Bullet train.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4bc5ab67",
   "metadata": {},
   "source": [
    "# Shinkansen Travel Experience "
   ]
  },
  {
   "attachments": {
    "image-2.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "4318d55a",
   "metadata": {},
   "source": [
    "![image-2.png](attachment:image-2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be61d011",
   "metadata": {},
   "source": [
    "##### The goal of the problem is to predict whether a passenger was satisfied or not considering his/her overall experience of traveling on the Shinkansen Bullet Train."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "dc35222d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Libraries to help with reading and manipulating data\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Libraries to help with data visualization\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from pylab import rcParams\n",
    "\n",
    "# Command to tell Python to actually display the graphs\n",
    "%matplotlib inline\n",
    "\n",
    "# this library contains a large number of probability distributions as well as a growing library of statistical functions\n",
    "import scipy.stats as stats  \n",
    "\n",
    "# sklearn library for predicitive modelling\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, f1_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import roc_auc_score,roc_curve,classification_report,confusion_matrix,plot_confusion_matrix\n",
    "\n",
    "# To build linear regression_model\n",
    "import statsmodels.api as sm\n",
    "\n",
    "# set working directory\n",
    "import os\n",
    "os.getcwd()\n",
    "os.chdir('C:\\\\Users\\\\Dubey\\\\Downloads')\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bedce000",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:90% !important; }<\\style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# this two lines of code is just to increase the size of the display\n",
    "from IPython.core.display import display, HTML \n",
    "display(HTML('<style>.container { width:90% !important; }<\\style>'))\n",
    "\n",
    "# set column number to see all columns\n",
    "pd.set_option('display.max_columns', 22)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d46518d5",
   "metadata": {},
   "source": [
    "## READ DATA : given in problem set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0ed5a44d",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"Data_dictionary_(1).csv\")\n",
    "stest = pd.read_csv(\"Surveydata_test.csv\")\n",
    "strain = pd.read_csv(\"Surveydata_train_(1).csv\")\n",
    "ttest = pd.read_csv(\"Traveldata_test.csv\")\n",
    "ttrain = pd.read_csv(\"Traveldata_train_(1).csv\")\n",
    "sample_submittion  = pd.read_csv(\"Sample_Submission_(2).csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5db1dec5",
   "metadata": {},
   "source": [
    "### data dictionaey"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "66e4c5f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Seat comfort</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Arrival time convenient</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "      <th>Catering</th>\n",
       "      <th>Unnamed: 8</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "      <th>Platform location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>extremely poor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>extremely poor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>extremely poor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>very inconvienient</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>poor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>poor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>poor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Inconvienient</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>need improvement</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>need improvement</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>need improvement</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>need improvement</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>manageable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>good</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>good</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>good</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>Convienient</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.0</td>\n",
       "      <td>excellent</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>excellent</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>excellent</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>very convienient</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>onboard wifi service</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>onboard entertainment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Online support</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ease of Online booking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0</td>\n",
       "      <td>extremely poor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>extremely poor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>extremely poor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>extremely poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.0</td>\n",
       "      <td>poor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>poor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>poor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>poor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0          Seat comfort  Unnamed: 2  Unnamed: 3  \\\n",
       "0         0.0        extremely poor         NaN         0.0   \n",
       "1         1.0                  poor         NaN         1.0   \n",
       "2         2.0      need improvement         NaN         2.0   \n",
       "3         3.0            acceptable         NaN         3.0   \n",
       "4         4.0                  good         NaN         4.0   \n",
       "5         5.0             excellent         NaN         5.0   \n",
       "6         NaN                   NaN         NaN         NaN   \n",
       "7         NaN  onboard wifi service         NaN         NaN   \n",
       "8         0.0        extremely poor         NaN         0.0   \n",
       "9         1.0                  poor         NaN         1.0   \n",
       "\n",
       "  Arrival time convenient  Unnamed: 5  Unnamed: 6          Catering  \\\n",
       "0          extremely poor         NaN         0.0    extremely poor   \n",
       "1                    poor         NaN         1.0              poor   \n",
       "2        need improvement         NaN         2.0  need improvement   \n",
       "3              acceptable         NaN         3.0        acceptable   \n",
       "4                    good         NaN         4.0              good   \n",
       "5               excellent         NaN         5.0         excellent   \n",
       "6                     NaN         NaN         NaN               NaN   \n",
       "7   onboard entertainment         NaN         NaN    Online support   \n",
       "8          extremely poor         NaN         0.0    extremely poor   \n",
       "9                    poor         NaN         1.0              poor   \n",
       "\n",
       "   Unnamed: 8 Unnamed: 9       Platform location  \n",
       "0         NaN          0      very inconvienient  \n",
       "1         NaN          1           Inconvienient  \n",
       "2         NaN          2        need improvement  \n",
       "3         NaN          3              manageable  \n",
       "4         NaN          4             Convienient  \n",
       "5         NaN          5        very convienient  \n",
       "6         NaN        NaN                     NaN  \n",
       "7         NaN        NaN  Ease of Online booking  \n",
       "8         NaN          0          extremely poor  \n",
       "9         NaN          1                    poor  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e2fa75ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>Seat comfort</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Arrival time convenient</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "      <th>Catering</th>\n",
       "      <th>Platform location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>extremely poor</td>\n",
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       "      <td>extremely poor</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>poor</td>\n",
       "      <td>1.0</td>\n",
       "      <td>poor</td>\n",
       "      <td>1.0</td>\n",
       "      <td>poor</td>\n",
       "      <td>Inconvienient</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>need improvement</td>\n",
       "      <td>2.0</td>\n",
       "      <td>need improvement</td>\n",
       "      <td>2.0</td>\n",
       "      <td>need improvement</td>\n",
       "      <td>need improvement</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>acceptable</td>\n",
       "      <td>3.0</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>3.0</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>manageable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>good</td>\n",
       "      <td>4.0</td>\n",
       "      <td>good</td>\n",
       "      <td>4.0</td>\n",
       "      <td>good</td>\n",
       "      <td>Convienient</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>excellent</td>\n",
       "      <td>5.0</td>\n",
       "      <td>excellent</td>\n",
       "      <td>5.0</td>\n",
       "      <td>excellent</td>\n",
       "      <td>very convienient</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>onboard wifi service</td>\n",
       "      <td>NaN</td>\n",
       "      <td>onboard entertainment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Online support</td>\n",
       "      <td>Ease of Online booking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>extremely poor</td>\n",
       "      <td>0.0</td>\n",
       "      <td>extremely poor</td>\n",
       "      <td>0.0</td>\n",
       "      <td>extremely poor</td>\n",
       "      <td>extremely poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>poor</td>\n",
       "      <td>1.0</td>\n",
       "      <td>poor</td>\n",
       "      <td>1.0</td>\n",
       "      <td>poor</td>\n",
       "      <td>poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>acceptable</td>\n",
       "      <td>3.0</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>3.0</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>acceptable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>good</td>\n",
       "      <td>4.0</td>\n",
       "      <td>good</td>\n",
       "      <td>4.0</td>\n",
       "      <td>good</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>excellent</td>\n",
       "      <td>5.0</td>\n",
       "      <td>excellent</td>\n",
       "      <td>5.0</td>\n",
       "      <td>excellent</td>\n",
       "      <td>excellent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>On-board service</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Leg room</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Baggage handling</td>\n",
       "      <td>Checkin service</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Seat Class</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cleanliness</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Online boarding</td>\n",
       "      <td>Overall_Experience</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Ordinary</td>\n",
       "      <td>0.0</td>\n",
       "      <td>extremely poor</td>\n",
       "      <td>0.0</td>\n",
       "      <td>extremely poor</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Green Car</td>\n",
       "      <td>1.0</td>\n",
       "      <td>poor</td>\n",
       "      <td>1.0</td>\n",
       "      <td>poor</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>need improvement</td>\n",
       "      <td>2.0</td>\n",
       "      <td>need improvement</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>3.0</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>good</td>\n",
       "      <td>4.0</td>\n",
       "      <td>good</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>excellent</td>\n",
       "      <td>5.0</td>\n",
       "      <td>excellent</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Seat comfort  Unnamed: 3 Arrival time convenient  Unnamed: 6  \\\n",
       "0         extremely poor         0.0          extremely poor         0.0   \n",
       "1                   poor         1.0                    poor         1.0   \n",
       "2       need improvement         2.0        need improvement         2.0   \n",
       "3             acceptable         3.0              acceptable         3.0   \n",
       "4                   good         4.0                    good         4.0   \n",
       "5              excellent         5.0               excellent         5.0   \n",
       "6                    NaN         NaN                     NaN         NaN   \n",
       "7   onboard wifi service         NaN   onboard entertainment         NaN   \n",
       "8         extremely poor         0.0          extremely poor         0.0   \n",
       "9                   poor         1.0                    poor         1.0   \n",
       "11            acceptable         3.0              acceptable         3.0   \n",
       "12                  good         4.0                    good         4.0   \n",
       "13             excellent         5.0               excellent         5.0   \n",
       "15      On-board service         NaN                Leg room         NaN   \n",
       "23            Seat Class         NaN             Cleanliness         NaN   \n",
       "24              Ordinary         0.0          extremely poor         0.0   \n",
       "25             Green Car         1.0                    poor         1.0   \n",
       "26                   NaN         2.0        need improvement         2.0   \n",
       "27                   NaN         3.0              acceptable         3.0   \n",
       "28                   NaN         4.0                    good         4.0   \n",
       "29                   NaN         5.0               excellent         5.0   \n",
       "\n",
       "            Catering       Platform location  \n",
       "0     extremely poor      very inconvienient  \n",
       "1               poor           Inconvienient  \n",
       "2   need improvement        need improvement  \n",
       "3         acceptable              manageable  \n",
       "4               good             Convienient  \n",
       "5          excellent        very convienient  \n",
       "6                NaN                     NaN  \n",
       "7     Online support  Ease of Online booking  \n",
       "8     extremely poor          extremely poor  \n",
       "9               poor                    poor  \n",
       "11        acceptable              acceptable  \n",
       "12              good                    good  \n",
       "13         excellent               excellent  \n",
       "15  Baggage handling         Checkin service  \n",
       "23   Online boarding      Overall_Experience  \n",
       "24    extremely poor                       1  \n",
       "25              poor                       0  \n",
       "26  need improvement                     NaN  \n",
       "27        acceptable                     NaN  \n",
       "28              good                     NaN  \n",
       "29         excellent                     NaN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# clean data dictionary \n",
    "\n",
    "# Count the number of NaN values in each column\n",
    "nan_count = data.isna().sum()\n",
    "\n",
    "# Calculate the percentage of NaN values in each column\n",
    "nan_percent = nan_count/len(data)*100\n",
    "\n",
    "# Select the columns with less than 25% NaN values\n",
    "data = data[nan_percent[nan_percent < 25].index]\n",
    "\n",
    "# Drop duplicate rows\n",
    "data = data.drop_duplicates()\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4b53b55",
   "metadata": {},
   "source": [
    "## ---------------------------------------------------------------------------understanding data--------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65b484c4",
   "metadata": {},
   "source": [
    "### survey train data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2bc3946c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Overall_Experience</th>\n",
       "      <th>Seat_comfort</th>\n",
       "      <th>Seat_Class</th>\n",
       "      <th>Arrival_time_convenient</th>\n",
       "      <th>Catering</th>\n",
       "      <th>Platform_location</th>\n",
       "      <th>Onboardwifi_service</th>\n",
       "      <th>Onboard_entertainment</th>\n",
       "      <th>Online_support</th>\n",
       "      <th>Onlinebooking_Ease</th>\n",
       "      <th>Onboard_service</th>\n",
       "      <th>Leg_room</th>\n",
       "      <th>Baggage_handling</th>\n",
       "      <th>Checkin_service</th>\n",
       "      <th>Cleanliness</th>\n",
       "      <th>Online_boarding</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>98800001</td>\n",
       "      <td>0</td>\n",
       "      <td>need improvement</td>\n",
       "      <td>Green Car</td>\n",
       "      <td>excellent</td>\n",
       "      <td>excellent</td>\n",
       "      <td>very convinient</td>\n",
       "      <td>good</td>\n",
       "      <td>need improvement</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>need improvement</td>\n",
       "      <td>need improvement</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>need improvement</td>\n",
       "      <td>good</td>\n",
       "      <td>need improvement</td>\n",
       "      <td>poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>98800002</td>\n",
       "      <td>0</td>\n",
       "      <td>poor</td>\n",
       "      <td>Ordinary</td>\n",
       "      <td>excellent</td>\n",
       "      <td>poor</td>\n",
       "      <td>need improvement</td>\n",
       "      <td>good</td>\n",
       "      <td>poor</td>\n",
       "      <td>good</td>\n",
       "      <td>good</td>\n",
       "      <td>excellent</td>\n",
       "      <td>need improvement</td>\n",
       "      <td>poor</td>\n",
       "      <td>need improvement</td>\n",
       "      <td>good</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>98800003</td>\n",
       "      <td>1</td>\n",
       "      <td>need improvement</td>\n",
       "      <td>Green Car</td>\n",
       "      <td>need improvement</td>\n",
       "      <td>need improvement</td>\n",
       "      <td>need improvement</td>\n",
       "      <td>need improvement</td>\n",
       "      <td>good</td>\n",
       "      <td>excellent</td>\n",
       "      <td>excellent</td>\n",
       "      <td>excellent</td>\n",
       "      <td>excellent</td>\n",
       "      <td>excellent</td>\n",
       "      <td>good</td>\n",
       "      <td>excellent</td>\n",
       "      <td>excellent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>98800004</td>\n",
       "      <td>0</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>Ordinary</td>\n",
       "      <td>need improvement</td>\n",
       "      <td>NaN</td>\n",
       "      <td>need improvement</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>need improvement</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>good</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>acceptable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>98800005</td>\n",
       "      <td>1</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>Ordinary</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>manageable</td>\n",
       "      <td>need improvement</td>\n",
       "      <td>good</td>\n",
       "      <td>excellent</td>\n",
       "      <td>good</td>\n",
       "      <td>good</td>\n",
       "      <td>good</td>\n",
       "      <td>good</td>\n",
       "      <td>good</td>\n",
       "      <td>good</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         ID  Overall_Experience      Seat_comfort Seat_Class  \\\n",
       "0  98800001                   0  need improvement  Green Car   \n",
       "1  98800002                   0              poor   Ordinary   \n",
       "2  98800003                   1  need improvement  Green Car   \n",
       "3  98800004                   0        acceptable   Ordinary   \n",
       "4  98800005                   1        acceptable   Ordinary   \n",
       "\n",
       "  Arrival_time_convenient          Catering Platform_location  \\\n",
       "0               excellent         excellent   very convinient   \n",
       "1               excellent              poor  need improvement   \n",
       "2        need improvement  need improvement  need improvement   \n",
       "3        need improvement               NaN  need improvement   \n",
       "4              acceptable        acceptable        manageable   \n",
       "\n",
       "  Onboardwifi_service Onboard_entertainment Online_support Onlinebooking_Ease  \\\n",
       "0                good      need improvement     acceptable   need improvement   \n",
       "1                good                  poor           good               good   \n",
       "2    need improvement                  good      excellent          excellent   \n",
       "3          acceptable      need improvement     acceptable         acceptable   \n",
       "4    need improvement                  good      excellent               good   \n",
       "\n",
       "    Onboard_service          Leg_room  Baggage_handling   Checkin_service  \\\n",
       "0  need improvement        acceptable  need improvement              good   \n",
       "1         excellent  need improvement              poor  need improvement   \n",
       "2         excellent         excellent         excellent              good   \n",
       "3        acceptable        acceptable        acceptable              good   \n",
       "4              good              good              good              good   \n",
       "\n",
       "        Cleanliness Online_boarding  \n",
       "0  need improvement            poor  \n",
       "1              good            good  \n",
       "2         excellent       excellent  \n",
       "3        acceptable      acceptable  \n",
       "4              good            good  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the head of dataset\n",
    "strain.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0b574624",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>ID</th>\n",
       "      <th>Overall_Experience</th>\n",
       "      <th>Seat_comfort</th>\n",
       "      <th>Seat_Class</th>\n",
       "      <th>Arrival_time_convenient</th>\n",
       "      <th>Catering</th>\n",
       "      <th>Platform_location</th>\n",
       "      <th>Onboardwifi_service</th>\n",
       "      <th>Onboard_entertainment</th>\n",
       "      <th>Online_support</th>\n",
       "      <th>Onlinebooking_Ease</th>\n",
       "      <th>Onboard_service</th>\n",
       "      <th>Leg_room</th>\n",
       "      <th>Baggage_handling</th>\n",
       "      <th>Checkin_service</th>\n",
       "      <th>Cleanliness</th>\n",
       "      <th>Online_boarding</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>94374</th>\n",
       "      <td>98894375</td>\n",
       "      <td>0</td>\n",
       "      <td>poor</td>\n",
       "      <td>Ordinary</td>\n",
       "      <td>good</td>\n",
       "      <td>good</td>\n",
       "      <td>Convinient</td>\n",
       "      <td>poor</td>\n",
       "      <td>poor</td>\n",
       "      <td>poor</td>\n",
       "      <td>poor</td>\n",
       "      <td>good</td>\n",
       "      <td>good</td>\n",
       "      <td>good</td>\n",
       "      <td>need improvement</td>\n",
       "      <td>good</td>\n",
       "      <td>poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94375</th>\n",
       "      <td>98894376</td>\n",
       "      <td>1</td>\n",
       "      <td>good</td>\n",
       "      <td>Ordinary</td>\n",
       "      <td>good</td>\n",
       "      <td>good</td>\n",
       "      <td>Convinient</td>\n",
       "      <td>need improvement</td>\n",
       "      <td>excellent</td>\n",
       "      <td>excellent</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>good</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94376</th>\n",
       "      <td>98894377</td>\n",
       "      <td>1</td>\n",
       "      <td>need improvement</td>\n",
       "      <td>Green Car</td>\n",
       "      <td>need improvement</td>\n",
       "      <td>need improvement</td>\n",
       "      <td>need improvement</td>\n",
       "      <td>good</td>\n",
       "      <td>excellent</td>\n",
       "      <td>good</td>\n",
       "      <td>good</td>\n",
       "      <td>good</td>\n",
       "      <td>good</td>\n",
       "      <td>good</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>good</td>\n",
       "      <td>acceptable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94377</th>\n",
       "      <td>98894378</td>\n",
       "      <td>0</td>\n",
       "      <td>need improvement</td>\n",
       "      <td>Ordinary</td>\n",
       "      <td>NaN</td>\n",
       "      <td>need improvement</td>\n",
       "      <td>Convinient</td>\n",
       "      <td>good</td>\n",
       "      <td>need improvement</td>\n",
       "      <td>good</td>\n",
       "      <td>good</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>good</td>\n",
       "      <td>good</td>\n",
       "      <td>good</td>\n",
       "      <td>excellent</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94378</th>\n",
       "      <td>98894379</td>\n",
       "      <td>0</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>Ordinary</td>\n",
       "      <td>poor</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>manageable</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>poor</td>\n",
       "      <td>good</td>\n",
       "      <td>good</td>\n",
       "      <td>poor</td>\n",
       "      <td>good</td>\n",
       "      <td>acceptable</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             ID  Overall_Experience      Seat_comfort Seat_Class  \\\n",
       "94374  98894375                   0              poor   Ordinary   \n",
       "94375  98894376                   1              good   Ordinary   \n",
       "94376  98894377                   1  need improvement  Green Car   \n",
       "94377  98894378                   0  need improvement   Ordinary   \n",
       "94378  98894379                   0        acceptable   Ordinary   \n",
       "\n",
       "      Arrival_time_convenient          Catering Platform_location  \\\n",
       "94374                    good              good        Convinient   \n",
       "94375                    good              good        Convinient   \n",
       "94376        need improvement  need improvement  need improvement   \n",
       "94377                     NaN  need improvement        Convinient   \n",
       "94378                    poor        acceptable        manageable   \n",
       "\n",
       "      Onboardwifi_service Onboard_entertainment Online_support  \\\n",
       "94374                poor                  poor           poor   \n",
       "94375    need improvement             excellent      excellent   \n",
       "94376                good             excellent           good   \n",
       "94377                good      need improvement           good   \n",
       "94378          acceptable            acceptable     acceptable   \n",
       "\n",
       "      Onlinebooking_Ease Onboard_service    Leg_room Baggage_handling  \\\n",
       "94374               poor            good        good             good   \n",
       "94375         acceptable      acceptable  acceptable       acceptable   \n",
       "94376               good            good        good             good   \n",
       "94377               good      acceptable        good             good   \n",
       "94378         acceptable            poor        good             good   \n",
       "\n",
       "        Checkin_service Cleanliness Online_boarding  \n",
       "94374  need improvement        good            poor  \n",
       "94375              good  acceptable            good  \n",
       "94376        acceptable        good      acceptable  \n",
       "94377              good   excellent            good  \n",
       "94378              poor        good      acceptable  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the tail of dataset\n",
    "strain.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0136acbf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 94379 entries, 0 to 94378\n",
      "Data columns (total 17 columns):\n",
      " #   Column                   Non-Null Count  Dtype \n",
      "---  ------                   --------------  ----- \n",
      " 0   ID                       94379 non-null  int64 \n",
      " 1   Overall_Experience       94379 non-null  int64 \n",
      " 2   Seat_comfort             94318 non-null  object\n",
      " 3   Seat_Class               94379 non-null  object\n",
      " 4   Arrival_time_convenient  85449 non-null  object\n",
      " 5   Catering                 85638 non-null  object\n",
      " 6   Platform_location        94349 non-null  object\n",
      " 7   Onboardwifi_service      94349 non-null  object\n",
      " 8   Onboard_entertainment    94361 non-null  object\n",
      " 9   Online_support           94288 non-null  object\n",
      " 10  Onlinebooking_Ease       94306 non-null  object\n",
      " 11  Onboard_service          86778 non-null  object\n",
      " 12  Leg_room                 94289 non-null  object\n",
      " 13  Baggage_handling         94237 non-null  object\n",
      " 14  Checkin_service          94302 non-null  object\n",
      " 15  Cleanliness              94373 non-null  object\n",
      " 16  Online_boarding          94373 non-null  object\n",
      "dtypes: int64(2), object(15)\n",
      "memory usage: 12.2+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# Get information about the train dataset\n",
    "print(strain.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9f8e87ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Overall_Experience</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9.437900e+04</td>\n",
       "      <td>94379.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.884719e+07</td>\n",
       "      <td>0.546658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.724501e+04</td>\n",
       "      <td>0.497821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>9.880000e+07</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.882360e+07</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9.884719e+07</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.887078e+07</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.889438e+07</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ID  Overall_Experience\n",
       "count  9.437900e+04        94379.000000\n",
       "mean   9.884719e+07            0.546658\n",
       "std    2.724501e+04            0.497821\n",
       "min    9.880000e+07            0.000000\n",
       "25%    9.882360e+07            0.000000\n",
       "50%    9.884719e+07            1.000000\n",
       "75%    9.887078e+07            1.000000\n",
       "max    9.889438e+07            1.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get basic statistics of numerical variables in the train dataset\n",
    "strain.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a627bb85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                         null_count  null_percentage\n",
      "ID                                0         0.000000\n",
      "Overall_Experience                0         0.000000\n",
      "Seat_comfort                     61         0.064633\n",
      "Seat_Class                        0         0.000000\n",
      "Arrival_time_convenient        8930         9.461851\n",
      "Catering                       8741         9.261594\n",
      "Platform_location                30         0.031787\n",
      "Onboardwifi_service              30         0.031787\n",
      "Onboard_entertainment            18         0.019072\n",
      "Online_support                   91         0.096420\n",
      "Onlinebooking_Ease               73         0.077348\n",
      "Onboard_service                7601         8.053698\n",
      "Leg_room                         90         0.095360\n",
      "Baggage_handling                142         0.150457\n",
      "Checkin_service                  77         0.081586\n",
      "Cleanliness                       6         0.006357\n",
      "Online_boarding                   6         0.006357\n"
     ]
    }
   ],
   "source": [
    "# Check for null values and show the percentage of nulls\n",
    "null_values = strain.isnull().sum().rename('null_count')\n",
    "null_percent = ((null_values / strain.shape[0]) * 100).rename('null_percentage')\n",
    "null_info = pd.concat([null_values,null_percent],axis=1)\n",
    "print(null_info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9994c025",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ID []\n",
      "Overall_Experience []\n"
     ]
    }
   ],
   "source": [
    "# Check for any unwanted characters in numerical columns in the train dataset\n",
    "for col in strain.select_dtypes(include='number').columns:\n",
    "    print(col, strain[col][~np.isreal(strain[col])].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f67ffb2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Overall_Experience</th>\n",
       "      <th>Seat_comfort</th>\n",
       "      <th>Seat_Class</th>\n",
       "      <th>Arrival_time_convenient</th>\n",
       "      <th>Catering</th>\n",
       "      <th>Platform_location</th>\n",
       "      <th>Onboardwifi_service</th>\n",
       "      <th>Onboard_entertainment</th>\n",
       "      <th>Online_support</th>\n",
       "      <th>Onlinebooking_Ease</th>\n",
       "      <th>Onboard_service</th>\n",
       "      <th>Leg_room</th>\n",
       "      <th>Baggage_handling</th>\n",
       "      <th>Checkin_service</th>\n",
       "      <th>Cleanliness</th>\n",
       "      <th>Online_boarding</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [ID, Overall_Experience, Seat_comfort, Seat_Class, Arrival_time_convenient, Catering, Platform_location, Onboardwifi_service, Onboard_entertainment, Online_support, Onlinebooking_Ease, Onboard_service, Leg_room, Baggage_handling, Checkin_service, Cleanliness, Online_boarding]\n",
       "Index: []"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for duplicate rows\n",
    "duplicate_rows = strain[strain.duplicated()]\n",
    "duplicate_rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c5aab178",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                         zero_count  zero_percentage\n",
      "ID                                0         0.000000\n",
      "Overall_Experience            42786        45.334237\n",
      "Seat_comfort                      0         0.000000\n",
      "Seat_Class                        0         0.000000\n",
      "Arrival_time_convenient           0         0.000000\n",
      "Catering                          0         0.000000\n",
      "Platform_location                 0         0.000000\n",
      "Onboardwifi_service               0         0.000000\n",
      "Onboard_entertainment             0         0.000000\n",
      "Online_support                    0         0.000000\n",
      "Onlinebooking_Ease                0         0.000000\n",
      "Onboard_service                   0         0.000000\n",
      "Leg_room                          0         0.000000\n",
      "Baggage_handling                  0         0.000000\n",
      "Checkin_service                   0         0.000000\n",
      "Cleanliness                       0         0.000000\n",
      "Online_boarding                   0         0.000000\n"
     ]
    }
   ],
   "source": [
    "# Check for zeros and show the percentage of zeros\n",
    "zero_values = strain.eq(0).sum().rename('zero_count')\n",
    "zero_percent = (zero_values / strain.shape[0]) * 100\n",
    "zero_percent = zero_percent.rename('zero_percentage')\n",
    "zero_info = pd.concat([zero_values,zero_percent],axis=1)\n",
    "print(zero_info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c86b2e20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1296 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "datacat=strain.select_dtypes(include='object')\n",
    "\n",
    "plt.figure(figsize = (20,18))\n",
    "feature_list = datacat.columns\n",
    "for i in range(len(feature_list)):\n",
    "    plt.subplot(5, 3, i + 1)\n",
    "    ax1=sns.countplot(x = datacat[feature_list[i]], data = datacat)\n",
    "    ax1.bar_label(ax1.containers[0])\n",
    "    plt.title('Counplot of {}'.format(feature_list[i]))\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e2f7192",
   "metadata": {},
   "source": [
    "### survey test data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b22a067d",
   "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>Seat_comfort</th>\n",
       "      <th>Seat_Class</th>\n",
       "      <th>Arrival_time_convenient</th>\n",
       "      <th>Catering</th>\n",
       "      <th>Platform_location</th>\n",
       "      <th>Onboardwifi_service</th>\n",
       "      <th>Onboard_entertainment</th>\n",
       "      <th>Online_support</th>\n",
       "      <th>Onlinebooking_Ease</th>\n",
       "      <th>Onboard_service</th>\n",
       "      <th>Leg_room</th>\n",
       "      <th>Baggage_handling</th>\n",
       "      <th>Checkin_service</th>\n",
       "      <th>Cleanliness</th>\n",
       "      <th>Online_boarding</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>99900001</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>Green Car</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>manageable</td>\n",
       "      <td>need improvement</td>\n",
       "      <td>excellent</td>\n",
       "      <td>good</td>\n",
       "      <td>excellent</td>\n",
       "      <td>excellent</td>\n",
       "      <td>excellent</td>\n",
       "      <td>excellent</td>\n",
       "      <td>good</td>\n",
       "      <td>excellent</td>\n",
       "      <td>poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>99900002</td>\n",
       "      <td>extremely poor</td>\n",
       "      <td>Ordinary</td>\n",
       "      <td>good</td>\n",
       "      <td>poor</td>\n",
       "      <td>manageable</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>poor</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>excellent</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>good</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>excellent</td>\n",
       "      <td>acceptable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>99900003</td>\n",
       "      <td>excellent</td>\n",
       "      <td>Ordinary</td>\n",
       "      <td>excellent</td>\n",
       "      <td>excellent</td>\n",
       "      <td>very convinient</td>\n",
       "      <td>excellent</td>\n",
       "      <td>excellent</td>\n",
       "      <td>excellent</td>\n",
       "      <td>need improvement</td>\n",
       "      <td>need improvement</td>\n",
       "      <td>need improvement</td>\n",
       "      <td>need improvement</td>\n",
       "      <td>good</td>\n",
       "      <td>need improvement</td>\n",
       "      <td>excellent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>99900004</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>Green Car</td>\n",
       "      <td>excellent</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>very convinient</td>\n",
       "      <td>poor</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>excellent</td>\n",
       "      <td>poor</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>need improvement</td>\n",
       "      <td>excellent</td>\n",
       "      <td>excellent</td>\n",
       "      <td>excellent</td>\n",
       "      <td>poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>99900005</td>\n",
       "      <td>excellent</td>\n",
       "      <td>Ordinary</td>\n",
       "      <td>extremely poor</td>\n",
       "      <td>excellent</td>\n",
       "      <td>need improvement</td>\n",
       "      <td>excellent</td>\n",
       "      <td>excellent</td>\n",
       "      <td>excellent</td>\n",
       "      <td>excellent</td>\n",
       "      <td>NaN</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>excellent</td>\n",
       "      <td>excellent</td>\n",
       "      <td>excellent</td>\n",
       "      <td>excellent</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         ID    Seat_comfort Seat_Class Arrival_time_convenient    Catering  \\\n",
       "0  99900001      acceptable  Green Car              acceptable  acceptable   \n",
       "1  99900002  extremely poor   Ordinary                    good        poor   \n",
       "2  99900003       excellent   Ordinary               excellent   excellent   \n",
       "3  99900004      acceptable  Green Car               excellent  acceptable   \n",
       "4  99900005       excellent   Ordinary          extremely poor   excellent   \n",
       "\n",
       "  Platform_location Onboardwifi_service Onboard_entertainment Online_support  \\\n",
       "0        manageable    need improvement             excellent           good   \n",
       "1        manageable          acceptable                  poor     acceptable   \n",
       "2   very convinient           excellent             excellent      excellent   \n",
       "3   very convinient                poor            acceptable      excellent   \n",
       "4  need improvement           excellent             excellent      excellent   \n",
       "\n",
       "  Onlinebooking_Ease   Onboard_service          Leg_room  Baggage_handling  \\\n",
       "0          excellent         excellent         excellent         excellent   \n",
       "1         acceptable         excellent        acceptable              good   \n",
       "2   need improvement  need improvement  need improvement  need improvement   \n",
       "3               poor        acceptable  need improvement         excellent   \n",
       "4          excellent               NaN        acceptable         excellent   \n",
       "\n",
       "  Checkin_service       Cleanliness Online_boarding  \n",
       "0            good         excellent            poor  \n",
       "1      acceptable         excellent      acceptable  \n",
       "2            good  need improvement       excellent  \n",
       "3       excellent         excellent            poor  \n",
       "4       excellent         excellent       excellent  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the head of dataset\n",
    "stest.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f67ef484",
   "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>Seat_comfort</th>\n",
       "      <th>Seat_Class</th>\n",
       "      <th>Arrival_time_convenient</th>\n",
       "      <th>Catering</th>\n",
       "      <th>Platform_location</th>\n",
       "      <th>Onboardwifi_service</th>\n",
       "      <th>Onboard_entertainment</th>\n",
       "      <th>Online_support</th>\n",
       "      <th>Onlinebooking_Ease</th>\n",
       "      <th>Onboard_service</th>\n",
       "      <th>Leg_room</th>\n",
       "      <th>Baggage_handling</th>\n",
       "      <th>Checkin_service</th>\n",
       "      <th>Cleanliness</th>\n",
       "      <th>Online_boarding</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35597</th>\n",
       "      <td>99935598</td>\n",
       "      <td>need improvement</td>\n",
       "      <td>Green Car</td>\n",
       "      <td>excellent</td>\n",
       "      <td>need improvement</td>\n",
       "      <td>manageable</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>need improvement</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>good</td>\n",
       "      <td>excellent</td>\n",
       "      <td>good</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>good</td>\n",
       "      <td>acceptable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35598</th>\n",
       "      <td>99935599</td>\n",
       "      <td>need improvement</td>\n",
       "      <td>Ordinary</td>\n",
       "      <td>need improvement</td>\n",
       "      <td>good</td>\n",
       "      <td>need improvement</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>excellent</td>\n",
       "      <td>excellent</td>\n",
       "      <td>good</td>\n",
       "      <td>good</td>\n",
       "      <td>good</td>\n",
       "      <td>good</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>good</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35599</th>\n",
       "      <td>99935600</td>\n",
       "      <td>good</td>\n",
       "      <td>Green Car</td>\n",
       "      <td>extremely poor</td>\n",
       "      <td>good</td>\n",
       "      <td>need improvement</td>\n",
       "      <td>need improvement</td>\n",
       "      <td>good</td>\n",
       "      <td>poor</td>\n",
       "      <td>need improvement</td>\n",
       "      <td>poor</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>poor</td>\n",
       "      <td>poor</td>\n",
       "      <td>excellent</td>\n",
       "      <td>need improvement</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35600</th>\n",
       "      <td>99935601</td>\n",
       "      <td>excellent</td>\n",
       "      <td>Ordinary</td>\n",
       "      <td>excellent</td>\n",
       "      <td>excellent</td>\n",
       "      <td>Inconvinient</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>excellent</td>\n",
       "      <td>good</td>\n",
       "      <td>excellent</td>\n",
       "      <td>excellent</td>\n",
       "      <td>excellent</td>\n",
       "      <td>excellent</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>excellent</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35601</th>\n",
       "      <td>99935602</td>\n",
       "      <td>good</td>\n",
       "      <td>Ordinary</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>good</td>\n",
       "      <td>manageable</td>\n",
       "      <td>poor</td>\n",
       "      <td>good</td>\n",
       "      <td>poor</td>\n",
       "      <td>poor</td>\n",
       "      <td>acceptable</td>\n",
       "      <td>good</td>\n",
       "      <td>good</td>\n",
       "      <td>need improvement</td>\n",
       "      <td>good</td>\n",
       "      <td>poor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             ID      Seat_comfort Seat_Class Arrival_time_convenient  \\\n",
       "35597  99935598  need improvement  Green Car               excellent   \n",
       "35598  99935599  need improvement   Ordinary        need improvement   \n",
       "35599  99935600              good  Green Car          extremely poor   \n",
       "35600  99935601         excellent   Ordinary               excellent   \n",
       "35601  99935602              good   Ordinary              acceptable   \n",
       "\n",
       "               Catering Platform_location Onboardwifi_service  \\\n",
       "35597  need improvement        manageable          acceptable   \n",
       "35598              good  need improvement          acceptable   \n",
       "35599              good  need improvement    need improvement   \n",
       "35600         excellent      Inconvinient          acceptable   \n",
       "35601              good        manageable                poor   \n",
       "\n",
       "      Onboard_entertainment Online_support Onlinebooking_Ease Onboard_service  \\\n",
       "35597      need improvement     acceptable         acceptable            good   \n",
       "35598             excellent      excellent               good            good   \n",
       "35599                  good           poor   need improvement            poor   \n",
       "35600             excellent           good          excellent       excellent   \n",
       "35601                  good           poor               poor      acceptable   \n",
       "\n",
       "         Leg_room Baggage_handling   Checkin_service Cleanliness  \\\n",
       "35597   excellent             good        acceptable        good   \n",
       "35598        good             good        acceptable        good   \n",
       "35599  acceptable             poor              poor   excellent   \n",
       "35600   excellent        excellent        acceptable   excellent   \n",
       "35601        good             good  need improvement        good   \n",
       "\n",
       "        Online_boarding  \n",
       "35597        acceptable  \n",
       "35598              good  \n",
       "35599  need improvement  \n",
       "35600              good  \n",
       "35601              poor  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the tail of dataset\n",
    "stest.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7a9dd164",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 35602 entries, 0 to 35601\n",
      "Data columns (total 16 columns):\n",
      " #   Column                   Non-Null Count  Dtype \n",
      "---  ------                   --------------  ----- \n",
      " 0   ID                       35602 non-null  int64 \n",
      " 1   Seat_comfort             35580 non-null  object\n",
      " 2   Seat_Class               35602 non-null  object\n",
      " 3   Arrival_time_convenient  32277 non-null  object\n",
      " 4   Catering                 32245 non-null  object\n",
      " 5   Platform_location        35590 non-null  object\n",
      " 6   Onboardwifi_service      35590 non-null  object\n",
      " 7   Onboard_entertainment    35594 non-null  object\n",
      " 8   Online_support           35576 non-null  object\n",
      " 9   Onlinebooking_Ease       35584 non-null  object\n",
      " 10  Onboard_service          32730 non-null  object\n",
      " 11  Leg_room                 35577 non-null  object\n",
      " 12  Baggage_handling         35562 non-null  object\n",
      " 13  Checkin_service          35580 non-null  object\n",
      " 14  Cleanliness              35600 non-null  object\n",
      " 15  Online_boarding          35600 non-null  object\n",
      "dtypes: int64(1), object(15)\n",
      "memory usage: 4.3+ MB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.560200e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.991780e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.027756e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>9.990000e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.990890e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9.991780e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.992670e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.993560e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ID\n",
       "count  3.560200e+04\n",
       "mean   9.991780e+07\n",
       "std    1.027756e+04\n",
       "min    9.990000e+07\n",
       "25%    9.990890e+07\n",
       "50%    9.991780e+07\n",
       "75%    9.992670e+07\n",
       "max    9.993560e+07"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get information about the train dataset\n",
    "print(stest.info())\n",
    "\n",
    "# Get basic statistics of numerical variables in the train dataset\n",
    "stest.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d27b9293",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "null check\n",
      "                         null_count  null_percentage\n",
      "ID                                0         0.000000\n",
      "Seat_comfort                     22         0.061794\n",
      "Seat_Class                        0         0.000000\n",
      "Arrival_time_convenient        3325         9.339363\n",
      "Catering                       3357         9.429246\n",
      "Platform_location                12         0.033706\n",
      "Onboardwifi_service              12         0.033706\n",
      "Onboard_entertainment             8         0.022471\n",
      "Online_support                   26         0.073030\n",
      "Onlinebooking_Ease               18         0.050559\n",
      "Onboard_service                2872         8.066963\n",
      "Leg_room                         25         0.070221\n",
      "Baggage_handling                 40         0.112353\n",
      "Checkin_service                  22         0.061794\n",
      "Cleanliness                       2         0.005618\n",
      "Online_boarding                   2         0.005618\n",
      "\n",
      " unwanted character check \n",
      " \n",
      "ID []\n",
      "\n",
      " duplicate data check \n",
      "\n",
      "\n",
      " zeros check \n",
      "\n",
      "                         zero_count  zero_percentage\n",
      "ID                                0              0.0\n",
      "Seat_comfort                      0              0.0\n",
      "Seat_Class                        0              0.0\n",
      "Arrival_time_convenient           0              0.0\n",
      "Catering                          0              0.0\n",
      "Platform_location                 0              0.0\n",
      "Onboardwifi_service               0              0.0\n",
      "Onboard_entertainment             0              0.0\n",
      "Online_support                    0              0.0\n",
      "Onlinebooking_Ease                0              0.0\n",
      "Onboard_service                   0              0.0\n",
      "Leg_room                          0              0.0\n",
      "Baggage_handling                  0              0.0\n",
      "Checkin_service                   0              0.0\n",
      "Cleanliness                       0              0.0\n",
      "Online_boarding                   0              0.0\n"
     ]
    }
   ],
   "source": [
    "# Check for null values and show the percentage of nulls\n",
    "print('null check')\n",
    "null_values = stest.isnull().sum().rename('null_count')\n",
    "null_percent = ((null_values / stest.shape[0]) * 100).rename('null_percentage')\n",
    "null_info = pd.concat([null_values,null_percent],axis=1)\n",
    "print(null_info)\n",
    "\n",
    "# Check for any unwanted characters in numerical columns in the train dataset\n",
    "print('\\n unwanted character check \\n ')\n",
    "for col in stest.select_dtypes(include='number').columns:\n",
    "    print(col, stest[col][~np.isreal(stest[col])].unique())\n",
    "\n",
    "# Check for duplicate rows\n",
    "print('\\n duplicate data check \\n')\n",
    "duplicate_rows = stest[stest.duplicated()]\n",
    "duplicate_rows\n",
    "\n",
    "# Check for zeros and show the percentage of zeros\n",
    "print('\\n zeros check \\n')\n",
    "zero_values = stest.eq(0).sum().rename('zero_count')\n",
    "zero_percent = (zero_values / stest.shape[0]) * 100\n",
    "zero_percent = zero_percent.rename('zero_percentage')\n",
    "zero_info = pd.concat([zero_values,zero_percent],axis=1)\n",
    "print(zero_info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "938ecb7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1296 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "datacat=stest.select_dtypes(include='object')\n",
    "\n",
    "plt.figure(figsize = (20,18))\n",
    "feature_list = datacat.columns\n",
    "for i in range(len(feature_list)):\n",
    "    plt.subplot(5, 3, i + 1)\n",
    "    ax1=sns.countplot(x = datacat[feature_list[i]], data = datacat)\n",
    "    ax1.bar_label(ax1.containers[0])\n",
    "    plt.title('Counplot of {}'.format(feature_list[i]))\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcc637b9",
   "metadata": {},
   "source": [
    "### Travel train data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "59f39c3e",
   "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>Gender</th>\n",
       "      <th>CustomerType</th>\n",
       "      <th>Age</th>\n",
       "      <th>TypeTravel</th>\n",
       "      <th>Travel_Class</th>\n",
       "      <th>Travel_Distance</th>\n",
       "      <th>DepartureDelay_in_Mins</th>\n",
       "      <th>ArrivalDelay_in_Mins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>98800001</td>\n",
       "      <td>Female</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>52.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Business</td>\n",
       "      <td>272</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>98800002</td>\n",
       "      <td>Male</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>48.0</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Eco</td>\n",
       "      <td>2200</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>98800003</td>\n",
       "      <td>Female</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>43.0</td>\n",
       "      <td>Business travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>1061</td>\n",
       "      <td>77.0</td>\n",
       "      <td>119.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>98800004</td>\n",
       "      <td>Female</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>44.0</td>\n",
       "      <td>Business travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>780</td>\n",
       "      <td>13.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>98800005</td>\n",
       "      <td>Female</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Business travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>1981</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         ID  Gender    CustomerType   Age       TypeTravel Travel_Class  \\\n",
       "0  98800001  Female  Loyal Customer  52.0              NaN     Business   \n",
       "1  98800002    Male  Loyal Customer  48.0  Personal Travel          Eco   \n",
       "2  98800003  Female  Loyal Customer  43.0  Business travel     Business   \n",
       "3  98800004  Female  Loyal Customer  44.0  Business travel     Business   \n",
       "4  98800005  Female  Loyal Customer  50.0  Business travel     Business   \n",
       "\n",
       "   Travel_Distance  DepartureDelay_in_Mins  ArrivalDelay_in_Mins  \n",
       "0              272                     0.0                   5.0  \n",
       "1             2200                     9.0                   0.0  \n",
       "2             1061                    77.0                 119.0  \n",
       "3              780                    13.0                  18.0  \n",
       "4             1981                     0.0                   0.0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the head of dataset\n",
    "ttrain.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e177b413",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>CustomerType</th>\n",
       "      <th>Age</th>\n",
       "      <th>TypeTravel</th>\n",
       "      <th>Travel_Class</th>\n",
       "      <th>Travel_Distance</th>\n",
       "      <th>DepartureDelay_in_Mins</th>\n",
       "      <th>ArrivalDelay_in_Mins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>94374</th>\n",
       "      <td>98894375</td>\n",
       "      <td>Male</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>32.0</td>\n",
       "      <td>Business travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>1357</td>\n",
       "      <td>83.0</td>\n",
       "      <td>125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94375</th>\n",
       "      <td>98894376</td>\n",
       "      <td>Male</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>44.0</td>\n",
       "      <td>Business travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>592</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94376</th>\n",
       "      <td>98894377</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63.0</td>\n",
       "      <td>Business travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>2794</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94377</th>\n",
       "      <td>98894378</td>\n",
       "      <td>Male</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>16.0</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Eco</td>\n",
       "      <td>2744</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94378</th>\n",
       "      <td>98894379</td>\n",
       "      <td>Male</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>54.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Eco</td>\n",
       "      <td>2107</td>\n",
       "      <td>28.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             ID Gender    CustomerType   Age       TypeTravel Travel_Class  \\\n",
       "94374  98894375   Male  Loyal Customer  32.0  Business travel     Business   \n",
       "94375  98894376   Male  Loyal Customer  44.0  Business travel     Business   \n",
       "94376  98894377   Male             NaN  63.0  Business travel     Business   \n",
       "94377  98894378   Male  Loyal Customer  16.0  Personal Travel          Eco   \n",
       "94378  98894379   Male  Loyal Customer  54.0              NaN          Eco   \n",
       "\n",
       "       Travel_Distance  DepartureDelay_in_Mins  ArrivalDelay_in_Mins  \n",
       "94374             1357                    83.0                 125.0  \n",
       "94375              592                     5.0                  11.0  \n",
       "94376             2794                     0.0                   0.0  \n",
       "94377             2744                     0.0                   0.0  \n",
       "94378             2107                    28.0                  28.0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the tail of dataset\n",
    "ttrain.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "21b619d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 94379 entries, 0 to 94378\n",
      "Data columns (total 9 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   ID                      94379 non-null  int64  \n",
      " 1   Gender                  94302 non-null  object \n",
      " 2   CustomerType            85428 non-null  object \n",
      " 3   Age                     94346 non-null  float64\n",
      " 4   TypeTravel              85153 non-null  object \n",
      " 5   Travel_Class            94379 non-null  object \n",
      " 6   Travel_Distance         94379 non-null  int64  \n",
      " 7   DepartureDelay_in_Mins  94322 non-null  float64\n",
      " 8   ArrivalDelay_in_Mins    94022 non-null  float64\n",
      "dtypes: float64(3), int64(2), object(4)\n",
      "memory usage: 6.5+ MB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Travel_Distance</th>\n",
       "      <th>DepartureDelay_in_Mins</th>\n",
       "      <th>ArrivalDelay_in_Mins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9.437900e+04</td>\n",
       "      <td>94346.000000</td>\n",
       "      <td>94379.000000</td>\n",
       "      <td>94322.000000</td>\n",
       "      <td>94022.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.884719e+07</td>\n",
       "      <td>39.419647</td>\n",
       "      <td>1978.888185</td>\n",
       "      <td>14.647092</td>\n",
       "      <td>15.005222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.724501e+04</td>\n",
       "      <td>15.116632</td>\n",
       "      <td>1027.961019</td>\n",
       "      <td>38.138781</td>\n",
       "      <td>38.439409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>9.880000e+07</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.882360e+07</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>1359.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9.884719e+07</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>1923.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.887078e+07</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>2538.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.889438e+07</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>6951.000000</td>\n",
       "      <td>1592.000000</td>\n",
       "      <td>1584.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ID           Age  Travel_Distance  DepartureDelay_in_Mins  \\\n",
       "count  9.437900e+04  94346.000000     94379.000000            94322.000000   \n",
       "mean   9.884719e+07     39.419647      1978.888185               14.647092   \n",
       "std    2.724501e+04     15.116632      1027.961019               38.138781   \n",
       "min    9.880000e+07      7.000000        50.000000                0.000000   \n",
       "25%    9.882360e+07     27.000000      1359.000000                0.000000   \n",
       "50%    9.884719e+07     40.000000      1923.000000                0.000000   \n",
       "75%    9.887078e+07     51.000000      2538.000000               12.000000   \n",
       "max    9.889438e+07     85.000000      6951.000000             1592.000000   \n",
       "\n",
       "       ArrivalDelay_in_Mins  \n",
       "count          94022.000000  \n",
       "mean              15.005222  \n",
       "std               38.439409  \n",
       "min                0.000000  \n",
       "25%                0.000000  \n",
       "50%                0.000000  \n",
       "75%               13.000000  \n",
       "max             1584.000000  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get information about the train dataset\n",
    "print(ttrain.info())\n",
    "\n",
    "# Get basic statistics of numerical variables in the train dataset\n",
    "ttrain.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "822b9f0f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "null check\n",
      "                        null_count  null_percentage\n",
      "ID                               0         0.000000\n",
      "Gender                          77         0.081586\n",
      "CustomerType                  8951         9.484101\n",
      "Age                             33         0.034965\n",
      "TypeTravel                    9226         9.775480\n",
      "Travel_Class                     0         0.000000\n",
      "Travel_Distance                  0         0.000000\n",
      "DepartureDelay_in_Mins          57         0.060395\n",
      "ArrivalDelay_in_Mins           357         0.378262\n",
      "\n",
      " duplicate data check \n",
      "\n",
      "\n",
      " zeros check \n",
      "\n",
      "                        zero_count  zero_percentage\n",
      "ID                               0         0.000000\n",
      "Gender                           0         0.000000\n",
      "CustomerType                     0         0.000000\n",
      "Age                              0         0.000000\n",
      "TypeTravel                       0         0.000000\n",
      "Travel_Class                     0         0.000000\n",
      "Travel_Distance                  0         0.000000\n",
      "DepartureDelay_in_Mins       53339        56.515750\n",
      "ArrivalDelay_in_Mins         52915        56.066498\n"
     ]
    }
   ],
   "source": [
    "# Check for null values and show the percentage of nulls\n",
    "print('null check')\n",
    "null_values = ttrain.isnull().sum().rename('null_count')\n",
    "null_percent = ((null_values / ttrain.shape[0]) * 100).rename('null_percentage')\n",
    "null_info = pd.concat([null_values,null_percent],axis=1)\n",
    "print(null_info)\n",
    "\n",
    "# Check for duplicate rows\n",
    "print('\\n duplicate data check \\n')\n",
    "duplicate_rows = ttrain[ttrain.duplicated()]\n",
    "duplicate_rows\n",
    "\n",
    "# Check for zeros and show the percentage of zeros\n",
    "print('\\n zeros check \\n')\n",
    "zero_values = ttrain.eq(0).sum().rename('zero_count')\n",
    "zero_percent = (zero_values / ttrain.shape[0]) * 100\n",
    "zero_percent = zero_percent.rename('zero_percentage')\n",
    "zero_info = pd.concat([zero_values,zero_percent],axis=1)\n",
    "print(zero_info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f98b64b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4cRx22GG88sorRATHHnss3/nOdwA4/fTTueWWW1hmmWVYZ511uOqqq1hvvfXa+aolaU62t7a3khY93bp1Y5VVVqFDhw507NiR4cOHM3LkSL7+9a/z7rvv0rFjRy655BL69OkDwD333MMJJ5zA+++/z9prr829994LwFFHHcWtt97KOuusw9NPPz3r+F/+8pd54YUXgOrv4tVXX52RI0e2+XUubZprc+s9RMaOmbkt1fAXx0XEzrUbS2/luo+ZnJmXZ2bvzOzdqdNS+cV2m7vgggvYbLPNZq3ff//9jBw5kpEjR7LDDjtwwAEHAHDxxRez+eab88QTT3DPPffwve99j/feew+A73//+1xzzTVNHv/cc8+ddTyTy5KkpdF3vvMd+vbty/PPP88TTzzBZpttxjHHHMPZZ5/NU089xRe+8AXOPfdcAG644QamT5/OU089xYgRI/j973/P2LFj6dixI7/5zW949tlnGTZsGBdffDHPPvssULXDTz75JCNHjmTfffflzDPPbM/LlaRFQnnS9saIeD4inouIHSJizYi4MyJGlZ9rlLIRERdGxOiIeDIitq05zuGl/Kgy51BDvFdEPFX2ubDMVSRJi6W7776bkSNHzvoi7OSTT+aMM85g5MiRnHnmmZx88slAlSD+5je/yaBBg3jmmWe44YYbZh3jiCOO4O9///scx77uuutm5YUOPPDAWXkmtY+6Jpgzc0L5+SpwE9UYyq+UoS8oP18txScA69fs3rXE5hbv2kRc7Wz8+PHcdtttHHPMMXNsmzp1KkOHDmX//fcHICJ46623yEymTZvGmmuuSceOVcf63XbbjVVWWaUtqy5J0mLhzTff5L777uPoo48GYLnllmP11VfnxRdfZOedq+/zd999d/76178CVXv79ttvM2PGDP773/+y3HLLseqqq9K5c2e23bbKd6yyyipsttlmTJhQ/Tm16qqrzjrf22+/jTkOSQKqien/npmbAlsDz+E8Q5LUIhHB1KlTgerv2Yan4/7yl79wwAEHsMEGGwCwzjrrzNpn5513Zs0112z2mJnJ9ddfz8EHH1zHmmte6pZgjoiVImKVhmVgD+BpYBDQ8A3t4cAtZXkQcFj5lnd74M0ylMYQYI+IWKM0unsAQ8q2qRGxfflW97CaY6kdnXDCCfzqV79imWXm/O918803s9tuu826aT3++ON57rnnWG+99dhyyy254IILmtyvsR/+8IdstdVWfPe732X69OkL/RokSVqUvfTSS3Tq1IkjjzySbbbZhmOOOYa3336bLbbYgltuqf4cuuGGGxg3rhplrF+/fqy00kp07tyZDTbYgJNOOmmOP9THjh3L448/znbbbTcr9sMf/pD111+fP//5z/ZglrTUi4jVgJ2BKwEy873MfINqPqEBpdgAqrmBoGaeoTJ3UMM8Q3tS5hnKzClAwzxDnSnzDJWnfa+uOZYkLVYigj322INevXpx+eWXA3D++efz/e9/n/XXX5+TTjqJX/7ylwC8+OKLTJkyhV122YVevXpx9dVXt/g8999/P+uuuy7du3evy3WoZerZg3ld4IGIeAJ4BLgtM/8OnA3sHhGjgM+WdYDBwBhgNPAH4JsAZXK/nwGPlteZDRP+lTJXlH3+yTwm+FP9NYyL06tXrya3X3vttbN9qzRkyBB69uzJv//9b0aOHMnxxx8/69us5vzyl7/k+eef59FHH2Xy5Mmcc845C/UaJEla1M2YMYPHHnuMb3zjGzz++OOstNJKnH322fTv359LLrmEXr168dZbb7HccssB8Mgjj9ChQwf+/e9/89JLL/Gb3/yGMWPGzDretGnTOPDAAzn//PNn67l81llnMW7cOA455BAuuuiiNr9OSVrEbAhMAv4YEY9HxBWlM1WbzjMUEcdGxPCIGD5p0qSFcFmStPA98MADPPbYY9x+++1cfPHF3HfffVx66aWcd955jBs3jvPOO2/W03gzZsxgxIgR3HbbbQwZMoSf/exnvPjiiy06T+M8k9pH3RLMmTkmM7cury0y86wSfz0zd8vM7pn52YZkcflW97jM3Cgzt8zM4TXH6p+ZG5fXH2viwzOzR9nn+PItr9rRgw8+yKBBg+jWrRsHHXQQQ4cO5dBDDwXgtdde45FHHmGfffaZVf6Pf/wjBxxwABHBxhtvzIYbbsjzzz8/13N07tyZiGD55ZfnyCOP5JFHHqnrNUltZebMmWyzzTbsu+++QPWozw9/+EM22WQTNttsMy688MLZyj/66KN07NiRG2+8cVbs5JNPZosttmCzzTbj29/+NpnJW2+9Rc+ePWe91l57bU444YS2vDTVUUR8PCJG1rymRsQJjge5ZOvatStdu3ad1du4X79+PPbYY2y66abccccdjBgxgoMPPpiNNtoIqB477Nu3L8suuyzrrLMOn/rUp2aNhff+++9z4IEHcsghhzQ7dt0hhxwya7gNSVqKdQS2BS7NzG2At/lwOAygbeYZco4hSYuDLl2q78fWWWcdvvCFL/DII48wYMCAWX9vfvGLX5yVz+natSt77rknK620EmuvvTY777wzTzzxxDzPMWPGDP72t7/x5S9/uX4Xohap9yR/Wsr88pe/ZPz48YwdO5aBAwey66678qc//QmAG2+8kX333ZcVVlhhVvkNNtiAu+66C4BXXnmFF154gY997GNzPcfEiVXngMzk5ptvpkePHnW6GqltNZ4c86qrrmLcuHE8//zzPPfccxx00EGzts2cOZNTTjmFPfbYY1bsH//4Bw8++CBPPvkkTz/9NI8++ij33nsvq6yyyqzJD0aOHMlHP/pRJ0BYgmTmC5nZMzN7Ar2Ad6jmPXA8yCXY//zP/7D++uvPmjn7rrvuYvPNN+fVV6upLT744AN+/vOf8/Wvfx2o2tuhQ4cC1XjKw4YNY9NNNyUzOfroo9lss8048cQTZzvHqFGjZi3fcsstbLrppm1xaZK0KBsPjM/Mh8v6jVQJZ+cZkqQab7/9Nm+99das5TvuuIMePXqw3nrrce+99wIwdOjQWcNa7LfffjzwwAPMmDGDd955h4cffni2e+Pm/N///R+bbropXbt2nWdZ1ZcJZrWZgQMHzvHYwumnn84//vEPttxyS3bbbTfOOecc1l57bQB22mknvvjFL3LXXXfRtWtXhgwZAlS9qLbccku23HJLXnvtNX70ox+1+bVIC1tTk2Neeuml/PjHP541LnntRAe/+93vOPDAA2eLRQTvvvsu7733HtOnT+f9999n3XXXpdaLL77Iq6++yk477VTnK1I72Q34Z2a+jONBLvF+97vfccghh7DVVlsxcuRIfvCDH3DttdeyySabsOmmm7Leeutx5JFHAnDccccxbdo0tthiCz7xiU9w5JFHstVWW/Hggw9yzTXXMHTo0FlPOQwePBiAU089lR49erDVVltxxx13cMEFF7Tn5UpSu8vM/wDjIuLjJbQb8CzOMyRJs3nllVfYcccd2XrrrenTpw/77LMPffv25Q9/+APf+9732HrrrfnBD34wa2zmzTbbjL59+7LVVlvRp08fjjnmmFmdCQ8++GB22GEHXnjhBbp27cqVV1456zxN5ZnUPmJpG1Wid+/e2fBIqCQtKvr168dpp53GW2+9xa9//WtuvfVW1lprLU488URuuukmOnXqxIUXXkj37t2ZMGECX/nKV7j77rs56qij2HfffenXrx8AJ510EldccQWZyfHHH89ZZ50123nOPPNMpk6dyq9//ev2uMylTkSMyMzebXi+/sBjmXlRRLyRmauXeABTMnP1iLgVODszHyjb7gJOAXYBVsjMn5f46cB/gXtK+c+W+E7AKZm579zqYnsrSWorbdneRkRPqnmAlqOaQ+hIqo5b1wMbAC8DX8rMyaX9vYjqyZ93gCMbhoKMiKOAH5TDntUwFGRE9AauAlakmmPoW3MbCtL2VpLUlpprczu2R2WWJL2+3/KZLaW2MuLcw9q7CpoPtZNj3nPPPbPi06dPZ4UVVmD48OH87W9/46ijjuL+++/nhBNO4JxzzpnVs7nB6NGjee655xg/vpobZvfdd+f++++frbfywIEDueaaa9rkutS2ImI54PPAaY23ZWZGRN2/UY6IY6mG3WCDDTZY6Me3zdWixvZWWvpk5kigqWT2bk2UTeC4Zo7TH+jfRHw44BiA0mLoX2du2d5VkGazwY+farNzmWCWpHbWMDnm4MGDeffdd5k6dSqHHnooXbt2nTVW8he+8IVZj7oPHz581njMr732GoMHD6Zjx46MGjWK7bffnpVXXhmAvfbai4ceemhWgvmJJ55gxowZ9OrVqx2uUm1gL6rey6+U9VcionNmTpyP8SB3aRS/h/kYDzIzLwcuh6pH1YJcjCRJkiRp8eAYzJLUzpqbHHP//ffn7rvvBuDee+9lk002AeCll15i7NixjB07ln79+nHJJZew//77s8EGG3DvvfcyY8YM3n//fe69997ZJka49tprHZ9qyXYwcG3NuuNBSpIkSZLqzh7MkrSIOvXUUznkkEM477zzWHnllbniiivmWr5fv34MHTqULbfckoigb9++fO5zn5u1/frrr581eZeWLBGxErA78P9qwmcD10fE0ZTxIEt8MLA3MJoyHiRAGSvyZ8CjpdyZmTm5LH+T2ceDvL1uFyNJkiRJWqyYYJakRcguu+zCLrvsAsDqq6/ObbfdNtfyV1111azlDh068Pvf/77ZsmPGjFkYVdQiKDPfBtZqFHsdx4OUJEmSJNWZCWZJ7cIJELSoacsJECRJkiRJWlI4BrMkSZIkSZIkqVVMMEuSJEmSJEmSWsUEsyRJkiRJkiSpVUwwS5IkSZIkSZJaxQSzJEmSJEmSJKlVTDBLkiRJkiRJklrFBLMkSZIkSZIkqVVMMEuSJEmSJEmSWsUEsyRJkiRJkiSpVUwwS5IkSZIkSZJaxQSzJEmSJEmSJKlVTDBLkiRJkiRJklrFBLMkSZIkSZIkqVVMMEuSJEmSJEmSWsUEsyRJkiRJkiSpVUwwS5IkSZIkSZJaxQSzJEmSJEmSJKlVTDBLkiRJkiRJklrFBLMkSZIkSZIkqVVMMEuStJiLiNUj4saIeD4inouIHSJizYi4MyJGlZ9rlLIRERdGxOiIeDIitq05zuGl/KiIOLwm3isinir7XBgR0R7XKUmSJEla9JhgliRp8XcB8PfM3BTYGngOOBW4KzO7A3eVdYC9gO7ldSxwKUBErAmcAWwH9AHOaEhKlzJfq9mvbxtckyRJi5yIGFu+dB0ZEcNLzC91JUlLNRPMkiQtxiJiNWBn4EqAzHwvM98A9gMGlGIDgP3L8n7A1VkZBqweEZ2BPYE7M3NyZk4B7gT6lm2rZuawzEzg6ppjSZK0NPpMZvbMzN5l3S91JUlLNRPMkiQt3jYEJgF/jIjHI+KKiFgJWDczJ5Yy/wHWLctdgHE1+48vsbnFxzcRn0NEHBsRwyNi+KRJkxbwsiRJWmz4pa4kaalW9wRzRHQoN7y3lvUNI+Lh8sjPdRGxXIkvX9ZHl+3dao5xWom/EBF71sT7ltjoiDh1jpNLkrTk6whsC1yamdsAb/NhzykAyk1q1rsimXl5ZvbOzN6dOnWq9+kkSWoPCdwRESMi4tgSa9Mvdf1CV5K0qGmLHszfoRoLssE5wHmZuTEwBTi6xI8GppT4eaUcEbE5cBCwBdXjQZeUpHUH4GKqx442Bw4uZSVJWpqMB8Zn5sNl/UaqhPMrpScU5eerZfsEYP2a/buW2NziXZuIS5K0NNoxM7elug89LiJ2rt3YFl/q+oWuJGlRU9cEc0R0BfYBrijrAexKdfMLcz4+1PBY0Y3AbqX8fsDAzJyemS8Bo6nGqeoDjM7MMZn5HjCwlJUkaamRmf8BxkXEx0toN+BZYBDQMGnQ4cAtZXkQcFiZeGh74M3S62oIsEdErFHGgdwDGFK2TY2I7Uu7fFjNsSRJWqpk5oTy81XgJqr7Ur/UlSQt1erdg/l84GTgg7K+FvBGZs4o67WP/Mx6TKhsf7OUn9/HiubgI0SSpCXct4A/R8STQE/gF8DZwO4RMQr4bFkHGAyMofrC9g/ANwEyczLwM+DR8jqzxChlrij7/BO4vf6XJEnSoiUiVoqIVRqWqb6MfRq/1JUkLeU61uvAEbEv8GpmjoiIXep1npbIzMuBywF69+5d9zEoJUlqS5k5EujdxKbdmiibwHHNHKc/0L+J+HCgx4LVUpKkxd66wE1V7peOwF8y8+8R8ShwfUQcDbwMfKmUHwzsTfUF7TvAkVB9qRsRDV/qwpxf6l4FrEj1ha5f6kqSFnl1SzADnwI+HxF7AysAqwIXUM2c27H0Uq595KfhMaHxEdERWA14neYfH2IucUmSJEmSFprMHANs3UT8dfxSV5K0FKvbEBmZeVpmds3MblST9A3NzEOAu4F+pVjjx4caHivqV8pniR8UEctHxIZAd+ARqm97u0fEhhGxXDnHoHpdjyRJkiRJkiRpdvXswdycU4CBEfFz4HHgyhK/ErgmIkYDk6kSxmTmMxFxPdWERTOA4zJzJkBEHE81flUHoH9mPtOmVyJJkiRJkiRJS7E2STBn5j3APWV5DNVMu43LvAt8sZn9zwLOaiI+mGpcK0mSJEmSJElSG6vbEBmSJEmSJEmSpCWbCWZJkiRJkiRJUquYYJYkSZIkSZIktYoJZkmSJEmSJElSq5hgliRJkiRJkiS1iglmSZIkSZIkSVKrmGCWJEmSJEmSJLWKCWZJkiRJkiRJUquYYJYkSZIkSZIktYoJZkmSJEmSJElSq5hgliRJkiRJkiS1iglmSZIkSZIkSVKrmGCWJEmSJEmSJLWKCWZJkiRJkiRJUquYYJYkSZIkSZIktYoJZkmSFnMRMTYinoqIkRExvMTWjIg7I2JU+blGiUdEXBgRoyPiyYjYtuY4h5fyoyLi8Jp4r3L80WXfaPurlCRJkiQtikwwS5K0ZPhMZvbMzN5l/VTgrszsDtxV1gH2ArqX17HApVAlpIEzgO2APsAZDUnpUuZrNfv1rf/lSJIkSZIWByaYJUlaMu0HDCjLA4D9a+JXZ2UYsHpEdAb2BO7MzMmZOQW4E+hbtq2amcMyM4Gra44lSZIkSVrKmWCWJGnxl8AdETEiIo4tsXUzc2JZ/g+wblnuAoyr2Xd8ic0tPr6J+Bwi4tiIGB4RwydNmrQg1yNJkiRJWkx0bO8KSJKkBbZjZk6IiHWAOyPi+dqNmZkRkfWuRGZeDlwO0Lt377qfT5IkSZLU/uzBLEnSYi4zJ5SfrwI3UY2h/EoZ3oLy89VSfAKwfs3uXUtsbvGuTcQlSZIkSTLBLEnS4iwiVoqIVRqWgT2Ap4FBwOGl2OHALWV5EHBYVLYH3ixDaQwB9oiINcrkfnsAQ8q2qRGxfUQEcFjNsSRJWupERIeIeDwibi3rG0bEwxExOiKui4jlSnz5sj66bO9Wc4zTSvyFiNizJt63xEZHxKlznFySpEWQCWZJkhZv6wIPRMQTwCPAbZn5d+BsYPeIGAV8tqwDDAbGAKOBPwDfBMjMycDPgEfL68wSo5S5ouzzT+D2NrguSZIWVd8BnqtZPwc4LzM3BqYAR5f40cCUEj+vlCMiNgcOArYA+gKXlKR1B+BiYC9gc+DgUlaSpEWaYzBLkrQYy8wxwNZNxF8HdmsinsBxzRyrP9C/ifhwoMcCV1aSpMVcRHQF9gHOAk4sT/fsCnylFBkA/AS4FNivLAPcCFxUyu8HDMzM6cBLETGaangrgNGlbSciBpayz9b5siRJWiD2YJYkSZIkqWXOB04GPijrawFvZOaMsj4e6FKWuwDjAMr2N0v5WfFG+zQXn01EHBsRwyNi+KRJkxbCJUmStGBMMEuSJEmSNA8RsS/wamaOaM96ZOblmdk7M3t36tSpPasiSRLgEBmSJEmSJLXEp4DPR8TewArAqsAFwOoR0bH0Uu4KTCjlJwDrA+MjoiOwGvB6TbxB7T7NxSVJWmTZg1mSJEmSpHnIzNMys2tmdqOapG9oZh4C3A30K8UOB24py4PKOmX70DIXwiDgoIhYPiI2BLpTTdT7KNA9IjaMiOXKOQa1waVJkrRA7MEsSZIkSVLrnQIMjIifA48DV5b4lcA1ZRK/yVQJYzLzmYi4nmryvhnAcZk5EyAijgeGAB2A/pn5TJteiSRJrWCCWZIkSZKk+ZCZ9wD3lOUxQJ8myrwLfLGZ/c8CzmoiPhgYvBCrKklS3TlEhiRJkiRJkiSpVUwwS5IkSZIkSZJapW4J5ohYISIeiYgnIuKZiPhpiW8YEQ9HxOiIuK5MXkCZ4OC6En84IrrVHOu0En8hIvasifctsdERcWq9rkWSJEmSJEmSNKd69mCeDuyamVsDPYG+EbE9cA5wXmZuDEwBji7ljwamlPh5pRwRsTnVZAhbAH2BSyKiQ0R0AC4G9gI2Bw4uZSVJkiRJkiRJbaBuCeasTCury5ZXArsCN5b4AGD/srxfWads3y0iosQHZub0zHwJGE01gUIfYHRmjsnM94CBpawkSZIkSZIkqQ3UdQzm0tN4JPAqcCfwT+CNzJxRiowHupTlLsA4gLL9TWCt2nijfZqLN1WPYyNieEQMnzRp0kK4MkmSJEmSJElSXRPMmTkzM3sCXal6HG9az/PNpR6XZ2bvzOzdqVOn9qiCJEmSJEmSJC1x6ppgbpCZbwB3AzsAq0dEx7KpKzChLE8A1gco21cDXq+NN9qnubgkSZIkSZIkqQ3ULcEcEZ0iYvWyvCKwO/AcVaK5Xyl2OHBLWR5U1inbh2ZmlvhBEbF8RGwIdAceAR4FukfEhhGxHNVEgIPqdT2SJEmSJEmSpNl1nHeRVusMDIiIDlSJ7Osz89aIeBYYGBE/Bx4HrizlrwSuiYjRwGSqhDGZ+UxEXA88C8wAjsvMmQARcTwwBOgA9M/MZ+p4PZIkSZIkSZKkGnVLMGfmk8A2TcTHUI3H3Dj+LvDFZo51FnBWE/HBwOAFrqwkSZIkSZIkab61yRjMkiRJkiRJkqQljwlmSZIkSZIkSVKrmGCWJGkJEBEdIuLxiLi1rG8YEQ9HxOiIuK5MiEuZNPe6En84IrrVHOO0En8hIvasifctsdERcWqbX5wkSZIkaZHVogRzRNzVkpgkSWq9BWxvvwM8V7N+DnBeZm4MTAGOLvGjgSklfl4pR0RsTjXB7hZAX+CSkrTuAFwM7AVsDhxcykqStFjy/laSpIVrrgnmiFghItYE1o6INSJizfLqBnRpkxpKkrSEW9D2NiK6AvsAV5T1AHYFbixFBgD7l+X9yjpl+26l/H7AwMycnpkvAaOpJuXtA4zOzDGZ+R4wsJSVJGmx4v2tJEn10XEe2/8fcAKwHjACiBKfClxUv2pJkrRUWdD29nzgZGCVsr4W8EZmzijr4/nwxrkLMA4gM2dExJulfBdgWM0xa/cZ1yi+XVOViIhjgWMBNthggxZUW5KkNuX9rSRJdTDXBHNmXgBcEBHfyszftVGdJElaqixIexsR+wKvZuaIiNilHvVrqcy8HLgcoHfv3tmedZEkqTHvbyVJqo959WAGIDN/FxGfBLrV7pOZV9epXpIkLXVa2d5+Cvh8ROwNrACsClwArB4RHUsv5q7AhFJ+ArA+MD4iOgKrAa/XxBvU7tNcXJKkxY73t5IkLVwtSjBHxDXARsBIYGYJJ2ADLEnSQtKa9jYzTwNOK/vvApyUmYdExA1AP6oxkw8Hbim7DCrrD5XtQzMzI2IQ8JeI+C3Vo8PdgUeoHh/uHhEbUiWWDwK+snCuWJKktuf9rSRJC1eLEsxAb2DzzPRxV0mS6mdhtrenAAMj4ufA48CVJX4lcE1EjAYmUyWMycxnIuJ64FlgBnBcZs4EiIjjgSFAB6B/Zj6zEOonSVJ78f5WkqSFqKUJ5qeB/wEm1rEukiQt7Raovc3Me4B7yvIYoE8TZd4FvtjM/mcBZzURHwwMbk2dJElaBHl/K0nSQtTSBPPawLMR8QgwvSGYmZ+vS60kSVo62d5KklR/treSJC1ELU0w/6SelZAkSYDtrSRJbeEn7V0BSZKWJC1KMGfmvfWuiCRJSzvbW0mS6s/2VpKkhWuZlhSKiLciYmp5vRsRMyNiar0rJ0nS0sT2VpKk+mttexsRK0TEIxHxREQ8ExE/LfENI+LhiBgdEddFxHIlvnxZH122d6s51mkl/kJE7FkT71tioyPi1DpcviRJC11LezCv0rAcEQHsB2xfr0pJkrQ0sr2VJKn+FqC9nQ7smpnTImJZ4IGIuB04ETgvMwdGxGXA0cCl5eeUzNw4Ig4CzgG+HBGbAwcBWwDrAf8XEZuUc1wM7A6MBx6NiEGZ+exCuGxJkuqmRT2Ya2XlZmDPeZWVJEmtY3srSVL9zU97W8pOK6vLllcCuwI3lvgAYP+yvF9Zp2zfrSahPTAzp2fmS8BooE95jc7MMZn5HjCwlJUkaZHWoh7MEXFAzeoyQG/g3brUSJKkpZTtrSRJ9bcg7W1EdABGABtT9Tb+J/BGZs4oRcYDXcpyF2AcQGbOiIg3gbVKfFjNYWv3Gdcovl0TdTgWOBZggw02aEm1JUmqqxYlmIHP1SzPAMbiN6mSJC1streSJNVfq9vbzJwJ9IyI1YGbgE0XduVaUIfLgcsBevfunW19fkmSGmvpGMxH1rsikiQt7WxvJUmqv4XR3mbmGxFxN7ADsHpEdCy9mLsCE0qxCcD6wPiI6AisBrxeE29Qu09zcbWTcePGcdhhh/HKK68QERx77LF85zvfAeB3v/sdF198MR06dGCfffbhV7/6FWPHjmWzzTbj4x//OADbb789l112GQB9+/Zl4sSJzJgxg5122mnWvs0dS5IWFy0dIqMr8DvgUyV0P/CdzBxfr4pJkrS0sb2VJKn+WtveRkQn4P2SXF6RajK+c4C7gX5UYyYfDtxSdhlU1h8q24dmZkbEIOAvEfFbqkn+ugOPAAF0j4gNqRLLBwFfWThXrdbq2LEjv/nNb9h2221566236NWrF7vvvjuvvPIKt9xyC0888QTLL788r7766qx9NtpoI0aOHDnHsa6//npWXXVVMpN+/fpxww03cNBBB3H33Xc3eyxJWhy0dJK/P1I1juuV1/+WmCRJWnhsbyVJqr/Wtredgbsj4kngUeDOzLwVOAU4MSJGU42xfGUpfyWwVomfCJwKkJnPANcDzwJ/B47LzJmlB/TxwBDgOeD6UlbtqHPnzmy77bYArLLKKmy22WZMmDCBSy+9lFNPPZXll18egHXWWWeex1p11VUBmDFjBu+99x7VnI+06liStChpaYK5U2b+MTNnlNdVQKc61kuSpKWR7a0kSfXXqvY2M5/MzG0yc6vM7JGZZ5b4mMzsk5kbZ+YXM3N6ib9b1jcu28fUHOuszNwoMz+embfXxAdn5iZl21kL/9K1IMaOHcvjjz/Odtttx4svvsj999/Pdtttx6c//WkeffTRWeVeeuklttlmGz796U9z//33z3aMPffck3XWWYdVVlmFfv36Acz1WJK0OGhpgvn1iDg0IjqU16FUY0dJkqSFx/ZWkqT6s73VfJs2bRoHHngg559/PquuuiozZsxg8uTJDBs2jHPPPZcvfelLZCadO3fmX//6F48//ji//e1v+cpXvsLUqVNnHWfIkCFMnDiR6dOnM3ToUIBmjyVJi4uWJpiPAr4E/AeYSDV+1BF1qpMkSUsr21tJkurP9lbz5f333+fAAw/kkEMO4YADDgCga9euHHDAAUQEffr0YZllluG1115j+eWXZ6211gKgV69ebLTRRrz44ouzHW+FFVZgv/3245ZbbpnrsSRpcdHSBPOZwOGZ2Skz16FqkH9av2pJkrRUsr2VJKn+bG/VYpnJ0UcfzWabbcaJJ544K77//vtz9913A9UQF++99x5rr702kyZNYubMmQCMGTOGUaNG8bGPfYxp06YxceJEoOqxfNttt7HpppvO9ViStLjo2MJyW2XmlIaVzJwcEdvUqU6SJC2tbG8lSao/21u12IMPPsg111zDlltuSc+ePQH4xS9+wVFHHcVRRx1Fjx49WG655RgwYAARwX333cePf/xjll12WZZZZhkuu+wy1lxzTV555RU+//nPM336dD744AM+85nP8PWvfx2g2WNJ0uKipQnmZSJijYZGOCLWnI99JUlSy9jeSpJUf7a3arEdd9yx2fGQ//SnP80RO/DAAznwwAPniK+77rrNTt633HLLNXksSVpctLQR/Q3wUETcUNa/CDijrSRJC5ftrSRJ9Wd7Oxe9vn91e1dBms2Icw9r7ypImocWJZgz8+qIGA7sWkIHZOaz9auWJElLH9tbSZLqz/ZWkqSFq8WPAZUG10ZXkqQ6mt/2NiJWAO4Dlqdq12/MzDMiYkNgILAWMAL4ama+FxHLA1cDvYDXgS9n5thyrNOAo4GZwLczc0iJ9wUuADoAV2Tm2QvjWiVJai/e30qStPAs094VkCRJC2Q6sGtmbg30BPpGxPbAOcB5mbkxMIUqcUz5OaXEzyvliIjNgYOALYC+wCUR0SEiOgAXA3sBmwMHl7KSJEmSJNUvwRwR60fE3RHxbEQ8ExHfKfE1I+LOiBhVfq5R4hERF0bE6Ih4MiK2rTnW4aX8qIg4vCbeKyKeKvtcGE6zKklaymRlWlldtryS6rHfG0t8ALB/Wd6vrFO271baz/2AgZk5PTNfAkYDfcprdGaOycz3qHpF71ffq5IkSZIkLS7q2YN5BvC9zNwc2B44rvR4OhW4KzO7A3eVdah6RnUvr2OBS2HWjL5nANtR3eSe0ZCULmW+VrNf3zpejyRJi6TS03gk8CpwJ/BP4I3MnFGKjAe6lOUuwDiAsv1NqmE0ZsUb7dNcvKl6HBsRwyNi+KRJkxbClUmSJEmSFnV1SzBn5sTMfKwsvwU8R3VDWttzqnGPqqtLT6xhwOoR0RnYE7gzMydn5hSqG+e+ZduqmTksM5NqPMmGY0mStNTIzJmZ2RPoSvVl7KbtVI/LM7N3Zvbu1KlTe1RBkiRJktTG2mQM5ojoBmwDPAysm5kTy6b/AOuW5fntOdWlLDeON3V+e1RJkpZ4mfkGcDewA9UXtQ2T+XYFJpTlCcD6AGX7alST/c2KN9qnubgkSZIkSfVPMEfEysBfgRMyc2rtttLzOOtdB3tUSZKWVBHRKSJWL8srArtTPTV0N9CvFDscuKUsDyrrlO1DS3s8CDgoIpaPiA2php56BHgU6B4RG0bEclQTAQ6q+4VJkiRJkhYLHeddpPUiYlmq5PKfM/NvJfxKRHTOzIllmItXS3xuPad2aRS/p8S7NlFekqSlSWdgQER0oPri+PrMvDUingUGRsTPgceBK0v5K4FrImI0MJkqYUxmPhMR1wPPUs2jcFxmzgSIiOOBIUAHoH9mPtN2lydJkiRJWpTVLcFcZqS/EnguM39bs6mh59TZzNmj6viIGEg1od+bJQk9BPhFzcR+ewCnZebkiJgaEdtTDb1xGPC7el2PJEmLosx8kmoYqsbxMVTjMTeOvwt8sZljnQWc1UR8MDB4gSsrSZIkSVri1LMH86eArwJPlZntAX5AlVi+PiKOBl4GvlS2DQb2BkYD7wBHApRE8s+oHtEFODMzJ5flbwJXASsCt5eXJEmSJEmSJKkN1C3BnJkPANHM5t2aKJ/Acc0cqz/Qv4n4cKDHAlRTkiRJkiRJktRKdZ/kT5IkSZIkSZK0ZDLBLEmSJEmSJElqFRPMkiRJkiRJkqRWMcEsSZIkSZIkSWoVE8ySJEmSJEmSpFYxwSxJkiRJkiRJahUTzJIkSZIkzUNErB8Rd0fEsxHxTER8p8TXjIg7I2JU+blGiUdEXBgRoyPiyYjYtuZYh5fyoyLi8Jp4r4h4quxzYURE21+pJEnzxwSzJEmSJEnzNgP4XmZuDmwPHBcRmwOnAndlZnfgrrIOsBfQvbyOBS6FKiENnAFsB/QBzmhISpcyX6vZr28bXJckSQvEBLMkSZIkSfOQmRMz87Gy/BbwHNAF2A8YUIoNAPYvy/sBV2dlGLB6RHQG9gTuzMzJmTkFuBPoW7atmpnDMjOBq2uOJUnSIssEsyRJkiRJ8yEiugHbAA8D62bmxLLpP8C6ZbkLMK5mt/ElNrf4+Cbijc99bEQMj4jhkyZNWvCLkSRpAZlgliRJkiSphSJiZeCvwAmZObV2W+l5nPU8f2Zenpm9M7N3p06d6nkqSZJaxASzJEmSJEktEBHLUiWX/5yZfyvhV8rwFpSfr5b4BGD9mt27ltjc4l2biEuStEgzwSxJkiRJ0jxERABXAs9l5m9rNg0CDi/LhwO31MQPi8r2wJtlKI0hwB4RsUaZ3G8PYEjZNjUiti/nOqzmWJIkLbI6tncFJEmSJElaDHwK+CrwVESMLLEfAGcD10fE0cDLwJfKtsHA3sBo4B3gSIDMnBwRPwMeLeXOzMzJZfmbwFXAisDt5SVJ0iLNBLMkSZIkSfOQmQ8A0czm3Zoon8BxzRyrP9C/ifhwoMcCVFOSpDbnEBmSJEmSJEmSpFYxwSxJkiRJkiRJahUTzJIkLcYiYv2IuDsino2IZyLiOyW+ZkTcGRGjys81Sjwi4sKIGB0RT0bEtjXHOryUHxURh9fEe0XEU2WfC8vEQ5IkSZIkmWCWJGkxNwP4XmZuDmwPHBcRmwOnAndlZnfgrrIOsBfQvbyOBS6FKiENnAFsB/QBzmhISpcyX6vZr28bXJckSZIkaTFgglmSpMVYZk7MzMfK8lvAc0AXYD9gQCk2ANi/LO8HXJ2VYcDqEdEZ2BO4MzMnZ+YU4E6gb9m2amYOK5MVXV1zLEmSJEnSUs4EsyRJS4iI6AZsAzwMrJuZE8um/wDrluUuwLia3caX2Nzi45uIN3X+YyNieEQMnzRp0oJdjCRJkiRpsWCCWZKkJUBErAz8FTghM6fWbis9j7PedcjMyzOzd2b27tSpU71PJ0mSJElaBJhgliRpMRcRy1Ill/+cmX8r4VfK8BaUn6+W+ARg/Zrdu5bY3OJdm4hLkiRJkmSCWZKkxVlEBHAl8Fxm/rZm0yDg8LJ8OHBLTfywqGwPvFmG0hgC7BERa5TJ/fYAhpRtUyNi+3Kuw2qOJUmLlaOOOop11lmHHj16zIr95Cc/oUuXLvTs2ZOePXsyePDgWduefPJJdthhB7bYYgu23HJL3n33XQBGjBjBlltuycYbb8y3v/1tqgdF5n4sSZKkJZUJZkmSFm+fAr4K7BoRI8trb+BsYPeIGAV8tqwDDAbGAKOBPwDfBMjMycDPgEfL68wSo5S5ouzzT+D2trgwSVrYjjjiCP7+97/PEf/ud7/LyJEjGTlyJHvvvTcAM2bM4NBDD+Wyyy7jmWee4Z577mHZZZcF4Bvf+AZ/+MMfGDVqFKNGjZrtmE0dS5IkaUnWsb0rIEmSWi8zHwCimc27NVE+geOaOVZ/oH8T8eFAjzn3kKTFy84778zYsWNbVPaOO+5gq622YuuttwZgrbXWAmDixIlMnTqV7bffHoDDDjuMm2++mb322qsudZYkSVrU2YNZkiRJ0lLtoosuYquttuKoo45iypQpALz44otEBHvuuSfbbrstv/rVrwCYMGECXbt+ODR9165dmTBhwlyPJUmStCQzwSxJkiRpqfWNb3yDf/7zn4wcOZLOnTvzve99D6iGyHjggQf485//zAMPPMBNN93EXXfd1apjSZIkLclMMEuSJElaaq277rp06NCBZZZZhq997Ws88sgjQNUzeeedd2bttdfmIx/5CHvvvTePPfYYXbp0Yfz48bP2Hz9+PF26dJnrsSRJkpZkJpglSZIkLbUmTpw4a/mmm26iR49qyPk999yTp556infeeYcZM2Zw7733svnmm9O5c2dWXXVVhg0bRmZy9dVXs99++831WJIkSUsyJ/mTJEmStFQ4+OCDueeee3jttdfo2rUrP/3pT7nnnnsYOXIkEUG3bt34/e9/D8Aaa6zBiSeeyCc+8Qkigr333pt99tkHgEsuuYQjjjiC//73v+y1116zJvg7+eSTmzyWJEnSkswEsyRJkqSlwrXXXjtH7Oijj262/KGHHsqhhx46R7x37948/fTTc8SvueaaBaugJEnSYqhuCeaI6A/sC7yamT1KbE3gOqAbMBb4UmZOiYgALgD2Bt4BjsjMx8o+hwM/Kof9eWYOKPFewFXAisBg4DuZmfW6HkmSJGlx868zt2zvKkhz2ODHT7V3FSRJ0kJUzzGYrwL6NoqdCtyVmd2Bu8o6wF5A9/I6FrgUZiWkzwC2A/oAZ0TEGmWfS4Gv1ezX+FySJEmSJEmSpDqqW4I5M+8DJjcK7wcMKMsDgP1r4ldnZRiwekR0BvYE7szMyZk5BbgT6Fu2rZqZw0qv5atrjiVJkiRJkiRJagP17MHclHUzs2Fq5f8A65blLsC4mnLjS2xu8fFNxJsUEcdGxPCIGD5p0qQFuwJJkiRJkiRJEtD2CeZZSs/jNhkzOTMvz8zemdm7U6dObXFKSZIkSZIkSVritXWC+ZUyvAXl56slPgFYv6Zc1xKbW7xrE3FJkiRJkiRJUhtp6wTzIODwsnw4cEtN/LCobA+8WYbSGALsERFrlMn99gCGlG1TI2L7iAjgsJpjSZIkSZIkSZLaQN0SzBFxLfAQ8PGIGB8RRwNnA7tHxCjgs2UdYDAwBhgN/AH4JkBmTgZ+BjxaXmeWGKXMFWWffwK31+taJEmSJElLt4joHxGvRsTTNbE1I+LOiBhVfq5R4hERF0bE6Ih4MiK2rdnn8FJ+VEQcXhPvFRFPlX0uLJ2pJEla5HWs14Ez8+BmNu3WRNkEjmvmOP2B/k3EhwM9FqSOkiRJkiS10FXARcDVNbFTgbsy8+yIOLWsnwLsBXQvr+2AS4HtImJN4AygN9WcRCMiYlBmTillvgY8TNUJqy92pJIkLQbabZI/SZIkSZIWF5l5HzC5UXg/YEBZHgDsXxO/OivDgNXLPER7Andm5uSSVL4T6Fu2rZqZw0oHrKtrjiVJ0iLNBLMkSZIkSa2zbpkjCOA/wLpluQswrqbc+BKbW3x8E/E5RMSxETE8IoZPmjRpwa9AkqQFZIJZkiRJkqQFVHoeZxuc5/LM7J2ZvTt16lTv00mSNE8mmCVJkiRJap1XyvAWlJ+vlvgEYP2acl1LbG7xrk3EJUla5JlgliRpMees9pIktZtBQEObeThwS038sNLubg+8WYbSGALsERFrlLZ5D2BI2TY1IrYv7exhNceSJGmRZoJZkqTF31VUM83XapjVvjtwV1mH2We1P5ZqxnpqZrXfDugDnNGQlObDWe0b9mt8LkmSlngRcS3wEPDxiBgfEUcDZwO7R8Qo4LNlHWAwMAYYDfwB+CZAZk4GfgY8Wl5nlhilzBVln38Ct7fFdUmStKA6tncFJEnSgsnM+yKiW6PwfsAuZXkAcA9wCjWz2gPDIqJhVvtdKLPaA0REw6z291BmtS/xhlntvemVJC1VMvPgZjbt1kTZBI5r5jj9gf5NxIcDPRakjpIktQd7MEuStGRyVntJkiRJUt2ZYJYkaQnnrPaSJEmSpHoxwSxJ0pLJWe0lSZIkSXVnglmSpCWTs9pLkiRJkurOSf4kSVrMlVntdwHWjojxwBlUs9hfX2a4fxn4Uik+GNibaob6d4AjoZrVPiIaZrWHOWe1vwpYkWpyPyf4kyRJkiQBJpglSVrsOau9JEmSJKm9OESGJEmSJEmSJKlVTDBLkiRJkiRJklrFBLMkSZIkSZIkqVVMMEuSJEmSJEmSWsUEsyRJkiRJkiSpVUwwS5IkSZIkSZJaxQSzJEmSJEmSJKlVTDBLkiRJkiRJklrFBLMkSZIkSZIkqVVMMEuSJEmSJEmSWsUEsyRJkiRJkiSpVUwwS5IkSZIkSZJaxQSzJEmSJEmSJKlVTDBLkiRJkiRJklrFBLMkSZIkSZIkqVVMMEuSJEmSJEmSWsUEsyRJkiRJkiSpVUwwS5IkSZIkSZJaxQSzJEmSJEmSJKlVFvsEc0T0jYgXImJ0RJza3vWRJGlJZHsrSVLbsM2VJC1uFusEc0R0AC4G9gI2Bw6OiM3bt1aSJC1ZbG8lSWobtrmSpMXRYp1gBvoAozNzTGa+BwwE9mvnOkmStKSxvZUkqW3Y5kqSFjsd27sCC6gLMK5mfTywXeNCEXEscGxZnRYRL7RB3TT/1gZea+9KLAni14e3dxXUdvzcLCxnRD2O+tF6HLQd2N4uWfy9sZDY3i51/OwsLAu/zV1S2ltoQZtre7tY8ffGQmB7u9Txc7OwtOE97uKeYG6RzLwcuLy966G5i4jhmdm7veshLU783GhRYnu7ePD3htQ6fna0qLC9XXz4e0Oaf35uFk+L+xAZE4D1a9a7lpgkSVp4bG8lSWobtrmSpMXO4p5gfhToHhEbRsRywEHAoHaukyRJSxrbW0mS2oZtriRpsbNYD5GRmTMi4nhgCNAB6J+Zz7RztdR6PuYlzT8/N6o729sljr83pNbxs6O6s81d4vh7Q5p/fm4WQ5GZ7V0HSZIkSZIkSdJiaHEfIkOSJEmSJEmS1E5MMEuSJEmSJEmSWsUEsxaKiJgZESNrXt3qeK6xEbF2vY4vLQoiIiPiTzXrHSNiUkTcOo/9dplXGUltIyKm1fHYV0VEv2a2nRQRz5f2+NGIOKwVx+8WEV9Z8JpKLRMRP4mIk8rymRHx2ZaUXYjn9zMlLeZq7kmfjogbIuIj7V2nBhFxRERc1Ch2ZM3983sR8VRZPrtOdWj295yWXjWfmyci4rGI+GQrj/P11rSPCyIi9o+IzdvgPHN8fjWnxXqSPy1S/puZPdu7EtIS5G2gR0SsmJn/BXYHJrRznSQt4iLi61S/L/pk5tSIWBX4QisO1Q34CvCXhVi9uYqIjpk5o63Op0VXZv64vevQwM+UtFiZdU8aEX8Gvg78dl47tddnJTP/CPyx1GEs8JnMfK1R3Tpk5sy2rpuWKrWfmz2BXwKfnt+DZOZlC7leLbE/cCvwbOMNtoFtzx7MqpuI6BUR90bEiIgYEhGdS/yeiDgvIoZHxHMR8YmI+FtEjIqIn9fsf3PZ95mIOLaZcxwaEY+Ub9x+HxEd2ur6pDYwGNinLB8MXNuwISL6RMRDEfF4RPwjIj7eeOeIWCki+pfPyOMRsV8b1VtSMyKiZ0QMi4gnI+KmiFgjIjaKiMdqynRvWI+IH5cek09HxOUREfM4xQ+Ab2TmVIDMnJqZA8qxZj0BFBG9I+Kesvzpmh5Uj0fEKsDZwE4l9t2IWCEi/lh6Vz0eEZ8p+x5R2us7y/GPj4gTS5lhEbFmKbdRRPy9tOv3R8SmJX5VRFwWEQ8Dv1p477QWBxHxw4h4MSIeAD5eE5/Vyy4izo6IZ8tn5tdNHMPPlJ8pqSn3Axs39/dw+awNioihwF0R0Tki7osPe0DvVModXD6nT0fEOQ0Hj4hpEXFWVL0+h0XEuiX+uYh4uJzr/xri86Mc+zcR8QSwQ1O/tyJi04h4pGafbhHxVFlu8j5caoFVgSkw55OxEXFRRBxRludom2P2J5HuiYhzyufuxZrPU4eIOLf8f34yIv5fic/x+StlryrrT0XEd2srGlVP688D55b9NirnPT8ihgPfaerzGBHLlPZ19ZpjjSrbOkXEX0v9Ho2IT9XvrV7ymGDWwrJifPiH9E0RsSzwO6BfZvYC+gNn1ZR/LzN7A5cBtwDHAT2AIyJirVLmqLJvb+DbNXEAImIz4MvAp8o3bjOBQ+p3iVKbGwgcFBErAFsBD9dsex7YKTO3AX4M/KKJ/X8IDM3MPsBnqBrflepcZ0lzdzVwSmZuBTwFnJGZ/wTejIiepcyRlB5NwEWZ+YnM7AGsCOzb3IGj6lm5SmaOmc86nQQcV9rSnYD/AqcC92dmz8w8j6qdzszckuoLrwHldxNU7fcBwCeo2vp3yu+mh4CGRyUvB75V2vWTgEtqzt8V+GRmnjif9dZiLCJ6AQcBPYG9qf7/NC6zFlVv4S3KZ+bnjcvgZ8rPlNRIRHQE9qL6nTC3v4e3pbpf/TTVEwZDyud2a2BkRKwHnAPsSvW76hMRsX/ZdyVgWGZuDdwHfK3EHwC2L5/ZgcDJrbiElYCHM3PrzHyAJn5vZebzwHIRsWHZ58vAdS24D5caa8jlPA9cAfxsboVb2DYDdCyfuxOAM0rsaODNzPwEVRv3tfJ/eI7PH9Vnrktm9iht5R9rD56Z/wAGAd8vbes/y6blMrN3Zv6GJj6PmfkBVQ7qC+V6tgNezsxXgAuA80r9Dizvh1rIITK0sMw2REZE9KD64/jOqDqGdAAm1pQfVH4+BTyTmRPLfmOA9YHXqZLKDY8grg90L/EGuwG9gEfLOVYEXl2oVyW1o8x8MqrxzA+m6s1cazWqm9HuQALLNnGIPYDPx4fjVK4AbAA8V58aS5qbiFgNWD0z7y2hAcANZfkK4MiIOJHqJrFPiX8mIk4GPgKsCTwD/O9CrtqDwG+jepz4b5k5Pubs1Lkj1Q0rmfl8RLwMbFK23Z2ZbwFvRcSbNfV7CtgqIlYGPgncUHPc5WuOfYOP/y6VdgJuysx3ACJiUBNl3gTeBa4svahmm2PAz5SfKamRFSNiZFm+H7gS+AdN/z0McGdmTi7LjwL9S4L25swcGRG7Avdk5iSYNezGzsDNwHt8+DtpBNVQOlB9wXNd6TW8HPBSK65jJvDXmvXmfm9dT/X77ezy88tUT4PM7T5caqx2iIwdgKtLPqc5c22ba/yt/BxBNUwUVPenW8WHY4GvRpXnaerzNwb4WET8DrgNuKOF13NdzXJzn8frqDpp/ZHqy+6GfT4LbF7Ttq5a2ly1gAlm1UtQJY53aGb79PLzg5rlhvWOEbEL1Yd7h8x8J6pHDldgdgEMyMzTFlalpUXQIODXwC5AbS/+n1HdgH6hJKHvaWLfAA7MzBfqXEdJC+6vVL07hgIjMvP10pvxEqB3Zo6LiJ8wZ1s4SxkfdlpEfKyZHpcz+PDptRVq9js7Im6j6kX6YFTj782Pxu14bRvfsZzzjbnM1fD2fJ5PS4nMnBERfag6FfQDjqfqSdgSfqakpc8c8wJFlSma4+/h0mtx1mclM++LiJ2phqe7KiJ+S5VIa877mZlleSYf5lZ+B/w2MweVe9qftOI63m34kmgev7euo/qi6W/VJeSoiNiSud+HS83KzIeiGvqpE7O3cVD+381H29zQdtV+PoLq6ZshjQs3/vxl5tURsTWwJ9V46l8CjmrBZdS2gc19Hh+iGkKnE9U4zg29sJeh6vH8bqO6teC0cogM1csLQKfyDRgRsWxEbDEf+68GTCnJ5U2B7ZsocxfQLyLWKedYMyI+uqAVlxYx/YGfZuZTjeKr8eGkf0c0s+8Q4FvlD2siYpu61FBSi2Tmm8CUKOPQAV8F7i3b3qX6zF7Kh48ANtxAvlZ6T7Rk5vdfAheXR/uJiJXjwxm9x1I9+QPVY3+UMhtl5lOZeQ5VD5JNgbeAVWqOez9lGKqI2ISq91eLvrzKauzalyLii2X/KDcMWrrdB+wfEStGNUbx5xoXKP/vV8vMwcB3qR6bncXPlJ8pqQVa9PdwuY98JTP/QPUExLbAI8CnI2LtqOb6OZjyO2Yuav9GP3wh1L/Z31tlSICZwOl82ANzQe/DtRQruZcOVE+Ov0zVm3f5qMYr3q2UmWvbPA9DgG+UnspExCZRjZM+x+evJLqXycy/Aj+i+kw21rhtbazJz2P5cugmqklAn8vMhifl7wC+1VAuPhxqSy1gD2bVRWa+Vx57uLA8vtgROJ/qcZ6W+Dvw9Yh4jqqRHNbEOZ6NiB8Bd0TEMsD7VOPZvbwQLkFaJGTmeODCJjb9imqIjB9RPTLUlJ9Rfe6eLJ+Rl5jLWJOSFrqPRMT4mvXfUv1xe1lEfAQYQzU2bIM/U40HdwdAZr4REX8Angb+Q5WompdLgZWpho96n6pt/E3Z9lOqxxl/xuxPPZwQ1QRjH1C107eX5ZlRTTB0FVXvqUujmkBoBnBEZk6fjx4dh5T9f0Q1pM9A4ImW7qwlT2Y+FhHXUf0/eJWm/3+vAtxSevAF0NSYwn6m/ExJc9PSv4d3Ab5fPufTgMMyc2JEnArcTfU76LbMvGUe5/sJVa/iKVRPUGw49+Jz14LfW9cB5zacZyHch2vpUzu0TACHlx704yLieqr/ey8Bj5cyLWmbm3MF1XAZj5UvfSZR9SDehUafP6AL8MfyuQVo6sn1gcAfIuLbNP2l8U9o/vN4HdXn6Yia2LepvlR+kuqzcx9V72m1QHz4VIckSZLaS1TjQ66Wmae3d12kJYGfKUmSpLZhD2ZJkqR2FhE3ARvR8vFlJc2FnylJkqS2Yw9mSZIkSZIkSVKrOMmfJEmSJEmSJKlVTDBLkiRJkiRJklrFBLMkSZIkSZIkqVVMMEtLuIhYNyL+EhFjImJERDwUEV9YCMfdJSJuXRh1lCSpvUXE/0TEwIj4Z2kvB0fEJvN5jP0jYvN61bGZc94UESMjYnREvFmWR0bEJ9uyHpIkLQwRsVZNW/afiJhQs77cAhx3z5rjTIuIF8ry1Quz/jXn+0lEnFSPY0uLoo7tXQFJ9RMRAdwMDMjMr5TYR4HPt0NdOmbmjLY+ryRJ81Lay5uo2suDSmxrYF3gxfk41P7ArcCzC7uOc9EvM2dGxC7ASZm5bxueW5KkhSozXwd6QpWkBaZl5q8XwnGHAEPKce+hajOH15aJiA6ZOXNBzyUtjezBLC3ZdgXey8zLGgKZ+XJm/i4iOkTEuRHxaEQ8GRH/D2b1TL4nIm6MiOcj4s/lxpuI6FtijwEHNBwzIlaKiP4R8UhEPB4R+5X4ERExKCKGAne16ZVLktRynwHeb9RePgF0qH1aJyIuiogjyvLZEfFsaUN/XXoMfx44t/SI2igiekbEsFLmpohYo+x7T0ScFxHDI+K5iPhERPwtIkZFxM9rzndoaVtHRsTvI6JDiU+LiN9ExBPADo0vJiLui4ieNesPRMTWpTfVNeVpplER8bWaMt+v+ZvgpwvtnZUkacGsGBEvRcSyABGxasN6aU8vKO3k0xHRp5Rp8v60KRExNiLOKfe4X4yIr5X28ImI+GtEfCQiVouIlyNimZrjjyt12Cgi/h7V00/3R8SmbfKuSIsYE8zSkm0L4LFmth0NvJmZnwA+AXwtIjYs27YBTgA2Bz4GfCoiVgD+AHwO6AX8T82xfggMzcw+VDfp50bESmXbtlS9qz690K5KkqSFqwcwoqWFI2It4AvAFpm5FfDzzPwHMAj4fmb2zMx/AlcDp5QyTwFn1BzmvczsDVwG3AIcV+pxRFSPB28GfBn4VGb2BGYCh5R9VwIezsytM/OBJqp4JXBEqesmwAolYQ6wFdUX0DsAP46I9SJiD6A70Ieq11iviNi5pe+HJEl19F/gHmCfsn4Q8LfMfL+sf6S0k98E+pfY3O5Pm/J6Zm6bmQPLsT+RmVsDzwFHZ+abwEig4Z52X2BIqcPlwLcysxdwEnDJgl6wtDhyiAxpKRIRFwM7Au8BLwNbRUS/snk1qpvL94BHMnN82Wck0A2YBryUmaNK/E/AsWXfPYDPx4djTK0AbFCW78zMyXW8LEmS2tqbwLvAlaWH8xxzEkTEasDqmXlvCQ0AbqgpMqj8fAp4JjMnlv3GAOtTtde9gEfLg0QrAq+WfWYCf51L/W4ATo+I7wNHAVfVbLslM/8L/Dci7qZKKu9I1ZY/XsqsTPU3wX1zOYckSW3lCuBkquEfjwS+VrPtWoDMvK/0bl6d5u9Pn2vm+NfVLPcoTxOtTtUeDqkp82Xgbqok9yURsTLwSeCG0lYDLN+qK5QWcyaYpSXbM8CBDSuZeVxErA0MB/5F9U3rkNodohrDcXpNaCbz/l0RwIGZ+UKjY20HvN3aykuS1EaeAfo1EZ/B7E/8rQCQmTPKY7i7lf2Op+oVPD8a2toPmL3d/YCq3Q2qMaFPa2Lfd+c2RmRmvhMRdwL7AV+iSlTP2ty4eDnXLzPz9/N3CZIk1V9mPhgR3cq9aofMfLp2c+PiNHN/Ohe196xXAftn5hNlWKxdSnwQ8IuIWJOqXR1K9UTRG6UHtbRUc4gMack2FFghIr5RE/tI+TkE+EbNWFabzOOxoeeBbhGxUVk/uGbbEOBbEbPGat5modRekqS2MRRYPiIanswhIraiukHdPCKWLz2idivbVgZWy8zBwHeBrctubwGrAJTHaadExE5l21eBht7MLXEX0C8i1innXDOqiXpb6grgQuDRzJxSE98vIlYow3zsAjxK1Y4fVa6LiOjScF5JkhYRVwN/Af7YKP5lgIjYkWoIyDdZsPvTVYCJ5T65YWgqMnMaVZt5AXBrZs7MzKnASxHxxXKeiGqSYGmpYw9maQmWmRkR+wPnRcTJwCSqb2dPoXp8thvwWGl4JwH7z+VY75Yb79si4h3gfspNNPAz4HzgyTLxwUtU41JJkrTIK+3lF4DzI+IUquEvxlLNR3A98DRV29YwhMQqwC1lfoIATizxgcAfIuLbVD2bDwcui4iPAGOoHuttaZ2ejYgfAXeUtvV9qnGaX27h/iMiYipz3og/SfV479rAzzLz38C/y5jPD5V78WnAoXw4JIckSe3tz8DPKUNi1Hg3Ih4HlqUaFgoW7P70dOBhqvvjh/nwnheqYTJu4MNezVAloS8tbfayVH8LPIG0lInMxk8TSJIkSVqcRcR6VJMibZqZH5TYT4BpmfnrdqyaJEnzrcwdtF9mfrUmdg9wUmYOb7eKSQLswSxJkiQtUSLiMOAs4MSG5LIkSYuriPgdsBewd3vXRVLT7MEsSZIkSZIkSWoVJ/mTJEmSJEmSJLWKCWZJkiRJkiRJUquYYJYkSZIkSZIktYoJZkmSJEmSJElSq5hgliRJkiRJkiS1iglmSZIkSZIkSVKrmGCWJEmSJEmSJLWKCWZJkiRJkiRJUquYYJYkSZIkSZIktYoJZmkRFhFHRMQDbXSuj0fEyIh4KyK+3RbnnB8R0S0iMiI6LuBx2uw9lSRJkiRJWtKZYJaKiPhKRAyPiGkRMTEibo+IHdu7Xi0VEfdExDELcIiTgbszc5XMvLDRsZ8p78u0iJgZEe/WrP9gwWq+8EXEnhFxX0mWT4qIeyPi8+1dL0mSJEmSpCWNCWYJiIgTgfOBXwDrAhsAlwD7tWO12tpHgWea2pCZW2Tmypm5MnA/cHzDemb+oqHcgvYuXhgioh9wA3A10JXq3/PHwOfas16SJEmSJElLIhPMWupFxGrAmcBxmfm3zHw7M9/PzP/NzO+XMstHxPkR8e/yOj8ili/b5hhyoQzlsHFZvioiLo6I20qP2ocjYqNGZb8dEWMi4rWIODcimvxsRsQnI+LRiHiz/PxkiZ8F7ARcVHoVX9TM/p8vvZHfKD2eNyvxocBnavbfpIXvXcOwFUdHxL+AoSV+Q0T8p9TzvojYosS3K/EONcf4QkQ8WZaXiYhTI+KfEfF6RFwfEWu2pC5l/wB+C/wsM6/IzDcz84PMvDczv9bMPhdExLiImBoRIyJip5ptfUqv9qkR8UpE/LbEV4iIP5U6vlH+LdZtaT0lSZIkSZKWFCaYJdgBWAG4aS5lfghsD/QEtgb6AD+aj3McBPwUWAMYDZzVaPsXgN7AtlS9po9qfICSaL0NuBBYiyqReltErJWZP2T2nsXHN7H/JsC1wAlAJ2Aw8L8RsVxm7tpo/xfn49oAPg1sBuxZ1m8HugPrAI8BfwbIzIeBt4Fda/b9CvCXsvwtYP9yvPWAKcDF81GPjwPrAzfOxz6PUv27rlnqcUNErFC2XQBckJmrAhsB15f44cBq5VxrAV8H/jsf55QkSZIkSVoimGCWqgTha5k5Yy5lDgHOzMxXM3MSVbL4q/Nxjpsy85Fyjj9TJTRrnZOZkzPzX1RDdRzcxDH2AUZl5jWZOSMzrwWep+VDP3wZuC0z78zM94FfAysCn5yP62jOT0rP7/8CZGb/zHwrM6cDPwG2Lj3FoUpyHwwQEasAe5cYVInaH2bm+Jp9+83H0BtrlZ8TW1rxzPxTZr5e3tPfAMtTJaoB3gc2joi1M3NaZg6ria8FbJyZMzNzRGZObek5JUmSJEmSlhQmmCV4HVh7HknM9YCXa9ZfLrGW+k/N8jvAyo22j2vBsRvXoaFslxbWYbb9M/ODct6W7j83s+ofER0i4uwyzMVUYGzZtHb5+RfggDLEyAHAY5nZUK+PAjeVYSfeAJ4DZlKNo9wSr5efnVta8Yg4KSKeK8N5vEHVM7mhrkcDmwDPl2Ew9i3xa4AhwMAyZMqvImLZlp5TkiRJkiRpSWGCWYKHgOlUQzM0599Uyc8GG5QYVEM+fKRhQ0T8TyvqsH4zx55bHRrKTijLOY9zzLZ/Ga94/Zr9F0Ttub9CNczHZ6mStd0aTgmQmc9SJbr3YvbhMaBKVO+VmavXvFbIzJbW8YVyjANbUriMt3wy8CVgjcxcHXizpq6jMvNgqqE+zgFujIiVyhjdP83Mzal6gO8LHNbCOkqSJEmSJC0xTDBrqZeZbwI/Bi6OiP0j4iMRsWxE7BURvyrFrgV+FBGdImLtUv5PZdsTwBYR0bOM3fuTVlTj+xGxRkSsD3wHuK6JMoOBTSLiKxHRMSK+DGwO3Fq2vwJ8bC7nuB7YJyJ2K71tv0eVWP9HK+o7N6uU475OlXj/RRNl/kJ1nTsDN9TELwPOioiPApT3e7+WnjgzEzgROD0ijoyIVcvEgTtGxOXN1HUGMAnoGBE/BlZt2BgRh0ZEp9Lb+40S/iAiPhMRW5bJCqdSDZnxQUvrKUmSJEmStKQwwSwBZezdE6km7ptE1Qv2eODmUuTnwHDgSeApqonrfl72fRE4E/g/YBTwQCuqcAswAhhJNZHflU3U8XWqnrLfo0rengzsm5mvlSIXUI1XPCUiLmxi/xeAQ4HfAa9Rjd38ucx8rxX1nZurqXooTwCeBYY1UeZaqon8htbUH6prGATcERFvlX23m5+TZ+aNVONNH0XVa/sVqn+rW5ooPgT4O/BiqfO7zD5cSV/gmYiYVup2UBln+n+oJhKcSjWMx71Uw2ZIkiRJkiQtVaLq8CepvUREAt0zc3R710WSJEmSJEmaH/ZgliRJkiRJkiS1Ssf2roAkza8yZEVT9srM+9u0MpIkSZIkSUsxh8iQJEmSJEmSJLWKQ2RIkiRJkiRJklplqRsiY+21185u3bq1dzUkSUuBESNGvJaZndq7HpIkSZIk1ctSl2Du1q0bw4cPb+9qSNIcZs6cSe/evenSpQu33norQ4cO5aSTTuK9996jV69eXHnllXTs2JFbbrmF008/nWWWWYaOHTty/vnns+OOO846ztSpU9l8883Zf//9ueiii3jnnXf44he/yD//+U86dOjA5z73Oc4+++x2vNKlR0S83N51kCRJkiSpnhwiQ5IWERdccAGbbbYZAB988AGHH344AwcO5Omnn+ajH/0oAwYMAGC33XbjiSeeYOTIkfTv359jjjlmtuOcfvrp7LzzzrPFTjrpJJ5//nkef/xxHnzwQW6//fa2uShJkiRJkrREM8EsSYuA8ePHc9ttt81KFr/++usst9xybLLJJgDsvvvu/PWvfwVg5ZVXJiIAePvtt2ctA4wYMYJXXnmFPfbYY1bsIx/5CJ/5zGcAWG655dh2220ZP358m1yXJEmSJElasplglqRFwAknnMCvfvUrllmm+rW89tprM2PGjFlD+tx4442MGzduVvmbbrqJTTfdlH322Yf+/fsDVa/n733ve/z6179u9jxvvPEG//u//8tuu+1Wx6uRJEmSJElLi7ommCNibEQ8FREjI2J4ia0ZEXdGxKjyc40Sj4i4MCJGR8STEbFtzXEOL+VHRcThNfFe5fijy74xZy0kadF26623ss4669CrV69ZsYhg4MCBfPe736VPnz6sssoqdOjQYdb2L3zhCzz//PPcfPPNnH766QBccskl7L333nTt2rXJ88yYMYODDz6Yb3/723zsYx+r70VJkiRJkqSlQltM8veZzHytZv1U4K7MPDsiTi3rpwB7Ad3LazvgUmC7iFgTOAPoDSQwIiIGZeaUUuZrwMPAYKAv4MCikhYrDz74IIMGDWLw4MG8++67TJ06lUMPPZQ//elP3H///QDccccdvPjii3Psu/POOzNmzBhee+01HnroIe6//34uueQSpk2bxnvvvcfKK688a0K/Y489lu7du3PCCSe05eVJkiRJkqQlWHsMkbEfMKAsDwD2r4lfnZVhwOoR0RnYE7gzMyeXpPKdQN+ybdXMHJaZCVxdcyxJWmz88pe/ZPz48YwdO5aBAwey66678qc//YlXX30VgOnTp3POOefw9a9/HYDRo0dT/dqDxx57jOnTp7PWWmvx5z//mX/961+MHTuWX//61xx22GGzkss/+tGPePPNNzn//PPb5RolSZIkSdKSqd4J5gTuiIgREXFsia2bmRPL8n+AdctyF2Bczb7jS2xu8fFNxOcQEcdGxPCIGD5p0qQFuR5JajPnnnsum222GVtttRWf+9zn2HXXXQH461//So8ePejZsyfHHXcc1113HXMbIWj8+PGcddZZPPvss2y77bb07NmTK664oq0uQ5IkSZIkLcGioRdcXQ4e0SUzJ0TEOlQ9j78FDMrM1WvKTMnMNSLiVuDszHygxO+iGjpjF2CFzPx5iZ8O/Be4p5T/bInvBJySmfvOrU69e/fOhkmzJEmqp4gYkZm927sekiRJkiTVS13HYM7MCeXnqxFxE9AHeCUiOmfmxDLMxaul+ARg/Zrdu5bYBKokc238nhLv2kR5SYuBf525ZXtXQZrNBj9+qr2rIEmSJEnSYqduQ2RExEoRsUrDMrAH8DQwCDi8FDscuKUsDwIOi8r2wJtlKI0hwB4RsUZErFGOM6RsmxoR20f1bPhhNceSJEmSJEmSJNVZPXswrwvcVMYF7Qj8JTP/HhGPAtdHxNHAy8CXSvnBwN7AaOAd4EiAzJwcET8DHi3lzszMyWX5m8BVwIrA7eUlSZIkSZIkSWoDdUswZ+YYYOsm4q8DuzURT+C4Zo7VH+jfRHw40GOBKytJkiRJkiRJmm91GyJDkiRJkiRJkrRkM8EsSZIkSZIkSWoVE8ySJEmSJEmSpFYxwSxJkiRJkiRJahUTzKqLmTNnss0227DvvvsCcMQRR7DhhhvSs2dPevbsyciRIwHITL797W+z8cYbs9VWW/HYY48B8PLLL7PtttvSs2dPtthiCy677DIA3nrrrVnH6NmzJ2uvvTYnnHBCe1yiJEmSJEmStNTr2N4V0JLpggsuYLPNNmPq1KmzYueeey79+vWbrdztt9/OqFGjGDVqFA8//DDf+MY3ePjhh+ncuTMPPfQQyy+/PNOmTaNHjx58/vOfZ7311puVnAbo1asXBxxwQFtdliRJkiRJkqQa9mDWQjd+/Hhuu+02jjnmmHmWveWWWzjssMOICLbffnveeOMNJk6cyHLLLcfyyy8PwPTp0/nggw/m2PfFF1/k1VdfZaeddlro1yBJkiRJkiRp3kwwa6E74YQT+NWvfsUyy8z+3+uHP/whW221Fd/97neZPn06ABMmTGD99defVaZr165MmDABgHHjxrHVVlux/vrrc8opp7DeeuvNdryBAwfy5S9/mYio8xVJkiRJkiRJaooJZi1Ut956K+ussw69evWaLf7LX/6S559/nkcffZTJkydzzjnnzPNY66+/Pk8++SSjR49mwIABvPLKK7NtHzhwIAcffPBCrb8kSZIkSZKkljPBrIXqwQcfZNCgQXTr1o2DDjqIoUOHcuihh9K5c2ciguWXX54jjzySRx55BIAuXbowbty4WfuPHz+eLl26zHbM9dZbjx49enD//ffPij3xxBPMmDFjjkS2JEmSJEmSpLZjglkL1S9/+UvGjx/P2LFjGThwILvuuit/+tOfmDhxIgCZyc0330yPHj0A+PznP8/VV19NZjJs2DBWW201OnfuzPjx4/nvf/8LwJQpU3jggQf4+Mc/Pus81157rb2XJUmSJEmSpHbWsb0roKXDIYccwqRJk8hMevbsyWWXXQbA3nvvzeDBg9l44435yEc+wh//+EcAnnvuOb73ve8REWQmJ510EltuueWs411//fUMHjy4Xa5FkiRJkiRJUiUys73r0KZ69+6dw4cPb+9qSEu9f5255bwLSW1ogx8/tdCPGREjMrP3Qj+wJEmSJEmLCHswL6Be37+6vasgzWHEuYe1dxUkSZIkSZK0FHAMZkmSJEmSJElSq5hgliRJkiRJkiS1iglmSZIkSZIkSVKrmGCWJEmSJEmSJLWKCWZJkiRJkiRJUquYYJYkSZIkSZIktYoJZkmSJEmSJElSq5hgliRJkiRJkiS1iglmSZIkSZIkSVKrmGCWJEmSJEmSJLWKCWZJkiRJkiRJUquYYJYkSZIkSZIktYoJZkmSJEmSJElSq5hgliRJkiRJkiS1iglmSZIkSZIkSVKrmGCWJEmSJEmSJLVK3RPMEdEhIh6PiFvL+oYR8XBEjI6I6yJiuRJfvqyPLtu71RzjtBJ/ISL2rIn3LbHREXFqva9FkiRJkiRJkvShtujB/B3guZr1c4DzMnNjYApwdIkfDUwp8fNKOSJic+AgYAugL3BJSVp3AC4G9gI2Bw4uZSVJkiRJkiRJbaCuCeaI6ArsA1xR1gPYFbixFBkA7F+W9yvrlO27lfL7AQMzc3pmvgSMBvqU1+jMHJOZ7wEDS1lJkiRJkiRJUhuodw/m84GTgQ/K+lrAG5k5o6yPB7qU5S7AOICy/c1Sfla80T7NxecQEcdGxPCIGD5p0qQFvCRJkiRJkiRJEtQxwRwR+wKvZuaIep2jpTLz8szsnZm9O3Xq1N7VkSRJkiRJkqQlQsc6HvtTwOcjYm9gBWBV4AJg9YjoWHopdwUmlPITgPWB8RHREVgNeL0m3qB2n+bikiRJkiRJkqQ6q1sP5sw8LTO7ZmY3qkn6hmbmIcDdQL9S7HDglrI8qKxTtg/NzCzxgyJi+YjYEOgOPAI8CnSPiA0jYrlyjkH1uh5JkiRJkiRJ0uzq2YO5OacAAyPi58DjwJUlfiVwTUSMBiZTJYzJzGci4nrgWWAGcFxmzgSIiOOBIUAHoH9mPtOmVyJJkiRJkiRJS7E2STBn5j3APWV5DNCniTLvAl9sZv+zgLOaiA8GBi/EqkqSJEmSJEmSWqhuQ2RIkiRJkiRJkpZsJpglSZIkSZIkSa1iglmSJEmSJEmS1CommCVJkiRJkiRJrWKCWZIkSZIkSZLUKiaYJUmSJEmSJEmtYoJZkiRJkiRJktQqJpglSZIkSZIkSa1iglmSJEmSJEmS1CommCVJkiRJkiRJrWKCWZIkSZIkSZLUKiaYJUmSJEmSJEmtYoJZkiRJkiRJktQqJpglSZIkSZIkSa1iglmSJEmSJEmS1CommCVJkiRJkiRJrWKCWZIkSZIkSZLUKiaYJUmSJEmSJEmtYoJZkiRJkiRJktQqJpglSZIkSZIkSa1iglmSJEmSJEmS1CommCVJkiRJkiRJrWKCWZIkSZIkSZLUKiaYJUmSJEmSJEmtYoJZkiRJkiRJktQqJpglSZIkSZIkSa1iglmSJEmSJEmS1CommCVJkiRJkiRJrWKCWZIkSZIkSZLUKiaYJUmSJEmSJEmtYoJZkiRJkiRJktQqdUswR8QKEfFIRDwREc9ExE9LfMOIeDgiRkfEdRGxXIkvX9ZHl+3dao51Wom/EBF71sT7ltjoiDi1XtciSZIkSZIkSZpTPXswTwd2zcytgZ5A34jYHjgHOC8zNwamAEeX8kcDU0r8vFKOiNgcOAjYAugLXBIRHSKiA3AxsBewOXBwKStJkiRJkiRJagN1SzBnZVpZXba8EtgVuLHEBwD7l+X9yjpl+24RESU+MDOnZ+ZLwGigT3mNzswxmfkeMLCUlSRJkiRJkiS1gbqOwVx6Go8EXgXuBP4JvJGZM0qR8UCXstwFGAdQtr8JrFUbb7RPc/Gm6nFsRAyPiOGTJk1aCFcmSZIkSZIkSaprgjkzZ2ZmT6ArVY/jTet5vrnU4/LM7J2ZvTt16tQeVZAkSZIkSZKkJU5dE8wNMvMN4G5gB2D1iOhYNnUFJpTlCcD6AGX7asDrtfFG+zQXlyRJkiRJkiS1gbolmCOiU0SsXpZXBHYHnqNKNPcrxQ4HbinLg8o6ZfvQzMwSPygilo+IDYHuwCPAo0D3iNgwIpajmghwUL2uR5IkSZIkSZI0u47zLtJqnYEBEdGBKpF9fWbeGhHPAgMj4ufA48CVpfyVwDURMRqYTJUwJjOfiYjrgWeBGcBx+f/bu/tg2+ryPuDfL6DBNxSFsQQwxgw2Q9CgItK0tkYz+NJGrI2aTFKu1pHYqKNJ0441qVitjYmmM4KtHZsg0mkTiZpoLQkyNtZWRcGIgKiVsXWEQaXiSIwvLczTP866zREvcNzcfc49l89nZs9e61m/tdaz79z9z/eu++yZW5Ok7YuTXJzk0CTnzcyn1vh5AAAAAADYZEsBc9v3z8yT7qy22cxcmeRR+6h/PhvzmG9b/3aSZ93OtV6b5LX7qF+U5KI7/QAAAAAAAOx3dxgwtz08yb2THNX2yCRdDh2R5Ng19wYAAAAAwAHszp5g/sUkL0vyg0k+nr8MmG9O8qb1tQUAAAAAwIHuDgPmmXljkje2fcnMnLtNPQEAAAAAsAtsaQbzzJzb9ieSPHTzOTNzwZr6AgAAAADgALfVH/n790l+JMkVSW5dypNEwAwAAAAAcDe1pYA5ySlJTpyZWWczAAAAAADsHodscd3VSf7KOhsBAAAAAGB32eoTzEcluabtx5J8Z29xZp6+lq4AAAAAADjgbTVgftU6mwAAAAAAYPfZUsA8M/913Y0AAAAAALC7bClgbvvnSfb+wN89k9wjyV/MzBHragwAAAAAgAPbVp9gvt/e7bZNckaS09bVFAAAAAAAB75Dvt8TZsMfJXny/m8HAAAAAIDdYqsjMp65afeQJKck+fZaOgIAAAAAYFfYUsCc5Kc3bd+S5H9lY0wGAAAAAAB3U1udwfy8dTcCAAAAAMDusqUZzG2Pa/uHbb+yvN7Z9rh1NwcAAAAAwIFrqz/y99Yk70nyg8vrPy01AAAAAADuprYaMB89M2+dmVuW1/lJjl5jXwAAAAAAHOC2GjB/te0vtD10ef1Ckq+uszEAAAAAAA5sWw2Y/0GSZyf5UpIbkvxMkueuqScAAAAAAHaBw7a47tVJ9szM15Kk7QOTvCEbwTMAAAAAAHdDW32C+ZF7w+UkmZmbkjxqPS0BAAAAALAbbDVgPqTtkXt3lieYt/r0MwAAAAAAB6GthsS/neQjbf9g2X9WkteupyUAAAAAAHaDLQXMM3NB28uTPHEpPXNmrllfWwAAAAAAHOi2POZiCZSFygAAAAAAJNn6DGYAAAAAAPguAmYAAAAAAFYiYAYAAAAAYCUCZgAAAAAAVrK2gLnt8W3/tO01bT/V9qVL/YFtL2n7ueX9yKXetue0vbbtlW0fvelae5b1n2u7Z1P9MW2vWs45p23X9XkAAAAAAPhu63yC+ZYk/2hmTkxyWpIXtT0xycuTvH9mTkjy/mU/SZ6a5ITldVaSNycbgXSSs5M8LsmpSc7eG0ova16w6bynrPHzAAAAAACwydoC5pm5YWb+bNn+8ySfTnJskjOSvG1Z9rYkz1i2z0hywWy4NMkD2h6T5MlJLpmZm2bma0kuSfKU5dgRM3PpzEySCzZdCwAAAACANduWGcxtH5rkUUk+muTBM3PDcuhLSR68bB+b5IubTrtuqd1R/bp91Pd1/7PaXt728htvvPGufRgAAAAAAJJsQ8Dc9r5J3pnkZTNz8+Zjy5PHs+4eZuYtM3PKzJxy9NFHr/t2AAAAAAB3C2sNmNveIxvh8n+YmXct5S8v4y2yvH9lqV+f5PhNpx+31O6oftw+6gAAAAAAbIO1Bcxtm+R3k3x6Zv7VpkPvSbJn2d6T5N2b6md2w2lJvr6M0rg4yeltj1x+3O/0JBcvx25ue9pyrzM3XQsAAAAAgDU7bI3X/utJ/n6Sq9pesdRekeR1SS5s+/wkX0jy7OXYRUmeluTaJN9M8rwkmZmb2r4myWXLulfPzE3L9i8lOT/JvZL88fICAAAAAGAbrC1gnpn/nqS3c/hJ+1g/SV50O9c6L8l5+6hfnuSku9AmAAAAAAArWvuP/AEAAAAAcHASMAMAAAAAsBIBMwAAAAAAKxEwAwAAAACwEgEzAAAAAAArETADAAAAALASATMAAAAAACsRMAMAAAAAsBIBMwAAAAAAKxEwAwAAAACwEgEzAAAAAAArETADAAAAALASATMAAAAAACsRMAMAAAAAsBIBMwAAAAAAKxEwAwAAAACwEgEzAAAAAAArETADAAAAALASATMAAAAAACsRMAMAAAAAsBIBMwAAAAAAKxEwAwAAAACwEgEzAAAAAAArETADAAAAALASATMAAAAAACsRMAMAAAAAsBIBMwAAAAAAKxEwAwAAAACwEgEzAAAAAAArETADAAAAALCStQXMbc9r+5W2V2+qPbDtJW0/t7wfudTb9py217a9su2jN52zZ1n/ubZ7NtUf0/aq5Zxz2nZdnwUAAAAAgO+1zieYz0/ylNvUXp7k/TNzQpL3L/tJ8tQkJyyvs5K8OdkIpJOcneRxSU5NcvbeUHpZ84JN5932XgAAAAAArNHaAuaZ+WCSm25TPiPJ25bttyV5xqb6BbPh0iQPaHtMkicnuWRmbpqZryW5JMlTlmNHzMylMzNJLth0LQAAAAAAtsF2z2B+8MzcsGx/KcmDl+1jk3xx07rrltod1a/bR32f2p7V9vK2l99444137RMAAAAAAJBkB3/kb3nyeLbpXm+ZmVNm5pSjjz56O24JAAAAAHDQ2+6A+cvLeIss719Z6tcnOX7TuuOW2h3Vj9tHHQAAAACAbbLdAfN7kuxZtvckefem+pndcFqSry+jNC5OcnrbI5cf9zs9ycXLsZvbnta2Sc7cdC0AAAAAALbBYeu6cNvfS/KEJEe1vS7J2Ulel+TCts9P8oUkz16WX5TkaUmuTfLNJM9Lkpm5qe1rkly2rHv1zOz94cBfSnJ+knsl+ePlBQAAAADANllbwDwzP3c7h560j7WT5EW3c53zkpy3j/rlSU66Kz0CAAAAALC6HfuRPwAAAAAAdjcBMwAAAAAAKxEwAwAAAACwEgEzAAAAAAArETADAAAAALASATMAAAAAACsRMAMAAAAAsBIBMwAAAAAAKxEwAwAAAACwEgEzAAAAAAArETADAAAAALASATMAAAAAACsRMAMAAAAAsBIBMwAAAAAAKxEwAwAAAACwEgEzAAAAAAArETADAAAAALASATMAAAAAACsRMAMAAAAAsBIBMwAAAAAAKxEwAwAAAACwEgEzAAAAAAArETADAAAAALASATMAAAAAACsRMAMAAAAAsBIBMwAAAAAAKxEwAwAAAACwEgEzAAAAAAArETADAAAAALASATMAAAAAACsRMAMAAAAAsJJdHzC3fUrbz7a9tu3Ld7ofAAAAAIC7i10dMLc9NMm/TvLUJCcm+bm2J+5sVwAAAAAAdw+7OmBOcmqSa2fm8zPzf5L8fpIzdrgnAAAAAIC7hcN2uoG76NgkX9y0f12Sx912Uduzkpy17H6j7We3oTe+f0cl+d873cTBoG/Ys9MtsH18b/aXs7uOq/7QOi4KAAAAB4rdHjBvycy8JclbdroP7ljby2fmlJ3uA3YT3xsAAABgJ+32ERnXJzl+0/5xSw0AAAAAgDXb7QHzZUlOaPvDbe+Z5GeTvGeHewIAAAAAuFvY1SMyZuaWti9OcnGSQ5OcNzOf2uG2WJ0xJvD9870BAAAAdkxnZqd7AAAAAABgF9rtIzIAAAAAANghAmYAAAAAAFayq2cws3Pa3prkqiRNcmuSF8/Mh1e4zguTfHNmLtjPLcJBY9P3ba/fn5nX7VQ/AAAAAHuZwcxK2n5jZu67bD85yStm5m/tcFtwUNr8fQMAAAA4kBiRwf5wRJKvJUnbJ7R9794Dbd/U9rnL9uvaXtP2yrZvWGqvavury/YH2v5m24+1/R9tH7/UD237+raXLef+4lI/pu0H217R9uq2j1/Wnr/sX9X2l7f3jwK2T9vHtv1w208u35v7tT287VuXv/+faPuTO90nAAAAcPAyIoNV3avtFUkOT3JMkife0eK2D0ryd5P86MxM2wfcztLDZubUtk9LcnaSn0ry/CRfn5nHtv2BJB9q+74kz0xy8cy8tu2hSe6d5OQkx87MSct9b+8+sJvs/b7t9RtJ/jDJ25M8Z2Yua3tEkm8leWmSmZlHtP3RJO9r+/CZ+fa2dw0AAAAc9ATMrOpbM3NykrT9a0kuaHvSHaz/epJvJ/nd5Qnn997Ounct7x9P8tBl+/Qkj2z7M8v+/ZOckOSyJOe1vUeSP5qZK9p+PsnD2p6b5D8ned8qHw4OMP//+7ZX20ckuWFmLkuSmbl5qf+NJOcutc+0/UKShye5cls7BgAAAO4WjMjgLpuZjyQ5KsnRSW7Jd/+9OnxZc0uSU5O8I8nfSfInt3O57yzvt+Yv/wGkSV4yMycvrx+emffNzAeT/M0k1yc5v+2ZM/O1JD+e5ANJXpjkd/bPpwQAAAAAbkvAzF22/Df8Q5N8NckXkpzY9geW8RRPWtbcN8n9Z+aiJL+cjRB4qy5O8g+XJ5XT9uFt79P2h5J8eWb+XTaC5Ee3PSrJITPzziS/nuTR++VDwoHns0mOafvYJFnmLx+W5L8l+fml9vAkD1nWAgAAAOx3RmSwqs0zYZtkz8zcmuSLbS9McnWS/5nkE8ua+yV5d9vDl/W/8n3c63eyMS7jz9o2yY1JnpHkCUn+cdv/m+QbSc5McmySt7bd+48n/3SVDwcHmNvOYP6TmXl52+ckObftvbIxf/mnkvybJG9ue1U2/kfBc2fmO99zRQAAAID9oDOz0z0AAAAAALALGZEBAAAAAMBKBMwAAAAAAKxEwAwAAAAAwEoEzAAAAAAArETADAAAAADASgTMAAAAAACsRMAMu0jbB7W9Ynl9qe31m/bvuZ/v9YS2772TNU9te3nba9p+ou1vL/VXtf3V/dkPAAAAAAeew3a6AWDrZuarSU5ONkLcJN+YmTfsPd72sJm5ZTt6aXtSkjcl+dsz85m2hyY5azvuDQAAAMCBwRPMsMu1Pb/tv2370SS/1fbUth9Znij+cNu/uqy7tO2PbTrvA21PaXuftue1/dhyzhlbvPU/SfLamflMkszMrTPz5n3094K2l7X9ZNt3tr33Un9W26uX+geX2o8tfVzR9sq2J9zFPx4AAAAA1kjADAeH45L8xMz8SpLPJHn8zDwqySuT/MtlzduTPDtJ2h6T5JiZuTzJryX5LzNzapKfTPL6tvfZwj1PSvLxLax718w8dmZ+PMmnkzx/qb8yyZOX+tOX2guTvHFmTk5ySpLrtnB9AAAAAHaIERlwcPiDmbl12b5/krctT/9Oknss9QuTvC/J2dkImt+x1E9P8vRNM5MPT/KQ/djbSW3/RZIHJLlvkouX+oeSnN/2wiTvWmofSfJrbY/LRjD9uf3YBwAAAAD7mSeY4eDwF5u2X5PkT2fmpCQ/nY3AODNzfZKvtn1kkudk44nmJGmSvzczJy+vh8zMp7dwz08lecwW1p2f5MUz84gk/3xTPy9M8utJjk/y8bYPmpn/mI2nmb+V5KK2T9zC9QEAAADYIQJmOPjcP8n1y/Zzb3Ps7dmYnXz/mblyqV2c5CVtmyRtH7XF+7w+ySvaPnw575C2L9zHuvsluaHtPZL8/N5i2x+ZmY/OzCuT3Jjk+LYPS/L5mTknybuTPHKLvQAAAACwAwTMcPD5rSS/0fYT+d4xOO9I8rPZGJex12uyMUbjyrafWvbv1BJQvyzJ77X9dJKrkzxsH0v/WZKPZmMkxmc21V/f9qq2Vyf5cJJPZmN0x9Vtr8jGjOcLttILAAAAADujM7PTPQAAAAAAsAt5ghkAAAAAgJXc9r/PA3yXts9L8tLblD80My/aiX4AAAAAOHAYkQEAAAAAwEqMyAAAAAAAYCUCZgAAAAAAViJgBgAAAABgJQJmAAAAAABW8v8ACoE8yW23djgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x1296 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "datacat=ttrain.select_dtypes(include='object')\n",
    "\n",
    "plt.figure(figsize = (20,18))\n",
    "feature_list = datacat.columns\n",
    "for i in range(len(feature_list)):\n",
    "    plt.subplot(5, 3, i + 1)\n",
    "    ax1=sns.countplot(x = datacat[feature_list[i]], data = datacat)\n",
    "    ax1.bar_label(ax1.containers[0])\n",
    "    plt.title('Counplot of {}'.format(feature_list[i]))\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ab2d259",
   "metadata": {},
   "source": [
    "### Travel test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6ee678c6",
   "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>Gender</th>\n",
       "      <th>CustomerType</th>\n",
       "      <th>Age</th>\n",
       "      <th>TypeTravel</th>\n",
       "      <th>Travel_Class</th>\n",
       "      <th>Travel_Distance</th>\n",
       "      <th>DepartureDelay_in_Mins</th>\n",
       "      <th>ArrivalDelay_in_Mins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>99900001</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.0</td>\n",
       "      <td>Business travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>532</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>99900002</td>\n",
       "      <td>Female</td>\n",
       "      <td>disloyal Customer</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Business travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>1425</td>\n",
       "      <td>9.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>99900003</td>\n",
       "      <td>Male</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>60.0</td>\n",
       "      <td>Business travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>2832</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>99900004</td>\n",
       "      <td>Female</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>29.0</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Eco</td>\n",
       "      <td>1352</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>99900005</td>\n",
       "      <td>Male</td>\n",
       "      <td>disloyal Customer</td>\n",
       "      <td>18.0</td>\n",
       "      <td>Business travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>1610</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         ID  Gender       CustomerType   Age       TypeTravel Travel_Class  \\\n",
       "0  99900001  Female                NaN  36.0  Business travel     Business   \n",
       "1  99900002  Female  disloyal Customer  21.0  Business travel     Business   \n",
       "2  99900003    Male     Loyal Customer  60.0  Business travel     Business   \n",
       "3  99900004  Female     Loyal Customer  29.0  Personal Travel          Eco   \n",
       "4  99900005    Male  disloyal Customer  18.0  Business travel     Business   \n",
       "\n",
       "   Travel_Distance  DepartureDelay_in_Mins  ArrivalDelay_in_Mins  \n",
       "0              532                     0.0                   0.0  \n",
       "1             1425                     9.0                  28.0  \n",
       "2             2832                     0.0                   0.0  \n",
       "3             1352                     0.0                   0.0  \n",
       "4             1610                    17.0                   0.0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the head of dataset\n",
    "ttest.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "23fa8409",
   "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>Gender</th>\n",
       "      <th>CustomerType</th>\n",
       "      <th>Age</th>\n",
       "      <th>TypeTravel</th>\n",
       "      <th>Travel_Class</th>\n",
       "      <th>Travel_Distance</th>\n",
       "      <th>DepartureDelay_in_Mins</th>\n",
       "      <th>ArrivalDelay_in_Mins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35597</th>\n",
       "      <td>99935598</td>\n",
       "      <td>Male</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Eco</td>\n",
       "      <td>1334</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35598</th>\n",
       "      <td>99935599</td>\n",
       "      <td>Female</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>53.0</td>\n",
       "      <td>Business travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>1772</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35599</th>\n",
       "      <td>99935600</td>\n",
       "      <td>Male</td>\n",
       "      <td>disloyal Customer</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Business travel</td>\n",
       "      <td>Eco</td>\n",
       "      <td>1180</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35600</th>\n",
       "      <td>99935601</td>\n",
       "      <td>Female</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>67.0</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Eco</td>\n",
       "      <td>420</td>\n",
       "      <td>23.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35601</th>\n",
       "      <td>99935602</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Eco</td>\n",
       "      <td>1680</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             ID  Gender       CustomerType   Age       TypeTravel  \\\n",
       "35597  99935598    Male     Loyal Customer   8.0  Personal Travel   \n",
       "35598  99935599  Female     Loyal Customer  53.0  Business travel   \n",
       "35599  99935600    Male  disloyal Customer  22.0  Business travel   \n",
       "35600  99935601  Female     Loyal Customer  67.0  Personal Travel   \n",
       "35601  99935602    Male                NaN  20.0  Personal Travel   \n",
       "\n",
       "      Travel_Class  Travel_Distance  DepartureDelay_in_Mins  \\\n",
       "35597          Eco             1334                     0.0   \n",
       "35598     Business             1772                     0.0   \n",
       "35599          Eco             1180                     0.0   \n",
       "35600          Eco              420                    23.0   \n",
       "35601          Eco             1680                     0.0   \n",
       "\n",
       "       ArrivalDelay_in_Mins  \n",
       "35597                   0.0  \n",
       "35598                   0.0  \n",
       "35599                   0.0  \n",
       "35600                  16.0  \n",
       "35601                   0.0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the tail of dataset\n",
    "ttest.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3a068168",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 35602 entries, 0 to 35601\n",
      "Data columns (total 9 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   ID                      35602 non-null  int64  \n",
      " 1   Gender                  35572 non-null  object \n",
      " 2   CustomerType            32219 non-null  object \n",
      " 3   Age                     35591 non-null  float64\n",
      " 4   TypeTravel              32154 non-null  object \n",
      " 5   Travel_Class            35602 non-null  object \n",
      " 6   Travel_Distance         35602 non-null  int64  \n",
      " 7   DepartureDelay_in_Mins  35573 non-null  float64\n",
      " 8   ArrivalDelay_in_Mins    35479 non-null  float64\n",
      "dtypes: float64(3), int64(2), object(4)\n",
      "memory usage: 2.4+ MB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Travel_Distance</th>\n",
       "      <th>DepartureDelay_in_Mins</th>\n",
       "      <th>ArrivalDelay_in_Mins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.560200e+04</td>\n",
       "      <td>35591.000000</td>\n",
       "      <td>35602.000000</td>\n",
       "      <td>35573.000000</td>\n",
       "      <td>35479.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.991780e+07</td>\n",
       "      <td>39.446995</td>\n",
       "      <td>1987.151761</td>\n",
       "      <td>14.880696</td>\n",
       "      <td>15.308802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.027756e+04</td>\n",
       "      <td>15.137554</td>\n",
       "      <td>1024.308863</td>\n",
       "      <td>37.895453</td>\n",
       "      <td>38.531293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>9.990000e+07</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.990890e+07</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>1360.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9.991780e+07</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>1929.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.992670e+07</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>2559.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.993560e+07</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>6868.000000</td>\n",
       "      <td>978.000000</td>\n",
       "      <td>970.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ID           Age  Travel_Distance  DepartureDelay_in_Mins  \\\n",
       "count  3.560200e+04  35591.000000     35602.000000            35573.000000   \n",
       "mean   9.991780e+07     39.446995      1987.151761               14.880696   \n",
       "std    1.027756e+04     15.137554      1024.308863               37.895453   \n",
       "min    9.990000e+07      7.000000        50.000000                0.000000   \n",
       "25%    9.990890e+07     27.000000      1360.000000                0.000000   \n",
       "50%    9.991780e+07     40.000000      1929.000000                0.000000   \n",
       "75%    9.992670e+07     51.000000      2559.000000               13.000000   \n",
       "max    9.993560e+07     85.000000      6868.000000              978.000000   \n",
       "\n",
       "       ArrivalDelay_in_Mins  \n",
       "count          35479.000000  \n",
       "mean              15.308802  \n",
       "std               38.531293  \n",
       "min                0.000000  \n",
       "25%                0.000000  \n",
       "50%                0.000000  \n",
       "75%               13.000000  \n",
       "max              970.000000  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get information about the train dataset\n",
    "print(ttest.info())\n",
    "\n",
    "# Get basic statistics of numerical variables in the train dataset\n",
    "ttest.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9a7d3d8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "null check\n",
      "                        null_count  null_percentage\n",
      "ID                               0         0.000000\n",
      "Gender                          30         0.084265\n",
      "CustomerType                  3383         9.502275\n",
      "Age                             11         0.030897\n",
      "TypeTravel                    3448         9.684849\n",
      "Travel_Class                     0         0.000000\n",
      "Travel_Distance                  0         0.000000\n",
      "DepartureDelay_in_Mins          29         0.081456\n",
      "ArrivalDelay_in_Mins           123         0.345486\n",
      "\n",
      " duplicate data check \n",
      "\n",
      "\n",
      " zeros check \n",
      "\n",
      "                        zero_count  zero_percentage\n",
      "ID                               0         0.000000\n",
      "Gender                           0         0.000000\n",
      "CustomerType                     0         0.000000\n",
      "Age                              0         0.000000\n",
      "TypeTravel                       0         0.000000\n",
      "Travel_Class                     0         0.000000\n",
      "Travel_Distance                  0         0.000000\n",
      "DepartureDelay_in_Mins       20028        56.255267\n",
      "ArrivalDelay_in_Mins         19848        55.749677\n"
     ]
    }
   ],
   "source": [
    "# Check for null values and show the percentage of nulls\n",
    "print('null check')\n",
    "null_values = ttest.isnull().sum().rename('null_count')\n",
    "null_percent = ((null_values / ttest.shape[0]) * 100).rename('null_percentage')\n",
    "null_info = pd.concat([null_values,null_percent],axis=1)\n",
    "print(null_info)\n",
    "\n",
    "\n",
    "# Check for duplicate rows\n",
    "print('\\n duplicate data check \\n')\n",
    "duplicate_rows = ttest[ttest.duplicated()]\n",
    "duplicate_rows\n",
    "\n",
    "# Check for zeros and show the percentage of zeros\n",
    "print('\\n zeros check \\n')\n",
    "zero_values = ttest.eq(0).sum().rename('zero_count')\n",
    "zero_percent = (zero_values / ttest.shape[0]) * 100\n",
    "zero_percent = zero_percent.rename('zero_percentage')\n",
    "zero_info = pd.concat([zero_values,zero_percent],axis=1)\n",
    "print(zero_info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "511f6dba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1296 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "datacat=ttest.select_dtypes(include='object')\n",
    "\n",
    "plt.figure(figsize = (20,18))\n",
    "feature_list = datacat.columns\n",
    "for i in range(len(feature_list)):\n",
    "    plt.subplot(5, 3, i + 1)\n",
    "    ax1=sns.countplot(x = datacat[feature_list[i]], data = datacat)\n",
    "    ax1.bar_label(ax1.containers[0])\n",
    "    plt.title('Counplot of {}'.format(feature_list[i]))\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a217db7",
   "metadata": {},
   "source": [
    "## merge data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "26ce0fcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "traind = pd.merge(ttrain, strain, on='ID', how='inner')\n",
    "testd = pd.merge(ttest, stest, on='ID', how='inner')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d97ec6b7",
   "metadata": {},
   "source": [
    "### encode data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "1df5fc43",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a dictionary that maps the current values to the new values\n",
    "mapping = {'good': 4, 'excellent': 5, 'acceptable':3, 'need improvement':2, 'poor':1 , 'extremely poor':0}\n",
    "\n",
    "a='Onlinebooking_Ease'\n",
    "\n",
    "# Use the replace() method to map the values in the column\n",
    "traind[a] = traind[a].replace(mapping)\n",
    "# Use the replace() method to map the values in the column\n",
    "testd[a] = testd[a].replace(mapping)\n",
    "\n",
    "a='Onboard_service'\n",
    "\n",
    "# Use the replace() method to map the values in the column\n",
    "traind[a] = traind[a].replace(mapping)\n",
    "# Use the replace() method to map the values in the column\n",
    "testd[a] = testd[a].replace(mapping)\n",
    "\n",
    "\n",
    "a='Leg_room'\n",
    "\n",
    "# Use the replace() method to map the values in the column\n",
    "traind[a] = traind[a].replace(mapping)\n",
    "# Use the replace() method to map the values in the column\n",
    "testd[a] = testd[a].replace(mapping)\n",
    "\n",
    "\n",
    "a='Baggage_handling'\n",
    "\n",
    "# Use the replace() method to map the values in the column\n",
    "traind[a] = traind[a].replace(mapping)\n",
    "# Use the replace() method to map the values in the column\n",
    "testd[a] = testd[a].replace(mapping)\n",
    "\n",
    "\n",
    "a='Checkin_service'\n",
    "\n",
    "# Use the replace() method to map the values in the column\n",
    "traind[a] = traind[a].replace(mapping)\n",
    "# Use the replace() method to map the values in the column\n",
    "testd[a] = testd[a].replace(mapping)\n",
    "\n",
    "\n",
    "a='Cleanliness'\n",
    "\n",
    "# Use the replace() method to map the values in the column\n",
    "traind[a] = traind[a].replace(mapping)\n",
    "# Use the replace() method to map the values in the column\n",
    "testd[a] = testd[a].replace(mapping)\n",
    "\n",
    "\n",
    "a='Online_boarding'\n",
    "\n",
    "# Use the replace() method to map the values in the column\n",
    "traind[a] = traind[a].replace(mapping)\n",
    "# Use the replace() method to map the values in the column\n",
    "testd[a] = testd[a].replace(mapping)\n",
    "\n",
    "\n",
    "a='Catering'\n",
    "\n",
    "# Use the replace() method to map the values in the column\n",
    "traind[a] = traind[a].replace(mapping)\n",
    "# Use the replace() method to map the values in the column\n",
    "testd[a] = testd[a].replace(mapping)\n",
    "\n",
    "a='Onboardwifi_service'\n",
    "\n",
    "# Use the replace() method to map the values in the column\n",
    "traind[a] = traind[a].replace(mapping)\n",
    "# Use the replace() method to map the values in the column\n",
    "testd[a] = testd[a].replace(mapping)\n",
    "\n",
    "\n",
    "a='Onboard_entertainment'\n",
    "\n",
    "# Use the replace() method to map the values in the column\n",
    "traind[a] = traind[a].replace(mapping)\n",
    "# Use the replace() method to map the values in the column\n",
    "testd[a] = testd[a].replace(mapping)\n",
    "\n",
    "a='Online_support'\n",
    "\n",
    "# Use the replace() method to map the values in the column\n",
    "traind[a] = traind[a].replace(mapping)\n",
    "# Use the replace() method to map the values in the column\n",
    "testd[a] = testd[a].replace(mapping)\n",
    "\n",
    "a='Arrival_time_convenient'\n",
    "\n",
    "# Use the replace() method to map the values in the column\n",
    "traind[a] = traind[a].replace(mapping)\n",
    "# Use the replace() method to map the values in the column\n",
    "testd[a] = testd[a].replace(mapping)\n",
    "\n",
    "# Create a dictionary that maps the current values to the new values\n",
    "mapping = {'Green Car': 1, 'Ordinary': 0}\n",
    "\n",
    "a='Seat_Class'\n",
    "\n",
    "# Use the replace() method to map the values in the column\n",
    "traind[a] = traind[a].replace(mapping)\n",
    "# Use the replace() method to map the values in the column\n",
    "testd[a] = testd[a].replace(mapping)\n",
    "\n",
    "# Create a dictionary that maps the current values to the new values\n",
    "mapping = {'Business': 1, 'Eco': 0}\n",
    "\n",
    "a='Travel_Class'\n",
    "\n",
    "# Use the replace() method to map the values in the column\n",
    "traind[a] = traind[a].replace(mapping)\n",
    "# Use the replace() method to map the values in the column\n",
    "testd[a] = testd[a].replace(mapping)\n",
    "\n",
    "# Create a dictionary that maps the current values to the new values\n",
    "mapping = {'Convinient': 4, 'very convinient': 5, 'manageable':3, 'need improvement':2, 'Inconvinient':1 , 'very inconvinient':0}\n",
    "\n",
    "a='Platform_location'\n",
    "\n",
    "# Use the replace() method to map the values in the column\n",
    "traind[a] = traind[a].replace(mapping)\n",
    "# Use the replace() method to map the values in the column\n",
    "testd[a] = testd[a].replace(mapping)\n",
    "\n",
    "# Create a dictionary that maps the current values to the new values\n",
    "mapping = {'Business travel': 1, 'Personal Travel': 0}\n",
    "\n",
    "a='TypeTravel'\n",
    "\n",
    "# Use the replace() method to map the values in the column\n",
    "traind[a] = traind[a].replace(mapping)\n",
    "# Use the replace() method to map the values in the column\n",
    "testd[a] = testd[a].replace(mapping)\n",
    "\n",
    "# Create a dictionary that maps the current values to the new values\n",
    "mapping = {'Loyal Customer': 1, 'disloyal Customer': 0}\n",
    "\n",
    "a='CustomerType'\n",
    "\n",
    "# Use the replace() method to map the values in the column\n",
    "traind[a] = traind[a].replace(mapping)\n",
    "# Use the replace() method to map the values in the column\n",
    "testd[a] = testd[a].replace(mapping)\n",
    "\n",
    "# Create a dictionary that maps the current values to the new values\n",
    "mapping = {'acceptable': 2, 'need improvement': 3, 'good': 4, 'poor': 1, 'excellent': 5, 'extremely poor': 0}\n",
    "\n",
    "# Use the replace() method to map the values in the 'Seat_comfort' column\n",
    "traind['Seat_comfort'] = traind['Seat_comfort'].replace(mapping)\n",
    "# Use the replace() method to map the values in the 'Seat_comfort' column\n",
    "testd['Seat_comfort'] = testd['Seat_comfort'].replace(mapping)\n",
    "\n",
    "# Create a dictionary that maps the current values to the new values\n",
    "mapping = {'Female': 1, 'Male': 0}\n",
    "\n",
    "# Use the replace() method to map the values in the  column\n",
    "traind['Gender'] = traind['Gender'].replace(mapping)\n",
    "# Use the replace() method to map the values in the column\n",
    "testd['Gender'] = testd['Gender'].replace(mapping)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de214fe2",
   "metadata": {},
   "source": [
    "### treat missing values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "449055b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill null values with the mean for int and float type columns\n",
    "traind = traind.fillna(traind.mean())\n",
    "testd = testd.fillna(testd.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "29b83284",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>9.884719e+07</td>\n",
       "      <td>27245.014865</td>\n",
       "      <td>98800001.0</td>\n",
       "      <td>9.882360e+07</td>\n",
       "      <td>98847190.0</td>\n",
       "      <td>98870784.5</td>\n",
       "      <td>98894379.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>5.070412e-01</td>\n",
       "      <td>0.499749</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustomerType</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>8.173316e-01</td>\n",
       "      <td>0.367617</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.173316e-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>3.941965e+01</td>\n",
       "      <td>15.113989</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.700000e+01</td>\n",
       "      <td>40.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TypeTravel</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>6.883727e-01</td>\n",
       "      <td>0.439941</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Travel_Class</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>4.771930e-01</td>\n",
       "      <td>0.499482</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Travel_Distance</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>1.978888e+03</td>\n",
       "      <td>1027.961019</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1.359000e+03</td>\n",
       "      <td>1923.0</td>\n",
       "      <td>2538.0</td>\n",
       "      <td>6951.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DepartureDelay_in_Mins</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>1.464709e+01</td>\n",
       "      <td>38.127262</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1592.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ArrivalDelay_in_Mins</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>1.500522e+01</td>\n",
       "      <td>38.366639</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1584.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Overall_Experience</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>5.466576e-01</td>\n",
       "      <td>0.497821</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seat_comfort</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>2.836935e+00</td>\n",
       "      <td>1.392621</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seat_Class</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>5.026012e-01</td>\n",
       "      <td>0.499996</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arrival_time_convenient</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>2.994991e+00</td>\n",
       "      <td>1.452279</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Catering</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>2.853511e+00</td>\n",
       "      <td>1.375454</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Platform_location</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>2.990864e+00</td>\n",
       "      <td>1.308025</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Onboardwifi_service</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>3.248227e+00</td>\n",
       "      <td>1.319311</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Onboard_entertainment</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>3.382510e+00</td>\n",
       "      <td>1.346061</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Online_support</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>3.519250e+00</td>\n",
       "      <td>1.307543</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Onlinebooking_Ease</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>3.470108e+00</td>\n",
       "      <td>1.305041</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Onboard_service</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>3.470799e+00</td>\n",
       "      <td>1.216418</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Leg_room</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>3.482994e+00</td>\n",
       "      <td>1.291644</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baggage_handling</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>3.696786e+00</td>\n",
       "      <td>1.155529</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Checkin_service</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>3.342400e+00</td>\n",
       "      <td>1.259793</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cleanliness</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>3.704078e+00</td>\n",
       "      <td>1.151951</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Online_boarding</th>\n",
       "      <td>94379.0</td>\n",
       "      <td>3.351901e+00</td>\n",
       "      <td>1.298020</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           count          mean           std         min  \\\n",
       "ID                       94379.0  9.884719e+07  27245.014865  98800001.0   \n",
       "Gender                   94379.0  5.070412e-01      0.499749         0.0   \n",
       "CustomerType             94379.0  8.173316e-01      0.367617         0.0   \n",
       "Age                      94379.0  3.941965e+01     15.113989         7.0   \n",
       "TypeTravel               94379.0  6.883727e-01      0.439941         0.0   \n",
       "Travel_Class             94379.0  4.771930e-01      0.499482         0.0   \n",
       "Travel_Distance          94379.0  1.978888e+03   1027.961019        50.0   \n",
       "DepartureDelay_in_Mins   94379.0  1.464709e+01     38.127262         0.0   \n",
       "ArrivalDelay_in_Mins     94379.0  1.500522e+01     38.366639         0.0   \n",
       "Overall_Experience       94379.0  5.466576e-01      0.497821         0.0   \n",
       "Seat_comfort             94379.0  2.836935e+00      1.392621         0.0   \n",
       "Seat_Class               94379.0  5.026012e-01      0.499996         0.0   \n",
       "Arrival_time_convenient  94379.0  2.994991e+00      1.452279         0.0   \n",
       "Catering                 94379.0  2.853511e+00      1.375454         0.0   \n",
       "Platform_location        94379.0  2.990864e+00      1.308025         0.0   \n",
       "Onboardwifi_service      94379.0  3.248227e+00      1.319311         0.0   \n",
       "Onboard_entertainment    94379.0  3.382510e+00      1.346061         0.0   \n",
       "Online_support           94379.0  3.519250e+00      1.307543         0.0   \n",
       "Onlinebooking_Ease       94379.0  3.470108e+00      1.305041         0.0   \n",
       "Onboard_service          94379.0  3.470799e+00      1.216418         0.0   \n",
       "Leg_room                 94379.0  3.482994e+00      1.291644         0.0   \n",
       "Baggage_handling         94379.0  3.696786e+00      1.155529         1.0   \n",
       "Checkin_service          94379.0  3.342400e+00      1.259793         0.0   \n",
       "Cleanliness              94379.0  3.704078e+00      1.151951         0.0   \n",
       "Online_boarding          94379.0  3.351901e+00      1.298020         0.0   \n",
       "\n",
       "                                  25%         50%         75%         max  \n",
       "ID                       9.882360e+07  98847190.0  98870784.5  98894379.0  \n",
       "Gender                   0.000000e+00         1.0         1.0         1.0  \n",
       "CustomerType             8.173316e-01         1.0         1.0         1.0  \n",
       "Age                      2.700000e+01        40.0        51.0        85.0  \n",
       "TypeTravel               0.000000e+00         1.0         1.0         1.0  \n",
       "Travel_Class             0.000000e+00         0.0         1.0         1.0  \n",
       "Travel_Distance          1.359000e+03      1923.0      2538.0      6951.0  \n",
       "DepartureDelay_in_Mins   0.000000e+00         0.0        12.0      1592.0  \n",
       "ArrivalDelay_in_Mins     0.000000e+00         0.0        13.0      1584.0  \n",
       "Overall_Experience       0.000000e+00         1.0         1.0         1.0  \n",
       "Seat_comfort             2.000000e+00         3.0         4.0         5.0  \n",
       "Seat_Class               0.000000e+00         1.0         1.0         1.0  \n",
       "Arrival_time_convenient  2.000000e+00         3.0         4.0         5.0  \n",
       "Catering                 2.000000e+00         3.0         4.0         5.0  \n",
       "Platform_location        2.000000e+00         3.0         4.0         5.0  \n",
       "Onboardwifi_service      2.000000e+00         3.0         4.0         5.0  \n",
       "Onboard_entertainment    2.000000e+00         4.0         4.0         5.0  \n",
       "Online_support           3.000000e+00         4.0         5.0         5.0  \n",
       "Onlinebooking_Ease       2.000000e+00         4.0         5.0         5.0  \n",
       "Onboard_service          3.000000e+00         4.0         4.0         5.0  \n",
       "Leg_room                 2.000000e+00         4.0         5.0         5.0  \n",
       "Baggage_handling         3.000000e+00         4.0         5.0         5.0  \n",
       "Checkin_service          3.000000e+00         3.0         4.0         5.0  \n",
       "Cleanliness              3.000000e+00         4.0         5.0         5.0  \n",
       "Online_boarding          2.000000e+00         4.0         4.0         5.0  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traind.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "98b801ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <td>35602.0</td>\n",
       "      <td>9.991780e+07</td>\n",
       "      <td>10277.556478</td>\n",
       "      <td>99900001.0</td>\n",
       "      <td>9.990890e+07</td>\n",
       "      <td>99917801.5</td>\n",
       "      <td>99926701.75</td>\n",
       "      <td>99935602.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <td>35602.0</td>\n",
       "      <td>5.079557e-01</td>\n",
       "      <td>0.499733</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustomerType</th>\n",
       "      <td>35602.0</td>\n",
       "      <td>8.178094e-01</td>\n",
       "      <td>0.367210</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.178094e-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>35602.0</td>\n",
       "      <td>3.944700e+01</td>\n",
       "      <td>15.135215</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.700000e+01</td>\n",
       "      <td>40.0</td>\n",
       "      <td>51.00</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TypeTravel</th>\n",
       "      <td>35602.0</td>\n",
       "      <td>6.939417e-01</td>\n",
       "      <td>0.437976</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Travel_Class</th>\n",
       "      <td>35602.0</td>\n",
       "      <td>4.811247e-01</td>\n",
       "      <td>0.499651</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Travel_Distance</th>\n",
       "      <td>35602.0</td>\n",
       "      <td>1.987152e+03</td>\n",
       "      <td>1024.308863</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1.360000e+03</td>\n",
       "      <td>1929.0</td>\n",
       "      <td>2559.00</td>\n",
       "      <td>6868.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DepartureDelay_in_Mins</th>\n",
       "      <td>35602.0</td>\n",
       "      <td>1.488070e+01</td>\n",
       "      <td>37.880016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.00</td>\n",
       "      <td>978.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ArrivalDelay_in_Mins</th>\n",
       "      <td>35602.0</td>\n",
       "      <td>1.530880e+01</td>\n",
       "      <td>38.464673</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.00</td>\n",
       "      <td>970.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seat_comfort</th>\n",
       "      <td>35602.0</td>\n",
       "      <td>2.831816e+00</td>\n",
       "      <td>1.394206</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seat_Class</th>\n",
       "      <td>35602.0</td>\n",
       "      <td>4.983428e-01</td>\n",
       "      <td>0.500004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arrival_time_convenient</th>\n",
       "      <td>35602.0</td>\n",
       "      <td>2.987266e+00</td>\n",
       "      <td>1.450781</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Catering</th>\n",
       "      <td>35602.0</td>\n",
       "      <td>2.850302e+00</td>\n",
       "      <td>1.372406</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Platform_location</th>\n",
       "      <td>35602.0</td>\n",
       "      <td>2.989576e+00</td>\n",
       "      <td>1.300988</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Onboardwifi_service</th>\n",
       "      <td>35602.0</td>\n",
       "      <td>3.251588e+00</td>\n",
       "      <td>1.316848</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Onboard_entertainment</th>\n",
       "      <td>35602.0</td>\n",
       "      <td>3.385795e+00</td>\n",
       "      <td>1.344949</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Online_support</th>\n",
       "      <td>35602.0</td>\n",
       "      <td>3.520801e+00</td>\n",
       "      <td>1.301673</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Onlinebooking_Ease</th>\n",
       "      <td>35602.0</td>\n",
       "      <td>3.476534e+00</td>\n",
       "      <td>1.305248</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Onboard_service</th>\n",
       "      <td>35602.0</td>\n",
       "      <td>3.455087e+00</td>\n",
       "      <td>1.222897</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Leg_room</th>\n",
       "      <td>35602.0</td>\n",
       "      <td>3.493662e+00</td>\n",
       "      <td>1.290992</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baggage_handling</th>\n",
       "      <td>35602.0</td>\n",
       "      <td>3.694280e+00</td>\n",
       "      <td>1.155384</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Checkin_service</th>\n",
       "      <td>35602.0</td>\n",
       "      <td>3.338055e+00</td>\n",
       "      <td>1.260465</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cleanliness</th>\n",
       "      <td>35602.0</td>\n",
       "      <td>3.711742e+00</td>\n",
       "      <td>1.150776</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Online_boarding</th>\n",
       "      <td>35602.0</td>\n",
       "      <td>3.354438e+00</td>\n",
       "      <td>1.300411</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           count          mean           std         min  \\\n",
       "ID                       35602.0  9.991780e+07  10277.556478  99900001.0   \n",
       "Gender                   35602.0  5.079557e-01      0.499733         0.0   \n",
       "CustomerType             35602.0  8.178094e-01      0.367210         0.0   \n",
       "Age                      35602.0  3.944700e+01     15.135215         7.0   \n",
       "TypeTravel               35602.0  6.939417e-01      0.437976         0.0   \n",
       "Travel_Class             35602.0  4.811247e-01      0.499651         0.0   \n",
       "Travel_Distance          35602.0  1.987152e+03   1024.308863        50.0   \n",
       "DepartureDelay_in_Mins   35602.0  1.488070e+01     37.880016         0.0   \n",
       "ArrivalDelay_in_Mins     35602.0  1.530880e+01     38.464673         0.0   \n",
       "Seat_comfort             35602.0  2.831816e+00      1.394206         0.0   \n",
       "Seat_Class               35602.0  4.983428e-01      0.500004         0.0   \n",
       "Arrival_time_convenient  35602.0  2.987266e+00      1.450781         0.0   \n",
       "Catering                 35602.0  2.850302e+00      1.372406         0.0   \n",
       "Platform_location        35602.0  2.989576e+00      1.300988         1.0   \n",
       "Onboardwifi_service      35602.0  3.251588e+00      1.316848         0.0   \n",
       "Onboard_entertainment    35602.0  3.385795e+00      1.344949         0.0   \n",
       "Online_support           35602.0  3.520801e+00      1.301673         1.0   \n",
       "Onlinebooking_Ease       35602.0  3.476534e+00      1.305248         0.0   \n",
       "Onboard_service          35602.0  3.455087e+00      1.222897         1.0   \n",
       "Leg_room                 35602.0  3.493662e+00      1.290992         0.0   \n",
       "Baggage_handling         35602.0  3.694280e+00      1.155384         1.0   \n",
       "Checkin_service          35602.0  3.338055e+00      1.260465         1.0   \n",
       "Cleanliness              35602.0  3.711742e+00      1.150776         1.0   \n",
       "Online_boarding          35602.0  3.354438e+00      1.300411         0.0   \n",
       "\n",
       "                                  25%         50%          75%         max  \n",
       "ID                       9.990890e+07  99917801.5  99926701.75  99935602.0  \n",
       "Gender                   0.000000e+00         1.0         1.00         1.0  \n",
       "CustomerType             8.178094e-01         1.0         1.00         1.0  \n",
       "Age                      2.700000e+01        40.0        51.00        85.0  \n",
       "TypeTravel               0.000000e+00         1.0         1.00         1.0  \n",
       "Travel_Class             0.000000e+00         0.0         1.00         1.0  \n",
       "Travel_Distance          1.360000e+03      1929.0      2559.00      6868.0  \n",
       "DepartureDelay_in_Mins   0.000000e+00         0.0        13.00       978.0  \n",
       "ArrivalDelay_in_Mins     0.000000e+00         0.0        13.00       970.0  \n",
       "Seat_comfort             2.000000e+00         3.0         4.00         5.0  \n",
       "Seat_Class               0.000000e+00         0.0         1.00         1.0  \n",
       "Arrival_time_convenient  2.000000e+00         3.0         4.00         5.0  \n",
       "Catering                 2.000000e+00         3.0         4.00         5.0  \n",
       "Platform_location        2.000000e+00         3.0         4.00         5.0  \n",
       "Onboardwifi_service      2.000000e+00         3.0         4.00         5.0  \n",
       "Onboard_entertainment    2.000000e+00         4.0         4.00         5.0  \n",
       "Online_support           3.000000e+00         4.0         5.00         5.0  \n",
       "Onlinebooking_Ease       2.000000e+00         4.0         5.00         5.0  \n",
       "Onboard_service          3.000000e+00         4.0         4.00         5.0  \n",
       "Leg_room                 2.000000e+00         4.0         5.00         5.0  \n",
       "Baggage_handling         3.000000e+00         4.0         5.00         5.0  \n",
       "Checkin_service          3.000000e+00         3.0         4.00         5.0  \n",
       "Cleanliness              3.000000e+00         4.0         5.00         5.0  \n",
       "Online_boarding          2.000000e+00         4.0         4.00         5.0  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testd.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb3b6d44",
   "metadata": {},
   "outputs": [],
   "source": [
    "traind.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f9ad8bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "testd.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c122779",
   "metadata": {},
   "source": [
    "# train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "88880e3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(66065, 24)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(28314, 24)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(66065,)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(28314,)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(35602, 24)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Assign the features and target variables\n",
    "X = traind.drop(['Overall_Experience'], axis=1)\n",
    "y = traind['Overall_Experience']\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "# Assign the features and target variables\n",
    "X_testdata = testd\n",
    "\n",
    "\n",
    "display(X_train.shape)\n",
    "display(X_test.shape)\n",
    "display(y_train.shape)\n",
    "display(y_test.shape)\n",
    "display(X_testdata.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "399ad0a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Model Building\n",
    "dTree = DecisionTreeClassifier(random_state =9, max_depth=15)\n",
    "dTree1 = DecisionTreeClassifier(random_state =9, max_depth=15, min_samples_split= 75, min_samples_leaf = 15)\n",
    "rf = RandomForestClassifier(n_estimators=220, max_depth= 20, max_features= 15, random_state = 100)\n",
    "rf1 = RandomForestClassifier(n_estimators=100, random_state = 100)\n",
    "bgg = BaggingClassifier(random_state=5)\n",
    "bgg1 = BaggingClassifier(base_estimator=dTree, n_estimators=50, random_state=500, max_features= 10)\n",
    "grad = GradientBoostingClassifier(random_state= 3000, learning_rate=0.1, n_estimators=100)\n",
    "ada1 = AdaBoostClassifier(base_estimator=rf, n_estimators=22, learning_rate=0.1, random_state= 88)\n",
    "ada2 = AdaBoostClassifier(base_estimator=dTree, n_estimators=20, learning_rate=0.1, random_state= 88)\n",
    "gb = GaussianNB()\n",
    "lr = LogisticRegression(random_state= 100, penalty='l2', tol=0.0001)\n",
    "lr1 = LogisticRegression(random_state= 70, penalty='l1', solver = 'saga', tol=0.0001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "583b8290",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9715583137818815"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.9425111632483161"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.997275410580489"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.9964580337546356"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.9813819723000076"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.9218799666994627"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.9983955195640657"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.7889351396352078"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.54320744721108"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.54320744721108"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = [dTree, dTree1, rf, rf1, bgg, bgg1, grad, ada1, ada2, gb, lr, lr1]\n",
    "\n",
    "for i in model: #accuracy on train set from training data\n",
    "    i.fit(X_train , y_train)\n",
    "    y_predict = i.predict(X_train)\n",
    "    display(accuracy_score(y_train, y_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "44a126ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9341315250406159"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.9300699300699301"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.9524263615172706"
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     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/plain": [
       "0.9498481316663134"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.9454686727414"
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     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/plain": [
       "0.9334957971321608"
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     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/plain": [
       "0.9212403757858303"
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     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
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       "0.9534152715970898"
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     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/plain": [
       "0.9429257611075793"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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   ],
   "source": [
    "for i in model:  #accuracy on test set from training data\n",
    "    i.fit(X_train , y_train)\n",
    "    y_predict_test = i.predict(X_test)\n",
    "    display(accuracy_score(y_test, y_predict_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "82a5fa4f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "##----- Trial and Error Block for Random Forest --------\n",
    "rf = RandomForestClassifier(n_estimators=230, max_depth= 20, max_features= 15, random_state = 100)\n",
    "rf.fit(X_train , y_train)\n",
    "y_predict = rf.predict(X_train)\n",
    "display(accuracy_score(y_train, y_predict))\n",
    "\n",
    "y_predict_test = rf.predict(X_test)\n",
    "display(accuracy_score(y_test, y_predict_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6c80fb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Trial block for ---SVM (gamma, c)------\n",
    "clt = SVC(gamma = 0.25, C = 2)\n",
    "clt.fit(X_train , y_train)\n",
    "y_predict = clt.predict(X_train)\n",
    "display(accuracy_score(y_train, y_predict))\n",
    "\n",
    "y_predict_test = clt.predict(X_test)\n",
    "display(accuracy_score(y_test, y_predict_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be2ea90f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Param tuning on Random Forest with cv = 5\n",
    "param_grid = {\n",
    "    'n_estimators': [200, 300],\n",
    "    'max_depth': [17, 20, 25],\n",
    "    'max_features' : [15, 17, 20] }\n",
    "\n",
    "\n",
    "rf = RandomForestClassifier(random_state = 100)\n",
    "grid_search = GridSearchCV(estimator = rf, param_grid = param_grid, cv = 5)\n",
    "\n",
    "\n",
    "grid_search.fit(X_train,y_train)\n",
    "y_predict = grid_search.predict(X_train)\n",
    "display(accuracy_score(y_train, y_predict))\n",
    "\n",
    "y_predict_test = grid_search.predict(X_test)\n",
    "display(accuracy_score(y_test, y_predict_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5bdd25b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Votting with the best models\n",
    "voting = VotingClassifier(estimators = [('RF', rf), ('AdaBoost', ada1)] , voting = 'hard')\n",
    "voting.fit(X_train, y_train)\n",
    "\n",
    "display(accuracy_score(y_train, voting.predict(X_train)))\n",
    "display(accuracy_score(y_test, voting.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63857ba1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#-------- 1st BEST model File ---------\n",
    "df_test1 = testd.copy()\n",
    "pred = ada1.predict(df_test)\n",
    "df_test1['Overall_Experience'] = pred\n",
    "sample = df_test1[['ID', 'Overall_Experience']]\n",
    "\n",
    "sample.to_csv('hacktt.csv', index=False)\n",
    "solver=pd.read_csv('hacktt.csv')\n",
    "solver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bda7b1a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#--------2nd BEST model File ---------\n",
    "df_test1 = testd.copy()\n",
    "pred = rf.predict(df_test1)\n",
    "df_test1['Overall_Experience'] = pred\n",
    "sample = df_test1[['ID', 'Overall_Experience']]\n",
    "sample.to_csv('hack.csv', index=False)\n",
    "solver=pd.read_csv('hack.csv')\n",
    "solver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f8b3501b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#--------3nd BEST model File ---------\n",
    "df_test1 = testd.copy()\n",
    "pred = dTree.predict(df_test1)\n",
    "df_test1['Overall_Experience'] = pred\n",
    "sample = df_test1[['ID', 'Overall_Experience']]\n",
    "sample.to_csv('solve2.csv', index=False)"
   ]
  }
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