Predicting_Restaurant_Closures_nyc.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "cb569ea2",
   "metadata": {},
   "source": [
    "## Predicting Restaurant Closures in NYC"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84dc51cd",
   "metadata": {},
   "source": [
    "## 1) Import required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3a096c5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pandas helps to load data\n",
    "import pandas as pd \n",
    "# Numpy helps perform computational operations\n",
    "import numpy as np\n",
    "# Matplotlib and Seaborn is used for data visualization\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns\n",
    "# To enable plots appear within Jupyter Notebook\n",
    "%matplotlib inline\n",
    "\n",
    "import sklearn\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "from nltk.stem import *\n",
    "import nltk.corpus\n",
    "from nltk.corpus import stopwords\n",
    "\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d92bfc58",
   "metadata": {},
   "source": [
    "## 2) Load the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9d3a82a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CAMIS</th>\n",
       "      <th>DBA</th>\n",
       "      <th>BORO</th>\n",
       "      <th>BUILDING</th>\n",
       "      <th>STREET</th>\n",
       "      <th>ZIPCODE</th>\n",
       "      <th>PHONE</th>\n",
       "      <th>CUISINE DESCRIPTION</th>\n",
       "      <th>INSPECTION DATE</th>\n",
       "      <th>ACTION</th>\n",
       "      <th>...</th>\n",
       "      <th>RECORD DATE</th>\n",
       "      <th>INSPECTION TYPE</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Community Board</th>\n",
       "      <th>Council District</th>\n",
       "      <th>Census Tract</th>\n",
       "      <th>BIN</th>\n",
       "      <th>BBL</th>\n",
       "      <th>NTA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50048586</td>\n",
       "      <td>BILL'S NEW YORK CITY</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>57</td>\n",
       "      <td>EAST   54 STREET</td>\n",
       "      <td>10022.0</td>\n",
       "      <td>2125182727</td>\n",
       "      <td>American</td>\n",
       "      <td>11/20/2017</td>\n",
       "      <td>Violations were cited in the following area(s).</td>\n",
       "      <td>...</td>\n",
       "      <td>03/23/2022</td>\n",
       "      <td>Cycle Inspection / Initial Inspection</td>\n",
       "      <td>40.760097</td>\n",
       "      <td>-73.973129</td>\n",
       "      <td>105.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10200.0</td>\n",
       "      <td>1035765.0</td>\n",
       "      <td>1.012900e+09</td>\n",
       "      <td>MN17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50071467</td>\n",
       "      <td>TGA NYC</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>320</td>\n",
       "      <td>WEST   36 STREET</td>\n",
       "      <td>10018.0</td>\n",
       "      <td>2126514842</td>\n",
       "      <td>American</td>\n",
       "      <td>10/17/2018</td>\n",
       "      <td>Violations were cited in the following area(s).</td>\n",
       "      <td>...</td>\n",
       "      <td>03/23/2022</td>\n",
       "      <td>Smoke-Free Air Act / Initial Inspection</td>\n",
       "      <td>40.753817</td>\n",
       "      <td>-73.993362</td>\n",
       "      <td>104.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11100.0</td>\n",
       "      <td>1090332.0</td>\n",
       "      <td>1.007590e+09</td>\n",
       "      <td>MN13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41617347</td>\n",
       "      <td>DUNKIN</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>1090</td>\n",
       "      <td>MORRIS PARK AVENUE</td>\n",
       "      <td>10461.0</td>\n",
       "      <td>7189750433</td>\n",
       "      <td>Donuts</td>\n",
       "      <td>08/09/2019</td>\n",
       "      <td>Violations were cited in the following area(s).</td>\n",
       "      <td>...</td>\n",
       "      <td>03/23/2022</td>\n",
       "      <td>Cycle Inspection / Initial Inspection</td>\n",
       "      <td>40.849397</td>\n",
       "      <td>-73.853349</td>\n",
       "      <td>211.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>25400.0</td>\n",
       "      <td>2094972.0</td>\n",
       "      <td>2.041080e+09</td>\n",
       "      <td>BX37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>41617347</td>\n",
       "      <td>DUNKIN</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>1090</td>\n",
       "      <td>MORRIS PARK AVENUE</td>\n",
       "      <td>10461.0</td>\n",
       "      <td>7189750433</td>\n",
       "      <td>Donuts</td>\n",
       "      <td>08/09/2019</td>\n",
       "      <td>Violations were cited in the following area(s).</td>\n",
       "      <td>...</td>\n",
       "      <td>03/23/2022</td>\n",
       "      <td>Cycle Inspection / Initial Inspection</td>\n",
       "      <td>40.849397</td>\n",
       "      <td>-73.853349</td>\n",
       "      <td>211.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>25400.0</td>\n",
       "      <td>2094972.0</td>\n",
       "      <td>2.041080e+09</td>\n",
       "      <td>BX37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41663033</td>\n",
       "      <td>EL ENCANTO CENTRO-AMERICANO RESTAURANT</td>\n",
       "      <td>Queens</td>\n",
       "      <td>14912</td>\n",
       "      <td>JAMAICA AVENUE</td>\n",
       "      <td>11435.0</td>\n",
       "      <td>7182629799</td>\n",
       "      <td>Latin American</td>\n",
       "      <td>10/27/2021</td>\n",
       "      <td>Violations were cited in the following area(s).</td>\n",
       "      <td>...</td>\n",
       "      <td>03/23/2022</td>\n",
       "      <td>Cycle Inspection / Initial Inspection</td>\n",
       "      <td>40.701927</td>\n",
       "      <td>-73.805260</td>\n",
       "      <td>412.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>21200.0</td>\n",
       "      <td>4213989.0</td>\n",
       "      <td>4.099970e+09</td>\n",
       "      <td>QN61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329115</th>\n",
       "      <td>50093803</td>\n",
       "      <td>COFFEE MONSTER</td>\n",
       "      <td>Queens</td>\n",
       "      <td>15708</td>\n",
       "      <td>NORTHERN BLVD</td>\n",
       "      <td>11354.0</td>\n",
       "      <td>9179080334</td>\n",
       "      <td>Coffee/Tea</td>\n",
       "      <td>12/03/2019</td>\n",
       "      <td>Violations were cited in the following area(s).</td>\n",
       "      <td>...</td>\n",
       "      <td>03/23/2022</td>\n",
       "      <td>Pre-permit (Operational) / Initial Inspection</td>\n",
       "      <td>40.763722</td>\n",
       "      <td>-73.808049</td>\n",
       "      <td>407.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>117100.0</td>\n",
       "      <td>4119352.0</td>\n",
       "      <td>4.052760e+09</td>\n",
       "      <td>QN51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329116</th>\n",
       "      <td>41648461</td>\n",
       "      <td>TROPIC POLLO</td>\n",
       "      <td>Queens</td>\n",
       "      <td>9059</td>\n",
       "      <td>CORONA AVENUE</td>\n",
       "      <td>11373.0</td>\n",
       "      <td>7185921868</td>\n",
       "      <td>Latin American</td>\n",
       "      <td>03/07/2019</td>\n",
       "      <td>No violations were recorded at the time of thi...</td>\n",
       "      <td>...</td>\n",
       "      <td>03/23/2022</td>\n",
       "      <td>Administrative Miscellaneous / Initial Inspection</td>\n",
       "      <td>40.742442</td>\n",
       "      <td>-73.873697</td>\n",
       "      <td>404.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>47100.0</td>\n",
       "      <td>4039574.0</td>\n",
       "      <td>4.015860e+09</td>\n",
       "      <td>QN29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329117</th>\n",
       "      <td>41272136</td>\n",
       "      <td>GOLDEN KRUST CARIBBEAN BAKERY &amp; GRILL</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>872</td>\n",
       "      <td>UTICA AVENUE</td>\n",
       "      <td>11203.0</td>\n",
       "      <td>7182825027</td>\n",
       "      <td>Caribbean</td>\n",
       "      <td>07/12/2019</td>\n",
       "      <td>Violations were cited in the following area(s).</td>\n",
       "      <td>...</td>\n",
       "      <td>03/23/2022</td>\n",
       "      <td>Cycle Inspection / Re-inspection</td>\n",
       "      <td>40.652110</td>\n",
       "      <td>-73.930435</td>\n",
       "      <td>317.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>86800.0</td>\n",
       "      <td>3102091.0</td>\n",
       "      <td>3.046750e+09</td>\n",
       "      <td>BK96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329118</th>\n",
       "      <td>41350902</td>\n",
       "      <td>SPA CASTLE CAFE</td>\n",
       "      <td>Queens</td>\n",
       "      <td>11-11</td>\n",
       "      <td>131 STREET</td>\n",
       "      <td>11356.0</td>\n",
       "      <td>7189396300</td>\n",
       "      <td>Asian/Asian Fusion</td>\n",
       "      <td>03/16/2022</td>\n",
       "      <td>Violations were cited in the following area(s).</td>\n",
       "      <td>...</td>\n",
       "      <td>03/23/2022</td>\n",
       "      <td>Cycle Inspection / Initial Inspection</td>\n",
       "      <td>40.787493</td>\n",
       "      <td>-73.837351</td>\n",
       "      <td>407.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>94500.0</td>\n",
       "      <td>4097283.0</td>\n",
       "      <td>4.040110e+09</td>\n",
       "      <td>QN49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329119</th>\n",
       "      <td>41559805</td>\n",
       "      <td>DUNKIN</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>640</td>\n",
       "      <td>SOUTH CONDUIT BOULEVARD</td>\n",
       "      <td>11208.0</td>\n",
       "      <td>3478733086</td>\n",
       "      <td>Donuts</td>\n",
       "      <td>11/18/2019</td>\n",
       "      <td>Violations were cited in the following area(s).</td>\n",
       "      <td>...</td>\n",
       "      <td>03/23/2022</td>\n",
       "      <td>Cycle Inspection / Initial Inspection</td>\n",
       "      <td>40.675675</td>\n",
       "      <td>-73.866069</td>\n",
       "      <td>305.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>120000.0</td>\n",
       "      <td>3095219.0</td>\n",
       "      <td>3.042390e+09</td>\n",
       "      <td>BK82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>329120 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           CAMIS                                     DBA       BORO BUILDING  \\\n",
       "0       50048586                    BILL'S NEW YORK CITY  Manhattan       57   \n",
       "1       50071467                                 TGA NYC  Manhattan      320   \n",
       "2       41617347                                  DUNKIN      Bronx     1090   \n",
       "3       41617347                                  DUNKIN      Bronx     1090   \n",
       "4       41663033  EL ENCANTO CENTRO-AMERICANO RESTAURANT     Queens    14912   \n",
       "...          ...                                     ...        ...      ...   \n",
       "329115  50093803                          COFFEE MONSTER     Queens    15708   \n",
       "329116  41648461                            TROPIC POLLO     Queens     9059   \n",
       "329117  41272136   GOLDEN KRUST CARIBBEAN BAKERY & GRILL   Brooklyn      872   \n",
       "329118  41350902                         SPA CASTLE CAFE     Queens    11-11   \n",
       "329119  41559805                                  DUNKIN   Brooklyn      640   \n",
       "\n",
       "                         STREET  ZIPCODE       PHONE CUISINE DESCRIPTION  \\\n",
       "0              EAST   54 STREET  10022.0  2125182727            American   \n",
       "1              WEST   36 STREET  10018.0  2126514842            American   \n",
       "2            MORRIS PARK AVENUE  10461.0  7189750433              Donuts   \n",
       "3            MORRIS PARK AVENUE  10461.0  7189750433              Donuts   \n",
       "4                JAMAICA AVENUE  11435.0  7182629799      Latin American   \n",
       "...                         ...      ...         ...                 ...   \n",
       "329115            NORTHERN BLVD  11354.0  9179080334          Coffee/Tea   \n",
       "329116            CORONA AVENUE  11373.0  7185921868      Latin American   \n",
       "329117             UTICA AVENUE  11203.0  7182825027           Caribbean   \n",
       "329118               131 STREET  11356.0  7189396300  Asian/Asian Fusion   \n",
       "329119  SOUTH CONDUIT BOULEVARD  11208.0  3478733086              Donuts   \n",
       "\n",
       "       INSPECTION DATE                                             ACTION  \\\n",
       "0           11/20/2017    Violations were cited in the following area(s).   \n",
       "1           10/17/2018    Violations were cited in the following area(s).   \n",
       "2           08/09/2019    Violations were cited in the following area(s).   \n",
       "3           08/09/2019    Violations were cited in the following area(s).   \n",
       "4           10/27/2021    Violations were cited in the following area(s).   \n",
       "...                ...                                                ...   \n",
       "329115      12/03/2019    Violations were cited in the following area(s).   \n",
       "329116      03/07/2019  No violations were recorded at the time of thi...   \n",
       "329117      07/12/2019    Violations were cited in the following area(s).   \n",
       "329118      03/16/2022    Violations were cited in the following area(s).   \n",
       "329119      11/18/2019    Violations were cited in the following area(s).   \n",
       "\n",
       "        ... RECORD DATE                                    INSPECTION TYPE  \\\n",
       "0       ...  03/23/2022              Cycle Inspection / Initial Inspection   \n",
       "1       ...  03/23/2022            Smoke-Free Air Act / Initial Inspection   \n",
       "2       ...  03/23/2022              Cycle Inspection / Initial Inspection   \n",
       "3       ...  03/23/2022              Cycle Inspection / Initial Inspection   \n",
       "4       ...  03/23/2022              Cycle Inspection / Initial Inspection   \n",
       "...     ...         ...                                                ...   \n",
       "329115  ...  03/23/2022      Pre-permit (Operational) / Initial Inspection   \n",
       "329116  ...  03/23/2022  Administrative Miscellaneous / Initial Inspection   \n",
       "329117  ...  03/23/2022                   Cycle Inspection / Re-inspection   \n",
       "329118  ...  03/23/2022              Cycle Inspection / Initial Inspection   \n",
       "329119  ...  03/23/2022              Cycle Inspection / Initial Inspection   \n",
       "\n",
       "         Latitude  Longitude Community Board Council District Census Tract  \\\n",
       "0       40.760097 -73.973129           105.0              4.0      10200.0   \n",
       "1       40.753817 -73.993362           104.0              3.0      11100.0   \n",
       "2       40.849397 -73.853349           211.0             13.0      25400.0   \n",
       "3       40.849397 -73.853349           211.0             13.0      25400.0   \n",
       "4       40.701927 -73.805260           412.0             27.0      21200.0   \n",
       "...           ...        ...             ...              ...          ...   \n",
       "329115  40.763722 -73.808049           407.0             20.0     117100.0   \n",
       "329116  40.742442 -73.873697           404.0             25.0      47100.0   \n",
       "329117  40.652110 -73.930435           317.0             41.0      86800.0   \n",
       "329118  40.787493 -73.837351           407.0             19.0      94500.0   \n",
       "329119  40.675675 -73.866069           305.0             42.0     120000.0   \n",
       "\n",
       "              BIN           BBL   NTA  \n",
       "0       1035765.0  1.012900e+09  MN17  \n",
       "1       1090332.0  1.007590e+09  MN13  \n",
       "2       2094972.0  2.041080e+09  BX37  \n",
       "3       2094972.0  2.041080e+09  BX37  \n",
       "4       4213989.0  4.099970e+09  QN61  \n",
       "...           ...           ...   ...  \n",
       "329115  4119352.0  4.052760e+09  QN51  \n",
       "329116  4039574.0  4.015860e+09  QN29  \n",
       "329117  3102091.0  3.046750e+09  BK96  \n",
       "329118  4097283.0  4.040110e+09  QN49  \n",
       "329119  3095219.0  3.042390e+09  BK82  \n",
       "\n",
       "[329120 rows x 26 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Load Dataset\n",
    "df = pd.read_csv(\"DOHMH_New_York_City_Restaurant_Inspection_Results (1).csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3056159",
   "metadata": {},
   "source": [
    "## 3) Cleaning the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7c1970bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 329120 entries, 0 to 329119\n",
      "Data columns (total 26 columns):\n",
      " #   Column                 Non-Null Count   Dtype  \n",
      "---  ------                 --------------   -----  \n",
      " 0   CAMIS                  329120 non-null  int64  \n",
      " 1   DBA                    327829 non-null  object \n",
      " 2   BORO                   329120 non-null  object \n",
      " 3   BUILDING               328537 non-null  object \n",
      " 4   STREET                 329108 non-null  object \n",
      " 5   ZIPCODE                324134 non-null  float64\n",
      " 6   PHONE                  329092 non-null  object \n",
      " 7   CUISINE DESCRIPTION    325162 non-null  object \n",
      " 8   INSPECTION DATE        329120 non-null  object \n",
      " 9   ACTION                 325163 non-null  object \n",
      " 10  VIOLATION CODE         321466 non-null  object \n",
      " 11  VIOLATION DESCRIPTION  323448 non-null  object \n",
      " 12  CRITICAL FLAG          329120 non-null  object \n",
      " 13  SCORE                  313863 non-null  float64\n",
      " 14  GRADE                  166354 non-null  object \n",
      " 15  GRADE DATE             161932 non-null  object \n",
      " 16  RECORD DATE            329120 non-null  object \n",
      " 17  INSPECTION TYPE        325163 non-null  object \n",
      " 18  Latitude               328798 non-null  float64\n",
      " 19  Longitude              328798 non-null  float64\n",
      " 20  Community Board        323336 non-null  float64\n",
      " 21  Council District       323344 non-null  float64\n",
      " 22  Census Tract           323344 non-null  float64\n",
      " 23  BIN                    321836 non-null  float64\n",
      " 24  BBL                    328310 non-null  float64\n",
      " 25  NTA                    323336 non-null  object \n",
      "dtypes: float64(9), int64(1), object(16)\n",
      "memory usage: 65.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56c1bbbf",
   "metadata": {},
   "source": [
    "### Removing Duplicate data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "72a60f96",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# of Restaurants in NYC:  28727\n",
      "# of duplicates:  0\n"
     ]
    },
    {
     "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>CAMIS</th>\n",
       "      <th>DBA</th>\n",
       "      <th>BORO</th>\n",
       "      <th>BUILDING</th>\n",
       "      <th>STREET</th>\n",
       "      <th>ZIPCODE</th>\n",
       "      <th>PHONE</th>\n",
       "      <th>CUISINE DESCRIPTION</th>\n",
       "      <th>INSPECTION DATE</th>\n",
       "      <th>ACTION</th>\n",
       "      <th>...</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Community Board</th>\n",
       "      <th>Council District</th>\n",
       "      <th>Census Tract</th>\n",
       "      <th>BIN</th>\n",
       "      <th>BBL</th>\n",
       "      <th>NTA</th>\n",
       "      <th>#_of_inspections</th>\n",
       "      <th>count_check</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50075009</td>\n",
       "      <td>#1 GARDEN CHINESE RESTAURANT</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>221</td>\n",
       "      <td>PROSPECT PARK WEST</td>\n",
       "      <td>11215.0</td>\n",
       "      <td>9172508950</td>\n",
       "      <td>Chinese</td>\n",
       "      <td>2019-12-16</td>\n",
       "      <td>Violations were cited in the following area(s).</td>\n",
       "      <td>...</td>\n",
       "      <td>40.660156</td>\n",
       "      <td>-73.980555</td>\n",
       "      <td>307.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>16900.0</td>\n",
       "      <td>3026939.0</td>\n",
       "      <td>3.011100e+09</td>\n",
       "      <td>BK40</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>41336841</td>\n",
       "      <td>#1 SABOR LATINO RESTAURANT</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>4120</td>\n",
       "      <td>WHITE PLAINS ROAD</td>\n",
       "      <td>10466.0</td>\n",
       "      <td>7186532222</td>\n",
       "      <td>Latin American</td>\n",
       "      <td>2022-03-17</td>\n",
       "      <td>Violations were cited in the following area(s).</td>\n",
       "      <td>...</td>\n",
       "      <td>40.891176</td>\n",
       "      <td>-73.858591</td>\n",
       "      <td>212.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>42200.0</td>\n",
       "      <td>2063603.0</td>\n",
       "      <td>2.048430e+09</td>\n",
       "      <td>BX44</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50086385</td>\n",
       "      <td>$1 PIZZA</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>832</td>\n",
       "      <td>6 AVENUE</td>\n",
       "      <td>10001.0</td>\n",
       "      <td>9175878888</td>\n",
       "      <td>Pizza</td>\n",
       "      <td>2022-02-22</td>\n",
       "      <td>Violations were cited in the following area(s).</td>\n",
       "      <td>...</td>\n",
       "      <td>40.746510</td>\n",
       "      <td>-73.990158</td>\n",
       "      <td>105.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7600.0</td>\n",
       "      <td>1015746.0</td>\n",
       "      <td>1.008300e+09</td>\n",
       "      <td>MN17</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50105915</td>\n",
       "      <td>$1 PIZZA SLICE</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>748</td>\n",
       "      <td>9 AVENUE</td>\n",
       "      <td>10019.0</td>\n",
       "      <td>5513581106</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1900-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>40.763714</td>\n",
       "      <td>-73.988766</td>\n",
       "      <td>104.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13300.0</td>\n",
       "      <td>1025172.0</td>\n",
       "      <td>1.010410e+09</td>\n",
       "      <td>MN15</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50106018</td>\n",
       "      <td>$1 SLICE PIZZA</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>3414</td>\n",
       "      <td>JEROME AVENUE</td>\n",
       "      <td>10467.0</td>\n",
       "      <td>3479930599</td>\n",
       "      <td>Pizza</td>\n",
       "      <td>2021-11-18</td>\n",
       "      <td>Violations were cited in the following area(s).</td>\n",
       "      <td>...</td>\n",
       "      <td>40.881253</td>\n",
       "      <td>-73.882900</td>\n",
       "      <td>207.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>42100.0</td>\n",
       "      <td>2017769.0</td>\n",
       "      <td>2.033270e+09</td>\n",
       "      <td>BX43</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      CAMIS                           DBA       BORO BUILDING  \\\n",
       "0  50075009  #1 GARDEN CHINESE RESTAURANT   Brooklyn      221   \n",
       "1  41336841    #1 SABOR LATINO RESTAURANT      Bronx     4120   \n",
       "2  50086385                      $1 PIZZA  Manhattan      832   \n",
       "3  50105915                $1 PIZZA SLICE  Manhattan      748   \n",
       "4  50106018                $1 SLICE PIZZA      Bronx     3414   \n",
       "\n",
       "               STREET  ZIPCODE       PHONE CUISINE DESCRIPTION  \\\n",
       "0  PROSPECT PARK WEST  11215.0  9172508950             Chinese   \n",
       "1   WHITE PLAINS ROAD  10466.0  7186532222      Latin American   \n",
       "2            6 AVENUE  10001.0  9175878888               Pizza   \n",
       "3            9 AVENUE  10019.0  5513581106                 NaN   \n",
       "4       JEROME AVENUE  10467.0  3479930599               Pizza   \n",
       "\n",
       "  INSPECTION DATE                                           ACTION  ...  \\\n",
       "0      2019-12-16  Violations were cited in the following area(s).  ...   \n",
       "1      2022-03-17  Violations were cited in the following area(s).  ...   \n",
       "2      2022-02-22  Violations were cited in the following area(s).  ...   \n",
       "3      1900-01-01                                              NaN  ...   \n",
       "4      2021-11-18  Violations were cited in the following area(s).  ...   \n",
       "\n",
       "    Latitude  Longitude Community Board  Council District Census Tract  \\\n",
       "0  40.660156 -73.980555           307.0              39.0      16900.0   \n",
       "1  40.891176 -73.858591           212.0              12.0      42200.0   \n",
       "2  40.746510 -73.990158           105.0               3.0       7600.0   \n",
       "3  40.763714 -73.988766           104.0               3.0      13300.0   \n",
       "4  40.881253 -73.882900           207.0              11.0      42100.0   \n",
       "\n",
       "         BIN           BBL   NTA  #_of_inspections  count_check  \n",
       "0  3026939.0  3.011100e+09  BK40                29            1  \n",
       "1  2063603.0  2.048430e+09  BX44                15            1  \n",
       "2  1015746.0  1.008300e+09  MN17                12            1  \n",
       "3  1025172.0  1.010410e+09  MN15                 1            1  \n",
       "4  2017769.0  2.033270e+09  BX43                 3            1  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Change the inspection date column to datetime format:\n",
    "df['INSPECTION DATE'] = pd.to_datetime(df['INSPECTION DATE'])\n",
    "\n",
    "# Add a column that counts how many data entries have the same id (CAMIS) (and therefore represent the same restaurant):\n",
    "df['#_of_inspections'] = df.groupby('CAMIS')['CAMIS'].transform('count')\n",
    "\n",
    "# Sort values by name (DBA), id (CAMIS), and inspection date:\n",
    "df_sorted = df.sort_values(by=['DBA', 'CAMIS','INSPECTION DATE'])\n",
    "\n",
    "# Drop duplicate values:\n",
    "df_filtered = df_sorted.drop_duplicates(subset=['DBA', 'CAMIS'], keep='last', inplace=False).copy()\n",
    "\n",
    "# Double check no duplicates remain:\n",
    "df_filtered['count_check'] = df_filtered.groupby('CAMIS')['CAMIS'].transform('count')\n",
    "print('# of Restaurants in NYC: ', len(df_filtered))\n",
    "print('# of duplicates: ', len(df_filtered[df_filtered['count_check'] > 1]))\n",
    "df_filtered.reset_index(inplace=True, drop = True)\n",
    "df_filtered.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2d8a237",
   "metadata": {},
   "source": [
    "### Remove Unnecessary Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cf82f97f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove columns that we will not be working with:\n",
    "df_filtered.drop(columns = ['VIOLATION DESCRIPTION','VIOLATION CODE', 'RECORD DATE', \n",
    "                                   'Community Board', 'Council District', 'Census Tract','BIN','BBL','NTA',\n",
    "                                   'count_check'], inplace = True, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22b27ff1",
   "metadata": {},
   "source": [
    "### Changing Phone Number format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "17ff0c72",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Update format of telephone numbers:\n",
    "df_filtered['PHONE']=df_filtered['PHONE'].astype(str).apply(\n",
    "    lambda x: '('+x[:3]+')'+' '+x[3:6]+'-'+x[6:10])\n",
    "\n",
    "# Rename telephone number column to match the name in the df_filtered dataset:\n",
    "df_filtered.rename(columns={'PHONE':'display_phone'}, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfea20f6",
   "metadata": {},
   "source": [
    "### Handling Missing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "89fbf21d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CAMIS                     0\n",
       "DBA                    1240\n",
       "BORO                      0\n",
       "BUILDING                101\n",
       "STREET                   12\n",
       "ZIPCODE                 468\n",
       "display_phone             0\n",
       "CUISINE DESCRIPTION    3958\n",
       "INSPECTION DATE           0\n",
       "ACTION                 3957\n",
       "CRITICAL FLAG             0\n",
       "SCORE                  4553\n",
       "GRADE                  7433\n",
       "GRADE DATE             8422\n",
       "INSPECTION TYPE        3957\n",
       "Latitude                 81\n",
       "Longitude                81\n",
       "#_of_inspections          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtered.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "915676eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace null values in ACTION column with 'Not Yet Inspected' as per guidence from dataset creators\n",
    "df_filtered['ACTION'].fillna(value='Not Yet Inspected', inplace=True)\n",
    "\n",
    "# Replace null values in CRITICAL FLAG column with 'Not Applicable' as per guidence from dataset creators\n",
    "df_filtered['CRITICAL FLAG'].fillna(value='Not Applicable', inplace=True)\n",
    "\n",
    "# Replace null values in INSPECTION TYPE column with 'Unknown' as per guidence from dataset creators\n",
    "df_filtered['INSPECTION TYPE'].fillna(value='Unknown', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8276fc0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    18535\n",
       "N     4950\n",
       "Z     4061\n",
       "B      887\n",
       "C      218\n",
       "P       76\n",
       "Name: GRADE, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use details from Action column to determine grade status for rows with null values:\n",
    "df_filtered['GRADE'].fillna(df_filtered['ACTION'], inplace=True)\n",
    "df_filtered['GRADE'].replace({'Violations were cited in the following area(s).':'Z',\n",
    "                                     'No violations were recorded at the time of this inspection.': 'A',\n",
    "                                     'Not Yet Inspected':'N',\n",
    "                                     'Establishment Closed by DOHMH. Violations were cited in the following area(s) and those requiring immediate action were addressed.':'P',\n",
    "                                     'Establishment re-opened by DOHMH.': 'P','Establishment re-closed by DOHMH': 'P'}, inplace = True)\n",
    "\n",
    "# Ensure GRADE column has only the grade letter scores and not any text from the Action column:\n",
    "df_filtered['GRADE'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a9e5ed0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use details from GRADE column to determine the SCORE for rows with null values:\n",
    "df_filtered['SCORE'].fillna(df_filtered['GRADE'], inplace=True)\n",
    "df_filtered['SCORE'].replace({'N':'PEND',\n",
    "                                     'Z': 'PEND',\n",
    "                                     'P':'PEND',\n",
    "                                     'A': 8,\n",
    "                                     'B': 21,\n",
    "                                     'C': 35}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6f1cab66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CAMIS                  0\n",
       "DBA                    0\n",
       "BORO                   0\n",
       "BUILDING               0\n",
       "STREET                 0\n",
       "ZIPCODE                0\n",
       "display_phone          0\n",
       "CUISINE DESCRIPTION    0\n",
       "INSPECTION DATE        0\n",
       "ACTION                 0\n",
       "CRITICAL FLAG          0\n",
       "SCORE                  0\n",
       "GRADE                  0\n",
       "GRADE DATE             0\n",
       "INSPECTION TYPE        0\n",
       "Latitude               0\n",
       "Longitude              0\n",
       "#_of_inspections       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replace null values in grade date column with 'TBD' as the restaurants with no grade date do not yet have a grade.\n",
    "df_filtered['GRADE DATE'].fillna(value='TBD', inplace=True)\n",
    "\n",
    "# Drop all other rows with NaN values:\n",
    "df_filtered.dropna(inplace=True)\n",
    "\n",
    "# Double check we do not have any more null values:\n",
    "df_filtered.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f62efa6",
   "metadata": {},
   "source": [
    "### Handling Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "83442dbc",
   "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>CAMIS</th>\n",
       "      <th>ZIPCODE</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>#_of_inspections</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.427900e+04</td>\n",
       "      <td>24279.000000</td>\n",
       "      <td>24279.000000</td>\n",
       "      <td>24279.000000</td>\n",
       "      <td>24279.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.710586e+07</td>\n",
       "      <td>10670.524115</td>\n",
       "      <td>40.729517</td>\n",
       "      <td>-73.943762</td>\n",
       "      <td>13.160138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.191804e+06</td>\n",
       "      <td>594.033711</td>\n",
       "      <td>0.068357</td>\n",
       "      <td>0.075284</td>\n",
       "      <td>10.523260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.007544e+07</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>40.499563</td>\n",
       "      <td>-74.248708</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.156782e+07</td>\n",
       "      <td>10022.000000</td>\n",
       "      <td>40.690223</td>\n",
       "      <td>-73.989193</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.004572e+07</td>\n",
       "      <td>10467.000000</td>\n",
       "      <td>40.735760</td>\n",
       "      <td>-73.959633</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.008627e+07</td>\n",
       "      <td>11226.000000</td>\n",
       "      <td>40.762756</td>\n",
       "      <td>-73.904043</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.012003e+07</td>\n",
       "      <td>12345.000000</td>\n",
       "      <td>40.912822</td>\n",
       "      <td>-73.700928</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              CAMIS       ZIPCODE      Latitude     Longitude  \\\n",
       "count  2.427900e+04  24279.000000  24279.000000  24279.000000   \n",
       "mean   4.710586e+07  10670.524115     40.729517    -73.943762   \n",
       "std    4.191804e+06    594.033711      0.068357      0.075284   \n",
       "min    3.007544e+07  10000.000000     40.499563    -74.248708   \n",
       "25%    4.156782e+07  10022.000000     40.690223    -73.989193   \n",
       "50%    5.004572e+07  10467.000000     40.735760    -73.959633   \n",
       "75%    5.008627e+07  11226.000000     40.762756    -73.904043   \n",
       "max    5.012003e+07  12345.000000     40.912822    -73.700928   \n",
       "\n",
       "       #_of_inspections  \n",
       "count      24279.000000  \n",
       "mean          13.160138  \n",
       "std           10.523260  \n",
       "min            1.000000  \n",
       "25%            5.000000  \n",
       "50%           10.000000  \n",
       "75%           18.000000  \n",
       "max           99.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check For Outliers:\n",
    "df_filtered.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a04c6056",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert outliers in number of inspections column to have a value of '70'\n",
    "df_filtered['#_of_inspections'] = np.where(df_filtered['#_of_inspections'].between(\n",
    "    70,98), 70, df_filtered['#_of_inspections'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72e3d407",
   "metadata": {},
   "source": [
    "## 4) Visualizing the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b7074489",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cpy=df_filtered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "78f208d3",
   "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>CAMIS</th>\n",
       "      <th>DBA</th>\n",
       "      <th>BORO</th>\n",
       "      <th>BUILDING</th>\n",
       "      <th>STREET</th>\n",
       "      <th>ZIPCODE</th>\n",
       "      <th>display_phone</th>\n",
       "      <th>CUISINE DESCRIPTION</th>\n",
       "      <th>INSPECTION DATE</th>\n",
       "      <th>ACTION</th>\n",
       "      <th>CRITICAL FLAG</th>\n",
       "      <th>SCORE</th>\n",
       "      <th>GRADE</th>\n",
       "      <th>GRADE DATE</th>\n",
       "      <th>INSPECTION TYPE</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>#_of_inspections</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50075009</td>\n",
       "      <td>#1 GARDEN CHINESE RESTAURANT</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>221</td>\n",
       "      <td>PROSPECT PARK WEST</td>\n",
       "      <td>11215.0</td>\n",
       "      <td>(917) 250-8950</td>\n",
       "      <td>Chinese</td>\n",
       "      <td>2019-12-16</td>\n",
       "      <td>Violations were cited in the following area(s).</td>\n",
       "      <td>Not Critical</td>\n",
       "      <td>10.0</td>\n",
       "      <td>A</td>\n",
       "      <td>12/16/2019</td>\n",
       "      <td>Cycle Inspection / Re-inspection</td>\n",
       "      <td>40.660156</td>\n",
       "      <td>-73.980555</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>41336841</td>\n",
       "      <td>#1 SABOR LATINO RESTAURANT</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>4120</td>\n",
       "      <td>WHITE PLAINS ROAD</td>\n",
       "      <td>10466.0</td>\n",
       "      <td>(718) 653-2222</td>\n",
       "      <td>Latin American</td>\n",
       "      <td>2022-03-17</td>\n",
       "      <td>Violations were cited in the following area(s).</td>\n",
       "      <td>Not Critical</td>\n",
       "      <td>40.0</td>\n",
       "      <td>Z</td>\n",
       "      <td>TBD</td>\n",
       "      <td>Cycle Inspection / Initial Inspection</td>\n",
       "      <td>40.891176</td>\n",
       "      <td>-73.858591</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50086385</td>\n",
       "      <td>$1 PIZZA</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>832</td>\n",
       "      <td>6 AVENUE</td>\n",
       "      <td>10001.0</td>\n",
       "      <td>(917) 587-8888</td>\n",
       "      <td>Pizza</td>\n",
       "      <td>2022-02-22</td>\n",
       "      <td>Violations were cited in the following area(s).</td>\n",
       "      <td>Critical</td>\n",
       "      <td>7.0</td>\n",
       "      <td>B</td>\n",
       "      <td>02/22/2022</td>\n",
       "      <td>Cycle Inspection / Re-inspection</td>\n",
       "      <td>40.746510</td>\n",
       "      <td>-73.990158</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50106018</td>\n",
       "      <td>$1 SLICE PIZZA</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>3414</td>\n",
       "      <td>JEROME AVENUE</td>\n",
       "      <td>10467.0</td>\n",
       "      <td>(347) 993-0599</td>\n",
       "      <td>Pizza</td>\n",
       "      <td>2021-11-18</td>\n",
       "      <td>Violations were cited in the following area(s).</td>\n",
       "      <td>Not Critical</td>\n",
       "      <td>11.0</td>\n",
       "      <td>A</td>\n",
       "      <td>11/18/2021</td>\n",
       "      <td>Pre-permit (Operational) / Initial Inspection</td>\n",
       "      <td>40.881253</td>\n",
       "      <td>-73.882900</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>41319107</td>\n",
       "      <td>&amp; MORE BY SHERATON</td>\n",
       "      <td>Queens</td>\n",
       "      <td>13520</td>\n",
       "      <td>39 AVENUE</td>\n",
       "      <td>11354.0</td>\n",
       "      <td>(718) 460-6666</td>\n",
       "      <td>American</td>\n",
       "      <td>2022-01-27</td>\n",
       "      <td>Violations were cited in the following area(s).</td>\n",
       "      <td>Not Critical</td>\n",
       "      <td>12.0</td>\n",
       "      <td>A</td>\n",
       "      <td>01/27/2022</td>\n",
       "      <td>Cycle Inspection / Re-inspection</td>\n",
       "      <td>40.759839</td>\n",
       "      <td>-73.831798</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27476</th>\n",
       "      <td>50103228</td>\n",
       "      <td>kokomo restaurant</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>65</td>\n",
       "      <td>KENT AVENUE</td>\n",
       "      <td>11249.0</td>\n",
       "      <td>(347) 743-9839</td>\n",
       "      <td>Caribbean</td>\n",
       "      <td>2022-01-12</td>\n",
       "      <td>Violations were cited in the following area(s).</td>\n",
       "      <td>Not Critical</td>\n",
       "      <td>19.0</td>\n",
       "      <td>N</td>\n",
       "      <td>TBD</td>\n",
       "      <td>Pre-permit (Operational) / Initial Inspection</td>\n",
       "      <td>40.722050</td>\n",
       "      <td>-73.959840</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27478</th>\n",
       "      <td>50007379</td>\n",
       "      <td>matchpointnyc</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>2781</td>\n",
       "      <td>SHELL ROAD</td>\n",
       "      <td>11223.0</td>\n",
       "      <td>(718) 769-0001</td>\n",
       "      <td>Soups/Salads/Sandwiches</td>\n",
       "      <td>2022-03-04</td>\n",
       "      <td>Violations were cited in the following area(s).</td>\n",
       "      <td>Not Critical</td>\n",
       "      <td>12.0</td>\n",
       "      <td>A</td>\n",
       "      <td>03/04/2022</td>\n",
       "      <td>Cycle Inspection / Re-inspection</td>\n",
       "      <td>40.584497</td>\n",
       "      <td>-73.974514</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27480</th>\n",
       "      <td>50107134</td>\n",
       "      <td>red bamboo</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>140</td>\n",
       "      <td>WEST    4 STREET</td>\n",
       "      <td>10012.0</td>\n",
       "      <td>(212) 260-1212</td>\n",
       "      <td>Chinese</td>\n",
       "      <td>2021-11-09</td>\n",
       "      <td>Violations were cited in the following area(s).</td>\n",
       "      <td>Not Critical</td>\n",
       "      <td>10.0</td>\n",
       "      <td>A</td>\n",
       "      <td>11/09/2021</td>\n",
       "      <td>Pre-permit (Operational) / Initial Inspection</td>\n",
       "      <td>40.731373</td>\n",
       "      <td>-74.000314</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27483</th>\n",
       "      <td>50008073</td>\n",
       "      <td>tutto il giorno</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>114</td>\n",
       "      <td>FRANKLIN STREET</td>\n",
       "      <td>10013.0</td>\n",
       "      <td>(212) 274-8100</td>\n",
       "      <td>Italian</td>\n",
       "      <td>2020-02-05</td>\n",
       "      <td>Violations were cited in the following area(s).</td>\n",
       "      <td>Critical</td>\n",
       "      <td>5.0</td>\n",
       "      <td>A</td>\n",
       "      <td>02/05/2020</td>\n",
       "      <td>Cycle Inspection / Re-inspection</td>\n",
       "      <td>40.718626</td>\n",
       "      <td>-74.006082</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27486</th>\n",
       "      <td>50086236</td>\n",
       "      <td>wok and roll kitchen</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>1821</td>\n",
       "      <td>CHURCH AVENUE</td>\n",
       "      <td>11226.0</td>\n",
       "      <td>(718) 282-1188</td>\n",
       "      <td>Other</td>\n",
       "      <td>2019-03-27</td>\n",
       "      <td>Violations were cited in the following area(s).</td>\n",
       "      <td>Not Critical</td>\n",
       "      <td>10.0</td>\n",
       "      <td>A</td>\n",
       "      <td>03/27/2019</td>\n",
       "      <td>Pre-permit (Operational) / Initial Inspection</td>\n",
       "      <td>40.649543</td>\n",
       "      <td>-73.962820</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24279 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          CAMIS                           DBA       BORO BUILDING  \\\n",
       "0      50075009  #1 GARDEN CHINESE RESTAURANT   Brooklyn      221   \n",
       "1      41336841    #1 SABOR LATINO RESTAURANT      Bronx     4120   \n",
       "2      50086385                      $1 PIZZA  Manhattan      832   \n",
       "4      50106018                $1 SLICE PIZZA      Bronx     3414   \n",
       "5      41319107            & MORE BY SHERATON     Queens    13520   \n",
       "...         ...                           ...        ...      ...   \n",
       "27476  50103228             kokomo restaurant   Brooklyn       65   \n",
       "27478  50007379                 matchpointnyc   Brooklyn     2781   \n",
       "27480  50107134                    red bamboo  Manhattan      140   \n",
       "27483  50008073               tutto il giorno  Manhattan      114   \n",
       "27486  50086236          wok and roll kitchen   Brooklyn     1821   \n",
       "\n",
       "                   STREET  ZIPCODE   display_phone      CUISINE DESCRIPTION  \\\n",
       "0      PROSPECT PARK WEST  11215.0  (917) 250-8950                  Chinese   \n",
       "1       WHITE PLAINS ROAD  10466.0  (718) 653-2222           Latin American   \n",
       "2                6 AVENUE  10001.0  (917) 587-8888                    Pizza   \n",
       "4           JEROME AVENUE  10467.0  (347) 993-0599                    Pizza   \n",
       "5               39 AVENUE  11354.0  (718) 460-6666                 American   \n",
       "...                   ...      ...             ...                      ...   \n",
       "27476         KENT AVENUE  11249.0  (347) 743-9839                Caribbean   \n",
       "27478          SHELL ROAD  11223.0  (718) 769-0001  Soups/Salads/Sandwiches   \n",
       "27480    WEST    4 STREET  10012.0  (212) 260-1212                  Chinese   \n",
       "27483     FRANKLIN STREET  10013.0  (212) 274-8100                  Italian   \n",
       "27486       CHURCH AVENUE  11226.0  (718) 282-1188                    Other   \n",
       "\n",
       "      INSPECTION DATE                                           ACTION  \\\n",
       "0          2019-12-16  Violations were cited in the following area(s).   \n",
       "1          2022-03-17  Violations were cited in the following area(s).   \n",
       "2          2022-02-22  Violations were cited in the following area(s).   \n",
       "4          2021-11-18  Violations were cited in the following area(s).   \n",
       "5          2022-01-27  Violations were cited in the following area(s).   \n",
       "...               ...                                              ...   \n",
       "27476      2022-01-12  Violations were cited in the following area(s).   \n",
       "27478      2022-03-04  Violations were cited in the following area(s).   \n",
       "27480      2021-11-09  Violations were cited in the following area(s).   \n",
       "27483      2020-02-05  Violations were cited in the following area(s).   \n",
       "27486      2019-03-27  Violations were cited in the following area(s).   \n",
       "\n",
       "      CRITICAL FLAG SCORE GRADE  GRADE DATE  \\\n",
       "0      Not Critical  10.0     A  12/16/2019   \n",
       "1      Not Critical  40.0     Z         TBD   \n",
       "2          Critical   7.0     B  02/22/2022   \n",
       "4      Not Critical  11.0     A  11/18/2021   \n",
       "5      Not Critical  12.0     A  01/27/2022   \n",
       "...             ...   ...   ...         ...   \n",
       "27476  Not Critical  19.0     N         TBD   \n",
       "27478  Not Critical  12.0     A  03/04/2022   \n",
       "27480  Not Critical  10.0     A  11/09/2021   \n",
       "27483      Critical   5.0     A  02/05/2020   \n",
       "27486  Not Critical  10.0     A  03/27/2019   \n",
       "\n",
       "                                     INSPECTION TYPE   Latitude  Longitude  \\\n",
       "0                   Cycle Inspection / Re-inspection  40.660156 -73.980555   \n",
       "1              Cycle Inspection / Initial Inspection  40.891176 -73.858591   \n",
       "2                   Cycle Inspection / Re-inspection  40.746510 -73.990158   \n",
       "4      Pre-permit (Operational) / Initial Inspection  40.881253 -73.882900   \n",
       "5                   Cycle Inspection / Re-inspection  40.759839 -73.831798   \n",
       "...                                              ...        ...        ...   \n",
       "27476  Pre-permit (Operational) / Initial Inspection  40.722050 -73.959840   \n",
       "27478               Cycle Inspection / Re-inspection  40.584497 -73.974514   \n",
       "27480  Pre-permit (Operational) / Initial Inspection  40.731373 -74.000314   \n",
       "27483               Cycle Inspection / Re-inspection  40.718626 -74.006082   \n",
       "27486  Pre-permit (Operational) / Initial Inspection  40.649543 -73.962820   \n",
       "\n",
       "       #_of_inspections  \n",
       "0                    29  \n",
       "1                    15  \n",
       "2                    12  \n",
       "4                     3  \n",
       "5                    34  \n",
       "...                 ...  \n",
       "27476                 5  \n",
       "27478                10  \n",
       "27480                 2  \n",
       "27483                11  \n",
       "27486                 2  \n",
       "\n",
       "[24279 rows x 18 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8b321aad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Replace the long description for Latin restuarants with 'Latin'\n",
    "df_cpy['CUISINE DESCRIPTION'].replace(\n",
    "    {'Latin (Cuban, Dominican, Puerto Rican, South & Central American)':'Latin'},inplace=True)\n",
    "\n",
    "# Create a list of the 20 most frequent cuisine types:\n",
    "cuisine_type = df_cpy['CUISINE DESCRIPTION'].value_counts()[:20].index.tolist()\n",
    "\n",
    "# Create a temporary dataframe including only restaurants that are in the 20 most popular cuisine types:\n",
    "df_cpy_cuis = df_cpy.loc[df_cpy['CUISINE DESCRIPTION'].isin(cuisine_type)].copy()\n",
    "\n",
    "# Create a horizontal bar chart comparing the # of each grade inspections in each cuisine type:\n",
    "plt.figure(figsize=(15,10))\n",
    "sns.countplot(data=df_cpy_cuis, y='CUISINE DESCRIPTION', \n",
    "              order = df_cpy_cuis['CUISINE DESCRIPTION'].value_counts().index,palette='Blues_r')\n",
    "# Add chart title and labels\n",
    "plt.title('# of Restaurants in Each Cuisine Type', fontsize=20)\n",
    "plt.xlabel('# of Restaurants', fontsize=14)\n",
    "plt.ylabel('Cuisine Type', fontsize=14);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5fea7cf",
   "metadata": {},
   "source": [
    "### 4.1) Borough"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bb1e058",
   "metadata": {},
   "source": [
    "While not all restaurants have an inspection grade yet (some are still pending or not graded yet), let's see how many restaurants in each borough have been given an inspection grade."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "dabc1c01",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Of Restaurants Per Borough: \n",
      " BORO\n",
      "Manhattan        7640\n",
      "Brooklyn         4778\n",
      "Queens           4344\n",
      "Bronx            1769\n",
      "Staten Island     685\n",
      "Name: GRADE, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Create a temporary dataframe that includes only the restaurants that have been given an inspection grade:\n",
    "df_filtered_temp = df_filtered.loc[df_filtered['GRADE'].isin(['A','B','C'])].copy()\n",
    "\n",
    "# List in descending order the number of inspected restaurants in each borough:\n",
    "print('# Of Restaurants Per Borough:','\\n',\n",
    "      df_filtered_temp['GRADE'].groupby(df_filtered_temp['BORO']).count().sort_values(ascending=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3175909",
   "metadata": {},
   "source": [
    "It looks like Manhattan has the most graded restaurants, followed by Brooklyn, Queens, the Bronx, and finally Staten Island"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7ee33a6",
   "metadata": {},
   "source": [
    "Let's now take a look at which grades the graded restaurants earned by each borough."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d901cc21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a bar chart comparing the # of each grade inspections in each borough:\n",
    "plt.figure(figsize=(15,8))\n",
    "sns.countplot(data=df_filtered_temp, x='BORO',hue='GRADE')\n",
    "\n",
    "# Add chart title and labels\n",
    "plt.title('Inspection Grade By Borough', fontsize=20)\n",
    "plt.xlabel('Borough', fontsize=14)\n",
    "plt.ylabel('# of Restaurants', fontsize=14);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38a4a0b4",
   "metadata": {},
   "source": [
    "It looks like the majority of restaurants have an 'A' inspection grade, and a 'C' grade is given to the least number of restaurants in each borough."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "752bc0ac",
   "metadata": {},
   "source": [
    "Many of the restaurants have had multiple inspections recorded. Let's see if certain boroughs are more likely to have many inspections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "abaec60f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define a function that will create ranges representing the value in the count column:\n",
    "def count_range(x):\n",
    "    '''This function will replace a numerical value with a corresponding range label.'''\n",
    "    if x == 1:\n",
    "        return '1'\n",
    "    elif 2<=x<=5:\n",
    "        return '2-5'\n",
    "    elif 6<=x<=10:\n",
    "        return '6-10'\n",
    "    elif 11<=x<=20:\n",
    "        return '11-20'\n",
    "    elif 21<=x<=30:\n",
    "        return '21-30'\n",
    "    else:\n",
    "        return '31+'\n",
    "\n",
    "# Add a new column that gives a range of the number of visits based on the '#_of_inspections' column:\n",
    "df_filtered['count_range'] = df_filtered['#_of_inspections'].apply(lambda x: count_range(x))\n",
    "\n",
    "# Create a bar chart comparing the % of inspection visits for each borough:\n",
    "x,y = 'BORO', 'count_range'\n",
    "(df_filtered.groupby(x)[y]\n",
    "                     .value_counts(normalize=True)\n",
    "                     .rename('Percent of Visits')\n",
    "                     .mul(100)\n",
    "                     .reset_index()\n",
    "                     .pipe((sns.catplot,'data'), x=x,y='Percent of Visits',hue=y,kind='bar',\n",
    "                           hue_order = ['1','2-5','6-10','11-20','21-30','31+'],height=6, aspect=2, legend=False))\n",
    "\n",
    "# Add chart title, labels, and legend\n",
    "plt.title('% of Inspection Visits By Borough', fontsize=20)\n",
    "plt.legend(loc='upper right', title = '# of Inspections', bbox_to_anchor=(1.1, 1))\n",
    "plt.xlabel('Borough', fontsize=14)\n",
    "plt.xticks(rotation='vertical');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34803c35",
   "metadata": {},
   "source": [
    "We see a similar trend across all boroughs, with 11-20 visits being most common, followed by 6-10 visits. Having only 1 visit is typically the least common. This is not too surprising as restaurants can expect to get an inspection at least once a year and therefore even restaurants with an A grading and no violations will likely have as many inspections as years they have been in business."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "685454a7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Of Restaurants Per Borough: \n",
      " BORO\n",
      "Manhattan        9575\n",
      "Brooklyn         6101\n",
      "Queens           5451\n",
      "Bronx            2286\n",
      "Staten Island     866\n",
      "Name: GRADE, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print('# Of Restaurants Per Borough:','\\n',\n",
    "      df_filtered['GRADE'].groupby(df_filtered['BORO']).count().sort_values(ascending=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8378cb38",
   "metadata": {},
   "source": [
    "### 4.2) Cuisine Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "21e11bc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Replace the long description for Latin restuarants with 'Latin'\n",
    "df_filtered['CUISINE DESCRIPTION'].replace(\n",
    "    {'Latin (Cuban, Dominican, Puerto Rican, South & Central American)':'Latin'},inplace=True)\n",
    "\n",
    "# Create a list of the 20 most frequent cuisine types:\n",
    "cuisine_type = df_filtered['CUISINE DESCRIPTION'].value_counts()[:20].index.tolist()\n",
    "\n",
    "# Create a temporary dataframe including only restaurants that are in the 20 most popular cuisine types:\n",
    "df_filtered_cuis = df_filtered.loc[df_filtered['CUISINE DESCRIPTION'].isin(cuisine_type)].copy()\n",
    "\n",
    "# Create a horizontal bar chart comparing the # of each grade inspections in each cuisine type:\n",
    "plt.figure(figsize=(15,10))\n",
    "sns.countplot(data=df_filtered_cuis, y='CUISINE DESCRIPTION', \n",
    "              order = df_filtered_cuis['CUISINE DESCRIPTION'].value_counts().index,palette='Blues_r')\n",
    "# Add chart title and labels\n",
    "plt.title('# of Restaurants in Each Cuisine Type', fontsize=20)\n",
    "plt.xlabel('# of Restaurants', fontsize=14)\n",
    "plt.ylabel('Cuisine Type', fontsize=14);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9a7318c",
   "metadata": {},
   "source": [
    "The top 5 most popular cuisines are American, Chinese, Coffee/Tea, Pizza and Italian"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a380d448",
   "metadata": {},
   "source": [
    "Let's now see how inspection grades vary by cuisine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0f2258ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a stacked bar chart comparing the # of each grade inspections in each cuisine type:\n",
    "df_plot = df_filtered_cuis.groupby(['GRADE', 'CUISINE DESCRIPTION']).size().reset_index().pivot(columns='GRADE', index='CUISINE DESCRIPTION', values=0)\n",
    "df_plot.plot(kind='bar', stacked=True,figsize=(15,8))\n",
    "\n",
    "# Add chart title and labels\n",
    "plt.title('Inspection Grade By Cuisine', fontsize=20)\n",
    "plt.ylabel('# of Inspection Grade', fontsize=14)\n",
    "plt.xlabel('Cuisine Type', fontsize=14);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdadd979",
   "metadata": {},
   "source": [
    "Overall, most restaurants appear to primarily consist of 'A' grades. In particular, there are a lot of 'A' grades for our most populous cuisine types: American, Coffee/Tea, Chinese, and Pizza. We also see a good amount of restaurants with pending grades ('Z') in these same categories.\n",
    "\n",
    "Let's see if any particular cuisines have a greater amount of inspection visits than others."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d1ac9bb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Add a new column that gives a range of the number of visits based on the '#_of_inspections' column:\n",
    "df_filtered_cuis['count_range'] = df_filtered_cuis['#_of_inspections'].apply(lambda x: count_range(x))\n",
    "\n",
    "# Create a bar chart comparing the % of inspection visits for each cuisine:\n",
    "x,y = 'CUISINE DESCRIPTION', 'count_range'\n",
    "(df_filtered_cuis.groupby(x)[y]\n",
    "                     .value_counts(normalize=True)\n",
    "                     .rename('Percent of Visits')\n",
    "                     .mul(100)\n",
    "                     .reset_index()\n",
    "                     .pipe((sns.catplot,'data'), x=x,y='Percent of Visits',hue=y,kind='bar',\n",
    "                           hue_order = ['1','2-5','6-10','11-20','21-30','31+'],height=6, aspect=2, legend=False))\n",
    "\n",
    "# Add chart title and legend\n",
    "plt.title('% of Inspection Visits By Cuisine', fontsize=20)\n",
    "plt.legend(loc='upper right', title='# of Inspections')\n",
    "plt.xticks(rotation='vertical');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4fe279e",
   "metadata": {},
   "source": [
    "It looks like 'Coffee/Tea', 'Juice, Smoothies, Fruit Salads', and 'Other' have more restaurants with fewer inspection visits, indicating they either have fewer violations or they consist of a lot of newer restaurants and therefore have not had as many visits yet. Latin American, Chinese, and Thai seem to have the greatest number of inspection visits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9672b2b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a pie chart that shows the breakout of each grade:\n",
    "plt.figure(figsize=(12,6))\n",
    "df_filtered_cuis['GRADE'].value_counts().plot(kind='pie')\n",
    "plt.title('% Of Each Grade', fontsize=20);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1602133a",
   "metadata": {},
   "source": [
    "The vast majority of our restaurants appear to have 'A' grades."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b8173ba",
   "metadata": {},
   "source": [
    "Now let's see if certain grades (including pending grades) are likely to have more or less inspection visits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e1b5c7e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a bar chart comparing the % of inspection visits for each grade:\n",
    "x,y = 'GRADE', 'count_range'\n",
    "(df_filtered_cuis.groupby(x)[y]\n",
    "                     .value_counts(normalize=True)\n",
    "                     .rename('Percent of Visits')\n",
    "                     .mul(100)\n",
    "                     .reset_index()\n",
    "                     .pipe((sns.catplot,'data'), x=x,y='Percent of Visits',hue=y,kind='bar', \n",
    "                           hue_order = ['1','2-5','6-10','11-20','21-30','31+'],height=6, aspect=2, legend=False))\n",
    "\n",
    "# Add chart title and legend:\n",
    "plt.title('% of Inspection Visits By Grade', fontsize=20)\n",
    "plt.legend(loc='upper right', title='# of Inspections');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7d2a981",
   "metadata": {},
   "source": [
    "Those restaurants that are not yet graded (N) are most likely to only have 2-5 visits, likely because they are new restaurants. We also see that restaurants with a 'B' or 'C' grade are likely to have more inspection visits than restaurants with an 'A' grade, likely because these restaurants have violations (leading to their 'B' or 'C' grades) and therefore needed extra inspections."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "159eca55",
   "metadata": {},
   "source": [
    "## 5) Classification Algorithms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0e74a0d",
   "metadata": {},
   "source": [
    "Many of our data columns are currently filled with categorical data, which our model will be unable to read. Therefore, we have to update the data to be numerical. We will use one-hot encoding to turn each category within a column into its own column with numerical data that our model is able to properly use."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50c1843b",
   "metadata": {},
   "source": [
    "#### One Hot Encoding of Categorical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "60cc74ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_inputs(df):\n",
    "    df = df.copy()\n",
    "    \n",
    "    # Drop CAMIS and DBA columns\n",
    "    df = df.drop(['CAMIS', 'DBA'], axis=1)\n",
    "    \n",
    "    # Drop columns with more than 25% missing values\n",
    "    missing_value_columns = df.columns[df.isna().mean() >= 0.25]\n",
    "    df = df.drop(missing_value_columns, axis=1)\n",
    "    \n",
    "    # Drop high-cardinality columns\n",
    "    high_cardinality_columns = [\n",
    "        'BUILDING',\n",
    "        'STREET',\n",
    "        'display_phone'\n",
    "    ]\n",
    "    df = df.drop(high_cardinality_columns, axis=1)\n",
    "    \n",
    "    # Convert ZIPCODE column into a string column\n",
    "    df['ZIPCODE'] = df['ZIPCODE'].astype(str)\n",
    "    \n",
    "    # Extract date features\n",
    "    df['INSPECTION DATE'] = pd.to_datetime(df['INSPECTION DATE'])\n",
    "    df['INSPECTION YEAR'] = df['INSPECTION DATE'].apply(lambda x: x.year)\n",
    "    df['INSPECTION MONTH'] = df['INSPECTION DATE'].apply(lambda x: x.month)\n",
    "    df['INSPECTION DAY'] = df['INSPECTION DATE'].apply(lambda x: x.day)\n",
    "    df = df.drop('INSPECTION DATE', axis=1)\n",
    "    \n",
    "    # One-hot encode remaining categorical columns\n",
    "    for column in df.select_dtypes('object').columns.drop('CRITICAL FLAG'):\n",
    "        dummies = pd.get_dummies(df[column], prefix=column)\n",
    "        df = pd.concat([df, dummies], axis=1)\n",
    "        df = df.drop(column, axis=1)\n",
    "    \n",
    "    # Split df into X and y\n",
    "    y = df['CRITICAL FLAG']\n",
    "    X = df.drop('CRITICAL FLAG', axis=1)\n",
    "    \n",
    "    # Train-test split\n",
    "    X_train, X_rem, y_train, y_rem = train_test_split(X, y, train_size=0.8, shuffle=True, random_state=1)\n",
    "    \n",
    "    # Scale X\n",
    "    scaler = StandardScaler()\n",
    "    scaler.fit(X_train)\n",
    "    X_train = pd.DataFrame(scaler.transform(X_train), index=X_train.index, columns=X_train.columns)\n",
    "    X_rem = pd.DataFrame(scaler.transform(X_rem), index=X_rem.index, columns=X_rem.columns)\n",
    "    \n",
    "    return X_train, X_rem, y_train, y_rem\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7f9467c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_inputs_rf(df):\n",
    "    df = df.copy()\n",
    "    \n",
    "    # Drop CAMIS and DBA columns\n",
    "    df = df.drop(['CAMIS', 'DBA'], axis=1)\n",
    "    \n",
    "    # Drop columns with more than 25% missing values\n",
    "    missing_value_columns = df.columns[df.isna().mean() >= 0.25]\n",
    "    df = df.drop(missing_value_columns, axis=1)\n",
    "    \n",
    "    # Drop high-cardinality columns\n",
    "    high_cardinality_columns = [\n",
    "        'BUILDING',\n",
    "        'STREET',\n",
    "        'display_phone'\n",
    "    ]\n",
    "    df = df.drop(high_cardinality_columns, axis=1)\n",
    "    \n",
    "    # Convert ZIPCODE column into a string column\n",
    "    df['ZIPCODE'] = df['ZIPCODE'].astype(str)\n",
    "    \n",
    "    # Extract date features\n",
    "    df['INSPECTION DATE'] = pd.to_datetime(df['INSPECTION DATE'])\n",
    "    df['INSPECTION YEAR'] = df['INSPECTION DATE'].apply(lambda x: x.year)\n",
    "    df['INSPECTION MONTH'] = df['INSPECTION DATE'].apply(lambda x: x.month)\n",
    "    df['INSPECTION DAY'] = df['INSPECTION DATE'].apply(lambda x: x.day)\n",
    "    df = df.drop('INSPECTION DATE', axis=1)\n",
    "    \n",
    "    # Split df into X and y\n",
    "    y = df['CRITICAL FLAG']\n",
    "    X = df.drop('CRITICAL FLAG', axis=1)\n",
    "    \n",
    "    # Train-test split\n",
    "    X_train, X_rem, y_train, y_rem = train_test_split(X, y, train_size=0.8, shuffle=True, random_state=1)\n",
    "    \n",
    "    # Scale X\n",
    "    scaler = StandardScaler()\n",
    "    scaler.fit(X_train)\n",
    "    X_train = pd.DataFrame(scaler.transform(X_train), index=X_train.index, columns=X_train.columns)\n",
    "    X_rem = pd.DataFrame(scaler.transform(X_rem), index=X_rem.index, columns=X_rem.columns)\n",
    "    \n",
    "    return X_train, X_rem, y_train, y_rem\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eef3c904",
   "metadata": {},
   "source": [
    "#### Divide dataset into train (80%), dev(10%) and test(10%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e8da4bfb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "X_train, X_rem, y_train, y_rem = preprocess_inputs(df_filtered_cuis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "32a5c875",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting remaining data into development and test dataset\n",
    "X_dev, X_test, y_dev, y_test = train_test_split(X_rem, y_rem, train_size=0.5, shuffle=True, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a69d0fe4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(15752, 956)\n",
      "(15752,)\n",
      "(1969, 956)\n",
      "(1969,)\n",
      "(1970, 956)\n",
      "(1970,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None, None)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(X_train.shape), print(y_train.shape)\n",
    "print(X_dev.shape), print(y_dev.shape)\n",
    "print(X_test.shape), print(y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ac42119",
   "metadata": {},
   "source": [
    "## Comparing Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4e9832b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, f1_score, classification_report\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import itertools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e0cb5b87",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define Confusion Matrix:\n",
    "def confusion_matrix_plot(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    '''This function will create a confusion matrix chart which shows the accuracy breakdown of the given model.\n",
    "    Inputs:\n",
    "        cm: confusion matrix function for tested and predicted y values\n",
    "        classes: variables to identify for each class (0 or 1)\n",
    "        normalize: if True will normalize the data, if False will not normalize the data\n",
    "        title: title of the chart\n",
    "        cmap: colors to plot\n",
    "    Returns:\n",
    "        A graph that represents the confusion matrix\n",
    "    '''\n",
    "   \n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    \n",
    "    # Add title and labels:\n",
    "    plt.title(title)\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "    \n",
    "    # Add axis scales and tick marks:\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "    \n",
    "    # Add labels to each cell:\n",
    "    thresh = cm.max() / 2.\n",
    "    # Iterate through confusion matrix and append labels to the plot:\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, cm[i, j],\n",
    "                 horizontalalignment='center',\n",
    "                 color='white' if cm[i, j] > thresh else 'black')\n",
    "        \n",
    "    # Add legend:\n",
    "    plt.colorbar()\n",
    "    plt.show()\n",
    "    \n",
    "# Use High Rating and Low Rating  for 0 and 1 classes \n",
    "class_names = ['Critical','Not Applicable','Non-Critical']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eaff0aa2",
   "metadata": {},
   "source": [
    "### 1) Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "39a50316",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import balanced_accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "0ba6e1b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Train Accuracy:  79.37742835418318 %\n",
      "Logistic Regession Dev Accuracy:  71.59572195861159 %\n",
      "                precision    recall  f1-score   support\n",
      "\n",
      "      Critical       0.64      0.73      0.68      1020\n",
      "Not Applicable       1.00      0.87      0.93        23\n",
      "  Not Critical       0.65      0.55      0.59       926\n",
      "\n",
      "      accuracy                           0.65      1969\n",
      "     macro avg       0.76      0.72      0.74      1969\n",
      "  weighted avg       0.65      0.65      0.64      1969\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/aashikaperunkolam/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the classifier, fit it on the training data and make predictions on the test set:\n",
    "logr = LogisticRegression()\n",
    "\n",
    "logr.fit(X_train, y_train)\n",
    "y_pred_dev_logr = logr.predict(X_dev)\n",
    "y_pred_train_logr = logr.predict(X_train)\n",
    "\n",
    "# Check the Accuracy:\n",
    "print('Logistic Regression Train Accuracy: ', balanced_accuracy_score(y_train, y_pred_train_logr)*100,'%')\n",
    "print('Logistic Regession Dev Accuracy: ', balanced_accuracy_score(y_dev, y_pred_dev_logr)*100,'%')\n",
    "\n",
    "# Print classification report:\n",
    "print(classification_report(y_dev, y_pred_dev_logr))\n",
    "\n",
    "# Confusion Matrix for LogReg:\n",
    "cm_logr = confusion_matrix(y_dev,y_pred_dev_logr)\n",
    "confusion_matrix_plot(cm_logr, classes=class_names, title='Logistic Regression Confusion Matrix')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "8a20d5df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Critical          1020\n",
       "Not Critical       926\n",
       "Not Applicable      23\n",
       "Name: CRITICAL FLAG, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_dev.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3a3bc63",
   "metadata": {},
   "source": [
    "### 2) Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "dbd031df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Train Accuracy:  98.70816846227822 %\n",
      "Random Forest Dev Accuracy:  67.25735393853913 %\n",
      "                precision    recall  f1-score   support\n",
      "\n",
      "      Critical       0.64      0.73      0.68      1020\n",
      "Not Applicable       0.89      0.74      0.81        23\n",
      "  Not Critical       0.65      0.54      0.59       926\n",
      "\n",
      "      accuracy                           0.64      1969\n",
      "     macro avg       0.73      0.67      0.69      1969\n",
      "  weighted avg       0.65      0.64      0.64      1969\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the classifier, fit it on the training data and make predictions on the test set:\n",
    "from sklearn.metrics import balanced_accuracy_score\n",
    "rforest = RandomForestClassifier(n_estimators=120, criterion = 'entropy', max_depth=None, max_features=10, \n",
    "                                 min_samples_split=8, random_state=123)\n",
    "rforest.fit(X_train, y_train)\n",
    "y_pred_dev_rf = rforest.predict(X_dev)\n",
    "y_pred_train_rf = rforest.predict(X_train)\n",
    "\n",
    "# Check the Accuracy:\n",
    "print('Random Forest Train Accuracy: ', balanced_accuracy_score(y_train, y_pred_train_rf)*100,'%')\n",
    "print('Random Forest Dev Accuracy: ', balanced_accuracy_score(y_dev, y_pred_dev_rf)*100,'%')\n",
    "\n",
    "# Print classification report:\n",
    "print(classification_report(y_dev, y_pred_dev_rf))\n",
    "\n",
    "# Confusion Matrix for Random Forest:\n",
    "cm_forest = confusion_matrix(y_dev,y_pred_dev_rf)\n",
    "confusion_matrix_plot(cm_forest, classes=class_names, title='Random Forest Confusion Matrix')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "82a8e8e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the 30 most influential features based on XGBoost model:\n",
    "influential_features = pd.Series(rforest.feature_importances_, index=X_train.columns)\n",
    "influential_features.nlargest(30).sort_values().plot(kind='barh', color='slategrey', figsize=(10,10))\n",
    "plt.title('Feature Importances with Random Forest');"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}