springs',\n",
       " 'Hepburn Springs',\n",
       " 'Huskisson',\n",
       " 'Inverloch',\n",
       " 'Lakes Entrance',\n",
       " 'Lorn',\n",
       " 'Macedon',\n",
       " 'Mayfield',\n",
       " 'Middleton Beach',\n",
       " 'Montville',\n",
       " 'Palm Cove',\n",
       " 'Paynesville',\n",
       " 'Penola',\n",
       " 'Phillip Island',\n",
       " 'Tanunda',\n",
       " 'Trentham East',\n",
       " 'Victor Harbor',\n",
       " 'Chatham-Kent',\n",
       " 'Consort',\n",
       " 'Vineland Station',\n",
       " 'Yorkton',\n",
       " ' Downtown Core',\n",
       " ' Downtown Core',\n",
       " ' Outram',\n",
       " ' Outram',\n",
       " ' Downtown Core',\n",
       " ' Outram',\n",
       " ' Rochor',\n",
       " ' Bukit Timah',\n",
       " ' Tanglin',\n",
       " ' Kallang',\n",
       " ' Kallang',\n",
       " ' Downtown Core',\n",
       " ' Downtown Core',\n",
       " ' Downtown Core',\n",
       " ' Outram',\n",
       " ' Rochor',\n",
       " ' Singapore River',\n",
       " ' Rochor',\n",
       " ' Outram',\n",
       " ' Rochor',\n",
       " ' Tourist Club Area  (Al Zahiyah)',\n",
       " ' Tourist Club Area  (Al Zahiyah)',\n",
       " 'Al Dhafrah',\n",
       " 'Al Dhafrah',\n",
       " 'Al Mushrif',\n",
       " ' Al Wahda',\n",
       " ' Al Wahda',\n",
       " ' Al Markaziya',\n",
       " ' Mussafah Sanaiya',\n",
       " 'Madinat Zayed',\n",
       " ' Madinat Zayed ',\n",
       " ' Al Mushrif',\n",
       " 'Mussafah Sanaiya',\n",
       " 'Najda',\n",
       " 'Najda',\n",
       " 'Najda',\n",
       " ' Al Maqtaa',\n",
       " ' Al Markaziya',\n",
       " ' Al Markaziya',\n",
       " ' Yas Island',\n",
       " 'Al Barari',\n",
       " 'Al Karama',\n",
       " 'Al Karama',\n",
       " 'Barsha 2',\n",
       " ' Al Safa',\n",
       " 'Deira City Centre Area',\n",
       " 'DIFC',\n",
       " 'Dubai Media City',\n",
       " 'Festival City',\n",
       " 'Jumeirah 1',\n",
       " ' Umm Suqeim',\n",
       " ' Barsha 1',\n",
       " 'Mankhool',\n",
       " ' Trade Centre Area',\n",
       " 'Satwa',\n",
       " 'Downtown Dubai',\n",
       " 'Downtown Dubai',\n",
       " 'Downtown Dubai',\n",
       " 'Trade Centre Area',\n",
       " 'Umm Hurair',\n",
       " 'Abu Shagara',\n",
       " 'Abu Shagara',\n",
       " 'Abu Shagara',\n",
       " 'Al Khan',\n",
       " 'Al Majaz',\n",
       " 'Al Majaz',\n",
       " 'Al Majaz',\n",
       " 'Al Mareija',\n",
       " 'Al Mareija',\n",
       " 'Al Nahda',\n",
       " 'Al Nahda',\n",
       " 'Al Nud',\n",
       " ' Al Khan',\n",
       " 'Halwan Suburb',\n",
       " ' Al Majaz 3',\n",
       " 'Muwailih Commercial',\n",
       " ' Al Nahda',\n",
       " 'University City',\n",
       " 'University City',\n",
       " 'University City',\n",
       " 'Agra Cantt',\n",
       " 'Agra Cantt',\n",
       " 'Agra Cantt',\n",
       " 'Civil Lines',\n",
       " 'Civil Lines',\n",
       " ' Tajganj',\n",
       " ' Tajganj',\n",
       " ' Tajganj',\n",
       " 'Khandari',\n",
       " ' Tajganj',\n",
       " ' Tajganj',\n",
       " 'Rakabganj',\n",
       " 'Tajganj',\n",
       " 'Tajganj',\n",
       " 'Tajganj',\n",
       " 'Tajganj',\n",
       " 'Tajganj',\n",
       " 'Tajganj',\n",
       " 'Tajganj',\n",
       " 'Tajganj',\n",
       " 'Ambavadi',\n",
       " 'Ashram Road',\n",
       " 'Bodakdev',\n",
       " 'Bodakdev',\n",
       " 'Bodakdev',\n",
       " 'Bodakdev',\n",
       " 'Bodakdev',\n",
       " 'C G Road',\n",
       " ' Satellite',\n",
       " 'Ellis Bridge',\n",
       " 'Ellis Bridge',\n",
       " 'Ghatlodia',\n",
       " 'Gurukul',\n",
       " 'Gurukul',\n",
       " 'Navrangpura',\n",
       " 'Navrangpura',\n",
       " 'Prahlad Nagar',\n",
       " 'Thaltej',\n",
       " ' Sola',\n",
       " 'Vastrapur',\n",
       " 'Vastrapur',\n",
       " 'Civil Lines',\n",
       " 'Civil Lines',\n",
       " 'Civil Lines',\n",
       " 'Civil Lines',\n",
       " 'Civil Lines',\n",
       " 'Civil Lines',\n",
       " 'Civil Lines',\n",
       " 'Civil Lines',\n",
       " 'Civil Lines',\n",
       " 'Civil Lines',\n",
       " 'Civil Lines',\n",
       " 'Civil Lines',\n",
       " 'Civil Lines',\n",
       " 'Civil Lines',\n",
       " 'Civil Lines',\n",
       " 'Civil Lines',\n",
       " 'Civil Lines',\n",
       " 'Civil Lines',\n",
       " 'Tagore Town',\n",
       " ' Civil Lines',\n",
       " 'Basant Nagar',\n",
       " 'Basant Nagar',\n",
       " 'Hathi Gate',\n",
       " 'INA Colony',\n",
       " 'INA Colony',\n",
       " 'INA Colony',\n",
       " 'Ranjit Avenue',\n",
       " 'Ranjit Avenue',\n",
       " 'Ranjit Avenue',\n",
       " 'Ranjit Avenue',\n",
       " 'Ranjit Avenue',\n",
       " 'Town Hall',\n",
       " 'Town Hall',\n",
       " 'Town Hall',\n",
       " 'Town Hall',\n",
       " 'Town Hall',\n",
       " 'Town Hall',\n",
       " 'Town Hall',\n",
       " 'White Avenue',\n",
       " 'White Avenue',\n",
       " 'White Avenue',\n",
       " 'Akashwani',\n",
       " 'Chicalthana',\n",
       " 'CIDCO',\n",
       " 'CIDCO',\n",
       " 'CIDCO',\n",
       " 'CIDCO',\n",
       " 'CIDCO',\n",
       " 'CIDCO',\n",
       " 'CIDCO',\n",
       " ' Nirala Bazar',\n",
       " 'Mondha',\n",
       " 'Nirala Bazar',\n",
       " 'Nirala Bazar',\n",
       " 'Nirala Bazar',\n",
       " 'Nirala Bazar',\n",
       " 'Nyay Nagar',\n",
       " ' Chicalthana',\n",
       " 'Shahgunj',\n",
       " 'Usmanpura',\n",
       " 'Usmanpura',\n",
       " ' Koramangala',\n",
       " 'Indiranagar',\n",
       " 'Indiranagar',\n",
       " 'Indiranagar',\n",
       " 'Indiranagar',\n",
       " 'Indiranagar',\n",
       " 'Indiranagar',\n",
       " 'JP Nagar',\n",
       " 'Koramangala 5th Block',\n",
       " 'Koramangala 5th Block',\n",
       " 'Koramangala 5th Block',\n",
       " 'Koramangala 6th Block',\n",
       " 'Koramangala 7th Block',\n",
       " 'Marathahalli',\n",
       " 'Marathahalli',\n",
       " 'New BEL Road',\n",
       " 'Residency Road',\n",
       " 'Sarjapur Road',\n",
       " 'Sarjapur Road',\n",
       " 'UB City',\n",
       " 'Arera Colony',\n",
       " 'Arera Colony',\n",
       " 'Arera Colony',\n",
       " 'Arera Colony',\n",
       " 'Arera Colony',\n",
       " ' Maharana Pratap Nagar',\n",
       " ' Maharana Pratap Nagar',\n",
       " ' Maharana Pratap Nagar',\n",
       " 'Gulmohar Colony',\n",
       " 'Kohefiza',\n",
       " 'Maharana Pratap Nagar',\n",
       " 'Maharana Pratap Nagar',\n",
       " 'Maharana Pratap Nagar',\n",
       " 'Maharana Pratap Nagar',\n",
       " 'Maharana Pratap Nagar',\n",
       " 'Peer Gate Area',\n",
       " 'Sayaji Hotel',\n",
       " 'TT Nagar',\n",
       " 'TT Nagar',\n",
       " 'TT Nagar',\n",
       " ' Sahid Nagar',\n",
       " ' Sahid Nagar',\n",
       " 'Chandrasekharpur',\n",
       " 'Chandrasekharpur',\n",
       " 'Chandrasekharpur',\n",
       " 'Chandrasekharpur',\n",
       " 'Gajapati Nagar',\n",
       " ' Jayadev Vihar',\n",
       " 'Patia',\n",
       " 'Patia',\n",
       " 'Patia',\n",
       " 'Patia',\n",
       " 'Patia',\n",
       " 'Patia',\n",
       " 'Patia',\n",
       " 'Patia',\n",
       " 'Patia',\n",
       " 'Sahid Nagar',\n",
       " 'Sahid Nagar',\n",
       " ' Nayapalli',\n",
       " 'Unit 4',\n",
       " 'Chandigarh Industrial Area',\n",
       " ' Chandigarh Industrial Area',\n",
       " ' Chandigarh Industrial Area',\n",
       " ' Chandigarh Industrial Area',\n",
       " ' Chandigarh Industrial Area',\n",
       " ' Chandigarh Industrial Area',\n",
       " 'Sector 26',\n",
       " 'Sector 26',\n",
       " 'Sector 28',\n",
       " 'Sector 35',\n",
       " 'Sector 35',\n",
       " 'Sector 35',\n",
       " 'Sector 35',\n",
       " 'Sector 35',\n",
       " 'Sector 7',\n",
       " 'Sector 7',\n",
       " 'Sector 8',\n",
       " 'Sector 8',\n",
       " 'Adyar',\n",
       " 'Anna Nagar East',\n",
       " 'Anna Nagar East',\n",
       " 'Ashok Nagar',\n",
       " '  Royapettah',\n",
       " 'Gopalapuram',\n",
       " 'Kilpauk',\n",
       " 'Kotturpuram',\n",
       " 'Mylapore',\n",
       " 'Nungambakkam',\n",
       " 'Nungambakkam',\n",
       " 'Perungudi',\n",
       " 'RA Puram',\n",
       " 'Ramapuram',\n",
       " 'Santhome',\n",
       " 'T. Nagar',\n",
       " 'T. Nagar',\n",
       " 'Velachery',\n",
       " 'Velachery',\n",
       " 'Velachery',\n",
       " 'Gandhipuram',\n",
       " 'Peelamedu',\n",
       " 'Peelamedu',\n",
       " 'Peelamedu',\n",
       " 'Peelamedu',\n",
       " 'Peelamedu',\n",
       " 'Podanur',\n",
       " 'Race Course',\n",
       " 'Race Course',\n",
       " 'Race Course',\n",
       " 'Race Course',\n",
       " 'Race Course',\n",
       " 'Race Course',\n",
       " 'Race Course',\n",
       " 'RS Puram',\n",
       " 'RS Puram',\n",
       " 'Saibaba Colony',\n",
       " 'Saibaba Colony',\n",
       " ' Peelamedu',\n",
       " 'Town Hall',\n",
       " 'Chukkuwala',\n",
       " 'Chukkuwala',\n",
       " 'Hathibarkala Salwala',\n",
       " 'Hathibarkala Salwala',\n",
       " 'Hathibarkala Salwala',\n",
       " 'Jakhan',\n",
       " 'Jakhan',\n",
       " 'Jakhan',\n",
       " 'Karanpur',\n",
       " 'Karanpur',\n",
       " 'Krishna Nagar',\n",
       " 'Malsi',\n",
       " ' Jakhan',\n",
       " 'Paltan Bazaar',\n",
       " 'Race Course',\n",
       " 'Rajpur',\n",
       " 'Rajpur',\n",
       " 'Rajpur',\n",
       " 'Rajpur',\n",
       " 'Rajpur',\n",
       " 'Badarpur Border',\n",
       " 'Badarpur Border',\n",
       " 'Badarpur Border',\n",
       " 'Badarpur Border',\n",
       " 'Badarpur Border',\n",
       " 'Badarpur Border',\n",
       " 'Badarpur Border',\n",
       " 'Badarpur Border',\n",
       " 'Badarpur Border',\n",
       " 'Badkal Lake',\n",
       " 'Charmwood Village',\n",
       " 'Charmwood Village',\n",
       " 'Charmwood Village',\n",
       " 'Charmwood Village',\n",
       " 'Charmwood Village',\n",
       " 'Charmwood Village',\n",
       " ' Sector 35',\n",
       " ' Sector 35',\n",
       " ' Sector 35',\n",
       " ' Sector 35',\n",
       " ' Sector 35',\n",
       " ' Sector 35',\n",
       " ' Sector 35',\n",
       " ' Sector 35',\n",
       " ' Sector 35',\n",
       " ' Sector 35',\n",
       " ' Sector 35',\n",
       " ' Sector 35',\n",
       " ' Sector 15',\n",
       " ' Sector 15',\n",
       " ' Sector 15',\n",
       " ' Sector 15',\n",
       " 'Dayal Bagh',\n",
       " ' Sector 12',\n",
       " ' Sector 12',\n",
       " ' Faridabad',\n",
       " ' Faridabad',\n",
       " ' Faridabad',\n",
       " 'Indraprastha Colony',\n",
       " 'K Hotel',\n",
       " 'NIT',\n",
       " 'NIT',\n",
       " 'NIT',\n",
       " 'NIT',\n",
       " ' Sector 21',\n",
       " ' Sector 12',\n",
       " ' Sector 12',\n",
       " ' Sector 12',\n",
       " 'Sector 10',\n",
       " 'Sector 10',\n",
       " 'Sector 10',\n",
       " 'Sector 10',\n",
       " 'Sector 10',\n",
       " 'Sector 10',\n",
       " 'Sector 10',\n",
       " 'Sector 10',\n",
       " 'Sector 11',\n",
       " 'Sector 11',\n",
       " 'Sector 11',\n",
       " 'Sector 11',\n",
       " 'Sector 12',\n",
       " 'Sector 12',\n",
       " 'Sector 12',\n",
       " 'Sector 12',\n",
       " 'Sector 12',\n",
       " 'Sector 15',\n",
       " 'Sector 15',\n",
       " 'Sector 15',\n",
       " 'Sector 15',\n",
       " 'Sector 15',\n",
       " 'Sector 15',\n",
       " 'Sector 15',\n",
       " 'Sector 15',\n",
       " 'Sector 15',\n",
       " 'Sector 15',\n",
       " 'Sector 15',\n",
       " 'Sector 15',\n",
       " 'Sector 15',\n",
       " 'Sector 15',\n",
       " 'Sector 15',\n",
       " 'Sector 15',\n",
       " 'Sector 15',\n",
       " 'Sector 15',\n",
       " 'Sector 15',\n",
       " 'Sector 15',\n",
       " 'Sector 15',\n",
       " 'Sector 15',\n",
       " 'Sector 15',\n",
       " 'Sector 15',\n",
       " 'Sector 15',\n",
       " 'Sector 15',\n",
       " 'Sector 15',\n",
       " 'Sector 15',\n",
       " 'Sector 15',\n",
       " 'Sector 15',\n",
       " 'Sector 15',\n",
       " 'Sector 16',\n",
       " 'Sector 16',\n",
       " 'Sector 16',\n",
       " 'Sector 16',\n",
       " 'Sector 16',\n",
       " 'Sector 16',\n",
       " 'Sector 16',\n",
       " 'Sector 16',\n",
       " 'Sector 16',\n",
       " 'Sector 16',\n",
       " 'Sector 16',\n",
       " 'Sector 16',\n",
       " 'Sector 16',\n",
       " 'Sector 16',\n",
       " 'Sector 16',\n",
       " 'Sector 16',\n",
       " 'Sector 16',\n",
       " 'Sector 17',\n",
       " 'Sector 17',\n",
       " 'Sector 17',\n",
       " 'Sector 17',\n",
       " 'Sector 17',\n",
       " 'Sector 17',\n",
       " 'Sector 17',\n",
       " 'Sector 17',\n",
       " 'Sector 17',\n",
       " 'Sector 17',\n",
       " 'Sector 19',\n",
       " 'Sector 19',\n",
       " 'Sector 21',\n",
       " 'Sector 21',\n",
       " 'Sector 21',\n",
       " 'Sector 21',\n",
       " 'Sector 21',\n",
       " 'Sector 21',\n",
       " 'Sector 21',\n",
       " 'Sector 21',\n",
       " 'Sector 21',\n",
       " 'Sector 21',\n",
       " ...]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "locality=[]\n",
    "for i in range(0,9551):\n",
    "    try:\n",
    "        locality.append(data['Locality'][i][1])\n",
    "    except IndexError as e:\n",
    "        locality.append(data['Locality'][i][0])\n",
    "    \n",
    "locality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "475427ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9551\n"
     ]
    }
   ],
   "source": [
    "print (len(locality))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a328b3d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['locality']=locality\n",
    "data=data.drop(columns=['Locality'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "745387be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1053"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['locality'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7028e264",
   "metadata": {},
   "source": [
    "The number of categories in locality column has slighty reduced (from 1208 to 1053)\n",
    "\n",
    "Column Price range needs to be changed to object datatype from int64 as it is a categorical column "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d1ab963f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Price range']= data['Price range'].astype(object)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1d64ce5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 9551 entries, 0 to 9550\n",
      "Data columns (total 15 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   City                  9551 non-null   object \n",
      " 1   Longitude             9551 non-null   float64\n",
      " 2   Latitude              9551 non-null   float64\n",
      " 3   Cuisines              9542 non-null   object \n",
      " 4   Average Cost for two  9551 non-null   int64  \n",
      " 5   Currency              9551 non-null   object \n",
      " 6   Has Table booking     9551 non-null   object \n",
      " 7   Has Online delivery   9551 non-null   object \n",
      " 8   Is delivering now     9551 non-null   object \n",
      " 9   Price range           9551 non-null   object \n",
      " 10  Aggregate rating      9551 non-null   float64\n",
      " 11  Rating text           9551 non-null   object \n",
      " 12  Votes                 9551 non-null   int64  \n",
      " 13  Country               9551 non-null   object \n",
      " 14  locality              9551 non-null   object \n",
      "dtypes: float64(3), int64(2), object(10)\n",
      "memory usage: 1.4+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0c322858",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Longitude</th>\n",
       "      <td>9551.0</td>\n",
       "      <td>64.126574</td>\n",
       "      <td>41.467058</td>\n",
       "      <td>-157.948486</td>\n",
       "      <td>77.081343</td>\n",
       "      <td>77.191964</td>\n",
       "      <td>77.282006</td>\n",
       "      <td>174.832089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latitude</th>\n",
       "      <td>9551.0</td>\n",
       "      <td>25.854381</td>\n",
       "      <td>11.007935</td>\n",
       "      <td>-41.330428</td>\n",
       "      <td>28.478713</td>\n",
       "      <td>28.570469</td>\n",
       "      <td>28.642758</td>\n",
       "      <td>55.976980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Average Cost for two</th>\n",
       "      <td>9551.0</td>\n",
       "      <td>1199.210763</td>\n",
       "      <td>16121.183073</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>800000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aggregate rating</th>\n",
       "      <td>9551.0</td>\n",
       "      <td>2.666370</td>\n",
       "      <td>1.516378</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>4.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Votes</th>\n",
       "      <td>9551.0</td>\n",
       "      <td>156.909748</td>\n",
       "      <td>430.169145</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>131.000000</td>\n",
       "      <td>10934.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       count         mean           std         min  \\\n",
       "Longitude             9551.0    64.126574     41.467058 -157.948486   \n",
       "Latitude              9551.0    25.854381     11.007935  -41.330428   \n",
       "Average Cost for two  9551.0  1199.210763  16121.183073    0.000000   \n",
       "Aggregate rating      9551.0     2.666370      1.516378    0.000000   \n",
       "Votes                 9551.0   156.909748    430.169145    0.000000   \n",
       "\n",
       "                             25%         50%         75%            max  \n",
       "Longitude              77.081343   77.191964   77.282006     174.832089  \n",
       "Latitude               28.478713   28.570469   28.642758      55.976980  \n",
       "Average Cost for two  250.000000  400.000000  700.000000  800000.000000  \n",
       "Aggregate rating        2.500000    3.200000    3.700000       4.900000  \n",
       "Votes                   5.000000   31.000000  131.000000   10934.000000  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a91e3ef4",
   "metadata": {},
   "source": [
    "The numerical features indicate skewness and outliers which we will look into in detail during visualization\n",
    "\n",
    "Converting latitude and longitude to categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8f5e5381",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       3.0\n",
       "1       3.0\n",
       "2       3.0\n",
       "3       3.0\n",
       "4       3.0\n",
       "       ... \n",
       "9546    4.0\n",
       "9547    4.0\n",
       "9548    4.0\n",
       "9549    4.0\n",
       "9550    4.0\n",
       "Name: Longitude, Length: 9551, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in data['Longitude']:\n",
    "    if -160<=i<=-100 :\n",
    "        data['Longitude'].replace({i:1},inplace=True)\n",
    "    elif -100<=i<=-40:\n",
    "        data['Longitude'].replace({i:2},inplace=True)\n",
    "    elif -40<=i<=20:\n",
    "        data['Longitude'].replace({i:3},inplace=True)\n",
    "    elif 20<=i<=80:\n",
    "        data['Longitude'].replace({i:4},inplace=True)\n",
    "    elif 80<=i<=140:\n",
    "        data['Longitude'].replace({i:5},inplace=True)\n",
    "    else:\n",
    "        data['Longitude'].replace({i:6},inplace=True)\n",
    "\n",
    "data['Longitude']\n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "70d2fdeb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       4.0\n",
       "1       4.0\n",
       "2       4.0\n",
       "3       4.0\n",
       "4       4.0\n",
       "       ... \n",
       "9546    6.0\n",
       "9547    6.0\n",
       "9548    6.0\n",
       "9549    6.0\n",
       "9550    6.0\n",
       "Name: Latitude, Length: 9551, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in data['Latitude']:\n",
    "    if -45<=i<=-30 :\n",
    "        data['Latitude'].replace({i:1},inplace=True)\n",
    "    elif -30<=i<=-15:\n",
    "        data['Latitude'].replace({i:2},inplace=True)\n",
    "    elif -15<=i<=0:\n",
    "        data['Latitude'].replace({i:3},inplace=True)\n",
    "    elif 0<=i<=15:\n",
    "        data['Latitude'].replace({i:4},inplace=True)\n",
    "    elif 15<=i<=30:\n",
    "        data['Latitude'].replace({i:5},inplace=True)\n",
    "    else:\n",
    "        data['Latitude'].replace({i:6},inplace=True)\n",
    "\n",
    "data['Latitude']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cb4f0be7",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Longitude']= data['Longitude'].astype(object)\n",
    "data['Latitude']= data['Latitude'].astype(object)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "868f42bb",
   "metadata": {},
   "source": [
    "Now that latitude and longitude are sorted\n",
    "\n",
    "Looking at Cuisines\n",
    "1. Imputing null values with bfill\n",
    "2. Try to reduce categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5ec39b1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City                    0\n",
       "Longitude               0\n",
       "Latitude                0\n",
       "Cuisines                0\n",
       "Average Cost for two    0\n",
       "Currency                0\n",
       "Has Table booking       0\n",
       "Has Online delivery     0\n",
       "Is delivering now       0\n",
       "Price range             0\n",
       "Aggregate rating        0\n",
       "Rating text             0\n",
       "Votes                   0\n",
       "Country                 0\n",
       "locality                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Cuisines'] = data['Cuisines'].fillna(method='bfill')\n",
    "data.isna().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8c41ea01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "North Indian                                  936\n",
       "North Indian, Chinese                         511\n",
       "Fast Food                                     355\n",
       "Chinese                                       354\n",
       "North Indian, Mughlai                         334\n",
       "                                             ... \n",
       "Healthy Food, North Indian, Italian, Salad      1\n",
       "Bengali, Fast Food                              1\n",
       "North Indian, Rajasthani, Asian                 1\n",
       "Chinese, Thai, Malaysian, Indonesian            1\n",
       "Italian, World Cuisine                          1\n",
       "Name: Cuisines, Length: 1825, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Cuisines'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "29805a47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0              [French,  Japanese,  Desserts]\n",
       "1                                  [Japanese]\n",
       "2       [Seafood,  Asian,  Filipino,  Indian]\n",
       "3                          [Japanese,  Sushi]\n",
       "4                         [Japanese,  Korean]\n",
       "                        ...                  \n",
       "9546                                [Turkish]\n",
       "9547      [World Cuisine,  Patisserie,  Cafe]\n",
       "9548                [Italian,  World Cuisine]\n",
       "9549                        [Restaurant Cafe]\n",
       "9550                                   [Cafe]\n",
       "Name: Cuisines, Length: 9551, dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Cuisines'] = data['Cuisines'].str.split(',')\n",
    "\n",
    "data['Cuisines']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "46017c2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['French',\n",
       " 'Japanese',\n",
       " 'Seafood',\n",
       " 'Japanese',\n",
       " 'Japanese',\n",
       " 'Chinese',\n",
       " 'Asian',\n",
       " 'Seafood',\n",
       " 'European',\n",
       " 'Filipino',\n",
       " 'Filipino',\n",
       " 'American',\n",
       " 'Filipino',\n",
       " 'Filipino',\n",
       " 'Korean',\n",
       " 'Cafe',\n",
       " 'Italian',\n",
       " 'Filipino',\n",
       " 'Cafe',\n",
       " 'Cafe',\n",
       " 'Seafood',\n",
       " 'American',\n",
       " 'Fast Food',\n",
       " 'Cafe',\n",
       " 'Bakery',\n",
       " 'Brazilian',\n",
       " 'Pizza',\n",
       " 'Japanese',\n",
       " 'Arabian',\n",
       " 'Japanese',\n",
       " 'Brazilian',\n",
       " 'Italian',\n",
       " 'Pizza',\n",
       " 'Bar Food',\n",
       " 'Mexican',\n",
       " 'International',\n",
       " 'Peruvian',\n",
       " 'American',\n",
       " 'Seafood',\n",
       " 'International',\n",
       " 'Italian',\n",
       " 'American',\n",
       " 'Seafood',\n",
       " 'Desserts',\n",
       " 'Juices',\n",
       " 'Beverages',\n",
       " 'Lebanese',\n",
       " 'Burger',\n",
       " 'Brazilian',\n",
       " 'Brazilian',\n",
       " 'Brazilian',\n",
       " 'Brazilian',\n",
       " 'Brazilian',\n",
       " 'Italian',\n",
       " 'Japanese',\n",
       " 'Bakery',\n",
       " 'Brazilian',\n",
       " 'Brazilian',\n",
       " 'Bar Food',\n",
       " 'Brazilian',\n",
       " 'Brazilian',\n",
       " 'Brazilian',\n",
       " 'Italian',\n",
       " 'Steak',\n",
       " 'Indian',\n",
       " 'Brazilian',\n",
       " 'Beverages',\n",
       " 'French',\n",
       " 'Brazilian',\n",
       " 'Brazilian',\n",
       " 'French',\n",
       " 'Sushi',\n",
       " 'BBQ',\n",
       " 'Sushi',\n",
       " 'Gourmet Fast Food',\n",
       " 'Brazilian',\n",
       " 'Italian',\n",
       " 'Brazilian',\n",
       " 'Brazilian',\n",
       " 'Brazilian',\n",
       " 'Brazilian',\n",
       " 'Lebanese',\n",
       " 'BBQ',\n",
       " 'American',\n",
       " 'Mexican',\n",
       " 'Mexican',\n",
       " 'Coffee and Tea',\n",
       " 'Fast Food',\n",
       " 'Fast Food',\n",
       " 'Asian',\n",
       " 'Pizza',\n",
       " 'Steak',\n",
       " 'Chinese',\n",
       " 'Chinese',\n",
       " 'American',\n",
       " 'American',\n",
       " 'American',\n",
       " 'Japanese',\n",
       " 'Japanese',\n",
       " 'Seafood',\n",
       " 'Italian',\n",
       " 'American',\n",
       " 'American',\n",
       " 'Southern',\n",
       " 'Mexican',\n",
       " 'Japanese',\n",
       " 'Breakfast',\n",
       " 'Breakfast',\n",
       " 'American',\n",
       " 'American',\n",
       " 'American',\n",
       " 'American',\n",
       " 'International',\n",
       " 'Sandwich',\n",
       " 'Italian',\n",
       " 'Asian',\n",
       " 'Mexican',\n",
       " 'International',\n",
       " 'Bar Food',\n",
       " 'Italian',\n",
       " 'Burger',\n",
       " 'Desserts',\n",
       " 'Chinese',\n",
       " 'American',\n",
       " 'International',\n",
       " 'Desserts',\n",
       " 'American',\n",
       " 'Breakfast',\n",
       " 'BBQ',\n",
       " 'American',\n",
       " 'Desserts',\n",
       " 'Italian',\n",
       " 'Mexican',\n",
       " 'Bar Food',\n",
       " 'Mexican',\n",
       " 'Steak',\n",
       " 'Indian',\n",
       " 'German',\n",
       " 'Vietnamese',\n",
       " 'Burger',\n",
       " 'Thai',\n",
       " 'Mexican',\n",
       " 'American',\n",
       " 'Italian',\n",
       " 'Breakfast',\n",
       " 'American',\n",
       " 'European',\n",
       " 'Latin American',\n",
       " 'American',\n",
       " 'American',\n",
       " 'American',\n",
       " 'Pizza',\n",
       " 'American',\n",
       " 'Pizza',\n",
       " 'Mexican',\n",
       " 'Italian',\n",
       " 'Sushi',\n",
       " 'Japanese',\n",
       " 'Breakfast',\n",
       " 'Brazilian',\n",
       " 'American',\n",
       " 'American',\n",
       " 'American',\n",
       " 'Italian',\n",
       " 'Mexican',\n",
       " 'American',\n",
       " 'American',\n",
       " 'Indian',\n",
       " 'Chinese',\n",
       " 'Chinese',\n",
       " 'American',\n",
       " 'Indian',\n",
       " 'Burger',\n",
       " 'American',\n",
       " 'Breakfast',\n",
       " 'American',\n",
       " 'Pizza',\n",
       " 'International',\n",
       " 'American',\n",
       " 'Mediterranean',\n",
       " 'BBQ',\n",
       " 'Pizza',\n",
       " 'American',\n",
       " 'Asian',\n",
       " 'Asian',\n",
       " 'American',\n",
       " 'American',\n",
       " 'Burger',\n",
       " 'Pizza',\n",
       " 'Japanese',\n",
       " 'Breakfast',\n",
       " 'Japanese',\n",
       " 'Japanese',\n",
       " 'Seafood',\n",
       " 'Steak',\n",
       " 'Chinese',\n",
       " 'BBQ',\n",
       " 'Italian',\n",
       " 'Pizza',\n",
       " 'American',\n",
       " 'Asian',\n",
       " 'American',\n",
       " 'Italian',\n",
       " 'American',\n",
       " 'American',\n",
       " 'Desserts',\n",
       " 'BBQ',\n",
       " 'Mexican',\n",
       " 'BBQ',\n",
       " 'American',\n",
       " 'Japanese',\n",
       " 'Mexican',\n",
       " 'Italian',\n",
       " 'American',\n",
       " 'Fast Food',\n",
       " 'Mexican',\n",
       " 'American',\n",
       " 'Japanese',\n",
       " 'Thai',\n",
       " 'Desserts',\n",
       " 'BBQ',\n",
       " 'American',\n",
       " 'American',\n",
       " 'Italian',\n",
       " 'Indian',\n",
       " 'Sushi',\n",
       " 'Burger',\n",
       " 'American',\n",
       " 'Italian',\n",
       " 'American',\n",
       " 'Asian',\n",
       " 'Fast Food',\n",
       " 'American',\n",
       " 'American',\n",
       " 'Mexican',\n",
       " 'Mexican',\n",
       " 'Chinese',\n",
       " 'Thai',\n",
       " 'American',\n",
       " 'American',\n",
       " 'Mexican',\n",
       " 'American',\n",
       " 'Japanese',\n",
       " 'American',\n",
       " 'BBQ',\n",
       " 'American',\n",
       " 'BBQ',\n",
       " 'Asian',\n",
       " 'Asian',\n",
       " 'Latin American',\n",
       " 'Italian',\n",
       " 'Chinese',\n",
       " 'Asian',\n",
       " 'American',\n",
       " 'French',\n",
       " 'Japanese',\n",
       " 'Burger',\n",
       " 'American',\n",
       " 'Chinese',\n",
       " 'Breakfast',\n",
       " 'American',\n",
       " 'American',\n",
       " 'Latin American',\n",
       " 'Seafood',\n",
       " 'Breakfast',\n",
       " 'Seafood',\n",
       " 'Mexican',\n",
       " 'American',\n",
       " 'Italian',\n",
       " 'American',\n",
       " 'Mexican',\n",
       " 'Desserts',\n",
       " 'American',\n",
       " 'Japanese',\n",
       " 'American',\n",
       " 'International',\n",
       " 'Mexican',\n",
       " 'Breakfast',\n",
       " 'American',\n",
       " 'American',\n",
       " 'Italian',\n",
       " 'American',\n",
       " 'American',\n",
       " 'American',\n",
       " 'Mexican',\n",
       " 'American',\n",
       " 'BBQ',\n",
       " 'American',\n",
       " 'BBQ',\n",
       " 'BBQ',\n",
       " 'Chinese',\n",
       " 'Bar Food',\n",
       " 'Seafood',\n",
       " 'American',\n",
       " 'BBQ',\n",
       " 'Sandwich',\n",
       " 'Thai',\n",
       " 'BBQ',\n",
       " 'American',\n",
       " 'Southern',\n",
       " 'American',\n",
       " 'German',\n",
       " 'German',\n",
       " 'Burger',\n",
       " 'Italian',\n",
       " 'Burger',\n",
       " 'Thai',\n",
       " 'Coffee and Tea',\n",
       " 'Pizza',\n",
       " 'Seafood',\n",
       " 'Chinese',\n",
       " 'Burger',\n",
       " 'American',\n",
       " 'American',\n",
       " 'Steak',\n",
       " 'Desserts',\n",
       " 'European',\n",
       " 'Greek',\n",
       " 'American',\n",
       " 'Italian',\n",
       " 'Japanese',\n",
       " 'Pizza',\n",
       " 'Mexican',\n",
       " 'American',\n",
       " 'BBQ',\n",
       " 'Japanese',\n",
       " 'Japanese',\n",
       " 'Breakfast',\n",
       " 'Italian',\n",
       " 'Italian',\n",
       " 'American',\n",
       " 'Spanish',\n",
       " 'Hawaiian',\n",
       " 'American',\n",
       " 'Irish',\n",
       " 'New American',\n",
       " 'Brazilian',\n",
       " 'Caribbean',\n",
       " 'Italian',\n",
       " 'Asian',\n",
       " 'Asian',\n",
       " 'American',\n",
       " 'German',\n",
       " 'American',\n",
       " 'American',\n",
       " 'Cajun',\n",
       " 'Southern',\n",
       " 'Mediterranean',\n",
       " 'American',\n",
       " 'Pub Food',\n",
       " 'Pub Food',\n",
       " 'Mexican',\n",
       " 'Burger',\n",
       " 'Italian',\n",
       " 'Asian',\n",
       " 'Italian',\n",
       " 'Tapas',\n",
       " 'Burger',\n",
       " 'Latin American',\n",
       " 'Seafood',\n",
       " 'European',\n",
       " 'Japanese',\n",
       " 'American',\n",
       " 'Italian',\n",
       " 'Seafood',\n",
       " 'Caribbean',\n",
       " 'Breakfast',\n",
       " 'Burger',\n",
       " 'Caribbean',\n",
       " 'Greek',\n",
       " 'Sandwich',\n",
       " 'Burger',\n",
       " 'American',\n",
       " 'Asian',\n",
       " 'Pizza',\n",
       " 'Italian',\n",
       " 'American',\n",
       " 'Chinese',\n",
       " 'Coffee and Tea',\n",
       " 'Pizza',\n",
       " 'American',\n",
       " 'Burger',\n",
       " 'American',\n",
       " 'American',\n",
       " 'Mexican',\n",
       " 'Asian',\n",
       " 'Indian',\n",
       " 'Desserts',\n",
       " 'New American',\n",
       " 'Indian',\n",
       " 'Mexican',\n",
       " 'American',\n",
       " 'Seafood',\n",
       " 'Seafood',\n",
       " 'American',\n",
       " 'Seafood',\n",
       " 'Seafood',\n",
       " 'American',\n",
       " 'Burger',\n",
       " 'Japanese',\n",
       " 'Asian',\n",
       " 'Asian',\n",
       " 'Breakfast',\n",
       " 'Hawaiian',\n",
       " 'Hawaiian',\n",
       " 'Hawaiian',\n",
       " 'Japanese',\n",
       " 'American',\n",
       " 'American',\n",
       " 'Hawaiian',\n",
       " 'Breakfast',\n",
       " 'Asian',\n",
       " 'American',\n",
       " 'Southern',\n",
       " 'Burger',\n",
       " 'Desserts',\n",
       " 'American',\n",
       " 'International',\n",
       " 'American',\n",
       " 'American',\n",
       " 'American',\n",
       " 'American',\n",
       " 'Breakfast',\n",
       " 'American',\n",
       " 'Breakfast',\n",
       " 'Breakfast',\n",
       " 'Coffee and Tea',\n",
       " 'Pizza',\n",
       " 'Cafe',\n",
       " 'American',\n",
       " 'American',\n",
       " 'Seafood',\n",
       " 'Burger',\n",
       " 'Burger',\n",
       " 'American',\n",
       " 'Asian',\n",
       " 'BBQ',\n",
       " 'Japanese',\n",
       " 'Asian',\n",
       " 'Chinese',\n",
       " 'Pizza',\n",
       " 'Burger',\n",
       " 'American',\n",
       " 'American',\n",
       " 'American',\n",
       " 'American',\n",
       " 'Mexican',\n",
       " 'Caribbean',\n",
       " 'Japanese',\n",
       " 'Asian',\n",
       " 'Pizza',\n",
       " 'Italian',\n",
       " 'American',\n",
       " 'BBQ',\n",
       " 'Latin American',\n",
       " 'American',\n",
       " 'Spanish',\n",
       " 'British',\n",
       " 'American',\n",
       " 'Breakfast',\n",
       " 'International',\n",
       " 'Breakfast',\n",
       " 'American',\n",
       " 'American',\n",
       " 'Italian',\n",
       " 'Sandwich',\n",
       " 'European',\n",
       " 'Breakfast',\n",
       " 'Desserts',\n",
       " 'International',\n",
       " 'Mexican',\n",
       " 'Seafood',\n",
       " 'Cuban',\n",
       " 'Bar Food',\n",
       " 'Mexican',\n",
       " 'Seafood',\n",
       " 'Burger',\n",
       " 'Asian',\n",
       " 'Pizza',\n",
       " 'Burger',\n",
       " 'Fast Food',\n",
       " 'American',\n",
       " 'Mexican',\n",
       " 'American',\n",
       " 'Greek',\n",
       " 'Mexican',\n",
       " 'Japanese',\n",
       " 'Italian',\n",
       " 'Japanese',\n",
       " 'Indian',\n",
       " 'Mexican',\n",
       " 'Southern',\n",
       " 'BBQ',\n",
       " 'Coffee and Tea',\n",
       " 'Indian',\n",
       " 'Italian',\n",
       " 'Desserts',\n",
       " 'Chinese',\n",
       " 'Asian',\n",
       " 'American',\n",
       " 'American',\n",
       " 'Burger',\n",
       " 'Japanese',\n",
       " 'Coffee and Tea',\n",
       " 'Sandwich',\n",
       " 'American',\n",
       " 'Pizza',\n",
       " 'Mexican',\n",
       " 'American',\n",
       " 'Chinese',\n",
       " 'Mexican',\n",
       " 'American',\n",
       " 'Thai',\n",
       " 'Japanese',\n",
       " 'Burger',\n",
       " 'Burger',\n",
       " 'Bar Food',\n",
       " 'Modern Australian',\n",
       " 'Pizza',\n",
       " 'Coffee and Tea',\n",
       " 'Cafe',\n",
       " 'Tea',\n",
       " 'Cafe',\n",
       " 'Cafe',\n",
       " 'Italian',\n",
       " 'Breakfast',\n",
       " 'Burger',\n",
       " 'Breakfast',\n",
       " 'Breakfast',\n",
       " 'Cafe',\n",
       " 'Asian',\n",
       " 'Bar Food',\n",
       " 'Coffee and Tea',\n",
       " 'Mediterranean',\n",
       " 'Modern Australian',\n",
       " 'Cafe',\n",
       " 'Breakfast',\n",
       " 'Modern Australian',\n",
       " 'Australian',\n",
       " 'Coffee and Tea',\n",
       " 'Japanese',\n",
       " 'Chinese',\n",
       " 'Italian',\n",
       " 'Asian',\n",
       " 'Chinese',\n",
       " 'American',\n",
       " 'French',\n",
       " 'American',\n",
       " 'French',\n",
       " 'French',\n",
       " 'Bakery',\n",
       " 'Italian',\n",
       " 'Italian',\n",
       " 'American',\n",
       " 'Cafe',\n",
       " 'Singaporean',\n",
       " 'Asian',\n",
       " 'Chinese',\n",
       " 'Singaporean',\n",
       " 'Western',\n",
       " 'American',\n",
       " 'American',\n",
       " 'Finger Food',\n",
       " 'Cafe',\n",
       " 'American',\n",
       " 'American',\n",
       " 'Italian',\n",
       " 'Turkish',\n",
       " 'Fast Food',\n",
       " 'Asian',\n",
       " 'Italian',\n",
       " 'Thai',\n",
       " 'Indian',\n",
       " 'Indian',\n",
       " 'Filipino',\n",
       " 'American',\n",
       " 'Pakistani',\n",
       " 'Lebanese',\n",
       " 'Indian',\n",
       " 'Indian',\n",
       " 'Indian',\n",
       " 'Indian',\n",
       " 'Chinese',\n",
       " 'American',\n",
       " 'Mediterranean',\n",
       " 'Indian',\n",
       " 'Indian',\n",
       " 'Indian',\n",
       " 'International',\n",
       " 'Continental',\n",
       " 'Indian',\n",
       " 'Indian',\n",
       " 'American',\n",
       " 'Cafe',\n",
       " 'Fast Food',\n",
       " 'Asian',\n",
       " 'Indian',\n",
       " 'Indian',\n",
       " 'Indian',\n",
       " 'Seafood',\n",
       " 'American',\n",
       " 'Cafe',\n",
       " 'American',\n",
       " 'Goan',\n",
       " 'Indian',\n",
       " 'South Indian',\n",
       " 'Indian',\n",
       " 'Indian',\n",
       " 'Lebanese',\n",
       " 'Fast Food',\n",
       " 'Pakistani',\n",
       " 'Pakistani',\n",
       " 'Pakistani',\n",
       " 'Fast Food',\n",
       " 'Bakery',\n",
       " 'Arabian',\n",
       " 'African',\n",
       " 'Chinese',\n",
       " 'American',\n",
       " 'Indian',\n",
       " 'American',\n",
       " 'Cafe',\n",
       " 'Fast Food',\n",
       " 'Cafe',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'Rajasthani',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'Mughlai',\n",
       " 'Rajasthani',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'South Indian',\n",
       " 'North Indian',\n",
       " 'Cafe',\n",
       " 'North Indian',\n",
       " 'Cafe',\n",
       " 'North Indian',\n",
       " 'Italian',\n",
       " 'Chinese',\n",
       " 'North Indian',\n",
       " 'Chinese',\n",
       " 'Continental',\n",
       " 'Ice Cream',\n",
       " 'North Indian',\n",
       " 'Pizza',\n",
       " 'Pizza',\n",
       " 'Cafe',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'Desserts',\n",
       " 'Fast Food',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'Italian',\n",
       " 'Cafe',\n",
       " 'Cafe',\n",
       " 'North Indian',\n",
       " 'Continental',\n",
       " 'North Indian',\n",
       " 'Cafe',\n",
       " 'Continental',\n",
       " 'North Indian',\n",
       " 'Cafe',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'American',\n",
       " 'Fast Food',\n",
       " 'North Indian',\n",
       " 'Italian',\n",
       " 'Fast Food',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'South Indian',\n",
       " 'Continental',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'Desserts',\n",
       " 'South Indian',\n",
       " 'Mughlai',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'Beverages',\n",
       " 'North Indian',\n",
       " 'Street Food',\n",
       " 'North Indian',\n",
       " 'Cafe',\n",
       " 'North Indian',\n",
       " 'Fast Food',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'Mithai',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'Fast Food',\n",
       " 'North Indian',\n",
       " 'Italian',\n",
       " 'North Indian',\n",
       " 'Pizza',\n",
       " 'North Indian',\n",
       " 'South Indian',\n",
       " 'North Indian',\n",
       " 'Italian',\n",
       " 'North Indian',\n",
       " 'Continental',\n",
       " 'Juices',\n",
       " 'South Indian',\n",
       " 'Mughlai',\n",
       " 'Maharashtrian',\n",
       " 'North Indian',\n",
       " 'Cafe',\n",
       " 'Mughlai',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'Asian',\n",
       " 'Italian',\n",
       " 'European',\n",
       " 'Modern Indian',\n",
       " 'Bakery',\n",
       " 'Pizza',\n",
       " 'Pizza',\n",
       " 'Continental',\n",
       " 'American',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'Continental',\n",
       " 'European',\n",
       " 'Asian',\n",
       " 'Pizza',\n",
       " 'Continental',\n",
       " 'Finger Food',\n",
       " 'Continental',\n",
       " 'Modern Indian',\n",
       " 'Cafe',\n",
       " 'Cafe',\n",
       " 'Fast Food',\n",
       " 'North Indian',\n",
       " 'Fast Food',\n",
       " 'North Indian',\n",
       " 'Chinese',\n",
       " 'Pizza',\n",
       " 'Cafe',\n",
       " 'Bakery',\n",
       " 'North Indian',\n",
       " 'Pizza',\n",
       " 'Fast Food',\n",
       " 'Fast Food',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'South Indian',\n",
       " 'Italian',\n",
       " 'Fast Food',\n",
       " 'Cafe',\n",
       " 'Chinese',\n",
       " 'Mediterranean',\n",
       " 'Cafe',\n",
       " 'Fast Food',\n",
       " 'Chinese',\n",
       " 'North Indian',\n",
       " 'Cafe',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'Chinese',\n",
       " 'Mughlai',\n",
       " 'Cafe',\n",
       " 'North Indian',\n",
       " 'Cafe',\n",
       " 'Italian',\n",
       " 'North Indian',\n",
       " 'Cafe',\n",
       " 'Chinese',\n",
       " 'Chinese',\n",
       " 'Asian',\n",
       " 'North Indian',\n",
       " 'American',\n",
       " 'Mexican',\n",
       " 'Japanese',\n",
       " 'Continental',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'American',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'Bakery',\n",
       " 'Cafe',\n",
       " 'Burger',\n",
       " 'Mexican',\n",
       " 'Mughlai',\n",
       " 'Italian',\n",
       " 'Burger',\n",
       " 'Desserts',\n",
       " 'European',\n",
       " 'North Indian',\n",
       " 'Continental',\n",
       " 'North Indian',\n",
       " 'Mexican',\n",
       " 'Cafe',\n",
       " 'Italian',\n",
       " 'Cafe',\n",
       " 'Italian',\n",
       " 'North Indian',\n",
       " 'South Indian',\n",
       " 'Biryani',\n",
       " 'European',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'Continental',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'Cafe',\n",
       " 'South Indian',\n",
       " 'Cafe',\n",
       " 'Pizza',\n",
       " 'Fast Food',\n",
       " 'Chinese',\n",
       " 'Continental',\n",
       " 'South Indian',\n",
       " 'Continental',\n",
       " 'North Indian',\n",
       " 'Parsi',\n",
       " 'Cafe',\n",
       " 'North Indian',\n",
       " 'Italian',\n",
       " 'South Indian',\n",
       " 'North Indian',\n",
       " 'Bakery',\n",
       " 'North Indian',\n",
       " 'Desserts',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'South Indian',\n",
       " 'Desserts',\n",
       " 'North Indian',\n",
       " 'Chinese',\n",
       " 'North Indian',\n",
       " 'Chinese',\n",
       " 'Mughlai',\n",
       " 'North Indian',\n",
       " 'Cafe',\n",
       " 'Bakery',\n",
       " 'Cafe',\n",
       " 'Burger',\n",
       " 'North Indian',\n",
       " 'Cafe',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'Cafe',\n",
       " 'Cafe',\n",
       " 'Cafe',\n",
       " 'Chinese',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'Bakery',\n",
       " 'Chinese',\n",
       " 'Mithai',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'Cafe',\n",
       " 'Fast Food',\n",
       " 'Raw Meats',\n",
       " 'Fast Food',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'Fast Food',\n",
       " 'North Indian',\n",
       " 'Chinese',\n",
       " 'Burger',\n",
       " 'Cafe',\n",
       " 'Desserts',\n",
       " 'Fast Food',\n",
       " 'American',\n",
       " 'American',\n",
       " 'North Indian',\n",
       " 'Chinese',\n",
       " 'Ice Cream',\n",
       " 'Fast Food',\n",
       " 'South Indian',\n",
       " 'North Indian',\n",
       " 'South Indian',\n",
       " 'Desserts',\n",
       " 'Fast Food',\n",
       " 'North Indian',\n",
       " 'Cafe',\n",
       " 'North Indian',\n",
       " 'Mithai',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'Mughlai',\n",
       " 'North Indian',\n",
       " 'Bakery',\n",
       " 'Cafe',\n",
       " 'Cafe',\n",
       " 'Fast Food',\n",
       " 'North Indian',\n",
       " 'Fast Food',\n",
       " 'Bakery',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'Ice Cream',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'Chinese',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'Bakery',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'Fast Food',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'Cafe',\n",
       " 'Pizza',\n",
       " 'North Indian',\n",
       " 'Bakery',\n",
       " 'North Indian',\n",
       " 'Fast Food',\n",
       " 'Bakery',\n",
       " 'Chinese',\n",
       " 'Fast Food',\n",
       " 'Cafe',\n",
       " 'Fast Food',\n",
       " 'Italian',\n",
       " 'Bakery',\n",
       " 'Burger',\n",
       " 'Cafe',\n",
       " 'Bakery',\n",
       " 'North Indian',\n",
       " 'Raw Meats',\n",
       " 'Ice Cream',\n",
       " 'Raw Meats',\n",
       " 'Fast Food',\n",
       " 'Juices',\n",
       " 'Beverages',\n",
       " 'North Indian',\n",
       " 'Bakery',\n",
       " 'Bakery',\n",
       " 'Fast Food',\n",
       " 'American',\n",
       " 'Cafe',\n",
       " 'North Indian',\n",
       " 'Ice Cream',\n",
       " 'Chinese',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'Cafe',\n",
       " 'North Indian',\n",
       " 'Chinese',\n",
       " 'Chinese',\n",
       " 'Chinese',\n",
       " 'North Indian',\n",
       " 'Chinese',\n",
       " 'North Indian',\n",
       " 'Fast Food',\n",
       " 'Chinese',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'Chinese',\n",
       " 'North Indian',\n",
       " 'Mithai',\n",
       " 'Cafe',\n",
       " 'Chinese',\n",
       " 'Cafe',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'Mithai',\n",
       " 'Street Food',\n",
       " 'Street Food',\n",
       " 'Bakery',\n",
       " 'North Indian',\n",
       " 'Mithai',\n",
       " 'Ice Cream',\n",
       " 'North Indian',\n",
       " 'North Indian',\n",
       " 'Asian',\n",
       " ...]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Cuisine=[]\n",
    "for i in range(0,9551):\n",
    "    Cuisine.append(data['Cuisines'][i][0])\n",
    "    \n",
    "    \n",
    "Cuisine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "40438005",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "119"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Cuisine']=Cuisine\n",
    "data=data.drop(columns=['Cuisines'])\n",
    "data['Cuisine'].nunique()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d6f5d6b",
   "metadata": {},
   "source": [
    "We see a drastic change in the categories in the cuisine column (from 1825 to 119)\n",
    "\n",
    "The idea was to keep only the first cuisine mentioned in the data as it is generally the main type of food a restaurant is famous for  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "cb09c7d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dividing data to numerical and categorical\n",
    "C_data = data.select_dtypes(include=['object'])\n",
    "N_data = data.select_dtypes(include=['int64', 'float64'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e1ed8bdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Currency</th>\n",
       "      <th>Has Table booking</th>\n",
       "      <th>Has Online delivery</th>\n",
       "      <th>Is delivering now</th>\n",
       "      <th>Price range</th>\n",
       "      <th>Rating text</th>\n",
       "      <th>Country</th>\n",
       "      <th>locality</th>\n",
       "      <th>Cuisine</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Makati City</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Phillipines</td>\n",
       "      <td>Poblacion</td>\n",
       "      <td>French</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Makati City</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Phillipines</td>\n",
       "      <td>Legaspi Village</td>\n",
       "      <td>Japanese</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mandaluyong City</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>Phillipines</td>\n",
       "      <td>Ortigas</td>\n",
       "      <td>Seafood</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mandaluyong City</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Phillipines</td>\n",
       "      <td>Ortigas</td>\n",
       "      <td>Japanese</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mandaluyong City</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Phillipines</td>\n",
       "      <td>Ortigas</td>\n",
       "      <td>Japanese</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               City Longitude Latitude          Currency Has Table booking  \\\n",
       "0       Makati City       3.0      4.0  Botswana Pula(P)               Yes   \n",
       "1       Makati City       3.0      4.0  Botswana Pula(P)               Yes   \n",
       "2  Mandaluyong City       3.0      4.0  Botswana Pula(P)               Yes   \n",
       "3  Mandaluyong City       3.0      4.0  Botswana Pula(P)                No   \n",
       "4  Mandaluyong City       3.0      4.0  Botswana Pula(P)               Yes   \n",
       "\n",
       "  Has Online delivery Is delivering now Price range Rating text      Country  \\\n",
       "0                  No                No           3   Excellent  Phillipines   \n",
       "1                  No                No           3   Excellent  Phillipines   \n",
       "2                  No                No           4   Very Good  Phillipines   \n",
       "3                  No                No           4   Excellent  Phillipines   \n",
       "4                  No                No           4   Excellent  Phillipines   \n",
       "\n",
       "           locality   Cuisine  \n",
       "0         Poblacion    French  \n",
       "1   Legaspi Village  Japanese  \n",
       "2           Ortigas   Seafood  \n",
       "3           Ortigas  Japanese  \n",
       "4           Ortigas  Japanese  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5c8b78c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column Name        : City\n",
      "------------------------------------------\n",
      "New Delhi       57.302900\n",
      "Gurgaon         11.705581\n",
      "Noida           11.307716\n",
      "Faridabad        2.627997\n",
      "Ghaziabad        0.261753\n",
      "                  ...    \n",
      "Huskisson        0.010470\n",
      "Panchkula        0.010470\n",
      "Forrest          0.010470\n",
      "Flaxton          0.010470\n",
      "Chatham-Kent     0.010470\n",
      "Name: City, Length: 141, dtype: float64\n",
      "\n",
      "\n",
      "Column Name        : Longitude\n",
      "------------------------------------------\n",
      "3.0    96.199351\n",
      "4.0     1.947440\n",
      "2.0     0.900429\n",
      "1.0     0.565386\n",
      "5.0     0.387394\n",
      "Name: Longitude, dtype: float64\n",
      "\n",
      "\n",
      "Column Name        : Latitude\n",
      "------------------------------------------\n",
      "4.0    96.984609\n",
      "2.0     1.099361\n",
      "1.0     0.816668\n",
      "6.0     0.502565\n",
      "5.0     0.387394\n",
      "3.0     0.209402\n",
      "Name: Latitude, dtype: float64\n",
      "\n",
      "\n",
      "Column Name        : Currency\n",
      "------------------------------------------\n",
      "Indian Rupees(Rs.)        90.587373\n",
      "Dollar ($)                 5.036122\n",
      "Pounds(Œ£)                 0.837609\n",
      "Brazilian Real(RD)         0.628206\n",
      "Emirati Diram(AED)         0.628206\n",
      "Rand(R)                    0.628206\n",
      "NewZealand ($)             0.418804\n",
      "Turkish Lira(TL)           0.345514\n",
      "Botswana Pula(P)           0.230342\n",
      "Indonesian Rupiah(IDR)     0.219872\n",
      "Qatari Rial(QR)            0.209402\n",
      "Sri Lankan Rupee(LKR)      0.209402\n",
      "Dollar($)                  0.010470\n",
      "Turkish (TL)               0.010470\n",
      "Name: Currency, dtype: float64\n",
      "\n",
      "\n",
      "Column Name        : Has Table booking\n",
      "------------------------------------------\n",
      "No     87.875615\n",
      "Yes    12.124385\n",
      "Name: Has Table booking, dtype: float64\n",
      "\n",
      "\n",
      "Column Name        : Has Online delivery\n",
      "------------------------------------------\n",
      "No     74.337766\n",
      "Yes    25.662234\n",
      "Name: Has Online delivery, dtype: float64\n",
      "\n",
      "\n",
      "Column Name        : Is delivering now\n",
      "------------------------------------------\n",
      "No     99.644016\n",
      "Yes     0.355984\n",
      "Name: Is delivering now, dtype: float64\n",
      "\n",
      "\n",
      "Column Name        : Price range\n",
      "------------------------------------------\n",
      "1    46.529159\n",
      "2    32.593446\n",
      "3    14.741912\n",
      "4     6.135483\n",
      "Name: Price range, dtype: float64\n",
      "\n",
      "\n",
      "Column Name        : Rating text\n",
      "------------------------------------------\n",
      "Average      39.126793\n",
      "Not rated    22.489792\n",
      "Good         21.987226\n",
      "Very Good    11.297246\n",
      "Excellent     3.151502\n",
      "Poor          1.947440\n",
      "Name: Rating text, dtype: float64\n",
      "\n",
      "\n",
      "Column Name        : Country\n",
      "------------------------------------------\n",
      "India             90.587373\n",
      "United States      4.544027\n",
      "United Kingdom     0.837609\n",
      "Brazil             0.628206\n",
      "UAE                0.628206\n",
      "South Africa       0.628206\n",
      "New Zealand        0.418804\n",
      "Turkey             0.355984\n",
      "Australia          0.251283\n",
      "Phillipines        0.230342\n",
      "Indonesia          0.219872\n",
      "Singapore          0.209402\n",
      "Qatar              0.209402\n",
      "Sri Lanka          0.209402\n",
      "Canada             0.041880\n",
      "Name: Country, dtype: float64\n",
      "\n",
      "\n",
      "Column Name        : locality\n",
      "------------------------------------------\n",
      "Connaught Place    1.277353\n",
      "Rajouri Garden     1.036541\n",
      "Shahdara           0.910899\n",
      "Defence Colony     0.900429\n",
      "Pitampura          0.889959\n",
      "                     ...   \n",
      " Anjuna            0.010470\n",
      "Arambol            0.010470\n",
      "Betalbatim         0.010470\n",
      "Calangute          0.010470\n",
      "Moda               0.010470\n",
      "Name: locality, Length: 1053, dtype: float64\n",
      "\n",
      "\n",
      "Column Name        : Cuisine\n",
      "------------------------------------------\n",
      "North Indian         31.326563\n",
      "Chinese               8.951942\n",
      "Fast Food             7.046383\n",
      "Bakery                6.501937\n",
      "Cafe                  6.460057\n",
      "                       ...    \n",
      "Australian            0.010470\n",
      "Cajun                 0.010470\n",
      "Peruvian              0.010470\n",
      "Tex-Mex               0.010470\n",
      "Gourmet Fast Food     0.010470\n",
      "Name: Cuisine, Length: 119, dtype: float64\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "column_names = C_data.columns\n",
    "for column_name in column_names:\n",
    "    print(\"Column Name        :\", column_name)\n",
    "    print(\"------------------------------------------\")\n",
    "    print(data[column_name].value_counts(normalize=True, dropna=False)*100)\n",
    "    print('\\n')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8d2515a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Checking categories with lesser percentage to others\n",
    "def change_to_others(x, value_counts_df):\n",
    "    for key, val in value_counts_df.to_dict().items():\n",
    "        if key == x and val < 1:\n",
    "            return 'others'\n",
    "    return x\n",
    "\n",
    "columns_not_to_be_considered = ['Latitude','Longitude','Currency','Is delivering now','Country','Price range']\n",
    "column_names = C_data.columns\n",
    "column_names = column_names.drop(columns_not_to_be_considered)\n",
    "columns_to_be_changed = column_names\n",
    "\n",
    "for column_name in columns_to_be_changed:\n",
    "    value_counts_df = data[column_name].value_counts(normalize=True) * 100\n",
    "    data[column_name] = data[column_name].apply(lambda x:change_to_others(x, value_counts_df))\n",
    "    \n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d0bcd664",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column Name        : City\n",
      "------------------------------------------\n",
      "New Delhi    57.302900\n",
      "others       17.055806\n",
      "Gurgaon      11.705581\n",
      "Noida        11.307716\n",
      "Faridabad     2.627997\n",
      "Name: City, dtype: float64\n",
      "\n",
      "\n",
      "Column Name        : Longitude\n",
      "------------------------------------------\n",
      "3.0    96.199351\n",
      "4.0     1.947440\n",
      "2.0     0.900429\n",
      "1.0     0.565386\n",
      "5.0     0.387394\n",
      "Name: Longitude, dtype: float64\n",
      "\n",
      "\n",
      "Column Name        : Latitude\n",
      "------------------------------------------\n",
      "4.0    96.984609\n",
      "2.0     1.099361\n",
      "1.0     0.816668\n",
      "6.0     0.502565\n",
      "5.0     0.387394\n",
      "3.0     0.209402\n",
      "Name: Latitude, dtype: float64\n",
      "\n",
      "\n",
      "Column Name        : Currency\n",
      "------------------------------------------\n",
      "Indian Rupees(Rs.)        90.587373\n",
      "Dollar ($)                 5.036122\n",
      "Pounds(Œ£)                 0.837609\n",
      "Brazilian Real(RD)         0.628206\n",
      "Emirati Diram(AED)         0.628206\n",
      "Rand(R)                    0.628206\n",
      "NewZealand ($)             0.418804\n",
      "Turkish Lira(TL)           0.345514\n",
      "Botswana Pula(P)           0.230342\n",
      "Indonesian Rupiah(IDR)     0.219872\n",
      "Qatari Rial(QR)            0.209402\n",
      "Sri Lankan Rupee(LKR)      0.209402\n",
      "Dollar($)                  0.010470\n",
      "Turkish (TL)               0.010470\n",
      "Name: Currency, dtype: float64\n",
      "\n",
      "\n",
      "Column Name        : Has Table booking\n",
      "------------------------------------------\n",
      "No     87.875615\n",
      "Yes    12.124385\n",
      "Name: Has Table booking, dtype: float64\n",
      "\n",
      "\n",
      "Column Name        : Has Online delivery\n",
      "------------------------------------------\n",
      "No     74.337766\n",
      "Yes    25.662234\n",
      "Name: Has Online delivery, dtype: float64\n",
      "\n",
      "\n",
      "Column Name        : Is delivering now\n",
      "------------------------------------------\n",
      "No     99.644016\n",
      "Yes     0.355984\n",
      "Name: Is delivering now, dtype: float64\n",
      "\n",
      "\n",
      "Column Name        : Price range\n",
      "------------------------------------------\n",
      "1    46.529159\n",
      "2    32.593446\n",
      "3    14.741912\n",
      "4     6.135483\n",
      "Name: Price range, dtype: float64\n",
      "\n",
      "\n",
      "Column Name        : Rating text\n",
      "------------------------------------------\n",
      "Average      39.126793\n",
      "Not rated    22.489792\n",
      "Good         21.987226\n",
      "Very Good    11.297246\n",
      "Excellent     3.151502\n",
      "Poor          1.947440\n",
      "Name: Rating text, dtype: float64\n",
      "\n",
      "\n",
      "Column Name        : Country\n",
      "------------------------------------------\n",
      "India             90.587373\n",
      "United States      4.544027\n",
      "United Kingdom     0.837609\n",
      "Brazil             0.628206\n",
      "UAE                0.628206\n",
      "South Africa       0.628206\n",
      "New Zealand        0.418804\n",
      "Turkey             0.355984\n",
      "Australia          0.251283\n",
      "Phillipines        0.230342\n",
      "Indonesia          0.219872\n",
      "Singapore          0.209402\n",
      "Qatar              0.209402\n",
      "Sri Lanka          0.209402\n",
      "Canada             0.041880\n",
      "Name: Country, dtype: float64\n",
      "\n",
      "\n",
      "Column Name        : locality\n",
      "------------------------------------------\n",
      "others             97.686106\n",
      "Connaught Place     1.277353\n",
      "Rajouri Garden      1.036541\n",
      "Name: locality, dtype: float64\n",
      "\n",
      "\n",
      "Column Name        : Cuisine\n",
      "------------------------------------------\n",
      "North Indian    31.326563\n",
      "others          12.407078\n",
      "Chinese          8.951942\n",
      "Fast Food        7.046383\n",
      "Bakery           6.501937\n",
      "Cafe             6.460057\n",
      "American         2.931630\n",
      "South Indian     2.743168\n",
      "Mithai           2.575647\n",
      "Street Food      2.470945\n",
      "Italian          2.460475\n",
      "Continental      2.460475\n",
      "Pizza            2.429065\n",
      "Mughlai          2.251073\n",
      "Ice Cream        1.863679\n",
      "Desserts         1.570516\n",
      "Burger           1.225003\n",
      "Biryani          1.172652\n",
      "Raw Meats        1.151712\n",
      "Name: Cuisine, dtype: float64\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "column_names = C_data.columns\n",
    "for column_name in column_names:\n",
    "    print(\"Column Name        :\", column_name)\n",
    "    print(\"------------------------------------------\")\n",
    "    print(data[column_name].value_counts(normalize=True, dropna=False)*100)\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "312848d5",
   "metadata": {},
   "source": [
    "Dropping Locality from the dataset after binning the catagories 90% of the data is categorised as 'others'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d4acee0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=data.drop(columns=['locality'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e6310fa5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Currency</th>\n",
       "      <th>Has Table booking</th>\n",
       "      <th>Has Online delivery</th>\n",
       "      <th>Is delivering now</th>\n",
       "      <th>Price range</th>\n",
       "      <th>Rating text</th>\n",
       "      <th>Country</th>\n",
       "      <th>Cuisine</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>others</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Phillipines</td>\n",
       "      <td>others</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>others</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Phillipines</td>\n",
       "      <td>others</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>others</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>Phillipines</td>\n",
       "      <td>others</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>others</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Phillipines</td>\n",
       "      <td>others</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>others</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Botswana Pula(P)</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Phillipines</td>\n",
       "      <td>others</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9546</th>\n",
       "      <td>others</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Turkish Lira(TL)</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>others</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9547</th>\n",
       "      <td>others</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Turkish Lira(TL)</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>others</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9548</th>\n",
       "      <td>others</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Turkish Lira(TL)</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>Good</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>Italian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9549</th>\n",
       "      <td>others</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Turkish Lira(TL)</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>others</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9550</th>\n",
       "      <td>others</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Turkish Lira(TL)</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>Cafe</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9551 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        City Longitude Latitude          Currency Has Table booking  \\\n",
       "0     others       3.0      4.0  Botswana Pula(P)               Yes   \n",
       "1     others       3.0      4.0  Botswana Pula(P)               Yes   \n",
       "2     others       3.0      4.0  Botswana Pula(P)               Yes   \n",
       "3     others       3.0      4.0  Botswana Pula(P)                No   \n",
       "4     others       3.0      4.0  Botswana Pula(P)               Yes   \n",
       "...      ...       ...      ...               ...               ...   \n",
       "9546  others       4.0      6.0  Turkish Lira(TL)                No   \n",
       "9547  others       4.0      6.0  Turkish Lira(TL)                No   \n",
       "9548  others       4.0      6.0  Turkish Lira(TL)                No   \n",
       "9549  others       4.0      6.0  Turkish Lira(TL)                No   \n",
       "9550  others       4.0      6.0  Turkish Lira(TL)                No   \n",
       "\n",
       "     Has Online delivery Is delivering now Price range Rating text  \\\n",
       "0                     No                No           3   Excellent   \n",
       "1                     No                No           3   Excellent   \n",
       "2                     No                No           4   Very Good   \n",
       "3                     No                No           4   Excellent   \n",
       "4                     No                No           4   Excellent   \n",
       "...                  ...               ...         ...         ...   \n",
       "9546                  No                No           3   Very Good   \n",
       "9547                  No                No           3   Very Good   \n",
       "9548                  No                No           4        Good   \n",
       "9549                  No                No           4   Very Good   \n",
       "9550                  No                No           2   Very Good   \n",
       "\n",
       "          Country  Cuisine  \n",
       "0     Phillipines   others  \n",
       "1     Phillipines   others  \n",
       "2     Phillipines   others  \n",
       "3     Phillipines   others  \n",
       "4     Phillipines   others  \n",
       "...           ...      ...  \n",
       "9546       Turkey   others  \n",
       "9547       Turkey   others  \n",
       "9548       Turkey  Italian  \n",
       "9549       Turkey   others  \n",
       "9550       Turkey     Cafe  \n",
       "\n",
       "[9551 rows x 11 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_C_data = data.select_dtypes(include=['object'])\n",
    "new_C_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0f709f5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 3600x5040 with 11 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting count plots for categorical data\n",
    "\n",
    "plt.figure(figsize=(50,70))\n",
    "plotnumber = 1\n",
    "for column in new_C_data:\n",
    "    if plotnumber <=12:\n",
    "        ax= plt.subplot(4,3,plotnumber)\n",
    "        sns.countplot(data=new_C_data,y=new_C_data[column])\n",
    "        plt.yticks(fontsize=30)\n",
    "        plt.ylabel(column,fontsize=30)\n",
    "        \n",
    "    plotnumber+=1\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "490f2ece",
   "metadata": {},
   "source": [
    "observations:\n",
    "\n",
    "Most of the Restaurants in the dataset are Indian\n",
    "\n",
    "Most of the Restaurants have an Average rating\n",
    "\n",
    "The price range is mostly category 1\n",
    "\n",
    "Most of the Restaurants do not have a table booking also most of the restaurants do not deliver"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3aa446fa",
   "metadata": {},
   "source": [
    "Now Lets plot graphs for numerical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "89869269",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Average Cost for two</th>\n",
       "      <th>Aggregate rating</th>\n",
       "      <th>Votes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1100</td>\n",
       "      <td>4.8</td>\n",
       "      <td>314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1200</td>\n",
       "      <td>4.5</td>\n",
       "      <td>591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4000</td>\n",
       "      <td>4.4</td>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1500</td>\n",
       "      <td>4.9</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1500</td>\n",
       "      <td>4.8</td>\n",
       "      <td>229</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Average Cost for two  Aggregate rating  Votes\n",
       "0                  1100               4.8    314\n",
       "1                  1200               4.5    591\n",
       "2                  4000               4.4    270\n",
       "3                  1500               4.9    365\n",
       "4                  1500               4.8    229"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "215806c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting graphs for numerical data\n",
    "plt.figure(figsize=(15,5))\n",
    "plotnumber = 1\n",
    "for column in N_data:\n",
    "    if plotnumber <=3:\n",
    "        ax= plt.subplot(1,3,plotnumber)\n",
    "        sns.distplot(N_data[column])\n",
    "        plt.xlabel(column,fontsize=20)\n",
    "    plotnumber+=1\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c969774",
   "metadata": {},
   "source": [
    "The numerical colums are all skewed indicating the presence of outliers\n",
    "\n",
    "Aggregate Rating is bi-modal\n",
    "\n",
    "Let's check for skewness and find the outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "18ae2cbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Longitude               -0.300020\n",
       "Latitude                -4.686081\n",
       "Average Cost for two    35.477915\n",
       "Price range              0.889618\n",
       "Aggregate rating        -0.954130\n",
       "Votes                    8.807637\n",
       "dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for skewness\n",
    "data.skew()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "62adfdb9",
   "metadata": {},
   "source": [
    "From the above observation we conclude that 'Averange Cost for two'and 'Votes' are highly skewed\n",
    "\n",
    "will try and reduce the skewness by checking for outliers "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "78ab141f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encoding the categorical columns\n",
    "L_encoder = LabelEncoder()\n",
    "for i in data.columns:\n",
    "    if data[i].dtypes=='object':\n",
    "        data[i]=L_encoder.fit_transform(data[i].values.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e0da7292",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Average Cost for two</th>\n",
       "      <th>Currency</th>\n",
       "      <th>Has Table booking</th>\n",
       "      <th>Has Online delivery</th>\n",
       "      <th>Is delivering now</th>\n",
       "      <th>Price range</th>\n",
       "      <th>Aggregate rating</th>\n",
       "      <th>Rating text</th>\n",
       "      <th>Votes</th>\n",
       "      <th>Country</th>\n",
       "      <th>Cuisine</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1100</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1</td>\n",
       "      <td>314</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1200</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1</td>\n",
       "      <td>591</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4.4</td>\n",
       "      <td>5</td>\n",
       "      <td>270</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1</td>\n",
       "      <td>365</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1</td>\n",
       "      <td>229</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4.4</td>\n",
       "      <td>5</td>\n",
       "      <td>336</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5</td>\n",
       "      <td>520</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4.2</td>\n",
       "      <td>5</td>\n",
       "      <td>677</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1</td>\n",
       "      <td>621</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1100</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1</td>\n",
       "      <td>532</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   City  Longitude  Latitude  Average Cost for two  Currency  \\\n",
       "0     4          2         3                  1100         0   \n",
       "1     4          2         3                  1200         0   \n",
       "2     4          2         3                  4000         0   \n",
       "3     4          2         3                  1500         0   \n",
       "4     4          2         3                  1500         0   \n",
       "5     4          2         3                  1000         0   \n",
       "6     4          2         3                  2000         0   \n",
       "7     4          2         3                  2000         0   \n",
       "8     4          2         3                  6000         0   \n",
       "9     4          2         3                  1100         0   \n",
       "\n",
       "   Has Table booking  Has Online delivery  Is delivering now  Price range  \\\n",
       "0                  1                    0                  0            2   \n",
       "1                  1                    0                  0            2   \n",
       "2                  1                    0                  0            3   \n",
       "3                  0                    0                  0            3   \n",
       "4                  1                    0                  0            3   \n",
       "5                  0                    0                  0            2   \n",
       "6                  1                    0                  0            3   \n",
       "7                  1                    0                  0            3   \n",
       "8                  1                    0                  0            3   \n",
       "9                  1                    0                  0            2   \n",
       "\n",
       "   Aggregate rating  Rating text  Votes  Country  Cuisine  \n",
       "0               4.8            1    314        6       18  \n",
       "1               4.5            1    591        6       18  \n",
       "2               4.4            5    270        6       18  \n",
       "3               4.9            1    365        6       18  \n",
       "4               4.8            1    229        6       18  \n",
       "5               4.4            5    336        6        5  \n",
       "6               4.0            5    520        6       18  \n",
       "7               4.2            5    677        6       18  \n",
       "8               4.9            1    621        6       18  \n",
       "9               4.8            1    532        6       18  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "90c18a2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking for outliers using boxplot\n",
    "plt.figure(figsize=(15,5))\n",
    "graph =1\n",
    "for column in N_data:\n",
    "    if graph <=3:\n",
    "        plt.subplot(1,3,graph)\n",
    "        ax=sns.boxplot(data=N_data[column])\n",
    "        plt.xlabel(column,fontsize=15)\n",
    "    graph+=1\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd57b9ea",
   "metadata": {},
   "source": [
    "From the above graphs we can say that most of the numerical columns have outliers which is leading to skewness of data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e6cd9652",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 102,  336,  341,  342,  343,  350,  352,  409,  429,  454,  458,\n",
       "         468,  469,  470,  472,  585,  591,  600,  644,  726,  727,  728,\n",
       "         731,  735,  736,  739,  743,  793,  805,  807,  811,  813,  814,\n",
       "         815,  817,  821,  824, 1144, 1147, 1163, 1235, 1247, 1248, 1251,\n",
       "        1252, 1253, 1255, 1256, 1258, 1464, 1554, 1555, 1839, 1861, 1866,\n",
       "        2300, 2302, 2304, 2307, 2311, 2314, 2338, 2344, 2402, 2404, 2406,\n",
       "        2409, 2410, 2411, 2412, 2413, 2414, 2416, 2480, 2483, 2484, 2485,\n",
       "        2488, 2490, 2492, 2495, 2499, 2635, 2880, 3005, 3008, 3013, 3016,\n",
       "        3018, 3025, 3026, 3028, 3033, 3041, 3049, 3055, 3056, 3057, 3060,\n",
       "        3068, 3075, 3080, 3083, 3085, 3093, 3094, 3095, 3101, 3107, 3110,\n",
       "        3112, 3115, 3116, 3117, 3118, 3119, 3316, 3319, 3336, 3337, 3338,\n",
       "        3438, 3588, 3589, 3599, 3601, 3605, 3976, 3983, 3984, 3986, 3992,\n",
       "        3993, 3994, 3998, 4087, 4162, 4178, 4320, 4386, 4638, 4639, 4649,\n",
       "        4673, 5007, 5026, 6144, 6317, 6534, 6659, 6701, 6709, 6712, 6848,\n",
       "        6921, 6997, 7033, 7045, 7087, 7863, 8394, 8434, 9157, 9161, 9165,\n",
       "        9167, 9169, 9171, 9276, 9277, 9278, 9279, 9279, 9280, 9280, 9281,\n",
       "        9282, 9283, 9283, 9284, 9285, 9286, 9287, 9288, 9289, 9290, 9291,\n",
       "        9291, 9292, 9293, 9294, 9295, 9296, 9296], dtype=int64),\n",
       " array([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 2, 0, 2, 0,\n",
       "        0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2], dtype=int64))"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using z-score to detect and treat the outliers\n",
    "features=N_data\n",
    "from scipy.stats import zscore\n",
    "import numpy as np\n",
    "z=np.abs(zscore(features))\n",
    "threshold=3\n",
    "outliers=np.where(z>3)\n",
    "outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c2492a7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Average Cost for two</th>\n",
       "      <th>Currency</th>\n",
       "      <th>Has Table booking</th>\n",
       "      <th>Has Online delivery</th>\n",
       "      <th>Is delivering now</th>\n",
       "      <th>Price range</th>\n",
       "      <th>Aggregate rating</th>\n",
       "      <th>Rating text</th>\n",
       "      <th>Votes</th>\n",
       "      <th>Country</th>\n",
       "      <th>Cuisine</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1100</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1</td>\n",
       "      <td>314</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1200</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1</td>\n",
       "      <td>591</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4.4</td>\n",
       "      <td>5</td>\n",
       "      <td>270</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1</td>\n",
       "      <td>365</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1</td>\n",
       "      <td>229</td>\n",
       "      <td>6</td>\n",
       "      <td>18</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9546</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>80</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>5</td>\n",
       "      <td>788</td>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9547</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>105</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4.2</td>\n",
       "      <td>5</td>\n",
       "      <td>1034</td>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9548</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>170</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3.7</td>\n",
       "      <td>2</td>\n",
       "      <td>661</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9549</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>120</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5</td>\n",
       "      <td>901</td>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9550</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>55</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5</td>\n",
       "      <td>591</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9362 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      City  Longitude  Latitude  Average Cost for two  Currency  \\\n",
       "0        4          2         3                  1100         0   \n",
       "1        4          2         3                  1200         0   \n",
       "2        4          2         3                  4000         0   \n",
       "3        4          2         3                  1500         0   \n",
       "4        4          2         3                  1500         0   \n",
       "...    ...        ...       ...                   ...       ...   \n",
       "9546     4          3         5                    80        13   \n",
       "9547     4          3         5                   105        13   \n",
       "9548     4          3         5                   170        13   \n",
       "9549     4          3         5                   120        13   \n",
       "9550     4          3         5                    55        13   \n",
       "\n",
       "      Has Table booking  Has Online delivery  Is delivering now  Price range  \\\n",
       "0                     1                    0                  0            2   \n",
       "1                     1                    0                  0            2   \n",
       "2                     1                    0                  0            3   \n",
       "3                     0                    0                  0            3   \n",
       "4                     1                    0                  0            3   \n",
       "...                 ...                  ...                ...          ...   \n",
       "9546                  0                    0                  0            2   \n",
       "9547                  0                    0                  0            2   \n",
       "9548                  0                    0                  0            3   \n",
       "9549                  0                    0                  0            3   \n",
       "9550                  0                    0                  0            1   \n",
       "\n",
       "      Aggregate rating  Rating text  Votes  Country  Cuisine  \n",
       "0                  4.8            1    314        6       18  \n",
       "1                  4.5            1    591        6       18  \n",
       "2                  4.4            5    270        6       18  \n",
       "3                  4.9            1    365        6       18  \n",
       "4                  4.8            1    229        6       18  \n",
       "...                ...          ...    ...      ...      ...  \n",
       "9546               4.1            5    788       11       18  \n",
       "9547               4.2            5   1034       11       18  \n",
       "9548               3.7            2    661       11       10  \n",
       "9549               4.0            5    901       11       18  \n",
       "9550               4.0            5    591       11        4  \n",
       "\n",
       "[9362 rows x 14 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data=data.drop(outliers[0])\n",
    "new_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "654e16b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City                     0.364465\n",
       "Longitude               -1.487551\n",
       "Latitude                -4.713679\n",
       "Average Cost for two     3.619524\n",
       "Currency                 1.304773\n",
       "Has Table booking        2.382516\n",
       "Has Online delivery      1.120081\n",
       "Is delivering now       16.505874\n",
       "Price range              0.933571\n",
       "Aggregate rating        -0.940907\n",
       "Rating text              0.434638\n",
       "Votes                    3.216882\n",
       "Country                  3.326986\n",
       "Cuisine                 -0.331155\n",
       "dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "aecb786e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.9685863874345548"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_loss=((9550-9362)/9550)*100\n",
    "data_loss"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4ddd693",
   "metadata": {},
   "source": [
    "Since the skewness has reduced and the data loss is negligible we will use the new_data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a3ca3c6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting heat map to check for multicollinerity and labelv//s feature relationship\n",
    "df_corr = new_data.corr().abs()\n",
    "plt.figure(figsize=(20,15))\n",
    "sns.heatmap(df_corr, annot= True, annot_kws ={'size':10})\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ceb27a0",
   "metadata": {},
   "source": [
    "We observe:\n",
    "\n",
    "Heat Map looks good there seems to be no multicollinearity however will double check it with VIFs\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08fea9a5",
   "metadata": {},
   "source": [
    "# Regression Model Building:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "301791ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dividing data into feature and label\n",
    "X= new_data.drop(columns=['Average Cost for two'])\n",
    "Y= new_data['Average Cost for two']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "1ead444e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Applying PowerTransformer\n",
    "pt = PowerTransformer()\n",
    "X_scaled = pd.DataFrame(pt.fit_transform(X), columns=X.columns)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "a382421a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>City</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Currency</th>\n",
       "      <th>Has Table booking</th>\n",
       "      <th>Has Online delivery</th>\n",
       "      <th>Is delivering now</th>\n",
       "      <th>Price range</th>\n",
       "      <th>Aggregate rating</th>\n",
       "      <th>Rating text</th>\n",
       "      <th>Votes</th>\n",
       "      <th>Country</th>\n",
       "      <th>Cuisine</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.712169</td>\n",
       "      <td>-0.038668</td>\n",
       "      <td>0.028613</td>\n",
       "      <td>-4.832333</td>\n",
       "      <td>2.746265</td>\n",
       "      <td>-0.586149</td>\n",
       "      <td>-0.060373</td>\n",
       "      <td>1.285629</td>\n",
       "      <td>1.864298</td>\n",
       "      <td>-0.204206</td>\n",
       "      <td>1.302234</td>\n",
       "      <td>1.559812</td>\n",
       "      <td>1.453945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.712169</td>\n",
       "      <td>-0.038668</td>\n",
       "      <td>0.028613</td>\n",
       "      <td>-4.832333</td>\n",
       "      <td>2.746265</td>\n",
       "      <td>-0.586149</td>\n",
       "      <td>-0.060373</td>\n",
       "      <td>1.285629</td>\n",
       "      <td>1.536945</td>\n",
       "      <td>-0.204206</td>\n",
       "      <td>1.656273</td>\n",
       "      <td>1.559812</td>\n",
       "      <td>1.453945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.712169</td>\n",
       "      <td>-0.038668</td>\n",
       "      <td>0.028613</td>\n",
       "      <td>-4.832333</td>\n",
       "      <td>2.746265</td>\n",
       "      <td>-0.586149</td>\n",
       "      <td>-0.060373</td>\n",
       "      <td>1.669821</td>\n",
       "      <td>1.430745</td>\n",
       "      <td>1.516896</td>\n",
       "      <td>1.218908</td>\n",
       "      <td>1.559812</td>\n",
       "      <td>1.453945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.712169</td>\n",
       "      <td>-0.038668</td>\n",
       "      <td>0.028613</td>\n",
       "      <td>-4.832333</td>\n",
       "      <td>-0.364131</td>\n",
       "      <td>-0.586149</td>\n",
       "      <td>-0.060373</td>\n",
       "      <td>1.669821</td>\n",
       "      <td>1.976313</td>\n",
       "      <td>-0.204206</td>\n",
       "      <td>1.385762</td>\n",
       "      <td>1.559812</td>\n",
       "      <td>1.453945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.712169</td>\n",
       "      <td>-0.038668</td>\n",
       "      <td>0.028613</td>\n",
       "      <td>-4.832333</td>\n",
       "      <td>2.746265</td>\n",
       "      <td>-0.586149</td>\n",
       "      <td>-0.060373</td>\n",
       "      <td>1.669821</td>\n",
       "      <td>1.864298</td>\n",
       "      <td>-0.204206</td>\n",
       "      <td>1.128534</td>\n",
       "      <td>1.559812</td>\n",
       "      <td>1.453945</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9357</th>\n",
       "      <td>1.712169</td>\n",
       "      <td>4.692261</td>\n",
       "      <td>9.674552</td>\n",
       "      <td>6.952984</td>\n",
       "      <td>-0.364131</td>\n",
       "      <td>-0.586149</td>\n",
       "      <td>-0.060373</td>\n",
       "      <td>1.285629</td>\n",
       "      <td>1.120993</td>\n",
       "      <td>1.516896</td>\n",
       "      <td>1.819959</td>\n",
       "      <td>2.635396</td>\n",
       "      <td>1.453945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9358</th>\n",
       "      <td>1.712169</td>\n",
       "      <td>4.692261</td>\n",
       "      <td>9.674552</td>\n",
       "      <td>6.952984</td>\n",
       "      <td>-0.364131</td>\n",
       "      <td>-0.586149</td>\n",
       "      <td>-0.060373</td>\n",
       "      <td>1.285629</td>\n",
       "      <td>1.222763</td>\n",
       "      <td>1.516896</td>\n",
       "      <td>1.976055</td>\n",
       "      <td>2.635396</td>\n",
       "      <td>1.453945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9359</th>\n",
       "      <td>1.712169</td>\n",
       "      <td>4.692261</td>\n",
       "      <td>9.674552</td>\n",
       "      <td>6.952984</td>\n",
       "      <td>-0.364131</td>\n",
       "      <td>-0.586149</td>\n",
       "      <td>-0.060373</td>\n",
       "      <td>1.669821</td>\n",
       "      <td>0.728899</td>\n",
       "      <td>0.397949</td>\n",
       "      <td>1.719768</td>\n",
       "      <td>2.635396</td>\n",
       "      <td>-0.055396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9360</th>\n",
       "      <td>1.712169</td>\n",
       "      <td>4.692261</td>\n",
       "      <td>9.674552</td>\n",
       "      <td>6.952984</td>\n",
       "      <td>-0.364131</td>\n",
       "      <td>-0.586149</td>\n",
       "      <td>-0.060373</td>\n",
       "      <td>1.669821</td>\n",
       "      <td>1.020715</td>\n",
       "      <td>1.516896</td>\n",
       "      <td>1.896768</td>\n",
       "      <td>2.635396</td>\n",
       "      <td>1.453945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9361</th>\n",
       "      <td>1.712169</td>\n",
       "      <td>4.692261</td>\n",
       "      <td>9.674552</td>\n",
       "      <td>6.952984</td>\n",
       "      <td>-0.364131</td>\n",
       "      <td>-0.586149</td>\n",
       "      <td>-0.060373</td>\n",
       "      <td>0.613582</td>\n",
       "      <td>1.020715</td>\n",
       "      <td>1.516896</td>\n",
       "      <td>1.656273</td>\n",
       "      <td>2.635396</td>\n",
       "      <td>-1.177600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9362 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          City  Longitude  Latitude  Currency  Has Table booking  \\\n",
       "0     1.712169  -0.038668  0.028613 -4.832333           2.746265   \n",
       "1     1.712169  -0.038668  0.028613 -4.832333           2.746265   \n",
       "2     1.712169  -0.038668  0.028613 -4.832333           2.746265   \n",
       "3     1.712169  -0.038668  0.028613 -4.832333          -0.364131   \n",
       "4     1.712169  -0.038668  0.028613 -4.832333           2.746265   \n",
       "...        ...        ...       ...       ...                ...   \n",
       "9357  1.712169   4.692261  9.674552  6.952984          -0.364131   \n",
       "9358  1.712169   4.692261  9.674552  6.952984          -0.364131   \n",
       "9359  1.712169   4.692261  9.674552  6.952984          -0.364131   \n",
       "9360  1.712169   4.692261  9.674552  6.952984          -0.364131   \n",
       "9361  1.712169   4.692261  9.674552  6.952984          -0.364131   \n",
       "\n",
       "      Has Online delivery  Is delivering now  Price range  Aggregate rating  \\\n",
       "0               -0.586149          -0.060373     1.285629          1.864298   \n",
       "1               -0.586149          -0.060373     1.285629          1.536945   \n",
       "2               -0.586149          -0.060373     1.669821          1.430745   \n",
       "3               -0.586149          -0.060373     1.669821          1.976313   \n",
       "4               -0.586149          -0.060373     1.669821          1.864298   \n",
       "...                   ...                ...          ...               ...   \n",
       "9357            -0.586149          -0.060373     1.285629          1.120993   \n",
       "9358            -0.586149          -0.060373     1.285629          1.222763   \n",
       "9359            -0.586149          -0.060373     1.669821          0.728899   \n",
       "9360            -0.586149          -0.060373     1.669821          1.020715   \n",
       "9361            -0.586149          -0.060373     0.613582          1.020715   \n",
       "\n",
       "      Rating text     Votes   Country   Cuisine  \n",
       "0       -0.204206  1.302234  1.559812  1.453945  \n",
       "1       -0.204206  1.656273  1.559812  1.453945  \n",
       "2        1.516896  1.218908  1.559812  1.453945  \n",
       "3       -0.204206  1.385762  1.559812  1.453945  \n",
       "4       -0.204206  1.128534  1.559812  1.453945  \n",
       "...           ...       ...       ...       ...  \n",
       "9357     1.516896  1.819959  2.635396  1.453945  \n",
       "9358     1.516896  1.976055  2.635396  1.453945  \n",
       "9359     0.397949  1.719768  2.635396 -0.055396  \n",
       "9360     1.516896  1.896768  2.635396  1.453945  \n",
       "9361     1.516896  1.656273  2.635396 -1.177600  \n",
       "\n",
       "[9362 rows x 13 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "0fb87dec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.6195240871572794"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "10ecf369",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City                    0.035590\n",
       "Longitude               1.678840\n",
       "Latitude                2.724882\n",
       "Currency                0.752558\n",
       "Has Table booking       2.382516\n",
       "Has Online delivery     1.120081\n",
       "Is delivering now      16.505874\n",
       "Price range             0.158229\n",
       "Aggregate rating       -0.615772\n",
       "Rating text            -0.055331\n",
       "Votes                  -0.021285\n",
       "Country                -1.327860\n",
       "Cuisine                -0.320406\n",
       "dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_scaled.skew()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed52f9df",
   "metadata": {},
   "source": [
    "After applying powertransform the skewness is under control and data looks good"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "4d4017aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vif</th>\n",
       "      <th>feature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.447507</td>\n",
       "      <td>City</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.441218</td>\n",
       "      <td>Longitude</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.260599</td>\n",
       "      <td>Latitude</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.460121</td>\n",
       "      <td>Currency</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.356344</td>\n",
       "      <td>Has Table booking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.220769</td>\n",
       "      <td>Has Online delivery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.012985</td>\n",
       "      <td>Is delivering now</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.718391</td>\n",
       "      <td>Price range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.413364</td>\n",
       "      <td>Aggregate rating</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.235265</td>\n",
       "      <td>Rating text</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4.843991</td>\n",
       "      <td>Votes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.385061</td>\n",
       "      <td>Country</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.023688</td>\n",
       "      <td>Cuisine</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         vif              feature\n",
       "0   1.447507                 City\n",
       "1   1.441218            Longitude\n",
       "2   1.260599             Latitude\n",
       "3   1.460121             Currency\n",
       "4   1.356344    Has Table booking\n",
       "5   1.220769  Has Online delivery\n",
       "6   1.012985    Is delivering now\n",
       "7   1.718391          Price range\n",
       "8   4.413364     Aggregate rating\n",
       "9   1.235265          Rating text\n",
       "10  4.843991                Votes\n",
       "11  1.385061              Country\n",
       "12  1.023688              Cuisine"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Using VIF to check for muliticolinearity:\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "vif= pd.DataFrame()\n",
    "vif['vif']=[variance_inflation_factor(X_scaled,i) for i in range(X_scaled.shape[1])]\n",
    "vif['feature']= X.columns\n",
    "vif"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4dbb434",
   "metadata": {},
   "source": [
    "All the features have vifs less than 5 we can go ahead and build our model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "37c29b03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At random state  212 the model performs well\n",
      "Acuraccy of training model : 53.66582488017721\n",
      "Accuracy of testing model : 53.847373303272114\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  213 the model performs well\n",
      "Acuraccy of training model : 53.730815320563785\n",
      "Accuracy of testing model : 53.668582201963574\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  239 the model performs well\n",
      "Acuraccy of training model : 53.51386808408365\n",
      "Accuracy of testing model : 54.15061483499192\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  241 the model performs well\n",
      "Acuraccy of training model : 53.6051373732954\n",
      "Accuracy of testing model : 53.86613525247987\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  247 the model performs well\n",
      "Acuraccy of training model : 53.560089068723784\n",
      "Accuracy of testing model : 53.95860975042521\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  253 the model performs well\n",
      "Acuraccy of training model : 53.53310739536761\n",
      "Accuracy of testing model : 54.189644469464504\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  261 the model performs well\n",
      "Acuraccy of training model : 53.52565073006878\n",
      "Accuracy of testing model : 54.15694238548221\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  279 the model performs well\n",
      "Acuraccy of training model : 53.80315709274568\n",
      "Accuracy of testing model : 53.54921938792205\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  283 the model performs well\n",
      "Acuraccy of training model : 53.51333818929598\n",
      "Accuracy of testing model : 54.26763294564275\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  287 the model performs well\n",
      "Acuraccy of training model : 53.538062469003286\n",
      "Accuracy of testing model : 54.16692022746885\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  289 the model performs well\n",
      "Acuraccy of training model : 53.59528972682155\n",
      "Accuracy of testing model : 54.02839060863185\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  291 the model performs well\n",
      "Acuraccy of training model : 53.556819664296576\n",
      "Accuracy of testing model : 53.80889428610023\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  296 the model performs well\n",
      "Acuraccy of training model : 53.75025111209948\n",
      "Accuracy of testing model : 53.71367307660324\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  306 the model performs well\n",
      "Acuraccy of training model : 53.603676022709976\n",
      "Accuracy of testing model : 53.70033895651062\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  307 the model performs well\n",
      "Acuraccy of training model : 53.79790042591823\n",
      "Accuracy of testing model : 53.632326149228774\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  317 the model performs well\n",
      "Acuraccy of training model : 53.508635122694706\n",
      "Accuracy of testing model : 54.22161703590091\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  319 the model performs well\n",
      "Acuraccy of training model : 53.63824664105241\n",
      "Accuracy of testing model : 53.62092367367517\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  332 the model performs well\n",
      "Acuraccy of training model : 53.59232558511242\n",
      "Accuracy of testing model : 54.04946555884251\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  337 the model performs well\n",
      "Acuraccy of training model : 53.62665671938909\n",
      "Accuracy of testing model : 53.83333854072034\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  340 the model performs well\n",
      "Acuraccy of training model : 53.55783927899609\n",
      "Accuracy of testing model : 53.76253489245055\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  350 the model performs well\n",
      "Acuraccy of training model : 53.588025961826766\n",
      "Accuracy of testing model : 54.01397143363816\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  351 the model performs well\n",
      "Acuraccy of training model : 53.70818640333559\n",
      "Accuracy of testing model : 53.67166597773676\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  374 the model performs well\n",
      "Acuraccy of training model : 53.656879990125475\n",
      "Accuracy of testing model : 53.84894469218845\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  380 the model performs well\n",
      "Acuraccy of training model : 53.77666046878904\n",
      "Accuracy of testing model : 53.661488301867436\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  382 the model performs well\n",
      "Acuraccy of training model : 53.6802324930368\n",
      "Accuracy of testing model : 53.874024405650786\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  384 the model performs well\n",
      "Acuraccy of training model : 53.71318125212501\n",
      "Accuracy of testing model : 53.667112482133504\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  392 the model performs well\n",
      "Acuraccy of training model : 53.68744691861629\n",
      "Accuracy of testing model : 53.807929086889196\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  395 the model performs well\n",
      "Acuraccy of training model : 53.56738595176744\n",
      "Accuracy of testing model : 54.028946174832036\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  401 the model performs well\n",
      "Acuraccy of training model : 53.58901827972635\n",
      "Accuracy of testing model : 54.07061019177461\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  403 the model performs well\n",
      "Acuraccy of training model : 53.81402643866886\n",
      "Accuracy of testing model : 53.513036071754414\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  406 the model performs well\n",
      "Acuraccy of training model : 53.751672787974314\n",
      "Accuracy of testing model : 53.59849622242305\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  407 the model performs well\n",
      "Acuraccy of training model : 53.64758608396818\n",
      "Accuracy of testing model : 53.86546798508398\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  411 the model performs well\n",
      "Acuraccy of training model : 53.72938272729142\n",
      "Accuracy of testing model : 53.75948008701927\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  416 the model performs well\n",
      "Acuraccy of training model : 53.55501485990077\n",
      "Accuracy of testing model : 54.12131277966588\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  419 the model performs well\n",
      "Acuraccy of training model : 53.7473078822285\n",
      "Accuracy of testing model : 53.66183530842058\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  438 the model performs well\n",
      "Acuraccy of training model : 53.68209193961294\n",
      "Accuracy of testing model : 53.65436622456228\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  444 the model performs well\n",
      "Acuraccy of training model : 53.7318446180145\n",
      "Accuracy of testing model : 53.58307414771808\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  449 the model performs well\n",
      "Acuraccy of training model : 53.60589926643724\n",
      "Accuracy of testing model : 53.93079243775514\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  460 the model performs well\n",
      "Acuraccy of training model : 53.5632562910926\n",
      "Accuracy of testing model : 54.12991339692099\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  462 the model performs well\n",
      "Acuraccy of training model : 53.5545539339514\n",
      "Accuracy of testing model : 53.90063509615807\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  470 the model performs well\n",
      "Acuraccy of training model : 53.73367721452744\n",
      "Accuracy of testing model : 53.74008181804448\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  478 the model performs well\n",
      "Acuraccy of training model : 53.70258878226299\n",
      "Accuracy of testing model : 53.76606660071238\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  484 the model performs well\n",
      "Acuraccy of training model : 53.65595263060201\n",
      "Accuracy of testing model : 53.834689637537124\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  499 the model performs well\n",
      "Acuraccy of training model : 53.67697897475754\n",
      "Accuracy of testing model : 53.76362544991275\n",
      "\n",
      "-----------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#traintestsplit\n",
    "lr = LinearRegression()\n",
    "for i in range(200,500):\n",
    "    x_train,x_test,y_train,y_test = train_test_split (X_scaled,Y, test_size=0.3, random_state = i)\n",
    "    lr.fit(x_train,y_train)\n",
    "    pred_train = lr.predict(x_train)\n",
    "    pred_test = lr.predict(x_test)\n",
    "    if round(r2_score(y_train,pred_train)*100)==round(r2_score(y_test,pred_test)*100):\n",
    "        print (\"At random state \",i,\"the model performs well\")\n",
    "        print (\"Acuraccy of training model :\",r2_score(y_train,pred_train)*100)\n",
    "        print (\"Accuracy of testing model :\",r2_score(y_test,pred_test)*100)\n",
    "        print (\"\\n-----------------------------------------\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1eae108c",
   "metadata": {},
   "source": [
    "Selecting the random state as 296 since the r2 score for test and train data is almost equal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "9d2974a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#traintestsplit\n",
    "x_train,x_test,y_train,y_test = train_test_split (X_scaled,Y, test_size=0.3, random_state = 296)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "7982627b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model Selection\n",
    "def model_selection (algorithm, x_train,y_train,x_test,y_test):\n",
    "    algorithm.fit(x_train,y_train)\n",
    "    pred_train = algorithm.predict(x_train)\n",
    "    pred_test = algorithm.predict(x_test)\n",
    "    mse = mean_squared_error(y_test,pred_test)\n",
    "\n",
    "    print (algorithm)\n",
    "    print (\"Acuraccy of training model :\",r2_score(y_train,pred_train))\n",
    "    print (\"Accuracy of testing model :\",r2_score(y_test,pred_test))\n",
    "    print (\"RMSE : \",sqrt(mse))\n",
    "    print (\"\\n-----------------------------------------\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "baa8f5f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearRegression()\n",
      "Acuraccy of training model : 0.5375025111209948\n",
      "Accuracy of testing model : 0.5371367307660324\n",
      "RMSE :  405.79984083250787\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "Ridge()\n",
      "Acuraccy of training model : 0.5375025017669923\n",
      "Accuracy of testing model : 0.5371341035495429\n",
      "RMSE :  405.8009924928267\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "Lasso()\n",
      "Acuraccy of training model : 0.5374402056225769\n",
      "Accuracy of testing model : 0.537117455871666\n",
      "RMSE :  405.80829005299887\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "DecisionTreeRegressor()\n",
      "Acuraccy of training model : 0.9947946622577705\n",
      "Accuracy of testing model : 0.6602126234988283\n",
      "RMSE :  347.68754481065173\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "RandomForestRegressor()\n",
      "Acuraccy of training model : 0.9726359484071169\n",
      "Accuracy of testing model : 0.8333157522256391\n",
      "RMSE :  243.51896701080966\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "SVR()\n",
      "Acuraccy of training model : 0.2969057678332996\n",
      "Accuracy of testing model : 0.3016514927128945\n",
      "RMSE :  498.45001993911296\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "AdaBoostRegressor()\n",
      "Acuraccy of training model : 0.7651005387453862\n",
      "Accuracy of testing model : 0.7397918590826806\n",
      "RMSE :  304.2607651160033\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
      "             colsample_bylevel=None, colsample_bynode=None,\n",
      "             colsample_bytree=None, early_stopping_rounds=None,\n",
      "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
      "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
      "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
      "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
      "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
      "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
      "             n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
      "             predictor=None, random_state=None, ...)\n",
      "Acuraccy of training model : 0.9729233910603619\n",
      "Accuracy of testing model : 0.8295079517970784\n",
      "RMSE :  246.28478183388518\n",
      "\n",
      "-----------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Training Model\n",
    "#Linear Regression\n",
    "lr= LinearRegression()\n",
    "model_selection(lr,x_train,y_train,x_test,y_test)\n",
    "\n",
    "reg=Ridge()\n",
    "model_selection(reg,x_train,y_train,x_test,y_test)\n",
    "\n",
    "lasso=Lasso()\n",
    "model_selection(lasso,x_train,y_train,x_test,y_test)\n",
    "\n",
    "\n",
    "#Decision Tree\n",
    "dtr = DecisionTreeRegressor()\n",
    "model_selection(dtr,x_train,y_train,x_test,y_test)\n",
    "\n",
    "#Random Forest \n",
    "rf= RandomForestRegressor()\n",
    "model_selection(rf,x_train,y_train,x_test,y_test)\n",
    "\n",
    "#Support Vector Regressor:\n",
    "svr = SVR()\n",
    "model_selection(svr,x_train,y_train,x_test,y_test)\n",
    "\n",
    "#AdaBoost\n",
    "ada = AdaBoostRegressor()\n",
    "model_selection(ada,x_train,y_train,x_test,y_test)\n",
    "\n",
    "#XGBoost\n",
    "xg = xgb.XGBRegressor()\n",
    "model_selection(xg,x_train,y_train,x_test,y_test)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cabf5b6",
   "metadata": {},
   "source": [
    "Hyperparameter Tunning Randon Forests and XGBoost as they have the best accuracy and lower RMSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "c1c9d1c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forests: \n",
      " {'max_depth': 15, 'max_features': 'auto', 'min_samples_leaf': 5, 'min_samples_split': 5, 'n_estimators': 75}\n",
      "XGBoost: \n",
      " {'gamma': 1, 'learning_rate': 0.05, 'max_depth': 7}\n"
     ]
    }
   ],
   "source": [
    "# Hyperparameter Tuning:\n",
    "#Random Forest\n",
    "parameters = {'n_estimators':(25,50,75,100),\n",
    "    'max_features':['auto','sqrt','log2'],\n",
    "    'max_depth': (8,12,15),\n",
    "    'min_samples_leaf': (5,8,10),\n",
    "    'min_samples_split':range(5,7,10)\n",
    "    }\n",
    "\n",
    "clf = GridSearchCV(rf,parameters)\n",
    "clf.fit(x_train,y_train)\n",
    "print (\"Random Forests: \\n\",clf.best_params_)\n",
    "\n",
    "#XGBoost\n",
    "params = {\n",
    "    'learning_rate': [0.1,0.01,0.05],\n",
    "    'max_depth': [7,10,12],\n",
    "    'gamma': [0, 0.5, 1]\n",
    "}\n",
    "\n",
    "clf = GridSearchCV(xg,params)\n",
    "clf.fit(x_train,y_train)\n",
    "print (\"XGBoost: \\n\",clf.best_params_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "d0fb7057",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestRegressor(max_depth=15, max_features='auto', min_samples_leaf=5,\n",
      "                      min_samples_split=5, n_estimators=75)\n",
      "Acuraccy of training model : 0.9063667833569533\n",
      "Accuracy of testing model : 0.8530948180716911\n",
      "RMSE :  228.6146571272869\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
      "             colsample_bylevel=None, colsample_bynode=None,\n",
      "             colsample_bytree=None, early_stopping_rounds=None,\n",
      "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
      "             gamma=1, gpu_id=None, grow_policy=None, importance_type=None,\n",
      "             interaction_constraints=None, learning_rate=0.05, max_bin=None,\n",
      "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
      "             max_delta_step=None, max_depth=7, max_leaves=None,\n",
      "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
      "             n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
      "             predictor=None, random_state=None, ...)\n",
      "Acuraccy of training model : 0.9388114612986578\n",
      "Accuracy of testing model : 0.8549583846216785\n",
      "RMSE :  227.15998279394805\n",
      "\n",
      "-----------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "rf1= RandomForestRegressor(n_estimators=75,max_features='auto', max_depth = 15, min_samples_leaf = 5, min_samples_split = 5)\n",
    "model_selection(rf1,x_train,y_train,x_test,y_test)\n",
    "\n",
    "xg1 = xgb.XGBRegressor(gamma=1, learning_rate=0.05, max_depth=7)\n",
    "model_selection(xg1,x_train,y_train,x_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4048522d",
   "metadata": {},
   "source": [
    "Accuracy for both Decision Tree and XGBoost has increased after Hyperparameter tunning\n",
    "Selecting XGBoost for prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "30400139",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving Model\n",
    "import pickle\n",
    "filename= 'Zomato_reg.pkl'\n",
    "pickle.dump(xg1,open(filename, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "a71eba1a",
   "metadata": {},
   "outputs": [
    {
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       "  </tbody>\n",
       "</table>\n",
       "<p>2809 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Orignal   Predicted\n",
       "0         550  652.528687\n",
       "1         300  342.186554\n",
       "2         350  383.758575\n",
       "3         400  282.980194\n",
       "4         400  275.181915\n",
       "...       ...         ...\n",
       "2804      200  264.103088\n",
       "2805      200  266.077179\n",
       "2806      200  339.548462\n",
       "2807      350  296.676697\n",
       "2808      150  201.283905\n",
       "\n",
       "[2809 rows x 2 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Final Prediction\n",
    "a= np.array(y_test)\n",
    "predicted=np.array (xg1.predict(x_test))\n",
    "\n",
    "df = pd.DataFrame({\"Orignal\":a, \"Predicted\":predicted}, index = range(len(a)))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "fd58de7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x24090265b50>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='Orignal',y='Predicted',data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f82bf775",
   "metadata": {},
   "source": [
    "Building Classification model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "fded634a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dividing data into feature and label\n",
    "x= new_data.drop(columns=['Price range'])\n",
    "y= new_data['Price range']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "663b3c47",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Applying PowerTransformer\n",
    "pt = PowerTransformer()\n",
    "x_scaled = pd.DataFrame(pt.fit_transform(x), columns=x.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "12aeef86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Average Cost for two</th>\n",
       "      <th>Currency</th>\n",
       "      <th>Has Table booking</th>\n",
       "      <th>Has Online delivery</th>\n",
       "      <th>Is delivering now</th>\n",
       "      <th>Aggregate rating</th>\n",
       "      <th>Rating text</th>\n",
       "      <th>Votes</th>\n",
       "      <th>Country</th>\n",
       "      <th>Cuisine</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.712169</td>\n",
       "      <td>-0.038668</td>\n",
       "      <td>0.028613</td>\n",
       "      <td>1.148984</td>\n",
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       "      <td>-0.586149</td>\n",
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       "      <td>1.864298</td>\n",
       "      <td>-0.204206</td>\n",
       "      <td>1.302234</td>\n",
       "      <td>1.559812</td>\n",
       "      <td>1.453945</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.712169</td>\n",
       "      <td>-0.038668</td>\n",
       "      <td>0.028613</td>\n",
       "      <td>1.266904</td>\n",
       "      <td>-4.832333</td>\n",
       "      <td>2.746265</td>\n",
       "      <td>-0.586149</td>\n",
       "      <td>-0.060373</td>\n",
       "      <td>1.536945</td>\n",
       "      <td>-0.204206</td>\n",
       "      <td>1.656273</td>\n",
       "      <td>1.559812</td>\n",
       "      <td>1.453945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.712169</td>\n",
       "      <td>-0.038668</td>\n",
       "      <td>0.028613</td>\n",
       "      <td>3.181609</td>\n",
       "      <td>-4.832333</td>\n",
       "      <td>2.746265</td>\n",
       "      <td>-0.586149</td>\n",
       "      <td>-0.060373</td>\n",
       "      <td>1.430745</td>\n",
       "      <td>1.516896</td>\n",
       "      <td>1.218908</td>\n",
       "      <td>1.559812</td>\n",
       "      <td>1.453945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.712169</td>\n",
       "      <td>-0.038668</td>\n",
       "      <td>0.028613</td>\n",
       "      <td>1.580936</td>\n",
       "      <td>-4.832333</td>\n",
       "      <td>-0.364131</td>\n",
       "      <td>-0.586149</td>\n",
       "      <td>-0.060373</td>\n",
       "      <td>1.976313</td>\n",
       "      <td>-0.204206</td>\n",
       "      <td>1.385762</td>\n",
       "      <td>1.559812</td>\n",
       "      <td>1.453945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.712169</td>\n",
       "      <td>-0.038668</td>\n",
       "      <td>0.028613</td>\n",
       "      <td>1.580936</td>\n",
       "      <td>-4.832333</td>\n",
       "      <td>2.746265</td>\n",
       "      <td>-0.586149</td>\n",
       "      <td>-0.060373</td>\n",
       "      <td>1.864298</td>\n",
       "      <td>-0.204206</td>\n",
       "      <td>1.128534</td>\n",
       "      <td>1.559812</td>\n",
       "      <td>1.453945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>9357</th>\n",
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       "      <td>4.692261</td>\n",
       "      <td>9.674552</td>\n",
       "      <td>-1.448082</td>\n",
       "      <td>6.952984</td>\n",
       "      <td>-0.364131</td>\n",
       "      <td>-0.586149</td>\n",
       "      <td>-0.060373</td>\n",
       "      <td>1.120993</td>\n",
       "      <td>1.516896</td>\n",
       "      <td>1.819959</td>\n",
       "      <td>2.635396</td>\n",
       "      <td>1.453945</td>\n",
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       "    <tr>\n",
       "      <th>9358</th>\n",
       "      <td>1.712169</td>\n",
       "      <td>4.692261</td>\n",
       "      <td>9.674552</td>\n",
       "      <td>-1.249657</td>\n",
       "      <td>6.952984</td>\n",
       "      <td>-0.364131</td>\n",
       "      <td>-0.586149</td>\n",
       "      <td>-0.060373</td>\n",
       "      <td>1.222763</td>\n",
       "      <td>1.516896</td>\n",
       "      <td>1.976055</td>\n",
       "      <td>2.635396</td>\n",
       "      <td>1.453945</td>\n",
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       "    <tr>\n",
       "      <th>9359</th>\n",
       "      <td>1.712169</td>\n",
       "      <td>4.692261</td>\n",
       "      <td>9.674552</td>\n",
       "      <td>-0.863370</td>\n",
       "      <td>6.952984</td>\n",
       "      <td>-0.364131</td>\n",
       "      <td>-0.586149</td>\n",
       "      <td>-0.060373</td>\n",
       "      <td>0.728899</td>\n",
       "      <td>0.397949</td>\n",
       "      <td>1.719768</td>\n",
       "      <td>2.635396</td>\n",
       "      <td>-0.055396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9360</th>\n",
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       "      <td>4.692261</td>\n",
       "      <td>9.674552</td>\n",
       "      <td>-1.147183</td>\n",
       "      <td>6.952984</td>\n",
       "      <td>-0.364131</td>\n",
       "      <td>-0.586149</td>\n",
       "      <td>-0.060373</td>\n",
       "      <td>1.020715</td>\n",
       "      <td>1.516896</td>\n",
       "      <td>1.896768</td>\n",
       "      <td>2.635396</td>\n",
       "      <td>1.453945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9361</th>\n",
       "      <td>1.712169</td>\n",
       "      <td>4.692261</td>\n",
       "      <td>9.674552</td>\n",
       "      <td>-1.700169</td>\n",
       "      <td>6.952984</td>\n",
       "      <td>-0.364131</td>\n",
       "      <td>-0.586149</td>\n",
       "      <td>-0.060373</td>\n",
       "      <td>1.020715</td>\n",
       "      <td>1.516896</td>\n",
       "      <td>1.656273</td>\n",
       "      <td>2.635396</td>\n",
       "      <td>-1.177600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9362 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          City  Longitude  Latitude  Average Cost for two  Currency  \\\n",
       "0     1.712169  -0.038668  0.028613              1.148984 -4.832333   \n",
       "1     1.712169  -0.038668  0.028613              1.266904 -4.832333   \n",
       "2     1.712169  -0.038668  0.028613              3.181609 -4.832333   \n",
       "3     1.712169  -0.038668  0.028613              1.580936 -4.832333   \n",
       "4     1.712169  -0.038668  0.028613              1.580936 -4.832333   \n",
       "...        ...        ...       ...                   ...       ...   \n",
       "9357  1.712169   4.692261  9.674552             -1.448082  6.952984   \n",
       "9358  1.712169   4.692261  9.674552             -1.249657  6.952984   \n",
       "9359  1.712169   4.692261  9.674552             -0.863370  6.952984   \n",
       "9360  1.712169   4.692261  9.674552             -1.147183  6.952984   \n",
       "9361  1.712169   4.692261  9.674552             -1.700169  6.952984   \n",
       "\n",
       "      Has Table booking  Has Online delivery  Is delivering now  \\\n",
       "0              2.746265            -0.586149          -0.060373   \n",
       "1              2.746265            -0.586149          -0.060373   \n",
       "2              2.746265            -0.586149          -0.060373   \n",
       "3             -0.364131            -0.586149          -0.060373   \n",
       "4              2.746265            -0.586149          -0.060373   \n",
       "...                 ...                  ...                ...   \n",
       "9357          -0.364131            -0.586149          -0.060373   \n",
       "9358          -0.364131            -0.586149          -0.060373   \n",
       "9359          -0.364131            -0.586149          -0.060373   \n",
       "9360          -0.364131            -0.586149          -0.060373   \n",
       "9361          -0.364131            -0.586149          -0.060373   \n",
       "\n",
       "      Aggregate rating  Rating text     Votes   Country   Cuisine  \n",
       "0             1.864298    -0.204206  1.302234  1.559812  1.453945  \n",
       "1             1.536945    -0.204206  1.656273  1.559812  1.453945  \n",
       "2             1.430745     1.516896  1.218908  1.559812  1.453945  \n",
       "3             1.976313    -0.204206  1.385762  1.559812  1.453945  \n",
       "4             1.864298    -0.204206  1.128534  1.559812  1.453945  \n",
       "...                ...          ...       ...       ...       ...  \n",
       "9357          1.120993     1.516896  1.819959  2.635396  1.453945  \n",
       "9358          1.222763     1.516896  1.976055  2.635396  1.453945  \n",
       "9359          0.728899     0.397949  1.719768  2.635396 -0.055396  \n",
       "9360          1.020715     1.516896  1.896768  2.635396  1.453945  \n",
       "9361          1.020715     1.516896  1.656273  2.635396 -1.177600  \n",
       "\n",
       "[9362 rows x 13 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "e9a6824e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City                     0.035590\n",
       "Longitude                1.678840\n",
       "Latitude                 2.724882\n",
       "Average Cost for two     0.084006\n",
       "Currency                 0.752558\n",
       "Has Table booking        2.382516\n",
       "Has Online delivery      1.120081\n",
       "Is delivering now       16.505874\n",
       "Aggregate rating        -0.615772\n",
       "Rating text             -0.055331\n",
       "Votes                   -0.021285\n",
       "Country                 -1.327860\n",
       "Cuisine                 -0.320406\n",
       "dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_scaled.skew()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "1856923f",
   "metadata": {},
   "source": [
    "The skewness is under control\n",
    "Lets check the vifs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "8ee95b08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vif</th>\n",
       "      <th>feature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.441947</td>\n",
       "      <td>City</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.439453</td>\n",
       "      <td>Longitude</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.252741</td>\n",
       "      <td>Latitude</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.932534</td>\n",
       "      <td>Average Cost for two</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.534416</td>\n",
       "      <td>Currency</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.514428</td>\n",
       "      <td>Has Table booking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.224200</td>\n",
       "      <td>Has Online delivery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.012746</td>\n",
       "      <td>Is delivering now</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.412981</td>\n",
       "      <td>Aggregate rating</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.227561</td>\n",
       "      <td>Rating text</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4.852723</td>\n",
       "      <td>Votes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.575014</td>\n",
       "      <td>Country</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.020381</td>\n",
       "      <td>Cuisine</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         vif               feature\n",
       "0   1.441947                  City\n",
       "1   1.439453             Longitude\n",
       "2   1.252741              Latitude\n",
       "3   1.932534  Average Cost for two\n",
       "4   1.534416              Currency\n",
       "5   1.514428     Has Table booking\n",
       "6   1.224200   Has Online delivery\n",
       "7   1.012746     Is delivering now\n",
       "8   4.412981      Aggregate rating\n",
       "9   1.227561           Rating text\n",
       "10  4.852723                 Votes\n",
       "11  1.575014               Country\n",
       "12  1.020381               Cuisine"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Using VIF to check for muliticolinearity:\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "Vif= pd.DataFrame()\n",
    "Vif['vif']=[variance_inflation_factor(x_scaled,i) for i in range(x_scaled.shape[1])]\n",
    "Vif['feature']= x_scaled.columns\n",
    "Vif"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62bcb479",
   "metadata": {},
   "source": [
    "Data looks good all the vifs are less than 10"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "110aae40",
   "metadata": {},
   "source": [
    "Lets look at the label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "332b6262",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "sns.countplot(y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1286ebea",
   "metadata": {},
   "source": [
    "Label is imbalanced\n",
    "Balancing Label using SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "7d5caca5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "#traintestsplit\n",
    "x_train_,x_test,y_train,y_test = train_test_split (x_scaled,y, test_size=0.3, random_state = 285)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "9c35daba",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,y_train = SMOTE().fit_resample(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "bd43c20e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y_train)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d2d28f7",
   "metadata": {},
   "source": [
    "The label is now balanced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "baa1f1a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At random state  303 the model performs well\n",
      "Acuraccy of training model : 79.61239127117351\n",
      "Accuracy of testing model : 79.92168031327874\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  304 the model performs well\n",
      "Acuraccy of training model : 80.16175797344728\n",
      "Accuracy of testing model : 79.88608045567818\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  306 the model performs well\n",
      "Acuraccy of training model : 80.05493667022738\n",
      "Accuracy of testing model : 79.81488074047704\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  307 the model performs well\n",
      "Acuraccy of training model : 79.59713108499923\n",
      "Accuracy of testing model : 80.38447846208615\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  308 the model performs well\n",
      "Acuraccy of training model : 80.94002746833512\n",
      "Accuracy of testing model : 80.66927732289071\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  309 the model performs well\n",
      "Acuraccy of training model : 80.22279871814436\n",
      "Accuracy of testing model : 80.06407974368102\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  319 the model performs well\n",
      "Acuraccy of training model : 80.23805890431863\n",
      "Accuracy of testing model : 80.06407974368102\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  320 the model performs well\n",
      "Acuraccy of training model : 80.55852281397833\n",
      "Accuracy of testing model : 80.74047703809185\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  322 the model performs well\n",
      "Acuraccy of training model : 78.89516252098275\n",
      "Accuracy of testing model : 79.45888216447135\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  323 the model performs well\n",
      "Acuraccy of training model : 79.76499313291622\n",
      "Accuracy of testing model : 79.88608045567818\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  326 the model performs well\n",
      "Acuraccy of training model : 79.64291164352205\n",
      "Accuracy of testing model : 80.27767888928445\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  329 the model performs well\n",
      "Acuraccy of training model : 81.29101175034336\n",
      "Accuracy of testing model : 80.98967604129584\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  336 the model performs well\n",
      "Acuraccy of training model : 79.59713108499923\n",
      "Accuracy of testing model : 79.92168031327874\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  340 the model performs well\n",
      "Acuraccy of training model : 80.34488020753852\n",
      "Accuracy of testing model : 79.77928088287646\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  346 the model performs well\n",
      "Acuraccy of training model : 80.4669616969327\n",
      "Accuracy of testing model : 79.60128159487361\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  347 the model performs well\n",
      "Acuraccy of training model : 80.23805890431863\n",
      "Accuracy of testing model : 79.92168031327874\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  348 the model performs well\n",
      "Acuraccy of training model : 78.54417823897451\n",
      "Accuracy of testing model : 78.99608401566394\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  353 the model performs well\n",
      "Acuraccy of training model : 80.71112467572105\n",
      "Accuracy of testing model : 80.66927732289071\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  357 the model performs well\n",
      "Acuraccy of training model : 79.71921257439341\n",
      "Accuracy of testing model : 79.81488074047704\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  358 the model performs well\n",
      "Acuraccy of training model : 78.9867236380284\n",
      "Accuracy of testing model : 78.78248487006051\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  362 the model performs well\n",
      "Acuraccy of training model : 80.83320616511521\n",
      "Accuracy of testing model : 80.562477750089\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  365 the model performs well\n",
      "Acuraccy of training model : 80.51274225545552\n",
      "Accuracy of testing model : 80.52687789248843\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  369 the model performs well\n",
      "Acuraccy of training model : 80.4669616969327\n",
      "Accuracy of testing model : 80.42007831968672\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  372 the model performs well\n",
      "Acuraccy of training model : 80.92476728216084\n",
      "Accuracy of testing model : 81.02527589889641\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  373 the model performs well\n",
      "Acuraccy of training model : 79.13932549977109\n",
      "Accuracy of testing model : 78.71128515485938\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  374 the model performs well\n",
      "Acuraccy of training model : 79.3529681062109\n",
      "Accuracy of testing model : 79.35208259166964\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  378 the model performs well\n",
      "Acuraccy of training model : 80.02441629787883\n",
      "Accuracy of testing model : 80.34887860448559\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  379 the model performs well\n",
      "Acuraccy of training model : 80.08545704257592\n",
      "Accuracy of testing model : 80.17087931648274\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  382 the model performs well\n",
      "Acuraccy of training model : 78.95620326567983\n",
      "Accuracy of testing model : 79.0316838732645\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  384 the model performs well\n",
      "Acuraccy of training model : 80.32962002136426\n",
      "Accuracy of testing model : 79.92168031327874\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  385 the model performs well\n",
      "Acuraccy of training model : 79.76499313291622\n",
      "Accuracy of testing model : 80.2064791740833\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  386 the model performs well\n",
      "Acuraccy of training model : 78.8188615901114\n",
      "Accuracy of testing model : 79.35208259166964\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  393 the model performs well\n",
      "Acuraccy of training model : 79.94811536700747\n",
      "Accuracy of testing model : 79.7436810252759\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  396 the model performs well\n",
      "Acuraccy of training model : 79.30718754768809\n",
      "Accuracy of testing model : 79.24528301886792\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  397 the model performs well\n",
      "Acuraccy of training model : 80.31435983518999\n",
      "Accuracy of testing model : 79.70808116767533\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  399 the model performs well\n",
      "Acuraccy of training model : 80.32962002136426\n",
      "Accuracy of testing model : 80.0996796012816\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  400 the model performs well\n",
      "Acuraccy of training model : 80.80268579276667\n",
      "Accuracy of testing model : 80.74047703809185\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  401 the model performs well\n",
      "Acuraccy of training model : 79.00198382420265\n",
      "Accuracy of testing model : 78.60448558205768\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  403 the model performs well\n",
      "Acuraccy of training model : 80.51274225545552\n",
      "Accuracy of testing model : 80.81167675329299\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  404 the model performs well\n",
      "Acuraccy of training model : 81.33679230886617\n",
      "Accuracy of testing model : 81.02527589889641\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  407 the model performs well\n",
      "Acuraccy of training model : 79.0325041965512\n",
      "Accuracy of testing model : 79.20968316126735\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  408 the model performs well\n",
      "Acuraccy of training model : 80.43644132458417\n",
      "Accuracy of testing model : 80.4556781772873\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  409 the model performs well\n",
      "Acuraccy of training model : 80.45170151075844\n",
      "Accuracy of testing model : 80.02847988608045\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  411 the model performs well\n",
      "Acuraccy of training model : 80.26857927666717\n",
      "Accuracy of testing model : 80.27767888928445\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  418 the model performs well\n",
      "Acuraccy of training model : 80.29909964901572\n",
      "Accuracy of testing model : 79.88608045567818\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  419 the model performs well\n",
      "Acuraccy of training model : 79.78025331909049\n",
      "Accuracy of testing model : 79.85048059807761\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  421 the model performs well\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acuraccy of training model : 80.22279871814436\n",
      "Accuracy of testing model : 80.4556781772873\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  425 the model performs well\n",
      "Acuraccy of training model : 80.07019685640164\n",
      "Accuracy of testing model : 80.02847988608045\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  427 the model performs well\n",
      "Acuraccy of training model : 79.10880512742256\n",
      "Accuracy of testing model : 78.71128515485938\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  428 the model performs well\n",
      "Acuraccy of training model : 80.43644132458417\n",
      "Accuracy of testing model : 80.27767888928445\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  432 the model performs well\n",
      "Acuraccy of training model : 79.74973294674194\n",
      "Accuracy of testing model : 79.6368814524742\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  437 the model performs well\n",
      "Acuraccy of training model : 78.6052189836716\n",
      "Accuracy of testing model : 79.3876824492702\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  438 the model performs well\n",
      "Acuraccy of training model : 78.84938196245994\n",
      "Accuracy of testing model : 78.99608401566394\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  440 the model performs well\n",
      "Acuraccy of training model : 78.94094307950556\n",
      "Accuracy of testing model : 78.78248487006051\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  443 the model performs well\n",
      "Acuraccy of training model : 79.76499313291622\n",
      "Accuracy of testing model : 80.2064791740833\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  445 the model performs well\n",
      "Acuraccy of training model : 80.52800244162978\n",
      "Accuracy of testing model : 81.02527589889641\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  449 the model performs well\n",
      "Acuraccy of training model : 80.146497787273\n",
      "Accuracy of testing model : 79.92168031327874\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  457 the model performs well\n",
      "Acuraccy of training model : 80.49748206928125\n",
      "Accuracy of testing model : 80.0996796012816\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  459 the model performs well\n",
      "Acuraccy of training model : 79.44452922325652\n",
      "Accuracy of testing model : 78.9248843004628\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  462 the model performs well\n",
      "Acuraccy of training model : 80.7874256065924\n",
      "Accuracy of testing model : 80.84727661089356\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  467 the model performs well\n",
      "Acuraccy of training model : 79.42926903708225\n",
      "Accuracy of testing model : 79.2808828764685\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  470 the model performs well\n",
      "Acuraccy of training model : 79.73447276056767\n",
      "Accuracy of testing model : 79.77928088287646\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  476 the model performs well\n",
      "Acuraccy of training model : 79.90233480848467\n",
      "Accuracy of testing model : 79.85048059807761\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  478 the model performs well\n",
      "Acuraccy of training model : 80.23805890431863\n",
      "Accuracy of testing model : 80.17087931648274\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  479 the model performs well\n",
      "Acuraccy of training model : 79.94811536700747\n",
      "Accuracy of testing model : 80.4556781772873\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  482 the model performs well\n",
      "Acuraccy of training model : 79.78025331909049\n",
      "Accuracy of testing model : 79.92168031327874\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  484 the model performs well\n",
      "Acuraccy of training model : 79.64291164352205\n",
      "Accuracy of testing model : 80.49127803488786\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  488 the model performs well\n",
      "Acuraccy of training model : 80.65008393102396\n",
      "Accuracy of testing model : 80.562477750089\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  493 the model performs well\n",
      "Acuraccy of training model : 78.74256065924004\n",
      "Accuracy of testing model : 78.74688501245996\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "At random state  495 the model performs well\n",
      "Acuraccy of training model : 79.81077369143904\n",
      "Accuracy of testing model : 80.02847988608045\n",
      "\n",
      "-----------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#traintestsplit\n",
    "lg = LogisticRegression()\n",
    "for i in range(300,500):\n",
    "    x_train,x_test,y_train,y_test = train_test_split (x_scaled,y, test_size=0.3, random_state = i)\n",
    "    lg.fit(x_train,y_train)\n",
    "    pred_train = lg.predict(x_train)\n",
    "    pred_test = lg.predict(x_test)\n",
    "    if round(accuracy_score(y_train,pred_train)*100)==round(accuracy_score(y_test,pred_test)*100):\n",
    "        print (\"At random state \",i,\"the model performs well\")\n",
    "        print (\"Acuraccy of training model :\",accuracy_score(y_train,pred_train)*100)\n",
    "        print (\"Accuracy of testing model :\",accuracy_score(y_test,pred_test)*100)\n",
    "        print (\"\\n-----------------------------------------\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "652c3fb6",
   "metadata": {},
   "source": [
    "Selecting 404 as random state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "ab5b21c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#traintestsplit\n",
    "x_train,x_test,y_train,y_test = train_test_split (x_scaled,y, test_size=0.3, random_state = 404)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "b4eada9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model Selection\n",
    "def model_selection (algorithm, x_train,y_train,x_test,y_test):\n",
    "    algorithm.fit(x_train,y_train)\n",
    "    pred_train = algorithm.predict(x_train)\n",
    "    pred_test = algorithm.predict(x_test)\n",
    "\n",
    "    \n",
    "    print(algorithm)\n",
    "    print (\"Acuraccy of training model :\",accuracy_score(y_train,pred_train))\n",
    "    print (\"Accuracy of testing model :\",accuracy_score(y_test,pred_test))\n",
    "    print (\"Confusion Matrix \\n\",confusion_matrix(y_test,pred_test))\n",
    "    print (\"\\n-----------------------------------------\\n\")\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "e13ed31f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression()\n",
      "Acuraccy of training model : 0.8133679230886617\n",
      "Accuracy of testing model : 0.8102527589889641\n",
      "Confusion Matrix \n",
      " [[1204  103    0    0]\n",
      " [ 143  712   53    2]\n",
      " [  25  116  249   21]\n",
      " [   8    1   61  111]]\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "KNeighborsClassifier()\n",
      "Acuraccy of training model : 0.906912864336945\n",
      "Accuracy of testing model : 0.846564613741545\n",
      "Confusion Matrix \n",
      " [[1219   84    4    0]\n",
      " [  75  786   48    1]\n",
      " [   3  110  277   21]\n",
      " [   1    7   77   96]]\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "DecisionTreeClassifier()\n",
      "Acuraccy of training model : 1.0\n",
      "Accuracy of testing model : 0.9661801352794589\n",
      "Confusion Matrix \n",
      " [[1298    9    0    0]\n",
      " [   5  890   15    0]\n",
      " [   0   21  371   19]\n",
      " [   0    0   26  155]]\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "RandomForestClassifier()\n",
      "Acuraccy of training model : 1.0\n",
      "Accuracy of testing model : 0.9697401210395158\n",
      "Confusion Matrix \n",
      " [[1300    7    0    0]\n",
      " [   3  895   12    0]\n",
      " [   0   26  379    6]\n",
      " [   0    0   31  150]]\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "SVC()\n",
      "Acuraccy of training model : 0.935602014344575\n",
      "Accuracy of testing model : 0.9070843716625133\n",
      "Confusion Matrix \n",
      " [[1258   47    2    0]\n",
      " [  13  864   31    2]\n",
      " [   3   81  311   16]\n",
      " [   0   10   56  115]]\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "AdaBoostClassifier()\n",
      "Acuraccy of training model : 0.9130169388066535\n",
      "Accuracy of testing model : 0.9038803844784621\n",
      "Confusion Matrix \n",
      " [[1273   33    1    0]\n",
      " [  18  878   13    1]\n",
      " [   0   40  355   16]\n",
      " [   0    8  140   33]]\n",
      "\n",
      "-----------------------------------------\n",
      "\n",
      "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
      "              colsample_bylevel=None, colsample_bynode=None,\n",
      "              colsample_bytree=None, early_stopping_rounds=None,\n",
      "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
      "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
      "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
      "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
      "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
      "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
      "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
      "              objective='multi:softprob', predictor=None, ...)\n",
      "Acuraccy of training model : 1.0\n",
      "Accuracy of testing model : 0.9747241011035955\n",
      "Confusion Matrix \n",
      " [[1300    7    0    0]\n",
      " [   2  897   11    0]\n",
      " [   0   23  377   11]\n",
      " [   0    0   17  164]]\n",
      "\n",
      "-----------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Logistic Regression\n",
    "lr= LogisticRegression()\n",
    "model_selection(lr,x_train,y_train,x_test,y_test)\n",
    "\n",
    "#KNN\n",
    "knn= KNeighborsClassifier()\n",
    "model_selection(knn,x_train,y_train,x_test,y_test)\n",
    "\n",
    "#Decision Tree\n",
    "dtc = DecisionTreeClassifier()\n",
    "model_selection(dtc,x_train,y_train,x_test,y_test)\n",
    "\n",
    "#Random Forest \n",
    "rfc= RandomForestClassifier()\n",
    "model_selection(rfc,x_train,y_train,x_test,y_test)\n",
    "\n",
    "#SVC\n",
    "svc= SVC()\n",
    "model_selection(svc,x_train,y_train,x_test,y_test)\n",
    "\n",
    "#AdaBoost\n",
    "ada = AdaBoostClassifier()\n",
    "model_selection(ada,x_train,y_train,x_test,y_test)\n",
    "\n",
    "#XGBoost\n",
    "xg = xgb.XGBClassifier()\n",
    "model_selection(xg,x_train,y_train,x_test,y_test)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65c3785e",
   "metadata": {},
   "source": [
    "Since XGBoost has the best accuracy lets Tune its parameters and check if we can make the model better"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "7c4cb971",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost: \n",
      " {'gamma': 1, 'learning_rate': 0.05, 'max_depth': 8}\n"
     ]
    }
   ],
   "source": [
    "# Hyperparameter Tuning :\n",
    "\n",
    "#XGBoost\n",
    "params = {\n",
    "    'learning_rate': [0.1,0.05,0.01],\n",
    "    'max_depth': [5,8,10],\n",
    "    'gamma': [0, 0.5, 1]\n",
    "}\n",
    "\n",
    "clf = GridSearchCV(xg,params)\n",
    "clf.fit(x_train,y_train)\n",
    "print (\"XGBoost: \\n\",clf.best_params_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "2043a850",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
      "              colsample_bylevel=None, colsample_bynode=None,\n",
      "              colsample_bytree=None, early_stopping_rounds=None,\n",
      "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
      "              gamma=1, gpu_id=None, grow_policy=None, importance_type=None,\n",
      "              interaction_constraints=None, learning_rate=0.05, max_bin=None,\n",
      "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
      "              max_delta_step=None, max_depth=8, max_leaves=None,\n",
      "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
      "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
      "              objective='multi:softprob', predictor=None, ...)\n",
      "Acuraccy of training model : 0.9897756752632382\n",
      "Accuracy of testing model : 0.9768600925596298\n",
      "Confusion Matrix \n",
      " [[1298    8    1    0]\n",
      " [   1  896   13    0]\n",
      " [   0   18  386    7]\n",
      " [   0    0   17  164]]\n",
      "\n",
      "-----------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#XGBoost\n",
    "xg1 = xgb.XGBClassifier(gamma=1,learning_rate=0.05,max_depth=8)\n",
    "model_selection(xg1,x_train,y_train,x_test,y_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2db3d64e",
   "metadata": {},
   "source": [
    "After observing the results of Hyperparameter Tunning Selecting XGBoost as the best model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "b7b186e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving Model\n",
    "import pickle\n",
    "filename= 'Zomato_clf.pkl'\n",
    "pickle.dump(xg1,open(filename, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "ae06ebd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    .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>Orignal</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
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       "      <th>...</th>\n",
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       "      <th>2804</th>\n",
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       "      <th>2805</th>\n",
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       "      <th>2806</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2807</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2808</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2809 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Orignal  Predicted\n",
       "0           2          2\n",
       "1           2          2\n",
       "2           3          2\n",
       "3           0          0\n",
       "4           3          3\n",
       "...       ...        ...\n",
       "2804        0          0\n",
       "2805        1          1\n",
       "2806        0          0\n",
       "2807        1          1\n",
       "2808        0          0\n",
       "\n",
       "[2809 rows x 2 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Final Prediction\n",
    "a= (np.array(y_test))\n",
    "predicted=(np.array (xg1.predict(x_test)))\n",
    "df1 = pd.DataFrame({\"Orignal\":a, \"Predicted\":predicted}, index = range(len(a)))\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d00dc511",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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