03_training_first_models.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loan Prediction 03 - Training and Validation of models with manual data imputation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We propose models of two different natures (regression based and tree based) to execute the credit analysis for the loan approval.\n",
    "\n",
    "- Logitic Regression\n",
    "- Ridge Regression\n",
    "- Random Forest\n",
    "- Extreme Gradient Boosting\n",
    "\n",
    "\n",
    "Since we only have the classes for the train_dataset, we will consider it as the entire data set. Hence, 20% of this data will be kept separate for final performance comparison.\n",
    "\n",
    "We will apply a GridSearch along with a Cross Validation of 10 folds to find each models best hyper parameters.\n",
    "\n",
    "For this type of problem, let us assume that the F1-Score is a good evaluation metric, since maximizing it can offer a good spot between the approved and rejected loans.\n",
    "\n",
    "That is, we want to have a good balance of false negatives (reject loans that should be approved) and false positives (approved loans that should be rejected). \n",
    "\n",
    "Finally, we can say that the bank will take risks, approving improper transactions aiming to maximize their profit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import sys\n",
    "from joblib import dump, load\n",
    "sys.path.append('utils')\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.style.use('seaborn')\n",
    "\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression, RidgeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "import metrics_utils \n",
    "import model_utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(491, 14)\n"
     ]
    }
   ],
   "source": [
    "df_import = pd.read_csv('dataset/train_loan_preprocessed.csv')\n",
    "print(df_import.shape)\n",
    "score = 'f1'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns_x = df_import.drop(columns=['Loan_Status']).columns\n",
    "column_y = ['Loan_Status']\n",
    "\n",
    "X_train, X_validation, y_train, y_validation = train_test_split(\n",
    "    df_import[columns_x], df_import[column_y],  test_size=0.20, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()\n",
    "scaler.fit(df_import[columns_x])\n",
    "dump(scaler, 'saves/minmax_scaler_manual_imputation.bin', compress=True)\n",
    "\n",
    "X_train_norm = pd.DataFrame(data=scaler.transform(X_train),columns=X_train.columns)\n",
    "X_validation_norm = pd.DataFrame(data=scaler.transform(X_validation),columns=X_train.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Base_Loan_Installment</th>\n",
       "      <th>Remaining_Income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>392.000000</td>\n",
       "      <td>392.000000</td>\n",
       "      <td>392.000000</td>\n",
       "      <td>392.000000</td>\n",
       "      <td>392.000000</td>\n",
       "      <td>392.000000</td>\n",
       "      <td>392.000000</td>\n",
       "      <td>392.000000</td>\n",
       "      <td>392.000000</td>\n",
       "      <td>392.000000</td>\n",
       "      <td>392.000000</td>\n",
       "      <td>392.000000</td>\n",
       "      <td>392.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.816327</td>\n",
       "      <td>0.650510</td>\n",
       "      <td>0.265306</td>\n",
       "      <td>0.219388</td>\n",
       "      <td>0.130102</td>\n",
       "      <td>0.253158</td>\n",
       "      <td>0.152486</td>\n",
       "      <td>0.335566</td>\n",
       "      <td>0.626701</td>\n",
       "      <td>0.864796</td>\n",
       "      <td>0.521684</td>\n",
       "      <td>0.244011</td>\n",
       "      <td>0.720834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.387713</td>\n",
       "      <td>0.477418</td>\n",
       "      <td>0.343690</td>\n",
       "      <td>0.414361</td>\n",
       "      <td>0.336846</td>\n",
       "      <td>0.165164</td>\n",
       "      <td>0.177773</td>\n",
       "      <td>0.155528</td>\n",
       "      <td>0.160690</td>\n",
       "      <td>0.342378</td>\n",
       "      <td>0.395194</td>\n",
       "      <td>0.124863</td>\n",
       "      <td>0.122087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.151568</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.245283</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.165756</td>\n",
       "      <td>0.674797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.199833</td>\n",
       "      <td>0.132350</td>\n",
       "      <td>0.311321</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.217668</td>\n",
       "      <td>0.730342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.299251</td>\n",
       "      <td>0.250640</td>\n",
       "      <td>0.407008</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.297359</td>\n",
       "      <td>0.788672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.949931</td>\n",
       "      <td>0.902673</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Gender     Married  Dependents   Education  Self_Employed  \\\n",
       "count  392.000000  392.000000  392.000000  392.000000     392.000000   \n",
       "mean     0.816327    0.650510    0.265306    0.219388       0.130102   \n",
       "std      0.387713    0.477418    0.343690    0.414361       0.336846   \n",
       "min      0.000000    0.000000    0.000000    0.000000       0.000000   \n",
       "25%      1.000000    0.000000    0.000000    0.000000       0.000000   \n",
       "50%      1.000000    1.000000    0.000000    0.000000       0.000000   \n",
       "75%      1.000000    1.000000    0.666667    0.000000       0.000000   \n",
       "max      1.000000    1.000000    1.000000    1.000000       1.000000   \n",
       "\n",
       "       ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "count       392.000000         392.000000  392.000000        392.000000   \n",
       "mean          0.253158           0.152486    0.335566          0.626701   \n",
       "std           0.165164           0.177773    0.155528          0.160690   \n",
       "min           0.000000           0.000000    0.000000          0.000000   \n",
       "25%           0.151568           0.000000    0.245283          0.666667   \n",
       "50%           0.199833           0.132350    0.311321          0.666667   \n",
       "75%           0.299251           0.250640    0.407008          0.666667   \n",
       "max           0.949931           0.902673    1.000000          1.000000   \n",
       "\n",
       "       Credit_History  Property_Area  Base_Loan_Installment  Remaining_Income  \n",
       "count      392.000000     392.000000             392.000000        392.000000  \n",
       "mean         0.864796       0.521684               0.244011          0.720834  \n",
       "std          0.342378       0.395194               0.124863          0.122087  \n",
       "min          0.000000       0.000000               0.000000          0.000000  \n",
       "25%          1.000000       0.000000               0.165756          0.674797  \n",
       "50%          1.000000       0.500000               0.217668          0.730342  \n",
       "75%          1.000000       1.000000               0.297359          0.788672  \n",
       "max          1.000000       1.000000               1.000000          1.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(392, 13)\n"
     ]
    }
   ],
   "source": [
    "display(X_train_norm.describe())\n",
    "print(X_train_norm.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Base_Loan_Installment</th>\n",
       "      <th>Remaining_Income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>99.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.797980</td>\n",
       "      <td>0.585859</td>\n",
       "      <td>0.178451</td>\n",
       "      <td>0.212121</td>\n",
       "      <td>0.101010</td>\n",
       "      <td>0.243335</td>\n",
       "      <td>0.167506</td>\n",
       "      <td>0.348961</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.787879</td>\n",
       "      <td>0.459596</td>\n",
       "      <td>0.253338</td>\n",
       "      <td>0.715653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.403551</td>\n",
       "      <td>0.495080</td>\n",
       "      <td>0.298572</td>\n",
       "      <td>0.410891</td>\n",
       "      <td>0.302876</td>\n",
       "      <td>0.154598</td>\n",
       "      <td>0.199347</td>\n",
       "      <td>0.159564</td>\n",
       "      <td>0.126804</td>\n",
       "      <td>0.410891</td>\n",
       "      <td>0.382505</td>\n",
       "      <td>0.133491</td>\n",
       "      <td>0.099381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003331</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.105121</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.071038</td>\n",
       "      <td>0.264518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.145740</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.253369</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.171220</td>\n",
       "      <td>0.671901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.201221</td>\n",
       "      <td>0.144989</td>\n",
       "      <td>0.315364</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.216758</td>\n",
       "      <td>0.722706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.302387</td>\n",
       "      <td>0.242483</td>\n",
       "      <td>0.407008</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.297814</td>\n",
       "      <td>0.768307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.989218</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.668488</td>\n",
       "      <td>0.951500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Gender    Married  Dependents  Education  Self_Employed  \\\n",
       "count  99.000000  99.000000   99.000000  99.000000      99.000000   \n",
       "mean    0.797980   0.585859    0.178451   0.212121       0.101010   \n",
       "std     0.403551   0.495080    0.298572   0.410891       0.302876   \n",
       "min     0.000000   0.000000    0.000000   0.000000       0.000000   \n",
       "25%     1.000000   0.000000    0.000000   0.000000       0.000000   \n",
       "50%     1.000000   1.000000    0.000000   0.000000       0.000000   \n",
       "75%     1.000000   1.000000    0.333333   0.000000       0.000000   \n",
       "max     1.000000   1.000000    1.000000   1.000000       1.000000   \n",
       "\n",
       "       ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "count        99.000000          99.000000   99.000000         99.000000   \n",
       "mean          0.243335           0.167506    0.348961          0.636364   \n",
       "std           0.154598           0.199347    0.159564          0.126804   \n",
       "min           0.003331           0.000000    0.105121          0.166667   \n",
       "25%           0.145740           0.000000    0.253369          0.666667   \n",
       "50%           0.201221           0.144989    0.315364          0.666667   \n",
       "75%           0.302387           0.242483    0.407008          0.666667   \n",
       "max           1.000000           1.000000    0.989218          1.000000   \n",
       "\n",
       "       Credit_History  Property_Area  Base_Loan_Installment  Remaining_Income  \n",
       "count       99.000000      99.000000              99.000000         99.000000  \n",
       "mean         0.787879       0.459596               0.253338          0.715653  \n",
       "std          0.410891       0.382505               0.133491          0.099381  \n",
       "min          0.000000       0.000000               0.071038          0.264518  \n",
       "25%          1.000000       0.000000               0.171220          0.671901  \n",
       "50%          1.000000       0.500000               0.216758          0.722706  \n",
       "75%          1.000000       0.750000               0.297814          0.768307  \n",
       "max          1.000000       1.000000               0.668488          0.951500  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(99, 13)\n"
     ]
    }
   ],
   "source": [
    "display(X_validation_norm.describe())\n",
    "print(X_validation_norm.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 45 candidates, totalling 450 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  56 tasks      | elapsed:    0.2s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Ellapsed time: 0.7257835865020752 seconds ---\n",
      "Best params:  {'C': 1, 'penalty': 'l1', 'solver': 'liblinear'}\n",
      "Best score (f1) 0.8674793450864312\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done 450 out of 450 | elapsed:    0.7s finished\n"
     ]
    }
   ],
   "source": [
    "logistic_regression_params = {\n",
    "    'C' : [1,10,100],\n",
    "    'penalty' : ['l1', 'l2', 'elasticnet'],\n",
    "    'solver' : ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga']\n",
    "}\n",
    "\n",
    "best_lr,best_lr_params,best_lr_score = model_utils.find_best_classification_model_with_cross_validation(\n",
    "    LogisticRegression(random_state=0, class_weight = 'balanced'),\n",
    "    logistic_regression_params,\n",
    "    X_train_norm.values,\n",
    "    y_train.values.ravel(),\n",
    "    metric = score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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//8t1va2tLUqVKlXo/SUkJGDx4sVYvHgx5s6di379+mHRokWwtLTEtGnTtG6rUqmwfv162NnZYcKECZqsHQBMTU0xfvx4KBQK/PHHH5rtoaGhAIBhw4ZpVbUsLCx0fgt/XYsWLXR+q9ixYwdSUlIwcuRI1KhRQ+s6pVKJgIAAXLp0CdevX9e6TtfvxMLCApaWllrbzMzMtJ6bWmG+DW/btg0KhQLjx4/XfGMEgPLly2u+Tai/ub2uUqVKub5tNG3aFBUrVsT58+cLfNy39ezZM2RlZQEAHB0dZd3X2J6r2tt+Nh88eIBjx46hYsWKGDJkiNZ1Hh4eaN++PZKTk3HgwIFc923Xrh18fX21tvXo0QPAq8qJHOqh4Nepq1CWlpb47LPPtK5Tf3N8c4jIwcFBq3Km1qRJE9SoUQPHjh2TFZdaXp/b/EyYMAGurq74+eefsX79ekyfPh22traYN28eTEwMuxpG9erV0aBBA8THxyM6OlrrOnUV8s0qC6C7p83ExAT9+/cHgLd+/RISEnDixAlUrlwZvXv31rquTZs28PDw0Hk/Z2dnnZUS9YyYt43nddu2bQMADB8+HOXLl9dsNzMz0xz/t27dqvO+kydP1vrs2dnZoXnz5nj27JmsXjldf9/Kli2rtX3t2rUwNzfHrFmzct3+iy++gJWVldbfqbel1+GhS5cuAYDOsrq1tTVq1aqFU6dO4ebNm3B1ddWs+9CgQYNctzcxMYGHh0ehXtgaNWqgZs2a2LVrFxISEtCiRQs0aNAAbm5uOg8Qcv31118AXh1Y9PFhTkhIwJIlS7S2WVtbY82aNbmGqW7duoXk5GRUqVIFy5cv17m/9957T6unJL/XtX79+lp/7N5Ut25dndvPnTsH4NVBWFe/j/r3dOPGDdSoUQNeXl5wcHBASEgILl68iGbNmsHDwwM1a9bMlZx07NgR69atQ7t27dC2bVvNeHJhZpGkpaXhzp07cHBw0Nlcp34v6lpjxNXVVWei5OjoqHm+BVm9enWuXq2WLVsWONz4NkQ/17fxbz+b6mNKgwYNdA5p+vj4YOfOnbh06ZJWiRmAzmGnDz74AAB0jsXnR9e+1A2Tut7T6iGeN3sTJEnCzp07ERoaiitXriAlJUVr6m9ew7YFyetzm59SpUph4cKF+PTTTzFjxgwoFAosWrSo0I2gKSkpWLNmTa7t/fv3L9TMnB49euDMmTPYsmULGjVqBAB4+fIl/vjjD5QqVQqdO3fOdZ8nT55g5cqViIqKQnx8fK5ewrx6ZAry+vtM1zHey8sLZ8+ezbX9+fPnWLNmDQ4ePIjbt28jPT1dq1/kbeN53cWLFwHo/rtavXp12NnZ4fbt23j+/LnWUGS5cuXg5OSU6z7qz0BKSkqBj92iRQssWrQI33//PaKiotCkSRN4eHigevXqWslaWloarl27hgoVKmDVqlU691WqVClZ/WR50WvSoj5429nZ6bxevV39Yqlvn9f0rtezyvyYmppizZo1WLp0Kfbt24d58+YBeJUJdu3aFWPGjNH6ZcqljldXs+Tb8PLywrp16wC8au7dv38/pk+fjqFDh2Lr1q1ar19ycjKAV0nBm4nO654/f675v/p11fX6mZqaoly5cnnuJ6/fhToO9begvKgPIpaWltiyZQt+/PFHHDp0SPONw8bGBoGBgRg2bJjmAD1hwgQ4OTlh+/btCAkJQUhICMzMzODn54fx48ejcuXKeT5eWloagLzfc+oDsK4PaF4HVjMzs0I39a1duzZXM2OlSpXyTVqsra1hbm6OrKwsPHz4sNAzokQ/17fxbz+bhT2m6Gry1/Wc1Qm73OesK4FWJyq6rlM/TnZ2ttb2OXPmYM2aNbCzs0OTJk3g4OCg+SYcGhqa671UWG87RbZq1apwcXFBbGwsatSogSZNmhT6vikpKTqPSV27di1U0vLJJ59g9uzZOHjwIJ48eQJbW1vs3bsXqamp6NSpU64qXHJyMrp3746EhATUq1cPnTt3hrW1NczMzPDs2TOsW7dOZ1NqYRT0t0jX+y8zMxNBQUG4ePEilEol2rdvD1tbW81natmyZW8dz+vUn/u8kkk7OzskJiYiLS1N67OU1+9A/b4taJ0c4FUlacuWLViyZAmOHTuGffv2AXg1bXzw4MHo27cvgP99CXj8+HG+f6fy+8JcWHpNWtQf3sePH+Ojjz7Kdf2jR4+0bqceInj8+LHO/SUlJRX6sa2trTFx4kRMnDgRd+7cwcmTJ7F582asX78eKSkpuRpV5VD/8vWRNb+pXLly6NGjB7KysjB9+nRMmzZN65eufq1atWqV75vhderXNSkpKVeTZ05ODpKTk/NMwPJqClPHERYWBldX10LF4ejoiNmzZ0OSJFy/fh0xMTHYsGEDli5dCpVKhVGjRgF49SEaMGAABgwYgKSkJJw5cwa7d+/G3r17cf36dezevTvPb+UFvYcSExO14te3Q4cOyb6PmZkZ6tevj1OnTiE6OrrQSYvo5wpA8y1U1wEvr29u/+az+foxRRf1MeXN4caiKCkpSbNg38aNG3PFvGvXrrfed2GaOXUJCQlBbGwsbGxs8M8//+Cnn34qVIMmADg5OeHq1atv9bjAqwpxp06dsG7dOoSFhWHgwIGaL0W61mbZsmULEhIS8NVXX2H48OFa150+fVrzRfBtFPZ99roDBw7g4sWL6N69O2bNmqV13f379wucBVNY6vfJo0ePdDbEG/oz8NFHH2HRokXIzs7GlStXcPz4caxbtw4zZszQfPlQv3516tTJc6hKX/Q6cKn+dqlrqlxKSgouX76MUqVKaUrb6qleZ86cyXV7lUqlsxxXGJUrV0ZAQADWr1+PMmXKICIiQnOdnCxTrV69egBejU8a6ltpr1698NFHH+HAgQNar0e1atVgZWWFc+fOafogCqL+Peh6Xc+dO5fr219hqF8DXfssiEKhwEcffYSgoCBN6fD138nrypcvj9atW2PRokXw8fHB3bt3ce3atTz3bWlpqZnFpGsoUf1efH1aYVGg7q349ddf8eLFi3xvq/62VhSeqzqBV0/1fZ2u6cdvyu+zqcvrxwhd71v1c1bPWivK4uLioFKp0Lhx41x/YB48eID4+Phc93mb41VhqWe5VK1aFbt27ULVqlWxePHid7paq/pzsHXrVty4cQNnzpxBtWrV4Onpmeu2d+7cAfCqx+RNJ0+e/FdxqN9nZ8+e1XmM17V/dTytW7cudDzq36ecvyPq2HTt8+bNm3j06BGqVKnyr0YTCsPMzAxubm74/PPPNRXTgwcPAnh1XKhatSquXbtWqGEnIP8vQPneT9atC9CpUyeYm5tj/fr1ml+o2qJFi5CWloZOnTppvjV7eHjgww8/xIkTJ3LNhd+8eXOhG4Xi4uK01v1QUzc8vt6IZGVlBYVCofOgmxc3Nze4u7vj8uXL+Pnnn3Nd//TpU2RkZBR6f7qYmppqFpwLDg7WbDczM0Pfvn3x6NEjzJw5Ey9fvsx138TERK0GWPXY/vLly7XK5pmZmVi4cOFbxdetWzdYWVlhyZIlOhs3VSqVVrL6zz//6PzWot6m/p1kZmbqTISysrI0JceCVvP89NNPIUkS5s6dq/UBePLkiebbzqefflrQU3ynOnTogCZNmuD27dsYPny4pkryuszMTGzYsAH/+c9/NNtEP1c3NzeYmJhg165dWslWcnKyzoqJnM+mLo6OjmjcuDESEhJy9U/89ddf2LVrF6ytrbWWLyiq1N+Sz5w5o/W7e/78OSZPnqwzKVMP5co5XhXGs2fPMHbsWJiYmGDhwoWoUKECgoODYWpqiq+//lozHGxoSqUS9evXx/Xr1/Hdd98B+F8i8yb16/fml2L1dPR/o1KlSvD29sbt27exceNGrev27dun8wu0Op43k4m7d+9iwYIFOh9H/fvMb0r6m9Sf52XLluHp06ea7dnZ2fjvf/8LSZIM9pm/cOGCZnjqdepRkNc/vwMHDkRGRgYmTZqkc7g2OTlZ0zsE/K8RX+57W9bwUH7n65k6dSqcnJwwYcIETJ8+HV27dkXbtm1ha2uLU6dOITY2FtWqVcPXX3+tFfTMmTMxZMgQDB8+HK1bt8aHH36Iq1ev4vjx4/Dz80NUVFSBza9Xr17FF198gTp16qB69eqwt7fHkydPEBERgaysLK0ZP2XLlkW9evVw+vRpjB07FlWrVoWJiQn8/f3zHfb44Ycf0K9fPyxYsAD79u2Dt7c3JEnC7du3cfz4cezZs0dn05McrVu3Rs2aNXHy5EkcPXoUTZs2BfCqa/zKlSvYtGkTDh8+DB8fHzg4OCApKQl37tzB2bNnMXr0aM2sHi8vL/Ts2RObN29G+/bt0bp1a806Le+//z7s7e1ll5NtbGzw448/YsSIEejRowd8fX1Ro0YNKBQKPHjwALGxsUhOTtbMyjh+/Dh++OEH1K9fH1WqVEH58uXx4MEDREREwMTEBIMHDwbwqvEuMDAQlStXRu3atVGxYkVkZGTgzz//xI0bN+Dv71/g6pWDBg1CVFQUIiIi0LlzZ/j5+eHly5fYu3cvkpKSMGTIEJ3f3EQyMTHBokWL8M033yAiIgItW7aEr68vqlWrBlNTUyQkJCAmJgZPnjzBoEGDNPcT/Vzt7e3RsWNHhIWFoUuXLmjWrBnS0tIQFRUFT09PrYMSIO+zmZdp06ahd+/emDt3Lo4fPw43NzfNOi0mJiaYPXu2UQwP2dnZoX379ti9eze6dOmCxo0bIzU1FX/++ScsLCxQs2bNXE3UVatWhYODA3bv3g0zMzNUrFgRCoUCnTt3fuu1cwBg4sSJuHfvHiZPnqypzLq6umL8+PGYPn06xo8f/85O4dCjRw+cO3cOp0+fhoWFRa6GarVu3bph1apVmDlzpmZY9fbt24iMjETr1q11Lq4ox9SpU9G7d29Mnz4dUVFRmnVaDh48iObNm+daiLFFixZwcnLCL7/8gitXrsDV1RX37t1DZGQkPv74Y53x+Pj4QKFQYN68ebh69Sref/99mJiYYOjQoXnG1bBhQwwcOBCrVq1Chw4d0KZNG7z33ns4cuQIrl+/Di8vLwwcOPBfPfe8hIaGYuvWrfD09ISzszOsrKxw584dHD58GKVKldLM2gJeDeldvHgRmzdvxsmTJ9GkSRN88MEHePbsGeLi4nD69Gn06NFDk5yqX499+/Zh2LBhqFWrFkxNTeHl5ZXvMUxW0qKeSqvLxIkTUbp0afTp0weVK1fGr7/+iv379+PFixf44IMPMHjwYAwdOjRXc5C3tzfWr1+P4OBgREZGAng1FLF27VrN9KiCDkhubm747LPPNH/snz17BltbW9SuXRtBQUG5plPPnTsXc+bMwbFjx7B7925IkgRHR8d8kxZnZ2ds374dv/zyCw4ePIj169ejVKlSqFSpEgYNGlTopuH8KBQKjBw5EsOGDUNwcLAmaTE3N8eyZcsQFhaG0NBQREZGIj09HTY2NnBycsJXX32Fjh07au3r+++/R7Vq1bBp0yZs2rQJ5cqVQ6tWrTBmzBj4+fm91XL4vr6+2LlzJ3799VccO3YMp0+fhrm5Oezt7eHj46NVtm3atCnu37+PU6dOISIiAmlpabC3t0fjxo0xYMAAzRTC0qVL4+uvv8aJEycQGxurWYzrww8/xPfff1+obxAWFhZYtWoVVq1ahV27dmH9+vUwNTWFq6srJk6ciA4dOsh+ru+CpaUlli1bhmPHjiE0NBSxsbGIjo6GJEmwt7dHo0aNNImJWlF4rjNnzkT58uWxe/du/Pbbb/jggw8QFBSEwYMHY8+ePVq3lfvZ1MXZ2Rnbtm3DsmXLEBUVhZMnT6Js2bJo2rQphg4d+lYzZ0SZNWsWnJ2dER4ejg0bNsDW1hb+/v4YOXIkRo4cmev2pqamWLJkCebPn4+9e/fi+fPnkCQJDRo0eOukZd26dTh48CD8/f1zrV7cp08fREdH48CBA1i9erXsk9m9jXbt2mHOnDlITU1F69at8zzvm6OjIzZs2ID58+fj1KlTOHr0KKpVq4Zp06ahYcOG/zppqV69OjZv3owFCxYgOjoaJ06cgKurK5YvX47ExMRcSYulpSXWrVuH+fPn48SJEzh16hScnfUCOwIAACAASURBVJ3xxRdfICgoSGc8SqUSc+bMwapVq7BhwwZkZGTA1NQ036QFeFUwqFWrFn777TeEhoYiOzsblStXxpgxYzBw4MC3nnVWkI4dOyI7OxuxsbH4+++/8fLlSzg4OKBDhw4YPHhwruUvpk+fjmbNmmHz5s04fvw4UlNTUa5cOc2SBW8uGzBlyhSYmZkhOjoakZGRmtOb5Je0KCRJxnq+71ivXr1w/vx5nD59Wm+rzZZ0t2/fRps2bdC+ffs8S5hERERFkWFXECqEFy9e6Gzc2b59O2JjY9G4cWMmLG/h0aNHuZq9Xrx4gdmzZwOAUfQAEBERvU74WZ7v3buHrl27olGjRqhcuTJycnJw6dIlnDlzBlZWVvn20VDe1qxZg927d8PLywt2dnZ4/PgxoqOj8eDBA/j5+fFM0kREJVRKSgomT56Ma9euQaFQYPbs2ahatSpGjx6NhIQEVKpUCcHBwf96NXlDED489OzZM8ydOxenTp3Co0ePkJWVhQoVKsDX1xfDhg17q94LenXmzJUrV+LKlStITk6GmZkZqlSpgg4dOqB///4GGwMlIqKi7dtvv4WnpycCAgKQmZmJly9fYsWKFShXrhw+++wzhISE4NmzZxg3bpzoUHMRnrQQERHRu5GamorOnTsjIiJCaxZpmzZtsG7dOtjb2yMxMRFBQUGaFXCLEuHDQ0RERPRuxMfHw9bWFhMmTMCVK1dQu3ZtTJo0CUlJSZpTBdjZ2clakf5dYtJSxEyqEig6BKJ/LfzlnYJvRGQEYh8cN9i+sx7fLPhGMplXqJbv9dnZ2bh06RKmTJmCevXqYebMmQgJCdG6jUKheOtTQxia8NlDRERE9G44OjrC0dFRc2qWTz75BJcuXUL58uU1K3MnJibC1tZWZJh5YtJCREQkgipH/5cC2NnZwdHRETdvvqryREdHo3r16vD398eOHTsAADt27ECLFi0M+tTfFoeHiIiISpApU6bg66+/RlZWFpydnTFnzhyoVCqMGjUKW7duRcWKFbXOgVeUcPZQEcOeFioO2NNCxYVBe1oeXtX7Ps0dXPS+z6KEw0NERERkFDg8REREJMIbp1qhgjFpISIiEkCSmLTIxeEhIiIiMgqstBAREYnA4SHZWGkhIiIio8BKCxERkQjsaZGNSQsREZEIhVjBlrRxeIiIiIiMAistREREInB4SDZWWoiIiMgosNJCREQkAqc8y8akhYiISACuiCsfh4eIiIjIKLDSQkREJAKHh2RjpYWIiIiMAistREREIrCnRTZWWoiIiMgosNJCREQkApfxl41JCxERkQgcHpKNw0NERERkFFhpISIiEoFTnmVjpYWIiIiMAistREREIrCnRTYmLURERCJweEg2Dg8RERGRUWClhYiISABJ4jotcrHSQkREREaBlRYiIiIR2IgrG5MWIiIiEdiIKxuHh4iIiMgosNJCREQkAoeHZGOlhYiIiIwCKy1EREQiqDjlWS4mLURERCJweEg2Dg8RERGRUWClhYiISAROeZaNlRYiIiIyCqy0EBERicCeFtlYaSEiIiKjwEoLERGRCOxpkY1JCxERkQhMWmTj8BAREREZBVZaiIiIBJAkrogrFystREREZBRYaSEiIhKBPS2yMWkhIiISgeu0yMbhISIiIjIKrLQQERGJwOEh2VhpISIiIqPASgsREZEI7GmRjUkLERGRCBweko3DQ0RERGQUWGkhIiISgcNDsrHSQkREREaBlRYiIiIR2NMiGystREREZBRYaSEiIhKBlRbZmLQQERGJwEZc2Tg8REREREaBlRYiIiIRODwkGystREREZBRYaSEiIhKBPS2yMWkhIiISgcNDsnF4iIiIiIwCKy1EREQicHhINlZaiIiIyCiw0kJERCQCe1pkY9JCREQkApMW2Tg8REREREaBlRYiIiIRJEnIw/r7+6Ns2bIwMTGBqakptm/fjuTkZIwePRoJCQmoVKkSgoODYW1tLSS+/LDSQkREVMKsWbMGYWFh2L59OwAgJCQEvr6+2L9/P3x9fRESEiI4Qt2YtBAREYmgUun/8pYiIiLQpUsXAECXLl1w8OBBfT1LvWLSQkREVMIMHjwY3bp1w+bNmwEASUlJsLe3BwDY2dkhKSlJZHh5Yk8LERGRCIJmD23cuBEODg5ISkrCwIEDUa1aNa3rFQoFFAqFkNgKwkoLERGRCJJK/5dCcHBwAACUL18erVq1wvnz51G+fHkkJiYCABITE2Fra2uwp/1vMGkhIiIqIdLT05GWlqb5//Hjx/HRRx/B398fO3bsAADs2LEDLVq0EBlmnjg8REREJIKA4aGkpCSMGDECAJCTk4MOHTrAz88PderUwahRo7B161ZUrFgRwcHB7zy2wmDSQkREVEI4Oztj586dubbb2NhgzZo1AiKSh0kLERGRCIIWlzNmTFqIiIhE4LmHZGMjLhERERkFVlqIiIhEYKVFNlZaiIiIyCiw0kJERCRCIReDo/9h0kJERCSApOLsIbk4PERERERGgZUWIiIiEdiIKxsrLURERGQUWGkhIiISgY24srHSQkREREaBlRYiIiIROHtINiYtREREIrARVzYODxEREZFRYKWFiIhIBFZaZGOlhYiIiIwCKy1EREQiSGzElYtJCxERkQgcHpKNw0NERERkFFhpoWLN+gNbdF8wDJYVrCFJwKmNhxC9ai8+mRAI15YeyMnMxpO7D7Ft3E94mZIuOlyiPE1dOAF+rRrjyeOnCPg4CAAw6rsR8GvVGFlZWYi/nYCpo2YjLSVNcKRUaFynRTZWWqhYU2WrsGfmBixq9Q1WdP0OPkGtYFejEq4fu4AfW3+DxW3H4/Gt+2g2vJPoUIny9cfmcIzoPUZrW8yRUwj4OAg9/fvjzs04DBoZJCg6oneDSQsVa6mPknHv4m0AQObzl3h0IwFWjja4fvQCVDmvxpPjYq/DyrG8wCiJCnY25i88S07R2hZz5CRycnIAABfOXITDB/YiQqO3Jan0fynmmLRQiVHOqQI+qFUF8eduaG1vEPAxrkWeExQVkX507t0exw9Fiw6D5FBJ+r8Uc+xp0SN3d3coFIo8rz979uw7jIZeZ1GmFAKXj8bu6euQkfZCs/3jEZ2hysnBXzuOC4yO6N8Z/FU/5GTnIHzbftGhEBkUkxY9io2NBQAEBwfDzs4OnTt3BgDs3LkTjx49EhlaiWZiZorAFaPx147juLTvlGa7e3c/uLTwwK+BswRGR/TvdOzZDn6tGuPzgJGiQyGZJE55lo3DQwZw6NAh9OnTB5aWlrC0tERgYCAiIiJEh1VidfvvZ0i8noDjK8M12z5qVhd+n3fAuiHzkPUyU2B0RG+vUXNvDBgRiFH9v8XLFxmiwyEyOFZaDKBMmTLYuXMn2rdvD4VCgV27dqFMmTKiwyqRKnu6wP3Tpnhw+S6+CJ8NANg/dws6fN8PphbmGLR+AoBXzbhhk34VGSpRvuYs/x4NGrmjnG057D0bihU/rMTAkUGwsDDH8s3BAF4148769gfBkVKhlYAeFH1TSBLXEda3+Ph4zJo1C2fPnoVCoYCHhwcmTpwIJyenAu87qUrgO4iQyLDCX94RHQKRXsQ+MFy/2/NZ/fS+z7KT1up9n0UJKy0G4OTkhOXLl4sOg4iIirISMEVZ39jTYgC3bt1C//790aFDBwDAlStXsGzZMsFRERFRkcIpz7IxaTGAKVOmYOzYsTAze1XIcnV1RXh4eAH3IiIiovxweMgAXrx4gbp162ptMzU1FRQNEREVSZzyLBsrLQZgY2ODu3fvahaa27t3L+zs7ARHRUREZNxYaTGAqVOnYsqUKbh58yaaNm0KJycnzJs3T3RYRERUlJSAHhR9Y9JiIKtXr0Z6ejpUKhUsLS0RFxcnOiQiIipKOHtINg4PGcDIka+W0y5TpgwsLS0BAF999ZXIkIiIiIweKy16dOPGDVy/fh2pqanYv/9/Jy5LS0tDRgaX2CYiotdweEg2Ji16dOvWLURGRiI1NRWHDx/WbC9btixmzJghMDIiIiLjx6RFj1q2bImWLVsiNjYW7u7uosMhIqIijGd5lo89LQawadMmpKSkaH5+9uwZJkyYIDAiIiIqcrgirmxMWgzg6tWrsLKy0vxsbW2Ny5cvC4yIiIjI+HF4yABUKhWePXsGa2trAEBycjJycnIER0VEREVKCaiM6BuTFgMYNGgQevbsiU8++QTAqxVxhw4dKjgqIiIi48akxQC6dOkCNzc3xMTEAACWLFmCGjVqCI6KiIiKFC4uJxt7WgwkOTkZpUuXRt++fWFra8sVcYmIiP4lJi0GsGTJEvzyyy8ICQkBAGRlZWHcuHGCoyIioiKFs4dkY9JiAAcOHMDy5ctRunRpAICDgwOeP38uOCoiIipKJJWk90txx6TFAMzNzaFQKKBQKAAA6enpgiMiIiIyfmzENYC2bdviu+++Q0pKCrZs2YJt27ahR48eosMiIqKipARURvSNSYsBDB48GMePH0fZsmVx69YtjBw5Eo0bNxYdFhERkVFj0mIgjRs3ZqJCRER547mHZGPSoke9e/fGxo0b4e7urulneV25cuUwePBg9OnTR0B0RERUpHB4SDYmLXq0ceNGAEBsbKzO658+fYpevXoxaSEiInoLnD1kIKdPn8a2bdsAAE+ePEFcXBxsbGywbt06wZEREVGRwHVaZGPSYgD5LS5nb28vMjQiIiKjxaTFALi4HBERFUSSJL1fijv2tBgAF5cjIqIClYDhHH1j0mIAuhaXCwgIEB0WERGRUWPSYgBcXI6IiArESotsTFoM5PXF5VQqFXbu3IlOnToJjoqIiMh4MWnRo7S0NGzYsAEPHz6Ev78/GjdujA0bNuDXX3+Fi4sLkxYiItIoCWdl1jcmLXo0btw4WFtbo379+vj999/x008/QZIkLF26FDVr1hQdHhERkVFj0qJH8fHxWL58OQAgICAATZo0QWRkJEqVKiU4MiIiKnJYaZGNSYsemZn97+U0NTWFo6MjExYiItKN50uUjUmLHl25cgUeHh4AXi0alJGRAQ8PD0iSBIVCgbNnzwqOkIiIyHgxadGjy5cviw6BiIiMBBtx5eMy/kRERGQUWGkhIiISgZUW2Zi0EBERicBGXNk4PERERFTC5OTkoEuXLvj8888BAHFxcQgICECrVq0watQoZGZmCo5QNyYtREREAkgqSe+Xwlq7di2qV6+u+XnevHkYMGAADhw4ACsrK2zdutUQT/lfY9JCRERUgjx48ACRkZHo3r07gFdLdMTExKBNmzYAgK5duyIiIkJkiHliTwsREZEIgnpaZs+ejXHjxuH58+cAgKdPn8LKykqzQKqjoyMePnwoJrgCsNJCREQkgIjhocOHD8PW1hZubm7v4BnqHystREREJcTZs2dx6NAhREVFISMjA2lpaZg1axZSUlKQnZ0NMzMzPHjwAA4ODqJD1YmVFiIiIhFUBrgUYOzYsYiKisKhQ4ewYMEC+Pj4YP78+fD29sa+ffsAAKGhofD399fjE9UfJi1EREQl3Lhx47Bq1Sq0atUKycnJCAgIEB2SThweIiIiEkASvLict7c3vL29AQDOzs5Fdprz65i0EBERicAVcWXj8BAREREZBVZaiIiIBBA9PGSMWGkhIiIio8BKCxERkQistMjGSgsREREZBVZaiIiIBGBPi3xMWoiIiARg0iIfh4eIiIjIKLDSQkREJAArLfKx0kJERERGgZUWIiIiESSF6AiMDpMWIiIiATg8JB+Hh4iIiMgosNJCREQkgKTi8JBcrLQQERGRUWClhYiISAD2tMjHpIWIiEgAibOHZOPwEBERERkFVlqIiIgE4PCQfKy0EBERkVFgpYWIiEgATnmWj5UWIiIiMgqstBAREQkgSaIjMD5MWoiIiATg8JB8HB4iIiIio8BKCxERkQCstMjHSgsREREZBSYtebh79y4yMzMBANHR0Vi5ciVSUlIER0VERMWFJOn/UtwxacnDyJEjAQAJCQmYOHEirl+/jvHjxwuOioiIigtJpdD7pbhj0pIPCwsLREZGonfv3pgzZw7i4+NFh0RERFRiMWnJQ2ZmJp48eYLDhw/Dx8dHdDhERFTMSJJC75fijklLHvr27YsWLVrA3NwcdevWRXx8PCwtLUWHRUREVGIpJKkktO78ezk5OcjKysJ7771n0MeZVCXQoPsnehfCX94RHQKRXsQ+OG6wfV+v1Ubv+6xxaZ/e91mUsNKSh7179yItLQ0AsGTJEgwfPhy3bt0SHBURERUXKkmh90txx6QlD0uXLoWlpSXOnz+Pw4cPo23btpg2bZrosIiIiEosJi15MDN7tVjwn3/+iR49eqBLly7IyMgQHBURERUXbMSVj0lLPvbt24fw8HA0atQIAJCVlSU4IiIiopKL5x7Kw+TJkxESEoIuXbrA2dkZt27dgqenp+iwiIiomCgJi8HpG2cPFTGcPUTFAWcPUXFhyNlDV5Tt9L5P12vhet9nUcJKSx5ycnIQGhqKK1euaPWyzJgxQ2BURERUXLBkIB97WvIwdepUREdH48CBA3B0dMSZM2dgYsKXi4iI9IPnHpKPf4Xz8Ndff2HevHmwsrLCiBEjsHHjRty8eVN0WERERCUWh4fyYGFhAYVCAVNTU7x8+RLW1tZISkoSHRYRERUTJWExOH1j0pKHcuXKISUlBY0bN8bnn38OGxsb2NnZiQ6LiIioxGLSkofly5fD3NwcY8aMwY4dO5Camopu3bqJDouIiIqJkrAYnL4xacmDhYUFAMDU1BSffvqp4GiIiKi44ewh+Zi0vKFnz55QKPLOfjdt2vQOoyEiIiI1Ji1vGDVqlOgQiIioBGAjrnxMWt7g6+srOgQiIiLSgeu05KF3795ITk7W/Pz06VP07dtXYERERFSc8CzP8rHSkof09HSUK1dO87ONjQ3S0tIERkRERMUJG3HlY6UlDyqVCi9evND8nJ6ejuzsbIERERERlWystOShXbt2GDJkCAIDX511+bfffkP79u0FR0VERMUFG3HlY9KSh2HDhqFChQrYs2cPAKBr167o3r27wR/3v/eOGPwxiAztxb2jokMgomKISUs+AgICEBAQIDoMIiIqhkpC46y+saeFiIiIjAIrLURERAKwp0U+Ji1EREQCcMazfBweysfJkyexceNGAEBSUhLu3r0rOCIiIqKSi0lLHlauXIkFCxZg1apVAICMjAyMHz9ecFRERFRcqCSF3i/FHZOWPISFhWHdunUoU6YMAKBixYpITU0VHBUREVHJxZ6WPLz33nswNzfX2qZQFP8sloiI3g1OeZaPSUseHB0dce7cOSgUCkiShJ9//hnVq1cXHRYRERUTKtEBGCEmLXmYNGkSxo0bh3/++Qf16tVDvXr1sHDhQtFhERERlVhMWvLg4OCAtWvXIi0tDZIk4f333xcdEhERFSMSODwkF5OWPBw7dkzn9iZNmrzjSIiIiAhg0pKnZcuWaf6fkZGBa9euoWbNmkxaiIhIL1RcXU42Ji15+O2337R+vnr1KtasWSMoGiIiKm5UHB6Sjeu0FJKLiwsuXrwoOgwiIqISi5WWPLze06JSqXDhwgWYmpoKjIiIiIoTEY24GRkZ6NOnDzIzM5GTk4M2bdpg5MiRiIuLw5gxY5CcnIzatWtj7ty5sLCweOfxFYRJSx5e72kxNTVF5cqVERwcLDAiIiKif8fCwgJr1qxB2bJlkZWVhcDAQPj5+WHVqlUYMGAA2rdvj++++w5bt25FYGCg6HBzYdKig0qlwtChQ+Hn5yc6FCIiKqZELC6nUChQtmxZAEB2djays7OhUCgQExOD+fPnAwC6du2KJUuWFMmkhT0tOpiYmGDBggWiwyAiItK7nJwcdO7cGY0aNUKjRo3g7OwMKysrmJm9qmM4Ojri4cOHgqPUjUlLHlxcXPD333+LDoOIiIopCQq9XwrD1NQUYWFhOHLkCM6fP4+bN28a+JnqD4eH8nDt2jX07NkT1apV05TSAGDTpk0CoyIiouJC9LmHrKys4O3tjXPnziElJQXZ2dkwMzPDgwcP4ODgIDg63Zi05OGbb74RHQIREZFePXnyBGZmZrCyssLLly/x559/4v/+7//g7e2Nffv2oX379ggNDYW/v7/oUHVi0vKGiRMnYvbs2fD19RUdChERFWMiKi2JiYkYP348cnJyIEkSPvnkEzRv3hw1atTA6NGjERwcjJo1ayIgIEBAdAVj0vKGy5cviw6BiIjIIFxdXbFjx45c252dnbF161YBEcnDpIWIiEgAnuVZPiYtb7h27ZrOoSFJkqBQKBAdHS0gKiIiKm5UzFlkY9LyhipVqiAkJER0GERERPQGJi1vsLCwQKVKlUSHQURExRzP8iwfF5d7g7m5uegQiIiISAdWWt6wZcsW0SEQEVEJIIkOwAgxaSEiIhJA9Iq4xojDQ0RERGQUWGkhIiISQKVgI65crLQQERGRUWClhYiISAA24srHSgsREREZBVZaiIiIBODsIfmYtBAREQnAcw/Jx+EhIiIiMgqstBAREQnAcw/Jx0oLERERGQVWWoiIiATglGf5mLQQEREJwEZc+Tg8REREREaBlRYiIiIBuE6LfKy0EBERkVFgpYWIiEgANuLKx6SFiIhIADbiysfhISIiIjIKrLQQEREJwEZc+VhpISIiIqPASgsREZEArLTIx0oLERERGQVWWoiIiASQOHtINiYtREREAnB4SD4ODxEREZFRYKWFiIhIAFZa5GOlhYiIiIwCKy1EREQC8NxD8jFpISIiEoDnHpKPw0NERERkFFhpISIiEoCNuPKx0kJERERGgZUWIiIiAVhpkY9JCxERkQCcPSQfh4eIiIjIKLDSQkREJACnPMvHSgsREREZBVZaiIiIBGAjrnystBAREZFRYKWFiIhIAM4eko9JCxERkQAqpi2ycXiIiIiIjAIrLURERAKwEVc+VlqIiIjIKLDSQkREJAA7WuRj0kJERCQAh4fk4/AQERERGQVWWoiIiATguYfkY6WFiIiIjAIrLURERAJwcTn5mLQQEREJwJRFPg4PERERkVFgpYWIiEgATnmWj5UWIiIiMgqstBAREQnARlz5mLQQEREJwJRFPg4PERERkVFgpYWIiEgANuLKx0oLERERGQVWWoiIiARgI658rLQQERGRUWClhYiISADWWeRj0kJERCQAG3HlY9JCRERUQty/fx/ffPMNkpKSoFAo0KNHD/Tv3x/JyckYPXo0EhISUKlSJQQHB8Pa2lp0uLmwp4WIiEgAyQD/CmJqaorx48cjPDwcmzdvxm+//Ybr168jJCQEvr6+2L9/P3x9fRESEvIOXgH5mLQQERGVEPb29qhduzYAwNLSEtWqVcPDhw8RERGBLl26AAC6dOmCgwcPigwzTxweIiIiEkB0T0t8fDwuX76MevXqISkpCfb29gAAOzs7JCUlCY5ONyYtREREAohcp+X58+cYOXIkJk6cCEtLS63rFAoFFAqFoMjyx+EhIiKiEiQrKwsjR45Ex44d0bp1awBA+fLlkZiYCABITEyEra2tyBDzxKSFiIhIAMkAlwIfU5IwadIkVKtWDQMHDtRs9/f3x44dOwAAO3bsQIsWLfTwDPWPw0NEREQlxJkzZxAWFgalUonOnTsDAMaMGYPPPvsMo0aNwtatW1GxYkUEBwcLjlQ3Ji1EREQCiOhp8fT0xNWrV3Vet2bNmnccjXxMWqhEuX4tBqlpacjJUSE7Oxs+vu1Eh0RUKCmpaZj6n2Bcv3kHUCgwY+JoVP3QCWOnzMG9Bw9R0dEB82dMgLXV+6JDpUISPXvIGDFpoRKnZasAJCU9FR0GkSz/CV6Bxt6eWDhrMrKysvDiZQZ+XrsZPp71MSSoB35ZtwUr12/BmOGDRYdKZDBsxCUiKuJS057jzF9/49OObQAA5ubmsHrfEoePRqNz25YAgM5tW+JQVLTIMEkmESviGjsmLVSiSJKEPeEbcSJmD4YM7iM6HKJCSbj3ADblrDF51gJ0HzAC380JRvqLl0h6mgy7Cq+mplYob4Okp8mCIyUyLA4P6Ym7u3u+i/GcPXv2HUZDeWnWvCvu3XsAO7vy2LtnE65evY6jx06IDosoX9k5Obh87Tomjh6GurVdMSd4BVau26J1m6K8IBjpxp4W+Zi06ElsbCwAIDg4GHZ2dpqpZDt37sSjR49EhkavuXfvAQDg0aMkhIXtQcOG9Zm0UJHnaF8BDnYVULe2KwCg9cdN8Mv6LShvUw6PHj+BXQVbPHr8BLblit5ZeYn0icNDenbo0CH06dMHlpaWsLS0RGBgICIiIkSHRQDKlCkNS8uymv+3atkMFy/qnvpHVJRUKG8LR3s73LoTDwCIOXMO1at8iI+b+CBsz6sT24XtOYjmTX1FhkkysadFPlZa9KxMmTLYuXMn2rdvD4VCgV27dqFMmTKiwyIADg522Pr7SgCAmZkpNm3agX37I8UGRVRIE0cPw7fT5iIrOwvOFT/AjImjIUkSxk6Zje279qGioz3mz5goOkySgcND8ikkSSr+qdk7FB8fj1mzZuHs2bNQKBTw8PDAxIkT4eTkVKj7m1lUMnCERIb34t5R0SEQ6YV5hWoG23f/Kp/qfZ9rbm/T+z6LElZa9MzJyQnLly8XHQYRERVxKtYMZGNPi57dunUL/fv3R4cOHQAAV65cwbJlywRHRUREZPyYtOjZlClTMHbsWJiZvSpiubq6Ijw8XHBURERU1Ig4y7Ox4/CQnr148QJ169bV2mZqaiooGiIiKqpEnDDR2LHSomc2Nja4e/euZpGnvXv3ws7OTnBURERExo+VFj2bOnUqpkyZgps3b6Jp06ZwcnLCvHnzRIdFRERFTElYV0XfmLToWcWKFbF69Wqkp6dDpVLB0tJSdEhERETFApMWPWvRogWaNm2Kdu3awcfHwV+43wAAEq9JREFUR3Q4RERURHFxOfnY06Jne/bsga+vLzZs2IAWLVpg+vTpOH36tOiwiIioiFFB0vuluGPSomelS5dGu3btsGTJEoSGhiItLQ1BQUGiwyIiIjJ6HB4ygJMnTyI8PBxHjx6Fm5sbgoODRYdERERFDBtx5WPSomf+/v6oWbMm2rZti2+++YYnSyQiItITJi16tnPnTs4YIiKiArERVz72tOhZWloaRowYAV9fX/j6+uLLL7/EgwcPRIdFRERk9Ji06NmECRPg7++Po0eP4ujRo2jevDkmTJggOiwiIipiJEnS+6W4Y9KiZ0+ePMGnn34KMzMzmJmZoVu3bnjy5InosIiIqIjhlGf5mLToWbly5RAWFoacnBzk5OQgLCwM5cqVEx0WERGR0WPSomezZ8/Gnj170LhxYzRp0gT79u3DnDlzRIdFRERFjMoAl+KOs4f0KCcnBwcOHMCKFStEh0JERFTssNKiR6ampti1a5foMIiIyAhIBvhX3LHSomceHh6YPn062rVrh9KlS2u2165dW2BURERU1JSExll9Y9KiZ5cvXwYALFq0SLNNoVBg7dq1okIiIiIqFpi06Nm6detEh0BEREagJKyrom9MWvTs6dOnWLp0Kc6cOQOFQgEPDw+MGDECNjY2okMjIiIyamzE1bMxY8bAxsYGP/74IxYtWgRbW1uMHj1adFhERFTEcMqzfKy06NmjR48wYsQIzc/Dhw/Hnj17BEZERERFUUmY7aNvrLToWePGjbF7926oVCqoVCqEh4ejSZMmosMiIiIyegqJnUB65e7ujhcvXsDE5FU+qFKpNFOfFQoFzp49m+/9zSwqGTxGIkN7ce+o6BCI9MK8QjWD7bulcxu97/Ng3D6977Mo4fCQnsXGxooOgYiIqFhi0mIA+/fv18we8vT0RMuWLUWHRERERQwHOuRjT4ueff/999i0aROUSiU++ugjbNy4EdOmTRMdFhERkdFjpUXPYmJisGfPHigUCgBA165d0b59e8FRERFRUcNl/OVjpUXPKleujHv37ml+vn//PipXriwwIiIiKop4wkT5WGnRs+fPn6Ndu3aoW7cuAODChQuoU6cOhg4dCgBYsWKFyPCIiIiMFpMWPRs5cqTm/5Ik4fTp0wgPD8eXX34pMCoiIipqVGzElY1Ji555eXnh0qVL+OOPP7Bv3z5UqlQJvXr1gpeXl+jQiIiIjBqTFj25desWdu/ejV27dsHGxgbt2rWDJEk86zMREenEOot8TFr0pG3btvD09MRPP/2kabxdvXq12KCIiKjI4uwh+Th7SE+WLFkCOzs79OvXD5MnT0Z0dDQXDiIiItIjnntIz9LT0xEREYHdu3cjJiYGnTt3RqtWrQp90kSee4iKA557iIoLQ557yLdSc73vMzrhsN73WZSw0qJnZcqUQceOHbFixQocOXIEtWrVws8//yw6LCIiIqPHSksRw0oLFQestFBxYchKi0/Fj/W+z5h7kXrfZ1HCRlwiIvr/9u4+psr6/+P46+BNKpiK2R+l/vra0nR4Q0bIvKuTMoXDjTaLljixhWgLTd0M7xJvZk2Xm2xWpOkszOYNHgXRhpg1CJxGWTZ13pS4DA3xZniDh/P5/cE4k5TsGHZx6fPh3PTic12fz3X8bL7P+/25rg8swEJc/1EeAgAAtkCmBQAACzwIewU1NjItAADAFsi0AABgAZ6D8R+ZFgAAYAtkWgAAsABPD/mPoAUAAAtQHvIf5SEAAGALZFoAALAA5SH/kWkBAAC2QKYFAAAL8HI5/xG0AABgAS8Lcf1GeQgAANgCmRYAACxAech/ZFoAAIAtkGkBAMACrGnxH0ELAAAWoDzkP8pDAADAFsi0AABgAcpD/iPTAgDAAyItLU0RERFyuVy+YxcuXFBSUpIiIyOVlJSkixcvWjjCv0fQAgCABcw9+HUno0eP1qpVq+ody8zMVEREhL766itFREQoMzPzXt3yv0bQAgDAAyIsLEzt2rWrd2z37t2Kj4+XJMXHxys/P9+Kof0jrGkBAMACTWVNS0VFhR599FFJUqdOnVRRUWHxiBpG0AIAgAWa4iPPDodDDofD6mE0iPIQAAAPsI4dO+rs2bOSpLNnzyo4ONjiETWMoAUAAAsY423033fD6XRq69atkqStW7fqxRdfbMzbbFQELQAAPCCmTZumhIQEnTx5UkOGDNHGjRuVnJyswsJCRUZGqqioSMnJyVYPs0EOY5rISiBIkpq3fNzqIQD/2tXfv7V6CECjaPFIt3t27f/r2KfRr/lbxcFGv2ZTwkJcAAAsQM7Af5SHAACALZBpAQDAAt4m+MhzU0emBQAA2AKZFgAALMCaFv8RtAAAYIGm8hp/O6E8BAAAbIFMCwAAFmiKew81dWRaAACALZBpAQDAAizE9R+ZFgAAYAtkWgAAsAAvl/MfQQsAABagPOQ/ykMAAMAWyLQAAGABXi7nPzItAADAFsi0AABgAda0+I+gBQAAC/D0kP8oDwEAAFsg0wIAgAUoD/mPTAsAALAFMi0AAFiAR579R9ACAIAFDAtx/UZ5CAAA2AKZFgAALEB5yH9kWgAAgC2QaQEAwAI88uw/Mi0AAMAWyLQAAGABnh7yH0ELAAAWoDzkP8pDAADAFsi0AABgATIt/iPTAgAAbIFMCwAAFiDP4j+HIT8FAABsgPIQAACwBYIWAABgCwQtAADAFghaAACALRC0AAAAWyBoAQAAtkDQgiatZ8+eiouLk8vlUkpKii5dunTHcxISEu6qr/z8fB07dszv80JDQ++qPzw48vPz1aNHDx0/ftzqodwR8xlNGUELmrRWrVrJ7XYrJydH7dq1U1ZW1h3P2bBhw131dbdBC3AnOTk56t+/v3Jzcxvleh6Pp1GuA9gNb8SFbfTr109Hjhzx/X3VqlXKy8tTdXW1hg8frtTUVEm13xRLS0v/ts3WrVu1evVqORwO9ejRQ6+++qoKCgq0b98+ffjhh8rIyJAkpaenq7KyUq1atdLChQv15JNPqqysTDNmzNCVK1fkdDr/408BdlNVVaUDBw5o3bp1SklJUWpqqkpKSrRixQoFBgbqt99+U3h4uObPn6+AgACFhoZqzJgxKiws1COPPKLly5crODhYiYmJevrpp3XgwAG5XC5FRkZq1qxZqqysVHBwsJYsWaK2bdsqNjZWu3fvVkBAgK5cuaKRI0cqPz9fZ86cYT7D/gzQhPXr188YY4zH4zFvvfWW2bt3rzHGmG+//dbMmTPHeL1eU1NTY5KTk82+ffvqndNQm6NHj5rIyEhTUVFhjDGmsrLSGGPMzJkzTV5enq/vcePGmZMnTxpjjPnhhx9MYmKiMcaYiRMnmuzsbGOMMZ9//rmvP+B23G63SUtLM8YY88orr5iffvrJFBcXm5CQEHPq1Cnj8XjM+PHjfXOve/fuxu12G2OMycjIMOnp6cYYY8aOHWveffdd33UnTpxotmzZYowxZuPGjWbSpEnGGGNSUlLMd999Z4wxJjc318yaNcsYw3zG/YHyEJq0a9euKS4uTgMHDlRFRYUGDhwoSSosLFRhYaHi4+M1atQonThxQr/++mu9cxtqU1xcrBEjRig4OFiS1L59+1v6raqqUmlpqaZMmaK4uDjNmzdP586dkySVlpYqOjpakhQXF3cP7x73g9zcXN98iYqK8pWI+vTpoy5duqhZs2aKjo7WgQMHJEkBAQGKioqSVDu/6o7XnV+ntLRULpfrlnZRUVHasWOHr++oqCjmM+4blIfQpNWtabl69apef/11ZWVlady4cTLGKDk5+W8X3TbU5rPPPrtjv8YYPfzww3K73bf9ucPh8O9G8EC6cOGCiouLdfToUTkcDtXU1MjhcGjo0KG3zKGG5tTNx1u3bn3HPp1Op5YvX64LFy7o0KFDGjBggK5evcp8xn2BTAtsoXXr1pozZ47WrFkjj8ejQYMGafPmzaqqqpIklZeXq6Kiot45DbUZMGCAdu7cqcrKSkm1/7FIUmBgoK9tUFCQOnfurLy8PEm1Qczhw4cl1a6Zqfu2vG3btnt857CzXbt2KS4uTnv27FFBQYH27t2rzp07a//+/Tp48KDKysrk9XqVl5en/v37S5K8Xq927dolSdq+fbvv+F/dPA+3b9+uZ599VlLtPA4JCdHixYv1/PPPq1mzZsxn3DcIWmAbvXr1Uo8ePZSTk6NBgwbJ5XIpISFBMTExSk1N9QUcdRpq89RTTyklJUWJiYmKjY3Ve++9J6k2rb569WrFx8fr1KlTWrp0qTZt2qTY2FhFR0crPz9fkjR79mytX79eMTExKi8v/88/B9hHTk6Ohg0bVu9YZGSkcnNz1bt3by1cuFAjR47U448/ruHDh0uS2rRpo4MHD8rlcqm4uFhvvvnmba89d+5cbdmyRTExMXK73Zo9e7bvZ1FRUdq2bVu9chLzGfcDhzHGWD0IoLFUVlZq9OjR2rNnj9VDARpUUlKiTz/9VB9//PEtP7v56TcA9ZFpwX2jvLxcCQkJmjBhgtVDAQDcA2RaAACALZBpAQAAtkDQAgAAbIGgBQAA2AJBC2BDTqdTI0aMUGxsrFwuV6NtxOd0OnX06FFJ0htvvKFTp079bfv8/HwdPHjwrvrasmWLby+ofyoxMZEnw4AHGG/EBWxqxYoV6t69u3755RclJCQoIiLCtzVBnZqaGjVr1uyurv/JJ5/csU1+fr5CQkLUp0+fu+oDAPxB0ALYXK9evRQYGKjTp0/r66+/1rZt23y7By9dulQdO3bUokWL9Pvvv+v69euKjo5WSkqKJGn//v1KT0+XJIWFhenmhwmdTqc++ugjde/eXeXl5Vq0aJFvfyeXy6VevXqpoKBARUVF2rhxo5KSkhQfH6/s7GytX79eNTU1CgoK0vz589WtWzdVV1dr0aJFKi4uVocOHdSzZ88G7+n48eNavHixb3+cCRMmaNSoUfXabN++XevWrdONGzckSTNnzlRERIS8Xq8WLFig4uJitWzZUm3atNGGDRtUUVGh6dOn+96cHBERoVmzZjXOPwKA/wRBC2BzxcXFun79up544gkdO3ZMP/74o9xut7p27SpJSkpK0uTJkxUWFqbq6mqNHz9evXv3VlhYmN5++20tW7ZM4eHh2rFjh7Kysm7bx4wZMzR06FBlZGRIks6fP6/g4GA5nU6FhIRo7NixkmqDoLy8PGVlZally5bau3evZs2apQ0bNujLL7/U6dOnlZubK4/Ho9dee02dO3e+pS+Px6PJkydr6tSpGjlypCT5tly4Wd0bjx0Oh06cOKHx48frm2++0eHDh1VSUqIdO3YoICBAFy9elFQb5HTt2lVr166VJN9xAPZB0ALYVGpqqh566CEFBQUpIyNDDz/8sCTpmWee8QUsV65c0b59+3T+/HnfeVVVVTp+/Lg6duyo1q1bKzw8XFLtq9/nzZt3Sz91OwSvWbPGd+yvZag6BQUFOnz4sMaMGSOpdo+bS5cuSap9C2x8fLxatGihFi1aKDY2Vt9///0t1zh58qQ8Ho8vYJGkDh063NKurKxM06dPV3l5uZo3b64///xT586dU5cuXeTxeDR79myFh4frhRdekCT17dtXa9eu1fvvv6/nnntOgwYN+ptPF0BTRNAC2FTdmpa/CgwM9P3Z6/XK4XBo06ZNatGiRb12dRvm3ezf7vZrjNFLL72kKVOm/Kvr/BPTpk3TO++8o2HDhsnr9apv3766fv26OnXqpNzcXJWUlKioqEjLli1Tdna2QkNDlZ2draKiIrndbmVmZuqLL7645+ME0Hh4egi4jwUFBal///7KzMz0HTtz5ozOnTunbt266dq1a9q/f78kaefOnb6syM0CAwMVGhrqK6tI8mVugoKCdPnyZd9xp9Mpt9utP/74Q1LtQuCff/5ZkjRgwAC53W55PB5du3ZNOTk5tx3z//73PzVv3ty3I7F0+/LQ5cuXfeWlzZs3q7q62je2q1evavDgwZoxY4batm2rsrIylZWVKSgoSNHR0UpLS9OhQ4fk9Xrv/CECaDLItAD3uWXLlmnJkiWKiYmRVBuELF68WJ06ddIHH3xQbyHuY4891uA10tPT5XK5FBAQIJfLpeTkZMXGxiotLU07d+70LcSdOnWqJk2apJqaGt24cUMjRoxQSEiIXn75ZR05ckRRUVHq0KGDevfu7VsUe7PmzZtr5cqVWrBggVauXCmHw6EJEyYoPj6+Xru0tDRNnjxZ7dq10+DBg9W+fXtJtUHZ3Llz5fF4VFNToyFDhqhfv37Kzs7W2rVrFRAQIK/Xq/T0dAUE8L0NsBP2HgIAALbA1wwAAGALBC0AAMAWCFoAAIAtELQAAABbIGgBAAC2QNACAABsgaAFAADYAkELAACwhf8HIYnbko9FwHMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    Rejected       0.81      0.65      0.72        34\n",
      "    Approved       0.83      0.92      0.88        65\n",
      "\n",
      "    accuracy                           0.83        99\n",
      "   macro avg       0.82      0.79      0.80        99\n",
      "weighted avg       0.83      0.83      0.82        99\n",
      "\n",
      "No Skill: ROC AUC=0.500\n",
      "Logistic Regression : ROC AUC=0.835\n"
     ]
    },
    {
     "data": {
      "image/png": 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WazKZuO22O3j99Ve5+OJBPPXU//DXvz5HSUkJXq+X66+fQIcOHXnkkanMmvUUJpOZvn0vICbmzFrhVIh/++333H337QBERkYxdeqfOXz4EC+9NBuTyYzVauX3v38Ep9PJo48+SHl5OYZhcO+9vzvjfI8//uSPJra1rGy7YHG+65trnFskMHYeKOCv73xLucdH/25J3HJlF+KiwgNdVp3x21ak/tIQtiJ1uVxERERgMplYuXIFK1euYObMc+/6FihOp5OoqCgAFi9eyPHjx3jggd+fcVxj3Hji8S9n4DW8TBs4pUZXc364I6KqNZ4bYxvWNbVh7TW0NnSXe3nuzS0MH9Cai1Lr58tzg9iKVKq2a9cOXnhhFmAQExPLo49ODXRJVdqw4QsWL16I1+shOTmFxx57MtAlBY3zXd9c49wi/uczDFZ/fZiIcAuX9m5BRLiFxyZeWKf7jAcThXgA9OnTj0WL3gh0GTUybNiVDBt2ZaDLEBGpVt7JMhZk7GDXoZM0j7cxqEcyVou5wQY4KMRFRCTE+QyDz745wpLPs3BXeOnXuTm3juiK1dLw524rxEVEJGSVuT28+G4mOw+eJNpm5baR3bm4u71B975/TCEuIiIhyxZuwWo107dTc24d2ZUmMcG15oe/KcRFRCSkHCss49u9xxl2YStMJhO/HdOL8LCGPfZdFYW4iIiEBMMwWPPtUd5avRd3uZdOLeNpmxxLRLgl0KUFjEJcRESC3vFCFws/2sH27AIiI6zckZ5KG/vZF59qTBTiIiIS1NZlHuWNlXtwlXvp3bEZt43sRkJs4xr7ropCXEREgprjRBkmE9w+qhuDe6U0yrHvqijERUQkqBiGwZY9x+jbqTlms4lfDm7P0Ata0jTu3FsyN0YN/054EREJGQXFbv76Tib/+953rPr6MABhVrMCvArqiYuISMAZhsH6bbm8vnIPZW4PPdolcEGXxECXFfQU4iIiElAFxW4WfbyTzKzjRIRbuHVkV9L6tNDYdw0oxEVEJKD2HD5JZtZxUtsmcPuobjSPjwx0SSFDIS4iIvWusMRNmNVMlC2MAd2SsIVb6dWhqXrf50kT20REpN4YhsGG7bk8Pncjr6/cA4DJZKJ3x2YK8J9BPXEREakXhaXlvPrxTrbsOUZ4mJn2KXEYhqHwrgWFuIiI+JVhGGzakcc/P91NSVkFXVs34fb0VJKaaOy7thTiIiLiV8cLXcxd9j0Wi4mbrujM0AtbYVbvu04oxEVExC/K3B4iI6w0bxLJ5PRUOraIIykhKtBlNSgKcRERqVNFznJe+2Q3+QVl/PHWC7FazAzqkRzoshokhbiIiNSZzTvzWPzJLoqdFXRqFY/T5SEuOjzQZTVYCnEREam1Ymc5//x0N5t25BFmNXPD0E4M798as1lj3/6kEBcRkVoxDIPn3tzKobwSOraIY3J6KinNogNdVqOgEBcRkZ/lh3u8TSYT117WgdzjTq4coN53fdKKbSIict627M5n2vxNFJWWA9C3U3NGXtxGAV7P1BMXEZEaKymr4I2Vu9mw3YHVYiLrSCH9tGVowCjERUSkRrbuPcaij3dSWFJOu+RY7khPpWViTKDLatQU4iL/4fK4ySl1kBJtx2aNqPZ4wzDwGl5cHneNjhcJZRkbsnl3zT4sZhPj0jow8uI2WMwakQ00hbgIpwL8ifUzcHrKMJvMxIfHnXNTBsMwKHCfBGDW5heZ0v9eBbk0aBd0SSQz6zgTR3SllXrfQcOvX6PWrl3LiBEjGD58OHPmzDnj9aNHjzJx4kTGjBnD1VdfzZo1a/xZjkiVckodOD1lAPgMH17De87jf/y6w5lHTqnDr/WJ1Denq4IFy3ewP6cIgJRm0Tx6y4UK8CDjt5641+tl+vTpLFiwALvdznXXXcfQoUPp1KlT5TEvv/wyV111FTfddBN79+7lrrvuYvXq1f4qSaRKKdF2zCYzPsOHPSqp2p61y+Nm1uYXcTjzsEclkRJtr8dqRfzru33HWfjRTgqK3VR4fNx1TY9AlyRV8FuIZ2Zm0rZtW1q3bg1Aeno6q1atOi3ETSYTJSUlABQXF5OUlOSvcqQROp8xbps1griwWMp9Fdzf764aHT+l/73nNYYuEuycLg9/e2sLn246iMVsYszg9owa1DbQZck5+C3EHQ4Hycn/XfDebreTmZl52jH33HMPd9xxB6+99hplZWUsWLDAX+VII/NzxrhPlhcCMHvLnBqNcdusEbSPb1OndYsEyqG8EmYv+ZYTRW7aJMUwOT2VNvbYQJcl1QjoxLaMjAyuvfZaJk+ezJYtW5gyZQrLli3DfI4ZjwkJUVitljqtIzFRH9S6EEztuOf4sdPGuA18WM1Vf9wrvKePcbvCi2ndrLnf6/ypYGrDUKU2/HmiY21EhFmZcGU7xg/rQphVM89ro74+h34LcbvdTm5ubuVjh8OB3X76uOGSJUuYO3cuAP369cPtdlNQUECzZs2qPG9BgbNO60xMjCU/v7hOz9kYBVs72jyxtRrjtpXX/+8TbG0YitSG52d79gnKXB76dzs1lPnk7f1pkdJEbVhLdf05PNcXAr+FeK9evcjOzubQoUPY7XYyMjJ4/vnnTzsmJSWFDRs2MHbsWLKysnC73TRt2tRfJUkjYrNGEB8eh9fw1vjSuMa4pbEoc3t45/MsPt9yhJjIMHp1bEZEmIWwOr7KKf7ntxC3Wq1MnTqVO++8E6/Xy7hx4+jcuTOzZ8+mZ8+eDBs2jEceeYTHH3+chQsXYjKZmDlz5jnHLUXOh8lkwmqy1jiQNcYtjcGO7BPMX76T40UuWiZGc0d6KhFhCu9Q5dcx8bS0NNLS0k577v7776/8d6dOnXjzzTf9WYKIiAAVHh9vrt7DZ98cwWwyMfoXbbn6F+019h3itGKbiEgjYLGYyDlWSovmp3rf7VPiAl2S1AGFuIhIA+Uu97Jt/wku7JqI2WTi7l/2JDJCY98NiUJcRKQB2nWwgPnLd3DspItHJ15Ip5bxxEWHB7osqWMKcRGRBsRd4eXdNVms2nwYTDDy4ja0tWu984ZKIS4i0kDsPnSS+ct3kFdQRnLTKO5IT6Vjy/hAlyV+pBAXEWkgvt6VT35BGSMuas21l3YgXLeONXgKcRGREHYor4SWidGYTSbGpnXgotQk9b4bEd0gKCISgsorvLy9ei9PLtjEmi1HAIgIsyjAGxn1xEVEQkzWkULmZewg94STpCaRtEzUxLXGSiEuIhIiKjxe3l+3n483HcQw4IoLWzEurSMR4Rr7bqwU4iIiIWLr3uN8tPEgiU1sTB6VStc2CYEuSQJMIS4iEsQqPD58PoOIcAv9uyYycURXftEjWb1vATSxTUQkaO3PKWL6wq94c/Ue4NTOfJf3a6kAl0rqiYuIBJkKj48P1+9n+YaD+AyDLm2a4DMMzNqqWX5CIS4iEkQO5BYzN+N7juSX0izOxuRR3Uht1zTQZUmQUoiLiASJwtJynl78NR6vjyH9WjJ+SEciI/RnWqqmT4eISID5fAZms4n46HCuG9KRlonR9FDvW2pAIS4iEiAer4+MDQfYcaCAKRP6YTabuHJA60CXJSFEIS4hw+Vxk1PqICXajs0aUe3xhmHgNby4PO4aHS9Snw46ipmfsYODeSUkxEZwrMhFUpPIQJclIUYhLiHB5XHzxPoZOD1lmE1m4sPjMJ1jpq5hGBS4TwIwa/OLTOl/r4JcgoLH62P5vw/w4ZfZeH0Gl/ZO4YahnYmy6c+xnD99aiQk5JQ6cHrKAPAZPryGF6up6o+v1/BW/tvhzCOn1EH7+DZ+r1OkOn97N5Nt+06QEBvBbSO70btjs0CXJCFMIS4hISXajtlkxmf4sEclVduzdnnczNr8Ig5nHvaoJFKi7fVYrUjVLu3dgvjocCYM60yULSzQ5UiIU4hLSLBZI4gPj8NreGt0adxmjWBK/3vPawxdxB8O55ew5PMs7hzdnZjIMAZ0S2JAt6RAlyUNhEJcQobJZMJqstY4kG3WCF1Cl4Dx+nx8vPEgS7/Yj8drsHlXHkP6tgx0WdLAKMRFROrY0WOlzMvYwf6cIuKjw7ltZDf6dm4e6LKkAVKIi4jUoQ3bc1mwfCcer49BPexMuKILMZEa+xb/UIiLiNSh1okxxEeHcdMVXejXJTHQ5UgDV6OtSEtLS9m5c6e/axERCTk+n8HHGw9yKK8EgFZJMTzz60EKcKkX1Yb42rVrGTVqFL/5zW8AyMzM5O677/Z7YSIiwS73hJOZ//yGtz/by9uf7a183mqpUf9IpNaq/aT97W9/4+233yYuLg6A3r17c+jQIb8XJiISrHw+g082HWTa/E3sPVLIRalJ3HV190CXJY1QjcbE7fbTF8oIC9MkDRFpnE4Uufj7B9vZe7iQmMgwfjW6O/1137cESLUhHhkZyYkTJyrXqd68eTOxsbF+L0xEJBhFhFs4drKM/t2SuOXKLsRFhQe6JGnEqg3xBx98kDvuuIPDhw8zadIk9u7dy0svvVQftYmIBIW8Aif5hS56tGtKtC2MaZMGEB+jVQAl8KoN8X79+rFw4UK+/vprDMPgggsuICEhoT5qExEJKJ9hsPrrwyxZk0WYxczMuwcRbQtTgEvQqHZi28yZM4mPj2fo0KEMGzaMhIQEZs6cWR+1iYgETN7JMp59fQuvr9xDmMXMLVd2JSpCS2tIcKn2E7lx48Yznvv3v//tl2IktLk8br9uOGIYBl7Di8vj1oYm4jc+w+DzLUd457Ms3BVe+nVuzq0juqr3LUGpyhBfsWIFK1as4OjRozz00EOVzxcXF2Oz2eqlOAkdrgoXT6yfgdNThtlkJj48rnIyZF0wDIMC90kAZm1+sUY7mYn8LAb8+3sHVouJW0d2Z2B3e51+lkXqUpUh3rp1awYNGsSWLVsYOHBg5fMxMTFccskl9VKchI5DRTk4PWUA+AwfXsOL1VR3lx69hrfy3w5nHjmlDu1QJnXGMAz25RTRsUU8ZrOJu0Z3x2IxkxCrL4oS3Kr8K9u9e3e6d+/OsGHDaNq0aX3WJCGodVwKZpMZn+HDHpVU5z1ll8fNrM0v4nDmYY9KIiXaXv0PidTAscIyFn60kx0HCvjjxP50aBFH8yaRgS5LpEaq7SrFx8ezZMkSdu7cidvtrnz+z3/+s18Lk7rnzzFrW5iNuLBYyn0V3N/vrro/vzWCKf3v9euYuzQuhmGw9tujvLV6L65yL707NlPPW0JOtSE+bdo0ysrK2Lx5M9dffz0ZGRkMGDCgPmqTOuTyuP06Zm02wcnyQgBmb5njlzFrmzVCl9ClTpwocrHgo51s33+CyAgrk0elckmvZI19S8ip9hazb7/9lueee464uDh++9vf8sYbb7Bv3776qE3qUE6p44wx67rk8Z05Zi0SrD7edJDt+0/Qs0NT/nzHRQzunaIAl5BUbU88PDwck8mExWLB5XIRHx/P8ePH66M2qUMp0Xa/jlnHNgnj4Y+f0Zi1BK2i0nJio8IwmUxce2kH2ifHMbCHZp5LaKs2xJs0aUJRURGXXHIJv/71r0lISCAxUfvkhhqbNYL48Di8htc/l7rDbBqzlqBkGAZffpfLG6v2cOPQTlzapwWREVYG9UwOdGkitVZtiL/88suEhYXx4IMPsnTpUoqKihg7dmx91CZ1zGQyYTVZ/RawGrOWYFNQ7GbRxzvJzDqOLdyC2axetzQsNbqcDmCxWCrDe82aNaSlpfm3MhGRn8kwDNZvy+WNlXtwuj10b5fA7Vel0ixeC1VJw3LOiW2ffvopixYtIjs7G4D169dz3XXXMWPGjBqdfO3atYwYMYLhw4czZ86csx6zfPlyRo0aRXp6+mkrw4mI/FyZWceZl7EDr2Fw64iuPHRDXwW4NEhV9sRnzJjBqlWr6NGjB2+99RZDhgxhyZIl3HPPPUyYMKHaE3u9XqZPn86CBQuw2+1cd911DB06lE6dOlUek52dzZw5c3jjjTc0YU5EasUwDDxeHwC9OzYjfVBb0vq00MIt0qBVGeJr165l6dKlxMTEkJ+fz9ChQ3n//ffp2LFjjU6cmZlJ27Ztad26NQDp6emsWrXqtBB/++23ufnmm4mPjwegWbNmtfldRKSRKixx8+qKXbRKjkRsbIYAACAASURBVOPaS9phMpkYl1azv1UioazKEI+MjCQmJgaAxMRE2rVrV+MAB3A4HCQn/3f2p91uJzMz87RjfrhMf+ONN+Lz+bjnnnu47LLLznnehIQorFZLjeuoicTE2Do9X7Cy/GdSj79+38bSjv6kNjw/hmGwdssR/vGvTIqdFXgNSGgajdVS7RIYcg76HNZefbVhlSFeUFDAW2+9Vfm4pKTktMc33HBDrd/c6/Vy4MABFi9eTG5uLrfccgsffvghcXFxVf5MQYGz1u/7Y4mJseTnF9fpOYOV12cA+OX3bUzt6C9qw/NTWFrO4hW7+GZ3PuFhZm4e3oXrr+zG8eMlgS4tpOlzWHt13Ybn+kJQZYgPGDCAzZs3Vz7u379/5WOTyVRtiNvtdnJzcysfOxwO7Hb7Gcf06dOHsLAwWrduTbt27cjOzqZ3797n/o1EpFFzujxMm7eRImcFXVo3YfKobiQlROkWMml0qgzxZ599tlYn7tWrF9nZ2Rw6dAi73U5GRgbPP//8acdcccUVZGRkMG7cOE6cOEF2dnblGLqISFWibFYu69uS2Kgwhl3YCrNWXZNGqu42fP7pia1Wpk6dyp133onX62XcuHF07tyZ2bNn07NnT4YNG8all17Kl19+yahRo7BYLEyZMoWEhAR/lSQiIeyrnXl8tcPB3WN6YjaZGHtZh0CXJBJwfgtxgLS0tDMWhbn//vsr/20ymXj00Ud59NFH/VmGiISwYmc5r32ym6925hFmNXM4r4Q2dk28EgE/h7icH3/u9w2nZvJ6DS8uj1trm0tI+HpXHotX7KLIWUGnlvFMTk8luWlUoMsSCRo1DvHCwsLK+7ml7vl7v2/DMChwnwRg1uYX/bIJikhdWrxiF59tOUKY1cwNQzsxvH9rTVwT+Ylqb6b87rvvGDp0KNdcc03l42nTpvm9sMbG3/t9//h82u9bQkHHlnF0bBHHk7cPYMRFbRTgImdRbU98xowZvPzyy/zhD38ATs06f+SRR/xeWGPj7/2+XR43sza/qP2+JWiVlFXwwRf7GXNpB6JsVgb1SGZg92SFt8g5VBvi5eXldO3a9bTnwsLC/FZQY+X3/b6tEdrvW4LWlj35vPrxLgpLy4mNCuPqS9pjMpnQnWMi51ZtiIeFhVFWVlY5PpuVlaUQ9xPt9y2NTamrgtc/3cOG7blYLSbGpXVg5MX6jIrUVLUhfvfdd3P77beTl5fHH//4R9asWcMzzzxTH7WJSAO240ABcz7cTmFJOe2SY7kjPZWWiTGBLkskpFQb4kOGDKFdu3asW7cOwzC444476NBBiyyISO1ERVgpc3sYe1kHrhrYBotZm5aInK9qQ3zZsmVceeWVTJw4sT7qEZEGLDPrOM3jbbRoHk3b5Fie+80lxERqeE7k56r2q+9HH33EkCFDmDp1Kt9++2191CQiDYzT5WH+8h389Z1vWfTxTgzj1I56CnCR2qm2J/5///d/nDhxgg8++IBp06ZRXl7O2LFjufPOO+ujPhEJcdv2HWfBRzspKHbTJimGW67sWqcLGYk0ZjUahGratCmTJk3i1VdfpX///mfsRiYi8lNlbg8LP9rBC29/S1FpOWMGt+fx2/rTOkmT10TqSrU9ccMwWLduHe+99x4bN25kyJAhLFq0qD5qE5EQ5vH62LrnGK2TYrgjPVWbloj4QbUhftlll9G+fXuuvfZaZsyYQVSUNh8QkbMrc3twFDhplxxHbFQ4D0/oh71pFFaLZp6L+EO1If7GG2/QqlWr+qhFRELY99knWLB8JxUeL0/9aiAxkWG671vEz6oM8a1bt9K3b1+ys7PJzs4+4/XBgwf7sy4RCRGucg/vfJ7FZ98cwWwyMWpQW2zhlkCXJdIoVBnib731Fn379uWll1464zWTyaQQFxF2Hihg/vIdHCt00bJ5NJPTU2mfEhfoskQajSpD/IelVV9//fV6K0ZEQodhGLzzeRbHi1ykD2rLNZe0J8yqsW+R+lTt/3G33HJLjZ4TkcbhRJELOHVF7o70VP44sT/j0joqwEUCoNr/60pLS0977PP5OHHihN8KEpHg5C738vqnu3nkHxs46CgGoEXzaDq00OVzkUCp8nL6/PnzmT9/PidPnjxt/LusrIwRI0bUS3EiEhx2HzrJ/Iwd5J0sI6VZFP9ZNVVEAqzKEB83bhzDhg1j+vTpTJs2rfL5mJgYmjZtWi/FiUhguSu8vLdmHys3HwJg5MVtGDO4PeFhmn0uEgyqDPH4+Hji4+OZN29efdYjIkHkgy/38+nmQ9ibRnFHeiqdWsYHuiQR+ZEqQ/yRRx5h5syZ3HDDDWfdrODNN9/0a2EiEhgVHh9WiwmTyUT6wLaEWcyMGthWvW+RIFRliN90000APPDAA/VWjIgE1t4jhczL2MEvL2nHwB7JRNnCGHNph0CXJSJVqDLEe/fuDcCgQYMqn/N4PBQVFWlMXKSBqfB4+de6/azYdBAMyD3hDHRJIlID1d5i9vvf/57i4mJcLhejR49m+PDhLFy4sB5KE5H6kHW0kCcXfMXHGw+SGB/JH26+QL1vkRBRbYjv2bOH2NhY1qxZw4ABA1i3bh3vvvtufdQmIn625/BJZiz+mpzjTq64sBV/mnwRXVo3CXRZIlJD1e5i5vV6Afjqq69IS0sjKioKs1krM4k0BB1bxjOoRzKX9k6ha5uEQJcjIuep2hBv3749d999N7t37+bBBx/E5XLVR10i4gcVHh8ffLkfgHFpHTGbTNw5unuAqxKRn6vaEJ81axZr1qyhe/fuREVFkZuby+9+97v6qE1E6tD+nCLmZ+zgyLFSkppEMvoX7YjQbWMiIa3aEI+MjGTQoEFkZmZy4MABevfuzZAhQ+qhNBGpCxUeHx+u38/yDQfxGQZD+rVk/JCOCnCRBqDaEF+/fj0PPfQQnTt3xjAMsrKyeP7550+79UxEglN5hZenXv2aw/klNIuLYNKoVHq00y2iIg1FtSH+/PPPs2jRIrp06QKcmq3+hz/8gffee8/vxYlI7YSHWejaugkdW8Zx/eWdiIyo9n95EQkh1f4fXVFRURngAJ07d66csS4iweego5gvMnOYcEVnTCYTE4Z3xnyWpZNFJPRVe69YQkICS5curXz8wQcf0KSJ7iMVCTYer4+lX+znz4s2s/Lrw+w+dBJAAS7SgFXbE3/yySd56KGHePLJJwHo0KEDL7zwgr/rEpHzcCivhHkZ33PQUUJCbASTruqm+75FGoEa3Sf+3nvvUVRUBEBcXJzfixKRmlux6SBLPs/C6zMY3CuFG4d1IsoWFuiyRKQeVBniubm5/M///A/79++ne/fuPPzwwyQk6Ju9SLCxhVuIjQpj0lXd6N2xeaDLEZF6VOWY+OOPP06zZs247777MAyDWbNm1WddIlIFr8/Hys2HcJV7ALisTwue/tVABbhII1RlT9zhcDB37lwA0tLSuO666+qtKBE5uyPHSpmf8T37c4opKHEzfkgnTCaTbh0TaaSq/D/fav3vSxaLVnYSCSSvz8eKTYd4f90+PF6DQT2SGTWwbaDLEpEAqzLEs7OzufHGG6t8/Oabb/q3sgbA5XGTU+ogJdqOzRpR7fGGYeA1vLg87hodL41DzvFS5i7bwf6cIuKjw7l1ZFf6dU4MdFkiEgSqDPGXXnqpPutocFweN0+sn4HTU4bZZCY+PA7TOe7XNQyDAvep+3pnbX6RKf3vVZALAE6Xh+zcIgb2sHPTFV2IidTMcxE5pcoQ19rotZNT6sDpKQPAZ/jwGl6spqrHLb3Gf1fBczjzyCl10D6+jd/rlOCUc7wUi8VMUpNIOraMZ/odF9OyeXSgyxKRIOPX2TBr167l6aefxufzMX78eO66666zHrdixQruu+8+lixZQq9evfxZUr1JibZjNpnxGT7sUUnV9qxdHjezNr+Iw5mHPSqJlGh7PVYrwcLnM/jkq0P8a90+2ifHMuXmCzCbTApwETkrv4W41+tl+vTpLFiwALvdznXXXcfQoUPp1KnTaceVlJTw6quv0qdPH3+VEhA2awTx4XF4DW+NLo3brBFM6X/veY2hS8NyJL+E5/75DXuPFBIbFcYV/VtryVQROSe/hXhmZiZt27aldevWAKSnp7Nq1aozQnz27Nn86le/Yt68ef4qJWBMJhNWk7XGgWyzRugSeiPk8xms3HyI99buo9zjY0C3JG6+sgtxUeGBLk1EglyNQnzTpk1kZWUxYcIEjh8/TmlpKW3anDtsHA4HycnJlY/tdjuZmZmnHbN9+3Zyc3MZMmRIjUM8ISEKq7Vub3lLTIyt0/P9wGI2+fX8waax/J517WSxm2UbDmCLsPK7m3ozuE/LQJcU0vQ5rD21Ye3VVxtWG+Lz5s3j008/5cSJE0yYMAG3280jjzzC66+/Xqs39vl8zJw5k2eeeea8fq6gwFmr9/2pxMRY8vOL6/ScP/D6DAC/nT+Y+LMdGyKfYXCi0EXzJpEA/PbanvTsYqfCVa52rAV9DmtPbVh7dd2G5/pCUO1WpEuXLmXx4sVERUUB0KJFC4qLqy/ObreTm5tb+djhcGC3/3eyVmlpKbt37+bWW29l6NChbN26lf/3//4f3333XbXnFglleSfLmPX6Fp755zc4XRUAdG2TQJNYzYMQkfNTbU/cZrMRFnb6fannut/5B7169SI7O5tDhw5ht9vJyMjg+eefr3w9NjaWjRs3Vj6eOHEiU6ZMaTCz00V+ymcYfPbNEd75fC/lFT4u6JJYebVGROTnqDbEk5OT2bp1KyaTCcMweOWVV+jYsWP1J7ZamTp1KnfeeSder5dx48bRuXNnZs+eTc+ePRk2bFid/AIioSD/ZBkLlu9g58GTRNusTBrZjYu722v0hVhEpCrVhvgf//hHHn74Yfbs2UOfPn3o06cPf/nLX2p08rS0NNLS0k577v777z/rsYsXL67ROUVC0SvLvmfv4UL6dW7OrSO6Eh+jS+ciUnvVhrjdbufVV1+lpKQEwzCIjdWsRZGacFd4iQg7dSfFLcO7cCS/lIE91PsWkbpTbYh/8cUXZ31+8ODBdV6MSENgGAZrth7lvbX7+MPNF9CyeTRt7LG0sesLsIjUrWpD/Mcbobjdbnbv3k1qaqpCXOQsjhe6WPjRDrZnFxAZYeXYyTItmSoiflNtiP/0fvBdu3axaNEivxUkEooMw2Dtt0d5a/VeXOVeendsxm0ju5Gg28ZExI/Oe9nVrl27sn37dn/UIhKyPt54kHc+zyIywsLto7oxuFeKxr5FxO/Oa0zc5/Px3XffYbHU7bKnIqHIMIzKoL60TwsO5ZdwXVpHmsbZAlyZiDQW5zUmbrFYaNu2LX/961/9WpRIsCsodrPo450M7pVC/25JxESGcdfVPQJdlog0MucMcZ/Px913381ll11WX/WIBDXDMPjyu1zeWLWHMreHKJuV/t2SAl2WiDRS5wxxs9nMCy+8oBAX4b+978ys49jCLdw2siuX9WkR6LJEpBGr9nJ6165d2bZtGz179qyPekSC0uH8Ema+9g1Ot4fUtgncPqobzeMjA12WiDRy1Yb47t27ueGGG+jQoQPR0f+93/XNN9/0a2EiwSSlWRTtUmK5sGsSQ/q20MxzEQkK1Yb4lClT6qMOkaBiGAb//t7ByRI3V13cFovZzEM39FV4i0hQqTLEH3vsMWbMmMGgQYPqsx6RgCssLefVj3eyZc8xIiOspPVpQZQtTAEuIkGnyhDfsWNHfdbRIBmGgdfw4vK4sVm1clewMwyDTTvy+Oenuykpq6Br6ybcnp5KlC0s0KWJiJzVea/YJjXj8rgpLC/CZ/iYtflFpvS/V0EexLw+H/9Yup3Nu/IJDzNz8/AuXH5BS8zqfYtIEKsyxHfv3n3WS+k/rFK1YcMGvxYW6nJKHfgMHwAOZx45pQ7ax7cJcFVSFYvZTGSElS6t4pmcnkpSQlSgSxIRqVaVId6uXTvmzJlTn7U0KCnRdswmMz7Dhz0qiZRoe6BLkp8ocpbz5Xc5jLyoDSaTiZuHd8FqNav3LSIho8oQDw8Pp2XLlvVZS4Nis0YQHx6H1/DqUnoQ2rwzj8Wf7KLYWUFKs2j6dmpOeJj2BBCR0FJliIeFaTJPbZlMJqwmqwI8iBQ7y/nnp7vZtCOPMKuZG4d2oneHZoEuS0TkZ6kyxN9+++36rEPE77bsyWfRRzspclbQsWUck0elktIsuvofFBEJUpqdLo3GiSI3TreX6y/vxJUDWmM2a+xbREKbQlwatMys43Rr04TwMAuXX9CSXh2bkdREa56LSMNgDnQBocTlcbO/8CAuj7tGxxuGgcfnqfHxUndKyiqY8+F2/vrOt7y/bj8AZpNJAS4iDYp64jXk8rh5Yv0MnJ4yzCYz8eFx51yG0zAMCtwnAbTYSz3buucYiz7eSWFpOe1TYrmkd0qgSxIR8QuFeA3llDpwesoA8Bk+vIYXq6nq5vMa3sp/a7GX+lHqquCNlXtYvy0Xq8XEuLQOjLy4DRazLjiJSMOkEK+hny7eUl3P2uVxM2vziziceVrspZ4cyS9l/bZc2ibHcmd6Ki0TYwJdkoiIXynEa+h8F2+xWSOY0v9eckodpETbdSndT5yuCso9PprERNCldRMevKEPqW0T1PsWkUZBIX4eznfxFps1QpfQ/Sgz6ziLPt5JctMofn/jqb2+e7bXwi0i0ngoxCXkOF0e3ly9hy8yc7CYTaT1bYHPMLBozXMRaWQU4hJStu0/zoLlOykodtMmKYbJ6am0sccGuiwRkYBQiEvIcLoqePn97ZRXeLnmknaM/kU7rBaNfYtI46UQl6BX5vYQGWElyhbGnempNI2z0TZZvW8REXVjJGiVuT28+vFOps3fRJnbA0C/LokKcBGR/1BPXILSjuwTzF++k+NFLlomRlPsLCcyQh9XEZEf019FCSqucg/vfJ7FZ98cwWwyMfoXbbn6F+0Js+qikYjITynEJaj8fel2MrOO06J5NHekp9I+JS7QJYmIBC2FuAScYRiVm8lcc0l7WiXG8MvB7QizWgJcmYhIcNM1SgmoXQcLmDZ/E44TTgA6tIjjuiEdFeAiIjWgnrgEhLvCy7trsli1+TCYYMfBAuxNowJdlohISFGIS73bfegk85fvIK+gjOSmUdyRnkrHlvGBLktEJOQoxKVeffldDvMzdgAw8qI2jLm0PeFhunQuIvJzKMSlXvXs0IyOLeO5/vJOdGql3reISG0oxMWvyiu8/GvdPrq1SaBPp+bER4fz2MQLA12WiEiDoBAXv8k6Usi8jB3knnCSnVNMn07NA12SiEiDohCXOlfh8fL+uv18vOkgGHBF/1aMS+sY6LJERBocv4b42rVrefrpp/H5fIwfP5677rrrtNcXLFjAO++8g8VioWnTpsyYMYOWLVv6syTxs+OFLl54eys5x50kNrExeVQqXdskBLosEZEGyW+LvXi9XqZPn87cuXPJyMhg2bJl7N2797RjUlNTeffdd/nwww8ZMWIEzz77rL/KkXoSHxNOuNXCsAtbMX3yxQpwERE/8luIZ2Zm0rZtW1q3bk14eDjp6emsWrXqtGMGDhxIZGQkAH379iU3N9df5Ygf7c8p4tONBwCwWsw8NvECbh7ehYhw3TomIuJPfgtxh8NBcnJy5WO73Y7D4ajy+CVLlnDZZZf5qxzxgwqPj3fXZPH0q1/z8nuZnCxxA2jJVBGRehIUE9uWLl3Ktm3beO2116o9NiEhCmsdh0RiYmyNjrOYTed1fEO29/BJ/vrGNxzILSYpIZL7buhH5/aafV5b+mzVntqw9tSGtVdfbei3ELfb7addHnc4HNjt9jOOW79+PX//+9957bXXCA8Pr/a8BQXOOq0zMTGW/PziGh3r9RkANT6+ITIMg/fX7SdjwwF8hsGQfi0ZP6QjbVolNOp2qQvn81mUs1Mb1p7asPbqug3P9YXAb5fTe/XqRXZ2NocOHaK8vJyMjAyGDh162jHff/89U6dO5eWXX6ZZs2b+KkXqkMlkIr+wjITYcB66sS+3juhKZERQXNAREWl0/PbX12q1MnXqVO688068Xi/jxo2jc+fOzJ49m549ezJs2DBmzZqF0+nk/vvvByAlJYW///3v/ipJfiaP18c3u/O5KPXUlZRbhnfBZDIpvEVEAsyvf4XT0tJIS0s77bkfAhtg4cKF/nx7qQMHHcXMz9jBwbwSLGYTF3ZNIsoWFuiyRESEIJnYJsHH4/Wx/N8H+PDLbLw+g0t7p5DatmmgyxIRkR9RiMsZDueVMC9jBwccxSTERnDbyG707qg5CyIiwUYhLmfYtv8EBxzFXNIrmQnDOuvyuYhIkFKICwBHj5WS2CSSMKuZKwe0pn1KrJZMFREJcn67xUxCg9fnI2NDNk8u2MQHX+4HwGw2KcBFREKAeuKN2JFjpczP+J79OcXER4fTsUV8oEsSEZHzoBBvhLw+Hys2HeL9dfvweA0G9bAz4YouxERq7FtEJJQoxBuhfUeLWPJ5FnHR4dw2oiv9uiQGuiQREfkZFOKNhM9n4Cr3EmWz0rlVE24f1Y1+nRPV+xYRCWEK8UYg94STeRnfE20L4/7remMymbi0d4tAlyUiIrWkEG/AfD6DlZsP8e7afVR4fFyUmkSFx0d4mPb7FhFpCBTiDZTjhJN5y3ew93AhsVFh/Gp0d/p3Swp0WSIiUocU4g1QeYWXGa99TbGzgv7dkrjlyi7ERVW/V7uIiIQWhXgD4vMZmM0mwsMsXH95J8Ks5srtQ0VEpOHRim0NgM84Nfb9p4Vf4a7wAnBJrxQFuIhIA6eeeIjLO1nGgowd7Dp0kmiblaPHSmmfEhfoskREpB4oxEOUzzD47JsjLPk8C3eFl36dm3PriK7Ex0QEujQREaknCvEQteijnazLzCHaZuW2kd25uLsdk8kU6LJERKQeKcRD1CW9Uigpq2DiiK40Ue9bRKRRUoiHiGOFZbyxcg83DutMYpNIurRuQpfWTQJdloiIBJBCPMgZhsGab4/y9uq9uMq9tEqM4drLOgS6LBERCQIK8SB2vNDFwo92sD27gMgIK5NHpXJJr+RAlyUiIkFCIR6ktu07zkvvb8NV7qVXh2ZMuqobCbEa+xYRkf9SiAeplokxRNvCmHBFZwb3StHMcxEROYNCPEgYhsEX3+XQPM5GarumJMRG8MyvB2K1aFE9ERE5O4V4ECgodrPo451kZh2nZWI00ydfhMlkUoCLiMg5NeoQd3nc7Dl+DJsnFpu1+vFmwzDwGl5cHneNjq/J+dZvy+X1lXsoc3vo3i6B269K1aVzERGpkUYb4i6PmyfWz8DpKcNsMhMfHnfO8DQMgwL3SQBmbX6RKf3vrVWQl5RVMG/Z93ybdZyIcAu3juxKWp8WCnAREamxRhviOaUOnJ4yAHyGD6/hxWqqujm8hrfy3w5nHjmlDtrHt/nZ7x8RZuFYoYvUtgncflU3mjeJ/NnnEhGRxqnRhnhKtB2zyYzP8GGPSqq2Z+3yuJm1+UUczjzsUUmkRJ//Np+FJW725RTRr3MiYVYzv5/Qj9ioMMzqfYuIyM/QaEPcZo0gPjwOA1+NLo3brBFM6X8vOaUOUqLt53Up3TAMNn7v4J+f7qbc4+OpOy8msUkk8dHhtf01RESkEWu0IQ6cmgFuttY4kG3WiPO+hF5YWs7iFbv4Znc+4WFmrr+8E83ibT+nXBERkdM06hD3J8Mw+GpnHq99spuSsgq6tG7C5FHdSEqICnRpIiLSQCjE/WjzzjzKK7xMuKIzwy5spbFvERGpUwrxOrbvaBEdWpy6Xe2WEV0pc3uwq/ctIiJ+oCXB6kiRs5yX39/GU69uZuueYwDERYUrwEVExG/UE68Dm3fmsfiTXRQ7K+jUMp7kZgpuERHxP4V4LZSUVfDaJ7vYtCOPMKuZG4Z2Ynj/1pjNGvsWERH/U4jXwrrMo2zakUfHFnFMTk8lpVl0oEsSEZFGRCF+nkrKKrCFW7BazAzv35qYyDAu6Zmi3reIiNQ7TWw7D1v25PPE3I0s//cBAKwWM5f2bqEAFxGRgFBPvAZKyip4Y+VuNmx3YLWYiAizBLokERERhXh1tu49xqKPd1JYUk77lFgmp3enZXONfYuISOA16hA3DIMKrxeXx33W9dMPOor525JMLGYT49I6MPLiNljMGoEQEZHg0GhD3OVxU1hehM/wMWvzi6ftZObx+rBazLSxx3LtZR3o17k5rRJjAlyxiIjI6RptiOeUOvAZPgAczjxySh3YI1J4c9Veyso9/GZMT0wmE1f/ol1gCxUREamCX68Nr127lhEjRjB8+HDmzJlzxuvl5eU88MADDB8+nPHjx3P48GF/lnOalGg7ZtOpX98elURBfhhPzNvEF9/lkH+yjDK3t95qERER+Tn8FuJer5fp06czd+5cMjIyWLZsGXv37j3tmHfeeYe4uDg+/fRTJk2axHPPPeevcs5gs0YQFxZLVFgU9hOX879LdlBUWs6Ywe15/Nb+RNka7UUKEREJEX4L8czMTNq2bUvr1q0JDw8nPT2dVatWnXbM6tWrufbaawEYMWIEGzZswDAMf5V0GpfHTVFFMc4KJ1uNZbSy23jitv5cM7g9Vosmr4mISPDzW3fT4XCQnJxc+dhut5OZmXnGMSkpKacKsVqJjY2loKCApk2bVnnehIQorNba36e95/ixyjFxc2Qp903qRGpSi1qftzFLTIwNdAkhT21Ye2rD2lMb1l59tWHIXTMuKHDWyXlsnljsUUk4nHnYo5KI8caTn19cJ+dujBITY9V+taQ2rD21Ye2pDWuvrtvwXF8I/Bbidrud3NzcyscOhwO73X7GMTk5OSQnJ+PxeCguLiYhIcFfJZ3GZo1gSv97cYUXYyuPPet94iIiIsHMb4O/vXr1Ijs7m0OHDlFeXk5GRgZDhw497ZihQ4fyr3/9C4AVK1YwcOBATKb6fgkFSwAADIVJREFUW4fcZo2gc7P2CnAREQlJfuuJW61Wpk6dyp133onX62XcuHF07tyZ2bNn07NnT4YNG8Z1113Hww8/zPDhw4mPj+cvf/mLv8oRERFpcExGfU0HryN1PVaj8Z+6oXasPbVh7akNa09tWHv1OSaue6lERERClEJcREQkRCnERUREQpRCXEREJEQpxEVEREKUQlxERCREKcRFRERClEJcREQkRIXcYi8iIiJyinriIiIiIUohLiIiEqIU4iIiIiFKIS4iIhKiFOIiIiIhSiEuIiISohpNiK9du5YRI0YwfPhw5syZc8br5eXlPPDAAwwfPpzx48dz+PDhAFQZ3KprwwULFjBq1CiuvvpqbrvtNo4cORKAKoNbdW34gxUrVtC1a1e+++67eqwudNSkHZcvX86oUaNIT0/noYcequcKg191bXj06FEmTpzImDFjuPrqq1mzZk0Aqgxejz76KIMGDWL06NFnfd0wjP/f3v0GRVW9cQD/7ixgDBJFEW4TU+lk1sBEkcAMw+Ku4B/AZRdWyigak2IsDI0Ch0HoheMwqDE4Flu+8C/9ASUs8AUz7CiZCI1hDCCJxto2I0sCyiKx7J/n94LxjgjF5Zewu/F8XrHsuec89xnYZ+65d8/Bzp07ERcXh3Xr1qGjo2N2AqF5wGaz0cqVK+n3338ni8VC69ato+7u7gltjh07Rjt27CAiotraWsrOznZGqC5LTA6bmppoZGSEiIgqKio4h/cQk0MiIrPZTK+99hqtX7+e2tranBCpaxOTx56eHkpKSqKbN28SEdGNGzecEarLEpPDgoICqqioICKi7u5uUigUzgjVZbW0tFB7ezslJCRM+f7p06dp06ZN5HA4qLW1lbRa7azEMS+uxNva2vDkk08iKCgIXl5eSEhIQENDw4Q2er0eGo0GALB69Wo0NTWBeB0cgZgcRkZGwtvbGwAQGhqK3t5eZ4TqssTkEADKysrw9ttvY8GCBU6I0vWJyWNlZSXS0tLg5+cHAHjkkUecEarLEpNDiUSC4eFhAIDZbMZjjz3mjFBd1vLly4W/r6k0NDRArVZDIpEgNDQUQ0ND6Ovru+9xzIsibjKZsGjRIuF1YGAgTCbTpDYymQwA4OHhAV9fXwwODs5pnK5MTA7vdvz4ccjl8rkIzW2IyWFHRwd6e3uxYsWKOY7OfYjJo8FgQE9PD1599VWkpqaisbFxrsN0aWJymJWVhe+//x5yuRzvvPMOCgoK5jpMt3ZvjhctWvSPn5n/r3lRxNncOnnyJNrb25GRkeHsUNyKw+FAcXEx8vLynB2K27Pb7bh27RqOHj2KvXv3YseOHRgaGnJ2WG6lrq4OGo0GjY2N+OKLL5CbmwuHw+HssNg95kURDwwMnDC1azKZEBgYOKnN9evXAQA2mw1msxkPP/zwnMbpysTkEADOnTsHnU6H8vJyeHl5zWWILm+6HN6+fRuXL19Geno6lEolLl68iM2bN/PDbfcQ+/+sVCrh6emJoKAgPPXUUzAYDHMcqesSk8Pjx49j7dq1AIAXX3wRFouFZydn4N4c9/b2TvmZ+W/NiyIeEhICg8EAo9GIsbEx1NXVQalUTmijVCrx7bffAhh/MjgyMhISicQZ4bokMTns7OxEYWEhysvL+R7kFKbLoa+vL5qbm6HX66HX6xEaGory8nKEhIQ4MWrXI+ZvMTY2Fi0tLQCAgYEBGAwGBAUFOSNclyQmhzKZDE1NTQCAq1evwmKxwN/f3xnhuiWlUomamhoQES5evAhfX99Zea7A47736II8PDxQWFiIjIwM2O12pKSk4JlnnkFZWRmCg4OxcuVKaLVafPTRR4iLi4Ofnx9KS0udHbZLEZPDkpISjIyMIDs7G8D4h4BOp3Ny5K5DTA7Z9MTkMTo6Gj/++CPi4+MhlUqRm5vLM2t3EZPD7du3o6CgAIcOHYJEIkFxcTFf2Nzlgw8+QEtLCwYHByGXy7FlyxbYbDYAwIYNGxATE4MzZ84gLi4O3t7e2LVr16zEwVuRMsYYY25qXkynM8YYY/9FXMQZY4wxN8VFnDHGGHNTXMQZY4wxN8VFnDHGGHNT8+IrZow5i1KphJeXl7AOekREBPLz8//xGLlcjoMHD2LJkiX/evzS0lJUVVUhICAAY2NjCAsLQ1FRETw9PWfcV0VFBex2O9LT09HR0QGj0Yg1a9YAGF8hLTk5GVVVVfdtkR+5XA4fHx94enrCarUiIyMDKSkp0x5XX18PmUzG369n8wIXccZm2b59+7B06VKnjZ+cnIwPP/wQFosFaWlpwuYgM3X3MZ2dnTh37pxQxKVSKU6ePHnfYr5j//79WLJkCbq6uqDVahETE4NHH330H4+pr69HWFgYF3E2L/B0OmNOUFNTA61WC7VaDY1Gg+bm5inblZWVYc2aNVCpVNBoNMKuUq2trXjjjTeQnJyM5ORkUXs9L1iwAGFhYejp6QEAnD59WtgreuPGjTAajQDGV+dKTU2FSqVCYmIiDh06BGD8qn7Pnj3o7+/Hp59+irNnzyIpKQm7du2CzWbDs88+C4vFgurqarz//vvCuFarFVFRUbh+/TqICDqdTjj3zZs3o7+/f9rYly1bBh8fH2EXqEuXLmHDhg3QaDRISEjA0aNHAQBnzpxBY2MjdDodkpKS8N133wEYX0JUq9VCo9HgzTff5CVY2X/HrGxwyhgjIiKFQkGrV68mlUpFKpWKGhsbiYhoYGBAaNPd3U0xMTHC6+joaLpy5Qr19/dTeHg4jY6OEtH4PuNWq5UGBwcpKSmJ/vzzTyIi6u3tpejoaDKbzZPG/+STT2j37t1ERHTr1i1KTEykEydOUF9fH4WHh9OVK1eIiOirr76iV155hYiIPv74Yzpw4IDQx509ue/uq7KykrZu3Sq0sVqttHTpUhodHaXh4WEKDw8Xjquvr6eNGzcSEdGJEyeoqKiI7HY7EREdOXKEcnNzp8zdnTwQETU3N1NiYiKNjY0JubBYLMLPq1atot9++42IiHJycujLL78U+jl//jxlZmYK7RsaGigtLW3KMRlzNzydztgsm2o6/dq1a8jJyUFfXx+kUilMJhMGBgYmrE394IMP4vHHH0deXh6ioqKwYsUKLFy4EBcuXMAff/yBTZs2CW0lEgmMRiOee+65SeNXV1fjhx9+gEQiQWxsLNRqNRoaGhAcHCzcd9dqtdi5cyf++usvLF++HKWlpRgeHkZkZCQiIiJmdL4+Pj5QKBSora1FWloaqqurodFoAAB6vR6XLl0SXtvtdjz00EN/21dWVhYcDgeMRiP27dsn3MsfGRlBUVERLl++DIlEghs3buDXX3/F008/PakPvV6Pzs5OrF+/HgBARLh9+/aMzokxV8VFnDEn2LZtGwoLC6FQKGC32/HCCy9gbGxsQhsPDw9UVVXhwoULOH/+PDQaDQ4ePAgiwvPPP48jR46IGuvOPXGx4uPjERYWhrNnz0Kn06GmpgbFxcUzOj+NRoM9e/Zg7dq1aG1tFfYiICJs2bIFarVaVD937onX1tZi+/btqK+vh7+/P/bu3QuZTIaSkhJIpVKkp6fDYrFM2QcRITU1FVlZWTM6B8bcAd8TZ8wJzGYznnjiCQBAZWUlrFbrpDbDw8MYHBxEREQEsrOzsXjxYnR3d+Oll17C1atX8dNPPwltf/nllxmNHxoaio6ODuH+eHV1NUJCQuDt7Q2DwYCAgACkpKTg3XffRVtb26TjfXx8YDab/7b/8PBwDAwMoLS0FKtWrcIDDzwAYPxp/YqKCmFvb4vFgq6urmnjTUxMREREBA4cOAAAGBoagkwmg1QqRVdXF37++Weh7cKFCyfEplAoUFNTA5PJBGD86r+9vX3aMRlzB3wlzpgT5OfnIzMzE35+foiJiYGvr++kNrdu3cLWrVsxOjoKIkJwcDBiY2Ph5eWFzz77DLt378bQ0BBsNhuCgoLw+eefix4/ICAAxcXF2LZtGxwOB/z9/VFSUgIAqKurw6lTp+Dp6QmJRDLlV+KioqJw+PBhqFQqREZGIjc3d8L7EokEarUa+/fvxzfffCP8PiUlBTdv3hSedCcivP7661i2bNm0Mefk5CA1NRUZGRl47733kJeXh6+//hqLFy/Gyy+/LLRTq9XIz8/HqVOn8NZbb0GlUiErKwuZmZlwOByw2WyIj49HcHCw6Hwx5qp4FzPGGGPMTfF0OmOMMeamuIgzxhhjboqLOGOMMeamuIgzxhhjboqLOGOMMeamuIgzxhhjboqLOGOMMeamuIgzxhhjbup/jMCksJ10lv0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "['saves/logistic_regression_manual_imputation.bin']"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_result = model_utils.predict(best_lr,X_validation_norm,y_validation);\n",
    "metrics_utils.evalute_model_performance(best_lr, 'Logistic Regression',X_validation_norm,y_validation,df_result)\n",
    "dump(best_lr, 'saves/logistic_regression_manual_imputation.bin', compress=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ridge Regressor Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 28 candidates, totalling 280 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Ellapsed time: 0.4194447994232178 seconds ---\n",
      "Best params:  {'alpha': 1, 'solver': 'auto'}\n",
      "Best score (f1) 0.861619472548948\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  56 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=-1)]: Done 280 out of 280 | elapsed:    0.4s finished\n"
     ]
    }
   ],
   "source": [
    "ridge_regression_params = {\n",
    "    'alpha' : [1,10,100,1000],\n",
    "    'solver' : ['auto', 'svd', 'lsqr', 'sag', 'cholesky','saga','sparse_cg']\n",
    "}\n",
    "\n",
    "best_ridge,best_ridge_params,best_ridge_score = model_utils.find_best_classification_model_with_cross_validation(\n",
    "    RidgeClassifier(random_state=0, class_weight = 'balanced'),\n",
    "    ridge_regression_params,\n",
    "    X_train_norm.values,\n",
    "    y_train.values.ravel(),\n",
    "    metric = score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    Rejected       0.76      0.65      0.70        34\n",
      "    Approved       0.83      0.89      0.86        65\n",
      "\n",
      "    accuracy                           0.81        99\n",
      "   macro avg       0.79      0.77      0.78        99\n",
      "weighted avg       0.80      0.81      0.80        99\n",
      "\n",
      "Could not print ROC AUC curve.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['saves/ridge_regression_manual_imputation.bin']"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_result = model_utils.predict(best_ridge,X_validation_norm,y_validation);\n",
    "metrics_utils.evalute_model_performance(best_ridge, 'Ridge Regression',X_validation_norm,y_validation,df_result)\n",
    "dump(best_ridge, 'saves/ridge_regression_manual_imputation.bin', compress=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 160 candidates, totalling 1600 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:    2.0s\n",
      "[Parallel(n_jobs=-1)]: Done 184 tasks      | elapsed:   10.3s\n",
      "[Parallel(n_jobs=-1)]: Done 434 tasks      | elapsed:   32.4s\n",
      "[Parallel(n_jobs=-1)]: Done 784 tasks      | elapsed:   54.9s\n",
      "[Parallel(n_jobs=-1)]: Done 1234 tasks      | elapsed:  1.6min\n",
      "[Parallel(n_jobs=-1)]: Done 1600 out of 1600 | elapsed:  2.4min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Ellapsed time: 144.93281507492065 seconds ---\n",
      "Best params:  {'criterion': 'gini', 'max_depth': 15, 'min_samples_split': 3, 'n_estimators': 150, 'oob_score': True}\n",
      "Best score (f1) 0.8896920773421302\n"
     ]
    }
   ],
   "source": [
    "random_forest_params = {\n",
    "    'n_estimators' : [50,100,150,200],\n",
    "    'min_samples_split': [2,3,4,5],\n",
    "    'max_depth':[5,8,10,13,15],\n",
    "    'criterion':['gini','entropy'],\n",
    "    'oob_score':[True]\n",
    "}\n",
    "\n",
    "best_random_forest,best_random_forest_params,best_random_forest_score = model_utils.find_best_classification_model_with_cross_validation(\n",
    "    RandomForestClassifier(random_state=0, class_weight = 'balanced'),\n",
    "    random_forest_params,\n",
    "    X_train_norm.values,\n",
    "    y_train.values.ravel(),\n",
    "    metric = score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    Rejected       0.91      0.59      0.71        34\n",
      "    Approved       0.82      0.97      0.89        65\n",
      "\n",
      "    accuracy                           0.84        99\n",
      "   macro avg       0.86      0.78      0.80        99\n",
      "weighted avg       0.85      0.84      0.83        99\n",
      "\n",
      "No Skill: ROC AUC=0.500\n",
      "Random Forest : ROC AUC=0.845\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "['saves/random_forest_manual_imputation.bin']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_result = model_utils.predict(best_random_forest,X_validation_norm,y_validation)\n",
    "metrics_utils.evalute_model_performance(model = best_random_forest, model_name = 'Random Forest', X = X_validation_norm, y = y_validation, df_result = df_result)\n",
    "dump(best_random_forest, 'saves/random_forest_manual_imputation.bin', compress=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 192 candidates, totalling 1920 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:    2.4s\n",
      "[Parallel(n_jobs=-1)]: Done 184 tasks      | elapsed:   11.9s\n",
      "[Parallel(n_jobs=-1)]: Done 434 tasks      | elapsed:   37.1s\n",
      "[Parallel(n_jobs=-1)]: Done 784 tasks      | elapsed:  1.1min\n",
      "[Parallel(n_jobs=-1)]: Done 1234 tasks      | elapsed:  1.6min\n",
      "[Parallel(n_jobs=-1)]: Done 1784 tasks      | elapsed:  2.3min\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Ellapsed time: 154.718275308609 seconds ---\n",
      "Best params:  {'learning_rate': 0.01, 'loss': 'deviance', 'max_depth': 2, 'min_samples_split': 2, 'n_estimators': 100}\n",
      "Best score (f1) 0.889667300331103\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done 1920 out of 1920 | elapsed:  2.6min finished\n"
     ]
    }
   ],
   "source": [
    "gboost_params = {\n",
    "    'loss':['deviance', 'exponential'],\n",
    "    'learning_rate':[0.01,0.1],\n",
    "    'n_estimators' : [50,100,150],\n",
    "    'min_samples_split': [2,3,4,5],\n",
    "    'max_depth':[2,3,5,8]\n",
    "}\n",
    "\n",
    "best_gboost,best_gboost_params,best_gboost_score = model_utils.find_best_classification_model_with_cross_validation(\n",
    "    GradientBoostingClassifier(random_state=0),\n",
    "    gboost_params,\n",
    "    X_train_norm.values,\n",
    "    y_train.values.ravel(),\n",
    "    metric = score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    Rejected       0.95      0.59      0.73        34\n",
      "    Approved       0.82      0.98      0.90        65\n",
      "\n",
      "    accuracy                           0.85        99\n",
      "   macro avg       0.89      0.79      0.81        99\n",
      "weighted avg       0.87      0.85      0.84        99\n",
      "\n",
      "No Skill: ROC AUC=0.500\n",
      "Gradient Boosting : ROC AUC=0.828\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "['saves/gradient_boosting_manual_imputation.bin']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_result = model_utils.predict(best_gboost,X_validation_norm,y_validation)\n",
    "metrics_utils.evalute_model_performance(model = best_gboost, model_name = 'Gradient Boosting', X = X_validation_norm, y = y_validation, df_result = df_result)\n",
    "dump(best_gboost, 'saves/gradient_boosting_manual_imputation.bin', compress=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extreme Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 144 candidates, totalling 1440 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  56 tasks      | elapsed:    0.6s\n",
      "[Parallel(n_jobs=-1)]: Done 656 tasks      | elapsed:    5.4s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Ellapsed time: 12.025429964065552 seconds ---\n",
      "Best params:  {'gamma': 1, 'learning_rate': 0.1, 'max_depth': 3, 'missing': -999, 'n_estimators': 50, 'objective': 'binary:logistic', 'subsample': 1}\n",
      "Best score (f1) 0.8822729533378662\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done 1425 out of 1440 | elapsed:   11.8s remaining:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 1440 out of 1440 | elapsed:   12.0s finished\n"
     ]
    }
   ],
   "source": [
    "xgb_params = {'objective':['binary:logistic'],\n",
    "              'learning_rate': [0.1,0.3,0.5],\n",
    "              'gamma':[0,1],\n",
    "              'max_depth': [3,4,6,10],\n",
    "              'subsample': [0.5, 1],\n",
    "              'n_estimators': [50,100,150],\n",
    "              'missing':[-999]}\n",
    "\n",
    "best_xgb,best_gboost_params,best_gboost_score = model_utils.find_best_classification_model_with_cross_validation(\n",
    "    XGBClassifier(seed=0),\n",
    "    xgb_params,\n",
    "    X_train_norm.values,\n",
    "    y_train.values.ravel(),\n",
    "    metric = score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    Rejected       0.95      0.62      0.75        34\n",
      "    Approved       0.83      0.98      0.90        65\n",
      "\n",
      "    accuracy                           0.86        99\n",
      "   macro avg       0.89      0.80      0.83        99\n",
      "weighted avg       0.87      0.86      0.85        99\n",
      "\n",
      "Could not print ROC AUC curve.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['saves/extreme_gradient_boosting_manual_imputation.bin']"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_result = model_utils.predict(best_xgb,X_validation_norm,y_validation)\n",
    "metrics_utils.evalute_model_performance(model = best_gboost, model_name = 'Extreme Gradient Boosting', X = X_validation_norm, y = y_validation, df_result = df_result)\n",
    "dump(best_gboost, 'saves/extreme_gradient_boosting_manual_imputation.bin', compress=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the Ridge Regression is capable to execute some feature selection and had the same performance as the Logistic Regression, we can assume that all variables must be important to this problem and that the correlation between the Loan Amount and the Loan Installment is not a problem.\n",
    "\n",
    "However, the tree based models showed better performance both durtion cross validation and the \"final\" validation, preseting better F1-Score and Accuracy.\n",
    "\n",
    "Let us see (in the next notebooks) how these models will perform when the missing data is filled by a Random Forest algorithm."
   ]
  }
 ],
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  "file_extension": ".py",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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   "nbconvert_exporter": "python",
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  "name": "python",
  "npconvert_exporter": "python",
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 },
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}