License Status Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  License Status Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. AAC (license was cancelled during term)\n",
    "2. AA (license was issued)\n",
    "3. INQ (license requires inquiry)\n",
    "4. REA (license revocation has been appealed)\n",
    "5. REV (license was revoked)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score, roc_auc_score, accuracy_score, classification_report\n",
    "\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import mutual_info_classif\n",
    "from collections import Counter\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "\n",
    "from sklearn.model_selection import RandomizedSearchCV, GridSearchCV\n",
    "import xgboost\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\siddhesh\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3072: DtypeWarning: Columns (1,2,3,9,10,11,13,14,16,27,28,29,31) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('5270fea685ab11ec.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>train_file.csv</th>\n",
       "      <th>LICENSE ID</th>\n",
       "      <th>ACCOUNT NUMBER</th>\n",
       "      <th>SITE NUMBER</th>\n",
       "      <th>LEGAL NAME</th>\n",
       "      <th>DOING BUSINESS AS NAME</th>\n",
       "      <th>ADDRESS</th>\n",
       "      <th>CITY</th>\n",
       "      <th>STATE</th>\n",
       "      <th>ZIP CODE</th>\n",
       "      <th>...</th>\n",
       "      <th>LICENSE TERM START DATE</th>\n",
       "      <th>LICENSE TERM EXPIRATION DATE</th>\n",
       "      <th>LICENSE APPROVED FOR ISSUANCE</th>\n",
       "      <th>DATE ISSUED</th>\n",
       "      <th>LICENSE STATUS CHANGE DATE</th>\n",
       "      <th>SSA</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>LOCATION</th>\n",
       "      <th>LICENSE STATUS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>35342-20020816</td>\n",
       "      <td>1256593</td>\n",
       "      <td>32811</td>\n",
       "      <td>1</td>\n",
       "      <td>CARMEN CAHUE</td>\n",
       "      <td>CLAUDIA'S BRIDAL SHOP</td>\n",
       "      <td>2625 S CENTRAL PARK AVE  1</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60623.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2002-08-16T00:00:00</td>\n",
       "      <td>2003-08-15T00:00:00</td>\n",
       "      <td>2002-08-21T00:00:00</td>\n",
       "      <td>2006-04-11T00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25</td>\n",
       "      <td>41.8436</td>\n",
       "      <td>-87.7146</td>\n",
       "      <td>{'latitude': '41.843612879431845', 'longitude'...</td>\n",
       "      <td>AAI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1358463-20051116</td>\n",
       "      <td>1639294</td>\n",
       "      <td>262311</td>\n",
       "      <td>29</td>\n",
       "      <td>ISLA TROPICAL, INC.</td>\n",
       "      <td>ISLA TROPICAL</td>\n",
       "      <td>2825 W MONTROSE AVE</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60618</td>\n",
       "      <td>...</td>\n",
       "      <td>2005-11-16T00:00:00</td>\n",
       "      <td>2006-11-15T00:00:00</td>\n",
       "      <td>2006-04-05T00:00:00</td>\n",
       "      <td>2006-06-12T00:00:00</td>\n",
       "      <td>2006-06-15T00:00:00</td>\n",
       "      <td>60</td>\n",
       "      <td>41.9611</td>\n",
       "      <td>-87.6996</td>\n",
       "      <td>{'latitude': '41.96113244107215', 'longitude':...</td>\n",
       "      <td>AAC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1980233-20090722</td>\n",
       "      <td>1980233</td>\n",
       "      <td>345008</td>\n",
       "      <td>1</td>\n",
       "      <td>DJS REMODELING</td>\n",
       "      <td>DJS REMODELING, INC.</td>\n",
       "      <td>1605  CLAVEY RD 1</td>\n",
       "      <td>HIGHLAND</td>\n",
       "      <td>IL</td>\n",
       "      <td>60035</td>\n",
       "      <td>...</td>\n",
       "      <td>2009-07-22T00:00:00</td>\n",
       "      <td>2011-07-15T00:00:00</td>\n",
       "      <td>2009-07-22T00:00:00</td>\n",
       "      <td>2009-07-22T00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AAI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1476582-20040211</td>\n",
       "      <td>1476582</td>\n",
       "      <td>273121</td>\n",
       "      <td>1</td>\n",
       "      <td>ALL-BRY CONSTRUCTION CO.</td>\n",
       "      <td>ALL-BRY CONSTRUCTION CO.</td>\n",
       "      <td>8  NORTH TRAIL</td>\n",
       "      <td>LEMONT</td>\n",
       "      <td>IL</td>\n",
       "      <td>60439</td>\n",
       "      <td>...</td>\n",
       "      <td>2004-02-11T00:00:00</td>\n",
       "      <td>2005-02-15T00:00:00</td>\n",
       "      <td>2004-02-10T00:00:00</td>\n",
       "      <td>2004-02-11T00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AAI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1141408-20080516</td>\n",
       "      <td>1896750</td>\n",
       "      <td>213785</td>\n",
       "      <td>1</td>\n",
       "      <td>MCDONOUGH MECHANICAL SERVICE</td>\n",
       "      <td>MCDONOUGH MECHANICAL SERVICE</td>\n",
       "      <td>4081  JOSEPH DR</td>\n",
       "      <td>WAUKEGAN</td>\n",
       "      <td>IL</td>\n",
       "      <td>60087</td>\n",
       "      <td>...</td>\n",
       "      <td>2008-05-16T00:00:00</td>\n",
       "      <td>2010-05-15T00:00:00</td>\n",
       "      <td>2008-06-04T00:00:00</td>\n",
       "      <td>2008-06-05T00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AAI</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     train_file.csv LICENSE ID ACCOUNT NUMBER SITE NUMBER  \\\n",
       "0    35342-20020816    1256593          32811           1   \n",
       "1  1358463-20051116    1639294         262311          29   \n",
       "2  1980233-20090722    1980233         345008           1   \n",
       "3  1476582-20040211    1476582         273121           1   \n",
       "4  1141408-20080516    1896750         213785           1   \n",
       "\n",
       "                     LEGAL NAME        DOING BUSINESS AS NAME  \\\n",
       "0                  CARMEN CAHUE         CLAUDIA'S BRIDAL SHOP   \n",
       "1           ISLA TROPICAL, INC.                 ISLA TROPICAL   \n",
       "2                DJS REMODELING          DJS REMODELING, INC.   \n",
       "3      ALL-BRY CONSTRUCTION CO.      ALL-BRY CONSTRUCTION CO.   \n",
       "4  MCDONOUGH MECHANICAL SERVICE  MCDONOUGH MECHANICAL SERVICE   \n",
       "\n",
       "                      ADDRESS      CITY STATE ZIP CODE  ...  \\\n",
       "0  2625 S CENTRAL PARK AVE  1   CHICAGO    IL  60623.0  ...   \n",
       "1         2825 W MONTROSE AVE   CHICAGO    IL    60618  ...   \n",
       "2           1605  CLAVEY RD 1  HIGHLAND    IL    60035  ...   \n",
       "3              8  NORTH TRAIL    LEMONT    IL    60439  ...   \n",
       "4             4081  JOSEPH DR  WAUKEGAN    IL    60087  ...   \n",
       "\n",
       "  LICENSE TERM START DATE LICENSE TERM EXPIRATION DATE  \\\n",
       "0     2002-08-16T00:00:00          2003-08-15T00:00:00   \n",
       "1     2005-11-16T00:00:00          2006-11-15T00:00:00   \n",
       "2     2009-07-22T00:00:00          2011-07-15T00:00:00   \n",
       "3     2004-02-11T00:00:00          2005-02-15T00:00:00   \n",
       "4     2008-05-16T00:00:00          2010-05-15T00:00:00   \n",
       "\n",
       "  LICENSE APPROVED FOR ISSUANCE          DATE ISSUED  \\\n",
       "0           2002-08-21T00:00:00  2006-04-11T00:00:00   \n",
       "1           2006-04-05T00:00:00  2006-06-12T00:00:00   \n",
       "2           2009-07-22T00:00:00  2009-07-22T00:00:00   \n",
       "3           2004-02-10T00:00:00  2004-02-11T00:00:00   \n",
       "4           2008-06-04T00:00:00  2008-06-05T00:00:00   \n",
       "\n",
       "  LICENSE STATUS CHANGE DATE  SSA LATITUDE LONGITUDE  \\\n",
       "0                        NaN   25  41.8436  -87.7146   \n",
       "1        2006-06-15T00:00:00   60  41.9611  -87.6996   \n",
       "2                        NaN  NaN      NaN       NaN   \n",
       "3                        NaN  NaN      NaN       NaN   \n",
       "4                        NaN  NaN      NaN       NaN   \n",
       "\n",
       "                                            LOCATION LICENSE STATUS  \n",
       "0  {'latitude': '41.843612879431845', 'longitude'...            AAI  \n",
       "1  {'latitude': '41.96113244107215', 'longitude':...            AAC  \n",
       "2                                                NaN            AAI  \n",
       "3                                                NaN            AAI  \n",
       "4                                                NaN            AAI  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(143142, 32)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['train_file.csv', 'LICENSE ID', 'ACCOUNT NUMBER', 'SITE NUMBER',\n",
       "       'LEGAL NAME', 'DOING BUSINESS AS NAME', 'ADDRESS', 'CITY', 'STATE',\n",
       "       'ZIP CODE', 'WARD', 'PRECINCT', 'WARD PRECINCT', 'POLICE DISTRICT',\n",
       "       'LICENSE CODE', 'LICENSE DESCRIPTION', 'LICENSE NUMBER',\n",
       "       'APPLICATION TYPE', 'APPLICATION CREATED DATE',\n",
       "       'APPLICATION REQUIREMENTS COMPLETE', 'PAYMENT DATE',\n",
       "       'CONDITIONAL APPROVAL', 'LICENSE TERM START DATE',\n",
       "       'LICENSE TERM EXPIRATION DATE', 'LICENSE APPROVED FOR ISSUANCE',\n",
       "       'DATE ISSUED', 'LICENSE STATUS CHANGE DATE', 'SSA', 'LATITUDE',\n",
       "       'LONGITUDE', 'LOCATION', 'LICENSE STATUS'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>LICENSE ID</th>\n",
       "      <th>ACCOUNT NUMBER</th>\n",
       "      <th>SITE NUMBER</th>\n",
       "      <th>LEGAL NAME</th>\n",
       "      <th>DOING BUSINESS AS NAME</th>\n",
       "      <th>ADDRESS</th>\n",
       "      <th>CITY</th>\n",
       "      <th>STATE</th>\n",
       "      <th>ZIP CODE</th>\n",
       "      <th>...</th>\n",
       "      <th>LICENSE TERM START DATE</th>\n",
       "      <th>LICENSE TERM EXPIRATION DATE</th>\n",
       "      <th>LICENSE APPROVED FOR ISSUANCE</th>\n",
       "      <th>DATE ISSUED</th>\n",
       "      <th>LICENSE STATUS CHANGE DATE</th>\n",
       "      <th>SSA</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>LOCATION</th>\n",
       "      <th>LICENSE STATUS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>35342-20020816</td>\n",
       "      <td>1256593</td>\n",
       "      <td>32811</td>\n",
       "      <td>1</td>\n",
       "      <td>CARMEN CAHUE</td>\n",
       "      <td>CLAUDIA'S BRIDAL SHOP</td>\n",
       "      <td>2625 S CENTRAL PARK AVE  1</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60623.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2002-08-16T00:00:00</td>\n",
       "      <td>2003-08-15T00:00:00</td>\n",
       "      <td>2002-08-21T00:00:00</td>\n",
       "      <td>2006-04-11T00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25</td>\n",
       "      <td>41.8436</td>\n",
       "      <td>-87.7146</td>\n",
       "      <td>{'latitude': '41.843612879431845', 'longitude'...</td>\n",
       "      <td>AAI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1358463-20051116</td>\n",
       "      <td>1639294</td>\n",
       "      <td>262311</td>\n",
       "      <td>29</td>\n",
       "      <td>ISLA TROPICAL, INC.</td>\n",
       "      <td>ISLA TROPICAL</td>\n",
       "      <td>2825 W MONTROSE AVE</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60618</td>\n",
       "      <td>...</td>\n",
       "      <td>2005-11-16T00:00:00</td>\n",
       "      <td>2006-11-15T00:00:00</td>\n",
       "      <td>2006-04-05T00:00:00</td>\n",
       "      <td>2006-06-12T00:00:00</td>\n",
       "      <td>2006-06-15T00:00:00</td>\n",
       "      <td>60</td>\n",
       "      <td>41.9611</td>\n",
       "      <td>-87.6996</td>\n",
       "      <td>{'latitude': '41.96113244107215', 'longitude':...</td>\n",
       "      <td>AAC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1980233-20090722</td>\n",
       "      <td>1980233</td>\n",
       "      <td>345008</td>\n",
       "      <td>1</td>\n",
       "      <td>DJS REMODELING</td>\n",
       "      <td>DJS REMODELING, INC.</td>\n",
       "      <td>1605  CLAVEY RD 1</td>\n",
       "      <td>HIGHLAND</td>\n",
       "      <td>IL</td>\n",
       "      <td>60035</td>\n",
       "      <td>...</td>\n",
       "      <td>2009-07-22T00:00:00</td>\n",
       "      <td>2011-07-15T00:00:00</td>\n",
       "      <td>2009-07-22T00:00:00</td>\n",
       "      <td>2009-07-22T00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AAI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1476582-20040211</td>\n",
       "      <td>1476582</td>\n",
       "      <td>273121</td>\n",
       "      <td>1</td>\n",
       "      <td>ALL-BRY CONSTRUCTION CO.</td>\n",
       "      <td>ALL-BRY CONSTRUCTION CO.</td>\n",
       "      <td>8  NORTH TRAIL</td>\n",
       "      <td>LEMONT</td>\n",
       "      <td>IL</td>\n",
       "      <td>60439</td>\n",
       "      <td>...</td>\n",
       "      <td>2004-02-11T00:00:00</td>\n",
       "      <td>2005-02-15T00:00:00</td>\n",
       "      <td>2004-02-10T00:00:00</td>\n",
       "      <td>2004-02-11T00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AAI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1141408-20080516</td>\n",
       "      <td>1896750</td>\n",
       "      <td>213785</td>\n",
       "      <td>1</td>\n",
       "      <td>MCDONOUGH MECHANICAL SERVICE</td>\n",
       "      <td>MCDONOUGH MECHANICAL SERVICE</td>\n",
       "      <td>4081  JOSEPH DR</td>\n",
       "      <td>WAUKEGAN</td>\n",
       "      <td>IL</td>\n",
       "      <td>60087</td>\n",
       "      <td>...</td>\n",
       "      <td>2008-05-16T00:00:00</td>\n",
       "      <td>2010-05-15T00:00:00</td>\n",
       "      <td>2008-06-04T00:00:00</td>\n",
       "      <td>2008-06-05T00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AAI</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>143137</th>\n",
       "      <td>2032383-20160716</td>\n",
       "      <td>2.46596e+06</td>\n",
       "      <td>63376</td>\n",
       "      <td>180</td>\n",
       "      <td>INTERPARK LLC</td>\n",
       "      <td>INTERPARK</td>\n",
       "      <td>505 N STATE ST 2-4</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60654</td>\n",
       "      <td>...</td>\n",
       "      <td>2016-07-16T00:00:00</td>\n",
       "      <td>2018-07-15T00:00:00</td>\n",
       "      <td>2016-06-23T00:00:00</td>\n",
       "      <td>2016-08-11T00:00:00</td>\n",
       "      <td>2018-01-09T00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.8911</td>\n",
       "      <td>-87.6279</td>\n",
       "      <td>{'latitude': '41.89105524147196', 'longitude':...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143138</th>\n",
       "      <td>40376-20020216</td>\n",
       "      <td>1.2112e+06</td>\n",
       "      <td>55051</td>\n",
       "      <td>1</td>\n",
       "      <td>YUN HEE HWANG</td>\n",
       "      <td>TOP CLEANERS</td>\n",
       "      <td>1201 W ROSCOE ST 1ST</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60657</td>\n",
       "      <td>...</td>\n",
       "      <td>2002-02-16T00:00:00</td>\n",
       "      <td>2003-02-15T00:00:00</td>\n",
       "      <td>2002-01-18T00:00:00</td>\n",
       "      <td>2002-01-22T00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.9434</td>\n",
       "      <td>-87.6592</td>\n",
       "      <td>{'latitude': '41.94338183750669', 'longitude':...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143139</th>\n",
       "      <td>2064176-20121116</td>\n",
       "      <td>2.1885e+06</td>\n",
       "      <td>357381</td>\n",
       "      <td>1</td>\n",
       "      <td>D. C. A. TOWING INCORPORATED</td>\n",
       "      <td>D. C. A. TOWING INCORPORATED</td>\n",
       "      <td>7052 W 64TH ST 1ST</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60638</td>\n",
       "      <td>...</td>\n",
       "      <td>2012-11-16T00:00:00</td>\n",
       "      <td>2014-11-15T00:00:00</td>\n",
       "      <td>2012-12-25T00:00:00</td>\n",
       "      <td>2012-12-26T00:00:00</td>\n",
       "      <td>2012-12-29T00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.7757</td>\n",
       "      <td>-87.798</td>\n",
       "      <td>{'latitude': '41.775709631013335', 'longitude'...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143140</th>\n",
       "      <td>1954052-20090202</td>\n",
       "      <td>1.95405e+06</td>\n",
       "      <td>340610</td>\n",
       "      <td>1</td>\n",
       "      <td>maria t bedoy</td>\n",
       "      <td>mari's cleaning services</td>\n",
       "      <td>4544 S CHRISTIANA AVE 1</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60632</td>\n",
       "      <td>...</td>\n",
       "      <td>2009-02-02T00:00:00</td>\n",
       "      <td>2009-06-15T00:00:00</td>\n",
       "      <td>2009-01-31T00:00:00</td>\n",
       "      <td>2009-02-02T00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39</td>\n",
       "      <td>41.8105</td>\n",
       "      <td>-87.7079</td>\n",
       "      <td>{'latitude': '41.81052250782054', 'longitude':...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143141</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>143142 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      ID   LICENSE ID ACCOUNT NUMBER SITE NUMBER  \\\n",
       "0         35342-20020816      1256593          32811           1   \n",
       "1       1358463-20051116      1639294         262311          29   \n",
       "2       1980233-20090722      1980233         345008           1   \n",
       "3       1476582-20040211      1476582         273121           1   \n",
       "4       1141408-20080516      1896750         213785           1   \n",
       "...                  ...          ...            ...         ...   \n",
       "143137  2032383-20160716  2.46596e+06          63376         180   \n",
       "143138    40376-20020216   1.2112e+06          55051           1   \n",
       "143139  2064176-20121116   2.1885e+06         357381           1   \n",
       "143140  1954052-20090202  1.95405e+06         340610           1   \n",
       "143141               NaN          NaN            NaN         NaN   \n",
       "\n",
       "                          LEGAL NAME        DOING BUSINESS AS NAME  \\\n",
       "0                       CARMEN CAHUE         CLAUDIA'S BRIDAL SHOP   \n",
       "1                ISLA TROPICAL, INC.                 ISLA TROPICAL   \n",
       "2                     DJS REMODELING          DJS REMODELING, INC.   \n",
       "3           ALL-BRY CONSTRUCTION CO.      ALL-BRY CONSTRUCTION CO.   \n",
       "4       MCDONOUGH MECHANICAL SERVICE  MCDONOUGH MECHANICAL SERVICE   \n",
       "...                              ...                           ...   \n",
       "143137                 INTERPARK LLC                     INTERPARK   \n",
       "143138                 YUN HEE HWANG                  TOP CLEANERS   \n",
       "143139  D. C. A. TOWING INCORPORATED  D. C. A. TOWING INCORPORATED   \n",
       "143140                 maria t bedoy      mari's cleaning services   \n",
       "143141                           NaN                           NaN   \n",
       "\n",
       "                           ADDRESS      CITY STATE ZIP CODE  ...  \\\n",
       "0       2625 S CENTRAL PARK AVE  1   CHICAGO    IL  60623.0  ...   \n",
       "1              2825 W MONTROSE AVE   CHICAGO    IL    60618  ...   \n",
       "2                1605  CLAVEY RD 1  HIGHLAND    IL    60035  ...   \n",
       "3                   8  NORTH TRAIL    LEMONT    IL    60439  ...   \n",
       "4                  4081  JOSEPH DR  WAUKEGAN    IL    60087  ...   \n",
       "...                            ...       ...   ...      ...  ...   \n",
       "143137          505 N STATE ST 2-4   CHICAGO    IL    60654  ...   \n",
       "143138        1201 W ROSCOE ST 1ST   CHICAGO    IL    60657  ...   \n",
       "143139          7052 W 64TH ST 1ST   CHICAGO    IL    60638  ...   \n",
       "143140     4544 S CHRISTIANA AVE 1   CHICAGO    IL    60632  ...   \n",
       "143141                         NaN       NaN   NaN      NaN  ...   \n",
       "\n",
       "       LICENSE TERM START DATE LICENSE TERM EXPIRATION DATE  \\\n",
       "0          2002-08-16T00:00:00          2003-08-15T00:00:00   \n",
       "1          2005-11-16T00:00:00          2006-11-15T00:00:00   \n",
       "2          2009-07-22T00:00:00          2011-07-15T00:00:00   \n",
       "3          2004-02-11T00:00:00          2005-02-15T00:00:00   \n",
       "4          2008-05-16T00:00:00          2010-05-15T00:00:00   \n",
       "...                        ...                          ...   \n",
       "143137     2016-07-16T00:00:00          2018-07-15T00:00:00   \n",
       "143138     2002-02-16T00:00:00          2003-02-15T00:00:00   \n",
       "143139     2012-11-16T00:00:00          2014-11-15T00:00:00   \n",
       "143140     2009-02-02T00:00:00          2009-06-15T00:00:00   \n",
       "143141                     NaN                          NaN   \n",
       "\n",
       "       LICENSE APPROVED FOR ISSUANCE          DATE ISSUED  \\\n",
       "0                2002-08-21T00:00:00  2006-04-11T00:00:00   \n",
       "1                2006-04-05T00:00:00  2006-06-12T00:00:00   \n",
       "2                2009-07-22T00:00:00  2009-07-22T00:00:00   \n",
       "3                2004-02-10T00:00:00  2004-02-11T00:00:00   \n",
       "4                2008-06-04T00:00:00  2008-06-05T00:00:00   \n",
       "...                              ...                  ...   \n",
       "143137           2016-06-23T00:00:00  2016-08-11T00:00:00   \n",
       "143138           2002-01-18T00:00:00  2002-01-22T00:00:00   \n",
       "143139           2012-12-25T00:00:00  2012-12-26T00:00:00   \n",
       "143140           2009-01-31T00:00:00  2009-02-02T00:00:00   \n",
       "143141                           NaN                  NaN   \n",
       "\n",
       "       LICENSE STATUS CHANGE DATE  SSA LATITUDE LONGITUDE  \\\n",
       "0                             NaN   25  41.8436  -87.7146   \n",
       "1             2006-06-15T00:00:00   60  41.9611  -87.6996   \n",
       "2                             NaN  NaN      NaN       NaN   \n",
       "3                             NaN  NaN      NaN       NaN   \n",
       "4                             NaN  NaN      NaN       NaN   \n",
       "...                           ...  ...      ...       ...   \n",
       "143137        2018-01-09T00:00:00  NaN  41.8911  -87.6279   \n",
       "143138                        NaN  NaN  41.9434  -87.6592   \n",
       "143139        2012-12-29T00:00:00  NaN  41.7757   -87.798   \n",
       "143140                        NaN   39  41.8105  -87.7079   \n",
       "143141                        NaN  NaN      NaN       NaN   \n",
       "\n",
       "                                                 LOCATION LICENSE STATUS  \n",
       "0       {'latitude': '41.843612879431845', 'longitude'...            AAI  \n",
       "1       {'latitude': '41.96113244107215', 'longitude':...            AAC  \n",
       "2                                                     NaN            AAI  \n",
       "3                                                     NaN            AAI  \n",
       "4                                                     NaN            AAI  \n",
       "...                                                   ...            ...  \n",
       "143137  {'latitude': '41.89105524147196', 'longitude':...            NaN  \n",
       "143138  {'latitude': '41.94338183750669', 'longitude':...            NaN  \n",
       "143139  {'latitude': '41.775709631013335', 'longitude'...            NaN  \n",
       "143140  {'latitude': '41.81052250782054', 'longitude':...            NaN  \n",
       "143141                                                NaN            NaN  \n",
       "\n",
       "[143142 rows x 32 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rename(columns = {'train_file.csv': 'ID'}, inplace = True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                                   NaN\n",
       "LICENSE ID                           NaN\n",
       "ACCOUNT NUMBER                       NaN\n",
       "SITE NUMBER                          NaN\n",
       "LEGAL NAME                           NaN\n",
       "DOING BUSINESS AS NAME               NaN\n",
       "ADDRESS                              NaN\n",
       "CITY                                 NaN\n",
       "STATE                                NaN\n",
       "ZIP CODE                             NaN\n",
       "WARD                                 NaN\n",
       "PRECINCT                             NaN\n",
       "WARD PRECINCT                        NaN\n",
       "POLICE DISTRICT                      NaN\n",
       "LICENSE CODE                         NaN\n",
       "LICENSE DESCRIPTION                  NaN\n",
       "LICENSE NUMBER                       NaN\n",
       "APPLICATION TYPE                     NaN\n",
       "APPLICATION CREATED DATE             NaN\n",
       "APPLICATION REQUIREMENTS COMPLETE    NaN\n",
       "PAYMENT DATE                         NaN\n",
       "CONDITIONAL APPROVAL                 NaN\n",
       "LICENSE TERM START DATE              NaN\n",
       "LICENSE TERM EXPIRATION DATE         NaN\n",
       "LICENSE APPROVED FOR ISSUANCE        NaN\n",
       "DATE ISSUED                          NaN\n",
       "LICENSE STATUS CHANGE DATE           NaN\n",
       "SSA                                  NaN\n",
       "LATITUDE                             NaN\n",
       "LONGITUDE                            NaN\n",
       "LOCATION                             NaN\n",
       "LICENSE STATUS                       NaN\n",
       "Name: 143141, dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[143141]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>LICENSE ID</th>\n",
       "      <th>ACCOUNT NUMBER</th>\n",
       "      <th>SITE NUMBER</th>\n",
       "      <th>LEGAL NAME</th>\n",
       "      <th>DOING BUSINESS AS NAME</th>\n",
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       "      <th>ZIP CODE</th>\n",
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       "      <th>SSA</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>LOCATION</th>\n",
       "      <th>LICENSE STATUS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>35342-20020816</td>\n",
       "      <td>1256593</td>\n",
       "      <td>32811</td>\n",
       "      <td>1</td>\n",
       "      <td>CARMEN CAHUE</td>\n",
       "      <td>CLAUDIA'S BRIDAL SHOP</td>\n",
       "      <td>2625 S CENTRAL PARK AVE  1</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60623.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2002-08-16T00:00:00</td>\n",
       "      <td>2003-08-15T00:00:00</td>\n",
       "      <td>2002-08-21T00:00:00</td>\n",
       "      <td>2006-04-11T00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25</td>\n",
       "      <td>41.8436</td>\n",
       "      <td>-87.7146</td>\n",
       "      <td>{'latitude': '41.843612879431845', 'longitude'...</td>\n",
       "      <td>AAI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1358463-20051116</td>\n",
       "      <td>1639294</td>\n",
       "      <td>262311</td>\n",
       "      <td>29</td>\n",
       "      <td>ISLA TROPICAL, INC.</td>\n",
       "      <td>ISLA TROPICAL</td>\n",
       "      <td>2825 W MONTROSE AVE</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60618</td>\n",
       "      <td>...</td>\n",
       "      <td>2005-11-16T00:00:00</td>\n",
       "      <td>2006-11-15T00:00:00</td>\n",
       "      <td>2006-04-05T00:00:00</td>\n",
       "      <td>2006-06-12T00:00:00</td>\n",
       "      <td>2006-06-15T00:00:00</td>\n",
       "      <td>60</td>\n",
       "      <td>41.9611</td>\n",
       "      <td>-87.6996</td>\n",
       "      <td>{'latitude': '41.96113244107215', 'longitude':...</td>\n",
       "      <td>AAC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1980233-20090722</td>\n",
       "      <td>1980233</td>\n",
       "      <td>345008</td>\n",
       "      <td>1</td>\n",
       "      <td>DJS REMODELING</td>\n",
       "      <td>DJS REMODELING, INC.</td>\n",
       "      <td>1605  CLAVEY RD 1</td>\n",
       "      <td>HIGHLAND</td>\n",
       "      <td>IL</td>\n",
       "      <td>60035</td>\n",
       "      <td>...</td>\n",
       "      <td>2009-07-22T00:00:00</td>\n",
       "      <td>2011-07-15T00:00:00</td>\n",
       "      <td>2009-07-22T00:00:00</td>\n",
       "      <td>2009-07-22T00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AAI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1476582-20040211</td>\n",
       "      <td>1476582</td>\n",
       "      <td>273121</td>\n",
       "      <td>1</td>\n",
       "      <td>ALL-BRY CONSTRUCTION CO.</td>\n",
       "      <td>ALL-BRY CONSTRUCTION CO.</td>\n",
       "      <td>8  NORTH TRAIL</td>\n",
       "      <td>LEMONT</td>\n",
       "      <td>IL</td>\n",
       "      <td>60439</td>\n",
       "      <td>...</td>\n",
       "      <td>2004-02-11T00:00:00</td>\n",
       "      <td>2005-02-15T00:00:00</td>\n",
       "      <td>2004-02-10T00:00:00</td>\n",
       "      <td>2004-02-11T00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AAI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1141408-20080516</td>\n",
       "      <td>1896750</td>\n",
       "      <td>213785</td>\n",
       "      <td>1</td>\n",
       "      <td>MCDONOUGH MECHANICAL SERVICE</td>\n",
       "      <td>MCDONOUGH MECHANICAL SERVICE</td>\n",
       "      <td>4081  JOSEPH DR</td>\n",
       "      <td>WAUKEGAN</td>\n",
       "      <td>IL</td>\n",
       "      <td>60087</td>\n",
       "      <td>...</td>\n",
       "      <td>2008-05-16T00:00:00</td>\n",
       "      <td>2010-05-15T00:00:00</td>\n",
       "      <td>2008-06-04T00:00:00</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AAI</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>143136</th>\n",
       "      <td>1743194-20070816</td>\n",
       "      <td>1.83568e+06</td>\n",
       "      <td>266103</td>\n",
       "      <td>3</td>\n",
       "      <td>GOODHEALTH MEDICAL PRODUCTS, INC.</td>\n",
       "      <td>GOODHEALTH MEDICAL PRODUCTS, INC.</td>\n",
       "      <td>2621 W 79TH ST</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60652</td>\n",
       "      <td>...</td>\n",
       "      <td>2007-08-16T00:00:00</td>\n",
       "      <td>2009-11-15T00:00:00</td>\n",
       "      <td>2007-09-12T00:00:00</td>\n",
       "      <td>2007-09-18T00:00:00</td>\n",
       "      <td>2008-03-17T00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.7498</td>\n",
       "      <td>-87.6887</td>\n",
       "      <td>{'latitude': '41.74984206916219', 'longitude':...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143137</th>\n",
       "      <td>2032383-20160716</td>\n",
       "      <td>2.46596e+06</td>\n",
       "      <td>63376</td>\n",
       "      <td>180</td>\n",
       "      <td>INTERPARK LLC</td>\n",
       "      <td>INTERPARK</td>\n",
       "      <td>505 N STATE ST 2-4</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60654</td>\n",
       "      <td>...</td>\n",
       "      <td>2016-07-16T00:00:00</td>\n",
       "      <td>2018-07-15T00:00:00</td>\n",
       "      <td>2016-06-23T00:00:00</td>\n",
       "      <td>2016-08-11T00:00:00</td>\n",
       "      <td>2018-01-09T00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.8911</td>\n",
       "      <td>-87.6279</td>\n",
       "      <td>{'latitude': '41.89105524147196', 'longitude':...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143138</th>\n",
       "      <td>40376-20020216</td>\n",
       "      <td>1.2112e+06</td>\n",
       "      <td>55051</td>\n",
       "      <td>1</td>\n",
       "      <td>YUN HEE HWANG</td>\n",
       "      <td>TOP CLEANERS</td>\n",
       "      <td>1201 W ROSCOE ST 1ST</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60657</td>\n",
       "      <td>...</td>\n",
       "      <td>2002-02-16T00:00:00</td>\n",
       "      <td>2003-02-15T00:00:00</td>\n",
       "      <td>2002-01-18T00:00:00</td>\n",
       "      <td>2002-01-22T00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.9434</td>\n",
       "      <td>-87.6592</td>\n",
       "      <td>{'latitude': '41.94338183750669', 'longitude':...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143139</th>\n",
       "      <td>2064176-20121116</td>\n",
       "      <td>2.1885e+06</td>\n",
       "      <td>357381</td>\n",
       "      <td>1</td>\n",
       "      <td>D. C. A. TOWING INCORPORATED</td>\n",
       "      <td>D. C. A. TOWING INCORPORATED</td>\n",
       "      <td>7052 W 64TH ST 1ST</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60638</td>\n",
       "      <td>...</td>\n",
       "      <td>2012-11-16T00:00:00</td>\n",
       "      <td>2014-11-15T00:00:00</td>\n",
       "      <td>2012-12-25T00:00:00</td>\n",
       "      <td>2012-12-26T00:00:00</td>\n",
       "      <td>2012-12-29T00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.7757</td>\n",
       "      <td>-87.798</td>\n",
       "      <td>{'latitude': '41.775709631013335', 'longitude'...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143140</th>\n",
       "      <td>1954052-20090202</td>\n",
       "      <td>1.95405e+06</td>\n",
       "      <td>340610</td>\n",
       "      <td>1</td>\n",
       "      <td>maria t bedoy</td>\n",
       "      <td>mari's cleaning services</td>\n",
       "      <td>4544 S CHRISTIANA AVE 1</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60632</td>\n",
       "      <td>...</td>\n",
       "      <td>2009-02-02T00:00:00</td>\n",
       "      <td>2009-06-15T00:00:00</td>\n",
       "      <td>2009-01-31T00:00:00</td>\n",
       "      <td>2009-02-02T00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39</td>\n",
       "      <td>41.8105</td>\n",
       "      <td>-87.7079</td>\n",
       "      <td>{'latitude': '41.81052250782054', 'longitude':...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>143141 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      ID   LICENSE ID ACCOUNT NUMBER SITE NUMBER  \\\n",
       "0         35342-20020816      1256593          32811           1   \n",
       "1       1358463-20051116      1639294         262311          29   \n",
       "2       1980233-20090722      1980233         345008           1   \n",
       "3       1476582-20040211      1476582         273121           1   \n",
       "4       1141408-20080516      1896750         213785           1   \n",
       "...                  ...          ...            ...         ...   \n",
       "143136  1743194-20070816  1.83568e+06         266103           3   \n",
       "143137  2032383-20160716  2.46596e+06          63376         180   \n",
       "143138    40376-20020216   1.2112e+06          55051           1   \n",
       "143139  2064176-20121116   2.1885e+06         357381           1   \n",
       "143140  1954052-20090202  1.95405e+06         340610           1   \n",
       "\n",
       "                               LEGAL NAME             DOING BUSINESS AS NAME  \\\n",
       "0                            CARMEN CAHUE              CLAUDIA'S BRIDAL SHOP   \n",
       "1                     ISLA TROPICAL, INC.                      ISLA TROPICAL   \n",
       "2                          DJS REMODELING               DJS REMODELING, INC.   \n",
       "3                ALL-BRY CONSTRUCTION CO.           ALL-BRY CONSTRUCTION CO.   \n",
       "4            MCDONOUGH MECHANICAL SERVICE       MCDONOUGH MECHANICAL SERVICE   \n",
       "...                                   ...                                ...   \n",
       "143136  GOODHEALTH MEDICAL PRODUCTS, INC.  GOODHEALTH MEDICAL PRODUCTS, INC.   \n",
       "143137                      INTERPARK LLC                          INTERPARK   \n",
       "143138                      YUN HEE HWANG                       TOP CLEANERS   \n",
       "143139       D. C. A. TOWING INCORPORATED       D. C. A. TOWING INCORPORATED   \n",
       "143140                      maria t bedoy           mari's cleaning services   \n",
       "\n",
       "                           ADDRESS      CITY STATE ZIP CODE  ...  \\\n",
       "0       2625 S CENTRAL PARK AVE  1   CHICAGO    IL  60623.0  ...   \n",
       "1              2825 W MONTROSE AVE   CHICAGO    IL    60618  ...   \n",
       "2                1605  CLAVEY RD 1  HIGHLAND    IL    60035  ...   \n",
       "3                   8  NORTH TRAIL    LEMONT    IL    60439  ...   \n",
       "4                  4081  JOSEPH DR  WAUKEGAN    IL    60087  ...   \n",
       "...                            ...       ...   ...      ...  ...   \n",
       "143136              2621 W 79TH ST   CHICAGO    IL    60652  ...   \n",
       "143137          505 N STATE ST 2-4   CHICAGO    IL    60654  ...   \n",
       "143138        1201 W ROSCOE ST 1ST   CHICAGO    IL    60657  ...   \n",
       "143139          7052 W 64TH ST 1ST   CHICAGO    IL    60638  ...   \n",
       "143140     4544 S CHRISTIANA AVE 1   CHICAGO    IL    60632  ...   \n",
       "\n",
       "       LICENSE TERM START DATE LICENSE TERM EXPIRATION DATE  \\\n",
       "0          2002-08-16T00:00:00          2003-08-15T00:00:00   \n",
       "1          2005-11-16T00:00:00          2006-11-15T00:00:00   \n",
       "2          2009-07-22T00:00:00          2011-07-15T00:00:00   \n",
       "3          2004-02-11T00:00:00          2005-02-15T00:00:00   \n",
       "4          2008-05-16T00:00:00          2010-05-15T00:00:00   \n",
       "...                        ...                          ...   \n",
       "143136     2007-08-16T00:00:00          2009-11-15T00:00:00   \n",
       "143137     2016-07-16T00:00:00          2018-07-15T00:00:00   \n",
       "143138     2002-02-16T00:00:00          2003-02-15T00:00:00   \n",
       "143139     2012-11-16T00:00:00          2014-11-15T00:00:00   \n",
       "143140     2009-02-02T00:00:00          2009-06-15T00:00:00   \n",
       "\n",
       "       LICENSE APPROVED FOR ISSUANCE          DATE ISSUED  \\\n",
       "0                2002-08-21T00:00:00  2006-04-11T00:00:00   \n",
       "1                2006-04-05T00:00:00  2006-06-12T00:00:00   \n",
       "2                2009-07-22T00:00:00  2009-07-22T00:00:00   \n",
       "3                2004-02-10T00:00:00  2004-02-11T00:00:00   \n",
       "4                2008-06-04T00:00:00  2008-06-05T00:00:00   \n",
       "...                              ...                  ...   \n",
       "143136           2007-09-12T00:00:00  2007-09-18T00:00:00   \n",
       "143137           2016-06-23T00:00:00  2016-08-11T00:00:00   \n",
       "143138           2002-01-18T00:00:00  2002-01-22T00:00:00   \n",
       "143139           2012-12-25T00:00:00  2012-12-26T00:00:00   \n",
       "143140           2009-01-31T00:00:00  2009-02-02T00:00:00   \n",
       "\n",
       "       LICENSE STATUS CHANGE DATE  SSA LATITUDE LONGITUDE  \\\n",
       "0                             NaN   25  41.8436  -87.7146   \n",
       "1             2006-06-15T00:00:00   60  41.9611  -87.6996   \n",
       "2                             NaN  NaN      NaN       NaN   \n",
       "3                             NaN  NaN      NaN       NaN   \n",
       "4                             NaN  NaN      NaN       NaN   \n",
       "...                           ...  ...      ...       ...   \n",
       "143136        2008-03-17T00:00:00  NaN  41.7498  -87.6887   \n",
       "143137        2018-01-09T00:00:00  NaN  41.8911  -87.6279   \n",
       "143138                        NaN  NaN  41.9434  -87.6592   \n",
       "143139        2012-12-29T00:00:00  NaN  41.7757   -87.798   \n",
       "143140                        NaN   39  41.8105  -87.7079   \n",
       "\n",
       "                                                 LOCATION LICENSE STATUS  \n",
       "0       {'latitude': '41.843612879431845', 'longitude'...            AAI  \n",
       "1       {'latitude': '41.96113244107215', 'longitude':...            AAC  \n",
       "2                                                     NaN            AAI  \n",
       "3                                                     NaN            AAI  \n",
       "4                                                     NaN            AAI  \n",
       "...                                                   ...            ...  \n",
       "143136  {'latitude': '41.74984206916219', 'longitude':...            NaN  \n",
       "143137  {'latitude': '41.89105524147196', 'longitude':...            NaN  \n",
       "143138  {'latitude': '41.94338183750669', 'longitude':...            NaN  \n",
       "143139  {'latitude': '41.775709631013335', 'longitude'...            NaN  \n",
       "143140  {'latitude': '41.81052250782054', 'longitude':...            NaN  \n",
       "\n",
       "[143141 rows x 32 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(143141, inplace = True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Function to convert all the date time colums datetime format\n",
    "def convert_date(dates):\n",
    "    for i in dates:\n",
    "        df[i] = pd.to_datetime(df[i], errors = 'coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = ['APPLICATION REQUIREMENTS COMPLETE', 'PAYMENT DATE', 'LICENSE TERM EXPIRATION DATE', 'LICENSE TERM START DATE','DATE ISSUED', 'LICENSE APPROVED FOR ISSUANCE', 'LICENSE STATUS CHANGE DATE']\n",
    "convert_date(dates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Function to extract Year\n",
    "def year(newc, c):\n",
    "    df[newc] = df[c].apply(lambda x:x.year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "year('PAYMENT YEAR', 'PAYMENT DATE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         2002.0\n",
       "1         2005.0\n",
       "2         2009.0\n",
       "3         2004.0\n",
       "4         2008.0\n",
       "           ...  \n",
       "143136    2007.0\n",
       "143137    2016.0\n",
       "143138    2002.0\n",
       "143139    2012.0\n",
       "143140    2009.0\n",
       "Name: PAYMENT YEAR, Length: 143141, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['PAYMENT YEAR']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Function to extract Month\n",
    "def month(newc, c):\n",
    "    df[newc] = df[c].apply(lambda x:x.month)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "month('PAYMENT MONTH', 'PAYMENT DATE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          8.0\n",
       "1         11.0\n",
       "2          7.0\n",
       "3          2.0\n",
       "4          6.0\n",
       "          ... \n",
       "143136     9.0\n",
       "143137     6.0\n",
       "143138     1.0\n",
       "143139    12.0\n",
       "143140     1.0\n",
       "Name: PAYMENT MONTH, Length: 143141, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['PAYMENT MONTH']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "year('APPLICATION REQUIREMENTS COMPLETE YEAR', 'APPLICATION REQUIREMENTS COMPLETE')\n",
    "year('DATE ISSUED YEAR', 'DATE ISSUED')\n",
    "year('LICENSE TERM START YEAR', 'LICENSE TERM START DATE')\n",
    "year('LICENSE TERM EXPIRATION YEAR', 'LICENSE TERM EXPIRATION DATE')\n",
    "year('LICENSE APPROVED YEAR', 'LICENSE APPROVED FOR ISSUANCE')\n",
    "year('LICENSE STATUS CHANGE YEAR', 'LICENSE STATUS CHANGE DATE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>ID</th>\n",
       "      <th>LICENSE ID</th>\n",
       "      <th>ACCOUNT NUMBER</th>\n",
       "      <th>SITE NUMBER</th>\n",
       "      <th>LEGAL NAME</th>\n",
       "      <th>DOING BUSINESS AS NAME</th>\n",
       "      <th>ADDRESS</th>\n",
       "      <th>CITY</th>\n",
       "      <th>STATE</th>\n",
       "      <th>ZIP CODE</th>\n",
       "      <th>...</th>\n",
       "      <th>LOCATION</th>\n",
       "      <th>LICENSE STATUS</th>\n",
       "      <th>PAYMENT YEAR</th>\n",
       "      <th>PAYMENT MONTH</th>\n",
       "      <th>APPLICATION REQUIREMENTS COMPLETE YEAR</th>\n",
       "      <th>DATE ISSUED YEAR</th>\n",
       "      <th>LICENSE TERM START YEAR</th>\n",
       "      <th>LICENSE TERM EXPIRATION YEAR</th>\n",
       "      <th>LICENSE APPROVED YEAR</th>\n",
       "      <th>LICENSE STATUS CHANGE YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>35342-20020816</td>\n",
       "      <td>1256593</td>\n",
       "      <td>32811</td>\n",
       "      <td>1</td>\n",
       "      <td>CARMEN CAHUE</td>\n",
       "      <td>CLAUDIA'S BRIDAL SHOP</td>\n",
       "      <td>2625 S CENTRAL PARK AVE  1</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60623.0</td>\n",
       "      <td>...</td>\n",
       "      <td>{'latitude': '41.843612879431845', 'longitude'...</td>\n",
       "      <td>AAI</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1358463-20051116</td>\n",
       "      <td>1639294</td>\n",
       "      <td>262311</td>\n",
       "      <td>29</td>\n",
       "      <td>ISLA TROPICAL, INC.</td>\n",
       "      <td>ISLA TROPICAL</td>\n",
       "      <td>2825 W MONTROSE AVE</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60618</td>\n",
       "      <td>...</td>\n",
       "      <td>{'latitude': '41.96113244107215', 'longitude':...</td>\n",
       "      <td>AAC</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>2006.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1980233-20090722</td>\n",
       "      <td>1980233</td>\n",
       "      <td>345008</td>\n",
       "      <td>1</td>\n",
       "      <td>DJS REMODELING</td>\n",
       "      <td>DJS REMODELING, INC.</td>\n",
       "      <td>1605  CLAVEY RD 1</td>\n",
       "      <td>HIGHLAND</td>\n",
       "      <td>IL</td>\n",
       "      <td>60035</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AAI</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1476582-20040211</td>\n",
       "      <td>1476582</td>\n",
       "      <td>273121</td>\n",
       "      <td>1</td>\n",
       "      <td>ALL-BRY CONSTRUCTION CO.</td>\n",
       "      <td>ALL-BRY CONSTRUCTION CO.</td>\n",
       "      <td>8  NORTH TRAIL</td>\n",
       "      <td>LEMONT</td>\n",
       "      <td>IL</td>\n",
       "      <td>60439</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AAI</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1141408-20080516</td>\n",
       "      <td>1896750</td>\n",
       "      <td>213785</td>\n",
       "      <td>1</td>\n",
       "      <td>MCDONOUGH MECHANICAL SERVICE</td>\n",
       "      <td>MCDONOUGH MECHANICAL SERVICE</td>\n",
       "      <td>4081  JOSEPH DR</td>\n",
       "      <td>WAUKEGAN</td>\n",
       "      <td>IL</td>\n",
       "      <td>60087</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AAI</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>NaN</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>143136</th>\n",
       "      <td>1743194-20070816</td>\n",
       "      <td>1.83568e+06</td>\n",
       "      <td>266103</td>\n",
       "      <td>3</td>\n",
       "      <td>GOODHEALTH MEDICAL PRODUCTS, INC.</td>\n",
       "      <td>GOODHEALTH MEDICAL PRODUCTS, INC.</td>\n",
       "      <td>2621 W 79TH ST</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60652</td>\n",
       "      <td>...</td>\n",
       "      <td>{'latitude': '41.74984206916219', 'longitude':...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>2008.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143137</th>\n",
       "      <td>2032383-20160716</td>\n",
       "      <td>2.46596e+06</td>\n",
       "      <td>63376</td>\n",
       "      <td>180</td>\n",
       "      <td>INTERPARK LLC</td>\n",
       "      <td>INTERPARK</td>\n",
       "      <td>505 N STATE ST 2-4</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60654</td>\n",
       "      <td>...</td>\n",
       "      <td>{'latitude': '41.89105524147196', 'longitude':...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>2018.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143138</th>\n",
       "      <td>40376-20020216</td>\n",
       "      <td>1.2112e+06</td>\n",
       "      <td>55051</td>\n",
       "      <td>1</td>\n",
       "      <td>YUN HEE HWANG</td>\n",
       "      <td>TOP CLEANERS</td>\n",
       "      <td>1201 W ROSCOE ST 1ST</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60657</td>\n",
       "      <td>...</td>\n",
       "      <td>{'latitude': '41.94338183750669', 'longitude':...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143139</th>\n",
       "      <td>2064176-20121116</td>\n",
       "      <td>2.1885e+06</td>\n",
       "      <td>357381</td>\n",
       "      <td>1</td>\n",
       "      <td>D. C. A. TOWING INCORPORATED</td>\n",
       "      <td>D. C. A. TOWING INCORPORATED</td>\n",
       "      <td>7052 W 64TH ST 1ST</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60638</td>\n",
       "      <td>...</td>\n",
       "      <td>{'latitude': '41.775709631013335', 'longitude'...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143140</th>\n",
       "      <td>1954052-20090202</td>\n",
       "      <td>1.95405e+06</td>\n",
       "      <td>340610</td>\n",
       "      <td>1</td>\n",
       "      <td>maria t bedoy</td>\n",
       "      <td>mari's cleaning services</td>\n",
       "      <td>4544 S CHRISTIANA AVE 1</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60632</td>\n",
       "      <td>...</td>\n",
       "      <td>{'latitude': '41.81052250782054', 'longitude':...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>143141 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      ID   LICENSE ID ACCOUNT NUMBER SITE NUMBER  \\\n",
       "0         35342-20020816      1256593          32811           1   \n",
       "1       1358463-20051116      1639294         262311          29   \n",
       "2       1980233-20090722      1980233         345008           1   \n",
       "3       1476582-20040211      1476582         273121           1   \n",
       "4       1141408-20080516      1896750         213785           1   \n",
       "...                  ...          ...            ...         ...   \n",
       "143136  1743194-20070816  1.83568e+06         266103           3   \n",
       "143137  2032383-20160716  2.46596e+06          63376         180   \n",
       "143138    40376-20020216   1.2112e+06          55051           1   \n",
       "143139  2064176-20121116   2.1885e+06         357381           1   \n",
       "143140  1954052-20090202  1.95405e+06         340610           1   \n",
       "\n",
       "                               LEGAL NAME             DOING BUSINESS AS NAME  \\\n",
       "0                            CARMEN CAHUE              CLAUDIA'S BRIDAL SHOP   \n",
       "1                     ISLA TROPICAL, INC.                      ISLA TROPICAL   \n",
       "2                          DJS REMODELING               DJS REMODELING, INC.   \n",
       "3                ALL-BRY CONSTRUCTION CO.           ALL-BRY CONSTRUCTION CO.   \n",
       "4            MCDONOUGH MECHANICAL SERVICE       MCDONOUGH MECHANICAL SERVICE   \n",
       "...                                   ...                                ...   \n",
       "143136  GOODHEALTH MEDICAL PRODUCTS, INC.  GOODHEALTH MEDICAL PRODUCTS, INC.   \n",
       "143137                      INTERPARK LLC                          INTERPARK   \n",
       "143138                      YUN HEE HWANG                       TOP CLEANERS   \n",
       "143139       D. C. A. TOWING INCORPORATED       D. C. A. TOWING INCORPORATED   \n",
       "143140                      maria t bedoy           mari's cleaning services   \n",
       "\n",
       "                           ADDRESS      CITY STATE ZIP CODE  ...  \\\n",
       "0       2625 S CENTRAL PARK AVE  1   CHICAGO    IL  60623.0  ...   \n",
       "1              2825 W MONTROSE AVE   CHICAGO    IL    60618  ...   \n",
       "2                1605  CLAVEY RD 1  HIGHLAND    IL    60035  ...   \n",
       "3                   8  NORTH TRAIL    LEMONT    IL    60439  ...   \n",
       "4                  4081  JOSEPH DR  WAUKEGAN    IL    60087  ...   \n",
       "...                            ...       ...   ...      ...  ...   \n",
       "143136              2621 W 79TH ST   CHICAGO    IL    60652  ...   \n",
       "143137          505 N STATE ST 2-4   CHICAGO    IL    60654  ...   \n",
       "143138        1201 W ROSCOE ST 1ST   CHICAGO    IL    60657  ...   \n",
       "143139          7052 W 64TH ST 1ST   CHICAGO    IL    60638  ...   \n",
       "143140     4544 S CHRISTIANA AVE 1   CHICAGO    IL    60632  ...   \n",
       "\n",
       "                                                 LOCATION LICENSE STATUS  \\\n",
       "0       {'latitude': '41.843612879431845', 'longitude'...            AAI   \n",
       "1       {'latitude': '41.96113244107215', 'longitude':...            AAC   \n",
       "2                                                     NaN            AAI   \n",
       "3                                                     NaN            AAI   \n",
       "4                                                     NaN            AAI   \n",
       "...                                                   ...            ...   \n",
       "143136  {'latitude': '41.74984206916219', 'longitude':...            NaN   \n",
       "143137  {'latitude': '41.89105524147196', 'longitude':...            NaN   \n",
       "143138  {'latitude': '41.94338183750669', 'longitude':...            NaN   \n",
       "143139  {'latitude': '41.775709631013335', 'longitude'...            NaN   \n",
       "143140  {'latitude': '41.81052250782054', 'longitude':...            NaN   \n",
       "\n",
       "       PAYMENT YEAR PAYMENT MONTH APPLICATION REQUIREMENTS COMPLETE YEAR  \\\n",
       "0            2002.0           8.0                                 2002.0   \n",
       "1            2005.0          11.0                                 2005.0   \n",
       "2            2009.0           7.0                                 2009.0   \n",
       "3            2004.0           2.0                                 2004.0   \n",
       "4            2008.0           6.0                                 2008.0   \n",
       "...             ...           ...                                    ...   \n",
       "143136       2007.0           9.0                                 2007.0   \n",
       "143137       2016.0           6.0                                 2016.0   \n",
       "143138       2002.0           1.0                                 2001.0   \n",
       "143139       2012.0          12.0                                 2012.0   \n",
       "143140       2009.0           1.0                                 2009.0   \n",
       "\n",
       "       DATE ISSUED YEAR LICENSE TERM START YEAR LICENSE TERM EXPIRATION YEAR  \\\n",
       "0                2006.0                  2002.0                       2003.0   \n",
       "1                2006.0                  2005.0                       2006.0   \n",
       "2                2009.0                  2009.0                       2011.0   \n",
       "3                2004.0                  2004.0                       2005.0   \n",
       "4                2008.0                  2008.0                       2010.0   \n",
       "...                 ...                     ...                          ...   \n",
       "143136           2007.0                  2007.0                       2009.0   \n",
       "143137           2016.0                  2016.0                       2018.0   \n",
       "143138           2002.0                  2002.0                       2003.0   \n",
       "143139           2012.0                  2012.0                       2014.0   \n",
       "143140           2009.0                  2009.0                       2009.0   \n",
       "\n",
       "       LICENSE APPROVED YEAR LICENSE STATUS CHANGE YEAR  \n",
       "0                     2002.0                        NaN  \n",
       "1                     2006.0                     2006.0  \n",
       "2                     2009.0                        NaN  \n",
       "3                     2004.0                        NaN  \n",
       "4                     2008.0                        NaN  \n",
       "...                      ...                        ...  \n",
       "143136                2007.0                     2008.0  \n",
       "143137                2016.0                     2018.0  \n",
       "143138                2002.0                        NaN  \n",
       "143139                2012.0                     2012.0  \n",
       "143140                2009.0                        NaN  \n",
       "\n",
       "[143141 rows x 40 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                                             2\n",
       "LICENSE ID                                     0\n",
       "ACCOUNT NUMBER                                 6\n",
       "SITE NUMBER                                    6\n",
       "LEGAL NAME                                     6\n",
       "DOING BUSINESS AS NAME                         9\n",
       "ADDRESS                                        6\n",
       "CITY                                           6\n",
       "STATE                                          6\n",
       "ZIP CODE                                      49\n",
       "WARD                                       50333\n",
       "PRECINCT                                   58285\n",
       "WARD PRECINCT                              50322\n",
       "POLICE DISTRICT                            55493\n",
       "LICENSE CODE                                   6\n",
       "LICENSE DESCRIPTION                            6\n",
       "LICENSE NUMBER                                 7\n",
       "APPLICATION TYPE                               6\n",
       "APPLICATION CREATED DATE                  109253\n",
       "APPLICATION REQUIREMENTS COMPLETE            649\n",
       "PAYMENT DATE                                2347\n",
       "CONDITIONAL APPROVAL                           6\n",
       "LICENSE TERM START DATE                      353\n",
       "LICENSE TERM EXPIRATION DATE                  31\n",
       "LICENSE APPROVED FOR ISSUANCE               9175\n",
       "DATE ISSUED                                    7\n",
       "LICENSE STATUS CHANGE DATE                 78653\n",
       "SSA                                       117783\n",
       "LATITUDE                                   48162\n",
       "LONGITUDE                                  48162\n",
       "LOCATION                                   48162\n",
       "LICENSE STATUS                             57246\n",
       "PAYMENT YEAR                                2347\n",
       "PAYMENT MONTH                               2347\n",
       "APPLICATION REQUIREMENTS COMPLETE YEAR       649\n",
       "DATE ISSUED YEAR                               7\n",
       "LICENSE TERM START YEAR                      353\n",
       "LICENSE TERM EXPIRATION YEAR                  31\n",
       "LICENSE APPROVED YEAR                       9175\n",
       "LICENSE STATUS CHANGE YEAR                 78653\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85899</th>\n",
       "      <td>1802721-20110616</td>\n",
       "      <td>REV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85900</th>\n",
       "      <td>2048542-20120816</td>\n",
       "      <td>IN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     ID      LICENSE ID ACCOUNT NUMBER SITE NUMBER LEGAL NAME  \\\n",
       "85895               NaN  LICENSE STATUS            NaN         NaN        NaN   \n",
       "85896  1985799-20091116             AAC            NaN         NaN        NaN   \n",
       "85897  1932144-20090416             AAI            NaN         NaN        NaN   \n",
       "85898    71173-20100816             REV            NaN         NaN        NaN   \n",
       "85899  1802721-20110616             REV            NaN         NaN        NaN   \n",
       "85900  2048542-20120816              IN            NaN         NaN        NaN   \n",
       "\n",
       "      DOING BUSINESS AS NAME ADDRESS CITY STATE ZIP CODE  ... LOCATION  \\\n",
       "85895                    NaN     NaN  NaN   NaN      NaN  ...      NaN   \n",
       "85896                    NaN     NaN  NaN   NaN      NaN  ...      NaN   \n",
       "85897                    NaN     NaN  NaN   NaN      NaN  ...      NaN   \n",
       "85898                    NaN     NaN  NaN   NaN      NaN  ...      NaN   \n",
       "85899                    NaN     NaN  NaN   NaN      NaN  ...      NaN   \n",
       "85900                    NaN     NaN  NaN   NaN      NaN  ...      NaN   \n",
       "\n",
       "      LICENSE STATUS PAYMENT YEAR PAYMENT MONTH  \\\n",
       "85895            NaN          NaN           NaN   \n",
       "85896            NaN          NaN           NaN   \n",
       "85897            NaN          NaN           NaN   \n",
       "85898            NaN          NaN           NaN   \n",
       "85899            NaN          NaN           NaN   \n",
       "85900            NaN          NaN           NaN   \n",
       "\n",
       "      APPLICATION REQUIREMENTS COMPLETE YEAR DATE ISSUED YEAR  \\\n",
       "85895                                    NaN              NaN   \n",
       "85896                                    NaN              NaN   \n",
       "85897                                    NaN              NaN   \n",
       "85898                                    NaN              NaN   \n",
       "85899                                    NaN              NaN   \n",
       "85900                                    NaN              NaN   \n",
       "\n",
       "      LICENSE TERM START YEAR LICENSE TERM EXPIRATION YEAR  \\\n",
       "85895                     NaN                          NaN   \n",
       "85896                     NaN                          NaN   \n",
       "85897                     NaN                          NaN   \n",
       "85898                     NaN                          NaN   \n",
       "85899                     NaN                          NaN   \n",
       "85900                     NaN                          NaN   \n",
       "\n",
       "      LICENSE APPROVED YEAR LICENSE STATUS CHANGE YEAR  \n",
       "85895                   NaN                        NaN  \n",
       "85896                   NaN                        NaN  \n",
       "85897                   NaN                        NaN  \n",
       "85898                   NaN                        NaN  \n",
       "85899                   NaN                        NaN  \n",
       "85900                   NaN                        NaN  \n",
       "\n",
       "[6 rows x 40 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['CITY'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.dropna( how='any',subset=['CITY'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>LICENSE ID</th>\n",
       "      <th>ACCOUNT NUMBER</th>\n",
       "      <th>SITE NUMBER</th>\n",
       "      <th>LEGAL NAME</th>\n",
       "      <th>DOING BUSINESS AS NAME</th>\n",
       "      <th>ADDRESS</th>\n",
       "      <th>CITY</th>\n",
       "      <th>STATE</th>\n",
       "      <th>ZIP CODE</th>\n",
       "      <th>...</th>\n",
       "      <th>LOCATION</th>\n",
       "      <th>LICENSE STATUS</th>\n",
       "      <th>PAYMENT YEAR</th>\n",
       "      <th>PAYMENT MONTH</th>\n",
       "      <th>APPLICATION REQUIREMENTS COMPLETE YEAR</th>\n",
       "      <th>DATE ISSUED YEAR</th>\n",
       "      <th>LICENSE TERM START YEAR</th>\n",
       "      <th>LICENSE TERM EXPIRATION YEAR</th>\n",
       "      <th>LICENSE APPROVED YEAR</th>\n",
       "      <th>LICENSE STATUS CHANGE YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>35342-20020816</td>\n",
       "      <td>1256593</td>\n",
       "      <td>32811</td>\n",
       "      <td>1</td>\n",
       "      <td>CARMEN CAHUE</td>\n",
       "      <td>CLAUDIA'S BRIDAL SHOP</td>\n",
       "      <td>2625 S CENTRAL PARK AVE  1</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60623.0</td>\n",
       "      <td>...</td>\n",
       "      <td>{'latitude': '41.843612879431845', 'longitude'...</td>\n",
       "      <td>AAI</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1358463-20051116</td>\n",
       "      <td>1639294</td>\n",
       "      <td>262311</td>\n",
       "      <td>29</td>\n",
       "      <td>ISLA TROPICAL, INC.</td>\n",
       "      <td>ISLA TROPICAL</td>\n",
       "      <td>2825 W MONTROSE AVE</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60618</td>\n",
       "      <td>...</td>\n",
       "      <td>{'latitude': '41.96113244107215', 'longitude':...</td>\n",
       "      <td>AAC</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>2006.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1980233-20090722</td>\n",
       "      <td>1980233</td>\n",
       "      <td>345008</td>\n",
       "      <td>1</td>\n",
       "      <td>DJS REMODELING</td>\n",
       "      <td>DJS REMODELING, INC.</td>\n",
       "      <td>1605  CLAVEY RD 1</td>\n",
       "      <td>HIGHLAND</td>\n",
       "      <td>IL</td>\n",
       "      <td>60035</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AAI</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1476582-20040211</td>\n",
       "      <td>1476582</td>\n",
       "      <td>273121</td>\n",
       "      <td>1</td>\n",
       "      <td>ALL-BRY CONSTRUCTION CO.</td>\n",
       "      <td>ALL-BRY CONSTRUCTION CO.</td>\n",
       "      <td>8  NORTH TRAIL</td>\n",
       "      <td>LEMONT</td>\n",
       "      <td>IL</td>\n",
       "      <td>60439</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AAI</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1141408-20080516</td>\n",
       "      <td>1896750</td>\n",
       "      <td>213785</td>\n",
       "      <td>1</td>\n",
       "      <td>MCDONOUGH MECHANICAL SERVICE</td>\n",
       "      <td>MCDONOUGH MECHANICAL SERVICE</td>\n",
       "      <td>4081  JOSEPH DR</td>\n",
       "      <td>WAUKEGAN</td>\n",
       "      <td>IL</td>\n",
       "      <td>60087</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AAI</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>NaN</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>143136</th>\n",
       "      <td>1743194-20070816</td>\n",
       "      <td>1.83568e+06</td>\n",
       "      <td>266103</td>\n",
       "      <td>3</td>\n",
       "      <td>GOODHEALTH MEDICAL PRODUCTS, INC.</td>\n",
       "      <td>GOODHEALTH MEDICAL PRODUCTS, INC.</td>\n",
       "      <td>2621 W 79TH ST</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60652</td>\n",
       "      <td>...</td>\n",
       "      <td>{'latitude': '41.74984206916219', 'longitude':...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>2008.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143137</th>\n",
       "      <td>2032383-20160716</td>\n",
       "      <td>2.46596e+06</td>\n",
       "      <td>63376</td>\n",
       "      <td>180</td>\n",
       "      <td>INTERPARK LLC</td>\n",
       "      <td>INTERPARK</td>\n",
       "      <td>505 N STATE ST 2-4</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60654</td>\n",
       "      <td>...</td>\n",
       "      <td>{'latitude': '41.89105524147196', 'longitude':...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>2018.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143138</th>\n",
       "      <td>40376-20020216</td>\n",
       "      <td>1.2112e+06</td>\n",
       "      <td>55051</td>\n",
       "      <td>1</td>\n",
       "      <td>YUN HEE HWANG</td>\n",
       "      <td>TOP CLEANERS</td>\n",
       "      <td>1201 W ROSCOE ST 1ST</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60657</td>\n",
       "      <td>...</td>\n",
       "      <td>{'latitude': '41.94338183750669', 'longitude':...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143139</th>\n",
       "      <td>2064176-20121116</td>\n",
       "      <td>2.1885e+06</td>\n",
       "      <td>357381</td>\n",
       "      <td>1</td>\n",
       "      <td>D. C. A. TOWING INCORPORATED</td>\n",
       "      <td>D. C. A. TOWING INCORPORATED</td>\n",
       "      <td>7052 W 64TH ST 1ST</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60638</td>\n",
       "      <td>...</td>\n",
       "      <td>{'latitude': '41.775709631013335', 'longitude'...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143140</th>\n",
       "      <td>1954052-20090202</td>\n",
       "      <td>1.95405e+06</td>\n",
       "      <td>340610</td>\n",
       "      <td>1</td>\n",
       "      <td>maria t bedoy</td>\n",
       "      <td>mari's cleaning services</td>\n",
       "      <td>4544 S CHRISTIANA AVE 1</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60632</td>\n",
       "      <td>...</td>\n",
       "      <td>{'latitude': '41.81052250782054', 'longitude':...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>143135 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      ID   LICENSE ID ACCOUNT NUMBER SITE NUMBER  \\\n",
       "0         35342-20020816      1256593          32811           1   \n",
       "1       1358463-20051116      1639294         262311          29   \n",
       "2       1980233-20090722      1980233         345008           1   \n",
       "3       1476582-20040211      1476582         273121           1   \n",
       "4       1141408-20080516      1896750         213785           1   \n",
       "...                  ...          ...            ...         ...   \n",
       "143136  1743194-20070816  1.83568e+06         266103           3   \n",
       "143137  2032383-20160716  2.46596e+06          63376         180   \n",
       "143138    40376-20020216   1.2112e+06          55051           1   \n",
       "143139  2064176-20121116   2.1885e+06         357381           1   \n",
       "143140  1954052-20090202  1.95405e+06         340610           1   \n",
       "\n",
       "                               LEGAL NAME             DOING BUSINESS AS NAME  \\\n",
       "0                            CARMEN CAHUE              CLAUDIA'S BRIDAL SHOP   \n",
       "1                     ISLA TROPICAL, INC.                      ISLA TROPICAL   \n",
       "2                          DJS REMODELING               DJS REMODELING, INC.   \n",
       "3                ALL-BRY CONSTRUCTION CO.           ALL-BRY CONSTRUCTION CO.   \n",
       "4            MCDONOUGH MECHANICAL SERVICE       MCDONOUGH MECHANICAL SERVICE   \n",
       "...                                   ...                                ...   \n",
       "143136  GOODHEALTH MEDICAL PRODUCTS, INC.  GOODHEALTH MEDICAL PRODUCTS, INC.   \n",
       "143137                      INTERPARK LLC                          INTERPARK   \n",
       "143138                      YUN HEE HWANG                       TOP CLEANERS   \n",
       "143139       D. C. A. TOWING INCORPORATED       D. C. A. TOWING INCORPORATED   \n",
       "143140                      maria t bedoy           mari's cleaning services   \n",
       "\n",
       "                           ADDRESS      CITY STATE ZIP CODE  ...  \\\n",
       "0       2625 S CENTRAL PARK AVE  1   CHICAGO    IL  60623.0  ...   \n",
       "1              2825 W MONTROSE AVE   CHICAGO    IL    60618  ...   \n",
       "2                1605  CLAVEY RD 1  HIGHLAND    IL    60035  ...   \n",
       "3                   8  NORTH TRAIL    LEMONT    IL    60439  ...   \n",
       "4                  4081  JOSEPH DR  WAUKEGAN    IL    60087  ...   \n",
       "...                            ...       ...   ...      ...  ...   \n",
       "143136              2621 W 79TH ST   CHICAGO    IL    60652  ...   \n",
       "143137          505 N STATE ST 2-4   CHICAGO    IL    60654  ...   \n",
       "143138        1201 W ROSCOE ST 1ST   CHICAGO    IL    60657  ...   \n",
       "143139          7052 W 64TH ST 1ST   CHICAGO    IL    60638  ...   \n",
       "143140     4544 S CHRISTIANA AVE 1   CHICAGO    IL    60632  ...   \n",
       "\n",
       "                                                 LOCATION LICENSE STATUS  \\\n",
       "0       {'latitude': '41.843612879431845', 'longitude'...            AAI   \n",
       "1       {'latitude': '41.96113244107215', 'longitude':...            AAC   \n",
       "2                                                     NaN            AAI   \n",
       "3                                                     NaN            AAI   \n",
       "4                                                     NaN            AAI   \n",
       "...                                                   ...            ...   \n",
       "143136  {'latitude': '41.74984206916219', 'longitude':...            NaN   \n",
       "143137  {'latitude': '41.89105524147196', 'longitude':...            NaN   \n",
       "143138  {'latitude': '41.94338183750669', 'longitude':...            NaN   \n",
       "143139  {'latitude': '41.775709631013335', 'longitude'...            NaN   \n",
       "143140  {'latitude': '41.81052250782054', 'longitude':...            NaN   \n",
       "\n",
       "       PAYMENT YEAR PAYMENT MONTH APPLICATION REQUIREMENTS COMPLETE YEAR  \\\n",
       "0            2002.0           8.0                                 2002.0   \n",
       "1            2005.0          11.0                                 2005.0   \n",
       "2            2009.0           7.0                                 2009.0   \n",
       "3            2004.0           2.0                                 2004.0   \n",
       "4            2008.0           6.0                                 2008.0   \n",
       "...             ...           ...                                    ...   \n",
       "143136       2007.0           9.0                                 2007.0   \n",
       "143137       2016.0           6.0                                 2016.0   \n",
       "143138       2002.0           1.0                                 2001.0   \n",
       "143139       2012.0          12.0                                 2012.0   \n",
       "143140       2009.0           1.0                                 2009.0   \n",
       "\n",
       "       DATE ISSUED YEAR LICENSE TERM START YEAR LICENSE TERM EXPIRATION YEAR  \\\n",
       "0                2006.0                  2002.0                       2003.0   \n",
       "1                2006.0                  2005.0                       2006.0   \n",
       "2                2009.0                  2009.0                       2011.0   \n",
       "3                2004.0                  2004.0                       2005.0   \n",
       "4                2008.0                  2008.0                       2010.0   \n",
       "...                 ...                     ...                          ...   \n",
       "143136           2007.0                  2007.0                       2009.0   \n",
       "143137           2016.0                  2016.0                       2018.0   \n",
       "143138           2002.0                  2002.0                       2003.0   \n",
       "143139           2012.0                  2012.0                       2014.0   \n",
       "143140           2009.0                  2009.0                       2009.0   \n",
       "\n",
       "       LICENSE APPROVED YEAR LICENSE STATUS CHANGE YEAR  \n",
       "0                     2002.0                        NaN  \n",
       "1                     2006.0                     2006.0  \n",
       "2                     2009.0                        NaN  \n",
       "3                     2004.0                        NaN  \n",
       "4                     2008.0                        NaN  \n",
       "...                      ...                        ...  \n",
       "143136                2007.0                     2008.0  \n",
       "143137                2016.0                     2018.0  \n",
       "143138                2002.0                        NaN  \n",
       "143139                2012.0                     2012.0  \n",
       "143140                2009.0                        NaN  \n",
       "\n",
       "[143135 rows x 40 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                                             1\n",
       "LICENSE ID                                     0\n",
       "ACCOUNT NUMBER                                 0\n",
       "SITE NUMBER                                    0\n",
       "LEGAL NAME                                     0\n",
       "DOING BUSINESS AS NAME                         3\n",
       "ADDRESS                                        0\n",
       "CITY                                           0\n",
       "STATE                                          0\n",
       "ZIP CODE                                      43\n",
       "WARD                                       50327\n",
       "PRECINCT                                   58279\n",
       "WARD PRECINCT                              50316\n",
       "POLICE DISTRICT                            55487\n",
       "LICENSE CODE                                   0\n",
       "LICENSE DESCRIPTION                            0\n",
       "LICENSE NUMBER                                 1\n",
       "APPLICATION TYPE                               0\n",
       "APPLICATION CREATED DATE                  109247\n",
       "APPLICATION REQUIREMENTS COMPLETE            643\n",
       "PAYMENT DATE                                2341\n",
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       "LICENSE TERM START DATE                      347\n",
       "LICENSE TERM EXPIRATION DATE                  25\n",
       "LICENSE APPROVED FOR ISSUANCE               9169\n",
       "DATE ISSUED                                    1\n",
       "LICENSE STATUS CHANGE DATE                 78647\n",
       "SSA                                       117777\n",
       "LATITUDE                                   48156\n",
       "LONGITUDE                                  48156\n",
       "LOCATION                                   48156\n",
       "LICENSE STATUS                             57240\n",
       "PAYMENT YEAR                                2341\n",
       "PAYMENT MONTH                               2341\n",
       "APPLICATION REQUIREMENTS COMPLETE YEAR       643\n",
       "DATE ISSUED YEAR                               1\n",
       "LICENSE TERM START YEAR                      347\n",
       "LICENSE TERM EXPIRATION YEAR                  25\n",
       "LICENSE APPROVED YEAR                       9169\n",
       "LICENSE STATUS CHANGE YEAR                 78647\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                                        143134\n",
       "LICENSE ID                                143135\n",
       "ACCOUNT NUMBER                             74318\n",
       "SITE NUMBER                                  447\n",
       "LEGAL NAME                                 66063\n",
       "DOING BUSINESS AS NAME                     72233\n",
       "ADDRESS                                    72885\n",
       "CITY                                        1312\n",
       "STATE                                         56\n",
       "ZIP CODE                                    2419\n",
       "WARD                                         101\n",
       "PRECINCT                                     152\n",
       "WARD PRECINCT                               2525\n",
       "POLICE DISTRICT                               53\n",
       "LICENSE CODE                                 238\n",
       "LICENSE DESCRIPTION                          130\n",
       "LICENSE NUMBER                             99947\n",
       "APPLICATION TYPE                               7\n",
       "APPLICATION CREATED DATE                    3958\n",
       "APPLICATION REQUIREMENTS COMPLETE           4198\n",
       "PAYMENT DATE                                5250\n",
       "CONDITIONAL APPROVAL                           3\n",
       "LICENSE TERM START DATE                     3914\n",
       "LICENSE TERM EXPIRATION DATE                 480\n",
       "LICENSE APPROVED FOR ISSUANCE               5111\n",
       "DATE ISSUED                                 4063\n",
       "LICENSE STATUS CHANGE DATE                  4104\n",
       "SSA                                          107\n",
       "LATITUDE                                   46664\n",
       "LONGITUDE                                  46667\n",
       "LOCATION                                   39752\n",
       "LICENSE STATUS                                 5\n",
       "PAYMENT YEAR                                  19\n",
       "PAYMENT MONTH                                 12\n",
       "APPLICATION REQUIREMENTS COMPLETE YEAR        19\n",
       "DATE ISSUED YEAR                              17\n",
       "LICENSE TERM START YEAR                       24\n",
       "LICENSE TERM EXPIRATION YEAR                  27\n",
       "LICENSE APPROVED YEAR                         19\n",
       "LICENSE STATUS CHANGE YEAR                    17\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                                        143134\n",
       "LICENSE ID                                143135\n",
       "ACCOUNT NUMBER                            143135\n",
       "SITE NUMBER                               143135\n",
       "LEGAL NAME                                143135\n",
       "DOING BUSINESS AS NAME                    143132\n",
       "ADDRESS                                   143135\n",
       "CITY                                      143135\n",
       "STATE                                     143135\n",
       "ZIP CODE                                  143092\n",
       "WARD                                       92808\n",
       "PRECINCT                                   84856\n",
       "WARD PRECINCT                              92819\n",
       "POLICE DISTRICT                            87648\n",
       "LICENSE CODE                              143135\n",
       "LICENSE DESCRIPTION                       143135\n",
       "LICENSE NUMBER                            143134\n",
       "APPLICATION TYPE                          143135\n",
       "APPLICATION CREATED DATE                   33888\n",
       "APPLICATION REQUIREMENTS COMPLETE         142492\n",
       "PAYMENT DATE                              140794\n",
       "CONDITIONAL APPROVAL                      143135\n",
       "LICENSE TERM START DATE                   142788\n",
       "LICENSE TERM EXPIRATION DATE              143110\n",
       "LICENSE APPROVED FOR ISSUANCE             133966\n",
       "DATE ISSUED                               143134\n",
       "LICENSE STATUS CHANGE DATE                 64488\n",
       "SSA                                        25358\n",
       "LATITUDE                                   94979\n",
       "LONGITUDE                                  94979\n",
       "LOCATION                                   94979\n",
       "LICENSE STATUS                             85895\n",
       "PAYMENT YEAR                              140794\n",
       "PAYMENT MONTH                             140794\n",
       "APPLICATION REQUIREMENTS COMPLETE YEAR    142492\n",
       "DATE ISSUED YEAR                          143134\n",
       "LICENSE TERM START YEAR                   142788\n",
       "LICENSE TERM EXPIRATION YEAR              143110\n",
       "LICENSE APPROVED YEAR                     133966\n",
       "LICENSE STATUS CHANGE YEAR                 64488\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                                                             NaN\n",
       "LICENSE ID                                              LICENSE ID\n",
       "ACCOUNT NUMBER                                      ACCOUNT NUMBER\n",
       "SITE NUMBER                                            SITE NUMBER\n",
       "LEGAL NAME                                              LEGAL NAME\n",
       "DOING BUSINESS AS NAME                      DOING BUSINESS AS NAME\n",
       "ADDRESS                                                    ADDRESS\n",
       "CITY                                                          CITY\n",
       "STATE                                                        STATE\n",
       "ZIP CODE                                                  ZIP CODE\n",
       "WARD                                                          WARD\n",
       "PRECINCT                                                  PRECINCT\n",
       "WARD PRECINCT                                        WARD PRECINCT\n",
       "POLICE DISTRICT                                    POLICE DISTRICT\n",
       "LICENSE CODE                                          LICENSE CODE\n",
       "LICENSE DESCRIPTION                            LICENSE DESCRIPTION\n",
       "LICENSE NUMBER                                      LICENSE NUMBER\n",
       "APPLICATION TYPE                                  APPLICATION TYPE\n",
       "APPLICATION CREATED DATE                  APPLICATION CREATED DATE\n",
       "APPLICATION REQUIREMENTS COMPLETE                              NaT\n",
       "PAYMENT DATE                                                   NaT\n",
       "CONDITIONAL APPROVAL                          CONDITIONAL APPROVAL\n",
       "LICENSE TERM START DATE                                        NaT\n",
       "LICENSE TERM EXPIRATION DATE                                   NaT\n",
       "LICENSE APPROVED FOR ISSUANCE                                  NaT\n",
       "DATE ISSUED                                                    NaT\n",
       "LICENSE STATUS CHANGE DATE                                     NaT\n",
       "SSA                                                            SSA\n",
       "LATITUDE                                                  LATITUDE\n",
       "LONGITUDE                                                LONGITUDE\n",
       "LOCATION                                                  LOCATION\n",
       "LICENSE STATUS                                                 NaN\n",
       "PAYMENT YEAR                                                   NaN\n",
       "PAYMENT MONTH                                                  NaN\n",
       "APPLICATION REQUIREMENTS COMPLETE YEAR                         NaN\n",
       "DATE ISSUED YEAR                                               NaN\n",
       "LICENSE TERM START YEAR                                        NaN\n",
       "LICENSE TERM EXPIRATION YEAR                                   NaN\n",
       "LICENSE APPROVED YEAR                                          NaN\n",
       "LICENSE STATUS CHANGE YEAR                                     NaN\n",
       "Name: 85901, dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[85901]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>ID</th>\n",
       "      <th>LICENSE ID</th>\n",
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       "      <th>PAYMENT YEAR</th>\n",
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       "      <th>DATE ISSUED YEAR</th>\n",
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       "      <th>LICENSE APPROVED YEAR</th>\n",
       "      <th>LICENSE STATUS CHANGE YEAR</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>35342-20020816</td>\n",
       "      <td>1256593</td>\n",
       "      <td>32811</td>\n",
       "      <td>1</td>\n",
       "      <td>CARMEN CAHUE</td>\n",
       "      <td>CLAUDIA'S BRIDAL SHOP</td>\n",
       "      <td>2625 S CENTRAL PARK AVE  1</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60623.0</td>\n",
       "      <td>...</td>\n",
       "      <td>{'latitude': '41.843612879431845', 'longitude'...</td>\n",
       "      <td>AAI</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1358463-20051116</td>\n",
       "      <td>1639294</td>\n",
       "      <td>262311</td>\n",
       "      <td>29</td>\n",
       "      <td>ISLA TROPICAL, INC.</td>\n",
       "      <td>ISLA TROPICAL</td>\n",
       "      <td>2825 W MONTROSE AVE</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60618</td>\n",
       "      <td>...</td>\n",
       "      <td>{'latitude': '41.96113244107215', 'longitude':...</td>\n",
       "      <td>AAC</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>2006.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1980233-20090722</td>\n",
       "      <td>1980233</td>\n",
       "      <td>345008</td>\n",
       "      <td>1</td>\n",
       "      <td>DJS REMODELING</td>\n",
       "      <td>DJS REMODELING, INC.</td>\n",
       "      <td>1605  CLAVEY RD 1</td>\n",
       "      <td>HIGHLAND</td>\n",
       "      <td>IL</td>\n",
       "      <td>60035</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AAI</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1476582-20040211</td>\n",
       "      <td>1476582</td>\n",
       "      <td>273121</td>\n",
       "      <td>1</td>\n",
       "      <td>ALL-BRY CONSTRUCTION CO.</td>\n",
       "      <td>ALL-BRY CONSTRUCTION CO.</td>\n",
       "      <td>8  NORTH TRAIL</td>\n",
       "      <td>LEMONT</td>\n",
       "      <td>IL</td>\n",
       "      <td>60439</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AAI</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1141408-20080516</td>\n",
       "      <td>1896750</td>\n",
       "      <td>213785</td>\n",
       "      <td>1</td>\n",
       "      <td>MCDONOUGH MECHANICAL SERVICE</td>\n",
       "      <td>MCDONOUGH MECHANICAL SERVICE</td>\n",
       "      <td>4081  JOSEPH DR</td>\n",
       "      <td>WAUKEGAN</td>\n",
       "      <td>IL</td>\n",
       "      <td>60087</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AAI</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>NaN</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>143136</th>\n",
       "      <td>1743194-20070816</td>\n",
       "      <td>1.83568e+06</td>\n",
       "      <td>266103</td>\n",
       "      <td>3</td>\n",
       "      <td>GOODHEALTH MEDICAL PRODUCTS, INC.</td>\n",
       "      <td>GOODHEALTH MEDICAL PRODUCTS, INC.</td>\n",
       "      <td>2621 W 79TH ST</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60652</td>\n",
       "      <td>...</td>\n",
       "      <td>{'latitude': '41.74984206916219', 'longitude':...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>2008.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143137</th>\n",
       "      <td>2032383-20160716</td>\n",
       "      <td>2.46596e+06</td>\n",
       "      <td>63376</td>\n",
       "      <td>180</td>\n",
       "      <td>INTERPARK LLC</td>\n",
       "      <td>INTERPARK</td>\n",
       "      <td>505 N STATE ST 2-4</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60654</td>\n",
       "      <td>...</td>\n",
       "      <td>{'latitude': '41.89105524147196', 'longitude':...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>2018.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143138</th>\n",
       "      <td>40376-20020216</td>\n",
       "      <td>1.2112e+06</td>\n",
       "      <td>55051</td>\n",
       "      <td>1</td>\n",
       "      <td>YUN HEE HWANG</td>\n",
       "      <td>TOP CLEANERS</td>\n",
       "      <td>1201 W ROSCOE ST 1ST</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60657</td>\n",
       "      <td>...</td>\n",
       "      <td>{'latitude': '41.94338183750669', 'longitude':...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143139</th>\n",
       "      <td>2064176-20121116</td>\n",
       "      <td>2.1885e+06</td>\n",
       "      <td>357381</td>\n",
       "      <td>1</td>\n",
       "      <td>D. C. A. TOWING INCORPORATED</td>\n",
       "      <td>D. C. A. TOWING INCORPORATED</td>\n",
       "      <td>7052 W 64TH ST 1ST</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60638</td>\n",
       "      <td>...</td>\n",
       "      <td>{'latitude': '41.775709631013335', 'longitude'...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143140</th>\n",
       "      <td>1954052-20090202</td>\n",
       "      <td>1.95405e+06</td>\n",
       "      <td>340610</td>\n",
       "      <td>1</td>\n",
       "      <td>maria t bedoy</td>\n",
       "      <td>mari's cleaning services</td>\n",
       "      <td>4544 S CHRISTIANA AVE 1</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60632</td>\n",
       "      <td>...</td>\n",
       "      <td>{'latitude': '41.81052250782054', 'longitude':...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>143134 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      ID   LICENSE ID ACCOUNT NUMBER SITE NUMBER  \\\n",
       "0         35342-20020816      1256593          32811           1   \n",
       "1       1358463-20051116      1639294         262311          29   \n",
       "2       1980233-20090722      1980233         345008           1   \n",
       "3       1476582-20040211      1476582         273121           1   \n",
       "4       1141408-20080516      1896750         213785           1   \n",
       "...                  ...          ...            ...         ...   \n",
       "143136  1743194-20070816  1.83568e+06         266103           3   \n",
       "143137  2032383-20160716  2.46596e+06          63376         180   \n",
       "143138    40376-20020216   1.2112e+06          55051           1   \n",
       "143139  2064176-20121116   2.1885e+06         357381           1   \n",
       "143140  1954052-20090202  1.95405e+06         340610           1   \n",
       "\n",
       "                               LEGAL NAME             DOING BUSINESS AS NAME  \\\n",
       "0                            CARMEN CAHUE              CLAUDIA'S BRIDAL SHOP   \n",
       "1                     ISLA TROPICAL, INC.                      ISLA TROPICAL   \n",
       "2                          DJS REMODELING               DJS REMODELING, INC.   \n",
       "3                ALL-BRY CONSTRUCTION CO.           ALL-BRY CONSTRUCTION CO.   \n",
       "4            MCDONOUGH MECHANICAL SERVICE       MCDONOUGH MECHANICAL SERVICE   \n",
       "...                                   ...                                ...   \n",
       "143136  GOODHEALTH MEDICAL PRODUCTS, INC.  GOODHEALTH MEDICAL PRODUCTS, INC.   \n",
       "143137                      INTERPARK LLC                          INTERPARK   \n",
       "143138                      YUN HEE HWANG                       TOP CLEANERS   \n",
       "143139       D. C. A. TOWING INCORPORATED       D. C. A. TOWING INCORPORATED   \n",
       "143140                      maria t bedoy           mari's cleaning services   \n",
       "\n",
       "                           ADDRESS      CITY STATE ZIP CODE  ...  \\\n",
       "0       2625 S CENTRAL PARK AVE  1   CHICAGO    IL  60623.0  ...   \n",
       "1              2825 W MONTROSE AVE   CHICAGO    IL    60618  ...   \n",
       "2                1605  CLAVEY RD 1  HIGHLAND    IL    60035  ...   \n",
       "3                   8  NORTH TRAIL    LEMONT    IL    60439  ...   \n",
       "4                  4081  JOSEPH DR  WAUKEGAN    IL    60087  ...   \n",
       "...                            ...       ...   ...      ...  ...   \n",
       "143136              2621 W 79TH ST   CHICAGO    IL    60652  ...   \n",
       "143137          505 N STATE ST 2-4   CHICAGO    IL    60654  ...   \n",
       "143138        1201 W ROSCOE ST 1ST   CHICAGO    IL    60657  ...   \n",
       "143139          7052 W 64TH ST 1ST   CHICAGO    IL    60638  ...   \n",
       "143140     4544 S CHRISTIANA AVE 1   CHICAGO    IL    60632  ...   \n",
       "\n",
       "                                                 LOCATION LICENSE STATUS  \\\n",
       "0       {'latitude': '41.843612879431845', 'longitude'...            AAI   \n",
       "1       {'latitude': '41.96113244107215', 'longitude':...            AAC   \n",
       "2                                                     NaN            AAI   \n",
       "3                                                     NaN            AAI   \n",
       "4                                                     NaN            AAI   \n",
       "...                                                   ...            ...   \n",
       "143136  {'latitude': '41.74984206916219', 'longitude':...            NaN   \n",
       "143137  {'latitude': '41.89105524147196', 'longitude':...            NaN   \n",
       "143138  {'latitude': '41.94338183750669', 'longitude':...            NaN   \n",
       "143139  {'latitude': '41.775709631013335', 'longitude'...            NaN   \n",
       "143140  {'latitude': '41.81052250782054', 'longitude':...            NaN   \n",
       "\n",
       "       PAYMENT YEAR PAYMENT MONTH APPLICATION REQUIREMENTS COMPLETE YEAR  \\\n",
       "0            2002.0           8.0                                 2002.0   \n",
       "1            2005.0          11.0                                 2005.0   \n",
       "2            2009.0           7.0                                 2009.0   \n",
       "3            2004.0           2.0                                 2004.0   \n",
       "4            2008.0           6.0                                 2008.0   \n",
       "...             ...           ...                                    ...   \n",
       "143136       2007.0           9.0                                 2007.0   \n",
       "143137       2016.0           6.0                                 2016.0   \n",
       "143138       2002.0           1.0                                 2001.0   \n",
       "143139       2012.0          12.0                                 2012.0   \n",
       "143140       2009.0           1.0                                 2009.0   \n",
       "\n",
       "       DATE ISSUED YEAR LICENSE TERM START YEAR LICENSE TERM EXPIRATION YEAR  \\\n",
       "0                2006.0                  2002.0                       2003.0   \n",
       "1                2006.0                  2005.0                       2006.0   \n",
       "2                2009.0                  2009.0                       2011.0   \n",
       "3                2004.0                  2004.0                       2005.0   \n",
       "4                2008.0                  2008.0                       2010.0   \n",
       "...                 ...                     ...                          ...   \n",
       "143136           2007.0                  2007.0                       2009.0   \n",
       "143137           2016.0                  2016.0                       2018.0   \n",
       "143138           2002.0                  2002.0                       2003.0   \n",
       "143139           2012.0                  2012.0                       2014.0   \n",
       "143140           2009.0                  2009.0                       2009.0   \n",
       "\n",
       "       LICENSE APPROVED YEAR LICENSE STATUS CHANGE YEAR  \n",
       "0                     2002.0                        NaN  \n",
       "1                     2006.0                     2006.0  \n",
       "2                     2009.0                        NaN  \n",
       "3                     2004.0                        NaN  \n",
       "4                     2008.0                        NaN  \n",
       "...                      ...                        ...  \n",
       "143136                2007.0                     2008.0  \n",
       "143137                2016.0                     2018.0  \n",
       "143138                2002.0                        NaN  \n",
       "143139                2012.0                     2012.0  \n",
       "143140                2009.0                        NaN  \n",
       "\n",
       "[143134 rows x 40 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(85901)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-87.71461847200001, -87.699626044, nan, ..., -87.68873291700001,\n",
       "       -87.79800922700001, -87.707872417], dtype=object)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['LONGITUDE'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                                                             NaN\n",
       "LICENSE ID                                              LICENSE ID\n",
       "ACCOUNT NUMBER                                      ACCOUNT NUMBER\n",
       "SITE NUMBER                                            SITE NUMBER\n",
       "LEGAL NAME                                              LEGAL NAME\n",
       "DOING BUSINESS AS NAME                      DOING BUSINESS AS NAME\n",
       "ADDRESS                                                    ADDRESS\n",
       "CITY                                                          CITY\n",
       "STATE                                                        STATE\n",
       "ZIP CODE                                                  ZIP CODE\n",
       "WARD                                                          WARD\n",
       "PRECINCT                                                  PRECINCT\n",
       "WARD PRECINCT                                        WARD PRECINCT\n",
       "POLICE DISTRICT                                    POLICE DISTRICT\n",
       "LICENSE CODE                                          LICENSE CODE\n",
       "LICENSE DESCRIPTION                            LICENSE DESCRIPTION\n",
       "LICENSE NUMBER                                      LICENSE NUMBER\n",
       "APPLICATION TYPE                                  APPLICATION TYPE\n",
       "APPLICATION CREATED DATE                  APPLICATION CREATED DATE\n",
       "APPLICATION REQUIREMENTS COMPLETE                              NaT\n",
       "PAYMENT DATE                                                   NaT\n",
       "CONDITIONAL APPROVAL                          CONDITIONAL APPROVAL\n",
       "LICENSE TERM START DATE                                        NaT\n",
       "LICENSE TERM EXPIRATION DATE                                   NaT\n",
       "LICENSE APPROVED FOR ISSUANCE                                  NaT\n",
       "DATE ISSUED                                                    NaT\n",
       "LICENSE STATUS CHANGE DATE                                     NaT\n",
       "SSA                                                            SSA\n",
       "LATITUDE                                                  LATITUDE\n",
       "LONGITUDE                                                LONGITUDE\n",
       "LOCATION                                                  LOCATION\n",
       "LICENSE STATUS                                                 NaN\n",
       "PAYMENT YEAR                                                   NaN\n",
       "PAYMENT MONTH                                                  NaN\n",
       "APPLICATION REQUIREMENTS COMPLETE YEAR                         NaN\n",
       "DATE ISSUED YEAR                                               NaN\n",
       "LICENSE TERM START YEAR                                        NaN\n",
       "LICENSE TERM EXPIRATION YEAR                                   NaN\n",
       "LICENSE APPROVED YEAR                                          NaN\n",
       "LICENSE STATUS CHANGE YEAR                                     NaN\n",
       "Name: 85901, dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[85901]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\siddhesh\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\pandas\\core\\frame.py:4174: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  errors=errors,\n"
     ]
    }
   ],
   "source": [
    "df.drop(85901, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\siddhesh\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\pandas\\core\\frame.py:3069: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self[k1] = value[k2]\n"
     ]
    }
   ],
   "source": [
    "df[['LATITUDE', 'LONGITUDE']] = df[['LATITUDE', 'LONGITUDE']].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Filling missing values with mean\n",
    "def lat_lon(latlonlist):\n",
    "    for i in latlonlist:\n",
    "        df[i] = df[i].fillna(df[i].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\siddhesh\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\ipykernel_launcher.py:4: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "latlonlist = ['LONGITUDE', 'LATITUDE']\n",
    "lat_lon(latlonlist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Filling missing values with round mean for year entries\n",
    "def round_mean(date_data):\n",
    "    for i in date_data:\n",
    "        df[i] = df[i].fillna(round(df[i].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\siddhesh\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\ipykernel_launcher.py:4: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "year_data = ['PAYMENT MONTH','PAYMENT YEAR', 'APPLICATION REQUIREMENTS COMPLETE YEAR','LICENSE TERM EXPIRATION YEAR', 'LICENSE TERM START YEAR', 'LICENSE APPROVED YEAR', 'LICENSE STATUS CHANGE YEAR']\n",
    "round_mean(year_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\siddhesh\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\ipykernel_launcher.py:1: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df['LICENSE VALIDITY'] = abs(df['LICENSE TERM EXPIRATION YEAR'] - df['LICENSE TERM START YEAR'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Extracted years from these datetime columns, dropping these columns\n",
    "drop_cols = ['PAYMENT DATE', 'DATE ISSUED', 'APPLICATION REQUIREMENTS COMPLETE', \n",
    "             'LICENSE TERM EXPIRATION DATE', 'APPLICATION CREATED DATE', 'SSA', 'ADDRESS', 'LOCATION',\n",
    "             'LICENSE TERM EXPIRATION YEAR', 'LICENSE TERM START DATE', 'LICENSE TERM START YEAR',\n",
    "             'LICENSE APPROVED FOR ISSUANCE', 'LICENSE STATUS CHANGE DATE','LICENSE ID', \n",
    "             ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(drop_cols, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>ACCOUNT NUMBER</th>\n",
       "      <th>SITE NUMBER</th>\n",
       "      <th>LEGAL NAME</th>\n",
       "      <th>DOING BUSINESS AS NAME</th>\n",
       "      <th>CITY</th>\n",
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       "      <th>WARD</th>\n",
       "      <th>PRECINCT</th>\n",
       "      <th>...</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>LICENSE STATUS</th>\n",
       "      <th>PAYMENT YEAR</th>\n",
       "      <th>PAYMENT MONTH</th>\n",
       "      <th>APPLICATION REQUIREMENTS COMPLETE YEAR</th>\n",
       "      <th>DATE ISSUED YEAR</th>\n",
       "      <th>LICENSE APPROVED YEAR</th>\n",
       "      <th>LICENSE STATUS CHANGE YEAR</th>\n",
       "      <th>LICENSE VALIDITY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>35342-20020816</td>\n",
       "      <td>32811</td>\n",
       "      <td>1</td>\n",
       "      <td>CARMEN CAHUE</td>\n",
       "      <td>CLAUDIA'S BRIDAL SHOP</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60623.0</td>\n",
       "      <td>22</td>\n",
       "      <td>28</td>\n",
       "      <td>...</td>\n",
       "      <td>41.843613</td>\n",
       "      <td>-87.714618</td>\n",
       "      <td>AAI</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>2010.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1358463-20051116</td>\n",
       "      <td>262311</td>\n",
       "      <td>29</td>\n",
       "      <td>ISLA TROPICAL, INC.</td>\n",
       "      <td>ISLA TROPICAL</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60618</td>\n",
       "      <td>33</td>\n",
       "      <td>23</td>\n",
       "      <td>...</td>\n",
       "      <td>41.961132</td>\n",
       "      <td>-87.699626</td>\n",
       "      <td>AAC</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1980233-20090722</td>\n",
       "      <td>345008</td>\n",
       "      <td>1</td>\n",
       "      <td>DJS REMODELING</td>\n",
       "      <td>DJS REMODELING, INC.</td>\n",
       "      <td>HIGHLAND</td>\n",
       "      <td>IL</td>\n",
       "      <td>60035</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>41.880289</td>\n",
       "      <td>-87.679310</td>\n",
       "      <td>AAI</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1476582-20040211</td>\n",
       "      <td>273121</td>\n",
       "      <td>1</td>\n",
       "      <td>ALL-BRY CONSTRUCTION CO.</td>\n",
       "      <td>ALL-BRY CONSTRUCTION CO.</td>\n",
       "      <td>LEMONT</td>\n",
       "      <td>IL</td>\n",
       "      <td>60439</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>41.880289</td>\n",
       "      <td>-87.679310</td>\n",
       "      <td>AAI</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1141408-20080516</td>\n",
       "      <td>213785</td>\n",
       "      <td>1</td>\n",
       "      <td>MCDONOUGH MECHANICAL SERVICE</td>\n",
       "      <td>MCDONOUGH MECHANICAL SERVICE</td>\n",
       "      <td>WAUKEGAN</td>\n",
       "      <td>IL</td>\n",
       "      <td>60087</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>41.880289</td>\n",
       "      <td>-87.679310</td>\n",
       "      <td>AAI</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143136</th>\n",
       "      <td>1743194-20070816</td>\n",
       "      <td>266103</td>\n",
       "      <td>3</td>\n",
       "      <td>GOODHEALTH MEDICAL PRODUCTS, INC.</td>\n",
       "      <td>GOODHEALTH MEDICAL PRODUCTS, INC.</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60652</td>\n",
       "      <td>18</td>\n",
       "      <td>14</td>\n",
       "      <td>...</td>\n",
       "      <td>41.749842</td>\n",
       "      <td>-87.688733</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143137</th>\n",
       "      <td>2032383-20160716</td>\n",
       "      <td>63376</td>\n",
       "      <td>180</td>\n",
       "      <td>INTERPARK LLC</td>\n",
       "      <td>INTERPARK</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60654</td>\n",
       "      <td>42</td>\n",
       "      <td>22</td>\n",
       "      <td>...</td>\n",
       "      <td>41.891055</td>\n",
       "      <td>-87.627898</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143138</th>\n",
       "      <td>40376-20020216</td>\n",
       "      <td>55051</td>\n",
       "      <td>1</td>\n",
       "      <td>YUN HEE HWANG</td>\n",
       "      <td>TOP CLEANERS</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60657</td>\n",
       "      <td>44</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>41.943382</td>\n",
       "      <td>-87.659191</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143139</th>\n",
       "      <td>2064176-20121116</td>\n",
       "      <td>357381</td>\n",
       "      <td>1</td>\n",
       "      <td>D. C. A. TOWING INCORPORATED</td>\n",
       "      <td>D. C. A. TOWING INCORPORATED</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60638</td>\n",
       "      <td>23</td>\n",
       "      <td>18</td>\n",
       "      <td>...</td>\n",
       "      <td>41.775710</td>\n",
       "      <td>-87.798009</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143140</th>\n",
       "      <td>1954052-20090202</td>\n",
       "      <td>340610</td>\n",
       "      <td>1</td>\n",
       "      <td>maria t bedoy</td>\n",
       "      <td>mari's cleaning services</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60632</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>41.810523</td>\n",
       "      <td>-87.707872</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>143134 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      ID ACCOUNT NUMBER SITE NUMBER  \\\n",
       "0         35342-20020816          32811           1   \n",
       "1       1358463-20051116         262311          29   \n",
       "2       1980233-20090722         345008           1   \n",
       "3       1476582-20040211         273121           1   \n",
       "4       1141408-20080516         213785           1   \n",
       "...                  ...            ...         ...   \n",
       "143136  1743194-20070816         266103           3   \n",
       "143137  2032383-20160716          63376         180   \n",
       "143138    40376-20020216          55051           1   \n",
       "143139  2064176-20121116         357381           1   \n",
       "143140  1954052-20090202         340610           1   \n",
       "\n",
       "                               LEGAL NAME             DOING BUSINESS AS NAME  \\\n",
       "0                            CARMEN CAHUE              CLAUDIA'S BRIDAL SHOP   \n",
       "1                     ISLA TROPICAL, INC.                      ISLA TROPICAL   \n",
       "2                          DJS REMODELING               DJS REMODELING, INC.   \n",
       "3                ALL-BRY CONSTRUCTION CO.           ALL-BRY CONSTRUCTION CO.   \n",
       "4            MCDONOUGH MECHANICAL SERVICE       MCDONOUGH MECHANICAL SERVICE   \n",
       "...                                   ...                                ...   \n",
       "143136  GOODHEALTH MEDICAL PRODUCTS, INC.  GOODHEALTH MEDICAL PRODUCTS, INC.   \n",
       "143137                      INTERPARK LLC                          INTERPARK   \n",
       "143138                      YUN HEE HWANG                       TOP CLEANERS   \n",
       "143139       D. C. A. TOWING INCORPORATED       D. C. A. TOWING INCORPORATED   \n",
       "143140                      maria t bedoy           mari's cleaning services   \n",
       "\n",
       "            CITY STATE ZIP CODE WARD PRECINCT  ...   LATITUDE  LONGITUDE  \\\n",
       "0        CHICAGO    IL  60623.0   22       28  ...  41.843613 -87.714618   \n",
       "1        CHICAGO    IL    60618   33       23  ...  41.961132 -87.699626   \n",
       "2       HIGHLAND    IL    60035  NaN      NaN  ...  41.880289 -87.679310   \n",
       "3         LEMONT    IL    60439  NaN      NaN  ...  41.880289 -87.679310   \n",
       "4       WAUKEGAN    IL    60087  NaN      NaN  ...  41.880289 -87.679310   \n",
       "...          ...   ...      ...  ...      ...  ...        ...        ...   \n",
       "143136   CHICAGO    IL    60652   18       14  ...  41.749842 -87.688733   \n",
       "143137   CHICAGO    IL    60654   42       22  ...  41.891055 -87.627898   \n",
       "143138   CHICAGO    IL    60657   44        9  ...  41.943382 -87.659191   \n",
       "143139   CHICAGO    IL    60638   23       18  ...  41.775710 -87.798009   \n",
       "143140   CHICAGO    IL    60632   14       10  ...  41.810523 -87.707872   \n",
       "\n",
       "       LICENSE STATUS PAYMENT YEAR PAYMENT MONTH  \\\n",
       "0                 AAI       2002.0           8.0   \n",
       "1                 AAC       2005.0          11.0   \n",
       "2                 AAI       2009.0           7.0   \n",
       "3                 AAI       2004.0           2.0   \n",
       "4                 AAI       2008.0           6.0   \n",
       "...               ...          ...           ...   \n",
       "143136            NaN       2007.0           9.0   \n",
       "143137            NaN       2016.0           6.0   \n",
       "143138            NaN       2002.0           1.0   \n",
       "143139            NaN       2012.0          12.0   \n",
       "143140            NaN       2009.0           1.0   \n",
       "\n",
       "       APPLICATION REQUIREMENTS COMPLETE YEAR DATE ISSUED YEAR  \\\n",
       "0                                      2002.0           2006.0   \n",
       "1                                      2005.0           2006.0   \n",
       "2                                      2009.0           2009.0   \n",
       "3                                      2004.0           2004.0   \n",
       "4                                      2008.0           2008.0   \n",
       "...                                       ...              ...   \n",
       "143136                                 2007.0           2007.0   \n",
       "143137                                 2016.0           2016.0   \n",
       "143138                                 2001.0           2002.0   \n",
       "143139                                 2012.0           2012.0   \n",
       "143140                                 2009.0           2009.0   \n",
       "\n",
       "        LICENSE APPROVED YEAR  LICENSE STATUS CHANGE YEAR LICENSE VALIDITY  \n",
       "0                      2002.0                      2010.0              1.0  \n",
       "1                      2006.0                      2006.0              1.0  \n",
       "2                      2009.0                      2010.0              2.0  \n",
       "3                      2004.0                      2010.0              1.0  \n",
       "4                      2008.0                      2010.0              2.0  \n",
       "...                       ...                         ...              ...  \n",
       "143136                 2007.0                      2008.0              2.0  \n",
       "143137                 2016.0                      2018.0              2.0  \n",
       "143138                 2002.0                      2010.0              1.0  \n",
       "143139                 2012.0                      2012.0              2.0  \n",
       "143140                 2009.0                      2010.0              0.0  \n",
       "\n",
       "[143134 rows x 27 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['DOING BUSINESS AS NAME'] = df['DOING BUSINESS AS NAME'].fillna('None')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                                            0\n",
       "ACCOUNT NUMBER                                0\n",
       "SITE NUMBER                                   0\n",
       "LEGAL NAME                                    0\n",
       "DOING BUSINESS AS NAME                        0\n",
       "CITY                                          0\n",
       "STATE                                         0\n",
       "ZIP CODE                                     43\n",
       "WARD                                      50327\n",
       "PRECINCT                                  58279\n",
       "WARD PRECINCT                             50316\n",
       "POLICE DISTRICT                           55487\n",
       "LICENSE CODE                                  0\n",
       "LICENSE DESCRIPTION                           0\n",
       "LICENSE NUMBER                                1\n",
       "APPLICATION TYPE                              0\n",
       "CONDITIONAL APPROVAL                          0\n",
       "LATITUDE                                      0\n",
       "LONGITUDE                                     0\n",
       "LICENSE STATUS                            57239\n",
       "PAYMENT YEAR                                  0\n",
       "PAYMENT MONTH                                 0\n",
       "APPLICATION REQUIREMENTS COMPLETE YEAR        0\n",
       "DATE ISSUED YEAR                              0\n",
       "LICENSE APPROVED YEAR                         0\n",
       "LICENSE STATUS CHANGE YEAR                    0\n",
       "LICENSE VALIDITY                              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Function to fill missing values with '0'\n",
    "def zeros(z):\n",
    "    for i in z:\n",
    "        df[i] = df[i].fillna(0)\n",
    "       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "z = ['WARD', 'PRECINCT', 'POLICE DISTRICT', 'ZIP CODE', 'WARD PRECINCT', 'LICENSE NUMBER']\n",
    "zeros(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>ACCOUNT NUMBER</th>\n",
       "      <th>SITE NUMBER</th>\n",
       "      <th>LEGAL NAME</th>\n",
       "      <th>DOING BUSINESS AS NAME</th>\n",
       "      <th>CITY</th>\n",
       "      <th>STATE</th>\n",
       "      <th>ZIP CODE</th>\n",
       "      <th>WARD</th>\n",
       "      <th>PRECINCT</th>\n",
       "      <th>...</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>LICENSE STATUS</th>\n",
       "      <th>PAYMENT YEAR</th>\n",
       "      <th>PAYMENT MONTH</th>\n",
       "      <th>APPLICATION REQUIREMENTS COMPLETE YEAR</th>\n",
       "      <th>DATE ISSUED YEAR</th>\n",
       "      <th>LICENSE APPROVED YEAR</th>\n",
       "      <th>LICENSE STATUS CHANGE YEAR</th>\n",
       "      <th>LICENSE VALIDITY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>35342-20020816</td>\n",
       "      <td>32811</td>\n",
       "      <td>1</td>\n",
       "      <td>CARMEN CAHUE</td>\n",
       "      <td>CLAUDIA'S BRIDAL SHOP</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60623.0</td>\n",
       "      <td>22</td>\n",
       "      <td>28</td>\n",
       "      <td>...</td>\n",
       "      <td>41.843613</td>\n",
       "      <td>-87.714618</td>\n",
       "      <td>AAI</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1358463-20051116</td>\n",
       "      <td>262311</td>\n",
       "      <td>29</td>\n",
       "      <td>ISLA TROPICAL, INC.</td>\n",
       "      <td>ISLA TROPICAL</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60618</td>\n",
       "      <td>33</td>\n",
       "      <td>23</td>\n",
       "      <td>...</td>\n",
       "      <td>41.961132</td>\n",
       "      <td>-87.699626</td>\n",
       "      <td>AAC</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1980233-20090722</td>\n",
       "      <td>345008</td>\n",
       "      <td>1</td>\n",
       "      <td>DJS REMODELING</td>\n",
       "      <td>DJS REMODELING, INC.</td>\n",
       "      <td>HIGHLAND</td>\n",
       "      <td>IL</td>\n",
       "      <td>60035</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>41.880289</td>\n",
       "      <td>-87.679310</td>\n",
       "      <td>AAI</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1476582-20040211</td>\n",
       "      <td>273121</td>\n",
       "      <td>1</td>\n",
       "      <td>ALL-BRY CONSTRUCTION CO.</td>\n",
       "      <td>ALL-BRY CONSTRUCTION CO.</td>\n",
       "      <td>LEMONT</td>\n",
       "      <td>IL</td>\n",
       "      <td>60439</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>41.880289</td>\n",
       "      <td>-87.679310</td>\n",
       "      <td>AAI</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1141408-20080516</td>\n",
       "      <td>213785</td>\n",
       "      <td>1</td>\n",
       "      <td>MCDONOUGH MECHANICAL SERVICE</td>\n",
       "      <td>MCDONOUGH MECHANICAL SERVICE</td>\n",
       "      <td>WAUKEGAN</td>\n",
       "      <td>IL</td>\n",
       "      <td>60087</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>41.880289</td>\n",
       "      <td>-87.679310</td>\n",
       "      <td>AAI</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>143136</th>\n",
       "      <td>1743194-20070816</td>\n",
       "      <td>266103</td>\n",
       "      <td>3</td>\n",
       "      <td>GOODHEALTH MEDICAL PRODUCTS, INC.</td>\n",
       "      <td>GOODHEALTH MEDICAL PRODUCTS, INC.</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60652</td>\n",
       "      <td>18</td>\n",
       "      <td>14</td>\n",
       "      <td>...</td>\n",
       "      <td>41.749842</td>\n",
       "      <td>-87.688733</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143137</th>\n",
       "      <td>2032383-20160716</td>\n",
       "      <td>63376</td>\n",
       "      <td>180</td>\n",
       "      <td>INTERPARK LLC</td>\n",
       "      <td>INTERPARK</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60654</td>\n",
       "      <td>42</td>\n",
       "      <td>22</td>\n",
       "      <td>...</td>\n",
       "      <td>41.891055</td>\n",
       "      <td>-87.627898</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143138</th>\n",
       "      <td>40376-20020216</td>\n",
       "      <td>55051</td>\n",
       "      <td>1</td>\n",
       "      <td>YUN HEE HWANG</td>\n",
       "      <td>TOP CLEANERS</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60657</td>\n",
       "      <td>44</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>41.943382</td>\n",
       "      <td>-87.659191</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143139</th>\n",
       "      <td>2064176-20121116</td>\n",
       "      <td>357381</td>\n",
       "      <td>1</td>\n",
       "      <td>D. C. A. TOWING INCORPORATED</td>\n",
       "      <td>D. C. A. TOWING INCORPORATED</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60638</td>\n",
       "      <td>23</td>\n",
       "      <td>18</td>\n",
       "      <td>...</td>\n",
       "      <td>41.775710</td>\n",
       "      <td>-87.798009</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143140</th>\n",
       "      <td>1954052-20090202</td>\n",
       "      <td>340610</td>\n",
       "      <td>1</td>\n",
       "      <td>MARIA T BEDOY</td>\n",
       "      <td>MARI'S CLEANING SERVICES</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60632</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>41.810523</td>\n",
       "      <td>-87.707872</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>143134 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      ID ACCOUNT NUMBER SITE NUMBER  \\\n",
       "0         35342-20020816          32811           1   \n",
       "1       1358463-20051116         262311          29   \n",
       "2       1980233-20090722         345008           1   \n",
       "3       1476582-20040211         273121           1   \n",
       "4       1141408-20080516         213785           1   \n",
       "...                  ...            ...         ...   \n",
       "143136  1743194-20070816         266103           3   \n",
       "143137  2032383-20160716          63376         180   \n",
       "143138    40376-20020216          55051           1   \n",
       "143139  2064176-20121116         357381           1   \n",
       "143140  1954052-20090202         340610           1   \n",
       "\n",
       "                               LEGAL NAME             DOING BUSINESS AS NAME  \\\n",
       "0                            CARMEN CAHUE              CLAUDIA'S BRIDAL SHOP   \n",
       "1                     ISLA TROPICAL, INC.                      ISLA TROPICAL   \n",
       "2                          DJS REMODELING               DJS REMODELING, INC.   \n",
       "3                ALL-BRY CONSTRUCTION CO.           ALL-BRY CONSTRUCTION CO.   \n",
       "4            MCDONOUGH MECHANICAL SERVICE       MCDONOUGH MECHANICAL SERVICE   \n",
       "...                                   ...                                ...   \n",
       "143136  GOODHEALTH MEDICAL PRODUCTS, INC.  GOODHEALTH MEDICAL PRODUCTS, INC.   \n",
       "143137                      INTERPARK LLC                          INTERPARK   \n",
       "143138                      YUN HEE HWANG                       TOP CLEANERS   \n",
       "143139       D. C. A. TOWING INCORPORATED       D. C. A. TOWING INCORPORATED   \n",
       "143140                      MARIA T BEDOY           MARI'S CLEANING SERVICES   \n",
       "\n",
       "            CITY STATE ZIP CODE WARD PRECINCT  ...   LATITUDE  LONGITUDE  \\\n",
       "0        CHICAGO    IL  60623.0   22       28  ...  41.843613 -87.714618   \n",
       "1        CHICAGO    IL    60618   33       23  ...  41.961132 -87.699626   \n",
       "2       HIGHLAND    IL    60035    0        0  ...  41.880289 -87.679310   \n",
       "3         LEMONT    IL    60439    0        0  ...  41.880289 -87.679310   \n",
       "4       WAUKEGAN    IL    60087    0        0  ...  41.880289 -87.679310   \n",
       "...          ...   ...      ...  ...      ...  ...        ...        ...   \n",
       "143136   CHICAGO    IL    60652   18       14  ...  41.749842 -87.688733   \n",
       "143137   CHICAGO    IL    60654   42       22  ...  41.891055 -87.627898   \n",
       "143138   CHICAGO    IL    60657   44        9  ...  41.943382 -87.659191   \n",
       "143139   CHICAGO    IL    60638   23       18  ...  41.775710 -87.798009   \n",
       "143140   CHICAGO    IL    60632   14       10  ...  41.810523 -87.707872   \n",
       "\n",
       "       LICENSE STATUS PAYMENT YEAR PAYMENT MONTH  \\\n",
       "0                 AAI       2002.0           8.0   \n",
       "1                 AAC       2005.0          11.0   \n",
       "2                 AAI       2009.0           7.0   \n",
       "3                 AAI       2004.0           2.0   \n",
       "4                 AAI       2008.0           6.0   \n",
       "...               ...          ...           ...   \n",
       "143136            NaN       2007.0           9.0   \n",
       "143137            NaN       2016.0           6.0   \n",
       "143138            NaN       2002.0           1.0   \n",
       "143139            NaN       2012.0          12.0   \n",
       "143140            NaN       2009.0           1.0   \n",
       "\n",
       "       APPLICATION REQUIREMENTS COMPLETE YEAR DATE ISSUED YEAR  \\\n",
       "0                                      2002.0           2006.0   \n",
       "1                                      2005.0           2006.0   \n",
       "2                                      2009.0           2009.0   \n",
       "3                                      2004.0           2004.0   \n",
       "4                                      2008.0           2008.0   \n",
       "...                                       ...              ...   \n",
       "143136                                 2007.0           2007.0   \n",
       "143137                                 2016.0           2016.0   \n",
       "143138                                 2001.0           2002.0   \n",
       "143139                                 2012.0           2012.0   \n",
       "143140                                 2009.0           2009.0   \n",
       "\n",
       "        LICENSE APPROVED YEAR  LICENSE STATUS CHANGE YEAR LICENSE VALIDITY  \n",
       "0                      2002.0                      2010.0              1.0  \n",
       "1                      2006.0                      2006.0              1.0  \n",
       "2                      2009.0                      2010.0              2.0  \n",
       "3                      2004.0                      2010.0              1.0  \n",
       "4                      2008.0                      2010.0              2.0  \n",
       "...                       ...                         ...              ...  \n",
       "143136                 2007.0                      2008.0              2.0  \n",
       "143137                 2016.0                      2018.0              2.0  \n",
       "143138                 2002.0                      2010.0              1.0  \n",
       "143139                 2012.0                      2012.0              2.0  \n",
       "143140                 2009.0                      2010.0              0.0  \n",
       "\n",
       "[143134 rows x 27 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['LEGAL NAME'] = df['LEGAL NAME'].str.upper()\n",
    "df['DOING BUSINESS AS NAME'] = df['DOING BUSINESS AS NAME'].str.upper()\n",
    "df['STATE'] = df['STATE'].str.upper()\n",
    "df['CITY'] = df['CITY'].str.upper()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['LEGAL NAME'] = df['LEGAL NAME'].str.replace('.', '', regex=False)\n",
    "df['DOING BUSINESS AS NAME'] = df['DOING BUSINESS AS NAME'].str.replace('.', '', regex=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['LEGAL NAME'] = df['LEGAL NAME'].str.replace(',', '', regex=False)\n",
    "df['DOING BUSINESS AS NAME'] = df['DOING BUSINESS AS NAME'].str.replace(',', '', regex=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>ACCOUNT NUMBER</th>\n",
       "      <th>SITE NUMBER</th>\n",
       "      <th>LEGAL NAME</th>\n",
       "      <th>DOING BUSINESS AS NAME</th>\n",
       "      <th>CITY</th>\n",
       "      <th>STATE</th>\n",
       "      <th>ZIP CODE</th>\n",
       "      <th>WARD</th>\n",
       "      <th>PRECINCT</th>\n",
       "      <th>...</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>LICENSE STATUS</th>\n",
       "      <th>PAYMENT YEAR</th>\n",
       "      <th>PAYMENT MONTH</th>\n",
       "      <th>APPLICATION REQUIREMENTS COMPLETE YEAR</th>\n",
       "      <th>DATE ISSUED YEAR</th>\n",
       "      <th>LICENSE APPROVED YEAR</th>\n",
       "      <th>LICENSE STATUS CHANGE YEAR</th>\n",
       "      <th>LICENSE VALIDITY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>35342-20020816</td>\n",
       "      <td>32811</td>\n",
       "      <td>1</td>\n",
       "      <td>CARMEN CAHUE</td>\n",
       "      <td>CLAUDIA'S BRIDAL SHOP</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60623.0</td>\n",
       "      <td>22</td>\n",
       "      <td>28</td>\n",
       "      <td>...</td>\n",
       "      <td>41.843613</td>\n",
       "      <td>-87.714618</td>\n",
       "      <td>AAI</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1358463-20051116</td>\n",
       "      <td>262311</td>\n",
       "      <td>29</td>\n",
       "      <td>ISLA TROPICAL INC</td>\n",
       "      <td>ISLA TROPICAL</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60618</td>\n",
       "      <td>33</td>\n",
       "      <td>23</td>\n",
       "      <td>...</td>\n",
       "      <td>41.961132</td>\n",
       "      <td>-87.699626</td>\n",
       "      <td>AAC</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1980233-20090722</td>\n",
       "      <td>345008</td>\n",
       "      <td>1</td>\n",
       "      <td>DJS REMODELING</td>\n",
       "      <td>DJS REMODELING INC</td>\n",
       "      <td>HIGHLAND</td>\n",
       "      <td>IL</td>\n",
       "      <td>60035</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>41.880289</td>\n",
       "      <td>-87.679310</td>\n",
       "      <td>AAI</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1476582-20040211</td>\n",
       "      <td>273121</td>\n",
       "      <td>1</td>\n",
       "      <td>ALL-BRY CONSTRUCTION CO</td>\n",
       "      <td>ALL-BRY CONSTRUCTION CO</td>\n",
       "      <td>LEMONT</td>\n",
       "      <td>IL</td>\n",
       "      <td>60439</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>41.880289</td>\n",
       "      <td>-87.679310</td>\n",
       "      <td>AAI</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1141408-20080516</td>\n",
       "      <td>213785</td>\n",
       "      <td>1</td>\n",
       "      <td>MCDONOUGH MECHANICAL SERVICE</td>\n",
       "      <td>MCDONOUGH MECHANICAL SERVICE</td>\n",
       "      <td>WAUKEGAN</td>\n",
       "      <td>IL</td>\n",
       "      <td>60087</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>41.880289</td>\n",
       "      <td>-87.679310</td>\n",
       "      <td>AAI</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143136</th>\n",
       "      <td>1743194-20070816</td>\n",
       "      <td>266103</td>\n",
       "      <td>3</td>\n",
       "      <td>GOODHEALTH MEDICAL PRODUCTS INC</td>\n",
       "      <td>GOODHEALTH MEDICAL PRODUCTS INC</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60652</td>\n",
       "      <td>18</td>\n",
       "      <td>14</td>\n",
       "      <td>...</td>\n",
       "      <td>41.749842</td>\n",
       "      <td>-87.688733</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143137</th>\n",
       "      <td>2032383-20160716</td>\n",
       "      <td>63376</td>\n",
       "      <td>180</td>\n",
       "      <td>INTERPARK LLC</td>\n",
       "      <td>INTERPARK</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60654</td>\n",
       "      <td>42</td>\n",
       "      <td>22</td>\n",
       "      <td>...</td>\n",
       "      <td>41.891055</td>\n",
       "      <td>-87.627898</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143138</th>\n",
       "      <td>40376-20020216</td>\n",
       "      <td>55051</td>\n",
       "      <td>1</td>\n",
       "      <td>YUN HEE HWANG</td>\n",
       "      <td>TOP CLEANERS</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60657</td>\n",
       "      <td>44</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>41.943382</td>\n",
       "      <td>-87.659191</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143139</th>\n",
       "      <td>2064176-20121116</td>\n",
       "      <td>357381</td>\n",
       "      <td>1</td>\n",
       "      <td>D C A TOWING INCORPORATED</td>\n",
       "      <td>D C A TOWING INCORPORATED</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60638</td>\n",
       "      <td>23</td>\n",
       "      <td>18</td>\n",
       "      <td>...</td>\n",
       "      <td>41.775710</td>\n",
       "      <td>-87.798009</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143140</th>\n",
       "      <td>1954052-20090202</td>\n",
       "      <td>340610</td>\n",
       "      <td>1</td>\n",
       "      <td>MARIA T BEDOY</td>\n",
       "      <td>MARI'S CLEANING SERVICES</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60632</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>41.810523</td>\n",
       "      <td>-87.707872</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>143134 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      ID ACCOUNT NUMBER SITE NUMBER  \\\n",
       "0         35342-20020816          32811           1   \n",
       "1       1358463-20051116         262311          29   \n",
       "2       1980233-20090722         345008           1   \n",
       "3       1476582-20040211         273121           1   \n",
       "4       1141408-20080516         213785           1   \n",
       "...                  ...            ...         ...   \n",
       "143136  1743194-20070816         266103           3   \n",
       "143137  2032383-20160716          63376         180   \n",
       "143138    40376-20020216          55051           1   \n",
       "143139  2064176-20121116         357381           1   \n",
       "143140  1954052-20090202         340610           1   \n",
       "\n",
       "                             LEGAL NAME           DOING BUSINESS AS NAME  \\\n",
       "0                          CARMEN CAHUE            CLAUDIA'S BRIDAL SHOP   \n",
       "1                     ISLA TROPICAL INC                    ISLA TROPICAL   \n",
       "2                        DJS REMODELING               DJS REMODELING INC   \n",
       "3               ALL-BRY CONSTRUCTION CO          ALL-BRY CONSTRUCTION CO   \n",
       "4          MCDONOUGH MECHANICAL SERVICE     MCDONOUGH MECHANICAL SERVICE   \n",
       "...                                 ...                              ...   \n",
       "143136  GOODHEALTH MEDICAL PRODUCTS INC  GOODHEALTH MEDICAL PRODUCTS INC   \n",
       "143137                    INTERPARK LLC                        INTERPARK   \n",
       "143138                    YUN HEE HWANG                     TOP CLEANERS   \n",
       "143139        D C A TOWING INCORPORATED        D C A TOWING INCORPORATED   \n",
       "143140                    MARIA T BEDOY         MARI'S CLEANING SERVICES   \n",
       "\n",
       "            CITY STATE ZIP CODE WARD PRECINCT  ...   LATITUDE  LONGITUDE  \\\n",
       "0        CHICAGO    IL  60623.0   22       28  ...  41.843613 -87.714618   \n",
       "1        CHICAGO    IL    60618   33       23  ...  41.961132 -87.699626   \n",
       "2       HIGHLAND    IL    60035    0        0  ...  41.880289 -87.679310   \n",
       "3         LEMONT    IL    60439    0        0  ...  41.880289 -87.679310   \n",
       "4       WAUKEGAN    IL    60087    0        0  ...  41.880289 -87.679310   \n",
       "...          ...   ...      ...  ...      ...  ...        ...        ...   \n",
       "143136   CHICAGO    IL    60652   18       14  ...  41.749842 -87.688733   \n",
       "143137   CHICAGO    IL    60654   42       22  ...  41.891055 -87.627898   \n",
       "143138   CHICAGO    IL    60657   44        9  ...  41.943382 -87.659191   \n",
       "143139   CHICAGO    IL    60638   23       18  ...  41.775710 -87.798009   \n",
       "143140   CHICAGO    IL    60632   14       10  ...  41.810523 -87.707872   \n",
       "\n",
       "       LICENSE STATUS PAYMENT YEAR PAYMENT MONTH  \\\n",
       "0                 AAI       2002.0           8.0   \n",
       "1                 AAC       2005.0          11.0   \n",
       "2                 AAI       2009.0           7.0   \n",
       "3                 AAI       2004.0           2.0   \n",
       "4                 AAI       2008.0           6.0   \n",
       "...               ...          ...           ...   \n",
       "143136            NaN       2007.0           9.0   \n",
       "143137            NaN       2016.0           6.0   \n",
       "143138            NaN       2002.0           1.0   \n",
       "143139            NaN       2012.0          12.0   \n",
       "143140            NaN       2009.0           1.0   \n",
       "\n",
       "       APPLICATION REQUIREMENTS COMPLETE YEAR DATE ISSUED YEAR  \\\n",
       "0                                      2002.0           2006.0   \n",
       "1                                      2005.0           2006.0   \n",
       "2                                      2009.0           2009.0   \n",
       "3                                      2004.0           2004.0   \n",
       "4                                      2008.0           2008.0   \n",
       "...                                       ...              ...   \n",
       "143136                                 2007.0           2007.0   \n",
       "143137                                 2016.0           2016.0   \n",
       "143138                                 2001.0           2002.0   \n",
       "143139                                 2012.0           2012.0   \n",
       "143140                                 2009.0           2009.0   \n",
       "\n",
       "        LICENSE APPROVED YEAR  LICENSE STATUS CHANGE YEAR LICENSE VALIDITY  \n",
       "0                      2002.0                      2010.0              1.0  \n",
       "1                      2006.0                      2006.0              1.0  \n",
       "2                      2009.0                      2010.0              2.0  \n",
       "3                      2004.0                      2010.0              1.0  \n",
       "4                      2008.0                      2010.0              2.0  \n",
       "...                       ...                         ...              ...  \n",
       "143136                 2007.0                      2008.0              2.0  \n",
       "143137                 2016.0                      2018.0              2.0  \n",
       "143138                 2002.0                      2010.0              1.0  \n",
       "143139                 2012.0                      2012.0              2.0  \n",
       "143140                 2009.0                      2010.0              0.0  \n",
       "\n",
       "[143134 rows x 27 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>ACCOUNT NUMBER</th>\n",
       "      <th>SITE NUMBER</th>\n",
       "      <th>LEGAL NAME</th>\n",
       "      <th>DOING BUSINESS AS NAME</th>\n",
       "      <th>CITY</th>\n",
       "      <th>STATE</th>\n",
       "      <th>ZIP CODE</th>\n",
       "      <th>WARD</th>\n",
       "      <th>PRECINCT</th>\n",
       "      <th>...</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>LICENSE STATUS</th>\n",
       "      <th>PAYMENT YEAR</th>\n",
       "      <th>PAYMENT MONTH</th>\n",
       "      <th>APPLICATION REQUIREMENTS COMPLETE YEAR</th>\n",
       "      <th>DATE ISSUED YEAR</th>\n",
       "      <th>LICENSE APPROVED YEAR</th>\n",
       "      <th>LICENSE STATUS CHANGE YEAR</th>\n",
       "      <th>LICENSE VALIDITY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [ID, ACCOUNT NUMBER, SITE NUMBER, LEGAL NAME, DOING BUSINESS AS NAME, CITY, STATE, ZIP CODE, WARD, PRECINCT, WARD PRECINCT, POLICE DISTRICT, LICENSE CODE, LICENSE DESCRIPTION, LICENSE NUMBER, APPLICATION TYPE, CONDITIONAL APPROVAL, LATITUDE, LONGITUDE, LICENSE STATUS, PAYMENT YEAR, PAYMENT MONTH, APPLICATION REQUIREMENTS COMPLETE YEAR, DATE ISSUED YEAR, LICENSE APPROVED YEAR, LICENSE STATUS CHANGE YEAR, LICENSE VALIDITY]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 27 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['LICENSE NUMBER'].isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extracting useful information from Legal Name & Business Name Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To find out the type of business, extracted the last word from the name\n",
    "df[\"LEGAL SUFFIX\"] = df[\"LEGAL NAME\"].str.split().str[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "INC             59315\n",
       "LLC             12289\n",
       "CORPORATION      3766\n",
       "CORP             3321\n",
       "CO               3026\n",
       "COMPANY          2007\n",
       "LTD              1754\n",
       "INCORPORATED     1292\n",
       "LIMITED           863\n",
       "JR                646\n",
       "CONSTRUCTION      526\n",
       "KIM               517\n",
       "SR                462\n",
       "CHICAGO           373\n",
       "MARTINEZ          354\n",
       "SERVICES          354\n",
       "LP                327\n",
       "LOPEZ             325\n",
       "SERVICE           311\n",
       "GARCIA            267\n",
       "LEE               256\n",
       "HERNANDEZ         249\n",
       "PEREZ             214\n",
       "WILLIAMS          214\n",
       "SMITH             212\n",
       "NGUYEN            210\n",
       "JOHNSON           206\n",
       "CENTER            201\n",
       "JONES             193\n",
       "COOLING           190\n",
       "Name: LEGAL SUFFIX, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['LEGAL SUFFIX'].value_counts().head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "62055"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inc = df['LEGAL NAME'].str.contains('INCORPORATED|INC|INC.', na=False).sum()\n",
    "inc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12464"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "llc = df['LEGAL NAME'].str.contains('LLC', na=False).sum()\n",
    "llc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "62055"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corp = df['LEGAL NAME'].str.contains('CO|CORP|CORPORTION', na=False).sum()\n",
    "inc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3091"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ltd = df['LEGAL NAME'].str.contains('LTD|LIMITED', na=False).sum()\n",
    "ltd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>ACCOUNT NUMBER</th>\n",
       "      <th>SITE NUMBER</th>\n",
       "      <th>LEGAL NAME</th>\n",
       "      <th>DOING BUSINESS AS NAME</th>\n",
       "      <th>CITY</th>\n",
       "      <th>STATE</th>\n",
       "      <th>ZIP CODE</th>\n",
       "      <th>WARD</th>\n",
       "      <th>PRECINCT</th>\n",
       "      <th>...</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>LICENSE STATUS</th>\n",
       "      <th>PAYMENT YEAR</th>\n",
       "      <th>PAYMENT MONTH</th>\n",
       "      <th>APPLICATION REQUIREMENTS COMPLETE YEAR</th>\n",
       "      <th>DATE ISSUED YEAR</th>\n",
       "      <th>LICENSE APPROVED YEAR</th>\n",
       "      <th>LICENSE STATUS CHANGE YEAR</th>\n",
       "      <th>LICENSE VALIDITY</th>\n",
       "      <th>LEGAL SUFFIX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1141408-20080516</td>\n",
       "      <td>213785</td>\n",
       "      <td>1</td>\n",
       "      <td>MCDONOUGH MECHANICAL SERVICE</td>\n",
       "      <td>MCDONOUGH MECHANICAL SERVICE</td>\n",
       "      <td>WAUKEGAN</td>\n",
       "      <td>IL</td>\n",
       "      <td>60087</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.679310</td>\n",
       "      <td>AAI</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>SERVICE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2129534-20111129</td>\n",
       "      <td>368042</td>\n",
       "      <td>1</td>\n",
       "      <td>QUALITY PLUMBING SERVICES INC</td>\n",
       "      <td>QUALITY PLUMBING SERVICES INC</td>\n",
       "      <td>WOOD DALE</td>\n",
       "      <td>IL</td>\n",
       "      <td>60191</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.679310</td>\n",
       "      <td>AAI</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>INC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1337432-20120216</td>\n",
       "      <td>260686</td>\n",
       "      <td>1</td>\n",
       "      <td>CENTRAL PRINT SERVICES INC</td>\n",
       "      <td>CENTRAL PRINT SERVICES</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60610</td>\n",
       "      <td>42</td>\n",
       "      <td>68</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.638001</td>\n",
       "      <td>AAC</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>INC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1573623-20060216</td>\n",
       "      <td>286310</td>\n",
       "      <td>1</td>\n",
       "      <td>M &amp; T CREDIT SERVICES LLC</td>\n",
       "      <td>M &amp; T CREDIT SERVICES LLC</td>\n",
       "      <td>BUFFALO</td>\n",
       "      <td>NY</td>\n",
       "      <td>14203</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.679310</td>\n",
       "      <td>AAI</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>LLC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1479862-20040728</td>\n",
       "      <td>248389</td>\n",
       "      <td>1</td>\n",
       "      <td>PHOENIX EVENT SERVICES</td>\n",
       "      <td>CHICAGO PARTY RENTAL</td>\n",
       "      <td>MCCOOK</td>\n",
       "      <td>IL</td>\n",
       "      <td>60525</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.679310</td>\n",
       "      <td>AAI</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>SERVICES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <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>142980</th>\n",
       "      <td>1953918-20111116</td>\n",
       "      <td>48119</td>\n",
       "      <td>21</td>\n",
       "      <td>BASTE FINANCIAL SERVICES INC</td>\n",
       "      <td>NADEEM BASTE</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60652</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.738423</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>INC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143039</th>\n",
       "      <td>1594616-20081116</td>\n",
       "      <td>290838</td>\n",
       "      <td>1</td>\n",
       "      <td>VEGA CLEANING SERVICES</td>\n",
       "      <td>VEGA CLEANING SERVICES</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60642</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.661147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>SERVICES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143087</th>\n",
       "      <td>1426269-20050216</td>\n",
       "      <td>44679</td>\n",
       "      <td>1</td>\n",
       "      <td>BUZY BEE BOARD-UP SERVICE</td>\n",
       "      <td>BUZY BEE BOARD -UP SERVICE INC</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60639</td>\n",
       "      <td>31</td>\n",
       "      <td>35</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.756040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>SERVICE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143101</th>\n",
       "      <td>1118850-20060816</td>\n",
       "      <td>208989</td>\n",
       "      <td>1</td>\n",
       "      <td>SADIE'S TRANSPORTATION SERVICE INC</td>\n",
       "      <td>SADIE'S TRANSPORTATION SERVICE INC</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60616</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.623728</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>INC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143135</th>\n",
       "      <td>1741866-20081016</td>\n",
       "      <td>304120</td>\n",
       "      <td>3</td>\n",
       "      <td>ARAMARK HEALTHCARE SUPPORT SERVICES LLC</td>\n",
       "      <td>ARAMARK HEALTHCARE SUPPORT SERVICES INC</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60624</td>\n",
       "      <td>28</td>\n",
       "      <td>59</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.712147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>LLC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6437 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      ID ACCOUNT NUMBER SITE NUMBER  \\\n",
       "4       1141408-20080516         213785           1   \n",
       "5       2129534-20111129         368042           1   \n",
       "32      1337432-20120216         260686           1   \n",
       "36      1573623-20060216         286310           1   \n",
       "39      1479862-20040728         248389           1   \n",
       "...                  ...            ...         ...   \n",
       "142980  1953918-20111116          48119          21   \n",
       "143039  1594616-20081116         290838           1   \n",
       "143087  1426269-20050216          44679           1   \n",
       "143101  1118850-20060816         208989           1   \n",
       "143135  1741866-20081016         304120           3   \n",
       "\n",
       "                                     LEGAL NAME  \\\n",
       "4                  MCDONOUGH MECHANICAL SERVICE   \n",
       "5                 QUALITY PLUMBING SERVICES INC   \n",
       "32                   CENTRAL PRINT SERVICES INC   \n",
       "36                    M & T CREDIT SERVICES LLC   \n",
       "39                       PHOENIX EVENT SERVICES   \n",
       "...                                         ...   \n",
       "142980             BASTE FINANCIAL SERVICES INC   \n",
       "143039                   VEGA CLEANING SERVICES   \n",
       "143087                BUZY BEE BOARD-UP SERVICE   \n",
       "143101       SADIE'S TRANSPORTATION SERVICE INC   \n",
       "143135  ARAMARK HEALTHCARE SUPPORT SERVICES LLC   \n",
       "\n",
       "                         DOING BUSINESS AS NAME       CITY STATE ZIP CODE  \\\n",
       "4                  MCDONOUGH MECHANICAL SERVICE   WAUKEGAN    IL    60087   \n",
       "5                 QUALITY PLUMBING SERVICES INC  WOOD DALE    IL    60191   \n",
       "32                       CENTRAL PRINT SERVICES    CHICAGO    IL    60610   \n",
       "36                    M & T CREDIT SERVICES LLC    BUFFALO    NY    14203   \n",
       "39                         CHICAGO PARTY RENTAL     MCCOOK    IL    60525   \n",
       "...                                         ...        ...   ...      ...   \n",
       "142980                             NADEEM BASTE    CHICAGO    IL    60652   \n",
       "143039                   VEGA CLEANING SERVICES    CHICAGO    IL    60642   \n",
       "143087           BUZY BEE BOARD -UP SERVICE INC    CHICAGO    IL    60639   \n",
       "143101       SADIE'S TRANSPORTATION SERVICE INC    CHICAGO    IL    60616   \n",
       "143135  ARAMARK HEALTHCARE SUPPORT SERVICES INC    CHICAGO    IL    60624   \n",
       "\n",
       "       WARD PRECINCT  ...  LONGITUDE LICENSE STATUS PAYMENT YEAR  \\\n",
       "4         0        0  ... -87.679310            AAI       2008.0   \n",
       "5         0        0  ... -87.679310            AAI       2011.0   \n",
       "32       42       68  ... -87.638001            AAC       2012.0   \n",
       "36        0        0  ... -87.679310            AAI       2006.0   \n",
       "39        0        0  ... -87.679310            AAI       2004.0   \n",
       "...     ...      ...  ...        ...            ...          ...   \n",
       "142980   18        6  ... -87.738423            NaN       2011.0   \n",
       "143039    1       18  ... -87.661147            NaN       2008.0   \n",
       "143087   31       35  ... -87.756040            NaN       2005.0   \n",
       "143101    3        7  ... -87.623728            NaN       2006.0   \n",
       "143135   28       59  ... -87.712147            NaN       2008.0   \n",
       "\n",
       "       PAYMENT MONTH APPLICATION REQUIREMENTS COMPLETE YEAR DATE ISSUED YEAR  \\\n",
       "4                6.0                                 2008.0           2008.0   \n",
       "5               11.0                                 2011.0           2011.0   \n",
       "32               2.0                                 2011.0           2012.0   \n",
       "36               2.0                                 2005.0           2006.0   \n",
       "39               7.0                                 2004.0           2004.0   \n",
       "...              ...                                    ...              ...   \n",
       "142980          11.0                                 2011.0           2011.0   \n",
       "143039           9.0                                 2008.0           2008.0   \n",
       "143087           2.0                                 2004.0           2005.0   \n",
       "143101           8.0                                 2006.0           2006.0   \n",
       "143135           9.0                                 2008.0           2008.0   \n",
       "\n",
       "       LICENSE APPROVED YEAR  LICENSE STATUS CHANGE YEAR  LICENSE VALIDITY  \\\n",
       "4                     2008.0                      2010.0               2.0   \n",
       "5                     2011.0                      2010.0               2.0   \n",
       "32                    2012.0                      2013.0               2.0   \n",
       "36                    2006.0                      2010.0               1.0   \n",
       "39                    2004.0                      2010.0               1.0   \n",
       "...                      ...                         ...               ...   \n",
       "142980                2011.0                      2012.0               2.0   \n",
       "143039                2008.0                      2010.0               2.0   \n",
       "143087                2005.0                      2010.0               1.0   \n",
       "143101                2006.0                      2010.0               1.0   \n",
       "143135                2008.0                      2010.0               2.0   \n",
       "\n",
       "       LEGAL SUFFIX  \n",
       "4           SERVICE  \n",
       "5               INC  \n",
       "32              INC  \n",
       "36              LLC  \n",
       "39         SERVICES  \n",
       "...             ...  \n",
       "142980          INC  \n",
       "143039     SERVICES  \n",
       "143087      SERVICE  \n",
       "143101          INC  \n",
       "143135          LLC  \n",
       "\n",
       "[6437 rows x 28 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['LEGAL NAME'].str.contains('SERVICE')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 5 major types of businesses, inc. llc, corp, ltd, other. Converting all the entries in Legal Name abd Business Name column with respect to these business types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['BUSINESS TYPE'] = 'OTHER' #inc, llc, corp, ltd, other"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['BUSINESS TYPE'] = np.where(df['LEGAL NAME'].str.contains('INC'), 'INC', df['BUSINESS TYPE'])\n",
    "df['BUSINESS TYPE'] = np.where(df['LEGAL NAME'].str.contains('INCORPORATED'), 'INC', df['BUSINESS TYPE'])\n",
    "df['BUSINESS TYPE'] = np.where(df['DOING BUSINESS AS NAME'].str.contains('INC'), 'INC', df['BUSINESS TYPE'])\n",
    "df['BUSINESS TYPE'] = np.where(df['DOING BUSINESS AS NAME'].str.contains('INCORPORATED'), 'INC', df['BUSINESS TYPE'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df['BUSINESS TYPE'] = np.where(df['LEGAL NAME'].str.contains('LLC'), 'LLC', df['BUSINESS TYPE'])\n",
    "df['BUSINESS TYPE'] = np.where(df['DOING BUSINESS AS NAME'].str.contains('LLC'), 'LLC', df['BUSINESS TYPE'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['BUSINESS TYPE'] = np.where(df['LEGAL NAME'].str.contains('CO'), 'CORP', df['BUSINESS TYPE'])\n",
    "df['BUSINESS TYPE'] = np.where(df['LEGAL NAME'].str.contains('CORP'), 'CORP', df['BUSINESS TYPE'])\n",
    "df['BUSINESS TYPE'] = np.where(df['LEGAL NAME'].str.contains('CORPORATION'), 'CORP', df['BUSINESS TYPE'])\n",
    "df['BUSINESS TYPE'] = np.where(df['DOING BUSINESS AS NAME'].str.contains('CO'), 'CORP', df['BUSINESS TYPE'])\n",
    "df['BUSINESS TYPE'] = np.where(df['DOING BUSINESS AS NAME'].str.contains('CORP'), 'CORP', df['BUSINESS TYPE'])\n",
    "df['BUSINESS TYPE'] = np.where(df['DOING BUSINESS AS NAME'].str.contains('CORPORATION'), 'CORP', df['BUSINESS TYPE'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['BUSINESS TYPE'] = np.where(df['LEGAL NAME'].str.contains('LTD'), 'LTD', df['BUSINESS TYPE'])\n",
    "df['BUSINESS TYPE'] = np.where(df['LEGAL NAME'].str.contains('LIMITED'), 'LTD', df['BUSINESS TYPE'])\n",
    "df['BUSINESS TYPE'] = np.where(df['DOING BUSINESS AS NAME'].str.contains('LTD'), 'LTD', df['BUSINESS TYPE'])\n",
    "df['BUSINESS TYPE'] = np.where(df['DOING BUSINESS AS NAME'].str.contains('LIMITED'), 'LTD', df['BUSINESS TYPE'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>ID</th>\n",
       "      <th>ACCOUNT NUMBER</th>\n",
       "      <th>SITE NUMBER</th>\n",
       "      <th>LEGAL NAME</th>\n",
       "      <th>DOING BUSINESS AS NAME</th>\n",
       "      <th>CITY</th>\n",
       "      <th>STATE</th>\n",
       "      <th>ZIP CODE</th>\n",
       "      <th>WARD</th>\n",
       "      <th>PRECINCT</th>\n",
       "      <th>...</th>\n",
       "      <th>LICENSE STATUS</th>\n",
       "      <th>PAYMENT YEAR</th>\n",
       "      <th>PAYMENT MONTH</th>\n",
       "      <th>APPLICATION REQUIREMENTS COMPLETE YEAR</th>\n",
       "      <th>DATE ISSUED YEAR</th>\n",
       "      <th>LICENSE APPROVED YEAR</th>\n",
       "      <th>LICENSE STATUS CHANGE YEAR</th>\n",
       "      <th>LICENSE VALIDITY</th>\n",
       "      <th>LEGAL SUFFIX</th>\n",
       "      <th>BUSINESS TYPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>35342-20020816</td>\n",
       "      <td>32811</td>\n",
       "      <td>1</td>\n",
       "      <td>CARMEN CAHUE</td>\n",
       "      <td>CLAUDIA'S BRIDAL SHOP</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60623.0</td>\n",
       "      <td>22</td>\n",
       "      <td>28</td>\n",
       "      <td>...</td>\n",
       "      <td>AAI</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>CAHUE</td>\n",
       "      <td>OTHER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1358463-20051116</td>\n",
       "      <td>262311</td>\n",
       "      <td>29</td>\n",
       "      <td>ISLA TROPICAL INC</td>\n",
       "      <td>ISLA TROPICAL</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60618</td>\n",
       "      <td>33</td>\n",
       "      <td>23</td>\n",
       "      <td>...</td>\n",
       "      <td>AAC</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>INC</td>\n",
       "      <td>INC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1980233-20090722</td>\n",
       "      <td>345008</td>\n",
       "      <td>1</td>\n",
       "      <td>DJS REMODELING</td>\n",
       "      <td>DJS REMODELING INC</td>\n",
       "      <td>HIGHLAND</td>\n",
       "      <td>IL</td>\n",
       "      <td>60035</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>AAI</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>REMODELING</td>\n",
       "      <td>INC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1476582-20040211</td>\n",
       "      <td>273121</td>\n",
       "      <td>1</td>\n",
       "      <td>ALL-BRY CONSTRUCTION CO</td>\n",
       "      <td>ALL-BRY CONSTRUCTION CO</td>\n",
       "      <td>LEMONT</td>\n",
       "      <td>IL</td>\n",
       "      <td>60439</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>AAI</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>CO</td>\n",
       "      <td>CORP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1141408-20080516</td>\n",
       "      <td>213785</td>\n",
       "      <td>1</td>\n",
       "      <td>MCDONOUGH MECHANICAL SERVICE</td>\n",
       "      <td>MCDONOUGH MECHANICAL SERVICE</td>\n",
       "      <td>WAUKEGAN</td>\n",
       "      <td>IL</td>\n",
       "      <td>60087</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>AAI</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>SERVICE</td>\n",
       "      <td>OTHER</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>143136</th>\n",
       "      <td>1743194-20070816</td>\n",
       "      <td>266103</td>\n",
       "      <td>3</td>\n",
       "      <td>GOODHEALTH MEDICAL PRODUCTS INC</td>\n",
       "      <td>GOODHEALTH MEDICAL PRODUCTS INC</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60652</td>\n",
       "      <td>18</td>\n",
       "      <td>14</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>INC</td>\n",
       "      <td>INC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143137</th>\n",
       "      <td>2032383-20160716</td>\n",
       "      <td>63376</td>\n",
       "      <td>180</td>\n",
       "      <td>INTERPARK LLC</td>\n",
       "      <td>INTERPARK</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60654</td>\n",
       "      <td>42</td>\n",
       "      <td>22</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>LLC</td>\n",
       "      <td>LLC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143138</th>\n",
       "      <td>40376-20020216</td>\n",
       "      <td>55051</td>\n",
       "      <td>1</td>\n",
       "      <td>YUN HEE HWANG</td>\n",
       "      <td>TOP CLEANERS</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60657</td>\n",
       "      <td>44</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>HWANG</td>\n",
       "      <td>OTHER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143139</th>\n",
       "      <td>2064176-20121116</td>\n",
       "      <td>357381</td>\n",
       "      <td>1</td>\n",
       "      <td>D C A TOWING INCORPORATED</td>\n",
       "      <td>D C A TOWING INCORPORATED</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60638</td>\n",
       "      <td>23</td>\n",
       "      <td>18</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>INCORPORATED</td>\n",
       "      <td>CORP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143140</th>\n",
       "      <td>1954052-20090202</td>\n",
       "      <td>340610</td>\n",
       "      <td>1</td>\n",
       "      <td>MARIA T BEDOY</td>\n",
       "      <td>MARI'S CLEANING SERVICES</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60632</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>BEDOY</td>\n",
       "      <td>OTHER</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>143134 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      ID ACCOUNT NUMBER SITE NUMBER  \\\n",
       "0         35342-20020816          32811           1   \n",
       "1       1358463-20051116         262311          29   \n",
       "2       1980233-20090722         345008           1   \n",
       "3       1476582-20040211         273121           1   \n",
       "4       1141408-20080516         213785           1   \n",
       "...                  ...            ...         ...   \n",
       "143136  1743194-20070816         266103           3   \n",
       "143137  2032383-20160716          63376         180   \n",
       "143138    40376-20020216          55051           1   \n",
       "143139  2064176-20121116         357381           1   \n",
       "143140  1954052-20090202         340610           1   \n",
       "\n",
       "                             LEGAL NAME           DOING BUSINESS AS NAME  \\\n",
       "0                          CARMEN CAHUE            CLAUDIA'S BRIDAL SHOP   \n",
       "1                     ISLA TROPICAL INC                    ISLA TROPICAL   \n",
       "2                        DJS REMODELING               DJS REMODELING INC   \n",
       "3               ALL-BRY CONSTRUCTION CO          ALL-BRY CONSTRUCTION CO   \n",
       "4          MCDONOUGH MECHANICAL SERVICE     MCDONOUGH MECHANICAL SERVICE   \n",
       "...                                 ...                              ...   \n",
       "143136  GOODHEALTH MEDICAL PRODUCTS INC  GOODHEALTH MEDICAL PRODUCTS INC   \n",
       "143137                    INTERPARK LLC                        INTERPARK   \n",
       "143138                    YUN HEE HWANG                     TOP CLEANERS   \n",
       "143139        D C A TOWING INCORPORATED        D C A TOWING INCORPORATED   \n",
       "143140                    MARIA T BEDOY         MARI'S CLEANING SERVICES   \n",
       "\n",
       "            CITY STATE ZIP CODE WARD PRECINCT  ... LICENSE STATUS  \\\n",
       "0        CHICAGO    IL  60623.0   22       28  ...            AAI   \n",
       "1        CHICAGO    IL    60618   33       23  ...            AAC   \n",
       "2       HIGHLAND    IL    60035    0        0  ...            AAI   \n",
       "3         LEMONT    IL    60439    0        0  ...            AAI   \n",
       "4       WAUKEGAN    IL    60087    0        0  ...            AAI   \n",
       "...          ...   ...      ...  ...      ...  ...            ...   \n",
       "143136   CHICAGO    IL    60652   18       14  ...            NaN   \n",
       "143137   CHICAGO    IL    60654   42       22  ...            NaN   \n",
       "143138   CHICAGO    IL    60657   44        9  ...            NaN   \n",
       "143139   CHICAGO    IL    60638   23       18  ...            NaN   \n",
       "143140   CHICAGO    IL    60632   14       10  ...            NaN   \n",
       "\n",
       "       PAYMENT YEAR PAYMENT MONTH APPLICATION REQUIREMENTS COMPLETE YEAR  \\\n",
       "0            2002.0           8.0                                 2002.0   \n",
       "1            2005.0          11.0                                 2005.0   \n",
       "2            2009.0           7.0                                 2009.0   \n",
       "3            2004.0           2.0                                 2004.0   \n",
       "4            2008.0           6.0                                 2008.0   \n",
       "...             ...           ...                                    ...   \n",
       "143136       2007.0           9.0                                 2007.0   \n",
       "143137       2016.0           6.0                                 2016.0   \n",
       "143138       2002.0           1.0                                 2001.0   \n",
       "143139       2012.0          12.0                                 2012.0   \n",
       "143140       2009.0           1.0                                 2009.0   \n",
       "\n",
       "       DATE ISSUED YEAR LICENSE APPROVED YEAR LICENSE STATUS CHANGE YEAR  \\\n",
       "0                2006.0                2002.0                     2010.0   \n",
       "1                2006.0                2006.0                     2006.0   \n",
       "2                2009.0                2009.0                     2010.0   \n",
       "3                2004.0                2004.0                     2010.0   \n",
       "4                2008.0                2008.0                     2010.0   \n",
       "...                 ...                   ...                        ...   \n",
       "143136           2007.0                2007.0                     2008.0   \n",
       "143137           2016.0                2016.0                     2018.0   \n",
       "143138           2002.0                2002.0                     2010.0   \n",
       "143139           2012.0                2012.0                     2012.0   \n",
       "143140           2009.0                2009.0                     2010.0   \n",
       "\n",
       "        LICENSE VALIDITY  LEGAL SUFFIX BUSINESS TYPE  \n",
       "0                    1.0         CAHUE         OTHER  \n",
       "1                    1.0           INC           INC  \n",
       "2                    2.0    REMODELING           INC  \n",
       "3                    1.0            CO          CORP  \n",
       "4                    2.0       SERVICE         OTHER  \n",
       "...                  ...           ...           ...  \n",
       "143136               2.0           INC           INC  \n",
       "143137               2.0           LLC           LLC  \n",
       "143138               1.0         HWANG         OTHER  \n",
       "143139               2.0  INCORPORATED          CORP  \n",
       "143140               0.0         BEDOY         OTHER  \n",
       "\n",
       "[143134 rows x 29 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "INC      45808\n",
       "CORP     43078\n",
       "OTHER    41155\n",
       "LLC       9802\n",
       "LTD       3291\n",
       "Name: BUSINESS TYPE, dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['BUSINESS TYPE'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(['LEGAL NAME','DOING BUSINESS AS NAME'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>ACCOUNT NUMBER</th>\n",
       "      <th>SITE NUMBER</th>\n",
       "      <th>CITY</th>\n",
       "      <th>STATE</th>\n",
       "      <th>ZIP CODE</th>\n",
       "      <th>WARD</th>\n",
       "      <th>PRECINCT</th>\n",
       "      <th>WARD PRECINCT</th>\n",
       "      <th>POLICE DISTRICT</th>\n",
       "      <th>...</th>\n",
       "      <th>LICENSE STATUS</th>\n",
       "      <th>PAYMENT YEAR</th>\n",
       "      <th>PAYMENT MONTH</th>\n",
       "      <th>APPLICATION REQUIREMENTS COMPLETE YEAR</th>\n",
       "      <th>DATE ISSUED YEAR</th>\n",
       "      <th>LICENSE APPROVED YEAR</th>\n",
       "      <th>LICENSE STATUS CHANGE YEAR</th>\n",
       "      <th>LICENSE VALIDITY</th>\n",
       "      <th>LEGAL SUFFIX</th>\n",
       "      <th>BUSINESS TYPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>35342-20020816</td>\n",
       "      <td>32811</td>\n",
       "      <td>1</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60623.0</td>\n",
       "      <td>22</td>\n",
       "      <td>28</td>\n",
       "      <td>22-28</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>AAI</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>CAHUE</td>\n",
       "      <td>OTHER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1358463-20051116</td>\n",
       "      <td>262311</td>\n",
       "      <td>29</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60618</td>\n",
       "      <td>33</td>\n",
       "      <td>23</td>\n",
       "      <td>33-23</td>\n",
       "      <td>17</td>\n",
       "      <td>...</td>\n",
       "      <td>AAC</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>INC</td>\n",
       "      <td>INC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1980233-20090722</td>\n",
       "      <td>345008</td>\n",
       "      <td>1</td>\n",
       "      <td>HIGHLAND</td>\n",
       "      <td>IL</td>\n",
       "      <td>60035</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>AAI</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>REMODELING</td>\n",
       "      <td>INC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1476582-20040211</td>\n",
       "      <td>273121</td>\n",
       "      <td>1</td>\n",
       "      <td>LEMONT</td>\n",
       "      <td>IL</td>\n",
       "      <td>60439</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>AAI</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>CO</td>\n",
       "      <td>CORP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1141408-20080516</td>\n",
       "      <td>213785</td>\n",
       "      <td>1</td>\n",
       "      <td>WAUKEGAN</td>\n",
       "      <td>IL</td>\n",
       "      <td>60087</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>AAI</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>SERVICE</td>\n",
       "      <td>OTHER</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>143136</th>\n",
       "      <td>1743194-20070816</td>\n",
       "      <td>266103</td>\n",
       "      <td>3</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60652</td>\n",
       "      <td>18</td>\n",
       "      <td>14</td>\n",
       "      <td>18-14</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>INC</td>\n",
       "      <td>INC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143137</th>\n",
       "      <td>2032383-20160716</td>\n",
       "      <td>63376</td>\n",
       "      <td>180</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60654</td>\n",
       "      <td>42</td>\n",
       "      <td>22</td>\n",
       "      <td>42-22</td>\n",
       "      <td>18</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>LLC</td>\n",
       "      <td>LLC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143138</th>\n",
       "      <td>40376-20020216</td>\n",
       "      <td>55051</td>\n",
       "      <td>1</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60657</td>\n",
       "      <td>44</td>\n",
       "      <td>9</td>\n",
       "      <td>44-9</td>\n",
       "      <td>19</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>HWANG</td>\n",
       "      <td>OTHER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143139</th>\n",
       "      <td>2064176-20121116</td>\n",
       "      <td>357381</td>\n",
       "      <td>1</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60638</td>\n",
       "      <td>23</td>\n",
       "      <td>18</td>\n",
       "      <td>23-18</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>INCORPORATED</td>\n",
       "      <td>CORP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143140</th>\n",
       "      <td>1954052-20090202</td>\n",
       "      <td>340610</td>\n",
       "      <td>1</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60632</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>14-10</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>BEDOY</td>\n",
       "      <td>OTHER</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>143134 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      ID ACCOUNT NUMBER SITE NUMBER      CITY STATE ZIP CODE  \\\n",
       "0         35342-20020816          32811           1   CHICAGO    IL  60623.0   \n",
       "1       1358463-20051116         262311          29   CHICAGO    IL    60618   \n",
       "2       1980233-20090722         345008           1  HIGHLAND    IL    60035   \n",
       "3       1476582-20040211         273121           1    LEMONT    IL    60439   \n",
       "4       1141408-20080516         213785           1  WAUKEGAN    IL    60087   \n",
       "...                  ...            ...         ...       ...   ...      ...   \n",
       "143136  1743194-20070816         266103           3   CHICAGO    IL    60652   \n",
       "143137  2032383-20160716          63376         180   CHICAGO    IL    60654   \n",
       "143138    40376-20020216          55051           1   CHICAGO    IL    60657   \n",
       "143139  2064176-20121116         357381           1   CHICAGO    IL    60638   \n",
       "143140  1954052-20090202         340610           1   CHICAGO    IL    60632   \n",
       "\n",
       "       WARD PRECINCT WARD PRECINCT POLICE DISTRICT  ... LICENSE STATUS  \\\n",
       "0        22       28         22-28              10  ...            AAI   \n",
       "1        33       23         33-23              17  ...            AAC   \n",
       "2         0        0             0               0  ...            AAI   \n",
       "3         0        0             0               0  ...            AAI   \n",
       "4         0        0             0               0  ...            AAI   \n",
       "...     ...      ...           ...             ...  ...            ...   \n",
       "143136   18       14         18-14               8  ...            NaN   \n",
       "143137   42       22         42-22              18  ...            NaN   \n",
       "143138   44        9          44-9              19  ...            NaN   \n",
       "143139   23       18         23-18               8  ...            NaN   \n",
       "143140   14       10         14-10               8  ...            NaN   \n",
       "\n",
       "       PAYMENT YEAR PAYMENT MONTH APPLICATION REQUIREMENTS COMPLETE YEAR  \\\n",
       "0            2002.0           8.0                                 2002.0   \n",
       "1            2005.0          11.0                                 2005.0   \n",
       "2            2009.0           7.0                                 2009.0   \n",
       "3            2004.0           2.0                                 2004.0   \n",
       "4            2008.0           6.0                                 2008.0   \n",
       "...             ...           ...                                    ...   \n",
       "143136       2007.0           9.0                                 2007.0   \n",
       "143137       2016.0           6.0                                 2016.0   \n",
       "143138       2002.0           1.0                                 2001.0   \n",
       "143139       2012.0          12.0                                 2012.0   \n",
       "143140       2009.0           1.0                                 2009.0   \n",
       "\n",
       "       DATE ISSUED YEAR  LICENSE APPROVED YEAR  LICENSE STATUS CHANGE YEAR  \\\n",
       "0                2006.0                 2002.0                      2010.0   \n",
       "1                2006.0                 2006.0                      2006.0   \n",
       "2                2009.0                 2009.0                      2010.0   \n",
       "3                2004.0                 2004.0                      2010.0   \n",
       "4                2008.0                 2008.0                      2010.0   \n",
       "...                 ...                    ...                         ...   \n",
       "143136           2007.0                 2007.0                      2008.0   \n",
       "143137           2016.0                 2016.0                      2018.0   \n",
       "143138           2002.0                 2002.0                      2010.0   \n",
       "143139           2012.0                 2012.0                      2012.0   \n",
       "143140           2009.0                 2009.0                      2010.0   \n",
       "\n",
       "       LICENSE VALIDITY  LEGAL SUFFIX  BUSINESS TYPE  \n",
       "0                   1.0         CAHUE          OTHER  \n",
       "1                   1.0           INC            INC  \n",
       "2                   2.0    REMODELING            INC  \n",
       "3                   1.0            CO           CORP  \n",
       "4                   2.0       SERVICE          OTHER  \n",
       "...                 ...           ...            ...  \n",
       "143136              2.0           INC            INC  \n",
       "143137              2.0           LLC            LLC  \n",
       "143138              1.0         HWANG          OTHER  \n",
       "143139              2.0  INCORPORATED           CORP  \n",
       "143140              0.0         BEDOY          OTHER  \n",
       "\n",
       "[143134 rows x 27 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extracting useful information from License Description column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Limited Business License           78172\n",
       "Home Repair                        12493\n",
       "Retail Food Establishment           7109\n",
       "Home Occupation                     5830\n",
       "Peddler, non-food                   3446\n",
       "                                   ...  \n",
       "Transportation Network Provider        1\n",
       "Guard Dog Service                      1\n",
       "Shared Housing Unit Operator           1\n",
       "Food - Shared Kitchen                  1\n",
       "License Manager                        1\n",
       "Name: LICENSE DESCRIPTION, Length: 129, dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['LICENSE DESCRIPTION'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Motor Vehicle Repair : Engine Only (Class II)',\n",
       "       'Motor Vehicle Repair; Specialty(Class I)',\n",
       "       'Motor Vehicle Repair: Engine/Body(Class III)'], dtype=object)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['LICENSE DESCRIPTION'][df['LICENSE DESCRIPTION'].str.contains('Motor Vehicle Repair')].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extracting useful information from the LICENSE DESCRIPTION column, above given 3 classes can be merged into one single class, converting other entries in the same way below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Repossessor Class B Employee', 'Repossessor Class A',\n",
       "       'Repossessor Class B'], dtype=object)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['LICENSE DESCRIPTION'][df['LICENSE DESCRIPTION'].str.contains('Repossessor')].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Peddler, food (fruits and vegtables only)', 'Peddler, non-food',\n",
       "       'Peddler,food - (fruits and vegetables only) - special',\n",
       "       'Peddler, non-food, special', 'Junk Peddler', 'Peddler License'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['LICENSE DESCRIPTION'][df['LICENSE DESCRIPTION'].str.contains('Peddler')].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Day Care Center 2 - 6 Years', 'Day Care Center Under 2 Years',\n",
       "       'Day Care Center Under 2 and 2 - 6 Years'], dtype=object)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['LICENSE DESCRIPTION'][df['LICENSE DESCRIPTION'].str.contains('Day Care Center')].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Itinerant Merchant, Class I', 'Itinerant Merchant, Class II'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['LICENSE DESCRIPTION'][df['LICENSE DESCRIPTION'].str.contains('Itinerant')].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Expediter - Class B Employee', 'Expediter - Class A',\n",
       "       'Expediter - Class B'], dtype=object)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['LICENSE DESCRIPTION'][df['LICENSE DESCRIPTION'].str.contains('Expediter')].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Single Room Occupancy Class II', 'Single Room Occupancy Class I'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['LICENSE DESCRIPTION'][df['LICENSE DESCRIPTION'].str.contains('Room Occupancy')].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Secondhand Dealer (No Valuable Objects)',\n",
       "       \"Secondhand Dealer - Children's Products\", 'Secondhand Dealer'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['LICENSE DESCRIPTION'][df['LICENSE DESCRIPTION'].str.contains('Secondhand Dealer')].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([\"Airport Pushcart Liquor O'Hare - Class A\",\n",
       "       'Airport Pushcart Liquor Midway - Class A',\n",
       "       'Liquor Airport Pushcart License'], dtype=object)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['LICENSE DESCRIPTION'][df['LICENSE DESCRIPTION'].str.contains('Pushcart')].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['LICENSE DESCRIPTION'] = df['LICENSE DESCRIPTION'].replace('Motor Vehicle Repair : Engine Only (Class II)', 'Motor Vehicle Repair')\n",
    "df['LICENSE DESCRIPTION'] = df['LICENSE DESCRIPTION'].replace('Motor Vehicle Repair: Engine/Body(Class III)', 'Motor Vehicle Repair')\n",
    "df['LICENSE DESCRIPTION'] = df['LICENSE DESCRIPTION'].replace('Motor Vehicle Repair; Specialty(Class I)', 'Motor Vehicle Repair')\n",
    "df['LICENSE DESCRIPTION'] = df['LICENSE DESCRIPTION'].replace('Day Care Center Under 2 Years', 'Day Care Center')\n",
    "df['LICENSE DESCRIPTION'] = df['LICENSE DESCRIPTION'].replace('Day Care Center 2 - 6 Years', 'Day Care Center')\n",
    "df['LICENSE DESCRIPTION'] = df['LICENSE DESCRIPTION'].replace('Day Care Center Under 2 and 2 - 6 Years', 'Day Care Center')\n",
    "df['LICENSE DESCRIPTION'] = df['LICENSE DESCRIPTION'].replace('Peddler, non-food', 'Peddler')\n",
    "df['LICENSE DESCRIPTION'] = df['LICENSE DESCRIPTION'].replace('Peddler, non-food, special', 'Peddler')\n",
    "df['LICENSE DESCRIPTION'] = df['LICENSE DESCRIPTION'].replace('Junk Peddler', 'Peddler')\n",
    "df['LICENSE DESCRIPTION'] = df['LICENSE DESCRIPTION'].replace('Peddler License', 'Peddler')\n",
    "df['LICENSE DESCRIPTION'] = df['LICENSE DESCRIPTION'].replace('Peddler, food (fruits and vegtables only)', 'Peddler')\n",
    "df['LICENSE DESCRIPTION'] = df['LICENSE DESCRIPTION'].replace('Peddler,food - (fruits and vegetables only) - special', 'Peddler')\n",
    "df['LICENSE DESCRIPTION'] = df['LICENSE DESCRIPTION'].replace('Single Room Occupancy Class I', 'Single Room Occupancy')\n",
    "df['LICENSE DESCRIPTION'] = df['LICENSE DESCRIPTION'].replace('Single Room Occupancy Class II', 'Single Room Occupancy')\n",
    "df['LICENSE DESCRIPTION'] = df['LICENSE DESCRIPTION'].replace('Tire Facilty Class I (100 - 1,000 Tires)', 'Tire Facilty')\n",
    "df['LICENSE DESCRIPTION'] = df['LICENSE DESCRIPTION'].replace('Tire Facility Class II (1,001 - 5,000 Tires)', 'Tire Facilty')\n",
    "df['LICENSE DESCRIPTION'] = df['LICENSE DESCRIPTION'].replace('Tire Facility Class III (5,001 - More Tires)', 'Tire Facilty')\n",
    "df['LICENSE DESCRIPTION'] = df['LICENSE DESCRIPTION'].replace('Repossessor Class A', 'Repossessor')\n",
    "df['LICENSE DESCRIPTION'] = df['LICENSE DESCRIPTION'].replace('Repossessor Class B', 'Repossessor')\n",
    "df['LICENSE DESCRIPTION'] = df['LICENSE DESCRIPTION'].replace('Repossessor Class B Employee', 'Repossessor')\n",
    "df['LICENSE DESCRIPTION'] = df['LICENSE DESCRIPTION'].replace('Expediter - Class B', 'Expediter')\n",
    "df['LICENSE DESCRIPTION'] = df['LICENSE DESCRIPTION'].replace('Expediter - Class A', 'Expediter')\n",
    "df['LICENSE DESCRIPTION'] = df['LICENSE DESCRIPTION'].replace('Expediter - Class B Employee', 'Expediter')\n",
    "df['LICENSE DESCRIPTION'] = df['LICENSE DESCRIPTION'].replace('Itinerant Merchant, Class II', 'Itinerant Merchant')\n",
    "df['LICENSE DESCRIPTION'] = df['LICENSE DESCRIPTION'].replace('Itinerant Merchant, Class I', 'Itinerant Merchant')\n",
    "df['LICENSE DESCRIPTION'] = df['LICENSE DESCRIPTION'].replace('Secondhand Dealer (No Valuable Objects)', 'Secondhand Dealer')\n",
    "df['LICENSE DESCRIPTION'] = df['LICENSE DESCRIPTION'].replace(\"Secondhand Dealer - Children's Products\", 'Secondhand Dealer')\n",
    "df['LICENSE DESCRIPTION'] = df['LICENSE DESCRIPTION'].replace(\"Airport Pushcart Liquor O'Hare\", 'Pushcart')\n",
    "df['LICENSE DESCRIPTION'] = df['LICENSE DESCRIPTION'].replace(\"Airport Pushcart Liquor Midway - Class A\", 'Pushcart')\n",
    "df['LICENSE DESCRIPTION'] = df['LICENSE DESCRIPTION'].replace(\"Liquor Airport Pushcart License\", 'Pushcart')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option(\"display.max_rows\", None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Limited Business License                                        78172\n",
       "Home Repair                                                     12493\n",
       "Retail Food Establishment                                        7109\n",
       "Home Occupation                                                  5830\n",
       "Peddler                                                          5325\n",
       "Motor Vehicle Repair                                             3280\n",
       "Massage Therapist                                                2521\n",
       "Tobacco                                                          2426\n",
       "Hazardous Materials                                              2246\n",
       "Street Performer                                                 2238\n",
       "Scavenger, Private                                               1737\n",
       "Residential Real Estate Developer                                1563\n",
       "Consumption on Premises - Incidental Activity                    1528\n",
       "Day Care Center                                                  1252\n",
       "Manufacturing Establishments                                     1097\n",
       "Regulated Business License                                       1093\n",
       "Mobile Food Dispenser                                             938\n",
       "Broker                                                            937\n",
       "Automatic Amusement Device Operator                               818\n",
       "Electronic Equipment Repair                                       683\n",
       "Tavern                                                            518\n",
       "Package Goods                                                     507\n",
       "Mobile Frozen Desserts Dispenser - Non-Motorized                  503\n",
       "Massage Establishment                                             488\n",
       "Tire Facilty                                                      452\n",
       "Public Place of Amusement                                         332\n",
       "Expediter                                                         330\n",
       "Outdoor Patio                                                     325\n",
       "Repossessor                                                       311\n",
       "Secondhand Dealer                                                 304\n",
       "Wholesale Food Establishment                                      295\n",
       "Commercial Garage                                                 293\n",
       "Hotel                                                             292\n",
       "Music and Dance                                                   235\n",
       "Accessory Garage                                                  227\n",
       "Valet Parking Operator                                            226\n",
       "Retail Computing Center                                           222\n",
       "Filling Station                                                   221\n",
       "Late Hour                                                         202\n",
       "Auctioneer                                                        187\n",
       "Kennels and Catteries                                             187\n",
       "Long-Term Care Facility                                           176\n",
       "Laundry, Late Hour                                                165\n",
       "Children's Activities Facilities                                  151\n",
       "Single Room Occupancy                                             143\n",
       "Motor Vehicle Services License                                    138\n",
       "Animal Care Facility                                              135\n",
       "Veterinary Hospital                                               131\n",
       "Itinerant Merchant                                                128\n",
       "Grooming Facility                                                 116\n",
       "Pet Shop                                                          114\n",
       "Children's Services Facility License                              109\n",
       "Board-Up Work                                                     108\n",
       "Body Piercing                                                     102\n",
       "Raffles                                                            89\n",
       "Vacation Rental                                                    88\n",
       "Mobile Food License                                                85\n",
       "Caterer's Liquor License                                           84\n",
       "Performing Arts Venue                                              78\n",
       "Explosives, Certificate of Fitness                                 78\n",
       "Tobacco Vending Machine Operator                                   72\n",
       "Laboratories                                                       69\n",
       "Navy Pier - Outdoor Fixed                                          66\n",
       "Tobacco Dealer Wholesale                                           64\n",
       "Hospital                                                           63\n",
       "Tobacco Vending, Individual                                        60\n",
       "Day Labor Agency                                                   57\n",
       "Navy Pier Vendor (Non-Food)                                        54\n",
       "Animal Care License                                                45\n",
       "Night Care Privilege                                               41\n",
       "Retail Food Est.-Supplemental License for Dog-Friendly Areas       38\n",
       "Animal Exhibition                                                  33\n",
       "Bed-And-Breakfast Establishment                                    33\n",
       "Not-For-Profit Club                                                23\n",
       "Humane Society                                                     21\n",
       "Public Place of Amusement-TCC                                      20\n",
       "Shared Kitchen User                                                20\n",
       "Caterer's Registration (Liquor)                                    20\n",
       "Wrigley Field                                                      18\n",
       "Pharmaceutical Representative                                      15\n",
       "Pawnbroker                                                         14\n",
       "Produce Merchant                                                   13\n",
       "Special Event Food                                                 10\n",
       "Food - Shared Kitchen - Supplemental                                9\n",
       "Riverwalk Venue Liquor License                                      9\n",
       "Weapons Dealer                                                      8\n",
       "Tobacco Sampler                                                     7\n",
       "Special Event Liquor                                                7\n",
       "Navy Pier - Mobile                                                  6\n",
       "Explosives                                                          6\n",
       "Bicycle Messenger Service                                           6\n",
       "Assisted Living/Shared Housing Establishment                        6\n",
       "Emerging Business                                                   5\n",
       "Industrial Private Event Venue                                      5\n",
       "Pushcart                                                            4\n",
       "Indoor Special Event                                                3\n",
       "Private Booting Operation                                           3\n",
       "Affiliation                                                         3\n",
       "Class A - Indoor Special Event                                      3\n",
       "Taxicab Two-Way Dispatch Service License                            2\n",
       "License Broker                                                      2\n",
       "Airport Pushcart Liquor O'Hare - Class A                            2\n",
       "Navy Pier Vendor (Food)                                             2\n",
       "Navy Pier Kiosk License                                             1\n",
       "Transportation Network Provider                                     1\n",
       "Guard Dog Service                                                   1\n",
       "License Manager                                                     1\n",
       "Shared Housing Unit Operator                                        1\n",
       "Food - Shared Kitchen                                               1\n",
       "Name: LICENSE DESCRIPTION, dtype: int64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['LICENSE DESCRIPTION'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option(\"display.max_rows\", None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                                            0\n",
       "ACCOUNT NUMBER                                0\n",
       "SITE NUMBER                                   0\n",
       "CITY                                          0\n",
       "STATE                                         0\n",
       "ZIP CODE                                      0\n",
       "WARD                                          0\n",
       "PRECINCT                                      0\n",
       "WARD PRECINCT                                 0\n",
       "POLICE DISTRICT                               0\n",
       "LICENSE CODE                                  0\n",
       "LICENSE DESCRIPTION                           0\n",
       "LICENSE NUMBER                                0\n",
       "APPLICATION TYPE                              0\n",
       "CONDITIONAL APPROVAL                          0\n",
       "LATITUDE                                      0\n",
       "LONGITUDE                                     0\n",
       "LICENSE STATUS                            57239\n",
       "PAYMENT YEAR                                  0\n",
       "PAYMENT MONTH                                 0\n",
       "APPLICATION REQUIREMENTS COMPLETE YEAR        0\n",
       "DATE ISSUED YEAR                              0\n",
       "LICENSE APPROVED YEAR                         0\n",
       "LICENSE STATUS CHANGE YEAR                    0\n",
       "LICENSE VALIDITY                              0\n",
       "LEGAL SUFFIX                                  0\n",
       "BUSINESS TYPE                                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option(\"display.max_rows\", 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataset contains null entries as well in the target variable, Using the NON NULL license status entries for training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    " train_df =df[df['LICENSE STATUS'].notna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(85895, 27)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df =df[df['LICENSE STATUS'].isna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(57239, 27)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "57239"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df['LICENSE STATUS'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df = test_df.drop('LICENSE STATUS', 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a dataframe with Null entries in the target variable, these entries will be predicted based on our trained model on non null entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>ID</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>85902</th>\n",
       "      <td>1516615-20120216</td>\n",
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       "    <tr>\n",
       "      <th>85903</th>\n",
       "      <td>69813-20110616</td>\n",
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       "    <tr>\n",
       "      <th>85904</th>\n",
       "      <td>1519227-20041116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85905</th>\n",
       "      <td>1922025-20100816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85906</th>\n",
       "      <td>1675683-20070816</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     ID\n",
       "85902  1516615-20120216\n",
       "85903    69813-20110616\n",
       "85904  1519227-20041116\n",
       "85905  1922025-20100816\n",
       "85906  1675683-20070816"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Final_Predict = pd.DataFrame({'ID': test_df.ID})\n",
    "Final_Predict.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df.drop('ID', 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = train_df.drop('ID', 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ACCOUNT NUMBER                0\n",
       "SITE NUMBER                   0\n",
       "CITY                          0\n",
       "STATE                         0\n",
       "ZIP CODE                      0\n",
       "                             ..\n",
       "LICENSE APPROVED YEAR         0\n",
       "LICENSE STATUS CHANGE YEAR    0\n",
       "LICENSE VALIDITY              0\n",
       "LEGAL SUFFIX                  0\n",
       "BUSINESS TYPE                 0\n",
       "Length: 26, dtype: int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ACCOUNT NUMBER                0\n",
       "SITE NUMBER                   0\n",
       "CITY                          0\n",
       "STATE                         0\n",
       "ZIP CODE                      0\n",
       "                             ..\n",
       "LICENSE APPROVED YEAR         0\n",
       "LICENSE STATUS CHANGE YEAR    0\n",
       "LICENSE VALIDITY              0\n",
       "LEGAL SUFFIX                  0\n",
       "BUSINESS TYPE                 0\n",
       "Length: 25, dtype: int64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df.drop('LEGAL SUFFIX', 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "                     ID\n",
       "85902  1516615-20120216\n",
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       "85905  1922025-20100816\n",
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  {
   "cell_type": "code",
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       "      <td>60618</td>\n",
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       "      <td>2009.0</td>\n",
       "      <td>2009.0</td>\n",
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       "      <td>0</td>\n",
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       "  ACCOUNT NUMBER SITE NUMBER      CITY STATE ZIP CODE WARD PRECINCT  \\\n",
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       "1         262311          29   CHICAGO    IL    60618   33       23   \n",
       "2         345008           1  HIGHLAND    IL    60035    0        0   \n",
       "3         273121           1    LEMONT    IL    60439    0        0   \n",
       "4         213785           1  WAUKEGAN    IL    60087    0        0   \n",
       "\n",
       "  WARD PRECINCT POLICE DISTRICT LICENSE CODE  ... LICENSE STATUS PAYMENT YEAR  \\\n",
       "0         22-28              10         1010  ...            AAI       2002.0   \n",
       "1         33-23              17         1315  ...            AAC       2005.0   \n",
       "2             0               0         1011  ...            AAI       2009.0   \n",
       "3             0               0         1010  ...            AAI       2004.0   \n",
       "4             0               0         1010  ...            AAI       2008.0   \n",
       "\n",
       "  PAYMENT MONTH APPLICATION REQUIREMENTS COMPLETE YEAR  DATE ISSUED YEAR  \\\n",
       "0           8.0                                 2002.0            2006.0   \n",
       "1          11.0                                 2005.0            2006.0   \n",
       "2           7.0                                 2009.0            2009.0   \n",
       "3           2.0                                 2004.0            2004.0   \n",
       "4           6.0                                 2008.0            2008.0   \n",
       "\n",
       "   LICENSE APPROVED YEAR LICENSE STATUS CHANGE YEAR  LICENSE VALIDITY  \\\n",
       "0                 2002.0                     2010.0               1.0   \n",
       "1                 2006.0                     2006.0               1.0   \n",
       "2                 2009.0                     2010.0               2.0   \n",
       "3                 2004.0                     2010.0               1.0   \n",
       "4                 2008.0                     2010.0               2.0   \n",
       "\n",
       "   LEGAL SUFFIX  BUSINESS TYPE  \n",
       "0         CAHUE          OTHER  \n",
       "1           INC            INC  \n",
       "2    REMODELING            INC  \n",
       "3            CO           CORP  \n",
       "4       SERVICE          OTHER  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "      <td>2012.0</td>\n",
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       "      <td>60636.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>7.0</td>\n",
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       "      <td>41.786485</td>\n",
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       "      <td>11.0</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2012.0</td>\n",
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       "      <th>85904</th>\n",
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       "      <td>60605</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4-8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1020</td>\n",
       "      <td>...</td>\n",
       "      <td>41.868269</td>\n",
       "      <td>-87.625695</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>CORP</td>\n",
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       "    <tr>\n",
       "      <th>85905</th>\n",
       "      <td>335243</td>\n",
       "      <td>1</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60827</td>\n",
       "      <td>9.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>9-33</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1607</td>\n",
       "      <td>...</td>\n",
       "      <td>41.655762</td>\n",
       "      <td>-87.608686</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <th>85906</th>\n",
       "      <td>283018</td>\n",
       "      <td>3</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IL</td>\n",
       "      <td>60632</td>\n",
       "      <td>15.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>15-26</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1010</td>\n",
       "      <td>...</td>\n",
       "      <td>41.816969</td>\n",
       "      <td>-87.699785</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>CORP</td>\n",
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       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ACCOUNT NUMBER SITE NUMBER     CITY STATE ZIP CODE  WARD PRECINCT  \\\n",
       "85902         280120           1  CHICAGO    IL    60610  42.0     67.0   \n",
       "85903          65043           1  CHICAGO    IL  60636.0  16.0      5.0   \n",
       "85904         281270           1  CHICAGO    IL    60605   4.0      8.0   \n",
       "85905         335243           1  CHICAGO    IL    60827   9.0     33.0   \n",
       "85906         283018           3  CHICAGO    IL    60632  15.0     26.0   \n",
       "\n",
       "      WARD PRECINCT POLICE DISTRICT LICENSE CODE  ...   LATITUDE  LONGITUDE  \\\n",
       "85902         42-67            18.0         1020  ...  41.890047 -87.637467   \n",
       "85903          16-5             7.0         1010  ...  41.786485 -87.676665   \n",
       "85904           4-8             1.0         1020  ...  41.868269 -87.625695   \n",
       "85905          9-33             5.0         1607  ...  41.655762 -87.608686   \n",
       "85906         15-26             9.0         1010  ...  41.816969 -87.699785   \n",
       "\n",
       "      PAYMENT YEAR PAYMENT MONTH  APPLICATION REQUIREMENTS COMPLETE YEAR  \\\n",
       "85902       2012.0           1.0                                  2011.0   \n",
       "85903       2012.0          11.0                                  2011.0   \n",
       "85904       2004.0          12.0                                  2004.0   \n",
       "85905       2010.0          11.0                                  2010.0   \n",
       "85906       2007.0           8.0                                  2007.0   \n",
       "\n",
       "       DATE ISSUED YEAR  LICENSE APPROVED YEAR  LICENSE STATUS CHANGE YEAR  \\\n",
       "85902            2012.0                 2012.0                      2012.0   \n",
       "85903            2012.0                 2012.0                      2012.0   \n",
       "85904            2004.0                 2004.0                      2010.0   \n",
       "85905            2010.0                 2008.0                      2012.0   \n",
       "85906            2007.0                 2007.0                      2008.0   \n",
       "\n",
       "       LICENSE VALIDITY  BUSINESS TYPE  \n",
       "85902               2.0            LLC  \n",
       "85903               2.0            INC  \n",
       "85904               1.0           CORP  \n",
       "85905               2.0          OTHER  \n",
       "85906               2.0           CORP  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the caregorical variables must be encoded before feeding the to the algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "enc = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = train_df.apply(lambda col: enc.fit_transform(col.astype(str)), axis=0, result_type='expand')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df = test_df.apply(lambda col: enc.fit_transform(col.astype(str)), axis=0, result_type='expand')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
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       "   ACCOUNT NUMBER  SITE NUMBER  CITY  STATE  ZIP CODE  WARD  PRECINCT  \\\n",
       "0           21179            0   193     16      1384    15        21   \n",
       "1            8446           97   193     16      1373    27        16   \n",
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       "\n",
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       "\n",
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       "0             2             10                                       2   \n",
       "1             5              2                                       5   \n",
       "2             9              9                                       9   \n",
       "3             4              4                                       4   \n",
       "4             8              8                                       8   \n",
       "\n",
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       "0                 4                      2                           8   \n",
       "1                 4                      6                           4   \n",
       "2                 7                      9                           8   \n",
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       "4                 6                      8                           8   \n",
       "\n",
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       "3                 1          1725              0  \n",
       "4                 7          7647              4  \n",
       "\n",
       "[5 rows x 26 columns]"
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     "execution_count": 99,
     "metadata": {},
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   "source": [
    "train_df.head()"
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  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
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       "       ACCOUNT NUMBER  SITE NUMBER  CITY  STATE  ZIP CODE  WARD  PRECINCT  \\\n",
       "85902           12084            0    59     13       289    37        64   \n",
       "85903           38247            0    59     13       339     8        45   \n",
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       "\n",
       "       WARD PRECINCT  POLICE DISTRICT  LICENSE CODE  ...  LATITUDE  LONGITUDE  \\\n",
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       "85903            339               23            16  ...      5605      14435   \n",
       "85904           1624                2            28  ...     11035       3780   \n",
       "85905           2430               21           161  ...        61       2168   \n",
       "85906            278               25            16  ...      7762      18852   \n",
       "\n",
       "       PAYMENT YEAR  PAYMENT MONTH  APPLICATION REQUIREMENTS COMPLETE YEAR  \\\n",
       "85902            12              0                                      11   \n",
       "85903            12              2                                      11   \n",
       "85904             4              3                                       4   \n",
       "85905            10              2                                      10   \n",
       "85906             7             10                                       7   \n",
       "\n",
       "       DATE ISSUED YEAR  LICENSE APPROVED YEAR  LICENSE STATUS CHANGE YEAR  \\\n",
       "85902                10                     12                          10   \n",
       "85903                10                     12                          10   \n",
       "85904                 2                      4                           8   \n",
       "85905                 8                      8                          10   \n",
       "85906                 5                      7                           6   \n",
       "\n",
       "       LICENSE VALIDITY  BUSINESS TYPE  \n",
       "85902                 5              2  \n",
       "85903                 5              1  \n",
       "85904                 1              0  \n",
       "85905                 5              4  \n",
       "85906                 5              0  \n",
       "\n",
       "[5 rows x 24 columns]"
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     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df.drop('LEGAL SUFFIX', axis = 1, inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8446</td>\n",
       "      <td>97</td>\n",
       "      <td>193</td>\n",
       "      <td>16</td>\n",
       "      <td>1373</td>\n",
       "      <td>27</td>\n",
       "      <td>16</td>\n",
       "      <td>1230</td>\n",
       "      <td>10</td>\n",
       "      <td>36</td>\n",
       "      <td>...</td>\n",
       "      <td>13442</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24060</td>\n",
       "      <td>0</td>\n",
       "      <td>466</td>\n",
       "      <td>16</td>\n",
       "      <td>928</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>10647</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10940</td>\n",
       "      <td>0</td>\n",
       "      <td>585</td>\n",
       "      <td>16</td>\n",
       "      <td>1185</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>10647</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2735</td>\n",
       "      <td>0</td>\n",
       "      <td>1145</td>\n",
       "      <td>16</td>\n",
       "      <td>990</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>10647</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ACCOUNT NUMBER  SITE NUMBER  CITY  STATE  ZIP CODE  WARD  PRECINCT  \\\n",
       "0           21179            0   193     16      1384    15        21   \n",
       "1            8446           97   193     16      1373    27        16   \n",
       "2           24060            0   466     16       928     0         0   \n",
       "3           10940            0   585     16      1185     0         0   \n",
       "4            2735            0  1145     16       990     0         0   \n",
       "\n",
       "   WARD PRECINCT  POLICE DISTRICT  LICENSE CODE  ...  LONGITUDE  \\\n",
       "0            689                2             7  ...      15394   \n",
       "1           1230               10            36  ...      13442   \n",
       "2              1                0             8  ...      10647   \n",
       "3              1                0             7  ...      10647   \n",
       "4              1                0             7  ...      10647   \n",
       "\n",
       "   LICENSE STATUS  PAYMENT YEAR  PAYMENT MONTH  \\\n",
       "0               1             2             10   \n",
       "1               0             5              2   \n",
       "2               1             9              9   \n",
       "3               1             4              4   \n",
       "4               1             8              8   \n",
       "\n",
       "   APPLICATION REQUIREMENTS COMPLETE YEAR  DATE ISSUED YEAR  \\\n",
       "0                                       2                 4   \n",
       "1                                       5                 4   \n",
       "2                                       9                 7   \n",
       "3                                       4                 2   \n",
       "4                                       8                 6   \n",
       "\n",
       "   LICENSE APPROVED YEAR  LICENSE STATUS CHANGE YEAR  LICENSE VALIDITY  \\\n",
       "0                      2                           8                 1   \n",
       "1                      6                           4                 1   \n",
       "2                      9                           8                 7   \n",
       "3                      4                           8                 1   \n",
       "4                      8                           8                 7   \n",
       "\n",
       "   BUSINESS TYPE  \n",
       "0              4  \n",
       "1              1  \n",
       "2              1  \n",
       "3              0  \n",
       "4              4  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\siddhesh\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "image/png": 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wRCRJnRkikqTODBFJUmeGiCSpM0NEktSZISJJ6swQkSR1ZohIkjozRCRJnRkikqTODBFJUmeGiCSpM0NEktTZwEIkyZwkFye5Nsk1Sd7Z6jsmWZbk+vZ3RqsnyQlJViS5Mslz+/pa2Npfn2RhX/15Sa5q65yQJIPaH0nSIw3ySGQNcFRV7Q7sAxyZZHfgaODCqpoHXNjmAQ4E5rXXIuAkePineI8B9gb2Ao7p+6nek4C39K23YID7I0kaZWAhUlW3VdW/tun7gOuAWcDBwJLWbAnwyjZ9MLC0ei4FdkiyC3AAsKyqVlfVXcAyYEFbtn1VXVpVBSzt60uSNAkm5ZpIkrnAc4DvAztX1W1t0S+Andv0LOCWvtVWttp49ZVj1Mfa/qIky5MsX7Vq1cbtjCTpYQMPkSTbAmcB76qqe/uXtSOIGvQYqurkqppfVfNnzpw56M1J0pQx0BBJ8mh6AXJaVX2jlX/ZTkXR/t7e6rcCc/pWn91q49Vnj1GXJE2SQd6dFeAU4Lqq+kzfonOAkTusFgLf7Ksf3u7S2ge4p532ugDYP8mMdkF9f+CCtuzeJPu0bR3e15ckaRJMH2DfLwIOA65K8sNW+yDwSeDMJEcANwOvbcvOAw4CVgAPAG8CqKrVST4GXN7aHVtVq9v024FTgW2A89tLkjRJBhYiVfUvwLq+t7HfGO0LOHIdfS0GFo9RXw7ssRHDlCRtBL+xLknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdDSxEkixOcnuSq/tqOyZZluT69ndGqyfJCUlWJLkyyXP71lnY2l+fZGFf/XlJrmrrnJAkg9oXSdLYBnkkciqwYFTtaODCqpoHXNjmAQ4E5rXXIuAk6IUOcAywN7AXcMxI8LQ2b+lbb/S2JEkDNrAQqarvAqtHlQ8GlrTpJcAr++pLq+dSYIckuwAHAMuqanVV3QUsAxa0ZdtX1aVVVcDSvr4kSZNksq+J7FxVt7XpXwA7t+lZwC197Va22nj1lWPUx5RkUZLlSZavWrVq4/ZAkvSwoV1Yb0cQNUnbOrmq5lfV/JkzZ07GJiVpSpjsEPllOxVF+3t7q98KzOlrN7vVxqvPHqMuSZpEkx0i5wAjd1gtBL7ZVz+83aW1D3BPO+11AbB/khntgvr+wAVt2b1J9ml3ZR3e15ckaZJMH1THSb4KvBjYKclKendZfRI4M8kRwM3Aa1vz84CDgBXAA8CbAKpqdZKPAZe3dsdW1cjF+rfTuwNsG+D89pIkTaKBhUhVHbqORfuN0baAI9fRz2Jg8Rj15cAeGzNGSdLG8RvrkqTODBFJUmeGiCSpM0NEktSZISJJ6swQkSR1ZohIkjozRCRJnRkikqTODBFJUmeGiCSpM0NEktSZISJJ6swQkSR1ZohIkjozRCRJnRkikqTODBFJUmeGiCSpM0NEktSZISJJ6swQkSR1ZohIkjozRCRJnRkikqTODBFJUmeGiCSpM0NEktSZISJJ6myzD5EkC5L8JMmKJEcPezySNJVs1iGSZBrwBeBAYHfg0CS7D3dUkjR1TB/2ADbSXsCKqroBIMkZwMHAtUMdlaQt2olHfWvYQxiId3z65Ru8TqpqAEOZHEleDSyoqje3+cOAvavqHaPaLQIWtdmnAT+Z1IE+0k7AHUMew6bC92It34u1fC/W2hTeiydV1cyxFmzuRyITUlUnAycPexwjkiyvqvnDHsemwPdiLd+LtXwv1trU34vN+poIcCswp29+dqtJkibB5h4ilwPzkuyWZCvgEOCcIY9JkqaMzfp0VlWtSfIO4AJgGrC4qq4Z8rAmYpM5tbYJ8L1Yy/diLd+LtTbp92KzvrAuSRquzf10liRpiAwRSVJnhsgk8zEtPUkWJ7k9ydXDHsuwJZmT5OIk1ya5Jsk7hz2mYUmydZLLkvyovRcfHfaYhinJtCQ/SHLusMeyLobIJPIxLb/nVGDBsAexiVgDHFVVuwP7AEdO4X8vHgT2rapnA3sCC5LsM9whDdU7geuGPYjxGCKT6+HHtFTVb4CRx7RMOVX1XWD1sMexKaiq26rqX9v0ffQ+NGYNd1TDUT33t9lHt9eUvPsnyWzgL4AvDXss4zFEJtcs4Ja++ZVM0Q8LjS3JXOA5wPeHPJShaadwfgjcDiyrqqn6XnwWeB/wuyGPY1yGiLSJSLItcBbwrqq6d9jjGZaqeqiq9qT3BIq9kuwx5CFNuiQvA26vqiuGPZb1MUQml49p0ZiSPJpegJxWVd8Y9ng2BVV1N3AxU/Pa2YuAVyS5id5p732T/MNwhzQ2Q2Ry+ZgWPUKSAKcA11XVZ4Y9nmFKMjPJDm16G+ClwI+HOqghqKoPVNXsqppL73Pioqp6w5CHNSZDZBJV1Rpg5DEt1wFnbiaPafmDS/JV4HvA05KsTHLEsMc0RC8CDqP3f5s/bK+Dhj2oIdkFuDjJlfT+p2tZVW2yt7fKx55IkjaCRyKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBDRlJHk/jFqH0nynr759yT5cbvN9vIkh7f6Je3pyyO34H69b/0HkjxhrO0k+VB7Gu2Vbb29x+tv1Nh2TnJue6LttUnOS/KsvnVWJ7mxTf9TW2fPJJVkQZs/uy1fkeSevnVfmOSmJDv1be/FI0+LHWvbG/9PQFuizfrncaU/pCT/id6X2/aqqnuTbA+8qq/J66tq+Rir3gEcBbx/VH8vAF4GPLeqHmwf2FtNoL8Rx9L7nsTnWn9/UlVX0Xu6LUlOBc6tqv4AOhT4l/b321X1qtb2xcB7quplfeMbZ9OP3PZ4jTV1eSQirfVB4G0jz62qqnuraskE1lsM/MckO46q7wLcUVUPtv7uqKqfb8B4dqH3kE7a+leO17h98/01wBuBlybZegO2tVHb1tRliEhAO+rYrqpuGKfZaX2ng/62r34/vSAZ/WNS3wHmJPl/Sb6Y5M8n2N+ILwCntB+s+lCSXdezGy8EbqyqnwKX0HuMeFcbum1NUYaINHGvr6o92+u9o5adACxMst1Iof0uxvOARcAq4GtJ3jjB/qiqC4AnA38PPB34QZKZ44zvUHoP66P9PXQ9+zPW4yqq47Y1RRkiEr1TV8D9SZ7ccf27gdOBI0fVH6qqS6rqGHrPTfurDex3dVWdXlWH0XuW1J+N1a79auZfAR9uT379PL1fBdxurPbNncCMvvkd6V3f2aBta2ozRKS1PgF8oZ3aIsm2I3dnTdBngLfSblhJ8rQk8/qW7wncPNHOkuyb5LFtejvgKcDP1tF8P+DKqppTVXOr6kn0Hi3/qnW0h94pr8Na/9OAN9B79PqGbltTmHdnaSp5bJKVffOjH7t+ErAtcHmS3wK/BT7dt/y0JL9q03dU1Uv6V66qO5KcDby7lbYFPt8ebb4GWEHv1NaE+qN3KuzEJGvo/Q/fl6rq8nXs26HA2aNqZwFvA5auY52PAScl+REQ4NvAyG9WbMi2NYX5FF9JUmeezpIkdWaISJI6M0QkSZ0ZIpKkzgwRSVJnhogkqTNDRJLU2f8HeGaUh4f1sLMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Target Variable')\n",
    "sns.countplot(train_df['LICENSE STATUS'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataset is quite imbalanced and may result in incorrect prediction so before feeding it to our model we first need to balance this data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    55400\n",
       "0    30200\n",
       "4      290\n",
       "3        3\n",
       "2        2\n",
       "Name: LICENSE STATUS, dtype: int64"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[\"LICENSE STATUS\"].value_counts()# 0 = AAC, 1= AAI, 2 = INQ, 3 = REA, 4 = REV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = train_df.drop(\"LICENSE STATUS\", axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = train_df['LICENSE STATUS']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    55400\n",
       "0    30200\n",
       "4      290\n",
       "3        3\n",
       "2        2\n",
       "Name: LICENSE STATUS, dtype: int64"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Splitting the data for traning and testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.3, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4553     1\n",
       "75659    1\n",
       "75149    1\n",
       "49218    0\n",
       "24484    0\n",
       "        ..\n",
       "6265     0\n",
       "54886    0\n",
       "76820    1\n",
       "860      1\n",
       "15795    1\n",
       "Name: LICENSE STATUS, Length: 60126, dtype: int32"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To create a balanced dataset: Oversampling, using SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "smote = SMOTE(k_neighbors=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_smote, y_train_smote = smote.fit_resample(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({1: 38856, 0: 38856, 4: 38856, 2: 38856, 3: 38856})"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Counter(y_train_smote)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\siddhesh\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Target Variable')\n",
    "sns.countplot(y_train_smote)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1296 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Finding corelation for feature selection\n",
    "plt.figure(figsize=(20,18))\n",
    "cor = X_train_smote.corr()\n",
    "sns.heatmap(cor, annot=True, cmap=plt.cm.CMRmap_r)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "def correlation(dataset, threshold):\n",
    "    col_corr = set()  # Set of all the names of correlated columns\n",
    "    corr_matrix = dataset.corr()\n",
    "    for i in range(len(corr_matrix.columns)):\n",
    "        for j in range(i):\n",
    "            if (corr_matrix.iloc[i, j]) > threshold: # we are interested in absolute coeff value\n",
    "                colname = corr_matrix.columns[i]  # getting the name of column\n",
    "                col_corr.add(colname)\n",
    "    return col_corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_features = correlation(X_train_smote, 0.8)\n",
    "len(set(corr_features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'APPLICATION REQUIREMENTS COMPLETE YEAR',\n",
       " 'DATE ISSUED YEAR',\n",
       " 'LICENSE APPROVED YEAR',\n",
       " 'LICENSE STATUS CHANGE YEAR',\n",
       " 'LICENSE VALIDITY',\n",
       " 'WARD PRECINCT'}"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_features #Highly Corelated Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dtermining mutual information for feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.09756966, 0.46435865, 0.76447849, 0.03570487, 1.03423002,\n",
       "       0.77508666, 0.83234762, 0.88879986, 0.84654408, 0.81303307,\n",
       "       0.92906816, 0.91369242, 0.31430142, 0.00125273, 0.83444949,\n",
       "       0.79768417, 0.77890631, 0.46050069, 0.78320386, 0.7926139 ,\n",
       "       0.76554841, 1.0997703 , 0.39689971, 0.4423116 ])"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mutual_info = mutual_info_classif(X_train_smote, y_train_smote)\n",
    "mutual_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LICENSE STATUS CHANGE YEAR    1.099770\n",
       "ACCOUNT NUMBER                1.097570\n",
       "ZIP CODE                      1.034230\n",
       "LICENSE DESCRIPTION           0.929068\n",
       "LICENSE NUMBER                0.913692\n",
       "                                ...   \n",
       "BUSINESS TYPE                 0.442312\n",
       "LICENSE VALIDITY              0.396900\n",
       "APPLICATION TYPE              0.314301\n",
       "STATE                         0.035705\n",
       "CONDITIONAL APPROVAL          0.001253\n",
       "Length: 24, dtype: float64"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mutual_info = pd.Series(mutual_info)\n",
    "mutual_info.index = X_train_smote.columns\n",
    "mutual_info.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mutual_info.sort_values(ascending=False).plot.bar(figsize=(20, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ACCOUNT NUMBER', 'ZIP CODE', 'PRECINCT', 'WARD PRECINCT',\n",
       "       'POLICE DISTRICT', 'LICENSE CODE', 'LICENSE DESCRIPTION',\n",
       "       'LICENSE NUMBER', 'LATITUDE', 'LICENSE STATUS CHANGE YEAR'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Select top 10 important features\n",
    "sel_ten_cols = SelectKBest(mutual_info_classif, k=10)\n",
    "sel_ten_cols.fit(X_train_smote, y_train_smote)\n",
    "X_train_smote.columns[sel_ten_cols.get_support()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_X_train_smote = X_train_smote[['ACCOUNT NUMBER', 'ZIP CODE', 'PRECINCT', 'WARD PRECINCT',\n",
    "       'POLICE DISTRICT', 'LICENSE CODE', 'LICENSE DESCRIPTION',\n",
    "       'LICENSE NUMBER', 'LATITUDE', 'LICENSE STATUS CHANGE YEAR']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_X_test = X_test[['ACCOUNT NUMBER', 'ZIP CODE', 'PRECINCT', 'WARD PRECINCT',\n",
    "       'POLICE DISTRICT', 'LICENSE CODE', 'LICENSE DESCRIPTION',\n",
    "       'LICENSE NUMBER', 'LATITUDE', 'LICENSE STATUS CHANGE YEAR']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "params={\n",
    " \"learning_rate\"    : [0.05, 0.10, 0.15, 0.20] ,\n",
    " \"max_depth\"        : [ 3, 4, 5, 6],\n",
    " \"min_child_weight\" : [ 1, 3, 5, 7 ],\n",
    " \"gamma\"            : [ 0.0, 0.1, 0.2 , 0.3, 0.4],\n",
    " \"colsample_bytree\" : [ 0.3, 0.4, 0.5 , 0.7],    \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier=xgboost.XGBClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_search=RandomizedSearchCV(classifier,param_distributions=params,n_iter=5,scoring='roc_auc',n_jobs=-1,cv=5,verbose=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 5 candidates, totalling 25 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Siddhesh\\AppData\\Roaming\\Python\\Python36\\site-packages\\sklearn\\model_selection\\_search.py:921: UserWarning: One or more of the test scores are non-finite: [nan nan nan nan nan]\n",
      "  category=UserWarning\n",
      "c:\\users\\siddhesh\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[20:47:23] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5,\n",
       "                   estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                           colsample_bylevel=None,\n",
       "                                           colsample_bynode=None,\n",
       "                                           colsample_bytree=None,\n",
       "                                           enable_categorical=False, gamma=None,\n",
       "                                           gpu_id=None, importance_type=None,\n",
       "                                           interaction_constraints=None,\n",
       "                                           learning_rate=None,\n",
       "                                           max_delta_step=None, max_depth=None,\n",
       "                                           min_child_weight=None, missing=nan,\n",
       "                                           monotone_constraints...\n",
       "                                           predictor=None, random_state=None,\n",
       "                                           reg_alpha=None, reg_lambda=None,\n",
       "                                           scale_pos_weight=None,\n",
       "                                           subsample=None, tree_method=None,\n",
       "                                           validate_parameters=None,\n",
       "                                           verbosity=None),\n",
       "                   n_iter=5, n_jobs=-1,\n",
       "                   param_distributions={'colsample_bytree': [0.3, 0.4, 0.5,\n",
       "                                                             0.7],\n",
       "                                        'gamma': [0.0, 0.1, 0.2, 0.3, 0.4],\n",
       "                                        'learning_rate': [0.05, 0.1, 0.15, 0.2],\n",
       "                                        'max_depth': [3, 4, 5, 6],\n",
       "                                        'min_child_weight': [1, 3, 5, 7]},\n",
       "                   scoring='roc_auc', verbose=3)"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search.fit(final_X_train_smote,y_train_smote)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=0.3,\n",
       "              enable_categorical=False, gamma=0.0, gpu_id=-1,\n",
       "              importance_type=None, interaction_constraints='',\n",
       "              learning_rate=0.05, max_delta_step=0, max_depth=5,\n",
       "              min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "              n_estimators=100, n_jobs=4, num_parallel_tree=1,\n",
       "              objective='multi:softprob', predictor='auto', random_state=0,\n",
       "              reg_alpha=0, reg_lambda=1, scale_pos_weight=None, subsample=1,\n",
       "              tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier = random_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[20:48:03] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=0.3,\n",
       "              enable_categorical=False, gamma=0.0, gpu_id=-1,\n",
       "              importance_type=None, interaction_constraints='',\n",
       "              learning_rate=0.05, max_delta_step=0, max_depth=5,\n",
       "              min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "              n_estimators=100, n_jobs=4, num_parallel_tree=1,\n",
       "              objective='multi:softprob', predictor='auto', random_state=0,\n",
       "              reg_alpha=0, reg_lambda=1, scale_pos_weight=None, subsample=1,\n",
       "              tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.fit(final_X_train_smote, y_train_smote)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "y_pred_xgb = classifier.predict(final_X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Accuracy :  94.36\n"
     ]
    }
   ],
   "source": [
    "acc_xgb = round( metrics.accuracy_score(y_test, y_pred_xgb) * 100 , 2 )\n",
    "print( 'Total Accuracy : ', acc_xgb )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create confusion matrix function to find out sensitivity and specificity\n",
    "def draw_cm(actual, predicted):\n",
    "    cm = confusion_matrix( actual, predicted, [1,0]).T\n",
    "    sns.heatmap(cm, annot=True,  fmt='.2f', xticklabels = [\"Yes\",\"No\"] , yticklabels = [\"Yes\",\"No\"] )\n",
    "    plt.ylabel('Predicted')\n",
    "    plt.xlabel('Actual')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Siddhesh\\AppData\\Roaming\\Python\\Python36\\site-packages\\sklearn\\utils\\validation.py:72: FutureWarning: Pass labels=[1, 0] as keyword args. From version 1.0 (renaming of 0.25) passing these as positional arguments will result in an error\n",
      "  \"will result in an error\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Confusion Matrix\n",
    "draw_cm(y_test, y_pred_xgb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KNN Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a KNN Classifier\n",
    "knn = KNeighborsClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = {'n_neighbors': [3, 4, 5, 10], \n",
    "              'weights': ['uniform', 'distance'],\n",
    "              'algorithm' : ['auto', 'ball_tree', 'kd_tree', 'brute'],\n",
    "              'leaf_size' : [10, 20, 30, 50]\n",
    "             }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_search=RandomizedSearchCV(knn,param_distributions=parameters,n_iter=5,scoring='roc_auc',n_jobs=-1,cv=5,verbose=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 5 candidates, totalling 25 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Siddhesh\\AppData\\Roaming\\Python\\Python36\\site-packages\\sklearn\\model_selection\\_search.py:921: UserWarning: One or more of the test scores are non-finite: [nan nan nan nan nan]\n",
      "  category=UserWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=KNeighborsClassifier(), n_iter=5, n_jobs=-1,\n",
       "                   param_distributions={'algorithm': ['auto', 'ball_tree',\n",
       "                                                      'kd_tree', 'brute'],\n",
       "                                        'leaf_size': [10, 20, 30, 50],\n",
       "                                        'n_neighbors': [3, 4, 5, 10],\n",
       "                                        'weights': ['uniform', 'distance']},\n",
       "                   scoring='roc_auc', verbose=3)"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search.fit(final_X_train_smote,y_train_smote)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='ball_tree', leaf_size=20)"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = random_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='ball_tree', leaf_size=20)"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(final_X_train_smote, y_train_smote)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_knn = knn.predict(final_X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Accuracy :  82.41\n"
     ]
    }
   ],
   "source": [
    "acc_knn = round( metrics.accuracy_score(y_test, y_pred_knn) * 100 , 2 )\n",
    "print( 'Total Accuracy : ', acc_knn )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Siddhesh\\AppData\\Roaming\\Python\\Python36\\site-packages\\sklearn\\utils\\validation.py:72: FutureWarning: Pass labels=[1, 0] as keyword args. From version 1.0 (renaming of 0.25) passing these as positional arguments will result in an error\n",
      "  \"will result in an error\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_cm(y_test, y_pred_knn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Siddhesh\\AppData\\Roaming\\Python\\Python36\\site-packages\\sklearn\\linear_model\\_logistic.py:765: 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",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a Logistic regression classifier\n",
    "logreg = LogisticRegression()\n",
    "\n",
    "# Train the model using the training sets \n",
    "logreg.fit(final_X_train_smote, y_train_smote)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = logreg.predict(final_X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Accuracy :  46.49\n"
     ]
    }
   ],
   "source": [
    "acc_logreg = round( metrics.accuracy_score(y_test, y_pred) * 100, 2 )\n",
    "print( 'Total Accuracy : ', acc_logreg )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Siddhesh\\AppData\\Roaming\\Python\\Python36\\site-packages\\sklearn\\utils\\validation.py:72: FutureWarning: Pass labels=[1, 0] as keyword args. From version 1.0 (renaming of 0.25) passing these as positional arguments will result in an error\n",
      "  \"will result in an error\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_cm(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = GaussianNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(final_X_train_smote,y_train_smote)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_gnb = model.predict(final_X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Accuracy :  72.46\n"
     ]
    }
   ],
   "source": [
    "acc_nb = round( metrics.accuracy_score(y_test, y_pred_gnb) * 100, 2 )\n",
    "print( 'Total Accuracy : ', acc_nb )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_parameters = [{'max_features': ['log2', 'sqrt','auto'], \n",
    "              'criterion': ['entropy', 'gini'],\n",
    "              'max_depth': [2, 3, 5, 10, 50], \n",
    "              'min_samples_split': [2, 3, 50, 100],\n",
    "              'min_samples_leaf': [1, 5, 8, 10]\n",
    "             }]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_search=RandomizedSearchCV(clf,param_distributions=dt_parameters,n_iter=3,scoring='roc_auc',n_jobs=-1,cv=5,verbose=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 3 candidates, totalling 15 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Siddhesh\\AppData\\Roaming\\Python\\Python36\\site-packages\\sklearn\\model_selection\\_search.py:921: UserWarning: One or more of the test scores are non-finite: [nan nan nan]\n",
      "  category=UserWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=DecisionTreeClassifier(), n_iter=3,\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions=[{'criterion': ['entropy', 'gini'],\n",
       "                                         'max_depth': [2, 3, 5, 10, 50],\n",
       "                                         'max_features': ['log2', 'sqrt',\n",
       "                                                          'auto'],\n",
       "                                         'min_samples_leaf': [1, 5, 8, 10],\n",
       "                                         'min_samples_split': [2, 3, 50, 100]}],\n",
       "                   scoring='roc_auc', verbose=3)"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search.fit(final_X_train_smote,y_train_smote)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=5, max_features='sqrt', min_samples_leaf=8,\n",
       "                       min_samples_split=3)"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = random_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=5, max_features='sqrt', min_samples_leaf=8,\n",
       "                       min_samples_split=3)"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(final_X_train_smote, y_train_smote)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_dt = clf.predict(final_X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Accuracy :  75.82\n"
     ]
    }
   ],
   "source": [
    "acc_dt = round( metrics.accuracy_score(y_test, y_pred_dt) * 100 , 2 )\n",
    "print( 'Total Accuracy : ', acc_dt )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Siddhesh\\AppData\\Roaming\\Python\\Python36\\site-packages\\sklearn\\utils\\validation.py:72: FutureWarning: Pass labels=[1, 0] as keyword args. From version 1.0 (renaming of 0.25) passing these as positional arguments will result in an error\n",
      "  \"will result in an error\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_cm(y_test, y_pred_dt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters_rf = {'n_estimators': [4, 6, 9, 10, 15], \n",
    "              'max_features': ['log2', 'sqrt','auto'], \n",
    "              'criterion': ['entropy', 'gini'],\n",
    "              'max_depth': [2, 3, 5, 10], \n",
    "              'min_samples_split': [2, 3, 5],\n",
    "              'min_samples_leaf': [1, 5, 8]\n",
    "             }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 5 candidates, totalling 25 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Siddhesh\\AppData\\Roaming\\Python\\Python36\\site-packages\\sklearn\\model_selection\\_search.py:921: UserWarning: One or more of the test scores are non-finite: [nan nan nan nan nan]\n",
      "  category=UserWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_iter=5,\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={'criterion': ['entropy', 'gini'],\n",
       "                                        'max_depth': [2, 3, 5, 10],\n",
       "                                        'max_features': ['log2', 'sqrt',\n",
       "                                                         'auto'],\n",
       "                                        'min_samples_leaf': [1, 5, 8],\n",
       "                                        'min_samples_split': [2, 3, 5],\n",
       "                                        'n_estimators': [4, 6, 9, 10, 15]},\n",
       "                   scoring='roc_auc', verbose=3)"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search=RandomizedSearchCV(rf,param_distributions=parameters_rf,n_iter=5,scoring='roc_auc',n_jobs=-1,cv=5,verbose=3)\n",
    "random_search.fit(final_X_train_smote,y_train_smote)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_depth=10, min_samples_leaf=8, n_estimators=10)"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = random_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_depth=10, min_samples_leaf=8, n_estimators=10)"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.fit(final_X_train_smote, y_train_smote)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_rf = rf.predict(final_X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Accuracy :  95.08\n"
     ]
    }
   ],
   "source": [
    "acc_rf = round( metrics.accuracy_score(y_test, y_pred_rf) * 100 , 2 )\n",
    "print( 'Total Accuracy : ', acc_rf )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Siddhesh\\AppData\\Roaming\\Python\\Python36\\site-packages\\sklearn\\utils\\validation.py:72: FutureWarning: Pass labels=[1, 0] as keyword args. From version 1.0 (renaming of 0.25) passing these as positional arguments will result in an error\n",
      "  \"will result in an error\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_cm(y_test, y_pred_rf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "scrolled": false
   },
   "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>Model</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>95.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>94.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>K - Nearest Neighbors</td>\n",
       "      <td>82.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>75.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Naive Bayes</td>\n",
       "      <td>72.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>46.49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Model  Score\n",
       "3          Random Forest  95.08\n",
       "5                XGBoost  94.36\n",
       "4  K - Nearest Neighbors  82.41\n",
       "2          Decision Tree  75.82\n",
       "1            Naive Bayes  72.46\n",
       "0    Logistic Regression  46.49"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models = pd.DataFrame({\n",
    "    'Model': ['Logistic Regression', 'Naive Bayes', 'Decision Tree', 'Random Forest', \n",
    "              'K - Nearest Neighbors', 'XGBoost'],\n",
    "    'Score': [acc_logreg, acc_nb, acc_dt, acc_rf,acc_knn, acc_xgb]})\n",
    "models.sort_values(by='Score', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "XGBoost outperforms other models and hence it will be used for our final prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Final Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_test_df = test_df[['ACCOUNT NUMBER', 'ZIP CODE', 'PRECINCT', 'WARD PRECINCT',\n",
    "       'POLICE DISTRICT', 'LICENSE CODE', 'LICENSE DESCRIPTION',\n",
    "       'LICENSE NUMBER', 'LATITUDE', 'LICENSE STATUS CHANGE YEAR']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_pred = classifier.predict(new_test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "Final_Predict['LICENSE STATUS'] = final_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(57239, 2)"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Final_Predict.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "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>ID</th>\n",
       "      <th>LICENSE STATUS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>85902</th>\n",
       "      <td>1516615-20120216</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85903</th>\n",
       "      <td>69813-20110616</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85904</th>\n",
       "      <td>1519227-20041116</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85905</th>\n",
       "      <td>1922025-20100816</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85906</th>\n",
       "      <td>1675683-20070816</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143136</th>\n",
       "      <td>1743194-20070816</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143137</th>\n",
       "      <td>2032383-20160716</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143138</th>\n",
       "      <td>40376-20020216</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143139</th>\n",
       "      <td>2064176-20121116</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143140</th>\n",
       "      <td>1954052-20090202</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>57239 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      ID  LICENSE STATUS\n",
       "85902   1516615-20120216               0\n",
       "85903     69813-20110616               0\n",
       "85904   1519227-20041116               0\n",
       "85905   1922025-20100816               0\n",
       "85906   1675683-20070816               0\n",
       "...                  ...             ...\n",
       "143136  1743194-20070816               0\n",
       "143137  2032383-20160716               0\n",
       "143138    40376-20020216               0\n",
       "143139  2064176-20121116               0\n",
       "143140  1954052-20090202               0\n",
       "\n",
       "[57239 rows x 2 columns]"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Final_Predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 0 = AAC, 1= AAI, 2 = INQ, 3 = REA, 4 = REV\n",
    "Final_Predict['LICENSE STATUS'] = Final_Predict['LICENSE STATUS'].replace(0, 'AAC')\n",
    "Final_Predict['LICENSE STATUS'] = Final_Predict['LICENSE STATUS'].replace(1, 'AAI')\n",
    "Final_Predict['LICENSE STATUS'] = Final_Predict['LICENSE STATUS'].replace(2, 'INQ')\n",
    "Final_Predict['LICENSE STATUS'] = Final_Predict['LICENSE STATUS'].replace(3, 'REA')\n",
    "Final_Predict['LICENSE STATUS'] = Final_Predict['LICENSE STATUS'].replace(4, 'REV')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAC    53397\n",
       "REV     3611\n",
       "AAI      231\n",
       "Name: LICENSE STATUS, dtype: int64"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Final_Predict['LICENSE STATUS'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "Final_Predict.to_csv('Final_Prediction.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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