Food Quality Assessment Women in AI Hackathon.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train=pd.read_excel('C:/Users/hp/Downloads/Food_QUalityA_ParticipantsData/Food_QUalityA_ParticipantsData/Data_Train.xlsx')\n",
    "test=pd.read_excel('C:/Users/hp/Downloads/Food_QUalityA_ParticipantsData/Food_QUalityA_ParticipantsData/Data_Test.xlsx')\n",
    "sample=pd.read_excel('C:/Users/hp/Downloads/Food_QUalityA_ParticipantsData/Food_QUalityA_ParticipantsData/Sample_Submission.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <td>31103489027986</td>\n",
       "      <td>26-04-2010</td>\n",
       "      <td>4744</td>\n",
       "      <td>8123</td>\n",
       "      <td>7715</td>\n",
       "      <td>RESTAURANT</td>\n",
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       "    <tr>\n",
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       "      <td>21-06-2009</td>\n",
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       "      <td>CANVASS RE-INSPECTION</td>\n",
       "      <td>31.0</td>\n",
       "      <td>Medium</td>\n",
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       "      <td>4</td>\n",
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       "      <th>4</th>\n",
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       "      <td>09-12-2015</td>\n",
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       "      <td>10409</td>\n",
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       "      <td>4</td>\n",
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       "               ID        Date  LicenseNo  FacilityID  FacilityName  \\\n",
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       "4     RESTAURANT    7876  id-11235901  id_1890134     81886.0   \n",
       "\n",
       "                  Reason  SectionViolations RiskLevel     Geo_Loc  \\\n",
       "0                CANVASS               33.0      High  locid16406   \n",
       "1              COMPLAINT               33.0      High    locid878   \n",
       "2                CANVASS                NaN      High   locid3368   \n",
       "3  CANVASS RE-INSPECTION               31.0    Medium  locid11839   \n",
       "4              COMPLAINT               30.0      High  locid12264   \n",
       "\n",
       "   Inspection_Results  \n",
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   "source": [
    "train.head()"
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       "               ID        Date  LicenseNo  FacilityID  FacilityName  \\\n",
       "0  47571900570810  19-03-2014      34218        2354          2185   \n",
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       "\n",
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       "4     RESTAURANT   16286  id-11235901  id_1890134     81874.0   \n",
       "\n",
       "                  Reason  SectionViolations RiskLevel     Geo_Loc  \n",
       "0                CANVASS               19.0       Low   locid1145  \n",
       "1                CANVASS               32.0      High  locid16450  \n",
       "2                CANVASS                NaN      High  locid12916  \n",
       "3                CANVASS                NaN      High   locid7292  \n",
       "4  CANVASS RE-INSPECTION               32.0      High   locid2048  "
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   "source": [
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       "   FACILITY CHANGED  FAIL  FURTHER INSPECTION REQUIRED  INSPECTION OVERRULED  \\\n",
       "0               0.1   0.1                          0.1                   0.1   \n",
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       "3               0.1   0.1                          0.1                   0.1   \n",
       "4               0.1   0.1                          0.1                   0.1   \n",
       "\n",
       "   PASS  PASS(CONDITIONAL)  SHUT-DOWN  \n",
       "0   0.1                0.1        0.1  \n",
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       "2   0.1                0.1        0.1  \n",
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   "source": [
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    "target"
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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>Date</th>\n",
       "      <th>LicenseNo</th>\n",
       "      <th>FacilityID</th>\n",
       "      <th>FacilityName</th>\n",
       "      <th>Type</th>\n",
       "      <th>Street</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>LocationID</th>\n",
       "      <th>Reason</th>\n",
       "      <th>SectionViolations</th>\n",
       "      <th>RiskLevel</th>\n",
       "      <th>Geo_Loc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>31103489027986</td>\n",
       "      <td>26-04-2010</td>\n",
       "      <td>4744</td>\n",
       "      <td>8123</td>\n",
       "      <td>7715</td>\n",
       "      <td>RESTAURANT</td>\n",
       "      <td>15522</td>\n",
       "      <td>id-11235901</td>\n",
       "      <td>id_1890134</td>\n",
       "      <td>81876.0</td>\n",
       "      <td>CANVASS</td>\n",
       "      <td>33.0</td>\n",
       "      <td>High</td>\n",
       "      <td>locid16406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10088999935915</td>\n",
       "      <td>21-06-2009</td>\n",
       "      <td>2973</td>\n",
       "      <td>12268</td>\n",
       "      <td>11664</td>\n",
       "      <td>GROCERY STORE</td>\n",
       "      <td>3057</td>\n",
       "      <td>id-11235901</td>\n",
       "      <td>id_1890134</td>\n",
       "      <td>81862.0</td>\n",
       "      <td>COMPLAINT</td>\n",
       "      <td>33.0</td>\n",
       "      <td>High</td>\n",
       "      <td>locid878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40148966010272</td>\n",
       "      <td>01-05-2013</td>\n",
       "      <td>18223</td>\n",
       "      <td>1112</td>\n",
       "      <td>969</td>\n",
       "      <td>RESTAURANT</td>\n",
       "      <td>14988</td>\n",
       "      <td>id-11235901</td>\n",
       "      <td>id_1890134</td>\n",
       "      <td>81883.0</td>\n",
       "      <td>CANVASS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High</td>\n",
       "      <td>locid3368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37157708563676</td>\n",
       "      <td>28-09-2015</td>\n",
       "      <td>20825</td>\n",
       "      <td>20007</td>\n",
       "      <td>19115</td>\n",
       "      <td>RESTAURANT</td>\n",
       "      <td>3661</td>\n",
       "      <td>id-11235901</td>\n",
       "      <td>id_1890134</td>\n",
       "      <td>81859.0</td>\n",
       "      <td>CANVASS RE-INSPECTION</td>\n",
       "      <td>31.0</td>\n",
       "      <td>Medium</td>\n",
       "      <td>locid11839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>47478049564374</td>\n",
       "      <td>09-12-2015</td>\n",
       "      <td>2136</td>\n",
       "      <td>16867</td>\n",
       "      <td>10409</td>\n",
       "      <td>RESTAURANT</td>\n",
       "      <td>7876</td>\n",
       "      <td>id-11235901</td>\n",
       "      <td>id_1890134</td>\n",
       "      <td>81886.0</td>\n",
       "      <td>COMPLAINT</td>\n",
       "      <td>30.0</td>\n",
       "      <td>High</td>\n",
       "      <td>locid12264</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               ID        Date  LicenseNo  FacilityID  FacilityName  \\\n",
       "0  31103489027986  26-04-2010       4744        8123          7715   \n",
       "1  10088999935915  21-06-2009       2973       12268         11664   \n",
       "2  40148966010272  01-05-2013      18223        1112           969   \n",
       "3  37157708563676  28-09-2015      20825       20007         19115   \n",
       "4  47478049564374  09-12-2015       2136       16867         10409   \n",
       "\n",
       "            Type  Street         City       State  LocationID  \\\n",
       "0     RESTAURANT   15522  id-11235901  id_1890134     81876.0   \n",
       "1  GROCERY STORE    3057  id-11235901  id_1890134     81862.0   \n",
       "2     RESTAURANT   14988  id-11235901  id_1890134     81883.0   \n",
       "3     RESTAURANT    3661  id-11235901  id_1890134     81859.0   \n",
       "4     RESTAURANT    7876  id-11235901  id_1890134     81886.0   \n",
       "\n",
       "                  Reason  SectionViolations RiskLevel     Geo_Loc  \n",
       "0                CANVASS               33.0      High  locid16406  \n",
       "1              COMPLAINT               33.0      High    locid878  \n",
       "2                CANVASS                NaN      High   locid3368  \n",
       "3  CANVASS RE-INSPECTION               31.0    Medium  locid11839  \n",
       "4              COMPLAINT               30.0      High  locid12264  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                       0\n",
       "Date                     0\n",
       "LicenseNo                0\n",
       "FacilityID               0\n",
       "FacilityName             0\n",
       "Type                  4700\n",
       "Street                   0\n",
       "City                     0\n",
       "State                    0\n",
       "LocationID              50\n",
       "Reason                   0\n",
       "SectionViolations    52044\n",
       "RiskLevel                0\n",
       "Geo_Loc                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'LocationID'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\Anaconda3\\3.7\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2656\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2657\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2658\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'LocationID'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-30-a5634fca1355>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'LocationID'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'LocationID'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfillna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'LocationID'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'SectionViolations'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'SectionViolations'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfillna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'SectionViolations'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'SectionViolations'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'SectionViolations'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobject\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Type'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Type'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfillna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Type'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\3.7\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2925\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2926\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2927\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2928\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2929\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\3.7\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2657\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2658\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2659\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2660\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2661\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'LocationID'"
     ]
    }
   ],
   "source": [
    "df['LocationID']=df['LocationID'].fillna(df['LocationID'].mean())\n",
    "df['SectionViolations']=df['SectionViolations'].fillna(df['SectionViolations'].mean())\n",
    "df['Type']=df['Type'].fillna(df['Type'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'Date', 'LicenseNo', 'FacilityID', 'FacilityName', 'Type',\n",
       "       'Street', 'City', 'State', 'LocationID', 'Reason', 'SectionViolations',\n",
       "       'RiskLevel', 'Geo_Loc'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop(['ID', 'Date', 'LicenseNo', 'FacilityID', 'Type',\n",
    "        'City', 'State', 'LocationID', 'Geo_Loc'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(196591, 5)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Reason'] = df['Reason'].str.replace('\\d+','')\n",
    "df['Reason'] = df['Reason'].str.lower()\n",
    "df['Reason'] = df['Reason'].str.replace('\\s+', ' ')\n",
    "df['Reason'] = df['Reason'].str.replace('\\'', '')\n",
    "df['Reason'] = df['Reason'].str.replace(r'[^A-Za-z0-9]+', ' ')\n",
    "df['Reason'] = df['Reason'].str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['SectionViolations'] = df['SectionViolations'].astype(object)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2=pd.get_dummies(df,drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(196591, 83)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train=df2.iloc[:147443,:]\n",
    "x_test=df2.iloc[147433:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc=StandardScaler()\n",
    "x_train=sc.fit_transform(x_train)\n",
    "x_test=sc.fit_transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_cv, y_train, y_cv = train_test_split(x_train, target, test_size=0.30, stratify=target, \n",
    "                                                random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original dataset shape Counter({4: 55617, 1: 19919, 5: 14442, 6: 8884, 3: 3323, 2: 994, 0: 31})\n",
      "Original dataset shape Counter({4: 23836, 1: 8537, 5: 6189, 6: 3807, 3: 1424, 2: 426, 0: 14})\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter\n",
    "print('Original dataset shape %s' % Counter(y_train))\n",
    "print('Original dataset shape %s' % Counter(y_cv))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original dataset shape Counter({4: 55617, 1: 55617, 5: 55617, 6: 55617, 3: 55617, 2: 55617, 0: 55617})\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import RandomOverSampler\n",
    "ros = RandomOverSampler()\n",
    "X_ros, y_ros = ros.fit_sample(x_train, y_train)\n",
    "print('Original dataset shape %s' % Counter(y_ros))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hp\\Anaconda3\\3.7\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:940: 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"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lc=LogisticRegression()\n",
    "lc.fit(X_ros,y_ros)\n",
    "lc_pred=lc.predict(x_cv)\n",
    "lc.score(X_ros,y_ros)\n",
    "lc_test_pred=lc.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1_score       : 0.7829448601722696\n",
      "Test accuracy score : 0.7829448601722696\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, f1_score\n",
    "\n",
    "print('f1_score       :', f1_score(y_cv, lc_pred, average='micro'))\n",
    "#print('Train accuracy score :', accuracy_score(y_train,lc_pred))\n",
    "print('Test accuracy score :', accuracy_score(y_cv, lc_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = pd.DataFrame(lc_test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds=preds.astype('object')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds=preds.replace({0 : 'FACILITY CHANGED', 1 : 'FAIL', 2 : 'FURTHER INSPECTION REQUIRED', 3 : 'INSPECTION OVERRULED',\n",
    "                                4 : 'PASS', 5: 'PASS(CONDITIONAL)', 6: 'SHUT-DOWN'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FURTHER INSPECTION REQUIRED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PASS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PASS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PASS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PASS(CONDITIONAL)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             0\n",
       "0  FURTHER INSPECTION REQUIRED\n",
       "1                         PASS\n",
       "2                         PASS\n",
       "3                         PASS\n",
       "4            PASS(CONDITIONAL)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds=pd.get_dummies(preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0_FACILITY CHANGED</th>\n",
       "      <th>0_FAIL</th>\n",
       "      <th>0_FURTHER INSPECTION REQUIRED</th>\n",
       "      <th>0_INSPECTION OVERRULED</th>\n",
       "      <th>0_PASS</th>\n",
       "      <th>0_PASS(CONDITIONAL)</th>\n",
       "      <th>0_SHUT-DOWN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0_FACILITY CHANGED  0_FAIL  0_FURTHER INSPECTION REQUIRED  \\\n",
       "0                   0       0                              1   \n",
       "1                   0       0                              0   \n",
       "2                   0       0                              0   \n",
       "3                   0       0                              0   \n",
       "4                   0       0                              0   \n",
       "\n",
       "   0_INSPECTION OVERRULED  0_PASS  0_PASS(CONDITIONAL)  0_SHUT-DOWN  \n",
       "0                       0       0                    0            0  \n",
       "1                       0       1                    0            0  \n",
       "2                       0       1                    0            0  \n",
       "3                       0       1                    0            0  \n",
       "4                       0       0                    1            0  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds.to_excel(\"FoodAssessmen.xlsx\", index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9785272952937746"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf=RandomForestClassifier()\n",
    "rf.fit(X_ros,y_ros)\n",
    "rf_pred=rf.predict(x_cv)\n",
    "rf.score(X_ros,y_ros)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample = pd.DataFrame()\n",
    "sample['Predictions'] = rf.predict(x_test)\n",
    "sample['Predictions'] = sample['Predictions'].astype(object)\n",
    "sample = sample.replace({'Predictions' : { 0 : 'FACILITY CHANGED', 1 : 'FAIL', 2 : 'FURTHER INSPECTION REQUIRED', 3 : 'INSPECTION OVERRULED',\n",
    "                                4 : 'PASS', 5: 'PASS(CONDITIONAL)', 6: 'SHUT-DOWN'}})\n",
    "sample = pd.get_dummies(sample)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predictions_FACILITY CHANGED</th>\n",
       "      <th>Predictions_FAIL</th>\n",
       "      <th>Predictions_FURTHER INSPECTION REQUIRED</th>\n",
       "      <th>Predictions_INSPECTION OVERRULED</th>\n",
       "      <th>Predictions_PASS</th>\n",
       "      <th>Predictions_PASS(CONDITIONAL)</th>\n",
       "      <th>Predictions_SHUT-DOWN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <th>1</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Predictions_FACILITY CHANGED  Predictions_FAIL  \\\n",
       "0                             0                 0   \n",
       "1                             0                 0   \n",
       "2                             0                 0   \n",
       "3                             0                 0   \n",
       "4                             0                 1   \n",
       "\n",
       "   Predictions_FURTHER INSPECTION REQUIRED  Predictions_INSPECTION OVERRULED  \\\n",
       "0                                        0                                 0   \n",
       "1                                        0                                 0   \n",
       "2                                        0                                 0   \n",
       "3                                        0                                 0   \n",
       "4                                        0                                 0   \n",
       "\n",
       "   Predictions_PASS  Predictions_PASS(CONDITIONAL)  Predictions_SHUT-DOWN  \n",
       "0                 1                              0                      0  \n",
       "1                 1                              0                      0  \n",
       "2                 1                              0                      0  \n",
       "3                 1                              0                      0  \n",
       "4                 0                              0                      0  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample.to_excel(\"FoodAssessment.xlsx\", index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample.to_excel('C:/Users/hp/Downloads/Food_QUalityA_ParticipantsData/sample.xlsx',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(49158, 7)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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