2016_pred.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem loan prediction 2017 - Lending Club\n",
    "## Testing model with 2016 Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as pl\n",
    "import datetime\n",
    "%matplotlib inline\n",
    "l_encoder = LabelEncoder()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Uploading and cleaning the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Uploading data from 2016\n",
    "data_2016 = pd.read_csv('data_files/LoanStats_2016Q4.csv', skiprows=1, low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(103548, 145)\n",
      "HEAD\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>member_id</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>funded_amnt</th>\n",
       "      <th>funded_amnt_inv</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>...</th>\n",
       "      <th>hardship_payoff_balance_amount</th>\n",
       "      <th>hardship_last_payment_amount</th>\n",
       "      <th>disbursement_method</th>\n",
       "      <th>debt_settlement_flag</th>\n",
       "      <th>debt_settlement_flag_date</th>\n",
       "      <th>settlement_status</th>\n",
       "      <th>settlement_date</th>\n",
       "      <th>settlement_amount</th>\n",
       "      <th>settlement_percentage</th>\n",
       "      <th>settlement_term</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>10.49%</td>\n",
       "      <td>162.49</td>\n",
       "      <td>B</td>\n",
       "      <td>B2</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13000.0</td>\n",
       "      <td>13000.0</td>\n",
       "      <td>13000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>5.32%</td>\n",
       "      <td>391.50</td>\n",
       "      <td>A</td>\n",
       "      <td>A1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>7.99%</td>\n",
       "      <td>563.98</td>\n",
       "      <td>A</td>\n",
       "      <td>A5</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14400.0</td>\n",
       "      <td>14400.0</td>\n",
       "      <td>14400.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>7.99%</td>\n",
       "      <td>291.92</td>\n",
       "      <td>A</td>\n",
       "      <td>A5</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>5.32%</td>\n",
       "      <td>210.81</td>\n",
       "      <td>A</td>\n",
       "      <td>A1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 145 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    id  member_id  loan_amnt  funded_amnt  funded_amnt_inv        term  \\\n",
       "0  NaN        NaN     5000.0       5000.0           5000.0   36 months   \n",
       "1  NaN        NaN    13000.0      13000.0          13000.0   36 months   \n",
       "2  NaN        NaN    18000.0      18000.0          18000.0   36 months   \n",
       "3  NaN        NaN    14400.0      14400.0          14400.0   60 months   \n",
       "4  NaN        NaN     7000.0       7000.0           7000.0   36 months   \n",
       "\n",
       "  int_rate  installment grade sub_grade       ...        \\\n",
       "0   10.49%       162.49     B        B2       ...         \n",
       "1    5.32%       391.50     A        A1       ...         \n",
       "2    7.99%       563.98     A        A5       ...         \n",
       "3    7.99%       291.92     A        A5       ...         \n",
       "4    5.32%       210.81     A        A1       ...         \n",
       "\n",
       "  hardship_payoff_balance_amount hardship_last_payment_amount  \\\n",
       "0                            NaN                          NaN   \n",
       "1                            NaN                          NaN   \n",
       "2                            NaN                          NaN   \n",
       "3                            NaN                          NaN   \n",
       "4                            NaN                          NaN   \n",
       "\n",
       "  disbursement_method  debt_settlement_flag debt_settlement_flag_date  \\\n",
       "0                Cash                     N                       NaN   \n",
       "1                Cash                     N                       NaN   \n",
       "2                Cash                     N                       NaN   \n",
       "3                Cash                     N                       NaN   \n",
       "4                Cash                     N                       NaN   \n",
       "\n",
       "  settlement_status settlement_date settlement_amount  settlement_percentage  \\\n",
       "0               NaN             NaN               NaN                    NaN   \n",
       "1               NaN             NaN               NaN                    NaN   \n",
       "2               NaN             NaN               NaN                    NaN   \n",
       "3               NaN             NaN               NaN                    NaN   \n",
       "4               NaN             NaN               NaN                    NaN   \n",
       "\n",
       "  settlement_term  \n",
       "0             NaN  \n",
       "1             NaN  \n",
       "2             NaN  \n",
       "3             NaN  \n",
       "4             NaN  \n",
       "\n",
       "[5 rows x 145 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " TAIL\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>member_id</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>funded_amnt</th>\n",
       "      <th>funded_amnt_inv</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>...</th>\n",
       "      <th>hardship_payoff_balance_amount</th>\n",
       "      <th>hardship_last_payment_amount</th>\n",
       "      <th>disbursement_method</th>\n",
       "      <th>debt_settlement_flag</th>\n",
       "      <th>debt_settlement_flag_date</th>\n",
       "      <th>settlement_status</th>\n",
       "      <th>settlement_date</th>\n",
       "      <th>settlement_amount</th>\n",
       "      <th>settlement_percentage</th>\n",
       "      <th>settlement_term</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>103543</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>10.49%</td>\n",
       "      <td>859.56</td>\n",
       "      <td>B</td>\n",
       "      <td>B3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103544</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>14.49%</td>\n",
       "      <td>564.56</td>\n",
       "      <td>C</td>\n",
       "      <td>C4</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103545</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>14.49%</td>\n",
       "      <td>329.33</td>\n",
       "      <td>C</td>\n",
       "      <td>C4</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103546</th>\n",
       "      <td>Total amount funded in policy code 1: 1465324575</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103547</th>\n",
       "      <td>Total amount funded in policy code 2: 521953170</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 145 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      id  member_id  \\\n",
       "103543                                               NaN        NaN   \n",
       "103544                                               NaN        NaN   \n",
       "103545                                               NaN        NaN   \n",
       "103546  Total amount funded in policy code 1: 1465324575        NaN   \n",
       "103547   Total amount funded in policy code 2: 521953170        NaN   \n",
       "\n",
       "        loan_amnt  funded_amnt  funded_amnt_inv        term int_rate  \\\n",
       "103543    40000.0      40000.0          40000.0   60 months   10.49%   \n",
       "103544    24000.0      24000.0          24000.0   60 months   14.49%   \n",
       "103545    14000.0      14000.0          14000.0   60 months   14.49%   \n",
       "103546        NaN          NaN              NaN         NaN      NaN   \n",
       "103547        NaN          NaN              NaN         NaN      NaN   \n",
       "\n",
       "        installment grade sub_grade       ...        \\\n",
       "103543       859.56     B        B3       ...         \n",
       "103544       564.56     C        C4       ...         \n",
       "103545       329.33     C        C4       ...         \n",
       "103546          NaN   NaN       NaN       ...         \n",
       "103547          NaN   NaN       NaN       ...         \n",
       "\n",
       "       hardship_payoff_balance_amount hardship_last_payment_amount  \\\n",
       "103543                            NaN                          NaN   \n",
       "103544                            NaN                          NaN   \n",
       "103545                            NaN                          NaN   \n",
       "103546                            NaN                          NaN   \n",
       "103547                            NaN                          NaN   \n",
       "\n",
       "       disbursement_method  debt_settlement_flag debt_settlement_flag_date  \\\n",
       "103543                Cash                     N                       NaN   \n",
       "103544                Cash                     N                       NaN   \n",
       "103545                Cash                     N                       NaN   \n",
       "103546                 NaN                   NaN                       NaN   \n",
       "103547                 NaN                   NaN                       NaN   \n",
       "\n",
       "       settlement_status settlement_date settlement_amount  \\\n",
       "103543               NaN             NaN               NaN   \n",
       "103544               NaN             NaN               NaN   \n",
       "103545               NaN             NaN               NaN   \n",
       "103546               NaN             NaN               NaN   \n",
       "103547               NaN             NaN               NaN   \n",
       "\n",
       "        settlement_percentage settlement_term  \n",
       "103543                    NaN             NaN  \n",
       "103544                    NaN             NaN  \n",
       "103545                    NaN             NaN  \n",
       "103546                    NaN             NaN  \n",
       "103547                    NaN             NaN  \n",
       "\n",
       "[5 rows x 145 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print (data_2016.shape)\n",
    "print('HEAD')\n",
    "display(data_2016.head())\n",
    "print('\\n TAIL')\n",
    "display(data_2016.tail())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are a few data descriptors at the end of the files than need to be remove. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>member_id</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>funded_amnt</th>\n",
       "      <th>funded_amnt_inv</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>...</th>\n",
       "      <th>hardship_payoff_balance_amount</th>\n",
       "      <th>hardship_last_payment_amount</th>\n",
       "      <th>disbursement_method</th>\n",
       "      <th>debt_settlement_flag</th>\n",
       "      <th>debt_settlement_flag_date</th>\n",
       "      <th>settlement_status</th>\n",
       "      <th>settlement_date</th>\n",
       "      <th>settlement_amount</th>\n",
       "      <th>settlement_percentage</th>\n",
       "      <th>settlement_term</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>103538</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29400.0</td>\n",
       "      <td>29400.0</td>\n",
       "      <td>29400.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>13.99%</td>\n",
       "      <td>683.94</td>\n",
       "      <td>C</td>\n",
       "      <td>C3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103539</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>12.79%</td>\n",
       "      <td>362.34</td>\n",
       "      <td>C</td>\n",
       "      <td>C1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103540</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>10.49%</td>\n",
       "      <td>515.74</td>\n",
       "      <td>B</td>\n",
       "      <td>B3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103541</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>12.79%</td>\n",
       "      <td>543.50</td>\n",
       "      <td>C</td>\n",
       "      <td>C1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103542</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>10.49%</td>\n",
       "      <td>515.74</td>\n",
       "      <td>B</td>\n",
       "      <td>B3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103543</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>10.49%</td>\n",
       "      <td>859.56</td>\n",
       "      <td>B</td>\n",
       "      <td>B3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103544</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>14.49%</td>\n",
       "      <td>564.56</td>\n",
       "      <td>C</td>\n",
       "      <td>C4</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103545</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>14.49%</td>\n",
       "      <td>329.33</td>\n",
       "      <td>C</td>\n",
       "      <td>C4</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103546</th>\n",
       "      <td>Total amount funded in policy code 1: 1465324575</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103547</th>\n",
       "      <td>Total amount funded in policy code 2: 521953170</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 145 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      id  member_id  \\\n",
       "103538                                               NaN        NaN   \n",
       "103539                                               NaN        NaN   \n",
       "103540                                               NaN        NaN   \n",
       "103541                                               NaN        NaN   \n",
       "103542                                               NaN        NaN   \n",
       "103543                                               NaN        NaN   \n",
       "103544                                               NaN        NaN   \n",
       "103545                                               NaN        NaN   \n",
       "103546  Total amount funded in policy code 1: 1465324575        NaN   \n",
       "103547   Total amount funded in policy code 2: 521953170        NaN   \n",
       "\n",
       "        loan_amnt  funded_amnt  funded_amnt_inv        term int_rate  \\\n",
       "103538    29400.0      29400.0          29400.0   60 months   13.99%   \n",
       "103539    16000.0      16000.0          16000.0   60 months   12.79%   \n",
       "103540    24000.0      24000.0          24000.0   60 months   10.49%   \n",
       "103541    24000.0      24000.0          24000.0   60 months   12.79%   \n",
       "103542    24000.0      24000.0          24000.0   60 months   10.49%   \n",
       "103543    40000.0      40000.0          40000.0   60 months   10.49%   \n",
       "103544    24000.0      24000.0          24000.0   60 months   14.49%   \n",
       "103545    14000.0      14000.0          14000.0   60 months   14.49%   \n",
       "103546        NaN          NaN              NaN         NaN      NaN   \n",
       "103547        NaN          NaN              NaN         NaN      NaN   \n",
       "\n",
       "        installment grade sub_grade       ...        \\\n",
       "103538       683.94     C        C3       ...         \n",
       "103539       362.34     C        C1       ...         \n",
       "103540       515.74     B        B3       ...         \n",
       "103541       543.50     C        C1       ...         \n",
       "103542       515.74     B        B3       ...         \n",
       "103543       859.56     B        B3       ...         \n",
       "103544       564.56     C        C4       ...         \n",
       "103545       329.33     C        C4       ...         \n",
       "103546          NaN   NaN       NaN       ...         \n",
       "103547          NaN   NaN       NaN       ...         \n",
       "\n",
       "       hardship_payoff_balance_amount hardship_last_payment_amount  \\\n",
       "103538                            NaN                          NaN   \n",
       "103539                            NaN                          NaN   \n",
       "103540                            NaN                          NaN   \n",
       "103541                            NaN                          NaN   \n",
       "103542                            NaN                          NaN   \n",
       "103543                            NaN                          NaN   \n",
       "103544                            NaN                          NaN   \n",
       "103545                            NaN                          NaN   \n",
       "103546                            NaN                          NaN   \n",
       "103547                            NaN                          NaN   \n",
       "\n",
       "       disbursement_method  debt_settlement_flag debt_settlement_flag_date  \\\n",
       "103538                Cash                     N                       NaN   \n",
       "103539                Cash                     N                       NaN   \n",
       "103540                Cash                     N                       NaN   \n",
       "103541                Cash                     N                       NaN   \n",
       "103542                Cash                     N                       NaN   \n",
       "103543                Cash                     N                       NaN   \n",
       "103544                Cash                     N                       NaN   \n",
       "103545                Cash                     N                       NaN   \n",
       "103546                 NaN                   NaN                       NaN   \n",
       "103547                 NaN                   NaN                       NaN   \n",
       "\n",
       "       settlement_status settlement_date settlement_amount  \\\n",
       "103538               NaN             NaN               NaN   \n",
       "103539               NaN             NaN               NaN   \n",
       "103540               NaN             NaN               NaN   \n",
       "103541               NaN             NaN               NaN   \n",
       "103542               NaN             NaN               NaN   \n",
       "103543               NaN             NaN               NaN   \n",
       "103544               NaN             NaN               NaN   \n",
       "103545               NaN             NaN               NaN   \n",
       "103546               NaN             NaN               NaN   \n",
       "103547               NaN             NaN               NaN   \n",
       "\n",
       "        settlement_percentage settlement_term  \n",
       "103538                    NaN             NaN  \n",
       "103539                    NaN             NaN  \n",
       "103540                    NaN             NaN  \n",
       "103541                    NaN             NaN  \n",
       "103542                    NaN             NaN  \n",
       "103543                    NaN             NaN  \n",
       "103544                    NaN             NaN  \n",
       "103545                    NaN             NaN  \n",
       "103546                    NaN             NaN  \n",
       "103547                    NaN             NaN  \n",
       "\n",
       "[10 rows x 145 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_2016 = data_2016.sort_values(by = 'id',na_position = 'first', ascending=True)\n",
    "display(data_2016.tail(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The data frame has 103548 rows (loans) with 145 columns\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>member_id</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>funded_amnt</th>\n",
       "      <th>funded_amnt_inv</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>...</th>\n",
       "      <th>hardship_payoff_balance_amount</th>\n",
       "      <th>hardship_last_payment_amount</th>\n",
       "      <th>disbursement_method</th>\n",
       "      <th>debt_settlement_flag</th>\n",
       "      <th>debt_settlement_flag_date</th>\n",
       "      <th>settlement_status</th>\n",
       "      <th>settlement_date</th>\n",
       "      <th>settlement_amount</th>\n",
       "      <th>settlement_percentage</th>\n",
       "      <th>settlement_term</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>103538</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29400.0</td>\n",
       "      <td>29400.0</td>\n",
       "      <td>29400.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>13.99%</td>\n",
       "      <td>683.94</td>\n",
       "      <td>C</td>\n",
       "      <td>C3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103539</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>12.79%</td>\n",
       "      <td>362.34</td>\n",
       "      <td>C</td>\n",
       "      <td>C1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103540</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>10.49%</td>\n",
       "      <td>515.74</td>\n",
       "      <td>B</td>\n",
       "      <td>B3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103541</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>12.79%</td>\n",
       "      <td>543.50</td>\n",
       "      <td>C</td>\n",
       "      <td>C1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103542</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>10.49%</td>\n",
       "      <td>515.74</td>\n",
       "      <td>B</td>\n",
       "      <td>B3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 145 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  member_id  loan_amnt  funded_amnt  funded_amnt_inv        term  \\\n",
       "103538  NaN        NaN    29400.0      29400.0          29400.0   60 months   \n",
       "103539  NaN        NaN    16000.0      16000.0          16000.0   60 months   \n",
       "103540  NaN        NaN    24000.0      24000.0          24000.0   60 months   \n",
       "103541  NaN        NaN    24000.0      24000.0          24000.0   60 months   \n",
       "103542  NaN        NaN    24000.0      24000.0          24000.0   60 months   \n",
       "\n",
       "       int_rate  installment grade sub_grade       ...        \\\n",
       "103538   13.99%       683.94     C        C3       ...         \n",
       "103539   12.79%       362.34     C        C1       ...         \n",
       "103540   10.49%       515.74     B        B3       ...         \n",
       "103541   12.79%       543.50     C        C1       ...         \n",
       "103542   10.49%       515.74     B        B3       ...         \n",
       "\n",
       "       hardship_payoff_balance_amount hardship_last_payment_amount  \\\n",
       "103538                            NaN                          NaN   \n",
       "103539                            NaN                          NaN   \n",
       "103540                            NaN                          NaN   \n",
       "103541                            NaN                          NaN   \n",
       "103542                            NaN                          NaN   \n",
       "\n",
       "       disbursement_method  debt_settlement_flag debt_settlement_flag_date  \\\n",
       "103538                Cash                     N                       NaN   \n",
       "103539                Cash                     N                       NaN   \n",
       "103540                Cash                     N                       NaN   \n",
       "103541                Cash                     N                       NaN   \n",
       "103542                Cash                     N                       NaN   \n",
       "\n",
       "       settlement_status settlement_date settlement_amount  \\\n",
       "103538               NaN             NaN               NaN   \n",
       "103539               NaN             NaN               NaN   \n",
       "103540               NaN             NaN               NaN   \n",
       "103541               NaN             NaN               NaN   \n",
       "103542               NaN             NaN               NaN   \n",
       "\n",
       "        settlement_percentage settlement_term  \n",
       "103538                    NaN             NaN  \n",
       "103539                    NaN             NaN  \n",
       "103540                    NaN             NaN  \n",
       "103541                    NaN             NaN  \n",
       "103542                    NaN             NaN  \n",
       "\n",
       "[5 rows x 145 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print ('The data frame has %d rows (loans) with %d columns' % (data_2016.shape[0], data_2016.shape[1]))\n",
    "data_2016.drop(data_2016.tail(5).index,inplace=True)\n",
    "display(data_2016.tail(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove the columns with only NaN and or with large number of NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>total_cu_tl</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>open_rv_12m</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_tl_90g_dpd_24m</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_bal_il</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_tl_op_past_12m</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>open_il_24m</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>open_il_12m</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>open_act_il</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>open_acc_6m</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>open_rv_24m</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tot_cur_bal</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acc_now_delinq</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_tl_nvr_dlq</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>application_type</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>policy_code</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tax_liens</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>collections_12_mths_ex_med</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_bc_tl</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tot_coll_amt</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_pymnt_amnt</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_bal_bc</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inq_fi</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_bc_sats</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_actv_rev_tl</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_actv_bc_tl</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_accts_ever_120_pd</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_op_rev_tl</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_rev_accts</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_rev_tl_bal_gt_0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>installment</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>int_rate</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>term</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>funded_amnt_inv</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>funded_amnt</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan_amnt</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hardship_flag</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>disbursement_method</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zip_code</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_il_tl</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_acc</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>out_prncp_inv</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>addr_state</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_pymnt</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>revol_bal</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pub_rec</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_pymnt_inv</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>initial_list_status</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_rec_prncp</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_bal_ex_mort</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_bc_limit</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>earliest_cr_line</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_rec_int</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>open_acc</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>out_prncp</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_credit_pull_d</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>all_util</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>88 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            0\n",
       "total_cu_tl                 0\n",
       "open_rv_12m                 0\n",
       "num_tl_90g_dpd_24m          0\n",
       "total_bal_il                0\n",
       "num_tl_op_past_12m          0\n",
       "open_il_24m                 0\n",
       "open_il_12m                 0\n",
       "open_act_il                 0\n",
       "open_acc_6m                 0\n",
       "open_rv_24m                 0\n",
       "tot_cur_bal                 0\n",
       "acc_now_delinq              0\n",
       "pct_tl_nvr_dlq              0\n",
       "pub_rec_bankruptcies        0\n",
       "application_type            0\n",
       "policy_code                 0\n",
       "tax_liens                   0\n",
       "collections_12_mths_ex_med  0\n",
       "num_bc_tl                   0\n",
       "tot_coll_amt                0\n",
       "last_pymnt_amnt             0\n",
       "max_bal_bc                  0\n",
       "inq_fi                      0\n",
       "num_bc_sats                 0\n",
       "num_actv_rev_tl             0\n",
       "num_actv_bc_tl              0\n",
       "num_accts_ever_120_pd       0\n",
       "num_op_rev_tl               0\n",
       "num_rev_accts               0\n",
       "num_rev_tl_bal_gt_0         0\n",
       "...                        ..\n",
       "installment                 0\n",
       "int_rate                    0\n",
       "term                        0\n",
       "funded_amnt_inv             0\n",
       "funded_amnt                 0\n",
       "loan_amnt                   0\n",
       "hardship_flag               0\n",
       "disbursement_method         0\n",
       "zip_code                    0\n",
       "num_il_tl                   0\n",
       "total_acc                   0\n",
       "out_prncp_inv               0\n",
       "addr_state                  0\n",
       "total_pymnt                 0\n",
       "revol_bal                   0\n",
       "pub_rec                     0\n",
       "total_pymnt_inv             0\n",
       "initial_list_status         0\n",
       "total_rec_prncp             0\n",
       "total_bal_ex_mort           0\n",
       "total_bc_limit              0\n",
       "inq_last_6mths              0\n",
       "earliest_cr_line            0\n",
       "delinq_2yrs                 0\n",
       "total_rec_int               0\n",
       "open_acc                    0\n",
       "out_prncp                   0\n",
       "title                       2\n",
       "last_credit_pull_d          4\n",
       "all_util                    6\n",
       "\n",
       "[88 rows x 1 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(data_2016.isnull().sum().sort_values()[data_2016.isnull().sum().sort_values()<10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(103543, 131)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2016 = data_2016.dropna(axis=1, how='all')\n",
    "data_2016.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def df_col_low_nan(df_data, min_nan): \n",
    "    # Returns a df with columns with less nan than min_nan\n",
    "    use_col = pd.DataFrame(df_data.isnull().sum().sort_values()[df_data.isnull().sum().sort_values()<min_nan])\n",
    "    use_col.reset_index(drop = False, inplace=True)\n",
    "    use_col.columns = (['col_name','num_NaN'])\n",
    "    new_data = pd.DataFrame()\n",
    "    for i in range(0,len(use_col.col_name)):\n",
    "        df = df_data[use_col.col_name[i]]\n",
    "        new_data = pd.concat([new_data, df], axis=1)\n",
    "    return new_data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(103543, 88)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2016 = df_col_low_nan(data_2016, 10)\n",
    "data_2016.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Index(['loan_amnt', 'hardship_flag', 'revol_bal', 'out_prncp', 'total_pymnt',\n",
       "       'disbursement_method', 'total_pymnt_inv', 'total_rec_late_fee',\n",
       "       'recoveries', 'collection_recovery_fee', 'last_pymnt_amnt',\n",
       "       'application_type', 'total_rec_int', 'addr_state', 'total_rec_prncp',\n",
       "       'verification_status', 'term', 'int_rate', 'grade', 'emp_length',\n",
       "       'home_ownership', 'purpose', 'issue_d', 'loan_status', 'pymnt_plan',\n",
       "       'debt_settlement_flag', 'annual_inc', 'last_credit_pull_d'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_loan_df = pd.read_csv('data_files/loans_2017_clean.csv')\n",
    "print(len(n_loan_df.columns))\n",
    "n_loan_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(103543, 28)\n"
     ]
    }
   ],
   "source": [
    "new_df = pd.DataFrame()\n",
    "for col in n_loan_df.columns:\n",
    "    df = data_2016[col]\n",
    "    new_df = pd.concat([new_df, df], axis=1)\n",
    "print(new_df.shape)\n",
    "n_loan_df = new_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning target feature (loan_status)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_loan_df = new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Current', 'Fully Paid', 'Late (16-30 days)', 'Charged Off',\n",
       "       'Late (31-120 days)', 'In Grace Period', 'Default'], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_loan_df.loan_status.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Current', 'Fully Paid', 'Problem Loan'], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removing fully paid loans\n",
    "#n_loan_df = n_loan_df[n_loan_df.loan_status.str.contains('Current')==False]\n",
    "\n",
    "# Collapsing all but Fully Paid into Problem Loan\n",
    "n_loan_df.loan_status = n_loan_df.loan_status.str.replace(r'(^.*Paid$)', 'Fully Paid')\n",
    "n_loan_df.loan_status = n_loan_df.loan_status.str.replace(r'(^.*Charged Off$)', 'Charged Off')\n",
    "n_loan_df.loan_status = n_loan_df.loan_status.str.replace(r'(^Late.*$)', 'Late')\n",
    "n_loan_df.loan_status = n_loan_df.loan_status.str.replace(r'(Late)|(Charged Off)|(In Grace Period)|(Default)', \n",
    "                                                          'Problem Loan')\n",
    "n_loan_df.loan_status.unique()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Fully Paid', 'Problem Loan'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removing fully paid loans\n",
    "n_loan_df = n_loan_df[n_loan_df.loan_status.str.contains('Current')==False]\n",
    "n_loan_df.loan_status.unique()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature cleaning and selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Custom functiones needed later\n",
    "def types_of_columns(df):\n",
    "    # creates two separate dataframes \n",
    "    # one with float column names and the other with object column names\n",
    "    non_num_cols = df.dtypes[df.dtypes == 'object']\n",
    "    float_cols   = df.dtypes[df.dtypes != 'object']\n",
    "    return non_num_cols, float_cols\n",
    "\n",
    "def column_checker (df1, df2, num):\n",
    "    # df1 dataframe whit the columns\n",
    "    # df2 dataframe with colum names\n",
    "    for col in range(0,len(df2)):\n",
    "        col = df2.index[col]\n",
    "        values = df1[col].unique()\n",
    "        if len(values) > num:\n",
    "           print('\\n', col, '\\n',len(values),'\\n',values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sorting the columns by type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "col_types_original = types_of_columns(n_loan_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Non-Numeric Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_amnt                  float64\n",
       "revol_bal                  float64\n",
       "out_prncp                  float64\n",
       "total_pymnt                float64\n",
       "total_pymnt_inv            float64\n",
       "total_rec_late_fee         float64\n",
       "recoveries                 float64\n",
       "collection_recovery_fee    float64\n",
       "last_pymnt_amnt            float64\n",
       "total_rec_int              float64\n",
       "total_rec_prncp            float64\n",
       "annual_inc                 float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nn_col_list = col_types_original[1]\n",
    "nn_col_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " hardship_flag \n",
      " 2 \n",
      " ['N' 'Y']\n",
      "\n",
      " disbursement_method \n",
      " 2 \n",
      " ['Cash' 'DirectPay']\n",
      "\n",
      " application_type \n",
      " 2 \n",
      " ['Individual' 'Joint App']\n",
      "\n",
      " addr_state \n",
      " 49 \n",
      " ['KS' 'CT' 'MI' 'FL' 'CO' 'MD' 'TX' 'UT' 'AZ' 'CA' 'NC' 'OR' 'AL' 'WA' 'MA'\n",
      " 'IL' 'IN' 'PA' 'MN' 'NY' 'DE' 'WY' 'OH' 'TN' 'VA' 'WI' 'MS' 'GA' 'OK' 'SC'\n",
      " 'NJ' 'HI' 'MO' 'NV' 'VT' 'NM' 'AK' 'KY' 'ID' 'RI' 'MT' 'LA' 'AR' 'DC' 'ME'\n",
      " 'NE' 'NH' 'SD' 'ND']\n",
      "\n",
      " verification_status \n",
      " 3 \n",
      " ['Source Verified' 'Not Verified' 'Verified']\n",
      "\n",
      " term \n",
      " 2 \n",
      " [' 36 months' ' 60 months']\n",
      "\n",
      " int_rate \n",
      " 57 \n",
      " ['  7.99%' ' 11.44%' ' 13.49%' ' 13.99%' ' 11.49%' '  8.24%' '  7.49%'\n",
      " ' 14.99%' ' 10.49%' ' 12.74%' ' 22.74%' ' 16.99%' ' 24.74%' ' 25.49%'\n",
      " ' 17.99%' ' 19.99%' ' 21.49%' ' 11.39%' ' 15.99%' '  7.24%' ' 26.24%'\n",
      " '  6.99%' ' 23.99%' ' 28.69%' ' 18.99%' '  5.32%' ' 30.89%' ' 29.49%'\n",
      " '  6.00%' ' 29.99%' ' 30.74%' ' 30.99%' ' 30.84%' ' 30.49%' ' 30.79%'\n",
      " ' 30.94%' ' 25.29%' ' 14.49%' ' 15.59%' '  9.49%' ' 12.79%' ' 29.96%'\n",
      " '  8.99%' ' 22.39%' ' 27.79%' ' 24.99%' ' 26.99%' ' 25.69%' ' 24.49%'\n",
      " '  8.59%' '  7.59%' ' 26.49%' ' 10.99%' ' 28.88%' ' 29.67%' ' 28.18%'\n",
      " ' 27.49%']\n",
      "\n",
      " grade \n",
      " 7 \n",
      " ['A' 'B' 'C' 'E' 'D' 'F' 'G']\n",
      "\n",
      " emp_length \n",
      " 12 \n",
      " ['10+ years' '6 years' '9 years' '8 years' '3 years' '< 1 year' '2 years'\n",
      " '1 year' '5 years' 'n/a' '7 years' '4 years']\n",
      "\n",
      " home_ownership \n",
      " 4 \n",
      " ['MORTGAGE' 'RENT' 'OWN' 'ANY']\n",
      "\n",
      " purpose \n",
      " 12 \n",
      " ['credit_card' 'debt_consolidation' 'home_improvement' 'other' 'car'\n",
      " 'small_business' 'major_purchase' 'vacation' 'moving' 'medical' 'house'\n",
      " 'renewable_energy']\n",
      "\n",
      " issue_d \n",
      " 3 \n",
      " ['Dec-2016' 'Nov-2016' 'Oct-2016']\n",
      "\n",
      " loan_status \n",
      " 2 \n",
      " ['Fully Paid' 'Problem Loan']\n",
      "\n",
      " pymnt_plan \n",
      " 2 \n",
      " ['n' 'y']\n",
      "\n",
      " debt_settlement_flag \n",
      " 2 \n",
      " ['N' 'Y']\n",
      "\n",
      " last_credit_pull_d \n",
      " 15 \n",
      " ['Oct-2017' 'Sep-2017' 'Jun-2017' 'Jul-2017' 'Feb-2017' 'Aug-2017'\n",
      " 'Mar-2017' 'Apr-2017' 'May-2017' 'Jan-2017' 'Dec-2016' 'Nov-2017'\n",
      " 'Nov-2016' 'Oct-2016' 'Sep-2016']\n"
     ]
    }
   ],
   "source": [
    "column_checker(n_loan_df, types_of_columns(n_loan_df)[0], 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dropping columns with repetitive or non-relevant information\n",
    "\n",
    "zip_code and addr_state have similar information - keeping addr_state\n",
    "\n",
    "grade and sub_grade also have similar information - keeping grade"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Converting dates and other numbers into actual numeric variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jpinzon/anaconda/lib/python3.6/site-packages/pandas/core/generic.py:3110: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self[name] = value\n"
     ]
    }
   ],
   "source": [
    "# int_rate\n",
    "n_loan_df.int_rate = n_loan_df.int_rate.str.replace('%', \" \").astype(float)\n",
    "\n",
    "# emp_length\n",
    "# Removing 2110 rows with n/a in emp_length\n",
    "n_loan_df = n_loan_df[n_loan_df.emp_length.str.contains('n/a')==False]\n",
    "# less than one year is now 0\n",
    "n_loan_df.emp_length = n_loan_df.emp_length.str.replace(r'( years)|( year)|(\\+ years)', \"\").str.replace('< 1','0').astype(float)\n",
    "\n",
    "# Dates\n",
    "# issue_d is now delta time since issued\n",
    "n_loan_df.issue_d = ((max(pd.to_datetime(n_loan_df['issue_d'])) - pd.to_datetime(n_loan_df['issue_d'])).dt.days/365).astype(int)\n",
    "\n",
    "# last_credit_pull_d is now delta time since last credit check\n",
    "n_loan_df.last_credit_pull_d = (max(pd.to_datetime(n_loan_df['last_credit_pull_d'])) - pd.to_datetime(n_loan_df['last_credit_pull_d'])).dt.days/365\n",
    "n_loan_df.last_credit_pull_d = n_loan_df.last_credit_pull_d[n_loan_df.last_credit_pull_d.isnull()==False]\n",
    "n_loan_df = n_loan_df[n_loan_df.last_credit_pull_d.isnull() == False]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " hardship_flag \n",
      " 2 \n",
      " ['N' 'Y']\n",
      "\n",
      " disbursement_method \n",
      " 2 \n",
      " ['Cash' 'DirectPay']\n",
      "\n",
      " application_type \n",
      " 2 \n",
      " ['Individual' 'Joint App']\n",
      "\n",
      " addr_state \n",
      " 49 \n",
      " ['KS' 'CT' 'MI' 'FL' 'CO' 'MD' 'TX' 'UT' 'AZ' 'CA' 'NC' 'OR' 'AL' 'WA' 'MA'\n",
      " 'IL' 'IN' 'PA' 'MN' 'NY' 'DE' 'WY' 'OH' 'TN' 'VA' 'MS' 'GA' 'OK' 'SC' 'WI'\n",
      " 'NJ' 'HI' 'NV' 'VT' 'NM' 'AK' 'MO' 'KY' 'ID' 'MT' 'LA' 'AR' 'DC' 'ME' 'NE'\n",
      " 'NH' 'RI' 'SD' 'ND']\n",
      "\n",
      " verification_status \n",
      " 3 \n",
      " ['Source Verified' 'Not Verified' 'Verified']\n",
      "\n",
      " term \n",
      " 2 \n",
      " [' 36 months' ' 60 months']\n",
      "\n",
      " grade \n",
      " 7 \n",
      " ['A' 'B' 'C' 'E' 'D' 'F' 'G']\n",
      "\n",
      " home_ownership \n",
      " 4 \n",
      " ['MORTGAGE' 'RENT' 'OWN' 'ANY']\n",
      "\n",
      " purpose \n",
      " 12 \n",
      " ['credit_card' 'debt_consolidation' 'home_improvement' 'other' 'car'\n",
      " 'small_business' 'vacation' 'major_purchase' 'medical' 'house' 'moving'\n",
      " 'renewable_energy']\n",
      "\n",
      " loan_status \n",
      " 2 \n",
      " ['Fully Paid' 'Problem Loan']\n",
      "\n",
      " pymnt_plan \n",
      " 2 \n",
      " ['n' 'y']\n",
      "\n",
      " debt_settlement_flag \n",
      " 2 \n",
      " ['N' 'Y']\n"
     ]
    }
   ],
   "source": [
    "column_checker(n_loan_df, types_of_columns(n_loan_df)[0], 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(22171, 28)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#n_loan_df = pd.read_csv('loans_2017.csv')\n",
    "n_loan_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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clGRFkguTTEuyD3AgsCDJdUl27+tfVQ8DXwYO65rmMOCsNt/uSb6VZHmSy5I8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\ne5JtWmtvGI9lAQAAa6aqmpre+5bpqzj9CUlmtdZeN4ZlwTI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JkiQJsFAkSZIkSZKkjIUi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l0pKyuruz937lxKSkp2276kpIRFixbRt29fevfuzaZNm1oUS3V1NaWlpQwcOJB3332X0aNH80//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oRz/iqquu2nLO3LlzmTp1KvPnz2fq1KksXbq0Qc+fmZmZmWUvL1PPImKtpEuB6ZJuyfS81l16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\nzMzMzAxwR5GZmZmZmZmZmSX+P9Fs5zFuOMDLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f8e37b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nn_col_list = types_of_columns(n_loan_df)[0].index.drop(['loan_status'])\n",
    "\n",
    "row = 4\n",
    "col = 3\n",
    "\n",
    "fig, axs = pl.subplots(row,col, figsize=(20,30))\n",
    "fig.subplots_adjust(wspace=0.5, hspace=0.25)\n",
    "\n",
    "for num, name in enumerate(nn_col_list):\n",
    "    #print (num, name)\n",
    "    i = num%row\n",
    "    j = num%col\n",
    "    graph = sns.countplot(y = n_loan_df[name], hue=n_loan_df['loan_status'], ax = axs[i,j], linewidth = 1.5)\n",
    "    graph.set(xlabel=name, ylabel='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " hardship_flag\n",
      "N    21827\n",
      "Y      344\n",
      "Name: loan_status, dtype: int64\n",
      "\n",
      " disbursement_method\n",
      "Cash         22165\n",
      "DirectPay        6\n",
      "Name: loan_status, dtype: int64\n",
      "\n",
      " application_type\n",
      "Individual    21436\n",
      "Joint App       735\n",
      "Name: loan_status, dtype: int64\n",
      "\n",
      " addr_state\n",
      "AK      38\n",
      "AL     298\n",
      "AR     158\n",
      "AZ     565\n",
      "CA    3268\n",
      "CO     513\n",
      "CT     290\n",
      "DC      51\n",
      "DE      65\n",
      "FL    1699\n",
      "GA     701\n",
      "HI     113\n",
      "ID      63\n",
      "IL     788\n",
      "IN     374\n",
      "KS     158\n",
      "KY     223\n",
      "LA     251\n",
      "MA     491\n",
      "MD     525\n",
      "ME      54\n",
      "MI     605\n",
      "MN     430\n",
      "MO     300\n",
      "MS     125\n",
      "MT      53\n",
      "NC     643\n",
      "ND      48\n",
      "NE     117\n",
      "NH     118\n",
      "NJ     732\n",
      "NM     116\n",
      "NV     308\n",
      "NY    1742\n",
      "OH     734\n",
      "OK     184\n",
      "OR     236\n",
      "PA     766\n",
      "RI      90\n",
      "SC     248\n",
      "SD      34\n",
      "TN     340\n",
      "TX    1988\n",
      "UT     192\n",
      "VA     567\n",
      "VT      43\n",
      "WA     417\n",
      "WI     272\n",
      "WY      37\n",
      "Name: loan_status, dtype: int64\n",
      "\n",
      " verification_status\n",
      "Not Verified       5876\n",
      "Source Verified    9290\n",
      "Verified           7005\n",
      "Name: loan_status, dtype: int64\n",
      "\n",
      " term\n",
      " 36 months    16735\n",
      " 60 months     5436\n",
      "Name: loan_status, dtype: int64\n",
      "\n",
      " grade\n",
      "A    2021\n",
      "B    5933\n",
      "C    7348\n",
      "D    4284\n",
      "E    1753\n",
      "F     632\n",
      "G     200\n",
      "Name: loan_status, dtype: int64\n",
      "\n",
      " home_ownership\n",
      "ANY            14\n",
      "MORTGAGE    11172\n",
      "OWN          2568\n",
      "RENT         8417\n",
      "Name: loan_status, dtype: int64\n",
      "\n",
      " purpose\n",
      "car                     213\n",
      "credit_card            3835\n",
      "debt_consolidation    13006\n",
      "home_improvement       1679\n",
      "house                   128\n",
      "major_purchase          599\n",
      "medical                 326\n",
      "moving                  205\n",
      "other                  1708\n",
      "renewable_energy         24\n",
      "small_business          265\n",
      "vacation                183\n",
      "Name: loan_status, dtype: int64\n",
      "\n",
      " pymnt_plan\n",
      "n    21852\n",
      "y      319\n",
      "Name: loan_status, dtype: int64\n",
      "\n",
      " debt_settlement_flag\n",
      "N    22019\n",
      "Y      152\n",
      "Name: loan_status, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "for col in nn_col_list:\n",
    "    series = n_loan_df.groupby(col)['loan_status'].count()\n",
    "    print ('\\n',series)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numeric Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loan_amnt 1186\n",
      "revol_bal 15872\n",
      "out_prncp 4046\n",
      "total_pymnt 21781\n",
      "total_pymnt_inv 21687\n",
      "total_rec_late_fee 604\n",
      "recoveries 535\n",
      "collection_recovery_fee 541\n",
      "last_pymnt_amnt 18565\n",
      "total_rec_int 20651\n",
      "total_rec_prncp 7347\n",
      "int_rate 57\n",
      "emp_length 11\n",
      "issue_d 1\n",
      "annual_inc 2271\n",
      "last_credit_pull_d 15\n"
     ]
    }
   ],
   "source": [
    "num_col_list = types_of_columns(n_loan_df)[1].index\n",
    "\n",
    "for col in num_col_list:\n",
    "    length = len(n_loan_df[col].unique())\n",
    "    print (col, length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "num_col_list = types_of_columns(n_loan_df)[1].index\n",
    "\n",
    "cor_df = pd.DataFrame()\n",
    "for i in range(0,len(num_col_list)):\n",
    "    cor_df = pd.concat([cor_df, n_loan_df[num_col_list[i]]],axis=1)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11045aef0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ToukbqaewrIppH261xg/z9+DBCVG4OqnJKqy0yevTh/qhEoJVOzJZ1Ei57h/d\niT41WjPqlevxmnIpNeVaVlOuu66IoG+kHqNJIbOgwi4/gPAAD0b2DEQlYFdaAVtScm3CfTydGNc3\nBH+tC+uSsth6IM8a1qeTjh4RWgB2pxWw7WAezdEpyJMxfYNRCUFiah7rk7NtwvUaZ64e2J4gnSur\nd2aycW+ONczFUc2kAe3w17qgKLB003FO5JQ3q3fswA42/PIZZsVM576X0/sy27p4YMdadq5dggI4\nObsw9Kqp6IPCKCnM4c+FMykvLQQh6NLvSroPGm+X/6cP9eOdn/ZxKKPELixA68KzN8aicXMkNb2Y\ntxbtxWiy7Fo2bVwkfaP0GKpMdulVAj58oB+5xbZ14807e+Lj6UxBaRV/7znFyu0Zdpo3Dg0lNlRL\nldHEl6sOczynDIARPQIY3MUfIWB9chZ/7joFWOr3hPh2BPi48vrCpGbP5flg/+6tLF0wB8Vspt+w\nMYycYHv/JyVu5PfFXyKEQKVWc9WtDxAeFUtBXjbfffImJUUFIKD/ZeMYOuqaRjU+eyQelRCsTMxo\n9H6eMiaSuEgdhmoT7y3Zz+FMy7WZPimGvlGW+3nqnARr/FsuC2NUnyCKyppugyVnhzQMJU1iNpv4\n9ds53PnYm2i0ej559SFievTHL6hucWoffQD3PPkuru6eHEzayi9fz2TKc7PJSj/C9vUrmPLcbNQO\njsyf+SxR3fqh8w9uRrF5ClavIG/ZT7R74oVzzuO1hUnkFxt4865ebE/N42Ru3YOtZ4QPgT5uPPTx\nVjoFeXLfqE4889VOa/iYuBBO5pbj5mx72/yWcJJlCRZDoP4C12A5h2uXfMpV97+Ch5eOhTMfJ7xL\nX3QB7a1xND7+XDvtDVzcPDi6P5E/F33IjY+8i9rBkaunvoqTsysmk5FFc54mNKYXgR2iaYrzoTdl\nbBQvfL2TvGID79/Xh4QDOTYGQu9OOoJ0btz/wRaiQjRMHRfFE58lYjIrzPsjlcOZpbg6qZlxfxy7\nDudzIqectxfttaavv8D1tHFRPDd/J7nFBmbe34ctKbZafTrpCNa5cc8si9aD46OYPjcRgDU7T/Fr\nwkkev7qzze9/ZFI0n/9xiOSjhVzeM5B+UXpr2Evf7Ca32MCM+/qw5YC9VpDOjXtryvXAuCgeqynX\n5/XKNev+OHbWlGvn4QK+WpOG2axw5+URdtdLCLiidxAL1x6huMLIHZeHk5pRQl6xwRqnssrE6p2Z\ndAr2tEn3fbWtAAAgAElEQVSr93KmR4SWr1YfxmRWuGFIKIcySigorWq0bggB4+ND+HLVYYrLq5ky\nNpL9J4rIKarTqqgy8dvWk8S097JLP7ZvMKkZxSxcdxS1SuCobt5DYjabWL/0Uybc+188vHQsmv0E\nYZ374uNvWxcnTXkdFzcPjqUk8vdPH3LdQ++iUqkZOO4ufEMiqKos58cPHqddp+42aQFm/ryfhydG\n8/DH2+z07xnViSUbj7N2TxaPTIxmVJ8gliek0zfSUmfueG8TMe00dumvGtCe4zlluDmrG5w/wZFT\nJSxcf4zbhoezO62AzPw6Y79rqDd+3i48N38n4QEe3DI8jDd+SCZI58rgLv68/kMSRpOZRybFsOdI\nITlFlaTnVfDR8gPcNiK82XN5PjCbTfw0fxZTnn4Hbx9fZrw4la69BxAQHGqNE9mlF117DUAIQcbx\nw8yf/QrPvDMflUrNhJun0C4sksqKct5/YQpRsb1t0tbywte7yC02MGtKHAkpuVbjGSCuk44gnSt3\nz9xMtPV+3g7A6p2ZLEs4yRPXdLbL8+dNJ/hp43HgAi5wfQntfHLplOQSQwhxVAihbyIsVAhxVtsm\nCCG+EkJce/qYdZw8cgCdXxA+voE4ODgS23co+3dtsonTvmMXXN0tD6x24TEUFVi8HTmZJwgJj8bJ\n2QW1Wk1YZCz7dmy00zgbypJ3YywpblEe2YWVGM0KG/dlExepswmLi9Sxdo/lTT41owQ3Fwe8PZwA\ni9emd0cf65v+mZJ1PBUvXSBeugDUDo5E9hxM2t4EmzhBYTG4uHkAENAhitJCyzkUQuDk7AqA2WTC\nbDIiTrMv/PnQy8wvJ6ugEqNJYX1yNv2ifW3C46P1/FVzng6cLMbdxQGthxMFpVVWb15FlYkTuWXo\nPJ2bLU9GfjmnarWSsunfiNafjWgBJB8rpKTCaJdnsM6N5KOFAOw8nG8TdqpeueJboVw7D+djNlu8\nVCkniux+S5CPKwUlBgrLqjGbFfYfLyKygQFYbjCRmV+B2WybVu/pTEZeBUaTgqLAiZwyIkM0TZxJ\nCNG7kVdsoKC0CpNZIelIATHtbA3Askoj6Xn2Ws6OKkL93UlMtZwvk1mhstrUpBZA9olUvPQB1rrY\nqftgjuzdahMnMLSuLvq3j6KsyOLxdNf44BtiMaSdXNzQ+oVQVmR7rQD2nyjGw8UBH08nu7Ae4Vqr\nR3TVjkwGxvgB0L+zL2t2ZjaaXq9xpl+0nt8b2QkkM7+CapOC2ayw7WAuPcK1DfR82LLf4mFNO1WK\nm7MDXm6OBGpdOZJVSpXRjFmBg+nF9OroA8Cpggo7r/WF4vjhFPT+wej9gnBwcKRn/HCSE23be2cX\nV+t6fVWGSuu4Oi+tjnZhkQC4uLrhH9Seonxbz3cttffYuqQs4mNsH3HxMb7W+znlZDEerg3vZ+kV\nPB9Ij2ErIix3jFAUxXzayP8Cigty8dLWPRw1Wl9OpjW93VXihpVEdo0DwC8olNVLv6S8tBgHRycO\nJm0jODSyzX/zmZJXbKBTsO1DVOfpbOOpyS82oPN0orC0ijsv78iCv9JwdVI3zIrRccEMjfXn8Cn7\n7qzSojw8vesaPw8vPaeON7193d6E1YRG97Z+NptNfD/jMYpyM+k2cAwBHaKaTHu+9HLreZjyigx2\nxojO09mmGy6v2IBO42zjyfLzdiEiwJMD6baGfpcO3k1q5RYbiGqgpdc4k1NUaRNH30CrIceyy+gf\nrWdzSi6Du/o1Gie3yF5L5+lMTrGtVmPlCm+kXACX9wqy+87D1ZHieg+7knIjQTrXJn97fXKKDAzt\n5o+rk5pqk5mIQM8mu6sBNG6ONt1txeXVhPjad983htbDmTKDkasHtidA60JGXgW/bUun2th0U1da\nlIeHV/26qCPrxMEm4+/ftpr2Ub3svi/OzyI3Iw3/9o23H7XXPL+k7jpo3BwprTRajfLc4kp0XhZj\nXa9xJruROpNfUsXUcZF89nsqrs72j8b8EgN+3i4AFJRWERZga8BrPZzIr1cXCkqr8PZwIj2vgqsG\ntMfdxYFqo5nYUC3Hskq52CgsyMXbp+5+8PLRc/zwfrt4e7b9w28/fk5pcSH3PvG6XXh+zilOHjtE\nh4iYZvUavcc0zuTWvzZFp7+fweIJH9EjgNR0+zb4fCFUl87kE+kxbCE13rsDQoivgWTgNiHEZiHE\nDiHEIiGEhxBilBBiUb00w4QQy2uObxJCJAkhkoUQb52FtIMQ4lshxH4hxGIhhFtNfi8KIbbV5DdX\nnMFy7EKI+4QQ24UQ2+fOnXuWZ8BCWsouEv9ZyZXX3gOAX1B7Bo+6nq/ef5r5M58lsF3Ev3Zwbu+O\nPhSVV5F2yr4x/2NHBg98mMATnyeetvE6HScO7WHv1tUMHDfZ+p1KpeaWx2dx94vzyDqeSm5m6+1j\nfb71anFxUvPMDV35bGUqFQZbr9OQ2MYNtdZk5s/7Gds3hFlT+jRq6J8rLk5qnmuiXDcM6YCpxkhp\nLfJKDGzen8sNQ0O5YUgoWYUVKErratSiUkGgjxtbD+Ty0fKDVBnNDGnCqD4XTh7aw/5taxgwZrLN\n91WGClYueItB4+/ByeXMjNhzpV+UnsLSKlIbGa/YEk4VVLAyMYPpk2J4ZFIMJ3LKaOWqcF7pFjeY\nZ96Zz13TX2HF4i9twgyVFXw56yWuunUaLm7u5+X3/LY1nbve38QDH20lv9Rw+gSS0yI9hq1DJ2Ay\ncAhYAoxUFKVMCPEU8BjwOjBXCOGuKEoZcAOwUAgRBLwF9AYKgFVCiEmKovx8BppRwN2KomwUQswD\npgHvAnMURXkFQAixABgH/NpcRoqizAVqLUKldkCwRqunqKBu8HlxQQ4arc4u/akTaSydP4PJj7yG\nm0fdG2CfwaPpM3g0AKuWzMNL22jP+AVBp3Emv8S2EckrsXiAavHROJNXUkW/KF/iOunpFaHD0UGF\nm7OahydE88GyFBsPzJqdmVwzsINNnh5eOkoK67pUSoty8fCyP4c5GUf488c5TLz3JVzd7bsDnV09\nCOkYy7GUHegDO9iFn089vVfdOdJ5OZPXyHnUa1wAS9epTlPniVWrBM/c0JW1e7LYvD/HJp1KJegf\nY2ts1NfSa2w9umDx9vh61WnpNc7kFjf/cDiZW87zX+8CIFjnyq3D7cd36Zsol6/GVqt+uZ69oSt/\n78liU4NyjewRQFyknufm72TJ88NswkorqtG4Olo/e7o5nFV32Z4jBew5UgDA0Fj/ZtMWl1fj5V6n\npXFzpPgMB+wXl1VTXF5tHZO791jhaY14Dy8dpUX162Ie7hr7upibeZS/F3/I+LtfxKVeXTSZjKxc\n8CaRPYdy1YTRdt7kWhq75sXl1Xi4OKBSCcxmBb3Ghbwa73NusQE/Lxf2Nqgzg7r40T/Gl75Repwc\nVHZjiX3qDXvQejhR2MAIKSitwsfDqUEcy8vihr3ZbNhr6da+akC7Fr9EtgXeWj2F+XWTkYrybXuM\nGhIR3Z287LcpLSnCw9MLk9HIl7NeoveAkXSLG3JavUbvsWID+vr3s9fp7+fCsrpz+fv2DG4cGnZa\n7TbhElqu5t/pwrn4OKYoyhYgHugMbBRC7MJiLHZQFMUIrATGCyEcgLHAL0AcsFZRlJyaON8Cp7+j\nLJxQFKV20N43wKCa48uEEAlCiCRgONDlXAsVHBpFXlY6+TmZGI3VJG1dR3T3/jZxCvOy+e6jV7ju\n7ifRB4TYhJUWF1jj7NuxgW79hnOh8fNywUElGNjZz24G5/bUPIZ1CwAsszfLDUYKS6v4bu0R7p+9\nhWkfJjBz6T6SjxbywTJLl7p3vQdB/UkMtfi360RhbgZFeacwGas5uPMfwrv0s4lTXJDDb1+9wRU3\nTUfrWzc5p7y0CEOFxUtprDZw/OAutP625/hC6AX5uOHv7YKDWjCkqx9bG8yiTUjJZXgPy3mMCtFQ\nXmmyPggfnhjNiZxyfmlkRniPcC3puWU239loxfrZzdhNOJDLiHpaZfW0mqLWOBLCMou0PvXLlXCa\nctXXeqSmXA1nuvfu6MM1Azvwynd7MFTbd7tm5Feg9XTGy90RlUoQ097rrLrDaidIaNwciQrRsPdY\nYZNx03PL0Wmc0Xo4oVYJYsO0pJw8szG7pZVGisqq0Ne8OEUEepJd2PwD2y+kE0W5mRTnZ2EyVpO6\n+x9CO/e1iVNSkMPvX7/ByBsfxbteXVQUhb8XzUbr144eQyay50gh3689yvdrj9qkj2mnoazSaNON\nXMvutAKrV/OKXoFWg33z/hxG9gy0Sz9v1WFufmsDt72zkdcWJrMrzXZMo5+3C45qgUoliIvUszut\noIFePvExFkMqPMCDCoOJoprVCjxdLUamj6cTPSN0dnXrYqBdeDQ5p9LJy7a09zu3/EWXXrbtfc6p\ndKtX+sSRg5iMVbh7aFAUhYWfv4N/UHuGjbmuWZ3ae2xorL/d/bwlJcd6P0eHWK7N6e5nbb02eEBM\n04ZsmyNUrf93gZAew9ah9mkmgNWKotzUSJyFwINAPrBdUZSSFm663bAzQhFCuAAfAX0URTkhhHgZ\ncDlXAbVazbibH2T+zGcty9UMvBL/4FC2rl0OQN9h4/j7128oLytm2bezAUtX5LQXPgTg+4//R3lp\nMWq1A+NveQjXmkHm50r7p17GvVtPHDTeRC9YStaCLyhYtfys8nj+plhUKsFfu09xMrecK3pZHhCr\ndmSy41A+vSJ8mDOtL4ZqEx8tb3psXi23DQ8n1N8dFGzGLdWiUqsZdvX9/Dz3ZRTFTOe+I9EFtGfP\npt8B6DZgNFtXLaSyvIS/l3xiSaNSc9P09ykrzmf19zMxK2ZQFDp1H0R457hmf8/50PtkxUH+e1sP\nVCrBmp0ZHM8pY1Qfy/i5ldsz2J6aR59IHXMf6Y+h2sSsny3jlDq392J4j0COnCpl1hRLvl//mUZi\nqsVAH9LVn3VJWXQNrRvU//FvB3n1dovWqh0WrTE1Wiu2Z7DtYB5xnXR88ahFa8bSujFRT17bhW5h\n3mjcHPn68QF88/cRVu3IZFisP+P6WgzejQ28e/+rKdfqmnKNrtH6fXsG22rK9XlNuWbUK9eImnLN\nrinX/D/T2J6ax5QxkTg6qHjt9h6NXi9FgdU7MrhxaChCCPakFZBbbKBnzRI0Ow8X4O7iwB2XR+Ds\nqEJRIC5Sz2e/p1JlNHP1wPa4OqkxKQp/JGY0anzWYlZgecJJJo8MR6USJKbmk11YaZ2Ete1gHh4u\nDkwdF4mzoxoFy4P2g19SMFSbWZ6QznWDO6BWCfJLq1hSMwu0KVRqNYMn3seyz19GMZuJiRuBLqA9\nyZstdbFr/9FsW7MQQ3kJ65Z+akmjUnH9I++TeXQ/B3asRRfQgYUzHgUgftSthMb0sdGYflVn3v2p\nbkb7a5N78P6SfeSVVPHZykM8d2NX7rg8gsMZJazcbplQsvVAHv2i9Mx/fACGarNN+uZwcVSjD/Dk\n6eu6klNUSUZ+BUNj/QFYl5RF0tFCYkO1vDa5J1VGM1+tPmRNO3VsFO4uDpjMCt+tTaOiyjLUoGeE\nDzcNDcXD1ZGHJza92sD5QK1Wc83kh/j07acwm030GzqawJAwNv65DICBIyawZ9t6tm1YhVrtgKOT\nM7c/+CJCCNIOJLF9w2oC24XzzrP3AjD2+rvp3CPeTufVyT1Rqyxt7vHsMsbEWV4IVmxLt9zPkXrm\nTe9PZbWZGUv2WdM9dV0XuoVp0bg5suCJgSz4K41VOzK5+8qOhAd6gqJcNBN5/u2IthqT8v8FIUQo\nsFxRlK5CCF8gERiuKMohIYQ7EKwoykEhhBo4DGwDFimK8qMQIhDYQl1X8h/AbEVRfhFCHMVi4Nm9\nWtZoHgEGKIqyWQjxObAf+AI4AIQC6pq8FyuK8rIQ4qua37n4NEVSGltbqrW5bnBd9+Se0QPbVKvb\n73Wzoa99bV2batVfruZMDMuWMG1c3cSQ86k1/qW/2lSr/nI1Y15sWy2AFa/U6Y1t47L9Vq9sb/xw\nVgsLnDXP3NDVevz8/F1tqvXq5DrD94Nfmp6g1hrUN6Auf3ZNm2qtfn2k9fjeWZvbVAvgs0f6nz5S\nG7CikVnYrUmt8Qcw+oU/21Sr3nI157VvN/WBO1rdmOr04VcXpH9adiW3Ioqi5AB3AN8LIfYAm4Ho\nmjATsBwYXfMfRVEygaeBv4HdQKKiKL+codwB4AEhxH5AC3ysKEoh8BmWSTB/YDFCJRKJRCKRSM4I\n2ZXcQhRFOQp0rff5LyxjBxuL+yCW7uT6330PfN9I3NDTaDba76AoyvPA8418f0dT+UkkEolEImkB\n/9JVNxpDGoYSiUQikUgkLeESmpUsDcOLGCGEDmhsQMYIRVGa3xRVIpFIJBKJ5CyRhuFFTI3x1/h0\nRolEIpFIJBcFLVxl5KLi0ukUl0gkEolEIpG0COkxlEgkEolEImkJl9DkE7mOoaQhskJIJBKJ5N/O\nee3bPfT4lFZ/dnZ87xO5jqFEIpFIJBKJ5MIhu5IlEolEIpFIWsClNPlEGoYSO5Zuan4P1NbgqgHt\nrcfnc5u687n93vncrut8an32e2qbat07upP1uK3LBRfuPJ5PraqszDbVcvIPtB6fz3K9+n1Sm2o9\nf1Os9bitt3IDm+3czivXvb6+TfNf9OwQ6/H5rB+Sc0MahhKJRCKRSCQtQVw6I/MunZJIJBKJRCKR\nSFqE9BhKJBKJRCKRtAQ5xlAikUgkEolEAiBUl45hKLuSJRKJRCKRSCSA9BhKJBKJRCKRtAw5+UQi\nkUgkEolEcqkhPYaSM+ZA0jZ+/e4jFLOZuCGjGTb2RpvwvTs2sXrpVwghUKnVjL9pGqGRXU+b76wp\ncaiE4M9dmfy8+YRd+F1XRNAzQkdVtYk5yw9w5FSpNUwl4K27epFfUsUbPyYDcP3gDozoGUhxefU5\nlzVk+jNo+g7EWFjAwam3nXM+tUwbF0nfKD2GKhPv/LSPQxkldnGevr4LkcEajGaFAyeKmfnzfkxm\nheHdA7hhSAeEEJQbjHzwSwpp9c7B+dBriiP7E/lryVwUxUxs/BX0G3mdTfi+7X+z9c+fAAUnZ1dG\nXjcNv+Bwa7jZbOKb96bj4aXj6vteOueyBGhdePbGWDRujqSmF/PWor0YTXU7VEUGa/hgSh9e+yGZ\nf5Kzrd83HBbUFlohejeevzHWLp+z1X3s6hgigzUI4GReOe8s3kdllem0ZazPhoQE3vpgDiaziavH\njuWeW2+xCd+2cycPP/s8wYEBAIwYMoSpd0zGYDBwx0OPUFVdjclk4vJhQ3ngrjubLdPZlK2puujh\n4sDj13QmyMeVKqOZ95bsa1InPNCDK3sFIQTsOlzApv05NuE6T2fGx4cQoHVh7Z4stqTkWsP6Runo\nGeGDokBOUSXLtpzEZLbd4ax3Rx+mjI1EJQQrEzNY9M8xu98wZUwkcZE6DNUm3luyn8OZlrJOnxRD\n3yg9hWVVTJ2TYI1/24hw+kfrMStQVFZ1RuezLZh1fx9LG7z7VKNt8J2XR9ArwgeD0cSHvx7kSJZt\nG/zmnb3ILzHw5qK9AHTwc+e+UZ1wcVKTXVTZrHZL6oebs5qnr++Kn7cLapVgcSPX5LxxCU0+kR7D\nNkYIcYcQIuhC/46WYjab+GXBbO6c/jrTX/ucXQl/k5VuexN27NyTR175lEde+ZRr73qCn758/4zy\nfm1hEtM/3cagLn6E6N1swnpG+BDo48ZDH2/lkxUHuW9UJ5vwMXEhnMwtt8vzt4ST/OfzRP7zeeJZ\nltRCweoVHHn+sXNK25C+kTqCdW7c8d4mZv68n4cnRjca769dp7hrxmbum7UFZ0cVo+Ms1eZUQQWP\nf5bIfR9s4du/j/DoVTEXhZ7ZbGLN4o+55v7/cufTH5GyYx25p2wXR/fSBXDjQ29yx1MfEn/Fjaz6\nYY5N+I51y/Dxb9fistwzqhNLNh7njvc2UVphZFSfultOJeCeUR1JPJRvl67+QutAm2idzC1nypwE\nptQzCM6ljJ/8dpApsxO4f3YC2YWVTIwPOaMy1mIymXhtxiw+euctfvl6Pr//+ReHjx61i9erWyyL\n533B4nlfMPWOyQA4OTnxxcz3+enLL1g073M2Jmxl9969TWqdbdmaqos3DQvlcGYJ989O4O3Fe5k2\nLqrR9ELA6N5BfL/2CJ+sSKVLBy/0GmebOBVVRv5IzLAxCAE8XR3oG6nniz8OMff3VISALh287DQe\nGB/FC1/v4v7ZWxjWzZ/2vu424XGddATpXLl75mY++CWFB8fX/dbVOzN5/utddnn+tOEY0z7cyoMf\nbSXhQK5d+PnitR+SmT53OwM7+zbSBmsJ9HHloU+28emKVO4d1dEmfExcMOl5tm3wlDGRfLv2CI9/\nnsjWZsrV0voxMb4dx7PLmDI7gSc+T+S+MZHnUvxWQahUrf53oZCGYdtzB9Biw1AIcUG9uyfSDqDz\nC0LnF4iDgyPd+w5j385NNnGcXVyt2wJVGSrPeAvz7MJKjGaFjfuyiYvU2YTFRepYu+cUAKkZJbi5\nOODt4QSAj6cTvTv68OeuUy0snT1lybsxlhS3Sl79O/uyZqdl54n9J4rxcHHAx9PJLt7Wg3nW45ST\nxfhqXADYd7yI0kqjJf3xInwbPPAulN6pYwfR6gPx1gegdnAkuucQDidtsYkTHBaDi5sHAEGh0ZQW\n1T0kSgpzSdu3jW7xV7S4LD3Ctayv8ZKt2pHJwBg/a9jE/u3YsDebwlJbj4xe40y/aL3Nd22l1Rxn\nWsZyQ5130NlRbRN2JrpJ+1NoHxxMu6AgHB0dGT1iOH9v2Nhk/PoIIXBzsxgMRqMRo9F4RluAtbQu\ndvDzYFdaAQAncsrx93ZpVCfIx4380ioKy6oxmxX2Hi8iMkRjE6fcYCIzvwJzA08gWAxrB7UKIcBR\nraK0wmgXJyOvglMFlRhNCuuSsoiPsa078TG+1rYo5WQxHq4OaGvaquRjhZRU2Pdg1L+mLk5qu/Dz\nRV0bnEOfTg3bYD3rkrIASxvs7uKAt3tdG9yrkTY4yMeVfceLANhzpLBJ3ZbWDwVwdbacN1cndaPn\nWHL2SMPwHBBCPCaESK75e1QIESqESK4X/oQQ4mUhxLVAH+BbIcQuIYRrE/kdFUK8LYRIEkJsFUJ0\nrPn+KyHEJ0KIBODtmjznCSHWCiHShBAP18vjdiHEHiHEbiHEggbptwshDgohxp1rmYsLcvHy8bV+\n9vLRU1xg/yaYnLiB9565i69mPs+1dz1xVhp5xQZ8PG2NEJ2nM3nFBuvn/GIDupqG487LO7LgrzQU\nxb6hHx0XzHv39GbauAv3BlmLXuNs052SW2yw82bUR60SjOwRwLbUPLuwUX2C2HbQ/vsLoVdSlIen\ntq5OeHjrKSlq+rclbVlFWEwf6+e/ls5lyIS7mu2COZOyaNwcKa00Wh/4ucWV6LwscXQaZwZ18ePX\nhJN2eU8dF2m3vV9baTXH2VyvJ67pzI/PDqadr5u1y+9MdbNzcwjwq7te/r6+ZOXk2MXblbyXq++4\niyn/eZJDR45YvzeZTFx7190MnTiJ+D596Na5c6uWDezrYtqpEgZ1thjeUSGaJg1DTzcHm6EjJeXV\neLo6nvb3AZRUGNmcksvDE6J4dFIMhmpzo0M1cuqXo8iArmFbpXEmt0Gc5spay+SR4Xz9xEAu6xZw\nRr+3LckvqWtfa/HxcLJpg/NKDFbj7c7LI/jmryOYG7TBJ3LLrC/5/RsY0PVpaf34ZfMJ2vu5s/Dp\nwcx9OJ6Plh84w5K2AUK0/t8FQhqGZ4kQojdwJ9APiAfuBbSNxVUUZTGwHbhFUZQeiqJUNJN1kaIo\nscAcYGa970OAAYqi1PZrRgNXAn2Bl4QQjkKILsDzwHBFUboDj9RLH1oTdyzwiRDCrmUVQtxXYzxu\nnzt3bvMn4DR07T2Ix9+Yx20PvczqpV+1KK/m6N3Rh6LyqkYb8D92ZPDAh5auhYKz8NxcLDw8MZqk\no4UkH7V90+4ermV0n2A+W3noX6d3PHUPSVtWMWT8HQAc3rsVNw9vAtp1bD5hC5k2NpLPV6bS8N2h\nX5SewtIqUhsZz9TaWrU4qFve0L/70z5ufOMfjmeXMSzW/4x0z4aYyEhWL/qBJV/N4+arr+aRZ5+3\nhqnVahbP+4I1ixeRnLKf1LS0lgs2oGFdXLjuKB6uDnzyYD8m9W/HoczWu161uDiqiArRMOfXA8z6\neT+ODoKuod6trtMU89ekcfu7G/l7T+v3fLQlvTr6UFRW3Wgb/NFvB7myVxBv3dmzVT2hDetHn0gd\nhzNKufHNf5gyO4EHxzfeFS05O+Tkk7NnELBUUZQyACHEEmBwK+T7fb3/M+p9v0hRFFO9z78pimIA\nDEKIbMAfGF4TLxdAUZT6A41+VBTFDKQKIdKwGJY2g10URZkL1FqEytJNtuPEADRaPUX5dd6Fovxc\nNNqm3wTDo7qxOCeTspIi3D3tx+s0hk7jTH6Jwea7vBIDunpvkD4aZ/JKqugX5UtcJz29InQ4Oqhw\nc1bz8IRoPliWQlFZnedgzc5MrhnY4Yz0W5tPHuwHwIH0Yvy8XNiLpWtFr3Emt9jQaJpbh4fh5e7I\nzJ/323wfFuDBY1fF8OxXuxrtLpkQH8KYPsHnTQ/A00tHSUFdnSgtzMXTS2cXLyfjCH8s/IBr7v8v\nru6W7r30tH0cTk7gyL7tGI1VVFVW8NuCdxl7m62XOb+k6rRlKS6vxsPFAZVKYDYr6DUu5BVZ4nQK\n1vBszcQPLzdH4qL0mEwK0e009I/xZVg3f5u82kKrdhJEXKTt/XIu9QPArMDaPVlcP6QDf+zIbFK3\nIX56X05l112vrJwc/H19beJ4uNeNmxvSP57XZsygoLAQrXedoaTx9CSuZ082JmylU3g4DWnNulhu\nMPHuT3UTThb8Z2CjaUvKjWjc6jyEnm6OZ9ytGBbgQWFplbVbN+VEMSF6N7sXJV+vundqvZczeQ3b\nqreSQEEAACAASURBVGIDei8XqC2rV/PXsSF/7z7FnZe37YvS6fDxtLSv9ckvrbJpg3WezuSXVBEf\nradPJ8ukHScHFa7Oah6aEMXsZQfIyKvg1YVJAAT6uHLDkFBr+tasH1f2CmLh+qMAZORXcKqggkCf\nRjvm2p5LaPKJNAxbB29sva+N93c0j9LEcVmDePXvGhOnv4YNnxDn5FcICYsiLzud/JxMNFo9u7eu\n5ab7n7GJk5uVjs4vCCEE6UdTMVZX4+ahaSLHOvy8XMgvMTCws5+dgbI9NY/RfYLZuC+HTkGelBuM\nFJZW8d3aI3y31tLN1aW9FxPi2/HBMsvsWW8PJ+tYq35RTRuvbU3tZIO+UTomxrfj7z1ZxLTTUFZp\nJL/E3pM5uk8QfTrpePKLHTbeH18vZ166pRtvLdprN8i7lmVb/o+98w6vqkgb+G/uTe/JTSeQBiGh\nd1CKiIKglLWgsoqgnwVQQMS2qGtZQN0VxIYC6qqwq4BgQ0GagPTeawhJgIT0Xm5u7p3vj3O5yU1C\nEkgCys7vefLknDPvzDsz59w573mnnePH7eeumj6A4BYx5GSmkJt1AU9vA8f3beKO0c/ZyeTnpPPD\n5zO5/cGp+AU2s13vN2yszXuYfOogu3/7rppRCLDlaHq9ynIgIYd+7QLZcDCNQV1CbMbYQ+9UjKF7\n7u42bD+RydZjGWw9lsHnq08DsGbmrTaZWzuHNLqui9zc0d4IvdznI9TPlZRsrdPhhrgAzmYU10vv\nRdrFtibp3DnOpaQSFODPynXrefvvL9vJZGZlYfDzQwjBoaPHsFgkPt7eZOfm4qDX4+XpSanRyPbd\nu3nkr6Oq6YDGfRbdXRwwmsyUmyVDuoVy6Ewuwb7VX/wp2cX4eTrj4+5Ifkk5bVt4893W6rNrayKv\n2EQzfzcc9IJysyQy2IOUrOqdO6EGN4J8XMgqMHJT+yDeXmo/+Wb78QyG9Qxj46E0YsO0stbVY2F3\nT2MDapVtSira4ADeq7IKwe6TWQzuFmrfBheV8d8Nifx3QyIAbVp4M7xnGB/8qHXlerk5kl9sQgB3\n97af4NWYz0d6Ximdo/04nJiLj4cTzatMnLmaXMvJIo2NMgwvn9+BL4QQb6FNr7gTrWt5khDCABQC\nQ4FVVvkCwLMe6d4HvGX9v+0y87Qe+E4IMVtKmSWE8KvkNRwphPgSiASigCsahKHX6xn+wFN8Putv\nWCwWuvW9jaBmEWz/7ScAet08jMO7f2fv1rXo9XocnZz56/iX6zVA/eVR7dHpBOsPXOBcZjGDuoQA\n2sD+vfHZdIn248MJPTCazPUaQzJ6QBQRQe4gqXOphEvR4oXXcO/QGQcvH2IXfkfaws/IWb3iitLa\neSKLnq39+XLqjRhNFt5ZVvFCmTGmE7OXHyWroIzJI2JJyy3l/XHdAdh8NJ1F688wekAUXm6OTBqu\ndZOYLZIn5+68qvpqQqfXc8vd41j2yd+xWCy07zkQ/5Bw9m/5BYBOvW9n26/fUFKUz9qlc21xRk+d\nU2N6DSnLglXxvHR/O8YOjOZ0SgGrdp+vt47KXMgpaRJdLo46urb0u+IyZheW8fzItrg5O4CAhNSC\nWpcRqgkHBwemPT2Zcc8+h9li4c7bh9AyMpIlP/wAwL0jRrB6w0aW/PAjer0eF2cn/vXq3xFCkJGV\nxcsz38RstiClhUE338xNN95Yp86GPostAtx5fmQbpISk9CJmLTvKQGv7UBkpYdXuFEb1j0QnYH9C\nDpn5RrpY63xvfDbuLg78320tcXbUISX0aO3PJz+fJCWrhGPJeTw6uCUWC6TllLDvdPXZ3R+vOMH0\nMZ3R67S2KTm9iNu7ax87v+w6z66TWXSP8efzKTdQarLwbqWldV4Y2ZYOkb54uTmy8NneLFyfwOq9\nqTw8qCVh/m5IKUnPvbK2qjF46f526HSC36xt8MDOWh2v2ZfK3tPZdG7pxwfju1NmsvBRPdrgPm0D\nuK2LNueytlnJDX0+/rM+gefuacv8Sb1AwKe/xvPivXUvkaaoHVHTwH1F7QghngEesZ5+KqWcY50I\nMhk4DyQAiVLK14QQdwMzgRLghprGGQohEoHFwBA0j+AoKWW8EOILYIV1rCJCiNeAQinlO9bzw8BQ\nKWWiEGIM8ByaF3GflHKsNX4p2gQYL+AZKWVd1k2NXcmNTeVlQu6ZsbFJdX370k2244NDau6Kaiw6\nrKzw3gyctrZJdVX2dF1NXVUnbDQ2jw2pWJKoqcsF164er6ausrTUJtXlFFRhrF3Nck3/+lCT6np5\nVMX6k0NeWdekugBW/uOWJtdREyNnbmrS9JdO62c7vorPx1Xt2018/cVGN6YiXn3rmvRPK4/hFSCl\nnA3MrnLtfeD9GmSXAcvqkey/pJQvVIk7tsr5a1XO21U6/hL4soZ010opx9VDv0KhUCgUiv9xlGGo\nUCgUCoVC0RCqbqP0J0YZhlcRIcR3aGP9KvOClDKiKfRV9TgqFAqFQqFQ1IYyDK8iUso7r3UeFAqF\nQqFQNC5CqFnJCoVCoVAoFAq4rtYxvH5MXIVCoVAoFApFg1AeQ4VCoVAoFIqGcB15DNU6hoqqqAdC\noVAoFH92rqqlljTzlUZ/d4ZP+4dax1ChUCgUCoXiz4baEk+hUCgUCoVCoXEddSUrw1BRjczvFje5\nDv8777Md12f/44YwYWhr2/HV3K7ram6/p7b6+3Poq6zr5PjRTaor5uOFtuOa9v5tTDpHV+wDffD2\nPk2qq8Mvm23Ho/+1pRbJhrPwuYrf1YP/3FyLZOOw6PmmrbtL8emqpt3m8tHBFdtcfvDj5e3xfblM\ntO7xrrhyrh/fp0KhUCgUCsW1QIjG/6uXWjFYCHFCCBEvhHixhnBvIcRPQogDQogjQoiH60pTGYYK\nhUKhUCgUfzKEEHrgI2AI0AYYJYRoU0XsSeColLIj0B+YJYRwqi1d1ZWsUCgUCoVC0QCu0eSTHkC8\nlDIBQAjxDTACOFpJRgKeQggBeADZQHltiSrDUKFQKBQKhaIhNIFhKIR4HHi80qX5Usr5lc6bAWcr\nnZ8DelZJ5kPgRyAF8ATuk1JaatOrDEOFQqFQKBSKPxhWI3B+nYK1cxuwHxgARANrhBC/SynzLxVB\njTFUKBQKhUKhaAjXZvLJeaB5pfMw67XKPAwslxrxwBmg1qnbyjBUKBQKhUKh+POxC2glhIi0Tii5\nH63buDLJwC0AQoggoDWQUFuiqitZUW+2nzjFnJ9+wSIlw7p3YXT/fnbh/9m4mdX7DwJgtlhISs/g\n51dewMvNrV7pJx7fw8bvP0VazLTtOYjut9xjF358zwZ2/7YMJDg5u3LzPeMJCI2k3FTGtx/9DXO5\nCYvFTMsOvblh8F/r1DdhaAw9WvtjLDPzr2VHiU8pqCbz4r1tiWnmRblFcuJsPnO+P4bZIhnQMZj7\n+oUjhKDYWM77Pxwn4UJhvcpZlbApf8OrR2/Kc3OafG27xtJVn7oL9nVh2v3t8XJz5NT5fN5eeoRy\ns6RDpC9vjO7IhewSADYfTWfR+jMAuLvYN0lxzb25uWPQFesC6BDpy4Q7YtDrBfnFJqYu2APA1Lva\n0DPWv9HKVZuuO29szpDuzeqsV7c27Qm8dzQIHXlbNpCzeoVduM7NjeDRj+HoH4gsN3Fh4aeUpZyr\nM92L7N+9jS/nzcFiMTPgtuGMuPchu/Dd2zaxZOF8hE6HXqfnoSeeJrZtRwB++X4x63/9EaRkwODh\n3P6X++vU59G1J82emAw6Hdm/riBj6SK7cL2HJ2FP/w2nkFBkWRln57yJMUl7FgwjRmK4bRgIQfaq\nH8n8YWm19NtH+DD6lih0AjYcTGPFzqrOEhg9IJKOUb4Yyy3M/+UUSelFBPu68tTwGJtMoLcLy7Yk\n8+ueVNu1Id1C7dLpEHlRl2DDwTR+2lG93kffEkWnKF+MJgvzV54kMa0IADdnPY8ObkWYvxsSWLDy\nFPEpBbQIdOeRQdE46nWY/wDb0545tod1y+cjLRY69BpEz4Ej7cKP7v6NHWuXARInZ1cG3juBwGZR\nAMx7/RGcnF0ROh06nZ6Hnp1Tp76k43v5/ccFSIuFNj0G0nWAfZt/Yu8G9v62HAk4ObvQ/67x+IdG\nUpCbwdpv5lBckIsQgrY9b6Nj32GNVQ1XhBBX388mpSwXQjwF/Arogc+llEeEEOOs4Z8A/wC+EEIc\nQtsm8AUpZWZt6SrDUFEvzBYLs35YwZz/G0OgtxePfjiPPnGxRAYF2mQeuKkPD9ykLdC6+ehxFm/e\nVm+j0GIxs2H5PO584g08vA18M2cqUW17YAhuYZPx8gvinglv4uLmQeKxPaxb+hH3T34HvYMjd42f\njpOzK2ZzOUs/fJGIuC6EhF/aW94jxkAzgxtjZ20lrrkXk0bEMunjXdXk1u+/wFtLjgAw7b52DOke\nyood57mQU8LUBXsoLC2ne4yBp++MqzF+fchZ8wtZPy6j+bOvXFH8q62rvnX36OBWLN+SzIaDaUwe\nEcvgblrdARxKzOGVrw5UizNhaIzdeYC3c4N0ubs4MGlEa/72731k5BnxcXe0xVm9N4Uftp/l46cq\nxmo3ha6IIHeGdG/GxLk7WfH6gEtXrBAE3j+G8++/jSknm/AX36Do4F7KLqTYRPwGD6f0XDIp897D\nMSiEoPvHcO69ty6dZiUsZjOfz53FSzPew+AfyLSnH6Frr76EtYi0ybTr1I2uvfoihCDpTDzvvfkS\ns+cv5mziadb/+iMz3v0MB0cH3nxlCl169CY4tPmlFep0NJvwDGdemoIpM52Wcz4lf/tmjGcTbSKB\n946mJOEUSdOn4RzWgtAJz3Bm2tM4h0diuG0Yp6Y8hjSVE/mPWeTv3EpZqr3hN2ZgFG8vOUJ2QRlv\njO7I3tPZpGSV2MI7RvoS5OvKs5/uJTrEg4cHRvPafw5yIaeEl788cLHaeX98d3afqlgE3M/TiXYR\nPva6bo3mrSWHNV0PdWJPfJa9rihfgn1dmLpgD9Ehnowd2JLXFmk6Rt8SxcEzObz/w3H0OoGzo2ZE\njLopguVbznLwTA4do3x57p629bqXTYHFYmbN0o+5d8J0PH0MLJw1hej2PfGv1AZ7G4IZNektXNw8\nSDi6m9WLP+TBZ2bbwu97aiZuHt711rfxu3mMePx1PLwNLHn/WSLb9sAvyL7Nv3P8TFzcPEg6voff\nvv2IkZPeQafT03voIwSGRVNWWszi96bSPKajXdz/FaSUvwC/VLn2SaXjFGDQ5aSpupIBIYSPEGJC\nHTIRQog63VBWucONl7uGUd9818Wxs+cIM/jRzOCHo4MDt3Rsz+9HL72C/doDhxjYqX29009LPoW3\nIQRvQzB6B0diOvcl4cgOO5nQyDhc3DwACA5vTWGu9tEjhMDJ2RXQXn4Wczmijv3Tb2gTwNp9qday\n5ePh4oCfZ/WlnXaezLIdHz+XT4CXCwBHk/MoLNVm/B9LziPAy7neZa1K0eEDlBdcchxwo9IYuupb\nd52ifNl0OB2A1XtT6R0XWE2mMm7OetpH+Npd69zSr0G6BnQMZvORDDLyjADkFplscQ4l5lJQbLJL\npyl0tQhw5/jZPIymWicC4hIRjSkjDVNmBpjN5O/ejnvHrnYyTsHNKDmhfaiY0lJxMPij9/SqNd2L\nxJ88SnBoGEEhzXBwdOTGfreye9sm+zy4uiGsY5uMpSW2cU7nzybSsnUbnF1c0OsdiGvXmZ1bNtaq\nzy0mjrKUc5RdSEGWl5O7aS1eN9jv7OHcIoLCA5pX1XguGaegEBx8fHFpHkHxiaNIoxEsZooO78O7\n903VdKTllJKRZ8RskWw/nkHXln524V1a+bH5iHavTqcW4ubigHeljwOAtuE+pOeWkpVvtF174OZI\nFm9MtNeVW0nXsQy6tjTYhXdtWVlXAe4uenzcHXF10tM6zJsNB9MAMFskxUYzoK0j4uqs1+rL+v9a\nkZp0Et+AEHz8tTY4tks/4g9tt5NpVqkNDo2IpSC3VsdTraQln8LbP9jW5rfq1JeEIzvtZEIiKvQF\ntWhNYZ7WHrt7+REYFg2Ak4sbfoFhFOY17e4+dXKNFrhuCpRhqOED1GoYAhFAgw2sa0AEjZDvjPwC\nAr0rvgQDvb3IyK/ZwCgtK2P7yXj6t6u6zualKczLwtOnolvPw9vf1gjUxJEda4iIrXhpWixm/jNr\nMgteHU2LmE4Eh7e+ZFwAfy9n0vNKbeeZ+Ub8azHu9DrBrZ2C2XWqep4Gdwtl18lL5/V6oz515+Xm\nSGFpORaLtMqUYvCukGnTwod5E3syY0wnwgPdAQjxcyWvqMwunSBvlwbpCvN3w9PVgXce7cpHT/bg\n1s4htZatKXQlphXSPsIHT1d7g6QqDj6+lOdUvNzKc7Jx9LE3lI3nk/Ho1B0Al/AoHP38cfC1N4Yu\nRXZWBgb/CuPczz+Q7KyManI7t27gmcfv4+1XpzLu6ZcAaB4ezfHDByjIz8NYWsr+3dvIykyrVZ+j\nIQBTZrrt3JSZgaMhwE6m9Ew83jdqBp9rTBxOgUE4+gdSmpSAe7uO6D29EM7OeHa7AUf/6h8W2QVl\ndse+Hvb3y9fDiewCYyUZI35VZHrF+rPtWEU9dGnpR05hGckZxVV02afjW+WjwdfTmaz8KvnxdCbA\nx4WCEhOPD2nF9DGdeHRwS5vHcNG6BEb1j+C9cd0Z1T+Sa4nWBlfcH0+f2tvgg9tXExnXzXYuECyZ\n+zJf/WsyB7auqlNfUX7VNt9AUS36ju5cQ3hsl2rX87PTyEhJILhFTA2xriI60fh/1wjVlazxFhAt\nhNgPrLFeG4L2QTddSrnYKhNnlfkS+A5YCLhb5Z+SUm6tS5EQYixwJ+CNtgbRIinl60KIN4BsKeUc\nq9wMIB04ALwO5ALtgSXAIWAy4Ar8RUp5WgjxBZAPdAOCgeellN9WzbeU8t0a8mRbK2nevHncFVC/\nroBLsfnYCTqEN693N/Llcjb+IEd2rmHkUxVdaDqdngemvoexpJAV/36TzNQk/EPCG03npBGxHErM\n5XBirt31jlG+DOnWjKfn7W40Xdc78Sn5PPDPzZSWmekRY+D1BzsydvZW9DpBq1BPO9kQg2uDdGlp\nevH8Z3twctTz/rjuHEvO43xWcd2RG0lXckYxizcm8dYjnRusI+fXnwgYOZoW06ZTlnIW49kksNTu\nibxcetzYnx439ufYoX0sWTifl2d+QLMWEQwf+SAzX56Ms7Mr4VGt0DXCum3pSxYROm4yrT74N6VJ\npyk5fQppMWM8m0T60kVETn8Xi7GE0oRTyEYuJ2j3rEu0H0s2JQHg5KBjeM8w3l56pFF1RAR58NXa\n05xOLWT0gCiG9Qzj283J3NI5hP+sP8Ouk1n0bO3PxBF/jn1+k08d5ND21fx18j9t10ZNfhtPH3+K\nCnJZOvdl/ALDaN6yXaPoOxd/kGO71nLXhDftrpcZS1j51dv0Hf4oTi5N8775X0QZhhovAu2klJ2E\nEHcD44COgD+wSwixySrzrJRyKIAQwg0YKKUsFUK0Ar5GM8rqQw+gHVBsTf9n4HNgOTBHaKNY77fK\ntbfmJQ5txfIE4FMpZQ8hxGRgIvC0Nd0QoA/aVPQfgW+r5rsmqqyVJDO/W1xNJsDLk/S8PNt5el4+\nAV41d2GtO3CYWzt1qLsWKuHhbbDrlijMy8TD21BNLiPlDOuWfMiIx17F1b26fmdXD8Jatifp+N4a\nDcNPrOPJTpzPJ9DbhSNoZfL3ciazUldSZR4cEIm3uyNzvj9mdz0y2INn7oxj2hf7KSgx1Rj3eiS7\noKzOussvNuHh4oBOJ7BYJP5eLmRZu1gvdqOB1lU/US/wcnMkI89IRr6RYN8KY9DJQd8gXRl5peQX\nmyg1WSg1WTiYmEN0iIfNMKzqQWwqXav2pLBqTwprZt56yXotz82x8/45+Pphys2xk7GUlpK2cIHt\nPHL6bDuvXG34GQLIqiSbnZmOXxUPXmXi2ncm/d0U8vNy8fL2YcBtwxlw23AAvv7iYzvvY02YsjLs\nvHyO/gGYqngoLSXFnHu34mUf+++llKVqYypzVv9MzuqfAQge87jWxV61TJW8dn6eTuQU2t+vnMIy\n/DydgQKrjDPZlWQ6RvmSmF5IvnVIQaCPCwHezswY26kGXc52xzkF9t7tnAIjBi8n22Ihfp5O5BQY\nkWgextOp2uS0nSczGdYzDIC+7QJZuE6bILrjRCYTq2m9emhtcEUdF+TW3Aannz/Dqq/f555xr9u1\nwRe9f+6ePrTqcAOpySdrNQzdvaq2+Vm416AvMyWR9Us/Ytijf7fTZzaXs/Krt4jpfBPR7W+4vMI2\nAddi8klTcf2UpPHoA3wtpTRLKdOAjUD3GuQcgQXWmT5L0fYprC9rpJRZUsoSNGOwj5QyEcgSQnRG\nGyi6T0p50a++S0qZKqU0AqeB1dbrh9C6ii/yvZTSIqU8CgRdRn7qJDasGeeysknJzsFUXs66A4fo\n06b6121haSn7ziTSt4aw2ghq3orczBTysi5gLjdxct/vRLW1X8A9PyeDn794k0GjpuAbUDHDs7gw\nD2OJ1uiWm4wkn9yPb1BYjXrGfbiDcR/uYMvRdJtRENfci6LScrtuqYsM6RZKt1YGZn5zmMqTBgO8\nnXn1gQ68vfRIk3if/sjUt+4OJOTQr51mGAzqEsJWa3edr0fFy7x1mBc6oc3gzSksI6NSVy7AiXN5\nDdK17VgG7SJ80FkH/Mc29yY5o8gW7+KYwos0lS4f99q7kQFKkxJwDAzGwRAAej1e3XpRdHCvnYzO\n1Q302lg07979KTl1AktpaU3JVSM6Jo4LKWdJv5BCucnE1k1r6dqrr53MhZSzSOuDfib+BCZTGZ5e\nWg9CXq7WzZ2ZfoFdWzfQu3/t49mLTx7HKbQ5jkEhCAcHfPrdSv72LfblcfdAOGj+Cb/bhlF0+ACW\nEu33pPfWJn84BgThdeNN5GxYQ1WCfV0J8HZGrxP0ig1gb7z9OLO98dn0aavdq+gQD4qN5eRVGmd6\nQ6w/245VGCfnMot5cu4unpm/h2fm77m0rri6dHlSbDSTW2Qir8hEdr6RED/tg6dtuI+tzcgpLCOu\nuVa/bVs0rKemoYS0iCEnI4Vcaxt8fO8mWrar0gZnp/PD5zO5Y/RU/AIr2uAyYyllpcW248Tj+wio\no8cmqHkr8jJTyc9Ow1xu4tT+34ls08NOpiAng5VfvcnAUU/btflSStYv+QC/wOZ0vmlEQ4uuqILy\nGF45U4A0NG+eDqhf66xRdV2Ci+efAmPRuoI/rxRe+TPYUuncgv09rCzXqAMUHPR6pgy/g2c+/wqz\nxcLQbl2ICgrku+3arM07e2m288bDx+jRKhpXp1r36K6GTq+n/11P8P3815DSQpset2IIbsHBrSsB\n6HDjEHau/obS4gJ+W65NuNLp9IyaMpui/GzWfD0Hi7SAlLTq2IeoNjXZ8hXsPKF13Xw59UaMJgvv\nLKvoOpoxphOzlx8lq6CMySNiScst5f1xWnoXl1YZPSAKLzdHJg3XDGCzRfLk3J016qqLFi+8hnuH\nzjh4+RC78DvSFn5WbZmSxqIxdNW37hasiuel+9sxdmA0p1MKWLVbc6X0axfI0J5hmC2SMpOFGd8c\nssX/6KcTfDKxl+189vJjPDwo+op1JWcUs+tkFvMn9cQiYeWu87YlRKbd144OkfZj+C7klDSJrr8/\n0AEvtzqMQ4uFjG++Imzic6DTkb91E2Wp5/Huq81kzvt9PU7BoQSP0XbIMqacI23Rp/W4Yxp6vQMP\nj5/KzJefxmKxcPOgoTQPj2LNz8sBGHjHXezYsoHf161E7+CAk5Mzk1+cbpuMMnvGNArz89A7OPDw\nhGdx9/CsTR1YzKR8PJuo6bNBpyNn9c8Yk8/gd7v2Is/+5QdcmofTfOrLICWlSWfsZlhHvDQDvZcX\nstxMytzZWIqqLwf11doEnrunLTodbDqUzvmsEgZ0DAZg/YELHEjIoVOUL+881oUyk4UFK+NtcZ0d\ndbSN8OHz1afrVX9frj3N8yPboROw8VAa57OKGdDJqmv/BfYnaDOLZz3WlbJyC/NXnqqIuy6B8UNj\ncNDpSM8rZf4vJwH4bFW8tgSOTmAqb/yu8stBp9dz693j+Pbjv2OxWGjfayD+IeHs36xNeO3U53a2\n/voNJUX5rFk6V4tjXZamuCCX7z+bDoDFYiGu601ExnW9pK6L+vr95XF+WPCadbmaWzAEt+DwNq3N\nb3fDEHat1dr8jcvnASD0Ou6bPJvUxGOc2LsBQ3A438zWOsx6DXmQiLj6dto1AddwskhjI+QfYO2k\na40QwgDslVKGCyHuAp4Abgf8gN1oew82A2ZLKW+yxnkXOCelnCWEeBht/SAhhIgAVkgpa/ShW8cY\nzkTrSi4BdgCPSCl3WxeoPITmjWwlpTQLIfpj34W9wXq+u3KYdYzhCuu4QoQQhVJKDyFE18r5rgc1\ndiU3Nv533mc7nrviRJPqmjC0YiLKwGlrm1RX5a7Cg0N6N6muDisrvC9XU9fVrMOm1nW19VXW1dRr\nVsZ8vNB2vO90087Y7Bxd0QV+8PY+tUg2nA6/bLYdj/7XllokG87C5yp+Vw/+c3Mtko3Douebtu4u\nxaerTtUt1AAeHdzKdvzBj5dezaIxmDjc1lt1VS218x+/2+jGVLPxU66Jtak8hoCUMksIscW6zMxK\n4CDapA+JNonjghAiCzALIQ4AXwBzgWVCiIeAVUBRzanXyE5gGdr2NYuklLut+SgTQvwG5EopzbUl\ncBkcrJzvmiafKBQKhUKhUIAyDG1IKasu6fJclXAT2ibUlak8w+IFq1wimjewNs5JKf9S9aJ10kkv\nwLbcvJRyA7Ch0nn/msKklGOr5NejlnwrFAqFQqFoJMR11JWsJp/8QRBCtAHigXVSyqb16ysUCoVC\noVDUgPIYNhFCiNuAt6tcPiOlvBOtK9oO60ziqKuQNYVCoVAoFI3JdeQxVIZhEyGl/BVtY2uF6+Wy\nRwAAIABJREFUQqFQKBTXM2odQ4VCoVAoFArF9YbyGCoUCoVCoVA0ANEIW0T+Ubh+SqJQKBQKhUKh\naBBqgWtFVdQDoVAoFIo/O1d1Nkjq53Mb/d0Z8siEazKjRXkMFQqFQqFQKBSAGmOoUCgUCoVC0SDE\ndTQrWRmGimqovZKvHLVXcsNReyU3Dmqv5Iaj9kpuHK7RXslXl+toHcPrx8RVKBQKhUKhUDQI5TFU\nKBQKhUKhaAjKY6hQKBQKhUKhuN5QHkOFQqFQKBSKhnAdLXCtDEOFQqFQKBSKBiBUV7JCoVAoFAqF\n4npDeQwVCoVCoVAoGoJax1Dxv8j2E6eY89MvWKRkWPcujO7fzy78Pxs3s3r/QQDMFgtJ6Rn8/MoL\neLm51Sv9xON72Pj9p0iLmbY9B9H9lnvswo/v2cDu35aBBCdnV26+ZzwBoZGUm8r49qO/YS43YbGY\nadmhNzcM/mud+iYMjaFHa3+MZWb+tewo8SkF1WRevLctMc28KLdITpzNZ873xzBbJAM6BnNfv3CE\nEBQby3n/h+MkXCisVzmrEjblb3j16E15bk6Tr23XWLrqU3fBvi5Mu789Xm6OnDqfz9tLj1BulnSI\n9OWN0R25kF0CwOaj6SxafwYAdxf7JimuuTc3dwy6Yl0AHSJ9mXBHDHq9IL/YxNQFewCYelcbesb6\nN1q5atN1543NGdK9WZ316tamPYH3jgahI2/LBnJWr7AL17m5ETz6MRz9A5HlJi4s/JSylHN1pnuR\n/bu38eW8OVgsZgbcNpwR9z5kF7572yaWLJyP0OnQ6/Q89MTTxLbtCMAv3y9m/a8/gpQMGDyc2/9y\nf536PLr2pNkTk0GnI/vXFWQsXWQXrvfwJOzpv+EUEoosK+PsnDcxJmnPgmHESAy3DQMhyF71I5k/\nLK2WfvsIH0bfEoVOwIaDaazYeb6azOgBkXSM8sVYbmH+L6dISi8i2NeVp4bH2GQCvV1YtiWZX/ek\n2q4N6RZql06HyIu6BBsOpvHTjur1PvqWKDpF+WI0WZi/8iSJaUUAuDnreXRwK8L83ZDAgpWniE8p\noEWgO48MisZRr8P8B9ie9syxPaxbPh9psdCh1yB6DhxpF35092/sWLsMkDg5uzLw3gkENosCYN7r\nj+Dk7IrQ6dDp9Dz07Jw69SUd38vvPy5AWiy06TGQrgPs2/wTezew97flSMDJ2YX+d43HPzSSgtwM\n1n4zh+KCXIQQtO15Gx37Dmusavif509pGAohfIC/Sinn1iITAdwopfxvHWlFACuklO0aMYtXTH3y\nLYToBjwkpZx0tfJltliY9cMK5vzfGAK9vXj0w3n0iYslMijQJvPATX144CZtgdbNR4+zePO2ehuF\nFouZDcvncecTb+DhbeCbOVOJatsDQ3ALm4yXXxD3THgTFzcPEo/tYd3Sj7h/8jvoHRy5a/x0nJxd\nMZvLWfrhi0TEdSEk/NILnfaIMdDM4MbYWVuJa+7FpBGxTPp4VzW59fsv8NaSIwBMu68dQ7qHsmLH\neS7klDB1wR4KS8vpHmPg6TvjaoxfH3LW/ELWj8to/uwrVxT/auuqb909OrgVy7cks+FgGpNHxDK4\nm1Z3AIcSc3jlqwPV4kwYGmN3HuDt3CBd7i4OTBrRmr/9ex8ZeUZ83B1tcVbvTeGH7Wf5+KmetmtN\noSsiyJ0h3Zsxce5OVrw+4NIVKwSB94/h/PtvY8rJJvzFNyg6uJeyCyk2Eb/Bwyk9l0zKvPdwDAoh\n6P4xnHvvrUunWQmL2cznc2fx0oz3MPgHMu3pR+jaqy9hLSJtMu06daNrr74IIUg6E897b77E7PmL\nOZt4mvW//siMdz/DwdGBN1+ZQpcevQkObX5phTodzSY8w5mXpmDKTKflnE/J374Z49lEm0jgvaMp\nSThF0vRpOIe1IHTCM5yZ9jTO4ZEYbhvGqSmPIU3lRP5jFvk7t1KWam/4jRkYxdtLjpBdUMYbozuy\n93Q2KVkltvCOkb4E+bry7Kd7iQ7x4OGB0bz2n4NcyCnh5S8PXKx23h/fnd2nKhYB9/N0ol2Ej72u\nW6N5a8lhTddDndgTn2WvK8qXYF8Xpi7YQ3SIJ2MHtuS1RZqO0bdEcfBMDu//cBy9TuDsqHmXRt0U\nwfItZzl4JoeOUb48d0/bet3LpsBiMbNm6cfcO2E6nj4GFs6aQnT7nvhXaoO9DcGMmvQWLm4eJBzd\nzerFH/LgM7Nt4fc9NRM3D+9669v43TxGPP46Ht4Glrz/LJFte+AXZN/m3zl+Ji5uHiQd38Nv337E\nyEnvoNPp6T30EQLDoikrLWbxe1NpHtPRLu5VR6fGGF5rfIAJdchEAHW7jf54RFBHvqWUu6+mUQhw\n7Ow5wgx+NDP44ejgwC0d2/P70UuvYL/2wCEGdmpf7/TTkk/hbQjB2xCM3sGRmM59STiyw04mNDIO\nFzcPAILDW1OYmwlog36dnF0B7eVnMZcj6tg//YY2Aazdl2otWz4eLg74eTpVk9t5Mst2fPxcPgFe\nLgAcTc6jsLRci5+cR4CXc73LWpWiwwcoL8i/4vhXW1d9665TlC+bDqcDsHpvKr3jAqvJVMbNWU/7\nCF+7a51b+jVI14COwWw+kkFGnhGA3CKTLc6hxFwKik126TSFrhYB7hw/m4fRZKm1/C4R0Zgy0jBl\nZoDZTP7u7bh37Gon4xTcjJIT2oeKKS0VB4M/ek+vWtO9SPzJowSHhhEU0gwHR0du7Hcru7dtss+D\nq5ttEL2xtMS2Ntv5s4m0bN0GZxcX9HoH4tp1ZueWjbXqc4uJoyzlHGUXUpDl5eRuWovXDfY7ezi3\niKDwgOZVNZ5LxikoBAcfX1yaR1B84ijSaASLmaLD+/DufVM1HWk5pWTkGTFbJNuPZ9C1pZ9deJdW\nfmw+ot2r06mFuLk44F3p4wCgbbgP6bmlZOUbbdceuDmSxRsT7XXlVtJ1LIOuLQ124V1bVtZVgLuL\nHh93R1yd9LQO82bDwTQAzBZJsdEMgARcnfVafVn/XytSk07iGxCCj7/WBsd26Uf8oe12Ms0qtcGh\nEbEUWNvgKyEt+RTe/sG2Nr9Vp74kHNlpJxMSUaEvqEVrCvO09tjdy4/AsGgAnFzc8AsMozCvaXf3\nqQshRKP/XSv+rIbhW0C0EGK/EOJf1r/DQohDQoj7Ksn0tcpMEUJECCF+F0Lstf7dWB9FQoixQogf\nhBAbhBCnhBCvWq+/IYR4upLcDCHEZCFEfyHERmucBCHEW0KIB4QQO635i7bKfyGEeF8IsdUqd9GH\nbpfvS+SpvxBihfX4NSHE59b8JQghJlmvvyWEeLJSnNeEEM9eVi1XIiO/gEDvii/BQG8vMvJrNjBK\ny8rYfjKe/u3a1Dv9wrwsPH0quvU8vP1tjUBNHNmxhojYipemxWLmP7Mms+DV0bSI6URweOtLxgXw\n93ImPa/Udp6Zb8S/FuNOrxPc2imYXaeq52lwt1B2nbx0Xq836lN3Xm6OFJaWY7FIq0wpBu8KmTYt\nfJg3sSczxnQiPNAdgBA/V/KKyuzSCfJ2aZCuMH83PF0deOfRrnz0ZA9u7RxSa9maQldiWiHtI3zw\ndLU3SKri4ONLeU7Fy608JxtHH3tD2Xg+GY9O3QFwCY/C0c8fB197Y+hSZGdlYPCvMM79/APJzsqo\nJrdz6waeefw+3n51KuOefgmA5uHRHD98gIL8PIylpezfvY2szLRa9TkaAjBlptvOTZkZOBoC7GRK\nz8TjfaNm8LnGxOEUGISjfyClSQm4t+uI3tML4eyMZ7cbcPSv/mGRXVBmd+zrYX+/fD2cyC4wVpIx\n4ldFplesP9uOVdRDl5Z+5BSWkZxRXEWXfTq+VT4afD2dycqvkh9PZwJ8XCgoMfH4kFZMH9OJRwe3\ntHkMF61LYFT/CN4b151R/SO5lmhtcMX98fSpvQ0+uH01kXHdbOcCwZK5L/PVvyZzYOuqOvUV5Vdt\n8w0U1aLv6M41hMd2qXY9PzuNjJQEglvE1BBLcSX8KbuSgReBdlLKTkKIu4FxQEfAH9glhNhklXlW\nSjkUQAjhBgyUUpYKIVoBXwPdak6+Gj2AdkCxNf2fgc+B5cAcoe2efb9Vrr01L3FANpAAfCql7CGE\nmAxMBC4alCFAHyAW+BH4tmq+60kscDPgCZwQQnwMLAbmAB9ZZe4FbqspshDiceBxgHnz5nFXQP26\nAi7F5mMn6BDevN7dyJfL2fiDHNm5hpFPVXSh6XR6Hpj6HsaSQlb8+00yU5PwDwlvNJ2TRsRyKDGX\nw4m5dtc7RvkypFsznp63u9F0Xe/Ep+TzwD83U1pmpkeMgdcf7MjY2VvR6wStQj3tZEMMrg3SpaXp\nxfOf7cHJUc/747pzLDmP81nFdUduJF3JGcUs3pjEW490brCOnF9/ImDkaFpMm05ZylmMZ5PAUrsn\n8nLpcWN/etzYn2OH9rFk4XxenvkBzVpEMHzkg8x8eTLOzq6ER7VC1wjrtqUvWUTouMm0+uDflCad\npuT0KaTFjPFsEulLFxE5/V0sxhJKE04hG7mcoN2zLtF+LNmUBICTg47hPcN4e+mRRtUREeTBV2tP\nczq1kNEDohjWM4xvNydzS+cQ/rP+DLtOZtGztT8TR1yjfX4vk+RTBzm0fTV/nfxP27VRk9/G08ef\nooJcls59Gb/AMJq3bJwRWufiD3Js11rumvCm3fUyYwkrv3qbvsMfxcmlad439UatY/iHog/wtZTS\nDKQJITYC3YGq7ixH4EMhRCfADFzO58UaKWUWgBBiOdBHSjlHCJElhOgMBAH7pJRZVvfvLillqlX+\nNLDams4hNAPuIt9LKS3AUSFE0GXkpyo/SymNgFEIkQ4ESSn3CSEChRChQACQI6U8W1NkKeV8YP7F\n08zvFleTCfDyJD0vz3aenpdPgFfNXVjrDhzm1k4dLqsAHt4Gu26JwrxMPLwN1eQyUs6wbsmHjHjs\nVVzdq+t3dvUgrGV7ko7vrdEw/MQ6nuzE+XwCvV04glYmfy9nMit1JVXmwQGReLs7Muf7Y3bXI4M9\neObOOKZ9sZ+CElONca9HsgvK6qy7/GITHi4O6HQCi0Xi7+VClrWL9WI3Gmhd9RP1Ai83RzLyjGTk\nGwn2rTAGnRz0DdKVkVdKfrGJUpOFUpOFg4k5RId42AzDqh7EptK1ak8Kq/aksGbmrZes1/LcHDvv\nn4OvH6bcHDsZS2kpaQsX2M4jp8+288rVhp8hgKxKstmZ6fhV8eBVJq59Z9LfTSE/Lxcvbx8G3Dac\nAbcNB+DrLz628z7WhCkrw87L5+gfgKmKh9JSUsy5dyte9rH/XkpZqjamMmf1z+Ss/hmA4DGPa13s\nVctUyWvn5+lETqH9/copLMPP0xkosMo4k11JpmOUL4npheRbhxQE+rgQ4O3MjLGdatDlbHecU2Dv\n3c4pMGLwcoLzlfJTYESieRhPp2qT03aezGRYzzAA+rYLZOG6BAB2nMhkYjWtVw+tDa6o44Lcmtvg\n9PNnWPX1+9wz7nW7Nvii98/d04dWHW4gNflkrYahu1fVNj8L9xr0ZaYksn7pRwx79O92+szmclZ+\n9RYxnW8iuv0Nl1dYRa1cPyZu3UwB0tC8ed2A6oOHLk3V6WIXzz8FxgIPo3kQL1K5dbJUOrdgb4xX\nlmvIgILK6Zgr6VgK3APch+ZBvGJiw5pxLiublOwcTOXlrDtwiD5tqn/dFpaWsu9MIn1rCKuNoOat\nyM1MIS/rAuZyEyf3/U5U2552Mvk5Gfz8xZsMGjUF34CKGZ7FhXkYS7RGt9xkJPnkfnyDwmrUM+7D\nHYz7cAdbjqbbjIK45l4UlZbbdUtdZEi3ULq1MjDzm8NUnjQY4O3Mqw904O2lR5rE+/RHpr51dyAh\nh37tNMNgUJcQtlq763w9Kn56rcO80AltBm9OYRkZlbpyAU6cy2uQrm3HMmgX4YPOOuA/trk3yRlF\ntngXxxRepKl0+bjX3o0MUJqUgGNgMA6GANDr8erWi6KDe+1kdK5uoNfGonn37k/JqRNYSktrSq4a\n0TFxXEg5S/qFFMpNJrZuWkvXXn3tZC6knEVaH/Qz8Scwmcrw9NJ6EPJytW7uzPQL7Nq6gd79B9Wq\nr/jkcZxCm+MYFIJwcMCn363kb99iXx53D4SD1lz53TaMosMHsJRovye9tzb5wzEgCK8bbyJnw5pq\nOoJ9XQnwdkavE/SKDWBvvP04s73x2fRpq92r6BAPio3l5FUaZ3pDrD/bjlUYJ+cyi3ly7i6emb+H\nZ+bvubSuuLp0eVJsNJNbZCKvyER2vpEQP+2Dp224j63NyCksI665Vr9tWzSsp6ahhLSIIScjhVxr\nG3x87yZatqvSBmen88PnM7lj9FT8Aiva4DJjKWWlxbbjxOP7CKijxyaoeSvyMlPJz07DXG7i1P7f\niWzTw06mICeDlV+9ycBRT9u1+VJK1i/5AL/A5nS+aURDi94oCKFr9L9rxZ/VY1iA1m0K8DvwhBDi\nS8AP6Ac8BzSrJAPgDZyTUlqEEGOAyxnpO1AI4QeUAH8BHrFe/w54A80b2VgTXSqXraEsBhagdbFX\nH7l9GTjo9UwZfgfPfP4VZouFod26EBUUyHfbtVmbd/bSxj1tPHyMHq2icXW6HLsbdHo9/e96gu/n\nv4aUFtr0uBVDcAsObl0JQIcbh7Bz9TeUFhfw2/JPtDg6PaOmzKYoP5s1X8/BIi0gJa069iGqTfda\n9e08oXXdfDn1RowmC+8sq+g6mjGmE7OXHyWroIzJI2JJyy3l/XFaeheXVhk9IAovN0cmDdcMYLNF\n8uTcnTXqqosWL7yGe4fOOHj5ELvwO9IWflZtmZLGojF01bfuFqyK56X72zF2YDSnUwpYtVtzpfRr\nF8jQnmGYLZIyk4UZ3xyyxf/opxN8MrGX7Xz28mM8PCj6inUlZxSz62QW8yf1xCJh5a7ztiVEpt3X\njg6R9mP4LuSUNImuvz/QAS+3OoxDi4WMb74ibOJzoNORv3UTZann8e6rzWTO+309TsGhBI95HABj\nyjnSFn1ajzumodc78PD4qcx8+WksFgs3DxpK8/Ao1vy8HICBd9zFji0b+H3dSvQODjg5OTP5xem2\nQfCzZ0yjMD8PvYMDD094FnePOpopi5mUj2cTNX026HTkrP4ZY/IZ/G7XXuTZv/yAS/Nwmk99GaSk\nNOmM3QzriJdmoPfyQpabSZk7G0tR9eWgvlqbwHP3tEWng02H0jmfVcKAjsEArD9wgQMJOXSK8uWd\nx7pQZrKwYGW8La6zo462ET58vvp0vervy7WneX5kO3QCNh5K43xWMQM6WXXtv8D+BG1m8azHulJW\nbmH+ylMVcdclMH5oDA46Hel5pcz/5SQAn62K15bA0QlM5Y3fVX456PR6br17HN9+/HcsFgvtew3E\nPySc/Zt/AaBTn9vZ+us3lBTls2aptiDIxWVpigty+f6z6QBYLBbiut5EZFzXS+q6qK/fXx7nhwWv\nWZeruQVDcAsOb9Pa/HY3DGHXWq3N37h8HgBCr+O+ybNJTTzGib0bMASH881sbWRWryEPEhFX39Fh\nitoQ8g+wdtKVIIT4L9ABWGm9NATNkzddSrlYCOEI/AoYgC+AFcAyq8wq4EkppUddy9UIIcaiGYPe\nQBiwSEr5eqXwT4BcKeWL1vP+2I9t3GA93105TAjxhVXvt1a5Qmt+7PItpXy3hjxVTuc1oFBK+Y41\n7DAwVEqZaD0/BGRKKW+ums4lqLErubHxv/M+2/HcFSeaVNeEoRUTUQZOW9ukuip3FR4c0rtJdXVY\nWeF9uZq6rmYdNrWuq62vsq6mXrMy5uOFtuN9p5t2xmbn6Iou8IO396lFsuF0+GWz7Xj0v7bUItlw\nFj5X8bt68J+ba5FsHBY937R1dyk+XXWqbqEG8OjgVrbjD3689GoWjcHE4bbeqqs6rTfj2/82ujEV\ncM9fr8nU5D+rxxApZVUP3XNVwk1A1UXDKg98e8Eql4g2saQ2zkkp/1L1onXSSS/AtgqolHIDsKHS\nef+awqSUY6vk16OWfFNFtnI6r1UJa1flvP5rxigUCoVCobh8rqPJJ9dPSa4yQog2QDywTkrZtJ9b\nCoVCoVAoFFeBP63HsLERQtwGvF3l8hkp5Z1oXdF2SCmPAlHXME8KhUKhUCj+AFzLBakbG2UYWpFS\n/oo2tu8Pwx8xTwqFQqFQKK5flGGoUCgUCoVC0RCUx1ChUCgUCoVCAajJJwqFQqFQKBSK648/7TqG\niiZDPRAKhUKh+LNzVft2s35a1ujvTsOwu69J/7TyGCoUCoVCoVAoADXGUKFQKBQKhaJhqMkniuuZ\nSR/vanId74+v2Mt42Kvrm1TXT69XbCSzYGXTrkX+2JCKrZ+up63jrtVWf4Neavot8VbPqChbU+ur\nrOtq3rPpXx+qRbLhvDyqYoOlq1muMe807ZZ4Xz5b8awPeWVdk+oCWPmPW5pcR03c9Y8NTZr+8lf6\n246v5vNxVRHXTwfs9VMShUKhUCgUCkWDUB5DhUKhUCgUigYg1HI1CoVCoVAoFIrrDeUxVCgUCoVC\noWgIOjX5RKFQKBQKhUIBavKJQqFQKBQKheL6Q3kMFQqFQqFQKBqAUOsYKv5XiGvuxV19WqATgm3H\nMli774JdeKCPCw/cHEnzADdW7DjP+gNauI+7E6NvicTT1REJbD2awcZDaTXq+HhiT3RCsGZvKt9u\nTqoW/viQVnRtZcBosvDe90c5nVqIv5czU+5qg4+7EyBZtSeFn7afA+D5kW1pZnADwN2l5kf8zLE9\nrF8+HykttO81iJ63jrQLP7r7N3auWwZInJxduXXkBAKbRdnCLRYzi2ZNwcPbwF2Pv1qjjglDY+jR\n2h9jmZl/LTtKfEpBNZlgXxem3d8eLzdHTp3P5+2lRyg3SzpE+vLG6I5cyC4BYPPRdBatP2Mr0zN3\nxV0zXfUlbMrf8OrRm/LcHE6OH31FadzXL5zFm6o/ExPuiKF7a3+MJjPvVCpvt1YGxt8Rg04nWLX7\nfLW4d/duwRO3x3DPjI3kF5vo1spgFz5vYi+e+ngnpnLLFesaPSCKId1DySsyAfD56nh2ncyidZiX\nna7pD3UkLMD9iu6Zh4sDU+9uQ6ifK2XlFmYtP0piWhFh/m68fH/7aulcJCrEg9u6hCIE7D+dw9Zj\nGXbhBk9nhvUKI9jXhQ0H09h+PNMW1qO1gc7RfkgJGXml/Lj9HGZLzbuANeR5dHPW8+K97Qj0cUGv\nE3z7exK/7k0lwNuZ50e2tUujfYQPDwyIQidg46E0ft55vpqeBwZE0jHSl7JyCwtWniIpvYhgX1cm\nDIuxyQR6u7B8SzKr96YyYWhrgv1cqqXTtaUf4+6IQScEq/aksPT36s/luNtj6B5jwGgyM2v5MU6n\nauWe8pc4erT2J7eojPEf7rDJj74lihti/bFIyCsqq7EurwYfTOiBTgjW7kvlu63J1cL/77aWdGmp\nlevDH4+TcKHQFqYT8M9Hu5KdX8bMxdp6mTfEBXDfTRGE+bvxwmd77dJqyLNxqefe0UHH7Me64uig\nOkEbA1WLiksiBIzsG84nK04x85vDdG1pINjXvsEsNpazbHMy6/bbG4wWKflu61lmLj7M7OVH6dsu\nsFrci7y26ABPfrSDfu0DaR7gZhfWtZWBUIMbT7y/nY9+Os74oa0BMFskn/96iic/2sGzC/ZwR/cw\nW9x/Lj3C5E92MfmTXdVefKAZdWu//Zi7n3idh1+cy/G9G8m8YN8YehuCuX/iW4x94SN6Dbqf1Ys/\ntAvfu/FH/IKa11p/zQxujJ21lTnfH2PSiNgaZR4d3IrlW5IZO2srhSXlDO4Wags7lJjDuA93MO7D\nHTZDDbSGdffJrGumq77krPmFMy8/c3mRqiz50L9DMC0C3O2udY8x0MzfjYdnW8s7XCuvTsBTw1rz\n0pf7eey9bdXiBng707WVgbSckgr54a3t0p7z/THMZkuDdS3fksz4D3cw/sMd7LLWX2JaoZ2uLi0N\nPPLutiu6Z6P6R3A6tYAnPtjBP789wgTr7+JcZrHtPlZFCBjSNZSvN5zhk19O0TbcG38vZzuZkrJy\nft2TYmcQAni6OtAjxp/Pfo1n/spTCAFtw71rzHOPGEODnscRvZqTnF7EuA928Oyne3j89hgc9AKz\nRTLvF/sF6h8e1JJZy47wt3/vo1dsAKEGV7vwDpG+BPu68vxne/n36njGDIwG4EJOCX//6gB//+oA\nry48gLHcwp74bADmrjhhC6vMk8Na88pX+3nig+307xBU/blsZSDU4Mr/zdnG+z8c56lhFc/Wmn2p\nvPzV/mp1sGxzEhM+2slTc3ey40RmtfCrxfT/HmTyxzvp2y6QMH/7NrhLSz9C/Fx58qMdfPLzSR6/\nPcYu/I4eYZzLLLa7lpxRxD+XHuZoUl41XQ15Ni713JvKLTz32V7GfWD33Pe6zGpoGDrR+H/XiD+k\nYSiE8BFCTKhDJkII8dd6pBUhhDjceLmrGyHEa0KIZ+uQ+YsQos0Vph8ghNghhNgnhOh7Zbmsm/BA\ndzLyjGQVGDFbJHvjs2kf4WsnU1hSTnJGEZYqnoP8YpOtsTCaLKTllODt7lSjnrScUsrNkk2H0+kZ\nG2AX1ivWn/VWo/PEuXzcXRzw9XAip7CM06nai7akzMzZzCIMns7V0u7TNrDatQtJJ/H1D8HHPxi9\ngyOxnftx+tB2O5lmkXG4uHkAEBoRS2FeRaNdkJtJwtFddOg1qMbyXGTtvlQAjp3Nx8PFAT/P6uXv\nFOXLpsPpAKzem0rvuOr5rYybs572Eb6s3J1yzXTVl6LDBygvyL+sOG4x9t7JjQfTuDHO/pm4MS6A\nNdbyHj+rPRN+nk60DvMmJbuECzkllJtltbjjbo/h01WnuPiktg7zpqik3C7tjpG+VH6Ur1RXTRhN\nFvvzcjNwZfcsPNCD/Qk5AJzNKCbIxwUfj5p/XxcJ9XMju7CM3CITFovkSHIeMVW8mMWSODODAAAg\nAElEQVRGM6nZJdV+z6C9pxz0OoQAR72Owip1d5Eb2gQ06HmUgKuzHgBXJz0FJSbMFkl2QVk171J+\ncRkZeVr7tON4Bl2i/ezCu7T0Y8sRTcfp1ELcnB3wdne0k2nbwoeM3FKy8o01luciKVklXLC2VRsP\npdErzt8uvFdcgO0D+fi5fDxctbYK4HBSLgUlpmppFhvNtmMXJ32t+puStNxSyi2SzUfS6dHavlw9\nYvzZcFDr7Tl5vqINBs3D3LWVwXa/L3I+s5iUrJIadTXk2ajtuS8tM1dNpmZ3tqJO/pCGIeAD1GoY\nAhFAnYbh5SCEuJpd638BrsgwBG4BDkkpO0spf2/EPNnh4+5EbqXujdyismqNan3w83Simb8bSVU8\nJlXJyjNWM+4Mns5k5pdWyOQbMVTxcgT6uBAd7MmJ8/ZGSNtwH3ILq3fPFORl4elb8RL38PGnIO/S\nXrFD21cTGdfNdr7+u/n0G/5InXtjpudV5Dsz31jNO+Pl5khhabntJZyZX4rBu0KmTQsf5k3syYwx\nnQgP1LwTIX6u5BWV8dzd9o/O1dTVlDj62xtXGVXyCWDwciajSnkNXs74V7leOe4NcQFk5hvtusD8\nvZwpN9sbaz1j7V+KV6ILNK/XJxN78sxdbfC4xHCGJRuTKt2Py7tnCRcK6NNGe1m2DvMiyMeFAK/q\nH0aV8XRzIL+4wjgpKDbh6Vq/33NBSTnbjmcyaXhrnv5/9s4zPI7qasDvWfW26laz3CW5CvcKtjEG\njCmmJbQAppseeqiBEAgEAwGDITbNOCTwAaaDscHghnvvvar3rlXZ+/2Y0WpXuyqomTj3fR49mp05\n95y5M3fOnDn33pkL+2GrtrscS2eirH5tao9frDpGty5BfPin05hz12hmf70H1cgtPs0p+MgvrSK8\ngf8ID/Ylr6Q+4MsvsREe7Cozqm8Uq3e79yw07Pp3aQeefJXVj9wGMg3r7YlrJ/fi/fvHcXpqbLOy\nHU1esY2IBvWKCPEj1ylodpa5/uw+vP/DgUbPjyfa0jaaavcWgTfvGOWsyj1t3pGIpf3/ThC/1cDw\nOaC3iGwWkRfMv+0isk1ELnOSOc2UucfMDC4XkY3m39iWGBKR6SLypYgsAX401z0gIutEZKuIPOUk\ne425bouIzG+h/ptMXVtE5FMRCTT37QLgBXP/e5t/C0Vkg1kPjzl2ERkM/B2YZpYNEJGzRGSVWe+P\nRSTYlB0mIktNnd+LSFwjOm8WkfUisn7OnDktqVaL8fW2cMPZfViw8hiVDTIm7YG/rxcPXzaQuQv3\nUWFzfWIcP6gLy7Z7HtfYUo7u28q21YsYf/50AA7sWEtgcBixiX3apLc59qcXc9XfV3DLrDV8seoY\nT/3hFAC8LEJSfAhfrTn+X2nrRODnY+GKCT2Y98MBt20NB4zHRQQwuFe4m9yv4as1x7n2xZXc+toa\n8ktsbl1vdZwxOLbVY6I+XHqY4ABv3rxjFBeOSWR/Rgl2p7uzt1f7dkP5+1hI6Wrlta/28Mrnu/Dx\nFgb2CGtXG3UMT47kQHoplz+3nBmz1nDH+X0J9POcTautbVtSyMsiDOkdwdo97g+Goxs8JHQU8344\nyDUzV/LT1szmhX9DDEuKpKisqtEHhI6gqXZvVzQcQjGw03YMw5e099+J4rcaGP4JOKCUGgysBgYD\npwCTMYKpOFNmuVJqsFLqZSAbOFMpNRS4DHj1V9gbClyqlJogImcBScBI0+4wERkvIgOAx4BJSqlT\ngLtbqHuBUmqEWWYXcINS6hfgS+ABc/8PAHOAO5VSw4D7gdmelCmlNgNPAB+ZxyfI3K/JZt3XA/eK\niA8wy6zXMOAd4JlGdM5RSg1XSg2/+eabHesLy6rMyR0GYUG+jgH1LcFiEW44uw/r9+ax9VBBs/KR\noX4uT/cAeSU2oqz1YxMjrX6OLh8vi/DwZQP5eWsWqxqMJbRYhDH9urDc7I5wJiQ0kpKCevnSwlxC\nQiPd5HLSD/H9h69y4Y2PExBkZA/SDu7kwPY1zHnqer5+/+8c3beVb+bPBKB/N9cxV11C6/c7yur6\n1A1Gd3uwvzcWcyxJlNWfvCJDptxW6+gaWbs3Dy8vwRroQ06RjZxim1sXYEfa2n3813UHt4XqXNfz\nGO20n3XkFduIblDfvGIbuQ3W15WNiwggNjyAN+8czfv3jyPa6sfs20dhq6nFt0FgdiirlKR4a6tt\ngXHd2BUoBd+tS6Ovea7OH9XVxZYCesYEOfT+2nM289OdzHhtDc9/vIPQIF8y8uuzZyOS3YOakvIa\nrIH1GcKQQB+P3Zue6BkbTGFpFeW2WuzK6FZvOBatjvySqja1x7OHxrNip3Hd1nXXJ5rj+bwajLty\n7oaMCPaloIH/KCitcsnsRYT4UVBaL5PaM5wj2aUumVQwsk/DGkxMcmkHnnxVsY2oBjIN690UP205\n8YFhpNWP/Ab1yi9xzerVyfRNtDIiOYo37xzNvRf3Z1DPMO6+0H2iWsMhFm31VU21+wZMaWm9Na78\nVgNDZ04F/qOUqlVKZQFLgREe5HyAuSKyDfiYX9dNu1gplW8un2X+bQI2An0xAsVJwMdKqVwAJ/nm\nGGhmALcBVwEDGgqYGb6xwMcishn4J+Axu+eB0Rh1XWmWvRboDqRgPDEtNtc/BnRtVIsHjmaXER3m\nR0SIL14WYWifCLYdbj7Aq+PKiT3IKqzgp61NZ+1iwvzx9hLGD+zC2gaD3tfszmXSYKOLJaWrlfLK\nWgrM7uG7pvXlWE45X6w65qZzcK9w0nLLPI4biu2WTEFuOoV5mdTWVLN70zJ6D3TpgqC4IJsv3nmW\nqX+4j4guCY7148+fzoyn5nHzn9/hvGsepFtSKudebQwn3XnUdaD15CHGKeyXaKWssob8Evdu7S0H\nCxg/0OgaOWtonGOyTLjTeLGUrlYsIhSXV1NQWkVOUSUb97s2v4601VgA0BGU793t8ntCagyrGnTz\nrdqdw5lmffsmWimzGfXdk1ZMQmQAseFGe6orezirjN//bRnXzFzJNTNXklNs47bX17B+Xz6BDbp5\nA3y9OJJT2mpb4BqsjOvfxTHpZN1e17YdHxlIZkFlq85ZkL+3Iyt4zvB4th0qdBmvdvopMW660vPL\niQjxIyzIB4tFGNAtlL0tDPqLyqtJiAp02OwZG0xukeegZ+XO7Da1x+yiSoaYYwXDgn1JjAp03Pzv\nu9jVrceEBxAV6oeXRRjVN5pNB1yvi00H8hlnjjPuHRdMha3G5eF2dL8ot4k2YAxDaRhwxEcGOnzV\nhEExbuVW787hDNNX9e1q1LvAw1AWF50R9ZNlxvRteoxqR9IlzB9vi3DqgC5u7XTd3lwmphrtKTnB\nSrlZrw+WHOKmV1YxY9ZqXlqwk22HCnnl811uuhtOAGxL22is3YcG+Xh6A8Xuhis6lJOoK/lkel3N\nPUAWRmbRAlQ2Le5CmdOyAH9TSv3TWUBE7mzlfr0HXKiU2iIi04GJHmQsQKGZAfy1CEZge4XLSpFB\nwA6l1JhW6ASM1Pwny49y23kpWARW784ls6CScf0NB7ZyZw4hAd48cOkA/H29sCvFxNQYnv1wG/GR\ngYxMiSItr9zxiomv1xx3C54Anrp6MBaL8MOmdI7mlDlmoC1cn876fXkMT45kzt1jsFXXOhxP/26h\nTBocx6HMUl6ZYTwnvP/jQTbsM7qExg+MafT1OBYvL864ZAafvvkEdrudQaPOJCquO5tXfgvA4HFT\nWfX9h1SUFfPDx7MdZa6+7x+/6vhlFlQw776x2KrtzPx0h2P9M9cO5qUFO8krqWLuwv08evlApp/Z\nmwPpJSxcn2bufxfOG9WVWruiqtrOMx9uc5R//as9PPz7gSfMVkvp9tCTBKUOwdsaRt/5n5E1/20K\nFn3ddCG763CAZduzOJJdxrkjjeD8m7VprN2Tx8jkKN6716zvAqO+drvita/28Oz0IVhE+H5jOkey\ny9xMOEzZFbO+3M0z1w5xrNt+uJDoUH/OHZnQals3np1E77gQFIqsgkpe+cJoswO6u3a9bj6Qz6xb\nR7TqnHWLDuLB3/VHKTiSXcaLn+50lPf3sTCsj+skDDAymAvXp3PFxJ5YBDYfLCC32MZQU3bj/nyC\n/L254ew++PlYUApGpkTx5jd7Sc+rYNfRIm6c0ge7HbIKKtyCsDrW7sljVEpUq9vjB0sO8sClA5hz\n12gQeOv7/RSXVzOgeyhnDnV9Xl6xI5sHLhmAxQLLtmWTllfB6acYwdlPWzLZcrCA1J7hvHDjUGzV\ndt5auN9R1tfHwsDuYby3yH2IgTHuMJe+ifW9AG98vYe/XjsEL4sxIeJodhlTRxjt8tt1aazbm8eI\n5CjeuWcMldV2Xl5Qf04e+t0AUnuGYw30Yf7945i/5CCLNmZw3Vl96BoViFKK7MJfc8tqX564MhWL\nCD9uyeBYTjlnDTV88KKN6WzYn8/QPpGOLPtrX+5pVt+olChunJKENdCHRxu8Pqktvqqxdh8R4seD\nlw5oOJG3GWejaQxRv2bUaCchIpHARqVUdxG5GLgFmApEYHSVjgISgJeUUhPMMi8Dx5VSL4rIdcA7\nSikRkR7A10opj3c3M1gbrpS6w/x9FvA0cIZSqlREEoBqIBr4DBijlMoTkYjGsoYi8iRQqpSaKSK5\nGBm9AuBbIE0pNV1EZpl1fNcs8wvwslLqYzEGF6QqpbY0ot+xzyISDWzA6OLeLyJB5rE5DOwErlZK\nrTK7lpOVUjs86XRC3fXGumZE2s6rt9Ynfc//85IOtfXVU5Mcy3O/29eEZNu56Zwkx/KZj/zQobYW\nPzv5hNjaes64DrWV+t1Kx/JZj3ZsvQAWPVNft46252yrM8/ZX/+zrQnJtvPYFfU3/86s17UzVzYh\n2Xbm3V/f1s95/McOtQXw3dNndLgNT1z89M8dqn/B4xMdy53YPjp1kF7hsh/bPZgKG3/GCRlo+Jvs\nSlZK5WF0jW4HxgBbgS3AEuBBpVSmua7WnNRxD8aYvGtFZAtG92/jqYKmbS8C/g2sMrt/PwFCzIDq\nGWCpaeOlFqp8HGN21EpcU9sfAg+Yr5zpjdHNfIOpewcwrYX7mwNMB/4jIluBVUBfpVQVcCnwvKlz\nM0Z3tUaj0Wg0mnbkZJp88pvtSlZKNXwVzQMNtldjjPtzJtVp+SFT7jBNzE5SSr2H0d3rvO4V4BUP\nsvOAeU3uuCH3pNPyG8AbHmRW4j4OskWDZRvus1JqCR7GXZoTVca3RKdGo9FoNBrNbzYw1Gg0Go1G\no/mvwPKb7IBtFf8zgaGInA0832D1IaXURW3Q+SjwuwarP1ZKeXwtzG9Nv0aj0Wg0Go0z/zOBoVLq\ne+D7dtb5DI28G/C/Qb9Go9FoNJp24AS+Xqa9+Z8JDDUajUaj0Wg6ghM5WaS9OXlCXI1Go9FoNBpN\nm/hNvsdQc0LRDUKj0Wg0/+10agqveM3Kdr93WkeN0+8x1Gg0Go1Go9GcOPQYQ41Go9FoNJq2cBKN\nMdSBocaN61/+pcNtvHNP/UdYpj7RsZ/E+/Yv9e9BP1k/U9eZtjrzs3Ed/fk9cP0E365rL+lQW/3m\nfepYPlnbR2fauuEfqzrU1tt/rP/UfEfXC1zr1pl09Of+nD/115n+XtM6dGCo0Wg0Go1G0wZOplnJ\nOjDUaDQajUajaQsn0ZdPTp6aaDQajUaj0WjahM4YajQajUaj0bSFk6grWWcMNRqNRqPRaDSADgw1\nGo1Go9Fo2oSIpd3/WmZXpojIHhHZLyJ/akRmoohsFpEdIrK0OZ26K1mj0Wg0Go2mLZyArmQR8QJe\nB84EjgPrRORLpdROJ5kwYDYwRSl1VES6NKdXB4YaNwZ2D+PKiT0RCyzfns2369LcZK6c2JNBPcOo\nqrbz9qL9HM0uA+DMIXGMHxSDUpCWW8bbi/ZTU6uYNjqR8YO6UFJe46Zrzl2jsIjw/cYMPl5+xG37\nLVOTGJEUia3azkuf7eRARikAf7ywLyOToygsq+K219c65HvGBHPHBSkE+HqRVVjpouu285IZmRKF\nraqWFz7dyf70Ejd7seH+PHL5IKyBPuxLK+b5j3dQU1v/taPkBCuvzhjOMx9tZ/n2bMd6SwO/0FZb\nqT3Due3cZLy8hOLyau6buwGA+y7uz6i+UZ1ma9yAaDddDbnt3GRGpERhq65lppP94UmR3HpuMhaL\nsHB9Gh8tcz2/l4zrxi1Tk7n0maUUl1e76U2a/T77774RVV3VpP2u9zyMdeQ4agoL2Hvr1c3ub3ME\nDRpMzFXXIxYLhUt/JO+bz1y2WwKDiL/xdny6xKKqq8h463VsaccQHx+6P/I04u2DeHlRvG4VuZ99\n5NFGW85ZoJ8Xf/r9QLqE+eNlET5ZfoTvN2YAcNHYRM4ZkdButiadEstl47sjIpTbanj1i90czDSu\nQU9tsTX1nDa6KxeN60ZCZCCX/LW+LSRGB3L/Jf3pE2/l3UX7+WTFUZdyA7uHccWEHohFWL49i+/W\np7vpvmJCDwb1DKequpZ3Fh3gaE69rzptYBdQcDyvnHdMX3XL1CRiwwPc9PzzzlGturYaq//F47px\nzvB4FHA4s5QXPt3pprezmHv3aCwiLNyQ7tEHz5iazIjkSGzVtby4YBcHMow63HNhP0amGD741tfW\nOOSvPqMXY/pGYVdQVOZ67ba3v//T7waQEBUIQLD//1xIMxLYr5Q6CCAiHwLTAOfGdCWwQCl1FEAp\nle2mpQG6K/m/GBGJF5FP2lvvHyb14uXPd/LYvM2MSokiPsLVSQ7qEUZMmD8Pv7uJeT8c4JpJvQAI\nC/Jl8pA4/vLBVp6YvxmLRRiVUn/TWLQxgyc/2MKTH2xx0ffE/C3MeG0NEwZ1ITE60GXb8KRIEiID\nufGV1bz65W7uOD/Fse2HTZk8Pn+z2/7ffWFf3l18gNteX8svO3NctiVEBjL9xV/4x+e7uGtaX4/1\nv3FKEgtWHmX6i79QWlHDlOHxjm0WgRun9GHD/ny3cheN7dZutoL8vblrWgqPz9/MTa+s5ul/b3WU\nWbQxnUfe29QpthZvSqeyqtZFT7foIJffI5IjSYgK5LqXTPsX9HUcqzvOT+HReZu56ZVVTEyNdSkb\nHerHsKRIsgoqHOssDaLrgw/dgap1f5hoSMHibzn02L3NyrUIsRB7zU0ce/EZDjz8R6yjT8U3vquL\nSNT5l1B59BCHHruX9DmziLnqegBUdTVHnnuSQ4/fx8HH7yN40GD8eyd5NNOWczZtdCJHs8uYMWsN\n97+1gZunJuPtJfSICeKcEQncOXuti5622MosqOC+uRu4+dXVfPDTIf54UT9HGU9t0ZmRyZEtsr39\nSBEPvb2RTKe2AFBSXs3rX+3lEw8BBMBVp/fk5c938fj7hq+K8+Srwv155L1NvP/jQa4+oydg+Koz\nBsfy9L+38cS/tmARHL7qn9/u46kPtvLUB1tddLXm2DVW/0irHxeOSeT219dy8yursViE01NjGj2O\nHc3j72/mllmrmZga4359J0USHxnADf9YxatfuPrgxZsyeOx9dx/86Yoj3Pb6Wu6YvZY1e3JdtrW3\nv3/u4x3c+cY67nxjHSsb+PvORCyW9v8TuVlE1jv93dzAbAJwzOn3cXOdM8lAuIj8LCIbROSa5uqi\nA8NWIAYn9NiJiLdSKl0pdWl7684urCCnyEatXbFmTy6De0e4bB/SO4JfdhkX4MHMUgL9vAkN8gHA\nyyL4eluwCPh6WygsbTrTA5BZUElNrWLZtmzG9HXNTo3uG8WPmzMB2HO8mCB/b8KDfQHYfqSQkgr3\noCEhMpDthwsB2HTANYD7YZORVdl1rJhgf28iQnzdyg/uFc4yMxO4aGMG4/rVZ96njUlkxY5st3pF\nWf3cMidtsTXplFhW7Mghp8gGQGFZfTZt2+FCShpk1zrKVnWNIiPf9WY9tl+02+/Fpv3dx4xzFBHi\nS0rXUNLzK8gsqKCmVrF0a5ZL2RlTk3lr4T6cvzw/rI9rW6stKQa73a0uDSnbvoWakuJm5VpCQK8+\nVGVlUp2TBbU1FK9ZQcjQES4yfvFdKdu5HYCqjDR8orvgZQ0FQNmMLLV4eSFe3rhU0Im2nDMFBPh5\nGfvr60VJRTW1dkW36CB2HyvCVu16zNpia+fRIkorjets19Eioq1+jjKe2qIzY/pHt8j2gYwSt+w+\nGG1xb1oxNXbPBzG7qJLcYsNXrd2by5De4a51auirfL0JDfTkq7ya9VWtOXZN1d/LIvj5WLCY//OK\nbU3a70jqfPDSbVmM7ufqx0b3i3b44N3HiwkOaOiD3c9/ua3+YdLf18ujrfby986cNrDZXtL/KpRS\nc5RSw53+5rRCjTcwDDgXOBt4XESSmyqgA8MWIiI9zAGe7wPbgatFZJWIbBSRj0Uk2JQbISK/iMgW\nEVkrIiEi4i8i74rINhHZJCKnm7KrRWSAk42fRWS4iASJyDtm+U0iMs3cPl1EvhSRJcCP5j5tN7d5\nicgLIrJORLaKyC3m+jgRWWYOPN0uIqc1V9f8knoHWVBa5bgw6wgP9iW/pN6J5ZfaCA/2pbCsioUb\n0nnhxmG8fPMIym217Dha5JA7Y3AcT/3hFK47s7dHu7nFNiKdbjpgBFw5RZUuMlENZBpyJLuMMWaQ\n1tBRZDejyxroQ2llDXbzRpRbXElkqCETafXj1AFd+GrNcTebt56XzNzv9rWbra5RgYQEeDPzxmG8\nfvtIJg+Ja7LOHWUrKtTP5VwDjnKO3x7OUaTVz+3c5TjZHNMvmtxim6NLso6uUa4ZhOhLr2yy3h2B\nd3gENfn1WY7q/Hy8wyNdZCqPHSZk+CgA/Hv1wScyGp8IU0Ys9PzLTJJnvUPpji1UHnRtF3W05Zx9\nseoY3boE8eGfTmPOXaOZ/fUelILDWaUM6hFGSIBPu9lyZsrweNbtzfNYH09EWf2atd0WnNtmQUkV\nYUGuusODfN38WZjpq77fkM7fbxjKSzcNp6KqxsVXASQnhLjZ+7XHrrH65xXb+GTFET548FQ+evg0\nyiprPPZCdDa5RTYiQ9yv71znOhS17BxeO7kX798/jtNTYz3baid/X8fA7mEtSkR0GBZp/7/mSQMS\nnX53Ndc5cxz4XilVppTKBZYBpzRZlV9RbQ0kYQzinADcAExWSg0F1gP3iogv8BFwt1LqFGAyUAHc\nDiil1CDgCmCeiPibsr8HI4AD4pRS64FHgSVKqZHA6cALIlKX3x8KXKqUmtBg324AipRSI4ARwE0i\n0hNjfMH3SqnBGI3BLRfvnK5evHhxqw9OoJ8XQ3pF8NA7G7h37nr8fCyMNgO0n7Zm8tA7G3jyX1so\nKms8w9Ae/OPzXZw7siuvzBhOQIOn1bZw27lmhqtB8mJUShSFpVXs8zD2qLV4WYSkeCuPzdvEw+9u\n4g+n9yQhMrD5gr9xWwB+PhaumNCDeT8c8LgvzljHTiB48LAO25fWkvf1Z3gFBtHzLzOJmDyVyiOH\nUHWZTWXn0BP3s++emwnolYRfQmLTylrB8ORIDqSXcvlzy5kxaw13nN+XQD8vjuaU89HSIzx3/ZB2\nt3lKr3DOGZ7A3IX72113ZxPo58Xg3hE89O5G7ntrA34+Xg5fVcfIlKbHTraFYH9vxvSL5uqZK7n8\nb8vx9/XijMGeA6j/Vub9cJBrZq7kp62ZnWJvwqAu/Lwtq1NseUQs7f/XPOuAJBHpacYflwNfNpD5\nAjhVRLxFJBAYBexqSun/3EjNNnJEKbVaRM4D+gMrze8j+gKrgBQgQym1DkApVQwgIqcCs8x1u0Xk\nCEa///8Bi4A/YwSIdeMFzwIuEJH7zd/+QN0AtsVKKU+PlmcBqSJS17UcihHIrgPeEREf4HOllFtg\naKan61LUavvhAse28GBfCho8hRWUVhER4gcYgVBEsB8FpVX07xZGbnGlI92/cX8+feKtrN6d6zKx\nYOn2LM4b5TpmC3A8STuTW2wjOtQfKHLI5DbT5XI8t9wx7uWq03u4bOsS6s+OJnQVl1cT7O+NxSLY\n7Yooqz95ZhdrUoKVRy4fBEBooA8jUqKorVX0TbQypl80ExuMEWqLrZyiSorLq6mstlNZbWfr4QJ6\nxwWTllcO4JZB7ChbuUU281zXU1fO8dvDOcortuFtsZjrDaJNm3ERAcSGB/DmnaPN9X7Mvn0Ud76x\nltwGukvWrSKgdwqlmzfQWdQU5OMdUR8U+EREUFPgmiWzV1aQ8dbrjt+9Z75BdbbrTcleXk75ru0E\npQ7BlnaM8DOmuGxvyzk7e2g8Hy47DODork+MDmLP8WIWbkjH18dCcoK1XWwB9IwN5t6L+vHIe5s9\ndh06c8Horkwdbgxz2pNW3KzttuDcNsNDfCksc9VdUFbl0vUbHuxLYWkV/buFkltko9T0VRv259En\nLoTVu3M5PTWGCYNi3MZWN7b/TR273GKbx/oP7RNBZkGF4yF5xY4c+ncLbYcj0jaiQv3IK3G/vqOc\nr+/QX3cOf9qSyXVn9nG31U7+HoyxyWP7d+GuN9dx0xTPY3pPRpRSNSJyB/A94AW8o5TaISIzzO1v\nKqV2ichCYCtgB95SSm1vSq/OGP46ysz/ghGgDTb/+iulbvi1ypRSaUCeiKQCl2FkEOv0X+Kkv5tS\nqi7CL/Okyyxzp1OZnkqpRUqpZcB4jPTyey0ZeBoTHkCU1Q8vc/LI5oOucejmgwWOsWK9YoMpr6qh\nqKya/BIbveJC8PU2mlW/bqFk5BuBTN0YRIChDcYsxoT54+0ljB/UhdW7XQcqr9mT63iSTulqpayy\n1i1QbUidLRHcZhfWBVT9Eq2UVda4dDPVseVgAePNLuizhsY5xihdM3MlV79g/C3fns2sL3fzy64c\n3ll0gCufX8Elf13WbrZW7cphYI8wx/ijvomhjtmUUD9mrKNt7UkrdjuGq3bnuP0+07TfN9FKmc2w\nvyetmITIAGLDjfM7ITWGVbtzOJxVxu//toxrZq7kmpkrySm2cdvraygorWL9PlcoFSkAACAASURB\nVNcALGjQYCqPHnKrS0dScWg/vjFx+ER1AS9vrKNOpWTTehcZS2AgeBnP1WETJlO+dyf2ygq8QqzG\nNkB8fAkakEpVutGzU/DjQhcdbTln2UWVDDGvo7BgXxKjAh1jQcOCfPhytetwh7bYig71489XpfL8\nxzscDyZN8eXq48x4bQ0zXlvDyp3ZLbLdWmLC/B2+amRyFJsPFLhs33Igv4GvqqWovJq8kip6xQXX\n+6pEYzwswE9bs/hkxVH2Z7gOc2jtteWp/tmFlfRLDMXPx7A/pHc4R3OaP7YdRZ0PnjAoxs0Hr96d\n4/DBfbsadWjOBzsH1Q3HEba3vwcY0iuc47llJ3ScJiLt/9cClFLfKqWSlVK9lVLPmOveVEq96STz\nghmnDFRK/aM5nTpj2DpWA6+LSB+l1H6zmzcB2APEicgIpdQ6EQnB6EpeDlwFLDEHfXYzZcEIBh8E\nQpVSddPgvgfuFJE7lVJKRIYopRqf+ldf5lYRWaKUqjbtpAFRwHGl1FwR8cPoin6/KUX/WnKQey/u\nj0WEFTuySM+rcGTDft6axdZDBaT2COO564ZSVVPLO4uMrqWDmaWs35fHn69KpdYOR3NKWWqm9n93\nWne6RQehFG5PgH+9ZjAWi7BoYzpHc8qYas7o+3Z9Ouv25jEiKZK3/zgGW3UtL39WnwF/8NIBpPYM\nwxrow/v3jeVfPx1i0cYMJg6K4byRRkZy5S7XICazoIJ5943FVm1n5qc7HOufuXYwLy3YSV5JFXMX\n7ufRywcy/czeHEgvYeF699f1tIS22DqaU866vXnMuWsUdgXfrUvjcJYRrD1y2UBSe4Z3iq0//W6A\n2+DxI9llnDvSyAh9szaNtXvyGJkcxXv3mvYXGPbtdsVrX+3h2elDzNdTpHMku7HnGoO6SQ51VOzf\nS8m6Vc0e624PPUlQ6hC8rWH0nf8ZWfPfpmDR182W84jdTub8t0h84HHjdTXLllCVdoyw088CoPCn\nRfjFdSXu5jtBKWxpx8h4ezYA3mHhxN90B1i8QISStb9QusVztrMt5+yDJQd54NIBzLlrNAi89f1+\nR1b+iatSsQb6tJutqyf1whro45htXmtX3G7OevbUFp1ZuyePUSlRzdq+cEwivx/fnYhgX+bcNZq1\ne3J56bNdhAf78vrtIwn080YpxcXjXGf+f/DTIe65qJ/pq7JJz69gwiDDVy3dlsXWw4UM6hnO36YP\noarG7vBVhzJL2bAvjyeuTMVuVxzNKWPZ9vqM78iUSNbsySWla33W9Z6L+v/qY9dY/XcfL2b59mxm\n3zGKWrviQHoJ36497jILtzP567VD8LIYE2eOZpcx1Xzd0bfr0gwfnBzFO/eMobLazssL6t+E8tDv\nBpDaMxxroA/z7x/H/CUHWbQxg+vO6kPXqECUUmQ3mFTU3v4eYPygGJZuPYHdyCcZohoOmNJ4RER6\nAF8rpQaavycBzwN1fRmPKaW+FJERGN3GARhB4WSgBngDGG4u36uU+snUE4MRwD2tlHrKXBcA/AMY\ni5HVPaSUOk9EpgPDlVJ3NNwnc5b0X4HzMbKHOcCF5t8DQDVQClyjlGoqBaOuf/mXVh+nlvLOPWMd\ny1OfWNKhtr79yyTH8pmP/NChthY/O/mkt3XWox1ra9Ez9ba2njOuQ20BpH630rG869pLOtRWv3mf\nOpZP1vbRmbZu+EfzDw1t4e0/jnEsd3S9wLVunck5j//Yofq/e/oMx3In+vtOfeN0+e7t7R5MBfYd\neEI+wKwzhi1EKXUYGOj0ewnGJI+GcuuA0R5UXNeI3iwanAelVAVwiwfZ94D3PO2TUsoOPGL+OTPP\n/NNoNBqNRqNpEh0YajQajUaj0bSFE/tq43ZFB4YajUaj0Wg0beEEfCu5ozh5QlyNRqPRaDQaTZvQ\nGUONRqPRaDSaNnCCv5Lbrpw8NdFoNBqNRqPRtAmdMdRoNBqNRqNpCy37tvF/Bfo9hpqG6Aah0Wg0\nmv92OjVSqzi4r93vnQG9kk5ItKm7kjUajUaj0Wg0gO5K1mg0Go1Go2kTYjl58mw6MNS4cdMrHfuZ\nKYC5d9d/aurcP3fsJ5K+eUp/Eq89bXXmJ/E6+hN14PqZuo7+BJ/z5/dO1vbRmbY62lc5+yn9SbzW\n4/xJvM7095rWoQNDjUaj0Wg0mrZwEr2uRgeGGo1Go9FoNG1A9JdPNBqNRqPRaDQnGzpjqNFoNBqN\nRtMWTqLJJydPTTQajUaj0Wg0bUJnDDUajUaj0Wjagh5jqNFoNBqNRqM52dAZQ40bA7qHcfmEHlhE\nWL4ji4Xr091kLp/Qg0E9wqmqqeXdRQc4mlMGwBmDYzltQAwisGx7Fj9uzgTg0lO7k9oznFq7nZxC\nm4uuf945CosIizZm8PGKI262bjknieFJkdiq7bz8+U4OZJQSZfXjvov7Exbki0KxcEM6X64+DsD1\nZ/VmZHIUNbWKjIIKF123nZfMyJQobFW1vPDpTvanl7jZiw3355HLB2EN9GFfWjHPf7yDmtr6rx0l\nJ1h5dcZwnvloO8u3Z9M1KpDHLh/kpqettlJ7hnPbucl4eQnF5dXcN3cDABeNTeScEQmdZuvicd3c\ndLnV9dxkRqREYauuZaaT/eFJkdx6bjIWi7BwfRofLTPO79WTenHOiHiKyqoBeGfRftbtzSOlq9VF\nb8iwkZRsWAtA0KDBxFx1PWKxULj0R/K++cxF1hIYRPyNt+PTJRZVXUXGW69jSzuG+PjQ/ZGnEW8f\nxMuL4nWryP3so2br1BRd73kY68hx1BQWsPfWq1uloy3nLNjfm/su6U98RABVNXZeXLCTw1lljnLN\nfba1LbYnnRLLZeO7IyKU22p49YvdbbY1bXRXLhrXjYTIQC7561KKy412kRgdyP2X9KdPvJV3F+3n\nkxVHXcp1hK8a1ieCC0YnEhsR4KLnn3eOatW11Vj9665jAb5dl8Znvxxr8jh2JHPvHo1FhIUb0vl4\nubsPnjE1mRHJkdiqa3lxwS4OZBh1uOfCfoxMiaKwrIpbX1vjkL/q9J5MGV5/fTfGsD4R3HxOUrv6\n/xOFnpWsOam5cmJPXvl8F0/M38zI5CjiGjjIgT3C6BLmz6PzNjH/x4NcNaknAPGRAZw2IIZnP9rG\nUx9sIbVnONGh/gDsPFrIk//azFMfbCWr0DVY+/O/tnDr62sYP6gLidGBLtuGJ0USHxnITa+uZtZX\nu7n9vBQAau2Kt77fx62vr+G+uRs4b0RXR9lNBwq4bfZa7nhjLel55S76EiIDmf7iL/zj813cNa2v\nx/rfOCWJBSuPMv3FXyitqGHK8HjHNovAjVP6sGF/vmPd8dxyZry2hhlOjrGttoL8vblrWgqPz9/M\nTa+s5ul/bwWgR0wQ54xI4M7ZazvNVsPPqXeLDnL5PSI5koSoQK57ybR/QV/Hsbrj/BQenbeZm15Z\nxcTUWJeyC1Ye5dbX1nDra2tYtzcPgMNZpS66Y6fPMAZ1i4XYa27i2IvPcODhP2IdfSq+8V1dZKPO\nv4TKo4c49Ni9pM+ZRcxV1wOgqqs58tyTHHr8Pg4+fh/Bgwbj3zvJ4zFqKQWLv+XQY/e2SUdbztkV\nE3twIKOEW2at4e+f7OA287qo46KxjQfzI5Mj22Q7s6CC++Zu4OZXV/PBT4f440X92mxr+5EiHnp7\nI5kNHuRKyqt5/au9fOIhWIGO8VVpeRXM/noP+9KKXXS15lg1Vn/n6/iWWWsY3TeK+Ab73pk8/v5m\nbpm1mompMe7Xd1Ik8ZEB3PCPVbz6xW7uOL++rS3elMFj72/2qPPzX45xx+y13NHAV9VhEbj13JR2\n9/8nDIul/f9OVFXaU5mIPCki95vL74nIpa3Q0UNErnT6PVxEXm3P/TwZEJH/iMhWEbmnvXXnFFWS\nW2yj1q5YtzeXwb3CXbYP7hXB6l05ABzMLCXQz5vQQB/iwgM4lFVKVY0du4K9acUM7RMBwM6jRdjN\nAONgpuvNP7OgkppaxbLt2YzuG+2ybXTfKJaYT/J7jhcT5O9NeLAvBaVVHMgw9FRU1XIst4zIED8A\nNh3Ix24a232syEXfD5syANh1rJhgf28iQnzd6j+4VzjLtmcDsGhjBuP6dXFsmzYmkRU7siksrWr2\nOLbF1qRTYlmxI4ecIiO7Wmg+eXeLDmL3sSJs1fZOsZWZX0FarmtwPbZftNvvxab93ceMcxQR4ktK\n11DS8yvILKigplaxdGuWW9mGNKxXXVQa0KsPVVmZVOdkQW0NxWtWEDJ0hIuoX3xXynZuB6AqIw2f\n6C54WUMNNbZKAMTLC/HyhjZ+7r5s+xZqSoqbF2yCtpyz7l2C2XywAIBjOeXEhPkTFmyUj7L6Mapv\nVKN2x/SPbpPtnUeLKK2sMcofLSLa6tdmWwcySsgqrHRbX1hWzd60Ymrsnk9YR/iqzIIKj/vSmmPV\nWP2dr2O7XbH1UCGnDujiprezqPPBS7dlMbqfa9sZ3S/akU3dfbyY4ADDBwNsP1JISUXTWcHGSE6w\nkp5f3u7+X9N2fosZwx6AIzBUSq1XSt3VEYbE4IQfAxH5VV36IhILjFBKpSqlXm7v/ckvqe/qLSit\nIizY9YILD/Yl3ykwMmR8ScurICk+hCB/b3y9LQzqEU5EsLsTHdffc3CQW2Rzu7gjQ/zIKa530rnF\nNiIb3Ii6hPnTKzaEPWnuN+ozh8a7/M4uctUV1UCXNdCH0soaR2CZW1xJZKghE2n149QBXfhqTcu6\nLNpiq2tUICEB3sy8cRiv3z6SyUPiACOjNqhHGCEBPp1iKzkhhMIy1yC4rpzjt9WPnCL3cxTVYH2O\nk02AaaMTefPOUdx7cX+C/T1fApnz/gl2O97hEdTk5zrWV+fn4x0e6SJbeewwIcNHAeDfqw8+kdH4\nRJgyYqHnX2aSPOsdSndsofLgPo/2OpO2nLODmSWc2t8IJFK6WokJ83cEaLeel8zc7xqvX5TVr022\nnZkyPN6R7W2trbbQ0b6qIb/2WDVWf+fr2M/HwsiUSKLD/H/9AWhnPPpgqx+5znUoatk5PH90V2bf\nPpJ7LvScUTb01p+/jvD/nYpI+/+dIFoUFInINWZ2aouIzDezekvMdT+KSJODkERkmIgsFZENIvK9\niMSZ6/uIyA+m3o0i0ht4DjhNRDaLyD0iMlFEvjblI0Tkc9PuahFJNdc/KSLviMjPInJQRBoNJM19\n3yMi7wPbgUQROUtEVpn78LGIBJuyI0TkF3P/1opIiIj4i8i7IrJNRDaJyOmm7GoRGeBk52cz2xlk\n7ttaU36auX26iHwpIkuAH0XkfRG50Kn8B3WyHlgEJJjH6DQR6S0iC83ju1xE+po6okXkUxFZZ/55\n/BCsiNwsIutFZP3ixYubOpVNkllQwcIN6dxzYT/uvrAfx3LKaPigP3VEAna75/Ktwd/Xi0cvG8jc\nhfuosNW6bLtsfHdqG8k0tIbbzk3mrYX73LpW6/D2ar8L2csiJMVbeWzeJh5+dxN/OL0nCZGBHM0p\n56OlR3ju+iGdYmvlzhxGJEc2r+RX8tWa41z74kpufW0N+SU2bp6a7FEu8ryLER8fj9sakvf1Z3gF\nBtHzLzOJmDyVyiOHUHWNTdk59MT97LvnZgJ6JeGXkNheVTkhfLj0MMEB3rx5xyguHJPI/owS7Eox\nKiWKwtIq9nkYB9fenNIrnHOGJzB34f4Ot9XetMRXdSTO1/Gz04dwIKPUEVieDHyzNo3rX/qF22ev\nJb/U1nyBVtKU/9e0nmYzVWaw8xgwVimVKyIRwDxgnlJqnohcD7wKXNhIeR9gFjBNKZUjIpcBzwDX\nAx8AzymlPhMRf4xA9U/A/Uqp88zyE53UPQVsUkpdKCKTgPeBwea2vsDpQAiwR0TeUEo1luNOAq5V\nSq0WkSizfpOVUmUi8hBwr4g8B3wEXKaUWiciVqACuBtQSqlBZgC2SESSTdnfA382A984pdR6EXkW\nWKKUul5EwoC1IlL3NfahQKpSKl9EJgD3AJ+LSCgwFri2kf2/APhaKTXYPEY/AjOUUvtEZBQwG5gE\nvAK8rJRaYQbv3wNuj29KqTnAnLqfO44UOraFB/tS2ODCLiitcnm6NmSMp/IVO7JZscPoUrlobCIF\nTk/rY/tFk9oznJcW7OT1PqPcKhUV6kdeiautvBIb0VZ/wOgSjrL6kVdsyHhZhEcuG8hPW7P4xewu\nqmPy4FhGJEexfLtr92WXUH92OOnKLXa1V1xeTbC/NxaLYLcroqz+5JlPtUkJVh4xJ5mEBvowIiWK\n2lrlsH17g3FebbGVU1RJcXk1ldV2KqvtbD1cQO+4YNLyylm4IR1fHwvJCdYOt/XDpkziIwIZ7hQc\n5hU1OEfFNnN8lus58rZYHOO2AKKdbDpnIb9bl8bT1wzGE/bKSvwSulFTkI93RH0Xl09EBDUFeQ1k\nK8h463XH794z36A6O8tVpryc8l3bCUodgi2tcwf7R553scvvtpyzcpsxyaeO+Q+MIyO/ggmDYhjT\nL5qRKa7dgReM7srU4caEpT1pxW2yDdAzNph7L+rHI+9tdutK/LW22kKEU4apvXzVxNQYxg+McWm7\ndfzaY5VbbGu0/gs3pLNwgzFZ5vqzejuGcpxIPPrgYhtRztd3aPPn0OX6Xp/O5RN6uskYeuvPX3v6\n/xPBb6Dzsd1oSU0mAR8rpXIBlFL5wBjg3+b2+cCpTZRPAQYCi0VkM0YQ1lVEQoAEpdRnpt5KpVR5\nE3ow7cw35ZcAkWbABvCNUspm7mc2ENOEniNKqdXm8migP7DS3L9rge7mfmcopdaZ9oqVUjXmPvzL\nXLcbOAIkA/8H1I2p/D3wibl8FvAnU/fPgD9Ql2FdbB5PlFJLgSQRiQauAD417TWJmd0cC3xs2vgn\nEGdungy8Zq7/ErDWZUObokuYP1FWP7wswojkKLaYY5nq2HIwn9FmsNUrNpgKWy1F5izCkADjWSMi\nxJchvSNZs9vo/hvQPYyzh8Xz2le7qapxTRnGhPnj7SWMH9jFIV/Hmt25TBocCxhdZmWVtQ4Hfve0\nvhzLKefzVa43+GF9IrhkXHf+8u+tfNFgplpdN2m/RCtllTXkl7iPFdxysIDxA41uurOGxjmczjUz\nV3L1C8bf8u3ZzPpyt4tDCgpwfc5qi61Vu3IY2CMMi0Xw87HQNzHUMZsyLMjHbQZeR9nKKignIcp1\nUPyq3Tluv8807fdNtFJmM+zvSSsmITKA2HDj/E5IjXGUdR6nNa5/F8ekk9hw15uxb1wC1bnZVBza\nj29MHD5RXcDLG+uoUynZtN5F1hIYCF7GOQibMJnyvTuxV1bgFWI1tgHi40vQgFSq0tPcjk9Hk/f1\nApffbTlnQf7ejgz1OcPj2XaokHJbLe8sOsCVz6/g6hdWuuj5cvVxxwSplTuz22Q7OtSPP1+VyvMf\n7yAtz91lt8ZWa+kIX/Xz1iz+8u+tHMl2HQvd2mursfqHBRmZ8OhQP8YN6MKSLZltPyCtpM4HTxgU\nw+oGPnj17hzOMH1w365GHQqaGWMd7hSMNzaueG96CQkRge3u/08YFmn/vxNEZ7yuRoAdSqkxLiuN\nwLA9cX7UqKXpupU5LQtGgHaFs4CIuL9/pAmUUmkikmd2b18GzHDSf4lSak8D/aMa7AcYGdA/AJcD\n17XQtAUorMseetg2WinlPpK6Cf798yH+eGE/RISVO7NJNzMRAEu3ZbHtcCGDeoTzzLVDqKqx897i\n+q6kW89NIcjfm1q74t8/H6SiykjvXzmxJ95ewr0X9Xez9/TVg7FYhMWb0jmaU8Y55oy+79ans25f\nHsOTI3nr7jHYqmt5+fNdAPTvFsoZg+M4lFnKrBnGJIR5Px5k/b48ZkxNxsfbwjMeslCZBRXMu28s\ntmo7Mz/d4Vj/zLWDeWnBTvJKqpi7cD+PXj6Q6Wf25kB6CQvXNx9E+PtYGGYOXm8PW0dzylm3N485\nd43CroysWt3rSJ64KhVroE+n2HrsylQsDRzUkewyzh1pZIS+WZvG2j15jEyO4r17TfsLDPt2u+K1\nr/bw7PQhWET4fmM6R7INvTeenUTvuBAUiqyCSl75wjivA7qHudbr/bnUlhrdopnz3yLxgceN19Us\nW0JV2jHCTj8LgMKfFuEX15W4m+8EpbClHSPj7dkAeIeFE3/THWDxAhFK1v5C6ZYNzZ7Tpuj20JME\npQ7B2xpG3/mfkTX/bQoWff2rdLTlnHWLDuLB3/VHKeN8vOiUPWyOtXvyGJUS1WrbV0/qhTXQxzH7\nvKnhGi21deGYRH4/vjsRwb7MuWs0a/fk8tJnuwgP9uX120cS6OeNUsrt1Ukd4auG9I7gigk9CG4w\njveei/r/6mPVVP3rruOaWsVrX+6mrLLZPECH8ddrh+BlMSbOHM0uY6r5Oqxv16Wxbm8eI5KjeOee\nMVRW23l5QX1be+h3A0jtGY410If5949j/pKDLNqYwQ1n96FXXAgo5XEiDxj+4Y1v97a7/9e0HVGN\nDZiqEzC6kj8Dxiil8syu5PcwsojzRWQ6RjfxRSLyJFCqlJopIu8BX2NkqnYCVyulVpldy8lKqR0i\nshqjK/lzEfEDvDC6Ol9SSk0w7U/E7FoWY3ZyjlLqaXP9y0qpIc52zTLbgfOUUoc91KcHRjfsQPN3\nNLABmKSU2i8iQUACcBjYTX1XcghGV/JdwACl1A1mF/Jisz42EbkdI5s6RCk1wNT/LGAF7lRKKREZ\nopTaZB634UqpO5z2LQZYC2Qqpdz7Whuvwy/msfhYRASje3qLiPwbo+v9BVNusFLK87sF6lE3vbKq\nGZG2M/fu+ueEc/+8pENtffPUJMfymY/80IRk21n87OST3tZZj3asrUXP1Nvade0lHWoLoN+8Tx3L\nW8/xOAy33Uj9rj6Td7K2j8601dG+ytlPdXS9wLVunck5j//Yofq/e/oMx3In+vtOTblV5+W0+yBR\nn8joE5I2bLYrWSm1A2NM4FIR2QK8BNwJXCciW4GrMcbdNVa+CqOL9Xmz/GaMrk/MsneZen4BYoGt\nQK054aPhq1ieBIaZ8s/R+Bi8FqOUygGmA/8x9a4C+pr7fRkwy9zvxRjdwLMBi4hswxhXOF0pVZet\n/AQj2/d/TiaeBnyArSKyw/zd2L5kAbuAd39lNa4CbjD3cwdQN2nlLmC4GJN1dlKfxdRoNBqNRqNx\no0VdyUqpeRgTTpyZ5EHuSafl6U7Lm4HxHuT3edLjYd3Ppnw+Hia5ONs1fw/0oLNu22GMMY/O65YA\nIzzIrsMYg9gQj928ZmDn3WBdBXCLB9n3MDKvDkQkEGNizH8a23+z7GGc6qCUOgRM8SCXixHcajQa\njUaj6SAqfdv/dUMtex9D+3PyTKP5L0dEJmNkC2cppYqak9doNBqNRqNpb07abyWLSCTgaeDEGUqp\n39wIVaXUDxizoR2IyNnA8w1EDymlLuq0HdNoNBqNRvM/w0kbGJrBn+eXo/2XoJT6HuPdgxqNRqPR\naDQdju5K1mg0Go1Go9EAOjDUaDQajUaj0Zg0+x5Dzf8cukFoNBqN5r+dTn0HYElJSbvfO0NCQn6b\n7zHUaDQajUaj0fxvoANDjUaj0Wg0Gg1wEs9K1rQeW1rHf5TcLyHRsfy3j7Z3qK2HL6t/n3lnfq5r\n761Xd6it5DfmnxBbJ9Pn1TrbnrOtzvz83sxPdjQh2Xbuv3SAY3nPTVc0Idl2UubWv/9/2lM/dait\nL/58umP5D39f0aG2AP714KkdbsMTLy1o+be2W8O9F/d3LH+5umPvLxeMTmxeSNMkOmOo0Wg0Go1G\nowF0YKjRaDQajUajMdGBoUaj0Wg0Go0G0IGhRqPRaDQajcZEB4YajUaj0Wg0GkAHhhqNRqPRaDQa\nEx0YajQajUaj0WgA/R5DTTOsWLuW51+bjd1u5+Kp53DDla7vKFu3eTN3P/4ECbFxAJxx2qnMuMZ4\np96UK64iMDAAL4sXXl5efPjm7CZt9YoNZvKQOCwCmw8WsHp3rsv2iBBfzhvZlZhwf5Zuy2LtnjzH\ntuFJkQzuHQ7AloMFrNubR3Pcdl4yI1OisFXV8sKnO9mfXuImc+/F/UhOsCLA8bxyXvhkJ5VVtY7t\nyQlWXp0xnGc+2s7y7dnN2gQI7D+ILr+/GsRC0cqfKVj0tct2S2AgsVffhE9UF1RNNZnz36Iq/XiL\ndHeWrZYcu9hwfx65fBDWQB/2pRXz/Mc7qKlVBPt7c98l/YmPCKCqxs6LC3ZyOKuMrlGBPHb5IBcd\nF41NJC4ioNW2Av28+NPvB9IlzB8vi/DJ8iN8vzHDofucEQkdXi/HsW7lx6263vMw1pHjqCksaPf3\nVfaICWbS4FhEYNuhQtbucb3m+iWGMjIlCgSqauz8sDGdnCJbi/UHDjiFmMuvAYuFouU/kb/wS5ft\nlsAgYqffgm90DPbqKjLf+ydV6cfxDo8g7vrb8LKGAlC47EcKf1zopn9I7whumpKExQKLN2bw6cqj\nbjI3TUliWFIEtmo7r3y+i4OZpfh4WXj2uiH4eFnwsgi/7MrmPz8fBmD6mb0ZkRxJTa3rF85Se4Zx\n9Rm9sIjw89Ysvlrjfp1cfUYvBvcKx1ZtZ853ex3nP9DPixunJNE1KhAFzP1uH/vTS7jjghTiwgMM\nGf8TfzvuERPExNRYLCJsO+zuR/smWhmRHIVgtofNGeSa7eGsoXH0ig2h3FbD+z8ebJG93VvX8uUH\nxv1l5IRzmHSe6/1l+8aVfP/pe4jFgpfFiwuuupWeyYaP+L+3XmDn5jUEW8O4/9m32l75NlKF74ne\nhXbjxLdEzW+W2tpann1lFnNeeJ6Y6GiuuPV2Jo4dS+8e3V3khg4axGvPPuNRx9svvUh4aGiztkTg\nrGHxfPjzIYoraph+Zi/2pZeQV1x/E6qsqmXxpgySEkJcykaF+jG4dzjvLT5ArV1x2fge7E8voaC0\nqlF7I5MjSYgMZPqLv9Av0cpd0/py1xvr3OTe/GYv5TYjELxlahLTRnflhFdD/wAAIABJREFUo2VH\nAONGf+OUPmzYn99s/Zwr2uXya0l79XmqC/Lp/qe/ULZ1I1WZ6Q6RiCkXUHn8KOn/fAWfmDhiLr+W\n468813IbHWyrpcfuxilJLFh5lJ+3ZnH3tL5MGR7P12vSuGJiDw5klPDUB1tJjA7kzgv68uDbGzme\nW86M19a4vAS6tLKmTbamjU7kaHYZT8zfQmiQD+/cM5Yft2TSNSqQc0YkcOfstXz91CSHno6oVx0X\nje3W9PlqhILF35L35ack3v94q8o3hgCTh8Tx8fLDlJTX8IczenEgvYS8kvprrqi8ig+XHsJWbadn\nbDBnDYvngyWHWmhAiLnyOo6//CzVBXl0f/QZSrdsoCojzSESOXUatmNHSJ/9Er6x8XS58jqOv/QM\nym4n++N/YTt6GPHzp8fjz1K+c5tLWYBbpibz5/mbySu2MfOm4azdk8ux3HLH9mF9IoiLCGDGrDUk\nJ1i59dwUHnh7A9W1dh6ft5nK6lq8LMJz1w1lw7589qYVs/lAPu//cBC7Ui4vuL52cm+e+7/t5JdU\n8ZdrBrNhfx7peRWO7af0Cic23J/75m6gd1wI08/sw5P/2gIYAePWQwW8+sVuvCyCn4/RWffal3sc\n5a88vSdTGzyodCYCTDoljk9XHKGkopqrTu/FgYwS8kvq/WhRWTX/t+wwtmo7PWKCOXNIPP/52WgP\nO44UsflgAVOGxbfInt1ey2fvz+LmB58nNCKaV5+8nQFDxhKTUH9/Seo/lAFDxiIipB89yL9mP82D\nz70LwPBTz2bs5Av5cM7z7XcQ2oBq9y8lnzh+813JIlLaynJ/FJHA9t6ftnAi96k1trfv3kO3hHi6\nxsfj4+PDlEkT+emXlc2Waw3xEQEUlNgoLKvGblfsOlpEcoMAsNxWS0Z+BXa7a9moED/S8yqoqVUo\nBcdyykjuam3S3pj+0fywycgc7TpWTLC/NxEh7k98dUEhgJ+Pl8u2aWMSWbEjm8ImAtCG+PfoTXVO\nFtW5OVBbS/H61QSdMsxFxjc2gYo9xpcqqrMy8I6Mwiuk6fp0pq2WHrvBvcJZZmZRF23MYFy/LgB0\n7xLM5oMFABzLKScmzJ+wYM9P2/27hbbJlgIC/IzzFuDrRUlFNbV2RbfoIHYfK8JW7dqYOqpeUVY/\nRvWN8ljH5ijbvoWakuJWlW2K2IgACkqrKCqrxq4Uu48V0Tve9ZpLz6twHKP0vHKCA3xarN+/Zx+q\nczKpzs2G2lpK1q0iePBwFxnfuK6U7za+fFSVmY5PZDReIaHUFhViO3oYAGWrxJaRhndYhJuNzPwK\nsgorqbErlu/IYmSDYzyybxQ/bc0EYG9aMUH+3oSb56Sy2ri2vSyCl1d9OnfzwQLsHu7yWYWV5BTZ\nqLUrVu/KYVifSJftw/pEsGKH0S4OZJQQ5O9FWJAPAb5epHQN5eetWQDU2pWLX6ljVErr2kd7ERsR\n8P/snXd4FNX6xz9nS3rbZNMTSAiE3jtIEREBpYi9gqgICggKFuR61R+IFbEL3nsFKwioINJBei8B\nQg0lkJDe+2aze35/7LLJphFIQXE+z5MnM3PeOd85M2dm3nlPWbLyi8kuMGKWcCo+m4hA+/qQmFFa\nHxIzCnB3Lo0tXU4vsGtNuRqXzp9G7x+Ej18QGo2WDt37c/yQ/fvF0ckZISzXpri4CEHpdWrSoh0u\nrvbHp1A3/OUdw1owBfhLOYbc2GO6Zu3ktDT8/fxs6/56X1JSKzbRRh0/wT1PPc2EV17l7IXY0gQh\nGDftJR54ZgLLVq2qsF9Z3Jy15BQabeu5BSW41/AllJptINTXBWcHNRq1ICLQHQ+X6vfVeziSkl1k\nW0/LMaD3cKzUdto9rfh5Rh9CfV34bbfl55x8PBy5pbVfpc1J1aHx0lGSWRphLMnMQOuls7MxXL6E\nW4euADg1boLWW49GV/GleKO0anLuPFy05BWVYDZLq00RPp4Wm/NJudzSylKvmod44O/lhG8V5762\nWit2x9HIz5XFr/RhweQefLHqNFJCbHIebcO8KtSx+irXhLsi+XpNTKVlvFG4O2vJLXPP5RUa7V70\n5WkbruNCUs2/0zVeOowZpc+Lksx0NOXrX/xF3Dp2AywfMlqfivVP46PHKTSMogtnK2ik5ZRer/Qc\nAz7u9tfLx93R1tRpsS+1UQn46JkufDu9N1HnLdHC6sgoE0nNyDWgK/fRoHN3JD2nuIxNMTp3R3y9\nnMgtNDJuSDNmje7AU4Ob2iKGV2ge4kF2Qc0/MOsDNydNufpQ/TO4TZiOC8nXFbcBICczDS/v0veL\np7cv2ZkV3y/HDuzgvVee4H9zX+O+p6Zdt159I6Ws878bxd/GMRRCuAkhNgkhDgkhjgkhRli3uwoh\n/hBCHBFCRAshHhBCTAaCgD+FEFX+mKYQIk8I8ZEQ4rg1b18hRIQQ4lAZm2ZX1oUQsUKIOUKIKCHE\nASFEJyHEOiHEOSHEeKtNfyHEFiHEMiHEKSHED8JCTY/pS2vex4UQb5bZXm/aQohx1jwPLFiw4Jqu\nS8tmzVi/+EeW/+drHh45kimv/9uWtujjeSz9ej5fvPM2i39byYEjR68p75qSnmtg98k0HugXxgN9\nw0jOKqzTm+qD5Sd4cM52LqXk07+tPwDP3hnJf9bG1EvzQea631E5u9Boxiy8br0dQ9xFKoRJ/4Za\nV1i8NRY3Zw1fTezOyJ6hnE3MrTRCUxd0ifThXEIeD76znfGf7mXisBa4OKq5lFrAkq0XeWdsxzrT\nqqpc3ZvrycorJqaS/op/F0J9XWgbpmPbseQ6zTdjzUrULi40fn0OXgPuoCguFmRp/ROOjgRPmErK\nkm8xFxVWndF1YJYwdf4Bnpy7m8ggDxr5utql39encRV7XhtqlSDM341NUYnMXBSFodjMsO4hdjY9\nW/qy+2RaFTn89QjVu9CmsVeN+1XXhrZdbuGld75hzOQ3Wbf8m3rXU/h79TEsAu6WUuYIIfTAHiHE\nSmAwkCClvBNACOEppcwWQrwA3CqlrO5ucwUOSCmnCiFeB/4tpZwohMgWQnSQUkYBTwBla+MlKWUH\nIcRHwEKgN+AERANfWW06Aq2BBGAn0FtK+UkNj+k1KWWGEEINbBJCtJNSXvGo6kVbSrkAuOIRSsNl\nS1TMX68nOaX0xk9OS8XP1775xM219GHap0d3Zn/8CZnZ2eg8PfH3tTSN+Oh0DLilN9GnTtGlfbtK\nC51XaMSjzNepu4v91+vVOHohk6MXLM14/dr6V7nvVxO7A3D6cg5+nk4cJxuwRKbScqruVG+WsOVo\nMvf3bcy6Q4k0C/ZghnWghKeLlq7N9ZhMV3duSrIy7SIiGp03xqxMe62iIpK/+9q2Hj5rrqU57hqp\nL62M3OKrnrucAiNuThpUKoHZLNF7OJFujdwUGEx8sPyEzfa76b1JzKj8pV9brTs6BbF4WywACRmF\nJGUWEurryun4HNYeTMBBqyIyuLTpvD7K1a+tPz1b+loGcfyFyC002kWE3Jy15BaWVLDTezpyR+dg\nlu+4eE1NhSVZmWi9S58XGp0PJRXqXyFJC+fb1pvM+QRjqrX+qdUET5hKzt6d5B2u2NcTQO/hZFv2\n8XC06x8Jlo9GvacjxF2xr2iTbyjhWGwWnZp6cynVMlhkQPsAujSzf9Z5l4lGers7kplrH+HLzDXg\n4+EAl6/YOJCZa0BiiTCeS7RE1/adSbNzDFUCukb68K9FUTxya3il5WwI8opKytWHyp/Beg9Hbu8U\nxC+7Ll1TfSiPh05PVkbpsyY7IxVPnU+V9k1atCPjP4nk52bj6n71fusNjVnpY3hDEMDbQoijwEYg\nGPAHjgG3CyHeFUL0kVJmX0OeZmCJdfl74Bbr8n+AJ6zO2QPAj2X2uTKs7hiwV0qZK6VMBQxCCC9r\n2j4pZbyU0gxEAWHXcEz3WyOUh7E4eK0aUNuO1i2ac/HyZeITEzEajazdvIX+PXvZ2aRlZNiic8dO\nnsIszXh5eFBQWEh+gaUTeEFhIbsPHKRpeNWHkpBRiM7dEU9XLSqVoGUjT2Iu1zzC4mLtR+bhoqV5\niAfHL2ZVajf+s72M/2wvO0+kMLCjZSR1y1AP8otK7DpZXyHI29m23LOlL3GpljI9/sFOHnvf8rc9\nOoVPV55i18nUqx5n0cXzaP0C0Pj4glqNR5ce5B89ZGejcnYBtaU8nr37UxhzGnNRUWXZ3RCtmp67\nI+cz6dvG0lQ0qFOg7fy4OmnQWPt0DekSxLELWZX2uaoLrZTsIjpGWJxjLzcHQvUuNifUy1XLyj32\nXQHqo1z/W3+Oh9/dwWPv10//3OslKbMQnZsDni5aVELQItSTc4n295y7s5YRPUNZvT++2sFclVEU\new6tXwBavaX+uXftSd6Rg3Y2dvWvzwAKYk7aIoMBo8dhSEwgc8PqKjUCfZzx83JCoxL0ae1fYVT1\nvtPp3NouALDMIJBvKCEzrxgPFy2ujpa4iINGRfsmOuKtg1Y6RngzqncjZi8+ZpdXgM4ZX09H1CpB\nj5a+HCo36OzQ2QxuaW2pFxGB7hQYTGTlG8nON5KRYyDQ+ixp3diLy+mlA2TahHmRkFFIxjWe37om\nKbMQLzcHPFy0qAS0CPHkfKJ9U7G7s4bhPUJZc+DyNfWtrozQ8OakJV8mIzWRkhIjUXu30KpjufdL\n8mXb+yU+NoYSoxEXt2vvb61wbfydIoaPAL5AZymlUQgRCzhJKc8IIToBQ4FZQohNUsq3rlPjis+/\nHPg3sBk4KKUs2/HhyuemuczylXVNORsAEzU8z0KIcGAa0FVKmSmEWIglIljv2pWhUauZMWkSE15+\nBZPJzMghg2kaHsbPK38H4P7hw9iwdRs/r/wdtVqNo6MD782ciRCCjMxMprz+huUgTCaG3DaAW7p1\nq1JLSthwKIEH+4UhhODo+UzScgx0tE5Bc/hcJq5OGsbcHoGjVoWU0DVSz9drYiguMTOqdyOcHdSY\npGTdwYQKgwrKs+90Ot2b61n0Yi8MRjMfLD9uS5s9ugNzfzlBRl4xL93XGhdHDQg4n5jLJytOXe/p\ntGA2k7r4W0ImTQeVipxd2yhOvIxnH8vI2Oztm3EICCJg9DgADAnxJH9/nVMx1JNWTc5dem4xX689\ny2sPtmHM7RGcS8hl7QFLKKWRrysv3dcKKeFiSj4flomyOZXre1VbrR82n2f6va1ZMLkHCPjPurPk\nFFiiIK8/0q5CX9SkzMJ6KVdtaPTyG7i264jGw4sW3/1K8nf/rTDt0PUgJWyKSuSePo1t05Ok5xho\n36R02qeerXxxdtDYHGazGb7fXLOpSDCbSflxISFTXrVNl1ScEI9nP8uo8+ytG3EIDCZw7ASQEkNC\nPEmLLA0Xzk2b49mzL4b4S7i8PgeAtF+WkB8dZSexYPUZ3ni0PSoh2BSVSFxqgW1U7NqDCRyMSadL\nM2++mtQDg9HEp9b7V+fmwJSRLVGpBELAzuOpHIixPOafGdoMrVrFm4+1t9NatPEcL93XBpWArceS\nuZxewIAOFqdzc1QSUeczad9Ex4dPd6a4xMyCMn1KF206z4S7ItGoVKRkF7Fg9RlbWo8WvuyuwUdl\nfSMl/BmVxD29GyGEIPpiFum5BtqFW+rD0QuZ9Gjpi5ODmts6WOuDlPz4p2VU8tCuwYT4uuDsoOHp\nIc3YfSKV6Co+0AHUajUjH5vE1++/Ypmupu9gAkLC2L3Z8n7pOWAYxw5s5+CODag0GrRaBx59bqZt\nMMoPX8zm3Kkj5OdlM2vKgwy6ezTd+g2pz1NULTeyT2BdI/7qhRFC5Ekp3YQQzwNNpZSThBC3YnHa\nwoFiIENKWSSEuAt4Sko5UghxDBgupaxybgUhhAQeklIuFkLMBPyllJOsaZ8C9wBPSinXWLfFAl2k\nlGlCiDHW5Yll04A2wDQp5V3W7Z9haa5eeLVjEkK0B77F0hzsCxwFXrbuW6/aZbA1JdcnjsGhtuU5\nS6LrVevVB9rYlm+fsbFetcpOtVLXc86VJ/LL726IVkOew/rWami9slpHh/SuV612a0ojlB8sO16N\nZe2Zdm9r2/Lppx+qxrL2NP/6J9vyiDer7K5dJ5SdrubR93bUqxbA9y/dcnWjemDuL3XzEVMVL4wq\nbfhauad+3y/De9jeLdc5c+j1kZhRWOfOVKC3c4OW4Qp/p4jhD8DvVgfnAHAldNMWeF8IYQaMwATr\n9gXAWiFEgpTy1gq5WcgHulmdwhQszcZl9e4G1tdhGao9JinlESHEYSxli8PSR7BBtBUUFBQUFBQU\n/vKOoZTSzfo/DehZiUkssK6S/T4FPq1B/i9UkXQL8I2U0lTGNqzM8kIsA0DKp22x/l3ZPvFajklK\nOaaK7fWuraCgoKCgoHDt1NfMCjeCv7xjeCMQQvwKRAADrmaroKCgoKCgoHCz8I9wDIUQe4HyM+g+\ndiUaWR4p5d038JiOVWavoKCgoKCg8NfkJgoY/jMcQyll9xt9DOX5Kx6TgoKCgoKCwrXzVx/Iey38\nneYxVFBQUFBQUFBQqEf+ERFDBQUFBQUFBYX64mb65ZO//DyGCg2OUiEUFBQUFP7uNOgcgBdT8uv8\n3dnYz1WZx1BBQUFBQUFB4e/GzRRkUxxDBQUFBQUFBYVacDM1JSuOoUIFMtasrHcN7yHDbcszF0VV\nY1l7Zo3uYFsuTk6sVy0H/0Db8uFzGfWq1THC+4ZozfqpfmdUmvlQW9vyzfyTeA35M3UN+fN7S7bG\n1qvWA/3CbMu5ubn1quXu7m5bvnf21nrVAlj2Wr9616iMKfP312v+857paltuyHtM4fpQHEMFBQUF\nBQUFhVpwMzUlK9PVKCgoKCgoKCgoAErEUEFBQUFBQUGhVtxMfQyViKGCgoKCgoKCggKgRAwVFBQU\nFBQUFGrFzdTHUHEMFRQUFBQUFBRqwU3kFypNyQoKCgoKCgoKChaUiKFCtew+eYp5v6zEJM0M79GN\nxwcOqGBzKOYc835dQYnZjKerK19OmgDAT1u28fuefQggIjCQ1x6+H0ettkqtZkHuDO0WjEoIDsak\nsy06xS5d7+HIqN6NCPJxZsPhRHYeT7WlOWnVjOwVir/OCSnh112XiEstqFJrx969vPvJZ5jMJkbd\neSdPPfqIXfr+w4eZPGMmwYEBANzWty8TxozGYDAwZtLzFBuNmEwmbu/fj+fGPlHtOYw6sJtF8+dh\nNpsYcMdwRtz/uF36gd3b+Pm7BQiVCrVKzePPTKFF6/YArP5tCZvXrQQpGTB4OENHPlitVkPrNQl0\n445OQQgBUecy2XUy1S7dx92RYT1CCNA5seVoMntOpdnSujX3oWOEN1JCanYRK/fEY6qkB/ezd0XS\nrbkeQ7GJ95ef4GxCxbnrAnROzHiwLR4uWmIu5/Du0uOUmCQD2gfwQN/GCCEoMJTwyYpTnE/KA+DF\nUa3o3kJfZdnqS7c6wvzdGNAhACHg2IUs9p1Os0tvGepJt+Z6EFBcYmbjoQRSsw1XzbemhEx9FY9u\nvSnJyuTMhMdqnV9M9H5WL/kKaTbR6ZYh9B3ygF36kb2b2bH2Z6SUODo5M+yRSQSERgCwa8MvHNyx\nBiEE/sHhjBzzIlqtQ5Vau3bt4oMPPsBsNjNy5EjGjBlTqd3x48cZO3Yss2fPZuBAy5x3P/30E7/+\n+isAI0eO5OGHH66wX4cmOp4Y1BSVEGyKSuS33XEVbMYOiqBjhA/FRhOfrTrNhTLXXCXg3bGdyMgt\nZs7P0Xb7DesewuiBEVWWraFoEerBqF6NEEKw51Qqm6KS7NL9vJx4uH84IXoX/th3mT+PWtK9XB14\n5NZw3F20SAm7T6ayLTq5Rpo1uc9eub81kcEelJglp+NymPfbSduzol24jmfvjEStFuQUGGt5Bq4f\npSlZ4R+ByWzmw2W/8vGEcfh5eTJ27if0adOa8AB/m01uQSHvL/uFj8Y/RYBOR0au5UGYkpXN0m07\n+PGV6Tg5aHlt4XdsPBTFnd27VqolBAzrEcI368+RU2Bk/J2RnIzLtnvpFRab+GNfPC0beVbY/85u\nwcQk5LB4ayxqlUCrrjoYbjKZmP3RxyyY+wEBvr48OG48t97Sm4iwMDu7Tu3a8vm779htc3Bw4L/z\n5uLi4oKxpITRz03ilu7daN+6NZVhNpn43xcf8trsj/HR+zFjylg69+hDSKNwm02bDl3o3KMPQggu\nXjjLx3NeY+6CJcTFnmPzupXM/ui/aLQa5vxrKp269SYgKLTKsjWknhAwpHMQP/x5gZzCEp4cFMGZ\nyzmk5ZS9ZiWsO5hA8xAPu33dnTV0i9Tz1eozlJgko3qH0rqxJ0cvZNnZdYv0IdjHhTEf7qJlqAeT\nR7Rg8pcVJ+N9anAzftl5iS1Hk3l+RAsGdwli1d7LJGUW8uLXB8krKqFrpA9T7m5p23/9oQRW7Inj\ny4ndK+RXn7pVIYCBHQNZuj2W3IISHr2tCecScknPLT2f2QXFLN56AYPRTHiAG4M6B/HD5gvV5nst\nZG5YTfrK5YRO+1et8zKbTaz68XNGT52Dh07P/Lcn0aJ9D/yCGttsdHp/xk57H2dXd84c28+K7z7m\nmRmfkJOZxp7NvzHpza/ROjiyZP4sovdvoWOvQZVqmUwm3n33XT7//HP8/f15/PHH6du3L02aNKlg\n9+mnn9K9e+k1P3v2LL/++ivffvstGo2GyZMn06dPH0JD7ev9U4Ob8daPR8nIMfDO2E4ciEknPq30\n47NjhDeB3i5M+nIfzYLcGTe4Ga8uPGxLH9o1hPi0Alwc7V+7Pu6OtA/XkZpdhK+n07Wf6DpCCLi3\nd2O+/OMMWfnFvDCqFdGxWSRnFdlsCopKWL7zEm3DvOz2NUvJij1xxKcV4KhV8eKo1pyOz7bbtzJq\nep9tjkrinZ8tE8LPeKANQ7pa7jNXJw2TRzTn1W8Ok5ptwMtVy9IbNEn4zcQ/uilZCOElhHj2KjZh\nQoiKn4+V20Vfza4uEUKMF0I8fhWbDkKIodeT/4mLlwjR6wnW+6DVaBjYsQPbjtn/WsP6Q4fp364t\nATodAN7ubrY0k9mMwWikxGSiqNiI3tPeOShLiN6F9BwDmXnFmMySYxcyaRlq7wDmF5VwOb0Qs9l+\nX0etijB/Vw7GZFh1JUVGU5Vax06eolFwMKFBQWi1WobcNoA/d+ys0r4sQghcXFwAKCkpoaSkBCGq\n/p3zs2dOEBAUgn9gMBqtll59B3Jg9zY7GydnF1sehqJCyxMauBwXS9PmrXB0ckKt1tCyTUf27az+\n1xcaUi/I24WMvGKy8o2YzZLjl7KJLOcAFhhMJGYUYq4kEqgSoFGrEAK0ahV5hSUVbHq28mXjYcuv\n1ZyMy8HNSYO3e8WoUYcmOluEef2hRHq39APgxKVs8oos+Z68lI2vh6Ntn2OxWeRWEWGoT92qCPB2\nJjOvmOx8I2YpORWXTUSQu51NQnohBqPZulyAm3PVEfjrIT/6CCW5OXWSV/yF03j7BeHtG4hGo6Vt\n1/6cOrLbzqZRRGucXS1lDG3Sgpys0gip2WzCaDRgMpkwFhtw9/SpUuv48eOEhoYSEhKCVqtl0KBB\nbN1ase4uWbKEAQMG4O1d+ks+sbGxtGnTBicnJzQaDZ06dWLz5s0V9k3KKCQlq4gSs2TniRS6Rtof\nT9dIH7ZYI2gxCbm4OGnwcrPUGW93Bzo39a4QgQMYc3sE320+f8P7qDX2cyUtx0B6rgGTWXL4bAZt\nw3R2NnlFJcSl5le4n3MKjDYn2WA0k5xViKdr1dHdK9T0Ptt3Jt22fCo+B18PiwM9oH0AO46n2gII\nWfk3LmJolnX/d6P4RzuGgBdQrWMIhAFXdQyvBSFEnURqpZRfSSm/vYpZB+C6HMPU7Bz8dKVfhn5e\nnqRmZ9vZXEpJJaewgGc//ZIxH8xj9b4DNtuHb+3H3W/OZtjr/4ebsxPdWzSvUsvDRUt2mZs6p8CI\nh2vNXno6N0fyDSWM6t2IZ++KZGTPULSaqqt2SloqAX6+tnV/X1+SU1Mr2EVFH2fUmLGMn/4SZy+U\nRmVMJhP3jn2SfiNG0qNLF9q1alWlVkZ6Kj56P9u6t96PjPSKWvt2beGFcQ/w7r9fZPyU1wAIbRzB\nqegj5OZkYygqIurAbtLTqm+eaUg9dxeNXdNNboER9xo6KrmFJew+lcbk4c2ZMrIlBqO50qZWvYcj\nKdmlUYe0HAP6ck6Wh4uWvKIS28sqLacIH8+KjtjgLkHsL/OCqY4boevurCW3sPR85hUacXeu+lHR\nNlxn11T5VyM3Kx1P79L7zMNLT05mWpX2B3eupVkbS4uCh05P70H3MveVx3h/+kM4ObvStHXnKvdN\nSUnB37+0JcPPz4+UlJQKNlu2bOHee++12x4REUFUVBRZWVkUFRWxc+dOkpMr1vu0MpHb9BwD3u72\n19rH3ZH0MtHyjBwDPlYn54nbm1qdP/u3fddIHzJyDVxMya+ybA2Fp4sDmXnFtvWs/GI8a/gMLou3\nmwMhPi5cTLl63azJfVYWtUowsEMA+2Ms91OI3gV3Zw0fPNWZz5/rxsCOgVXuW99IKev870bxT3cM\n3wEihBBRQoj3rX/RQohjQogHytj0sdpMtUYGtwshDln/etVESAgxRgixUgixGdhk3TZdCLFfCHFU\nCPFmGdvHrduOCCG+qybPN4QQ06zLW4QQ7woh9gkhzggh+gghHIC3gAesx/9AVXldLyazmdNxl/lw\n3JPMG/8036zfZHEWCwrYHn2c5a+/yu9v/YsiQzFrDxysa3kAVCoI9HZh3+k0vlh1huISM33b+F19\nx2poGRnJhqVL+GXh/3h41CienzHTlqZWq1n2v/+ycdlSok+dJOb8+doWgW69+jN3wRKm/etdfv5u\nAQDBjcIYft+jvD3zeeb8ayqNmzRDpaqbW7ah9crjpFXRPMSDz34/zce/nUSrEbQp1zxVl7RvomNI\nl2C+Xnu23jQaUjfU14W2YTq2HatZP66/OudPRXFoxzoGjXoSgMI2oD50AAAgAElEQVT8XE5F7Wbq\n24uY/t6PFBuKOLJnU600PvzwQyZNmlShToeHh/P4448zceJEJk2aRGRkJGq1ulZaZenc1JvsguIK\nHz4OGhWjejViybbYOtO60ThoVDwxqCm/7o6zRbbrkskjWnAsNovoWEuXE7VK0CzIg5mLDvPqN4d5\n9Nbwq+Rw8yGEGCyEOC2EOCuEeKUau65CiBIhxL1V2Vzhn97H8BWgjZSygxDiHmA80B7QA/uFENus\nNtOklHcBCCFcgNullEVCiGbAT0CXGup1AtpJKTOEEIOAZkA3LN2LVgoh+gLpwEygl5QyTQjhXXV2\nFdBIKbtZm47/LaUcKIR4HegipZxY1U5CiHHAOID58+dzb6hlwIWvpwcpmaV9vlKysvH1tG/e9fPy\nxNPVBWdHB5wdHegQEU5MQgIAgd7e6NwsTcv92rXh2IWLDO5S+Vd/ToHR7uvUw0VLTg2bBXLyjXZN\nGccvZtG3bdWOoZ/el6SU0ihacmoq/r6+djZurq625b49ezD7o4/IzMpC51XqvHi4u9O1Y0d27t1H\ns3J9ma7g7eNLelpp5CIjLQVvH99KbQFatu1IykcJ5GRn4eHpxYA7hjPgjuEA/LTwS7to4I3Wyy0o\nwcOl9Jq5u9hHvKojPMCNrLxiCgyWJv9TcTmE6F1sD3zb8ecW4+fpxHEskWq9h6NdH0aw1B03Jw0q\nlcBslug9nEgv0zc1PMCNF+5uyYyFUVc9vq+s/Q1PX85pUF2A3EL7iKubs5bcSprX9Z6O3NE5mOU7\nLlJUXHWXiRuNu5cP2Rml91lOVhoeuoqDfZLiz7Pi23k89vwsXNwsXRHOnTyMTh+Aq7vlfmvVqTeX\nzp2gfY/bKtXy8/Ozi/KlpKTg52dfd0+ePMmMGTMAyMrKYufOnWg0Gvr378/IkSMZOXIkAJ9//nmF\nfQH0ZSKEPh6OZOTa14f0XAM+ZaJd3h6OpOcW0725L12b6ekU4YNWo8LFUc3k4S34bXccfl5OfPBU\nF1ueN5LsgmJ0bqXNuF6uDnatOFdDpRKMHdSUgzHpHL2QWaXd8B4hDO0SDNTsPrvCowPC8XTVMu+3\nk7ZtqdlF5BQYKTKaKTKaORqbSbDepcbHXJfciACfEEINfA7cDsRj8VtWSilPVGL3LrC+Jvn+0yOG\nZbkF+ElKaZJSJgNbgcpGSmiBr4UQx4ClQNXtiBXZIKXMsC4Psv4dBg4BLbA4igOApVLKNIAy9jXh\nF+v/g1iawGuElHKBlLKLlLLLuHHjbNtbNgolLi2NhPQMjCUlbDwcRZ829sXt26Y1R87HWvsRFnPi\n4iXC/P0J8NJx/OIlioqLkVJyIOYsYf5VOxmX0wrw8XBE5+aAWiVoG67jVHzN+jrlFZWQnV9sa4KI\nCHQnJavqkZptWjTnYnw88QmJGI1G1mzaTP/e9oHftPR0Wyj/2ImTmM0SL09PMrKyyMm1jJorMhjY\nc+AA4Y0bVakVEdmSpIQ4UpISKDEa2bVtI5179LGzSUqIs2ldOHsao7EYdw+LA56dZbn8aSlJ7N+1\nhd79K+98fyP0EjIK8HZ3xMtVi0olaN3IkzM1vGbZBUaC9S5o1Jb+jeEBbqRVMrp254kUW/NQy1AP\n8otKyMgtrmB35HymLUo8qFOgbXS0r6cj/36kHe8uPc7l9KpHqV9h/Gd7Gf/Z3gbXBUjKLETn5oCn\nixaVELQI9eRcov0ITXdnLSN6hrJ6f7xds99fkeCw5mSkXCYzLYmSEiPH9m+hRfsedjZZ6Sks/vIt\n7nlyOnr/ENt2T28/4s6fpNhQhJSS86ei8A2s+j5r1aoVcXFxXL58GaPRyPr16+nbt6+dzcqVK/n9\n99/5/fffue2223j55Zfp378/ABkZlnqflJTE5s2bGTx4cAWNQG9n/Dyd0KgEvVv5VegecCAmnf7t\nLB/VzYLcKTCUkJVXzI9bLvDMp3t49vO9zPv1BNGxWXyy8hSXUvN5ct5unv18L89+vteuGfpGcCkl\nH72nI97ulmdwx6beRF+s2sErz0P9wkjOKmTLVaLYK/fEX/N9NqRLEF2a+fD24mg7B2z3yVTahHmh\nUgkctSpahFYcmHiT0w04K6U8L6UsBhYDIyqxmwQsB1IqSavAPz1ieD1MBZKxRBZVQPXDruwp25FE\nAHOklPPLGgghJtXi2K48WUzUwbXVqNW8eM9Ipnz1NWazmbu6d6NJYAC/7LR0IB/VuydhAf70aNmc\nx96bi0oIhvXoToR1ipdb27dl9Afz0KhURIYEM6JXjyq1zBJW7Y1n9MAmqFSCgzEZpGQV2Tp47z+T\njpuThgl3ReKoVSOBXi19+WTFKQxGM6v2Xua+Po1RqwQZecX8svNS1eXSaJgx5XnGT5uOyWzm7qFD\naBoezs8rVgBw/4gRrN+ylZ9XrEStVuPk6MD7/34dIQSp6enMfHsOJpMZKc0MuvVW+vWqujeBWq3h\niQkv8vbMKZjNZm4ddBehjZuw4Q+LD3/7naPYu3ML2zetQa3R4ODgyPOvzLINDpk7ewZ5OdmoNRqe\neHYarm7uVWo1tJ6UsPZAAg/1D0clIOp8Jmk5Bjo1tQS5D53NwNVJw5N3NMVRq0JK6NZcz1d/nCEh\nvZCTl7J5anBTzGZIzizk8LmK30D7TqfTvbmeRS/2wmA088Hy0sFPs0d3YO4vJ0jPLebrtWd57cE2\njLk9gnMJuaw9cBmAxwY0wcNFy+ThLQDLwKTnvtgHWEY3tgvXVdCsb93qzuemqETu6dMYlRAci80k\nPcdA+yaWYzxyPpOerXxxdtDYXqZmM3y/ufZdGa7Q6OU3cG3XEY2HFy2++5Xk7/5L5vpV15WXWq3m\nzoee49t5MzCbzXTqPQi/oDD2b7Xk17XfXWz54wcK8nNZ9cNnAKjUasa/9hmhTVrQunMfvpr1HCq1\nmsDQpnTpM6RKLY1Gw/Tp05k0aRImk4nhw4cTERHBsmXLACr0KyzPSy+9RHZ2NhqNhpdffhl394r1\n/j/rzjLzobaoVILNR5KITytgUCfLdVh/KJFDZzPoFOHNZ892w2A08cWq09d13m4UZgnLd1xi/NDm\nqATsPZ1GUmYRvVpaWhx2nUzF3VnDi6Na4+SgRkpJv7b+zPn5GEE+LnSN1JOQXsD0eywzNKzaF8/J\nuOzqJGt8nz0/ogXJWUV8Mt4Sq9lxIoXvN1/gUmoB+8+ks2Byd8wS1uy/zLN3Vd2XvT4x10PIsGxr\nnpUFUsoFZdaDgbLzJsUDdtMsCCGCgbuBW6k82FVR92aae+daEUL4AIeklI2FEKOAZ7AM1PAGDmA5\nwcHAXCllP+s+HwHxUsoPhRBPAP+TUgohRBiwSkrZpgqtMZRp0rU2Jf8fcJuUMs968YyAL/Ar0FNK\nmS6E8K4qaiiEeAPIk1J+IITYgqXJ+4AQQg8ckFKGWZvIh0spR9fwtMiMNStraHr9eA8ZblueuSiq\nXrVmje5gWy5OTqxXLQf/0s7PlTk6dUnHiNJeBg2pNeunY/WqNfOhtrbl22dsrFctgA1vD2wwvbJa\nHyw7Xo1l7Zl2b+kUSkeH9K5XrXZrSkf1L9kaW69aD/QLsy3n5lac864uKesg3ju7+hkB6oJlN2iq\nlSnzq59KqbbMe6bUH2nAe6zq6SLqgajzmXXuTHVooqu2DNb+goOllE9Z1x8DupftOiaEWAp8KKXc\nI4RYiMVPWVZdvv/oiKHV8dppnWZmDXAUOAJI4CUpZZIQIh0wCSGOAAuBL4Dl1mli1mIfBbwW7fVC\niJbAbmu0Jg94VEp5XAgxG9gqhDBhaWoeU4ti/gm8IoSIwhKhXFKLvBQUFBQUFBT+GlwGyk64GWLd\nVpYuwGKrn6EHhgohSqSUv1WV6T/aMQSQUpafimZ6uXQjln5/ZWlXZvllq10sUGm00Jq+EItjWXbb\nx8DHldguAhZVe+AWuzfKLPcvs5yGtY+hNdpYo/CxgoKCgoKCwrVzg1pf9wPNhBDhWBzCByk3vZ6U\n0jZUu0zEsEqnEBTHUEFBQUFBQUHhb4eUskQIMRFYB6ixdG07LoQYb03/6nryVRzDOkYIcQeWYeFl\nuSClvLsWeb4G3Fdu81Ip5ezrzVNBQUFBQUGhbrhRwzWklKuB1eW2VeoQSinH1CRPxTGsY6SU67B4\n73WZ52xAcQIVFBQUFBT+gtTHqOQbhTKPoYKCgoKCgoKCAqBEDBUUFBQUFBQUasVNFDBUIoYKCgoK\nCgoKCgoW/tETXCtUilIhFBQUFBT+7jToBNf7TqfV+buzW3N9g5bhCkrEUEFBQUFBQUFBAVD6GCoo\nKCgoKCgo1ArzTdTWpjiGChXI3r653jU8+5T+mMwnK07Vq9bkES1syw35W7hHh95Sr1rtVu+4IVoN\neQ5v5t9KPv30Q/Wq1fzrn2zLDfn7xQ35u8yDZ26qV621s26zLT/y7o5qLOuGH16u3/u4KuavjqnX\n/J8Z2sy2vGjjuXrVGj0wol7zr4qbqVue0pSsoKCgoKCgoKAAKBFDBQUFBQUFBYVacRMFDJWIoYKC\ngoKCgoKCggUlYqigoKCgoKCgUAtupp/EUxxDBQUFBQUFBYVacDM5hkpTsoKCgoKCgoKCAqBEDBUU\nFBQUFBQUasVNFDBUHEOF6tkdfZwPf/oZs1kyok9vRg+9o4LNwVNnmLtkKSUmE15ubsx/6QVbmsls\nZvT/zcFX58VHk5+rVuvi6UPsWPE1ZmmmVbfb6XzrvXbppw9t4fCWX5CAg6MT/e6egD4onNysVDYt\nnkdBXhYIQevud9D+lmFXLduzd0XSrbkeQ7GJ95ef4GxCbgWbV+5vTWSwByVmyem4HOb9dhKTWeLm\npOHFe1oR5O1McYmZD385QWxyfqU6bp27E/zM86BSkbFuFalLv7dLV7u5EzLlVRwCg5DFxcTNm4Ph\n4gUAfEbch88dw0AIMtauJG3F0quWqyH0anLuAnROzHiwLR4uWmIu5/Du0uOUmCQujmpeub8Nfl5O\nqFWCZdsvsu5QIr6ejrx0X+tqy1YT3RE9Qri7dyOCfVy4Z9ZWcgqMAIT6ujDtnlY0DfLgm/VnWbbj\n0l9Cy6V1e/wffBxUKrK3/0nG2pV26SoXVwLGPIODrz9mYzFJC+dTnBCPRudN4NhnUXt4ApC1bRNZ\nm9ZWW6aY6P2sXvIV0myi0y1D6DvkAbv0I3s3s2Ptz0gpcXRyZtgjkwgItcwLt2vDLxzcsQYhBP7B\n4Ywc8yJarUO1etURMvVVPLr1piQrkzMTHruuPDo382bC0EhUKsHagwn8vO1iBZsJd0bSNdIHg9HE\nh8tPcjbRch2n3t2S7s31ZOUXM/7TvTb7Pq39eHRAOKG+rnb5tAv34rHbmqBSCbYcSeb3vfEVtB6/\nrQntI3QUG83MX33G9kxwcVTz9JBmhOhdkMCC1TGcTchlVO9G3Nren1xrvbnRXDh5kC2/LsAszbTt\nPohuA++zSz958E/2b1qOROLg6MzAe5/FN7iJLd1sNvHD3Km4efpw99P/vqreueMH2LBsPtJspn3v\nO+g16H679Oh9f7J7w1JA4uDowuAHn8M/xKJXVJDHHz98TGriRQSCOx+dQkiTlrU/CQp//6ZkIYSX\nEOLZq9iECSEerkFeYUKI6Lo7ur83JrOZ935YzMdTJrLk/15n3b79nE9ItLPJLSjgvR9+4sOJE1jy\n1uvMGf+UXfrijZsJCwy4qpbZbGLbr/O568l/8/CLnxETtZ2MZPuXqYe3PyPHv81DL3xCl9se4M/l\nnwOgUqnpfddYHp72Ofc+9x7Hdq2usG95ukX6EOzjwpgPdzHvt5N2k2CXZXNUEmM/2s24j/fgqFUx\npGsQAA/1D+NcYi7PfLqX95Yd59m7mlcupFIR/OwLXHh9GmfGP4pXv4E4hobZmfjd/xiF52OIeW4M\ncR/OIuiZ5wFwbByOzx3DiJn6NGeeG4N7t944BAZXfyIbQK+m5+6pwc34Zeclxny4i7zCEgZ3sZy7\nET1CuZSSz/hP9zLtPwcZNzQSjVpgMssKE+028it9OddUN/piNi//9xBJmYV223MLjHz++xmWba/o\nPFxvGWutJQT+Dz9B/MfvcuH1abh361XhnPsMHYEh7iKxb75M0v++xO/B0QBIs5mUpd8T++/pXHz7\nX+huHVRt/TCbTaz68XMemzyLiW9+zbH9f5KSYH98Or0/Y6e9z8Q35tPvzkdY8d3HAORkprFn82+M\nf+0zJr6xALPZRPT+LdWX7SpkbljNhZkvXN2wGp4b1pyZ30Yx7pM99G/rT6NyzlzXSB+CfJwZ+9Fu\nPv7tFBOHl96nGw4nMnNRVIU8Y1Py+L+fjhF9Mctu+5jbI3hv6XFe+s8herbyJdjH2S69fRMdAd5O\nvLjgIP9dd5YnBjW1pT12WxOOnM9k+n8O8er/DpOQXmBLW3MggRkLo5ixsOKxNCRms4nNy7/k7nFv\nMublLzh1eCvpSfbPUU/vAO6f+A6jX/qcHoMeZMPPn9mlH962Em//0Brrrfv5Cx547i3G/esrThzY\nSmqivZ6X3p9Hp77L0699yS1DHmTNj5/Y0jYsm09Eq86Mf30BT834DH1AzXTrCynr/u9G8bd3DAEv\noFrHEAgDruoYXgtCiAaLtjakVlmOX4glxM+XYF9ftBoNg7p1YVvUETubdXv3079TBwJ8vAHw9vCw\npSVnZLLzaDQj+lz9lxBS4mLw1Afg6ROAWqOlWfs+XDi+z84mMKwlTi5uAPg3ak5+djoArh7e+IZY\nohoOTi7o/ELIz86oVq9nK182HrY4uSfjcnBz0uDtXjH6se9Mum35VHwOvh5OADT2cyPqfCYAcakF\n+Hs54eVWcX+XyJYUJ8RTnJSALCkha9tGPHra/7qBY6Mw8o4cBMAQfwkH/0A0XjqcQsMoOH0CaTCA\n2UR+9GE8e/ertlwNoVfTc9ehiY5t0SkArD+USO+WfgBIwNlRDYCzg5rcQiMmsyQjt7hCVE7v4XjN\nuucSc0nOKqqwPSvfyJnLOZTU4LerGkrLKbwpxtQkjGkpYDKRu383bh262Nk4BIZQcMryvVqclIDW\nxxe1uyem7CwMl2IBkIYiDImX0Xh5V6kVf+E03n5BePsGotFoadu1P6eO7LazaRTRGmdXdwBCm7Qg\nJyvNlmY2mzAaDZhMJozFBtw9faot29XIjz5CSW5OrfJITC8kKbOIEpNk67FkerbU26X3bOnLpqgk\nwHL/ujlp8Lbep9GxWeQWVozUxaUWEJ9WUGF7clYRqdkGTGbJnpOpdG5mX/7OzbzZbq3vZxNycXFU\n4+WqxdlBTYtQT7YcTQbAZJYUGEy1Knd9kHTpDF76QLz0lmdwi459ORe9x84mKLz0GRzYuAW52aX1\nIzcrjfMn9tO2x6Aa6SXEnkHnG4ROH4hao6VV577EHLWvjyFNWuHs4m7VbkFOluV5XFSYz6Wz0bTv\nZWnBUmu0tuO6UZilrPO/G8XN4Bi+A0QIIaKEEO9b/6KFEMeEEA+UseljtZlqjQxuF0Icsv71qomQ\nEGKMEGKlEGIzsMm6bboQYr8Q4qgQ4s0yto9btx0RQnxXTZ4LhRBfCSEOCCHOCCHuqkxLCNFfCLFF\nCLFMCHFKCPGDEEJYbbsKIXZZtfYJIdyt+6+w7hMjhLh6XL8cqZlZ+Ot0tnU/nY7UTPuv6EvJyeQW\nFDD+vbk8/tbb/LGr9EHy0ZKlTLr3blTi6tUsLzsdN8/Sh7qbpw/5OelV2p/cv4FGzTtV2J6TkUxa\nwnn8G0VWq6f3cCQlu/SFnpZjsHNCyqNWCQZ2CGB/jOWYziflcksri6PTPMQDfy8nfCvZX+vja3np\nWzGmpaL18bWzKbpwFs9eFgfMObIlDn7+aPV+FF08j2ub9qjdPRCOjrh36YlW71dtuRpCrybnzsNF\nS15RCWarY5SWU4SPp8Vmxe44Gvm5sviVPiyY3IMvVp2u8uv4VFz2NenWFQ2lpfHSYcworeclmelo\nvHR2Nob4i7h17AaAU1gEWh89Gp29A6jx0eMUGkbRhbNVauVmpePpXVoXPLz05GSmVWl/cOdamrXp\narHV6ek96F7mvvIY709/CCdnV5q27lzzgtYTqeWukU+5a+Tj7mhnk1qJTU1JzzHYljNyDejKfQh6\nuzmSnlNcxqYYnbsjfl5O5BYYeWZoM2aP6cBTg5viqC19Jt7RKZA5T3Tk6SHNuJHkZaXj7lVaP9w8\n9eRmV/0Mjt67nvAWpR8xW35dQN9hY7G+lq5KblY6HrrSZ767l57crKr1juxaT4S1zmWnJeHi5smq\n7z7iv3Mm8scP8yg2VPxAU7g+bgbH8BXgnJSyA7AH6AC0BwYC7wshAq0226WUHaSUHwEpwO1Syk7A\nA8AnlWddKZ2Ae6WU/YQQg4BmQDerbmchRF8hRGtgJjBAStkeeP4qeYZZ87gT+EoI4VRey7reEZgC\ntAKaAL2FEA7AEuB5q9ZA4Eq7VjfgHqAdcJ8Qwj4UYUUIMc7qmB5YsGDBNZwKMJnMnLp4iY+ef45P\npk7mf6tWczEpme1HjqFzd6dlWONryq8mxJ89ysn9G+k1dLTd9mJDIWu/e5dbhj2Fg5NLnWpOHtGC\nY7FZRMdaHOPFW2Nxc9bw1cTujOwZytnE3Ov+wkv5+XvUbm40+/Qb9MPvofBcDNJswhB3kZSl3xM+\n6yPC/+9Dis7HIM3mWpelofXK0yXSh3MJeTz4znbGf7qXicNa4GKNIJbnrxhZaWgy1qxE7eJC49fn\n4DXgDoriYkGWXhfh6EjwhKmkLPkWc1Fh1RldA+dPRXFoxzoGjXoSgML8XE5F7Wbq24uY/t6PFBuK\nOLKnfn+n+GZBpRKEBbix8XAiry2MwmA0M6xHCAAbDycyZf4BZnxzmKy84qvk9NfhUsxRovesp8+w\nMQCcP74PF3cv/EObVr/jdRJ75ghHdq3n1hFjAUv0OinuLJ36DOXJVz9D6+DE7vU/14t2TbmZmpJv\ntsEntwA/SSlNQLIQYivQFSjfXqEFPhNCdABMQPXhJXs2SCmvtFMOsv4dtq67YXEU2wNLpZRpAGXs\nq+JnKaUZiBFCnAeudGjaUG7ffVLKeAAhRBQWhzIbSJRS7rdq5VjTr+yfbl3/Bcv5OVBeXEq5ALji\nEcrs7ZsB8NV5kZyZabNLyczEV+dlt6+fToenmyvOjo44OzrSIbIZMfHxnL4Yx/YjR9l1LBqDsYT8\nokJe//ob3nr6iUpPgJunD3llmiXystNx9ajYVJWWGMufyz5n2JOv4+Ra2mxtMpWw9rt3iOzYj4i2\nPSvVAPhqYncATl/Owc/TieNYIlJ6D0fSykQEyvLogHA8XbXM++2kbVuBwcQHy0/Y1r+b3pvEjIov\nZWN6ql3UTav3xZieamdjLiwg/qM5tvUW3yylODEBgMz1f5C5/g8AAkaPw5hmv++N0MvILb7qucsp\nMOLmpEGlEpjNEr2HE+nZFps7OgWxeFssAAkZhSRlFhLq68rp+BzUKvtow/AeIQztYuk3dy3X7Hq5\nnvpRG0qyMtF6l9Zzjc6HkqxMOxtzUSFJC+fb1pvM+QRjqjUqrFYTPGEqOXt3knd4f7Va7l4+ZGeU\nXs+crDS7iM0VkuLPs+LbeTz2/Cxc3Cz32LmTh9HpA3B1t9z/rTr15tK5E7Tvcdu1FbiO8fV0si3r\nPRztonoA6bkGq43lOvpWYlNTykYavd0dySznyGXkGfDxcIDLV2wcyMw1ILFEGM8l5gGw73SazTHM\nKTPo5M8jSYzsdeP6ybl5+ZCbVVo/8rLTKu0ukJpwgQ1LPmHUuDdxtj6DL184wbnovVw4cYCSkmKK\niwpZ/f0HDH10WpV67l4+dhHr3Kw03L0q6qVcvsDqHz7mgWffstVHdy89Hl56gsMtr8oWHW9h9/qr\nD8xTqBk3Q8TwepgKJGNx4LoA1zK0ruzQUwHMsUYiO0gpm0op/3sdx1P+2+DKevlhrmWfaCau7thX\nlW+NaBXWmLjkFC6npmEsKWH9vgP0ad/OzqZvh3ZExZyjxGSiyFDM8fMXCA8M4Ll7RrLq/TmseHc2\ns8c9SZcWzat0CgH8QpqRnZZITkYyphIjMUe2E9aqm51NbmYqa76dw8AHp+DlW9rJXkrJn0s/RecX\nSoe+I6ot0/jP9jL+s73sPJHCwI6BALQM9SC/qISM3Ipf7EO6BNGlmQ9vL462+4JzddKgUQubzbEL\nWZVGtwrOnMIhKBStfyBCo8Gr70By9uy0s1G5uiE0lkvpfccw8qOPYC609HFSe1pexFpffzx69SNz\ny4Zqy9cQejU9d0fOZ9K3jcVJHdQpkF0nLS+dlOwiOkZYmkK93BwI1bvYnOoXR7Wyy2Plnvhrvma1\noSG1AIpiz6H1C0Cr9wW1GveuPW39P6+gcnYBtSWi6tlnAAUxJ22RwYDR4zAkJpC5YfVVtYLDmpOR\ncpnMtCRKSowc27+FFu172Nlkpaew+Mu3uOfJ6ej9Q2zbPb39iDt/kmJDEVJKzp+KwjewUW2LX2uC\nfFzw1zmhUQv6tfVnzyn7pvE9J1O5rYNl8FuLEA/yDSVkXGdkLkDnjK+nI2qVoEdLXw6etf/ePxST\nQR9rfW8a5E6hwURWvpHsfCPpOQYCvS2DVVo39uKytQ+jl6vWtn+XyNr12awtAaGRZKUmkJ2ehKnE\nyKnD22jSurudTU5mCiu/eZshj7yIzq/0GdznrjGMe2MRT73+P+58/CVCm7Wr1ikECGocSWZKAllp\nFr0TB7fRrK19fczOSGH5glkMHz0NnzL10c3TG3edL+nJlpHhsaej0Afc2PoopazzvxvFzRAxzAXc\nrcvbgWeEEIsAb6AvMB0ILmMD4AnESynNQojRQOXtWFdnHfB/QogfpJR5QohgwAhsBn4VQsyVUqYL\nIbyvEjW8z3rM4ViaiE9jaTauCaeBQCFEVynlfiGEO6VNybcLIbyt6yOBsddSOI1azfSHH2TyvE8x\nm80M692LiOAglm/ZBsA9/fsSHhRIzzateOSNWQghGNGnN99LGNEAACAASURBVBHBVxk5WwkqtZo+\nI8ax8j9vIM1mWna9DZ+ARkTvXgNAm55D2L9xMYaCXLb+aomeqFQq7n9+LomxJzl9aAs+AY1Z/NEU\nAHoMfpSwlpW2nAOw73Q63ZvrWfRiLwxGMx8sP25Lmz26A3N/OUF6bjHPj2hBclYRn4y39LXacSKF\n7zdfoJGvKy/d1wop4WJKPh+WiR7aYTaR8OVcmsyaCyoVmev/wHDpAt5DLQ5sxuoVOIU2JvTFmSAl\nRRcvEP/xO7bdw16bjdrDA1liIuGLuZjz86o/kQ2gV9Nz9/Xas7z2YBvG3B7BuYRc1h6whFJ+2Hye\n6fe2ZsHkHiDgP+vOklNgpHVjT27vFGin1S3SxzYAqKa6I3uGcn/fxni7ObBgcg/2nU5j7q8n0bk5\n8Plz3XBx1CClZFTvRjw1z76z+7WWsdZaZjMpPy4kZMqrIFRk79xCcUI8nv0GApC9dSMOgcEEjp0A\nUmJIiCdpkSW479y0OZ49+2KIv4TL65YIcNovS8iPrnx0q1qt5s6HnuPbeTMwm8106j0Iv6Aw9m9d\nBUDXfnex5Y8fKMjPZdUPltGmKrWa8a99RmiTFrTu3IevZj2HSq0mMLQpXfoMqVSnpjR6+Q1c23VE\n4+FFi+9+Jfm7/5K5ftU15fHFqtPMHt0RlQrWH0zkYko+Q7tanj+r919m35l0ukbq+d8LPTEUm5n7\nS+l9+sr9rWkXrsPDRct303vz/ebzrDuYSK+Wvky4KxJPV/t4wcIN53j5/jaoBGw9lszltAKb07kp\nKomo85l0iNAxd1xnikvMdiPsv914nmfvikSjVpGSVcT81WcAeKh/OI39XZHSvr/kjUClVnPrPeNZ\nPv91pNlMm+63ow9szJGdlo+O9r2HsmfdYoryc9i07AvLPio1j7w477r1Bt0/gcWfz8RsNtO+5yB8\ngxpzaLulxaJTnzvZseZHCvNzWbvYqqdWMfZlS8+vO+4bz4qF72EqKUGnD+DOx6bW9hQoWBE30iut\nK4QQP2LpR7fGumkIlujYLCnlEiGEFosT5wMsBFYBy602a4HnpJRuQogwYJWUsk0VOmOALlLKiWW2\nPQ9cmaMlD3hUSnnO6nBOxxLZOyylHFNFnguBIiyRSw/gBSnlqvJaQoj+wDQp5ZXBKZ8BB6SUC4UQ\nXYFPAWcsTuBA4F4szqAnEAJ8L6V8k6tja0quTzz7DLAtf7LiVL1qlZ1q5PYZG+tVa8PbA23LR4fe\nUo1l7Wm3escN0WrIc1jfWg2tV1br9NMP1atW869/si0v2Rpbr1oP9AuzLR8dcvVZCGpDuzWlEfDB\nM+u3n+PaWaVN5Y+8u6May7rhh5fr9z6uivLTRNU1zwwtHVizaOO5etUaPTDiymLNRsHUEesPJda5\nMzWoU2CDluEKN0PEECll+aloppdLNwIDytmUbRN92WoXC1TqFFrTF2JxLMtu+xj4uBLbRcCiag+8\nlI1SyvHVaUkptwBbyqxPLLO8H7CLwVv7GMZLKUfW8BgUFBQUFBQUroObIch2hX9qH0MFBQUFBYX/\nZ++8w6Oq8j7+OZPek8kkISFAQjqEFrqoNEFERWxrV1RERLCLr3V1LbvWtQAqKAqsbRV0LYigiEiv\nofdQQ0gvkzaTZM77x0wmczOTEExbs+fDk4c795TvPeWe+7u/c+69CoWiHh3CY9jSCCEuBl6ut/uo\nlPLKZuT5JHBtvd1fNjTF3FxceTcVCoVCoVC0PB3IYagMQ1dIKX/CuiaxJfN8EXixJfNUKBQKhUKh\naEmUYahQKBQKhULRDNrzE3YtjTIMFQqFQqFQKJpBRzIM1cMnCoVCoVAoFAqgg7zHUNGiqA6hUCgU\nij87bfoOwO82nmrxa+flg6Pb5T2GymOoUCgUCoVCoQDUGkOFQqFQKBSKZtGRZl+VYahw4oNlrft5\nJIDJ4+o+kdSWnyF74bNdrar11A297Nu3vLq2kZjNZ9GjdZ8ea0ut215rXa0Fj9Rp3dnAt4xbkg8f\nGGrfvuut1tWbd3+d1hXP/dqqWv/560j7ttFobFWtgIC6T9G35Wfq2vLze239eca2pCN95rK96rAD\n2YVqKlmhUCgUCoVCYUV5DBUKhUKhUCiagXpdjUKhUCgUCoWiw6E8hgqFQqFQKBTNoAM5DJXHUKFQ\nKBQKhUJhRXkMFQqFQqFQKJpBR/IYKsNQoVAoFAqFohl0pIdPlGGoaJSj+7byy5K5SIuF3kPGMnjM\ntZrwvVt+ZePPiwGJp5cPY/4yjfDO3QF4/7k78PTyQeh06HRu3PrImy414qMCOHza+T1rnUK8eeL6\nXgT6enAos4SXv9xDdY315Jt2WSKDkgyYzDW8univJr1OwOx7B5NXUsnTC3do8nzy+lTmLz+Cj5cb\nF6dFIQSkHylk3b5cTbzQAC8uHxJNpxBvVu3MZsP+PHvYoKRQ+sXpkRJyiyv5dsMpaizOg8Ird6ah\nE7BqZzbfb8p0Cr9lVCx9uodgqrYwd+khjueU0SnEh+kTEu1xwoO8Wbz2BD9tzbLvu2RAFDeOjG03\nrV4xwdw0qjs6Ab/tyuYHF3o3jYqlT2wI5moL836s05t2uVZvydoTLN+WxbTLkuik93bKJ7VbMDcM\nj0HoBL/vzubHLaed4twwPIZesSGYq2qYv/wIJ3LLABjTL5ILUsNBwqn8cuYvP0x1jeTu8Ql0CvEB\nwNfLTZPX87f2RScEv+/JZpkLreuHx9ArJgRzdQ0fOWiN7tuJC3pGIASs3p3NL+lnAOgfr2fCkC50\n0vvw0ufad2jOuXcwOh2s2JbF4rUnnLTuGpdA/wQ9pioLb32zj4wzpXi46Xjp9n54uOlw0wnW7cvh\ns1XHAJg0Jo6BiaFU10jOFFQ45Qewbt06XnvtNSwWCxMnTmTSpEku4+3Zs4c77riDF198kYsusr4X\n7rPPPuPrr78GYOLEidx4440u035w/xB0OsGyraf59+rjTuH3XJrIwMRQTFU1vL54H4ezrOfug1em\nMDjJQFGZmanvbLTHv6BnODePiqVLmB/3v7fZpebZiH7wcQIHDaO6qJCD99zyh/KAxsedWhoat7qE\n+fLI1T2Ijwrko+WH+WqNtc093HW8cVd/PNzbb2VXc8rl7+3Ow1f3IErvg7nawutL9nIsu4xogy9P\nXd+rRbUAeseGMO3SRNzcBCXlVTw8b6s9na5dPiDX8VBrDBUNYrHUsOLLd7nm7ue44/E57Nv2G3ln\ntBewoNBO3HDfP7j9/2Yz9OLrWf7FLE34ddNfYtLMdzRGYbTBVxPnviuSXepPHpfAkrUnmPT6Okor\nqhk3IAqAQYmhdA71ZdLr63jzm31O6a88r6v9gl2fEzllCOCS/lF8tuoo7y09RM9uQRgCvTTxKszV\n/LT1tMYgBAjwcWdQooEPfzrM3B8PIQT07BbkUuvVr/bw2PztDE0JIyrURxPWJzaEiBAfHvlgG/N/\nOsztY+IAOFNYwVMLdvDUgh08vXAHpmoLWw4V2NPpAzxJjQkmr7iy3bRuvag7ry/ew+MfbWdIsrNe\n79gQOoX4MPPDbXy0/DC3Oeg9s3AHzyzcwV8XWfW2Hrbqzfn+gD3MkZtGxvLPb/bx9MJ0BicZiNRr\ntXrFBBMR4s0TH29n4S8Z3DLaasQG+3kyum8nnv90F8/8awc6AYOTDAC8v/QQz32yk+c+2clWh/IC\nvPXNPp5ZlM6gRGet1JhgwoO9eXLBdhb9ksFNo6xaUaE+XNAzgpe+2MVzn+ygd2wIYUFWIzczv4I5\n3x/gUGYJ9Xnukx1Mn72JC1Ij6FLvnOgfrydS78PUdzYy+7sD3HNpEgBVNRaeXpDOA+9v5oH3N5MW\nF0pi50AA0o8UMGPOZu5/bzOZBeVOejU1Nbz88su8/fbbfPnll/z0009kZGS4jPfOO+8wePBg+77D\nhw/z9ddfs3DhQj799FPWrFnDyZMnndICPLUwnSlvb2BErwi6hvlpwgYmhhIV6sMd/1zPW9/sZ/qE\nJHvYiu1ZPLUg3Sm/YzmlPP/ZLnYfL3Kp1xQKVyzl6FMP/eH0cPZxp5aGxi1jeRWzvzvIV79rjeWq\naguPfrhNYwy3Nc0p1w0jYjiSZeTudzbyyld7mHaZtU1P5ZUzddZGps7Slqs5Wn7e7tx3RRJPL0rn\nrrc28PynOzXprjyva7PqoTlI2fJ/7YUyDFsRIcS6JsR5QAjhe7Z4TdSbJISIaom8ALKOHyQkLJJg\nQyfc3D1ITruQw7s2aOJ0jk3B29cfgKiYZIxFea6y0tAtwl/z29/bHX2Ap1O8vt1DWL07B4Dl27IY\nlhIOwNAeYfy83erV2neyRJPeEOjF4GQDP2529mLVEhbkTUGpmaKyKiwWyZ4TxSRGB2rilJtqyCqo\nwOLCE6gT4O6mQwjwcNNRWlHtUie32ESNRbJhfy794/WasLQEPWv2WMt2JKsUX293gvw8NHF6dgsm\np6iS/BKTfd9NI2P54rdj1D+qttTKLqy0623cn0taXD29eD1rHfW8XOh1DSa3np4rcoorySuxam06\nmEe/uBBNeN84vd3bm3GmFF9Pd4J8rVpuOoGnuw6dAE93N4pKzU75D0wM1fyu1dp8MI++3etpddez\nwVHLy6oVGeLD0exSzNUWLBIOZpaQZmuDM4UVZBdpDetasosqqbZIft+TzaBkgyZsULKBX3davY4H\nM0vw83YnxN/axyurauzlc3Orc5GkZxTap7MOnnI2RPfs2UOXLl2Ijo7Gw8ODsWPH8ttvvznF++KL\nLxg1ahR6fV27Hjt2jNTUVLy9vXF3dyctLY2VK1e6LNeZwkqqayS/7cpmaIq2XENTwuze1P2nbOeu\nrVy7jxVhrKhyyu9kbjmn8pwN3XOhbPcOqo3OdXIuNDbuONLQuFVUVsXBzBKqXYwpleaaZh1bc2lO\nubqF+5OeUQhY2yoi2Jtgf+f0LaE1qk8n1uzJJbfYOm4UldX1l9qxX9F8lGHYikgpz2tCtAeAJ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37cQFPSMQAlbvzuaX9DMA9I/XM2FIFzrpfXjp812avObdPwSdECzbepovfz/upDV1fCIDE0Mx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\nTIMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11044b588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = pl.subplots(figsize=(10, 8))\n",
    "corr = cor_df.corr()\n",
    "sns.heatmap(corr, mask=np.zeros_like(corr, dtype=np.bool), cmap=sns.diverging_palette(250, 15, as_cmap=True),\n",
    "            square=True, ax=ax, annot=True, vmin=0, vmax=1, linewidths=2, xticklabels=1, yticklabels=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JzS3Hw+ND6mRmP/AS0D7KtSRJkiRp0jKhJGnSiIhDgW8Cn8nMl+tfKyOOKpvL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\nSZIm5f8DrNpwiOgCdSkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112041ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "row1 = 4\n",
    "col1 = 4\n",
    "\n",
    "fig, axs = pl.subplots(row1,col1, figsize=(20,15))\n",
    "fig.subplots_adjust(wspace=0.5, hspace=0.25)\n",
    "\n",
    "for num, name in enumerate(num_col_list):\n",
    "    i = num//row1\n",
    "    j = num%col1\n",
    "    graph = sns.boxplot(y = n_loan_df[name], ax = axs[i,j], linewidth = 1.5)\n",
    "    graph.set(xlabel=name, ylabel='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_loan_df = n_loan_df[n_loan_df.annual_inc.isnull()==False]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Removing outgroups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2166 outliers found in more than one feature will be remove\n",
      "(22171, 28) (20005, 28)\n"
     ]
    }
   ],
   "source": [
    "n_loans = n_loan_df.reset_index(drop=True)\n",
    "# ADAPTE FROM CUSTOMER SEGMENTATION PROJECT\n",
    "outliers_all = np.array([], dtype='int64')\n",
    "for column in types_of_columns(n_loans)[1].index:\n",
    "    Q1 = np.percentile(n_loans[column], 25)\n",
    "    Q3 = np.percentile(n_loans[column], 75)\n",
    "    step = (Q3 - Q1)*2\n",
    "    out_list_feat=list(n_loans[~((n_loans[column] >= Q1 - step) & (n_loans[column] <= Q3 + step))].index)\n",
    "    outliers_all = np.append(outliers_all, out_list_feat)\n",
    "\n",
    "from collections import Counter\n",
    "out_count = Counter(outliers_all)\n",
    "out_customer =[]\n",
    "for customer, count in out_count.items():\n",
    "    if count > 1:\n",
    "        out_customer.append(customer.astype(int))\n",
    "outliers  = list(sorted(out_customer))\n",
    "\n",
    "print(\"%d outliers found in more than one feature will be remove\" % len(outliers))\n",
    "\n",
    "n_loan_no_out = n_loans.drop(n_loans.index[outliers])\n",
    "    \n",
    "print(n_loan_df.shape, n_loan_no_out.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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t29dvWZIkSdKBTChJkka1MWPG9FuWJEmSdCC/NUuSRjVHKEmSJEmNM6EkSZIk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\nTttyNXBgko8k+ZNJ/Pf9ImBNV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      "text/plain": [
       "<matplotlib.figure.Figure at 0x111936630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "row1 = 4\n",
    "col1 = 4\n",
    "\n",
    "fig, axs = pl.subplots(row1,col1, figsize=(20,15))\n",
    "fig.subplots_adjust(wspace=0.5, hspace=0.25)\n",
    "\n",
    "for num, name in enumerate(num_col_list):\n",
    "    i = num//row1\n",
    "    j = num%col1\n",
    "    graph = sns.boxplot(y = n_loan_no_out[name], ax = axs[i,j], linewidth = 1.5)\n",
    "    graph.set(xlabel=name, ylabel='')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Encoding categorical columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>out_prncp</th>\n",
       "      <th>total_pymnt</th>\n",
       "      <th>total_pymnt_inv</th>\n",
       "      <th>total_rec_late_fee</th>\n",
       "      <th>recoveries</th>\n",
       "      <th>collection_recovery_fee</th>\n",
       "      <th>last_pymnt_amnt</th>\n",
       "      <th>total_rec_int</th>\n",
       "      <th>total_rec_prncp</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>issue_d</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>last_credit_pull_d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20005.000000</td>\n",
       "      <td>20005.000000</td>\n",
       "      <td>20005.000000</td>\n",
       "      <td>20005.000000</td>\n",
       "      <td>20005.000000</td>\n",
       "      <td>20005.000000</td>\n",
       "      <td>20005.000000</td>\n",
       "      <td>20005.0</td>\n",
       "      <td>20005.000000</td>\n",
       "      <td>20005.000000</td>\n",
       "      <td>20005.000000</td>\n",
       "      <td>20005.000000</td>\n",
       "      <td>20005.000000</td>\n",
       "      <td>20005.0</td>\n",
       "      <td>2.000500e+04</td>\n",
       "      <td>20005.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>13730.401150</td>\n",
       "      <td>14067.530617</td>\n",
       "      <td>1846.497848</td>\n",
       "      <td>11437.501772</td>\n",
       "      <td>11433.225143</td>\n",
       "      <td>0.491094</td>\n",
       "      <td>1.171669</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8411.390145</td>\n",
       "      <td>999.173784</td>\n",
       "      <td>10436.665221</td>\n",
       "      <td>14.573761</td>\n",
       "      <td>6.015396</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.841395e+04</td>\n",
       "      <td>0.257233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8772.652652</td>\n",
       "      <td>15832.011124</td>\n",
       "      <td>4997.831977</td>\n",
       "      <td>9524.770693</td>\n",
       "      <td>9522.050195</td>\n",
       "      <td>4.307834</td>\n",
       "      <td>165.719742</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8794.373054</td>\n",
       "      <td>970.939466</td>\n",
       "      <td>9114.401200</td>\n",
       "      <td>5.287224</td>\n",
       "      <td>3.720080</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.398435e+04</td>\n",
       "      <td>0.242047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1000.000000</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>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.320000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.400000e+03</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7000.000000</td>\n",
       "      <td>5434.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3848.250000</td>\n",
       "      <td>3847.900000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>649.960000</td>\n",
       "      <td>296.870000</td>\n",
       "      <td>2800.000000</td>\n",
       "      <td>11.390000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.000000e+04</td>\n",
       "      <td>0.084932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>12000.000000</td>\n",
       "      <td>10247.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8608.630725</td>\n",
       "      <td>8606.390000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5828.110000</td>\n",
       "      <td>689.850000</td>\n",
       "      <td>8000.000000</td>\n",
       "      <td>13.490000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.000000e+04</td>\n",
       "      <td>0.167123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>19250.000000</td>\n",
       "      <td>17889.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16367.429021</td>\n",
       "      <td>16365.840000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13268.560000</td>\n",
       "      <td>1395.800000</td>\n",
       "      <td>15000.000000</td>\n",
       "      <td>17.990000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.500000e+04</td>\n",
       "      <td>0.336986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>40000.000000</td>\n",
       "      <td>477970.000000</td>\n",
       "      <td>36716.940000</td>\n",
       "      <td>41077.541813</td>\n",
       "      <td>41000.920000</td>\n",
       "      <td>154.860000</td>\n",
       "      <td>23439.240000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40880.360000</td>\n",
       "      <td>9181.670000</td>\n",
       "      <td>40000.000000</td>\n",
       "      <td>30.990000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.500000e+06</td>\n",
       "      <td>1.167123</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          loan_amnt      revol_bal     out_prncp   total_pymnt  \\\n",
       "count  20005.000000   20005.000000  20005.000000  20005.000000   \n",
       "mean   13730.401150   14067.530617   1846.497848  11437.501772   \n",
       "std     8772.652652   15832.011124   4997.831977   9524.770693   \n",
       "min     1000.000000       0.000000      0.000000      0.000000   \n",
       "25%     7000.000000    5434.000000      0.000000   3848.250000   \n",
       "50%    12000.000000   10247.000000      0.000000   8608.630725   \n",
       "75%    19250.000000   17889.000000      0.000000  16367.429021   \n",
       "max    40000.000000  477970.000000  36716.940000  41077.541813   \n",
       "\n",
       "       total_pymnt_inv  total_rec_late_fee    recoveries  \\\n",
       "count     20005.000000        20005.000000  20005.000000   \n",
       "mean      11433.225143            0.491094      1.171669   \n",
       "std        9522.050195            4.307834    165.719742   \n",
       "min           0.000000            0.000000      0.000000   \n",
       "25%        3847.900000            0.000000      0.000000   \n",
       "50%        8606.390000            0.000000      0.000000   \n",
       "75%       16365.840000            0.000000      0.000000   \n",
       "max       41000.920000          154.860000  23439.240000   \n",
       "\n",
       "       collection_recovery_fee  last_pymnt_amnt  total_rec_int  \\\n",
       "count                  20005.0     20005.000000   20005.000000   \n",
       "mean                       0.0      8411.390145     999.173784   \n",
       "std                        0.0      8794.373054     970.939466   \n",
       "min                        0.0         0.000000       0.000000   \n",
       "25%                        0.0       649.960000     296.870000   \n",
       "50%                        0.0      5828.110000     689.850000   \n",
       "75%                        0.0     13268.560000    1395.800000   \n",
       "max                        0.0     40880.360000    9181.670000   \n",
       "\n",
       "       total_rec_prncp      int_rate    emp_length  issue_d    annual_inc  \\\n",
       "count     20005.000000  20005.000000  20005.000000  20005.0  2.000500e+04   \n",
       "mean      10436.665221     14.573761      6.015396      0.0  7.841395e+04   \n",
       "std        9114.401200      5.287224      3.720080      0.0  6.398435e+04   \n",
       "min           0.000000      5.320000      0.000000      0.0  2.400000e+03   \n",
       "25%        2800.000000     11.390000      2.000000      0.0  5.000000e+04   \n",
       "50%        8000.000000     13.490000      6.000000      0.0  7.000000e+04   \n",
       "75%       15000.000000     17.990000     10.000000      0.0  9.500000e+04   \n",
       "max       40000.000000     30.990000     10.000000      0.0  6.500000e+06   \n",
       "\n",
       "       last_credit_pull_d  \n",
       "count        20005.000000  \n",
       "mean             0.257233  \n",
       "std              0.242047  \n",
       "min              0.000000  \n",
       "25%              0.084932  \n",
       "50%              0.167123  \n",
       "75%              0.336986  \n",
       "max              1.167123  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_loan_no_out.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>hardship_flag</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>out_prncp</th>\n",
       "      <th>total_pymnt</th>\n",
       "      <th>disbursement_method</th>\n",
       "      <th>total_pymnt_inv</th>\n",
       "      <th>total_rec_late_fee</th>\n",
       "      <th>recoveries</th>\n",
       "      <th>collection_recovery_fee</th>\n",
       "      <th>...</th>\n",
       "      <th>grade</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>purpose</th>\n",
       "      <th>issue_d</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>pymnt_plan</th>\n",
       "      <th>debt_settlement_flag</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>last_credit_pull_d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5938.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8342.110922</td>\n",
       "      <td>0</td>\n",
       "      <td>8342.11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>57000.0</td>\n",
       "      <td>0.084932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10377.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5267.905588</td>\n",
       "      <td>0</td>\n",
       "      <td>5267.91</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>0.084932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8400.0</td>\n",
       "      <td>0</td>\n",
       "      <td>19885.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9023.279968</td>\n",
       "      <td>0</td>\n",
       "      <td>9023.28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>0.167123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7175.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3383.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7536.773657</td>\n",
       "      <td>0</td>\n",
       "      <td>7536.77</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>65000.0</td>\n",
       "      <td>0.084932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4662.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6334.533998</td>\n",
       "      <td>0</td>\n",
       "      <td>6334.53</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34278.0</td>\n",
       "      <td>0.419178</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   loan_amnt  hardship_flag  revol_bal  out_prncp  total_pymnt  \\\n",
       "0     8000.0              0     5938.0        0.0  8342.110922   \n",
       "1     5000.0              0    10377.0        0.0  5267.905588   \n",
       "2     8400.0              0    19885.0        0.0  9023.279968   \n",
       "3     7175.0              0     3383.0        0.0  7536.773657   \n",
       "4     6000.0              0     4662.0        0.0  6334.533998   \n",
       "\n",
       "   disbursement_method  total_pymnt_inv  total_rec_late_fee  recoveries  \\\n",
       "0                    0          8342.11                 0.0         0.0   \n",
       "1                    0          5267.91                 0.0         0.0   \n",
       "2                    0          9023.28                 0.0         0.0   \n",
       "3                    0          7536.77                 0.0         0.0   \n",
       "4                    0          6334.53                 0.0         0.0   \n",
       "\n",
       "   collection_recovery_fee         ...          grade  emp_length  \\\n",
       "0                      0.0         ...              0        10.0   \n",
       "1                      0.0         ...              1        10.0   \n",
       "2                      0.0         ...              1        10.0   \n",
       "3                      0.0         ...              2        10.0   \n",
       "4                      0.0         ...              2        10.0   \n",
       "\n",
       "   home_ownership  purpose  issue_d  loan_status  pymnt_plan  \\\n",
       "0               1        1        0   Fully Paid           0   \n",
       "1               3        1        0   Fully Paid           0   \n",
       "2               1        1        0   Fully Paid           0   \n",
       "3               1        2        0   Fully Paid           0   \n",
       "4               3        1        0   Fully Paid           0   \n",
       "\n",
       "   debt_settlement_flag  annual_inc  last_credit_pull_d  \n",
       "0                     0     57000.0            0.084932  \n",
       "1                     0     60000.0            0.084932  \n",
       "2                     0     70000.0            0.167123  \n",
       "3                     0     65000.0            0.084932  \n",
       "4                     0     34278.0            0.419178  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for col in (nn_col_list):\n",
    "    n_loan_no_out[col]=l_encoder.fit_transform(n_loan_no_out[col])\n",
    "n_loan_no_out.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_loan_no_out.loan_status = l_encoder.fit_transform(n_loan_no_out.loan_status)\n",
    "n_loan_no_out.loan_status.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20005, 28)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>hardship_flag</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>out_prncp</th>\n",
       "      <th>total_pymnt</th>\n",
       "      <th>disbursement_method</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5938.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>57000.0</td>\n",
       "      <td>0.084932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10377.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5267.905588</td>\n",
       "      <td>0</td>\n",
       "      <td>5267.91</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>0.084932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8400.0</td>\n",
       "      <td>0</td>\n",
       "      <td>19885.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9023.279968</td>\n",
       "      <td>0</td>\n",
       "      <td>9023.28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>0.167123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7175.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3383.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7536.773657</td>\n",
       "      <td>0</td>\n",
       "      <td>7536.77</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>65000.0</td>\n",
       "      <td>0.084932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4662.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6334.533998</td>\n",
       "      <td>0</td>\n",
       "      <td>6334.53</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34278.0</td>\n",
       "      <td>0.419178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>14235.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10587.252371</td>\n",
       "      <td>0</td>\n",
       "      <td>10587.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>0.084932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6288.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2035.976448</td>\n",
       "      <td>0</td>\n",
       "      <td>2035.98</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42000.0</td>\n",
       "      <td>0.084932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5206.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12899.775532</td>\n",
       "      <td>0</td>\n",
       "      <td>12899.78</td>\n",
       "      <td>20.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>68000.0</td>\n",
       "      <td>0.336986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3500.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3214.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3537.607458</td>\n",
       "      <td>0</td>\n",
       "      <td>3537.61</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>145000.0</td>\n",
       "      <td>0.084932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>128691.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10490.860871</td>\n",
       "      <td>0</td>\n",
       "      <td>10490.86</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99000.0</td>\n",
       "      <td>0.084932</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    loan_amnt  hardship_flag  revol_bal  out_prncp   total_pymnt  \\\n",
       "0      8000.0              0     5938.0        0.0   8342.110922   \n",
       "1      5000.0              0    10377.0        0.0   5267.905588   \n",
       "2      8400.0              0    19885.0        0.0   9023.279968   \n",
       "3      7175.0              0     3383.0        0.0   7536.773657   \n",
       "4      6000.0              0     4662.0        0.0   6334.533998   \n",
       "6     10000.0              0    14235.0        0.0  10587.252371   \n",
       "7      2000.0              0     6288.0        0.0   2035.976448   \n",
       "8     12000.0              0     5206.0        0.0  12899.775532   \n",
       "9      3500.0              0     3214.0        0.0   3537.607458   \n",
       "10    10000.0              0   128691.0        0.0  10490.860871   \n",
       "\n",
       "    disbursement_method  total_pymnt_inv  total_rec_late_fee  recoveries  \\\n",
       "0                     0          8342.11                 0.0         0.0   \n",
       "1                     0          5267.91                 0.0         0.0   \n",
       "2                     0          9023.28                 0.0         0.0   \n",
       "3                     0          7536.77                 0.0         0.0   \n",
       "4                     0          6334.53                 0.0         0.0   \n",
       "6                     0         10587.25                 0.0         0.0   \n",
       "7                     0          2035.98                 0.0         0.0   \n",
       "8                     0         12899.78                20.8         0.0   \n",
       "9                     0          3537.61                 0.0         0.0   \n",
       "10                    0         10490.86                 0.0         0.0   \n",
       "\n",
       "    collection_recovery_fee         ...          grade  emp_length  \\\n",
       "0                       0.0         ...              0        10.0   \n",
       "1                       0.0         ...              1        10.0   \n",
       "2                       0.0         ...              1        10.0   \n",
       "3                       0.0         ...              2        10.0   \n",
       "4                       0.0         ...              2        10.0   \n",
       "6                       0.0         ...              1         9.0   \n",
       "7                       0.0         ...              0         6.0   \n",
       "8                       0.0         ...              2        10.0   \n",
       "9                       0.0         ...              0         8.0   \n",
       "10                      0.0         ...              1        10.0   \n",
       "\n",
       "    home_ownership  purpose  issue_d  loan_status  pymnt_plan  \\\n",
       "0                1        1        0            0           0   \n",
       "1                3        1        0            0           0   \n",
       "2                1        1        0            0           0   \n",
       "3                1        2        0            0           0   \n",
       "4                3        1        0            0           0   \n",
       "6                3        2        0            0           0   \n",
       "7                3        1        0            0           0   \n",
       "8                1        2        0            0           0   \n",
       "9                1        2        0            0           0   \n",
       "10               1        1        0            0           0   \n",
       "\n",
       "    debt_settlement_flag  annual_inc  last_credit_pull_d  \n",
       "0                      0     57000.0            0.084932  \n",
       "1                      0     60000.0            0.084932  \n",
       "2                      0     70000.0            0.167123  \n",
       "3                      0     65000.0            0.084932  \n",
       "4                      0     34278.0            0.419178  \n",
       "6                      0     40000.0            0.084932  \n",
       "7                      0     42000.0            0.084932  \n",
       "8                      0     68000.0            0.336986  \n",
       "9                      0    145000.0            0.084932  \n",
       "10                     0     99000.0            0.084932  \n",
       "\n",
       "[10 rows x 28 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(n_loan_no_out.shape)\n",
    "n_loan_no_out.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Spliting the data for the model\n",
    "\n",
    "Spliting into features and target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loans original data 20005, target: 20005, features: 20005\n"
     ]
    }
   ],
   "source": [
    "target = n_loan_no_out.loan_status.values\n",
    "features = n_loan_no_out.drop(['loan_status'], axis=1).values\n",
    "print ('Loans original data %d, target: %d, features: %d' % (n_loan_no_out.shape[0], target.shape[0], features.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building an initial model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import itertools\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=pl.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    #print(cm)\n",
    "\n",
    "    pl.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    pl.title(title)\n",
    "    pl.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    pl.xticks(tick_marks, classes, rotation=45)\n",
    "    pl.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        pl.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    pl.tight_layout()\n",
    "    pl.ylabel('True label')\n",
    "    pl.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# USING TRAIN, VALIDATION, TEST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import load_model\n",
    "classifier = load_model('data_files/classifier_opt.hdf5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_2016 = sc.fit_transform(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Predicting on the test set \n",
    "pred_2016 = classifier.predict(X_2016)\n",
    "pred_2016 = (pred_2016 > 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[14733,     0],\n",
       "       [  364,  4908]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cnf_matrix = confusion_matrix(target, pred_2016, labels=[0, 1])\n",
    "cnf_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n"
     ]
    },
    {
     "data": {
      "image/png": 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DOsGaWWGqPL86wZpZUar/TS4nWDMrhAC11oley1Tlt2dmLZagpkZlL42eTrpe0kJJz5SU\nbSHpAUkvpJ+bl+w7P30M4HlJB5WUD5A0Ne27QqmZLamjpFtT+QRJvRuLyQnWzApT4flgbwCG1Cs7\nD3gwIvoCD6Zt0uT/w4Cd0jFXp48EAIwETgL6pqXunMOBNyNie+Ay4OeNBeQEa2aFkcpfGhMRDwFv\n1CseCtyY1m8EDi8pHx0RyyJiFjATGCSpO9AlIsZHNpfrTfWOqTvXbcD+aiTzuw/WzAohmjxMq6uk\nSSXboyJiVCPHdIuI+Wl9AdAtrfcExpfUm5PKlqf1+uV1x8yGVZNZvQ1sCSxa28WdYM2sGE2f7GXR\nuky4HREhKZ8vDKyFuwjMrDDr4Ztcr6Zf+0k/F6bytX0QYG5ar1++2jGS2gObAq83dHEnWDMrTCX7\nYNdiLHBCWj8BuLOkfFgaGdCH7GHWxNSdsFjSnql/9fh6x9Sd6yjgb9HIN7fcRWBmhaj0XASSbgEG\nk/XVzgEuBi4BxkgaDrwMHAMQEdMkjQGmAyuA0yOiNp3qNLIRCRsB49ICcB3we0kzyR6mDWssJidY\nMyuGhCr4LZiIOHYtu/ZfS/0RwIg1lE8C+q+hfClwdFNicoI1s8K0a60f2yqTE6yZFcLTFZqZ5ciT\nvZiZ5cEfPTQzy49bsGZmOany/OoEa2bFyB5yVXeGdYI1s2Ko+ifcdoI1s4L4kzFmZvnxiwZmZjkQ\nqKa6+wicYM2sOG21BSupS0MHRsTiyodjZm1H9b9p0FALdhoQZKMp6tRtB7BtjnGZWZWTqOhsWi3R\nWhNsRGyztn1mZhVR5S3YsnqYJQ2T9P203kvSgHzDMrO2QDUqe2mNGk2wkn4D7Accl4reA67JMygz\nayPWwzdjilTOKIK9ImI3SU8CRMQbkjrkHJeZVTup7Y4iKLFcUg3Zgy0kbQmszDUqM2sTqv1NrnL6\nYK8Cbge2kvQj4BHg57lGZWZtQ43KX1qhRluwEXGTpCeAA1LR0RHxTL5hmVnVE6hd60yc5Sr3Ta52\nwHKyboLqfrfNzNaftt5FIOkC4BagB9ALuFnS+XkHZmZVTuUP0SpnmJak70qaJukZSbdI2lDSFpIe\nkPRC+rl5Sf3zJc2U9Lykg0rKB0iamvZdoXXoKC6nNXo8sHtEXBgRFwCDgBObe0Ezs1UqNExLUk/g\nv4CBEdGf7LfuYcB5wIMR0Rd4MG0jqV/avxMwBLhaUrt0upHASUDftAxp7u2Vk2Dns3pXQvtUZma2\nbir7kKs9sJGk9sDGwDxgKHBj2n8jcHhaHwqMjohlETELmAkMktQd6BIR4yMigJtKjmmyhiZ7uYys\nz/UNYJqk+9L2gcDjzb2gmRnUNUyb9Nt3V0mTSrZHRcQogIiYK+mXwCvA+8D9EXG/pG4RUdcgXAB0\nS+s9gfEl55qTypan9frlzdLQQ666kQLTgLtLysevoa6ZWdM1bfjVoogYuKYdqW91KNAHeAv4k6Sv\nl9aJiJAUzQ21ORqa7OW69RmImbU1FX0F9gBgVkS8BiDpz8BewKuSukfE/PTr/8JUfy5QOqFVr1Q2\nN63XL2+WckYRbCdptKSnJc2oW5p7QTOzOpLKXhrxCrCnpI3TU//9gWeBscAJqc4JwJ1pfSwwTFJH\nSX3IHmZNTN0JiyXtmc5zfMkxTVbOONgbgJ8AvwQOBr5Bem3WzKzZBGpXmWH1ETFB0m3AZGAF8CQw\nCugMjJE0HHgZOCbVnyZpDDA91T89ImrT6U4jy3sbAePS0izlJNiNI+I+Sb+MiBeBC1NH80XNvaiZ\nGVDRV2Aj4mLg4nrFy8has2uqPwIYsYbySUD/SsRUToJdliZ7eVHSqWT9EZtU4uJm1oa14mkIy1VO\ngv0u0IlsEO8IYFPgm3kGZWZtQ2udSLtc5Uz2MiGtLuHDSbfNzNZdW23BSrqDBh5mRcQRuURkZm2D\naLXTEJaroRbsb9ZbFMnHd/k0I//5wPq+rOWkdsyviw7BKuXNhY3XaYZqn3C7oRcNHlyfgZhZW9N6\nJ9IuV7nzwZqZVV5bbcGameVKOMHWkdQxIpblGYyZtSWCdu0ar9aKlTMXwSBJU4EX0vbOkq7MPTIz\nq34VmnC7pSrnReArgEOB1wEi4ilgvzyDMrM2oK6LoIoTbDldBDUR8XK94RS1a6tsZla2Vpo4y1VO\ngp0taRAQ6Zs1ZwKertDM1pGgpro/Ul1Ogv0WWTfBtsCrwF9TmZnZumnrLdiIWEj29UUzs8rxMC2Q\ndC1rmJMgIk7OJSIzazvaeoIl6xKosyHwZWB2PuGYWdvhPlgi4tbSbUm/Bx7JLSIzaxuEE+wa9OHD\nb4ubmTVfW+8ikPQmH/bB1gBvAOflGZSZVT8h1JZbsOmztTvz4XfBV0aEvyhrZpVR5S3YBv/5SMn0\nnoioTYuTq5lVRht4Vbac9vkUSbvmHomZtT1tNcFKqus+2BV4XNLzkiZLelLS5PUTnplVrzRMq9yl\nsbNJm0m6TdJzkp6V9B+StpD0gKQX0s/NS+qfL2lmym0HlZQPkDQ17btC6/Bdm4b6YCcCuwGHNffk\nZmYNqmzL9NfAvRFxlKQOwMbA94EHI+ISSeeRPaA/V1I/sjdUdwJ6AH+V9MmIqAVGAicBE4B7gCHA\nuOYE1FCCFUBEvNicE5uZNaiCr8pK2hTYFzgRICI+AD6QNBQYnKrdCPwDOBcYCoxOHxGYJWkmMEjS\nS0CXiBifznsTcDg5JNitJJ21tp0R8avmXNDMbJWmJdiukiaVbI+KiFFpvQ/wGvA7STsDTwDfBrpF\nxPxUZwEfjuHvCYwvOdecVLY8rdcvb5aGEmw7oDOpJWtmVllN/mTMoogYuJZ97cm6NM+MiAmSfk29\n8foREZLW60iohhLs/Ij48XqLxMzalsrOpjUHmBMRE9L2bWQJ9lVJ3SNivqTuwMK0fy6wTcnxvVLZ\n3LRev7xZGno055armeWocqMIImIB2ccBdkhF+wPTgbHACansBODOtD4WGCapo6Q+QF9gYupOWCxp\nzzR64PiSY5qsoRbs/s09qZlZWSo7iuBM4I9pBMG/gW+QNSLHSBoOvAwcAxAR0ySNIUvCK4DT0wgC\ngNOAG4CNyB5uNesBFzSQYCPijeae1MysLBVMsBExBVhTH+0aG4sRMQIYsYbySUD/SsTUnNm0zMzW\nnb9oYGaWF0+4bWaWH7dgzcxy4gRrZpYDAXIXgZlZDpr8Jler4wRrZsVxF4GZWQ7kUQRmZvlxC9bM\nLCd+yGVmlhO3YM3McuA+WDOzHLkFa2aWE/fBmpnlQH7RwMwsP+4iMDPLg9xFYGaWCwE1bsGameXD\nXQRmZjlxF4GZWQ4kdxGYmeWmyluw1X13ZtaySeUvZZ1O7SQ9KemutL2FpAckvZB+bl5S93xJMyU9\nL+mgkvIBkqamfVdIze8odoKtsKVLl7LHfgexy96D6b/HPlz805+v2nflb/+XTw3ci/577MM5F/1o\nteNemT2HTXr05pdXXLW+Q7Y1qF25koEXj+Swy/8IwFOvLGDvn1zLLhdexdDL/8ji95euqnvJXQ+x\nw7m/pt/5V3Df1JmrykePn8ouF17FrhddzRf/5/csWvLuer+Pli0N0yp3Kc+3gWdLts8DHoyIvsCD\naRtJ/YBhwE7AEOBqSXVvPYwETgL6pmVIc+/QXQQV1rFjRx78y+107tyZ5cuXs89BX+LgL+zP++8v\nZezd45jy6N/p2LEjC197bbXjvvf9H3DwAfsXFLXVd8UD49mx+1YsXroMgFN+dyc//8pBfG7H3vzu\nocn8ctyj/PiI/Zk+dyFjJj7D0z85nXlvLeGgS2/k2Uv+i4jguzePY+qI0+m6SSfOHXM/Vz04kYsP\n36/gO2tBREXf5JLUCzgEGAGclYqHAoPT+o3AP4BzU/noiFgGzJI0Exgk6SWgS0SMT+e8CTgcGNec\nmNyCrTBJdO7cGYDly5ezfPlyJHHNdTdw7nf/i44dOwKw9VZbrTrm/+66h94f35Z+n9qhkJhtdXPe\neJt7nprBN/fdbVXZjFdfZ98dPg7AATttxx1PZI2ksU8+xzGD+tNxg/b02Wpzttt6Cyb+ey4REBG8\nu2w5EcGS95fRY7NNCrmfFq1pXQRdJU0qWU6ud7bLgXOAlSVl3SJiflpfAHRL6z2B2SX15qSynmm9\nfnmzOMHmoLa2ll0/ux/dtu/HAft9jj0GDmDGiy/y8GPj2fPzQxj8xaE8/sSTALzzzjv84vIrufi8\nswuO2uqcdcu9XHLMgdSUPOHu12Nrxj75HAC3TZrG7DfeBmDem0vYZotNV9XrtUUX5r25mA3at+Oq\n4w9ll4uuZpvv/pLp815bLWEbQJqusNwFFkXEwJJl1KozSYcCCyPiibVdLSICiPzv60O5JVhJ10ta\nKOmZvK7RUrVr144nH/k7s6c/xeOTn+SZ6c+yYkUtb7z5Fo89OI5f/PfFfOXEk4gIfvizS/nOaaeu\navVase6a8jxbb9KJAb17rFb+v8OHMvJvjzPoh9ew5P1ldGjkV9vlK2q55u+PM+lHpzL7srP5TK9u\nXHLXw3mG3vqISj7k2hs4LP2KPxr4vKQ/AK9K6g6Qfi5M9ecC25Qc3yuVzU3r9cubJc8+2BuA3wA3\n5XiNFm2zzTZl8D57c+9f/0avHt054kuHIIlBA3ajpkYsev11Jj4xmdvH3sW5F/+Yt95+mxrVsOGG\nG3LGycOLDr9N+tcLr/CXKc8z7ukXWLp8BYuXLuP4397OTaccyb1nHw/AjAWLuOfpFwDosfkmq1qz\nAHPeWEyPzbsw5ZUFAGy39RYAHDVoJ35x9yPr+W5agQoN04qI84HzASQNBs6OiK9LuhQ4Abgk/bwz\nHTIWuFnSr4AeZA+zJkZEraTFkvYEJgDHA1c2N67cEmxEPCSpd17nb6leW7SIDdpvwGabbcr777/P\nX//+T875zpl07tSJvz/8CPvt+1lmzHyRD5Yvp+uWW/LQvX9ZdewPf/YLOnfq5ORaoJ8e/QV+evQX\nAPjHc7P41b3/4qZTjmTh4nfYuktnVq5cyU//8hCnDB4IwJd23ZHjfnsb3z1oL+a9tYSZC99g0Cd6\n8urb7/DsvNd4bfG7bNWlE3+d9iI79uha5K21QOUPv1oHlwBjJA0HXgaOAYiIaZLGANOBFcDpEVGb\njjmNrIG4EdnDrWY94IIWMIogdVSfDLDtNr0aqd3yzV/wKieeeia1K2tZuTI4+suHceiQA/nggw8Y\nfvq3+fSe+9Jhgw24YeSVrMPwOlvPRo+fysi/PQ7A4QM+xYn77ArATj235qjdd+LTF/yG9u1quOLr\nh9CupoYem3fhoqGD2e+S69mgXTu23XJTrv/PLxd5Cy1TDp+MiYh/kI0WICJeB9Y4PCciRpCNOKhf\nPgnoX4lYlPX75iO1YO+KiLKCHbjrLvH4Px/ILR5bv1b+eWTRIViF7PGj3zJp1tyKtggG7tAnJlz9\n47Lrtz/g+CciYmAlY8hb4S1YM2urPB+smVl+aqr7kzF5DtO6BXgM2EHSnNTJbGaWqZtNq9ylFcpz\nFMGxeZ3bzKqEuwjMzHJS5SNpnGDNrCB+yGVmlptqHwvuBGtmxRBuwZqZ5cNdBGZm+Wmlw6/K5QRr\nZsVxC9bMLAdS1b/J5QRrZsXxKAIzs5y4i8DMLAdqvXMMlMsJ1syK4xasmVlO3AdrZpYHv2hgZpYf\nt2DNzHLguQjMzPIi1M4vGpiZ5aPKW7DVfXdm1nKJrA+23KWhU0nbSPq7pOmSpkn6dirfQtIDkl5I\nPzcvOeZ8STMlPS/poJLyAZKmpn1XaB0mrXWCNbOCpFEE5S4NWwF8LyL6AXsCp0vqB5wHPBgRfYEH\n0zZp3zBgJ2AIcLWkuv6KkcBJQN+0DGnuHTrBmllxKtSCjYj5ETE5rS8BngV6AkOBG1O1G4HD0/pQ\nYHRELIuIWcBMYJCk7kCXiBgfEQHcVHJMk7kP1syKU9OkNl5XSZNKtkdFxKj6lST1BnYFJgDdImJ+\n2rUA6JbWewLjSw6bk8qWp/X65c3iBGtmxSijZVrPoogY2PAp1Rm4HfhORCwu7T6NiJAUzYq1mZxg\nzaw4FRxFIGkDsuT6x4j4cyp+VVL3iJiffv1fmMrnAtuUHN4rlc1N6/XLm8V9sGZWnMqNIhBwHfBs\nRPyqZNdthornAAAGqElEQVRY4IS0fgJwZ0n5MEkdJfUhe5g1MXUnLJa0Zzrn8SXHNJlbsGZWoIq9\nKrs3cBwwVdKUVPZ94BJgjKThwMvAMQARMU3SGGA62QiE0yOiNh13GnADsBEwLi3N4gRrZgVRUx9y\nrVVEPMLas/X+azlmBDBiDeWTgP6ViMsJ1swK5MlezMwqr+5NrirmBGtmxanu/OoEa2ZFqu4M6wRr\nZgVp8osGrY4TrJkVxwnWzCwvTrBmZvlwC9bMLC9OsGZmlafKvcnVUjnBmlmB3II1M8vFOnzuqlVw\ngjWz4jjBmpnlQbiLwMwsL27BmpnlwLNpmZnlyQnWzCwfbsGameXBs2mZmeXHCdbMLAd+yGVmlqfq\nTrDVPdOCmbVsUvlLo6fSEEnPS5op6bz1EH2jnGDNrCBNSK6NJFhJ7YCrgIOBfsCxkvqth5tokBOs\nmRVITVgaNAiYGRH/jogPgNHA0JyCLluL6oN9YspTi2o23frlouNYD7oCi4oOwiqirfxdfrzSJ3zi\nySn3qfPmXZtwyIaSJpVsj4qIUWm9JzC7ZN8cYI91jXFdtagEGxFbFR3D+iBpUkQMLDoOW3f+u2y+\niBhSdAx5cxeBmVWDucA2Jdu9UlmhnGDNrBo8DvSV1EdSB2AYMLbgmFpWF0EbMqrxKtZK+O+yBYiI\nFZLOAO4D2gHXR8S0gsNCEVF0DGZmVcldBGZmOXGCNTPLiROsmVlOnGDXA0k7SPoPSRukV/qslfPf\no5XDD7lyJukI4KdkY/LmApOAGyJicaGBWbNI+mREzEjr7SKituiYrOVyCzZHkjYAvgIMj4j9gTvJ\nBkOfK6lLocFZk0k6FJgi6WaAiKh1S9Ya4gSbvy5A37R+B3AXsAHwVanKZxuuIpI6AWcA3wE+kPQH\ncJK1hjnB5igilgO/Ao6QtE9ErAQeAaYAny00OGuSiHgX+CZwM3A22cQjq5JskbFZy+UEm7+HgfuB\n4yTtGxG1EXEz0APYudjQrCkiYl5EvBMRi4BTgI3qkqyk3STtWGyE1tL4VdmcRcRSSX8EAjg//U+4\nDOgGzC80OGu2iHhd0inApZKeI3s9c7+Cw7IWxgl2PYiINyVdC0wna/ksBb4eEa8WG5mti4hYJOlp\nsln0vxARc4qOyVoWD9Naz9IDkUj9sdaKSdocGAN8LyKeLjoea3mcYM3WgaQNI2Jp0XFYy+QEa2aW\nE48iMDPLiROsmVlOnGDNzHLiBGtmlhMn2CohqVbSFEnPSPqTpI3X4VyDJd2V1g+TdF4DdTeTdFoz\nrvFDSWeXW16vzg2SjmrCtXpLeqapMZqtKyfY6vF+ROwSEf2BD4BTS3cq0+S/74gYGxGXNFBlM6DJ\nCdasLXCCrU4PA9unltvzkm4CngG2kXSgpMckTU4t3c4AkoZIek7SZOCIuhNJOlHSb9J6N0l3SHoq\nLXsBlwDbpdbzpane/5P0uKSnJf2o5FwXSJoh6RFgh8ZuQtJJ6TxPSbq9Xqv8AEmT0vkOTfXbSbq0\n5NqnrOsfpNm6cIKtMpLak726OTUV9QWujoidgHeBC4EDImI3ssm/z5K0IXAt8CVgAPCxtZz+CuCf\nEbEzsBswDTgPeDG1nv+fpAPTNQcBuwADJO0raQDZt+p3Ab4I7F7G7fw5InZP13sWGF6yr3e6xiHA\nNekehgNvR8Tu6fwnSepTxnXMcuG5CKrHRpKmpPWHgevIZux6OSLGp/I9gX7Ao2kq2g7AY8COwKyI\neAEgzRB18hqu8XngeFg1Rd/b6XXRUgem5cm03Zks4W4C3BER76VrjC3jnvpL+glZN0Rnsm/e1xmT\nXjd+QdK/0z0cCHympH9203TtGWVcy6zinGCrx/sRsUtpQUqi75YWAQ9ExLH16q123DoS8LOI+G29\na3ynGee6ATg8Ip6SdCIwuGRf/VcQI137zIgoTcRI6t2Ma5utM3cRtC3jgb0lbQ/ZLP2SPgk8B/SW\ntF2qd+xajn8Q+FY6tp2kTYElZK3TOvcB3yzp2+0paWvgIeBwSRtJ2oSsO6IxmwDz06d3vlZv39GS\nalLMnwCeT9f+VqqPpE+mLxGYFcIt2DYkIl5LLcFbJHVMxRdGxAxJJwN3S3qPrIthkzWc4tvAKEnD\ngVrgWxHxmKRH0zCocakf9lPAY6kF/Q7Z1IyTJd0KPAUsBB4vI+SLgAnAa+lnaUyvABPJPslzapp3\n93/J+mYnK7v4a8Dh5f3pmFWeJ3sxM8uJuwjMzHLiBGtmlhMnWDOznDjBmpnlxAnWzCwnTrBmZjlx\ngjUzy8n/B1kOE8SjGoLtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12191fdd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pl.figure()\n",
    "plot_confusion_matrix(cnf_matrix, classes=n_loan_no_out.loan_status.unique(),\n",
    "                      cmap=pl.cm.Reds, normalize=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.96424361493123767"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "f1_score(target, pred_2016.round(), average='binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 98.1805%, Loss = 0.1244\n"
     ]
    }
   ],
   "source": [
    "loss, accuracy = classifier.evaluate(X_2016, target,batch_size=128, verbose=0)\n",
    "print(\"Accuracy = {:.4f}%, Loss = {:.4f}\".format(accuracy* 100, loss))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False],\n",
       "       [False],\n",
       "       [False],\n",
       "       ..., \n",
       "       [False],\n",
       "       [ True],\n",
       "       [ True]], dtype=bool)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_2016"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}