SentimentAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['business_id', 'name', 'neighborhood', 'address', 'city', 'state',\n",
       "       'postal_code', 'latitude', 'longitude', 'stars', 'review_count',\n",
       "       'is_open', 'categories'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "businesses = pd.read_csv('yelp_business.csv')\n",
    "businesses.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "174567"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(businesses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>business_id</th>\n",
       "      <th>name</th>\n",
       "      <th>neighborhood</th>\n",
       "      <th>address</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>stars</th>\n",
       "      <th>review_count</th>\n",
       "      <th>is_open</th>\n",
       "      <th>categories</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FYWN1wneV18bWNgQjJ2GNg</td>\n",
       "      <td>\"Dental by Design\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"4855 E Warner Rd, Ste B9\"</td>\n",
       "      <td>Ahwatukee</td>\n",
       "      <td>AZ</td>\n",
       "      <td>85044</td>\n",
       "      <td>33.330690</td>\n",
       "      <td>-111.978599</td>\n",
       "      <td>4.0</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>Dentists;General Dentistry;Health &amp; Medical;Or...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>He-G7vWjzVUysIKrfNbPUQ</td>\n",
       "      <td>\"Stephen Szabo Salon\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"3101 Washington Rd\"</td>\n",
       "      <td>McMurray</td>\n",
       "      <td>PA</td>\n",
       "      <td>15317</td>\n",
       "      <td>40.291685</td>\n",
       "      <td>-80.104900</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>Hair Stylists;Hair Salons;Men's Hair Salons;Bl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KQPW8lFf1y5BT2MxiSZ3QA</td>\n",
       "      <td>\"Western Motor Vehicle\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"6025 N 27th Ave, Ste 1\"</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>AZ</td>\n",
       "      <td>85017</td>\n",
       "      <td>33.524903</td>\n",
       "      <td>-112.115310</td>\n",
       "      <td>1.5</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>Departments of Motor Vehicles;Public Services ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8DShNS-LuFqpEWIp0HxijA</td>\n",
       "      <td>\"Sports Authority\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"5000 Arizona Mills Cr, Ste 435\"</td>\n",
       "      <td>Tempe</td>\n",
       "      <td>AZ</td>\n",
       "      <td>85282</td>\n",
       "      <td>33.383147</td>\n",
       "      <td>-111.964725</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>Sporting Goods;Shopping</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PfOCPjBrlQAnz__NXj9h_w</td>\n",
       "      <td>\"Brick House Tavern + Tap\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"581 Howe Ave\"</td>\n",
       "      <td>Cuyahoga Falls</td>\n",
       "      <td>OH</td>\n",
       "      <td>44221</td>\n",
       "      <td>41.119535</td>\n",
       "      <td>-81.475690</td>\n",
       "      <td>3.5</td>\n",
       "      <td>116</td>\n",
       "      <td>1</td>\n",
       "      <td>American (New);Nightlife;Bars;Sandwiches;Ameri...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              business_id                        name neighborhood  \\\n",
       "0  FYWN1wneV18bWNgQjJ2GNg          \"Dental by Design\"          NaN   \n",
       "1  He-G7vWjzVUysIKrfNbPUQ       \"Stephen Szabo Salon\"          NaN   \n",
       "2  KQPW8lFf1y5BT2MxiSZ3QA     \"Western Motor Vehicle\"          NaN   \n",
       "3  8DShNS-LuFqpEWIp0HxijA          \"Sports Authority\"          NaN   \n",
       "4  PfOCPjBrlQAnz__NXj9h_w  \"Brick House Tavern + Tap\"          NaN   \n",
       "\n",
       "                            address            city state postal_code  \\\n",
       "0        \"4855 E Warner Rd, Ste B9\"       Ahwatukee    AZ       85044   \n",
       "1              \"3101 Washington Rd\"        McMurray    PA       15317   \n",
       "2          \"6025 N 27th Ave, Ste 1\"         Phoenix    AZ       85017   \n",
       "3  \"5000 Arizona Mills Cr, Ste 435\"           Tempe    AZ       85282   \n",
       "4                    \"581 Howe Ave\"  Cuyahoga Falls    OH       44221   \n",
       "\n",
       "    latitude   longitude  stars  review_count  is_open  \\\n",
       "0  33.330690 -111.978599    4.0            22        1   \n",
       "1  40.291685  -80.104900    3.0            11        1   \n",
       "2  33.524903 -112.115310    1.5            18        1   \n",
       "3  33.383147 -111.964725    3.0             9        0   \n",
       "4  41.119535  -81.475690    3.5           116        1   \n",
       "\n",
       "                                          categories  \n",
       "0  Dentists;General Dentistry;Health & Medical;Or...  \n",
       "1  Hair Stylists;Hair Salons;Men's Hair Salons;Bl...  \n",
       "2  Departments of Motor Vehicles;Public Services ...  \n",
       "3                            Sporting Goods;Shopping  \n",
       "4  American (New);Nightlife;Bars;Sandwiches;Ameri...  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "businesses.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>business_id</th>\n",
       "      <th>name</th>\n",
       "      <th>neighborhood</th>\n",
       "      <th>address</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>stars</th>\n",
       "      <th>review_count</th>\n",
       "      <th>is_open</th>\n",
       "      <th>categories</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PfOCPjBrlQAnz__NXj9h_w</td>\n",
       "      <td>\"Brick House Tavern + Tap\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"581 Howe Ave\"</td>\n",
       "      <td>Cuyahoga Falls</td>\n",
       "      <td>OH</td>\n",
       "      <td>44221</td>\n",
       "      <td>41.119535</td>\n",
       "      <td>-81.475690</td>\n",
       "      <td>3.5</td>\n",
       "      <td>116</td>\n",
       "      <td>1</td>\n",
       "      <td>American (New);Nightlife;Bars;Sandwiches;Ameri...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>o9eMRCWt5PkpLDE0gOPtcQ</td>\n",
       "      <td>\"Messina\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"Richterstr. 11\"</td>\n",
       "      <td>Stuttgart</td>\n",
       "      <td>BW</td>\n",
       "      <td>70567</td>\n",
       "      <td>48.727200</td>\n",
       "      <td>9.147950</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Italian;Restaurants</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>XOSRcvtaKc_Q5H1SAzN20A</td>\n",
       "      <td>\"East Coast Coffee\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"737 West Pike St\"</td>\n",
       "      <td>Houston</td>\n",
       "      <td>PA</td>\n",
       "      <td>15342</td>\n",
       "      <td>40.241548</td>\n",
       "      <td>-80.212815</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Breakfast &amp; Brunch;Gluten-Free;Coffee &amp; Tea;Fo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>fNMVV_ZX7CJSDWQGdOM8Nw</td>\n",
       "      <td>\"Showmars Government Center\"</td>\n",
       "      <td>Uptown</td>\n",
       "      <td>\"600 E 4th St\"</td>\n",
       "      <td>Charlotte</td>\n",
       "      <td>NC</td>\n",
       "      <td>28202</td>\n",
       "      <td>35.221647</td>\n",
       "      <td>-80.839345</td>\n",
       "      <td>3.5</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>Restaurants;American (Traditional)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>l09JfMeQ6ynYs5MCJtrcmQ</td>\n",
       "      <td>\"Alize Catering\"</td>\n",
       "      <td>Yonge and Eglinton</td>\n",
       "      <td>\"2459 Yonge St\"</td>\n",
       "      <td>Toronto</td>\n",
       "      <td>ON</td>\n",
       "      <td>M4P 2H6</td>\n",
       "      <td>43.711399</td>\n",
       "      <td>-79.399339</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>Italian;French;Restaurants</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Gu-xs3NIQTj3Mj2xYoN2aw</td>\n",
       "      <td>\"Maxim Bakery &amp; Restaurant\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"9665 Bayview Avenue, Unit 1-4\"</td>\n",
       "      <td>Richmond Hill</td>\n",
       "      <td>ON</td>\n",
       "      <td>L4C 9V4</td>\n",
       "      <td>43.867565</td>\n",
       "      <td>-79.412662</td>\n",
       "      <td>3.5</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>French;Food;Bakeries;Restaurants</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1K4qrnfyzKzGgJPBEcJaNQ</td>\n",
       "      <td>\"Chula Taberna Mexicana\"</td>\n",
       "      <td>Leslieville</td>\n",
       "      <td>\"1058 Gerrard Street E\"</td>\n",
       "      <td>Toronto</td>\n",
       "      <td>ON</td>\n",
       "      <td>M4M 3A6</td>\n",
       "      <td>43.669256</td>\n",
       "      <td>-79.335902</td>\n",
       "      <td>3.5</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>Tiki Bars;Nightlife;Mexican;Restaurants;Bars</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Dj0S-Oe4ytRJzMGUPgYUkw</td>\n",
       "      <td>\"Panera Bread\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"38295 Chestnut Ridge Rd\"</td>\n",
       "      <td>Elyria</td>\n",
       "      <td>OH</td>\n",
       "      <td>44035</td>\n",
       "      <td>41.343078</td>\n",
       "      <td>-82.067140</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Soup;Salad;Sandwiches;Restaurants</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>gAy4LYpsScrj8POnCW6btQ</td>\n",
       "      <td>\"Toast Cafe\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"2429 Hwy 160 W\"</td>\n",
       "      <td>Fort Mill</td>\n",
       "      <td>SC</td>\n",
       "      <td>29708</td>\n",
       "      <td>35.047287</td>\n",
       "      <td>-80.990559</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>Food;American (Traditional);Coffee &amp; Tea;Resta...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>nbhBRhZtdaZmMMeb2i02pg</td>\n",
       "      <td>\"Sunnyside Grill\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"2777 Steeles Avenue W\"</td>\n",
       "      <td>Toronto</td>\n",
       "      <td>ON</td>\n",
       "      <td>M3J 3K5</td>\n",
       "      <td>43.781816</td>\n",
       "      <td>-79.490433</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Restaurants;Breakfast &amp; Brunch</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1_3nOM7s9WqnJWTNu2-i8Q</td>\n",
       "      <td>\"Le Bistro Balmoral\"</td>\n",
       "      <td>Ville-Marie</td>\n",
       "      <td>\"305 Rue Sainte-Catherine O\"</td>\n",
       "      <td>Montreal</td>\n",
       "      <td>QC</td>\n",
       "      <td>H2X 2A1</td>\n",
       "      <td>45.506772</td>\n",
       "      <td>-73.566725</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>Arts &amp; Entertainment;Festivals;Restaurants;Fre...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>FXHfcFVEfI1vVngW2gVOpw</td>\n",
       "      <td>\"Bampot House of Tea &amp; Board Games\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"201 Harbord Street\"</td>\n",
       "      <td>Toronto</td>\n",
       "      <td>ON</td>\n",
       "      <td>M5S 1H6</td>\n",
       "      <td>43.661582</td>\n",
       "      <td>-79.408878</td>\n",
       "      <td>4.0</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>Coffee &amp; Tea;Restaurants;Food;Mediterranean;Te...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>tRVx2c89coruPRwYhGTcTw</td>\n",
       "      <td>\"Yuzu\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"13603 Madison Ave\"</td>\n",
       "      <td>Lakewood</td>\n",
       "      <td>OH</td>\n",
       "      <td>44107</td>\n",
       "      <td>41.476846</td>\n",
       "      <td>-81.786971</td>\n",
       "      <td>3.5</td>\n",
       "      <td>78</td>\n",
       "      <td>1</td>\n",
       "      <td>Nightlife;Izakaya;Comfort Food;Cocktail Bars;A...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>BnuzcebyB1AfxH0kjNWqSg</td>\n",
       "      <td>\"Carrabba's Italian Grill\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"245 Lancaster Ave\"</td>\n",
       "      <td>Frazer</td>\n",
       "      <td>PA</td>\n",
       "      <td>19355</td>\n",
       "      <td>40.041003</td>\n",
       "      <td>-75.542497</td>\n",
       "      <td>3.5</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>Restaurants;Italian;Seafood</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>rDMptJYWtnMhpQu_rRXHng</td>\n",
       "      <td>\"McDonald's\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"719 E Thunderbird Rd\"</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>AZ</td>\n",
       "      <td>85022</td>\n",
       "      <td>33.607070</td>\n",
       "      <td>-112.064382</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>Fast Food;Burgers;Restaurants</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1WBkAuQg81kokZIPMpn9Zg</td>\n",
       "      <td>\"Charr An American Burger Bar\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"777 E Thunderbird Rd, Ste 107\"</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>AZ</td>\n",
       "      <td>85022</td>\n",
       "      <td>33.607310</td>\n",
       "      <td>-112.063404</td>\n",
       "      <td>3.0</td>\n",
       "      <td>232</td>\n",
       "      <td>1</td>\n",
       "      <td>Burgers;Restaurants</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>-ooEO2YqDQVYNHnSF2BPfw</td>\n",
       "      <td>\"Restaurant Lucca\"</td>\n",
       "      <td>Rosemont-La Petite-Patrie</td>\n",
       "      <td>\"12 Rue Dante\"</td>\n",
       "      <td>Montréal</td>\n",
       "      <td>QC</td>\n",
       "      <td>H2S 1J5</td>\n",
       "      <td>45.532896</td>\n",
       "      <td>-73.613604</td>\n",
       "      <td>4.5</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>Italian;Restaurants</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Pd52CjgyEU3Rb8co6QfTPw</td>\n",
       "      <td>\"Flight Deck Bar &amp; Grill\"</td>\n",
       "      <td>Southeast</td>\n",
       "      <td>\"6730 S Las Vegas Blvd\"</td>\n",
       "      <td>Las Vegas</td>\n",
       "      <td>NV</td>\n",
       "      <td>89119</td>\n",
       "      <td>36.066914</td>\n",
       "      <td>-115.170848</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>Nightlife;Bars;Barbeque;Sports Bars;American (...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>4srfPk1s8nlm1YusyDUbjg</td>\n",
       "      <td>\"Subway\"</td>\n",
       "      <td>Southeast</td>\n",
       "      <td>\"6889 S Eastern Ave, Ste 101\"</td>\n",
       "      <td>Las Vegas</td>\n",
       "      <td>NV</td>\n",
       "      <td>89119</td>\n",
       "      <td>36.064652</td>\n",
       "      <td>-115.118954</td>\n",
       "      <td>2.5</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>Fast Food;Restaurants;Sandwiches</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>n7V4cD-KqqE3OXk0irJTyA</td>\n",
       "      <td>\"GameWorks\"</td>\n",
       "      <td>Southeast</td>\n",
       "      <td>\"6587 Las Vegas Blvd S, Ste 171\"</td>\n",
       "      <td>Las Vegas</td>\n",
       "      <td>NV</td>\n",
       "      <td>89119</td>\n",
       "      <td>36.068259</td>\n",
       "      <td>-115.178877</td>\n",
       "      <td>3.0</td>\n",
       "      <td>349</td>\n",
       "      <td>1</td>\n",
       "      <td>Arcades;Arts &amp; Entertainment;Gastropubs;Restau...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>c_XbaJqhm-5ycSBOkVUBmg</td>\n",
       "      <td>\"Don Taco\"</td>\n",
       "      <td>Ville-Marie</td>\n",
       "      <td>\"1855 Rue Saint Catherine Ouest, H3H 1M2\"</td>\n",
       "      <td>Montreal</td>\n",
       "      <td>QC</td>\n",
       "      <td>H3H 1M2</td>\n",
       "      <td>45.493216</td>\n",
       "      <td>-73.580379</td>\n",
       "      <td>3.5</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>Restaurants;Mexican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>spDZkD6cp0JUUm6ghIWHzA</td>\n",
       "      <td>\"Kitchen M\"</td>\n",
       "      <td>Unionville</td>\n",
       "      <td>\"8515 McCowan Road\"</td>\n",
       "      <td>Markham</td>\n",
       "      <td>ON</td>\n",
       "      <td>L3P 5E5</td>\n",
       "      <td>43.867918</td>\n",
       "      <td>-79.283687</td>\n",
       "      <td>3.0</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>Restaurants;Chinese</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>EJFdWX908N8Yc2XG0Lky8A</td>\n",
       "      <td>\"River Moon Cafe\"</td>\n",
       "      <td>Lawrenceville</td>\n",
       "      <td>\"104 43rd St\"</td>\n",
       "      <td>Pittsburgh</td>\n",
       "      <td>PA</td>\n",
       "      <td>15201</td>\n",
       "      <td>40.472735</td>\n",
       "      <td>-79.963265</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>Cafes;Restaurants</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>F31RycVVooeIOp9jsXmg6g</td>\n",
       "      <td>\"The Bluebird Cafe\"</td>\n",
       "      <td>Cannonmills</td>\n",
       "      <td>\"5 Canonmills\"</td>\n",
       "      <td>Edinburgh</td>\n",
       "      <td>MLN</td>\n",
       "      <td>EH3 5HA</td>\n",
       "      <td>55.962444</td>\n",
       "      <td>-3.197662</td>\n",
       "      <td>4.5</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>Breakfast &amp; Brunch;Diners;Restaurants;Cafes;Br...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>iPa__LOhse-hobC2Xmp-Kw</td>\n",
       "      <td>\"McDonald's\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"1635 E Camelback Rd\"</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>AZ</td>\n",
       "      <td>85016</td>\n",
       "      <td>33.508765</td>\n",
       "      <td>-112.046240</td>\n",
       "      <td>3.0</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>Restaurants;Burgers;Fast Food</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>o1fTwfqN0sDFNpV1CkOPPg</td>\n",
       "      <td>\"Crazy Mocha Coffee\"</td>\n",
       "      <td>Lawrenceville</td>\n",
       "      <td>\"4032 Butler St\"</td>\n",
       "      <td>Pittsburgh</td>\n",
       "      <td>PA</td>\n",
       "      <td>15201</td>\n",
       "      <td>40.469295</td>\n",
       "      <td>-79.961273</td>\n",
       "      <td>3.5</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>Restaurants;Sandwiches;Coffee &amp; Tea;Food</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>-nHkhiuerqmfBG3v2v9O-g</td>\n",
       "      <td>\"Bruegger's Bagels\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"5217 Clairton Blvd\"</td>\n",
       "      <td>Pittsburgh</td>\n",
       "      <td>PA</td>\n",
       "      <td>15236</td>\n",
       "      <td>40.344194</td>\n",
       "      <td>-79.970963</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Breakfast &amp; Brunch;Bagels;Restaurants;Bakeries...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>kKx8iCJkomVQBdWHnmmOiA</td>\n",
       "      <td>\"Little Caesars Pizza\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"10720 E Southern Ave\"</td>\n",
       "      <td>Mesa</td>\n",
       "      <td>AZ</td>\n",
       "      <td>85209</td>\n",
       "      <td>33.394877</td>\n",
       "      <td>-111.600194</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Restaurants;Pizza</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>B5EZlEDH6AVDk8tQGHAGqg</td>\n",
       "      <td>\"Villa Tap\"</td>\n",
       "      <td>Sherman</td>\n",
       "      <td>\"2302 Packers Ave\"</td>\n",
       "      <td>Madison</td>\n",
       "      <td>WI</td>\n",
       "      <td>53704</td>\n",
       "      <td>43.123389</td>\n",
       "      <td>-89.355390</td>\n",
       "      <td>4.0</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>American (Traditional);Nightlife;Dive Bars;Res...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>VXH7zXcZzXlmAVN8GSjGRQ</td>\n",
       "      <td>\"Thai Express\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"4700 Keele Street\"</td>\n",
       "      <td>Toronto</td>\n",
       "      <td>ON</td>\n",
       "      <td>M3J 1P3</td>\n",
       "      <td>43.774878</td>\n",
       "      <td>-79.494619</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Imported Food;Specialty Food;Thai;Ethnic Food;...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>YhV93k9uiMdr3FlV4FHjwA</td>\n",
       "      <td>\"Caviness Studio\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"\"</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>AZ</td>\n",
       "      <td>85001</td>\n",
       "      <td>33.449967</td>\n",
       "      <td>-112.070223</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Marketing;Men's Clothing;Restaurants;Graphic D...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>aFBCmJUYrPeol_P75QX7Jw</td>\n",
       "      <td>\"Fraticelli's Authentic Italian Grill\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"10 Vogell Road\"</td>\n",
       "      <td>Richmond Hill</td>\n",
       "      <td>ON</td>\n",
       "      <td>L4B 3K4</td>\n",
       "      <td>43.864333</td>\n",
       "      <td>-79.382411</td>\n",
       "      <td>3.0</td>\n",
       "      <td>88</td>\n",
       "      <td>1</td>\n",
       "      <td>Restaurants;Italian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>F0fEKpTk7gAmuSFI0KW1eQ</td>\n",
       "      <td>\"Cafe Mastrioni\"</td>\n",
       "      <td>Spring Valley</td>\n",
       "      <td>\"4250 S Rainbow Blvd, Ste 1007\"</td>\n",
       "      <td>Las Vegas</td>\n",
       "      <td>NV</td>\n",
       "      <td>89103</td>\n",
       "      <td>36.111057</td>\n",
       "      <td>-115.241688</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Italian;Restaurants</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>dTWfATVrBfKj7Vdn0qWVWg</td>\n",
       "      <td>\"Flavor Cuisine\"</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>\"8 Glen Watford Drive\"</td>\n",
       "      <td>Toronto</td>\n",
       "      <td>ON</td>\n",
       "      <td>M1S 2C1</td>\n",
       "      <td>43.787061</td>\n",
       "      <td>-79.276166</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>Restaurants;Chinese;Food Court</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>HAX1zec191t7QkT2sBZ76A</td>\n",
       "      <td>\"La Isla Cuban Restaurant\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"1816 Galerea Blvd, Ste D\"</td>\n",
       "      <td>Charlotte</td>\n",
       "      <td>NC</td>\n",
       "      <td>28270</td>\n",
       "      <td>35.137223</td>\n",
       "      <td>-80.734594</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>Restaurants;Cuban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>VdlPZg2NAu8t8GkdbPLecg</td>\n",
       "      <td>\"Tandoori Times Indian Bistro\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"8140 N Hayden Rd, Ste H115\"</td>\n",
       "      <td>Scottsdale</td>\n",
       "      <td>AZ</td>\n",
       "      <td>85258</td>\n",
       "      <td>33.555212</td>\n",
       "      <td>-111.900456</td>\n",
       "      <td>3.5</td>\n",
       "      <td>263</td>\n",
       "      <td>1</td>\n",
       "      <td>Restaurants;Gluten-Free;Indian;Seafood</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>c7X2SdKxVJMaOnFROO8WEg</td>\n",
       "      <td>\"Finga Lickin' Caribbean Eatery\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"2838 The Plz\"</td>\n",
       "      <td>Charlotte</td>\n",
       "      <td>NC</td>\n",
       "      <td>28205</td>\n",
       "      <td>35.236823</td>\n",
       "      <td>-80.801084</td>\n",
       "      <td>4.5</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>Pizza;Food;Internet Cafes;Restaurants;Caribbean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>1nhf9BPXOBFBkbRkpsFaxA</td>\n",
       "      <td>\"Mirage Grill &amp; Lounge\"</td>\n",
       "      <td>Yonge and Eglinton</td>\n",
       "      <td>\"117 Eglinton Avenue E\"</td>\n",
       "      <td>Toronto</td>\n",
       "      <td>ON</td>\n",
       "      <td>M4P 1H4</td>\n",
       "      <td>43.707465</td>\n",
       "      <td>-79.394285</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>Breakfast &amp; Brunch;Food;Coffee &amp; Tea;Canadian ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>QkG3KUXwqZBW18A9k1xqCA</td>\n",
       "      <td>\"Red Lobster\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"2810 North 75th Ave\"</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>AZ</td>\n",
       "      <td>85035</td>\n",
       "      <td>33.478735</td>\n",
       "      <td>-112.221379</td>\n",
       "      <td>2.5</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>American (Traditional);Restaurants;Seafood</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>sJ0MYSAIVK28cMzh-s-NPA</td>\n",
       "      <td>\"Amaya Express\"</td>\n",
       "      <td>Downtown Core</td>\n",
       "      <td>\"Eaton Centre, 220 Yonge St\"</td>\n",
       "      <td>Toronto</td>\n",
       "      <td>ON</td>\n",
       "      <td>M5B 2L7</td>\n",
       "      <td>43.654067</td>\n",
       "      <td>-79.380081</td>\n",
       "      <td>2.5</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>Indian;Restaurants;Fast Food</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>gIVjdDWRnS1yDVpPF3oUeg</td>\n",
       "      <td>\"Pizza Pizza\"</td>\n",
       "      <td>Milliken</td>\n",
       "      <td>\"7665 Kennedy Road\"</td>\n",
       "      <td>Markham</td>\n",
       "      <td>ON</td>\n",
       "      <td>L3R 0L7</td>\n",
       "      <td>43.841217</td>\n",
       "      <td>-79.303936</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Italian;Chicken Wings;Fast Food;Restaurants;Pizza</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>veXxt8rGY_RJPpA5QkHT9Q</td>\n",
       "      <td>\"Barrio\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"2466 Fairmount Blvd\"</td>\n",
       "      <td>Cleveland Heights</td>\n",
       "      <td>OH</td>\n",
       "      <td>44106</td>\n",
       "      <td>41.500586</td>\n",
       "      <td>-81.592278</td>\n",
       "      <td>3.5</td>\n",
       "      <td>97</td>\n",
       "      <td>1</td>\n",
       "      <td>Food;Restaurants;Tacos;Food Trucks;Mexican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>T5CdfrZWw-uW9Y5L_sddqQ</td>\n",
       "      <td>\"Police Station Pizza\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"7235 Steubenville Pike\"</td>\n",
       "      <td>Oakdale</td>\n",
       "      <td>PA</td>\n",
       "      <td>15071</td>\n",
       "      <td>40.442828</td>\n",
       "      <td>-80.186293</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>Pizza;Restaurants</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>5XejqzaFmtkZMstJS5Iy-w</td>\n",
       "      <td>\"D'Lish Cafe\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"503 W Thomas Rd\"</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>AZ</td>\n",
       "      <td>85013</td>\n",
       "      <td>33.480301</td>\n",
       "      <td>-112.080586</td>\n",
       "      <td>4.0</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>Vegan;American (New);Restaurants;Sandwiches;Sa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>Wpt0sFHcPtV5MO9He7yMKQ</td>\n",
       "      <td>\"McDonald's\"</td>\n",
       "      <td>Eastside</td>\n",
       "      <td>\"3020 E Desert Inn Rd\"</td>\n",
       "      <td>Las Vegas</td>\n",
       "      <td>NV</td>\n",
       "      <td>89121</td>\n",
       "      <td>36.130013</td>\n",
       "      <td>-115.109310</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>Restaurants;Fast Food;Burgers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>WUiDaFQRZ8wKYGLvmjFjAw</td>\n",
       "      <td>\"China Buffet\"</td>\n",
       "      <td>University City</td>\n",
       "      <td>\"8630 University Executive Park Dr\"</td>\n",
       "      <td>Charlotte</td>\n",
       "      <td>NC</td>\n",
       "      <td>28262</td>\n",
       "      <td>35.306173</td>\n",
       "      <td>-80.752672</td>\n",
       "      <td>3.5</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>Buffets;Restaurants;Sushi Bars;Chinese</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>nAFI7JZEhmvUKMKSA_5Chg</td>\n",
       "      <td>\"Nesenbach\"</td>\n",
       "      <td>Rathaus</td>\n",
       "      <td>\"Dorotheenstr. 6\"</td>\n",
       "      <td>Stuttgart</td>\n",
       "      <td>BW</td>\n",
       "      <td>70173</td>\n",
       "      <td>48.775453</td>\n",
       "      <td>9.180737</td>\n",
       "      <td>2.5</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>Cafes;Nightlife;Wine Bars;Bars;Restaurants</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>5q6Xh-UcJa78bp6dzyaE7w</td>\n",
       "      <td>\"Duck Donuts\"</td>\n",
       "      <td>Dilworth</td>\n",
       "      <td>\"1710 Kenilworth Ave, Ste 220\"</td>\n",
       "      <td>Charlotte</td>\n",
       "      <td>NC</td>\n",
       "      <td>28203</td>\n",
       "      <td>35.202624</td>\n",
       "      <td>-80.844419</td>\n",
       "      <td>4.5</td>\n",
       "      <td>373</td>\n",
       "      <td>1</td>\n",
       "      <td>Breakfast &amp; Brunch;Food;Coffee &amp; Tea;Donuts;Re...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>MTH-AcNyWfsBa9sXp04HcQ</td>\n",
       "      <td>\"Firehouse Subs\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"3420 E Baseline Rd, Ste 101\"</td>\n",
       "      <td>Mesa</td>\n",
       "      <td>AZ</td>\n",
       "      <td>85204</td>\n",
       "      <td>33.379621</td>\n",
       "      <td>-111.757160</td>\n",
       "      <td>3.5</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>Food;Delis;Fast Food;Sandwiches;Restaurants</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>nigYwB_m1TQ1WosjSWi-Hw</td>\n",
       "      <td>\"Simply Burgers\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"1455 W Elliot Rd\"</td>\n",
       "      <td>Gilbert</td>\n",
       "      <td>AZ</td>\n",
       "      <td>85233</td>\n",
       "      <td>33.349658</td>\n",
       "      <td>-111.821238</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>Burgers;Restaurants</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                business_id                                    name  \\\n",
       "4    PfOCPjBrlQAnz__NXj9h_w              \"Brick House Tavern + Tap\"   \n",
       "5    o9eMRCWt5PkpLDE0gOPtcQ                               \"Messina\"   \n",
       "10   XOSRcvtaKc_Q5H1SAzN20A                     \"East Coast Coffee\"   \n",
       "14   fNMVV_ZX7CJSDWQGdOM8Nw            \"Showmars Government Center\"   \n",
       "15   l09JfMeQ6ynYs5MCJtrcmQ                        \"Alize Catering\"   \n",
       "19   Gu-xs3NIQTj3Mj2xYoN2aw             \"Maxim Bakery & Restaurant\"   \n",
       "25   1K4qrnfyzKzGgJPBEcJaNQ                \"Chula Taberna Mexicana\"   \n",
       "28   Dj0S-Oe4ytRJzMGUPgYUkw                          \"Panera Bread\"   \n",
       "29   gAy4LYpsScrj8POnCW6btQ                            \"Toast Cafe\"   \n",
       "30   nbhBRhZtdaZmMMeb2i02pg                       \"Sunnyside Grill\"   \n",
       "32   1_3nOM7s9WqnJWTNu2-i8Q                    \"Le Bistro Balmoral\"   \n",
       "33   FXHfcFVEfI1vVngW2gVOpw     \"Bampot House of Tea & Board Games\"   \n",
       "40   tRVx2c89coruPRwYhGTcTw                                  \"Yuzu\"   \n",
       "44   BnuzcebyB1AfxH0kjNWqSg              \"Carrabba's Italian Grill\"   \n",
       "45   rDMptJYWtnMhpQu_rRXHng                            \"McDonald's\"   \n",
       "46   1WBkAuQg81kokZIPMpn9Zg          \"Charr An American Burger Bar\"   \n",
       "50   -ooEO2YqDQVYNHnSF2BPfw                      \"Restaurant Lucca\"   \n",
       "52   Pd52CjgyEU3Rb8co6QfTPw               \"Flight Deck Bar & Grill\"   \n",
       "53   4srfPk1s8nlm1YusyDUbjg                                \"Subway\"   \n",
       "54   n7V4cD-KqqE3OXk0irJTyA                             \"GameWorks\"   \n",
       "55   c_XbaJqhm-5ycSBOkVUBmg                              \"Don Taco\"   \n",
       "57   spDZkD6cp0JUUm6ghIWHzA                             \"Kitchen M\"   \n",
       "64   EJFdWX908N8Yc2XG0Lky8A                       \"River Moon Cafe\"   \n",
       "68   F31RycVVooeIOp9jsXmg6g                     \"The Bluebird Cafe\"   \n",
       "72   iPa__LOhse-hobC2Xmp-Kw                            \"McDonald's\"   \n",
       "75   o1fTwfqN0sDFNpV1CkOPPg                    \"Crazy Mocha Coffee\"   \n",
       "76   -nHkhiuerqmfBG3v2v9O-g                     \"Bruegger's Bagels\"   \n",
       "80   kKx8iCJkomVQBdWHnmmOiA                  \"Little Caesars Pizza\"   \n",
       "81   B5EZlEDH6AVDk8tQGHAGqg                             \"Villa Tap\"   \n",
       "86   VXH7zXcZzXlmAVN8GSjGRQ                          \"Thai Express\"   \n",
       "88   YhV93k9uiMdr3FlV4FHjwA                       \"Caviness Studio\"   \n",
       "90   aFBCmJUYrPeol_P75QX7Jw  \"Fraticelli's Authentic Italian Grill\"   \n",
       "91   F0fEKpTk7gAmuSFI0KW1eQ                        \"Cafe Mastrioni\"   \n",
       "96   dTWfATVrBfKj7Vdn0qWVWg                        \"Flavor Cuisine\"   \n",
       "97   HAX1zec191t7QkT2sBZ76A              \"La Isla Cuban Restaurant\"   \n",
       "101  VdlPZg2NAu8t8GkdbPLecg          \"Tandoori Times Indian Bistro\"   \n",
       "103  c7X2SdKxVJMaOnFROO8WEg        \"Finga Lickin' Caribbean Eatery\"   \n",
       "104  1nhf9BPXOBFBkbRkpsFaxA                 \"Mirage Grill & Lounge\"   \n",
       "107  QkG3KUXwqZBW18A9k1xqCA                           \"Red Lobster\"   \n",
       "113  sJ0MYSAIVK28cMzh-s-NPA                         \"Amaya Express\"   \n",
       "116  gIVjdDWRnS1yDVpPF3oUeg                           \"Pizza Pizza\"   \n",
       "117  veXxt8rGY_RJPpA5QkHT9Q                                \"Barrio\"   \n",
       "118  T5CdfrZWw-uW9Y5L_sddqQ                  \"Police Station Pizza\"   \n",
       "120  5XejqzaFmtkZMstJS5Iy-w                           \"D'Lish Cafe\"   \n",
       "122  Wpt0sFHcPtV5MO9He7yMKQ                            \"McDonald's\"   \n",
       "126  WUiDaFQRZ8wKYGLvmjFjAw                          \"China Buffet\"   \n",
       "127  nAFI7JZEhmvUKMKSA_5Chg                             \"Nesenbach\"   \n",
       "128  5q6Xh-UcJa78bp6dzyaE7w                           \"Duck Donuts\"   \n",
       "130  MTH-AcNyWfsBa9sXp04HcQ                        \"Firehouse Subs\"   \n",
       "132  nigYwB_m1TQ1WosjSWi-Hw                        \"Simply Burgers\"   \n",
       "\n",
       "                  neighborhood                                    address  \\\n",
       "4                          NaN                             \"581 Howe Ave\"   \n",
       "5                          NaN                           \"Richterstr. 11\"   \n",
       "10                         NaN                         \"737 West Pike St\"   \n",
       "14                      Uptown                             \"600 E 4th St\"   \n",
       "15          Yonge and Eglinton                            \"2459 Yonge St\"   \n",
       "19                         NaN            \"9665 Bayview Avenue, Unit 1-4\"   \n",
       "25                 Leslieville                    \"1058 Gerrard Street E\"   \n",
       "28                         NaN                  \"38295 Chestnut Ridge Rd\"   \n",
       "29                         NaN                           \"2429 Hwy 160 W\"   \n",
       "30                         NaN                    \"2777 Steeles Avenue W\"   \n",
       "32                 Ville-Marie               \"305 Rue Sainte-Catherine O\"   \n",
       "33                         NaN                       \"201 Harbord Street\"   \n",
       "40                         NaN                        \"13603 Madison Ave\"   \n",
       "44                         NaN                        \"245 Lancaster Ave\"   \n",
       "45                         NaN                     \"719 E Thunderbird Rd\"   \n",
       "46                         NaN            \"777 E Thunderbird Rd, Ste 107\"   \n",
       "50   Rosemont-La Petite-Patrie                             \"12 Rue Dante\"   \n",
       "52                   Southeast                    \"6730 S Las Vegas Blvd\"   \n",
       "53                   Southeast              \"6889 S Eastern Ave, Ste 101\"   \n",
       "54                   Southeast           \"6587 Las Vegas Blvd S, Ste 171\"   \n",
       "55                 Ville-Marie  \"1855 Rue Saint Catherine Ouest, H3H 1M2\"   \n",
       "57                  Unionville                        \"8515 McCowan Road\"   \n",
       "64               Lawrenceville                              \"104 43rd St\"   \n",
       "68                 Cannonmills                             \"5 Canonmills\"   \n",
       "72                         NaN                      \"1635 E Camelback Rd\"   \n",
       "75               Lawrenceville                           \"4032 Butler St\"   \n",
       "76                         NaN                       \"5217 Clairton Blvd\"   \n",
       "80                         NaN                     \"10720 E Southern Ave\"   \n",
       "81                     Sherman                         \"2302 Packers Ave\"   \n",
       "86                         NaN                        \"4700 Keele Street\"   \n",
       "88                         NaN                                         \"\"   \n",
       "90                         NaN                           \"10 Vogell Road\"   \n",
       "91               Spring Valley            \"4250 S Rainbow Blvd, Ste 1007\"   \n",
       "96                 Scarborough                     \"8 Glen Watford Drive\"   \n",
       "97                         NaN                 \"1816 Galerea Blvd, Ste D\"   \n",
       "101                        NaN               \"8140 N Hayden Rd, Ste H115\"   \n",
       "103                        NaN                             \"2838 The Plz\"   \n",
       "104         Yonge and Eglinton                    \"117 Eglinton Avenue E\"   \n",
       "107                        NaN                      \"2810 North 75th Ave\"   \n",
       "113              Downtown Core               \"Eaton Centre, 220 Yonge St\"   \n",
       "116                   Milliken                        \"7665 Kennedy Road\"   \n",
       "117                        NaN                      \"2466 Fairmount Blvd\"   \n",
       "118                        NaN                   \"7235 Steubenville Pike\"   \n",
       "120                        NaN                          \"503 W Thomas Rd\"   \n",
       "122                   Eastside                     \"3020 E Desert Inn Rd\"   \n",
       "126            University City        \"8630 University Executive Park Dr\"   \n",
       "127                    Rathaus                          \"Dorotheenstr. 6\"   \n",
       "128                   Dilworth             \"1710 Kenilworth Ave, Ste 220\"   \n",
       "130                        NaN              \"3420 E Baseline Rd, Ste 101\"   \n",
       "132                        NaN                         \"1455 W Elliot Rd\"   \n",
       "\n",
       "                  city state postal_code   latitude   longitude  stars  \\\n",
       "4       Cuyahoga Falls    OH       44221  41.119535  -81.475690    3.5   \n",
       "5            Stuttgart    BW       70567  48.727200    9.147950    4.0   \n",
       "10             Houston    PA       15342  40.241548  -80.212815    4.5   \n",
       "14           Charlotte    NC       28202  35.221647  -80.839345    3.5   \n",
       "15             Toronto    ON     M4P 2H6  43.711399  -79.399339    3.0   \n",
       "19       Richmond Hill    ON     L4C 9V4  43.867565  -79.412662    3.5   \n",
       "25             Toronto    ON     M4M 3A6  43.669256  -79.335902    3.5   \n",
       "28              Elyria    OH       44035  41.343078  -82.067140    2.0   \n",
       "29           Fort Mill    SC       29708  35.047287  -80.990559    3.5   \n",
       "30             Toronto    ON     M3J 3K5  43.781816  -79.490433    5.0   \n",
       "32            Montreal    QC     H2X 2A1  45.506772  -73.566725    3.0   \n",
       "33             Toronto    ON     M5S 1H6  43.661582  -79.408878    4.0   \n",
       "40            Lakewood    OH       44107  41.476846  -81.786971    3.5   \n",
       "44              Frazer    PA       19355  40.041003  -75.542497    3.5   \n",
       "45             Phoenix    AZ       85022  33.607070 -112.064382    1.0   \n",
       "46             Phoenix    AZ       85022  33.607310 -112.063404    3.0   \n",
       "50            Montréal    QC     H2S 1J5  45.532896  -73.613604    4.5   \n",
       "52           Las Vegas    NV       89119  36.066914 -115.170848    4.0   \n",
       "53           Las Vegas    NV       89119  36.064652 -115.118954    2.5   \n",
       "54           Las Vegas    NV       89119  36.068259 -115.178877    3.0   \n",
       "55            Montreal    QC     H3H 1M2  45.493216  -73.580379    3.5   \n",
       "57             Markham    ON     L3P 5E5  43.867918  -79.283687    3.0   \n",
       "64          Pittsburgh    PA       15201  40.472735  -79.963265    4.0   \n",
       "68           Edinburgh   MLN     EH3 5HA  55.962444   -3.197662    4.5   \n",
       "72             Phoenix    AZ       85016  33.508765 -112.046240    3.0   \n",
       "75          Pittsburgh    PA       15201  40.469295  -79.961273    3.5   \n",
       "76          Pittsburgh    PA       15236  40.344194  -79.970963    2.5   \n",
       "80                Mesa    AZ       85209  33.394877 -111.600194    2.5   \n",
       "81             Madison    WI       53704  43.123389  -89.355390    4.0   \n",
       "86             Toronto    ON     M3J 1P3  43.774878  -79.494619    3.0   \n",
       "88             Phoenix    AZ       85001  33.449967 -112.070223    5.0   \n",
       "90       Richmond Hill    ON     L4B 3K4  43.864333  -79.382411    3.0   \n",
       "91           Las Vegas    NV       89103  36.111057 -115.241688    1.5   \n",
       "96             Toronto    ON     M1S 2C1  43.787061  -79.276166    3.0   \n",
       "97           Charlotte    NC       28270  35.137223  -80.734594    3.0   \n",
       "101         Scottsdale    AZ       85258  33.555212 -111.900456    3.5   \n",
       "103          Charlotte    NC       28205  35.236823  -80.801084    4.5   \n",
       "104            Toronto    ON     M4P 1H4  43.707465  -79.394285    2.0   \n",
       "107            Phoenix    AZ       85035  33.478735 -112.221379    2.5   \n",
       "113            Toronto    ON     M5B 2L7  43.654067  -79.380081    2.5   \n",
       "116            Markham    ON     L3R 0L7  43.841217  -79.303936    4.0   \n",
       "117  Cleveland Heights    OH       44106  41.500586  -81.592278    3.5   \n",
       "118            Oakdale    PA       15071  40.442828  -80.186293    3.0   \n",
       "120            Phoenix    AZ       85013  33.480301 -112.080586    4.0   \n",
       "122          Las Vegas    NV       89121  36.130013 -115.109310    2.0   \n",
       "126          Charlotte    NC       28262  35.306173  -80.752672    3.5   \n",
       "127          Stuttgart    BW       70173  48.775453    9.180737    2.5   \n",
       "128          Charlotte    NC       28203  35.202624  -80.844419    4.5   \n",
       "130               Mesa    AZ       85204  33.379621 -111.757160    3.5   \n",
       "132            Gilbert    AZ       85233  33.349658 -111.821238    3.0   \n",
       "\n",
       "     review_count  is_open                                         categories  \n",
       "4             116        1  American (New);Nightlife;Bars;Sandwiches;Ameri...  \n",
       "5               5        1                                Italian;Restaurants  \n",
       "10              3        0  Breakfast & Brunch;Gluten-Free;Coffee & Tea;Fo...  \n",
       "14              7        1                 Restaurants;American (Traditional)  \n",
       "15             12        0                         Italian;French;Restaurants  \n",
       "19             34        1                   French;Food;Bakeries;Restaurants  \n",
       "25             39        1       Tiki Bars;Nightlife;Mexican;Restaurants;Bars  \n",
       "28              4        1                  Soup;Salad;Sandwiches;Restaurants  \n",
       "29              6        0  Food;American (Traditional);Coffee & Tea;Resta...  \n",
       "30              3        1                     Restaurants;Breakfast & Brunch  \n",
       "32              8        0  Arts & Entertainment;Festivals;Restaurants;Fre...  \n",
       "33             55        1  Coffee & Tea;Restaurants;Food;Mediterranean;Te...  \n",
       "40             78        1  Nightlife;Izakaya;Comfort Food;Cocktail Bars;A...  \n",
       "44             25        0                        Restaurants;Italian;Seafood  \n",
       "45             10        1                      Fast Food;Burgers;Restaurants  \n",
       "46            232        1                                Burgers;Restaurants  \n",
       "50             11        1                                Italian;Restaurants  \n",
       "52             13        1  Nightlife;Bars;Barbeque;Sports Bars;American (...  \n",
       "53              6        1                   Fast Food;Restaurants;Sandwiches  \n",
       "54            349        1  Arcades;Arts & Entertainment;Gastropubs;Restau...  \n",
       "55             33        1                                Restaurants;Mexican  \n",
       "57             80        1                                Restaurants;Chinese  \n",
       "64              5        0                                  Cafes;Restaurants  \n",
       "68             16        1  Breakfast & Brunch;Diners;Restaurants;Cafes;Br...  \n",
       "72             34        1                      Restaurants;Burgers;Fast Food  \n",
       "75             16        1           Restaurants;Sandwiches;Coffee & Tea;Food  \n",
       "76              5        1  Breakfast & Brunch;Bagels;Restaurants;Bakeries...  \n",
       "80              4        1                                  Restaurants;Pizza  \n",
       "81             34        1  American (Traditional);Nightlife;Dive Bars;Res...  \n",
       "86              5        1  Imported Food;Specialty Food;Thai;Ethnic Food;...  \n",
       "88              4        1  Marketing;Men's Clothing;Restaurants;Graphic D...  \n",
       "90             88        1                                Restaurants;Italian  \n",
       "91              3        0                                Italian;Restaurants  \n",
       "96              6        1                     Restaurants;Chinese;Food Court  \n",
       "97              4        0                                  Restaurants;Cuban  \n",
       "101           263        1             Restaurants;Gluten-Free;Indian;Seafood  \n",
       "103            21        1    Pizza;Food;Internet Cafes;Restaurants;Caribbean  \n",
       "104             6        0  Breakfast & Brunch;Food;Coffee & Tea;Canadian ...  \n",
       "107            37        1         American (Traditional);Restaurants;Seafood  \n",
       "113            34        1                       Indian;Restaurants;Fast Food  \n",
       "116             8        1  Italian;Chicken Wings;Fast Food;Restaurants;Pizza  \n",
       "117            97        1         Food;Restaurants;Tacos;Food Trucks;Mexican  \n",
       "118             9        0                                  Pizza;Restaurants  \n",
       "120            37        0  Vegan;American (New);Restaurants;Sandwiches;Sa...  \n",
       "122            20        1                      Restaurants;Fast Food;Burgers  \n",
       "126            76        1             Buffets;Restaurants;Sushi Bars;Chinese  \n",
       "127             7        1         Cafes;Nightlife;Wine Bars;Bars;Restaurants  \n",
       "128           373        1  Breakfast & Brunch;Food;Coffee & Tea;Donuts;Re...  \n",
       "130            82        1        Food;Delis;Fast Food;Sandwiches;Restaurants  \n",
       "132             5        0                                Burgers;Restaurants  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "restaurants = businesses[businesses['categories'].str.contains(\"Restaurants\")]\n",
    "restaurants.head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [],
   "source": [
    "business_attr = pd.read_csv('yelp_business_attributes.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "152041"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(business_attr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "152041"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "business_attr['business_id'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['business_id', 'name', 'neighborhood', 'address', 'city', 'state',\n",
       "       'postal_code', 'latitude', 'longitude', 'stars', 'review_count',\n",
       "       'is_open', 'categories', 'AcceptsInsurance', 'ByAppointmentOnly',\n",
       "       'BusinessAcceptsCreditCards', 'BusinessParking_garage',\n",
       "       'BusinessParking_street', 'BusinessParking_validated',\n",
       "       'BusinessParking_lot', 'BusinessParking_valet',\n",
       "       'HairSpecializesIn_coloring', 'HairSpecializesIn_africanamerican',\n",
       "       'HairSpecializesIn_curly', 'HairSpecializesIn_perms',\n",
       "       'HairSpecializesIn_kids', 'HairSpecializesIn_extensions',\n",
       "       'HairSpecializesIn_asian', 'HairSpecializesIn_straightperms',\n",
       "       'RestaurantsPriceRange2', 'GoodForKids', 'WheelchairAccessible',\n",
       "       'BikeParking', 'Alcohol', 'HasTV', 'NoiseLevel', 'RestaurantsAttire',\n",
       "       'Music_dj', 'Music_background_music', 'Music_no_music', 'Music_karaoke',\n",
       "       'Music_live', 'Music_video', 'Music_jukebox', 'Ambience_romantic',\n",
       "       'Ambience_intimate', 'Ambience_classy', 'Ambience_hipster',\n",
       "       'Ambience_divey', 'Ambience_touristy', 'Ambience_trendy',\n",
       "       'Ambience_upscale', 'Ambience_casual', 'RestaurantsGoodForGroups',\n",
       "       'Caters', 'WiFi', 'RestaurantsReservations', 'RestaurantsTakeOut',\n",
       "       'HappyHour', 'GoodForDancing', 'RestaurantsTableService',\n",
       "       'OutdoorSeating', 'RestaurantsDelivery', 'BestNights_monday',\n",
       "       'BestNights_tuesday', 'BestNights_friday', 'BestNights_wednesday',\n",
       "       'BestNights_thursday', 'BestNights_sunday', 'BestNights_saturday',\n",
       "       'GoodForMeal_dessert', 'GoodForMeal_latenight', 'GoodForMeal_lunch',\n",
       "       'GoodForMeal_dinner', 'GoodForMeal_breakfast', 'GoodForMeal_brunch',\n",
       "       'CoatCheck', 'Smoking', 'DriveThru', 'DogsAllowed',\n",
       "       'BusinessAcceptsBitcoin', 'Open24Hours', 'BYOBCorkage', 'BYOB',\n",
       "       'Corkage', 'DietaryRestrictions_dairy-free',\n",
       "       'DietaryRestrictions_gluten-free', 'DietaryRestrictions_vegan',\n",
       "       'DietaryRestrictions_kosher', 'DietaryRestrictions_halal',\n",
       "       'DietaryRestrictions_soy-free', 'DietaryRestrictions_vegetarian',\n",
       "       'AgesAllowed', 'RestaurantsCounterService'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "restaurant_attr = restaurants.merge(business_attr, left_on='business_id', right_on='business_id', how = 'inner')\n",
    "restaurant_attr.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3221032"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(restaurants['review_count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>business_id</th>\n",
       "      <th>weekday</th>\n",
       "      <th>hour</th>\n",
       "      <th>checkins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>537718</th>\n",
       "      <td>--6MefnULPED_I942VcFNA</td>\n",
       "      <td>Fri</td>\n",
       "      <td>21:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1109174</th>\n",
       "      <td>--6MefnULPED_I942VcFNA</td>\n",
       "      <td>Mon</td>\n",
       "      <td>1:00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3243238</th>\n",
       "      <td>--6MefnULPED_I942VcFNA</td>\n",
       "      <td>Mon</td>\n",
       "      <td>23:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3075089</th>\n",
       "      <td>--6MefnULPED_I942VcFNA</td>\n",
       "      <td>Wed</td>\n",
       "      <td>19:00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2362177</th>\n",
       "      <td>--6MefnULPED_I942VcFNA</td>\n",
       "      <td>Sat</td>\n",
       "      <td>22:00</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    business_id weekday   hour  checkins\n",
       "537718   --6MefnULPED_I942VcFNA     Fri  21:00         1\n",
       "1109174  --6MefnULPED_I942VcFNA     Mon   1:00         3\n",
       "3243238  --6MefnULPED_I942VcFNA     Mon  23:00         1\n",
       "3075089  --6MefnULPED_I942VcFNA     Wed  19:00         2\n",
       "2362177  --6MefnULPED_I942VcFNA     Sat  22:00        11"
      ]
     },
     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "business_checkin = pd.read_csv('yelp_checkin.csv')\n",
    "# restaurant_checkin = pd.merge(restaurant_attr['business_id'], business_checkin, \n",
    "#          left_on = 'business_id', right_on = 'business_id', how = 'inner')\n",
    "business_checkin.sort_values('business_id').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Total # of restaurants**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(54618, 53373, 51282)"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(restaurants), len(restaurant_attr), restaurant_checkin['business_id'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Restaurants in different states**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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>state</th>\n",
       "      <th>business_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>ON</td>\n",
       "      <td>13501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AZ</td>\n",
       "      <td>10598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>NV</td>\n",
       "      <td>7135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>QC</td>\n",
       "      <td>4981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>OH</td>\n",
       "      <td>4814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>NC</td>\n",
       "      <td>3850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>PA</td>\n",
       "      <td>3647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>BW</td>\n",
       "      <td>1783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>WI</td>\n",
       "      <td>1565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>EDH</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>IL</td>\n",
       "      <td>632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>SC</td>\n",
       "      <td>213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>MLN</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CHE</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>HLD</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>NYK</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>FIF</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ELN</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>WLN</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>C</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>NY</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>NI</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>ST</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>VS</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>IN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ESX</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BY</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>CO</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>KHL</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AK</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>XGL</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>WHT</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ABE</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>VA</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>RCC</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>PKN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>CA</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>GLG</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>HU</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>ZET</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   state  business_id\n",
       "31    ON        13501\n",
       "6     AZ        10598\n",
       "27    NV         7135\n",
       "34    QC         4981\n",
       "30    OH         4814\n",
       "25    NC         3850\n",
       "32    PA         3647\n",
       "8     BW         1783\n",
       "41    WI         1565\n",
       "14   EDH         1460\n",
       "21    IL          632\n",
       "36    SC          213\n",
       "24   MLN           96\n",
       "12   CHE           66\n",
       "19   HLD           63\n",
       "29   NYK           59\n",
       "17   FIF           31\n",
       "15   ELN           21\n",
       "42   WLN           20\n",
       "10     C           19\n",
       "28    NY           10\n",
       "0     01           10\n",
       "26    NI            8\n",
       "37    ST            6\n",
       "39    VS            3\n",
       "22    IN            3\n",
       "16   ESX            3\n",
       "9     BY            3\n",
       "13    CO            2\n",
       "23   KHL            1\n",
       "5     AK            1\n",
       "43   XGL            1\n",
       "2     30            1\n",
       "3      6            1\n",
       "40   WHT            1\n",
       "4    ABE            1\n",
       "38    VA            1\n",
       "35   RCC            1\n",
       "7      B            1\n",
       "1      3            1\n",
       "33   PKN            1\n",
       "11    CA            1\n",
       "18   GLG            1\n",
       "20    HU            1\n",
       "44   ZET            1"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "restaurants.groupby('state')['business_id'].count().reset_index().sort_values('business_id', ascending = False)\n",
    "# sort_values('')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Read all reviews**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5261668"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews = pd.read_csv('yelp_review.csv')\n",
    "len(reviews)\n",
    "# reviews.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Filter reviews by restaurants**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "restaurant_reviews = reviews[reviews.business_id.isin(restaurants['business_id'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3221418"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(restaurant_reviews)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Random sample 100k reviews**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "restaurant_reviews_sample = restaurant_reviews.sample(n=100000, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "restaurant_reviews_sample.to_csv('./yelp_restaurant_reviews_sample.csv', header = True, index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
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       "      <td>2016-09-21</td>\n",
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      ],
      "text/plain": [
       "                review_id                 user_id             business_id  \\\n",
       "0  ApmUOTRfk3pB6USCiSHl0Q  LMxsgATZnl_Vw0Kze7HdQQ  S_bG5WWbtNXhhY9Y0GTcSg   \n",
       "1  TPBUEeKPKH1SfCDZQlFKkQ  UOKFUQlGWmMEjwiGFwACdQ  JQ4E1Im1dHMpDa1s4vJMoQ   \n",
       "2  wdYB-bpUYt_lVodoKi56Ig  -od707p4FHGul0gte29AoQ  Yv4P4qUwd7F-qQ4Y4eD1JQ   \n",
       "3  RMeMYKXgKjCa2gJ03APC7g  SdxHtbvFQJAgPUkQfE4CIQ  Q2ZNaN3p8s_-XXjBWyY2qA   \n",
       "4  nXCAYS5UQnuw-CbhGnf5pQ  30SOROHP54Tn6ELl_YgeXg  UFyzcr1y2r3y-e_tNpA9VA   \n",
       "\n",
       "   stars        date                                               text  \\\n",
       "0      4  2016-09-21  Had the cheese quesadilla and a chai tea. It w...   \n",
       "1      4  2010-08-15  After looking at the other reviews about this ...   \n",
       "2      3  2016-12-21  So I may not be the best person the review thi...   \n",
       "3      5  2014-06-05  I'm here for a VMWare presentation lunch.  The...   \n",
       "4      2  2013-03-27  Was driving by Madison and thought we'd get so...   \n",
       "\n",
       "   useful  funny  cool  \n",
       "0       0      0     0  \n",
       "1       2      0     1  \n",
       "2       0      0     0  \n",
       "3       0      1     0  \n",
       "4       0      0     0  "
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "restaurant_reviews_sample = pd.read_csv('yelp_restaurant_reviews_sample.csv')\n",
    "restaurant_reviews_sample.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Extract year**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "restaurant_reviews_sample['review_year'] = restaurant_reviews_sample['date'].apply(lambda x:int(x[:4]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Look at distribution of reviews by year**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "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>review_year</th>\n",
       "      <th>review_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2005</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2006</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2007</td>\n",
       "      <td>425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008</td>\n",
       "      <td>1080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009</td>\n",
       "      <td>2040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2010</td>\n",
       "      <td>3657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2011</td>\n",
       "      <td>5797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2012</td>\n",
       "      <td>6857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2013</td>\n",
       "      <td>9115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2014</td>\n",
       "      <td>13006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2015</td>\n",
       "      <td>17510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2016</td>\n",
       "      <td>19696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2017</td>\n",
       "      <td>20683</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    review_year  review_id\n",
       "0          2005         15\n",
       "1          2006        119\n",
       "2          2007        425\n",
       "3          2008       1080\n",
       "4          2009       2040\n",
       "5          2010       3657\n",
       "6          2011       5797\n",
       "7          2012       6857\n",
       "8          2013       9115\n",
       "9          2014      13006\n",
       "10         2015      17510\n",
       "11         2016      19696\n",
       "12         2017      20683"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "restaurant_reviews_sample.groupby(['review_year'])['review_id'].size().reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Look at distribution of reviews by star**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "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>stars</th>\n",
       "      <th>review_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>11395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>9447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>14090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>27817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>37251</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   stars  review_id\n",
       "0      1      11395\n",
       "1      2       9447\n",
       "2      3      14090\n",
       "3      4      27817\n",
       "4      5      37251"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "restaurant_reviews_sample.groupby(['stars'])['review_id'].size().reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create 1-0 sentiment flag**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "restaurant_reviews_sample['sentiment'] = restaurant_reviews_sample['stars'].apply(lambda x: 1 if x >= 4 else 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Drop reviews older than 2010**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "relevant_reviews = restaurant_reviews_sample[(restaurant_reviews_sample['review_year'] > 2010)]\n",
    "#                                              & (restaurant_reviews_sample['stars'] != 3)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Text Preprocessing "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Removing stop words**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:15: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    }
   ],
   "source": [
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "# nltk.download('stopwords')\n",
    "\n",
    "english_stop_words = stopwords.words('english')\n",
    "def remove_stop_words(corpus):\n",
    "    removed_stop_words = []\n",
    "    for review in corpus:\n",
    "        removed_stop_words.append(\n",
    "            ' '.join([word for word in review.split() \n",
    "                      if word not in english_stop_words])\n",
    "        )\n",
    "    return removed_stop_words\n",
    "\n",
    "relevant_reviews['cleaned_text'] = relevant_reviews['text'].apply(lambda x: remove_stop_words(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Stemming/Lemmatization**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package wordnet to\n",
      "[nltk_data]     /Users/arunavsaikia/nltk_data...\n",
      "[nltk_data]   Unzipping corpora/wordnet.zip.\n",
      "/Library/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "nltk.download('wordnet')\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "\n",
    "def get_lemmatized_text(corpus):\n",
    "    lemmatizer = WordNetLemmatizer()\n",
    "    return [' '.join([lemmatizer.lemmatize(word) for word in review.split()]) for review in corpus]\n",
    "\n",
    "relevant_reviews['cleaned_text'] = relevant_reviews['cleaned_text'].apply(lambda x: get_lemmatized_text(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Train/Validation/Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "X_train_set, X_nottrain, y_train, y_nottrain = train_test_split(relevant_reviews['text'], \n",
    "                                                    relevant_reviews['sentiment'], \n",
    "                                                    test_size=0.30, \n",
    "                                                    random_state=42)\n",
    "\n",
    "X_test_set, X_val_set, y_test, y_val = train_test_split(X_nottrain, \n",
    "                                                    y_nottrain, \n",
    "                                                    test_size=0.5, \n",
    "                                                    random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    40338\n",
       "0    21746\n",
       "Name: sentiment, dtype: int64"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    9959\n",
       "0    5331\n",
       "Name: sentiment, dtype: int64"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_val.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    10151\n",
       "0     5139\n",
       "Name: sentiment, dtype: int64"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1-gram Binary**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "cv = CountVectorizer(binary=True)\n",
    "cv.fit(X_train_set)\n",
    "X_train = cv.transform(X_train_set)\n",
    "X_test = cv.transform(X_test_set)\n",
    "X_val = cv.transform(X_val_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1-gram Count**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "cv = CountVectorizer(binary=False)\n",
    "cv.fit(X_train)\n",
    "X_train = cv.transform(X_train_set)\n",
    "X_test = cv.transform(X_test_set)\n",
    "X_val = cv.transform(X_val_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2-gram Binary**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "cv = CountVectorizer(binary=True, ngram_range=(1,2))\n",
    "cv.fit(X_train)\n",
    "X_train = cv.transform(X_train_set)\n",
    "X_test = cv.transform(X_test_set)\n",
    "X_val = cv.transform(X_val_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2-gram Count**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "cv = CountVectorizer(binary=False, ngram_range=(1,2))\n",
    "cv.fit(X_train)\n",
    "X_train = cv.transform(X_train_set)\n",
    "X_test = cv.transform(X_test_set)\n",
    "X_val = cv.transform(X_val_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1-gram Tf-Idf**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "cv = TfidfVectorizer()\n",
    "cv.fit(X_train)\n",
    "X_train = cv.transform(X_train_set)\n",
    "X_test = cv.transform(X_test_set)\n",
    "X_val = cv.transform(X_val_set)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2-gram Tf-Idf**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "cv = TfidfVectorizer(ngram_range=(1,2))\n",
    "cv.fit(X_train)\n",
    "X_train = cv.transform(X_train_set)\n",
    "X_test = cv.transform(X_test_set)\n",
    "X_val = cv.transform(X_val_set)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15290, 1166319)"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiple Iterations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Baseline Logistic Regression**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For C = 0.01, Accuracy: 0.6777632439502943, F1-score: 0.8013867053654211\n",
      "For C = 0.05, Accuracy: 0.8225637671680837, F1-score: 0.8770785193239997\n",
      "For C = 0.25, Accuracy: 0.8821451929365598, F1-score: 0.9129552700222201\n",
      "For C = 0.5, Accuracy: 0.8913015042511445, F1-score: 0.9188793440062475\n",
      "For C = 1, Accuracy: 0.8983649444081099, F1-score: 0.9236288578730096\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, f1_score\n",
    "\n",
    "for c in [0.01, 0.05, 0.25, 0.5, 1]:\n",
    "    \n",
    "    lr = LogisticRegression(solver = 'lbfgs', \n",
    "                            penalty = 'l2', \n",
    "                            C=c,\n",
    "                           max_iter= 300)\n",
    "    lr.fit(X_train, y_train)\n",
    "    print (\"For C = %s, Accuracy: %s, F1-score: %s\" \n",
    "           % (c, accuracy_score(y_val, lr.predict(X_val)), f1_score(y_val, lr.predict(X_val))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Accuracy: 0.8992805755395683, F1-score: 0.9255751014884979\n"
     ]
    }
   ],
   "source": [
    "final_model = LogisticRegression(solver = 'lbfgs', \n",
    "                            penalty = 'l2', \n",
    "                            C=1,\n",
    "                           max_iter= 300)\n",
    "final_model.fit(X_train, y_train)\n",
    "print (\"Final Accuracy: %s, F1-score: %s\" \n",
    "           % (accuracy_score(y_test, final_model.predict(X_test)), f1_score(y_test, final_model.predict(X_test))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Positive\n",
      "('amazing', 1.2724722837440068)\n",
      "('delicious', 1.1737709794276843)\n",
      "('excellent', 1.1305861486631892)\n",
      "('perfect', 0.978954089385003)\n",
      "('wonderful', 0.9611709684293177)\n",
      "('awesome', 0.9413331159338744)\n",
      "('fantastic', 0.9162454708127878)\n",
      "('great', 0.8480660120741754)\n",
      "('perfection', 0.8475683063288447)\n",
      "('gem', 0.8158036922826443)\n",
      "('outstanding', 0.8004041710135689)\n",
      "('incredible', 0.7707504782283447)\n",
      "('glad', 0.7450359382588764)\n",
      "('disappoint', 0.7311391578098594)\n",
      "('notch', 0.7120859527587003)\n",
      "Best Negative\n",
      "('worst', -1.5827269516329685)\n",
      "('mediocre', -1.3689471266299473)\n",
      "('horrible', -1.3361157643800066)\n",
      "('terrible', -1.2331055550072083)\n",
      "('rude', -1.2229319988732033)\n",
      "('bland', -1.185029372274528)\n",
      "('ok', -1.1605387989842268)\n",
      "('overpriced', -1.0890524856738717)\n",
      "('disappointing', -1.001755375281107)\n",
      "('average', -0.9410541741997671)\n",
      "('poor', -0.9359040881242008)\n",
      "('unfortunately', -0.890757970045028)\n",
      "('okay', -0.8764995803719366)\n",
      "('tasteless', -0.8649364302789874)\n",
      "('meh', -0.8481321085128276)\n"
     ]
    }
   ],
   "source": [
    "feature_to_coef = {\n",
    "    word: coef for word, coef in zip(\n",
    "        cv.get_feature_names(), final_model.coef_[0]\n",
    "    )\n",
    "}\n",
    "print(\"Best Positive\")\n",
    "for best_positive in sorted(\n",
    "    feature_to_coef.items(), \n",
    "    key=lambda x: x[1], \n",
    "    reverse=True)[:15]:\n",
    "    print (best_positive)\n",
    "\n",
    "print(\"Best Negative\")\n",
    "\n",
    "for best_negative in sorted(\n",
    "    feature_to_coef.items(), \n",
    "    key=lambda x: x[1])[:15]:\n",
    "    print (best_negative)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Baseline SVM**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For C = 0.01, Accuracy: 0.8585349901896665, F1-score: 0.8986172955237872\n",
      "For C = 0.05, Accuracy: 0.8939175931981688, F1-score: 0.9208934841982053\n",
      "For C = 0.25, Accuracy: 0.907782864617397, F1-score: 0.9304047384007897\n",
      "For C = 0.5, Accuracy: 0.9076520601700457, F1-score: 0.9301405105877696\n",
      "For C = 1, Accuracy: 0.9079790712884238, F1-score: 0.9302533088782036\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "for c in [0.01, 0.05, 0.25, 0.5, 1]:\n",
    "    \n",
    "    svm = LinearSVC(C=c, max_iter=400)\n",
    "    svm.fit(X_train, y_train)\n",
    "    print (\"For C = %s, Accuracy: %s, F1-score: %s\" \n",
    "           % (c, accuracy_score(y_val, svm.predict(X_val)), f1_score(y_val, svm.predict(X_val))))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Accuracy: 0.9045127534336167, F1-score: 0.9292018232955096\n"
     ]
    }
   ],
   "source": [
    "final_model = LinearSVC(C=0.25, max_iter=400)\n",
    "\n",
    "final_model.fit(X_train, y_train)\n",
    "print (\"Final Accuracy: %s, F1-score: %s\" \n",
    "           % (accuracy_score(y_test, final_model.predict(X_test)), f1_score(y_test, final_model.predict(X_test))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Baseline Naive Bias**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Baseline NBSVM** (cite)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TIPS"
   ]
  }
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