income-level-prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ddc42108",
   "metadata": {},
   "source": [
    "# Udacity Machine Learning Engineer Nanodegree Capstone Project \n",
    "\n",
    "## Income Level Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "97dc7703",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Required Libraries\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV\n",
    "from sklearn.metrics import roc_auc_score, accuracy_score, roc_curve\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "\n",
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "# This module contains helper functions for exploring and preprocessing data\n",
    "import utils\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "268258fd",
   "metadata": {},
   "source": [
    "## 1. Exploratory Data Analysis\n",
    "### Loading and Visualising The Dataset\n",
    "\n",
    "Train and test data sets are in different files. I have loaded these data files separately and then combined train and test data sets into a single pandas DataFrame in order to make things simpler during exploratory data analysis step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fa040107",
   "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>AGE</th>\n",
       "      <th>WORKCLASS</th>\n",
       "      <th>FNLWGT</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>EDUCATION-NUM</th>\n",
       "      <th>MARITAL-STATUS</th>\n",
       "      <th>OCCUPATION</th>\n",
       "      <th>RELATIONSHIP</th>\n",
       "      <th>RACE</th>\n",
       "      <th>SEX</th>\n",
       "      <th>CAPITAL-GAIN</th>\n",
       "      <th>CAPITAL-LOSS</th>\n",
       "      <th>HOURS-PER-WEEK</th>\n",
       "      <th>NATIVE-COUNTRY</th>\n",
       "      <th>INCOME</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AGE         WORKCLASS  FNLWGT  EDUCATION  EDUCATION-NUM  \\\n",
       "0   39         State-gov   77516  Bachelors             13   \n",
       "1   50  Self-emp-not-inc   83311  Bachelors             13   \n",
       "2   38           Private  215646    HS-grad              9   \n",
       "3   53           Private  234721       11th              7   \n",
       "4   28           Private  338409  Bachelors             13   \n",
       "\n",
       "       MARITAL-STATUS         OCCUPATION   RELATIONSHIP   RACE     SEX  \\\n",
       "0       Never-married       Adm-clerical  Not-in-family  White    Male   \n",
       "1  Married-civ-spouse    Exec-managerial        Husband  White    Male   \n",
       "2            Divorced  Handlers-cleaners  Not-in-family  White    Male   \n",
       "3  Married-civ-spouse  Handlers-cleaners        Husband  Black    Male   \n",
       "4  Married-civ-spouse     Prof-specialty           Wife  Black  Female   \n",
       "\n",
       "   CAPITAL-GAIN  CAPITAL-LOSS  HOURS-PER-WEEK NATIVE-COUNTRY INCOME  \n",
       "0          2174             0              40  United-States  <=50K  \n",
       "1             0             0              13  United-States  <=50K  \n",
       "2             0             0              40  United-States  <=50K  \n",
       "3             0             0              40  United-States  <=50K  \n",
       "4             0             0              40           Cuba  <=50K  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_names = ['AGE', 'WORKCLASS', 'FNLWGT', 'EDUCATION', 'EDUCATION-NUM', 'MARITAL-STATUS', \n",
    "                'OCCUPATION', 'RELATIONSHIP', 'RACE','SEX', 'CAPITAL-GAIN', 'CAPITAL-LOSS',     \n",
    "                'HOURS-PER-WEEK', 'NATIVE-COUNTRY', 'INCOME']\n",
    "\n",
    "train_data = pd.read_csv(\"data/adult.data\", header=None, names=column_names, skipinitialspace=True)\n",
    "\n",
    "test_data = pd.read_csv(\"data/adult.test\", header=None, skiprows=1, names=column_names, skipinitialspace=True)\n",
    "\n",
    "# checking train data\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ba8c44df",
   "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>AGE</th>\n",
       "      <th>WORKCLASS</th>\n",
       "      <th>FNLWGT</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>EDUCATION-NUM</th>\n",
       "      <th>MARITAL-STATUS</th>\n",
       "      <th>OCCUPATION</th>\n",
       "      <th>RELATIONSHIP</th>\n",
       "      <th>RACE</th>\n",
       "      <th>SEX</th>\n",
       "      <th>CAPITAL-GAIN</th>\n",
       "      <th>CAPITAL-LOSS</th>\n",
       "      <th>HOURS-PER-WEEK</th>\n",
       "      <th>NATIVE-COUNTRY</th>\n",
       "      <th>INCOME</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25</td>\n",
       "      <td>Private</td>\n",
       "      <td>226802</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>89814</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>336951</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Protective-serv</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44</td>\n",
       "      <td>Private</td>\n",
       "      <td>160323</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>7688</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18</td>\n",
       "      <td>?</td>\n",
       "      <td>103497</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>?</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AGE  WORKCLASS  FNLWGT     EDUCATION  EDUCATION-NUM      MARITAL-STATUS  \\\n",
       "0   25    Private  226802          11th              7       Never-married   \n",
       "1   38    Private   89814       HS-grad              9  Married-civ-spouse   \n",
       "2   28  Local-gov  336951    Assoc-acdm             12  Married-civ-spouse   \n",
       "3   44    Private  160323  Some-college             10  Married-civ-spouse   \n",
       "4   18          ?  103497  Some-college             10       Never-married   \n",
       "\n",
       "          OCCUPATION RELATIONSHIP   RACE     SEX  CAPITAL-GAIN  CAPITAL-LOSS  \\\n",
       "0  Machine-op-inspct    Own-child  Black    Male             0             0   \n",
       "1    Farming-fishing      Husband  White    Male             0             0   \n",
       "2    Protective-serv      Husband  White    Male             0             0   \n",
       "3  Machine-op-inspct      Husband  Black    Male          7688             0   \n",
       "4                  ?    Own-child  White  Female             0             0   \n",
       "\n",
       "   HOURS-PER-WEEK NATIVE-COUNTRY  INCOME  \n",
       "0              40  United-States  <=50K.  \n",
       "1              50  United-States  <=50K.  \n",
       "2              40  United-States   >50K.  \n",
       "3              40  United-States   >50K.  \n",
       "4              30  United-States  <=50K.  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking test data\n",
    "test_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c78c9575",
   "metadata": {},
   "source": [
    "`INCOME` is the target/output variable and it is not in a binary format as it can be seen above. Another problem with this variable is that there is an inconsistency between train and test datasets. In train set it is labeled as `<=50K` and `>50K` while it is labeled as `<=50K.` and `>50K.` in test set. To facilitate the EDA process we can fix this problem at this point by making them consistent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "375655b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 48842 entries, 0 to 16280\n",
      "Data columns (total 15 columns):\n",
      " #   Column          Non-Null Count  Dtype \n",
      "---  ------          --------------  ----- \n",
      " 0   AGE             48842 non-null  int64 \n",
      " 1   WORKCLASS       48842 non-null  object\n",
      " 2   FNLWGT          48842 non-null  int64 \n",
      " 3   EDUCATION       48842 non-null  object\n",
      " 4   EDUCATION-NUM   48842 non-null  int64 \n",
      " 5   MARITAL-STATUS  48842 non-null  object\n",
      " 6   OCCUPATION      48842 non-null  object\n",
      " 7   RELATIONSHIP    48842 non-null  object\n",
      " 8   RACE            48842 non-null  object\n",
      " 9   SEX             48842 non-null  object\n",
      " 10  CAPITAL-GAIN    48842 non-null  int64 \n",
      " 11  CAPITAL-LOSS    48842 non-null  int64 \n",
      " 12  HOURS-PER-WEEK  48842 non-null  int64 \n",
      " 13  NATIVE-COUNTRY  48842 non-null  object\n",
      " 14  INCOME          48842 non-null  object\n",
      "dtypes: int64(6), object(9)\n",
      "memory usage: 6.0+ MB\n",
      "None\n",
      "\n",
      "Target variable percentage: 0.2393\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>AGE</th>\n",
       "      <th>WORKCLASS</th>\n",
       "      <th>FNLWGT</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>EDUCATION-NUM</th>\n",
       "      <th>MARITAL-STATUS</th>\n",
       "      <th>OCCUPATION</th>\n",
       "      <th>RELATIONSHIP</th>\n",
       "      <th>RACE</th>\n",
       "      <th>SEX</th>\n",
       "      <th>CAPITAL-GAIN</th>\n",
       "      <th>CAPITAL-LOSS</th>\n",
       "      <th>HOURS-PER-WEEK</th>\n",
       "      <th>NATIVE-COUNTRY</th>\n",
       "      <th>INCOME</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AGE         WORKCLASS  FNLWGT  EDUCATION  EDUCATION-NUM  \\\n",
       "0   39         State-gov   77516  Bachelors             13   \n",
       "1   50  Self-emp-not-inc   83311  Bachelors             13   \n",
       "2   38           Private  215646    HS-grad              9   \n",
       "3   53           Private  234721       11th              7   \n",
       "4   28           Private  338409  Bachelors             13   \n",
       "\n",
       "       MARITAL-STATUS         OCCUPATION   RELATIONSHIP   RACE     SEX  \\\n",
       "0       Never-married       Adm-clerical  Not-in-family  White    Male   \n",
       "1  Married-civ-spouse    Exec-managerial        Husband  White    Male   \n",
       "2            Divorced  Handlers-cleaners  Not-in-family  White    Male   \n",
       "3  Married-civ-spouse  Handlers-cleaners        Husband  Black    Male   \n",
       "4  Married-civ-spouse     Prof-specialty           Wife  Black  Female   \n",
       "\n",
       "   CAPITAL-GAIN  CAPITAL-LOSS  HOURS-PER-WEEK NATIVE-COUNTRY INCOME  \n",
       "0          2174             0              40  United-States  <=50K  \n",
       "1             0             0              13  United-States  <=50K  \n",
       "2             0             0              40  United-States  <=50K  \n",
       "3             0             0              40  United-States  <=50K  \n",
       "4             0             0              40           Cuba  <=50K  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data[\"INCOME\"] = test_data['INCOME'].apply(lambda x: '<=50K' if x=='<=50K.' else '>50K')\n",
    "\n",
    "data_df = pd.concat([train_data, test_data])\n",
    "\n",
    "print(data_df.info())\n",
    "\n",
    "# Checking the percentage of target variable (income)\n",
    "print(f\"\\nTarget variable percentage: {utils.check_class_balance(data_df, 'INCOME'):.4f}\")\n",
    "\n",
    "# Visualising data\n",
    "data_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ee9ebbe",
   "metadata": {},
   "source": [
    "There are 48842 data points with 15 columns including target column.\n",
    "\n",
    "At first glance at the dataset, it seems that there are both numerical (`int64`) and categorical (object-`string`) variables and also it looks like none of the variables have missing values. \n",
    "\n",
    "Also by calculating the the fraction of all data points that have a 'Class' label of 1 - income more than $50K - it is understood that the data is imbalanced. This may result in overfitting towards data labeled as 0, which accounts for some false negatives; cases in which data labeled (1) is incorrectly predicted as labeled (0). Even if a model labels all of the data as 0, it will still have 76% accuracy. We must take this issue to account while developing the model and choosing the performance evaluation metrics.\n",
    "\n",
    "\n",
    "\n",
    "We should move forward with in-depth analysis of the variables to see if these first impressions are valid or not."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27f42e93",
   "metadata": {},
   "source": [
    "### Categorical Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "eef9423c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of categorical variables: 9\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>WORKCLASS</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>MARITAL-STATUS</th>\n",
       "      <th>OCCUPATION</th>\n",
       "      <th>RELATIONSHIP</th>\n",
       "      <th>RACE</th>\n",
       "      <th>SEX</th>\n",
       "      <th>NATIVE-COUNTRY</th>\n",
       "      <th>INCOME</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>State-gov</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Private</td>\n",
       "      <td>11th</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          WORKCLASS  EDUCATION      MARITAL-STATUS         OCCUPATION  \\\n",
       "0         State-gov  Bachelors       Never-married       Adm-clerical   \n",
       "1  Self-emp-not-inc  Bachelors  Married-civ-spouse    Exec-managerial   \n",
       "2           Private    HS-grad            Divorced  Handlers-cleaners   \n",
       "3           Private       11th  Married-civ-spouse  Handlers-cleaners   \n",
       "4           Private  Bachelors  Married-civ-spouse     Prof-specialty   \n",
       "\n",
       "    RELATIONSHIP   RACE     SEX NATIVE-COUNTRY INCOME  \n",
       "0  Not-in-family  White    Male  United-States  <=50K  \n",
       "1        Husband  White    Male  United-States  <=50K  \n",
       "2  Not-in-family  White    Male  United-States  <=50K  \n",
       "3        Husband  Black    Male  United-States  <=50K  \n",
       "4           Wife  Black  Female           Cuba  <=50K  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make a list of categorical variables\n",
    "cat_vars = utils.check_var_types(data_df, \"categorical\")      \n",
    "\n",
    "# visualise the categorical variables\n",
    "data_df[cat_vars].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6cc1f5a",
   "metadata": {},
   "source": [
    "#### Cardinality of the Categorical Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "92016e93",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable: WORKCLASS - Number of unique values: 9\n",
      "Unique values:\n",
      "['State-gov' 'Self-emp-not-inc' 'Private' 'Federal-gov' 'Local-gov' '?'\n",
      " 'Self-emp-inc' 'Without-pay' 'Never-worked']\n",
      "\n",
      "Variable: EDUCATION - Number of unique values: 16\n",
      "Unique values:\n",
      "['Bachelors' 'HS-grad' '11th' 'Masters' '9th' 'Some-college' 'Assoc-acdm'\n",
      " 'Assoc-voc' '7th-8th' 'Doctorate' 'Prof-school' '5th-6th' '10th'\n",
      " '1st-4th' 'Preschool' '12th']\n",
      "\n",
      "Variable: MARITAL-STATUS - Number of unique values: 7\n",
      "Unique values:\n",
      "['Never-married' 'Married-civ-spouse' 'Divorced' 'Married-spouse-absent'\n",
      " 'Separated' 'Married-AF-spouse' 'Widowed']\n",
      "\n",
      "Variable: OCCUPATION - Number of unique values: 15\n",
      "Unique values:\n",
      "['Adm-clerical' 'Exec-managerial' 'Handlers-cleaners' 'Prof-specialty'\n",
      " 'Other-service' 'Sales' 'Craft-repair' 'Transport-moving'\n",
      " 'Farming-fishing' 'Machine-op-inspct' 'Tech-support' '?'\n",
      " 'Protective-serv' 'Armed-Forces' 'Priv-house-serv']\n",
      "\n",
      "Variable: RELATIONSHIP - Number of unique values: 6\n",
      "Unique values:\n",
      "['Not-in-family' 'Husband' 'Wife' 'Own-child' 'Unmarried' 'Other-relative']\n",
      "\n",
      "Variable: RACE - Number of unique values: 5\n",
      "Unique values:\n",
      "['White' 'Black' 'Asian-Pac-Islander' 'Amer-Indian-Eskimo' 'Other']\n",
      "\n",
      "Variable: SEX - Number of unique values: 2\n",
      "Unique values:\n",
      "['Male' 'Female']\n",
      "\n",
      "Variable: NATIVE-COUNTRY - Number of unique values: 42\n",
      "Unique values:\n",
      "['United-States' 'Cuba' 'Jamaica' 'India' '?' 'Mexico' 'South'\n",
      " 'Puerto-Rico' 'Honduras' 'England' 'Canada' 'Germany' 'Iran'\n",
      " 'Philippines' 'Italy' 'Poland' 'Columbia' 'Cambodia' 'Thailand' 'Ecuador'\n",
      " 'Laos' 'Taiwan' 'Haiti' 'Portugal' 'Dominican-Republic' 'El-Salvador'\n",
      " 'France' 'Guatemala' 'China' 'Japan' 'Yugoslavia' 'Peru'\n",
      " 'Outlying-US(Guam-USVI-etc)' 'Scotland' 'Trinadad&Tobago' 'Greece'\n",
      " 'Nicaragua' 'Vietnam' 'Hong' 'Ireland' 'Hungary' 'Holand-Netherlands']\n",
      "\n",
      "Variable: INCOME - Number of unique values: 2\n",
      "Unique values:\n",
      "['<=50K' '>50K']\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Checking the values for each categorical variable\n",
    "utils.check_unique_values(data_df[cat_vars])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19ac9bd8",
   "metadata": {},
   "source": [
    "Compared to the amount of data we have, it can be said that most of the variables show low cardinality, that is, they don't have many different labels. The `NATIVE-COUNTRY` column has a relatively high number of labels and it might be good to analyse this variable deeper.\n",
    "\n",
    "When we look at the unique values, for some columns there are '?' values. This means that actually we have some missing values to handle. \n",
    "\n",
    "We can continue with analysing missing values and frequency of labels for each categorical variable. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1a88212",
   "metadata": {},
   "source": [
    "#### Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8ff0e58a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WORKCLASS         0.057307\n",
       "OCCUPATION        0.057512\n",
       "NATIVE-COUNTRY    0.017546\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a data frame with missing values\n",
    "data_df_with_na = data_df.copy()\n",
    "\n",
    "for var in data_df_with_na.columns.tolist():\n",
    "    data_df_with_na.loc[data_df_with_na[var]=='?', var] = np.nan\n",
    "    train_data.loc[train_data[var]=='?', var] = np.nan\n",
    "    test_data.loc[test_data[var]=='?', var] = np.nan\n",
    "\n",
    "# make a list of the variables that contain missing values\n",
    "vars_with_na = [var for var in data_df_with_na.columns if data_df_with_na[var].isnull().sum() > 0]\n",
    "\n",
    "# determine percentage and number of missing values\n",
    "data_df_with_na[vars_with_na].isnull().mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4436208c",
   "metadata": {},
   "source": [
    "Another dataframe is created which contains null values instead of '?'. From this dataframe we can see that only 3 categorical variables have missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47fff7b3",
   "metadata": {},
   "source": [
    "#### Rare  Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9a2f0088",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WORKCLASS\n",
      "Never-worked    0.000205\n",
      "Without-pay     0.000430\n",
      "Rares / All Labels Count: 2/9\n",
      "\n",
      "EDUCATION\n",
      "1st-4th      0.005057\n",
      "Preschool    0.001699\n",
      "Rares / All Labels Count: 2/16\n",
      "\n",
      "MARITAL-STATUS\n",
      "Married-AF-spouse    0.000758\n",
      "Rares / All Labels Count: 1/7\n",
      "\n",
      "OCCUPATION\n",
      "Armed-Forces       0.000307\n",
      "Priv-house-serv    0.004955\n",
      "Rares / All Labels Count: 2/15\n",
      "\n",
      "RELATIONSHIP\n",
      "No Rare Labels\n",
      "\n",
      "RACE\n",
      "Amer-Indian-Eskimo    0.009623\n",
      "Other                 0.008313\n",
      "Rares / All Labels Count: 2/5\n",
      "\n",
      "SEX\n",
      "No Rare Labels\n",
      "\n",
      "NATIVE-COUNTRY\n",
      "Cambodia                      0.000573\n",
      "Canada                        0.003726\n",
      "China                         0.002498\n",
      "Columbia                      0.001740\n",
      "Cuba                          0.002825\n",
      "Dominican-Republic            0.002109\n",
      "Ecuador                       0.000921\n",
      "El-Salvador                   0.003173\n",
      "England                       0.002600\n",
      "France                        0.000778\n",
      "Germany                       0.004218\n",
      "Greece                        0.001003\n",
      "Guatemala                     0.001802\n",
      "Haiti                         0.001536\n",
      "Holand-Netherlands            0.000020\n",
      "Honduras                      0.000409\n",
      "Hong                          0.000614\n",
      "Hungary                       0.000389\n",
      "India                         0.003092\n",
      "Iran                          0.001208\n",
      "Ireland                       0.000758\n",
      "Italy                         0.002150\n",
      "Jamaica                       0.002170\n",
      "Japan                         0.001884\n",
      "Laos                          0.000471\n",
      "Nicaragua                     0.001003\n",
      "Outlying-US(Guam-USVI-etc)    0.000471\n",
      "Peru                          0.000942\n",
      "Philippines                   0.006040\n",
      "Poland                        0.001781\n",
      "Portugal                      0.001372\n",
      "Puerto-Rico                   0.003767\n",
      "Scotland                      0.000430\n",
      "South                         0.002355\n",
      "Taiwan                        0.001331\n",
      "Thailand                      0.000614\n",
      "Trinadad&Tobago               0.000553\n",
      "Vietnam                       0.001761\n",
      "Yugoslavia                    0.000471\n",
      "Rares / All Labels Count: 39/42\n",
      "\n",
      "INCOME\n",
      "No Rare Labels\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for var in cat_vars:\n",
    "    rares = utils.analyse_rare_labels(data_df, var, 0.01)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dac5f20",
   "metadata": {},
   "source": [
    "Some of the categorical variables show multiple labels that are present in less than 1% of the houses. Labels that are under-represented in the dataset tend to cause over-fitting of machine learning models. Hence these labels will be handled during feature engineering."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54354533",
   "metadata": {},
   "source": [
    "#### Relationship Between Categorical Variables and the Target Variable\n",
    "\n",
    "We can visualize the relationship between categorical variables and the target variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3d059a66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4EAAAFYCAYAAADtBPkCAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAABQt0lEQVR4nO3dd7yU1bX/8c+XoqCIFQ0CBqJY0FhBjSYmamK7Cmo0YolYEss1SiwxavKLxlxN0xi78erFEruxd4MixgZo7A1jiaixF+yC6/fH3gPD4QAHmDnPmWe+79frvM559hTW4znOzHr23mspIjAzMzMzM7Pm0KnoAMzMzMzMzKz9OAk0MzMzMzNrIk4CzczMzMzMmoiTQDMzMzMzsybiJNDMzMzMzKyJOAk0MzMzMzNrIl3q+eSSXgQmA1OBKRExWNISwGVAf+BF4AcR8W6+/5HA3vn+B0XErXl8HeA8oDtwEzAy5tDbYqmllor+/fvX/JzMzMzMzMwawYMPPvhWRPRqOV7XJDDbOCLeqjo+AhgdEb+TdEQ+/rmkQcBwYFVgWeDvklaMiKnAmcA+wP2kJHAL4ObZ/aP9+/dnwoQJtT8bMzMzMzOzBiDppdbGi1gOOgw4P/98PrBt1filEfFZRLwAPAesK6k30DMi7suzfxdUPcbMzMzMzMzmQr2TwABuk/SgpH3y2DIR8RpA/r50Hu8DvFz12El5rE/+ueX4TCTtI2mCpAlvvvlmDU/DzMzMzMysHOq9HHTDiHhV0tLA7ZKens191cpYzGZ85sGIs4GzAQYPHjzbPYNmZmZmZmbNqK5JYES8mr+/IelqYF3gdUm9I+K1vNTzjXz3SUC/qof3BV7N431bGTczMzMzsybxxRdfMGnSJD799NOiQ+lwunXrRt++fenatWub7l+3JFDSwkCniJicf94MOBa4DhgB/C5/vzY/5DrgYkl/IhWGGQiMi4ipkiZLWh94ANgdOLVecZuZmZmZWcczadIkFllkEfr374/U2mLB5hQRvP3220yaNIkBAwa06TH1nAlcBrg6/4K6ABdHxC2SxgOXS9ob+DewI0BEPCHpcuBJYApwQK4MCrA/01tE3MwcKoOamZmZmVm5fPrpp04AWyGJJZdckrmpiVK3JDAingfWaGX8bWDTWTzmOOC4VsYnAKvVOkYzMzMzM2scTgBbN7f/XYpoEWFmZmZmZmYFaY9m8R3GmePH1u259x+yUd2e28zMzMzM5l+PHj348MMPefHFFxkwYACnnHIKBx54IAA/+clPGDx4MHvssQcAJ5xwAueccw5dunShc+fOHHrooey+++58/vnnHH744Vx//fV06tSJQYMGcfrpp9O3b6plKYnddtuNCy+8EIApU6bQu3dv1ltvPW644QbOO+88fvazn9Gnz/SudxdffDGDBg1qt/8Ongk0MzMzM7Oms/TSS3PyySfz+eefz3TbWWedxe233864ceN4/PHHGTt2LBGpA91RRx3F5MmTefbZZ5k4cSLbbrst22+//bTbF154YR5//HE++eQTAG6//fYZEj6AnXbaiYcffnjaV3smgOAk0MzMzMzMmlCvXr3YdNNNOf/882e67fjjj+eMM86gZ8+eACy66KKMGDGCjz/+mFGjRnHSSSfRuXNnAPbcc08WXHBB7rjjjmmP33LLLbnxxhsBuOSSS9h5553b4YzazkmgmZmZmZk1pSOOOIITTzyRqVOnThubPHkykydPZvnll5/p/s899xzLLbfctOSwYvDgwTzxxBPTjocPH86ll17Kp59+yqOPPsp66603w/0vu+wy1lxzzWlflVnD9tJUewLNzMzMzMwqBgwYwLrrrsvFF188bSwiZlltc1a3tRxfffXVefHFF7nkkkvYaqutZrr/TjvtxGmnnVaDM5g3TgLNrO5clKmx1ev359+dmZl1BEcddRQ77LADG22U3pd69uzJwgsvzPPPP8/Xvva1Ge67wgor8NJLLzF58mQWWWSRaeMPPfQQ22yzzQz3HTp0KIcddhhjxozh7bffrv+JzAUvBzUzMzMzs6a18sorM2jQIG644YZpY0ceeSQHHHAAH3zwAQAffPABZ599NgsvvDAjRozgkEMOmbaE9IILLuDjjz9mk002meF599prL371q1/x9a9/vf1Opo2cBJqZmZmZWVP7xS9+waRJk6Yd77///my88cYMGTKE1VZbjW9/+9sstNBCAPz2t7+lW7durLjiigwcOJArrriCq6++eqZlon379mXkyJGt/nst9wTee++99Tu5VqhSyrRsBg8eHBMmTJhhzEvSzIrh//cam5eDmplZR/DUU0+xyiqrFB1Gh9Xafx9JD0bE4Jb39UygmZmZmZlZE3ESaGZmZmZm1kScBJqZmZmZmTURJ4FmZmZmZmZNxEmgmZmZmZlZE3ESaGZmZmZm1kS6FB2AmZmZmZnZ3Kp1C6P2aF00ZswYhg0bxoABAwDYfvvt+dWvfgXALbfcwsiRI5k6dSo/+tGPOOKIIwDYY4892Hrrrdlhhx1455132HTTTTnooIPYc8895zkOJ4FmZmZmZmbz6PPPP+eLL75g4YUXbtP9v/Wtb3HDDTfMMDZ16lQOOOAAbr/9dvr27cuQIUMYOnQogwYNmnaf999/n80335x99tlnvhJA8HJQMzMzMzOzufbUU09x6KGHstJKK/Hss8/O13ONGzeOFVZYga997WsssMACDB8+nGuvvXba7R9++CFbbrklu+yyC/vvv//8hu6ZQDMzMzMzs7b46KOPuPzyyzn33HOJCPbcc08effRRFllkEQAOPvhg7rzzzpkeN3z48GnLO++77z7WWGMNll12WU444QRWXXVVXnnlFfr16zft/n379uWBBx6YdnzIIYfwox/9iIMPPrgm5+Ek0MzMzMzMrA169+7N6quvzjnnnMPKK6880+0nnXTSbB+/9tpr89JLL9GjRw9uuukmtt12WyZOnEhEzHRfSdN+3mSTTbj22ms57LDDWHrppef7PLwc1MzMzMzMrA2uvPJK+vTpw3bbbcexxx7LSy+9NMPtBx98MGuuueZMX7/73e8A6NmzJz169ABgq6224osvvuCtt96ib9++vPzyy9OeZ9KkSSy77LLTjocPH87+++/PVlttxeTJk+f7PDwTaGZmZmZm1gabbbYZm222GW+//TZ//etfGTZsGEsttRTnnHMO/fv3n+NM4H/+8x+WWWYZJDFu3Di+/PJLllxySRZbbDEmTpzICy+8QJ8+fbj00ku5+OKLZ3jsT3/6U1577TW22247brrpJhZYYIF5Pg8ngWZmZmZm1nDao6XDrCy55JKMHDmSkSNHMm7cODp37tymx1155ZWceeaZdOnShe7du3PppZciiS5dunDaaaex+eabM3XqVPbaay9WXXXVmR7/+9//nj333JMf/vCHXHLJJXTqNG8LO50EmpmZmZmZzaN11123zff9yU9+wk9+8pNWb9tqq63YaqutZho/77zzZjgeNWrUXMXXGu8JNDMzMzMzayJOAs3MzMzMzJqIk0AzMzMzM7Mm4iTQzMzMzMysiTgJNDMzMzMzayJOAs3MzMzMzJqIW0SYmZmZmVnD+eST0TV9vu7dN53v59hjjz246667WHTRRYHU3mHNNdckIhg5ciQ33XQTCy20EOeddx5rr702AD169ODDDz8E4KabbmLkyJGMHj2a5ZZbbr7jmRUngWZmZmZmZm3w7rvvsvjii8/2Pn/84x/ZYYcdZhi7+eabmThxIhMnTuSBBx5g//3354EHHpjhPqNHj+bAAw/ktttuq2sCCF4OamZmZmZm1iaDBw9ml1124Y477iAi2vy4a6+9lt133x1JrL/++rz33nu89tpr026/++67+fGPf8yNN97I8ssvX4/QZ1D3JFBSZ0n/lHRDPl5C0u2SJubvi1fd90hJz0l6RtLmVePrSHos33aKJNU7bjMzMzMzs2rPPvssu+yyC6eddhqDBg3i+OOP59VXX53hPr/4xS9YffXVOfjgg/nss88AeOWVV+jXr9+0+/Tt25dXXnkFgM8++4xhw4ZxzTXXsPLKK7fLebTHTOBI4Kmq4yOA0RExEBidj5E0CBgOrApsAZwhqXN+zJnAPsDA/LVFO8RtZmZmZmY2TefOndl666256qqrGDt2LM8//zzLLbcc48aNA+C3v/0tTz/9NOPHj+edd97h97//PUCrs4aVea2uXbuywQYbcO6557bbedR1T6CkvsB/AccBh+ThYcB38s/nA2OAn+fxSyPiM+AFSc8B60p6EegZEffl57wA2Ba4uZ6xm5m11Znjx9btufcfslHdntvMzMzm3vvvv89ll13GqFGj6Nq1K+eeey6rr746AL179wZgwQUXZM899+SEE04A0szfyy+/PO05Jk2axLLLLgtAp06duPzyy/nud7/L8ccfz1FHHVX3c6j3TOCfgcOBL6vGlomI1wDy96XzeB/g5ar7TcpjffLPLcdnImkfSRMkTXjzzTdrcgJmZmZmZmYAu+22G2uvvTbPP/88F1xwAWPHjmXEiBF069YNYNo+v4jgmmuuYbXVVgNg6NChXHDBBUQE999/P4suuui0hBFgoYUW4oYbbuCiiy5qlxnBus0EStoaeCMiHpT0nbY8pJWxmM34zIMRZwNnAwwePLjtOzXNzMzMzKyh1KKlw9z6wQ9+wHnnnUeXLq2nUbvuuitvvvkmEcGaa67JWWedBcBWW23FTTfdxAorrMBCCy3EqFGjZnrsEksswS233MJGG23EUkstxbBhw+p2HvVcDrohMFTSVkA3oKekvwKvS+odEa9J6g28ke8/CehX9fi+wKt5vG8r42ZmZmZmZu1m6NChs739jjvuaHVcEqeffnqrt1V6BAL069ePF154Yd4DbKO6LQeNiCMjom9E9CcVfLkjInYDrgNG5LuNAK7NP18HDJe0oKQBpAIw4/KS0cmS1s9VQXeveoyZmZmZmZnNhSKaxf8OuFzS3sC/gR0BIuIJSZcDTwJTgAMiYmp+zP7AeUB3UkEYF4UxMzMzMzObB+2SBEbEGFIVUCLibaDVBbwRcRypkmjL8QnAavWL0MzMzMzMOrqIwC3DZzY3jeuhffoEmpmZmZmZzZdu3brx9ttvz3XCU3YRwdtvvz2tQmlbFLEc1MzMzMzMbK707duXSZMm4VZwM+vWrRt9+/ad8x0zJ4FmZmZmZtbhde3alQEDBhQdRil4OaiZmZmZmVkTcRJoZmZmZmbWRJwEmpmZmZmZNREngWZmZmZmZk3ESaCZmZmZmVkTcRJoZmZmZmbWRJwEmpmZmZmZNREngWZmZmZmZk3ESaCZmZmZmVkTcRJoZmZmZmbWRJwEmpmZmZmZNREngWZmZmZmZk3ESaCZmZmZmVkTcRJoZmZmZmbWRJwEmpmZmZmZNREngWZmZmZmZk3ESaCZmZmZmVkT6VJ0AGaWnDl+bF2ed/8hG9Xlec3MzMysMXkm0MzMzMzMrIk4CTQzMzMzM2siTgLNzMzMzMyaiJNAMzMzMzOzJuIk0MzMzMzMrIk4CTQzMzMzM2siTgLNzMzMzMyaiJNAMzMzMzOzJuJm8SXiZuNmZmZmZjYnngk0MzMzMzNrIk4CzczMzMzMmoiTQDMzMzMzsybiJNDMzMzMzKyJ1C0JlNRN0jhJj0h6QtKv8/gSkm6XNDF/X7zqMUdKek7SM5I2rxpfR9Jj+bZTJKlecZuZmZmZmZVZPWcCPwM2iYg1gDWBLSStDxwBjI6IgcDofIykQcBwYFVgC+AMSZ3zc50J7AMMzF9b1DFuMzMzMzOz0qpbEhjJh/mwa/4KYBhwfh4/H9g2/zwMuDQiPouIF4DngHUl9QZ6RsR9ERHABVWPMTMzMzMzs7lQ1z2BkjpLehh4A7g9Ih4AlomI1wDy96Xz3fsAL1c9fFIe65N/bjne2r+3j6QJkia8+eabNT0XMzMzMzOzMqhrEhgRUyNiTaAvaVZvtdncvbV9fjGb8db+vbMjYnBEDO7Vq9dcx2tmZmZmZlZ27VIdNCLeA8aQ9vK9npd4kr+/ke82CehX9bC+wKt5vG8r42ZmZmZmZjaX6lkdtJekxfLP3YHvAk8D1wEj8t1GANfmn68DhktaUNIAUgGYcXnJ6GRJ6+eqoLtXPcbMzMzMzMzmQpc6Pndv4Pxc4bMTcHlE3CDpPuBySXsD/wZ2BIiIJyRdDjwJTAEOiIip+bn2B84DugM35y9rJ598Mrpuz929+6Z1e24zMzMzM5tZ3ZLAiHgUWKuV8beBVj/5R8RxwHGtjE8AZref0MzMzMzMzNqgXfYEmpmZmZmZWcfgJNDMzMzMzKyJtCkJlDTTprDWxszMzMzMzKxjm+2eQEndgIWApSQtzvSefT2BZescm5nVQL0K+7ioj5mZmVljmlNhmH2Bn5ISvgeZngR+AJxev7DMzMzMzMysHmabBEbEycDJkg6MiFPbKSYzMzMzMzOrkza1iIiIUyVtAPSvfkxEXFCnuMzMzMzMzKwO2pQESroQWB54GKg0cA/ASaCZmZmZmVkDaWuz+MHAoIiIegZjZmZmZmZm9dXWJPBx4CvAa3WMxczMzKzNzhw/tm7Pvf+Qjer23GZmRWtrErgU8KSkccBnlcGIGFqXqMzMzMzMzKwu2poEHlPPIMzMzMzMzKx9tLU66F31DsTMzMzMzMzqr63VQSeTqoECLAB0BT6KiJ71CszMzMzMyqteezq9n9Nszto6E7hI9bGkbYF16xGQmZmZmZmZ1U+neXlQRFwDbFLbUMzMzMzMzKze2rocdPuqw06kvoHuGWhmZmZmZtZg2loddJuqn6cALwLDah6NmZmZmZmZ1VVb9wTuWe9AzMzMzMzMrP7atCdQUl9JV0t6Q9Lrkv4mqW+9gzMzMzMzM7PaamthmFHAdcCyQB/g+jxmZmZmZmZmDaStSWCviBgVEVPy13lArzrGZWZmZmZmZnXQ1iTwLUm7Seqcv3YD3q5nYGZmZmZmZlZ7bU0C9wJ+APwHeA3YAXCxGDMzMzMzswbT1hYRvwFGRMS7AJKWAE4gJYdmZmZmZmbWINo6E7h6JQEEiIh3gLXqE5KZmZmZmZnVS1uTwE6SFq8c5JnAts4impmZmZmZWQfR1kTuROBeSVcCQdofeFzdojIzMzMzM7O6aFMSGBEXSJoAbAII2D4inqxrZGZmZmZmZlZzbV7SmZM+J35mZmZmZmYNrK17As3MzMzMzKwEnASamZmZmZk1ESeBZmZmZmZmTaRuSaCkfpLulPSUpCckjczjS0i6XdLE/L269cSRkp6T9IykzavG15H0WL7tFEmqV9xmZmZmZmZlVs+ZwCnAoRGxCrA+cICkQcARwOiIGAiMzsfk24YDqwJbAGdI6pyf60xgH2Bg/tqijnGbmZmZmZmVVt2SwIh4LSIeyj9PBp4C+gDDgPPz3c4Hts0/DwMujYjPIuIF4DlgXUm9gZ4RcV9EBHBB1WPMzMzMzMxsLrTLnkBJ/YG1gAeAZSLiNUiJIrB0vlsf4OWqh03KY33yzy3HzczMzMzMbC7VPQmU1AP4G/DTiPhgdndtZSxmM97av7WPpAmSJrz55ptzH6yZmZmZmVnJ1TUJlNSVlABeFBFX5eHX8xJP8vc38vgkoF/Vw/sCr+bxvq2MzyQizo6IwRExuFevXrU7ETMzMzMzs5LoUq8nzhU8zwWeiog/Vd10HTAC+F3+fm3V+MWS/gQsSyoAMy4ipkqaLGl90nLS3YFT6xW3dVxnjh9bl+fdf8hGdXleMzMzM7OOqG5JILAh8EPgMUkP57GjSMnf5ZL2Bv4N7AgQEU9Iuhx4klRZ9ICImJoftz9wHtAduDl/mZmZmZmZ2VyqWxIYEf+g9f18AJvO4jHHAce1Mj4BWK120ZmZmZmZmTWnes4EmpmZdXheam5mZs2mXVpEmJmZmZmZWcfgJNDMzMzMzKyJOAk0MzMzMzNrIk4CzczMzMzMmoiTQDMzMzMzsybiJNDMzMzMzKyJOAk0MzMzMzNrIk4CzczMzMzMmoiTQDMzMzMzsybiJNDMzMzMzKyJOAk0MzMzMzNrIk4CzczMzMzMmoiTQDMzMzMzsybiJNDMzMzMzKyJOAk0MzMzMzNrIk4CzczMzMzMmkiXogMwM7NZ++ST0XV53u7dN63L85qZmVnH55lAMzMzMzOzJuIk0MzMzMzMrIk4CTQzMzMzM2si3hNoZmaFqNd+R/CeRzMzs9nxTKCZmZmZmVkTcRJoZmZmZmbWRLwc1MzMrMTOHD+2Ls+7/5CN6vK8ZmZWf54JNDMzMzMzayJOAs3MzMzMzJqIk0AzMzMzM7Mm4iTQzMzMzMysiTgJNDMzMzMzayJOAs3MzMzMzJqIk0AzMzMzM7Mm4iTQzMzMzMysiTgJNDMzMzMzayJ1SwIl/Z+kNyQ9XjW2hKTbJU3M3xevuu1ISc9JekbS5lXj60h6LN92iiTVK2YzMzMzM7Oyq+dM4HnAFi3GjgBGR8RAYHQ+RtIgYDiwan7MGZI658ecCewDDMxfLZ/TzMzMzMzM2qhLvZ44IsZK6t9ieBjwnfzz+cAY4Od5/NKI+Ax4QdJzwLqSXgR6RsR9AJIuALYFbq5X3GZmZmYdwZnjx9btufcfslHdntvMOr723hO4TES8BpC/L53H+wAvV91vUh7rk39uOd4qSftImiBpwptvvlnTwM3MzMzMzMqgoxSGaW2fX8xmvFURcXZEDI6Iwb169apZcGZmZmZmZmXR3kng65J6A+Tvb+TxSUC/qvv1BV7N431bGTczMzMzM7N50N5J4HXAiPzzCODaqvHhkhaUNIBUAGZcXjI6WdL6uSro7lWPMTMzMzMzs7lUt8Iwki4hFYFZStIk4Gjgd8DlkvYG/g3sCBART0i6HHgSmAIcEBFT81PtT6o02p1UEMZFYczMzMzMzOZRPauD7jyLmzadxf2PA45rZXwCsFoNQzMzMzMzM2taHaUwjJmZmZmZmbUDJ4FmZmZmZmZNxEmgmZmZmZlZE3ESaGZmZmZm1kTqVhim2Xzyyei6PG/37q3W0TEzMzMzM5snngk0MzMzMzNrIk4CzczMzMzMmoiTQDMzMzMzsybiPYFmZmZmZjV25vixdXne/YdsVJfntebimUAzMzMzM7Mm4iTQzMzMzMysiTgJNDMzMzMzayJOAs3MzMzMzJqIk0AzMzMzM7Mm4iTQzMzMzMysiTgJNDMzMzMzayLuE2hmZlYHn3wyum7P3b37pnV7bjMzKz/PBJqZmZmZmTURJ4FmZmZmZmZNxMtBzczMzJpMvZYre6myWWPwTKCZmZmZmVkT8UygmZmZWQueKTOzMvNMoJmZmZmZWRNxEmhmZmZmZtZEvBzUzMzMzKxBuAep1YJnAs3MzMzMzJqIZwLNzMzMrDQ8U2Y2Z04CzczMzMzMsjPHj63bc+8/ZKO6PffccBJoZg3NZdzNzMzM5o73BJqZmZmZmTURzwSamZnZXPMsvJlZ43ISaE3PG8jNzMzMrJl4OaiZmZmZmVkTcRJoZmZmZmbWRBomCZS0haRnJD0n6Yii4zEzMzMzM2tEDZEESuoMnA5sCQwCdpY0qNiozMzMzMzMGk9DJIHAusBzEfF8RHwOXAoMKzgmMzMzMzOzhqOIKDqGOZK0A7BFRPwoH/8QWC8iftLifvsA++TDlYBn2jHMpYC32vHfa09lPjfw+TU6n1/jKvO5gc+v0fn8GleZzw18fo2uvc/vqxHRq+Vgo7SIUCtjM2WvEXE2cHb9w5mZpAkRMbiIf7veynxu4PNrdD6/xlXmcwOfX6Pz+TWuMp8b+PwaXUc5v0ZZDjoJ6Fd13Bd4taBYzMzMzMzMGlajJIHjgYGSBkhaABgOXFdwTGZmZmZmZg2nIZaDRsQUST8BbgU6A/8XEU8UHFZLhSxDbSdlPjfw+TU6n1/jKvO5gc+v0fn8GleZzw18fo2uQ5xfQxSGMTMzMzMzs9polOWgZmZmZmZmVgNOAs3MzMzMzJqIk0AzMzMzM7Mm0hCFYczMrPFJ6gwcFBEnFR1LPUg6pJXh94EHI+Lhdg7HbAaSLoyIH85prNFIWgLYD/gUOCciPig4JLOG4JnAuSTpkNl9FR2ftY2khSV1yj+vKGmopK5Fx1UrkiZIOkDS4kXHUmtKdpP0q3y8nKR1i46rHiR1ktSz6DhqJSKmAsOKjqOOBpM+jPbJX/sA3wH+V9LhBcZlbSRpZFvGGtSq1Qf5osw6BcVSS38DepB6SN8n6WsFx1MXkk6WtEHRcdSbpMUlrV50HLUm6RFJR0lavuhYKpwEzr1F5vDV0CRNlvRBK1+TJZXp6tpYoJukPsBoYE/gvEIjqq3hwLLAeEmXStpckooOqkbOAL4B7JyPJwOnFxdObUm6WFJPSQsDTwLPSPpZ0XHV0D2STpP0LUlrV76KDqpGlgTWjohDI+JQUlLYC9gI2KPIwGpF0vWSrmvxdaGkkZK6FR1fDYxoZWyP9g6iliQdKWkysHr1ezrwBnBtweHVwpIRcVREHAIcAtwl6TFJm0m6vOjgaugh4JeSnpP0R0mDiw6oViSNye97SwCPAKMk/anouGpsKDAFuFzSeEmHSVquyIDcIsKakqSHImJtSQcC3SPiD5L+GRFrFR1bLeXZzq2BM4Evgf8DTo6IdwoNbD5U/e6m/b4kPRIRaxQdWy1Iejgi1pS0K+kq/c9JywlLcWVU0p2tDEdEbNLuwdSYpKeANSLi83y8IPBwRKxSltcXSSeTEttL8tBOwH+A7kDPRl1aKGlnYBfgm8DdVTctAkyNiO8WEliN5PeCcyJir6JjqTVJ9wC7RsSL+Viki6DvAotGxGsFhldzOVH6Puli73IRMbDgkOZb5fVR0o+AfhFxtKRHy/K+15KkgcD/I/3ddi4qDu8JnEf5iufepOUV065+lu0FVtLSzHh+/y4wnFqSpG8Au5J+j1Cy/x/ycoo9ga1Iy2UuIn3AuQNYs7jI5tsXeRlTAEjqRUpwy6JrXpq8LXBaRHwhqTRX6yJi46JjqKOLgfslVWZXtgEuqZrVLYO1ImKjquPrJY2NiI0kPVFYVPPvXuA1YCngxKrxycCjhURUQxHxpaRSXChrxV7AApWDSLMbr+TDjwuJqL5WAFYG+lOe15UuknoDPwB+UXQw9SKpP+kcdwKmAoVuEyjVh952diHwNLA5cCwpmXiq0IhqSNJQ0hvhsqQlI18lnd+qs3tcAxkJHAlcHRFP5D0Erc1QNCRJDwLvAecCR0TEZ/mmByRtWFhgtXEKcDWwtKTjgB2AXxYbUk39BXiRtCRmrKSvAg2/FFvSbhHxV81i73RENPzSn4j4jaSbgQ0BAftFxIR8867FRVZTvSQtV7kgmJczLZVv+7y4sOZPRLwEvJRn4F+NiE8BJHUn7TV7scDwauV+SUMiYnzRgdRSRDxTdAztQdLvge2BfwGXAb+JiPcKDap2jgVuBe6JiPH5M9nEgmOqKUkPAF2By4EdI+L5gkPyctB5VTV1/WhErJ6v3N9ahiVNkJbXAZsAf8/nuTGwc0TsU3Bo8y3PIv0uIsq0z2oGkr7W8gVG0oCIeKGomGpJ0srApqQP2qMjojQXYFojqUtETCk6jvkhad+I+Iuko1u7PSJ+3d4x1UN+fVmGqousJVpBgaStgLNIH0QFDAD+GxgD/Dgi/lxYcDUgaQKwQdWS3gVIH0yHFBvZ/JP0JLAi8BLwEen3F2VdclcmeYnrL4EzI+KtouOxuZOXYx8eEb8rOpZqTgLnkaRxEbGupLGkN8D/AOMiohRVqSRNiIjBORlcKy8lGRcRpajCKOmOsiTsransm2sx9mBENHwlOEnrA09ExOR8vAgwKCIeKDay2pC0DHA8sGxEbClpEPCNiDi34NBsDvIe46OB10lLfUr5ITvvdVyZdH5PV2bNyqCyJ7fFWCn2HOdVBTPJs6DWwZXlPbw1klYk1S5YJiJWy9tZhkbE/xQcWs1Uls0XHUc1Lwedd2crld//JXAdqTzxr4oNqabek9SDVEXzIklvkKoalcU/JV0HXEG6IgpARFxVXEjzL8+QrQosKmn7qpt6UrW3s8GdCVQnuB+1MtbIzgNGMX1fxLOkpT+lSAJLvp96JLBSRLxddCB1tg5pP1IXUsVJIuKCYkOqmTclDY2I6wAkDQNKMfMSES/lfYHfykN3R8QjRcZkc6WUy3mz/wV+RtoOQUQ8KulioDRJIHC7pMNI7+fVnzsLK9TnJHAeRcQ5+cexQClm/1oYRmq8ejBpL8uipDXbZbEE8DZpyWtFAA2dBAIrkaqBLkYqSlExGfhxEQHVgaJqCUOepS7Ta9lSEXG5pCMBImKKpKlFB1VDZd5P/TKpOXxpSboQWB54mDTbCem1syxJ4H6kC5+VtjMvA7sXGE/NKPU7/DHT3+f+KunsiDi1wLCs7TYG9pP0IuVbzrtQRIzTjJ2syjTxAKmAEcABVWNBgTlEmT44tStJxwN/qGzKzbOCh0ZEKQpURMRHVYfnFxZInUTEnkXHUA8RcS1wraRvRMR9RcdTJ89LOog0+wdpOXbhG6xr6CNJSzK9+un6lCuxWCEidpQ0LCLOz1d7by06qBp5Hhgj6UagUoypFEVvqgwmLb8u5V6SiPgXsH5eCaPKsvOS2BtYr/L+nguN3Ac4CWwMWxYdQB29pdREvfK+twOpWm9pRMSAomNoyUngvNsyIo6qHETEu3nDfEMngZL+ERHfVGokW/0mX7ni1LOg0GqqrOvPJZ3K9BfRnVveHhEHtXtQtbcfqULoL0nnOhpo+IJFVQ4hLTFfXqn/VS9SBdSy+CJ/f0/SaqT91P2LC6em/p2/FqCqZH3JPA58hZJ9QKvIF2COJrXTCUn/AI4tyRJfMX32FqbvW7UGkJfzfhMYGBGjcnukHkXHVSMHAGcDK0t6BXgB2K3YkGpL0kKk9/flImIfpV6BK0XEDYXFVNKLeXUn6VFgSKX0fi4jPSEiytJCodQk3UVefx7TG44/HhGrFRvZ/JE0Yna3R0TpZnXLKC9vXYn0Ae2ZiPhiDg9pGErNgP8GrE7a+9gD+H8R8ZdCA7M2kXQnqc/oOGac7RxaVEy1JOl20jaPv+ahXYHvRIM3iwfI7VlGkFrsiLTt47xGr+jaLHJl5cGkxGFFScsCV0REo7d9mkapp2qnks3AAyDpMuBBYPc8+dAduK9lIap2jclJ4LyRdDgwlPQhJkhrfa+LiD8UGlgN5FK2jzZ6QjQ7ksZHxJBKq488NlNVOOs4JB0eEX+onu2s1uiznC0K+cyk0YsWlZmkP0fETyVdT+t/m6VIkAAkfbu18Yi4q71jqYfWKjBWqmUXFVMtSVqbNMsJqTDMP4uMx9pO0sPAWsBDVZ9bHi3DnsCyb7GCGaruV3/uLLTysJeDzqP8YfQxpvcq+01ElGJfSy608YiqGgKXUKnXn+dlIj8HBjFjBcZGbotRKR4yYbb3alzbzOa2MhQtAqYttzuG1FA9gLtJr5+NvNzuwvz9hEKjaAdlSfZm405Jw0kNnSEtxb6xwHjqQcCXeCloo/k8IkJS5XPLwkUHVEOl3GLVwud59q/y+1ueqtUURfBMoLVK0h3AENKSn+pStqW4oi3pa6T15xsA75LXn0fEi0XGVSuSbiOVIT6MtIduBPBmRPy80MBqQNJavnrduEq+3G474KbKNoEyaaL94pOBhZm+X64T098DG/o8Jf0K2JG0HFvAtqTlhA29F75Z5PYCA4HvAb8lrUC7uAzVXZthi5Wk75GS2kHAbaQLoXtExJjCYnISOHea6I2w1Et+Ksq6/ryypKl6qYikuyKi1d9rI8l7knqTejxeGhFPFBxSzUn6L2buo1eKFi1lXm4naRSp7cxY4FLg1ogoW5lza1CSngLWiohP83F30tLCVYqNzNoqJxKbkT5z3hoRtxccUk2UeYtVtbwSZn3S7+/+iCi0B6mXg86liPhm/r5I0bHUU0TcJemrpCpUf89VjToXHVetSFoQ+D654XGlN01ZPmgzvQLjazmheBXoW2A8NRMRG0v6CvAD4GxJPYHLynI1W9JZwEKknlDnkJajjSs0qNoq7XK7iNhTUldSKfddgDMk3R4RPyo4tJqS1BlYhqrPEGXZOiBpQ+DhiPhI0m7A2sCfS3J+L5IuLH2ajxcE/lVYNDZX8kXrOyLidkkrAStJ6trohcOUPoBdAjwKfJeSbbGqqHptuTG/thwl6eSIeKmwmDwTOPeapHDKj0ll95eIiOVzKduzImLTgkOrCUm3kHqvPUhVyeyIOLGwoGpI0takvVb9SD2gegK/jojrCg2sxiR9HTgc2CkiSlGSvzJ7W/W9B3BVRGxWdGy1ULXc7kvSFd/OlGS5XUVOBLcA9gS+FRG9Cg6pZiQdSGqh8DrpdwjlaVhdWZa2Bql67YXAucD2JVlFcQ1pm8ftpP/3vgf8A3gDGr+4VtlJehD4FrA4cD9pf/zHEbFroYHVQGsrRMqmxWvLBcD/UfBri2cC50GTFE45AFgXeAAgIiZKWrrYkGqqb0RsUXQQ9RLT+868T5pRKg1JqwA7kWaQ3iYtuzu00KBq65P8/eNcAvwdoMM1mZ1XZV5FIWkLYDjp/7kxpJncHxQZUx2MJJWob+RCPrMzJRffGAacHBHnzqn1TgO5On9VjCkoDps3ioiPJe0NnJoLFJZlf/z9koZExPiiA6mj6teWUzrCa4uTwHnXG3hCUikLpwCfRcTnlWWSSn3LyjRtfK+kr0fEY0UHUktlb6OQjSItHdksIl4tOpg6uEHSYsAfSDPVkJKJUshLf3YFBkTEbyT1A3pHRBmWvO5BuiixbxmLw2Qvky4uldVkSUeSGlVvlJe+di04ppqIiPMlLQCsTHp/eCYiPi84LGs7SfoG6fVz7zxWls/xGwP7SXqR9Jm6UmejFCsMsg732lKWP54i/LroAOrsLklHAd3zRuT/Bq4vOKZa+iawh6QXSCV6y/KCU+o2CvlF818RcXLRsdSapCHAyxHxm3zcA3gMeBo4qcjYauwM0jLCTYDfAB8Cp5OWqTW0iBguaRnge/kC2riIeKPgsGpCqdE4wPPAGEk3MmOz+D8VEljt7UTaz7l3RPxH0nLAHwuOqSZyyf2/kPYBChggad+IuLnYyKyNRgJHAldHxBO5yvmdBcdUK1sWHUA76HCvLd4TOB9aK5xSliqTed/j3lRVoQLOiZL8weTf3UyK3KBrbZP3cw4t2xVsSQ8B342IdyRtRJpROhBYE1glInYoMr5akfRQRKytDtQwt1Yk7UjqFTiG9Lr5LeBnEXFlkXHVgqSjZ3d7RJT9wmjDk/Q0sHVEPJePlwdujIiVi43M5pakr0TEf4qOo5YkrUF6zQS4OyIeKTKeepK0ddW2ncJ4JnAeVRdOAZYH+gBnkZrHN7yI+BL43/xVOpVkL+9z7DaHuzccpV5sO0bEe/l4cVI7hc0LDaw2XgLukXQdMy7FbvSZiM4R8U7+eSfg7Ij4G/A3SQ8XF1bNfZFndCsNc3sxvcBIo/slqdfVGzDt3P4ONHwS2FqSly8W9oiIDwoIqS4kbQ/8HlialMiXqf3TG5UEMHueXBTGGs5NpMq1pSBpJPBj4Ko89FdJZ0cJeiDOwrGAk8AGVurCKZIeY+Y9Ze+Tlhn+T6MXBZA0FDgRWJb0JvhV0lLKsjQm7VVJAAEi4t0S/X2+mr86AWUqMtJZUpfcV25T0kWmijK9Vp9CKk6xtKTjSAV+fllsSDXTqcXyz7dJf6elIeliYD9SVeUHgUUl/SkiSrFkkrQXd5uIeGqO92w8T0i6idSeJUiN48fnxJeIuGp2D7YORUUHUGN7A+tFxEcAkn4P3Eeqbl5GHeL3V6YPFu2t7IVTbia9yV+cj4fn7x8A5wHbFBBTLf2G1LDz7xGxlqSNgZ0LjqmWplZXr83LX0vx91mZkZC0cOUNoyQuIe3FfYtUIfRuAEkrUJJCHHnm6AVSW49NSW+E25boA/ctkm4l/S4hzeiWbb/VoIj4QNKupNmIn5OSwbIkga+X6O+xpW6k1h6VkvRvklYzbUN6f3AS2DjKtkpLVLXryj93iESpTvYtOgBwEjg/yl44ZcOI2LDq+DFJ90TEhrnJZaP7IiLeltRJUqeIuDNfeSqLXwD/kHRXPt6IGWeWGlaujnYu0ANYLu8j2Dci/rvYyOZPRBwnaTSp8vBtVftvO5H2Bja83F7nxIj4BqngTalExM8kfR/YkPQB5uyIuHoOD2s0XXMfxG2B0yLiC0mluMCUTZB0GXANMxa+afgEKSL2LDoGmz+SvkmqRXFGXm7eIyJeKDquGhgFPCCp8nq5Lel9vjRy7ZBDgeUi4sdK/bdXKnJvoJPAeXcEafr6MVJGfxMlKuMO9JC0XkQ8ACBpXdKHboApxYVVM+/l6ot3AxdJeoNynBcAEXGLpLVJs50CDo6ItwoOq1b+DGwOXAcQEY/kQioNLyLub2Xs2SJiqaPbcqJ0VVkKTVWLiL/lPbldACQtUbXXswz+ArwIPAKMzasMSrMnEOgJfEwqilZRilkySaNovXXQXgWEY3MpF2caDKxESpq6An8lXXRqaBHxJ0ljSJXbBewZEWXpgVgxirRq4hv5eBJwBQXuDXR1UGtVLlf/f6TET6Q3+R8BTwD/FRGXFxjefJO0MGnJXSdSz51FgYsafa9jRVUvtq9FxLG5FPFXytCLTdIDEbFeGatLNgNJk4GFSRddPqVEhTck7Uva8P8JqdhN5dy+VmhgdVa1l9U6sHzxpaIbsB3wakn6x5ZeLhC2FvBQ1Xvfo43c2krSeRGxR/55REScX3BIdSNpQkQM7kifXTwTOJdmUTBlmkb+n7FaRIwHvi5pUdLFgveqbm7oBBAgIj6qavFxfqXFR9Fx1VB1L7ZjgcnA3yhBLzbgZUkbAKHU+PggpvdHtA4uIspUzKelw4BVSzTrPo2k3SLir1X9Altq6Oq8kg6PiD9IOpXWZ8saPlHK1YankXQJqXqtNYbPIyIqy6/zxexGV50AjQRKmwQCn0vqzvTK2MtTteS8CE4C597WRQfQXiT9F6laZrdKAZyIOLbQoGqk7C0+SFW21pb0T5hWHXSBooOqkf2Ak0m/s0nAbaRqvdZgJB0TEccUHUcN/Yu0lLCMFsrfy5rEVy4kTSg0ivY1EFiu6CCszS6X9BdgsfwZZi8av0BMMy1HPBq4Begn6SLSMt49igzISeBcquovNwB4LSI+zcfdgWWKjK2WJJ1FetPfmLTXcQeg4ZcSVil1iw9K3Istz7LsWnQcVhNDgWOKDqKGjgTulfQAMxYVafhZJNLFMoAnI+KKQiOpg4i4Pn+fNhNRtobceSl29Yfu/5Cqu1oDiIgTciHCD0j7An8VEbcXHNb86ivpFNLS+crP05TktROAiLhd0kNMr9UwsuhVI6XqX9TOrmDGD9VT81hZbBARuwPv5pL83wD6FRxTLX0WEZ9XDkrY4qPSi22Z3IvtH8DxxYZUG5L+IKmnpK6SRkt6qyQVa5tR2UqA/wW4A7ifVACg8lUGW+WqoEcWHUg7uqnoAGopIhaJiJ5VXyu2XCJqHZekg4GnIuJnEXFYCRJAgJ+RXiMnVP1cttdOACRtCHwaETcCiwFH5W1JhfFM4LzrUp1E5J6BZVluB6mwAcDHkpYlNT0eUGA8tVbqFh8RcZGkB5m+vLVMvdg2i4jDJW1HWg66I3AnqUqadXCSNoyIe/LhOq2MNbIpETGrPXON7hbgLWBhSdXVQEtT2KcVpbpIkT+EPpz3xO8GrA2cXFnhZB1eT+BWSe8AlwJXRsTrBcc0X1orBFO2GfgqZwJr5LZWPyMVX7yA6X07251nAufdm5KGVg4kDSO9QZbFDZIWIzUAfohUEvzSIgOqsSNIjXKrW3z8stCIaq9S7KYT0L3gWGqpa/6+FXBJycrvN4NTKz9ExJctxxrcnZL2kdRb0hKVr6KDqoU8+7AocGOL2aRFSpoAQuPvt2rpTNKF3TWAw4GXSB9CrQFExK8jYlXSdpZlSRezy1jYp1Qz8FWm5LZIw4BTIuJkCt5j7RYR8yhX9bmI9D+igJeBH0bEvwoNrA4kLQh0i4j3i47F2kbSr0gzZH8j/X1uC1wREf9TZFy1IOl3pPP5hLSvczHghohYr8CwbA4kfQPYAPgpcFLVTT2B7crQ4kNSa02bS9siQtLWRTY6rpfcY/WbpC0C90TEQwWHVBOSHsoFw34FvBIR51bGio7N2k7SV0jv78OBRcpSlb6iuoVCmUi6i7SiYk9gI9JExMMR8fXCYnISOH9yw3FFxOSiY6m1XIa/P1XLhiOioa8aNkuLD0lPAWu1KFz0UESsUmxktSFpceCDiJiay2QvUtLlI6UhaSNSoan9SJV4KyYD10fExEICs3lWxgSi6gJapTn8tpTnAlqH+xBqbSdpf2AnoBdwJXBZRDxZbFS1J+m/I+KMouOotZy87wKMj4i7c//m7xT5udpJYA1IuiEiStU6QtKFpGpwD5OK3kC6ot3QlZrmtAm3LHsjJN0M7Fzp75iX9v61hH+nZ0fEPkXHYXMmaXREbCrp8oj4QdHx1Fsz/G2W8Yp9mS+gdcQPodZ2eRXMpRHxcNGx1Mqclst7u0d9uTBMbfQpOoA6GAwMipJdJWiWFh+k8vRPSLqdNPP5PeAflfLLjZ7MVxlcdADWZr0lfRv4uqS1aFF0oyxL7qo0w9/mvkUHUAcvAt2AT/PxgqT+jw0vr5b4E8ywlNcJYAcnqWdEfAD8IR/PkDg1eKL0IOkzikg9K9/NPy8G/JsSFSRs0aJlAVJ9gw/zXutCOAmsjX8WHUAdPA58BXit6EDq5ArS/qSKSouPIcWEU3NX56+KMQXFUVOSOgHrR8S9eeiNIuOxufIrUkGmvuQPolUC2KTdI6qvUv5tStq+xXFf4H3gsYgowzk3ywW0Y4HS7ecsqYuBrZkxYaoIoGH3HEfEAJjWm/q6iLgpH28JfLfI2GotImYoAiNpW1Jdg8J4Oai1StKdwJqkBvHVTY+HzuoxjUTSwxGxZouxR8pQnKKlvH+uX0Q8WnQstSDpvoj4RtFx2LyR9P8i4jdFx1EPknaMFo3UWxtrZJJuJPWNvTMPfYfUF3FF4NiIuLCg0GpC0ojZ3d5aSftGVMalvNa4JD0YEeu0GJsQEaVeUSHp/ohYv6h/3zOB80jS1sBvgK+S/juWrVfSMUUHUGdvShoaEddB+Vp8SBoDDCX9bT5MOt+7StLD7DZJ3weuKtty5WYQEb/JFyYGkpbdVcbHFhdVzRxJWlEwp7FG9iWwSqU/maRlSK0H1gPGAg2dBEbE+bnn74p56JmI+KLImOqkjEt5S62yr3pOYw3qLUm/JPX7DWA3Un/q0mixiqITactAoZ9hnATOuz8D25OWwJTug2hE3FV0DHW2H3CRpNOoavFRbEg1tWhEfCDpR8CoiDhaUilmAoFDgIWBqZI+oXwXYEot/02OJC0LfRhYH7iPBl4OmpcubQX0qSwbzHoCU4qJqm76t2hQ/QawYkS8I6nhkyVJ3wHOJ+0NFNBP0oiSXKSYoeq3pJWh8at+l52kbqS+v0vlC2iV5aA9SW3KymBn4GjSNpYgXVDaudCIam+bqp+nkF5jhhUTSuIkcN69DDxexgQQQNL6pAbOq5A2sHYGPirLB+3cz3H9Erf46CKpN/AD4BdFB1NLLdfVW8MZSdp7e39EbJw/iP664Jjm16vABNLs+4NV45OBgwuJqH7ulnQD02c3vw+Mza1a3issqto5EdgsIp4BkLQicAmwzmwf1QBmVfUbF4fp6PYl9VddlvT6UkkCPwBOLyimmsrFbUZK6hERHxYdTz1ExJ5Fx9CSk8B5dzhwU+67U71nrmXBg0Z1GqkR6RWkKevdScu3SiUiPswfaErVOoG06f9W4B8RMV7S14BS9GGTJGBXYEBeWtgP6B0R4woOzdrm04j4VBKSFoyIpyWtVHRQ8yMiHpH0OCl5KMWesdk4gJT4bUj6MHoB8Ld8QXTjIgOrka6VBBAgIp6V1LXIgGqolFW/yy4iTgZOlnRgRJxadDz1kGeozwF6AMtJWgPYNyL+u9jIaicX0TqV9NoZwD+AkRExqaiYnATOu+OAD0l7WhYoOJa6iIjnJHWOiKnAKEn3zvFBjal0LT5yIYorqo6fJ31wK4MzSPuSNiHty/2QdDW0LJVdy25S7lt5DXC7pHdJM2kNLSKmSlpS0gIR8XnR8dRLTiCuzF9lNEHSuUzf27grM87uNrKyV/0uuy8lLVbV/3dxUj/gMjRWPwnYHLgOpl1Y26jYkGpuFKnS6475eLc89r2iAnISOO+WiIjNig6ijj7Om+MflvQH0pvGwgXHVC9lbPExjaSHImLtouOoofUiYm1J/wSIiHfz36o1gIjYLv94TK5CvChwS4Eh1dJLwD2SrgM+qgyWaIVIpbjB74GlSTOBZduTuz9ptvMg0rmNJV14aliSrifNPCwCPCmplFW/m8CPI2La8s/83vdjGvzvsyIiXk4LfaaZOqv7NqheETGq6vg8ST8tKhhwEjg//i5ps4i4rehA6uSHpH2APyHtaelHeWaSWjpU0uplaaHQCs35Lg3lC0mdyVW1JPUizQxaB5f7PD4aEatBKQtQvZq/OpE+cJfRH4BtIuKpogOph4j4jNTH8k+SekdEGWbNTig6AKuJTpJUWc6b3wfLcgH05bwkNPJF3YOAsr3GvCVpN9IeY0iFbwqtgOo+gfNI0mTSzNhnwBeU72poqbXWQgFo+BYKkkZGxMmSNoyIe/LY/0TEL4uOrVYk7QrsBKxNquK3A/DLMvViKzNJFwFHRsS/i46lXiQtQno/KF2BA0n3RMSGRcfRHsq2ikLSAOC1iPg0H3cHlomIFwsNzNpE0h9JlV3PIl0E3Q94OSIOLTKuWpC0FHAyqUG8gNtI++VK0yZC0nKkehvfIP3+7iWd40uFxeQk0FpT9j6IlUa5uVx9v0oLhYhYvejY5oekhyNizbJ9eGkpV5TclPR3ObqssxJlJOkO0v7Nccy4ZLLhl6RJWo20l2yJPPQWsHtEPFFcVLUl6WTSvrJrmHFJ4VVFxVQvZWuoLmkCsEFlz2qecbknIryfugHklRT7Mv297zbgnFy3wWyueTnoXJI02w/WEfFQe8VSZ3+mxH0QKW8LhackvQj0atEXsJLEN3SSW2UiqTx2F0hX2Mo8s1QGklYAlmHmdhDfBl5p/4jq4mzgkIi4E6b1nPtfYIMCY6q1nsDHQPWe+ABKlwSSfndl0qW6aFFEfO791I0jIr6UdB5wR3UF2zLIs9QHkntYVsbLcHGwIm9d+TEzn+NeRcXkJHDunZi/dyOVW36E9AF7deAB4JsFxVVrpe6DSPogWroWChGxs6SvkM6tNC+e1SQdSGoq+zpp47hIH0LLkuCW1Z+Bo1ruvZX0Een3eW4RQdXYwpUEECAixuT+eaXREXtd1ZqkbwIDI+KM/MGtR0S8UHRcNfCmpKERcR2ApGGk2WprAJKGAn8k7QMcIGlN4NiSJErXkN4Drqe8e/yvBe4G/k4HKXrj5aDzSNKlwHER8Vg+Xg04LCL2KDSwGpE0hLQctHR9EPNm6oMi4qSiY6mnfIV3xXz4TER8UWQ8tSLpOVKF0NLsFWgGkh6vFIRp5bbHIuLr7R1TrUm6GniI6e0FdgMGR8S2hQVVI5IOj4g/SDqVXJSpWkQcVEBYNSfpaNIF3pUiYkVJywJXlGEfpKTlgYtIbZECmERarvxcoYFZm0h6kNQaaUxlmXIZtrEASHogItYrOo56qmzXKTqOap4JnHcrVxJAgIh4PF+VKYvS9kHM/byGkvrSlJKkb5OaOL9IminrJ2lERIwtNLDaeBl4v+ggbK51m81t3dstivrai7TK4Cqmtxcoy8xZZd/thEKjqL/tgLVIyTwR8Wou9NPwIuJfwPqSepAmASYXHZPNlSkR8X6LNgplcXK+AHMbM048lGWLFcANkraKiJuKDqTCSeC8e0rSOcBfSVfUdgOeLDakmip7H8R7JZ0GXMaMxSnK8oLzJ2Czyr4BSSuSyhKvU2hU80FSpXLr88AYSTdSslnqkhsv6ccRMcM+K0l7U5Jm3BHxLnCQpEWBL8v0ITsirs/fzy86ljr7PCJCUqUMf2mW80paBjgeWDYitpQ0CPhGRJRhKXYzeFzSLkBnSQNJbRTuLTimWvk6qTXZJkxfDhr5uKHlbgJBujB4lKTPgCn55kILLno56DyS1I3UVPZbpF/sQ0D/iNi70MBqRNLvSJuPS9kHMTepbikiouFfcKD1JSKNvmwkXyWclYiIY9stGJtr+QPo1cDnTE/6BpNWGmwXEf8pKrZaycvo/4/pPQLfB/aKiFIkuQCSBpOKaVUqRwPQyK8t1SQdBgwEvgf8ljS7e3FEnFpoYDUg6WZgFPCLiFhDUhfgn2VYit0MJC1E+n+vcoH+VuA3kXpbNjRJTwOrVxcusvpzEjgf8vLPXUg9y14AroyI0woNqkZa9EHsEFcsrO0kjSJdTavsTdqVVBmu4ZemSdqxZU/A1sasY5K0MVDZG/hERNxRZDy1lCvyHhARd+fjbwJnlCVBApD0DPAz4DGqCjgU2euq1iR9j/RBW8CtEXF7wSHNF0ldImKKpPERMaS69UVH3KdkrSvze5+ky4ADI+KNomNpD5KOiYhjCo/DSeDcycvqhgM7A2+TlhMeFhFfLTQwmytlXxYjaUHgAFK12srepDNKcsVwph6IZe+LaI2htUbqrY01Mkn/iIiyVMFuCpXXR0ljgO8Dt+fj9YHfR8S3i43Q2qLM7335b3N1YDwzbvMoQ+XTmXSU35v3BM69p0klXrepVNSSdHCxIdVXR7liUWPnkZfF5ONnSQl9wyeBuaHsg7kSY2n2yUnaEtgK6CPplKqbejJ9ttqsSOMk/YW0/zZIq0TGVPrLlmTP8dF5P/xoStgsXtL2wO+BpUkX0Co9Vht5FUylksghwHXA8pLuAXoBOxQWlbVJk7z3zW67Rxl1iOo+TgLn3vdJM4F3SroFuJQO8suso6HAMUUHUWNLRcTlko4EyEtlOkTflvmVG8o+ovI1UH+VVJlwR1LSHqReO68Dpb4QYw1jzfy95QeaDShJkQNStdOVga7MWMChFEkg8AfSRd6n5njPxtGrqrDW1cBNpM8tnwHfBR6d1QOtQ6i89w1lxiJakynBe1++cH36rFoIlYWk7YCb8oqsDlGkz0ngXIqIq4Grc8WwbUn/Ay4j6Uzg6pIWUiljkvuRpCXJ/a7yspgytR3oDTwhaRwzVj9t5KUVT5L2Ni5AKtYgoB9pRveGAuMyAyAiNi46hnawRskLibxesgQQoDPQg5nfyxcqIBabSxHxCPCIpItJv8NS9f8t8YXrloYCf5Y0FrhU0q0RUehMrvcE1oCkJUizEzuVpbpkNUmdIuLLOd+zceTlWaeSClQ8Tl4WExGluCKa+wTOJCLuau9YakXSSaQPModUSu9L6gmcAHwSESOLjM9M0kjSRYnJwP8CawNHlOnioKT/BU6KiDK1RJpG0snAV4BrKMly146y/8jmT2v9f4FS9P+VdAcwBKi+cB0RMay4qGpPUldgS9JWgW+S9uf+qLB4nARaaySdD4yMiPfy8eLAiRGxV6GB1VAuj70S6cW0FFfUcuuS/YAVSNX7zi36SlOtSJoIrBgtXrQkdQaejoiBxURmlkh6JJfe35xUmOn/AaPK9AFc0lPA8qSK2J8xfc9cKSqg5srKLUUjv/dVVwO1xiXpQWCXaNH/NyI6xNLC+dHiwrVICdLOEbFqQSHVTU4EtyAtrf9WRPQqKhYvB7VZWb2SAEJqgiyp4d9E8qb/1qwoqaGv9mbnA1+QihdtCQwCyjJDFi0TwDw4tdLY2axgleV2W5GSv0cklWY5fT6XfYHStINoqQxtdFqxadEBWE10rSSAABHxbE4oGl5E3FXVdu0HpItMZxUaVI1J2oJUU2RjYAxwDulcC+Mk0Galk6TFI+JdmLbktQx/L9vM5rYyFDcYVNmvI+lc0tKKsnhS0u4RcUH1oKTdSFV7zYr2oKTbgAHAkZIWoaqXXqOLiJB0UhlmHlqSdHhE/EHSqeS94tUi4qACwqqJiHin6BisJh7M7+vV/X8fnM39O7xZtF1TSfdX70EqJrlvR2nXVYYP9VYfJwL3SrqS9Ib4A+C4YkOafyW9yltt2pLWXPG0yFhq7QDgKkl7kd74grSHoDuwXZGBmWV7kyqEPh8RH+fiU2V7zblf0pCIGF90IDVWKQYzodAozGZtP9L74EFU9f8tNKL51zRt1yJieNExtOQ9gTZLuYH6JqQXm9FlKARQVSa7VRHR0H31cpuLyqZqkRKkjylHrysAJG0CrEo6pyciYnTBIZlNI6kP8FWqLrKWoXBDhaQnSdUJXyK91pRqT6BZR5TbKDxatjYKuW3CcFIbnUrbtXMiYkChgdWQpH9ExDclTWbGVQaFfy7zTKDNzhLARxExSlIvSQMi4oWig5pPi+TvK5Fmka7Lx9uQrqo1tIjoXHQM9RYRdwB3FB2HWUuSfk+q+vYkqYclpDf9hn9tqbJl0QHUU16edhjQnxkT+dJV/rbGUdY2Cs3Qdi0ivpm/LzKn+7Y3zwRaqyQdDQwGVoqIFSUtC1wRERsWHFpN5H07369qNbAI6fy2KDYyM2tUkp4hFdXqEPs96knS0kC3ynFZPphKeoRUkOJBpifyRERD772yxjeLNgqN3v93JmVsu9ZRZ3I9E2izsh2wFvAQQES8mhOlslgO+Lzq+HPSlV8zs3n1PNCVqv5yZSNpKGnP+LLAG6Slr0+RlmiXwZSIOLPoIMxa8euiA2gPuZDRX/JXKXTUmVwngTYrn+dKcAGQp+rL5EJgnKSrScu1tiM1YTUzm1cfAw9LGs2MjcYbtrJkK34DrA/8PSLWkrQxqbJfQ8uzDwDXS/pv4Gpm/B26wqYVosz9f5tMb+AJSR1mJtfLQa1Vkg4DBgLfA34L7EVqSnpKoYHVkKR1SA1JAcZGxD+LjMfMGpukEa2NR8T57R1LvUiaEBGD87LJtfIV7nERsW7Rsc0PSS+QLgi2VlI5IuJr7RySGQCSLmPG/r8vRURZ+v82DUnfbm08Iu5q71gqnATaLEn6HrAZ6U3x1oi4veCQakpSZ2AZZtz832Gm6c3MOhpJfycVcPgtsBRpSeiQiNigyLjMykrSY1X9f7sA4yJi7YLDsjbqyDO5TgKtVZL2johzq447A7+MiFKsSZd0IHA08Dpp87/LnJvZfJE0kJQcDWLGoikNP4tU2cuStwZ8AnQiNateFLgoIt4uNMAakXQA6Xzey8eLAztHRKP3Y7MGJemh6qSv5bF1bB15JtdJoLVK0sXAYqTmx0sCo4C7IuKwIuOqFUnPAeuV5YOLmRVP0j9IF5dOIrWd2ZP0Pnt0oYHVQPUHT0l/i4jvFx1TPUh6OCLWbDH2z4hYq6CQrMk1Q//fMuvIM7kuDGOtiohdJO1Emrr+mHQl9J6Cw6qll4H3iw7CzEqle0SMlqSIeAk4RtLdpMSw0VXvlWv4mc3Z6JR/f5WiaJ2BBQqOyZpYM/T/LbkvKj9ExBSptW3HxXASaK3Ky5pGAn8DVgF+mK+GflxsZDXzPDBG0o3MWAHuT8WFZGYN7tPcD2qipJ8ArwBLFxxTrcQsfi6bW4HLJZ1FOs/9gFuKDcnMGtgakj7IPwvono8Ln8n1clBrlaSngQMqV7WBQ4C9IqIUvaAktXplvix7Hs2s/UkaQuqZtxiplUJP4I8RcX+RcdVC1ZK06uVo0AE+yNRSTuL3BTYlndttwDkRMXW2DzQzazBOAq1VknpGxActxgZGxMSiYjIz66jyssHfRcTPio7FzMxsTjoVHYB1LJIOB4iIDyTt2OLmPQsIqS4k9ZL0R0k3Sbqj8lV0XGbWeCR1yTNF66gjbfiwuSZpoKQrJT0p6fnKV9FxmZnVmpNAa2l41c9Htrhti/YMpM4uAp4GBgC/Bl4ExhcZkJk1rHH5+z+BayX9UNL2la8iA7O5Ngo4E5gCbAxcAFxYaERmZnXgJNBa0ix+bu24kS2Z+yB+ERF3RcRewPpFB2VmDW0J4G1gE2BrUpuIrQuNyOZW94gYTdou81JEHEP6fZqZlYqrg1pLs6sAV6YNpJWSva9J+i/gVaBvgfGYWeNaWtIhwOOk18nqC2Zlet1sBmWu8GpmNo2TQGupUsq2uowt+bhbcWHV3P9IWhQ4FDiVVMXv4GJDMrMG1RnoQeurJZwENpafAgsBB5EqvG4CjCgyIDOzenB1UDMzs/kg6aGIWLvoOMzMzNrKM4HWVCSdymyuzEfEQe0YjpmVQ5n2SzclSdfN7vaIGNpesZiZtQcngdZsJlT9/Gug1abxZmZzYdOiA7D59g3gZeAS4AGc2JtZyXk5qDUtSf+MiLWKjsPMzIolqTPwPWBnYHXgRuCSiHii0MDMzOrELSKsmfkKiJmZERFTI+KWiBhBahf0HDBG0oEFh2ZmVhdeDmpmZmZNT9KCwH+RZgP7A6cAVxUZk5lZvXg5qDUVSZOZPgO4EPBx5SYgIqJnIYGZmVlhJJ0PrAbcDFwaEY8XHJKZWV05CTQzM7OmJulL4KN8WP3ByBcIzayUnASamZmZmZk1EReGMTMzMzMzayJOAs3MzMzMzJqIk0AzMystSX0lXStpoqR/STpZ0gL5tnUljZX0jKSnJZ0jaaF825aSJkh6Kt92Qh4/T9IOLf6ND/P3/pI+kfSwpCclnSWpU76tl6QvJO2bj0+vul/lMQ9L2qH635C0gKQ/59gn5nPpW/Vvh6QTq44Pk3RMXf+jmplZw3MSaGZmpSRJpBL/10TEQGBFoAdwnKRlgCuAn0fESsAqwC3AIpJWA04DdouIVUhVI59v4z/7r4hYk9RwfBCwbR7fEbif1H6AiDgg32+rymPy15Utnu94YBFgxXwO1wBX5XMD+AzYXtJSbYzPzMzMSaCZmZXWJsCnETEKUkNw4GBgL+BQ4PyIuC/fFhFxZUS8DhwOHBcRT+fbpkTEGXPzD0fEFOBeYIU8tHP+N/tK6tOW58izknsCB+fYyefyWT43gCnA2fm8zMzM2sRJoJmZldWqwIPVAxHxAfBvUnL2YGsPIs38zeq2NskJ3KbAY5L6AV+JiHHA5cBObXyaFYB/55irTSCdW8XpwK6SFp2fmM3MrHk4CTQzs7ISM/Z8qx5XK+Nt0drzVY8tL+lh4B7gxoi4GRhOSv4ALiUvCW2D2cU/bTwniRcAB7Xxec3MrMk5CTQzs7J6AhhcPSCpJ9APeA5YZzaPm9VtbwOLVz3fEsBbVbdX9vetFRHH5LGdgT0kvQhcB6whaWAb4n8O+KqkRVqMrw082WLsz8DewMJteF4zM2tyTgLNzKysRgMLSdodQFJn4ETgPOAEYISk9Sp3lrSbpK8AfwSOkrRiHu8k6ZB8tzHATpUKo8AewJ2zCkDSSsDCEdEnIvpHRH/gt6TZwdmKiI+A84E/5djJ57IQcEeL+75Dmm3ce07Pa2Zm5iTQzMxKKSIC2A7YUdJE4FngU+CoXABmOHBCbhHxFPAt4IOIeBT4KXBJHn8c6J2f8wbgbuDBvOxzQ+DnswljZ+DqFmN/o+1LQo/MMT+bz2FHYLt8bi2dCLhKqJmZzZFafx8xMzMzMzOzMvJMoJmZmZmZWRNxEmhmZmZmZtZEnASamZmZmZk1ESeBZmZmZmZmTcRJoJmZmZmZWRNxEmhmZmZmZtZEnASamZmZmZk1kf8PLpa/xq8CwmEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4gAAAEGCAYAAAAnlbtPAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAApwUlEQVR4nO3de5RU1Z3o8e9PQMEH+EIHaQxESRSJoqJxkomJkvF1HVDHRDRRghpyWSaiiXfGxywnNzMamcR4NT4yRBRxfDEmPqJodNDEzERBNEbxEWEUYyuj+EKMQgR/94/aDUVTNA10dTfw/axVq079ztnn7NO9u7p+tffZJzITSZIkSZI26egKSJIkSZI6BxNESZIkSRJggihJkiRJKkwQJUmSJEmACaIkSZIkqeja0RVob9tvv33279+/o6shSZIkSR3iscceeyMze9dat9EliP3792fmzJkdXQ1JkiRJ6hAR8dKq1jnEVJIkSZIEmCBKkiRJkgoTREmSJEkSsBFegyhJkiRpw/Hhhx/S2NjIokWLOroqnU737t1paGigW7durS5jgihJkiRpvdXY2MhWW21F//79iYiOrk6nkZm8+eabNDY2MmDAgFaXc4ipJEmSpPXWokWL2G677UwOm4kItttuuzXuWTVBlCRJkrReMzmsbW1+LiaIkiRJkiTABFGSJEnSRm7LLbcEYO7cuUQEP/7xj5et++Y3v8mkSZOWvf7hD3/IbrvtxuDBg9lrr72YPHkyAH/+858544wz2GWXXRg4cCAjRoygsbFxWbmI4MQTT1z2esmSJfTu3ZsjjzwSgEmTJtG7d2+GDBmy7PHMM8/U87RrcpKatXDVow91dBU6jbH7HdjRVZAkSZLazA477MCll17KN77xDTbddNMV1v3kJz/h/vvvZ8aMGfTs2ZMFCxZw++23A3DuueeycOFCnn/+ebp06cK1117LMcccw/Tp04kItthiC2bNmsUHH3xAjx49uP/+++nbt+8K+z/uuOO4/PLL2+tUa7IHUZIkSZKK3r17M2zYMK677rqV1l144YVceeWV9OzZE4BevXoxatQo3n//fa699louueQSunTpAsDo0aPZbLPNeOCBB5aVP/zww7n77rsBuOmmmzj++OPb4YzWjAmiJEmSJFU5++yzufjii1m6dOmy2MKFC1m4cCG77LLLStvPmTOHnXfeeVni2GTo0KE8/fTTy16PHDmSm2++mUWLFvHkk0/y6U9/eoXtb7nllhWGmH7wwQdtfGar5xBTSZIkSaoyYMAA9t9/f2688cZlscxc5aygq1rXPL7nnnsyd+5cbrrpJo444oiVtt+gh5hGRPeImBERv4+IpyPi/5b4thFxf0TMLs/bVJU5JyLmRMQfIuLQqvi+EfFUWXdZlJ9yRGwWEbeU+PSI6F+v85EkSZK08Tj33HMZP348H330EQA9e/Zkiy224IUXXlhp21133ZWXXnqJhQsXrhB//PHHGTRo0Aqx4cOHc9ZZZ3XK4aVQ3yGmi4GDM3MvYAhwWEQcAJwNTMvMgcC08pqIGASMBPYADgOujIguZV9XAWOAgeVxWImfArydmbsClwDj63g+kiRJkjYSu+22G4MGDeKuu+5aFjvnnHM47bTTePfddwF49913mTBhAltssQWjRo3i29/+9rJhqZMnT+b999/n4IMPXmG/J598Mueffz6f+tSn2u9k1kDdEsSseK+87FYeCYwAmq74vA44qiyPAG7OzMWZ+SIwB9g/IvoAPTPz4cxMYHKzMk37uhUYFuFdMiVJkiStu/POO2+FW1WMHTuWgw46iP3224/Bgwfz+c9/ns033xyA73//+3Tv3p1PfOITDBw4kH//93/ntttuW2noaUNDA+PGjat5vObXIP72t7+t38mtQlRyrjrtvNID+BiwK3BFZv59RLyTmVtXbfN2Zm4TEZcDj2Tmv5X4ROAeYC5wUWZ+scQ/B/x9Zh4ZEbOAwzKzsaz7b+DTmflGs3qModIDyc4777zvSy+9tE7n5W0ulvM2F5IkSepIzz77LLvvvntHV6PTqvXziYjHMnNore3rOotpZi7NzCFAA5XewMEtbF6r5y9biLdUpnk9JmTm0Mwc2rt379XUWpIkSZI2Tu1ym4vMfAf4FZVrB18rw0Ypz6+XzRqBflXFGoBXS7yhRnyFMhHRFegFvFWPc5AkSZKkDV09ZzHtHRFbl+UewBeB54A7gVFls1HAHWX5TmBkmZl0AJXJaGZk5jxgYUQcUK4vPKlZmaZ9HQs8kPUcMytJkiRJG7B63gexD3BduQ5xE2BKZt4VEQ8DUyLiFOCPwJcAMvPpiJgCPAMsAU7LzKY7U44FJgE9qFyXeE+JTwSuj4g5VHoOR9bxfCRJkiRpg1a3BDEznwT2rhF/Exi2ijIXABfUiM8EVrp+MTMXURJMSZIkSdK6aZdrECVJkiRJnV89h5hKkiRJUrtq61vStcdt3X71q18xYsQIBgwYAMAxxxzD+eefD8C9997LuHHjWLp0Kaeeeipnn302AF/72tc48sgjOfbYY3nrrbcYNmwYp59+OqNHj16nupggSpIkSVIb+/Of/8yHH37IFlts0artP/e5z3HXXXetEFu6dCmnnXYa999/Pw0NDey3334MHz6cQYMGLdtmwYIFHHrooYwZM2adk0NwiKkkSZIktZlnn32W73znO3zyk5/k+eefX6d9zZgxg1133ZWPf/zjbLrppowcOZI77rhj2fr33nuPww8/nBNOOIGxY8eua9UBexAlSZIkaZ386U9/YsqUKUycOJHMZPTo0Tz55JNstdVWAJx55pk8+OCDK5UbOXLksiGjDz/8MHvttRc77bQTP/zhD9ljjz145ZVX6Ndv+a3iGxoamD59+rLX3/72tzn11FM588wz2+xcTBAlSZIkaR306dOHPffck6uvvprddtttpfWXXHJJi+X32WcfXnrpJbbcckumTp3KUUcdxezZs6l1i/fKreErDj74YO644w7OOussdthhh3U/ERxiKkmSJEnr5NZbb6Vv374cffTRfO973+Oll15aYf2ZZ57JkCFDVnpcdNFFAPTs2ZMtt9wSgCOOOIIPP/yQN954g4aGBl5++eVl+2lsbGSnnXZa9nrkyJGMHTuWI444goULF7bJudiDKEmSJEnr4JBDDuGQQw7hzTff5N/+7d8YMWIE22+/PVdffTX9+/dfbQ/i//zP/7DjjjsSEcyYMYOPPvqI7bbbjq233prZs2fz4osv0rdvX26++WZuvPHGFcqeccYZzJs3j6OPPpqpU6ey6aabrtO5mCBKkiRJ2mC0x20pVmW77bZj3LhxjBs3jhkzZtClS5dWlbv11lu56qqr6Nq1Kz169ODmm28mIujatSuXX345hx56KEuXLuXkk09mjz32WKn8+PHjGT16NCeeeCI33XQTm2yy9gNFo9a41g3Z0KFDc+bMmeu0j7a+t8r6rCP/ACVJkqRnn32W3XffvaOr0WnV+vlExGOZObTW9l6DKEmSJEkCTBAlSZIkSYUJoiRJkiQJMEGUJEmSJBUmiJIkSZIkwARRkiRJklR4H0RJkiRJG4wPPpjWpvvr0WPYOu/ja1/7Gr/+9a/p1asXAJMmTWLIkCFkJuPGjWPq1KlsvvnmTJo0iX322QeALbfckvfeew+AqVOnMm7cOKZNm8bOO++8zvVpiQmiJEmSJK2Dt99+m2222abFbX7wgx9w7LHHrhC75557mD17NrNnz2b69OmMHTuW6dOnr7DNtGnT+Na3vsV9991X9+QQHGIqSZIkSetk6NChnHDCCTzwwANkZqvL3XHHHZx00klEBAcccADvvPMO8+bNW7b+N7/5DV//+te5++672WWXXepR9ZWYIEqSJEnSOnj++ec54YQTuPzyyxk0aBAXXnghr7766grbnHfeeey5556ceeaZLF68GIBXXnmFfv36LdumoaGBV155BYDFixczYsQIbr/9dnbbbbd2OxcTREmSJElaB126dOHII4/k5z//OQ899BAvvPACO++8MzNmzADg+9//Ps899xyPPvoob731FuPHjweo2dsYEQB069aNz3zmM0ycOLH9TgQTREmSJElaZwsWLGDChAkMHz6c559/nokTJ7LnnnsC0KdPHyKCzTbbjNGjRy9LHBsaGnj55ZeX7aOxsZGddtoJgE022YQpU6bw6KOPcuGFF7bbeZggSpIkSdI6+OpXv8o+++zDCy+8wOTJk3nooYcYNWoU3bt3B1h2XWFmcvvttzN48GAAhg8fzuTJk8lMHnnkEXr16kWfPn2W7XfzzTfnrrvu4oYbbmi3nkRnMZUkSZK0wWiL21KsqS9/+ctMmjSJrl1rp1df+cpXmD9/PpnJkCFD+MlPfgLAEUccwdSpU9l1113ZfPPNufbaa1cqu+2223Lvvfdy4IEHsv322zNixIi6nkvdEsSI6AdMBv4C+AiYkJmXRsR3ga8D88um52bm1FLmHOAUYClwemb+ssT3BSYBPYCpwLjMzIjYrBxjX+BN4LjMnFuvc5IkSZKk5oYPH97i+gceeKBmPCK44ooraq5rugciQL9+/XjxxRfXvoJroJ5DTJcA38nM3YEDgNMiYlBZd0lmDimPpuRwEDAS2AM4DLgyIrqU7a8CxgADy+OwEj8FeDszdwUuAcbX8XwkSZIkaYNWtwQxM+dl5uNleSHwLNC3hSIjgJszc3FmvgjMAfaPiD5Az8x8OCvT/EwGjqoqc11ZvhUYFk3T/kiSJEmS1ki7TFITEf2BvYHpJfTNiHgyIq6JiG1KrC/wclWxxhLrW5abx1cok5lLgAXAdvU4B0mSJEmd05rcnH5jsjY/l7oniBGxJfAz4IzMfJfKcNFdgCHAPODipk1rFM8W4i2VaV6HMRExMyJmzp8/v0YRSZIkSeuj7t278+abb5okNpOZvPnmm8tmUm2tus5iGhHdqCSHN2TmzwEy87Wq9T8F7iovG4F+VcUbgFdLvKFGvLpMY0R0BXoBbzWvR2ZOACYADB061JYjSZIkbSAaGhpobGzEjqCVde/enYaGhtVvWKWes5gGMBF4NjN/VBXvk5nzysujgVll+U7gxoj4EbATlcloZmTm0ohYGBEHUBmiehLw46oyo4CHgWOBB9KvDiRJkqSNRrdu3RgwYEBHV2ODUc8exM8CJwJPRcQTJXYucHxEDKEyFHQu8A2AzHw6IqYAz1CZAfW0zFxayo1l+W0u7ikPqCSg10fEHCo9hyPreD6SJEmStEGrW4KYmf9J7WsEp7ZQ5gLgghrxmcDgGvFFwJfWoZqSJEmSpKJdZjGVJEmSJHV+JoiSJEmSJMAEUZIkSZJUmCBKkiRJkgATREmSJElSYYIoSZIkSQJMECVJkiRJhQmiJEmSJAkwQZQkSZIkFSaIkiRJkiTABFGSJEmSVJggSpIkSZIAE0RJkiRJUmGCKEmSJEkCTBAlSZIkSYUJoiRJkiQJMEGUJEmSJBUmiJIkSZIkwARRkiRJklSYIEqSJEmSABNESZIkSVJhgihJkiRJAkwQJUmSJEmFCaIkSZIkCTBBlCRJkiQVdUsQI6JfRDwYEc9GxNMRMa7Et42I+yNidnnepqrMORExJyL+EBGHVsX3jYinyrrLIiJKfLOIuKXEp0dE/3qdjyRJkiRt6OrZg7gE+E5m7g4cAJwWEYOAs4FpmTkQmFZeU9aNBPYADgOujIguZV9XAWOAgeVxWImfArydmbsClwDj63g+kiRJkrRBq1uCmJnzMvPxsrwQeBboC4wAriubXQccVZZHADdn5uLMfBGYA+wfEX2Anpn5cGYmMLlZmaZ93QoMa+pdlCRJkiStmXa5BrEM/dwbmA7smJnzoJJEAjuUzfoCL1cVayyxvmW5eXyFMpm5BFgAbFfj+GMiYmZEzJw/f34bnZUkSZIkbVjqniBGxJbAz4AzMvPdljatEcsW4i2VWTGQOSEzh2bm0N69e6+uypIkSZK0UaprghgR3agkhzdk5s9L+LUybJTy/HqJNwL9qoo3AK+WeEON+AplIqIr0At4q+3PRJIkSZI2fPWcxTSAicCzmfmjqlV3AqPK8ijgjqr4yDIz6QAqk9HMKMNQF0bEAWWfJzUr07SvY4EHynWKkiRJkqQ11LWO+/4scCLwVEQ8UWLnAhcBUyLiFOCPwJcAMvPpiJgCPENlBtTTMnNpKTcWmAT0AO4pD6gkoNdHxBwqPYcj63g+kiRJkrRBq1uCmJn/Se1rBAGGraLMBcAFNeIzgcE14osoCaYkSZIkad20yyymkiRJkqTOzwRRkiRJkgSYIEqSJEmSChNESZIkSRJggihJkiRJKkwQJUmSJEmACaIkSZIkqWhVghgR01oTkyRJkiStv7q2tDIiugObA9tHxDYsv/F9T2CnOtdNkiRJktSOWkwQgW8AZ1BJBh9jeYL4LnBF/aolSZIkSWpvLSaImXkpcGlEfCszf9xOdZIkSZIkdYDV9SACkJk/jojPAP2ry2Tm5DrVS5IkSZLUzlqVIEbE9cAuwBPA0hJOwARRkiRJkjYQrUoQgaHAoMzMelZGkiRJktRxWnsfxFnAX9SzIpIkSZKkjtXaHsTtgWciYgawuCmYmcPrUitJkiRJUrtrbYL43XpWQpIkSZLU8Vo7i+mv610RSZIkSVLHau0spgupzFoKsCnQDfhTZvasV8UkSZIkSe2rtT2IW1W/joijgP3rUSFJkiRJUsdo7SymK8jM24GD27YqkiRJkqSO1NohpsdUvdyEyn0RvSeiJEmSJG1AWjuL6d9ULS8B5gIj2rw2kiRJkqQO09prEEfXuyKSJEmSpI7VqmsQI6IhIm6LiNcj4rWI+FlENKymzDVl+1lVse9GxCsR8UR5HFG17pyImBMRf4iIQ6vi+0bEU2XdZRERJb5ZRNxS4tMjov8an70kSZIkaZnWTlJzLXAnsBPQF/hFibVkEnBYjfglmTmkPKYCRMQgYCSwRylzZUR0KdtfBYwBBpZH0z5PAd7OzF2BS4DxrTwXSZIkSVINrU0Qe2fmtZm5pDwmAb1bKpCZDwFvtXL/I4CbM3NxZr4IzAH2j4g+QM/MfDgzE5gMHFVV5rqyfCswrKl3UZIkSZK05lqbIL4REV+NiC7l8VXgzbU85jcj4skyBHWbEusLvFy1TWOJ9S3LzeMrlMnMJcACYLtaB4yIMRExMyJmzp8/fy2rLUmSJEkbttYmiCcDXwb+B5gHHAuszcQ1VwG7AEPKfi4u8Vo9f9lCvKUyKwczJ2Tm0Mwc2rt3ix2fkiRJkrTRam2C+E/AqMzsnZk7UEkYv7umB8vM1zJzaWZ+BPwU2L+sagT6VW3aALxa4g014iuUiYiuQC9aP6RVkiRJktRMaxPEPTPz7aYXmfkWsPeaHqxcU9jkaKBphtM7gZFlZtIBVCajmZGZ84CFEXFAub7wJOCOqjKjyvKxwAPlOkVJkiRJ0lpo1X0QgU0iYpumJDEitl1d2Yi4CfgCsH1ENAL/CHwhIoZQGQo6F/gGQGY+HRFTgGeAJcBpmbm07GoslRlRewD3lAfAROD6iJhDpedwZCvPRZIkSZJUQ2sTxIuB30bErVSSuy8DF7RUIDOPrxGe2ML2F9TaZ2bOBAbXiC8CvtRytSVJkiRJrdWqBDEzJ0fETOBgKpPDHJOZz9S1ZpIkSZKkdtXaHkRKQmhSKEmSJEkbqNZOUiNJkiRJ2sCZIEqSJEmSABNESZIkSVJhgihJkiRJAkwQJUmSJEmFCaIkSZIkCTBBlCRJkiQVJoiSJEmSJMAEUZIkSZJUmCBKkiRJkgATREmSJElSYYIoSZIkSQJMECVJkiRJhQmiJEmSJAkwQZQkSZIkFSaIkiRJkiTABFGSJEmSVJggSpIkSZIAE0RJkiRJUmGCKEmSJEkCTBAlSZIkSUXXjq6A1m8ffDCto6vQafToMayjqyBJkiStk7r1IEbENRHxekTMqoptGxH3R8Ts8rxN1bpzImJORPwhIg6tiu8bEU+VdZdFRJT4ZhFxS4lPj4j+9ToXSZIkSdoY1HOI6STgsGaxs4FpmTkQmFZeExGDgJHAHqXMlRHRpZS5ChgDDCyPpn2eArydmbsClwDj63YmkiRJkrQRqFuCmJkPAW81C48ArivL1wFHVcVvzszFmfkiMAfYPyL6AD0z8+HMTGByszJN+7oVGNbUuyhJkiRJWnPtPUnNjpk5D6A871DifYGXq7ZrLLG+Zbl5fIUymbkEWABsV+ugETEmImZGxMz58+e30alIkiRJ0oals8xiWqvnL1uIt1Rm5WDmhMwcmplDe/fuvZZVlCRJkqQNW3sniK+VYaOU59dLvBHoV7VdA/BqiTfUiK9QJiK6Ar1YeUirJEmSJKmV2jtBvBMYVZZHAXdUxUeWmUkHUJmMZkYZhrowIg4o1xee1KxM076OBR4o1ylKkiRJktZC3e6DGBE3AV8Ato+IRuAfgYuAKRFxCvBH4EsAmfl0REwBngGWAKdl5tKyq7FUZkTtAdxTHgATgesjYg6VnsOR9ToXSZIkSdoY1C1BzMzjV7Gq5t3EM/MC4IIa8ZnA4BrxRZQEU5IkSZK07jrLJDWSJEmSpA5mgihJkiRJAkwQJUmSJEmFCaIkSZIkCTBBlCRJkiQVJoiSJEmSJMAEUZIkSZJUmCBKkiRJkgATREmSJElSYYIoSZIkSQJMECVJkiRJhQmiJEmSJAkwQZQkSZIkFSaIkiRJkiTABFGSJEmSVJggSpIkSZIAE0RJkiRJUmGCKEmSJEkCTBAlSZIkSYUJoiRJkiQJMEGUJEmSJBUmiJIkSZIkwARRkiRJklSYIEqSJEmSgA5KECNibkQ8FRFPRMTMEts2Iu6PiNnleZuq7c+JiDkR8YeIOLQqvm/Zz5yIuCwioiPOR5IkSZI2BB3Zg3hQZg7JzKHl9dnAtMwcCEwrr4mIQcBIYA/gMODKiOhSylwFjAEGlsdh7Vh/SZIkSdqgdKYhpiOA68rydcBRVfGbM3NxZr4IzAH2j4g+QM/MfDgzE5hcVUaSJEmStIY6KkFM4L6IeCwixpTYjpk5D6A871DifYGXq8o2lljfstw8LkmSJElaC1076LifzcxXI2IH4P6IeK6FbWtdV5gtxFfeQSUJHQOw8847r2ldJUmSJGmj0CE9iJn5anl+HbgN2B94rQwbpTy/XjZvBPpVFW8AXi3xhhrxWsebkJlDM3No79692/JUJEmSJGmD0e4JYkRsERFbNS0DhwCzgDuBUWWzUcAdZflOYGREbBYRA6hMRjOjDENdGBEHlNlLT6oqI0mSJElaQx0xxHRH4LZyR4quwI2ZeW9EPApMiYhTgD8CXwLIzKcjYgrwDLAEOC0zl5Z9jQUmAT2Ae8pDkiRJkrQW2j1BzMwXgL1qxN8Ehq2izAXABTXiM4HBbV1HSZIkSdoYddQkNZLq4KpHH+roKnQaY/c7sKOrIEmStN7pTPdBlCRJkiR1IBNESZIkSRJggihJkiRJKkwQJUmSJEmACaIkSZIkqTBBlCRJkiQBJoiSJEmSpMIEUZIkSZIEmCBKkiRJkgoTREmSJEkSYIIoSZIkSSpMECVJkiRJgAmiJEmSJKkwQZQkSZIkASaIkiRJkqTCBFGSJEmSBJggSpIkSZIKE0RJkiRJEmCCKEmSJEkqunZ0BSRJG4erHn2oo6vQaYzd78COroIkSTXZgyhJkiRJAkwQJUmSJEmFCaIkSZIkCfAaREkbqA8+mNbRVeg0evQY1tFVkCRJ64n1PkGMiMOAS4EuwNWZeVEHV0mSpBb5BcZyneELDCdQWs4JlDoX2+Zyts32s14PMY2ILsAVwOHAIOD4iBjUsbWSJEmSpPXT+t6DuD8wJzNfAIiIm4ERwDMdWitJkqT1kL3by3WG3m0tZ9tcrt5tMzKzrgeop4g4FjgsM08tr08EPp2Z32y23RhgTHn5SeAP7VrRDdv2wBsdXQmpBtumOjPbpzor26Y6K9tm2/pYZvautWJ970GMGrGVMt7MnABMqH91Nj4RMTMzh3Z0PaTmbJvqzGyf6qxsm+qsbJvtZ72+BhFoBPpVvW4AXu2gukiSJEnSem19TxAfBQZGxICI2BQYCdzZwXWSJEmSpPXSej3ENDOXRMQ3gV9Suc3FNZn5dAdXa2Pj0F11VrZNdWa2T3VWtk11VrbNdrJeT1IjSZIkSWo76/sQU0mSJElSGzFBlCRJkiQBJogqIuKSiDij6vUvI+LqqtcXR8S3I+KuVZS/OiIGleVz615hbXQiYmlEPBERv4+IxyPiMyXePyJmreU+fxURTpm9AYiIoyMiI2K31Ww3NSK2bqNjzo2Ip0qbvC8i/mId97fathwR763LMdbkWFo7rW2LbXSsL6zq/3ILZb4bEWeV5e9FxBfbqC6TIuLF8j79RET8toVta9Y7IoZHxNltUR91fhHREBF3RMTsiPjviLg0IjaNiCERcUTVdsvarNqHCaKa/BZo+sC9CZWbke5Rtf4zQLdVFc7MUzPzmfLSBFH18EFmDsnMvYBzgO93dIXUqRwP/CeV2axXKTOPyMx32vC4B5U2OZMN+L0vIrp0dB3WI61qi2sjItp0csHMPD8z/6MNd/l/yvv0kMz8zFrU587MvKgN66NOKiIC+Dlwe2YOBD4BbAlcAAwBjlh16TU+lu9fa8gEUU3+i5IgUkkMZwELI2KbiNgM2B34HbBlRNwaEc9FxA3lD3xZT0xEXAT0KN8e3lDWfTUiZpTYv/qHqjbQE3i7ebD0ivym9DAu62Us6/6uqrfnomblNomI6yLin9uh7mpjEbEl8FngFMqH8ojoExEPlfedWRHxuRKfGxHbl+XbI+KxiHg6IsZU7e+9iLigtJVHImLHVlTjIWDXiNg/In4bEb8rz58s++wSET8sbfDJiPjWas5pj6r3zScjYmDzc46IaaWdPxURI0q8f0Q8GxE/Led1X0T0KOv2Lef0MHBa1b66RMQPIuLRcqxvlPgXIuLBiLgReKoVP4ON3ira4hci4tcRMSUino+IiyLiK+X3+1RE7FK26x0RPyu/h0cj4rMl/t2ImBAR9wGTWzj2dyPimvL/+IWIOL1q3XkR8YeI+A/gk1XxSRFxbFk+vxx3Vjle9f/38aW+zzf9La3Bz+TzsbxX8XcRsVWz9fuV+Mcj4msRcXlV3a4qbfCFsp9rSvueVFX++PJznBUR49ekbupQBwOLMvNagMxcCpwJnAr8C3BcaTPHle0HraJt1/yMWd7HvxcR04G/bNcz2wCYIAqAzHwVWBIRO1NJFB8Gmv6ohgJPAn8G9gbOAAYBH6fyj7B6P2ezvKfnKxGxO3Ac8NnMHAIsBb7SHuekDU7TFw/PAVcD/1Rjm9eBv87Mfai0u8sAIuJw4Cjg06W351+qynQFbgCez8x/qGP9VT9HAfdm5vPAWxGxD3AC8MvyvrMX8ESNcidn5r5U3uNOj4jtSnwL4JHSVh4Cvt6KOhxJJYl6DjgwM/cGzgcuLOvHAAOAvTNzTyptriX/G7i01H8o0Nhs/SLg6NLWDwIubvpADwwErsjMPYB3gL8t8WuB0zOz+YelU4AFmbkfsB/w9YgYUNbtD5yXmYNWU19VHMXKbREqbXAc8CngROATmbk/lfeypi8LLgUuKb+Hvy3rmuwLjMjME1Zz/N2AQ6n83v4xIrpFxL5UktW9gWOo/I5ruTwz98vMwUAPKm26SddS3zOAf2zh+D+oSgab2vhZwGmlLX8O+KBp46h8ifeTcm4v1NjfNlQSiTOBXwCXUPkS+1NRGYa4EzC+bDME2C8ijmqhfuo89gAeqw5k5rvAXOCfgVvKZ8lbyupabbulz5hbALMy89OZ+Z/1PpkNzXp9H0S1uaZexM8APwL6luUFVIagAszIzEaAiHgC6E9lKM2qDKPyj+3R8tmlB5UP8dKa+qD8AyAi/hKYHBGDm23TDbg8IoZQ+UfxiRL/InBtZr4PkJlvVZX5V2BKZl5Qx7qrvo4H/l9Zvrm8/gVwTUR0ozKE6Yka5U6PiKPLcj8qidWbVL4Ma7o+6jHgr1s49oMRsZTKl2j/APQCrotKj1+yfGj+F4GfZOYSWKkN1vIwcF5ENAA/z8zZzdYHcGFEHAh8ROX9uqmn88Wq830M6B8RvYCtM/PXJX49cHhZPgTYs6knqZzDwPJzmJGZL66mrlquVlu8G3g0M+cBRMR/A/eVbZ6ikuBDpY0MWp7n07Oqt+3OzFyWWLXg7sxcDCyOiNeptInPAbc1vf9FxJ2rKHtQRPwdsDmwLfA0lb8jqAwFhNKeWjj+/8nMW5vF/gv4UUkYf56ZjeUcd6dyX7tDypfUtfwiMzMingJey8ynyjk8XerxMeBXmTm/xG8ADgRub6GO6hyCyntka+O12nZLnzGXAj9r60pvLEwQVa3pOsRPURli+jLwHeBd4JqyzeKq7Zey+jYUwHWZeU7bVlUbs8x8OCrDBHs3W3Um8BqVb+s3odLLAqv+hwOVdn9QRFycmYtWsY06qdLrdzAwOCIS6ELld/13VD4o/i/g+oj4QWZOrir3BSofyP8yM9+PiF8B3cvqD3P5TYKXAl3LsKWmb7vvzMzzy/JBmflG1X7/H/BgZh4dEf2BXzWtolkbjIhPU/mCAiq9jU82rcvMG8vQqP8F/DIiTs3MB6qKf4VK+983Mz+MiLlV9W/+Pt2j1vGrqwJ8KzN/2ax+XwD+tIoyaqaFtjiVFX8nH1W9/ojl/0c3odIeV0gEywffP5Xlo1neg3dqjWqs6n90ize9jojuwJXA0Mx8OSK+y/L2VL3fZfuMiGup9Eq+mpmrvF4sMy+KiLupXFP2SCyfFGdeOcbewKoSxOqfU/OfYVdgSUvnpU7taZaPbgAgInpS+bJuaY3ta7Xtlj5jLirDVrUWHGKqav9FZUjJW5m5tHzDvTWVYaYPr8F+Pizf2gNMA46NiB0AImLbiPhYG9ZZG6GozA7YhUpvT7VewLzM/IjKMK6m613vA06OiM1L+W2rykyk8gHu36ONJ4BQuzgWmJyZH8vM/pnZD3iRSnL4emb+lMrveJ9m5XoBb5fkcDfggJYOUt4TmybfOL+FTXsBr5Tlr1XF7wP+d1Mbi4htM3N61T5X6NWJiI8DL2TmZcCdwJ41jvN6SQ4PotKT0lL93wEWRMRflVD1UP9fAmOb3rcj4hMRsUVL+1NNq2qLf7Wack3uA77Z9KKMhFhBZt5W1WZmtnK/DwFHR0SP0iP5NzW2aUoG34jKdZTH1timeV1Gl3q0OJlIROySmU9l5ngqkzk1ze76DpUvQC4sX0asjenA5yNi+/IlzvHAr1dTRp3DNGDziDgJlk0kczEwicoXvVutuugK+/AzZh2YIKraU1RmL32kWWxB9TfkrTABeDIibsjKzKb/ANwXEU8C9wN92qrC2qg0XYP4BHALMKrGt4NXAqMi4hEqw0v/BJCZ91L5kD2zlF9huuzM/BHwOJWeJt8X1y/HA7c1i/2MyoeMJyLid1S+pb602Tb3UukZfJLK9ayP0Db+Bfh+RPwXy7+ggMr1ZH+k8t74eyrXSLbkOGBWaa+7sfLkJDcAQyNiJpVk77lW1G00cEVUJqmp7qW6GngGeDwqt774VxxhtDZW1RZX97tucjqV3+mTEfEMletQ11lmPk7lPfOJUp/f1NjmHeCnVP7n3w48upaHq74G8YmI2BQ4IyoTyPyeSru7p+q4r1FJWK8oPeprpAzbPQd4EPg98Hhm3rGWdVc7KqM0jga+FBGzgeepjPo5l8rvc1CsOElNrX34GbNOYvkoGkmSJEnSxsxvyiVJkiRJgAmiJEmSJKkwQZQkSZIkASaIkiRJkqTCBFGSJEmSBJggSpK0TiJiaZmOfVZE/CIitm62/vcRcVOzWLeIuCgiZpdyMyLi8LJubkQ8VXWrgMva8XQkSRs573MkSdK6+SAzhwBExHXAacAF5fXuVL6MPTAitsjMP5Uy/0Tlfl2DM3NxROwIfL5qnwet4f1nJUlqE/YgSpLUdh4G+la9PgG4HrgPGA4QEZsDXwe+lZmLoXLD8Myc0s51lSRpJSaIkiS1gYjoAgwD7qwKHwfcAtwEHF9iuwJ/zMx3W9jdg1VDTM+sS4UlSarBIaaSJK2bHhHxBNAfeAy4HyAi9gPmZ+ZLEdEIXBMR27Rynw4xlSR1CHsQJUlaN03XIH4M2JTKNYhQ6THcLSLmAv8N9AT+FpgD7BwRW7V/VSVJapkJoiRJbSAzFwCnA2dFxGbAl4A9M7N/ZvYHRgDHZ+b7wETgsojYFCAi+kTEVzuo6pIkLWOCKElSG8nM3wG/B74MvJKZr1StfggYFBF9gH8A5gPPRMQs4Pbyukn1NYiT26f2kiRBZGZH10GSJEmS1AnYgyhJkiRJAkwQJUmSJEmFCaIkSZIkCTBBlCRJkiQVJoiSJEmSJMAEUZIkSZJUmCBKkiRJkgD4/zFXPs2LditvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for var in cat_vars:\n",
    "    plt.figure(figsize=(15,4))\n",
    "    if var in (\"NATIVE-COUNTRY\", \"EDUCATION\", \"OCCUPATION\"):\n",
    "        plt.xticks(rotation=90)\n",
    "    sns.countplot(data=data_df, x=var, hue=\"INCOME\", palette=\"Set3\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a43cee76",
   "metadata": {},
   "source": [
    "### Numerical Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f21d10e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of numerical variables: 6\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>AGE</th>\n",
       "      <th>FNLWGT</th>\n",
       "      <th>EDUCATION-NUM</th>\n",
       "      <th>CAPITAL-GAIN</th>\n",
       "      <th>CAPITAL-LOSS</th>\n",
       "      <th>HOURS-PER-WEEK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>77516</td>\n",
       "      <td>13</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>83311</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>215646</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>234721</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>338409</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AGE  FNLWGT  EDUCATION-NUM  CAPITAL-GAIN  CAPITAL-LOSS  HOURS-PER-WEEK\n",
       "0   39   77516             13          2174             0              40\n",
       "1   50   83311             13             0             0              13\n",
       "2   38  215646              9             0             0              40\n",
       "3   53  234721              7             0             0              40\n",
       "4   28  338409             13             0             0              40"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make a list of categorical variables\n",
    "num_vars = utils.check_var_types(data_df, \"numerical\")      \n",
    "\n",
    "# visualise the numerical variables\n",
    "data_df[num_vars].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d481ee2",
   "metadata": {},
   "source": [
    "Checking unique values for each numerical variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2d7d5d6f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable: AGE - Number of unique values: 74\n",
      "Variable: FNLWGT - Number of unique values: 28523\n",
      "Variable: EDUCATION-NUM - Number of unique values: 16\n",
      "Variable: CAPITAL-GAIN - Number of unique values: 123\n",
      "Variable: CAPITAL-LOSS - Number of unique values: 99\n",
      "Variable: HOURS-PER-WEEK - Number of unique values: 96\n"
     ]
    }
   ],
   "source": [
    "utils.check_unique_values(data_df[num_vars], verbose=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "effc7cdc",
   "metadata": {},
   "source": [
    "There might be a relationship between numerical `EDUCATION-NUM` and categorical `EDUCATION` variables. They are both about education and contains same number of unique values. When it is checked as below, we can see that they are keeping the same data in different formats.\n",
    "\n",
    "It can also be said that `EDUCATION` is an ordinal variable, it has categories with a particular order associated with it. Since `EDUCATION-NUM` already keeps the ordinal encoded data, we can drop `EDUCATION` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a4d77388",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=data_df, x=\"EDUCATION-NUM\", hue=\"INCOME\", palette=\"Set3\")\n",
    "data_df.groupby([\"EDUCATION-NUM\", \"EDUCATION\"])[\"EDUCATION\"].count()\n",
    "\n",
    "# creating a list to keep the columns to drop\n",
    "cols_to_drop = [\"EDUCATION\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03d5c948",
   "metadata": {},
   "source": [
    "#### Analysing Numerical Variables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed286cce",
   "metadata": {},
   "source": [
    "In order to check the correlation between numerical variables and the target variable, I added an encoded target column for income variable. Correlation Matrix of numerical variables, including target variable, can be seen below. It shows  Feature-to-Feature and Feature-to-Label Pearson Correlations.\n",
    "\n",
    "We can say that there isn't a very high linear correlation between any of numerical variables and the target variable.\n",
    "\n",
    "`FNLWGT` variable shows a negative correlation close to zero with target variable and it can be dropped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "89698a56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Encoding the target variable\n",
    "data_df[\"TARGET\"] = data_df[\"INCOME\"].apply(lambda x: 1 if x == '>50K' else 0)\n",
    "\n",
    "num_vars.append(\"TARGET\")\n",
    "\n",
    "#Generating the correlation matrix\n",
    "corr = data_df[num_vars].corr()\n",
    "\n",
    "#Generating the correlation heat-map\n",
    "sns.heatmap(corr, annot=True, cmap=\"YlGnBu\");\n",
    "cols_to_drop.append(\"FNLWGT\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1082f343",
   "metadata": {},
   "source": [
    "Histograms below show that the variables are not normally distributed. To maximise the performance of models, we need to account for non-Gaussian distributions and transform these variables in the feature engineering step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4aa4ac89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(nrows=2, ncols=3, figsize=(20, 10))\n",
    "fig.suptitle(\"Distributions of Numerical Variables\", fontsize=15, fontweight=\"bold\")\n",
    "\n",
    "for i in range(0,6):\n",
    "    var = num_vars[i]\n",
    "    if i < 3:\n",
    "        axs[0, i].hist(data_df[var], bins=30, fc=\"#96cac1\")\n",
    "        axs[0, i].set_title(var, fontweight=\"bold\")\n",
    "        axs[0, i].set_ylabel(\"Number of Records\")\n",
    "\n",
    "    else:\n",
    "        axs[1, i-3].hist(data_df[var], bins=30, fc=\"#96cac1\")\n",
    "        axs[1, i-3].set_title(var, fontweight=\"bold\")\n",
    "        axs[1, i-3].set_ylabel(\"Number of Records\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "706cdf65",
   "metadata": {},
   "source": [
    "## 2. Feature Engineering\n",
    "\n",
    "### Separate Dataset into Train and Test Sets\n",
    "\n",
    "After exploring data, it can be preprocessed and be made ready for modelling. But before beginning to process the data, it is important to separate the data into train and test sets. When we engineer features, some techniques learn parameters from data and this may cause data leakage from test set to train set. It is important to learn parameters only from the train set in order to avoid over-fitting. \n",
    "\n",
    "Since we already have separate DataFrames for train and test sets, we can continue with these datasets in the feature engineering step.\n",
    "\n",
    "We can start processing data with dropping columns that we have determined during exploration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2915f004",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 32561 entries, 0 to 32560\n",
      "Data columns (total 13 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   AGE             32561 non-null  float64\n",
      " 1   WORKCLASS       30725 non-null  object \n",
      " 2   EDUCATION-NUM   32561 non-null  float64\n",
      " 3   MARITAL-STATUS  32561 non-null  object \n",
      " 4   OCCUPATION      30718 non-null  object \n",
      " 5   RELATIONSHIP    32561 non-null  object \n",
      " 6   RACE            32561 non-null  object \n",
      " 7   SEX             32561 non-null  object \n",
      " 8   CAPITAL-GAIN    32561 non-null  float64\n",
      " 9   CAPITAL-LOSS    32561 non-null  float64\n",
      " 10  HOURS-PER-WEEK  32561 non-null  float64\n",
      " 11  NATIVE-COUNTRY  31978 non-null  object \n",
      " 12  INCOME          32561 non-null  object \n",
      "dtypes: float64(5), object(8)\n",
      "memory usage: 3.2+ MB\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 16281 entries, 0 to 16280\n",
      "Data columns (total 13 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   AGE             16281 non-null  float64\n",
      " 1   WORKCLASS       15318 non-null  object \n",
      " 2   EDUCATION-NUM   16281 non-null  float64\n",
      " 3   MARITAL-STATUS  16281 non-null  object \n",
      " 4   OCCUPATION      15315 non-null  object \n",
      " 5   RELATIONSHIP    16281 non-null  object \n",
      " 6   RACE            16281 non-null  object \n",
      " 7   SEX             16281 non-null  object \n",
      " 8   CAPITAL-GAIN    16281 non-null  float64\n",
      " 9   CAPITAL-LOSS    16281 non-null  float64\n",
      " 10  HOURS-PER-WEEK  16281 non-null  float64\n",
      " 11  NATIVE-COUNTRY  16007 non-null  object \n",
      " 12  INCOME          16281 non-null  object \n",
      "dtypes: float64(5), object(8)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "# drop unnecessary columns from train and test sets\n",
    "train_data = train_data.drop(cols_to_drop, axis=1)\n",
    "train_data.info()\n",
    "\n",
    "test_data = test_data.drop(cols_to_drop, axis=1)\n",
    "test_data.info()\n",
    "\n",
    "# refresh numerical and categorical variable lists\n",
    "cat_vars = [col for col in cat_vars if col not in cols_to_drop]\n",
    "num_vars = [col for col in num_vars if col not in cols_to_drop]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9bc2b1bc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Target variable percentage for train set: 0.2408\n",
      "\n",
      "Target variable percentage for test set: 0.2362\n",
      "\n",
      "Train set size: (32561, 14)\n",
      "Test set size: (16281, 14)\n"
     ]
    }
   ],
   "source": [
    "# generate train and test sets\n",
    "X_train = train_data\n",
    "X_test = test_data\n",
    "\n",
    "# encode the target variable\n",
    "train_data[\"TARGET\"] = train_data[\"INCOME\"].apply(lambda x: 1 if x == '>50K' else 0)\n",
    "test_data[\"TARGET\"] = test_data[\"INCOME\"].apply(lambda x: 1 if x == '>50K' else 0)\n",
    "\n",
    "y_train = train_data[\"TARGET\"]\n",
    "y_test = test_data[\"TARGET\"]\n",
    "\n",
    "# check the percentage of target variable in train & test sets\n",
    "print(f\"\\nTarget variable percentage for train set: {utils.check_class_balance(train_data, 'TARGET'):.4f}\")\n",
    "print(f\"\\nTarget variable percentage for test set: {utils.check_class_balance(test_data, 'TARGET'):.4f}\")\n",
    "\n",
    "print(f\"\\nTrain set size: {X_train.shape}\\nTest set size: {X_test.shape}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2b59cdf",
   "metadata": {},
   "source": [
    "### Pre-Process Categorical Variables\n",
    "\n",
    "#### Handling Missing Values\n",
    "\n",
    "Only categorical variables have missing values, there are some alternative ways to handle these. Some of them are: \n",
    "1. Dropping rows with missing values, \n",
    "2. Imputing with the most frequent category occured in variable\n",
    "3. Creating a new category for null values\n",
    "\n",
    "Each of these have both advantages and disadvantages. To prevent data loss (1. method) and not to bias the predictions in favor of most occurred category (2. method), I decided to replace missing values with the new category \"Missing\". Since the percentages of missing data is low, this method seems appropriate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "50f7eee6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking train and test sets for missing variables:\n",
      "\n",
      "*Train\n",
      "WORKCLASS         0\n",
      "OCCUPATION        0\n",
      "NATIVE-COUNTRY    0\n",
      "dtype: int64\n",
      "\n",
      "*Test\n",
      "WORKCLASS         0\n",
      "OCCUPATION        0\n",
      "NATIVE-COUNTRY    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "X_train.loc[:, vars_with_na] = X_train[vars_with_na].fillna('Missing')\n",
    "X_test.loc[:, vars_with_na] = X_test[vars_with_na].fillna('Missing')\n",
    "\n",
    "print(\"Checking train and test sets for missing variables:\")\n",
    "print(f\"\\n*Train\\n{X_train[vars_with_na].isnull().sum()}\\n\\n*Test\\n{X_test[vars_with_na].isnull().sum()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6668132f",
   "metadata": {},
   "source": [
    "#### Removing Rare Labels\n",
    "\n",
    "As mentioned earlier labels that are under-represented in the dataset tend to cause over-fitting. So, after grouping those categories within variables that are present in less than 1% of the observations, they will be replaced by the string \"Rare\" if there is more than one rare label."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "318d0144",
   "metadata": {},
   "outputs": [],
   "source": [
    "for var in cat_vars:  \n",
    "    rares = utils.analyse_rare_labels(X_train, var, 0.01, verbose=False).index\n",
    "  \n",
    "    X_train[var] = np.where(X_train[var].isin(rares), 'Rare', X_train[var])\n",
    "    \n",
    "    X_test[var] = np.where(X_test[var].isin(rares), 'Rare', X_test[var])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "863056ef",
   "metadata": {},
   "source": [
    "#### Encoding Categorical Variables\n",
    "\n",
    "We need to transform the strings of the categorical variables into numbers. One-hot encoding method is used for this purpose. I used scikit-learn's `OneHotEncoder` encoder, as it can handle test data with new labels not found in training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "aebca2c7",
   "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>WORKCLASS_Federal-gov</th>\n",
       "      <th>WORKCLASS_Local-gov</th>\n",
       "      <th>WORKCLASS_Missing</th>\n",
       "      <th>WORKCLASS_Private</th>\n",
       "      <th>WORKCLASS_Rare</th>\n",
       "      <th>WORKCLASS_Self-emp-inc</th>\n",
       "      <th>WORKCLASS_Self-emp-not-inc</th>\n",
       "      <th>WORKCLASS_State-gov</th>\n",
       "      <th>MARITAL-STATUS_Divorced</th>\n",
       "      <th>MARITAL-STATUS_Married-civ-spouse</th>\n",
       "      <th>...</th>\n",
       "      <th>RACE_Asian-Pac-Islander</th>\n",
       "      <th>RACE_Black</th>\n",
       "      <th>RACE_Rare</th>\n",
       "      <th>RACE_White</th>\n",
       "      <th>SEX_Female</th>\n",
       "      <th>SEX_Male</th>\n",
       "      <th>NATIVE-COUNTRY_Mexico</th>\n",
       "      <th>NATIVE-COUNTRY_Missing</th>\n",
       "      <th>NATIVE-COUNTRY_Rare</th>\n",
       "      <th>NATIVE-COUNTRY_United-States</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   WORKCLASS_Federal-gov  WORKCLASS_Local-gov  WORKCLASS_Missing  \\\n",
       "0                    0.0                  0.0                0.0   \n",
       "1                    0.0                  0.0                0.0   \n",
       "\n",
       "   WORKCLASS_Private  WORKCLASS_Rare  WORKCLASS_Self-emp-inc  \\\n",
       "0                0.0             0.0                     0.0   \n",
       "1                0.0             0.0                     0.0   \n",
       "\n",
       "   WORKCLASS_Self-emp-not-inc  WORKCLASS_State-gov  MARITAL-STATUS_Divorced  \\\n",
       "0                         0.0                  1.0                      0.0   \n",
       "1                         1.0                  0.0                      0.0   \n",
       "\n",
       "   MARITAL-STATUS_Married-civ-spouse  ...  RACE_Asian-Pac-Islander  \\\n",
       "0                                0.0  ...                      0.0   \n",
       "1                                1.0  ...                      0.0   \n",
       "\n",
       "   RACE_Black  RACE_Rare  RACE_White  SEX_Female  SEX_Male  \\\n",
       "0         0.0        0.0         1.0         0.0       1.0   \n",
       "1         0.0        0.0         1.0         0.0       1.0   \n",
       "\n",
       "   NATIVE-COUNTRY_Mexico  NATIVE-COUNTRY_Missing  NATIVE-COUNTRY_Rare  \\\n",
       "0                    0.0                     0.0                  0.0   \n",
       "1                    0.0                     0.0                  0.0   \n",
       "\n",
       "   NATIVE-COUNTRY_United-States  \n",
       "0                           1.0  \n",
       "1                           1.0  \n",
       "\n",
       "[2 rows x 45 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>WORKCLASS_Federal-gov</th>\n",
       "      <th>WORKCLASS_Local-gov</th>\n",
       "      <th>WORKCLASS_Missing</th>\n",
       "      <th>WORKCLASS_Private</th>\n",
       "      <th>WORKCLASS_Rare</th>\n",
       "      <th>WORKCLASS_Self-emp-inc</th>\n",
       "      <th>WORKCLASS_Self-emp-not-inc</th>\n",
       "      <th>WORKCLASS_State-gov</th>\n",
       "      <th>MARITAL-STATUS_Divorced</th>\n",
       "      <th>MARITAL-STATUS_Married-civ-spouse</th>\n",
       "      <th>...</th>\n",
       "      <th>RACE_Asian-Pac-Islander</th>\n",
       "      <th>RACE_Black</th>\n",
       "      <th>RACE_Rare</th>\n",
       "      <th>RACE_White</th>\n",
       "      <th>SEX_Female</th>\n",
       "      <th>SEX_Male</th>\n",
       "      <th>NATIVE-COUNTRY_Mexico</th>\n",
       "      <th>NATIVE-COUNTRY_Missing</th>\n",
       "      <th>NATIVE-COUNTRY_Rare</th>\n",
       "      <th>NATIVE-COUNTRY_United-States</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   WORKCLASS_Federal-gov  WORKCLASS_Local-gov  WORKCLASS_Missing  \\\n",
       "0                    0.0                  0.0                0.0   \n",
       "1                    0.0                  0.0                0.0   \n",
       "\n",
       "   WORKCLASS_Private  WORKCLASS_Rare  WORKCLASS_Self-emp-inc  \\\n",
       "0                1.0             0.0                     0.0   \n",
       "1                1.0             0.0                     0.0   \n",
       "\n",
       "   WORKCLASS_Self-emp-not-inc  WORKCLASS_State-gov  MARITAL-STATUS_Divorced  \\\n",
       "0                         0.0                  0.0                      0.0   \n",
       "1                         0.0                  0.0                      0.0   \n",
       "\n",
       "   MARITAL-STATUS_Married-civ-spouse  ...  RACE_Asian-Pac-Islander  \\\n",
       "0                                0.0  ...                      0.0   \n",
       "1                                1.0  ...                      0.0   \n",
       "\n",
       "   RACE_Black  RACE_Rare  RACE_White  SEX_Female  SEX_Male  \\\n",
       "0         1.0        0.0         0.0         0.0       1.0   \n",
       "1         0.0        0.0         1.0         0.0       1.0   \n",
       "\n",
       "   NATIVE-COUNTRY_Mexico  NATIVE-COUNTRY_Missing  NATIVE-COUNTRY_Rare  \\\n",
       "0                    0.0                     0.0                  0.0   \n",
       "1                    0.0                     0.0                  0.0   \n",
       "\n",
       "   NATIVE-COUNTRY_United-States  \n",
       "0                           1.0  \n",
       "1                           1.0  \n",
       "\n",
       "[2 rows x 45 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.drop(\"INCOME\", axis=1, inplace=True)\n",
    "X_test.drop(\"INCOME\", axis=1, inplace=True)\n",
    "cat_vars.remove(\"INCOME\")\n",
    "\n",
    "cat_encoder = OneHotEncoder(handle_unknown=\"ignore\", sparse=False)\n",
    "cat_encoded_train = cat_encoder.fit_transform(X_train[cat_vars])\n",
    "\n",
    "# get one-hot encoded variable names from encoder\n",
    "new_cat_var_names = cat_encoder.get_feature_names(cat_vars)\n",
    "\n",
    "# create a dataframe of one-hot encoded variables\n",
    "cat_encoded_train_df = pd.DataFrame(cat_encoded_train, columns=new_cat_var_names)\n",
    "display(cat_encoded_train_df.head(2))\n",
    "\n",
    "# perform one-hot encoding on test data\n",
    "# for test data encoders transform method must be used instead of fit_transform\n",
    "cat_encoded_test = cat_encoder.transform(X_test[cat_vars])\n",
    "cat_encoded_test_df = pd.DataFrame(cat_encoded_test, columns=new_cat_var_names)\n",
    "cat_encoded_test_df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de7f402d",
   "metadata": {},
   "source": [
    "### Pre-Process Numerical Variables\n",
    "\n",
    "#### Transformation\n",
    "\n",
    "While data exploration we observed that the numerical variables are not normally distributed. Hence, they will be log transformed in order to get a more Gaussian-like distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9769694a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# before processing, dropping target column from train and test sets, \n",
    "# updating numerical variable list\n",
    "X_train.drop(\"TARGET\", axis=1, inplace=True)\n",
    "X_test.drop(\"TARGET\", axis=1, inplace=True)\n",
    "num_vars.remove(\"TARGET\")\n",
    "\n",
    "for var in num_vars:\n",
    "    X_train[var] = X_train[var].apply(lambda x: np.log(x + 1))\n",
    "    X_test[var] = X_test[var].apply(lambda x: np.log(x + 1))\n",
    "    if any(X_train[var] <= 0):\n",
    "        X_train[var] = np.log(X_train[var]+1)\n",
    "        X_test[var] = np.log(X_test[var]+1)\n",
    "    else:\n",
    "        X_train[var] = np.log(X_train[var])\n",
    "        X_test[var] = np.log(X_test[var])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "322a09f1",
   "metadata": {},
   "source": [
    "#### Scaling\n",
    "\n",
    "We need to standardize the scale of the numerical columns in order to consistently compare the values of different features. Also many machine learning models require the values to be on the same scale.  We can use a MinMaxScaler to transform the numerical values so that they all fall between 0 and 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e399b2a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create scaler\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "#  fit  the scaler to the train set\n",
    "scaler.fit(X_train[num_vars]) \n",
    "\n",
    "# transform the train and test sets\n",
    "X_train[num_vars] = scaler.transform(X_train[num_vars])\n",
    "\n",
    "X_test[num_vars] = scaler.transform(X_test[num_vars])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2eea4d76",
   "metadata": {},
   "source": [
    "### Merge Datasets\n",
    "\n",
    "Before starting modelling we have to merge one-hot encoded dataframe with transformed and scaled numerical features and form the final train and test sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "87182d96",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = pd.merge(X_train[num_vars].reset_index(), cat_encoded_train_df, left_index=True, right_index=True)\\\n",
    "            .drop('index', axis=1)\n",
    "\n",
    "X_test = pd.merge(X_test[num_vars].reset_index(), cat_encoded_test_df, left_index=True, right_index=True)\\\n",
    "            .drop('index', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee266f42",
   "metadata": {},
   "source": [
    "### SMOTE for Handling Imbalanced Data\n",
    "\n",
    "One approach to deal with imbalanced datasets is oversampling the minority class. This can be achived by synthesizing new examples from the existing examples and is referred as the Synthetic Minority Over-sampling Technique - SMOTE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "96b66658",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "X_train_balanced, y_train_balanced = SMOTE().fit_resample(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "311b4282",
   "metadata": {},
   "source": [
    "## 3. Modelling\n",
    "\n",
    "Because of the imbalance, using only accuracy for evaluation of the model would not be sufficient as the model would be prone to make false negative predictions. False negative predictions would cause a low recall value.\n",
    "\n",
    "To be able to keep both false positives and false negatives under control and to evaluate the model, we can use ROC - Receiver Operating Characteristic - Curve and AUC - Area Under the Curve - Score (Precision and recall can be used too, but AUC combines these metrics, however, these metrics will also be observed).\n",
    "\n",
    "We can also visualize errors using scikit-learn's `classification_report` and `confusion_matrix`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30d81ea4",
   "metadata": {},
   "source": [
    "### Training \n",
    "\n",
    "3 different classifiers will be initialized:\n",
    "\n",
    "1. Random Forest\n",
    "2. Adaptive Boosting\n",
    "3. Gradient Boosting\n",
    "\n",
    "After training these models mostly using their default parameters, I will try to improve the performance of best performing one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3cacb3cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_and_evaluate(model, model_name, X_train, y_train, X_test, y_test, score_train=False):\n",
    "    \"\"\"Function that trains and evaluates the model.\n",
    "    \n",
    "    Fits the initialized model to train data. Then makes predictions \n",
    "    on test data and calculates evaluation metrics.\n",
    "    Args:\n",
    "        model (classification model): an initialized model\n",
    "        model_name (string): name of the model\n",
    "        X_train (DataFrame): input variables of train set\n",
    "        y_train (Series): target variable of train set \n",
    "        X_test (DataFrame): input variables of test set\n",
    "        y_test (Series): target variable of test set\n",
    "        score_train: if True calculates evaluation metrics for train set, default False\n",
    "    Returns:\n",
    "        dict: a dictionary that contains evaluation metrics\n",
    "    \"\"\"\n",
    "    results = {}\n",
    "    \n",
    "    print(f\"** {model_name.upper()} **\")\n",
    "    model.fit(X_train, y_train)\n",
    "    print(\"Finished  model training. Starting predictions and evaluation.\")\n",
    "    \n",
    "    y_pred = model.predict(X_test)\n",
    "    y_pred_prob = model.predict_proba(X_test)[:,1]\n",
    "    \n",
    "    if score_train:\n",
    "        y_pred_train = model.predict(X_train)\n",
    "        y_pred_prob_train = model.predict_proba(X_train)[:,1]\n",
    "\n",
    "        acc_train = accuracy_score(y_train, y_pred_train)\n",
    "        results[\"Accuracy_train\"] = acc_train\n",
    "        print(f\"\\nAccuracy Train: {acc_train:.4f}\")\n",
    "\n",
    "        roc_auc_train = roc_auc_score(y_train, y_pred_prob_train)\n",
    "        results[\"ROC_AUC_train\"] = roc_auc_train\n",
    "        print(f\"AUC Score Train: {roc_auc_train:.4f}\\n\")\n",
    "    \n",
    "    acc = accuracy_score(y_test, y_pred)\n",
    "    results[\"Accuracy\"] = acc\n",
    "    print(f\"Accuracy: {acc:.4f}\")\n",
    "    \n",
    "    roc_auc = roc_auc_score(y_test, y_pred_prob)\n",
    "    results[\"ROC_AUC\"] = roc_auc\n",
    "    print(f\"AUC Score: {roc_auc:.4f}\\n\")\n",
    "    \n",
    "    results[\"Report\"] = classification_report(y_test, y_pred, output_dict=True)\n",
    "    results[\"Conf_Matrix\"] = confusion_matrix(y_test, y_pred)\n",
    "    \n",
    "    results[\"Params\"] = model.get_params()\n",
    "    print(*results[\"Report\"][\"1\"].items(), \"\\n\")\n",
    "    plot_confusion_matrix(model, X_test, y_test, cmap=\"YlGnBu\", normalize=\"true\");\n",
    "    plt.show()\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ee6448a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "** RANDOMFOREST **\n",
      "Finished  model training. Starting predictions and evaluation.\n",
      "\n",
      "Accuracy Train: 0.9825\n",
      "AUC Score Train: 0.9987\n",
      "\n",
      "Accuracy: 0.8372\n",
      "AUC Score: 0.8895\n",
      "\n",
      "('precision', 0.651010101010101) ('recall', 0.6703068122724909) ('f1-score', 0.6605175506021009) ('support', 3846) \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "** ADAPTIVEBOOSTING **\n",
      "Finished  model training. Starting predictions and evaluation.\n",
      "\n",
      "Accuracy Train: 0.8812\n",
      "AUC Score Train: 0.9590\n",
      "\n",
      "Accuracy: 0.8477\n",
      "AUC Score: 0.9154\n",
      "\n",
      "('precision', 0.6513741134751773) ('recall', 0.7641705668226729) ('f1-score', 0.7032782962431203) ('support', 3846) \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "** GRADIENTBOOSTING **\n",
      "Finished  model training. Starting predictions and evaluation.\n",
      "\n",
      "Accuracy Train: 0.9030\n",
      "AUC Score Train: 0.9712\n",
      "\n",
      "Accuracy: 0.8585\n",
      "AUC Score: 0.9215\n",
      "\n",
      "('precision', 0.6817538896746818) ('recall', 0.7519500780031201) ('f1-score', 0.71513353115727) ('support', 3846) \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "base_results = {}\n",
    "\n",
    "rfc = RandomForestClassifier(n_estimators=500, random_state=0)\n",
    "abc = AdaBoostClassifier(n_estimators=500, random_state=0)\n",
    "gbc = GradientBoostingClassifier(n_estimators=500,  random_state=0)\n",
    "\n",
    "models = [rfc, abc, gbc]\n",
    "model_names = [\"RandomForest\", \"AdaptiveBoosting\", \"GradientBoosting\"]\n",
    "\n",
    "for clf, name in zip(models, model_names):\n",
    "#     print(f\"** {name.upper()} **\")\n",
    "    base_results[name] = train_and_evaluate(clf, name, X_train_balanced, y_train_balanced,\n",
    "                                            X_test, y_test, score_train=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8c4571f",
   "metadata": {},
   "source": [
    "Gradient Boosting performed best; it has the best accuracy and AUC score. Also it shows relatively balanced precision and recall values which results with the highest F1-Score, that is the harmonic mean of these two metrics.\n",
    "\n",
    "Random Forest performed worst, it also overfitted to training data.\n",
    "\n",
    "I will try to improve Gradient Boosting model by hyperparameter tuning.\n",
    "\n",
    "## 4. Tuning The Chosen Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c951c0cf",
   "metadata": {},
   "source": [
    "Gradient Boosting Classifier’s performance is tried to be improved using hyperparameter tuning with grid search."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ba74b3bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 2 folds for each of 24 candidates, totalling 48 fits\n",
      "\n",
      "Accuracy: 0.8673\n",
      "AUC Score: 0.9242\n",
      "\n",
      "('precision', 0.722942577401429) ('recall', 0.7103484139365575) ('f1-score', 0.7165901639344262) ('support', 3846) \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "parameters = {'n_estimators':[200, 500, 700], \n",
    "              'learning_rate':[0.2, 0.15, 0.1, 0.05],\n",
    "              'min_samples_split' : np.arange(2, 6, 2),\n",
    "             }\n",
    "\n",
    "gbc = GradientBoostingClassifier()\n",
    "\n",
    "model = GridSearchCV(estimator = gbc, param_grid = parameters, scoring='roc_auc',\n",
    "                     cv = 2, n_jobs = -1, verbose=3)\n",
    "\n",
    "model.fit(X_train_balanced, y_train_balanced)\n",
    "\n",
    "y_pred_tuned = model.predict(X_test)\n",
    "y_pred_prob_tuned = model.predict_proba(X_test)[:,1]\n",
    "\n",
    "acc_tuned = accuracy_score(y_test, y_pred_tuned)\n",
    "print(f\"\\nAccuracy: {acc_tuned:.4f}\")\n",
    "\n",
    "roc_auc_tuned = roc_auc_score(y_test, y_pred_prob_tuned)\n",
    "print(f\"AUC Score: {roc_auc_tuned:.4f}\\n\")\n",
    "\n",
    "print(*classification_report(y_test, y_pred_tuned, output_dict=True)[\"1\"].items(), \"\\n\")\n",
    "plot_confusion_matrix(model, X_test, y_test, cmap=\"YlGnBu\", normalize=\"true\");\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ed833719",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'learning_rate': 0.15, 'min_samples_split': 4, 'n_estimators': 700}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "181a5576",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "** GRADIENTBOOSTINGTUNED **\n",
      "Finished  model training. Starting predictions and evaluation.\n",
      "\n",
      "Accuracy Train: 0.9177\n",
      "AUC Score Train: 0.9788\n",
      "\n",
      "Accuracy: 0.8673\n",
      "AUC Score: 0.9242\n",
      "\n",
      "('precision', 0.7230606301297325) ('recall', 0.7100884035361414) ('f1-score', 0.7165158074248984) ('support', 3846) \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'Accuracy_train': 0.9177184466019418,\n",
       " 'ROC_AUC_train': 0.9787519850153434,\n",
       " 'Accuracy': 0.8672685952951293,\n",
       " 'ROC_AUC': 0.9242147675452655,\n",
       " 'Report': {'0': {'precision': 0.910828534868842,\n",
       "   'recall': 0.9158825894652192,\n",
       "   'f1-score': 0.9133485705120494,\n",
       "   'support': 12435},\n",
       "  '1': {'precision': 0.7230606301297325,\n",
       "   'recall': 0.7100884035361414,\n",
       "   'f1-score': 0.7165158074248984,\n",
       "   'support': 3846},\n",
       "  'accuracy': 0.8672685952951293,\n",
       "  'macro avg': {'precision': 0.8169445824992873,\n",
       "   'recall': 0.8129854965006803,\n",
       "   'f1-score': 0.814932188968474,\n",
       "   'support': 16281},\n",
       "  'weighted avg': {'precision': 0.8664728219748788,\n",
       "   'recall': 0.8672685952951293,\n",
       "   'f1-score': 0.8668514998878136,\n",
       "   'support': 16281}},\n",
       " 'Conf_Matrix': array([[11389,  1046],\n",
       "        [ 1115,  2731]]),\n",
       " 'Params': {'ccp_alpha': 0.0,\n",
       "  'criterion': 'friedman_mse',\n",
       "  'init': None,\n",
       "  'learning_rate': 0.15,\n",
       "  'loss': 'deviance',\n",
       "  'max_depth': 3,\n",
       "  'max_features': None,\n",
       "  'max_leaf_nodes': None,\n",
       "  'min_impurity_decrease': 0.0,\n",
       "  'min_impurity_split': None,\n",
       "  'min_samples_leaf': 1,\n",
       "  'min_samples_split': 4,\n",
       "  'min_weight_fraction_leaf': 0.0,\n",
       "  'n_estimators': 700,\n",
       "  'n_iter_no_change': None,\n",
       "  'random_state': 0,\n",
       "  'subsample': 1.0,\n",
       "  'tol': 0.0001,\n",
       "  'validation_fraction': 0.1,\n",
       "  'verbose': 0,\n",
       "  'warm_start': False}}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbc_tuned = GradientBoostingClassifier(learning_rate= 0.15, min_samples_split= 4, n_estimators= 700,\n",
    "                                       random_state=0)\n",
    "train_and_evaluate(gbc_tuned, \"GradientBoostingTuned\", X_train_balanced, y_train_balanced,\n",
    "                                            X_test, y_test, score_train=True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}