analysis_and_models.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dependencies\n",
    "# Analysis\n",
    "import pandas as pd\n",
    "import sqlite3\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "\n",
    "# ML\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix, classification_report, accuracy_score, balanced_accuracy_score\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from imblearn.over_sampling import RandomOverSampler"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Data Loading and EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Establish connection to sqlite db\n",
    "cnx = sqlite3.connect('Resources/bank_churners.db')\n",
    "customer_churn_df = pd.read_sql_query('SELECT * FROM bank_churners', cnx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CLIENTNUM', 'Attrition_Flag', 'Customer_Age', 'Gender',\n",
       "       'Dependent_count', 'Education_Level', 'Marital_Status',\n",
       "       'Income_Category', 'Card_Category', 'Months_on_book',\n",
       "       'Total_Relationship_Count', 'Months_Inactive_12_mon',\n",
       "       'Contacts_Count_12_mon', 'Credit_Limit', 'Total_Revolving_Bal',\n",
       "       'Avg_Open_To_Buy', 'Total_Amt_Chng_Q4_Q1', 'Total_Trans_Amt',\n",
       "       'Total_Trans_Ct', 'Total_Ct_Chng_Q4_Q1', 'Avg_Utilization_Ratio',\n",
       "       'Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1',\n",
       "       'Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_2'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check column names\n",
    "customer_churn_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10127 entries, 0 to 10126\n",
      "Data columns (total 23 columns):\n",
      " #   Column                                                                                                                              Non-Null Count  Dtype  \n",
      "---  ------                                                                                                                              --------------  -----  \n",
      " 0   CLIENTNUM                                                                                                                           10127 non-null  int64  \n",
      " 1   Attrition_Flag                                                                                                                      10127 non-null  object \n",
      " 2   Customer_Age                                                                                                                        10127 non-null  int64  \n",
      " 3   Gender                                                                                                                              10127 non-null  object \n",
      " 4   Dependent_count                                                                                                                     10127 non-null  int64  \n",
      " 5   Education_Level                                                                                                                     10127 non-null  object \n",
      " 6   Marital_Status                                                                                                                      10127 non-null  object \n",
      " 7   Income_Category                                                                                                                     10127 non-null  object \n",
      " 8   Card_Category                                                                                                                       10127 non-null  object \n",
      " 9   Months_on_book                                                                                                                      10127 non-null  int64  \n",
      " 10  Total_Relationship_Count                                                                                                            10127 non-null  int64  \n",
      " 11  Months_Inactive_12_mon                                                                                                              10127 non-null  int64  \n",
      " 12  Contacts_Count_12_mon                                                                                                               10127 non-null  int64  \n",
      " 13  Credit_Limit                                                                                                                        10127 non-null  float64\n",
      " 14  Total_Revolving_Bal                                                                                                                 10127 non-null  int64  \n",
      " 15  Avg_Open_To_Buy                                                                                                                     10127 non-null  float64\n",
      " 16  Total_Amt_Chng_Q4_Q1                                                                                                                10127 non-null  float64\n",
      " 17  Total_Trans_Amt                                                                                                                     10127 non-null  int64  \n",
      " 18  Total_Trans_Ct                                                                                                                      10127 non-null  int64  \n",
      " 19  Total_Ct_Chng_Q4_Q1                                                                                                                 10127 non-null  float64\n",
      " 20  Avg_Utilization_Ratio                                                                                                               10127 non-null  float64\n",
      " 21  Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1  10127 non-null  float64\n",
      " 22  Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_2  10127 non-null  float64\n",
      "dtypes: float64(7), int64(10), object(6)\n",
      "memory usage: 1.8+ MB\n"
     ]
    }
   ],
   "source": [
    "# Check datatypes\n",
    "customer_churn_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CLIENTNUM                                                                                                                             0\n",
       "Attrition_Flag                                                                                                                        0\n",
       "Customer_Age                                                                                                                          0\n",
       "Gender                                                                                                                                0\n",
       "Dependent_count                                                                                                                       0\n",
       "Education_Level                                                                                                                       0\n",
       "Marital_Status                                                                                                                        0\n",
       "Income_Category                                                                                                                       0\n",
       "Card_Category                                                                                                                         0\n",
       "Months_on_book                                                                                                                        0\n",
       "Total_Relationship_Count                                                                                                              0\n",
       "Months_Inactive_12_mon                                                                                                                0\n",
       "Contacts_Count_12_mon                                                                                                                 0\n",
       "Credit_Limit                                                                                                                          0\n",
       "Total_Revolving_Bal                                                                                                                   0\n",
       "Avg_Open_To_Buy                                                                                                                       0\n",
       "Total_Amt_Chng_Q4_Q1                                                                                                                  0\n",
       "Total_Trans_Amt                                                                                                                       0\n",
       "Total_Trans_Ct                                                                                                                        0\n",
       "Total_Ct_Chng_Q4_Q1                                                                                                                   0\n",
       "Avg_Utilization_Ratio                                                                                                                 0\n",
       "Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1    0\n",
       "Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_2    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for missing values\n",
    "customer_churn_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Attrition_Flag</th>\n",
       "      <th>Customer_Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Dependent_count</th>\n",
       "      <th>Education_Level</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Income_Category</th>\n",
       "      <th>Card_Category</th>\n",
       "      <th>Months_on_book</th>\n",
       "      <th>Total_Relationship_Count</th>\n",
       "      <th>Months_Inactive_12_mon</th>\n",
       "      <th>Contacts_Count_12_mon</th>\n",
       "      <th>Credit_Limit</th>\n",
       "      <th>Total_Revolving_Bal</th>\n",
       "      <th>Avg_Open_To_Buy</th>\n",
       "      <th>Total_Amt_Chng_Q4_Q1</th>\n",
       "      <th>Total_Trans_Amt</th>\n",
       "      <th>Total_Trans_Ct</th>\n",
       "      <th>Total_Ct_Chng_Q4_Q1</th>\n",
       "      <th>Avg_Utilization_Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Existing Customer</td>\n",
       "      <td>45</td>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "      <td>High School</td>\n",
       "      <td>Married</td>\n",
       "      <td>Less than $40K</td>\n",
       "      <td>Blue</td>\n",
       "      <td>36</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3544.0</td>\n",
       "      <td>1661</td>\n",
       "      <td>1883.0</td>\n",
       "      <td>0.831</td>\n",
       "      <td>15149</td>\n",
       "      <td>111</td>\n",
       "      <td>0.734</td>\n",
       "      <td>0.469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Attrited Customer</td>\n",
       "      <td>58</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Single</td>\n",
       "      <td>$40K - $60K</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3421.0</td>\n",
       "      <td>2517</td>\n",
       "      <td>904.0</td>\n",
       "      <td>0.992</td>\n",
       "      <td>992</td>\n",
       "      <td>21</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Attrited Customer</td>\n",
       "      <td>46</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>Doctorate</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>$80K - $120K</td>\n",
       "      <td>Blue</td>\n",
       "      <td>38</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>8258.0</td>\n",
       "      <td>1771</td>\n",
       "      <td>6487.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1447</td>\n",
       "      <td>23</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Existing Customer</td>\n",
       "      <td>34</td>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "      <td>Uneducated</td>\n",
       "      <td>Single</td>\n",
       "      <td>Less than $40K</td>\n",
       "      <td>Blue</td>\n",
       "      <td>24</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1438.3</td>\n",
       "      <td>0</td>\n",
       "      <td>1438.3</td>\n",
       "      <td>0.827</td>\n",
       "      <td>3940</td>\n",
       "      <td>82</td>\n",
       "      <td>0.952</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Existing Customer</td>\n",
       "      <td>49</td>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "      <td>Uneducated</td>\n",
       "      <td>Married</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Blue</td>\n",
       "      <td>41</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3128.0</td>\n",
       "      <td>749</td>\n",
       "      <td>2379.0</td>\n",
       "      <td>0.598</td>\n",
       "      <td>4369</td>\n",
       "      <td>59</td>\n",
       "      <td>0.735</td>\n",
       "      <td>0.239</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Attrition_Flag  Customer_Age Gender  Dependent_count Education_Level  \\\n",
       "0  Existing Customer            45      F                3     High School   \n",
       "1  Attrited Customer            58      M                0         Unknown   \n",
       "2  Attrited Customer            46      M                3       Doctorate   \n",
       "3  Existing Customer            34      F                2      Uneducated   \n",
       "4  Existing Customer            49      F                2      Uneducated   \n",
       "\n",
       "  Marital_Status Income_Category Card_Category  Months_on_book  \\\n",
       "0        Married  Less than $40K          Blue              36   \n",
       "1         Single     $40K - $60K          Blue              45   \n",
       "2       Divorced    $80K - $120K          Blue              38   \n",
       "3         Single  Less than $40K          Blue              24   \n",
       "4        Married         Unknown          Blue              41   \n",
       "\n",
       "   Total_Relationship_Count  Months_Inactive_12_mon  Contacts_Count_12_mon  \\\n",
       "0                         4                       3                      3   \n",
       "1                         3                       1                      3   \n",
       "2                         6                       3                      3   \n",
       "3                         6                       2                      2   \n",
       "4                         3                       5                      2   \n",
       "\n",
       "   Credit_Limit  Total_Revolving_Bal  Avg_Open_To_Buy  Total_Amt_Chng_Q4_Q1  \\\n",
       "0        3544.0                 1661           1883.0                 0.831   \n",
       "1        3421.0                 2517            904.0                 0.992   \n",
       "2        8258.0                 1771           6487.0                 0.000   \n",
       "3        1438.3                    0           1438.3                 0.827   \n",
       "4        3128.0                  749           2379.0                 0.598   \n",
       "\n",
       "   Total_Trans_Amt  Total_Trans_Ct  Total_Ct_Chng_Q4_Q1  Avg_Utilization_Ratio  \n",
       "0            15149             111                0.734                  0.469  \n",
       "1              992              21                0.400                  0.736  \n",
       "2             1447              23                0.000                  0.214  \n",
       "3             3940              82                0.952                  0.000  \n",
       "4             4369              59                0.735                  0.239  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop unnecessary columns\n",
    "customer_churn_df = customer_churn_df.drop(columns= ['CLIENTNUM', 'Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1', 'Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_2'])\n",
    "customer_churn_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10127, 20)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Review the shape of the df\n",
    "df_row_count = customer_churn_df.shape[0]\n",
    "customer_churn_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Customer_Age</th>\n",
       "      <th>Dependent_count</th>\n",
       "      <th>Months_on_book</th>\n",
       "      <th>Total_Relationship_Count</th>\n",
       "      <th>Months_Inactive_12_mon</th>\n",
       "      <th>Contacts_Count_12_mon</th>\n",
       "      <th>Credit_Limit</th>\n",
       "      <th>Total_Revolving_Bal</th>\n",
       "      <th>Avg_Open_To_Buy</th>\n",
       "      <th>Total_Amt_Chng_Q4_Q1</th>\n",
       "      <th>Total_Trans_Amt</th>\n",
       "      <th>Total_Trans_Ct</th>\n",
       "      <th>Total_Ct_Chng_Q4_Q1</th>\n",
       "      <th>Avg_Utilization_Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10127.000000</td>\n",
       "      <td>10127.000000</td>\n",
       "      <td>10127.000000</td>\n",
       "      <td>10127.000000</td>\n",
       "      <td>10127.000000</td>\n",
       "      <td>10127.000000</td>\n",
       "      <td>10127.000000</td>\n",
       "      <td>10127.000000</td>\n",
       "      <td>10127.000000</td>\n",
       "      <td>10127.000000</td>\n",
       "      <td>10127.000000</td>\n",
       "      <td>10127.000000</td>\n",
       "      <td>10127.000000</td>\n",
       "      <td>10127.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>46.325960</td>\n",
       "      <td>2.346203</td>\n",
       "      <td>35.928409</td>\n",
       "      <td>3.812580</td>\n",
       "      <td>2.341167</td>\n",
       "      <td>2.455317</td>\n",
       "      <td>8631.953698</td>\n",
       "      <td>1162.814061</td>\n",
       "      <td>7469.139637</td>\n",
       "      <td>0.759941</td>\n",
       "      <td>4404.086304</td>\n",
       "      <td>64.858695</td>\n",
       "      <td>0.712222</td>\n",
       "      <td>0.274894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.016814</td>\n",
       "      <td>1.298908</td>\n",
       "      <td>7.986416</td>\n",
       "      <td>1.554408</td>\n",
       "      <td>1.010622</td>\n",
       "      <td>1.106225</td>\n",
       "      <td>9088.776650</td>\n",
       "      <td>814.987335</td>\n",
       "      <td>9090.685324</td>\n",
       "      <td>0.219207</td>\n",
       "      <td>3397.129254</td>\n",
       "      <td>23.472570</td>\n",
       "      <td>0.238086</td>\n",
       "      <td>0.275691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>26.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1438.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>510.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2555.000000</td>\n",
       "      <td>359.000000</td>\n",
       "      <td>1324.500000</td>\n",
       "      <td>0.631000</td>\n",
       "      <td>2155.500000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0.582000</td>\n",
       "      <td>0.023000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>46.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4549.000000</td>\n",
       "      <td>1276.000000</td>\n",
       "      <td>3474.000000</td>\n",
       "      <td>0.736000</td>\n",
       "      <td>3899.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>0.702000</td>\n",
       "      <td>0.176000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>52.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>11067.500000</td>\n",
       "      <td>1784.000000</td>\n",
       "      <td>9859.000000</td>\n",
       "      <td>0.859000</td>\n",
       "      <td>4741.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>0.818000</td>\n",
       "      <td>0.503000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>73.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>34516.000000</td>\n",
       "      <td>2517.000000</td>\n",
       "      <td>34516.000000</td>\n",
       "      <td>3.397000</td>\n",
       "      <td>18484.000000</td>\n",
       "      <td>139.000000</td>\n",
       "      <td>3.714000</td>\n",
       "      <td>0.999000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Customer_Age  Dependent_count  Months_on_book  \\\n",
       "count  10127.000000     10127.000000    10127.000000   \n",
       "mean      46.325960         2.346203       35.928409   \n",
       "std        8.016814         1.298908        7.986416   \n",
       "min       26.000000         0.000000       13.000000   \n",
       "25%       41.000000         1.000000       31.000000   \n",
       "50%       46.000000         2.000000       36.000000   \n",
       "75%       52.000000         3.000000       40.000000   \n",
       "max       73.000000         5.000000       56.000000   \n",
       "\n",
       "       Total_Relationship_Count  Months_Inactive_12_mon  \\\n",
       "count              10127.000000            10127.000000   \n",
       "mean                   3.812580                2.341167   \n",
       "std                    1.554408                1.010622   \n",
       "min                    1.000000                0.000000   \n",
       "25%                    3.000000                2.000000   \n",
       "50%                    4.000000                2.000000   \n",
       "75%                    5.000000                3.000000   \n",
       "max                    6.000000                6.000000   \n",
       "\n",
       "       Contacts_Count_12_mon  Credit_Limit  Total_Revolving_Bal  \\\n",
       "count           10127.000000  10127.000000         10127.000000   \n",
       "mean                2.455317   8631.953698          1162.814061   \n",
       "std                 1.106225   9088.776650           814.987335   \n",
       "min                 0.000000   1438.300000             0.000000   \n",
       "25%                 2.000000   2555.000000           359.000000   \n",
       "50%                 2.000000   4549.000000          1276.000000   \n",
       "75%                 3.000000  11067.500000          1784.000000   \n",
       "max                 6.000000  34516.000000          2517.000000   \n",
       "\n",
       "       Avg_Open_To_Buy  Total_Amt_Chng_Q4_Q1  Total_Trans_Amt  Total_Trans_Ct  \\\n",
       "count     10127.000000          10127.000000     10127.000000    10127.000000   \n",
       "mean       7469.139637              0.759941      4404.086304       64.858695   \n",
       "std        9090.685324              0.219207      3397.129254       23.472570   \n",
       "min           3.000000              0.000000       510.000000       10.000000   \n",
       "25%        1324.500000              0.631000      2155.500000       45.000000   \n",
       "50%        3474.000000              0.736000      3899.000000       67.000000   \n",
       "75%        9859.000000              0.859000      4741.000000       81.000000   \n",
       "max       34516.000000              3.397000     18484.000000      139.000000   \n",
       "\n",
       "       Total_Ct_Chng_Q4_Q1  Avg_Utilization_Ratio  \n",
       "count         10127.000000           10127.000000  \n",
       "mean              0.712222               0.274894  \n",
       "std               0.238086               0.275691  \n",
       "min               0.000000               0.000000  \n",
       "25%               0.582000               0.023000  \n",
       "50%               0.702000               0.176000  \n",
       "75%               0.818000               0.503000  \n",
       "max               3.714000               0.999000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Descriptive statistics\n",
    "customer_churn_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Existing Customer    8500\n",
      "Attrited Customer    1627\n",
      "Name: Attrition_Flag, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Check for class imbalance\n",
    "print(customer_churn_df['Attrition_Flag'].value_counts())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar plot to visualize existing and attrited customers\n",
    "attrition_df = customer_churn_df['Attrition_Flag'].value_counts().reset_index()\n",
    "attrition_df = attrition_df.rename(columns= {'index': 'Attrition_Flag', 'Attrition_Flag': 'count'})\n",
    "\n",
    "# Set plot style\n",
    "sns.set(style=\"darkgrid\")\n",
    "\n",
    "sns.barplot(x= 'Attrition_Flag',\n",
    "            y= 'count',\n",
    "            data= attrition_df)\n",
    "\n",
    "# Save figure\n",
    "plt.savefig('Figures/attrition_bar.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar plot to visualize the education level of the customers\n",
    "education_df = customer_churn_df['Education_Level'].value_counts().reset_index()\n",
    "education_df = education_df.rename(columns= {'index': 'Education_Level', 'Education_Level': 'count'})\n",
    "\n",
    "# Set plot style\n",
    "sns.set(style=\"darkgrid\")\n",
    "\n",
    "sns.barplot(x= 'count',\n",
    "            y = 'Education_Level',\n",
    "            data= education_df)\n",
    "\n",
    "# Save figure\n",
    "plt.savefig('Figures/education_bar.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='count', ylabel='Income_Category'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar plot to visualize income of customers\n",
    "income_df = customer_churn_df['Income_Category'].value_counts().reset_index()\n",
    "income_df = income_df.rename(columns= {'index': 'Income_Category', 'Income_Category': 'count'})\n",
    "\n",
    "# Plot style\n",
    "sns.set(style=\"darkgrid\")\n",
    "\n",
    "sns.barplot(x= 'count',\n",
    "            y = 'Income_Category',\n",
    "            data= income_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x1500 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot histograms of numeric columns\n",
    "fig = customer_churn_df.hist(bins=50, figsize=(20,15))\n",
    "\n",
    "# Save figure\n",
    "plt.savefig('Figures/histogram.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(9313, 20)\n",
      "814 outliers removed\n"
     ]
    }
   ],
   "source": [
    "# Remove outliers using z-score\n",
    "# Create df with only numeric columns\n",
    "numeric_cols = customer_churn_df.select_dtypes(include=[np.number]).columns\n",
    "\n",
    "# Calculate z-score (absolute value)\n",
    "z_scores = stats.zscore(customer_churn_df[numeric_cols])\n",
    "abs_z_scores = np.abs(z_scores)\n",
    "\n",
    "# New df including values with a z-score < 3\n",
    "filtered_entries = (abs_z_scores < 3).all(axis=1)\n",
    "customer_churn_df = customer_churn_df[filtered_entries]\n",
    "\n",
    "# Review filtered df to check difference in row count\n",
    "print(customer_churn_df.shape)\n",
    "filtered_df_row_count = customer_churn_df.shape[0]\n",
    "print(f'{df_row_count - filtered_df_row_count} outliers removed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace 'Existing Customer' with 0 and 'Attrited Customer' with 1\n",
    "customer_churn_df['Attrition_Flag'] = customer_churn_df['Attrition_Flag'].replace({'Existing Customer': 0, 'Attrited Customer': 1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Seperate features from the target variable\n",
    "X = customer_churn_df.drop(columns= ['Attrition_Flag'])\n",
    "y = customer_churn_df['Attrition_Flag']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encode categorical variables\n",
    "le = LabelEncoder()\n",
    "\n",
    "categorical_features = ['Gender', 'Education_Level', 'Marital_Status', 'Income_Category', 'Card_Category']\n",
    "\n",
    "for feature in categorical_features:\n",
    "    X[feature] = le.fit_transform(X[feature])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer_Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Dependent_count</th>\n",
       "      <th>Education_Level</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Income_Category</th>\n",
       "      <th>Card_Category</th>\n",
       "      <th>Months_on_book</th>\n",
       "      <th>Total_Relationship_Count</th>\n",
       "      <th>Months_Inactive_12_mon</th>\n",
       "      <th>Contacts_Count_12_mon</th>\n",
       "      <th>Credit_Limit</th>\n",
       "      <th>Total_Revolving_Bal</th>\n",
       "      <th>Avg_Open_To_Buy</th>\n",
       "      <th>Total_Amt_Chng_Q4_Q1</th>\n",
       "      <th>Total_Trans_Amt</th>\n",
       "      <th>Total_Trans_Ct</th>\n",
       "      <th>Total_Ct_Chng_Q4_Q1</th>\n",
       "      <th>Avg_Utilization_Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3421.0</td>\n",
       "      <td>2517</td>\n",
       "      <td>904.0</td>\n",
       "      <td>0.992</td>\n",
       "      <td>992</td>\n",
       "      <td>21</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1438.3</td>\n",
       "      <td>0</td>\n",
       "      <td>1438.3</td>\n",
       "      <td>0.827</td>\n",
       "      <td>3940</td>\n",
       "      <td>82</td>\n",
       "      <td>0.952</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3128.0</td>\n",
       "      <td>749</td>\n",
       "      <td>2379.0</td>\n",
       "      <td>0.598</td>\n",
       "      <td>4369</td>\n",
       "      <td>59</td>\n",
       "      <td>0.735</td>\n",
       "      <td>0.239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>33304.0</td>\n",
       "      <td>1833</td>\n",
       "      <td>31471.0</td>\n",
       "      <td>0.428</td>\n",
       "      <td>1448</td>\n",
       "      <td>29</td>\n",
       "      <td>0.381</td>\n",
       "      <td>0.055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5723.0</td>\n",
       "      <td>1873</td>\n",
       "      <td>3850.0</td>\n",
       "      <td>0.851</td>\n",
       "      <td>2732</td>\n",
       "      <td>63</td>\n",
       "      <td>0.853</td>\n",
       "      <td>0.327</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer_Age  Gender  Dependent_count  Education_Level  Marital_Status  \\\n",
       "1            58       1                0                6               2   \n",
       "3            34       0                2                5               2   \n",
       "4            49       0                2                5               1   \n",
       "5            43       1                4                6               3   \n",
       "7            37       0                2                3               2   \n",
       "\n",
       "   Income_Category  Card_Category  Months_on_book  Total_Relationship_Count  \\\n",
       "1                1              0              45                         3   \n",
       "3                4              0              24                         6   \n",
       "4                5              0              41                         3   \n",
       "5                0              0              34                         5   \n",
       "7                4              0              36                         4   \n",
       "\n",
       "   Months_Inactive_12_mon  Contacts_Count_12_mon  Credit_Limit  \\\n",
       "1                       1                      3        3421.0   \n",
       "3                       2                      2        1438.3   \n",
       "4                       5                      2        3128.0   \n",
       "5                       2                      2       33304.0   \n",
       "7                       2                      2        5723.0   \n",
       "\n",
       "   Total_Revolving_Bal  Avg_Open_To_Buy  Total_Amt_Chng_Q4_Q1  \\\n",
       "1                 2517            904.0                 0.992   \n",
       "3                    0           1438.3                 0.827   \n",
       "4                  749           2379.0                 0.598   \n",
       "5                 1833          31471.0                 0.428   \n",
       "7                 1873           3850.0                 0.851   \n",
       "\n",
       "   Total_Trans_Amt  Total_Trans_Ct  Total_Ct_Chng_Q4_Q1  Avg_Utilization_Ratio  \n",
       "1              992              21                0.400                  0.736  \n",
       "3             3940              82                0.952                  0.000  \n",
       "4             4369              59                0.735                  0.239  \n",
       "5             1448              29                0.381                  0.055  \n",
       "7             2732              63                0.853                  0.327  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Review the features data\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1\n",
       "3    0\n",
       "4    0\n",
       "5    0\n",
       "7    0\n",
       "Name: Attrition_Flag, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Review the first five encoded entries for the target variable\n",
    "y[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split training and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale features\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# Fit the training data to the standard scaler\n",
    "X_scaler = scaler.fit(X_train)\n",
    "\n",
    "# Transform training and testing data using the scaler\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Fitting models and making predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=1)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# KNN\n",
    "knn = KNeighborsClassifier(n_neighbors=3)\n",
    "knn.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Random Forest\n",
    "rf = RandomForestClassifier(n_estimators=500, random_state=78)\n",
    "rf.fit(X_train, y_train)\n",
    "\n",
    "# Logistic Regression\n",
    "lr = LogisticRegression(solver='lbfgs', random_state=1)\n",
    "lr.fit(X_train_scaled, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict the test set results\n",
    "y_pred_knn = knn.predict(X_test_scaled)\n",
    "y_pred_rf = rf.predict(X_test)\n",
    "y_pred_lr = lr.predict(X_test_scaled)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# KNN analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN Classification Report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.97      0.94      1554\n",
      "           1       0.78      0.53      0.63       309\n",
      "\n",
      "    accuracy                           0.90      1863\n",
      "   macro avg       0.85      0.75      0.79      1863\n",
      "weighted avg       0.89      0.90      0.89      1863\n",
      "\n",
      "Accuracy Score: 0.7521897764616211\n"
     ]
    }
   ],
   "source": [
    "# Classification report for KNN model\n",
    "print(\"KNN Classification Report\")\n",
    "print(classification_report(y_test, y_pred_knn))\n",
    "\n",
    "# Calculating the accuracy score\n",
    "knn_acc_score = balanced_accuracy_score(y_test, y_pred_knn)\n",
    "print(f'Accuracy Score: {knn_acc_score}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 700x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create confusion matrix for KNN model\n",
    "knn_cm = confusion_matrix(y_test, y_pred_knn)\n",
    "\n",
    "# Confusion matrix vizualization\n",
    "fig, ax = plt.subplots(figsize= (7, 4))\n",
    "sns.heatmap(knn_cm, annot=True, fmt='g', \n",
    "            cmap='Blues', xticklabels=['Existing', 'Attrited'], yticklabels=['Existing', 'Attrited'])\n",
    "ax.set_title(f'KNN \\nAccuracy: {knn_acc_score:.4%}\\n', fontsize= 14)\n",
    "ax.set_ylabel('Actual', fontsize= 13)\n",
    "ax.set_xlabel('Predicted', fontsize= 13)\n",
    "ax.xaxis.set_ticks_position('top')\n",
    "ax.xaxis.set_label_position('top')\n",
    "\n",
    "# Save figure\n",
    "plt.savefig('Figures/KNN.png', bbox_inches='tight')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Random Forest analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Classification Report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.99      0.98      1554\n",
      "           1       0.95      0.84      0.89       309\n",
      "\n",
      "    accuracy                           0.97      1863\n",
      "   macro avg       0.96      0.92      0.94      1863\n",
      "weighted avg       0.97      0.97      0.97      1863\n",
      "\n",
      "Accuracy Score: 0.9162074696055278\n"
     ]
    }
   ],
   "source": [
    "# Classification report for random forest\n",
    "print(\"Random Classification Report\")\n",
    "print(classification_report(y_test, y_pred_rf))\n",
    "\n",
    "# Calculate the accuracy score\n",
    "rf_acc_score = balanced_accuracy_score(y_test, y_pred_rf)\n",
    "print(f'Accuracy Score: {rf_acc_score}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 700x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create confusion matrix for random forest\n",
    "rf_cm = confusion_matrix(y_test, y_pred_rf)\n",
    "\n",
    "# Confusion matrix vizualization\n",
    "f, ax = plt.subplots(figsize= (7, 4))\n",
    "sns.heatmap(rf_cm, annot=True, fmt='g', \n",
    "            cmap='Blues', xticklabels=['Existing', 'Attrited'], yticklabels=['Existing', 'Attrited'])\n",
    "ax.set_title(f'Random Forest \\nAccuracy: {rf_acc_score:.4%}\\n', fontsize= 14)\n",
    "ax.set_ylabel('Actual', fontsize= 13)\n",
    "ax.set_xlabel('Predicted', fontsize= 13)\n",
    "ax.xaxis.set_ticks_position('top')\n",
    "ax.xaxis.set_label_position('top')\n",
    "\n",
    "# Save figure\n",
    "plt.savefig('Figures/RandomForest.png', bbox_inches='tight')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Logistic Regression analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Classification Report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.96      0.94      1554\n",
      "           1       0.75      0.55      0.63       309\n",
      "\n",
      "    accuracy                           0.89      1863\n",
      "   macro avg       0.83      0.76      0.79      1863\n",
      "weighted avg       0.89      0.89      0.89      1863\n",
      "\n",
      "Accuracy Score: 0.7564193874873486\n"
     ]
    }
   ],
   "source": [
    "# Classification report for logistic regression model\n",
    "print(\"Logistic Regression Classification Report\")\n",
    "print(classification_report(y_test, y_pred_lr))\n",
    "\n",
    "# Calculate the accuracy score\n",
    "lr_acc_score = balanced_accuracy_score(y_test, y_pred_lr)\n",
    "print(f'Accuracy Score: {lr_acc_score}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 700x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create confusion matrix for logistic regression model\n",
    "lr_cm = confusion_matrix(y_test, y_pred_lr)\n",
    "\n",
    "# Confusion matrix vizualization\n",
    "f, ax = plt.subplots(figsize= (7, 4))\n",
    "sns.heatmap(lr_cm, annot=True, fmt='g', \n",
    "            cmap='Blues', xticklabels=['Existing', 'Attrited'], yticklabels=['Existing', 'Attrited'])\n",
    "ax.set_title(f'Logistic Regression \\nAccuracy: {lr_acc_score:.4%}\\n', fontsize= 14)\n",
    "ax.set_ylabel('Actual', fontsize= 13)\n",
    "ax.set_xlabel('Predicted', fontsize= 13)\n",
    "ax.xaxis.set_ticks_position('top')\n",
    "ax.xaxis.set_label_position('top')\n",
    "\n",
    "# Save figure\n",
    "plt.savefig('Figures/LogisticRegression.png', bbox_inches='tight')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Logistic regression model with adjusted weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instatiate logistic regression model\n",
    "lr_adjusted = LogisticRegression(class_weight='balanced', max_iter=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(class_weight='balanced', max_iter=1000)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the model\n",
    "lr_adjusted.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predictions using the scale test data\n",
    "ypred_lr_adj = lr_adjusted.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Classification Report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.84      0.90      1554\n",
      "           1       0.52      0.87      0.65       309\n",
      "\n",
      "    accuracy                           0.84      1863\n",
      "   macro avg       0.74      0.85      0.77      1863\n",
      "weighted avg       0.89      0.84      0.86      1863\n",
      "\n",
      "Accuracy Score: 0.8427267847557702\n"
     ]
    }
   ],
   "source": [
    "# Classification report for adjusted logistic regression model\n",
    "print(\"Logistic Regression Classification Report\")\n",
    "print(classification_report(y_test, ypred_lr_adj))\n",
    "\n",
    "# Calculate the accuracy score\n",
    "lr_adj_acc_score = accuracy_score(y_test, ypred_lr_adj)\n",
    "print(f'Accuracy Score: {lr_adj_acc_score}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 700x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create confusion matrix for adjusted weights logistic regression model\n",
    "lr_adj_cm = confusion_matrix(y_test, ypred_lr_adj)\n",
    "\n",
    "# Confusion matrix vizualization\n",
    "f, ax = plt.subplots(figsize= (7, 4))\n",
    "sns.heatmap(lr_adj_cm, annot=True, fmt='g', \n",
    "            cmap='Blues', xticklabels=['Existing', 'Attrited'], yticklabels=['Existing', 'Attrited'])\n",
    "ax.set_title(f'Logistic Regression (adjusted weights) \\nAccuracy: {lr_adj_acc_score:.4%}\\n', fontsize= 14)\n",
    "ax.set_ylabel('Actual', fontsize= 13)\n",
    "ax.set_xlabel('Predicted', fontsize= 13)\n",
    "ax.xaxis.set_ticks_position('top')\n",
    "ax.xaxis.set_label_position('top')\n",
    "\n",
    "# Save figure\n",
    "plt.savefig('Figures/LR_Adj_Weights.png', bbox_inches='tight')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Logistic Regression Model with Resampled Training Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiate the random oversampler model\n",
    "# Assign a random_state parameter of 1 to the model\n",
    "ros = RandomOverSampler(random_state=1)\n",
    "\n",
    "# Fit the original training data to the random_oversampler model\n",
    "X_train_ros, y_train_ros = ros.fit_resample(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    6222\n",
       "0    6222\n",
       "Name: Attrition_Flag, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count the distinct values of the resampled labels data\n",
    "y_train_ros.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiate the Logistic Regression model\n",
    "log_model_resampled = LogisticRegression(solver='lbfgs', max_iter=500, random_state=1)\n",
    "\n",
    "# Fit the model using the resampled training data\n",
    "log_model_resampled.fit(X_train_ros, y_train_ros)\n",
    "\n",
    "# Make a prediction using the testing data\n",
    "ros_predictions = log_model_resampled.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Resampled Classification Report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.82      0.89      1554\n",
      "           1       0.49      0.85      0.62       309\n",
      "\n",
      "    accuracy                           0.83      1863\n",
      "   macro avg       0.73      0.84      0.76      1863\n",
      "weighted avg       0.89      0.83      0.84      1863\n",
      "\n",
      "Accuracy Score: 0.8287707997852926\n"
     ]
    }
   ],
   "source": [
    "# Print the classification report for the model\n",
    "print(\"Logistic Regression Resampled Classification Report\")\n",
    "print(classification_report(y_test, ros_predictions))\n",
    "\n",
    "# Print the balanced_accuracy score of the model \n",
    "log_model_resample_acc = accuracy_score(y_test, ros_predictions)\n",
    "print(f'Accuracy Score: {log_model_resample_acc}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 700x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a confusion matrix for the model\n",
    "ros_lr_cm = confusion_matrix(y_test, ros_predictions)\n",
    "\n",
    "# Confusion matrix vizualization\n",
    "f, ax = plt.subplots(figsize= (7, 4))\n",
    "sns.heatmap(ros_lr_cm, annot=True, fmt='g', \n",
    "            cmap='Blues', xticklabels=['Existing', 'Attrited'], yticklabels=['Existing', 'Attrited'])\n",
    "ax.set_title(f'Logistic Regression (resampled) \\nAccuracy: {log_model_resample_acc:.4%}\\n', fontsize= 14)\n",
    "ax.set_ylabel('Actual', fontsize= 13)\n",
    "ax.set_xlabel('Predicted', fontsize= 13)\n",
    "ax.xaxis.set_ticks_position('top')\n",
    "ax.xaxis.set_label_position('top')\n",
    "\n",
    "# Save figure\n",
    "plt.savefig('Figures/LR_ROS.png', bbox_inches='tight')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Optimize models using feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.20640194651528845, 'Total_Trans_Amt'),\n",
       " (0.17119322985567162, 'Total_Trans_Ct'),\n",
       " (0.10884790787908374, 'Total_Revolving_Bal'),\n",
       " (0.09968714381400937, 'Total_Ct_Chng_Q4_Q1'),\n",
       " (0.06627854358512064, 'Total_Relationship_Count'),\n",
       " (0.0626707311785822, 'Avg_Utilization_Ratio'),\n",
       " (0.059628971939898724, 'Total_Amt_Chng_Q4_Q1'),\n",
       " (0.034369109473407156, 'Credit_Limit'),\n",
       " (0.03253871403979667, 'Customer_Age'),\n",
       " (0.03226063690395316, 'Avg_Open_To_Buy'),\n",
       " (0.024713628537590973, 'Months_on_book'),\n",
       " (0.024655629078544003, 'Months_Inactive_12_mon'),\n",
       " (0.01867607291528816, 'Contacts_Count_12_mon'),\n",
       " (0.013706529888164872, 'Dependent_count'),\n",
       " (0.012158549493576002, 'Education_Level'),\n",
       " (0.0114144213360089, 'Income_Category'),\n",
       " (0.009270527304585626, 'Marital_Status'),\n",
       " (0.009216881151447493, 'Gender'),\n",
       " (0.0023108251099822593, 'Card_Category')]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Feature importances\n",
    "importances = rf.feature_importances_\n",
    "\n",
    "# Sort the features by their importance\n",
    "ft_imp_arr = sorted(zip(rf.feature_importances_, X.columns), reverse=True)\n",
    "ft_imp_arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtQAAAG1CAYAAADZbWOSAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAADYIUlEQVR4nOzdeVzN6fv48VeLSitZm2QLKYrsygzCIPs2IhkyQ/YllJ0YiqzZQo19GRUygxnLWMcw9rHmk2VG9i1baTu/P/p1vpqS06ZTXc/Ho8fDeS/3fV+ncJ27677fGgqFQoEQQgghhBAiSzTzegBCCCGEEELkZ5JQCyGEEEIIkQ2SUAshhBBCCJENklALIYQQQgiRDZJQCyGEEEIIkQ2SUAshhBBCCJENklALIYQQQgiRDZJQCyGEEEIIkQ2SUAshhMgT8lyxnCXvpxB5RxJqIYQo5Ly9vbGysvro165du3K0v7i4OObMmcPu3btztN2cdu/ePaysrAgLC8vroXzSwYMH8fLyyuthCFFoaef1AIQQQuS9UqVKsXTp0nTPlS9fPkf7evz4MWvXrmXOnDk52m5htnbt2rweghCFmiTUQggh0NHRoXbt2nk9DCGEyJek5EMIIYTKDhw4QNeuXbG1tcXR0ZFZs2bx7t27NNf07t0be3t7atasSZs2bdi4cSOQXEbRokULACZMmICTkxOQXHaS8ucU/y25OHXqFFZWVmzdupXmzZvj4ODA8ePHAThz5gx9+vShVq1aNGjQAC8vL54/f65sKykpicWLF+Pk5ETNmjVxcnJiwYIFxMfHZyp+KysrtmzZgre3N3Xr1qVBgwbMmjWL2NhY/Pz8aNSoEQ0bNmTSpEm8f/8+1X0bN27Ey8sLe3t7HBwclPd9aM+ePXTt2hV7e3scHR2ZOnUq0dHRyvMBAQG0atWKpUuX0rBhQ1q2bEmHDh04ffo0p0+fxsrKilOnTgFw/fp1hg0bRqNGjahRowZffvllmj6trKzYtGkTkyZNokGDBtjb2zNixAiePn2aaly//PILXbt2pVatWjRr1ox58+YRFxenPB8REcGgQYOoU6cOderUYejQofz777+p2tiwYQNt2rTB1taWL7/8kunTp/PmzZtMvf9CqCtJqIUQQgCQkJCQ5uvDhW67d+9m6NChVK5cmWXLljFs2DDCw8MZMmSI8rrDhw8zdOhQatSowfLlywkICMDc3JyZM2dy7tw5SpcurSwtGTx48EfLTDKycOFCvLy88PLyonbt2vz111/069cPPT09Fi1axMSJEzl9+jR9+/ZVJo+rV69m06ZNDB06lODgYHr16sWaNWtYuXJlpvv39/dHR0eHpUuX0qlTJzZs2EDnzp158OAB8+bNw8XFhZCQEDZs2JDqvsWLF/Ps2TMWLVrEd999x08//cS4ceOU55cvX87o0aOpVasWS5YsYejQofz666+4ubmlSoLv37/P/v37WbBgAaNGjWLhwoXY2NhgY2PDtm3bqFGjBo8fP8bV1ZWYmBh8fX1ZvXo1bdu2ZcOGDWnKQxYuXEhSUhILFixg/PjxHD58mNmzZyvPb926lTFjxmBtbc3SpUsZNGgQmzdvZvr06QDcvn0bFxcXnj17hq+vLz/88AP//vsvvXr14tmzZ0ByQu7n54erqytBQUEMHTqUXbt2MWvWrEy//0KoIyn5EEIIQVRUFDVq1EhzfOTIkcqE2d/fny+//BJ/f3/l+YoVK9KvXz+OHDlCs2bN+N///kfnzp2ZNGmS8hp7e3saNmzIX3/9RZ06dbC2tgaSa7NtbGwyPVYXFxfatGmjfD1//nwqVapEYGAgWlpaANSqVYt27doRGhqKq6srp0+fpkaNGnTr1g2ABg0aULRoUQwNDTPdv6WlJT4+PgDUr1+fkJAQ4uPj8ff3R1tbmy+//JJDhw5x7ty5VPeZmpqycuVKtLW1adq0KZqamsyZM4ebN29SunRpVqxYQY8ePZg2bZrynmrVquHq6kpYWBi9e/cGkj/4eHl54eDgoLwuJY6Usp0LFy5gbW3N4sWLleccHBw4efIkf/31Fx4eHqn6+LCe/dKlS+zbtw9IntlPmRX/4YcflNe8f/+eHTt2EBcXx9KlS9HT02Pt2rXKvho3bkzLli1Zs2YNXl5enDp1CnNzc1xdXdHU1KRBgwbo6+vz4sWLTL//QqgjSaiFEEJQqlQpVqxYkeZ4mTJlALh16xYPHz5k0KBBJCQkKM/Xr18fQ0NDTpw4QbNmzfjuu+8AePfuHf/88w+3b9/m77//Bsh0ecXHWFlZKf8cExPDxYsXGTBgAAqFQjk2CwsLLC0tOXHiBK6urjRs2JD58+fTu3dvWrVqxVdffUWfPn2y1L+9vb3yz9ra2hQvXpyaNWuirf1//6UWK1aM169fp7qvXbt2qa5p3bo1c+bM4cyZM3zxxRfExcXRoUOHVPfUq1cPc3NzTp06pUyoITkJzkiTJk1o0qQJ8fHx3L59mzt37nDjxg2eP39OsWLFUl3739r5smXLEhMTAyTPPj99+pSWLVumuqZfv37069cPgD///JOGDRuip6enfP8NDQ2pV68ef/zxBwCNGjVi27ZtdO3ala+//ppmzZrRoUMHNDQ0MoxDiPxCEmohhBDo6Ohga2v70fMvX74EYMaMGcyYMSPN+cePHwPw/Plzpk2bxoEDB9DQ0KBChQrUrVsXyLl9kkuUKKH886tXr0hKSmL16tWsXr06zbW6uroAfPfddxgYGBAaGoqfnx++vr5Uq1aNiRMn0rhx40z1n96sdtGiRT95X+nSpdON49WrVxgYGABQsmTJNPeVLFkyTXKe3nUfSinh2LRpE+/evcPMzAw7Ozvl+5HR2DU1NZXfq5Tv+4fv+X+9fPmSPXv2sGfPnjTnTE1NAXB2diYpKYnNmzezdOlSFi9ejLm5OZ6enrRr1y7DWITIDyShFkII8UnGxsYAjB8/ngYNGqQ5b2JiAsDYsWOJjIzkxx9/pE6dOujo6BATE8P27dszbF9DQ4PExMRUx/672DE9BgYGaGho0K9fv3QTs5RkUVNTE1dXV1xdXXn27BlHjhxh5cqVDB8+nD/++AMdHZ1P9pVdKclpipSFf6ampsr37+nTp1haWqa67smTJ1hYWGSqr1WrVrF27VqmT59O69atMTIyAqB79+6Zaifl+/7hAk9IjuXKlSvUrl0bIyMjHBwc6N+/f5r7P5yRb9++Pe3bt+f169ccP36c1atXM27cOOrVq6f8TYgQ+ZUsShRCCPFJlStXpkSJEty7dw9bW1vlV9myZZk/fz5Xr14F4OzZs7Ru3ZpGjRopk9SjR48CybOmgLLO+UMGBga8ePEi1c4Y/61BTo+hoSE2NjbcunUr1biqVq3K0qVLlTteuLi4KBfAlShRgq5du+Lq6srr168/204Thw4dSvX6119/RUNDg0aNGlGrVi10dHTSPOzmzJkz3L9/nzp16mTYtqZm6v/Oz549S5UqVejevbsymX706BERERHK74MqKleuTPHixTl48GCq47t37+b777/n/fv3NGjQgP/9739YW1sr3/+aNWuydu1a9u/fD8CoUaMYNmwYAEZGRrRt25YhQ4aQmJio/O2GEPmZzFALIYT4JC0tLUaPHs3UqVPR0tKiefPmvHr1iuXLl/Po0SPlgkY7Ozt2795NjRo1KFu2LOfPnycwMBANDQ1lXW5Kgnfy5EksLS2pVasWzZs3Z8OGDUycOJEePXpw8+ZNgoOD002+/2vMmDEMHDgQT09POnbsSGJiIsHBwVy8eJHBgwcDybXewcHBlCxZEnt7ex49esSPP/5IgwYNlGUJue3SpUuMHTuWTp06cePGDZYsWcI333yjnH0eOHAgS5cupUiRIrRo0YJ79+6xePFiqlSpQteuXTNs29jYmPPnz3Py5ElsbGyws7Nj+fLlrFq1itq1a3P37l0CAwOJi4tTfh9UoaWlxfDhw/Hx8WH69Om0atWKO3fusGjRInr16oWpqSlDhgzBxcWFQYMG0atXL3R1ddm2bRsHDhxgyZIlQHIN9bRp0/Dz8+Orr77i1atXLF26lIoVK1K9evWsv6lCqAlJqIUQQqikR48eGBgYsGbNGrZt24a+vj516tTB399fmRT6+voyc+ZMZs6cCSTvAjJjxgzCw8M5c+YMkDyr3L9/f7Zt28bhw4c5ceIEjo6OeHl5sWHDBn777Tdq1KjB0qVLcXFx+eS4mjRpQlBQEEuXLmXEiBEUKVKEGjVq8OOPPyoX3I0cORIdHR1CQ0NZtmwZRkZGODk54enpmTtvVjq+/fZbHj16xLBhwyhevDgeHh4MGjRIeX748OGULFmSjRs3sn37dooVK0abNm0YNWrUJ2u0XV1duXz5Mt9//z1z5sxh0KBBvHjxgvXr17Ns2TLMzMzo1KkTGhoaBAYGEh0drSwz+RRXV1f09fUJCgoiJCSEMmXK4O7uzsCBAwGoXr06mzZtYuHChYwfPx6FQkG1atVYtmyZcs9xFxcX4uPj2bp1K5s3b0ZPT4/GjRszbtw4ihQpksV3VAj1oaHIqVUiQgghhEiXlZUVw4YNY/jw4Xk9FCFELpAaaiGEEEIIIbJBEmohhBBCCCGyQUo+hBBCCCGEyAaZoRZCCCGEECIbJKEWQgghhBAiGyShFkIIIYQQIhskoRZCCCGEECIb5MEuQnwGCoWCpKSCv/5XU1OjwMcpMRYMEmPBURjilBjzhqamBhoaGipdKwm1EJ+BhoYGr169IyEhKa+Hkmu0tTUpXtygQMcpMRYMEmPBURjilBjzjqmpAVpaqiXUUvIhhBBCCCFENkhCLYQQQgghRDZIyYcQn4mWVsH+/JoSX0GOU2IsGCTGgqMwxCkxflpSUt6vU5InJX5G3t7e7NixI8Nrbty48dFz9+/f5/z587Rr106l/sLCwpgwYUKGbX7Iysoqw/NdunTB19dXpbby0k8//cSUKVP49ttvmThxYo60+fvvv2NhYUGVKlWy1oBCASoubBBCCCGE6hRJCTx/EZvjSXVyDbVqSb4k1J/R69eviY2NVb5u0qQJEydOxNnZWXmsVKlSH73fzc0Nc3NzlZPazCbUT548Uf55z549zJ49m+PHjyuP6enpYWRkpFJbecnFxYWXL1/y/Plzjh49ip6eXrbai4qKwsnJifXr19OwYcOsN/SHK0Rfy9ZYhBBCCPEBE2tw2MSLF29zfEFjZhJqKfn4jIyMjNIkpEZGRhkm0Z/Th+NIGae6jE1VkZGRnD9/nmXLljF8+HD27NlD165ds9Vmjn3mjL4GL87nTFtCCCGEUBsFtyAnHzp8+DDffPMN9vb2NGnSBF9fX96/fw8kz06fPn2aHTt24OTkBMDDhw8ZO3YsDg4O1KhRg6ZNm7Jw4UKSknJvyxk3NzcmTpxIjx49qFevHjt37iQuLo758+fTsmVLatasScOGDRkzZgwvXrwA4N69e1hZWbF371569OiBra0tLVq0ICQkRNnus2fPGDFiBA0bNsTOzg4XFxdOnz6d6fGFhYVhbGxMs2bNqFevHlu2bEl1PmUsR44coWvXrtja2tKhQwcuXLjA9u3bad68OXXq1MHT05P3799z7949WrRoAUDfvn0JCAjIxrsnhBBCiIJIEmo1ceDAAQYPHkzTpk0JDQ1l5syZ7N27l7FjxwIQEBCAvb09bdu2VSaigwYN4vnz5wQFBbFv3z6+++47Vq5cyaFDh3J1rGFhYfTt25ctW7bQtGlT5s6dy88//8wPP/zAr7/+ip+fHydOnGDFihWp7vP19cXDw4OdO3fSuHFjpkyZwr///gvA9OnTiY2NZePGjezevZtKlSoxZMgQ3r17p/K4EhMT2bVrFy1btkRbW5t27dpx6dIlrl69muZaHx8fxo4dy86dO9HT02PgwIHs3buXlStX4uvry6+//sr27dsxMzNj+/btQPL3wN3dPRvvnBBCCCFyg5aWJtraOfuVGVLyoSYCAwNp1aoVQ4cOBaBy5cooFAoGDx5MZGQklpaWFClSBD09PUxNTYmNjaVTp060bt0ac3NzIHn2eNWqVdy4cYOWLVvm2litra3p0KGD8rWtrS1ff/01DRo0AMDc3JwmTZqkqd3u37+/crbXy8uL7du3c/HiRSwsLPjnn3+oVq0a5cuXR1dXl0mTJtGhQwe0tLRUHtfRo0d58uSJsia9devWzJo1i61bt+Lj45NmLA4ODgB07twZHx8fpk2bRoUKFbCyssLGxoaIiAi0tLQwNTUFwMTEBAMDg0y+W0IIIYTIbcbGRfO0f0mo1URERESa3Tvq168PJO/8YWlpmeqcnp4effr0Yd++faxbt467d+9y/fp1Hj9+nKslHwAVKlRI9bpTp06cPHmSBQsWcOfOHSIjI7l16xb16tVLdd2HMaTUaMfHxwMwbNgwxo0bx/79+6lXrx5NmjTB2dkZXV1dlccVGhpKsWLFaNy4MQDFixencePG7N69m/Hjx2NoaKi8tlKlSso/Fy2a/JfQwsJCeUxXV5e4uDiV+xZCCCFE3nn1KobExJzNf4yNi6q8KFFKPtSEQqFI87z4xMREALS1037uiYmJwcXFhRUrVmBoaEinTp3YtGkTZcuWzfWx/nfXjOnTpzNy5EhiY2Np1qwZ/v7+6W7tp6Ojk+ZYyoK/Vq1acezYMWbPnk3ZsmVZs2YNrVu35ubNmyqN6fnz5xw+fJiXL19iZ2eHjY0NNjY2HDt2jHfv3rFr165U16f3nmpqyl8HIYQQIj9KTEwiISFnvzJDZqjVRLVq1Th79izffvut8tiZM2cA0sxOAxw7dowrV65w4sQJSpYsCcDLly959uxZzu1KoYIXL16wZcsWFi5cmGr7v1u3bqGvr69SGymLGjt16oSzszPOzs7ExMTQpEkTDh8+TNWqVT/ZRnh4OPHx8Sxbtozy5csrjyclJeHu7s7WrVtxdXXNfICQ5oOOEEIIIcSHJKFWEwMGDGD06NEsW7YMZ2dn7ty5w8yZM2nevLkyoTYwMCAqKoqHDx8qZ6LDw8Np3bo1Dx48YMGCBcTHx3/WUoWUrQAPHjxIjRo1lAsLr1y5Qq1atVRqQ0dHh4sXL3LmzBmmTJlCyZIlOXLkCG/fvsXe3l6lNkJDQ7G3t0+3drx3794EBARw9uxZypQpk6n4AOUHg4iICGxsbLK+F7eJddbuE0IIIUT61OT/Vkmo1UTbtm1JTEwkMDCQFStWYGpqSvv27RkxYoTyGhcXF7y8vOjYsSMnT55kwoQJrF27lkWLFlGmTBmcnZ0xMzPj4sWLn23c2traLF68GF9fXzp06ICJiYly27yVK1eqvEvH4sWLmTNnDoMHD+b169dUrlyZ+fPnp6nDTs/ly5eJiIjA398/3fO9e/dm9erVbN26lZEjR2YqPkiuxe7WrRtz587l7t27TJ48OdNtoFCAw6bM3yeEEEKIDCmSEuTR40IUFrmxYEKdaGlpYmxctEDHKTEWDBJjwVEY4pQYPy0pSZErCbU8KVEINZSyYKKgKwxxSowFg8RYcBSGOCVG9SYJdSHw6NEj2rRpk+E1NjY2bNqkfiUJ+XnsQgghhCgcJKEuBEqWLMnOnTszvCYz+z1/Tvl57EIIIYQoHCShLgS0tLTSPIwlv8jPYxdCCCFE4SBPshBCCCGEECIbJKEWQgghhBAiGyShFkIIIYQQIhukhlqNeXt7s2PHjgyvuXHjxkfP3b9/n/Pnz9OuXTuV+gsLC2PChAkZtvkhKyurDM936dIFX19fldrKK/fu3WPNmjUcPXqUp0+fUrJkSZo2bYqHh0eqpyr+/vvvWFhYUKVKlSz3pepelvlVSnwFOU6JsWCQGAuOghBnbu2hLD4vebCLGnv9+jWxsbHK102aNGHixIk4Ozsrj5UqVeqj97u5uWFubq5yUpvZhPrJkyfKP+/Zs4fZs2dz/Phx5TE9Pb2sP6b7Mzh37hwDBw6kfv369OvXD3Nzc+7evcv8+fN5+fIlW7ZsoUyZMkRFReHk5MT69etp2LBh1jpTKEBDI2cDEEIIke8pkhJ4/iL2o0m1trYmxYsb8OLF23y7R/OnqGuM8mCXAsLIyChNQmpkZJRhEv05fTiOlHGqy9g+JS4uDk9PTxwcHFi8eDEa/z/ZLVeuHLa2tnz99dcEBAQwa9YscuQzp4YG/OEK0dey35YQQoiCwcQaDYdNaGpqyCx1PicJdT52+PBhli9fzs2bNzEwMKB9+/aMHj0aXV1d3NzcOH36NACnT5/m0KFDPHz4EH9/f/744w+io6MpWbIknTt3ZuTIkWhq5s6vy9zc3LCwsODmzZvcvn2byZMn4+zsTEBAAHv37uXhw4cYGBjg6OjIlClTKF68OPfu3aNFixYsWrSI4OBgrl+/TunSpRk8eDDdu3cH4NmzZ8yYMYNTp04RExODjY0NY8aMoUGDBiq/d/fv32fFihXKZDqFsbExq1evpkSJEsqxAPTt25dhw4YxfPjwrL0Z0dfgxfms3SuEEEIItSUJdT514MABhg8fzrBhw/D19eXu3btMnz6dqKgoAgICCAgIwMPDg7JlyzJ16lQABg0aRIkSJQgKCsLQ0JDDhw8za9YsbG1tadmyZa6NNSwsjHnz5lG9enVKlizJ3LlzOXjwIL6+vpQrV46bN2/i5eXFihUrmDhxovI+X19fpk6dSsWKFfnxxx+ZMmUKDRs2xMLCgunTp/P+/Xs2btyIjo4OK1euZMiQIRw9ehR9ff1Pjunvv/9GX1//o3Xgtra2ACQmJrJ9+3Z69OhBQEAAjo6OOfOmCCGEEP9fRmUFBaFO/FMKQoySUOdTgYGBtGrViqFDhwJQuXJlFAoFgwcPJjIyEktLS4oUKYKenh6mpqbExsbSqVMnWrdujbm5OZA8e7xq1Spu3LiRqwm1tbU1HTp0UL5OKalImU02NzenSZMmaWq3+/fvr5wd9vLyYvv27Vy8eBELCwv++ecfqlWrRvny5dHV1WXSpEl06NABLS0tlcYUHR2NkZFRmtnp/9LS0sLU1BQAExMTDAwMVI5bCCGEUIWxcdEcuSa/y88xSkKdT0VERKTZvaN+/fpA8s4flpaWqc7p6enRp08f9u3bx7p167h79y7Xr1/n8ePHJCXl7gKA/z7psFOnTpw8eZIFCxZw584dIiMjuXXrFvXq1Ut13YcxpNRox8fHAzBs2DDGjRvH/v37qVevHk2aNMHZ2Vnlx5AXL16c6OhoFArFJ5NqIYQQIje9ehVDYmL6/xdraWlibFw0w2vyO3WN0di4qMqz5vl3br2QSy8RTExMBEBbO+3npJiYGFxcXFixYgWGhoZ06tSJTZs2UbZs2Vwfq56eXqrX06dPZ+TIkcTGxtKsWTP8/f3T3dpPR0cnzbGUBYKtWrXi2LFjzJ49m7Jly7JmzRpat27NzZs3VRpTnTp1iI2N5fr16+meDw4OVpbKCCGEELkpMTGJhIT0v1ISzIyuye9f6hpjZsgMdT5VrVo1zp49y7fffqs8dubMGYA0s9MAx44d48qVK5w4cYKSJUsC8PLlS549e5Yzu1io6MWLF2zZsoWFCxem2v7v1q1bKtU+Q/IOHfPnz6dTp044Ozvj7OxMTEwMTZo04fDhw1StWvWTbTRu3Jhy5cqxYsUKlixZkurc8+fPCQ4OpkmTJgAygy2EEEKIDElCnU8NGDCA0aNHs2zZMpydnblz5w4zZ86kefPmyoTawMCAqKgoHj58qJyJDg8Pp3Xr1jx48IAFCxYQHx9PXFzcZxt3ylaABw8epEaNGsTGxrJx40auXLlCrVq1VGpDR0eHixcvcubMGaZMmULJkiU5cuQIb9++xd7eXuU2fvjhBzw8PBgyZAj9+/enbNmyREREsGjRIvT09PD09ARQJvoRERHY2NhkfW9tE+us3SeEEKJgkv8XCgxJqPOptm3bkpiYSGBgICtWrMDU1JT27dszYsQI5TUuLi54eXnRsWNHTp48yYQJE1i7di2LFi2iTJkyODs7Y2ZmxsWLFz/buLW1tVm8eDG+vr506NABExMTGjZsyJgxY1i5ciXv3r1TqZ3FixczZ84cBg8ezOvXr6lcuTLz589PU4edkUaNGrF161ZWrVqFp6cnL168oEyZMjRr1gwPDw/lTH7x4sXp1q0bc+fO5e7du0yePDnzgSsU4LAp8/cJIYQo0BRJCbIHdQEgT0oU4jNRt8UWOU1dF5XkJImxYJAYC46CEOenHj2urk8RzEnqGqM8KVEINZSy2KKgKwxxSowFg8RYcBSWOIX6koRapPHo0SPatGmT4TU2NjZs2qR+JQz5eexCCCGEyJ8koRZplCxZkp07d2Z4jar7PX9u+XnsQgghhMifJKEWaWhpaaV5GEt+kZ/HLoQQQoj8SR7sIoQQQgghRDZIQi2EEEIIIUQ2SEIthBBCCCFENkgNtRCfiap7WeZXKfEV5DglxoIhN2L81F7CQoiCTRLqD3h7e7Njx44Mr7lx48ZHz92/f5/z58/Trl07lfoLCwtjwoQJGbb5oXv37tGiRYs0x/X19alUqRIDBw785JZxOcnb25uoqCg2bNjwyWsDAgLYsWMHhw4d+gwj+78+ly5dqnytoaGBiYkJjo6OeHt7U7p0aZXbcnJyokuXLgwfPjxrg1EoMDYumrV785nCEKfEWDDkZIyKpASev4iVpFqIQkoS6g9MmjQJT09P5esmTZowceJEnJ2dVbrfy8sLc3NzlRPqrAoICMDe3h4AhULBkydPCAwMZPTo0ZiZmVGrVq1c7T8r3N3dcXV1/ez9li1blpCQEAASExN5+PAhvr6+DB48mNDQ0M83EA0N+MMVoq99vj6FEJ+HiTUaDpvQ1NSQhFqIQkoS6g8YGRlhZGSU5lipUqXyaETpMzExSTWm0qVL4+/vT/369dm7d69aJtQGBgYYGBh89n61tLRSvVdly5Zl/Pjx9OrVi4iICKpVq/b5BhN9DV6c/3z9CSGEEOKzKLhFcrng8OHDfPPNN9jb29OkSRN8fX15//49AG5ubpw+fZodO3bg5OQEwMOHDxk7diwODg7UqFGDpk2bsnDhQpKScv7xqJqammhra6Op+X/f0sjISL7//nvleD09PXny5AmQXG5ia2vLq1evUrXTunVrFi1aBMCDBw8YO3Ysjo6O1K5dmwEDBny0PMXNzY1Ro0alOnb27FmsrKy4e/cuAQEByvfl3r17WFlZsXfvXnr06IGtrS0tWrRQziSnWLduHU5OTtjZ2dGvXz+WLl2qbCM79PX10xwLDQ2lc+fO2NnZUbt2bdzc3Lhy5Uq2+xJCCCFEwScJtYoOHDjA4MGDadq0KaGhocycOZO9e/cyduxY4P/KMNq2batMDAcNGsTz588JCgpi3759fPfdd6xcuTLH64ijo6Px9fUlJiaG9u3bA8mP4O7duzcWFhaEhISwcuVK3rx5g4uLC+/evaNNmzZoa2vz66+/Ktu5ePEid+7coXPnzrx584ZevXrx6NEjVqxYwdatW9HX16dPnz7cv38/zRi6dOnC77//zps3b5THwsPDqVOnzkcftOLr64uHhwc7d+6kcePGTJkyhX///ReATZs2sWDBAoYMGcKuXbto2LAhy5Yty/Z79eLFC5YuXYq9vb1ydnr//v1MmzaNfv36sXfvXtatW0dsbCyTJk3Kdn9CiMJDS0sTbW31+Ppw4WVej0XilBjza4yZISUfKgoMDKRVq1YMHToUgMqVK6NQKBg8eDCRkZFYWlpSpEgR9PT0MDU1JTY2lk6dOtG6dWvMzc2B5FncVatWcePGDVq2bJnlsXz//fdoaWkBkJSUREJCAnZ2dgQHB2NjYwPAli1bKF26NFOnTlXet2jRIho1asS+ffvo2rUrbdq0ITw8nB49egD/lwBXrFiRzZs38+LFC8LCwjA1NQXA39+fli1bsmnTJsaNG5dqTG3atGHmzJns37+fLl26EBcXx759+5QfONLTv39/5SJLLy8vtm/fzsWLF7GwsCAoKIi+ffvSvXt3AAYPHszVq1czPWt8//59Zb15UlISsbGx6Orqsnr1auU1xYoVY9asWXTu3BkAc3NzevTowbRp0zLVlxCicFPHhZzqOKbcUBjilBjVmyTUKoqIiEiz2LB+/fpA8s4flpaWqc7p6enRp08f9u3bx7p167h79y7Xr1/n8ePH2S75mDVrFrVq1SImJoZt27axZ88eBgwYQKNGjZTXXL16lcjISGUymeL9+/dERkYCybPKffv25eHDh5QsWZI9e/YwevRoZbwVK1ZUJtMAurq62NnZpVv2oa+vT5s2bdi9ezddunTh6NGjxMbG0rZt24/G8eF7llK7Hh8fz4sXL4iKiqJ27dqprq9bt26mE+rSpUsrdyFJSkri5cuXhIWFMWDAAIKDg2nQoAH169fH1NSU5cuXc/fuXW7fvs21a9dypTRHCFFwvXoVQ2Kievy7oaWlibFxUbUaU24oDHFKjHnH2LioyttrSkKtIoVCgYaGRqpjiYmJAGhrp30bY2JicHV1JSYmhrZt29KpUyemTJmSIztdlClTRllGMW3aNGJjYxk1ahTr1q2jbt26QHLy2KhRo3RnWVOS1/r161OuXDl+/vlnLC0teffunTIBTi/elJjTixega9eufPvttzx58oTw8HBatmyJoaHhR+PQ0dFJc0yhUCjbVyiyv1peW1s7TcmJvb09p06dYuPGjTRo0IBffvmF8ePH0759e+zs7OjevTsRERH4+Phku38hROGRmJhEQoL6JAOgnmPKDYUhTolRvUkNtYqqVavG2bNnUx07c+YMQJrZaYBjx45x5coVNmzYwIgRI3B2dsbQ0JBnz57lSKL4ocmTJ1OmTBnGjx9PTEwMAFWrViUyMhIzMzMqVKhAhQoVMDExYfbs2URERADJ+zJ37tyZX3/9lV9++YWWLVsqk+1q1apx+/Ztnj17puzn/fv3XL58mSpVqqQ7jvr162Nubs7OnTs5fPgwXbp0yVI8RkZGmJubc+HChVTHL126lKX20qNQKJTfh5UrV9K9e3f8/PxwdXWlfv36ylrunP5eCSGEEKLgkRlqFQ0YMIDRo0ezbNkynJ2duXPnDjNnzqR58+bKhNrAwICoqCgePnxI2bJlgeS65NatW/PgwQMWLFhAfHw8cXFxOTo2AwMDZs6cSf/+/Vm8eDHe3t707t2bbdu2MWbMGIYOHYqGhgbz5s3j6tWrVK1aVXlvly5dWL58Of/73/8ICAhQHu/QoQMrV65k1KhRjBs3Dh0dHZYvX867d+/o2bPnR8fSuXNnVqxYQbFixXBwcMhyTN9//z1+fn5YWlpSp04dfv/9d/bu3YuZmVmm2klMTFTubALw5s0btm3bxj///IOXlxcAZmZmnDt3jitXrmBkZMShQ4fYuHEjAHFxcejq6mY5jlRMrHOmHSGEepG/20IUepJQq6ht27YkJiYSGBjIihUrMDU1pX379owYMUJ5jYuLC15eXnTs2JGTJ08yYcIE1q5dy6JFiyhTpgzOzs6YmZlx8eLFHB+fg4MDXbt2Zf369bRr1w5bW1s2btzI/Pnz6d27N1paWtSuXZt169ZRokQJ5X3m5uY0aNCAW7dupUqAjY2N2bhxI35+fvTr1w9IrmHesmULFhYWHx1Hly5dWLp0Ka6urqm28MusXr16ER0dzcKFC3nx4gUNGjSgS5cuaX5L8CkPHz6kSZMmytf6+vpYWlri5+enXBg6ZcoUpk6dSp8+fdDR0aF69erMnTuX0aNHc/HiRRo0aJDlOJQUCnDYlP12hBBqSZGUIA91EaIQ01DI77SFGjp69ChVq1ZNNSM9ZcoU/vnnH9atW5eHI8s6dVtskdPUdVFJTpIYC4bciDEpSaFWCbW2tibFixvw4sXbfFuTqorCEKfEmHdMTQ1kUaLI33bt2kVkZCTTp0+nVKlS/PXXX4SHh+frrezy82KLzCgMcUqMBUNhiFEI8XlIQq0GHj16RJs2bTK8xsbGhk2bCk/JwJQpU/D19WXo0KG8evWK8uXLM3HiRLp27cqePXs++dCVvn37KrcAFEIIIYTITZJQq4GSJUuyc+fODK/JsYVx+USxYsXw9fVN91zTpk0/+X4ZGxvnwqiEEEIIIdKShFoNaGlpffTx3CItAwMDDAwM8noYQgghhBCA7EMthBBCCCFEtkhCLYQQQgghRDZIyYcQn4mqW+/kVynxFeQ4JcaCoSDHJoTIG/k6ofb29mbHjh0ZXnPjxo2Pnrt//z7nz5+nXbt2KvUXFhbGhAkTMmwzPffu3WPNmjUcPXqUp0+fUrJkSZo2bYqHhwdlypRRXvf7779jYWHx0Ud7f8zBgwfZvHkzV65c4f3791SsWJGePXvyzTffKB+u4ubmhrm5+UcX+n1u+/fvZ9OmTVy7do24uDgqVKhAp06dcHV1RUdHJ917nj9/TseOHenZsyfDhw/PVH8KhYLQ0FBCQkKIjIxEoVBQpUoVunfvTrdu3dDQ0Ej3vuXLl3Py5Ek2bNiQ6Rj/MwCMjYtmr418ojDEKTEWAIrEj/69F0KIzMrXCfWkSZPw9PRUvm7SpAkTJ07E2dlZpfu9vLwwNzdXOaHOinPnzjFw4EDq16/PnDlzMDc35+7du8yfP59evXqxZcsWypQpQ1RUFB4eHqxfvz5TCfXcuXPZtGkTgwcPZty4cejp6XHixAl8fX35+++/+eGHH3IttqyaMWMGO3fuZPDgwUyePBltbW3OnDlDQEAA+/btIzg4ON1Fh1OmTEn1GHFVJSYmMnToUC5evMjw4cNxcHAgMTGREydO4Ofnx6FDhwgICEBLSyvVfWvXrmXJkiXUr18/y7EqaWjAH64QfS37bQkhssfEGhw2oakpCbUQImfk64TayMgIIyOjNMdKlSqVRyNKLS4uDk9PTxwcHFi8eLFyNqRcuXLY2try9ddfExAQwKxZs8jKAyuPHj1KUFAQK1aswMnJSXm8YsWKGBoaMn78eLp27UrdunVzLKbs+vnnn9m2bRvr16+nXr16yuMVK1bkyy+/pGPHjvj5+eHj45Pqvm3btnH79u0sfW/XrFnDqVOnCA0NpXLlysrjlpaWNGjQgO7duxMUFMTAgQOB5H3BJ02axNmzZ6lUqVIWI01H9DV4cT7n2hNCCCGEWijQhWSHDx/mm2++wd7eniZNmuDr68v79++B5BKI06dPs2PHDmUy+vDhQ8aOHYuDgwM1atSgadOmLFy4kKSkrD1J6/Dhw9y/f58hQ4ak+dWisbExq1evZsiQIdy7d48WLVoAyQ8kCQgIUKn9zZs3Y21tnSqZTtG+fXvWrl2LtbW18tjbt2+ZOHEi9erVo27dunh7e/Pu3TsATp06hZWVFUeOHKF9+/bUrFmTdu3a8fvvvyvvT0xMZOHChTRp0oRatWoxfPhwfvjhB9zc3FR+T9avX0/Tpk1TJdMpypQpQ//+/dmxYwevX79WHr99+zb+/v7Mmzfvo+UgH5OUlMTGjRvp1q1bqmQ6RfXq1enSpQsbNmxQfp+vXLmCiYkJ4eHh1KpVK1P9CSGEEKLwydcz1Bk5cOAAw4cPZ9iwYfj6+nL37l2mT59OVFQUAQEBBAQE4OHhQdmyZZk6dSoAgwYNokSJEgQFBWFoaMjhw4eZNWsWtra2tGzZMtNj+Pvvv9HX18fKyird87a2tkByorp9+3Z69OhBQEAAjo6OKrV/+fJlWrVqle45LS0tGjdunOrYb7/9xqBBgwgLC+PmzZuMHj0aMzMzRo4cqbxm3rx5TJo0iRIlSrBgwQLGjh3L0aNHMTAwwN/fnx07duDj44OlpSWbN29mw4YNKpdExMXFcenSJcaOHfvRaxo1asTChQu5fPkyjRs3Jj4+Hk9PTwYMGECNGjVU6udD//zzD48fP85wlr5Ro0b89NNP3Lt3j/Lly+Pk5JTuhxQhRMGiqamBtnbBnFcqDItLoXDEKTHmDwU2oQ4MDKRVq1YMHToUgMqVK6NQKBg8eDCRkZFYWlpSpEgR9PT0MDU1JTY2lk6dOtG6dWvMzc2B5FnsVatWcePGjSwl1NHR0RgZGX1y4YuWlhampqYAmJiYqPzQkpcvX2bqiYC2traMGTMGgPLly+Po6Mjly5dTXTNq1ChlIj5q1Cg6depEREQE1atXZ/PmzUyYMIGvv/4aSK5pPn9e9RKGly9folAoMDEx+eg1xYoVA+DZs2cALFmyBF1dXb7//nuV+/nQixcvAFTus3z58lnqRwiR/xga6uX1EHJdgV9c+v8VhjglRvVWYBPqiIiINIsNU2ZSb9y4gaWlZapzenp69OnTh3379rFu3Tru3r3L9evXefz4cZZLPooXL050dDQKhSJXVpObmpry8uVLla//bz2wiYkJUVFRqY59WBZhaGgIQHx8PJGRkcTGxlK7du1U19etW5fr16+r1H+xYsXQ0NDIcMzR0dFAci386dOn2bJlCzt27EizYFBVKR9UVOlTHlcuROHy5k0s8fGJeT2MXKGlpYmxcVFevYohMTFr/4flB4UhTokx7xgbF1V51rzAJtTpJbGJicn/cGprpw07JiYGV1dXYmJiaNu2LZ06dWLKlCm4urpmeQx16tRh5cqVXL9+PVUtc4rg4GDu3LmTZgGequzt7blw4UK655KSkvDw8KBz587KXU9USUrTq1FWKBTK9ywriyc/bNvOzo6//vrrozPOp06dQlNTE1tbW+bNm8e7d+/o2LGj8nxMTAyBgYEEBwerNDtuYWFBmTJl+Ouvvz66+8upU6cwNjamYsWKWYpLCJE/JSUpSEhQn/+8c0NiYlKBjxEKR5wSo3rLv8Uqn1CtWjXOnj2b6tiZM2cA0sxOAxw7dowrV66wYcMGRowYgbOzM4aGhjx79izLSWTjxo0pV64cK1asSHPu+fPnBAcHExcXB5ClGexvvvmG69evc+jQoTTnfv75Z44cOUKJEiUyP/B0VKhQAT09vTQJ/KVLlzLVTr9+/Th27BgnT55UHhs3bhwjR47k/PnzrFu3DmdnZ0xNTRk7dix79+5l586dyq/SpUvj4uLCzp07VepPU1MTNzc3duzYwc2bN5XH+/bty+TJkzl16hQ7d+6kd+/eWZ4FF0IIIUThVmBnqAcMGMDo0aNZtmwZzs7O3Llzh5kzZ9K8eXNlQm1gYEBUVBQPHz6kbNmyAISHh9O6dWsePHjAggULiI+PVya9maWjo8MPP/yAh4cHQ4YMoX///pQtW5aIiAgWLVqEnp6ech9tfX19ILlUxcbGJs12gOlxdHTExcWFUaNGMXToUOVOIYcOHWLZsmX06tWLhg0bZmns/1W0aFHc3NxYsmQJpUqVwtLSktDQUC5cuECDBg1UbsfZ2Znz588zePBgBg8eTMuWLXFxcWHy5Mm4uLhgbGzMpEmTAChRokSaDwTa2tqYmJhQoUIFlfscMGAAly5dok+fPowYMQJHR0cGDBjApEmT2L59OxYWFspa+1xlkva3FEKIPCB/F4UQOazAJtRt27YlMTGRwMBAVqxYgampKe3bt2fEiBHKa1xcXPDy8qJjx46cPHmSCRMmsHbtWhYtWkSZMmVwdnbGzMyMixcvZnkcjRo1YuvWraxatQpPT09evHhBmTJlaNasGR4eHpQsWRJIrrfu1q0bc+fO5e7du0yePFml9mfMmEGtWrX46aefCAoKIiEhgUqVKjFlyhS6du2a5XGnZ+TIkcTHxzN58mRiYmJo3rw5LVq0UG5FqKpJkybRsGFDNm7cSHBwMO/fv8fCwoKBAwfy+++/4+7uztSpU6lTp06OjFtTU5MlS5YQFhbG9u3bWbBgAQqFgkqVKtGtWzd27drFgAEDmD59erq/vcgRCgU4bMqdtoUQmadIJCkp6yVsQgjxIQ1FdopiRaGyf/9+6tatq1zoB+Du7k7ZsmWZPXt2jvQRHx/P7t27qV69OjY2NjnS5qe8e/eOkJAQWrVqhZmZWa71o26LLXKaui4qyUkSY8GQEuOLF2/zbb3mp2hra1K8uEGBjhEKR5wSY94xNTWQRYki5wUFBbF582bGjx+PoaEhBw8e5M8//yQ4ODjH+ihSpEiOz6x/ir6+Pn379s31fvLzYovMKAxxSoxCCCE+JAl1Fjx69Ig2bdpkeI2NjQ2bNmXtV/yrV69m+fLlGV7j7e1Nz549s9R+Vvn7++Pr60u/fv2IjY2lSpUqLF68mEaNGuXJmH18fNixY0eG1yxevJivvvoqx/oUQgghhPgvKfnIgsTERO7du5fhNbq6usqFjpkVHR39yf2lTU1NVVq4+LnkxZifP3+e6hHl6SldujRFi6rHRvHq9qusnKauv7LLSRJjwSAxFhyFIU6JMe9IyUcu09LSytQuE5llYmKS4ZP91FFejNnU1DRVPbcQQgghRF4osPtQCyGEEEII8TlIQi2EEEIIIUQ2SEIthBBCCCFENkgNtRCfiaoLG/KrlPgKcpzqGmNSkkIeUiKEEHkoTxNqb2/vT257duPGjY+eu3//PufPn6ddu3Yq9RcWFsaECRMybPND9+7dUz7O+0P6+vpUqlSJgQMHfnL7vA+5ublhbm6Or6+vSte/e/eOHTt24OrqCiS/X1FRUWzYsEHlPrMjICCAHTt2cOjQoY9eY2VlxZw5c3J87+gzZ86wdu1azp8/z5s3byhXrhydO3fm22+/RUdHJ0f7+pTM/pylS6HA2Fg9dhvJbYUhTnWLUZGUwPMXsZJUCyFEHsnThHrSpEl4enoqXzdp0oSJEyfi7Oys0v1eXl6Ym5tnL9FRQUBAAPb29gAoFAqePHlCYGAgo0ePxszMjFq1auVKv8HBwYSFhSkT6kmTJpGYmJgrfWXV8ePHc3z7vo0bN+Lr64ubmxuDBw/G2NiYc+fO4efnx59//smqVavQ0tLK0T4zkiM/Zxoa8IcrRF/LuYEJAWBijYbDJjQ1NSShFkKIPJKnCbWRkVGaZMzIyIhSpUrl0YjSZ2JikmpMpUuXxt/fn/r167N3795cS6j/u0W4Ou07nSKnv1c3btxgzpw5eHt74+bmpjxuYWGBubk5rq6u/PLLL3Ts2DFH+/0soq/Bi/N5PQohhBBC5DD1KgT8j8OHD/PNN99gb29PkyZN8PX15f3790By+cTp06fZsWMHTk5OADx8+JCxY8fi4OBAjRo1aNq0KQsXLiQpKec3CdfU1ERbWxtNzf97CyMjI/n++++V4/X09OTJkycfbePQoUO4uLhgb2+Pra0t3bt3548//gCSZ8WXLl1KVFQUVlZW3Lt3L02SGRkZiYeHBw0bNqRu3bqMGDGC+/fvK8+7ubnh5+fHxIkTqVevHnXq1MHLy4u3b98qrwkKCqJly5bUrFkTJycnli1bliaRX716NU2bNsXOzg43Nzfu3LmjPGdlZUVYWBiQXJIyZswYZs+eTd26dWncuDG+vr7ExcWp/L5u374dY2NjevXqleZcvXr1WLduHc2aNVMe27lzJx07dsTOzg4nJydWrlyp/H7fu3cPKysrTp06laqdD8ccEBCAm5sbq1ev5quvvsLW1pa+ffty69Yt5Xv4358zIYQQQogPqe2ixAMHDjB8+HCGDRuGr68vd+/eZfr06URFRREQEEBAQAAeHh6ULVuWqVOnAjBo0CBKlChBUFAQhoaGHD58mFmzZmFra0vLli1zbGzR0dEEBAQQExND+/btgeTHkffu3Zt27drh7e1NTEwMAQEBuLi4sHv3bvT19VO1cfnyZYYOHcq4ceOYN28eb9++ZeHChYwdO5bDhw/j7u7Ou3fv2LNnDyEhIWkeYBIVFUXPnj1xcHBg3bp1xMXF4efnR58+fQgPD8fQ0BCADRs24O7uzvbt27l27RpeXl6UL1+eoUOHcujQIVauXMmiRYuoVKkSFy5cYPz48ZQrV45OnTop+zl79iyBgYHExcUxfvx4Jk2a9NHHqv/22280a9aMLVu28O+//zJp0iRiYmKYMWOGSu/t33//ja2tLdra6f9oNmrUSPnntWvXMn/+fLy9vXF0dOTvv//Gx8eHly9f4u3trVJ/AOfPn6do0aKsWrWKt2/f4uXlxYwZM1i3bl26P2dCqKOcWiiprgsvc5LEWHAUhjglxvxBbRPqwMBAWrVqxdChQwGoXLkyCoWCwYMHExkZiaWlJUWKFEFPTw9TU1NiY2Pp1KkTrVu3xtzcHEieXVy1ahU3btzIVkL9/fffK2t2k5KSSEhIwM7OjuDgYGxsbADYsmULpUuXTpV0LVq0iEaNGrFv3740i/a0tLSYPHmysj4aoG/fvri7u/Ps2TPMzMzQ19dHS0sr3bKKzZs3o6+vj7+/v3KR3pIlS3ByciI8PJzevXsDYGlpyZgxYwCoVKkSv/zyC+fOnQPgn3/+QVdXl3LlyvHFF1/wxRdfULp0ab744gtlP9ra2sybN09ZbuLi4sLChQs/+l6ZmJgwb948ihYtSrVq1Xj8+DE//PAD48aNUyb5GXn58iUWFhafvE6hULB69Wr69OmjfA8rVqzIy5cv8fPzU/7cqCIhIYG5c+dSrFgxIPnnZt68eQAUK1Ys1c+ZEOoqpxdKqtvCy9wgMRYchSFOiVG9qW1CHRERkWYRWP369YHkOltLS8tU5/T09OjTpw/79u1j3bp13L17l+vXr/P48eNsl3zMmjWLWrVqERMTw7Zt29izZw8DBgxINVt69epVIiMjlYsXU7x//57IyMg0bVpbW2NiYsLq1au5ffs2d+7c4dq15AVrqiw8jIiIoGbNmql2vChRogSVKlVKtYvJf98nIyMjXr16BUDHjh0JDQ3l66+/xsrKCkdHR1q1apUqoS5ZsmSq2m1jY2NiY2M/Oi5bW1uKFv2/vxD29vbEx8dz+/ZtbG1tPxmXqakpL1++/OR1z58/5+nTp9StWzfV8fr16xMfH8+tW7coUaLEJ9uB5BhTkmlIfo/i4+NVulcIdfHqVQyJidkvb9PS0sTYuGiOtaeOJMaCozDEKTHmHWPjoirPmqttQq1QKNDQ0Eh1LCXRTK8cICYmBldXV2JiYmjbti2dOnViypQpqWaAs6pMmTJUqFABgGnTphEbG8uoUaNYt26dMqFLSkqiUaNGTJs2Lc396S0m/Ouvv3B3d6dp06bUq1ePdu3aERMTo/LManrvDyS/R0WKFFG+zmiLOVNTU3bt2sX58+c5ceIEx48fJzg4WFlqA2R6N40P+waUH2ZUbcfe3p7Q0FASExPTvcfLywtbW9uPbleY3s/IhzXh6SXKn3sbPiFyQ2JiEgkJOfcfUU63p44kxoKjMMQpMao3tS1WqVatGmfPnk117MyZM0DaWVeAY8eOceXKFTZs2MCIESNwdnbG0NCQZ8+epVlkl12TJ0+mTJkyjB8/npiYGACqVq1KZGQkZmZmVKhQgQoVKmBiYsLs2bOJiIhI00ZQUBANGzZk6dKl9OvXD0dHRx48eAD8XwKYXsKcolq1aly6dCnVgr+nT59y9+7ddN+f9OzatYstW7YoFzT+9NNP9OjRgz179qj8XvzXlStXUs2wp9QnV6pUSaX7u3Xrxps3b9i8eXOac2fOnGHnzp3o6+tTokQJSpQoke7PSJEiRShfvrwyuX/z5o3y/D///JOVsIQQQgghPkptZ6gHDBjA6NGjWbZsGc7Ozty5c4eZM2fSvHlzZcJoYGBAVFQUDx8+pGzZsgCEh4fTunVrHjx4wIIFC4iPj8/ULhOqMDAwYObMmfTv35/Fixfj7e1N79692bZtG2PGjGHo0KFoaGgwb948rl69StWqVdO0YWZmxoEDBzhz5gxly5bl1KlTLF68GEA5Xn19faKjo7l9+zblypVLdX+vXr3YsmULY8eOZciQIcpFicWLF1d5v+T379/j5+eHgYEB9erV4+HDh5w+fVpZWpMVUVFRTJ8+nX79+nHr1i2WLFlCnz59UpWBZMTS0pKRI0cyZ84cHjx4QMeOHdHV1eXPP/9k0aJFNG/enI4dO6KhoYG7uzuLFy+mXLlyNGnShEuXLrF06VJ69uyJkZERhoaGWFhY8OOPP1KxYkViYmKYM2dOpmek0/s5yxIT66zfK8THyM+VEELkObVNqNu2bUtiYiKBgYGsWLECU1NT2rdvz4gRI5TXuLi44OXlRceOHTl58iQTJkxg7dq1LFq0iDJlyuDs7IyZmRkXL17M8fE5ODjQtWtX1q9fT7t27bC1tWXjxo3Mnz+f3r17o6WlRe3atVm3bl26tbwjRozg6dOneHh4AFClShVmz57NuHHjuHTpEpaWlnz99df89NNPdOzYkY0bN6a638LCgg0bNuDv70/Pnj3R0dHB0dGRefPmYWxsrFIM33zzDdHR0SxfvpwHDx5gYmJC69atGTt2bJbfl9q1a6OhoUG3bt0wNjamb9++DB48OFNtDBw4kMqVK7NhwwbCwsKIjY3FwsICDw8PXF1dleUc3333HTo6Oqxbt445c+ZQtmxZvv/+ewYMGACg/FDzww8/0LlzZ7744gtGjBih/OCiqv/+nGXpoTIKBTikvzOKENmlSEqQh7oIIUQe0lDkdD2EKLQ+96PR8xt1W2yR09R1UUlOUtcYk5IUOZZQa2trUry4AS9evM23tYyfIjEWHIUhTokx75iaGuT/RYlCFDT5ebFFZhSGOAtDjEIIIVRXKBPqR48efXSXiBQ2NjYffXiJyLw9e/YwadKkDK/p27cvo0eP/kwjEkIIIYTIGYUyoS5ZsiQ7d+7M8BpdXd3PM5gCxNfX96PnmjZt+sn3XNXabyGEEEIIdVIoE2otLS3lvtLi8zAwMMDAwCCvhyGEEEIIkePUdh9qIYQQQggh8gNJqIUQQgghhMiGQlnyIUReUHXrnfwqJb6CHGdexZiT2+IJIYTIeYU+oX7z5g2Ojo4YGBhw+PDhTD9FLzvu3btHixYtWL9+PQ0bNkxz3snJiS5dujB8+HAAzp49i0KhoF69emnudXNzw9zcHF9fX06dOkXfvn05ePBgmicsZsW7d+/YsWMHrq6uQN7sNx0QEMDSpUtTHdPS0sLExIT69eszfvz4TMV68+ZNoqKiaNasGQBWVlbMmTOHrl275uSw/49CgbGxak+LzO8KQ5yfO0ZFUgLPX8RKUi2EEGqq0CfUv/zyCyVKlODp06fs379f5cd254XevXszZ84c6tWrh5mZGcePH8fExCTNdfb29hw/fhxTU9Mc6Tc4OJiwsDBlQj1p0iQSExNzpO3MKFu2LCEhIcrX8fHxXLt2jZkzZzJ48GDCw8PR0NBQqa1BgwbRpUsXZUJ9/PhxjIyMcmPYyTQ04A9XiL6We32IgsnEGg2HTWhqakhCLYQQaqrQJ9ShoaE0adKER48esXXrVrVOqD+kpaVFqVKl0j2no6Pz0XNZ8d+HaeZq4pmB9GL+4osveP36NV5eXkRERGBlZZWltnPy/fqo6Gvw4nzu9yOEEEKIz6rgFjuqIDIykosXL+Lo6EibNm04ffo0kZGRQHJZQ48ePVJd//DhQ6ytrTl58iSQPKvZtWtX7OzsaNeuHSEhIVhZWXHv3r0cH2tKojhhwgS8vb25d+8eVlZWnDp1Ks21p06dUo4jLCwMKyurdL+ioqIAOHToEC4uLtjb22Nra0v37t35448/gP8rtYiKilK26e3tjZubW6r30cPDg4YNG1K3bl1GjBjB/fv3lefd3Nzw8/Nj4sSJ1KtXjzp16uDl5cXbt29z5L1JKdPR0tICIC4ujvnz59OyZUtq1qxJw4YNGTNmDC9evACSS2mioqJYunSpMg4rKyvCwsKUbe7cuZOOHTtiZ2eHk5MTK1euJClJnownhBBCiLQK9Qx1SEgI+vr6fPXVVyQkJKCjo8OWLVuYPHkyXbp0oW/fvty9e1e5Z3V4eDhlypShYcOGXLt2jUGDBvHtt9/i7+/P9evXmT59eq6N9fjx4zRp0oSJEyfStWtXoqOjVbrP2dmZL7/8Uvn62bNnDBgwgC+//BJzc3MuX77M0KFDGTduHPPmzePt27csXLiQsWPHcvjwYdzd3Xn37h179uwhJCQkTRlJVFQUPXv2xMHBgXXr1hEXF4efnx99+vQhPDwcQ0NDADZs2IC7uzvbt2/n2rVreHl5Ub58eYYOHZqt9+XGjRssX74cW1tbKleuDMDcuXM5ePAgvr6+lCtXjps3b+Ll5cWKFSuYOHEiISEhdOnSBWdnZwYNGpSmzbVr1zJ//ny8vb1xdHTk77//xsfHh5cvX+Lt7Z2t8QqRVZ9rIaQsLi0YCkOMUDjilBjzh0KbUCckJLB7926aN29O0aLJC4yaNm3Krl278PT0pEGDBlhYWLB7926GDRsGwO7du+nUqROampqsXbuWmjVrMn78eAAqV67Ms2fPmDVrVq6MN6UkwcjICCMjI5UTaj09PfT09AB4//49w4YNo0KFCvj4+ADJs7qTJ09W1kdD8iPA3d3defbsGWZmZujr63+0xGTz5s3o6+vj7++vnClesmQJTk5OhIeH07t3bwAsLS0ZM2YMAJUqVeKXX37h3LlzmXoP7t+/j729vfJ1XFwchoaGODk5MW7cODQ1k/8i2tra8vXXX9OgQQMAzM3NadKkCTdu3ADA1NQULS0t9PX1KVasWKo+FAoFq1evpk+fPsr3pGLFirx8+RI/Pz+GDh2aZyUvonD73AshZXFpwVAYYoTCEafEqN4KbUJ95MgRnjx5grOzs/KYs7Mz+/fv55dffqF79+507txZmVBfu3aNiIgIlixZAsDVq1dxcHBI1Wa9evUyNYYiRYoAfLSUICkpCW3tnPkWKRQKvL29efr0Kdu3b1cmv9bW1piYmLB69Wpu377NnTt3uHYteeGcKgsPIyIiqFmzZqrdUUqUKEGlSpWUCSwkJ9QfMjIy4tWrV5mKoXTp0sqdRe7du8fcuXPR19dnzJgxqWbOO3XqxMmTJ1mwYAF37twhMjKSW7duqfT9ef78OU+fPqVu3bqpjtevX5/4+Hhu3bpFrVq1MjVuIXLCq1cxJCbmftmRlpYmxsZFP1t/eUFiLDgKQ5wSY94xNi6q8qx5oU2oU+plR4wYkebc1q1b6d69O126dGHp0qVcunSJvXv3Ym9vT6VKlYDkmd3s1tSm7NDx+vXrdM9HR0enu4tHVixatIijR4+ybdu2VMnnX3/9hbu7O02bNqVevXq0a9eOmJgYlUsxFApFujtrJCYmKj8wADmyHaG2tray/KZChQoEBQXRuXNnBg4cyLZt25R9TJ8+nT179tC5c2eaNWvG4MGDCQoK4tGjRyrFk56UDxc59QFHiMxKTEwiIeHz/UfzufvLCxJjwVEY4pQY1Vv+LVbJhufPn3PkyBG6du3Kzp07U311796dv//+mytXrmBubk6DBg3Yt28fe/bsoUuXLso2qlevzsWLF1O1+9/Xn6Knp0fVqlX566+/0py7fPky7969w9bWNmtBfmDHjh2sXr2aBQsWUKVKlVTngoKCaNiwIUuXLqVfv344Ojry4MED4P+Sy4y2oqtWrRqXLl0iLi5Oeezp06fcvXs3zax0TitZsiQ//PADV69eVf7m4MWLF2zZsoXp06cr682tra25devWR5PlD5UoUYISJUpw9uzZVMfPnDlDkSJFKF++fK7EIoQQQoj8q1BOt+3atYuEhAS+++67NEmfh4cHO3bsYMuWLcyaNYuuXbvi4+NDQkJCqvIQd3d3OnfujL+/P926dSMyMpLFixcDGSeg/zVw4EAmTpxIiRIlaNWqFVpaWly7do2FCxfSvHnzVAm1vr4+kZGRyt0qVHH69GmmTJmCp6cntra2PHnyRHnOyMgIMzMzDhw4wJkzZyhbtiynTp1SxpGSJOvr6xMdHc3t27fTPDylV69ebNmyhbFjxzJkyBDlosTixYt/li0ImzZtSseOHfnxxx9xdnamWrVqGBkZcfDgQWrUqEFsbCwbN27kypUrqUo1DAwMuHPnDk+fPqVkyZLK4xoaGri7u7N48WLKlStHkyZNuHTpEkuXLqVnz57Zq582sc5OqKKwkp8bIYRQe4UyoQ4LC8PBwSHdGVQLCwtatWrFL7/8gre3N61bt8bHx4eWLVumSqaqVavG0qVLWbBgAWvXrqVSpUq4uroSEBCQqtThUzp27Iienh7r1q0jKCiI9+/fY2ZmRocOHdLsQOHu7s6aNWu4desWkyZNUjnW+Ph45s6dy9y5c1OdmzNnDiNGjODp06d4eHgAUKVKFWbPns24ceO4dOkSlpaWfP311/z000907NiRjRs3pnm/NmzYgL+/Pz179kRHRwdHR0fmzZuHsbGxyu9DdkycOJHjx48zefJktm/fzuLFi/H19aVDhw6YmJgot81buXIl7969Q19fX7mV382bNwkPD0/V3nfffYeOjg7r1q1jzpw5lC1blu+//54BAwZkfZAKBThsymakorBSJCXIQ12EEEKNaShU+T24SOPSpUtoa2tjY2OjPLZ7924mTpzI+fPnpdZWpKFuiy1ymrouKslJeRVjUpLisyXU2tqaFC9uwIsXb/NtLeOnSIwFR2GIU2LMO6amBrIoMbddv36duXPn4ufnh7W1NXfv3iUgIIB27dpJMi3SlZ8XW2RGYYizMMQohBBCdZL5ZVGPHj14/Pgxs2fP5tGjR5QoUYJ27doxYsQIHj16RJs2bTK838bGhk2bpARg9erVLF++PMNrvL296dmz52cakRBCCCFE5kjJRy5ITEz85OPHdXV1KVu27GcakfqKjo7m5cuXGV5jampaIB6mom6/yspp6voru5wkMRYMEmPBURjilBjzjpR85DEtLS3lfskiYyYmJjm217YQQgghRF4olPtQCyGEEEIIkVMkoRZCCCGEECIbJKEWQgghhBAiG9Sqhtrb25sdO3ZkeM2NGzc+eu7+/fucP39e5Sf0hYWFMWHChAzb/JiffvqJKVOm8O233zJx4sRM35+e33//HQsLizSPB/+UgwcPsnnzZq5cucL79++pWLEiPXv25JtvvkFTM/kzk5ubG+bm5vj6+ubIWLNr//79bNq0iWvXrhEXF0eFChXo1KkTrq6u6OjopHvP8+fP6dixIz179mT48OGZ6k+hUBAaGkpISAiRkZEoFAqqVKlC9+7d6dat20efbrl8+XJOnjzJhg0bMh3jf6m6sCG/SomvIMeZmzF+zr2mhRBC5Cy1SqgnTZqEp6en8nWTJk2YOHFiqkd+Z8TLywtzc/PP8sjrsLAwKlWqxM6dOxkzZgx6enrZai8qKgoPDw/Wr1+fqYR67ty5bNq0icGDBzNu3Dj09PQ4ceIEvr6+/P333/zwww/ZGldumDFjBjt37mTw4MFMnjwZbW1tzpw5Q0BAAPv27SM4OBgDA4M0902ZMiXVo9NVlZiYyNChQ7l48SLDhw/HwcGBxMRETpw4gZ+fH4cOHSIgIAAtLa1U961du5YlS5ZQv379LMeqpFBgbFw0++3kA4UhztyIUZGUwPMXsZJUCyFEPqRWCbWRkVGa7dGMjIwoVapUHo0ofZGRkZw/f55ly5YxfPhw9uzZQ9euXbPVZlZ2Lzx69ChBQUGsWLECJycn5fGKFStiaGjI+PHj6dq1K3Xr1s3W2HLSzz//zLZt21i/fj316tVTHq9YsSJffvklHTt2xM/PDx8fn1T3bdu2jdu3b2fpZ2HNmjWcOnWK0NBQKleurDxuaWlJgwYN6N69O0FBQQwcOBCAR48eMWnSJM6ePUulSpWyGOl/aGjAH64QfS1n2hMFi4k1Gg6b0NTUkIRaCCHyIbVKqD/l8OHDLF++nJs3b2JgYED79u0ZPXo0urq6uLm5cfr0aQBOnz7NoUOHePjwIf7+/vzxxx9ER0dTsmRJOnfuzMiRI5WlEFkRFhaGsbExzZo1o169emzZsiVVQn3v3j1atGjBqlWrWLx4MTdv3qRixYrMnDmTmzdvsnz5cqKjo2nevDmzZ8/myZMntGjRAoC+ffsybNgwlUoaNm/ejLW1dapkOkX79u0pXbo01tbWymNv375l4sSJ/PbbbygUClq1asXUqVPR19fn1KlT9O3bl1WrVjFv3jzu3LlDhQoVGDt2LM2bNweSZ3qXLFlCaGgor1+/5quvvqJs2bJcv35d5ZKI9evX07Rp01TJdIoyZcrQv39/li1bxrhx45Qfrm7fvo2/vz9r167NdKlHUlISGzdupFu3bqmS6RTVq1enS5cubNiwge+++w5NTU2uXLmCiYkJ4eHhLFu2jKioqEz1+VHR1+DF+ZxpSwghhBBqI98UOx44cIDBgwfTtGlTQkNDmTlzJnv37mXs2LEABAQEYG9vT9u2bQkJCQFg0KBBPH/+nKCgIPbt28d3333HypUrOXToUJbHkZiYyK5du2jZsiXa2tq0a9eOS5cucfXq1TTX+vj4MHbsWHbu3Imenh4DBw5k7969rFy5El9fX3799Ve2b9+OmZkZ27dvV8bh7u6u0lguX76Mvb19uue0tLRo3Lgx+vr6ymO//fYbJUuWJCwsjLlz57Jnzx5Wr16d6r558+YxadIkwsLCsLCwYOzYsbx9+xYAf39/tm3bxtSpUwkLC6N06dKZqi2Oi4vj0qVLGc6YN2rUiLi4OC5fvgxAfHw8np6eDBgwgBo1aqjcV4p//vmHx48ff7LPx48fKx/G4+TkxPz587GwsMh0f0IIIYQofPLNDHVgYCCtWrVi6NChAFSuXBmFQsHgwYOJjIzE0tKSIkWKoKenh6mpKbGxsXTq1InWrVtjbm4OJC/MW7VqFTdu3KBly5ZZGsfRo0d58uSJsq67devWzJo1i61bt6YpU+jfvz8ODg4AdO7cGR8fH6ZNm0aFChWwsrLCxsaGiIgItLS0MDU1BZIfdJJe/XB6Xr58ibGxscpjt7W1ZcyYMQCUL18eR0dHZeKaYtSoUTRu3Fj5506dOhEREUH16tXZvHkzEyZM4OuvvwaSa5rPn1d9xvXly5coFIoMH+RSrFgxAJ49ewbAkiVL0NXV5fvvv1e5nw+9ePECQOU+y5cvn6V+hMgJ6rCgUxaXFgyFIUYoHHFKjPlDvkmoIyIi0iw2TFksduPGDSwtLVOd09PTo0+fPuzbt49169Zx9+5drl+/zuPHj0lKyvpjLUNDQylWrJgy6SxevDiNGzdm9+7djB8/HkNDQ+W1H9bfFi2avIjpw1lPXV1d4uLisjwWU1PTTz62+0P/rQc2MTFJU87wYVlESizx8fFERkYSGxtL7dq1U11ft25drl+/rlL/xYoVQ0NDI8MxR0dHA8m186dPn2bLli3s2LEjzYJBVaV8UFGlz8x8OBEiN6jTgk51GktukRgLjsIQp8So3vJNQq1QKNJsbZaYmAiAtnbaMGJiYnB1dSUmJoa2bdvSqVMnpkyZgqura5bH8Pz5cw4fPkx8fDx2dnbK40lJSSgUCnbt2pWq/fTGlZ3a7f+yt7fnwoUL6Z5LSkrCw8ODzp07K2fTVUlK09uyTqFQKGPJyuLJD9u2s7Pjr7/++uiM86lTp9DU1MTW1pZ58+bx7t07OnbsqDwfExNDYGAgwcHBKs2OW1hYUKZMGf7666+P7hZz6tQpjI2NqVixYpbiEiKnvHoVQ2Ji1j/w5wQtLU2MjYuqxVhyi8RYcBSGOCXGvGNsXFTlWfN8k1BXq1aNs2fP8u233yqPnTlzBiDN7DTAsWPHuHLlCidOnKBkyZJA8izls2fPspwUhoeHEx8fz7Jly1KVBiQlJeHu7s7WrVuznLB/bB/kjHzzzTe4u7tz6NChNAsTf/75Z44cOcKAAQOyNJ7/qlChAnp6ely4cCHVQsdLly59dN/o9PTr1w9PT09OnjypnOUfN24ccXFx9OvXj3Xr1uHs7IypqSljx47Fw8Mj1f1ubm58/fXXuLm5qdSfpqYmbm5uLFu2jN69e1O1alUgefFn+fLl6dChAzt37qRfv35ZngUXIqckJiaRkKAe/5mo01hyi8RYcBSGOCVG9ZZvilUGDBjAb7/9xrJly7h9+za///47M2fOpHnz5sqE2sDAgKioKB4+fEjZsmWB5CQ4KiqKM2fOMGTIEOLj47NcZhEaGoq9vT0tW7akWrVqyq/q1avTu3dvIiIiOHv2bJbaTlk8GBERwevXr1W6x9HRERcXF0aNGkVgYCD/+9//+N///seqVauYMmUKvXr1omHDhlkaz38VLVoUNzc3lixZwoEDB5Q7b3xshvxjnJ2d6dOnD4MHDyYwMJDIyEhcXFyIiIjAxcWFuLg4Jk2aBECJEiWoUKFCqi9tbW1MTEyoUKGCyn0OGDCAL7/8kj59+rBp0ybu3LnDgAEDOHz4MH379qVUqVLK2nwhhBBCiMzKNzPUbdu2JTExkcDAQFasWIGpqSnt27dnxIgRymtcXFzw8vKiY8eOnDx5kgkTJrB27VoWLVpEmTJlcHZ2xszMjIsXL2a6/8uXLxMREYG/v3+653v37s3q1avZunUrI0eOzHT7xYsXp1u3bsydO5e7d+8yefJkle6bMWMGtWrV4qeffiIoKIiEhAQqVarElClTsr039n+NHDmS+Ph4Jk+eTExMDM2bN6dFixa8f/8+U+1MmjSJhg0bsnHjRoKDg3n//j0WFhYMHDiQ33//HXd3d6ZOnUqdOnVyZNyamposWbKEsLAwtm/fzoIFC1AoFFSqVIlu3bqxa9cuBgwYwPTp09P9bUeOMbH+9DWicJKfDSGEyNc0FNkpihWFyv79+6lbt65yoR+Au7s7ZcuWZfbs2TnSR3x8PLt376Z69erY2NjkSJuf8u7dO0JCQmjVqhVmZma504lCkfxwFyE+Ql2elKitrUnx4ga8ePE23/7q9VMkxoKjMMQpMeYdU1ODgldDLfJeUFAQmzdvVu5mcvDgQf7880+Cg4NzrI8iRYrk+Mz6p+jr69O3b9/c7URDQ+0WW+Q0dV1UkpNyM8akJEWeJ9NCCCGyRhJqkh813aZNmwyvsbGxYdOmTZ9lPKtXr2b58uUZXuPt7U3Pnj0/y3hS+Pv74+vrS79+/YiNjaVKlSosXryYRo0a5cmYfXx82LFjR4bXLF68mK+++irH+syO/LzYIjMKQ5yFIUYhhBCqk5IPkrffS3lK3sfo6uoqFzrmtujo6E/uL21qaqp8NLc6yIsxP3/+/JMLOEuXLq3cAzyvqduvsnKauv7KLidJjAWDxFhwFIY4Jca8IyUfmaSlpZWpXSNym4mJSYZP9lNHeTFmU1PTVPXcQgghhBB5Id9smyeEEEIIIYQ6koRaCCGEEEKIbJCEWgghhBBCiGyQGmohPhNVFzbkVynx5XScsp2cEEIIdScJdSGWkJDApk2b2LVrF7dv30ZHRwcbGxsGDhxI48aNc7SvsLAwJkyYwI0bNwBwcnKiS5cuDB8+HIVCwc6dO/nqq68oUaKESu25ublhbm6Or69vuucDAgLYsWMHhw4dypHx3r9/n/Pnz9OuXbsstYdCgbGxeuw2kttyOk51eeCJEEII8TGSUBdScXFx9O/fnwcPHjB8+HDs7e2JjY0lNDQUd3d35syZQ+fOnXOt/5CQEHR1dQH466+/8Pb25uDBgznWvru7O66urlm+39nZmS+//FL52svLC3Nz86wn1Boa8IcrRF/L8pgKJRNrNBw2oampIQm1EEIItSUJdSG1ZMkSrl+/zi+//JJqf+1Jkybx7t07Zs+eTatWrTAwMMiV/j/c7i43tkI3MDDI1tj19PTQ09PLwRGRnEy/OJ+zbQohhBAizxXsok6Rrvj4eLZv30737t3TfVjNyJEjWbNmDXp6elhZWbFw4UKaN2+Oo6Mjt27dIi4ujnnz5vHll19ib2/PN998w/Hjx1O1sX//fjp06ICdnR19+vTh/v37qc47OTkREBDAqVOnlI/9btGiBWFhYTkSY0BAAE5OTgDcu3cPKysrjhw5QteuXbG1taVDhw5cuHCB7du307x5c+rUqYOnpyfv378Hkks+rKysgOTyktOnT7Njxw5lm0IIIYQQKWSGuhD6999/efnyJbVr1073fOnSpSldurTy9bZt21i9ejWJiYlUrlwZT09Pbt68ybx58yhbtiy///47Hh4eLF26lGbNmnHu3DmGDx/O0KFDad++PWfOnGHmzJnp9mVvb09AQADDhw9n+/btVKtWLTdCBpIfVT5z5kzKlCmDt7c3AwcOpGbNmqxcuZK7d+8yZswY7O3t6dOnT6r7AgIC8PDwoGzZskydOjXXxic+Tl0WdObWwkt1IjEWDIUhRigccUqM+YMk1IVQdHQ0gMpPNuzUqRO2trYA3L17l59//pmQkBDlsf79+3P9+nWCgoJo1qwZGzdupE6dOgwfPhyASpUqERERwfr169O0raOjoxyHqalpzpdZfKB///44ODgA0LlzZ3x8fJg2bRoVKlTAysoKGxsbIiIi0txXrFgxihQpgp6enjyZMY+o24JOdRtPbpAYC4bCECMUjjglRvUmCXUhlJIUvnz5UqXrP3ws+9WrVwGUZRop4uPjMTY2BiAiIgJHR8dU5+3t7dNNqD+nSpUqKf9ctGjyX1oLCwvlMV1dXeLi4j77uMSnvXoVQ2JiUl4PAy0tTYyNi6rNeHKDxFgwFIYYoXDEKTHmHWPjoirPmktCXQhZWFhQsmRJzp8/j7Ozc5rzd+7cwcfHBy8vL4BUs8YpCwg3bdqUZtGfpqZmmutSFClSJMfGn1Xa2ml/3D8cs1BfiYlJJCSozz+y6jae3CAxFgyFIUYoHHFKjOpNsolCSFNTk+7duxMWFsajR4/SnF+zZg0XLlzA3Nw8zbmqVasC8PjxYypUqKD8CgsLIzQ0FABra2vOnTuX6r6///77o+PR0NDITjhCCCGEEHlKZqgLKQ8PD44dO4aLiwsjR46kTp06REdHs3XrVsLCwvD398fQ0DDNfVWrVqV58+ZMmzaNqVOnUq1aNX777TcCAwP54YcfgOQ9oHv06IGfnx/ffPMNf//9N5s2bfroWPT19QG4fv06xYsXV3m7u0ePHnH06NE0xz/cPzqnGBgYEBUVxcOHD9PdGUUlJtY5O6jCQN4zIYQQ+YAk1IVU0aJF2bhxI8HBwaxevZr79++jq6tLjRo1WLduHQ0aNPjovQsXLmThwoVMmzaN6OhoLCwsmDlzJt26dQOSZ6hXr17NvHnz2LhxI1WrVsXDwwN/f/9026tWrRpNmzZl1KhRjBkzBnd3d5Vi+OOPP/jjjz/SHL9y5YpK92eGi4sLXl5edOzYkZMnT6KlpZW5BhQKcPj4hwrxcYqkBHmoixBCCLWmociNp2oIIdJQt8UWOS23FpUkJSnUJqHW1takeHEDXrx4m2/r/D5FYiwYCkOMUDjilBjzjqmpgSxKFELd5OfFFplRWOIUQgghUkhCLdSKj48PO3bsyPCaxYsX89VXX32mEQkhhBBCZEwSaqFWhg0bxrfffpvhNR8+xVEIIYQQIq9JQi3UiqmpqTyNUAghhBD5iuxDLYQQQgghRDZIQi2EEEIIIUQ2SEIthBBCCCFENkgNdT6RkJDApk2b2LVrF7dv30ZHRwcbGxsGDhxI48aNc6SP+Ph4Nm3aRL9+/XKkvbxy584dWrdujbW1NTt37szr4SipupdlfpUSX3bjVKd9p4UQQghVSEKdD8TFxdG/f38ePHjA8OHDsbe3JzY2ltDQUNzd3ZkzZw6dO3fOdj8///wzc+bMyfcJdVhYGJUqVeLatWtcuHCB2rVr5/WQQKHA2LhoXo/is8hunIqkBJ6/iJWkWgghRL4hCXU+sGTJEq5fv84vv/xC2bJllccnTZrEu3fvmD17Nq1atcLAwCBb/RSEh2YmJiayc+dO+vTpQ3h4OFu3blWPhFpDA/5whehreT0S9WZijYbDJjQ1NSShFkIIkW9IQq3m4uPj2b59O927d0+VTKcYOXIkPXv2RE9PDysrK+bMmUPXrl2V552cnOjSpQvDhw8nMTGRBQsW8PPPP/Ps2TPKlSvHt99+S69evQgLC2PChAkAWFlZsX79eho2bMjhw4dZvnw5N2/exMDAgPbt2zN69Gh0dXWV186fP5+ffvqJCxcuUKZMGWU7fn5+PHr0iHr16jF37lzldniRkZH4+vpy5swZDAwMaNiwId7e3pQqVQoANzc3LCwsuHnzJrdv32by5Mkqz8AfP36cR48e4eDgQFxcHKtWrWLChAmYmJgor4mJicHX15d9+/YRHx9P27ZtiY2NpUiRIvj6+gJw7tw55s+fz99//42pqSnNmzfH09MTQ0PDTH4HPxB9DV6cz/r9QgghhFBLBbuoswD4999/efny5UdnWUuXLo2dnR1aWlqfbGvz5s3s27ePhQsX8uuvv9KnTx+mT5/OmTNncHZ2ZuLEiUByUmpvb8+BAwcYPHgwTZs2JTQ0lJkzZ7J3717Gjh2bqt1Zs2bh6urKzz//TJUqVfD09GTFihXMmzePlStXcunSJVavXg3Ao0eP6N27NxYWFoSEhLBy5UrevHmDi4sL7969U7YZFhZG37592bJlC02bNlX5/QoNDaVcuXLUrFkTZ2dn3r9/n+bJi15eXpw4cYKFCxeydetW3rx5wy+//KI8f/36dfr164ejoyPh4eH4+/tz5coV3N3dC8QsvhBCCCFylsxQq7no6GiAVDOsWfXPP/+gr6+PhYUFpUqVok+fPlSuXJlKlSqhp6eHkZERgHKmODAwkFatWjF06FAAKleujEKhYPDgwURGRmJpaQlAly5daN26NQAuLi4cOnSI0aNHY2dnB4CjoyMREREAbNmyhdKlSzN16lTluBYtWkSjRo3Yt2+fcnbd2tqaDh06ZCq+ly9fcujQIfr3768cr42NDVu3blXWhf/777/8+uuvrFmzBgcHBwDmzp3LuXPnlO0EBQXRuHFjhgwZAkDFihWZP38+LVu25PTp0zRs2DBT4xKZp64LOHNq4aU6kxgLhsIQIxSOOCXG/EESajWXUibx8uXLbLfl6urKgQMH+Oqrr6hZsyaOjo60bduWEiVKpHt9REQE7dq1S3Wsfv36ANy4cUOZUFeqVEl5Xk9PDwALCwvlMV1dXeLi4gC4evUqkZGR2Nvbp2r3/fv3REZGKl9XqFAh0/GFh4cTHx+Ps7Oz8pizszP+/v6cPHmSxo0bc/XqVYBU/evq6mJra6t8ffXqVe7evZtmjJBcriIJde5T9wWc6j6+nCAxFgyFIUYoHHFKjOpNEmo1Z2FhQcmSJTl//nyqRDHFnTt38PHxwcvLC0i7sDA+Pl7554oVK/Lbb79x+vRpTpw4wcGDB1m5ciVz5syhS5cuadpWKBRoaGikOpaYmAiAtvb//eh8+OcU/70vRVJSEo0aNWLatGlpzqXMkMP/JeaZERYWBkC3bt2Ux1Lej61bt9K4cWNlaUxSUtJH20lKSqJDhw54eHikOSePRf88Xr2KITHx49+jvKKlpYmxcVG1HV9OkBgLhsIQIxSOOCXGvGNsXFTlWXNJqNWcpqYm3bt3Z+PGjXz33XeUKVMm1fk1a9Zw4cIFzM3NKVKkCK9fv1aee/PmDc+fP1e+Xr9+PSVKlKBdu3Y4Ojoyfvx4+vfvz549e+jSpUuaJLhatWqcPXuWb7/9VnnszJkzAMrZ6cyqWrUqe/bswczMDB0dHSB59t3Ly4v+/fvTqFGjLLV77do1rl27hoeHR5pZ9blz53Lw4EGePHmClZUVGhoaXLhwga+++gpI/tBx9epVZd9Vq1bl5s2bqWbJb926xdy5cxkzZkyqxF/kjsTEJBIS1Ocf1f9S9/HlBImxYCgMMULhiFNiVG+SUOcDHh4eHDt2DBcXF0aOHEmdOnWIjo5m69athIWF4e/vj6GhIfb29mzbto369etTpEgRFi1alGr2+NmzZyxbtgw9PT2qV69OZGQkV69eVSbM+vr6AFy+fJkqVaowYMAARo8ezbJly3B2dubOnTvMnDmT5s2bZzmh7t27N9u2bWPMmDEMHToUDQ0N5s2bx9WrV6latWqW36PQ0FCKFi2Ku7t7mnrzQYMGcezYMUJCQhg8eDBt27Zl5syZ+Pj4ULp0aVavXs2DBw+UHyjc3d1xdXVl6tSp9O3bl7dv3zJjxgzevn1LxYoVszxGTKyzfm9hIe+REEKIfEgS6nygaNGibNy4keDgYFavXs39+/fR1dWlRo0arFu3jgYNGgAwffp0ZsyYgYuLC6ampvTv3z/VzhnDhg0jISGBmTNn8vTpU0qVKkXv3r0ZNGgQAI0aNaJWrVq4uLgwb9482rZtS2JiIoGBgaxYsQJTU1Pat2/PiBEjshyLhYUFGzduZP78+fTu3RstLS1q167NunXrPlrL/SlxcXHs3r2bDh06pLt4s379+tjZ2bF9+3YGDRrEzJkzmTVrFsOHD0ehUNC+fXtq165NkSJFAKhduzZr1qxh8eLFdO3alaJFi9KoUSO8vLyUs+qZplCAw6as3VvIKJISZA9qIYQQ+YqGQvYBE4XI+/fvOXbsGI0aNUq1p3Tr1q3p2LGjckeT3KButWE5Ladq4NT50ePa2poUL27Aixdv8+2vJT9FYiwYCkOMUDjilBjzjqmpgdRQC5EeHR0dfHx8qF+/PkOGDEFLS4uQkBDu379PmzZtcrXv/FwblhmFJU4hhBAihSTUQu117NiRf//9N8NrTpw4oawBz4iGhgaBgYHMmzePnj17kpiYiI2NDcHBwVmuCxdCCCFE4SYJtVB7K1euTLX9X3qKFlV970pra2uCg4OzOywhhBBCCEASapEPfPHFF3k9BCGEEEKIj8q/z3gUQgghhBBCDUhCLYQQQgghRDZIQi2EEEIIIUQ2SA21EJ+JqntZ5lcp8WUnTnXeg1oIIYT4GEmos+jNmzc4OjpiYGDA4cOHs/4EvWx49OgRK1eu5MiRIzx+/BhTU1Pq16/P999/T/Xq1T/7eDJy7949WrRokeE1w4YNY/jw4dnqx8nJiaioKOXrIkWKYG5uTo8ePfjuu++y1Xa2KBQYG6u+E0l+lp04FUkJPH8RK0m1EEKIfEUS6iz65ZdfKFGiBE+fPmX//v20a9fus/Z//fp1+vXrR9WqVZkxYwaVK1fm8ePHbNmyhR49euDr6/vZx5QRMzMzjh8/rnwdHBzMnj17CAkJUR5TZR9pVbi7u+Pu7g5AbGwsFy9eZPLkyRQtWhRXV9cc6SPTNDTgD1eIvpY3/ecHJtZoOGxCU1NDEmohhBD5iiTUWRQaGkqTJk149OgRW7du/azJa0JCAiNHjqRWrVqsWLECTc3kX7Gbm5tjb29PqVKlmDRpEnZ2dlhYWHy2cWVES0uLUqVKKV/r6+unOZZT9PX1U7VrYWHBqVOnCA0NzbuEGpKT6Rfn865/IYQQQuSKgl3UmUsiIyO5ePEijo6OtGnThtOnTxMZGQmAt7c3PXr0SHX9w4cPsba25uTJkwAcP36crl27YmdnR7t27QgJCcHKyop79+6p1P/Ro0e5c+cOI0eOVCbTHxo+fDiampps27YNgICAAHr16kVgYCCNGjWifv36TJgwgTdv3ijvef36NVOmTKFRo0bUrVuXvn378vfffyvPBwQE4ObmxurVq/nqq6+wtbWlb9++3Lp1K3NvXgZevnzJjBkzaNq0KXZ2dvTq1YszZ87kSNv/ffCLm5sb3t7eqY55e3vj5uYGwJAhQ+jbt2+q87du3cLKyorr16/nyJiEEEIIUTDIDHUWhISEoK+vz1dffUVCQgI6Ojps2bKFyZMn06VLF/r27cvdu3epUKECAOHh4ZQpU4aGDRty7do1Bg0axLfffou/vz/Xr19n+vTpmer//Pnz6OvrY21tne55PT097O3tOXfunPJYSnIcFBTEmzdvmDRpEqNGjWLNmjUoFAq+//57ihQpQmBgIIaGhuzatYtevXrx008/YWNjo+y3aNGirFq1irdv3+Ll5cWMGTNYt25dFt7F1BITE3F3dyc+Ph4/Pz9KlSrFxo0b6devH1u2bMHW1jbLbV+6dIndu3czatQole/p1q0bQ4cO5f79+8oHy+zcuZMaNWqoXX16QaPOizdzYuGlupMYC4bCECMUjjglxvwhUwn1X3/9lanG69evn6nr84OEhAR2795N8+bNlbOeTZs2ZdeuXXh6etKgQQMsLCzYvXs3w4YNA2D37t106tQJTU1N1q5dS82aNRk/fjwAlStX5tmzZ8yaNUvlMbx8+RJjY2M0NDQ+ek2xYsVSLc7T0NBg0aJFlClTBoCpU6fy/fffc+vWLR49esT58+c5efIkpqamAIwZM4Zz586xfv16fH19lbHPnTuXYsWKAcmzvPPmzVN53Bk5fvw4V65cYffu3VSrVk05xosXLxIUFMSiRYtUbiswMFD5aPH4+Hji4+OpVasWzs7OKrfRtGlTSpYsye7duxk0aBBJSUmEh4fn7cLGQiI/LN7MD2PMLomxYCgMMULhiFNiVG+ZSqiHDBmiLBNQKBQfTehSzl27VvAWYB05coQnT56kSs6cnZ3Zv38/v/zyC927d6dz587KhPratWtERESwZMkSAK5evYqDg0OqNuvVq5epMRQrVozXr19neM2rV68oXry48nXFihWVyTSAvb09ABEREcpSk//uwhEXF8f79++Vr0uWLKlMpgGMjIyIj4/P1Ng/JiIiAiMjI2UyDckfAurVq8exY8cy1ZaLi4uydCMhIYE7d+6wcOFCevfuTWhoqEo7smhra9OxY0d27drFoEGD+PPPP3n69Cnt27fPXGAi0169iiExMSmvh5EuLS1NjI2LqvUYs0tiLBgKQ4xQOOKUGPOOsXFRlWfNM5VQh4eH079/f16+fImfn1+autTCICwsDIARI0akObd161a6d+9Oly5dWLp0KZcuXWLv3r3Y29tTqVIlIHlxXlJS9n5Y6tWrx6pVq7hy5Qo1atRIc/79+/dcvHiR7t27K48VKVIk1TUpY0gZj6GhoTK2D32YfObm1oAf+4CWlJSEtnbmKpNMTEyU5TYAlpaWmJiY4Orqyh9//EGzZs2UfX7ovx8OunXrRlBQEJcvXyY8PJwWLVqk+kAhckdiYhIJCerzD2p68sMYs0tiLBgKQ4xQOOKUGNVbpjIVMzMzgoKC6Nq1K3/++SdeXl65NS619Pz5c44cOULXrl3p379/qnPr1q0jJCREmeQ2aNCAffv2sWfPHoYMGaK8rnr16ly8eDHVvf99/SmOjo5Uq1YNf39/Vq9enSbhXL58ObGxsXzzzTfKY7dv3+b169cYGRkByfXQANbW1hQpUoQ3b94QFxdH1apVlfdMnjyZ6tWr06dPn0yNLyusrKx49eoVERERqWapz549S5UqVXKsn5QPEkWKFEkzy//PP/+gp6enfG1paYm9vT179uzh999/Z+7cuTk2DiGEEEIUHJlelGhubs748eOZMWMG/fr1S1VGUNDt2rWLhIQEvvvuOywtLVOd8/DwYMeOHWzZsoVZs2bRtWtXfHx8SEhISFUe4u7uTufOnfH396dbt25ERkayePFigAxroj+kra3NggULcHd359tvv2Xw4MFYWlry5MkTtm7dSnh4OLNmzaJixYrKe969e8f48eMZPXo0z549w8fHB2dnZ8qVK4eZmRnW1taMGjWKyZMn88UXX7B161ZCQ0OVtci5zdHRESsrKzw9PZk8eTIlS5Zk48aNREREMG3atEy19e7dO548eQIkz0L/888/zJ49m9KlS9O4cWMA6tSpw5o1azh06BBVq1Zlx44dREREYGdnl6qtbt26MWvWLIyNjWnSpEn2gjRJfxGp+P/k/RFCCJFPZWmXj65du1KjRo1CV/IRFhaGg4NDmmQakvc6btWqFb/88gve3t60bt0aHx8fWrZsqZwVBqhWrRpLly5lwYIFrF27lkqVKuHq6kpAQECasoyMpCSBQUFBzJgxg4cPH2JiYkLDhg3ZunUrNWvWTHW9mZkZ1apVo3fv3mhra9OhQwfGjh0LJJd9BAcHM2/ePEaPHk1MTAyWlpYEBAQoE9Dcpq2tzY8//oifnx/Dhw8nLi6OGjVqsHbtWmrXrp2ptoKDg5UfBDQ1NSlevDh169bF399f+TPbr18//v33X8aNG4eGhgbOzs7069cv1c4oAG3btmXWrFl07twZLS2trAeoUIDDpqzfX0gokhLkoS5CCCHyHQ3FfwtJRa66dOkS2trayq3oIHkXkIkTJ3L+/PlM1wurIiAggB07dnDo0KEcb7ugu3fvHq1atWLv3r2pZvyzQt0WW+S0nFhUkpSkUOuEWltbk+LFDXjx4m2+rfP7FImxYCgMMULhiFNizDumpga5syhRZN/169eZO3cufn5+WFtbc/fuXQICAmjXrl2uJNMiax48eMClS5fYvHkzX375ZbaTacjfiy0yo7DEKYQQQqSQDO4z69GjB48fP2b27Nk8evSIEiVK0K5dO0aMGMGjR49o06ZNhvfb2NiwaZN6lQ6cP38ed3f3DK9p2bJltvas9vDw4NSpUxleExISkm45Tla8ePECb29vKlasyNKlS3OkTSGEEEIUTFLyoUYSExM/+fhxXV1dypYt+5lGpJr379/z8OHDDK/R19enVKlSWe7j0aNHxMbGZniNmZlZrm7tl13q9qusnKauv7LLSRJjwSAxFhyFIU6JMe9IyUc+paWllWr/5PxCV1c318ddmHaTEUIIIUT+kn8fmi6EEEIIIYQakIRaCCGEEEKIbJCEWgghhBBCiGyQhPozcHJywsrKih9//DHd81OnTsXKyoqAgIAc6/P333/nf//7HwCnTp3Cysrqkwse8wMrKyvCwsJytY+AgACcnJxyvF0tLU20tQvul6pP+hRCCCEKGlmU+JkUKVKEffv20b9//1THExIS+O2333I0GYmKisLDw4P169dTpUqVHGtXZINCgbFxwX6yqCIpIa+HIIQQQuQJSag/k8aNG3Ps2DEePHiAmZmZ8viff/6Jvr5+jj7GXXZCVEMaGvCHK0Rfy+uR5A4TazTk0epCCCEKKUmoPxM7OzsiIyPTzFLv2bOHtm3bsnfvXuWx8+fPs3DhQq5cuYK2tjYtWrTAy8sLExMTILmExMXFhb///pvjx4+jo6NDp06dGD9+PA8fPqRFixYA9O3bl2HDhtGgQQMAjhw5wtatW7l9+zYVKlRg3LhxNGvWDIA7d+4wc+ZMLly4QFJSEnXq1GH8+PFYWVmpHOPhw4dZvnw5N2/exMDAgPbt2zN69Gh0dXWB5HKNmTNnsnfvXs6ePYuJiQl9+vRh0KBBmXovb926Ra9evbh8+TIVKlRgwoQJODo6qjyOly9fsnjxYg4dOsSLFy+oUaMGnp6e1KtXL93+1q9fz9y5c1m0aBEtW7bM1FhTib4GL85n/X4hhBBCqCVJqD+jtm3bpkqo4+LiOHDgAGvXrlUm1JcuXcLNzY1vvvmGqVOn8uzZM2bOnIm7uzvbt29HUzO57D0gIIBx48bh6enJ8ePHmTVrFjY2NnTo0IHt27fTo0cPAgICcHR05PLly0ByYujj40Pp0qXx9/dn1KhRnDhxAgMDA8aMGYOVlRWhoaEkJCTg5+fHsGHD2L9/v0qxHThwgOHDhzNs2DB8fX25e/cu06dPJyoqKlVt+Ny5c5kyZQpTp05l165dLFiwgLp16340mU3PunXrmDRpErNnz2bXrl0MGDCAkJAQatas+clxJCYm4u7uTnx8PH5+fpQqVYqNGzfSr18/tmzZgq2tbaq+Nm/ejL+/P0uXLlV++BAZU3UT/PwoJTaJMX+TGAuOwhCnxJg/SEL9GbVt25agoCBl2ceJEycoXrw4NjY2ymuCg4OxsrJi6tSpAFSpUoX58+fTsWNHjh07RtOmTQH48ssv6du3LwAVK1YkJCSEc+fO0blzZ0xNTQEwMTHBwMBA2fbEiRNp2LAhAEOHDuXAgQNERkZiZ2fHP//8g6OjI+XKlUNbW5vZs2dz69YtkpKSlEl8RgIDA2nVqhVDhw4FoHLlyigUCgYPHkxkZKTykeBdunShU6dOAIwaNYrNmzdz9uzZTCXUvXr1wsXFRdnGn3/+ydq1a/H39//kOO7du8eVK1fYvXs31apVA5IXhV68eJGgoCAWLVqk7Oenn37Cz8+PZcuW8eWXX6o8vsKuoNeKg8RYUEiMBUdhiFNiVG+SUH9GNWvWxMLCQjlLvWfPHtq3b5/qmoiIiFTlC5BcKmFsbMyNGzeUCXVKgprCyMiI+Pj4DPuvVKmS8s/GxsYAysd5jx49mtmzZ7NlyxYaNWrEl19+Sdu2bVVKplPG3a5du1TH6tevD8CNGzeU4/3vuA0NDT857v/6b/Jdq1Yt/vzzT5XGERUVhZGRkTKZBtDQ0KBevXocO3ZMeezx48dMnz4dbW1typUrl6nxFXavXsWQmKg+j47NSVpamhgbF5UY8zmJseAoDHFKjHnH2LioPHpcXaWUffTu3ZuDBw+yffv2VOcVCkW6O34kJSVRpEgR5WsdHZ0013xqMWJ6yXHKPa6urrRp04YjR45w8uRJFixYQEBAADt37qRkyZKfjCu9cScmJgKgrf1/P2ZZGfen4khMTFS2+6lxZPT+fjhODQ0NVq9ezeLFi5kwYQKbN29W+cNFYZeYmERCgvr8g5gbJMaCQWIsOApDnBKjepMM4TNr27YtFy9eJCQkBAsLizQzttWqVePMmTOpjl2/fp03b96kufZjMrsF39OnT/Hx8SE+Pp6uXbsyb948wsPDefLkCadPn1apjWrVqnH27NlUx1LiUHXcqrpy5Uqq1+fOnaNq1aoqjcPKyopXr14RERGR6pqzZ8+m2mKwVKlSODo6MmvWLC5fvsy6detyNAYhhBBCFBwyQ/2ZWVtbU6FCBRYsWJDu7hb9+vXD1dUVHx8fXF1defbsGT4+PtjY2NC4cWOV+tDX1weSyx8+rM/+mGLFinH48GH++ecfPD09MTQ0JCQkhCJFilCzZk2V+hwwYACjR49m2bJlODs7K3cNad68eY4n1GvXrqV8+fLUqlWLrVu3EhERwfz581UaR4UKFbCyssLT05PJkydTsmRJNm7cSEREBNOmTUvTV7Vq1fjuu+9YtGgRzZs3p2LFilkfuIl11u9VdwU5NiGEEOITJKHOA23btmXFihU4OzunOWdvb68sNejcuTOGhoa0bNkST0/PVCUfGSlevDjdunVj7ty53L17l1atWmV4vba2NqtXr8bPz49+/foRExODtbU1q1atonz58irHlJiYSGBgICtWrMDU1JT27dszYsQIle7PjCFDhrBhwwamTJlClSpVWLVqlbI+/FPj0NbW5scff8TPz4/hw4cTFxdHjRo1WLt2LbVr1/5of7/++isTJkxg06ZNWSv9UCiggO/TrEhKQENT/kkRQghR+Ggo5CkgQnwW6rbYIqdpaGhQrJg+L168zbc1cJ+ira1J8eIGEmM+JzEWHIUhTokx75iaGsiiRCHUTX5ebKEKbW1ZkiGEEKJwkoRaZOj8+fO4u7tneE3Lli2ZN29etvrx8PDg1KlTGV4TEhKS4/XYQgghhBDZJQm1yJCNjQ07d+7M8JqURZDZMWPGDOWe2B9jZmaW7X6EEEIIIXKaJNQiQ7q6ulSoUCHX+ylTpkyu9yGEEEIIkRuk6FEIIYQQQohskIRaCCGEEEKIbJCEWgghhBBCiGyQGmohPhNV97LMrzL7yHshhBCioMjzhNrJyYmoqCi8vb3p379/mvNTp05l27ZtDBs2jOHDh+dIn7///jsWFhZUqVKFU6dO0bdvXw4ePEi5cuVypP3/unfvHi1atGD9+vU0bNgwV/r4r3fv3rFjxw5cXV0B8Pb2Jioqig0bNnyW/j90+/ZtunTpws8//5zqPX7w4AHz5s3j1KlTxMXFYWdnh7e3N1WrVv3sY8x1CgXGxkXzehS5SpGUkNdDEEIIIfJEnifUAEWKFGHfvn1pEuqEhAR+++23HJ35ioqKwsPDg/Xr11OlSpUca1fdBAcHExYWpkyoJ02aRGJi4mcfx40bNxg0aBAxMTGpjsfFxTFw4EBMTU0JDAxEV1eXZcuW8e233/Lzzz9jamr62ceaqzQ04A9XiL6W1yPJHSbWaBTwR6sLIYQQH6MWCXXjxo05duwYDx48SLXX8J9//om+vj5Fi+bczF5hedL6f+M0MjL67GNYsWIFK1euxNLSkgcPHqQ6d+bMGSIiIjh69Khyy7y5c+fSoEEDDh06RPfu3T/7eHNd9DV4cT6vRyGEEEKIHKYWRZ12dnZ88cUX7Nu3L9XxPXv20LZt21Qz1OfPn6dv377UrVuXhg0bMnHiRKKjo5XnnZycWLVqFcOHD8fe3p6GDRsye/ZsEhISlKUXAH379iUgIEB535EjR+jQoQM1a9akXbt2HD58WHnuzp07DBgwgLp162Jvb8+AAQO4ceNGluMNCwvDycmJHTt20KpVK2rWrEm3bt04f/7/kq2HDx8yduxYHBwcqFGjBk2bNmXhwoUkJf3fo6svX75M//79sbe3x8HBgalTp/Lu3TsCAgJYunQpUVFRWFlZce/ePby9vXFzc0OhUNCiRYs0TzYMDw+nVq1avHnzBoDQ0FDatm2LnZ0dbdu2Zd26dan6VsWxY8eYN28eXl5eac5VrVqVVatWpdl/WqFQpPp+ZuTevXtYWVlx5MgRunbtiq2tLR06dODChQts376d5s2bU6dOHTw9PXn//r3yvuz8DAkhhBBC/JdazFADtG3bNlXZR1xcHAcOHGDt2rXs3bsXgEuXLuHm5sY333zD1KlTefbsGTNnzsTd3Z3t27ejqZn8+SAgIIBx48bh6enJ8ePHmTVrFjY2NnTo0IHt27fTo0cPAgICcHR05PLlywCsX78eHx8fSpcujb+/P6NGjeLEiRMYGBgwZswYrKysCA0NJSEhAT8/P4YNG8b+/fuzHO/jx4/ZunUr8+bNo0iRIkyfPh0vLy9+/fVXNDQ0GDRoECVKlCAoKAhDQ0MOHz7MrFmzsLW1pWXLlty7dw83NzecnJzYtm0bb968YcKECUydOpUZM2bw7t079uzZQ0hISKryCQ0NDTp37kxoaChjx45VflgJDw+nVatWGBoasm3bNubPn8/UqVOpVasWV69eZebMmTx69Ijx48erHOPmzZsB0n2keKlSpWjatGmqY+vXr+f9+/c4Ojpm6r308fFh5syZlClTBm9vbwYOHEjNmjVZuXIld+/eZcyYMdjb29OnT59s/wx17tw5U2MrjAry4suU2CTG/E1iLDgKQ5wSY/6gVgl1UFCQsuzjxIkTFC9eHBsbG+U1wcHBWFlZMXXqVACqVKnC/Pnz6dixI8eOHVMmaF9++SV9+/YFoGLFioSEhHDu3Dk6d+6sTC5NTEwwMDBQtj1x4kTlgsGhQ4dy4MABIiMjsbOz459//sHR0ZFy5cqhra3N7NmzuXXrFklJScoELLPi4+OZPn061tbWAAwaNIihQ4fy5MkTjI2N6dSpE61bt8bc3BwANzc3Vq1axY0bN2jZsiU//fQTJiYm+Pr6UqRIEQBmzZrF6dOnMTAwQF9fHy0tLUqVKpWm7y5durBs2TL++usvGjRowNOnTzl58iSrV68GYPny5QwaNIj27dsDYGFhwZs3b5gxYwYjR45EV1c3SzFn5LfffmPhwoW4ublRvXr1TN3bv39/HBwcAOjcuTM+Pj5MmzaNChUqYGVlhY2NDREREf+vvfuOquroHj7+pXcQxN6jgqioqFgTjVjBhr0gRomFGI1d4SGiqFERO0axNyzR2FvyGBMTTYz9p4kNY2yIJSqCKFLP+4cv5/EGFJB2ue7PWqwF58yZmX3vPbgdZuYAufMZEm+n64svQWLUFRKj7ngf4pQYtZvWJNQ1a9akXLly6ij1gQMH1IQuTURERLrRS0dHR6ytrbl69aqaDFWuXFmjjJWVFUlJSW9tv1KlSur31tbWALx8+RKA0aNHM2PGDDZv3kyjRo346KOPcHd3f+dkOs3r/Uyb45yUlISpqSn9+vXju+++Y926ddy6dYsrV67w8OFDddrF1atXqVGjhppMA7i6uuLq6pppu2XLlsXV1ZW9e/fSoEED9u3bR7FixWjUqBFPnjzh/v37LFy4kMWLF6vXpKamkpCQQGRkZLrXN6c2b97MtGnT8PDwwN/fP9vXv/7epc23L1eunHrMxMSExMREIG8/Q+KV2Nh4UlKyNz2osDAw0Mfa2kxiLOQkRt3xPsQpMRYca2uzLI+aa01CDf+b9tG3b18OHz7Mtm3bNM4ripLhjh+pqakaiaWxsXG6MpktRswoOU67xsvLi3bt2vHzzz9z/Phx5s2bR2hoKLt27cLe3j5LsWXkTf2Mj4/Hy8uL+Ph43N3d6dy5M5MmTVJ37AAwNDTM0e4n3bp1Y8aMGUyaNIk9e/bQuXNn9PX11YTd399fHfV93euLRnPDnDlzWLFiBd7e3gQEBLxTTIaG6T/Gb/rPTl5+hsQrKSmpJCdrzy/EvCAx6gaJUXe8D3FKjNpNqyaruLu7c/78eb799lvKlSuXbpTQwcGB06dPaxy7cuUKcXFxWR41zW7C9ujRI6ZOnUpSUhJdu3YlJCSEPXv28M8//3Dy5Mls1ZVVR48e5eLFi2zYsIEvvvgCDw8PLC0tefz4sZrUValShUuXLmlshXfo0CGaNWtGfHx8pnG2bduW5ORkvvnmGy5evKhOZShatChFixbl9u3bVKhQQf26ePEiCxYsyNU4Q0JCWLFiBRMmTODLL7/MlweD5MZnSAghhBDidVo1Qu3k5ESFChWYN28eQ4cOTXd+wIABeHl5MXXqVLy8vHj8+DFTp06levXqNG7cOEttmJubA6/+9P/6/Ow3KVKkCEeOHOH27duMHTsWS0tLvv32W4yMjKhZs2b2AsyikiVLAq8WCrZt25Z79+4xb948kpKS1KkLffv2Zf369UyePJmBAwcSHR3NnDlzaNq0KWZmZpibmxMTE8ONGzcyfGCNmZkZ7dq1Y/78+bi4uKjTJvT09Bg0aBDz5s2jdOnSNG/enIiICIKCgvj4448zHLl9FydOnGDlypV4e3vTqVMn/vnnH/Wcubm5xvz23JQbn6F3ZuOUt/UXJF2OTQghhMiEViXU8GqUeunSpXh4eKQ75+LiwooVK1i4cCGenp5YWlrSqlUrxo4dq/Hn+rextbWlW7duzJ49m1u3btG6deu3ljc0NGTFihUEBwczYMAA4uPjcXJyYvny5ZQvX/6dYsxMrVq18Pf3Z+3atSxYsIASJUrg4eFBqVKlOH/+PAAlSpRg9erVzJkzhy5dumBtbY2HhwdjxowBoE2bNmzdupVOnToRHh6eYTtdu3Zl+/bt6Rba+fj4YGJiwoYNGwgODqZo0aJ07dqV0aNH51qM+/btA2DDhg3pnt6Ym0/F/Lfc+Ay9E0UBHX/wiZKajJ6+1v1KEUIIIfKcniITQ4XIF9q22CK36enpUaSIOdHRzwvtHLjMGBrqY2trITEWchKj7ngf4pQYC46dnUXhXJQohC4rzIstssLQUKuWZAghhBD5RhLqHDh37hw+Pj5vLdOqVat0TyUszA4cOEBAQMBby/Tv3z/H00M6derEnTt33lrm119/VefECyGEEEIUFEmoc6B69ers2rXrrWV0LeFr3rx5pjGn7eOdE2FhYZnu+5y257QQQgghREGShDoHTExMqFChQkF3I19ZWFjk2Q4crytdunSetyGEEEIIkRtk0qMQQgghhBA5IAm1EEIIIYQQOSAJtRBCCCGEEDmgVQl1cnIy69ato2vXrri4uNCwYUMGDhzI8ePHc7WdqKgo9u/fn2v1vXjxgo0bc/+hHZGRkUyZMgU3Nzdq1aqFm5sbQUFBPHjwINfbykxOYwwICMDPzy/d8e3bt9OxY0fq1KlDmzZtWL58ucbj1HWJgYE+hoba/6Wvn/ePgBdCCCF0idYsSkxMTGTgwIHcu3ePESNG4OLiwsuXL9m+fTs+Pj7MnDkz3RP93tXEiRMpU6YM7du3z5X6Vq9ezY4dO/Dy8sqV+gDOnj3LkCFDcHV1ZebMmZQpU4Zbt24xd+5c+vTpw+bNmylRokSutZeZd40xJSWFOXPm8O2339KlSxeNc3v37mXy5MlMnjyZhg0bcvHiRQIDA0lMTGT48OG52f2CpyhYWxeOXUmU1GSeRL8kNVWe+SSEEEJkhdYk1IsWLeLKlSvs37+fkiVLqscDAgJ48eIFM2bMoHXr1vmyw0R25fbDJhMTExk7dixNmjRh4cKF6Om9GjEsW7Yszs7OtGnThtDQUKZPn56r7b7Nu8R4/fp1/P39uXPnToa7dmzatIkuXbrQo0cPAMqXL8+NGzfYunWr7iXUenrwmxfEXC7onrydjRN6TTair68nCbUQQgiRRVqRUCclJbFt2za6d++ukUynGTlyJL169cLU1JSnT5+ycOFCfvzxR6Kjo6lRowZjx46lfv36AISGhnLy5EmaNWvGhg0biI6OxsXFhSlTpvDBBx/g7e3NyZMnATh58iQ//vgj9+/fZ86cOfz222/ExMRgb2+Pp6cnI0eORF//1ayYP//8k7lz5/J///d/mJmZ0apVK/z8/Fi1ahWLFy8GwNHRkcOHD2NmZkZQUBAnTpwgPj6e6tWrM2bMGBo0aJCl1+PIkSNERUWxdOlSNZlOY21tzYoVKyhatKh67Ny5c8yfP5+LFy9iaGhIy5YtmThxIjY2NgC4ubnRpUsXRowYoV7j7e1NmTJlmDVrFidOnKB///4sX76ckJAQbt68SYUKFRg3bhwtWrQgNDQ0XYxly5bNNI6TJ0/i5OTE0qVLGTVqVLrz48aNw87OLt3xmJiYLL1OAH5+fujp6VG8eHG2bNlCcnIynp6eDB48mClTpnD8+HFKlChBQEAAzZs3B+Dly5eEhYWxd+9eHj58SOXKlRk+fDitWrUCYMeOHSxevJgRI0awZMkS7t27h6OjI19++SUuLi5Z7lv6wC5D9Ll3v14IIYQQWkkrEuo7d+7w9OlT6tSpk+H54sWLU7x4cVJSUvDx8SEpKYng4GCKFStGeHg4AwYMYPPmzTg7OwOvEkwzMzOWL1/O8+fPmThxIkFBQaxbt47Q0FB8fX0pWbIkgYGBAAwdOpSiRYuyatUqLC0tOXLkCNOnT8fZ2ZlWrVoRGRmJt7c3bm5ufPPNN8TFxeHv709gYCBBQUG8ePGCAwcO8O2332JnZ8eoUaNISEggPDwcY2NjwsLCGDZsGL/88kuWHvTyxx9/YG5ujqOjY4bn0+IEuHDhAt7e3vTs2ZPAwEAeP37MtGnT8PHxYdu2bep/CLIiJCSEgIAAihYtyrx58xg3bhy//PILPj4+6WLMij59+rz1fL169TR+jo2NZfPmzXz44YdZ7jO8mjrSs2dPtm7dyqlTpwgICOD7779n3LhxjB8/npCQEPz8/Pjtt9/Q09NjzJgxXLp0icDAQCpVqsT+/fsZPnw4X3/9NS1btgTg4cOHbNmyhZCQEIyMjJgyZQoTJ07k+++/T/efHF1kYJD95RVp17zLtYWFxKgbJEbd8T7EKTEWDlqRUKeNSKaNqL7JsWPHuHjxInv37sXBwQGAwMBAzp8/z6pVq1iwYAHwanHj7NmzKVKkCPBqNDbt8d9FihTByMgIU1NT7OzsePnyJZ07d6Zt27aUKVNGLb98+XKuXr1Kq1at2Lp1KzY2NsyaNQsjIyMApk+fzsmTJ7GwsMDc3BwDAwOKFSsGwO3bt3FwcKB8+fKYmJgQEBBAx44dMTAwyPLrYWVllaXEbfXq1Tg6Oqr/OahSpQpz586lU6dOHD16VB2VzYpRo0bRuHFj9fvOnTsTERGBi4tLuhhz2/Pnzxk2bBgJCQlMmDAhW9daW1sTEBCAgYEBFSpUYO7cuTRq1Eidc9+3b19++uknHj16RGxsLIcPHyYsLIwWLVoAMHz4cK5evUpYWJiaUCclJTFlyhScnJyAV//p+vzzz/nnn38oXrx47gWupXIy37uwzBXPCYlRN0iMuuN9iFNi1G5akVCnjXg+ffr0reUiIiKwsrJSk2kAPT096tevz9GjR9Vj9vb2ajINYGVl9cbHWJuamtKvXz++++471q1bx61bt7hy5QoPHz4kNTUVgKtXr1KjRg01mQZwdXXF1dU1wzqHDx/O+PHjOXToEPXr1+fDDz/Ew8MDExOTt8aXxtbWlpiYGBRFyTSpjoiIoGnTphrHHB0dsba25urVq9lKqD/44AP1e0tLS4BMH/+dG/755x+GDh3KnTt3WLVqFeXKlcvW9eXLl9f4z4qZmZlGHWmve0JCAlevXgXSj47Xr1+fuXPnahyrXLmy+r2VlRWQP6+HNoiNjSclJTVb1xgY6GNtbfZO1xYWEqNukBh1x/sQp8RYcKytzbI8aq4VCXW5cuWwt7fn3LlzeHh4pDt/8+ZNpk6dSt26dTNMMFNTUzE0/F8oxsbGWW47Pj4eLy8v4uPjcXd3p3PnzkyaNEljNwtDQ8Ns/Zm/devWHD16lKNHj/Lbb7+xcuVKFi5cyNatW6latWqm19etW5ewsDCuXLmijpC+bvXq1epr8qakOzU1VeM/AP9eVJhRYpjR65bbCy7/7fr16wwaNIjk5GTCw8PfOM3lbV6PM012prpA+s8QFMzroS1SUlJJTn63X2o5ubawkBh1g8SoO96HOCVG7aYVk1X09fXp3r07O3bsyHCP5ZUrV/J///d/VKtWjdjYWCIiIjTOnzlzhipVqrxT20ePHuXixYts2LCBL774Ag8PDywtLXn8+LGaPFWpUoVLly5p7I986NAhmjVrRnx8vEZCm5iYyMyZM7lz5w4eHh5Mnz6dQ4cOoa+vz5EjR7LUp8aNG1O2bFmWLl2a7tyTJ09YvXo1iYmJADg4OHD69GmNMleuXCEuLk4dYTUyMuLZs2fq+dTUVCIjI7P2Av1/eTFv+M6dO3zyySeYm5uzdevWd0qmsyvtrxtnzpzROH769Ol3/gwJIYQQ4v2mFSPUAL6+vhw9epTevXszcuRI6tatS0xMDFu2bGHHjh3MmTOHjz/+GEdHR8aOHcuXX36Jvb094eHhREREMHny5Cy3ZWFhwd27d7l//766q8iePXto27Yt9+7dY968eSQlJalJa9++fVm/fj2TJ09m4MCBREdHM2fOHJo2bYqZmRnm5ubExMRw48YNypYty/nz5zl9+jSTJk3C3t6en3/+mefPn2d5hwhjY2O++uorfH19GTZsGAMHDqRkyZJERESwYMECTE1NGTt2LAADBgzAy8uLqVOn4uXlxePHj5k6dSrVq1dX50PXrVuXAwcO0KZNG+zt7VmzZo1Ggp0V/44xo1Hh7PrPf/5DYmIic+fOxdDQkH/++Uc9l1dztatUqULz5s0JCgoCoGLFiuzfv5/Dhw+rc/DzjE36vzZoncLQRyGEEELLaE1CbWZmRnh4OKtXr2bFihVERUVhYmJCjRo1WLdunbrl3Jo1awgODmbEiBEkJiZSo0YN1q5d+8YdQjLSu3dvJk6cSKdOnTh+/Dj+/v6sXbuWBQsWUKJECTw8PChVqhTnz58HoESJEqxevZo5c+bQpUsXrK2t8fDwYMyYMQC0adOGrVu30qlTJ8LDw1m4cCEzZ87ks88+49mzZ3zwwQfMnTtX3dovKxo1asSWLVtYvnw5Y8eOJTo6mhIlSvDxxx/j6+uLvb09AC4uLqxYsYKFCxfi6emJpaUlrVq1YuzYsWrSO3r0aGJiYhg8eDBmZmb06NEDDw+PbE1f+HeMtWvXzvK1GXnw4IG6fWHnzp3TnU+b65wX5s+fz7x58/jyyy+JjY2latWqhIaG0rp16zxrE0WBJrn/NM28oKQmyx7UQgghRDboKe/LpFAhCpi2LbZ4k9RU5Z0SakNDfWxtLYiOfl5o58BlRmLUDRKj7ngf4pQYC46dnUXhWpQoxPugMC+2EEIIIcSbSUKdjx48eEC7du3eWqZ69eps3KjdUwM6derEnTt33lrm119/zdJDbN7kwIEDBAQEvLVM//79GT169Du3IYQQQgiRGyShzkf29vbs2rXrrWWyuld1QQoLC8t0P2Yzs5xtzt68efNMXytra+sctSGEEEIIkRskoc5HaU/yK+xKly6d521YWFhgYWGR5+0IIYQQQuSUVuxDLYQQQgghRGElCbUQQgghhBA5IAm1EEIIIYQQOSBzqIXIJ1ndyzIvvOve0kIIIYTInCTUecTNzY27d++qPxsZGWFvb4+bmxsjRozA1ta2AHuXNY6OjsycOZOuXbvmSn2KorBr1y6aNWtG0aJFc6XO/JCUlMTGjRsZMGDAu1eiKFhb52znk5xQUpN5Ev1SkmohhBAiD0hCnYd8fHzw8fEB4OXLl0RERBASEsKpU6fYvHkzlpaWBdzD/HXq1Cn8/Pw4fPhwQXclW/bt28fMmTNzllDr6cFvXhBzOdf6lWU2Tug12Yi+vp4k1EIIIUQekIQ6D5mbm1OsWDH153LlyuHk5ET79u1ZtWoVI0eOLMDe5b/C+pT7XOt3zGWIPpc7dQkhhBBCa8iixHxWunRpWrduzb59+wB49uwZkyZNolGjRtSrV4/+/fvzxx9/qOVDQ0Pp06cPy5Yto1GjRri6uuLv709cXJxaJit1eHt7s2LFCpo1a4azszP9+/fn77//Vsvcv3+fzz77DBcXFz7++GP279+fru8//fQTXbt2pVatWrRu3ZoFCxaQmJionnd0dGTr1q0MHDiQWrVq8dFHH7Fs2TIATpw4Qf/+/QFo2bIlO3bsyNLrlZycTGhoKG5ubtSuXZuuXbvyyy+/qOevX7+Or68vDRs2pF69enzxxRdERUWp5729vfHz89Oo08/PD29vbwAiIyNxdHTk4MGD9OjRA2dnZ1q2bMm3334LwI4dO/D391fjO3HiRJb6LYQQQoj3h4xQFwAHBwd2795NXFwcgwcPxsjIiGXLlmFpacnu3bvp06cPW7dupXr16gBqcrxq1Sri4uIICAhg1KhRrFy5EkVRslTHuXPnMDMzY/ny5Tx//pyJEycSFBTEunXrSE5OZtCgQVhaWhIeHk5iYiJBQUEaff7ll18YOXIk/v7+NG3alNu3bzNt2jRu3LjBwoUL1XKzZ89m0qRJBAYGsnv3bubNm0e9evVwcXEhNDSUESNGsG3bNhwcHLL0Ws2YMYMDBw4QGBhIzZo12blzJ8OGDWPXrl2YmZnRq1cvmjRpwrp160hMTCQ4OJh+/fqxZ8+ebE2pmTVrFoGBgVSsWJE1a9YwadIkGjZsiIeHB8+ePWPGjBkcO3YMGxubLNepbfJ6UWRa/QW5+DKvSYy6QWLUHe9DnBJj4SAJdQFIe2T2jz/+yLlz5zh+/Dh2dnYAjBkzhrNnz7J+/XpmzZoFgJ6eHgsWLKBEiRIABAYGMnjwYP7++28ePHiQpTqSk5OZPXs2RYoUAV6N3IaEhABw/Phxrl27xqFDhyhfvjwAM2fOxNPTU+1zWFgY3bt3p0+fPgCUL1+eoKAgPvnkEyIjIylbtiwAXbp0oXPnzgCMGjWKTZs2cebMGerXr68mo3Z2dpiammb6OsXFxbF161a+/PJLPDw8ABg5ciSpqak8f/6cnTt3Ym5uzpw5czA2NgZg0aJFuLm5sWfPHvr27Zvl92TgwIG0bNkSgIkTJ7Jt2zbOnz9Phw4dsLKyAtCYvlMY5deiyIJcfJlfJEbdIDHqjvchTolRu0lCXQCePXsGwJ07dwDURC5NYmIiCQkJ6s8VK1ZUk2kAFxcXACIiIoiMjMxSHfb29moyDWBlZUVSUpJaj42NjZpMAzg5OWFm9r8P9qVLl7hw4QI7d+5Uj6XNLb5+/bqaUFeuXFmjH5aWlmo72XXjxg2SkpKoU6eOxvHRo0cDsHjxYmrWrKkm0wBFixalUqVKXL16NVttvd7vtAT6XfutrWJj40lJSc2z+g0M9LG2NsvzdgqSxKgbJEbd8T7EKTEWHGtrsyyPmktCXQAuXrxIxYoVMTIywtLSMsP5xK8niUZGRhrnUlNffdgMDAxITU3NUh2vf5+RjBbeGRr+7+ORmprKoEGD6NKlS7pyr4/cZtTOuy7q+3fcGdWrp6eX7nhKSorGtf9uP6NEOTf7ra1SUlJJTs77X1T51U5Bkhh1g8SoO96HOCVG7VZ4J6sUUvfv3+fw4cN07NgRBwcH4uLiSExMpEKFCurXihUrNLaWu3HjhjqqDa/mQ8OrUeSs1vE21atXJzY2lmvXrr2xzapVq/L3339rtPHgwQNmz57N8+fPs9RORsnv21SoUAEjIyONBZYA3bt3Z+XKlTg4OHDhwgWNhZGPHj3i1q1b6oizkZGRRhwAt2/fzlY/sttvIYQQQrxfZIQ6D7148YJ//vkHeLUP9dWrV1mwYAFly5Zl4MCBmJqa4uTkxKhRo/jyyy8pXbo0W7ZsYfv27axevVqjngkTJjB69GgeP37M1KlT8fDwoGzZspQqVSpLdbxNw4YNqV27NhMmTGDy5MkYGBgwffp09PX/9/+twYMHM2rUKEJDQ+nQoQP3799X28vq3GJzc3MArly5gq2tLRYWFm8tb2ZmRr9+/Vi4cCF2dnZUrVqV7du389dff9GiRQuMjY3ZvHkz48aNY9iwYeqiRFtbW9q3bw9A3bp1WblyJT/++CNVq1Zl586dREREUKtWrSz1+fV+//nnn1SpUiVL878zZOP0btflVEG1K4QQQrwnJKHOQ6tXr1aTWnNzc0qWLEmbNm3w8fFRk8nVq1cTEhLC6NGjiY+Pp3LlyoSGhtK4cWO1nlKlSuHg4EDfvn0xNDSkY8eOjBs3Dng17SMrdbyNvr4+y5YtY/r06fj4+GBqasrQoUPV+dkA7dq1Y/78+Sxbtoxly5ZhY2NDixYtGD9+fJZfDwcHB5o3b86oUaMYM2aM+tCbtxkzZgyGhoZMmTKF2NhYHB0dWb58uToCvWHDBubMmUOvXr0wNjamadOmhISEqAs/BwwYwJ07dxg/fjx6enp4eHgwYMAAzp49m+V+N2rUiNq1a9O7d29CQkJwd3fP8rUqRYEmG7N/XS5RUpPloS5CCCFEHtFTdG2iqI4JDQ1l586d/PjjjwXdFZFDBbnYIjVVyfOE2tBQH1tbC6KjnxfaOXCZkRh1g8SoO96HOCXGgmNnZyGLEoXQNoV5sYUQQggh3kwSalEgpk6dqrEFX0YWLlxIs2bN8qlHQgghhBDvRqZ8iALx5MmTdLtv/Fvx4sU19sIu7LTtT1m5TVv/ZJebJEbdIDHqjvchTomx4MiUD6H17Ozs1Cc7CiGEEEIUZrIPtRBCCCGEEDkgCbUQQgghhBA5IFM+hMgnWZ2HlRvyY5s8IYQQQrwiCXUB8fb25uTJk288f+zYsQyfQOjm5kaXLl0YMWJEXnZPlZSUxMaNGxkwYABQMPtia8Ne3H5+fty9e5cNGza8WwWKgrV1/i2wVFKTeRL9UpJqIYQQIh9IQl2A3N3dCQgIyPBc0aJF87k3Gdu3bx8zZ85UE2ofHx+8vLwKtlOFkZ4e/OYFMZfzvi0bJ/SabERfX08SaiGEECIfSEJdgExNTTMchdYm/95V0cLCQn1susimmMsQfa6geyGEEEKIXCaLErXYs2fPmDhxIvXr16dx48asXbtW4/yOHTtwdHTUOHbixAkcHR2JjIxUj23YsIG2bdtSq1YtPDw82L17t3ruzJkzDBw4kHr16lGzZk06dOjAvn371Pr9/f0BcHR05MSJE4SGhuLm5qZef+/ePcaNG0fTpk2pU6cOn376KVevXlXP+/n5MX78eIKDg2ncuDG1a9dm2LBh/PPPP7n2OgFcv36dwYMH4+LiwocffsjYsWPVNnbs2IGzszOxsbEa17Rt25YFCxYA8ODBA0aPHk39+vVp2LAhvr6+3Lx5M1f7KIQQQgjdJCPUWmzUqFFERUURFhaGhYUFs2bN4u7du9mqY9WqVSxatIiAgAAaNWrE0aNH8ff3x97enipVquDj40Pfvn2ZMmUKycnJrFy5En9/fxo1aoSHhwfPnj1jxowZHDt2DBsbG41533FxcfTp04dy5cqxdOlSjI2N+frrr+nXrx+7d++mdOnSABw8eJCOHTsSHh5OVFQU48aNY/78+cyYMSNXXqcHDx7Qt29f2rdvj5+fH/Hx8YSGhtK7d2/27t1Lu3btmDZtGt9//z09evQA4Pz589y8eRNPT09evHiBt7c31apVIzw8HH19fdasWUPPnj3Zu3cvJUqUyJV+5rf8XAT5env53W5+khh1g8SoO96HOCXGwkES6gK0d+9evv/++3THW7RowfDhwzl27Bhr166lfv36AMydO5cWLVpkq421a9fSv39/evbsCYCXlxcvX74kJSWFxMREhg8fzqeffoq+/qsP8dChQ9mxYwc3b96kfv36WFlZAWQ4NWXPnj1ER0ezY8cO9SEtc+bMoVWrVmzcuJHx48cDYGlpydSpUzEyMqJy5cp07tyZn3/+OVtxvM3mzZspXrw4gYGB6rEFCxbQqFEjvvvuO7p27Uq7du3Ys2ePmlDv2bOHunXrUrFiRbZt20Z0dDRz587FyMgIgK+++ooTJ06wdevWfFsAmtvycxGkNrSbnyRG3SAx6o73IU6JUbtJQl2A3NzcGDduXLrj5ubmnDlzBgBnZ2f1uL29PeXKlcty/U+ePOHhw4fUrl1b4/inn36qft+tWzfCw8P566+/uHnzJpcvv1o0l5KSkmn9ERERVKxYUeOJhyYmJtSqVUtj2keFChXURBXAysqKpKSkLMeRmUuXLnH9+nVcXFw0jickJHD9+nUAunTpQv/+/bl//z729vYcOHCA0aNHq9fHxcXRoEGDN15fGMXGxpOSkn+PcDUw0Mfa2izf281PEqNukBh1x/sQp8RYcKytzeTR44WBhYUFFSpUeGuZ1FTND5ahYfq3TFEU9PT0AEhOTlaPGxsbA6jn/u369ev06dOH6tWr07RpU1q2bImtra06ipuZ19t9XUpKikY/0/qRV1JTU2nUqBGTJ09Ody5thN3V1ZWyZcuyb98+KleuzIsXL3B3d1evr1SpEkuXLk13vbm5eZ72PS+lpKSSnJz/v5gKqt38JDHqBolRd7wPcUqM2q3wTlbRcdWrVwfg7Nmz6rHY2Fhu376t/pw26vvs2TP12K1bt9TvLS0tKV68OH/88YdG3V988QXTp09n8+bNFC1alLVr1zJ48GCaN2/Oo0ePgP/t7vGmZBzAwcGBGzdu8PjxY/VYQkICf/75J1WqVMl2zO+qatWqXL9+nVKlSlGhQgUqVKiAjY0NM2bMICIiAngVh6enJ99//z379++nVatWarLt4OBAVFQUVlZW6vVlypRh7ty5nDp1Kt/iEEIIIUThJCPUBejly5dv3O2iRIkStGvXjqlTp2JsbIy9vT3z5s0jMTFRLVOnTh309fVZsGABAwcO5Pr166xevVqjniFDhjBv3jwqVqxI3bp1OXr0KIcPH2bVqlX8+eef3L9/n59//pkqVapw8eJFpk+fDqC2kzZCm1GS3LFjR8LCwhg1ahTjx4/H2NiYJUuW8OLFC3r16pVrrxO8eq1++eWXdMednZ3p27cv33zzDWPGjOHzzz9HT0+PkJAQLl26RNWqVdWyXbp0YcmSJfz111+Ehoaqxzt16sTy5csZPnw4EyZMwMrKirCwMH7++efcnT9t45R7dWlDO0IIIYQAJKEuUAcPHuTgwYMZnps3bx7BwcHMnj2b0aNHk5qaSq9evXjy5Ilaply5ckydOpWwsDC2bt1KjRo1+M9//sNnn32mlunXrx8JCQksWrSIf/75h4oVKzJ//nwaNWpE3bp1+fvvv5kwYQKJiYlUrFiRMWPGsGjRIi5cuECzZs1o1KgRtWvXpnfv3oSEhGj00dramvDwcIKDg9UHv9SrV4/Nmzdna653Vjx+/JjBgwenO75mzRqaNGlCeHg4c+fOpW/fvhgYGFCnTh3WrVun8YCcMmXK0KBBA/7++2+aNGmiHreysiI8PJzZs2czaNAgUlJScHJyYtWqVRoJeY4oCjTZmDt1ZaW51GR5qIsQQgiRT/SUfz+5QwiRJ/JzsUVqqpLvCbWhoT62thZERz8vtHPgMiMx6gaJUXe8D3FKjAXHzs5CFiUKoW0K82ILIYQQQryZJNSiwBw4cICAgIC3lunfv7+6vZ0QQgghhDaShFoUmObNm7Nr1663lrG2ts6fzgghhBBCvCNJqEWBsbCwwMLCoqC7IYQQQgiRI7IPtRBCCCGEEDkgCbUQQgghhBA5IAm1EEIIIYQQOSBzqIXIJ1ndyzInCmL/aSGEEOJ9Jwl1HvHz8+Pu3bts2LChoLuS565cucLq1av5/fffefr0KSVLlqRdu3YMGjQoW7t0REVFce7cOdq3b5+HvS0gioK1tVneN5OazJPol5JUCyGEEPlIEmqRI4cOHWLMmDF06NCBRYsWUbRoUa5evcrs2bM5evQoGzZswNLSMkt1TZw4kTJlyuhmQq2nB795QczlvGvDxgm9JhvR19eThFoIIYTIR5JQi3f26NEj/Pz88PLyws/PTz1erlw5HB0dcXd3Z8OGDXz22WcF2EstEnMZos8VdC+EEEIIkctkUWI+cHNzY/ny5YwYMQIXFxcaNmzIjBkzSE5OVsv8+eefDBw4EBcXF5o0aUJgYCAvXrwAICUlhbVr19K2bVucnZ1p27YtW7duVa89ceIE1atX5/fff8fDwwNnZ2d69erFjRs3WLp0KU2aNKFBgwZMmzYNRfnfyOVPP/1E165dqVWrFq1bt2bBggUkJiZmOa69e/cSHx+Pr69vunPlypVj3bp19OzZEwBFUVi5ciXu7u7UrFmTevXqMXToUO7cuQOAt7c3J0+eZOfOnbi5uQGQmJhISEgIH330ES4uLvTs2ZNjx45ptHPs2DE1hvbt2/Ptt9/i6OhIZGQkAC9fvmTBggW0bNkSZ2dnPD09+eGHH9Trd+zYgZubG1999RX169fH19cXT09P/P39Ndr55ZdfqFmzJk+ePMny6yOEEEKI94OMUOeT0NBQxo8fz9ixYzl27BjTp0+nevXqeHp6EhkZibe3N25ubnzzzTfExcXh7+9PYGAgc+bMYdasWezevZtJkybh7OzMr7/+ytSpU0lISMDb2xt4lXTPmjWLGTNmYGJiwogRI+jduzcfffQRGzZs4NSpU0yePJkPP/yQFi1a8MsvvzBy5Ej8/f1p2rQpt2/fZtq0ady4cYOFCxdmKaY//viDSpUqUaRIkQzP16tXT/1+3bp1LFu2jODgYDXhnTRpErNmzeLrr78mNDQUX19fSpYsSWBgIAD+/v5cu3aNkJAQSpYsyU8//YSvry+LFy/m448/5vLlywwdOpRPPvmEOXPmcOXKFaZMmaLRhzFjxnDp0iUCAwOpVKkS+/fvZ/jw4Xz99de0bNkSgLt37/LgwQN27tzJy5cvOX78OAsWLGDy5MmYmpoCsHv3blq0aIGdnV123vYCkR+LH9/WbkG1nx8kRt0gMeqO9yFOibFwkIQ6n3z00Uf0798fgIoVK/Ltt99y9uxZPD092bp1KzY2NsyaNQsjIyMApk+fzsmTJ4mLi2Pz5s34+fnRsWNH9fo7d+4QFhZGv3791DZGjhxJnTp1AGjTpg3r169n2rRpmJmZUblyZUJDQ7l27RotWrQgLCyM7t2706dPHwDKly9PUFAQn3zyCZGRkZQtWzbTmGJiYrK86LB8+fLMmjVLHX0uU6YM7u7u7N+/H4AiRYpgZGSEqakpdnZ23Lp1i3379vHtt9/i7OwMwMCBA7ly5QqrVq3i448/Zu3atdSsWZMJEyYA8MEHH/D48WOmT58OwPXr1zl8+DBhYWG0aNECgOHDh3P16lXCwsLUhBpg2LBhlCtXDoBixYoREhLCDz/8QIcOHYiLi+OHH35gwYIFWYq1oOXH4kdtbj8/SIy6QWLUHe9DnBKjdpOEOp9UrlxZ42crKyuSkpIAuHr1KjVq1FCTaQBXV1dcXV25cOECSUlJGqO9APXr12fNmjU8fvxYPVapUiX1ezMzM+zt7TEz+9+H08TEhISEBAAuXbrEhQsX2Llzp3o+bTrI9evXs5RQ29raEhUVlWk5eDXt5fz58yxatIhbt25x/fp1rl27RokSJTIsf+nSJQD1PyFpkpKS1CT+0qVLNGnSRON8/fr11e+vXr0KkOFrN3fuXI1jFStWVL8vUqQIbm5u7Nq1iw4dOnDw4EGsrKz46KOPshRrQYuNjSclJTXf2zUw0Mfa2qzA2s8PEqNukBh1x/sQp8RYcKytzbI8ai4JdT4xNjZOdywtgTU0NERPTy/D69LK/Pt8amqqem2a178H0Nd/84cgNTWVQYMG0aVLl3TnihUr9sbrXufi4sL+/fuJjo7G1tY23fng4GBMTEwYNWoUK1asIDQ0lK5du9KgQQO8vb05fPiwOkL9b2lxb9y4EQsLiwzjMjAwUF+H7EhNTU33WqVN7UjTrVs3fH19efToEXv27KFTp07prtFWKSmpJCcX3C+kgm4/P0iMukFi1B3vQ5wSo3YrvJNVdEiVKlW4dOkSKSkp6rFDhw7RrFkzPvjgAwwNDTl9+rTGNadPn6ZYsWLY2Ni8U5tVq1bl77//pkKFCurXgwcPmD17Ns+fP89SHe7u7lhYWLBs2bJ0527evMmmTZswMDAAYOnSpQwfPpwpU6bQq1cv6tSpw82bNzUWSf67fwAPHz7U6OOOHTvYvn07ANWqVeP8+fMa173+s4ODAwBnzpzRKHP69GmqVKny1tg+/PBDihUrxrZt2zhz5gxdu3Z9a3khhBBCvL8Kx5Cbjuvbty/r169n8uTJDBw4kOjoaObMmUPTpk2xsrKiZ8+eLFq0CBsbG2rVqsWxY8fYtGkTY8aMeePIdmYGDx7MqFGjCA0NpUOHDty/f58vv/yS0qVLZ3mE2s7OjsmTJzNx4kRiY2Pp3bs3RYoU4fz588ybN4+qVavi4+MDQKlSpfj1119xc3NDX1+f3bt389///hd7e3u1PgsLC+7evcv9+/epWrUqLVq0YPLkyQQGBuLg4MB///tfli1bxldffQWAj48Pnp6ezJkzh27dunH9+nV1QaWenh5VqlShefPmBAUFAa+mdezfv5/Dhw9nOh9aX18fT09PwsLCqFmzZqYJeJbYOOW8joKsXwghhBAZkoRaC5QoUYLVq1czZ84cunTpgrW1NR4eHowZMwaAgIAAbG1tmTt3Lo8ePaJChQoEBgaqW9K9i3bt2jF//nyWLVvGsmXLsLGxoUWLFowfPz5b9XTs2JGSJUuyatUqhg0bRmxsLKVLl8bT05NPP/1Una4xe/Zspk6dSrdu3bCwsKB27doEBQUxZcoUdRFk7969mThxIp06deL48ePMnz+f+fPnM3nyZGJiYihXrhzTpk2jW7duwKsR6MWLFzNv3jzWrl1LpUqV8PLyIjQ0VJ2PPn/+fObNm8eXX35JbGwsVatWJTQ0lNatW2caW9euXQkLC8ud0WlFgSYbc15PZs2kJstDXYQQQoh8pqe86W/uQmi5CxcuYGhoSPXq1dVje/fu5T//+Q/nzp3L8ZznU6dOMXjwYI4ePYqVlVVOu5sviy1SU5UCS6gNDfWxtbUgOvp5oZ0DlxmJUTdIjLrjfYhTYiw4dnYWsihR6L4rV64we/ZsgoODcXJy4tatW4SGhtK+ffscJdPXr18nIiKCsLAwunTpkivJNBTuxRZCCCGEeDNJqEU6586dU+c+v0mrVq0ICQnJpx5lrEePHjx8+JAZM2bw4MEDihYtSvv27fniiy9yVO/Nmzfx9/enVq1ajB49Opd6K4QQQghdJVM+RDoJCQncv3//rWXMzc2zvHhRvKJtf8rKbdr6J7vcJDHqBolRd7wPcUqMBUemfIgcMTExoUKFCgXdDSGEEEKIQkH2oRZCCCGEECIHJKEWQgghhBAiB2TKhxD5JKvzsLKjILfJE0IIIcQrMkJdCLm5ueHo6MiaNWsyPB8YGIijoyOhoaHv3Ia3tzd+fn7qzz/99BN//fXXO1+fFRcuXMDX15cGDRrg7OxM27ZtmTt3LnFxcWqZpKQk1q5dm616o6Ki2L9/f7auyXWKgrW1Gba2Frn6ZWdrir7+uz0tUwghhBC5Q0aoCykjIyO+++47Bg4cqHE8OTmZ//73v+/8SPI0oaGhGBgYAHD37l18fX1Zv3597jyCOwPXrl3D29ubvn37MmrUKCwsLLhy5QozZ87k/PnzrF+/HoB9+/Yxc+ZMBgwYkOW6J06cSJkyZWjfvn2e9D1L9PTgNy+IuZx7ddo4oddkI/r6ejJKLYQQQhQgSagLqcaNG3P06FHu3btHqVKl1OO///475ubmmJmZ5aj+IkWKqN/nx86KO3bsoHz58kycOFE9Vq5cOUxNTRk0aBBXrlyhWrVq+dKXPBNzGaLPFXQvhBBCCJHLZMpHIVWrVi1Kly7Nd999p3H8wIEDuLu7a4xQb9++HU9PT2rVqkWdOnXw9vbm4sWL6nk3NzdmzJiBh4cHDRs25Pfff1enbERGRtKyZUsA+vfvr04j+fHHH+nduzcuLi44OzvTvXt3fvvtt3eOR09Pj7t37xIREaFxvHHjxuzfv59KlSqxY8cO/P39AXB0dOTEiRMoisLKlStxd3enZs2a1KtXj6FDh3Lnzh3g1dSTkydPsnPnTtzc3NR4/z0d5t9TVFatWkWrVq2oWbMmbm5ufP3114U7mRdCCCFEnpER6kLM3d1dY9pHYmIiP/zwA2vXruXgwYMAHDp0iMmTJzN9+nRcXV159OgR06dPJyAggF27dql1bd68mWXLlmFlZYWjo6N6vFSpUmzbto0ePXoQGhpK06ZN+fPPP/n8888ZP348ISEhPH/+nPnz5zNu3DiOHDmCsbFxtmPp1asX27dvp1OnTtSuXZsGDRrg6upKw4YN1WkmHh4ePHv2jBkzZnDs2DFsbGxYt24dy5YtIzg4GEdHRyIjI5k0aRKzZs3i66+/JjQ0FF9fX0qWLElgYGCW+vLjjz8SFhbGggULqFSpEv/3f//HhAkTKFu2LJ07d852bHktLxY7vou0fmhLf/KCxKgbJEbd8T7EKTEWDpJQF2Lu7u6sWrVKnfbx66+/YmtrS/Xq1dUyRYoUYfr06Xh6egJQpkwZevToweTJkzXqat68OU2aNEnXhoGBAXZ2dgDY2NhgYWGBgYEBX375JV5eXmq5/v374+Pjw+PHjzWmoGRVhQoV2LNnD2vXruXw4cMsX76c5cuXY21tzfjx4+nZsyempqZYWVkBqE9pLF++PLNmzVJHn8uUKYO7u7u6CLFIkSIYGRlhamqqxpGZ27dvY2JiQtmyZSldujSlS5emePHilC5dOttx5Qdr65xN78lt2tafvCAx6gaJUXe8D3FKjNpNEupCrGbNmpQrV04dpT5w4AAdOnTQKOPq6oqdnR1Llizh1q1b3Lhxg8uXL5Oaqvloz+w8GdHJyQkbGxtWrFjBjRs3uHnzJpcvv1psl5KS8s7xlChRgokTJzJx4kTu3bvHr7/+yqZNm5g0aRIlSpSgefPm6a5xc3Pj/PnzLFq0iFu3bnH9+nWuXbtGiRIl3rkfnTp1Yvv27bRp0wZHR0eaNm1K69attTahjo2NJyWl4B/VamCgj7W1mdb0Jy9IjLpBYtQd70OcEmPBsbY2k0ePvy/Spn307duXw4cPs23bNo3z+/fvZ8KECXTo0IFatWrRvXt3IiIimDp1qkY5U1PTLLd56tQpfHx8aN68OfXr16d9+/bEx8fz+eefv3McISEhfPjhhzRu3Bh4NdWke/fudOrUidatW/Pzzz9nmFCvWLGC0NBQunbtSoMGDfD29ubw4cOZbpP37/nQSUlJ6vd2dnbs3r2bc+fO8euvv3Ls2DFWr17NiBEjGD58+DvHmFdSUlJJTtaeX0Da1p+8IDHqBolRd7wPcUqM2k0S6kLO3d2d5cuX8+2331KuXDkqV66scT4sLIzu3bsTFBSkHjt8+DDwKqnMyvZ6/y6zatUqGjZsyOLFi9VjGzZsUOt8F7/99hsRERFqQp3G2NgYU1NTihYtmmFfli5dyvDhwxkyZIhG/97WDyMjI549e6b+nJqaSmRkJBUrVgRg9+7dxMXF4eXlRb169fjiiy/48ssvOXDggFYm1EIIIYQoWJJQF3JOTk5UqFCBefPmMXTo0HTnS5UqxdmzZ7l48SJWVlb8+OOPhIeHA68WMZqYmGTahrm5OQARERFUr16dUqVK8cMPP3D69GlKlizJiRMnWLhwoVrnuxg9ejSfffYZI0eOpF+/fpQuXZqoqCi2bt3K8+fP6dWrl0Zf/vzzT6pUqaLOHXdzc0NfX5/du3fz3//+F3t7e7VuCwsL7t69y/379ylZsiR169blwIEDtGnTBnt7e9asWaORYCckJBAcHIyFhQX169fn/v37nDx5EldX13eKTWXjlLPr87o+IYQQQrwTSah1gLu7O0uXLsXDwyPduUmTJhEYGEi/fv0wNjamWrVqzJ49m9GjR3P+/HkaNGiQaf22trZ069aN2bNnc+vWLb744gsePXqEr68vAFWqVGHGjBmMHz+eCxcupBslz4pmzZqxYcMGVqxYwciRI4mNjcXGxoYPP/yQLVu2qAlyo0aNqF27Nr179yYkJITZs2czdepUunXrhoWFBbVr1yYoKIgpU6YQGRlJ2bJl6d27NxMnTqRTp04cP36c0aNHExMTw+DBgzEzM6NHjx54eHioo9o9e/YkJiaGJUuWcO/ePWxsbGjbti3jxo3LdlwqRYEmG9/9+jdVm5osD3URQgghCpieIpvrCpEv8mKxRWqqojUJtaGhPra2FkRHPy+0c+AyIzHqBolRd7wPcUqMBcfOzkIWJQqhbQrzYgshhBBCvJkk1CJPrVixgiVLlry1jJ+fnzpHWgghhBCisJGEWuSpnj170qZNm7eWyeoDV4QQQgghtJEk1CJP2djYYGNjU9DdEEIIIYTIM4X3oelCCCGEEEJoAUmohRBCCCGEyAFJqIUQQgghhMgBmUMt8p2iKOzcuZOdO3dy7do14uLiKFmyJM2aNWPo0KGUKFEiz9qOjIykZcuWrF+/noYNG+ZZOxnJ6l6WWaVNe1ALIYQQ7zNJqEW+SklJ4fPPP+fs2bP4+voSGBiIhYUF165dY8mSJXTr1o1du3ZpPDpcJygK1tZmuVtlajJPol9KUi2EEEIUMEmoRb5as2YNR48eZevWrdSoUUM9Xrp0aRo0aICHhwerV69mwoQJBdjLPKCnB795Qczl3KnPxgm9JhvR19eThFoIIYQoYJJQi3yjKAobN26kU6dOGsl0GjMzM8LDwylWrBgADx48YNasWRw9ehQDAwNcXFzw8/OjYsWKwKsHwqSkpGBvb8+uXbt48eIFTZs2JSgoSK0jIiKC6dOnc+HCBUqUKMHgwYPTtbt9+3ZWrlzJ3bt3KVOmDL1798bb2xt9fX11isjIkSPZuHEjJiYm7Nq1C2tr6+y/ADGXIfpc9q8TQgghhFaTRYki30RGRhIVFUWTJk3eWKZMmTIYGxvz4sULvL29SUlJITw8nA0bNmBra0vPnj158OCBWv7gwYM8ffqU8PBwFi9ezJkzZ5g/fz4Az549Y8CAAVhaWrJt2zYCAwPTPbXxm2++ITg4mM8//5z9+/czatQoVqxYwZw5czTK7dmzh3Xr1rFw4cJ3S6aFEEIIobNkhFrkm0ePHgHpn4zo6+vLiRMn1J9Lly7NgAEDiI6OZu7cuRgZGQHw1VdfceLECbZu3cqIESMAsLS0ZOrUqRgZGVG5cmU6d+7Mzz//DMD+/fuJj48nODgYKysrqlatyn/+8x8+//xzta0lS5YwdOhQOnToAEC5cuWIi4sjKCiIkSNHquX69u1LlSpV8uBVyZncXuiYE2l90aY+5TaJUTdIjLrjfYhTYiwcJKEW+cbW1haAp0+fahwPCgri5cuXAGzYsIEff/yRS5cuERcXR4MGDTTKJiQkcP36dfXnChUqqAk3gJWVFUlJScCr6R4VK1bEyspKPe/i4qJ+/+TJE+7fv8/ChQtZvHixejw1NZWEhAQiIyMxMTFR29FGub3QMTdoY59ym8SoGyRG3fE+xCkxajdJqEW+KVeuHMWKFePkyZO0b99ePf76NnlpjylPTU2lUqVKLF26NF095ubm6vfGxsZvbVNRNBfsGRr+7yOfmpoKgL+/f4bTUEqVKsXDhw8BMDU1fWs7BSU2Np6UlNSC7gbwamTB2tpMq/qU2yRG3SAx6o73IU6JseBYW5tledRcEmqRbwwMDOjfvz9ff/01ffr0oVq1aunK3Lt3DwAHBwd2796NlZWVOkUkOTmZMWPG0K5dOzw8PDJtz8nJie3bt/PkyRO1jj/++EM9X7RoUYoWLcrt27fp06ePevzAgQMcOnSI4ODgHMWbH1JSUklO1p5fPqCdfcptEqNukBh1x/sQp8So3SShFvlq0KBBXLp0ib59+zJkyBA+/vhjLC0tiYiIIDw8nF9//ZVu3brRqVMnli9fzvDhw5kwYQJWVlaEhYXx888/q/OnM9O+fXuWLl3K2LFjmThxIrGxscyYMUM9r6enx6BBg5g3bx6lS5emefPmREREEBQUxMcff5zp6He22ThpZ11CCCGEyBFJqEW+0tfXZ8GCBRw8eJDt27ezfv16YmNjsbe3p379+oSHh+Pq6gpAeHg4s2fPZtCgQaSkpODk5MSqVauoWrVqltoyNzdn/fr1TJ06lT59+mBjY8PIkSPx8/NTy/j4+GBiYsKGDRsIDg6maNGidO3aldGjR+du4IoCTTbmbpWpybIHtRBCCKEF9JR/TzIVQuSJ3J4bpm2PHjc01MfW1oLo6OeF9k92mZEYdYPEqDvehzglxoJjZ2chc6iF0DaFeW6YEEIIId6s8G74J4QQQgghhBaQhFoIIYQQQogckIRaCCGEEEKIHJCEWgghhBBCiByQhFoIIYQQQogckIRaCCGEEEKIHJBt84TIJxntZalte0kLIYQQIvtkhFrLJScns27dOrp27YqLiwsNGzZk4MCBHD9+PNfb2rFjB46Ojtm+LjIykilTpuDm5katWrVwc3MjKCiIBw8eZKue6Ohotm3blu32CwVFwdraDFtbC40vO1tT9PX1Crp3QgghhMgBSai1WGJiIp988gnr1q3D29ubnTt3sm7dOqpUqYKPjw+7du0q6C5y9uxZPD09efDgATNnzuTAgQNMmzaN8+fP06dPn2wl1bNnz2bPnj152NsCpKcHv3nBwbr/+/rNCz19Q0mohRBCiEJOpnxosUWLFnHlyhX2799PyZIl1eMBAQG8ePGCGTNm0Lp1aywsLAqkf4mJiYwdO5YmTZqwcOFC9PReJYZly5bF2dmZNm3aEBoayvTp07NUn6Lo+NSHmMsQfa6geyGEEEKIXCYj1FoqKSmJbdu20b17d41kOs3IkSNZuXIlpqamXLt2jWHDhtGwYUNq1qxJ69atWbdunVo2NDSU3r17M2bMGOrWrUtQUBAAhw4domPHjtSqVYt+/foRFRWVrT4eOXKEqKgohg0bpibTaaytrVmxYgXDhg1Tj23fvh1PT09q1apFnTp18Pb25uLFiwD4+fmxc+dOTp48qU47URSFFStW0LJlS2rXrk3nzp3TjWD/+eefeHl5Ubt2bVq2bMmePXuoXr06J06cACAlJYW1a9fStm1bnJ2dadu2LVu3blWvP3HiBI6OjqxYsYKGDRvSpUsXfH196d+/v0Y7f//9N46Ojly5ciVbr5EQQgghdJ+MUGupO3fu8PTpU+rUqZPh+eLFi1O8eHHi4+MZOHAgjRo1YtOmTRgaGrJ9+3ZmzJhBgwYNcHJyAuDcuXM4Ozuze/duUlJSOHv2LCNGjODzzz+nQ4cOnD59mmnTpmWrj3/88Qfm5uZvnHft7Oysfn/o0CEmT57M9OnTcXV15dGjR0yfPp2AgAB27dpFQEAAL1++5P79+4SGhgIwf/589u7dS2BgIJUrV+bUqVNMmTKFZ8+e4eXlxYMHD/jkk09o2bIlQUFB3L17lylTppCSkqK2O2vWLHbv3s2kSZNwdnbm119/ZerUqSQkJODt7a2WO3LkCN988w3x8fFERUXx+eefExUVRenSpQHYtWsXNWrUoFq1atl6jbIio8WKhVFaHLoST0YkRt0gMeqO9yFOibFwkIRaS8XExABgY2Pz1nLx8fH079+fvn37YmlpCcDw4cNZtmwZV69eVRNqgC+++AIrKyvg1XSSunXrMmLECAAqVapEREQE69evz1Yfrays0o1OZ6RIkSJMnz4dT09PAMqUKUOPHj2YPHkyAFZWVpiammJkZESxYsV48eIFa9euZfbs2bRo0QKA8uXLc/fuXVatWoWXlxfffPMN1tbWfPXVVxgZGVGlShUmTZrEZ599BkBcXBybN2/Gz8+Pjh07AlCxYkXu3LlDWFgY/fr1U/vn4+NDxYoVAahatSr29vbs3buXoUOHkpqayp49exg0aFCWX5vssLY2y5N6C4quxZMRiVE3SIy6432IU2LUbpJQayk7OzsAnj59mmm5vn37cuDAAa5cucKtW7e4fPkyAKmpqWq5okWLqsk0QEREBE2bNtWoy8XFJVsJta2tLTExMSiKkmlS7erqip2dHUuWLOHWrVvcuHGDy5cva/TxdX/99RcJCQlMnDgRf39/9XhycjKJiYm8fPmSS5cuUaNGDYyMjNTz9evXV7//+++/SUpKol69ehp1169fnzVr1vD48WP1WFoyDWBoaEinTp3YvXs3Q4cO5ffff+fRo0d06NAhS69LdsXGxpOSkvHrUJgYGOhjbW2mM/FkRGLUDRKj7ngf4pQYC461tVmWR80lodZS5cqVw97ennPnzuHh4ZHu/M2bN5k6dSq+vr74+flha2tLy5Ytady4Mc7OzjRv3lyjvKmpabo6/r0I8PXENCvq1q1LWFgYV65c0RgJT7N69Wq1n/v372fChAl06NCBWrVq0b17dyIiIpg6dWqGdaf1bcGCBXzwwQfpzhsbG2NgYPDGhPz1Ov6d7KddY2j4v4+/iYmJRplu3bqxatUq/vzzT/bs2UPLli0pUqTIG9vKiZSUVJKTtecXSE7pWjwZkRh1g8SoO96HOCVG7VZ4J6voOH19fbp3786OHTsy3Hpu5cqV/N///R8nT57k6dOnbNmyhWHDhtG6dWt1usjbds1wcnLi7NmzGsf++OOPbPWxcePGlC1blqVLl6Y79+TJE1avXk1iYiIAYWFhdO/eneDgYLy8vHB1deXOnTsa/Xw98f3ggw8wNDQkKiqKChUqqF8///wzq1atQl9fn2rVqnHx4kWSkpLU686fP5+ujtOnT2v07fTp0xQrVuyt02kqV66Mi4sLBw4c4KeffqJr167Zem2EEEII8f6QhFqL+fr6UqFCBXr37s2uXbu4ffs2f/zxBwEBAWzfvp1p06ZRuXJl4uPjOXjwIFFRURw7dowxY8YAqMlsRnx8fLhy5QrBwcHcuHGDPXv2sHHjxmz1z9jYmK+++opffvmFYcOGcerUKe7cucPhw4f55JNPMDU1ZezYsQCUKlWKs2fPcvHiRW7fvs3atWsJDw/X6Ke5uTkPHz7kzp07WFlZ0bt3bxYsWMCuXbu4c+cOO3fuJCQkBHt7ewD69u3Ls2fPmDRpEtevX+f48ePqiLeenh5WVlb07NmTRYsWsXfvXm7dusXGjRvZtGkTPj4+mU5T6datGxs3bsTY2JgPP/wwW69NhmycwNblf1826Uf1hRBCCFH4yJQPLWZmZkZ4eDirV69mxYoVREVFYWJiQo0aNVi3bh0NGjRAURQuXrxIcHAwcXFx6mK/w4cPc+HCBfr06ZNh3U5OTqxYsYKQkBDCw8OpWrUqvr6+zJkzJ1t9bNSoEVu2bGH58uWMHTuW6OhoSpQowccff4yvr6+a/E6aNInAwED69euHsbEx1apVY/bs2YwePZrz58/ToEEDPD09OXToEB06dODQoUP4+/tjZ2fHokWLePjwISVLlmT48OEMGTIEeDUvfOXKlcyYMYPOnTtTsmRJ+vTpw+zZs9XpKwEBAdja2jJ37lwePXpEhQoVCAwMpGfPnpnG5u7uri6kNDAwyNbrko6iQJP0/2FRUpPl0eNCCCFEIaen6PzTNISu+uuvv4iJidFYdHj27Fn69OnDkSNHKFWqVI7qj4yMpHXr1hw8eFBj0eK7ymixRWqqojMJtaGhPra2FkRHPy+0c+AyIzHqBolRd7wPcUqMBcfOzkIWJQrd9+DBA4YMGcJXX32Fq6srDx8+ZObMmTRo0CBHyfS9e/e4cOECmzZt4qOPPsqVZBoK92ILIYQQQryZJNQinQcPHtCuXbu3lqlevXq251zntqZNmxIQEMCyZcuYNGkSVlZWuLm5MW7cuBzVGx0djZ+fHxUrVmTx4sW51FshhBBC6CqZ8iHSSUlJITIy8q1lTExMMnwkungzbdpbM68YGOjrfJwSo26QGHXH+xCnxFgw9PX1svTwOpCEWgghhBBCiByRbfOEEEIIIYTIAUmohRBCCCGEyAFJqIUQQgghhMgBSaiFEEIIIYTIAUmohRBCCCGEyAFJqIUQQgghhMgBSaiFEEIIIYTIAUmohRBCCCGEyAFJqIUQQgghhMgBSaiFEEIIIYTIAUmohRBCCCGEyAFJqIUQQgghhMgBSaiFyERqaiqLFi3io48+onbt2vj4+HDr1q03lo+Ojmbs2LG4urri6urKpEmTePHihUaZgwcP4uHhgbOzMx07duSXX37Jdh25KbdjTE1NZeXKlbRt25Y6derQvn17tm3bplHHzp07cXR0TPf1tna1KUYANze3dP0fN25cturIbbkdZ0bvUdpXVFQUoP3v5evXffrpp4SGhqY7V9jvydevyyhGXbgnX7/uTe+jtt2TuR2jNt6Paf3NTpzXrl1jyJAhNGzYkMaNG/PFF1+o/U+jbfdkphQhxFuFhoYqjRs3Vo4cOaJcvnxZ8fHxUVq3bq0kJCRkWL5fv35Kjx49lD///FP57bfflBYtWigTJkxQzx8/flypUaOGsmHDBuWvv/5SZs2apdSsWVP566+/slyHtse4ZMkSxdXVVTlw4IBy69Yt5ZtvvlFq1Kih7NixQy0zc+ZMpV+/fsrDhw81vpKTkwtFjM+ePVMcHR2Vn376SaP/sbGxWa6jMMT57/fn1q1bSrNmzZSxY8eqZbT9vVQURYmPj1fGjBmjODg4KIsWLdI4pwv3ZGYx6sI9mVmM2nhP5naM2ng/ZjfOJ0+eKE2bNlVGjRqlREREKH/88YfSr18/xd3dXXn58qWiKNp5T2ZGEmoh3iIhIUFxcXFRNm3apB6LiYlRatWqpezbty9d+bNnzyoODg4aN/3Ro0cVR0dH5f79+4qiKIqPj48yatQojet69eqlTJo0Kct15Ka8iLFZs2bK0qVLNa77z3/+o/Tt21f9eeDAgcr06dNzO5wM5UWMZ86cURwcHJSYmJgM28zv91FR8ibOf5s0aZLi5uamvHjxQj2mze+lorx6r9q1a6e0bNlSqV+/frokpbDfk4qSeYyF/Z5UlMxj1LZ7Mi9i/LeCvh8VJftxbt26Valbt66aPCuKoty7d09xcHBQfvvtN0VRtO+ezAqZ8iHEW1y5coXnz5/TqFEj9Zi1tTXVq1fn1KlT6cqfPn2aYsWKUblyZfVYgwYN0NPT48yZM6SmpnL27FmN+gAaNmzI6dOns1RHbsuLGGfNmoWnp2e6a2NiYtTvr169SpUqVXI3mDfI7RjhVf+LFSuGtbV1hm3m9/sIeRPn6y5dusS2bdsIDAzEzMxMPa7N7yXA0aNHad26Nbt27cLKykrjnC7ck5B5jIX9noS3xwjad0/mRYyv04b7EbIfZ+PGjfn6668xMTFJdy4mJkYr78msMCyQVoUoJO7fvw9AqVKlNI4XL16ce/fupSv/4MGDdGWNjY0pUqQI9+7dIzY2lhcvXlCyZMk31pdZHbktt2PU19encePGGucjIyPZv38/vXv3BuDJkyc8evSIU6dOsWHDBp4+fUrt2rUZN24clSpVys3wgNyPESAiIgJzc3NGjBjBuXPnsLOzo2vXrvTv3x99ff18fx8hb+J83aJFi6hXrx7NmzdXj2n7ewkwcuTIN9anC/ckvD1GXbgn4e0xgvbdk3kR4+u04X6E7MdZtmxZypYtq3Fs2bJlmJiY4OrqqpX3ZFbICLUQbxEfHw+8ulFfZ2JiQkJCQobl/1329fIvX77MtL7M6shtuR3jv/3zzz8MGTKEokWL8tlnnwGv/uEDMDAwIDg4mPnz5/PixQv69u3Lo0ePchxTRn2G3I3x2rVrPHv2DA8PD1atWkWvXr1YuHChuogov9/HtDYhb97Lv//+myNHjjB06FCN49r+XmZGF+7J7CqM92RWaNs9mZfvo7bcj5DzONevX8+mTZsYM2YMRYsW1cp7MitkhFqItzA1NQUgMTFR/R4gISFB409sr5dPTExMdzwhIQFzc3P1T1z/LvN6fZnVkdtyO8bX/f333wwZMoSkpCQ2bNiAjY0NAI0aNeLkyZPqzwBff/01LVq0YMeOHQwZMiRXYnu9z5C7Ma5Zs4aEhAQsLS2BV6vvnz9/ztKlSxkxYkS+v49p/Ya8eS/37NlD6dKl+fDDDzWOa/t7mRlduCezo7Dek1mhbfdkXr6P2nI/wrvHqSgKCxcuZOnSpQwdOpQBAwYA2nlPZoWMUAvxFml/Unr48KHG8YcPH6b7cxRAyZIl05VNTEzk6dOnlChRgiJFimBubv7W+jKrI7fldoxpzpw5Q+/evTExMWHLli2UL19e45rXf+EDmJubU7ZsWR48eJCjeDKSFzEaGRmp/3CncXBw4MWLF8TExOT7+wh5914CHD58GHd3d/T09NLVo83vZWZ04Z7MqsJ8T2aFtt2TefU+gvbcj/BucSYlJTF+/HjCwsKYMGECY8aMUc9p4z2ZFZJQC/EW1apVw9LSkhMnTqjHYmNjuXTpEvXr109X3tXVlfv372vsv5l2bd26ddHT06Nu3bqcPHlS47oTJ05Qr169LNWR23I7RoALFy4waNAgqlatyqZNm9LNddu0aRMNGzZU/7QHEBcXx82bN/NkMU1ux5iamoqbmxtLly7VuO6PP/7A3t4eW1vbfH8fIW/eS4Bnz55x7dq1dIuEQPvfy8zowj2ZFYX9nsyMNt6TefE+gnbdj/BucU6YMIHvvvuOuXPn8umnn2qc08Z7MksKZG8RIQqRefPmKQ0aNFB++OEHdX/NNm3aKAkJCUpycrLy8OFDJT4+XlEURUlNTVV69+6tdOnSRTl//rxy/PhxpUWLFoqfn59a39GjRxUnJydl9erVyl9//aUEBwcrtWrVUrf/yUod2hxjUlKS0rp1a6Vly5bK7du3NfZBffz4saIoihIVFaW4uroqI0aMUCIiIpQLFy4oAwYMUFq1aqW2o80xKoqizJo1S6lbt666r++WLVuUWrVqKd98802W6ygMcSqKopw8eVJxcHDIcDsqbX8v/61FixbptiIr7PdkZjHqwj2ZWYyKop33ZG7HqCjadz8qSvbi3L59u+Lg4KCsXLky3V7ZaWW08Z7MjCTUQmQiOTlZmT17ttKoUSOlTp06yuDBg5U7d+4oiqIod+7cURwcHJTt27er5R89eqSMGDFCqVOnjtKwYUNl8uTJGvttKoqi7Ny5U2ndurXi7OysdOnSRd17Mzt1aGuMaXvBZvTVokULtY5Lly4pPj4+Sr169ZS6desqI0aMUKKiogpFjIryKklZsmSJ0rJlS6VGjRpK27Zt1X+4s1pHYYhTURRl//79ioODwxsfRqHt7+Xr3pSkFPZ78nX/jlFX7sm3xago2nlP5sVnVdvuR0XJXpwDBw584+fx9ddC2+7JzOgpiqIUzNi4EEIIIYQQhZ/MoRZCCCGEECIHJKEWQgghhBAiByShFkIIIYQQIgckoRZCCCGEECIHJKEWQgghhBAiByShFkIIIYQQIgckoRZCCCGEECIHJKEWQgghhBAiByShFkIIIYQQIgckoRZCCCGEECIHJKEWQgghhBAiByShFkIIIYQQIgf+H543mANc6viqAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the features by importance\n",
    "importances_df = pd.DataFrame(sorted(zip(rf.feature_importances_, X.columns), reverse=True))\n",
    "importances_df.set_index(importances_df[1], inplace=True)\n",
    "importances_df.drop(columns=1, inplace=True)\n",
    "importances_df.rename(columns={0: 'Feature Importances'}, inplace=True)\n",
    "importances_sorted = importances_df.sort_values(by='Feature Importances')\n",
    "importances_sorted.plot(kind='barh', color='orange', title= 'Features Importances', legend=False)\n",
    "\n",
    "# Save figure\n",
    "plt.savefig('Figures/feature_importances.png', bbox_inches='tight')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Retrain models with optimized features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>Attrition_Flag</th>\n",
       "      <th>Customer_Age</th>\n",
       "      <th>Total_Relationship_Count</th>\n",
       "      <th>Credit_Limit</th>\n",
       "      <th>Total_Revolving_Bal</th>\n",
       "      <th>Avg_Open_To_Buy</th>\n",
       "      <th>Total_Amt_Chng_Q4_Q1</th>\n",
       "      <th>Total_Trans_Amt</th>\n",
       "      <th>Total_Trans_Ct</th>\n",
       "      <th>Total_Ct_Chng_Q4_Q1</th>\n",
       "      <th>Avg_Utilization_Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>58</td>\n",
       "      <td>3</td>\n",
       "      <td>3421.0</td>\n",
       "      <td>2517</td>\n",
       "      <td>904.0</td>\n",
       "      <td>0.992</td>\n",
       "      <td>992</td>\n",
       "      <td>21</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>6</td>\n",
       "      <td>1438.3</td>\n",
       "      <td>0</td>\n",
       "      <td>1438.3</td>\n",
       "      <td>0.827</td>\n",
       "      <td>3940</td>\n",
       "      <td>82</td>\n",
       "      <td>0.952</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>3</td>\n",
       "      <td>3128.0</td>\n",
       "      <td>749</td>\n",
       "      <td>2379.0</td>\n",
       "      <td>0.598</td>\n",
       "      <td>4369</td>\n",
       "      <td>59</td>\n",
       "      <td>0.735</td>\n",
       "      <td>0.239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>5</td>\n",
       "      <td>33304.0</td>\n",
       "      <td>1833</td>\n",
       "      <td>31471.0</td>\n",
       "      <td>0.428</td>\n",
       "      <td>1448</td>\n",
       "      <td>29</td>\n",
       "      <td>0.381</td>\n",
       "      <td>0.055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>4</td>\n",
       "      <td>5723.0</td>\n",
       "      <td>1873</td>\n",
       "      <td>3850.0</td>\n",
       "      <td>0.851</td>\n",
       "      <td>2732</td>\n",
       "      <td>63</td>\n",
       "      <td>0.853</td>\n",
       "      <td>0.327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10122</th>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>4</td>\n",
       "      <td>3640.0</td>\n",
       "      <td>659</td>\n",
       "      <td>2981.0</td>\n",
       "      <td>0.938</td>\n",
       "      <td>3756</td>\n",
       "      <td>70</td>\n",
       "      <td>0.842</td>\n",
       "      <td>0.181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10123</th>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>6</td>\n",
       "      <td>12050.0</td>\n",
       "      <td>1821</td>\n",
       "      <td>10229.0</td>\n",
       "      <td>0.630</td>\n",
       "      <td>2381</td>\n",
       "      <td>40</td>\n",
       "      <td>0.481</td>\n",
       "      <td>0.151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10124</th>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>3</td>\n",
       "      <td>2951.0</td>\n",
       "      <td>2437</td>\n",
       "      <td>514.0</td>\n",
       "      <td>0.765</td>\n",
       "      <td>2519</td>\n",
       "      <td>36</td>\n",
       "      <td>0.565</td>\n",
       "      <td>0.826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10125</th>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "      <td>4</td>\n",
       "      <td>2872.0</td>\n",
       "      <td>2035</td>\n",
       "      <td>837.0</td>\n",
       "      <td>0.613</td>\n",
       "      <td>1770</td>\n",
       "      <td>47</td>\n",
       "      <td>0.741</td>\n",
       "      <td>0.709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10126</th>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>3</td>\n",
       "      <td>2786.0</td>\n",
       "      <td>1793</td>\n",
       "      <td>993.0</td>\n",
       "      <td>0.803</td>\n",
       "      <td>3646</td>\n",
       "      <td>68</td>\n",
       "      <td>0.659</td>\n",
       "      <td>0.644</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9313 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Attrition_Flag  Customer_Age  Total_Relationship_Count  Credit_Limit  \\\n",
       "1                   1            58                         3        3421.0   \n",
       "3                   0            34                         6        1438.3   \n",
       "4                   0            49                         3        3128.0   \n",
       "5                   0            43                         5       33304.0   \n",
       "7                   0            37                         4        5723.0   \n",
       "...               ...           ...                       ...           ...   \n",
       "10122               0            50                         4        3640.0   \n",
       "10123               1            38                         6       12050.0   \n",
       "10124               1            49                         3        2951.0   \n",
       "10125               0            54                         4        2872.0   \n",
       "10126               0            43                         3        2786.0   \n",
       "\n",
       "       Total_Revolving_Bal  Avg_Open_To_Buy  Total_Amt_Chng_Q4_Q1  \\\n",
       "1                     2517            904.0                 0.992   \n",
       "3                        0           1438.3                 0.827   \n",
       "4                      749           2379.0                 0.598   \n",
       "5                     1833          31471.0                 0.428   \n",
       "7                     1873           3850.0                 0.851   \n",
       "...                    ...              ...                   ...   \n",
       "10122                  659           2981.0                 0.938   \n",
       "10123                 1821          10229.0                 0.630   \n",
       "10124                 2437            514.0                 0.765   \n",
       "10125                 2035            837.0                 0.613   \n",
       "10126                 1793            993.0                 0.803   \n",
       "\n",
       "       Total_Trans_Amt  Total_Trans_Ct  Total_Ct_Chng_Q4_Q1  \\\n",
       "1                  992              21                0.400   \n",
       "3                 3940              82                0.952   \n",
       "4                 4369              59                0.735   \n",
       "5                 1448              29                0.381   \n",
       "7                 2732              63                0.853   \n",
       "...                ...             ...                  ...   \n",
       "10122             3756              70                0.842   \n",
       "10123             2381              40                0.481   \n",
       "10124             2519              36                0.565   \n",
       "10125             1770              47                0.741   \n",
       "10126             3646              68                0.659   \n",
       "\n",
       "       Avg_Utilization_Ratio  \n",
       "1                      0.736  \n",
       "3                      0.000  \n",
       "4                      0.239  \n",
       "5                      0.055  \n",
       "7                      0.327  \n",
       "...                      ...  \n",
       "10122                  0.181  \n",
       "10123                  0.151  \n",
       "10124                  0.826  \n",
       "10125                  0.709  \n",
       "10126                  0.644  \n",
       "\n",
       "[9313 rows x 11 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create new dataframe to only include the important features\n",
    "# Create a list with the columns to drop based on feature importance array\n",
    "columns_to_drop = ft_imp_arr[10:]\n",
    "\n",
    "# List with only columns names\n",
    "columns_to_drop = [i[1] for i in columns_to_drop]\n",
    "\n",
    "# New dataframe using feature importance\n",
    "churn_df_ftimp = customer_churn_df.drop(columns = columns_to_drop)\n",
    "churn_df_ftimp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Seperate features from the target variable\n",
    "X = churn_df_ftimp.drop(columns= ['Attrition_Flag'])\n",
    "y = churn_df_ftimp['Attrition_Flag']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>Customer_Age</th>\n",
       "      <th>Total_Relationship_Count</th>\n",
       "      <th>Credit_Limit</th>\n",
       "      <th>Total_Revolving_Bal</th>\n",
       "      <th>Avg_Open_To_Buy</th>\n",
       "      <th>Total_Amt_Chng_Q4_Q1</th>\n",
       "      <th>Total_Trans_Amt</th>\n",
       "      <th>Total_Trans_Ct</th>\n",
       "      <th>Total_Ct_Chng_Q4_Q1</th>\n",
       "      <th>Avg_Utilization_Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>58</td>\n",
       "      <td>3</td>\n",
       "      <td>3421.0</td>\n",
       "      <td>2517</td>\n",
       "      <td>904.0</td>\n",
       "      <td>0.992</td>\n",
       "      <td>992</td>\n",
       "      <td>21</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34</td>\n",
       "      <td>6</td>\n",
       "      <td>1438.3</td>\n",
       "      <td>0</td>\n",
       "      <td>1438.3</td>\n",
       "      <td>0.827</td>\n",
       "      <td>3940</td>\n",
       "      <td>82</td>\n",
       "      <td>0.952</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>49</td>\n",
       "      <td>3</td>\n",
       "      <td>3128.0</td>\n",
       "      <td>749</td>\n",
       "      <td>2379.0</td>\n",
       "      <td>0.598</td>\n",
       "      <td>4369</td>\n",
       "      <td>59</td>\n",
       "      <td>0.735</td>\n",
       "      <td>0.239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>43</td>\n",
       "      <td>5</td>\n",
       "      <td>33304.0</td>\n",
       "      <td>1833</td>\n",
       "      <td>31471.0</td>\n",
       "      <td>0.428</td>\n",
       "      <td>1448</td>\n",
       "      <td>29</td>\n",
       "      <td>0.381</td>\n",
       "      <td>0.055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>37</td>\n",
       "      <td>4</td>\n",
       "      <td>5723.0</td>\n",
       "      <td>1873</td>\n",
       "      <td>3850.0</td>\n",
       "      <td>0.851</td>\n",
       "      <td>2732</td>\n",
       "      <td>63</td>\n",
       "      <td>0.853</td>\n",
       "      <td>0.327</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer_Age  Total_Relationship_Count  Credit_Limit  Total_Revolving_Bal  \\\n",
       "1            58                         3        3421.0                 2517   \n",
       "3            34                         6        1438.3                    0   \n",
       "4            49                         3        3128.0                  749   \n",
       "5            43                         5       33304.0                 1833   \n",
       "7            37                         4        5723.0                 1873   \n",
       "\n",
       "   Avg_Open_To_Buy  Total_Amt_Chng_Q4_Q1  Total_Trans_Amt  Total_Trans_Ct  \\\n",
       "1            904.0                 0.992              992              21   \n",
       "3           1438.3                 0.827             3940              82   \n",
       "4           2379.0                 0.598             4369              59   \n",
       "5          31471.0                 0.428             1448              29   \n",
       "7           3850.0                 0.851             2732              63   \n",
       "\n",
       "   Total_Ct_Chng_Q4_Q1  Avg_Utilization_Ratio  \n",
       "1                0.400                  0.736  \n",
       "3                0.952                  0.000  \n",
       "4                0.735                  0.239  \n",
       "5                0.381                  0.055  \n",
       "7                0.853                  0.327  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Review the new features data\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split training and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale features\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# Fit the training data to the standard scaler\n",
    "X_scaler = scaler.fit(X_train)\n",
    "\n",
    "# Transform training and testing data using the scaler\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=1)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# KNN\n",
    "knn = KNeighborsClassifier(n_neighbors=3)\n",
    "knn.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Random Forest\n",
    "rf = RandomForestClassifier(n_estimators=500, random_state=78)\n",
    "rf.fit(X_train, y_train)\n",
    "\n",
    "# Logistic Regression\n",
    "lr = LogisticRegression(solver='lbfgs', random_state=1)\n",
    "lr.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict the test set results\n",
    "y_pred_knn = knn.predict(X_test_scaled)\n",
    "y_pred_rf = rf.predict(X_test)\n",
    "y_pred_lr = lr.predict(X_test_scaled)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# KNN analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN Classification Report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.97      0.95      1554\n",
      "           1       0.82      0.68      0.74       309\n",
      "\n",
      "    accuracy                           0.92      1863\n",
      "   macro avg       0.88      0.82      0.85      1863\n",
      "weighted avg       0.92      0.92      0.92      1863\n",
      "\n",
      "Accuracy Score: 0.8246835601204534\n"
     ]
    }
   ],
   "source": [
    "# Classification report for KNN model\n",
    "print(\"KNN Classification Report\")\n",
    "print(classification_report(y_test, y_pred_knn))\n",
    "\n",
    "# Calculating the accuracy score\n",
    "knn_acc_score = balanced_accuracy_score(y_test, y_pred_knn)\n",
    "print(f'Accuracy Score: {knn_acc_score}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 700x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create confusion matrix for KNN model\n",
    "knn_cm = confusion_matrix(y_test, y_pred_knn)\n",
    "\n",
    "# Confusion matrix vizualization\n",
    "f, ax = plt.subplots(figsize= (7, 4))\n",
    "sns.heatmap(knn_cm, annot=True, fmt='g', \n",
    "            cmap='Blues', xticklabels=['Existing', 'Attrited'], yticklabels=['Existing', 'Attrited'])\n",
    "ax.set_title(f'KNN \\nAccuracy: {knn_acc_score:.4%}\\n', fontsize= 14)\n",
    "ax.set_ylabel('Actual', fontsize= 13)\n",
    "ax.set_xlabel('Predicted', fontsize= 13)\n",
    "ax.xaxis.set_ticks_position('top')\n",
    "ax.xaxis.set_label_position('top')\n",
    "\n",
    "# Save figure\n",
    "plt.savefig('Figures/KNN_optimized.png', bbox_inches='tight')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Random Forest analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Classification Report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.98      0.98      1554\n",
      "           1       0.91      0.87      0.89       309\n",
      "\n",
      "    accuracy                           0.97      1863\n",
      "   macro avg       0.94      0.93      0.94      1863\n",
      "weighted avg       0.96      0.97      0.96      1863\n",
      "\n",
      "Balanced accuracy Score: 0.9285276955179868\n"
     ]
    }
   ],
   "source": [
    "# Classification report for random forest\n",
    "print(\"Random Classification Report\")\n",
    "print(classification_report(y_test, y_pred_rf))\n",
    "\n",
    "# Calculate the accuracy score\n",
    "rf_acc_score = balanced_accuracy_score(y_test, y_pred_rf)\n",
    "print(f'Balanced accuracy Score: {rf_acc_score}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlIAAAG2CAYAAAC04mh6AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAABp3klEQVR4nO3deXzM1/7H8dcIEYmtiSX2tRFbrKnYNamqfWtLK6jgUkWV2mtrq6rUvhNLUbHETpVay0XRqrahaiexxhIiiyTz+8MvcztNZpqOMFN5P+9jHj9zzvl+5zNzf633Pd/zPV+D0Wg0IiIiIiL/WCZ7FyAiIiLyb6UgJSIiImIjBSkRERERGylIiYiIiNhIQUpERETERgpSIiIiIjZSkBIRERGxkYKUiIiIiI0UpERERERslNneBYj8202fPp0ZM2ak2ufm5oanpyf169fnvffeI3v27M+4uv8ZMmQI69atY+fOnRQuXNhudfzZ2rVrGTp06N+O+/33359BNU/m/v37PHr0CHd3d3uXIiLPkIKUSDpp164d1apVM703Go3cuHGD7du3s3DhQn7++WeWLl2Kk5OTHat0TA0bNqRhw4b2LsNm+/fvZ+DAgUyZMoUaNWrYuxwReYYUpETSSeXKlWnZsmWK9m7duhEUFMTBgwfZvXs3r7zyih2qc2xlypRJ9bf7t/jpp5+4ffu2vcsQETvQGimRpyxTpky8/vrrABw9etTO1YiISHrSjJTIM+Dq6ppq++HDh1m8eDE///wz9+7dw9XVlXLlytGjRw9q1aplGufv78+LL75IUFAQ06ZNIywsDCcnJ/z8/Pjwww8pXry42XlXrlzJsmXLuHjxIvny5aNTp06pfn5iYiJff/01a9as4fz582TOnJkKFSrQtWtX6tevbxqXvJZpyZIl7Ny5k2+++YaoqCjKlCnDoEGDqFChAlOnTmXz5s1ER0dTtmxZhgwZgo+Pz5P/eH9x/Phx5syZw48//sjDhw8pXLgwzZo1o3v37mTNmtU0rkyZMrz11ls4OTkRGhqKs7Mz48aNIyAggLi4OBYsWMCmTZu4cuUKbm5u1KhRg969e+Pl5WX2+8yePZtvv/2Wy5cvkzlzZsqWLUuXLl3w9/cHoGPHjvzwww8AdOrUiUKFCrFr1650/94i4pgUpESegR07dgBQoUIFs7a+ffvi7e1N9+7dyZ49O6dPn2bNmjV0796d7du3U6hQIdP4U6dO0aNHD1q0aEHLli0JCwsjJCSEU6dO8e2335rWXn355ZfMmzePqlWr8uGHH3Lr1i2mTJmSoqakpCR69+7Nrl27qFGjBgMGDCA6Opp169bxn//8hyFDhtClSxezY4YMGULevHl59913iYyMZMGCBbz77rt4e3uTkJBAjx49uHv3LgsWLKBnz55s3749TQvsY2JiLF4a+/Pi7a1btzJgwADc3d0JDAzEw8ODAwcOMH36dL7//nuWLFmCi4uLafyGDRvInz8/gwcP5tKlS1SvXp34+HiCgoI4fvw4LVu25J133uH69euEhITw5ptvsnDhQqpWrQrAuHHjWL58OW+++SadOnXi/v37hISE0KtXL+bOnUv9+vXp2bMnuXLlYseOHfTs2ZOKFSv+7fcVkeeIUUSeyLRp04xeXl7GpUuXGiMjI02vmzdvGsPCwoyff/650cvLy9i6dWtjQkKC6bhWrVoZa9eubYyOjjY737Jly4xeXl7GhQsXmtpefvllo5eXl3HLli1mY4cNG2b08vIy7t+/32g0Go0XL140li1b1vj222+bfVZYWJixfPnyRi8vL+Ply5eNRqPRuG7dOqOXl5dx8ODBxqSkJNPY+/fvG1977TVjuXLljBcvXjQajUZjaGio0cvLy9i8eXNjfHy8aezYsWONXl5expYtW5p93oQJE4xeXl7G//73v1Z/u+TzWnv9ua7q1asb/fz8jLdu3TI7z6RJk4xeXl7G6dOnm9qSjz99+rTZ2Pnz5xu9vLyMW7duNWu/ceOGsUaNGsYmTZqY2ipXrmzs1q2b2birV68aX3nlFbPPSv7/gUOHDln9viLy/NGMlEg6+eSTT/jkk09StLu6uvLmm2/Sv39/szv2Vq9eTVRUlNllv/j4eAwGAwDR0dFm53FxcaFRo0ZmbRUrVmTNmjXcvHkTgF27dpGYmEjnzp3NPqts2bLUrVvX7JLTtm3bAOjXr5/pMwGyZ89Oz549GTRoEN9++y3du3c39TVq1IgsWbKY3pcuXRqA1157zezzihUrBsD169dT/7H+omXLlrRq1crqmAMHDhAVFcX777+Ph4eHWV+vXr346quv2Lp1K7179za1Fy1alBdffNFs7JYtW8iZMyc1atQwmwVzcnKiXr16bNiwgbNnz1KqVCk8PT354YcfCA4OplGjRhQuXBhPT0/TDKOIiIKUSDrp2rUrderUwWg0EhkZybJlywgLC6NPnz4EBQWlGJ85c2auXLnCrFmzOH/+PFeuXOHKlSskJiYCj7dP+LMXXnghxdYJzs7OwOPLdACXLl0CHgeIv3rxxRfNgtSlS5fInj07np6eKcYmrxO6cuWKWXvevHlTfIfU2pPrTK7r7xQpUsRsTVhqkr/bX4MRQNasWSlatCjnz583a8+TJ0+KsefPnycmJoaaNWta/Kzw8HBKlSrF2LFj6devH1988QVffPEFRYsWpXbt2jRt2hRfX9+0fDURec4pSImkk9KlS5uFgcaNG9OjRw/Gjx/PzZs3GTx4sNn4uXPnMmnSJAoVKkT16tWpUaMGZcqUISEhgV69eqU4f6ZMT3aT7V9DjdFoNJuJ+rPkMJcc1JIlB6e/snSe9JQcLC19VlJSUprqTUxMpFixYowePdriZ3l7ewNQtWpVvvvuOw4dOsT333/P4cOHCQkJYcWKFXTp0oUhQ4bY+G1E5HmhICXylGTJkoVJkybRsmVLFi5cSIUKFWjatCkAV69eZfLkyfj6+rJw4UKzALBx40abPzP5ktqZM2dMYSDZxYsXzd4XLVqUc+fOce3atRSzUmfOnAGgYMGCNteS3pJn2U6fPp1iL67Y2FguX75MkSJF/vY8hQsX5tatW7z00kspgtaPP/5ITEwMLi4uxMXF8fvvv5MrVy7q1atHvXr1ALh8+TJBQUEsWbKE3r1723W3ehGxP+0jJfIU5c6dm/Hjx2MwGBg9ejTXrl0D4O7duxiNRkqWLGkWomJiYli6dCkACQkJ//jzGjZsSJYsWQgODiY+Pt7Ufu7cOXbv3m02Nnm91ZQpU8wuIz58+JB58+bh5OTkUJuH1q5dm+zZs7N06VIiIyPN+ubOnUtMTEyKNWSpadSoEVFRUSxcuNCs/fr167z77rsMGDCATJkycefOHdq1a5di3VuRIkXInz8/BoPBNEuY/H/TeilTRJ4fmpESecr8/Pzo2LEjX331FcOGDSM4OJjSpUtTrFgxQkNDyZo1K15eXty4cYN169aZFo7fv3//H39WwYIF+eCDD/jiiy948803adWqFQ8ePGDp0qXkzJnTLIC0bNmSbdu2sW7dOiIiIggICCAmJoZ169Zx4cIFPvzwwzTN8DwrOXLkYNSoUQwePJgWLVrQrl07PDw8OHjwIDt27KB8+fJ069btb8/TvXt3du/ezZdffskvv/yCn58fUVFRhISEEBUVxcSJE3FxccHT05M33niDlStX0rVrV/z9/TEYDOzfv58jR44QGBhoulEgeS3WihUruHHjxr96l3YR+WcUpESegQ8//JD9+/dz4MABvv76azp06MCCBQuYOHEiW7ZsYdWqVeTLl4/q1avz3nvv0aFDB/bv32/TZ3Xt2pUCBQoQHBzM5MmTyZ07N507dyYuLo45c+aYxjk5OTFr1iyWLFnC+vXrmThxItmyZaNixYoMHz7cdCnLkbRo0YICBQowb948vvrqK+Lj4ylatCj9+vUjKCjIbENOS9zc3Pj666+ZN28e27ZtY/fu3eTMmZOyZcsyfvx4/Pz8TGNHjhxJyZIlWbduHZMmTSIxMZGSJUsyYsQI3n77bdO4pk2bsmPHDvbs2cPBgwdp2LChxU1YReT5YjD+9dYgEREREUkTrZESERERsZGClIiIiIiNFKREREREbKQgJSIiImIjBSkRERERGylIiYiIiNhIQUoyPKPRiL+/P2XKlGHGjBn2Lue5ExERwaBBg/Dz86NKlSq8/vrrbNiwIdWxv//+O3379sXPz48KFSrg7+/P2LFj07w5aXx8PDNmzKBhw4ZUqFCBmjVrMnjwYK5fv55i7IYNGyhTpkyqr78+Qy88PJwhQ4ZQp04dypcvT506dRg2bFiq5z1//jzvv/8+NWrUoEKFCjRu3JjFixen2PU8Pj6eESNGULVqVerVq8eUKVNS3c3+/fffT/Wh1yLiGLQhp2R4hw4dIjw8HFdXV1atWsW7776Lk5OTvct6LoSHh/Pmm28SGRlJ69atqVChAkeOHGHQoEGEhYUxdOhQ09hz587Rvn17nJyc6NChAwUKFOD48eMsW7aMQ4cOsXLlyr/d5LJ///7s2LGDunXr0qVLFy5dusSyZcs4fPgwa9euxd3d3TT2999/B+DTTz9N8bDj5Of6Ady+fZt27doRFRVFu3btKFWqFH/88QcrV65k//79rFmzhnz58gFw5coV2rdvT0xMDB06dKBIkSLs2LGDcePGcf78ecaMGWM6b3BwMGvXrqV79+4YjUbmzZtHjhw56Nq1q2nMiRMn2L59O6GhoTb8+iLyTBhFMrj+/fsby5cvb5wyZYrRy8vLuGPHDnuX9Nzo06eP0cvLy7hixQqz9vHjxxu9vLyMhw8fNrUFBQUZy5cvb/z999/Nxi5ZssTo5eVlnD9/vtXP+uWXX4xeXl7Grl27mrWvW7fO6OXlZZw4caJZe1BQkLFmzZp/+x1GjBhh9PLyMu7bt8+s/bvvvjN6eXkZR48ebWr7+OOPjV5eXsYtW7aYje3UqZPRy8vLeObMGVPbq6++auzbt6/pfZ8+fYyvvfaa2XGBgYHG/v37/22NImI/urQnGdr9+/f57rvv8PHxoUWLFsDj56XJk4uPj2fPnj0UK1aMdu3amfX17NkTgJUrV5rGHj16lGrVquHl5WU2tlWrVgAcOXLE6udduHABgJdfftmsPfnBy2FhYWbtv//+Oy+++OLffo8DBw5QpEgR6tata9YeEBBAjhw5zOpKrqFBgwap1nDq1ClT29WrVylWrJjpffHixYmIiDC937NnDz/99BP9+vX72xpFxH4UpCRD27x5M7GxsdSuXZsSJUpQpkwZDhw4wOXLl1OMvXDhAoMHD6ZOnTpUqlSJJk2aMG/ePOLj483G/fe//6Vr16689NJLVKtWjXbt2vHtt9+a+g8fPkyZMmWYPn16is/o2LEjZcqUMb1fu3YtZcqUYcuWLbz++utUqFCB1157jbi4OAB27NhBUFAQNWrUoHz58tSoUYOePXvy66+/pji3tbqio6OpUqUKTZs2TfV3aty4MY0aNTK9T15LZM2dO3eIi4ujTJkyGAwGs76cOXPi7u5uqjNz5sxs3ryZTz75JMV5bt26BUCmTNb/dVWqVCkA/vjjD7P28+fPA5A/f35TW2RkJDdv3jQFqfj4+BT/PSZbtmwZc+fOTdH+8OFDYmJizOoqUaIEAGfOnPnbGvLkycO9e/dM7+/cuYOHhwcASUlJfPnll7Rv396hHhwtIikpSEmGlrz2pHHjxsDjh88ajUZCQkLMxp06dYo2bdqwfft2mjdvztChQ/Hy8uLLL780W+ezevVqgoKCuHDhAu+88w79+/cnISGBvn37smrVKpvr/OijjyhRogQfffQRb775JlmzZmXJkiX07t2bR48e0bt3b0aMGEHDhg3Zv38/Xbt2JTo6Os11ubm58dprr3HmzBl+++03s88+ceIE586do3Xr1qa2L774gi+++MJqzcnrmR48eJCiLzExkfv373Pjxg3gcUgqUqSI2dqkZAsXLgSgRo0aVj+vbNmydOzYkdWrV7N06VKuXLnC4cOHGTx4MNmzZ6dLly6msckzQ1evXqVNmzZUrlwZHx8fXn/9dQ4ePGh23gIFCphC2p8tWbKEhIQEs7r+85//UKJECYYMGcLBgwe5cuUKy5YtY+XKldSsWZNq1aqZxtaqVYtt27bx448/8uOPP7J9+3bTrNe6desIDw+nV69eVr+ziDgAe19bFLGX06dPG728vIytWrUytV26dMno5eVl9PPzM8bFxZnaO3ToYCxfvrzx5MmTZufo37+/0cvLy3j69Gnj/fv3jZUrVza++uqrxvv375vGxMTEGF955RVj7dq1jQkJCcZDhw4Zvby8jNOmTUtRU2BgoNHLy8v0PjQ01Ojl5WV8/fXXjUlJSab2hIQEY40aNYytWrUyJiQkmJ0jef3Rt99+azQajWmu68iRI0YvLy/j2LFjzc43evRoo7e3t/Hq1atp+l3/rFmzZkYfHx/jlStXzNq/+eYbo5eXl9Hb29vq8cnrm+rXr2988ODB337epUuXjG+99ZbRy8vL9KpcubLxyJEjZuOCg4ONXl5exho1ahjnzp1r3LlzpzE4ONhYq1YtY9myZY07d+60+jn//e9/jeXLlzdWrVrVGBERkeK7ValSxayGdu3apaj/xo0bxtatW5vGtG/f3nj79m1jTEyMsV69esaZM2f+7fcVEfvTXXuSYSXPRv35claRIkWoXLkyx48fZ/v27TRr1ozbt29z9OhR/P398fb2NjvH0KFD6dGjB8WKFWPPnj08fPiQt956i+zZs5vGuLi4MG/ePJycnP728pQlL7/8stnlMScnJ/bt20dMTIzZHYYPHz4kS5Yspj/D40t6aamrevXqFC9enC1btjB48GCcnJyIj49n69at1KpVC09Pz39cd58+fejTpw/dunVj6NChlCxZkqNHjzJu3Dhy585tqjE169atY/jw4bi6ujJt2jTc3NysftaZM2d4++23iYmJoWvXrlStWpVr166xcOFCunXrxqxZs6hVqxYAPj4+9OzZkzZt2pitU2rUqBHNmjVjzJgxNGjQINX/vvbv30/v3r1JSkriiy++oECBAqa+efPm8eWXX1KsWDEGDhxInjx5OHr0KMuXL6dz584sXLiQnDlzApA3b15Wr17N2bNncXJyomTJkhgMBubNm0diYiLvvPMOjx49YtKkSWzbtg0nJydatWpFz549yZxZ/+oWcRT6p1EypEePHrFx40YAKlWqxJUrV0x9fn5+HD9+nBUrVtCsWTMiIiIwGo2pXt7JkycPefLkATCdI7VxyWtnbJU3b94Ubc7Ozhw7doxvvvmGS5cucfnyZVOtgGnfon9SV9u2bfnyyy85cOAA9erVY/fu3dy9e5c2bdrYVPerr77KZ599xueff0737t0ByJ07N4MGDWLLli2cPn061eNmzpzJtGnTyJEjB3PmzMHHx+dvP2v27Nncu3ePyZMn06RJE1N7kyZNaN68OYMHD2bnzp04OztTvXp1qlevnuIchQoVomHDhmzYsIEzZ86kWPi+Zs0aRo8eDcCkSZMICAgw9T148ICZM2eSL18+Vq9eTa5cuQBo2LAh5cqVY9CgQcyePZvBgwebjnFycjL7jLt37zJv3jwGDBiAq6srM2bMYNWqVYwfPx6AQYMGkSVLFnr06PG3v4eIPBsKUpIh7dmzh8jISAACAwNTHXP06FHOnDlj2iTxrwum/yqt49Jyjr9KbV+rkSNHsnLlSkqXLk2lSpWoX78+3t7eKfYr+id1tWrViilTprBhwwbq1avHunXryJkzp+muM1u0bduWZs2acerUKTJlykSZMmVwdnZmxowZZrNB8Djgjhw5krVr15I/f37mzZuXYhbQkt9//x03NzfTerdk7u7uvPLKK4SEhHDu3Lm/PV/ygu8/rzEDmDx5MnPmzCFHjhxMnz6dmjVrmvWfP3+e2NhY2rZtawpRyZo3b87o0aNTrL/6q9mzZ+Ph4cEbb7wBwPr162nUqJHp92/cuDFr165VkBJxIApSkiElX9br0aNHqrMdq1evZs+ePYSEhJhu1U++8+rPTp06xbx582jXrh2FCxc2jatTp47ZuI0bN3Lw4EH69etnCkWxsbEpzpd8h9rfOXr0KCtXrqRZs2ZMnDjRLCQdP37cbGxa68qfPz/58uWjbt267N69m9u3b7N//37atm1L1qxZ01TXX+3du5e7d+/SsmVLKlWqZGo/c+YMERERpq0N4PEC9AEDBvDtt99SpkwZ5s+fb3aX299xdnbGaDSSmJiY4tJX8uxc8mxdr169+OOPP9i0aRMuLi5mY8+ePQuYb8o5duxYvvrqKwoWLMi8efNS3TYheVPPxMTEFH1Go5GkpCTT56fmypUrfP3110ycONFU/40bN0wznvA4FF67ds3yjyAiz5zu2pMM5+bNm3z//ffkzp2b9957j1deeSXFq2/fvsDjGQE3NzcqV67Mvn37UoSpZcuWsWXLFnLkyEGtWrXIli0bq1atIiYmxjQmPj6eefPmsWvXLjw8PEzh4K9bFBw7doxLly6l6TvcvXsXAC8vL7MQdfv2bdasWQP87y/0tNaVrG3btkRHRzN+/HgePXpE27Zt01RTarZu3cqQIUNM+yslf+748eNxcXHhrbfeMrVPnTqVb7/9Fh8fH5YvX/6PQhRA/fr1efjwIatXrzZrv379Otu3bydv3rymAJQ3b14uXbpk2scq2eHDh9m3bx/169c3/SYrV67kq6++omjRooSEhFjce+rFF1+kUKFCbNu2LcWjY1avXm3aZsOSKVOmULZsWbNtJjw9Pc224rh06ZJNa9VE5OnRjJRkOOvXrychIYE2bdpYnGkpX748vr6+HDlyhM2bNzNixAg6duzIG2+8YXp8yaFDh/jmm2946623KFeuHABDhgxh1KhRtGnThtatW5MtWzY2bNjAH3/8weTJk8mcOTNFihThpZde4tChQ/Tv35+aNWty/vx5Vq5cScmSJTl37tzffoeqVauSO3du5syZw8OHDylcuDBXrlwhNDTU9Fy6qKgo4PGapLTUlezll1/G3d2d9evXU6pUqVRn7JKfldeyZUurdXbt2pXt27cTFBREhw4dcHFxYdOmTRw/fpxPP/3U9GiViIgIgoODMRgMNGzYkF27dqU4V548eUxB5PLly/z4448ULVqUKlWqmD5r165dfPzxx/z8889UrVqV69evs2LFCtP6peTv2adPH/bt28f48eP5/fff8fHx4cyZM4SEhJAvXz5GjhwJPF6wP2nSJODxppqHDh1KUZebmxuvvPIKmTJl4pNPPqFHjx60bduW9u3bkydPHn766Sc2bNhAqVKlTLObf3Xy5Ek2b97MV199ZdbevHlz5syZw9SpUwH47rvv6NOnj9XfXESeLYPR2lyzyHOoSZMmnDt3jh07dljd7PC7777jvffeo0KFCoSGhnL27FmmTZvGoUOHiI2NpVixYrRv35527dqZrWHau3cvCxYs4Ndff8XJyYmyZcvSs2dPs9mIW7du8eWXX7J7924ePnxImTJleO+99zhw4ABfffWV6Tlwa9euZejQoYwbNy7Fgu/ffvuNSZMm8euvvxIfH0/+/PmpXbs2Xbp04bXXXqN69eosXrz4H9WV7PPPP2fRokUMHDiQbt26pehP3owzuU5rfvnlF6ZOncovv/xCUlISZcuWpUePHmafu379erNF2Kl56aWXWLp0qdnv0rp1az7//HPTmAcPHjBnzhy2bdvGtWvXcHV1pWrVqvTq1StFILx16xbTpk0zrZdzd3enQYMG9O7d2zQbduTIEYtr6JIVKlTILPj99ttvzJo1i6NHjxIdHU3+/Pl59dVX6dWrFzly5Ej1HEFBQWTOnJl58+aZtcfHxzNx4kQ2b95MpkyZaNOmDX369DHdmSki9qcgJSIpjB8/nqVLl7J79+5U7xgUEZHHtEZKRMzcu3eP9evXExAQoBAlIvI3tEZKRIDHz+375ptvOH78OFFRURbX84iIyP9oRkpEAMiaNSv79u3DaDQyYcIEypYta++SREQcntZIiYiIiNhIM1IiIiIiNlKQEhEREbGRgpSIiIiIjRSkRERERGykICUiIiJiIwUpERERERspSImIiIjYSEFKRERExEYKUiIiIiI2UpASERERsZGClIiIiIiNFKREREREbKQgJSIiImIjBSkRERERGylIiYiIiNhIQUpERETERgpSIiIiIjZSkBIRERGxkYKUiIiIiI0UpERERERspCAlIiIiYiMFKREREREbKUiJiIiI2EhBSkRERMRGClIi/3JDhgyhTJkyZq+yZctStWpV3njjDdasWfPUa/D396djx46m9x07dsTf3/8fnyc+Pp7r16+nW13Tp0+nTJkyXLlyJd3OKSLyZ5ntXYCIpI+hQ4fywgsvAGA0Gnnw4AEbN25k+PDh3Llzh+7duz+zWnr27ElMTMw/OiY8PJygoCB69OhBmzZtnlJlIiLpS0FK5DnxyiuvULhwYbO2119/nSZNmjB79mw6d+6Ms7PzM6mldu3a//iYK1eucOHChfQvRkTkKdKlPZHnmIuLC/7+/kRHR/PHH3/YuxwRkeeOgpTIc85gMACQmJiIv78/H330EUOHDsXHx4d69epx69YtAI4dO8Y777xDlSpVqFKlCkFBQZw4cSLF+bZu3UrLli3x8fGhWbNmHDp0KMWY1NZInT9/ng8++AA/Pz+qVatGhw4dTMeuXbuWTp06AY8vUZYpU8Z03N27d/n444+pW7cuFSpUoHHjxixZsgSj0Wh2/kuXLtGnTx98fX2pUaMGkydPTjFGRCS96dKeyHMsKSmJH374AWdnZ0qVKgXAli1bKFGiBMOGDePWrVvkyZOH77//nnfffRdvb2/ef/994uPjWbt2LR06dGDRokVUr14deBx4hg4dSuXKlRk4cCAXL16kZ8+eJCUlUahQIYt1XLhwgTfeeAMnJyc6dOiAh4cHq1evplu3bixZsgRfX1969uzJnDlzaNeuHdWqVQMgOjqaDh06cP36dd5++208PT05dOgQn332GRcuXGDUqFEA3Lp1i/bt2xMfH0/nzp1xdXVlxYoV3L179+n+wCKS4SlIiTwnoqKiuH37NvB49ik8PJzFixdz6tQp3nnnHdzc3ACIjY1l9uzZ5M+fH3gctkaPHk3FihVZtmwZTk5OAAQGBtKqVSs+/fRT1q9fT2JiIhMnTqRixYosXbrUtN6qYsWKDBo0yGptU6ZMISYmho0bN5oCXfPmzWnYsCELFy5k5syZ1KpVizlz5lC5cmVatmwJQHBwMBcvXiQ0NNQ0S/X2228zadIk5s6dS7t27fD29iY4OJjbt28TGhpK+fLlAWjdujXNmzfn/v376fkzi4iYUZASeU60bt06RZuzszMdO3ZkwIABpraiRYuaQhRAWFgYV65c4e233+bevXtmx7/88sssXryYa9eucePGDSIjI+ndu7fZovXmzZszbtw4i3UlJSWxd+9e6tatawpRADlz5mT58uXkzJnT4rE7duzAy8uLvHnzmkIiPF5YP3fuXHbv3o23tzf79u2jYsWKphAF4O7uTvPmzVm0aJHF84uIPCkFKZHnxIQJE8iTJw8AmTJlImfOnJQqVYqsWbOajfPw8DB7f+nSJQC++OILvvjii1TPffXqVa5duwY8DmJ/lilTJooXL26xrrt37/Lw4UOKFSuWoq906dJWv9PFixeJi4ujZs2aFuuCx1snBAQEpOgvWbKk1fOLiDwpBSmR50TVqlVTbH+QmuRLd8mSkpIAeP/996lcuXKqx5QsWdK0UWZcXFyKfmuLuhMTEwFSBLq0SEpKolq1avTu3TvV/nz58gGPF9SnVlfydxMReVoUpEQyuORF4q6urtSqVcus78SJE9y7dw8XFxeKFCkCkGKvJ6PRyOXLl80u2/3ZCy+8QLZs2UwzX3+2aNEiwsPD+eijjyzWFh0dnaKue/fucfDgQdMsV+HChVPdgyq1zxQRSU/a/kAkg6tQoQJ58+Zl6dKlREdHm9ofPHhAv379GDp0KE5OTpQrV45ChQqxYsUKs13Lt27dSmRkpMXzZ86cmTp16rB3717TpTiA+/fvmxaTw/9myv48i+Tv78+pU6fYs2eP2Tlnz57N+++/b9ob69VXX+WPP/5g3759Zudfv379P/9BRET+Ac1IiWRwWbJkYcSIEfTr1482bdrw+uuvkzVrVlavXk1ERAQTJ04kc+bH/6oYMWIE7733Hu3ataNt27Zcv36d5cuXkzt3bquf0b9/fw4ePMgbb7xBhw4dyJ49O2vWrOH+/fumhfDJj7fZuHEjRqOR1q1b06NHD7Zv307v3r1p3749L774IseOHWPDhg3Uq1ePevXqAdClSxc2btxInz596Ny5M+7u7qxcuVKX9kTkqdOMlIjQqFEjFi5cSP78+Zk1axZTp07Fzc2N2bNn06xZM9O4l19+mblz5+Li4sKkSZP47rvvGDt27N8u6i5ZsiSrVq2iUqVKBAcHM3XqVNzd3Vm+fDne3t4AlCpVio4dO/Lrr7/y2WefERERQe7cuVm5ciVt2rRh27ZtfPrpp/z888/06tWLadOmkSnT43+FZc+ena+//ppGjRqxcuVKZsyYga+vL++9997T+9FERACDUVv/ioiIiNhEM1IiIiIiNlKQEhEREbGRgpSIiIiIjRSkRERERGykICVPpGPHjpQpU8bi6+bNm1aPP3z4MGXKlOHKlStp+rw//vjDbE+hMmXKsHbt2if5CiL/Og8ePKBSpUrUqlWL+Ph4s76HDx+yfPly0/tHjx6xePFiq+dbu3at6aHQABEREWzZsuWJavzrOUWeVwpS8sQaN27M/v37U3399bluf1WlShX2799PgQIF0vRZPXr04JdffjG9379/P02aNHmi+kX+bbZs2YKHhwcPHjxgx44dZn0LFy4kODjY9H7z5s1WHyoN0KRJE/bv3296P3jwYL7//vv0LVrkOaUNOeWJubi4kDdvXpuOdXZ2tvlY4ImOFfm3Cg0NpU6dOly/fp2QkBCaNm1q6vvrjjZp2eHGxcUFFxeXdK9TJCPQjJQ8VT///DPlypVj0aJFprYpU6ZQrVo1Ll++nOLS3okTJ3j77bepUqUKvr6+9OnTh4iICODx40LCw8OZMWMGHTt2BMwv7Q0ZMoSBAwcyfvx4atasSaVKlejVq5fZ5cVLly7RvXt3qlSpQp06dVi4cCENGzbU5UH51zh79iw///wztWvX5rXXXuOHH37g7NmzAEyfPp0ZM2YQHh5u+mdj6NChwON/Vg4fPsz06dNp3749/fv3p2rVqowZM8bsMlzHjh354YcfWLduHf7+/gDEx8czYcIE6tatS5UqVXjzzTfNZrAAduzYQfPmzfHx8SEwMND0z63I805BSp6qSpUq0aNHD6ZOncqlS5c4evQo8+bNY8yYMaaH4CZLSkqiR48e+Pr6snHjRhYvXkxERATDhg0DYM2aNXh6ehIUFMT06dNT/bxvvvmGu3fvsmzZMmbMmMGxY8eYPHkyADExMbzzzjskJSWxYsUKpkyZwrp167h8+fLT/RFE0tGaNWtwdXWlXr16vPLKKzg7O7NixQoAgoKCCAoKwtPTk/379/Paa6+Z/vnZv38/VapUAeCnn37Cw8ODDRs20LlzZ7PzT58+nSpVqtC4cWPWrFkDwNChQ/n++++ZMGEC69ato3HjxvTs2dO0XvHHH3+kT58+vPrqq2zYsIGWLVsyb968Z/SLiNiXLu3JE9u0aRPffvttivaXX36ZSZMm8d5777Fv3z6GDx9OREQELVu2NHvsSLL79+9z584d8uXLR+HChTEYDEyZMsX0QFx3d3ecnJxwdXW1+Gy37Nmz8/HHH5MlSxZKlSpFy5Yt2bt3L/D44bq3b99m7dq1puMnTpxIixYt0ueHEHnKEhIS2LRpEy+//DLZsmUDoH79+mzYsIEBAwbg5uaGq6srTk5OpsveOXLkAFJeBu/bt6+p78cffzS1586dmyxZsuDi4oK7uzsXL15k8+bNrFmzhooVKwKPn2146tQpgoODadCgAcuWLaNq1ar06dMHgBIlSnD69Gm++uqrp/uDiDgABSl5Yv7+/nz44Ycp2l1dXQHInDkzEyZMoEWLFnh4eDBixIhUz5MrVy66devGJ598wowZM6hVqxb16tWjUaNGaa6lWLFiZMmSxfQ+R44cPHr0CICwsDBKlChhFsLKlClj+stExNHt3buXmzdvmt1g0aRJE3bs2MGWLVt4/fXX03QeDw+PNP//fVhYGACdOnUya3/06BE5c+YE4PTp09SuXdusv0qVKgpSkiEoSMkTc3Nzo1ixYlbHnD59mqSkJG7evMmpU6eoWrVqquM+/PBD3n77bfbu3cvBgwcZPXo0c+fOZf369Tg7O/9tLdbGODk5kZSU9LfnEHFUyWv5+vbtm6IvJCQkzUHqnywsT16svnz5ctzc3Mz6kh8a/edxyf78P2hEnmdaIyVP3Y0bNxg1ahTdu3enefPmDB48mOjo6BTjzp07x6hRo/Dw8OCtt95i2rRpLFiwgLNnz3Lq1KknrsPb25uLFy9y9+5ds8+8f//+E59b5Gm7ffs2e/fupU2bNqxfv97s9frrr/PLL7/w22+/YTAYzI776/t/6sUXXwQe/3NcrFgx02vt2rWEhoYCULZsWbPLg4DZNiUizzMFKXlisbGx3Lx5M9VXXFwcw4YNI1++fLz33nsMHTqUhw8fprqvTe7cudm8eTMjR47k7NmznD9/ntDQUHLlykXJkiWBx7NfFy5c4NatW/+4zmbNmvHCCy8wcOBATp06xfHjxxk4cCDw5H/ZiDxtGzZsICEhgW7duuHl5WX26tmzJ05OTqxYsQJXV1fu3bvH+fPnefTokekS+6+//kpsbGyaPsvNzY3w8HCuXbvGiy++yMsvv8yoUaPYuXMnly9fJjg4mLlz55puGAkKCuLUqVOMHz+e8+fPs3HjRrNNQUWeZwpS8sS++eYb6tSpk+pr4cKF/Pe//+Wzzz7D2dmZ3LlzM2LECFavXs3OnTvNzuPu7s6CBQsIDw/nzTffpHXr1kRERLBo0SKyZ88OPL41e8+ePQQFBf3jOp2dnVmwYAHx8fG8+eab9OnThzZt2gC6DCGOb+3atdSqVYtSpUql6CtSpAgNGzZky5Yt1KpVi7x589KiRQvCwsLw8/OjUqVKtG/fnt27d6fps9q3b8/p06dp0aIFiYmJTJ48mUaNGjFq1CiaNGlCaGgon3zyCW3btgUez0jNnz+fw4cP06JFCxYvXkzPnj3T9fuLOCqDMS27tYk8B65cucKFCxeoU6eOqe369evUq1eP5cuXU716dTtWJyIi/0aakZIMIy4ujv/85z8EBwdz+fJlwsLCGDFiBMWLF6dSpUr2Lk9ERP6FNCMlGcq2bduYM2cO58+fx8XFhZo1azJo0CAKFixo79JERORfSEFKRERExEa6tCciIiJiIwUpERERERspSImIiIjYSEFKRERExEYKUuLQAgICCAgIsHcZIs8t/TMm8mQUpERERERspCAlIiIiYiMFKREREREbKUiJiIiI2EhBSkRERMRGClIiIiIiNlKQEhEREbFRhnxocbYqve1dgshz7c6RGfYuQeS55pL52X3Wk/ydGfPT8//vgmf4X4WIiIj86xh08coaBSkRERGxzGCwdwUOTTFTRERExEaakRIRERHLdGnPKgUpERERsUyX9qxSkBIRERHLNCNllYKUiIiIWKYZKasUpERERMQyzUhZpV9HRERExEaakRIRERHLdGnPKgUpERERsUyX9qxSkBIRERHLNCNllYKUiIiIWKYZKasUpERERMQyzUhZpZgpIiIiYiPNSImIiIhlurRnlYKUiIiIWKYgZZWClIiIiFiWSWukrFGQEhEREcs0I2WVgpSIiIhYprv2rFLMFBEREYc2a9YsOnbsaLH/o48+wt/f36wtKSmJadOmUbduXSpVqkRQUBAXL140G3Py5EkCAwOpXLkyDRo0IDg4+B/XpiAlIiIilhky2f5KB4sXL2batGkW+7/77jtWr16don3WrFmEhITw6aefsnLlSgwGA927dyc+Ph6AO3fu0KVLF4oXL05oaCh9+vRh6tSphIaG/qP6FKRERETEMoPB9tcTuH79Ot26dWPq1KmUKFEi1TE3btxgxIgRvPTSS2bt8fHxLFy4kD59+lC/fn28vb2ZPHky169fZ8eOHQCsWrUKZ2dnRo8eTalSpWjbti3vvPMO8+fP/0d1KkiJiIiIZXaakfrtt9/IlSsXGzdupFKlSin6jUYjQ4YMoWXLlimC1KlTp4iOjsbPz8/UljNnTsqVK8eRI0cAOHr0KL6+vmTO/L/l4n5+fpw/f57IyMg016nF5iIiImLZE8wsBQQEWO3fuXOnxT5/f/8U657+bPHixdy8eZM5c+Ywd+5cs75r164BUKBAAbP2fPnycfXqVdMYLy+vFP0AEREReHh4WK09mYKUiIiIWOaA2x+cOnWKGTNmsHz5cpydnVP0x8TEAKToy5o1K/fu3QMgNjY21X6AuLi4NNeiICUiIiJPhbUZJ1vFxcXx4Ycf8u677+Lt7Z3qGBcXF+DxWqnkPycfmy1bNtOY5IXnf+4HcHV1TXM9ClIiIiJimYPtI/Xzzz/zxx9/MGPGDGbOnAnAo0ePSEhIoEqVKowZM4bixYsDjxejFy1a1HTsjRs3TOHL09OTGzdumJ07+X3+/PnTXI+ClIiIiFjmYJf2fHx82L59u1nb0qVL2b59O0uXLsXDwwNnZ2eyZ8/O4cOHTUEqKiqKsLAwAgMDAfD19SUkJITExEScnJwAOHjwICVKlEjz+ihQkBIRERFrHGxGysXFhWLFipm15cqVi8yZM5u1BwYGMnHiRNzd3SlUqBATJkzA09OThg0bAtC2bVsWLFjA8OHD6datGydOnGDJkiWMGTPmH9WjICUiIiKWOdiMVFr17duXhIQEPvroI2JjY/H19SU4ONi0wNzDw4MFCxYwduxYWrduTd68eRk0aBCtW7f+R59jMBqNxqfxBRxZtiq97V2CyHPtzpEZ9i5B5Lnm8gynQbI1n2XzsTGbeqVjJY7p3xkzRURERByALu2JiIiIZQ62RsrRKEiJiIiIZf/SNVLPioKUiIiIWKYZKasUpERERMQyzUhZpV9HRERExEaakRIRERHLdGnPKgUpERERscigIGWVgpSIiIhYpCBlnYKUiIiIWKYcZZWClIiIiFikGSnrdNeeiIiIiI00IyUiIiIWaUbKOgUpERERsUhByjoFKREREbFIQco6BSkRERGxTDnKKgUpERERsUgzUtbprj0RERERG2lGSkRERCzSjJR1ClIiIiJikYKUdQpSIiIiYpGClHUOGaTWr19vsc/Z2Zn8+fNTuXJlnJycnl1RIiIiGZFylFUOGaRmzZrFlStXSEpKIkeOHADcv38fg8GA0WgEoESJEixatAhPT097lioiIvJc04yUdQ55195bb71FqVKl2LhxI0eOHOHIkSNs3bqV8uXLM3LkSPbt20eRIkWYMGGCvUsVERGRDMwhg9SiRYsYM2YMXl5epraSJUvy0UcfMXfuXPLly0e/fv04cOCAHasUERF5/hkMBptfGYFDXtq7f/8+2bNnT9Hu4uLCvXv3AMiZMydxcXHPujQREZEMJaMEIls55IxU9erVmTBhAvfv3ze1RUVFMWnSJKpUqQLA9u3bKVGihL1KFBERyRgMT/DKABxyRmrkyJF07tyZevXqUaJECYxGIxcuXOCFF15gwYIFHDhwgC+//JLJkyfbu1QREZHnmmakrHPIIFWkSBG2bt3Kli1bOHnyJE5OTnTq1ImmTZvi7OxM1qxZ2bRpEyVLlrR3qSIiIs81BSnrHDJIweP1UG3btk21r1ChQs+4GhEREbGXWbNmcfDgQZYuXWpq27VrFzNnzuTcuXO88MILNGrUiPfffx8XFxcAkpKSmDFjBqtXryYqKopq1aoxatQoihUrZjrHyZMnGTt2LL/++iu5c+emY8eOdO3a9R/V5pBBKjIyksmTJ3Ps2DEePXpk2jsq2c6dO+1UmYiISMZi7xmpxYsXM23aNHx9fU1tR48epXfv3vTr149GjRpx8eJFRo4cyd27dxk3bhzwOHyFhIQwbtw48ufPz4QJE+jevTubN2/G2dmZO3fu0KVLF1555RXGjBnD8ePHGTNmDLlz57Y4kZMahwxSI0eO5OjRo7Rq1cq0IaeIiIg8e/YKUtevX2f48OEcO3Ysxc1lISEh+Pn58Z///AeAYsWK8cEHHzBs2DDGjBkDwMKFCxk4cCD169cHYPLkydStW5cdO3bQtGlTVq1ahbOzM6NHjyZz5syUKlWKixcvMn/+/H9/kDpw4AAzZ86kdu3a9i5FREQkY7PThNRvv/1Grly52LhxIzNnziQ8PNzUFxQURKZMKTceSEhI4MGDB1y5coXo6Gj8/PxMfTlz5qRcuXIcOXKEpk2bcvToUXx9fcmc+X9RyM/Pj7lz5xIZGYmHh0ea6nTIIOXq6kqBAgXsXYaIiEiG9yQzUgEBAVb7rS3V8ff3x9/fP9W+cuXKmb2Pj49n0aJFlC9fHnd3d44ePQqQIkvky5ePq1evAnDt2jWzjb+T+wEiIiLSHKQcch+pVq1aERwcTGJior1LERERydAcfWfzhIQEBg0axJkzZxg1ahQAMTExADg7O5uNzZo1q2kz79jY2FT7gX+04bdDzkjdunWLb775ht27d1O0aNEUX/Srr76yU2UiIiKSVk/75rAHDx7Qr18/Dh8+zLRp06hUqRKA6c69+Ph405/hcUDKli2baUx8fLzZ+ZIDlKura5prcMgg5eTkRLNmzexdhoiISIZn77v2LLlx4wbdu3fnypUrzJ8/32w9VPIlvRs3blC0aFGzY7y9vQHw9PTkxo0bKc4JkD9//jTX4ZBBKvnWRXn+FM6fmyOrh/HmB/P5/tgfpva9Swbwkk/KR/7U7zSRH365AEDxQh580qcFtauWxtXFmbCzEXw8ewt7fjhtGu/q4sxHPZvQpmEV3HO5EXb2KqNnbjIbI5IRGY1GQlevImTFMq5cvoK7hzsNGvjTq8/7pmebXr9+nSlffsGB/ftJSHhEhYo+fPDhIMqWLfc3Z5fnmgPmqHv37tG5c2cePHjA119/TZkyZcz6vb29yZ49O4cPHzYFqaioKMLCwggMDATA19eXkJAQEhMTcXJyAuDgwYOUKFEizeujwIGC1Pr162nSpAnOzs6sX7/e6thWrVo9k5okfRUt8AIbZ75H7hzmU6YGg4HyLxZk0uIdbNj1s1nfb2ciAHghpys7FvTjTtRDBk5YQ1R0LO+0qsnmWb15rcc09h87A8CMj9rTwr8So6Zv4uS5a3R7vTYbZ7zHK10nmwKZSEa0eOECpk+dTOcuXanhV5NLly4ya/pUzpz5g7kLFvHwYTRBnTuQJUsWRowag3PWrMybM4ue3bqwZv0m8ubNZ++vIHbiiDNS48aN4/LlyyxYsAB3d3du3rxp6nN3d8fZ2ZnAwEAmTpyIu7s7hQoVYsKECXh6etKwYUMA2rZty4IFCxg+fDjdunXjxIkTLFmyxLR9Qlo5TJAaMmQIdevWxcPDgyFDhlgcZzAYFKT+ZQwGA4HNazDug9ap9r9YLB9u2bLyzf7fLIadji38yOeRg/qdJhJx8x4A3x08xQ8rh/JBp1fYf+wMLlmz8EajakxYtJ2ZK/YAsPfoaU5tHkPXtnUUpCTDSkpKInjBPF5/ox3vfzAAAL+atcidOzcD+/cj7Ldf+X7fXu7eucP6zd+YQlP58hVo/2Ybjv7wA42barlFRuVoQSopKYmtW7fy6NEjOnfunKJ/586dFC5cmL59+5KQkMBHH31EbGwsvr6+BAcHm9Zde3h4sGDBAsaOHUvr1q3JmzcvgwYNonXr1P+ussRhgtSpU6dS/bP8+1V8sSDThrVj3urv2XX4FOun9zLrr1SmMAC//B6e2uEAhF+/w7Rlu0whCh5fqjh3+SZexR9fy3bO4kSmTAbuP4g1jUlMTOLegxjcc7ul51cS+Vd58OABTZu14LXGTczaixV7fDn98uXL7NyxnVdebWQ285Qnb16+2/39M61VHI8jBKnPP//c9OdMmTJx4sSJvz3GycmJgQMHMnDgQItjfHx8WLly5RPV5pDbH3Tq1In79++naI+MjNRs1L/Q5Wt3qNBiDIO/XMvDmEcp+n3KFOLu/YdMGNiWK7vHc+fQZNZNf5cXi/3vX+ihO35ixLSNZse9kNOVutVfJOzs48t/UQ9iWbz+IO+93YAaPiXIlT0b73f0p3zpgoRs+eHpfkkRB5YzZ06GDh9BlarVzNp3frcdgBIlSnLu3FlKlCjJjGlTCKhfh2qVyhPUOZA/Tv9uj5JF/jUcZkZq7969/PLLLwD88MMPzJ49O8XthxcvXjTb2VT+He5EPeRO1EOL/T5ehcmdw5Vbdx7Qrv88ihRwZ3iPxny38AP82n/O1T/NQiXLlMnAnFEdyJ4tK18u/s7U/snsLVT2LsKeJQNMbWNmbSZ0x0/p+6VE/uWO//Qji4Ln83LAK+TJm5eEhASWfbWYQoWLMOrjT4mPj2fWjGl0facTq9dt/Ed3McnzxRFmpByZwwSpQoUK8fHHH2M0GjEYDGzdutVs+3eDwYCrqyuDBg2yY5XyNIyYtoHP52/j4M/nHjf8dJZDP5/j+NqPeO+tBnw0bYPZ+MyZMxH8SSda+Feiz9gQfgy7BEDeF7Kz76sPeZSQSJfhS4i4cZfX6pRn+H8a8zAmnmnLdj3rrybikH48dpS+7/WkcJGijPl4LLGx/7scPnvuAlzdHl8KL1++As2bvErI18tMa6skA1KOssphglTp0qVNG3f5+/uzZs0a3N3d7VyVPAsnTqecZbwQHsmp89ep6FXIrD13jmyEfNmdutVK02/cKhas2W/qe6d1LYoUcKdCyzGcvfT4Do59Rx9vsfBxn+Ys23SY2/ein+I3EXF832zdwsjhQyhevASz5wWTK3duMv3/UorqvjVMIQqgQMGClCxZit9PnbRXueIANCNlnUOukdq1a5dZiHr06BG//PIL0dH6S/B5kzlzJgKb1+ClisVT9GXLmoXIuw9M7wvnz83uxQOo4VOCzkMXM3fVPrPxRQu4cz0yyhSikn1/7AxZnbNQqkiep/IdRP4tFi9cwNBBA/CpVJmFXy0nT968AOTIkQN3D48UuzwDPEpIIOufdoaWjMfRHxFjbw4ZpK5evUpQUBAnTpwgLi6OVq1a8cYbb+Dv78/Jk/pfRs+ThIQkRrzblLH9Wpm1V/YuTKkiedn3/5t25nBzYcucPhTIm4tmvWawZvuPKc51+sJ18r6Q3WyROkDNyiVJTEzi0tXbT+17iDi61atCmPzlBBo2eo0584LJkSOHWX+dOvU4fOi/3Lnzv39OLpw/x8UL56latfqzLlcciMFg+ysjcJhLe382btw47t+/j7u7O99++y0RERF8/fXXrFmzhgkTJrBw4UJ7lyjpaOzcrcwdHci8MYGEbD1KsYLujHi3Kb/8Ec7SjYcBGNGzCV7F8/PJ7C08epRoNoMVF5/Az79fYfH6g/RsV48NM3rx6dythF+/S4CfN/06BjB31T6uR6a8E1QkI7h18yYTx4+jYMFCvPV2ICdPhpn1Fy5SlB7vvsfuXd/Rs3tXerz7HgkJj5g+ZTL5PT1p8/rrdqpcxPE5ZJA6dOgQS5YsoXDhwkyePJl69epRtWpVXnjhBdq0aWPv8iSdfbXhEDGxj+jXKYBVk7sTHRPPxl0/M3L6RhITkwBoFVAZgBHvNmXEu03Njr8YEYl301Hcj44lIGgyn/Rtyef9W+Pq4syZSzd5f9xKFq3777P+WiIO4/vv9xIbG0tERDhdOnVI0f/xp+No2boNS5aFMGXSRIYPGYiTkxN+NWsxcPAw3Nyy26FqcRQZ5RKdrQxGo9Fo7yL+qkqVKmzZsoWCBQtSq1Yt3n//fdq1a8fZs2dp3749R44ceaLzZ6vSO50qFZHU3Dkyw94liDzXXJ7hNIjXoG02H3v6i9fSsRLH5JAzUuXKlWP16tXky5ePO3fuUL9+feLj45k/f77pqc0iIiLy9GlGyjqHDFKDBw+mZ8+e3Llzh+7du+Pp6cno0aP57rvvCA4Otnd5IiIiGYZylHUOGaR8fHw4cOAA9+/fJ2fOnAB07tyZ999/nxdeeMHO1YmIiGQcmTIpSVnjMEEqIiKCAgUKYDAYiIiIMLU/ePB4H6GsWbMSExNDTEwMBQsWtFeZIiIiIiYOE6QCAgLYv38/Hh4e+Pv7p3pNNvnxMdpLSkRE5NnQpT3rHCZILVmyhFy5cgHw1Vdf2bkaERERAS02/zsOE6Reeukl058LFSpEoUKFUh23e/fuZ1WSiIhIhqccZZ1DPiKmVatWbN261awtJiaGESNG0KtXLztVJSIikvHoWXvWOWSQ6tChAwMHDmT48OHExsZy/PhxWrZsye7du5kyZYq9yxMREckwFKSsc5hLe3/Wr18/6tSpw5AhQ2jcuDE3b96kefPmDBkyxLSOSkRERMTeHHJGCiB//vwULFiQmzdvYjQa8fT0xM3Nzd5liYiIZCgGg+2vjMAhg9TixYtp0aIFMTExbNq0iS+//JIVK1bQtm1bwsLC/v4EIiIiki50ac86hwxSEyZMoEuXLoSEhFCiRAlee+01Nm7cSN68eXnzzTftXZ6IiEiGoRkp6xxmjVRkZCQeHh4ArFixAh8fH7P+fPnyMWvWLBYuXGiP8kRERDKkjDKzZCuHmZGqU6cOkZGRAKYQNWDAAFMbQFRUFFOnTrVLfSIiIhmRZqSsc5ggZTQaU7Tt2rWLhw8f/u04EREREXtwmEt7aaUpRhERkWdHf+9a968LUiIiIvLsKEdZpyAlIiIiFmlGyjqHClL6L0tERMSx6K9m6xwqSH366adkzZrV9P7Ro0dMmDDBtKN5XFycvUoTERHJkBxhkmPWrFkcPHiQpUuXmtpOnjzJ2LFj+fXXX8mdOzcdO3aka9eupv6kpCRmzJjB6tWriYqKolq1aowaNYpixYql+Rxp4TB37fn6+nLz5k2uXLlielWpUoU7d+6Y3t+8eZPq1avbu1QRERF5RhYvXsy0adPM2u7cuUOXLl0oXrw4oaGh9OnTh6lTpxIaGmoaM2vWLEJCQvj0009ZuXIlBoOB7t27Ex8fn+ZzpIXDzEj9OWWKiIiIY7DXhNT169cZPnw4x44do0SJEmZ9q1atwtnZmdGjR5M5c2ZKlSrFxYsXmT9/Pm3btiU+Pp6FCxcycOBA6tevD8DkyZOpW7cuO3bsoGnTpn97jrRymBkpERERcTz2etbeb7/9Rq5cudi4cSOVKlUy6zt69Ci+vr5kzvy/+SA/Pz/Onz9PZGQkp06dIjo6Gj8/P1N/zpw5KVeuHEeOHEnTOdLKYWakRERExPE8SR4KCAiw2r9z506Lff7+/vj7+6fad+3aNby8vMza8uXLB0BERATXrl0DoECBAinGXL16NU3nSH5s3d9RkBIRERGLHGGx+V/Fxsbi7Oxs1pZ8s1pcXBwxMTEAqY65d+9ems6RVgpSIiIiYtGTBClrM05PwsXFxbRoPFly+HF1dcXFxQWA+Ph405+Tx2TLli1N50grrZESERGRfxVPT09u3Lhh1pb8Pn/+/KZLeqmN8fT0TNM50kpBSkRERCwyGGx/PS2+vr4cO3aMxMREU9vBgwcpUaIEHh4eeHt7kz17dg4fPmzqj4qKIiwszLSN0t+dI60UpERERMQie921Z03btm158OABw4cP58yZM6xdu5YlS5bQo0cP4PHaqMDAQCZOnMjOnTs5deoUH3zwAZ6enjRs2DBN50grrZESERERixxwrTkeHh4sWLCAsWPH0rp1a/LmzcugQYNo3bq1aUzfvn1JSEjgo48+IjY2Fl9fX4KDg00LzNNyjrQwGI1GY7p+u3+BbFV627sEkefanSMz7F2CyHPN5RlOg/hPO2jzsbv61kzHShyTLu2JiIiI2EiX9kRERMQiR7y050gUpERERMSiTEpSVilIiYiIiEXKUdYpSImIiIhFjviIGEeiICUiIiIWZVKOskp37YmIiIjYSDNSIiIiYpEu7VmnICUiIiIWKUdZpyAlIiIiFhlQkrJGQUpEREQs0mJz6xSkRERExCKtkbJOd+2JiIiI2EgzUiIiImKRJqSsU5ASERERi/SsPesUpERERMQi5SjrFKRERETEIi02ty5NQcrb2/sf/5AGg4GwsDCbihIRERHHoBxlXZqCVKtWrZRIRURERP4iTUHq888/f9p1iIiIiAPSYnPr0n0fqaSkJO7fv8+3336b3qcWERGRZ8zwBK+MwKbF5vfv32fcuHFs376dhw8fYjQaUx138uTJJypORERE7EtLe6yzaUZqwoQJrF27ltKlS1OzZk2MRiPNmzenVq1aODs7kzVrVqZPn57etYqIiMgzlslg+ysjsClI7dmzh4YNGxISEsLEiRMB6NSpE8HBwaxcuRInJyfOnj2broWKiIjIs2cwGGx+ZQQ2Banbt29Tu3ZtAF544QXy58/Pzz//DDzeKuGNN95g8+bN6VeliIiIiAOyKUi5ubmRmJhoel+0aFF+//130/vSpUsTERHx5NWJiIiIXRkMtr8yApuClI+PD9u2bTOFqdKlS3P06FHTovNz587h7OycflWKiIiIXejSnnU2BamgoCCOHTtGo0aNuHfvHq1bt+bcuXMEBQUxevRoli5dSo0aNdK7VhEREXnGtNjcOpuCVM2aNZk3bx4lSpQgZ86c+Pj4MHr0aI4fP05ISAjlypVj6NCh6V2riIiIPGOakbLO5g0569Spw/z5800/VPv27Tl8+DA//PADK1euJH/+/OlWpIiIiNiHvTbkfPToEZMnT6ZBgwZUqVKFt99+mx9//NHUf/LkSQIDA6lcuTINGjQgODjY7PikpCSmTZtG3bp1qVSpEkFBQVy8ePEJq0opXXc2d3Z2JmfOnOl5ShEREcmAZs+eTWhoKJ9++inr16+nZMmSdO/enevXr3Pnzh26dOlC8eLFCQ0NpU+fPkydOpXQ0FDT8bNmzSIkJIRPP/2UlStXYjAY6N69O/Hx8elap007m3t7e6dpyk47m4uIiPy72etZezt37qRZs2bUqVMHgCFDhrB69WqOHz/OhQsXcHZ2ZvTo0WTOnJlSpUpx8eJF5s+fT9u2bYmPj2fhwoUMHDiQ+vXrAzB58mTq1q3Ljh07aNq0abrVaVOQatWqVYoglZCQQGRkJD/99BOenp688cYb6VKgiIiI2I+9ljrlzp2b3bt3ExgYSIECBVi5ciXOzs6ULVuWNWvW4OvrS+bM/4sxfn5+zJ07l8jISMLDw4mOjsbPz8/UnzNnTsqVK8eRI0fsH6Q+//xzi33Xrl3jrbfewtXV1eaiRERExDE8yaLxgIAAq/07d+602Dd8+HA++OADAgICcHJyIlOmTEydOpWiRYty7do1vLy8zMbny5cPgIiICK5duwZAgQIFUoy5evWqLV/FonRdIwXg6enJ22+/zaJFi9L71CIiIvKM2WtDzrNnz5IzZ05mzpzJypUradOmDYMHD+bUqVPExsam2K8ya9asAMTFxRETEwOQ6pi4uLgnK+wvbJqR+jsuLi7pnvhERETk2XuSNVLWZpysCQ8PZ+DAgSxevJjq1asDULFiRc6cOcP06dNxcXFJsWg8OSC5urri4uICQHx8vOnPyWOyZctmU02WpOuM1KNHjzhx4gSLFy+mRIkS6XlqERERySBOnDjBo0ePqFixoll7pUqVuHDhAp6enty4ccOsL/l9/vz5TZf0Uhvj6emZrrU+tbv2pk6dalNBIiIi4jjssdg8OQj9/vvv+Pj4mNpPnz5NsWLFqFy5MiEhISQmJuLk5ATAwYMHKVGiBB4eHuTIkYPs2bNz+PBhihYtCkBUVBRhYWEEBgama63pdtceQKZMmciTJw9NmjShTJkyT1yciIiI2Jc9dij38fGhevXqDB48mFGjRuHp6cn69es5ePAgX3/9NUWKFGHBggUMHz6cbt26ceLECZYsWcKYMWOAx2ujAgMDmThxIu7u7hQqVIgJEybg6elJw4YN07VWgzH5ScPpLD4+3mEfXPzw0VP5yiLy/y7dirF3CSLPNe8Cz+7O+D7rbN8TcnrrsjYfe+/ePaZMmcKePXu4d+8eXl5e9O/fn5deegl4fPlv7NixhIWFkTdvXoKCgsxmmxITE5k0aRJr164lNjYWX19fRo4cSeHChW2uKTU2BamAgACGDx+Ov79/qv2bN2/mk08+4fDhw09c4NOgICXydClIiTxdzzJI9V1/yuZjp7XyTsdKHFOaLu3dvn2bs2fPmt6Hh4dz4sQJcuTIkWJsUlISO3bsSPct2EVEROTZy5Qxnj1sszQFqaxZszJgwABu3rwJPL5eOnfuXObOnZvqeKPRSJMmTdKvShEREREHlKYg5ebmxuzZszl9+jRGo5Fhw4bx5ptvUqVKlRRjM2XKhLu7OzVr1kz3YkVEROTZ0oyUdWm+a698+fKUL18eeLz9+quvvppie3YRERF5vtjjrr1/E5s25OzduzfZsmXj888/5+7du6b22bNn88knn3D9+vX0qk9ERETsKJPB9ldGYFOQOnnyJG3atGHZsmWmBwMCxMTEsGbNGtq2bculS5fSrUgRERGxD3s9a+/fwqYgNWnSJHLnzs23336Lt/f/bm3s378/W7duJWvWrEycODHdihQRERH7yGQw2PzKCGwKUidOnKBTp04UKlQoRV+hQoXo0KEDR48efeLiRERERByZTY+IMRqNxMRY3nDPaDQSGxtrc1EiIiLiGGyacclAbPp9KleuzMqVK7l3716KvujoaFatWkXlypWftDYRERGxM62Rss6mGanevXvToUMHXn31VVq2bEnRokXJmjUrly5dYsuWLdy8eZMvvvgivWsVERGRZyyjrHWylU1BysfHh0WLFjF+/HiWLl3Knx/XV65cOb744gsqVaqUbkWKiIiIfShHWWdTkAKoXr06q1ev5vbt24SHh5OUlESBAgVISEhg/fr1DBs2jO3bt6dnrSIiIvKMZZT9oGxlc5BK5u7ujpubG99++y1Tpkzh8OHDJCUlkTnzE59aRERExKE9Udr56aefWLduHVu3biU6Ohqj0YinpydvvPEGb775ZnrVKCIiInaiNVLW/eMgdf36dTZs2MDatWu5ePEiRqORTJke3/zXr18//vOf/5jei4iIyL+bcpR1aQpS8fHxfPfdd6xdu5aDBw+SmJhI1qxZ8ff3p2HDhpQpU4bWrVvz4osvKkSJiIg8R7RGyro0Bam6desSFRVFrly5aNq0KQEBAdSrV49s2bIBEB4e/lSLFBEREfswoCRlTZqC1L1793B1daVRo0bUqFGDqlWrmkKUiIiIPL80I2VdmoLUsmXL2LRpE9u2bWPVqlUYDAYqVqzIq6++SsOGDXFycnradYqIiIg4nDQFqerVq1O9enVGjBjB3r172bRpE3v27OHnn39m4sSJFClSBIPBwMOHD592vSIiIvIMaUbKOoPxz9uS/wPR0dFs376dTZs2cfjwYRITE8mUKRMvvfQSbdq0oVGjRmTNmjW9600XDx/Z9JVFJI0u3bL8UHMReXLeBVyf2WdN2HPO5mMHNiiZjpU4JpuD1J9FRkayefNmNm3axK+//gpAjhw5OHLkyBMX+DQoSIk8XQpSIk/XswxSX+61PUgNqP/8B6l02X7cw8ODzp0707lzZy5evMjGjRvZvHlzepxaRERE7Ej7SFmXLjNS/zaakRJ5ujQjJfJ0PcsZqSnfn7f52H51S6RjJY5Ju2eKiIiI2EhPFhYRERGLdNeedQpSIiIiYpHWSFmnICUiIiIWZdIjYqzSGikRERGxyGCw/fWk1q9fT5MmTahYsSJNmzblm2++MfWdPHmSwMBAKleuTIMGDQgODjY7NikpiWnTplG3bl0qVapEUFAQFy9efPKi/kJBSkRERCzKZLD99SQ2bNjAsGHDaNeuHZs3b6ZJkyb079+fn376iTt37tClSxeKFy9OaGgoffr0YerUqYSGhpqOnzVrFiEhIXz66aesXLkSg8FA9+7diY+Pf8JfxJy2PxCRdKftD0Serme5/cGcgxdsPrZnzeI2HWc0GgkICKBRo0YMHjzY1N61a1deeuklAJYvX86uXbvInPnxKqVJkyaxfft2tm3bRnx8PH5+fgwcOJC33noLgKioKOrWrctnn31G06ZNbf5Of6UZKREREbEok8Fg88tW586dIzw8nObNm5u1BwcH06NHD44ePYqvr68pRAH4+flx/vx5IiMjOXXqFNHR0fj5+Zn6c+bMSbly5dL9qStabC4iIiIWPclap4CAAKv9O3fuTLX9woULADx8+JCuXbsSFhZG4cKFeffdd/H39+fatWt4eXmZHZMvXz4AIiIiuHbtGgAFChRIMebq1au2fBWLNCMlIiIiFtljRurBgwcADB48mGbNmrFw4UJq165Nr169OHjwILGxsTg7O5sdkzVrVgDi4uKIiXm8vCC1MXFxcTbXlRrNSImIiIhFTzIjZWnG6e9kyZIFeLwmqnXr1gCULVuWsLAwFi1ahIuLS4pF48kBydXVFRcXFwDi4+NNf04eky1bNptqskQzUiIiIuJQPD09AVJcvitdujRXrlzB09OTGzdumPUlv8+fP7/pkl5qY5LPnV4UpERERMSiTE/wslW5cuVwc3Pj559/Nms/ffo0RYsWxdfXl2PHjpGYmGjqO3jwICVKlMDDwwNvb2+yZ8/O4cOHTf1RUVGEhYVRvXr1J6gsJV3aExEREYsMdnhGjIuLC926dWPmzJnkz58fHx8ftmzZwoEDB1i8eDGlS5dmwYIFDB8+nG7dunHixAmWLFnCmDFjgMdrowIDA5k4cSLu7u4UKlSICRMm4OnpScOGDdO1VgUpERERscheD4jp1asX2bJlY/LkyVy/fp1SpUoxffp0atSoAcCCBQsYO3YsrVu3Jm/evAwaNMi0ngqgb9++JCQk8NFHHxEbG4uvry/BwcEpFqA/KW3IKSLpThtyijxdz3JDzmXHrth8bGC1wulYiWPSjJSIiIhYpEcWW6fF5iIiIiI20oyUiIiIWGSHteb/KgpSIiIiYpE97tr7N1GQEhEREYu0Bsg6BSkRERGxSDNS1ilIiYiIiEWKUdZpxk5ERETERpqREhEREYt0ac86BSkRERGxSJeurFOQEhEREYs0I2WdgpSIiIhYpBhlnYKUiIiIWKQJKescIkh5e3uneerw5MmTT7kaERERkbRxiCD12WefmYJUREQE8+bNo127dlSpUoUsWbJw4sQJvv76a9599107VyoiIpKxZNLFPasMRqPRaO8i/qxjx460bNmS119/3ax948aNLFmyhNDQ0Cf+jIePHOorizx3Lt2KsXcJIs817wKuz+yzNv963eZjm1XIn46VOCaHu6vxxIkT+Pr6pmj38fHhzJkzdqhIREQk4zI8wX8yAocLUkWLFmXz5s0p2leuXEnp0qXtUJGIiEjGZTDY/soIHGKN1J/17duXvn37cvDgQSpWrIjRaOTHH3/k5MmTzJ8/397liYiIZChaI2Wdw81INWzYkOXLl5MvXz7279/PgQMHKF68OKtXr8bPz8/e5YmIiIiYONyMFEDVqlWpWrWqvcsQERHJ8DLKJTpbOdyMFMDevXvp1KkTderUITw8nGnTprF+/Xp7lyUiIpLhaI2UdQ4XpA4cOEDv3r0pWLAgUVFRJCUlkZiYyLBhw9Jl6wMRERFJO921Z53DBanp06czYMAAPv/8c5ycnAD44IMPGDBgAIsWLbJzdSIiIhlLJoPtr4zA4YLU77//jr+/f4r2V199lcuXL9uhIhERkYxLM1LWOVyQypEjB9evp9xF9Y8//iBXrlx2qEhEREQkdQ4XpJo3b87YsWP57bffMBgMREdHs3fvXj755BOaNGli7/JEREQyFC02t87htj/o168f165do23btgC0bt0ao9FIgwYN6Nevn32Lk6cmMTGRJYuCWR+6hhs3rlO0WHE6d+lK0+YtTGP27dnNvDmz+OP07+TKnZtXGjbivb7v4+aW3Y6Vizgeo9HI9s1r2bJuJdcjrpDrBXdeqlWft4PexdUtOy0bVLF4bIXK1Rk75fHmxw8fRrN49mQO799DTEw03uUr0a3PQIoWL/Wsvoo4gIxyic5WDvfQ4mQXL17k5MmTJCUl4eXlRenSpTEajRjSIeLqocWOZ+qkiSz7agm9+vSlXPkK7P9+L8uWLOaz8RNo3LQ5u77bwYcf9KW670u83bEzCQmPWDB3Dk5OTixZHkLmzA73vwkyND202L7WrljM0vkzaN2+Ez5VX+Jq+GW+XjiL4qW8+PjLOZwO+yXFMQe/38W6kCUMGDGOegGvAfDJkL78cepXOvfoh6ubGyFL5nL39m1mLAklR04ttbCnZ/nQ4n2nb9t8bD0v93SsxDE53N8+AQEBhIaGUqxYMYoVK2Zqv379Oi1atODw4cN2rE6ehocPown5ehmBnTrTpWt3AGr41eTkb7+x4utlNG7anDmzZlCyVGlmzp1PlizOAFStWp1mrzVk4/q1tHn9TXt+BRGHkZSUxJrlC2nUoi2d/tMXgMrV/ciZKzdfjB7Emd/DKFPex+yYm9evsn1TKE1atTOFqFO//czRQ98z4vNpVPerC0A5nyr8p30zvlm/ijc7dX+2X0zsxhFmpM6fP0+bNm0YMWIEbdq0AeDkyZOMHTuWX3/9ldy5c9OxY0e6du1qOiYpKYkZM2awevVqoqKiqFatGqNGjTLLFunBIYLU1q1b+f777wEIDw/n448/JmvWrGZjwsPD02U2ShyPs3NWFi8LIU+ePGbtWbJkITo6GoDz587RvkMHU4gCcPfwoETJkuzbu0dBSuT/PYyOpkHDJtT1f82svWDhogBci7jCi97lzfqCZ36Js4sLHbv3NrX99MNBXFyyUaV6TVNbrtzulK9cjaOH9ytIZSD2/qv30aNHfPjhhzx8+NDUdufOHbp06cIrr7zCmDFjOH78OGPGjCF37tympUGzZs0iJCSEcePGkT9/fiZMmED37t3ZvHkzzs7Olj7uH3OIIFWlShVCQkJIvsoYERFBlixZTP0GgwFXV1fGjx9vrxLlKcqcOTNlvL2Bx2s7IiNvsWHdWg4fOsiI0Z8A8IL7C0SEh5sd9+jRI65dvcqj+PhnXrOIo8qeIwf/eX9IivaD3+8CoFiJ0mbtJ389zsF9O+k7eAyuf1pvePnSefIXLIzTXy6bFyhUhL07vnkKlYukbvr06bi5uZm1rVq1CmdnZ0aPHk3mzJkpVaoUFy9eZP78+bRt25b4+HgWLlzIwIEDqV+/PgCTJ0+mbt267Nixg6ZNm6ZbfQ4RpAoUKMBXX30FQMeOHZkxY4a2OsigvtmymeFDBgJQp259GjVuDEDLVm1YMG8Oi4Ln07J1W+LiYpk5bSrR0Q9wdX12awVE/o1O/nqctV8vpkadlylawnyh+LqQr8jnWZAGDc3vio5+cB9XV/O/vACyZXMj5uGDp1qvOBZ7TkgdOXKElStXsn79eho0aGBqP3r0KL6+vmbrY/38/Jg7dy6RkZGEh4cTHR2Nn5+fqT9nzpyUK1eOI0eOPH9B6s+WLl1q7xLEjir4+LBg8VIuXjjP7BnTeafDWywNWU2PXr1JSExk1vRpTJv8JZkzZ6HN62/QwD+Ac2fO2LtsEYf124kfGTv0fTwLFabPoFFmfTdvXOPIf/cS1GtAipknY1JSqsspjBgxGBxu5xx5ijI9wbW9gIAAq/07d+602BcVFcWgQYP46KOPKFCggFnftWvX8PLyMmvLly8f8Piq1rVr1wBSHJcvXz6uXr2a5vrTwiGCVNmyZdm/fz8eHh54e3tbXQt18uTJZ1iZPGtFixajaNFiVKvuS+EiRenR9R127thOk2bNef+DAfTs1ZsrVy6TL28+cuTMSdd3Asmp2UuRVO3buY1pn4+iUNFijP5iVoo77Q7t2wUYqOvfKMWxbtlzEHHlYor22JiHuGbXliMZib1mpEaPHk3lypVp3rx5ir7Y2NgU65yS11bHxcURE/P4zuHUxty7dy9d63SIIPXZZ5+RI0cOAMaNG2fnauRZux0Zyf79+6hTpx7uHh6m9vIVKgBw7dpVjh75gfj4OGrVrkupUo/XeCQkJPDH6dO0aNXaLnWLOLK1IUv4au5UyvtUZdjYybhlz5FizJGD+yhfqSq53T1S9BUqUpyfjhwkKSmJTJn+NwN1NfwyRYqVfKq1i4N5giRlbcbJmvXr13P06FE2bdqUar+Liwvxf1kfGxcXB4CrqysuLi4AxMfHm/6cPCZbtmw21WSJQwSp1q3/9xfhr7/+SufOnSlatKgdK5Jn6eHDh4waPpTeffvR9T89Te3/3b8fAK8y3ny3fRt7d+9m4zfbTTcibFgXyv2oKPwDGtqlbhFHtW3jGpbMmULtl1/lg2Gfmt28k8xoNHLm1G80bdM+1XNU8fVj9bIF/HTkv1SrUQeAe3dv89vxY7zRsWuqx8jzyR7bH4SGhhIZGWm2Lgpg1KhRBAcHU7BgQW7cuGHWl/w+f/78JCQkmNr+nCdu3LiB9//f3JReHCJI/dn69evp0qWLvcuQZ6hwkSI0a9GSeXNmkcnJifIVKhL2268smDubWrXrULtOXTw9PVm7ZjUjhw+hVZvX+eP070yd9CWNGjeharXq9v4KIg7jTuQtgmd+Sb78BWjWuj3nTpsvh/AsVJhcud25ef0q0dEPKFI89dml8pWqUaFydSZ9OpzOPd4nR67chCyeg1v2HLzW4o1n8VUkA5s4cSKxsbFmba+++ip9+/alSZMmbNmyhZCQEBITE3FycgLg4MGDlChRAg8PD3LkyEH27Nk5fPiwKUhFRUURFhZGYGBgutbqcEGqQYMGLFu2jN69e5Nd1+EzjBGjP6FYsRJsWBfKnJnTyZM3L28FdqJ7j3cxGAyUftGLaTPnMG3KJPr1fhePPHno9p8eBHXvYe/SRRzKscP7iY+L5cb1qwztG5Siv+/gMQQ0bsHdO493q86ePafFcw395EuCZ37J4jlTMBqNlK1QiYGjviB7DsvHyPPHHvtI5c+fP9V2Dw8PChUqRNu2bVmwYAHDhw+nW7dunDhxgiVLljBmzBjg8dqowMBAJk6ciLu7O4UKFWLChAl4enrSsGH6XsVwuEfEvPXWW/z0008YDAY8PDxSbMxp6/XWP9MjYkSeLj0iRuTpepaPiDlyzvbF2b4l0+9moDJlyjBu3DjTzuYnTpxg7NixhIWFkTdvXoKCgsxmmxITE5k0aRJr164lNjYWX19fRo4cSeHChdOtJnDAIDVjxgyr/b1797banxYKUiJPl4KUyNP1TIPU+ScIUiWe/7uqHe7SXo0aNahcuXKKxZFxcXHs2bPHPkWJiIhkUI7wrD1H5nC7qnXq1In79++naD9z5gwDBw60Q0UiIiIZl8Fg+ysjcIgZqcWLF5ueo2c0Gqldu3aq43x8fFJtFxEREbEHhwhSgYGB5M6dm6SkJIYNG8bQoUNNG3TC/x5a/Odn5oiIiMjTl0EmlmzmEEEqc+bMtGrVCngcmho3bmy2Eyk83sU6LCxMs1IiIiLPkpKUVQ63RmrYsGE8fPgwRfuVK1fo2LGjHSoSERHJuAxP8J+MwCFmpJYvX87ChQuBx2uk2rZta/ZsJ3i8I2nBggXtUZ6IiEiGlVEWjdvKIYJUmzZtuHPnDkajkZkzZ/Laa6/h5uZmNsbNzc3iInQRERF5OpSjrHOIIJUtWzbTRpsGg4GuXbuaPZ355MmTfP3110yfPp0ff/zRXmWKiIiImHGIIPVnyYEqLi7O9FDCX375hUyZMqX783FERETkb2hKyiqHC1Lnzp0jJCSEDRs2cO/ePQwGA23btqVnz57p/nwcERERsS6jLBq3lUMEqYSEBLZv305ISAg//PADzs7O1K9fn8aNGzNo0CDeeecdhSgRERE70GJz6xwiSDVo0IAHDx7g5+fH559/ziuvvEL27NkB9FgYERERO1KOss4hgtT9+/fx8PDA09MTNze3FA8sFhERETtRkrLKIYLUgQMH2Lp1K6GhoYSEhODq6oq/vz+NGzfGoDlFERERcVAGo9FotHcRf3b27FnWrFnDpk2buHXrlmmxebdu3ShevHi6fMbDRw71lUWeO5duxdi7BJHnmncB12f2Wb+FR9t8bPlCbn8/6F/O4YJUssTERPbs2cO6devYs2cPSUlJ1KpViwULFjzxuRWkRJ4uBSmRp+tZBqmwCNuDVLmCz3+QcohLe6lxcnIiICCAgIAAbt++zYYNG1i7dq29yxIREclQtMDGOoedkXqaNCMl8nRpRkrk6XqWM1Inr9o+I1W2gGakREREJAPThpzWZbJ3ASIiIiL/VpqREhEREYu0C5F1ClIiIiJikXKUdQpSIiIiYpmSlFUKUiIiImKRFptbpyAlIiIiFmmNlHW6a09ERETERpqREhEREYs0IWWdgpSIiIhYpiRllYKUiIiIWKTF5tZpjZSIiIhYZDDY/noSd+/eZeTIkdSrV4+qVavy1ltvcfToUVP/yZMnCQwMpHLlyjRo0IDg4GCz45OSkpg2bRp169alUqVKBAUFcfHixScrKhUKUiIiIuJw+vfvz88//8ykSZNYs2YN5cuXp2vXrpw9e5Y7d+7QpUsXihcvTmhoKH369GHq1KmEhoaajp81axYhISF8+umnrFy5EoPBQPfu3YmPj0/XOg1Go9GYrmf8F3j4KMN9ZZFn6tKtGHuXIPJc8y7g+sw+68KtWJuPLZ7HxabjLl68yKuvvsqKFSuoWrUqAEajkUaNGtG0aVNcXFxYvnw5u3btInPmx6uUJk2axPbt29m2bRvx8fH4+fkxcOBA3nrrLQCioqKoW7cun332GU2bNrX5O/2V1kiJiIiIZU9wiS4gIMBq/86dO1Ntf+GFF5g3bx4VKlT4XxkGA0ajkXv37vHrr7/i6+trClEAfn5+zJ07l8jISMLDw4mOjsbPz8/UnzNnTsqVK8eRI0fSNUjp0p6IiIhYZHiC/9gqZ86c1K9fH2dnZ1PbN998w6VLl6hTpw7Xrl3D09PT7Jh8+fIBEBERwbVr1wAoUKBAijFXr161ua7UaEZKRERELHqSReOWZpz+qWPHjjFs2DACAgLw9/dn3LhxZiELIGvWrADExcURE/N4eUFqY+7du5cuNSXTjJSIiIhYZHiCV3r47rvv6Nq1Kz4+PkyaNAkAFxeXFIvG4+LiAHB1dcXF5fHarNTGZMuWLZ0qe0xBSkRERBzSsmXL6NOnD/Xq1WP+/PmmgOTp6cmNGzfMxia/z58/v+mSXmpj/npJ8EkpSImIiIhF9tpH6uuvv+aTTz6hQ4cOTJkyxewyna+vL8eOHSMxMdHUdvDgQUqUKIGHhwfe3t5kz56dw4cPm/qjoqIICwujevXqT1bYXyhIiYiIiBXP/uLe+fPn+eyzz2jYsCE9evQgMjKSmzdvcvPmTe7fv0/btm158OABw4cP58yZM6xdu5YlS5bQo0cP4PHaqMDAQCZOnMjOnTs5deoUH3zwAZ6enjRs2ND2nyIV2kdKRNKd9pESebqe5T5S4Xdt38CyUG7nvx+Uijlz5jB58uRU+1q3bs3nn3/OiRMnGDt2LGFhYeTNm5egoCACAwNN4xITE5k0aRJr164lNjYWX19fRo4cSeHChW2qyRIFKRFJdwpSIk/XswxSEU8QpAraGKT+TbT9gYiIiFj0pGudnndaIyUiIiJiI81IiYiIiEVPskN5RqAgJSIiIpYpR1mlICUiIiIWKUdZpyAlIiIiFmmxuXUKUiIiImKR1khZp7v2RERERGykGSkRERGxTBNSVilIiYiIiEXKUdYpSImIiIhFWmxunYKUiIiIWKTF5tYpSImIiIhFmpGyTnftiYiIiNhIQUpERETERrq0JyIiIhbp0p51ClIiIiJikRabW6cgJSIiIhZpRso6BSkRERGxSDnKOi02FxEREbGRZqRERETEMk1JWaUgJSIiIhZpsbl1ClIiIiJikRabW6cgJSIiIhYpR1mnICUiIiKWKUlZpbv2RERERGykGSkRERGxSIvNrVOQEhEREYu02Nw6g9FoNNq7CBEREZF/I62REhEREbGRgpSIiIiIjRSkRERERGykICUiIiJiIwUpERERERspSImIiIjYSEFKRERExEYKUiIiIiI2UpASERERsdH/AWfn4VdENQf8AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 700x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create confusion matrix for random forest\n",
    "rf_cm = confusion_matrix(y_test, y_pred_rf)\n",
    "\n",
    "# Confusion matrix vizualization\n",
    "f, ax = plt.subplots(figsize= (7, 4))\n",
    "sns.heatmap(rf_cm, annot=True, fmt='g', \n",
    "            cmap='Blues', xticklabels=['Existing', 'Attrited'], yticklabels=['Existing', 'Attrited'])\n",
    "ax.set_title(f'Random Forest \\nAccuracy: {rf_acc_score:.4%}\\n', fontsize= 14)\n",
    "ax.set_ylabel('Actual', fontsize= 13)\n",
    "ax.set_xlabel('Predicted', fontsize= 13)\n",
    "ax.xaxis.set_ticks_position('top')\n",
    "ax.xaxis.set_label_position('top')\n",
    "\n",
    "# Save figure\n",
    "plt.savefig('Figures/RF_optimized.png', bbox_inches='tight')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Logistic Regression analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Classification Report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.96      0.93      1554\n",
      "           1       0.71      0.52      0.60       309\n",
      "\n",
      "    accuracy                           0.88      1863\n",
      "   macro avg       0.81      0.74      0.77      1863\n",
      "weighted avg       0.88      0.88      0.88      1863\n",
      "\n",
      "Accuracy Score: 0.7376641551398833\n"
     ]
    }
   ],
   "source": [
    "# Classification report for logistic regression model\n",
    "print(\"Logistic Regression Classification Report\")\n",
    "print(classification_report(y_test, y_pred_lr))\n",
    "\n",
    "# Calculate the accuracy score\n",
    "lr_acc_score = balanced_accuracy_score(y_test, y_pred_lr)\n",
    "print(f'Accuracy Score: {lr_acc_score}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 700x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create confusion matrix for logistic regression model\n",
    "lr_cm = confusion_matrix(y_test, y_pred_lr)\n",
    "\n",
    "# Confusion matrix vizualization\n",
    "f, ax = plt.subplots(figsize= (7, 4))\n",
    "sns.heatmap(lr_cm, annot=True, fmt='g', \n",
    "            cmap='Blues', xticklabels=['Existing', 'Attrited'], yticklabels=['Existing', 'Attrited'])\n",
    "ax.set_title(f'Logistic Regression \\nAccuracy: {lr_acc_score:.4%}\\n', fontsize= 14)\n",
    "ax.set_ylabel('Actual', fontsize= 13)\n",
    "ax.set_xlabel('Predicted', fontsize= 13)\n",
    "ax.xaxis.set_ticks_position('top')\n",
    "ax.xaxis.set_label_position('top')\n",
    "\n",
    "# Save figure\n",
    "plt.savefig('Figures/LR_optimized.png', bbox_inches='tight')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "PythonData",
   "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": "3.7.16"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}