Predicting Loan Payback.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting Loan Payback\n",
    "\n",
    "In this project, I will be using a publicly available dataset from LendingClub. LendingClub is a peer-to-peer lending company. \n",
    "Investors are often interested in learning how risky (i.e. the probability of loan payback) a borrower is. This project attempts to predict the people who have high probability of paying back their loans. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "plt.style.use('ggplot')\n",
    "import seaborn as sns\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Description of each feature in the dataset:\n",
    "\n",
    "- **credit.policy:** 1 if the customer meets the credit underwriting criteria of LendingClub.com, and 0 otherwise.\n",
    "- **purpose:** The purpose of the loan, such as credit_card, debt_consolidation, educational etc.\n",
    "- **int.rate:** The interest rate of the loan as a proportion.\n",
    "- **installment:** The monthly installments owed by the borrower if the loan is funded.\n",
    "- **log.annual.inc:** The natural log of the self-reported annual income of the borrower.\n",
    "- **dti:** The debt-to-income ratio of the borrower.\n",
    "- **fico:** The FICO credit score of the borrower.\n",
    "- **days.with.cr.line:** The number of days the borrower has had a credit line.\n",
    "- **revol.bal:** The borrower's revolving balance (i.e. amount unpaid at the end of the credit card billing cycle).\n",
    "- **revol.util:** The borrower's revolving line utilization rate (i.e. the amount of the credit line used relative to total credit available).\n",
    "- **inq.last.6mths:** The borrower's number of inquiries by creditors in the last 6 months.\n",
    "- **delinq.2yrs:** The number of times the borrower had been 30+ days past due on a payment in the past 2 years.\n",
    "- **pub.rec:** The borrower's number of derogatory public records.\n",
    "- **no.fully.paid:** Indicates whether the loan was not fully paid back. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dat = pd.read_csv(\"loan_data.csv\", encoding = 'unicode_escape')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>credit.policy</th>\n",
       "      <th>purpose</th>\n",
       "      <th>int.rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>log.annual.inc</th>\n",
       "      <th>dti</th>\n",
       "      <th>fico</th>\n",
       "      <th>days.with.cr.line</th>\n",
       "      <th>revol.bal</th>\n",
       "      <th>revol.util</th>\n",
       "      <th>inq.last.6mths</th>\n",
       "      <th>delinq.2yrs</th>\n",
       "      <th>pub.rec</th>\n",
       "      <th>not.fully.paid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>0.1189</td>\n",
       "      <td>829.10</td>\n",
       "      <td>11.350407</td>\n",
       "      <td>19.48</td>\n",
       "      <td>737</td>\n",
       "      <td>5639.958333</td>\n",
       "      <td>28854</td>\n",
       "      <td>52.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>0.1071</td>\n",
       "      <td>228.22</td>\n",
       "      <td>11.082143</td>\n",
       "      <td>14.29</td>\n",
       "      <td>707</td>\n",
       "      <td>2760.000000</td>\n",
       "      <td>33623</td>\n",
       "      <td>76.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>0.1357</td>\n",
       "      <td>366.86</td>\n",
       "      <td>10.373491</td>\n",
       "      <td>11.63</td>\n",
       "      <td>682</td>\n",
       "      <td>4710.000000</td>\n",
       "      <td>3511</td>\n",
       "      <td>25.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>0.1008</td>\n",
       "      <td>162.34</td>\n",
       "      <td>11.350407</td>\n",
       "      <td>8.10</td>\n",
       "      <td>712</td>\n",
       "      <td>2699.958333</td>\n",
       "      <td>33667</td>\n",
       "      <td>73.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>0.1426</td>\n",
       "      <td>102.92</td>\n",
       "      <td>11.299732</td>\n",
       "      <td>14.97</td>\n",
       "      <td>667</td>\n",
       "      <td>4066.000000</td>\n",
       "      <td>4740</td>\n",
       "      <td>39.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   credit.policy             purpose  int.rate  installment  log.annual.inc  \\\n",
       "0              1  debt_consolidation    0.1189       829.10       11.350407   \n",
       "1              1         credit_card    0.1071       228.22       11.082143   \n",
       "2              1  debt_consolidation    0.1357       366.86       10.373491   \n",
       "3              1  debt_consolidation    0.1008       162.34       11.350407   \n",
       "4              1         credit_card    0.1426       102.92       11.299732   \n",
       "\n",
       "     dti  fico  days.with.cr.line  revol.bal  revol.util  inq.last.6mths  \\\n",
       "0  19.48   737        5639.958333      28854        52.1               0   \n",
       "1  14.29   707        2760.000000      33623        76.7               0   \n",
       "2  11.63   682        4710.000000       3511        25.6               1   \n",
       "3   8.10   712        2699.958333      33667        73.2               1   \n",
       "4  14.97   667        4066.000000       4740        39.5               0   \n",
       "\n",
       "   delinq.2yrs  pub.rec  not.fully.paid  \n",
       "0            0        0               0  \n",
       "1            0        0               0  \n",
       "2            0        0               0  \n",
       "3            0        0               0  \n",
       "4            1        0               0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9578, 14)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9578 entries, 0 to 9577\n",
      "Data columns (total 14 columns):\n",
      "credit.policy        9578 non-null int64\n",
      "purpose              9578 non-null object\n",
      "int.rate             9578 non-null float64\n",
      "installment          9578 non-null float64\n",
      "log.annual.inc       9578 non-null float64\n",
      "dti                  9578 non-null float64\n",
      "fico                 9578 non-null int64\n",
      "days.with.cr.line    9578 non-null float64\n",
      "revol.bal            9578 non-null int64\n",
      "revol.util           9578 non-null float64\n",
      "inq.last.6mths       9578 non-null int64\n",
      "delinq.2yrs          9578 non-null int64\n",
      "pub.rec              9578 non-null int64\n",
      "not.fully.paid       9578 non-null int64\n",
      "dtypes: float64(6), int64(7), object(1)\n",
      "memory usage: 1.0+ MB\n"
     ]
    }
   ],
   "source": [
    "dat.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The only categorical variable is 'purpose'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>credit.policy</th>\n",
       "      <th>int.rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>log.annual.inc</th>\n",
       "      <th>dti</th>\n",
       "      <th>fico</th>\n",
       "      <th>days.with.cr.line</th>\n",
       "      <th>revol.bal</th>\n",
       "      <th>revol.util</th>\n",
       "      <th>inq.last.6mths</th>\n",
       "      <th>delinq.2yrs</th>\n",
       "      <th>pub.rec</th>\n",
       "      <th>not.fully.paid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9.578000e+03</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>0.804970</td>\n",
       "      <td>0.122640</td>\n",
       "      <td>319.089413</td>\n",
       "      <td>10.932117</td>\n",
       "      <td>12.606679</td>\n",
       "      <td>710.846314</td>\n",
       "      <td>4560.767197</td>\n",
       "      <td>1.691396e+04</td>\n",
       "      <td>46.799236</td>\n",
       "      <td>1.577469</td>\n",
       "      <td>0.163708</td>\n",
       "      <td>0.062122</td>\n",
       "      <td>0.160054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>0.396245</td>\n",
       "      <td>0.026847</td>\n",
       "      <td>207.071301</td>\n",
       "      <td>0.614813</td>\n",
       "      <td>6.883970</td>\n",
       "      <td>37.970537</td>\n",
       "      <td>2496.930377</td>\n",
       "      <td>3.375619e+04</td>\n",
       "      <td>29.014417</td>\n",
       "      <td>2.200245</td>\n",
       "      <td>0.546215</td>\n",
       "      <td>0.262126</td>\n",
       "      <td>0.366676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>15.670000</td>\n",
       "      <td>7.547502</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>612.000000</td>\n",
       "      <td>178.958333</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.103900</td>\n",
       "      <td>163.770000</td>\n",
       "      <td>10.558414</td>\n",
       "      <td>7.212500</td>\n",
       "      <td>682.000000</td>\n",
       "      <td>2820.000000</td>\n",
       "      <td>3.187000e+03</td>\n",
       "      <td>22.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.122100</td>\n",
       "      <td>268.950000</td>\n",
       "      <td>10.928884</td>\n",
       "      <td>12.665000</td>\n",
       "      <td>707.000000</td>\n",
       "      <td>4139.958333</td>\n",
       "      <td>8.596000e+03</td>\n",
       "      <td>46.300000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.140700</td>\n",
       "      <td>432.762500</td>\n",
       "      <td>11.291293</td>\n",
       "      <td>17.950000</td>\n",
       "      <td>737.000000</td>\n",
       "      <td>5730.000000</td>\n",
       "      <td>1.824950e+04</td>\n",
       "      <td>70.900000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.216400</td>\n",
       "      <td>940.140000</td>\n",
       "      <td>14.528354</td>\n",
       "      <td>29.960000</td>\n",
       "      <td>827.000000</td>\n",
       "      <td>17639.958330</td>\n",
       "      <td>1.207359e+06</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       credit.policy     int.rate  installment  log.annual.inc          dti  \\\n",
       "count    9578.000000  9578.000000  9578.000000     9578.000000  9578.000000   \n",
       "mean        0.804970     0.122640   319.089413       10.932117    12.606679   \n",
       "std         0.396245     0.026847   207.071301        0.614813     6.883970   \n",
       "min         0.000000     0.060000    15.670000        7.547502     0.000000   \n",
       "25%         1.000000     0.103900   163.770000       10.558414     7.212500   \n",
       "50%         1.000000     0.122100   268.950000       10.928884    12.665000   \n",
       "75%         1.000000     0.140700   432.762500       11.291293    17.950000   \n",
       "max         1.000000     0.216400   940.140000       14.528354    29.960000   \n",
       "\n",
       "              fico  days.with.cr.line     revol.bal   revol.util  \\\n",
       "count  9578.000000        9578.000000  9.578000e+03  9578.000000   \n",
       "mean    710.846314        4560.767197  1.691396e+04    46.799236   \n",
       "std      37.970537        2496.930377  3.375619e+04    29.014417   \n",
       "min     612.000000         178.958333  0.000000e+00     0.000000   \n",
       "25%     682.000000        2820.000000  3.187000e+03    22.600000   \n",
       "50%     707.000000        4139.958333  8.596000e+03    46.300000   \n",
       "75%     737.000000        5730.000000  1.824950e+04    70.900000   \n",
       "max     827.000000       17639.958330  1.207359e+06   119.000000   \n",
       "\n",
       "       inq.last.6mths  delinq.2yrs      pub.rec  not.fully.paid  \n",
       "count     9578.000000  9578.000000  9578.000000     9578.000000  \n",
       "mean         1.577469     0.163708     0.062122        0.160054  \n",
       "std          2.200245     0.546215     0.262126        0.366676  \n",
       "min          0.000000     0.000000     0.000000        0.000000  \n",
       "25%          0.000000     0.000000     0.000000        0.000000  \n",
       "50%          1.000000     0.000000     0.000000        0.000000  \n",
       "75%          2.000000     0.000000     0.000000        0.000000  \n",
       "max         33.000000    13.000000     5.000000        1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "credit.policy        0\n",
       "purpose              0\n",
       "int.rate             0\n",
       "installment          0\n",
       "log.annual.inc       0\n",
       "dti                  0\n",
       "fico                 0\n",
       "days.with.cr.line    0\n",
       "revol.bal            0\n",
       "revol.util           0\n",
       "inq.last.6mths       0\n",
       "delinq.2yrs          0\n",
       "pub.rec              0\n",
       "not.fully.paid       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking null values\n",
    "dat.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the number of paid fully and not paid fully examples\n",
    "notpaid = dat[dat[\"not.fully.paid\"] == 1].shape[0]\n",
    "paid = dat[dat[\"not.fully.paid\"] == 0].shape[0]\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.countplot(dat[\"not.fully.paid\"])\n",
    "plt.xticks((0, 1), [\"Paid fully\", \"Not paid fully\"])\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.title(\"Class counts\", fontsize=25)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of not paid fully examples = 1533\n",
      "Number of paid fully examples = 8045\n",
      "Proportion of not paid fully to paid fully examples = 19.06%\n"
     ]
    }
   ],
   "source": [
    "print(f\"Number of not paid fully examples = {notpaid}\") \n",
    "print(f\"Number of paid fully examples = {paid}\")\n",
    "print(f\"Proportion of not paid fully to paid fully examples = {(notpaid / paid) * 100:.2f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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57mbMTcpKqiStUwL3PPCegb6I+sTSmSromT6xHNlnn3v7rH+0z/lL4PMjvEbH9rkWTzO0aaz+2Oeadk/p8wJw+ADnW4aq80X35+lMlzSbqqTtKhZd0nZcn7/bffX1vbDv371+PFd/oXZPGfY48KZhXufu4+7K3KmTHmPe6ZH+Qp1MLOB4v+ra5yML+e/k7Po4ZzN3GqtZzD+N1TnUCUs/xzinz3V7tOu++gpDG1x3vwXEeWK93VXMncbqBap/z93TwF1GnwGAu47x4T6f6VHmnUbsD8w/KO+I7wmqHqEX9Nn/78w/9VbJAAPw+vBh9aiWCmVZ/i/wSqqBMs+nmkpmJtWv8keAa6iqMl9VluU+ZVk+P8Tj3kg1ZVJS/ec9hmoIigT+qSzLcwfZfQeqkrZfUf0yX6Fefhfwbarplfr2BN0LOIGqdOYeql/oy1J9wX8P2LYsy6OGEns/n+Vkqmreb9QxQFWCcDvVl83uzFutSFkNFLo5cBRVqdszVF9O91NV3W1WluWXBzjfC1QleCcAf2Zu8vI/wPZlWQ40uOto+TTV7AI3UH3pTqIaH6szBMpvqaZyOp2qxGsGVbX5TKoSz89S3S+/GmkAZVleTFVC88P6uAXV3/VzVKWd9wzhMN+vn0ejA0K3vammUvsdVQngU8B1wHvLsty3LMvZA+x3ANV1vZnqfhhDVWK1Z1mWoz71VVmW/0717+J/63M9V5/7CGCnsuoI0N9+X6C6d8+jul9XrOO9nup+3qIsy2l9dhvxPVGW5dNlWe5N1VbxXKppq8ZQTZ31MNUPmH8DJpcDDMArFWVZNh2DJGmEiqK4hKq674KyLPdZyGOdTdXu7JxyMR52oiiKE6mS/V+WZblNs9FIvWNJmyS1VFEUL2duB4TTm4xF0qJn0iZJLVRPeXY61f/jv16YalpJ7TB2wZtIkhYXRVGcBuxB1fZuOaqG90c2GpSknrCkTZLaZTzVgLrPUXUM2KmsBleWtITrSUeEiBhHNS3P8lSlez/IzBMi4myqEak7U3zsl5k3R0RBNe7OLlTDDuyXmTfVx9qXuYM7fjIzF9Rbyp4WkiSpTfod5LxX1aPPAm/JzL9HxLLANRHxs3rdMZn5gz7b7wxMrh9bUrXb2DIiVqfqMbQ59cCTETElMx9lENOm9e21LUmStPiZMGHCgOt6krRlZmcQQajGlFqWwUvAdgW+U+93fUSsGhFrUQ1seFlmPgIQEZcBO9E1zYgkSdKSqGcdESJiGarBCDcAvpqZv46IDwKfiojjqQYL/WhmPks1yvr9XbtPrZcNtLzvuQ4GDgbITMaPH78IPpEkSVLv9Cxpy8wXgE0jYlXgRxGxMdW0OQ9S9YA6k2pOyJPovy63HGR533OdydwJessZM2Ys/AeQJElaxBqvHu2WmY9FxFXATpnZmabm2Yj4NtVcg1CVoK3dtdskYFq9fJs+y69alPFKkrQ0K8uSmTNnMnv2bIqi3/bxGqayLBkzZgzjxo0b1jXtSdIWEWsAz9cJ2wrAdsBnImKtzJxe9xbdjWriaYApwKERcSFVR4TH6+0uBT4dEavV2+1AVVonSZIWgZkzZ7LssssydqxDu46mWbNmMXPmTFZYYYUFb1zr1ThtawFXRsQfqCbcvSwzfwKcHxG3ALdQjT30yXr7n1JNpnsX1eTVhwDUHRA+UR/jt8BJnU4JkiRp9M2ePduEbREYO3Yss2fPHtY+S8OE8aVDfkiSNDJPP/00K664YtNhLJH6u7Z1m7Z+60ydEUGSJKkFTNokSdJioyxLPvzhD7PRRhvx1re+dYHbH3nkkXzmM58B4Nprr2WzzTZb1CHO501vehO//vWv+1139dVXs+WWW47KeaykliRJPTNx4kSuueYa1ltvvX7X/+Y3v+Hqq6/mhhtuaE217K9+9auenMeSNkmStNiYOnUqa6+9dmsStl4yaZMkScO25ZZbcsYZZ7Dddtvxyle+kg984APMnDkTgPPPP5+tt96aV7/61ey33348+OCDAOy+++4AbL/99kyePJmLL754nmNecMEFHHPMMdx4441MnjyZ0047je9973vstttu82w3ceJE7rnnnkHjO/300znooIPmWXbcccdx/PHHz7ftrFmzmDhxIt/61rfYaqut2Hjjjfn0pz89p3fn3XffzTvf+U5e/epXs/HGG3PYYYfxxBNPzNl/s80249prrwXgmWee4bDDDmOjjTbizW9+M7fccssCr+VQmbRJkqQRueSSSzjvvPO47rrruO2228hMrrnmGk4++WTOOOMMbrrpJiZNmsQhhxwCwEUXXQTAZZddxp133smuu+46z/H23ntvTj75ZDbbbDPuvPNOjj766PnOOVS77747V155JY8//jhQJWZTpkzhne9854D7XHrppfz85z/nZz/7Gf/93//N97//faBqZ3fEEUfwu9/9jquuuor77ruPL37xi/0e49RTT2X69Olce+21nHvuuXOOMRpM2iRJ0ogccMABvOxlL2O11VZj++2359Zbb+VHP/oRe+21F695zWtYfvnlOfbYY7nxxhu5//77F3zAUbTmmmuy1VZb8ZOf/ASAK6+8ktVXX53Xvva1A+7zoQ99iFVXXZW1116bAw44YE5J4Prrr8+b3vQmlltuOcaPH89BBx3E9ddf3+8xLrnkEg4//HBWXXVVJk2axH777Tdqn8mkTZIkjcgaa6wx5/UKK6zA008/zUMPPcSkSZPmLF9ppZVYbbXV5lSR9tIee+wxp3Tvoosu4h3veMeg23fP+zlp0iQeeughAB5++GHe//73s9lmm7Hhhhvy4Q9/mEce6X9s/4cffni+44wWkzZJkjRq1lxzTaZOnTrn/dNPP82jjz7Ky172shEdb8UVV+SZZ56Z8/7hhx8e8r477rgjt912G3/+85+5/PLL57SpG0j3YPwPPPAAa665JgCf+tSnWG655bj88su5/fbb+cIXvsBAkxOsscYa8x1ntCz1Q35MP+bARs+/1qlnNXp+SZJG02677cYhhxzCbrvtxuTJkznllFN43etex9prrw1USc1999034JAffW200Ubccccd/PGPf2SDDTbgc5/73JBjGTduHG9961s59NBD2XTTTZk4ceKg259++ulssskm/P3vf+eb3/wmhx12GABPPfUUL3nJS1hllVV44IEHOOOMMwY8xtve9ja+8pWvzDnO2WefPeR4F8SSNkmSNGre9KY3ccwxx3DwwQfz+te/nnvvvZevfe1rc9YfddRRHHnkkbzqVa9iypQpPPDAA0yePHnAEqn111+fI488kr322os3vvGNbLHFFsOKZ4899uC2226brwPC0Ucfzcc//vF5lm2//fbssMMO7LDDDuy8885EBAAf+chHuPnmm3nlK1/J/vvvP+igv0cfffSc9nTvfve7B+34MFxL/dyjlrRJkjSwts89+sADD/DP//zP3Hzzzay88sr9bjNr1izWWWcdrr/++jklgr3g3KOSJEnA7Nmz+frXv86uu+46YMLWJkt9mzZJkrTkefrpp9lkk02YNGkS5513XtPhjAqTNkmStMRZccUVufPOO4e07dixY0e1l+eiYvWoJElSC5i0SZIktYBJmyRJUguYtEmSJLWAHREkSdJiYbTHTh3KWKhXXnklxx9/PLNnz2bvvffm0EMPHdUYRpMlbZIkaan0wgsv8PGPf5zzzjuPK6+8kh//+MfccccdTYc1IJM2SZK0VPrd737HuuuuyzrrrMNyyy3HrrvuyqWXXtp0WAMyaZMkSUulBx98sDNtFABrrbUWDz74YIMRDc6kTZIkLZX6m3+9KPqd9nOxYNImSZKWSmuttRbTpk2b83769OmsueaaDUY0OJM2SZK0VNp000255557uO+++3juuee4+OKL2WGHHZoOa0AO+SFJkhYLQxmiYzSNHTuWT37yk+yzzz7Mnj2bPffckw033LCnMQyHSZskSVpqbbvttmy77bZNhzEkVo9KkiS1gEmbJElSC5i0SZIktYBJmyRJUguYtEmSJLWASZskSVILOOSHJElaLOx3znWjeryz933DArc56qijuPzyyxk/fjy/+MUvRvX8o82SNkmStNSKCM4///ymwxgSkzZJkrTU2mqrrVh11VWbDmNITNokSZJawKRNkiSpBUzaJEmSWsCkTZIkqQUc8kOSJC0WhjJEx2g75JBDuO6663jkkUfYbLPNOProo9l77717HsdQmLRJkqSl1te+9rWmQxgyq0clSZJawKRNkiSpBXpSPRoR44CrgeXrc/4gM0+IiPWAC4HVgZuA92TmcxGxPPAdYDPgb8CemXlvfaxjgfcBLwCHZ+alvfgMkiQtjcqybDqEJdZwr22vStqeBd6SmZsAmwI7RcRWwGeAL2TmZOBRqmSM+vnRzNwA+EK9HRGxEbAX8GpgJ+BrEbFMjz6DJElLnTFjxjBr1qymw1jizJo1izFjhpeG9aSkLTNL4O/122XrRwm8BdinXn4OcCJwOrBr/RrgB8B/RURRL78wM58F7omIu4AtgNGdYVaSJAEwbtw4Zs6cybPPPktRFE2Hs0Qoy5IxY8Ywbty4Ye3Xs96jdYnYjcAGwFeB/wMey8xO+j4VmFi/ngjcD5CZsyLiceAl9fLruw7bvU/3uQ4GDq73Z/z48QPGNX3kH2lUDBabJElSR8+Stsx8Adg0IlYFfgS8qp/NOpW7/aXy5SDL+57rTODMzvoZM2YMP+AeWZxjkyRJvTVhwoQB1/W892hmPgZcBWwFrBoRncRxEjCtfj0VWBugXv9i4JHu5f3sI0mStMTqSdIWEWvUJWxExArAdsBtwJXAO+vN9gUurl9Pqd9Tr/9F3S5uCrBXRCxf9zydDPymF59BkiSpSb0qaVsLuDIi/gD8FrgsM38C/DtwVN2h4CXAN+vtvwm8pF5+FPBRgMy8FUjgT8DPgQ/V1a6SJElLtGIpGH+lnDZt4BrU6ccc2MNQ5rfWqWc1en5JkrT4qNu09dtN1xkRJEmSWsCkTZIkqQVM2iRJklrApE2SJKkFTNokSZJawKRNkiSpBUzaJEmSWsCkTZIkqQVM2iRJklrApE2SJKkFTNokSZJawKRNkiSpBUzaJEmSWsCkTZIkqQVM2iRJklrApE2SJKkFTNokSZJawKRNkiSpBUzaJEmSWsCkTZIkqQVM2iRJklrApE2SJKkFTNokSZJawKRNkiSpBUzaJEmSWsCkTZIkqQVM2iRJklrApE2SJKkFTNokSZJawKRNkiSpBUzaJEmSWsCkTZIkqQVM2iRJklrApE2SJKkFTNokSZJawKRNkiSpBUzaJEmSWsCkTZIkqQVM2iRJklrApE2SJKkFTNokSZJawKRNkiSpBUzaJEmSWsCkTZIkqQVM2iRJklpgbC9OEhFrA98BXgbMBs7MzC9FxInAQcBf600/lpk/rfc5Fngf8AJweGZeWi/fCfgSsAxwVmae0ovPIEmS1KSeJG3ALOAjmXlTRKwM3BgRl9XrvpCZp3VvHBEbAXsBrwYmAJdHxCvq1V8FtgemAr+NiCmZ+aeefApJkqSG9CRpy8zpwPT69ZMRcRswcZBddgUuzMxngXsi4i5gi3rdXZl5N0BEXFhva9ImSZKWaL0qaZsjItYFXgf8GtgaODQi3gvcQFUa9yhVQnd9125TmZvk3d9n+Zb9nONg4GCAzGT8+PEDxjN9pB9klAwWmyRJUkdPk7aIeBHwQ+DIzHwiIk4HPgGU9fPngAOAop/dS/rvOFH2XZCZZwJndtbPmDFjFKJfNBbn2CRJUm9NmDBhwHU9S9oiYlmqhO38zLwIIDMf6lr/DeAn9dupwNpdu08CptWvB1ouSZK0xOpV79EC+CZwW2Z+vmv5WnV7N4C3A3+sX08BvhsRn6fqiDAZ+A1VCdzkiFgPeICqs8I+vfgMkiRJTepVSdvWwHuAWyLi5nrZx4C9I2JTqirOe4H3A2TmrRGRVB0MZgEfyswXACLiUOBSqiE/vpWZt/boM0iSJDWmKMv5moQtacpp0wauQZ1+zIE9DGV+a516VqPnlyRJi4+6TVt/bfudEUGSJKkNTNokSZJawKRNkiSpBUzaJEmSWsCkTZIkqQVM2iRJklrApE2SJKkFTNokSV3jLmEAACAASURBVJJawKRNkiSpBUzaJEmSWsCkTZIkqQVM2iRJklrApE2SJKkFTNokSZJawKRNkiSpBUzaJEmSWsCkTZIkqQVM2iRJklrApE2SJKkFTNokSZJawKRNkiSpBUzaJEmSWsCkTZIkqQVM2iRJklrApE2SJKkFTNokSZJawKRNkiSpBUzaJEmSWsCkTZIkqQVM2iRJklrApE2SJKkFTNokSZJawKRNkiSpBUzaJEmSWsCkTZIkqQVM2iRJklrApE2SJKkFTNokSZJawKRNkiSpBUzaJEmSWsCkTZIkqQVM2iRJklrApE2SJKkFTNokSZJawKRNkiSpBcb24iQRsTbwHeBlwGzgzMz8UkSsDnwPWBe4F4jMfDQiCuBLwC7A08B+mXlTfax9gePqQ38yM8/pxWeQJElqUq9K2mYBH8nMVwFbAR+KiI2AjwJXZOZk4Ir6PcDOwOT6cTBwOkCd5J0AbAlsAZwQEav16DNIkiQ1pidJW2ZO75SUZeaTwG3ARGBXoFNSdg6wW/16V+A7mVlm5vXAqhGxFrAjcFlmPpKZjwKXATv14jNIkiQ1qSfVo90iYl3gdcCvgTUzczpUiV1EvLTebCJwf9duU+tlAy3ve46DqUroyEzGjx8/YDzTR/pBRslgsUmSJHX0NGmLiBcBPwSOzMwnImKgTYt+lpWDLJ9HZp4JnNlZP2PGjBFE2xuLc2ySJKm3JkyYMOC6nvUejYhlqRK28zPzonrxQ3W1J/Xzw/XyqcDaXbtPAqYNslySJGmJ1pOkre4N+k3gtsz8fNeqKcC+9et9gYu7lr83IoqI2Ap4vK5GvRTYISJWqzsg7FAvkyRJWqL1qnp0a+A9wC0RcXO97GPAKUBGxPuA+4A96nU/pRru4y6qIT/2B8jMRyLiE8Bv6+1OysxHevMRJEmSmlOU5XxNwpY05bRpA9egTj/mwB6GMr+1Tj2r0fNLkqTFR92mrb82/L3vPSppLn80SJKGymmsJEmSWsCkTZIkqQVM2iRJklrApE2SJKkFhpy0RcTRAyw/avTCkSRJUn+GU9J2/ADLjxuNQCRJkjSwBQ75ERFvqV8uExFvZt6xQ14OPLkoApMkSdJcQxmn7Zv18zjgW13LS+BB4LDRDkqSJEnzWmDSlpnrAUTEdzLzvYs+JEmSJPU15BkRuhO2iBjTZ93s0QxKkiRJ8xpy0hYRrwe+CryWqqoUqvZtJbDM6IcmSZKkjuHMPXoOcAlwAPD0oglHkiRJ/RlO0rYO8PHMLBdVMJIkSerfcMZp+xGww6IKRJIkSQMbTknbOOBHEXEN1VAfc9irVJIkadEaTtL2p/ohSZKkHhvOkB//uSgDkSRJ0sCGM+THWwZal5m/GJ1wJEmS1J/hVI9+s8/7NYDlgKlUc5BKkiRpERlO9eh63e8jYhngOJwwXpIkaZEbzpAf88jMF4BPAf82euFIkiSpPyNO2mrbA847KkmStIgNpyPC/VTzjHasSDV22yGjHZQkSZLmNZyOCO/u8/4p4I7MfGIU45EkSVI/htMR4ZcAETEGWBN4KDOtGpUkSeqB4VSPrgx8FdgTWBZ4PiIuBA7PzMcXUXySJElieB0RvgKsBLwGWKF+XhH48iKIS5IkSV2G06ZtJ+Dlmfl0/f6OiNgf+L/RD0uSJEndhlPSNpNqFoRu44FnRy8cSZIk9Wc4JW1nAZdFxOeBvwDrAB8GvrEoApMkSdJcw0naPgU8ALwLmABMAz6bmX3nJJUkSdIoG0716JeA2zNzu8zcKDO3A26LiC8uotgkSZJUG07StjdwQ59lNwL7jF44kiRJ6s9wkrYSWKbPsmWGeQxJkiSNwHASrl8Bn6hnROjMjHBivVySJEmL0HA6IhwB/ASYHhF/Af4BmA68bVEEJkmSpLmGXNKWmVOB1wO7AqcCuwGb1cslSZK0CA2npI16gvjr64ckSZJ6xE4EkiRJLWDSJkmS1AImbZIkSS1g0iZJktQCJm2SJEktYNImSZLUAsMa8mOkIuJbwL8AD2fmxvWyE4GDgL/Wm30sM39arzsWeB/wAnB4Zl5aL9+JauL6ZYCzMvOUXsQvSZLUtJ4kbcDZwH8B3+mz/AuZeVr3gojYCNgLeDUwAbg8Il5Rr/4qsD0wFfhtREzJzD8tysAlSZIWBz2pHs3Mq4FHhrj5rsCFmflsZt4D3AVsUT/uysy7M/M54MJ6W0mSpCVer0raBnJoRLwXuAH4SGY+Ckxk3hkXptbLAO7vs3zL/g4aEQcDBwNkJuPHjx8wgOkjDn10DBablnzef5KkoWoyaTsd+ARQ1s+fAw4Ain62Lem/VLDs78CZeSZwZmebGTNmLHSwi8riHJuWfN5/krR4mTBhwoDrGkvaMvOhzuuI+Abwk/rtVGDtrk0nAdPq1wMtlyRJWqI1lrRFxFqZ2akdejvwx/r1FOC7EfF5qo4Ik4HfUJXATY6I9YAHqDor7NPbqCVJkprRqyE/LgC2AcZHxFTgBGCbiNiUqorzXuD9AJl5a0Qk8CdgFvChzHyhPs6hwKVUQ358KzNv7UX8kiRJTSvKst9mYUuSctq0gWtRpx9zYA9Dmd9ap57V6PnVLO8/SVK3uk1bf+37nRFBkiSpDUzaJEmSWsCkTZIkqQVM2iRJklrApE2SJKkFTNokSZJawKRNkiSpBUzaJEmSWsCkTZIkqQVM2iRJklrApE2SJKkFTNokSZJawKRNkiSpBUzaJEmSWsCkTZIkqQVM2iRJklrApE2SJKkFTNokSZJawKRNkiSpBUzaJEmSWsCkTZIkqQVM2iRJklrApE2SJKkFTNokSZJawKRNkiSpBUzaJEmSWsCkTZIkqQVM2iRJklrApE2SJKkFTNokSZJawKRNkiSpBUzaJEmSWsCkTZIkqQVM2iRJklrApE2SJKkFxjYdgNpt+jEHNnr+tU49q9HzS5LUK5a0SZIktYBJmyRJUguYtEmSJLWASZskSVILmLRJkiS1gEmbJElSC5i0SZIktYBJmyRJUguYtEmSJLVAT2ZEiIhvAf8CPJyZG9fLVge+B6wL3AtEZj4aEQXwJWAX4Glgv8y8qd5nX+C4+rCfzMxzehG/JElS03pV0nY2sFOfZR8FrsjMycAV9XuAnYHJ9eNg4HSYk+SdAGwJbAGcEBGrLfLIJUmSFgM9Sdoy82rgkT6LdwU6JWXnALt1Lf9OZpaZeT2wakSsBewIXJaZj2Tmo8BlzJ8ISpIkLZGanDB+zcycDpCZ0yPipfXyicD9XdtNrZcNtHw+EXEwVSkdmcn48eMHDGL6SKMfJYPF1gZev4Xj9ZMkDVWTSdtAin6WlYMsn09mngmc2dlmxowZoxTa6FucY2sDr9/C8fpJ0uJlwoQJA65rsvfoQ3W1J/Xzw/XyqcDaXdtNAqYNslySJGmJ12TSNgXYt369L3Bx1/L3RkQREVsBj9fVqJcCO0TEanUHhB3qZZIkSUu8Xg35cQGwDTA+IqZS9QI9BciIeB9wH7BHvflPqYb7uItqyI/9ATLzkYj4BPDberuTMrNv5wZJkqQlUk+Stszce4BV2/azbQl8aIDjfAv41iiGJkmS1ArOiCBJktQCJm2SJEktYNImSZLUAiZtkiRJLWDSJkmS1AImbZIkSS1g0iZJktQCJm2SJEktYNImSZLUAiZtkiRJLWDSJkmS1AImbZIkSS1g0iZJktQCJm2SJEktYNImSZLUAiZtkiRJLWDSJkmS1AImbZIkSS1g0iZJktQCJm2SJEktYNImSZLUAiZtkiRJLWDSJkmS1AImbZIkSS1g0iZJktQCJm2SJEktYNImSZLUAiZtkiRJLWDSJkmS1AImbZIkSS1g0iZJktQCJm2SJEktYNImSZLUAiZtkiRJLWDSJkmS1AImbZIkSS1g0iZJktQCY5sOYGm33znXNXr+s/d9Q6PnlyRJQ2NJmyRJUguYtEmSJLWASZskSVILmLRJkiS1gEmbJElSC5i0SZIktYBJmyRJUgs0Pk5bRNwLPAm8AMzKzM0jYnXge8C6wL1AZOajEVEAXwJ2AZ4G9svMm5qIW1Lzph9zYGPnXuvUsxo7t6Sl0+JS0vbmzNw0Mzev338UuCIzJwNX1O8BdgYm14+DgdN7HqkkSVIDFpekra9dgXPq1+cAu3Ut/05mlpl5PbBqRKzVRICSJEm91Hj1KFAC/xMRJfD1zDwTWDMzpwNk5vSIeGm97UTg/q59p9bLpncfMCIOpiqJIzMZP378gCefPuCapcNg12Yomr5+Cxt/07x+C6fJ69f2ayepfRaHpG3rzJxWJ2aXRcSfB9m26GdZ2XdBnfid2Vk/Y8aMUQhzydT2a9P2+Jvm9Rs5r52kRWHChAkDrmu8ejQzp9XPDwM/ArYAHupUe9bPD9ebTwXW7tp9EjCtd9FKkiQ1o9GkLSJWioiVO6+BHYA/AlOAfevN9gUurl9PAd4bEUVEbAU83qlGlSRJWpI1XdK2JnBNRPwe+A3w35n5c+AUYPuIuBPYvn4P8FPgbuAu4BvAIb0PWZIkqfcabdOWmXcDm/Sz/G/Atv0sL4EP9SA0SZKkxcri0BFBktRjTQ5MDA5OLI1E09WjkiRJGgKTNkmSpBYwaZMkSWoBkzZJkqQWMGmTJElqAXuPSkux/c65rtHzn73vGxo9vzRS9r5VEyxpkyRJagGTNkmSpBYwaZMkSWoBkzZJkqQWMGmTJElqAZM2SZKkFjBpkyRJagGTNkmSpBYwaZMkSWoBkzZJkqQWMGmTJElqAecelSRJPeXcrSNjSZskSVILmLRJkiS1gEmbJElSC5i0SZIktYBJmyRJUguYtEmSJLWASZskSVILmLRJkiS1gEmbJElSC5i0SZIktYDTWEmSem6/c65r9Pxn7/uGRs8vjYQlbZIkSS1g0iZJktQCVo+q1axikSQtLSxpkyRJagGTNkmSpBYwaZMkSWoB27RJ0gjYnlJSr1nSJkmS1AImbZIkSS1g0iZJktQCtmmTJElLlSbbpC5Me1RL2iRJklrApE2SJKkFrB6VJKllHHJm6WRJmyRJUgu0sqQtInYCvgQsA5yVmac0HJIkSdIi1bqStohYBvgqsDOwEbB3RGzUbFSSJEmLVuuSNmAL4K7MvDsznwMuBHZtOCZJkqRFqijLsukYhiUi3gnslJkH1u/fA2yZmYd2bXMwcDBAZm7WSKCSJEkjU/S3sI0lbf19kHkyz8w8MzM3z8zN6+0X20dE3Nh0DG1+eP28fl67dj68fl4/r9+gj361MWmbCqzd9X4SMK2hWCRJknqijb1HfwtMjoj1gAeAvYB9mg1JkiRp0WpdSVtmzgIOBS4FbqsW5a3NRrVQzmw6gJbz+i0cr9/Iee0Wjtdv4Xj9Fk4rr1/rOiJIkiQtjVpX0iZJkrQ0MmmTJElqAZM2SZKkFjBpk5YiETEmIqLpOCRJw2dHhIZExERgHbqGXcnMq5uLaPEXEasPtj4zH+lVLG0WEVdn5j83HUdbRcS5mfmeBS3T/CLis8AngWeAnwObAEdm5nmNBtYiEbEOMDkzL4+IFYCxmflk03EtriLiEvoMwN8tM/+1h+EstDaO09Z6EfEZYE/gT8AL9eISMGkb3I1U16kA/gF4tH69KnAfsF5zobXKZRFxNPA94KnOQpPeIXt195uIWAZwuryh2SEz/y0i3k41UPoewJWASdsQRMRBVFM0rg6sTzW4/BnAtk3GtZg7rekARpNJWzN2AzbMzGebDqRNMnM9gIg4A5iSmT+t3+8MbNdkbC1zQP38oa5lJfDyBmJpjYg4FvgYsEJEPFEvLoDnaOmYTw1Ytn7eBbggMx+xtn5YPgRsAfwaIDPvjIiXNhvS4i0zf9l0DKPJpK0Zd1P952XSNjL/mJkf6LzJzJ9FxCeaDKhNOsmvhiczTwZOjoiTM/PYpuNpqUsi4s9U1aOHRMQawMyGY2qTZzPzuU6iGxFjGaTqT3NFxGTgZGAjYFxneWa26seqSVszngZujogr6ErcMvPw5kJqlRkRcRxVlUoJvBv4W7MhtUdErAgcBfxDZh5c/2e2YWb+pOHQWiEzj7VN6shk5kfr5iFPZOYLEfEUsGvTcbXILyOiU9q7PXAIcEnDMbXFt4ETgC8Abwb2Z5CJ2RdX9h5txhTgE8C1VO20Og8Nzd7AGsCP6sca9TINzbepqvT+qX4/lapxuIYgIk4B/hc4DjimfhzdaFAtERF7ALPqhK3zw2tCw2G1yUeBvwK3AO8Hfkp1H2rBVsjMK4AiM/+SmScCb2k4pmGzpK0BmXlORCwHvKJedHtmPt9kTG1RN/o+NjOPaDqWFls/M/eMiL0BMvOZiGjdL84GvR3bpI7Uf2Tm9yPijcCOVI3ETwe2bDasdsjM2cA3gG/UveknZabVo0MzMyLGAHdGxKHAA0Dr2gNa0taAiNgGuBP4KvA14I6IcAiGIcjMF7Cn3sJ6rh4qoASIiPWxfeVwdNqkavg6veXfCpyemRcDyzUYT6tExFURsUqdsN0MfDsiPt90XC1xJLAicDjVd8h7gH0bjWgELGlrxueour7fDhARrwAuwGRkqH4XEVOA7zPvkBUXNRdSq5xANUbW2hFxPrA1sF+jEbWLbVJH7oGI+DpVb+/PRMTyWHgwHC/OzCci4kDg25l5QkT8oemg2iAzf1u//DtVe7ZWMmlrxrKdhA0gM++ICH+5D93qVB0PutsjlIBJ2xBk5mURcROwFVVD3CMyc0bDYbXJlPqh4QtgJ+C0zHwsItaiahOooRlbX7MAPt50MG1SF44cw/wdiFrVrs2krRk3RMQ3gXPr9+/CjghDlpmt/ZW0GBlHNTjxWGCjiLD34xDVbVJXoOp9e/sCd9Acmfl0RDwMvJGqicis+llDcxJwKXBNZv42Il6O12+ovk81EPE3mFtN3zombc34INUgiYdTlXRcTdW2TUMQEeOA91GNTN893s4BA+6kObpm5LgVmF0vdkaOIYqIt1E1oF8OWC8iNgVOatt0OE2IiBOAzYENqXoxL0vVg3TrJuNqi8z8PlXy0Xl/N/CO5iJqlVmZeXrTQSwsk7YG1L3OPl8/NHznAn+m6n12ElVJ5W2NRtQuzsixcE6kGpX+KoDMvDkiHLB4aN4OvA64CSAzp0XEys2G1B7+YF0ol0TEIVTDRHW3RW3V9H02AO2hiMj6+ZaI+EPfR9PxtcgGmfkfwFOZeQ5VT7TXNBxTm9j7ceHMyszH+yxz2IWhea4eoqLTc3mlhuNpm3OBl1H9YP0l1dyjThY/NPtStWnrHh/1hkYjGgFL2nqrM7bYvzQaRft1xrR7LCI2Bh4E1m0unHaIiK9QfVna+3Hh/DEi9gGWqWeTOJzqi0ALlnXv0VXryc8PoGpjpKHZIDP3iIhd67aV36Vq46YFWFKm7zNp66HMnF6/PCQz/717Xd3O6N/n30v9ODMiVqMaCXwK8CLg+GZDaoXOr8obmb/3oyVFQ3cYVc+9Z6mG6rmUaoYTLUBmnlZPv/QEVbu24zPzsobDahN/sI5QPULDB4HOmKhXAV9v28D2Jm3N2J75E7Sd+1mmfmTmWfXLq4FWTfbbpLoqmYg4IjO/1L0uIpxhYogy82mqpM0hF0agTtJM1Eam84P1P/AH63CdTtUspNPp7z31sgMbi2gETNp6KCI+SDXB78v7tGFbmWouQw1BRHwa+GxmPla/Xw34SGY6B9/Q7At8qc+y/fpZpn5ExObAx6hKOLrHe3ptUzG1RUTsDnyGavqgon6UmblKo4G1RNcP1l/iD9bh+sfM3KTr/S8i4veNRTNCJm299V3gZ8DJVBP/djzZth4sDds5Mz/WeZOZj0bELjhx8qDquUb3oRqmort6dBWqwYo1NOdTNWi+hblDpmhoPgu8LTPt7T0C9QwS72D+HwwnNRVTi7wQEetn5v8B1GPctW68NpO2Hqp7nD0O7A0QES+l6rb9ooh4UWbe12R8LbJMRCzfGbKiHuh0+YZjaoNrgenAeKqp1DqeBOy9PHR/zUxnRBiZh0zYFsrFVN8hN+J8wcN1DHBlRNxNVcK7Di2czsqkrQH14JyfByYAD1PdPLdRjb2jBTsPuCIivk3VgP4A4JxmQ1r8ZeZfgL8Ab4iINYF/rFfdlpmzmousdU6IiLOAvr1vnUZtwW6IiO8BP8ZrNxKTMnOnpoNoo8y8ou7tvSFV0vbnNo5V6Thtzfgk1byPd9TdkLfFNm1DlpmfpbqGr6JKdD9RL9MQRMQewG+APajmMPx1RLyz2ahaZX9gU6o5NN9WPxzGZ2hWoRpyZge8diNxbUQ4JuUwRMRb6ufdqcb03ABYH3hrvaxVLGlrxvOZ+beIGBMRYzLzynrIDw1RZv4c+Hl/6yLiusx8Q49DapPjqBrlPgwQEWsAlwM/aDSq9tgkM/3iHAHnDR6ZiLiFqlZhLLB/XcX3LHM7ctgJZmD/D/gF1Q+EvkqgVaW8Jm3NeCwiXkQ1ZMX59QTKVk+NnnEL3mSpNqaTsNX+hqXuw3F9RGyUmX9qOpC2iIh/y8zPdg3wPA8Hdl4gSyNHKDNPqF+elJn3dK9r4/RzJm3N2BWYCXyYat7MF1PNoanR4UCxg/t5RFxKNTAsVJPH/7TBeNrmjcC+EXEPlnYMVafzQeumDVoc1O1RiYitgFsz88n6/crARlRtVTW4HwKv77PsB8BmDcQyYiZtDcjMp7re2oBePZWZx9RtOd5IlXCcmZk/ajisNrEh+DBl5iX185z/7yJiDPCizHyiscDa53TmTTye6meZukTEK6naPr+4Txu2VWhhrYxJWw9FxJMMUgrkAJOjpmg6gMVd3Vuv37YctgkcXGb+JSLeCEzOzG/XbQJf1HRcbVDPlfkBqvGxbqT6Iv18Zp7abGStUWTmnO+QzJwdEX6PD25DqurlVZm3XduTwEGNRLQQ/GP3UGauDBARJ1HNGXcuVYLxLqpZETQEEfGZ/uZu7Vr2ngbCWpK07tdnL0XECcDmVF8G36aaGuc8YOsm42qJjTLziYh4F1WV/L9TJW8mbUNzd0QcTlW6BtUMO3c3GM9iLzMvBi6OiDdk5nVNx7OwTNqasWNmbtn1/vSI+DXVaOFasEHnbs3MP/Y8oiWLbQIH93bgdcBNAJk5rW5bpAVbtp64ezfgvzLz+Yjwfhu6DwBfpuoBXlKNFXhwoxG1x+8i4kNUVaVzfphm5gHNhTR8Jm3NeKH+pXkh1T+8vWnhdBq95tytWkw8l5llJ9mIiJWaDqhFvg7cC/weuDoi1gFs0zYEEbEM8K7M3KvpWFrqXODPwI5UHf/exdwOMq1hN/9m7EM1qOlD9WOPepkG912qNglTmDsw59uAzTLz3U0GtoSxTeDgMiK+DqwaEQdRjXH3jYZjaoXM/HJmTszMXTKzrHtFvrnpuNogM1+gGnlAI7NBZv4H8FTdIeatQOvGW7SkrQGZeS/+4xuJMjPvrYu45xERq2fmI00EtQSyTeAgMvO0iNieqoRoQ+D4zLys4bBaISKOH2CVQx4Nzf9GxH8B36PqOQpAZt7UXEit8Xz9/FhEbEzVrnzd5sIZGZO2HnKAyYX2XapeQDdSXb/uEqESeHkTQbXFIL2XO+OMrQK2CVyQiPgw8H0TtRHpHu5oHNW/59ZVUTXon+rn7iS3BN7SQCxtc2ZErEbVHnAKVY/vgX5ELLaKsrQNaK9ExNsy85KI2Le/9d1jGElaPNW9RwN4hKpd6g8y86Fmo2qniFgemJKZOzYdi9QGJm1qjYgYdABJqwgGFxGrD7be6uXhiYjXUs0m8Q5gamZu13BIrVOXfPwmMyc3HUsbDFS9nJlWLy9ARHwa+GxmPla/Xw34SGYe12xkw2P1aA9FxCUMPrjuv/YwnDb6XP08jmqcrN9TVe29Fvg11Qj/GthA1coFVi+PxMNU7WL+Bry04VhaoWvic4BlgDWwPdtwWL08cjtn5sc6bzLz0YjYhaq6tDVM2nrrtKYDaLPMfDNARFwIHJyZt9TvNwaObjK2NsjMOZMj16Vuk3Eg3WGrh57Zkyrh+AFwkJPHD1n3xOezgIcyc1ZTwbRNZn6u+31EnEbVPksLtkxELJ+ZzwJExArA8g3HNGwmbT2Umb/svI6I5YBX1G9vz8zn+99L/XhlJ2GDquF8RGzaZEBtEhEHAkcAk4Cbga2Aa4Ftm4yrRdYBjszMm5sOpG3qKcBeT1UqXgLXAL9rNqpWWxFLyIfqPOCKiPg21b13AC2c+9s2bQ2IiG2obpZ7qaqm1gb2zcyrGwyrNSLiAqpqgvOo/vG9m2ri6b0bDawl6iqqfwSuz8xN6wmV/zMz92w4tNaIiE2AN9Vvf5WZv28ynrao22Ttwdx5b3ej6on7yeaiao+Bqpcz87+ai6o9ImJnqh+nBfA/mXlpwyENmyVtzfgcsENm3g4QEa8ALgA2azSq9tgf+CBVaRHA1cydi08LNjMzZ0YE/7+9O4+StKrPOP7tQfCAwmAiCKjIEpSwMxHkRCIqagwBRJYnKKAZD+rJMSxiDKLgLC5AWEyGhMiiQBA8PkRWBUV2EBVm2BGirFEwssgyogjDdP64b0v1MD1d3TNTt9+u53NOn6r37Sr66R66+lf3vvf+mumCuyW9qXaotmh6P36MFwuPb0g62fYJFWO1xQeArW0/CyDpKEo7sBRt3cn08lKwfQlwSe0cSyNFWx0rDhVsALZ/1vTjiy40BcdXgYs7f47RtV9KWh04H/iBpCeAhytnapP9gbfYfgZA0tHAj4AUbaN7gHId5bPN8cuBe6ulaZlML4/fIvtUrgSsSOmOsFq9VGOXoq2OuZK+RumFBmV6b17FPK0iaVfgGMov3vrN9Wyzs/q2O7bf39ydKelKYCrwvYqR2maA4b2CXyCtv5aoY0PxPwB3SvpBc/xuSuERXVjM9PLpkjK93AXbq3YeS9oN2LZSnHFL0VbHPwCfAA6kvNhfA5xYNVG7zKD8sl0FYPsWSevVkCCZbwAADdhJREFUDNRWnYtjomunAT+RdF5zvBvwtYp52mBuczsPOK/j/FW9j9JqmV5eRmyfL+kztXOMVYq2Cpolx8cDxzdbL7xuaBlydGWB7ack1c4Rfcj28ZKuokxRDQDTbWeKagm67fYi6du291jeeVrsATK9PC6Sdu84nELZ67N1KzFTtFXQvODvSvn53wI8Kulq24dUDdYed0j6IGXfnY0oI5bXV84UfUDSFOA225tRRjhi2cr2FUu22OllSXMg/atHsUvH/QWUAvh9daKMX4q2OqbafrrZL+s02zMk3VY7VIscAHyO8gJ2NvB9Mj0QPWB7oaRbJa1r+39r55mEWjfy0WPnkenlcbE9vXaGZSFFWx0vk7Q2pen052qHaRNJK1D2FPs0+dlFHWtTRjtuoKOtUBbCxPI22jRzppdfqmMRzGK1bXQyRVsdsymjQ9fZvlHSBsDPK2dqBdsvSMp+dlHTrNoBJrGswl06mV5+qaFFMG8FNgG+1RzvRQt3bUhHhGgdScdR+maew/CRjnNHfFLEMiRpLcoK5kHgRtv/VznSpCDpPbYvrZ2jrSTdZHta7RwTUbO90XuGWkY2e6NeOtTTui0y0laBpDWAjwLr0fFvYPsjtTK1zJ8AjwPv7Dg3yIt7F0UsN821qJ8HrqCMDJ0gabbtr9dNNnEt0n6p0wAwaHsLgBRssRytA6wK/KY5fmVzrlVStNVxAXAtcBnDN+mM7kwBDrL9JICkV1Fag0X0wqcpe2U9DiDpTymrl1O0jWzn0R8Sy0Cml0d2FHBTs3sDwA7AzGppxilFWx2r2D60dogW22KoYAOw/YSkrWsGir7yS2B+x/F84BeVsrSC7QdrZ2i7ZhHWGbb3XcLD8ndlZKdTBkkOphRrnwfWqphnXFK01fEdSTvZvrh2kJaaIulVtp8AaDYozv/L0SsPUToiXECZ8nsfcIOkQ6Bsvlsz3ES0SN/HTkPTo63q/1hDswhrDUkr2X5uhMdkenlkJwILgZVtX9jM0Hwb2KZurLHJH7o6DgI+K+k54PnmXF64uncccL2k/6b8IRDwpbqRoo/cy/Bd6C9oblddzGODl/Z9jHF7APihpAsZvggrbxRG9xbb0yTdDH+coVmpdqixStFWQV7Alo7t/5I0l7IQYQDY3fZPK8eKPmE7W34sJUlrUtoxAZCNirv2cPMxhbxJGKvnmynmQfjjgsCFdSONXbb8qETSrsDbmsOrbH+nZp6I6I6kN1M2dn4Dw1d/b1EtVEs0r3vHUVbtPUL5Gd5le9OqwVpG0qqU2Znf1s7SFpL2Af4OmAacAewJHG77nKrBxmhK7QD9SNJRlCnSnzYfBzXnImLiOws4DdiD0s9w6CNG9wVgO+BnttcHdgR+WDdSe0jarJneu4PSlWOepBS8XbB9FvDPwJHAr4Dd2lawQaZHa9kJ2Mr2QgBJZwA3A5+pmioiuvGo7Qtrh2ip520/LmmKpCm2r5R0dO1QLXIycIjtKwEkvR04BfjLmqHawvbdwN21cyyNFG31rM6Lm/xNrRkkIsZkhqRTgcuBPwydTEeOrjwp6ZXANcBZkh4BFlTO1CavGCrYAGxfJekVNQNFb6Voq+NI4OamrcYA5dq2w+pGioguTQc2BlbkxQuZ05GjO+8DngU+CexDecM6u2qidrlP0hHAmc3xvsD9FfNEj6Voq8D2N5tdmbehFG2HpndhRGtsaXvz2iHayPYzAJJWAy6qHKeNPgLMorxBGKCMWE6vmih6KqtHK5D0fuAK2081x6sDb7d9ft1kETEaSacAX8k2M2Mn6eOUkbXfU0YphzbX3aBqsIiWyEhbHTNsnzd0YPtJSTOAFG0RE9/2wIcl3U+5pm1Y0/NYon8CNrX9WO0gbSLpX20fLOkiXtpZYpByffRJtn/c+3TRSyna6ljcViv5t4hoh/fWDtBi9wK/qx2ihYauYTt2hM+/Gvg6sElv4kQtKRTqmCvpeOA/KO+SDgDm1Y0UEUsiaTXbTzO8WXyMzWGUFnQ/YfjK2wPrRZr4bM9rbq8e6TFNW8SY5FK01XEAcATwreb4UuDwenEiogtnAztT3mANUqZFhwwCuS5rdCcBVwC308IWQrVJ2oiy+8AmDG8DtoHtLOzoA1mIMAFJOsH2AbVzRMTYSdrU9p21c0xEkq63nY1gx0nSdcAM4CuULhzTgQHbM6oGi57JSNvE9NbaASJi3M6k9DeMl7pS0sco2310To/+ZuSnRIeVbV8uacD2g8BMSddSCrnoAynaIiKWrYHRH9K3PtjcHsbwVZCZWu7Os5KmAD+X9I/AQ8CalTNFD6VhfETEspVrTkZ2KGVz4vWB04BbgT3rRmqVg4FVgAOBv6B0RPhQ1UTRUynaJqa8U4+Iyehw209L2h54N3A68J91I7XKIGX6/ULgzcAbKQ3jo0+kaKtA0l6jnPu3HsaJiGUrWy+M7IXm9m+Br9q+AFipYp62OYsyQrkHZSXzzpQFCdEnck1bHYcB54x0zvbpvQ4UEd2RNEBpdr6B7dmS1gXWsn0DgO3tqgac2B6SdBLwLuBoSS8ngwdj8ajtC2uHiHpStPWQpL8BdgJeK2lOx6dWAxbUSRURY3QiZY+xd1L6aM4Hvg1sUzNUS4jSUeLYpn3f2sCnK2dqkxmSTgUuZ/jq23PrRYpeStHWWw8Dc4FdGd4BYT7wySqJImKs3mJ7mqSbAWw/ISlTfF2w/Tvg3I7jXwG/qpeodaYDGwMr8uLmxIN0/ExjckvR1kO2bwVulXQ2ZbHBxpRfuP+xnetgItrheUkr0KwSlbQG2d0/emNL25vXDhH15FqCOt5NaZw8B/h34J5m6jQiJr45wHnAmpK+BFwHfLlupOgTP5aUpvB9LG2sKpB0N7Cz7Xua4w2B79reuG6yiOiGpI2BHSkj5pfbvqtypOgDku4CNgTup1zTNgAM2t6iarDomUyP1vHIUMHWuA94pFaYiBizXwPXUl5DV5Y0zfZNlTPF5Pfe2gGirhRtPSRp9+bunZIuBky5LmYv4MZqwSKia5K+APw95RKHoamKQcpq0ojlpuk3Gn0sRVtvdW6C+Gtgh+b+o8Creh8nIsZBwIZZPBQRvZairYdsT6+dISKW2h3A6uSShojosSxEqEDSGyn99l5jezNJWwC72v5i5WgRMQpJbwYuoBRvnRuc7lotVET0hWz5UccplLZVzwPYvg3Yu2qiiOjWGcDRwFHAcR0fERHLVaZH61jF9g2SOs+ljVVEOzxme87oD4uIWLZStNXxWLM329CO6nuSVi4RbTFP0pHAhQyfHs2WHxGxXKVoq+MTwMnAxpIeomyUuE/dSBHRpa2b2+06zmXLj4hY7rIQoYckHbLIqZUp1xU+A2D7+J6HioiIiFbISFtvrdrcvgnYhrICbQDYD7imVqiI6J6kqcAM4G3NqauB2bafqpcqIvpBirYesj0LQNKlwDTb85vjmcA5FaNFRPe+TtnuY2gl0X7AacDuIz4jImIZSNFWx7pA527qzwHr1YkSEWO0oe09Oo5nSbqlWpqI6Bsp2uo4E7hB0nmUC5jfT9n7KSImvt9L2t72dQCS3gr8vnKmiOgDWYhQiaRpwF81h9fYvrlmnojojqStKG+ypjanngA+3GySHRGx3GSkrZJmT6fs6xTRPncB/wJsSOlB+hSwG5CiLSKWqxRtERFjcwHwJOVN10OVs0REH0nRFhExNq+z/d7aISKi/6RhfETE2FwvafPaISKi/2QhQkREFyTdTlnt/TJgI+A+Su/RAWDQ9hYV40VEH8j0aEREd3auHSAi+ltG2iIiIiJaINe0RURERLRAiraIiIiIFkjRFhEREdECWYgQEZOWpAeA1wAvAM8AFwMH2P5tzVwREeORkbaImOx2sf1KYBqwDXD4WJ4sKW9uI2JCyOrRiJi0mpG2/W1f1hwfA/w5sNki52cCf2Z7X0nrAfcD+wMzgAeADzXnPg7MpOzNdqzt45rnvxw4GlDzpQ0cavsPkl4NnA5sDywE7gR2sL1Q0jrACcDbgN8CX7E9Z/n8NCKi7TLSFhF9QdLrgZ2Am7t8yg6UAu+vO869g7Kx7nuAz0h6V3P+c8B2wFbAlsC2vDii9yngl8AalKnazwKDkqYAFwG3Aq8FdgQOltT59SIi/ijD/hEx2Z0vaQHwFPBd4MvAfl08b6btZwCkoQE0ZjXnbpd0GvAB4DJgH8q1co80j58FnAQcATwPrA28wfY9wLXNY7YF1rA9u/lv3yfpFGBv4PtL9y1HxGSUoi0iJrvdhqZBh3QUYUvyi1HOPQgM9SBdpznu/Nw6zf1jKFOqlzZf92TbRwFvANaR9GTH81agKeoiIhaVoi0i+tEzwCodx2st5jGLu+D39cDdzf11gYeb+w9TirA7F/2c7fmUKdJPSdoUuFLSjZQC8H7bGy3F9xERfSRFW0T0o1uAvSVdQrkGbU/ge1087whJHwXWB6YD+zbnvwkc3hRjg8DngW8ASNqZUujdCzxN2X7kBeAG4GlJhwJzgOco19CtbPvGZfFNRsTkkoUIEdGPjgA2BJ4AZgFnd/m8q4F7gMspq0cvbc5/EZgL3AbcDtzUnIOycOEyyurQHwEn2r7K9gvALpTFC/cDjwGnAlOX6juLiEkrW35ERIyiYxuQFW0vqBwnIvpURtoiIiIiWiBFW0REREQLZHo0IiIiogUy0hYRERHRAinaIiIiIlogRVtEREREC6Roi4iIiGiBFG0RERERLfD/lmXIwjVNN7EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Class counts by purpose\n",
    "plt.figure(figsize=(10,7))\n",
    "sns.countplot('purpose', hue='not.fully.paid', data=dat)\n",
    "plt.title(\"Class counts by purpose\", fontsize=25)\n",
    "plt.xlabel(\"Purpose\")\n",
    "#Plt.ylabel(\"Count\")\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observation:\n",
    "- People borrowed for debt_consolidation, credit_card, and all_other purposes have higher rate of paying back than rest of the purposes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking credit policy outcome based on FICO score\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.hist(dat['fico'].loc[dat['credit.policy']==1], bins=30, label='Credit.Policy=1')\n",
    "plt.hist(dat['fico'].loc[dat['credit.policy']==0], bins=30, label='Credit.Policy=0')\n",
    "plt.legend()\n",
    "plt.xlabel('FICO')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observation:\n",
    "- It's no surprise that people with higher FICO score match the credit policy more than those who have lower FICO score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking paid/not paid based on FICO score\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.hist(dat['fico'].loc[dat['not.fully.paid']==0], bins=30, label='not.fully.paid=0')\n",
    "plt.hist(dat['fico'].loc[dat['not.fully.paid']==1], bins=30, label='not.fully.paid=1')\n",
    "plt.legend()\n",
    "plt.xlabel('FICO')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observation:\n",
    "- People with higher FICO score are inclined to fully pay back their loans, thus are less risky borrowers. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# FICO score vs Interest rate\n",
    "sns.jointplot(data=dat, x='fico', y='int.rate', color='Blue', size=6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observation:\n",
    "- Interest rate decreases with increasing FICO score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 945.625x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# FICO score vs Interest rate based on credit policy\n",
    "sns.lmplot(x='fico', y='int.rate', data=dat, hue='credit.policy', col='not.fully.paid', palette='Set1', size=6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before applying any machine learning model, the categorical variable 'purpose' needs to be converted to numerical variables so that we can build our feature matrix. To do so, we will be using pandas pd.get_dummies function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>credit.policy</th>\n",
       "      <th>int.rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>log.annual.inc</th>\n",
       "      <th>dti</th>\n",
       "      <th>fico</th>\n",
       "      <th>days.with.cr.line</th>\n",
       "      <th>revol.bal</th>\n",
       "      <th>revol.util</th>\n",
       "      <th>inq.last.6mths</th>\n",
       "      <th>delinq.2yrs</th>\n",
       "      <th>pub.rec</th>\n",
       "      <th>not.fully.paid</th>\n",
       "      <th>purpose_credit_card</th>\n",
       "      <th>purpose_debt_consolidation</th>\n",
       "      <th>purpose_educational</th>\n",
       "      <th>purpose_home_improvement</th>\n",
       "      <th>purpose_major_purchase</th>\n",
       "      <th>purpose_small_business</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1189</td>\n",
       "      <td>829.10</td>\n",
       "      <td>11.350407</td>\n",
       "      <td>19.48</td>\n",
       "      <td>737</td>\n",
       "      <td>5639.958333</td>\n",
       "      <td>28854</td>\n",
       "      <td>52.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1071</td>\n",
       "      <td>228.22</td>\n",
       "      <td>11.082143</td>\n",
       "      <td>14.29</td>\n",
       "      <td>707</td>\n",
       "      <td>2760.000000</td>\n",
       "      <td>33623</td>\n",
       "      <td>76.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1357</td>\n",
       "      <td>366.86</td>\n",
       "      <td>10.373491</td>\n",
       "      <td>11.63</td>\n",
       "      <td>682</td>\n",
       "      <td>4710.000000</td>\n",
       "      <td>3511</td>\n",
       "      <td>25.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1008</td>\n",
       "      <td>162.34</td>\n",
       "      <td>11.350407</td>\n",
       "      <td>8.10</td>\n",
       "      <td>712</td>\n",
       "      <td>2699.958333</td>\n",
       "      <td>33667</td>\n",
       "      <td>73.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1426</td>\n",
       "      <td>102.92</td>\n",
       "      <td>11.299732</td>\n",
       "      <td>14.97</td>\n",
       "      <td>667</td>\n",
       "      <td>4066.000000</td>\n",
       "      <td>4740</td>\n",
       "      <td>39.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   credit.policy  int.rate  installment  log.annual.inc    dti  fico  \\\n",
       "0              1    0.1189       829.10       11.350407  19.48   737   \n",
       "1              1    0.1071       228.22       11.082143  14.29   707   \n",
       "2              1    0.1357       366.86       10.373491  11.63   682   \n",
       "3              1    0.1008       162.34       11.350407   8.10   712   \n",
       "4              1    0.1426       102.92       11.299732  14.97   667   \n",
       "\n",
       "   days.with.cr.line  revol.bal  revol.util  inq.last.6mths  delinq.2yrs  \\\n",
       "0        5639.958333      28854        52.1               0            0   \n",
       "1        2760.000000      33623        76.7               0            0   \n",
       "2        4710.000000       3511        25.6               1            0   \n",
       "3        2699.958333      33667        73.2               1            0   \n",
       "4        4066.000000       4740        39.5               0            1   \n",
       "\n",
       "   pub.rec  not.fully.paid  purpose_credit_card  purpose_debt_consolidation  \\\n",
       "0        0               0                    0                           1   \n",
       "1        0               0                    1                           0   \n",
       "2        0               0                    0                           1   \n",
       "3        0               0                    0                           1   \n",
       "4        0               0                    1                           0   \n",
       "\n",
       "   purpose_educational  purpose_home_improvement  purpose_major_purchase  \\\n",
       "0                    0                         0                       0   \n",
       "1                    0                         0                       0   \n",
       "2                    0                         0                       0   \n",
       "3                    0                         0                       0   \n",
       "4                    0                         0                       0   \n",
       "\n",
       "   purpose_small_business  \n",
       "0                       0  \n",
       "1                       0  \n",
       "2                       0  \n",
       "3                       0  \n",
       "4                       0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create dummy variables from the feature purpose\n",
    "dat1 = pd.get_dummies(dat, columns=[\"purpose\"], drop_first=True)\n",
    "dat1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9578, 19)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat1.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Extract 'feature matrix' and 'target array'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9578, 18)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract Feature Matrix\n",
    "X = dat1.drop('not.fully.paid', axis=1)\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9578,)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract the target array\n",
    "y = dat1['not.fully.paid']\n",
    "y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train Test split\n",
    "Let's split the dataset into training set (70%) and testing set (30%)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Trees"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A decision tree is a flowchart-like tree structure where an internal node represents feature(or attribute), the branch represents a decision rule, and each leaf node represents the outcome. The topmost node in a decision tree is known as the root node. It learns to partition on the basis of the attribute values. It partitions the tree in a recursive manner, called recursive partitioning. This flowchart-like structure helps you in decision making."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dtree = DecisionTreeClassifier()\n",
    "dtree.fit(X_train, y_train)\n",
    "prediction = dtree.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n",
      "[[1988  418]\n",
      " [ 361  107]]\n"
     ]
    }
   ],
   "source": [
    "# Get the confusion matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion = confusion_matrix(y_test, prediction)\n",
    "print(\"Confusion matrix:\\n{}\".format(confusion))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The confusion matrix is a two-by two array where the rows correspond to the true classes and the columns correspond to the predicted classes. There are several ways to summarize information from the confusion matrix such as precision, recall, f1-score. \n",
    "\n",
    "- **Precision:** measures how many of the samples predicted as positive are actually positive, i.e. (TP)/(TP+FP).\n",
    "- **Recall:** measures how many of the positive samples are captured by the positive predictions, i.e. (TP)/(TP+FN).\n",
    "- **f1-score:** is the harmonic mean of precision and recall, i.e. (2*TP)/(2*TP+FP+FN).\n",
    "\n",
    "We can use the 'classification_report' function to compute these three at once and print them in a nice format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.83      0.84      2406\n",
      "           1       0.20      0.23      0.22       468\n",
      "\n",
      "    accuracy                           0.73      2874\n",
      "   macro avg       0.53      0.53      0.53      2874\n",
      "weighted avg       0.74      0.73      0.74      2874\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report \n",
    "print(classification_report(y_test, prediction)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature importance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature importances:\n",
      "[0.0266347  0.12727021 0.09306285 0.1154289  0.11530411 0.07529836\n",
      " 0.10758409 0.09930575 0.1248075  0.03797122 0.02436481 0.00914465\n",
      " 0.00918231 0.0121965  0.00359327 0.00610897 0.00390508 0.00883673]\n"
     ]
    }
   ],
   "source": [
    "# Calculate feature importances\n",
    "importances = dtree.feature_importances_\n",
    "print(\"Feature importances:\")\n",
    "print(dtree.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TJEmSpEpYwEmSJElSJZZ2HaAWaw5YMS+Pc+E8PMaSo1bOw6NIkiRJqo0tcJIkSZJUCQs4SZIkSaqEBZwkSZIkVcICTpIkSZIqYQEnSZIkSZVwFMoN0HyMmOlomZIkSVL/2AInSZIkSZWwgJMkSZKkSljASZIkSVIlLOAkSZIkqRIWcJIkSZJUCQs4SZIkSaqEBZwkSZIkVWKqeeAiYk/gcGAJcHRmHjq2fg/gncDOwD6Z+Yl2+X2A9wFbAGuAN2Xmx+YvviRJkiRtPOZsgYuIJcARwF7ATsC+EbHT2Ga/Bp4JfGRs+ZXA0zPzHsCewDsjYqsbG1qSJEmSNkbTtMDtBpydmecARMSJwN7AT4YbZOa57bq/jO6YmT8b+f6CiPgdcBvgDzc6uSRJkiRtZKa5Bm4b4LyR+6vaZeskInYDbgb8Yl33lSRJkiRN1wJXJiwbrMsPiYg7AB8CnpGZf5mw/kDgQIDMZNmyZevy8Iviwq4DjJjr+elL1sV6HZcuXdrL98wkZp1/teQEsy6UWrLWkhPMulBqyVpLTjDrQqklay0559s0BdwqYLuR+9sCF0z7AyJiC+CzwKsz838nbZOZRwJHtncHq1evnvbhN0q1PD+LlXPZsmXVPCdmnX+15ASzLpRastaSE8y6UGrJWktOMOtCqSVrLTnXx/Lly2dcN00BdzqwY0RsD5wP7APsN80PjoibAScBx2fmx6fZR5IkSZI02ZzXwGXmtcBBwCnAWc2iPDMiDomIFQARsWtErAKeDLw/Is5sdw9gD+CZEXFGe7vPgvwmkiRJkrSBm2oeuMw8GTh5bNlrRr4/naZr5fh+JwAn3MiMkiRJkiSmG4VSkiRJktQDFnCSJEmSVAkLOEmSJEmqhAWcJEmSJFXCAk6SJEmSKmEBJ0mSJEmVmGoaAWmhrDlgxY1+jAvnIceSo1bOuU1NWSVJkrRhsgVOkiRJkiphASdJkiRJlbCAkyRJkqRKWMBJkiRJUiUs4CRJkiSpEhZwkiRJklQJCzhJkiRJqoQFnCRJkiRVwgJOkiRJkiphASdJkiRJlbCAkyRJkqRKWMBJkiRJUiUs4CRJkiSpEhZwkiRJklQJCzhJkiRJqoQFnCRJkiRVwgJOkiRJkiphASdJkiRJlVjadQBJ82vNASvm5XEunIfHWHLUynl4FEmSJA3ZAidJkiRJlbCAkyRJkqRKWMBJkiRJUiUs4CRJkiSpEhZwkiRJklQJCzhJkiRJqoQFnCRJkiRVwgJOkiRJkiphASdJkiRJlbCAkyRJkqRKWMBJkiRJUiUs4CRJkiSpEhZwkiRJklQJCzhJkiRJqsTSaTaKiD2Bw4ElwNGZeejY+j2AdwI7A/tk5idG1j0DeHV7942Zedx8BJckSZKkjc2cLXARsQQ4AtgL2AnYNyJ2Gtvs18AzgY+M7Xtr4LXAA4DdgNdGxK1ufGxJkiRJ2vhM04VyN+DszDwnM68BTgT2Ht0gM8/NzB8Bfxnb97HAFzPz4sy8BPgisOc85JYkSZKkjc40XSi3Ac4bub+KpkVtGpP23WZ8o4g4EDgQIDNZtmzZlA+/eC7sOsCIuZ6fvmSd5nU067qr5fWH6Z7XG2vp0qW9/J8xiVkXRi1Za8kJZl0otWStJSeYdaHUkrWWnPNtmgKuTFg2mPLxp9o3M48EjhyuX7169ZQPv3Gq5fmpJSeYdaEsRtZly5ZV85yYdWHUkrWWnGDWhVJL1lpyglkXSi1Za8m5PpYvXz7jumkKuFXAdiP3twUumPJnrwIePrbvaVPuK2kDt+aAFTf6MeajxXHJUSvn4VEkSZIW3jQF3OnAjhGxPXA+sA+w35SPfwrw7yMDlzwGeMU6p5QkSZIkzT2ISWZeCxxEU4yd1SzKMyPikIhYARARu0bEKuDJwPsj4sx234uBN9AUgacDh7TLJEmSJEnraKp54DLzZODksWWvGfn+dJrukZP2/QDwgRuRUZIkSZLEdNMISJIkSZJ6wAJOkiRJkiphASdJkiRJlbCAkyRJkqRKWMBJkiRJUiUs4CRJkiSpEhZwkiRJklQJCzhJkiRJqoQFnCRJkiRVwgJOkiRJkiphASdJkiRJlVjadQBJqsGaA1bc6Me4cB5yLDlq5Tw8iiRJqpUtcJIkSZJUCQs4SZIkSaqEBZwkSZIkVcICTpIkSZIqYQEnSZIkSZWwgJMkSZKkSljASZIkSVIlLOAkSZIkqRIWcJIkSZJUCQs4SZIkSaqEBZwkSZIkVcICTpIkSZIqYQEnSZIkSZWwgJMkSZKkSljASZIkSVIlLOAkSZIkqRIWcJIkSZJUCQs4SZIkSaqEBZwkSZIkVcICTpIkSZIqYQEnSZIkSZWwgJMkSZKkSljASZIkSVIlLOAkSZIkqRIWcJIkSZJUCQs4SZIkSaqEBZwkSZIkVcICTpIkSZIqsXSajSJiT+BwYAlwdGYeOrZ+E+B4YBfgIuApmXluRNwUOBq4X/uzjs/M/5jH/JIkSZK00ZizBS4ilgBHAHsBOwH7RsROY5vtD1ySmTsAhwFvbpc/GdgkM+9FU9w9NyL+ap6yS5IkSdJGZZoulLsBZ2fmOZl5DXAisPfYNnsDx7XffwJ4VEQUYADcPCKWApsB1wCXzUtySZIkSdrITNOFchvgvJH7q4AHzLRNZl4bEZcCW9MUc3sDvwE2B16cmReP/4CIOBA4sN2fZcuWreOvsfAu7DrAiLmen75kneZ1NOu6q+X1h3qybkiv/3xZunRpL/8XT1JL1lpyglkXSi1Za8kJZl0otWStJed8m6aAKxOWDabcZjdgDbAcuBXwtYj4UmaeM7phZh4JHDncb/Xq1VPE2njV8vzUkhPMulBqyVpLTli8rMuWLavmeaklay05wawLpZasteQEsy6UWrLWknN9LF++fMZ103ShXAVsN3J/W+CCmbZpu0tuCVwM7Ad8PjP/nJm/A74B3H/q5JIkSZKk60zTAnc6sGNEbA+cD+xDU5iNWgk8A/gW8CTgy5k5iIhfA4+MiBNoulA+EHjnfIWXJEmSpI3JnC1wmXktcBBwCnBWsyjPjIhDImJFu9kxwNYRcTZwMPDydvkRwC2AH9MUgsdm5o/m+XeQJEmSpI3CVPPAZebJwMljy14z8v3VNFMGjO93xaTlkiRJkqR1N801cJIkSZKkHrCAkyRJkqRKWMBJkiRJUiUs4CRJkiSpEhZwkiRJklQJCzhJkiRJqsRU0whIkuqx5oAVc280hwvnIceSo1bOw6NIkqRRtsBJkiRJUiUs4CRJkiSpEhZwkiRJklQJCzhJkiRJqoQFnCRJkiRVwgJOkiRJkiphASdJkiRJlbCAkyRJkqRKWMBJkiRJUiUs4CRJkiSpEhZwkiRJklQJCzhJkiRJqoQFnCRJkiRVwgJOkiRJkiphASdJkiRJlbCAkyRJkqRKWMBJkiRJUiUs4CRJkiSpEhZwkiRJklQJCzhJkiRJqoQFnCRJkiRVwgJOkiRJkiphASdJkiRJlbCAkyRJkqRKWMBJkiRJUiUs4CRJkiSpEhZwkiRJklQJCzhJkiRJqoQFnCRJkiRVwgJOkiRJkiphASdJkiRJlbCAkyRJkqRKWMBJkiRJUiWWTrNRROwJHA4sAY7OzEPH1m8CHA/sAlwEPCUzz23X7Qy8H9gC+Auwa2ZePV+/gCRJkiRtLOZsgYuIJcARwF7ATsC+EbHT2Gb7A5dk5g7AYcCb232XAicAz8vMewAPB/48b+klSZIkaSMyTQvcbsDZmXkOQEScCOwN/GRkm72B17XffwJ4T0QU4DHAjzLzhwCZedE85ZYkSZKkjc40Bdw2wHkj91cBD5hpm8y8NiIuBbYG7gIMIuIU4DbAiZn5lvEfEBEHAge2+7Ns2bJ1/T0W3IVdBxgx1/PTl6zTvI5mXXe1vP5QT9YN6fWHurLOh6VLl/byc2NcLTnBrAullqy15ASzLpRastaSc75NU8CVCcsGU26zFNgd2BW4Ejg1Ir6XmaeObpiZRwJHDvdbvXr1FLE2XrU8P7XkBLMulFqy1pITzDrJsmXLqnheaskJZl0otWStJSeYdaHUkrWWnOtj+fLlM66bZhTKVcB2I/e3BS6YaZv2urctgYvb5V/NzNWZeSVwMnC/qZNLkiRJkq4zTQvc6cCOEbE9cD6wD7Df2DYrgWcA3wKeBHw5M4ddJ/8tIjYHrgEeRjPIiSRJkiRpHc3ZApeZ1wIHAacAZzWL8syIOCQiVrSbHQNsHRFnAwcDL2/3vQR4B00ReAbw/cz87Pz/GpIkSZK04ZtqHrjMPJmm++PosteMfH818OQZ9j2BZioBSZIkSdKNMM01cJIkSZKkHrCAkyRJkqRKWMBJkiRJUiUs4CRJkiSpEhZwkiRJklQJCzhJkiRJqoQFnCRJkiRVwgJOkiRJkiphASdJkiRJlbCAkyRJkqRKWMBJkiRJUiUs4CRJkiSpEhZwkiRJklQJCzhJkiRJqoQFnCRJkiRVwgJOkiRJkiphASdJkiRJlbCAkyRJkqRKWMBJkiRJUiUs4CRJkiSpEhZwkiRJklQJCzhJkiRJqoQFnCRJkiRVwgJOkiRJkiphASdJkiRJlbCAkyRJkqRKWMBJkiRJUiUs4CRJkiSpEhZwkiRJklQJCzhJkiRJqoQFnCRJkiRVwgJOkiRJkiphASdJkiRJlbCAkyRJkqRKWMBJkiRJUiUs4CRJkiSpEhZwkiRJklQJCzhJkiRJqoQFnCRJkiRVwgJOkiRJkiqxdJqNImJP4HBgCXB0Zh46tn4T4HhgF+Ai4CmZee7I+jsCPwFel5lvm5/okiRJkrRxmbMFLiKWAEcAewE7AftGxE5jm+0PXJKZOwCHAW8eW38Y8LkbH1eSJEmSNl7TdKHcDTg7M8/JzGuAE4G9x7bZGziu/f4TwKMiogBExOOBc4Az5yeyJEmSJG2cpulCuQ1w3sj9VcADZtomM6+NiEuBrSPiKuBlwN8AL53pB0TEgcCB7f4sW7Zs6l9gsVzYdYARcz0/fck6zeto1nVXy+sP9WTdkF5/qCvrfFi6dGkvPzfG1ZITzLpQaslaS04w60KpJWstOefbNAVcmbBsMOU2rwcOy8wrImLGH5CZRwJHDvdbvXr1FLE2XrU8P7XkBLMulFqy1pITzDrJsmXLqnheaskJZl0otWStJSeYdaHUkrWWnOtj+fLlM66bpgvlKmC7kfvbAhfMtE1ELAW2BC6maal7S0ScC7wIeGVEHDRtcEmSJEnS9aZpgTsd2DEitgfOB/YB9hvbZiXwDOBbwJOAL2fmAHjocIOIeB1wRWa+Zx5yS5IkSdJGZ84WuMy8FjgIOAU4q1mUZ0bEIRGxot3sGJpr3s4GDgZevlCBJUmSJGljNdU8cJl5MnDy2LLXjHx/NfDkOR7jdeuRT5K0gVpzwIq5N5rCfAzasuSolfPwKJIkLbxproGTJEmSJPWABZwkSZIkVcICTpIkSZIqYQEnSZIkSZWwgJMkSZKkSljASZIkSVIlLOAkSZIkqRIWcJIkSZJUCQs4SZIkSaqEBZwkSZIkVcICTpIkSZIqYQEnSZIkSZWwgJMkSZKkSljASZIkSVIlLOAkSZIkqRIWcJIkSZJUCQs4SZIkSaqEBZwkSZIkVcICTpIkSZIqYQEnSZIkSZWwgJMkSZKkSljASZIkSVIlLOAkSZIkqRIWcJIkSZJUCQs4SZIkSaqEBZwkSZIkVWJp1wEkSeq7NQesuNGPceE85Fhy1Mp5eBRJUs1sgZMkSZKkSljASZIkSVIlLOAkSZIkqRIWcJIkSZJUCQs4SZIkSaqEo1BKkrQBccRMSdqw2QInSZIkSZWwgJMkSZKkSljASZIkSVIlLOAkSZIkqRIWcJIkSZJUCQs4SZIkSaqEBZwkSZIkVWKqeeAiYk/gcGAJcHRmHjq2fhPgeGAX4CLgKZl5bkT8DXAocDPgGuBfM/PL85hfkiRJkjYac7bARcQS4AhgL2AnYN+I2Glss/2BSzJzB+Aw4M3t8tXA4zLzXsAzgA/NV3BJkiRJ2thM04VyN+DszDwnM68BTgT2Httmb+C49vtPAI+KiJKZP8jMC9rlZwKbtq11kiRJkqR1NE0Xym2A80burwIeMNM2mXltRFwKbE3TAo9BW5EAACAASURBVDf0ROAHmfmn8R8QEQcCB7b7s2zZsql/gcVyYdcBRsz1/PQl6zSvo1nXXS2vP9STdUN6/aGerH3JCfVk3ZBe//mydOnSXh43TFJL1lpyglkXSi1Za8k536Yp4MqEZYN12SYi7kHTrfIxk35AZh4JHDncb/Xq1ZM2U6uW56eWnGDWhVJL1lpyglkXSi1Za8kJi5d12bJl1TwvtWStJSeYdaHUkrWWnOtj+fLlM66bpgvlKmC7kfvbAhfMtE1ELAW2BC5u728LnAQ8PTN/MXVqSZIkSdJapmmBOx3YMSK2B84H9gH2G9tmJc0gJd8CngR8OTMHEbEV8FngFZn5jfmLLUmSJEkbnzlb4DLzWuAg4BTgrGZRnhkRh0TEinazY4CtI+Js4GDg5e3yg4AdgP8XEWe0t9vO+28hSZIkSRuBqeaBy8yTgZPHlr1m5PurgSdP2O+NwBtvZEZJkiRJEtNdAydJkiRJ6gELOEmSJEmqhAWcJEmSJFXCAk6SJEmSKmEBJ0mSJEmVsICTJEmSpEpYwEmSJElSJSzgJEmSJKkSFnCSJEmSVAkLOEmSJEmqhAWcJEmSJFXCAk6SJEmSKmEBJ0mSJEmVWNp1AEmStHFac8CKG/0YF85DjiVHrZyHR5GkxWELnCRJkiRVwgJOkiRJkiphASdJkiRJlbCAkyRJkqRKWMBJkiRJUiUs4CRJkiSpEk4jIEmSNIv5mO4AFmfKA6dmkDZ8FnCSJEladBab0vqxC6UkSZIkVcICTpIkSZIqYQEnSZIkSZWwgJMkSZKkSljASZIkSVIlLOAkSZIkqRJOIyBJkiTNwikP1Ce2wEmSJElSJSzgJEmSJKkSFnCSJEmSVAkLOEmSJEmqhIOYSJIkSRuA+RhsBRxwpe8s4CRJkiQtKkf2XH92oZQkSZKkSljASZIkSVIlLOAkSZIkqRIWcJIkSZJUiakGMYmIPYHDgSXA0Zl56Nj6TYDjgV2Ai4CnZOa57bpXAPsDa4AXZOYp85ZekiRJkjYic7bARcQS4AhgL2AnYN+I2Glss/2BSzJzB+Aw4M3tvjsB+wD3APYE3ts+niRJkiRpHU3ThXI34OzMPCczrwFOBPYe22Zv4Lj2+08Aj4qI0i4/MTP/lJm/BM5uH0+SJEmStI6m6UK5DXDeyP1VwANm2iYzr42IS4Gt2+X/O7bvNuM/ICIOBA5s92f58uXT5l88n/1u1wmmZ9aFUUvWWnKCWRdKLVlryQlmXSi1ZK0lJ5h1odSStZacUFfWnpmmBa5MWDaYcptp9iUzj8zM+2fm/dt9NthbRHyv6wwbUk6zmrWWnGY1ay05zWrWWnKa1ay15LwRt4mmKeBWAduN3N8WuGCmbSJiKbAlcPGU+0qSJEmSpjBNF8rTgR0jYnvgfJpBSfYb22Yl8AzgW8CTgC9n5iAiVgIfiYh3AMuBHYHvzFd4SZIkSdqYzNkCl5nXAgcBpwBnNYvyzIg4JCJWtJsdA2wdEWcDBwMvb/c9E0jgJ8DngX/OzDXz/2tU5ciuA0yplpxg1oVSS9ZacoJZF0otWWvJCWZdKLVkrSUnmHWh1JK1lpzzqgwGN7gkTZIkSZLUQ9NcAydJkiRJ6gELOEmSJEmqhAWcJEmSJFXCAk6SJEmSKjHNNAKaJxFx88z8Y9c5NgQRcXPgqsz8S3v/JsCmmXllt8kaEXG3zPxpRNxv0vrM/P5iZ1I3ImKTzPzTXMv6ICLuDTy0vfu1zPxhl3mmFRG3ArbLzB91nWVcRNw6My/uOsc0Rv+vRsRdgLsBn8vMP3ccrToRscVs6zPzssXKMq12uqjfZObV7f3NgNtl5rmdBqtURNx6tvV9/L8QEdtn5i/nWqbuWcAtgoh4MHA0cAvgju1B0nMz8/ndJmtExBNmW5+Z/7VYWdbBqcCjgSva+5sDXwAe3FmitR0MHAi8fcK6AfDIxY0zt/aA7X00H9j3jIidgRWZ+caOo10nIt5N8/xNlJkvWMQ40/oWMF7IT1rWqYh4IXAAMPx7PyEijszMd3cYa0YRcRqwguZz7Azg9xHx1cw8uNNgN/TtiDgDOJamGOrz0M//Azy0LYhPBb4LPAV4aqepRkTErK9vZr5jsbLM4Uya/1WFZh7cy9vvb0Ezp+4du4s2o4+z9mfomnbZrt3EmVlEbA68BLhjZh4QETsCd83Mz3QcbdT3uP49MG4A/PXixpnKJ7nhZ9MngF06yDKriLgNzWfWXzFSz2Tms7vKtJgs4BbHYcBjaSY8JzN/GBF7dBtpLY+bZd2A6w/o+mTTzBwWb2TmFe0/9F7IzAPbb/cans0ciohNO4g0jaOAfwXeD5CZP4qIjwC9KeBoDiirEBG3B7YBNouI+3L9h/gWNCcc+mZ/4AHDXgIR8WaaQrOXBRywZWZeFhHPAY7NzNdGRO9a4IC70Jxsejbw7oj4GPDBzPxZt7EmKpl5ZUTsD7w7M98SET/oOtSYW7Zf70pTWKxs7z+OpgDthczcDiAi3gt8PjNXtvcfB/Tp83/U0sy8ZngnM6+JiJt1GWgWx9IUSA9q76+iKTZ7U8Bl5vZdZ5hWRNwNuAew5dhJ/S2Avh6zfAr4GvAlmpMNGxULuEWSmedFxOii3rzZMvNZXWdYD3+MiPsNuyJGxC7AVR1nmuSb3PBs1qRlfbB5Zn5n7H16bVdhJsnM47rOsA4eCzwT2BYYbRW4HHhlF4HmUFj7/9IaJp857oulEXEHIIBXdR1mJm2L2xeBL0bEI4ATgOdHxA+Bl2fmtzoNuLYSEQ+iaXHbv13Wq+OEzHw9QER8AbhfZl7e3n8dzQF83+w22tsmMz8dEa/tMtAsfh8RK0aKzb2B1R1nmsmdM/MpEbEvQGZeFRG9/X/VtmrvyEgxlJm9OeFAc0Lk74GtWPuk/uU0rVx9tHlmvqzrEF3p1T/mDdh5bTfKQXs26wXAWR1nuk5EPC0zT5ipa0qPuqSMehHw8Yi4oL1/B5quPr0w1voyWqz1tfUFYHVE3Jm2i2JEPAn4TbeRJmu7TrwM2Im1PxB70zW1LTaPi4gnZuYnu84zhWNpuvud1N5/PHBMh3nmcghwCvD1zDw9Iv4a+HnHmW4gIrYGngb8I3Ah8C80rUb3oSk4+nSW/oXAK4CTMvPM9jn9SseZZnJH4JqR+9fQdKXqm4sj4uU0hfuA5r1wSbeRZvQ84MMRcQRN1lXA07uNNKNr2mv0hp9XdwZ6d10xQNtL4IU0J/POAB5I07uhT59XnwI+FREP6tlJpdl8JiL+NjNP7jpIFyzgFsfzgMNpDuhX0Vyr1Yvr31o3b7/ecsK6Xl6v0R6w3Y3mrFEBftqzC+1HW1/eNrL8cpoDpD76Z+BI4G4RcT7wS5qDjT76MPAx4O9o/r6eAfy+00Qz+0xE7McN++kf0lmiCTLzHe11ZbvT/E09KzP71n3uOpn5cUZaXDLzHOCJ3SWa0beADwGPz8xVI8u/GxH/2VGmG4iIJcDjMnPFcFn7nPbxulJontPvtCccBsA/AH1sod8PeD3wufb+/wD7dhdnZpn5C+CBEXELmu60l3edaRavBT4PbBcRHwYeQvOZ20cvpOnu+7+Z+Yj22OX1HWeaydkR8UrquK7shcArI+IaYHj8N8jMWQcQ2lBYwC2Ou2bmWheBR8RDgG90lGctmfn+9tsvZeZamdqcfbUr1/+TuW9EkJnHdxvpOsto+uJ/hrUvYh7QrzPu12kP1h7djkR3k55/eG+dmcdExAsz86vAVyPiq12HmsGngEtprtfo5RligIh4IHDmSLfkW0bEAzLz2x1Hm6iiC9jvOtPAJZn55sUOM5PMXNN2Ra9CZr4pIj7H9aOm9vKEQ2aupjk51nsRcTvg34HlmblXROwEPCgze9cSn5lfjIjv07RmFeCF7XPdR1dn5tURMRyB+KcRcdeuQ82gmuvKMnNSo8NGwwJucbybG17zNGlZ12rJSUR8CLgzTXeE4T+ZAdCXAu4W7dfhhfafovmQ6dWF9qMiYhOaFoy/orm+COhfS1FreLbtNxHxd8AFNK2dfbRtZu7ZdYgpvI+1/9b/OGFZn9RyoPGFiHhyZv4BrrsW5sTMfGzHuSb5QUSspGnZvG7Km76NRNxOG/OjzLwn0MspWSLi7Zn5kpEWwrVk5qyjP3fkgzRdqYfXlP6MpqdD7wq49uTyGZn52Yh4Gk1LzOGZ+auus02wKiK2Av6b5lrYS2g+s/qoquvKImIF1w8KdFrPRiFdUBZwC6i9GPzBwG3Gri/bAljSTaobqiXnmPsDO/V1SO4KL7SHSlqKWm+MiC1phpF+N8179cXdRprRNyPiXpn5f10HmUMZ/Xtq5wLr82dELQcatxkWbwCZeUlE3LbLQLO4NXARa1+b07uRiNv35g8j4o6Z+euu88zgY+3X93SaYt0sy8yMiFcAZOa1EdHXkyPvA+4dzbRM/wp8gOYE7sM6TTVBZv5D++3rIuIrwJY03T/7qJrryiLiUJoT5B9uF70wInbPzJd3GGvR9PnDeUNwM5qWmKWsfX3ZZcCTOkk0WS05R/0YuD09HWRjRC0X2kM9LUWMnGW7FHhEl1mmsDvwzIj4JU1hXGj66e/cbawbOCciXkBzYATNdbrndJhnLrUcaKwZLTQi4k709Npi4CXZw8mFZ3AH4MyI+A5rtxaumHmXxZOZ32m/ntp1lnXwx3bQneHAIA+k+R/bR9dm5qAdKfNdbZf6Z3QdaibtNaa3o7m2HJrjlz6efBi9ruwarv+86uN1ZX8L3Ccz/wIQEccBPwAs4HTjjFyb88GeNusD9eQcswz4SfvhfV1rUV8+vEfUcqE91NNSRDs63uE0cwD9hWagiBe31/H1zV5dB5jS84B3Aa+mea+eSjMZfa9ExOVcf13pKyPiTzRdavt6oPEq4Osj12juQQ+f11ZNk473dRCItbSjI76JG46Ye5fOQs3sYJoRUu8cEd8AbkN/T+Je3rYUPg3Yoy2Qbtpxpoki4l9oBl25kObzCpr/YX07iVfjdWVbAcOTTlt2GWSxWcAtjisj4q00kyT2csjz1gcjYlJf/b7lBHhd1wGmUcuF9q1aWooAPgIcQVMQA+wDfBR4QGeJZpCZv4qI3YEdM/PYdvCNW8y132LLzN/RPI+9VtsBRmZ+vp1KZDjYwot7PNhCNZOOtycea/BB4I00oxHvBTyL6w/ie6O9rnBTmi6Iw9Gd/7+eje486ik0I3zun5m/jYg7Am/tONNMXkgzmNFFXQeZSzRz6T0V2D4z3xAR2wF3GLYo98x/0Fy3+xWa9+se9HeU73lnAbc4hkOe/z39HvL8pSPfb0ozoEWvJnIequjDm3ZUv15eaD+mlpYiaK7X+tDI/RMi4qDO0swimkl7709zUHQszVniE2iGve5cRPxbZr4lIt7N5MEWejmMfEScmpmPmmtZVyLibu1oc8NBYIaDFtyx7VLZu/8JWdGk4233vncDd6e5DGAJ8McetsBunpmnRMTb2mH6Xx0RX+s61Lj2usK3Z+aDgDO7zjOXzPwt8I6R+7+mP4OYjTuP/nZFHfdemhMMjwTeAFxBc7J01y5DTZKZH22nvtmVpoB7Wfu+2ChYwC2OKoY8z8zvjS36Rt9yRsTXM3P3kW5UQ33tPtV7EbFFZl5GM0ddr0XErdtvvxLN5Lgn0rwPngJ8trNgs/sH4L60RXxmXhARfWpFehnwFuAX9HeC4etExKY0c1cua0d0HE7RsQWwvLNgN/QSmmkO3j5h3YAeTeI7FHVNOv4emhbjj9OcIHk6sGOniSb7U9uq8YuIeB5wPtDXQWy+EBFPBP6r591nZyrgr8jMPnajOwc4LSI+y9qXfLxj5l0684DMvF9E/ACuG3TpZl2HGjXh5Nhwfs3lEbG8jyfHFoIF3OKoYsjzkYNjgJsAu9BcaNsbmbl7+7VPB8C1+whN6/D3WHvOOtr7f91FqBmMZ3zuyLoBzRnDvrmmvdh+ODDAzbsONObCdmCNZ9H/AWGgec1fRFOsfY/r3wuX0Zwp7oXMPKD9WsNzOlTFpONDmXl2RCzJzDXAsRHxza4zTfBimi7TL6C5Fm5Lmi6qfXQwzcmRayPiavp9YrSWAh6awUp+TVNo9qoYmuDP7fWEw8+r29C/Lr8H01xHXM3JsYVgAbc4ahnyfPTg+Fqa0ZL27zSRFlxm/n37tU9n1yeqIeMEGRHvB7aKiANoDt6O6jjTqPfRDGn918B3R5YX+lfAk5mHA4dHxL9k5ru7zjOTiJh1nq++za3WqmLS8daVbcvAGRHxFpoRift2coTM/DZAOxz/gZl5VceRZlTbidFKCvjRaYVuSVMQX9FxpNm8CzgJuG1EvIlmEJtXdxtpbZk5HARqr8y8enRd20Njo1AGg163klevPZPxgsw8rOssG7qI+MywGNH0RrohTFRLd4SIuH1f+79HxN8Aj6Epik7JzC92HOkGIuJ9mflPXeeYVvu/9e9oJ54fLu9Lt6SIOHaW1YPM7E0rTPtcPoemZ8jnMvObI+tenZlv7CzcDNpW4wtpWjReTNOy9d7MPLvTYGPa/6/H0IzoCE3m5/R4MKsqRMT/0Ay4czTwW5oC/pmZee9Og00QEfekadke9nJaDTw9M3t5rWFE3A14FM3n1amZeVbHkSaKiO9n5v3mWrahsgVugWXmmmhmiq+ygOvzQfEEB3QdoFKTuiEM1dQd4RiaA/reycwvRsS3af/nRsSt+zbfVk3FW+vTwNXA/9G/Lj5k5rO6zrAO3g9sDnyHZvTJr2bmwe26J9CMotg3q2m6J18NvL4tQjfpONMkxwIvysyvAETEw2lGpuxdoTFJjw+I/5HmUo+DaAr47WgGXuujI4GDx94DRwEP7jLULC4EvkbzebVZRNyvTydyI+L2wDY02e7L2tdBb95ZsEVmAbc4vhkR76EZiXJ0wtHe/EHMorcHxeMys++TevdSZdfozCgze/k+jYjnAocAV9EUGr3smlihbXs6xcVa2u7zr6UZ4hrgq8AhmdmnUel2Gz6X7WfVeyPiv4B9Wfua2D45laYFZtgdbTPgC/TvoPiPwwN3gMw8LSL63IVuLT0t3hiZs/Zq+j8n4M0nvAd6190XICLeADyTZlCrYRe9vp3IfSxNxm0ZGYmUZiC2V3YRqAsWcItj+IFyyMiyvv1BTNS3g+KI+D8mDHVOv+csq1bfWmDHBtq5gb61arVeCtyjx3N/1epzEfGYzPxC10Hm8AHgx0C09/+RplVm1mvkFtl1Aytk5rXAgRHxGuDL9HDOwtamo9cSZeYVEdHHs+/fjogjaOapHI6Y+5WI2BkgM3/UZbhxbevGbjRZT+/T//9REfEQmvlg78TaXaj7eGLsnIj4fzTdKKEZ6fWXHeaZTQB3zsxrug4yk8w8DjguIp6YmZ/sOk9XLOAWQd9bOCo7KPYat8XVtxbYSSNlDvW1VesXwJVdh9gA/S9wUjsB8Z/p74h5d87M0a5dr4+IMzpLM9l3I2LPzPz8cEFmHhIRF9AMctNHfxzt2hURu9C0cvfN/duv4ycXH0bzP2sPeiIingMMC/dC0532kMz8QLfJJjqGpuvk94A1HWeZy7NpWgn/i+Z5/R+aUX/76MfAVsDvug4yl8z8ZDuy+z1o5i4eLj9k5r02HBZwHelZn+JqDopHuk1oEfStBbbSUShfQdON+tusPQdQLyfIrsjbgQcB/9fzOauuiojdM/PrcF3LQa8Kjcx82gzLj6YZJKKPXgR8vC0yAe5A07rVK5n50K4zrIN/Be6bmRfBdfMCfpOmFblvLs3Mz3UdYhqZeQnNNBI1+A/gBxHxY9b+vFrRXaTJ2qlNNqeZ/uZomhEzv9NpqEVkAdedf6Ing27UeFA8wySef+zh2fcqtK0YP8rMe3adZVrt4EDDs9enZeZnuswzi/fTnNHu5WAbFfs58OOeF2/Q/K8/rr0WrgAXA8/oNtL0+taNeigzT29Hy7srzfP608wczrlKRPxNH0d7BYiInfvWdbK1iuY6oqHLgfM6yjLRyKjJX4mIt9K0ao0WGn05MU5EvDMzXxQRn2bCpR99LIqA44A3U8fn1YMzc+eI+FFmvj4i3k7zftgoWMB1ZDjJa99UdFA8aRLPHTpNVLHM/EtE/DAi7piZv+46z1wi4lBgV+DD7aIXRsRDMvMVHcaaybUjI/pp/vwGOC0iPsfaB3C9mEZgKDPPAO4dEVu09y/rONK66ls36uu0BduPZ1j9ZqCXBRxN62GfppEY/n86n+aavU/RFBx7078WjfFRk+8/8n3fxhYYXvP2tk5TrJvVmfmurkNMadiT4cqIWA5cBFTXILG+LOAWQUScmpmPmmtZ1yo7KK5mEs+K3AE4MyK+w9qjpfbxLOHfAvfJzL8ARMRxwA9ouiv2zVci4kCaYe9HC40+XVtao1+2t5sxMghH37Td0F4L7A4MIuLrNKNQXtRtsun0rRv1Oujr6Jn0aQ7A1nAC71+0t6FPdZBlVn0fU2BUZn6v/fY+mXn46LqIeCHNiLR9872I+A9gJT1t2RzxmYjYCngr8H2aAr6vXb7nnQXcAmpnhN8cWBYRt2LtuSqWdxZsZjUdFF8ZETcDzoiIt9Ccje/lsLwV6ftQzOO2oumOBs0kvn21X/t19O+oV9eW1igza3m/nkgzaMFwIJOn0kwp8+jOEs2gsgGt5tKLrrUR8Umaa8g+N/xs7ZuK/pauExH/DrwlM//Q3r8V8JLMfHW3ySZ6BnD42LJnTljWB/dtvz5wZFnfWjYByMw3tN9+MiI+QzMybZ+mZ1lQFnAL67k0XSWW0wwUMizgLgOO6CrUHGo5KP5HmuveapjEsxa/Bn7TToxLRGwG3K7bSDMaXmj9FZq/qz3o54mGKq8x7bOIOAg4MTNXR8SdaYbkvxfwM2D/zJypS11Xbj1yoAHwxoh4fGdpZvd9mv+ll9D8XW1F838BPOmwvt5HM+LguyLi48AHM/OnHWeaqP1/Oulard4dvAN7ZeZ1c35l5iUR8bdAbwq4iNiX5gTe9hGxcmTVLWm6+/VODS2cETHjFCwRQWZuFNfBWcAtoLbJ/PCI+JfMfHfXeaZQ00HxcDTKq6iv5aivPs7ak+CuaZft2k2cmWXmRyPiNJpsBXhZ3wZamO1DBthoPmQWwD9l5nva798FHJaZJ0XEw2kGjHlIZ8km+0pE7ANke/9JwGc7zDObzwMrM/NkgIjYC3h0Zr6k21g3FBGbZOafZll27uKnuqHM/BLwpXYQm32BL0bEecBRwAmjA6/0wEtHvt+U5qTotR1lmcuS0de7PeG4SceZxn2TpnfQMta+du9yoI+D2BARtwP+HViemXtFxE7AgzLzmI6jjXpc+/W2NMcsX27vPwI4jY1kIBMLuEWQme+OiAcDf8XaE04e31moCWo4KB6KiF8y+UyhZ4jX39LRyTsz85q2m2rvtGczP0pzsPnHubbvyONmWTdgI/mQWQCjn1u3zcyTADLztIi45Qz7dOm5wMFcP6DBEpo5zA6mf/PW7ZqZzxveyczPRcQbZtuhQ98C7jfTsszszUTp7XWQT6PpOfIDmuvMd6fpWvfw7pKtbeSaraFvREQfr9MCOAE4NSKOpfl/+myaERR7oz3R/Cua6U5q8UGaXg2vau//jKbLd28KuMx8FkDbbXKnzPxNe/8O9Ld327yzgFsEEfEh4M7AGVw/4eQA6FUBV8lB8dDoyFObAk8GZr1+Q3P6fUSsyMyVABGxN7C640wzeTvNnE+HtoOufAz4zLD7Zx8MP2Q07z4RER8EDqGZyPtFNMXwo7i+u19vZGYfi8qZrI6IV9McHA9oio5edfWKiNsD2wCbRcR9Wfva8s07CzaD+P/bu/Moy6r67OPfboygKCqCqEEEUVAkIAg2vrYSCQoqjsBjUAQJryMqStSIOIAT4sAbQYNikFnDg4qgEdFoMwvEBpnEIRggzkEUEARsrPePfa59q7uquumm797n1vNZ665b59yqtZ7urr737LP3/v2kLwOPpwzgnz+42AROkfS9esmWtsQeyLmUz9mHV4ozI9sfkXQFZS/pHOD9ts+qHGtKPWt7tI5tSzoQwPYiSa02St9w6P8TwK+BTWqFGbUM4EZjG8pdgiY2Vc+g+YvigSkquP1zV93tPTXyjInXAidLGixP+xmlPUNzbJ8DnCNpNcrm6ldRCgU084E4VJp7Sq2Vu+8L2wdJeiXlZtPGlGVTrwa+QikQEituD0rFzNO643O7cy3ZiVIAYn1g+P/QrcA7p/qByj5p+ztTvWB7m6nOV7SQxStbFlGWoe5bLc00uvf9s2zvSFn227o+tT26rZsxnoC/DD5bLQxytqSzKJ8FE5S/4wV1I41OBnCjcRXlLtYvl/WNNfXhonhgqJknLL5T2Kc73c2xfS2wnaQHAHNs37qsn6mp2/PwfMpNh61pbPkM+X1cZWwfR1nq00uSLrO91bK/c7S6KpP7184xE9vHUxqj72r7S7XzTGd4D+xU+2Eb3QO7GfB6upYXwHlAU7OEALbvlnS7pAf1pepgj9oe/SOlhcDGki4A1qXs222O7Td0/7ee3p06erCkfjbIAG401gF+0M1qDffVaK6/Vg8uigeGNwQP7hSqTpTxYvsPg68lbd1i/xdJpwDzKHdfP0VpOt9Uie4+lubuO0kPb3Xf7rAWB28AkjahFLLYkMn7tZupQihpT9snARtONcvd0Mx2H/fAHk+pkj1o5LwHZenn7tUSTe8O4EpJ32Jy39I31Ys0rd60PbK9UNL2wKaUpak/aqzQziTdjZAW/y+tchnAjcbBtQMsjz5cFA/0odTtmHgdZSa2NccCL+vuZjat6we5L/BEyn5NoMlmvuPgGKCpxtOSDrP9T8s614hTgU9TmuG2+n9rcOH7gKoplqGne2A3tb3l0PECSZdXSzOzf6fdaq5LegVlpVDzbY+6f+9TgFO6VTnNkXS+7fmSbmVyMbs5tFcYd0J8QQAAIABJREFUapXJAG4EuqWJfdCni+LVKW+AGzL5TvH7amUaR7ZbHLxB2ZtzoKQNbL9a0uMoFx9fqx1sCicCP6Ts3XkfZZ/WNVUTjSnbTQ3eOs8ClhysPWeKcy1YZPuo2iFmYvsz3XPTM9w93QN7maTtbF8EIGkecEHlTFPqltL2xY3AXV09gUO6bSqttTwYeAFlBZYl/ZkymLPtZgpE2Z7fPc/qbQoZwK1CPbxL0KeL4tMpG2sXMrQsNVaOpL8GHs3kQfG59RJN61jKv/2gb93PKLMHLf6uPtb27pJeaPt4SZ8HmqyW1ieSTrT9imWdq0XS6yj7iR7TVcsbeCCNXhQDX5X0ekoRk+Hl/jfVizSZpCNmer2hJXR9vLicB+wlaXCxvgFwjaQrKdcsW9SLNlnPWgl9m1Itc7A94X7AN5ncd7UJXeuDjwAf6a4B3w0cRqmc2YQlqqUupaX3q1UpA7hVqId3Cfp0Uby+7Z1rhxgnkg6j3Hn7AZPbXbQ4gNvY9ksl7QFg+4+S5izrhyoZ7B/4vaTNgV9RZo5j5Txx+KC7q/3kSlmm8nngTOBQ4B1D529t+AJj7+75bUPnJoCWLooHvcqeRim6cUp3vPvQa9W1PkM4jT59pvapldAaw3vLbf9BUnMtLwYkbUipKfBSyrXA26sGWtqgWupUn/mtvV+tMhnAxbA+XRRfKOlvbF9ZO8gYeRFlxrUPM5p3dQV3BqWON6bdmdijJT0EeBelutcDKHc1YwV0/YneSekDdkt3eg5wF3B0tWBLm7B9naT9lnxB0totDuJsb1Q7w7IMls51rSSeOSiwIOnTlFmNpgw1mp6kxT2w3exLL/SsldBtwwXBJD0Z+GPlTFOSdDHwV5Sb97vb/mnlSEvpw/vUKGQAF8P6dFE8H3hlt4ziThYvS21miUcP/ZTyxt3qvzkA3U2FT1OK7TxK0smUu/GvrJlrBt+2/TvKTOZjACTlA2gF2T4UOFTSobYPrJ1nBp8HdmHqu8VN3SWWtIPt70xV7h6aLXn/SMoyxcFA+AHdudYMr2BZA3gx8ItKWcZGz1oJvRk4VdLg3/0RlNmtFu1t+4e1QywvSS8AntEdnt3olp9VIgO4AHp5Ufyc2gHGhaQjKReUt1PKHH+byftfWtlTAoDtCUn7A88GtqNcGO9v+8a6yab1JUpLjmFfpK3lfn30NUlr2r5N0p6Uv+NPtDKLYHuX7rkPg/Xtge8wden7Vkvef5hSdGPQuHd7Gqz4vGSvOklfAP6jUpxx0ptWQrb/U9LjWVya/4cNl+b/paTDWTwoOgd4X4v99iR9GNgWOLk7tb+kpzV+Y+9ekwFcAP27KLZ9fbfnZT3ye7yyBo1aF1KW+A1baulPIy4CHmO72TLS3Qf2E4EHLTGzsRZD7QRihR0FbClpS8oejWOAEygX8tUtMUOwlJb6K9p+b/fcm9L3to+VdCal8AbAO/rQAxB4HKU4SKyEPrUSkrTXEqe2koTtE6oEmtnngKtYPBh+BaU+wpSz85U9F3jSoN2VpOOBy4AM4GLWaf6ieEDSG4H3Ar8GBr3qJoAsobyHhvaU7G/7E8OvdYP6Fj0TeI2k6ylNXFtcQrspZQndg5k8s3ErbfbW65tF3Y2nF1Jm3o6RtPcyf2p0BjMEa1CWd11O+T3dAriYsgy8CX0sed+tGtmR8pn1PkkbSHqK7UtqZxs2RRXqX9FmC4lekfRQyjXAfMrf7/mUmaIl98a1YNuhr9cA/g64lHLDqTUb2x7uUXeIpO9XS7NsD2bxMuoH1QwyahnAxbA+XBQP7E8puNHim3Vf7Q18Yolzr5ziXAuaX0Jr+3TgdElPtf3d2nnG0K1dQZM9gWd0M/J/VTnTXwxmCCT9G/DqQcGlrhLpW2tmm0Kre4dm8i+Um3c7UPor3kpZrrztTD80Kt1SrguAdbv+X3Hv+jfKvuLBYOPllIqkO1ZLNA3bbxw+lvQgSn/QFv1R0nzb50P5PabRgiuUCr+DZdRzKMs+Z8XsG2QAF5M1f1E85H8ofeBiJXVVR18GbCRpeAnlA4EmB8it7HNaTi+WdDXlQ/AbwJbAm22fVDdW772U8nu7r+1fSdoA+GjlTFN5/HC1XNtXSXpSzUBL6mnJ+3m2t5Z0GYDt30m6b+1QQ46g7HO9kKX3wMbKW9v2+4eOPyDpRdXS3DO3U5bStui1wAndIBPgdyxuL9IU21+QdDblps0c4J96soz6XpEBXPxFzy6KfwqcLenfmVxwo7mlPj1wIfBLYB0mbwy/Fbhiyp+Ie+LZtt8u6cWU3oq7AwuADOBWQvdBffjQ8Q20uSTpGkn/Svn3nqDMGF5TN9LUJG1C2Vu4nu3NJW0BvMD2BypHm8qfulnXQdXkdVm8nL4Ff+paCKw/VfPx1opD9dACSX8PuDveDWhy+4ekr7J4Ge1cSv9CT/8TdUiaS1nZtKWktQBs37KMH6ummx38vu0zukJWb5fUTCGrVS0DuOirG7rHfbtHrKDuze564Km1s4ypwbK+5wJfsH2T1GSxtF6RtB1wJPAEynvAasAfbLe2D2If4HWUZd9Qln0dVS/OjD5LaeL9GQDbV0j6PNDiAO4I4DTgYZI+SLmAf1fdSJPsQlnOtwMNNRgfI68BDmDxUsTVKP3WDqBs/VirWrKlfWzo60XA9bZ/VivMdGz/WdIbypftDtyGDBeyehulAEszhaxWtQzgopd6uuSnSZLOtz1/is32gz2QLX0Q9tFXJf2QsoTy9d1MQfbErLxPAn9PaTi7DbAXDS5Lsn1H12T667Z/VDvPMtzf9iVL3GBYVCvMTGyfLGkhpSDEHOBFtpuZ2ewqOP+bpGtsX147z7ixPeO+TUlPtH31qPLMxPY5tTPcA9+S9FbKfsLbBidt3zT9j1QzXMjqiAYLWa1SGcBFL3UXwW+nlGn/S0l22ztUC9VTtud3z30sZNA82++QdBhwi+27Jd0GvLB2rnFg+78krWb7buBYSRfWzrSkrtHsRymzhBt1+9/eZ/sFdZNN6UZJG7N4WeJulOXVTemWel1he3Og6abDUw3eJO0ymxoOV3IilfceTnFTdJJGb47+Q/e839C5CeAxFbIsS9OFrFa1DOCir06m3CHahbLpdm/gf6sm6jlJ76Ms7/qu7duW9f1xjzwB2FDS8Htui/u1+uT2rmjF9yV9hDLQWLNypqm8F3gKcDaA7e9L2rBmoBnsBxwNPF7Sz4H/plT3a0q31OtySRt0ex/7ZlsgA7hVa07tAIObot1n668og8o5lP9TTd4wtb1R7Qz3QF8KWa0SGcBFXz20my7fv1uecI6kPi1TaNF1lDfDI7s7h+cB53bl8GMFSToR2Bj4PnB3d3qCDOBW1iso+17eALwFeBSLS4q3ZJHtm1ve97hEH7ivU4rszKUsodqVoWIxDXkEcLWkS5i81KupmU1Jq9u+c4nTH6oSZnaZduargp1szxs6PkrSxcBHagWajqQ1gNezuL/eecCnW2yF0aNCVqtEBnDRV3/qnn8p6XnAL4D1K+bpPdufAz4n6eGAKL2qXk2jdwp7ZBtgM9stXVD03lClsT8CLe+JvUrSy4DVJD0OeBOl8mtLBv/HN6XMDp1OmSl4BWVWvkUt/5sP+y5LL+Wb6lyMr7slvZzSu24C2IPFN/NacwKlAvWR3fEelJnD3aslmoaklwCHAQ+jvF/Nqn37GcBFX32g61Pyj5Q3mrUod+FjBXWlzjcDfk2567YbcGnVUOPhKuDhNLiXqI8kXcnM+0q2GGGc5fFG4CBKu5PPA2fRWFXHQVEoSd8EtrZ9a3d8MKVITItuAH45mBmQdD9gvbqRFutuhP01cD9JW7F4Sd9awP2rBRsDkuYA69v+nxm+7a5R5VkOLwM+0T0mgAu6cy3a1PaWQ8cLJLVahOcjwPNbKl40ShnARS8NbQC/GXhmzSxj5KGUJWm/B24CbrTdZAW6nlkH+EG31Gu4Z2FTS716ZJfaAZZXt6n+ENtvowziWrcBky987wI2rBNlmU4F/s/Q8d3duW3rxFnKTsArKStDhpeg3gq8s0agcdFVHvwKpVH6dN+z3Qgjzcj2dfSncNVlkrazfRGApHmUAWeLfj1bB28AcyYmsqonxkMqe907JD2BcvHxFmA121mauhIkTdmTpmelpWMFSfpOX6rjSjqIsnz6NMpMwYuBU2wfWjXYFCR93/aTljh3+RKzB9VJ2tX2l2rnGDeSPgUcZ/s/a2dZFkmbUHqWrWd7c0lbAC+w3dRMPICkayhLqQfFgTYArgH+TFme2MwKB0mfoKxu+QqTb45+uVqoEcoMXIyTVPZaCZJ2AZ4OPAN4CPAdylLKWAkZqI2OpKNtv7p2jiVcJukMyuzQcLGN5i4ybH9Q0pmU9wGAfWxfVjPTDP5X0gtsnwHQ9YK6sXKmqXxb0uGU91WAcyhtJG6umGkcPBN4jaTrKf+vBvufmhlgDPkspdH0ZwBsXyHp8zS2lLqzc+0A98BawO3As4fOTQDNvbeuChnAxdiw/d7aGXruOZSCBZ+w/YvaYfouDdKr+EztAFNYG/gtMDwL1+xFhu1L6cfe19cCJ0v6ZHf8M0rRldYcQ9kHOyhD+grgWOAl1RKNh+fUDnAP3N/2JUtUom1ye4Lt6yU9hFLV9z5D55t7T7C9T+0MNWUAF73UVR9a0s3AlbZ/M+o848D2fsv+rlheaZA+erYX1s4whbnA/rZ/D9BdHH28bqT+s30tsJ2kBwBzBoVXBiTtbfv4Oukm2dj2cHuLQyR9v1qaMdENNLZk8WzxeVM1TW/EjZI2pruRJ2k3Gi1qJen9lL2b17L4xuMEk29ANUHS+pQidk+jZDyf8l77s6rBRiQDuOirfYGnUvoVAfwtcBGwiaT32T6xVrBx0uiStAgkfZWZq1G2UiRmi8HgDcD277qqhHEvsP2HaV7aH2hhAPdHSfNtnw8g6WmU1hexEiTtD7yKxTPZJ3WfV0fO8GO17AccDTxe0s+B/6Y0826RKDcdWqriOZ1jKZV9By0O9uzOPataohHKAC766s/AE2z/GkDSepRNwvMoywAzgLt3tLgkLQLgp5QN7Cd1x3tQmtGfVSvQNOZKeojt3wFIWpt89o7CnGV/y0i8Dji+a3sD8Dtg74p5xsW+wDzbtwFIOozSX6+5AZztnwI7SloTmNvwbDGU5b4PBvqwkmld28cOHR8n6c3V0oxYPkSirzYcDN46vwE2sX2TpD9N90OxfCTNBR7Q6JK0CICtbD9j6Pirks613VqJ9o8DF0r6ImXGUMAH60aaFVopsX0NpV/VxpQL45uBFwFX1Aw1BuYwuRn23bQzaJ/SYLA5hVZmiwEOpRReuor2297cKGlP4Avd8R6U/cazQgZw0VfnSfoai5vM7gac293h+v30PxbT6apivZbyQbgQeJCkw21/tG6yiCmtK+kx3d1tJG0ErFs501JsnyDpe5Q9JHOAl9j+QeVYs0ErF/OnUz6TLgV+XjnLODkWuFjSad3xiygFY/qold9VKAPJw4ArKSudWvYPwCeB/0e5YXNhd25WyAAu+mo/ShWv+ZQ3v+OBL9meII29V9Rmtm+R9HLg68A/UQZyGcBFi94CnC3pp93xhsBr6sWZXjdgy6BttFppPry+7T6VZu8F24dLOpvF1wAtt7xYllZmiwFutH1E7RDLw/YNQIszgyORRt7RW92+t6dQ3vwuSfXJlSPpauBJlE3Bn7R9TouNcSMGJK0OPL47/KHtO2f6/hgf3fv/h4BH2n6OpM2Ap9puahZG0tHAkbavrJ1lHEhaq7vRuPYUL08At9i+e4rXmiXpMttNFDbqehbeCZzB5CWUzbURkHQ8U1T4tT0rZuHm1g4QsSJUGqpcQlk6KcpSit3qpuq9z1CKQKxJWY76aOCWqokiZvZk4InAlsBLJe1VOU+MznGUgjWP7I5/DLRYwGA+sFDSjyRdIelKSdn/tuI+3z0vBL439FhIWab6K0kfqpRtRbUyWwywFbAd5ebIx7vHx6ommt5SFX4p+WeFLKGMvjoI2HYw6yZpXeA/gC9WTdVvnxpeOiHpBrIcNRol6URKYYjvs7iYwQRwQrVQMUrr2LakAwFsL5LU4sxLnxpON8/2Lt3zRlO9Lmk1SiXFZooZLWu22PYbqgYcYrtPn/mzusLvrPmDxtiZu8SSyd+SGeWV9V+STgWOtX1Nt59wUe1QEdPYhrJvM/sAZqfbJD2Uxc2Rt6NUeGyK7etrZxhX3ZK5xwFrDM7ZPhd4QrVQUzuOUnTloO74x8ApNFR0RdKetk+SdMBUr9s+fNSZlsOsrvCbC97oq29IOkvSKyW9Evh3SuGNWHFbAD8BjpF0kaRXS1qrdqiIaVxF6QMXs9MBlH06G0u6gDLz+sa6kWJUJP1fSs/Xs4BDuueDa2aawTq2TVfV0fYiJrdAaMGa3fMDp3k0x/YJwK7Ar4H/pVT4/UsP4G6AP7ZSxCR6S9KuwNMoFajOtX3aMn4klpOkZ1B6qzyYsiz1/bb/q26qiMUkLaAU3bmE9vsVxSog6T7AppTPgB/ZTg/QWULSlcC2wEW2nyTp8cAhtl9aOdpSumqZuwLfsr11N1t8mO3t6ya75yQdaPvQ2jmWh6RLbW9dO8eqkiWU0Vu2vwR8qXaOcdHtHXgesA+lJPvHgZOBp1NmNzepFi5iaQfXDhD1SNod+IbtqyW9C9ha0gdarJYXq8Qdtu+QhKTVbf9Q0qa1Q01jydnidSkF2Ppod0qz7z5oqb/evS4DuOgVSbcydc+UOcCE7Sz5W3E/ARYAH7V94dD5L3YzchHNsH1O7QxR1bttnyppPrATpVLeUcC8urFiRH4m6cHAV4BvSfod8IvKmaZk+1JJ2zMes8V9GhSN9RLDDOCiV2w3uRZ7TGxh+w9TvWD7TaMOEzEVSefbnj/FzZzcxJldBnuIngccZft0SQdXzBMjZPvF3ZcHd8upHwR8o2KkaY3ZbPFYD4r6JAO4iBhYJGk/Sl+t4apes6IpZvSD7fndc27mzG4/l/QZYEfgsK6pewqzzSJdkYpHAbd2j80pveBaM06zxX2agetT1nssb3YRMXAiparfTsA5wPqUD8WIiNaIUnlw566Z79rA2+pGilGR9H7gCuBI2m84vdRsMXDfinlWxqm1Awx0vUBnOvd3I4wzcpmBi4iBx9reXdILbR8v6fOUC6SIiKbYvl3StcBOknYCzrP9zdq5YmQEbGz7rtpBlkNvZoslbUKZHVzP9uaStgBeYPsDALY/VDXgZE8cPugKsT15cGz7ppEnGqEmf4EioorBpurfS9qcsqdgw3pxIiKmJml/SpXch3WPkySlD9zscRWlzU0f9Gm2+LPAgXTXA7avAP6+aqIlSDqw2wO9haRbJN3aHf8GOL1yvJHJDFxEDBzd7Sl4F6Xk8QOAd9eNFBExpX2BebZvA5B0GPBdypK6GH+HApdJuorG+0D2bLb4/rYvkTR8blGtMFPp+tAdKulQ2wfWzlNLBnARs5ykA4YO9+meP9U9rzniOBERy2MOi/cW0X091kULYpLjgcOAK4E/V84yo262+FXAl7tTJ0k62naLNxtulLQxXbVJSbsBv6wbaVrvlPQSYD4l73m2v1I508hkABcRg2p+mwLbUmbfAJ4PnFslUUTEzI4FLpZ0GmXg9kLgmLqRYoRutH1E7RDLqU+zxfsBRwOPl/Rz4L+BPetGmtangMcCX+iOXyvpWbb3q5hpZDKAi5jlbB8CIOmbwNa2b+2OD6ahilMREQO2D5d0NuXuO8A+ti+rGClGa6GkQyk3HIeXULbYRqA3s8W2fwrsKGlNYO7geqBR2wOb2x7MFh5PmZGdFTKAi4iBDYDhil53kSImEdG2OZQldE1eEMcqs1X3vN3QuQlghwpZlqU3s8WSHgzsRfnsv89gL5ztN1WMNZ0fUa5bru+OH0VpLTErZAAXEQMnApd0HzITwIsp+wwiIpoi6T3A7sCXKBfFx0o6dVDuPMab7WfO9LqkvW038fnVs9nirwMX0fDeQklfpVyjPAi4RtIl3fE84MKa2UYpA7iIAMD2ByWdCTy9O9Xyh0xEzG57AFvZvgNA0oeBS4EM4AJgf9q7AdmH2eI1bB+w7G+rqtWG7SOVAVxE/EW3f6DFPQQREcOuA9YA7uiOVweurZYmWtPMIKlns8UnSnoV8DUm7y1spim27XMGX0t6NPA42/8h6X7MonHNrPmDRkRExNi4E7ha0rcoy6eeBZwv6Qhods9OjM5E7QBD+jRbfBfwUeAgFv8dTgCPqZZoGt1A89WUxugbA+sDnwb+rmauUckALiIiIvrmtO4xcHalHNGmZmbg6Nds8QHAY23fWDvIctgPeApwMYDtn0h6WN1Io5MBXERERPSGpNWAZ9lutT9V1HdB7QBD+jRbfDVwe+0Qy+lO23cNKmVKug9tzbyuUnMmJmbNnzUiIiLGgKSzgOfbvmuZ3xxjR9J6wIeAR9p+jqTNgKfabq48v6S9Z3q9lWqZAF0V6icCC5i8B66lQSYAkj4C/J7S9uCNwOuBH9g+qGqwEckMXERERPTNdcAFks4AbhuctH14tUQxSsdR+qsNLtZ/DJxCY/3Vejhb/JXu0QfvAPaltDx4DaUFwr9WTTRCGcBFRERE3/yie8wFHlg5S4zeOrYt6UAA24sk3V071JJs3y1pXUn37cNsse3jJd0X2KQ79SPbf6qZaTq2/wx8tnvMOhnARURERK/YPqR2hqjqNkkPpdvzJGk74Oa6kaZ1HT2ZLZb0t5T+eddRCsE8qmuKfm7NXMMkXckMe91sbzHCONVkABcRERG9ImkBU1zE2d6hQpwYvQOAM4CNJV0ArAvsVjfStPo0W/xx4Nm2fwQgaRPgC8CTq6aabJfueb/u+cTu+eX0pwDLSksRk4iIiOgVScMXlGsAuwKLbL+9UqQYsa7q4KaUmaJml/r1iaQrlpzBmupcCyRdYPtpyzo3rjIDFxEREb1ie+ESpy6QdE6VMDFyknYHvmH7aknvAraW9AHbl9bOtqSezRZ/T9IxTJ7VWvL/WivWlDTf9vkAkv4PsGblTCOTAVxERET0iqS1hw7nAtsAD68UJ0bv3bZPlTQf2An4GHAUMK9urCm9dejrv8wWV8qyLK+jLE18E2Vm81zgX6ommt6+wOckPYgyQL4Z+Ie6kUYnA7iIiIjom4UsntVYRCm6sG+1NDFqg4qTzwOOsn26pIMr5plWn2aLbd8JHN49mtb9vW4paS1gju1JRWy64ivN9Ni7t2UAFxEREX2zGaVx73zKQO484HtVE8Uo/VzSZ4AdgcMkrU6ZiW1On2aLJT0NOBh4NENjBNuPqZVpWWzfMs1L+1Mqao6lDOAiIiKib44HbgGO6I73oOzb2b1aohglATsDH7P9e0mPAN5WOdN0+jRbfAzwFkrm5vrq3UNzagdYlTKAi4iIiL7Z1PaWQ8cLJF1eLU2MlO3bJV0L7CRpJ+A829+snWsafZotvtn2mbVD3EvGusx+BnARERHRN5dJ2s72RQCS5gEXVM4UIyJpf+BVwJe7UydJOtr2kRVjTaf52WJJW3dfLpD0Ucrf652D11us7rkcMgMXERER0ZB5wF6SbuiONwCukXQlMNFi36q4V+0LzLN9G4Ckw4DvAi0O4PowW/zxJY63Gfp6Amix5cGyjPUNnQzgIiIiom92rh0gqprD5D1ad9PujEvzs8W2n7k839dSZUdJ6wEfAh5p+zmSNgOeavsYANtvqBpwFcsALiIiInrF9vW1M0RVxwIXSzqNMnB7IaUAR4vGaba4pcqOx1F+Dw7qjn8MnEK7vwf3qiZLrkZERERETMX24cA+wE3Ab4F9bP9z3VTT2hnYCNi+e2wEPBfYBXh+xVwroqVZznVsG/gzgO1F9L9y5nLLDFxERERE9NEcygV8SwOLScZstrilyo63SXooXSZJ2wE3z/wj4yMzcBERERHRG5LeQ1nK9xBgHeBYSe+qm2pWaGmgfABwBrCxpAuAE4A31o00OpmBi4iIiIg+2QPYyvYdAJI+DFwKfKBqqvHXTPEV25dK2h7YlDKw/JHtP1WONTKZgYuIiIiIPrkOWGPoeHXg2jpRxoek9SQdI+nM7ngzSfsOXm+psqOk3YH72b4aeBFwylA/u7GXAVxERERE9MmdwNWSjpN0LHAV8AdJR0g6Yhk/G9M7DjgLeGR3/GPgzdXSzOzdtm+VNB/YibKk9qjKmUYmA7iIiIiI6JPTgHcCC4CzKaXkzwQWdo9YMX2q7DjI9TzgKNunA/etmGeksgcuIiIiInpB0mrAs2zvWTvLGOpTZcefS/oMsCNwmKTVmUUTU7PmDxoRERER/Wb7bmBdSbNmtmWE+lTZUZTlnjvb/j2wNvC2upFGZ87EREstHSIiIiIiptfNvGxNGWzcNjjfNfiOlSDpPvSksqOkLYGnd4fn2b68Zp5RygxcRERERPTJL4CvUa5jHzj0iJXQp8qOkvYHTgYe1j1OktTqbOG9LnvgIiIiIqI3bB9SO8OYerftU4cqO36MUtlxXt1YU9oXmGf7NgBJhwHfBY6smmpEMoCLiIiIiN6QtICu0MYw2ztUiDNOlqrsKOnginlmMofJFTLv7s7NChnARURERESfvHXo6zWAXYFFlbKMkz5VdjwWuFjSaZSB2wuBY+pGGp0UMYmIiIiIXpN0ju3ta+foM0n3B3YGrrT9E0mPAP7G9jcrR5tStz9vfnd4nu3LauYZpczARURERERvSFp76HAusA3w8Epxxobt2yVdC+wkaSfKoKjJwduQOZTG47Nm+SS0Oy0aERERETGVhcD3useFlP5l+1ZNNAb6VNlR0nuA44GHAOsAx0p6V91Uo5MZuIiIiIjok82A11OWz00A51EGc7Fy+lTZcQ9gK9t3AEj6MHAp8IGqqUYkM3ARERER0SfHA08AjqAMLp4AnFg10XjoU2XH6ygFbAZWB64nHGZUAAAC60lEQVStE2X0MgMXEREREX2yqe0th44XSLq8Wprx0afKjncCV0v6FmUW9lnA+ZKOALD9pprhVrXMwEVEREREn1wmabvBgaR5wAUV84wF24cD+wA3Ab8F9rH9z3VTTes04J3AAuBs4CDgTMr+yIX1Yo1GZuAiIiIiok/mAXtJuqE73gC4RtKVwITtLepFGwtNV3aUtBrwLNt71s5SS2bgIiIiIqJPdgY2ArbvHhsBzwV2AZ5fMVev9aWyo+27gXUl3bd2lloyAxcRERERvWH7+toZxlSfKjteB1wg6QzgtsHJbhno2MsALiIiIiIirqNUdryjO265suMvusdc4IGVs4zcnImJidoZIiIiIiKiIklfAbYFJlV2BH4D41/ZsU8yAxcREREREad1j4GzK+VYJkkLKIPMSWzvUCHOyGUAFxERERExi/WwsuNbh75eA9gVWFQpy8hlCWVERERExCwn6Szg+bbvqp1lRUg6x/b2tXOMQmbgIiIiIiLiOnpS2VHS2kOHc4FtgIdXijNyGcBFRERERESfKjsuZPEeuEWUwee+1dKMWAZwERERERGznO1Dame4BzYDXg/MpwzkzgO+VzXRCGUAFxERERExy/WssuPxwC3AEd3xHsCJwO7VEo1QBnAREREREdGnyo6b2t5y6HiBpMurpRmxDOAiIiIiImY52wuXOHWBpHOqhFm2yyRtZ/siAEnzgAsqZxqZDOAiIiIiIma5nlV2nAfsJemG7ngD4BpJVwITtreoF23VywAuIiIiIiL6VNlx59oBasoALiIiIiIielPZ0fb1tTPUlAFcRERERETM6sqOfZIBXEREREREzOrKjn0yt3aAiIiIiIio7jJJ2w0OZltlxz6ZMzGxVL++iIiIiIiYRSRdA2wKTKrsCPyZWVDZsU+yhDIiIiIiImZ1Zcc+yQxcRERERERET2QPXERERERERE9kABcREREREdETGcBFRERERET0RAZwERERERERPfH/AepG9UVei8ZgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot feature importances\n",
    "indices = np.argsort(importances)[::-1] \n",
    "names = [dat1.columns[i] for i in indices]\n",
    "plt.figure(figsize=(15,6))\n",
    "plt.title(\"Feature Importance\", fontsize=25)\n",
    "plt.bar(range(X.shape[1]), importances[indices])\n",
    "plt.xticks(range(X.shape[1]), names, rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rforests = RandomForestClassifier(n_estimators=1000)\n",
    "rforests.fit(X_train, y_train)\n",
    "predictionRF = rforests.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n",
      "[[2392   14]\n",
      " [ 460    8]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.99      0.91      2406\n",
      "           1       0.36      0.02      0.03       468\n",
      "\n",
      "    accuracy                           0.84      2874\n",
      "   macro avg       0.60      0.51      0.47      2874\n",
      "weighted avg       0.76      0.84      0.77      2874\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Get the confusion matrix and classification report\n",
    "confusionRF = confusion_matrix(y_test, predictionRF)\n",
    "print(\"Confusion matrix:\\n{}\".format(confusionRF))\n",
    "print(classification_report(y_test, predictionRF))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature importance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature importances:\n",
      "[0.01614731 0.10356501 0.11201967 0.11072693 0.11147538 0.07917281\n",
      " 0.11346606 0.11111163 0.11367952 0.05417326 0.01434057 0.00943806\n",
      " 0.00833981 0.01459846 0.00600076 0.00762208 0.0051938  0.00892887]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate feature importances\n",
    "importances = rforests.feature_importances_\n",
    "print(\"Feature importances:\")\n",
    "print(rforests.feature_importances_)\n",
    "\n",
    "# Plot feature importances\n",
    "indices = np.argsort(importances)[::-1]               \n",
    "names = [dat1.columns[i] for i in indices]\n",
    "plt.figure(figsize=(15,6))\n",
    "plt.title(\"Feature Importance\", fontsize=25)\n",
    "plt.bar(range(X.shape[1]), importances[indices])\n",
    "plt.xticks(range(X.shape[1]), names, rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "gboosting = GradientBoostingClassifier(random_state=0, max_depth=1)\n",
    "gboosting.fit(X_train, y_train)\n",
    "predictionGB = gboosting.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n",
      "[[2405    1]\n",
      " [ 468    0]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      1.00      0.91      2406\n",
      "           1       0.00      0.00      0.00       468\n",
      "\n",
      "    accuracy                           0.84      2874\n",
      "   macro avg       0.42      0.50      0.46      2874\n",
      "weighted avg       0.70      0.84      0.76      2874\n",
      "\n"
     ]
    }
   ],
   "source": [
    "confusionGB = confusion_matrix(y_test, predictionGB)\n",
    "print(\"Confusion matrix:\\n{}\".format(confusionGB))\n",
    "print(classification_report(y_test, predictionGB))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SJEmNsICTJEmSpEas7DtAK9YfduCSPM6FS/AYK45bswSPIkmSJKk1XoGTJEmSpEZYwEmSJElSIyzgJEmSJKkRFnCSJEmS1AgLOEmSJElqhAWcJEmSJDXCAk6SJEmSGmEBJ0mSJEmNsICTJEmSpEasnGWjiDgAOApYAbw1M185Z/1zgacC1wC/AJ6SmT/u1q0HvtVt+pPMPHCJskuSJEnSdmXRAi4iVgDHAH8MrAXOiIg1mfmdic3OAvbNzMsj4q+BVwOP79ZdkZn3WOLckiRJkrTdmeUK3H2AczPzPICIOBF4JHBtAZeZp01s/5/AIUsZUpIkSZI0WwG3G/DTiftrgfsusP2hwMkT93eMiDOpzStfmZkfnbtDRBwOHA6QmaxatWqGWNvWhX0HmLAtnp+VK1cO8nWYxqzLo5WsreQEsy6XVrK2khPMulxaydpKTjDrcmklays5l9osBVyZsmw0bcOIOATYF3jwxOLbZOYFEXF74LMR8a3M/MHkfpl5LHDs+LHXrVs3Q6zt17Z4flatWrVNfs5SMOvyaCVrKznBrMullayt5ASzLpdWsraSE8y6XFrJ2krOLbF69ep5180yCuVaYI+J+7sDF8zdKCL2B14EHJiZV42XZ+YF3dfzgNOBfWYJLUmSJEna2CxX4M4A7hQRtwPOBw4CnjC5QUTsA7wFOCAzfz6x/KbA5Zl5VUSsAh5IHeBEkiRJkrSZFr0Cl5nXAEcApwDfrYvynIg4MiLGUwK8BtgZ+EBEnB0Ra7rldwXOjIhvAKdR+8B9B0mSJEnSZptpHrjMPAk4ac6yl0x8v/88+30J+L2tCShJkiRJqmbpAydJkiRJGgALOEmSJElqhAWcJEmSJDXCAk6SJEmSGmEBJ0mSJEmNsICTJEmSpEZYwEmSJElSIyzgJEmSJKkRFnCSJEmS1AgLOEmSJElqhAWcJEmSJDXCAk6SJEmSGmEBJ0mSJEmNsICTJEmSpEZYwEmSJElSIyzgJEmSJKkRFnCSJEmS1AgLOEmSJElqhAWcJEmSJDXCAk6SJEmSGmEBJ0mSJEmNsICTJEmSpEZYwEmSJElSIyzgJEmSJKkRFnCSJEmS1AgLOEmSJElqhAWcJEmSJDXCAk6SJEmSGmEBJ0mSJEmNsICTJEmSpEZYwEmSJElSIyzgJEmSJKkRFnCSJEmS1AgLOEmSJElqhAWcJEmSJDXCAk6SJEmSGmEBJ0mSJEmNsICTJEmSpEZYwEmSJElSIyzgJEmSJKkRFnCSJEmS1AgLOEmSJElqhAWcJEmSJDXCAk6SJEmSGmEBJ0mSJEmNsICTJEmSpEZYwEmSJElSIyzgJEmSJKkRFnCSJEmS1AgLOEmSJElqxMpZNoqIA4CjgBXAWzPzlXPWPxd4KnAN8AvgKZn5427dE4EXd5u+IjNPWKLskiRJkrRdWfQKXESsAI4BHg7sBRwcEXvN2ewsYN/M3Bv4IPDqbt+bAS8F7gvcB3hpRNx06eJLkiRJ0vZjlitw9wHOzczzACLiROCRwHfGG2TmaRPb/ydwSPf9w4BTM/Pibt9TgQOA9219dEmSJEnavszSB2434KcT99d2y+ZzKHDyFu4rSZIkSZrHLFfgypRlo2kbRsQhwL7Agzdn34g4HDgcIDNZtWrVDLG2rQv7DjBhWzw/K1euHOTrMI1Zl0crWVvJCWZdLq1kbSUnmHW5tJK1lZxg1uXSStZWci61WQq4tcAeE/d3By6Yu1FE7A+8CHhwZl41se8fztn39Ln7ZuaxwLHd3dG6detmiLX92hbPz6pVq7bJz1kKZl0erWRtJSeYdbm0krWVnGDW5dJK1lZyglmXSytZW8m5JVavXj3vulkKuDOAO0XE7YDzgYOAJ0xuEBH7AG8BDsjMn0+sOgX454mBS/4EeMHs0SVJkiRJY4v2gcvMa4AjqMXYd+uiPCcijoyIA7vNXgPsDHwgIs6OiDXdvhcDL6cWgWcAR44HNJEkSZIkbZ6Z5oHLzJOAk+Yse8nE9/svsO/bgbdvaUBJkiRJUjXLKJSSJEmSpAGY6Qqc2rL+sAMX32gRSzHq5orj1izBo0iSJEka8wqcJEmSJDXCAk6SJEmSGmEBJ0mSJEmNsICTJEmSpEZYwEmSJElSIyzgJEmSJKkRFnCSJEmS1AgLOEmSJElqhAWcJEmSJDXCAk6SJEmSGmEBJ0mSJEmNsICTJEmSpEZYwEmSJElSIyzgJEmSJKkRFnCSJEmS1AgLOEmSJElqhAWcJEmSJDXCAk6SJEmSGmEBJ0mSJEmNsICTJEmSpEZYwEmSJElSIyzgJEmSJKkRFnCSJEmS1AgLOEmSJElqhAWcJEmSJDXCAk6SJEmSGmEBJ0mSJEmNsICTJEmSpEZYwEmSJElSIyzgJEmSJKkRFnCSJEmS1AgLOEmSJElqhAWcJEmSJDXCAk6SJEmSGrGy7wDavq0/7MCtfowLlyDHiuPWLMGjSJIkScvLK3CSJEmS1AgLOEmSJElqhAWcJEmSJDXCAk6SJEmSGmEBJ0mSJEmNsICTJEmSpEZYwEmSJElSIyzgJEmSJKkRFnCSJEmS1AgLOEmSJElqhAWcJEmSJDXCAk6SJEmSGmEBJ0mSJEmNsICTJEmSpEasnGWjiDgAOApYAbw1M185Z/1+wOuBvYGDMvODE+vWA9/q7v4kMw9ciuCSJEmStL1ZtICLiBXAMcAfA2uBMyJiTWZ+Z2KznwBPAv5uykNckZn3WIKskiRJkrRdm+UK3H2AczPzPICIOBF4JHBtAZeZP+rW/XYZMkqSJEmSmK2A2w346cT9tcB9N+Nn7BgRZwLXAK/MzI/O3SAiDgcOB8hMVq1atRkPv21c2HeACYs9P0PJOsvr2FLWpbBy5cpBvr+naSVrKznBrMullayt5ASzLpdWsraSE8y6XFrJ2krOpTZLAVemLBttxs+4TWZeEBG3Bz4bEd/KzB9MbpCZxwLHjh973bp1m/Hw259Wnp9WcsK2y7pq1apmnpdWsraSE8y6XFrJ2kpOMOtyaSVrKznBrMullayt5NwSq1evnnfdLKNQrgX2mLi/O3DBrD88My/ovp4HnA7sM+u+kiRJkqQNZrkCdwZwp4i4HXA+cBDwhFkePCJuClyemVdFxCrggcCrtzSsJEmSJG3PFr0Cl5nXAEcApwDfrYvynIg4MiIOBIiIe0fEWuBxwFsi4pxu97sCZ0bEN4DTqH3gvrPpT5EkSZIkLWameeAy8yTgpDnLXjLx/RnUppVz9/sS8HtbmVGSJEmSxGx94CRJkiRJA2ABJ0mSJEmNsICTJEmSpEZYwEmSJElSIyzgJEmSJKkRFnCSJEmS1AgLOEmSJElqhAWcJEmSJDXCAk6SJEmSGmEBJ0mSJEmNsICTJEmSpEZYwEmSJElSIyzgJEmSJKkRFnCSJEmS1AgLOEmSJElqhAWcJEmSJDXCAk6SJEmSGmEBJ0mSJEmNsICTJEmSpEZYwEmSJElSIyzgJEmSJKkRFnCSJEmS1AgLOEmSJElqhAWcJEmSJDXCAk6SJEmSGmEBJ0mSJEmNsICTJEmSpEZYwEmSJElSIyzgJEmSJKkRFnCSJEmS1AgLOEmSJElqhAWcJEmSJDXCAk6SJEmSGmEBJ0mSJEmNsICTJEmSpEZYwEmSJElSIyzgJEmSJKkRFnCSJEmS1AgLOEmSJElqhAWcJEmSJDXCAk6SJEmSGmEBJ0mSJEmNsICTJEmSpEZYwEmSJElSIyzgJEmSJKkRFnCSJEmS1AgLOEmSJElqhAWcJEmSJDXCAk6SJEmSGmEBJ0mSJEmNWDnLRhFxAHAUsAJ4a2a+cs76/YDXA3sDB2XmByfWPRF4cXf3FZl5wlIElyRJkqTtzaJX4CJiBXAM8HBgL+DgiNhrzmY/AZ4EvHfOvjcDXgrcF7gP8NKIuOnWx5YkSZKk7c8sTSjvA5ybmedl5tXAicAjJzfIzB9l5jeB387Z92HAqZl5cWZeApwKHLAEuSVJkiRpuzNLE8rdgJ9O3F9LvaI2i2n77jZ3o4g4HDgcIDNZtWrVjA+/7VzYd4AJiz0/Q8k6y+vYUtalsHLlykG+v6dpJWsrOcGsy6WVrK3kBLMul1aytpITzLpcWsnaSs6lNksBV6YsG834+DPtm5nHAseO169bt27Gh98+tfL8tJITtl3WVatWNfO8tJK1lZxg1uXSStZWcoJZl0srWVvJCWZdLq1kbSXnlli9evW862ZpQrkW2GPi/u7ABTP+7K3ZV5IkSZI0YZYrcGcAd4qI2wHnAwcBT5jx8U8B/nli4JI/AV6w2SklSZIkSYsXcJl5TUQcQS3GVgBvz8xzIuJI4MzMXBMR9wY+AtwUeEREvCwz75aZF0fEy6lFIMCRmXnxMv0u0rJaf9iBW/0YS9Hnb8Vxa5bgUSRJktSimeaBy8yTgJPmLHvJxPdnUJtHTtv37cDbtyKjJEmSJInZ+sBJkiRJkgbAAk6SJEmSGmEBJ0mSJEmNsICTJEmSpEZYwEmSJElSIyzgJEmSJKkRFnCSJEmS1AgLOEmSJElqhAWcJEmSJDXCAk6SJEmSGmEBJ0mSJEmNsICTJEmSpEZYwEmSJElSI1b2HUDS0lp/2IFL8jgXLsFjrDhuzRI8iiRJksa8AidJkiRJjbCAkyRJkqRGWMBJkiRJUiMs4CRJkiSpERZwkiRJktQICzhJkiRJaoQFnCRJkiQ1wgJOkiRJkhphASdJkiRJjbCAkyRJkqRGWMBJkiRJUiMs4CRJkiSpERZwkiRJktQICzhJkiRJaoQFnCRJkiQ1wgJOkiRJkhphASdJkiRJjbCAkyRJkqRGWMBJkiRJUiMs4CRJkiSpERZwkiRJktQICzhJkiRJaoQFnCRJkiQ1wgJOkiRJkhphASdJkiRJjbCAkyRJkqRGWMBJkiRJUiMs4CRJkiSpERZwkiRJktQICzhJkiRJaoQFnCRJkiQ1wgJOkiRJkhphASdJkiRJjbCAkyRJkqRGWMBJkiRJUiMs4CRJkiSpERZwkiRJktSIlbNsFBEHAEcBK4C3ZuYr56zfAXgncC/gIuDxmfmjiLgt8F3g/3Wb/mdmPn2JskuSJEnSdmXRAi4iVgDHAH8MrAXOiIg1mfmdic0OBS7JzDtGxEHAq4DHd+t+kJn3WOLckiRJkrTdmaUJ5X2AczPzvMy8GjgReOScbR4JnNB9/0HgoRFRli6mJEmSJGmWJpS7AT+duL8WuO9822TmNRHxS+Dm3brbRcRZwKXAizPz83N/QEQcDhze7c+qVas265fYFi7sO8CExZ6foWSd5XU06+Zr5fWH2Z7XrbVy5cpB/s2YxqzLo5WsreQEsy6XVrK2khPMulxaydpKzqU2SwE37UraaMZtfgbcJjMvioh7AR+NiLtl5qWTG2bmscCx4/3WrVs3Q6ztVyvPTys5wazLZVtkXbVqVTPPiVmXRytZW8kJZl0urWRtJSeYdbm0krWVnFti9erV866bpQnlWmCPifu7AxfMt01ErAR2AS7OzKsy8yKAzPwa8APgzjMnlyRJkiRda5YrcGcAd4qI2wHnAwcBT5izzRrgicCXgccCn83MUUTcglrIrY+I2wN3As5bsvSSJEmStB1ZtIDr+rQdAZxCnUbg7Zl5TkQcCZyZmWuAtwHviohzgYupRR7AfsCREXENsB54emZevBy/iKT2rD/swK1+jKXo87fiuDVL8CiSJEnLb6Z54DLzJOCkOcteMvH9lcDjpuz3IeBDW5lRkiRJksSMBZwkbe+8WihJkoZglkFMJEmSJEkDYAEnSZIkSY2wgJMkSZKkRljASZIkSVIjLOAkSZIkqREWcJIkSZLUCAs4SZIkSWqEBZwkSZIkNcICTpIkSZIaYQEnSZIkSY2wgJMkSZKkRljASZIkSVIjLOAkSZIkqREWcJIkSZLUCAs4SZIkSWqEBZwkSZIkNcICTpIkSZIaYQEnSZIkSY2wgJMkSZKkRljASZIkSVIjLOAkSZIkqREWcJIkSZLUCAs4SZIkSWrEyr4DSJKW1vrDDtzqx7hwCXKsOG7NEjyKJEmaZAEnSerFUhSaYLEpSdq+2IRSkiRJkhrhFThJkhZhs1RJ0lB4BU6SJEmSGmEBJ0mSJEmNsICTJEmSpC7+MoEAACAASURBVEbYB06SpOsQ++tJ0nWbV+AkSZIkqREWcJIkSZLUCAs4SZIkSWqEBZwkSZIkNcICTpIkSZIaYQEnSZIkSY1wGgFJktQLpzyQpM3nFThJkiRJaoQFnCRJkiQ1wgJOkiRJkhphASdJkiRJjbCAkyRJkqRGWMBJkiRJUiMs4CRJkiSpERZwkiRJktQICzhJkiRJaoQFnCRJkiQ1wgJOkiRJkhqxcpaNIuIA4ChgBfDWzHzlnPU7AO8E7gVcBDw+M3/UrXsBcCiwHnhmZp6yZOklSZKW2frDDlySx7lwCR5jxXFrluBRJLVs0StwEbECOAZ4OLAXcHBE7DVns0OBSzLzjsC/Aq/q9t0LOAi4G3AA8Kbu8SRJkiRJm2mWJpT3Ac7NzPMy82rgROCRc7Z5JHBC9/0HgYdGROmWn5iZV2XmD4Fzu8eTJEmSJG2mWZpQ7gb8dOL+WuC+822TmddExC+Bm3fL/3POvrvN/QERcThweLc/q1evnjX/tvOJM/tOMDuzLo9WsraSE8y6XFrJ2kpOMOtyaSVrKzmX0CCPxeZh1uXRStZWci6lWa7AlSnLRjNuM8u+ZOaxmblvZu7b7XOdvUXE1/rOcF3KaVaztpLTrGZtJadZzdpKTrOatZWcW3GbapYCbi2wx8T93YEL5tsmIlYCuwAXz7ivJEmSJGkGszShPAO4U0TcDjifOijJE+ZsswZ4IvBl4LHAZzNzFBFrgPdGxL8Aq4E7AV9dqvCSJEmStD1Z9ApcZl4DHAGcAny3LspzIuLIiBiPq/s24OYRcS7wXOAfun3PARL4DvBJ4G8yc/3S/xpNObbvADNqJSeYdbm0krWVnGDW5dJK1lZyglmXSytZW8kJZl0urWRtJeeSKqPRJl3SJEmSJEkDNEsfOEmSJEnSAFjASZIkSVIjLOAkSZIkqREWcJIkSZLUiFmmEdBWiIibZebFfefYHBFxo8z8dd85ZhURNwX2yMxv9p1lmoj4feAPurufz8xv9JlnIRFxI+CKzPxtd/96wI6ZeXm/ybScIuImC63PzEu3VZZZRcQOmXnVYss0u266oJ9l5pXd/Z2AW2bmj3oN1qCIuEtmfi8i7jltfWZ+fVtnWszk3/+IuDNwF+DkzPxNz9E2ERG3y8wfLrZM1z0RcbOF1rd2zL2lLOCW31ci4mzgeOofwsEO+xkRDwDeCuwM3KYrPJ6Wmc/oN9mmIuJ04EDqe/hs4BcR8bnMfG6vweaIiGcBhwEf7ha9OyKOzcyje4y1kM8A+wO/6u7fEPgU8IDeEs0jIm4IPA+4TWYeFhF3AvbMzI/3HA2AiHj0Qusz88MLrd/GzgFGQKHO2XlZ9/3O1Pk/b9NftHl9GZh7cDxtWS8i4mjqczpVZj5zG8aZ1QfY+LO+vlt2737iTNcVF2+mFpd3j4i9gQMz8xU9R5v0XOBw4HVT1o2AP9q2cWbyH8AfdCdFPwOcCTwe+IteU033ITb9rH8QuFcPWaaKiAWPRzLzX7ZVlllFxC2oxyy3ZaJGyMyn9JVpiq+x4f/VXCPg9ts2Tj8s4JbfnakHxE8Bjo6I9wPvyMzv9xtrqn8FHkadmJ3M/EZE7NdvpHntkpmXRsRTgeMz86URMcQrcIcC9x1f0YyIV1EPModawO2YmePijcz8VVcoDdHx1D/k9+/ur6UebA6igAMescC6ERuK+t5l5h4AEfEm4JOZuaa7/whgUH8DIuJWwG7AThGxDxv+id+EesJhKM7sO8AWWJmZV4/vZObVEXGDPgPN4zjg74G3AGTmNyPivcBgCrjMPLz79uHjK5pjEbFjD5FmUTLz8og4FDg6M18dEWf1HWpSRNwFuBuwy5yTZDcBhva83rj7uif1JMia7v4jqMXyEH0M+DzwaeoJnMHJzNv1nWEILOCWWXfF7VTg1Ih4CPBu4BkR8Q3gHzLzy70GnCMzfxoRk4sG+QEGVkbErYEAXtR3mAUUNn4O1zP9rNFQ/Doi7jlu3hMR9wKu6DnTfO6QmY+PiIMBMvOKiBjMc5uZT+47wxa4z+QV98z894h4aZ+BpngY8CRgd2DyDPZlwAv7CDRNZp7Qd4Yt8IuIOHCigH8ksK7nTNPcMDO/Oud/1TV9hVnEl9j0StG0ZUNQIuL+1Ctuh3bLhnacuCfwZ8CubHyS7DLqlaPByMyXAUTEp4B7ZuZl3f1/pJ5sHKIbZubz+w4xq+5q8Z2YKN4zc6jF8ZIa2gfzOicibg4cAvwlcCHwt9SzMPegfoCHdCbhp10zylF31vWZwHd7zjSfI4FTgC9k5hkRcXvgv3rONM3x1Ga0H+nuPwp4W495FvNs4AMRcUF3/9bUJjRDdHXXR2cEEBF3AAbT/ykiDsnMd8/XjGaIzWeAiyPiH6gnmkbUv12X9BtpY11hdEJEPCYzP9R3nsV0TZKeD+zFxgcZQ2xC93TgPRFxDPX1Xwv8Vb+RplrXfd7Hn/3HAj/rN9LG5lwpnizWhnaleNKzgBcAH8nMc7r/q6f1nGkjmfkx4GMRcf+hnQBfwG2AqyfuX01tojhEH4+IP83Mk/oOspiuBdazqCfzzgbuR23hNMS/rUvOAm75fRl4F/CozFw7sfzMiPi3njLN5+nAUdR/OmupfZ8G1/8NIDM/wMQZrMw8D3hMf4mmy8x/6frrPYh65e3JmTmoJimTumL4LtSznAX43hA7sHdeCnwS2CMi3gM8kHplZihu1H298ZR1Q+0L+wTgZcDJ3f3/AA7uL86CPh4RT2DTvhpH9pZouvcA7wf+F/Vv7BOBX/SaaB6Z+QPgfhGxM7U53WV9Z5rH3wDHAneJiPOBH1JPNgzJ5JXi104sv4xaJA1KRKwAHpGZB46Xdf9Xh9hXE+DciHghw+6rNfYu4KvdidwR8OfAUK/QPwt4YURcDYz/948yc8GBrnryLGrT1P/MzId0xy4v6znTNmMBt/z2nG/gksx81bYOs4g9M3OjzsoR8UDgiz3lmVcjHW2JiPsB50w0SbxxRNw3M7/Sc7SF3JsNz+s+EUFmvrPfSJvKzFMj4uvUs24FeFZmDqa5V2a+pfv205m50Weo+1wNTvf8/U3fOWb0MeCX1H6Qg7nyOsXNM/NtEfGszPwc8LmI+FzfoaaJiFsC/wyszsyHR8RewP0zc1CtBrrCYv9u1MTrDbTQXEXtj/txNh5wYcSwWt4AkJnruybzrRh8X62xzPyniDiZDaNRD/ZEbmZOO+E4VFdm5pURMR6B+HsRsWffobYVC7jl96mIeFxm/g9c2173xMx8WM+5pjmaTdvlT1s2BK388X4zGz9/v56ybDAi4l3AHajNEcbP6wgYXAHXFUFnZ+YnIuIQ6lnDozLzx31nm2Pwn6uIeF1mPm/iDPFGMnPBETV7sntmHtB3iBmMz2L/LCL+F3AB9arMEL2D2ux73K/4+9Srh4Mq4CJiB2qLi9tS+0MDg7v6unP3dTyAxceoRdyQB7A4KyLWUFu3XDuV0MBGzB1roq9WNxXPNzPz7sDgpo6YJiIOZMPgVacPZWTnKdZGxK7AR6njTFxC/fu6XbCAW363GBdvAJl5SUT8Tp+B5uo6LT8AuMWc/jo3AVb0k2pRTfzxpjZDuvaAuJtfZ8ifu32BvYY83cWENwO/30138ffA26mF5oN7TdVp7HP1/u7rG3tNsXm+FBG/l5nf6jvIIl4REbtQp7w4mvr6P6ffSPNalZkZES8AyMxrImKIJ8gGf/W10QEsbgZcxMZ9iAY1Yu6EJvpqdf/zvxERt8nMn/SdZzER8UrqCYf3dIueFREPysx/6DHWVJn55923/xgRpwG7ULtVbBeGfCB5XbF+8oMbEb/L8Pq/3IB6tnAlG/fXuRR4bC+JFtfEH2/gvIh4JrXYgNqn8Lwe8yzm28CtGNiAAPO4JjNH3Uh5b+iaqT2x71ATmvlcZeZXu6+f6TvLZngQ8KSI+CH1IL5Q+2rs3W+sjU2cvf4l8JA+s8zg193AW+PBQe5HzT00rVx9hbYGsHhetjMJ8mRfravZ8PkfYl+tWwPnRMRX2fjK5oHz79KbPwXukZm/BYiIE4CzgMEVcHBt381bUvvBQj1+GXyhvBQs4Jbfi4AvTPR52I86uedgTPTLeMcAm59tJCIuY0N/ghdGxFXUJkpD/eP9dOANwIupuT/DwF7/OVYB3+n+0Vx7Znug/2gu664UHALs1/0hv37Pma7V0udqrBvZ75/YdMTEO/cWan4P7zvALLqR/I6izlf4W+rAVs/p+nENzXOpoyTfISK+CNyCgZ1s6LRy9RXaGsDiKxFxNrUZ7clDbonRWF+t1gbW2BUYF/K79BlkIRHxt9TBzC6k/m2F+hkb1Em85WIBt8wy85PdEMLjgRaeM6SBFua4PCJeQ50kc5DDXTf2R5vM/DlwUN85NsM/9h1gMzyeOmrioZn53xFxG+A1PWea5h0RMa1f2WA+VxPeQZ0M+bXUAunJbPjHOCiZ+eOIeBBwp8w8vhvYaOfF9uvBe4FjqAfuUP8evA+4b2+Jpuj66uxIbYI8HoX2/w10FNomrr5CWwNYAHcG9geeAhwdEe8H3pGZ3+831qaizvn5F8DtMvPlEbEHcOtxa4Ih6U7mteL/UvtCnkb9XO3HAEdN7TyLOvjeRX0H6YMF3DKJiLt0I+KMByoYd6y8TdekcoidWcfDXf8ZAx/uOiI+k5kPXWxZXyLif2fmqyPiaKYPCjHIoZlb+keTmf/NxETOXTPlwQ22AvzdxPc7UgdfGOqkwzfMzFMi4rXdkPIvjojP9x1qmqgTjO9LLTaOp159fTd1OokhKZn5ron7746II3pLM4+ur87rMvP+wDl951lEE1dfx7r/90P8n7+R7orbqdQBIR5C/Tw9IyK+AfxDDmvetTdRTy79EfBy4FfUEyX37jPUNF1T5KOBu1Kb1q8Afj3AFkNk5vu6qY/uTS3gnt/9rx2inzLMJt7bhAXc8nkedZj7101ZN2KYEw0OfrjriNiROr/Wqm5Ez/HQzDcBVvcWbFPPB14N/ICBTYQ8TUR8ITMfNNFEdWyoTVPn+6f4q8wcVJOPzPzanEVfHNrnasJV3ZntH0TE04HzgUENujThz4F96A6MM/OCiBjMFfqIuFn37WlRJ0c/kfrZejzwid6CLexTEfEY4MNDbD4XETfJzEupc6lpiXX9Hw8B/pLaLO1vqU1q70EdeGVI0x/cNzPvGRFnwbUDxN2g71DzeCP1yvsHqCed/gq4U6+J5phy0WE8b/HqiFg90IsO5wGnR8Qn2LjLx7/Mv8t1hwXcMsnMw7qvQ++0PqmF4a6fBjybWqx9jQ0F3KXUs29DcWE3YM2TGf7ABWTmg7qvgzkAnsHg/ynCRgfyANcD7kXtaD1Ez6E2Q3wmtS/cLtTmVEN0dTeIzXjAjRsttsM29jU2nv/raRPrRtSrBkPzXOoJsmsi4kqGdwLnvdQWInOfW7r7t+8j1HXIl6l99h6VmWsnlp8ZEf/WU6b5/Kbr9zz+/N+CgTb3BsjMcyNiRWauB46PiC/1nWmO51L757d00eEn3e0G3W27YgG3TCJiwXmTBjqvyuCHu87Mo4CjIuJvM/PovvMs4M3U4WxvD5w5sbzggcaSaeCfImx8sHkNdbSsQ3tNNI/sJpjvho4/PDOv6DnSQjIi3gLsGhGHUQvN43rOdK3MHNLVipkM/QROZv5Z97W557YRe8535TUzX7WtwyziDcBHgN+JiH+iDrbz4n4jzevy7urg2RHxauooz4M64ZSZ48HVHp6ZV06u61o+Dc7EVB03pp5o+lXPkbapMhoNrpXEdUJEHL/A6lFmDuqsdncm65mZ+a99Z5lFl/d/0U3kOl4+tEvnEfHmzPzrvnNsjYj4+PjAaUgi4j+oHe7fCvw39Z/ikzLz93sN1rCu+czbqKMPQm1G9dShDroQEX8M/Am1OD4lM0/tOdJMIuJWA+5XMlgTzbumGmgzr8Hr/p8+ldri5uTM/NLEuhdn5it6C7eAiLgL8FDq5/8zmfndniNN1bXGuZB6leg51JYNb8rMc3sNNkVEfD0z77nYsiGIiLtTrxiPW7msA/4qM4feh3dJeAVumWTmk/vOsDkyc31EHAg0UcAB/w5cCXyLYTebaLp46xzWd4B5/CW1SeIR1H+Ke1AHCBm8AR/AHw88OzNPA4iIP6SOTDnIojgzT42Ir9D9L4uImzUyj9XbqCegBm9gB2/TmneNDbWZVwveAtwQ+Cp19MnPZeZzu3WPpo5MO0QXAp+nfv53ioh7DrSIX0dt8n0l8LKuYN6h50wbiYhbAbtRn8d92Hh8gRv2FmxhxwLPnfP/6jjgAX2G2lYs4JZZ1yTxpdShWAE+BxyZmUMcOedLEfFG6kiUk5NNDvEP4u5DHDL6uigzBzmp98TcalfS3jw7Qz2A//X4nyFAZp4eEYNslhIRTwOOBK6gnsRppnlyZg7xtZ9qQMVba33KW3Kf8f/T7hjgTRHxYeBgNu5nOBgR8XLgSdSBwsZNyYZaxH+G2lpk/Ld0J+BTDKvQeBj1+dydidGdqQMGvbCPQDO40ZT/V4NqmrqcLOCW39uBbwPR3f9L6lnuBfvI9WT8x+TIiWVD/YN4ckT8SWZ+qu8g1wUR8S2mTHfAgOdXiogHUuet+102bkbrAfyW+0pEHEOdp2w8YuJpEbE3QGZ+s89wc/wdcLehzqs5Z/CaTQz1SmF3Jv4+1Nf/jIFeKd7EgK9qt+DaASAy8xrg8Ih4CfBZhjm3ItRjqjtk5tV9B5nBjpP9szLzVxExqKtamXkCcEJEPCYzP9R3nhmdFxH/h9qMEuoIqj/sMc82ZQG3/O6QmZPNul4WEWf3lmYBjZ3d/E/gI93ks79heKOltWZwfdxm8DZq08mvAet7zrKJRg/g9+2+zi3YH0w9oN+P4fgBcHnfIRYwbaTEsUFeKYyIpwLjA/dCbU53ZGa+vd9kMxnqVe0WnBkRB2TmJ8cLMvPIiLiAOiDXEH0b2BX4ed9BZvDryeadEXEvasuBwcnMD3WjkN+NOm/pePmR8+/Vm6dQW998mPr36j+oI39vFyzglt8VEfGgzPwCXHvVYJAf3GkG3Kb8dcD9gW8Ncb6i1kw0R2zJLzPz5L5DLKC5A/jM/IO+M2yGF1CbfX+FjecAemZ/kTZodKTEvwf2ycyL4Np5wb5EbUkyaAO+qj14mXnIPMvfSh0kaoj+L3BWRHybjT//B/YXaV7PBj7QFcQAt6a2bhicbrqIG1KnP3ordXTPr/Yaah6ZeQl1ypvtkgXc8vtr6mXpXagHchcDT+w30mb5a4Y5iMV/Ad+2eFta80yO/eshXdmcGInutIh4DfXs2+Q/8EGccGj0AH4TEbH3wJpOjr2FeqVo0AMZAXQDRI2vXp6emR/vM88C1rLxJNmXAT/tKctUXauLb2bm3fvOsj0YcNPUE4BX0cDnPzPP6EbM3JN6HPi9zBzPu0tE/PGARtB9QGbuHRHfzMyXRcTrqP9jByMiXp+Zz46If2dK14+BFvFLzgJumWXm2cDvR8RNuvuX9hxps4wnJB+gnwGnR8TJbHzwPqhpBBo0bXLsO/aaaFNzR6Lbd+L7QfbZbOgAfppnM8zJvK+ZGClvsCLilcC9gfd0i54VEQ/MzBf0GGsjETF+Hs+n9oP8GPWz9EgGdvY9M38bEd+IiNtk5k/6zrMdGGrT1HWZ+Ya+Q8yqK9i+Pc/qVwFDKeDGLcQuj4jVwEXA0E5Gjvu8vbbXFD2zgFtmXROUlwIPAkYR8QXqKJQX9ZtsUxHxmcx86GLLBuKH3e0GTHTA1tYb+uTYjfXVbOIAfiFDm7NywmkRcTh1SpHJkzhD61v4p8A9MvO3ABFxAnAWtQnoUIwn8P5Bdxv7WA9ZZnFr4JyI+Cobj5i8XZx535YG3DT1axHxf4E1DLAFxmYa0kifH4+IXYHXAF+nnsgZVDPazPxa9+09MvOoyXUR8SzqaO/XeRZwy+9EasfK8UAmf0Edpn//3hLNERE7Uts8r4qIm7Lx/B+rewu2gMxsbdj4VlweETcAzo6IV1OvdA5yWN6I+Gfg1Zn5P939mwLPy8wX95tsEy0cwAMQER+i9nc6eZx3wJ7QfZ18HgfZt5A62MK4sNylzyDTNPj3tLW8TWhs4KV9uq/3m1g2yBYYMxhMV5DMfHn37Yci4uPUETSHOO0V1O5IR81Z9qQpy66TLOCW380mPhAAr4iIR/WWZrqnUZtJraYOvDAu4C4Fjukr1DQRcQRwYmaui4g7UKdk+D3g+8ChmTlfEwXN5i+p/d5amBz74Zl57fw0mXlJRPwpMLQCDgZ+AD/hzdRRvN4QER8A3pGZ3+s501QN9TEcD7ZwGvVv634MsHgH6DJO61MytIPinwA/6yZGJiJ2Am7Zb6TrhK9T/+ZfQn2v7kp9rmFgJ0daa4kxdBEx79RWEUFmDqYfXEQcTD2Bd7uIWDOx6sbUJp/bBQu45XdaRBwEZHf/scAnesyzie4S9FER8beZeXTfeRbx15n5xu77NwD/mpkfiYg/pA5q8MDekl0HTIxGeQXDP8u9IiJ2yMyr4NqDuB16zjRNMwfwmflp4NPdoEsHA6dGxE+B44B3T3a878tCBxrAoA40ADLzfRFxOrUZbQGeP9BBIaDOrTe2I/XkzTU9ZVnIB9h4EuT13bJ79xPnOuOTwJrMPAkgIh4O7J+Zz+s31qYi4pbAPwOrM/PhEbEXcP/MfFvP0TYx+X9qnmU/2vapNvGI7uvvUD9bn+3uPwQ4nWENZPIlauugVWzcJ/4yYIgDbi0LC7jl9zTguWzodLmCOifIcxnYvGWZeXREPAC4LRtPjPzO3kJtavI9+zuZ+RGAzDw9Im48zz6aUUT8kOln4Adz5nXCu4HPRMTx1MxPoY5MNiiNHcCP++0eQr0aexa1796DqM1V/rC/ZNd6xALrRgzrQIPuDPH7qAfGv15s+z5N9C0Z+2JEDLE/ycrJCZwz8+qu6be2zr0z8+njO5l5ckS8fKEdevQOagucF3X3v0/tnjK4Ag74MnDP+ZZl5oInpbaFzHwyQNdscq/M/Fl3/9YMrCVWd6L5x9SppLZbFnDLLDObKSoi4l3AHYCz2TAx8ggYUgH3wYh4B3AkdSLvZ1MP2B7KhqYe2nKTIzruCDwOWLBfRF8y89UR8U1qf9ICvDwzT+k51iZaOoCPiA8Dd6GecHrE+J848P6IOLO/ZBuMDzQa8jrqnE+v7AbdeD/w8XHzvyGZ0wfqetS/B7fqKc5CfhERB2bmGoCIeCSwrudM1wXrIuLF1JNjI+qJnKE2SVuVmRkRLwDIzGsiYv1iO21LEXErYDdgp4jYh43HF7hhb8EWdtuJv/sAFwJ37ivMQlqY9mg5WcBp0r7UMy+D6VA7V2a+KCKeRD0gvgO1ydzhwEepA8RoK0wZHfX13cipL+kjz3wiYgVwSmbuT232M2TNHMADb8zMz05bkZn7Tlu+rU0MeT/V0KYSyczPAZ/r3rN/RJ1X8+3Ug7ihGU8+D7Xp5I+AQ3tLM7+nA++JiHFz+rXUKU+0dQ6mjpr9ke7+f3TLhujXXWuBEVx7MD+0wTYeRh1UY3dg8u/SZcALp+0wAKdHxCnUY6wRdVqh0/qNNK8Wpj1aNhZwPYiIszJzn8W33Oa+TT3b+rPFNuxTZr6D2nxCS2xikmzYcAZ+cFeRM3N9RFweEbsMeIQsoI0D+Ml+ZdP6mA2sX9ng3o+L6fpnPoJayN+TATb17ewFPINu2hvg88AgrrxOyswfAPeLiJ2BkpmXLbaPFteNMvmsvnPM6HnUKQTuEBFfBG5BHWNgMDLzBOCEiHhMZn6o7zyzyMwjuv8Bf9AtOnbcVWWIhj7t0XKygOvBQIs3qB1Cv9NdJZicV6WJuXUi4lZD7lvUiMkOweMz8NFPlEVdCXwrIk5l47mgntlfpOkaOIBvpl9Za0PeR8T7gftSrxQfQ53IfahTNJxAHX14PEHywdTmtI/rLdECMvNX4+8j4p6NzgE2GBFxZ+pANrdl437wQxuFlMz8WkQ8GNiT2jTx/w1hkKVJEf+/vTuPsqyqzz7+7cYAioIiiBpEEBVFA4Jit7GVSERQcUQegyJIeB1RUV41IhpBUWwH3ggalAShATE8aBA04hBtpkYgNjPiEAwQ5yAyCAI21vvHPjd9q7k1dHfd2ufcej5r3XXrnKpa6+nqqnvPPnvv30/72D4F2HLQyoG2rRboaW7YteY1fxKdaXs0DBnADZmkxbb/bqpzLXFY7QBr6XigrU1HO6FjpZn/jZZVdB2kCxfwHdxX1utfeQDwJMp+TaCVjcdPAF7V3CFuu21sb993vFTSFdXSrJ43UWa3Y82dDnyW0ri51b+vze/lacBpzYxsG/UGEw+smmIaJF1ge5Gk2xlfyGweLSu41+c1lJVCXWh7NOMygBu+XYFVB2vPH3CuumapV2fZzuBtLUlaj/ICuCXj78B+sFamiTTLU7qg9RfwXdtX1jgZ+CFln8kHKXtgr62aaLDzgEMkbWH79ZIeRxkofa12sAEuk7TQ9kUAkhYAyypnmhbbGbytvRW2j60dYppeTFnRYEl/ogzmbLs1xcxsf655bv2qAduLmucuLVG/Cbin2U9+eLNNoY2thIYiA7ghkfQmyl6CxzSV8noeRMveELt450XSybZfM9W5WG1nUjaCL6dvGW0bdajlQRcu4Lv0pt3zWNt7SXqJ7SWSTgVaV4WUMoBfzsq+ZT+jzHS06f+/ZwGwr6TeRfAWwLWSrqK8F2xXL9p4kv4ceDTjbzSdVy/RSPiqpDdTipj0b6O4uV6kwZpS8h8DPta8pr4fWEypRNgKko6e7PNtWu6/SgXa+2jj7wDwHUoV6t5S6vsD32J8j8iRlQHc8JwKnE1p4vuevvO3t+0PoaN3Xp7Uf9DceXlqpSyjZHPbu9cOMU1daXnQ+gv4LtwhHqC33+UWSU8GfkWZOW6brW2/UtLeiiGoCQAAIABJREFUALb/IGneVN9USSf+9iUtpsy+/IDxLW8ygFs7+zXP7+o7Nwa07aYYAJK2pOzRfiXl9+DdVQPdV6+v4jMpBYJOa4736vtcW/Qq0A56bWrr78D6/ftgbf9eUlvbM8y4DOCGZ8z29ZIOXPUTkjZu2yCuK5qeL++l9FW5rTk9D7gHOK5asNFxoaS/sH1V7SBT6UrLAzp0Ad/XFH2cFu4rAzhO0kOA91Gq0T2Qche+be5pitj0yp1vTUtnt5tZjS54KWUWu5U/x66yvVXtDNMl6WLgzyg3w/ay/dPKke6jt8y/aX30nF6RFUmfpcwUtUaX/u/73NFfvEjSU4E/VM40azKAG55TgT0YfFejrXczWs/2kcCRko60fUjtPCNoEfDaZnni3axcRtuapVM9XWl5QIcu4Bk/K7g+8DLgF5WyTOU7tn9HmXV5DICkVl2ENAP1z1IK2DxK0hcod+NfWzPXCPgp5eK9rX9HnSJpF9vfHdRCBFrXRqRnP9s/rB1imh5JeW/q3bh/YHOulSS9GHh2c3hOy5b793s7cLqk3nvUIyizsXNCBnBDYnuP5rlVFxQj5GuSNrB9h6R9KKXZP9WhO8ht9fzaAVZD61sedO0CftVeRZK+CPx7pThT+TLl777fl2jRUmrbY5IOAp4HLKTcEDnI9k11k3WTpGMoN0LupJQO/w7j92q1Zk9Rx+wMfJfB7URa1Uakzy8lHcXKgca5wAdb2hf0o5QCQb2G2DvT0qrfkj4K7AR8oTl1kKRntvGGue3/kPQEVraS+GHbWkkMUwZwQ7LK7MB9pF/NWjsW2F7S9pR178cDJ1FeGGMN2b6h2U+4GS1/fehCy4MRuIB/HKWQRWs0b9hPAjZaZcZgQ/raCbTIRcBjbLe+5UUH9JqKL6csm+13n6W/MT22P9A8d6mdyOeBq1l50+41lP3GA2cRa7J9gqSzKUWCAN7T4p61LwCe0mt1I2kJcBnQugGcpH1XObWDJGyfVCXQLGv1BVrH9WYH1qcs7bqCcvG2HXAxZalarLkVzcXxSygzb8dL2m/K74pJSXor8AHg10CvV9kY5fe2VSQ9lJJ1ESXjBZQ7sKvujautMxfwAyrR/or2tTzZhrI8/cGMnzG4nXb2AXsO8AZJN1Aazrd2WXLb9e0pOsj2p/o/19woiTXQ0TYiW9vu7/l1uKTLq6WZRLMS47mU94EPStpC0tNtX1I72wQezMrlnhvVDDKFnfo+Xh/4a+BSys38kZcB3JD0Zgck/Qvw+l5RiKZa2jtrZhsRtzcFTfYBnt3MGv1Z5Uyj4CBKcYC2DYIG+RfK/qfem/irKVW+nlst0WCtv4BvlsgsAzZteuq0lu0zgTMlPcP292rnmYYuLUvuiv2AT61y7rUDzsX0tHHv8FT+IGmR7QugvIbR3gIW/0i5IboLpWfl7ZQl4DtN9k2VHMnK5Z7zKEtUWzf7BmD7rf3Hkjai9AedEzKAG74n9Ff0s321pKfUDDQiXgm8CjjA9q8kbQF8vHKmUfDflD5wXbCx7Q/1HR8h6aXV0kysCxfwR1P2jl3IffeVtdXLJF1DuWj7BrA98Hbbp9SNNV725c6cppLrq4CtJPUvoXwQ0IWbTq3U0TYibwROai7aAX7HyjYIbbPA9o6SLgOw/TtJ69YONYjtL0o6hzK4nAf8XYuXe67qTsqy/zkhA7jhu1bSPwOnUJYm7QNcWzdS9zUvKEf1Hd/IHJk2H7KfAudI+jfGFwdo4xKapZL+BnBz/AqgdcsUO3IB/8emhcDmg5rPtrQ4xPNsv1vSyyi99fYCllJea2M0XQj8EtiE8UWMbgeurJJohEh6PGV/+Wa2nyxpO+DFto+oHG0cSfMpK0W2l7QhgO3bpvi2mv7YrBLqVSLelJVbFFqlmcm83PZZTYG4d0tqZYE4SV9l5ZL/+ZRee574O0ZLBnDDtz/wJsrSNChLvo6tF2c0SFoIHAM8EVgXWAf4ve02r9fughubx7rNo83eABzMyiUT61D6whxMWaK4YbVk3bMHZenpLrSvwexEekumXwB80fbNUquKkMYMay4ibwCeUTvLiPonShPvzwHYvlLSqUCrBnC2/yTpLeXDVg/ceo4GzgAeJunDlJuN76sbaUL9BeLeRSkW09YCcZ/o+3gFcIPtn9UKM9sygBsy23c1TRu/bvtHtfOMkE8Df0Np4vk0YF/m0NT5sHRpKY3tSfdtSHqS7WtmK0+XNVUx/0XStbavqJ1nmr4q6YeUJZRvbu5qt3r/XqwdSRfYXjSg2E5vX2lu2qydB9i+ZJUbIStqhZnCtyW9k7Lv+Y7eSds3T/wtddj+gqTllCIb84CX2m7rSqz+AnFHt7lAnO1za2eoKQO4IWsaIn6cMpuxVbP/7YO2X1w3WffZ/k9J69i+FzhB0oW1M3VdcxH8bkqZ9v8tyW57l2qh1tzJdGc/VysMGrxJ2qONjVxtv0fSYuA22/dKugN4Se1cMTy2FzXPXSy60QU3SdqalUv9XkFZstpGf9s8H9h3bgx4TIUsE2qWe15p+8lAFxqPt75A3IAbOOPMlRs5GcAN3weApwPnANi+XNKWNQONiDubTcCXS/oY5U1mg8qZRsEXKHc096BsEt8P+J+qidbcvNoBRsROQOsGcI0nAltK6n8vy17YESfpg5TtCN+zfcdUXx/TdiBwHPAEST8H/otS3bd1bG9VO8N0NMs9r5C0RbNXv+1aXyCudwOneR34FeVm7TzK7+qcubmTAdzwrbB9a/ZmzLjXUPY8vQV4B/AoVpaTjzX30GbJxEHN8oRzJXV1mUIa+64mSevZvnuV0x+pEmYKkk4GtgYuB+5tTo+RAdxccD3lIvOY5m78+cB5TYuJWE2r9IH7OqUY0HzK0sQ96SsY1haS1gfezMo+oOcDn21pG5RHANdIuoTxyz1btxKrYwXidrO9oO/4WEkXAx+rFWg2ZQA3fFdLehWwjqTHAW+jVNKKtdBXEekPQGf2bXXAH5vnX0p6IfALYPOKeWJ2fY/7LjsddK4NngZsazsD9TnG9ueBz0t6OCBKb9XXM4fuvs+w3s9tG8qM+5mUGY3XUGY62+gkSvXRY5rjvSkzMXtVSzSxzlyjSHo5sBh4GOV3oM37S++V9GpKT9gxyu/AvZN/y+jIAG743gocSinJfirwTVpW0alLJF3F5GufW9McuaOOaPrq/F/KG+OGlBnOVpE0D9jc9n9P8mX3zFaermsuhP8cuL+kHVi5/HRD4AHVgk3uauDhtHePTgxJ05pnW+DXlJmXVwCXVg3VYb3iVZK+Bexo+/bm+DBKobA22sb29n3HSyW1tQDTjcAve7ODku4PbFY30oQ+BryoxUVW+r0K+FTzGAOWNefmhAzghqjZ/Hm47XdRBnGx9vaoHWCU9RWruBV4Ts0sk2mqZH2F0nx6oq9ZOIuRum434LWU2db+5VK3A++tEWgaNgF+0CxL6u9Z2LplSTHjHkpZQn8LcDNwk+22Vkvski0Yf+PrHmDLOlGmdJmkhbYvApC0gHIB30anA3/Zd3xvc26nOnEm9euODN6wfT1zuHDVvLGxrD4ZJknf7WgFvwigvVUIJX0GONH2f9TOMiok7Wn7y7VzTIekgX2J5npp6blE0hMpNx/eAaxjO8u914KkQylLUs+gzGi8DDjN9pFVgw0g6VrKks9eYZAtgGspDbLH2rQaR9Lltp+yyrkrVplBbAVJn6KsbPgK42+M/Wu1UBPoSuP5YckM3PBdJuksyt2W/s2rrftj6DpJx9l+fe0cI6itVQifA7xB0g2Uv63eWv3WvHF30HckHQU8uzk+l9L25NaKmQbKQG3ukrQH8CzK7+lDgO9SllLGWrD9YUlnU362APvbvqxmpknsXjvAavgfSS+2fRZA02PtpsqZJrIhcCfwvL5zY0Abr1k70Xh+WDKAG76Ngd8C/bNwbf1j6LrP1Q4wimx/oHaGCTy/doARdDxlb1mvbO5rgBOAl1dLtIo0cw7K3/55wKds/6J2mFFi+1I6sJ/Q9g2SHkKpQH2/vvNtzP5G4AuSPt0c/4zy2to6tvevnWE1dKnx/IzLAG745gMH2b4FoHnB+WTdSKPJ9vLaGbquqUC1qluBq2z/ZrbzTKZ5A9+elXeLzx/UiDpWy9a2+9txHC7p8mppBkgz57B94NRfFaNM0oco+3avY+WNnDHG3yxvBdvXAQslPRCY1ysS0yNpP9tL6qQbT9LmlAJmz6T8PC+gXMP+rGqwwbrUeH7GZQA3fNv1Bm8Atn/XVHmLtSDpq0xejTKFDNbMAcAzKH2AAP4KuAh4vKQP2j65VrBVSToIeB0rZ7NPaZbRHjPJt8Xk/iBpke0LACQ9k9KqI6LVsoR+zhHlhlNnqg3b/v0EnzoIaMUAjrLi4lRWtmPYpzm3a7VEE+tM4/lhyABu+OZLeojt3wFI2pj83GfCTykbbU9pjvemNHf9Zq1AI+JPwBNt/xpA0maUTcILKEuWWjOAoww2F9i+A0DSYkrPsgzg1tybgCVNKwmA3wH7VcwTMV1ZQj+3XA08GGjVypA1NG/qL5k1m9o+oe/4RElvr5ZmErZ/CjxX0gbA/DbPbA5DBhLD90ngQklfoswYCfhw3UgjYQfbz+47/qqk82y3teR5V2zZG7w1fgM83vbNkv440TdVMo/xTTvvpV1vhF10LaUP0NaUi6NbgZcCV9YMFTERSfOBB2YJ/ZxzJKVI3NV0v41Im8rB3yRpH+CLzfHelDoOrdW7iTtAm2Y2Z1wGcENm+yRJ36esy54HvNz2DyrHGgWbSnpMcwcGSVsBm1bONArOl/Q1VjZvfQVwXnOH65aJv62KE4CLJZ3RHL+UUoQj1tyZlP/nS4GfV84SMVBTae6NlJs2y4GNJB1l++N1k8UsWgIsBq6irBzpsjbdePxb4NPA/6MMLC9sznVRm36uMy4DuFnQDNgyaJtZ7wDOkfTT5nhL4A314oyMAykVBxdRXvyWAF+2PUbLGnvbPkrSOazM2uaS112xue0uleeOuWlb27dJejXwdeDvKAO5DODmjptsH107xAxpTQNy2zcCXZzFHKRNM5szLo28o7MkrQc8oTn8oe27J/v6mJ5m39vTKS9+l7St+qSkDZuLt40HfHoMuM32vQM+F1OQdBxwjO2rameJmIika4CnUIotfNr2uW1tjBzD0fSrvBs4i/FLKFvXRqB5T/0I8Ejbz5e0LfAM261bMSJpCQMqp9vu3CycpMtsj2zRwPm1A0SshacCTwK2B14pad/KeTpPpaHKJZSlk6IsUXxF3VT3cWrzvBz4ft9jOWXp368kfaRStq5bBCyX9CNJV0q6SlL2v0XbfI5StGoDyhLvRwO3VU0Us20HYCFlYPTJ5vGJqokmdiKlwNojm+MfA60sDMKAyumUn3UXtWZmcxiyhDI6SdLJlEILl7OykMUYcFK1UKPhUGCn3qybpE2Bfwe+VDVVH9t7NM9bDfq8pHUoFcpS0Gb1pTl6dMFn+pfPSbqRli3xjuGy3aX/701sW9IhALZXSGrrKpHOVE6fambT9luqBhyyVv6nREzD0yj7ILIGeGbNX2XJ5G9p8Ux9s7zjccD6vXO2zwOeWC1Uh9m+oXaGiGn4T0mnAyfYvrZ5H1hRO1QMn6R9bJ8i6eBBn7d91GxnmoY7JD2UlQ2nF1Iq/LZRlyqnn0gpZnZoc/xj4DTmSDGz1l6YRUzhakofuJhZ35D0TUmvlfRa4N8oRQJaR9L/ofSm+yZwePN8WM1METErtgN+Ahwv6SJJr5e0Ye1QMSs2aJ4fNMGjjQ6m7NXbWtIyykqht9aNNJjtk4A9gV8D/0OpnP6//V+bm6ZtsYlt01Qhtb2C8a2FRlqKmEQnSVpK2cR+Cd3vAdMqkvYEnkmp7Hie7TOm+JYqJF0F7ARcZPspkp4AHG77lZWjRcQskfRsSs+qB1OWen/I9n/WTRW1STrE9pG1c/RIuh+wDeV99Ue229ZXdVokXWp7x9o5AJoq1HsC37a9YzOzudj2znWTzY4soYyuOqx2gFFl+8vAl2vnmIa7bN8lCUnr2f6hpG1qh4qI4Wr2ub4Q2J/SQuaTwBeAZ1FWDDy+Wrhoi70ozb6rk7QX8A3b10h6H7CjpCPaWDFzGtrUW23Vmc1NKQXY5oQM4KKTbJ9bO8MokXQ7g3umzAPGbLdxedLPJD0Y+ArwbUm/A35ROVNEDN9PgKXAx21f2Hf+S82MXESbBhrvt326pEXAbpRqmccCC+rGWiOtWbZn+1JJOzMCM5trIgO46BRJF9heNGDA0eaBRuvZbuvegQnZflnz4WHNktqNgG9UjBQRs2M7278f9Anbb5vtMNFKrRlosHJf1guBY22fKemwinlGwojNbK62DOCiU2wvap47N+CImddsqH4UcHvzeDKlF1xEjK4Vkg6k9AHtr0DbuWbDMTRtmoH7uaTPAc8FFktaj+4WEWzTz3WUZjZXW1d/gSJijpP0IeBK4Bja38g1ImbOyZQqxLsB5wKbU27gRPScXjtAH1GqJO/eNMneGHhX3UiDNT12Jzv317MYZyr3mdkE1q2YZ1ZlBi4iukrA1rbvqR0kImbVY23vJekltpdIOpVygRxzhKTHU2ZbNrP9ZEnbAS+2fQSA7Y9UDdjH9p2SrgN2k7QbcL7tb9XONYEn9R80BYOe2ju2ffOsJ5rYKM1srrY58w+NiJFzNaV0eETMLb1CBbdIejJl/+uW9eJEBf8EHELzu2D7SuBvqiaagKSDKFVSH9Y8TpHUqj5wkg5pagtsJ+k2Sbc3x78BzqwcbyKdmdkchszARURXHQlcJulq0gswYi45rtn/+j5KGfEHAu+vGylm2QNsXyKp/9yKWmGmcACwwPYdAJIWA9+jLP9vhaZn3pGSjrR9SO0809Gxmc0ZlwFcRHTVEmAxcBXwp8pZImLIJB3cd7h/8/yZ5nmDWY4Tdd0kaWuaapOSXgH8sm6kCc1j5X4tmo/bVAyk33slvRxYRPnZnm/7K5UzDdTMbL4O+Nfm1CmSjrPdmoHxMGUAFxFddZPto2uHiIhZ06s+vA2wE2X2DeBFwHlVEkUtBwLHAU+Q9HPgv4B96kaa0AnAxZLOoAzcXgIcXzfShD4DPBb4YnP8Rkm72j6wYqaJtH5mc5gygIuIrlou6UjKRVz/Esq0EYgYQbYPB5D0LWBH27c3x4fRrqqDMWS2fwo8V9IGwPze70Ib2T5K0jmUWS2A/W1fVjHSZHYGnmy7N7O5hLLKpY26NLM54zKAi4iu2qF5Xth3bgzYpUKWiJg9WwD91WfvIUVM5hRJDwb2pfy/36+3F67ljdznUZb7t3mQ8SPK39cNzfGjKO162qhLM5szLgO4iOgk28+Z7POS9rO9ZLbyRMSsORm4pLlwGwNeRtkTG3PH14GL6MAeaEl/D+wFfJky0DhB0um9lgdtIOmrlL+ljYBrJV3SHC8ALqyZbSIdm9mccRnARcSoOohc1EWMHNsflnQ28Kzm1Jy6cAsA1rd98NRf1gp7AzvYvgtA0keBS4HWDOCAT9QOsBa6MLM54zKAi4hRNadezCPmkmava/a7zl0nS3od8DXG74FuU6PpnuuB9YG7muP1gOuqpRnA9rm9jyU9Gnic7X+XdH9aOlbowszmMLXyPyUiYgaM1Q4QERFDcQ/wceBQVr7WjwGPqZZoYncD10j6NiXjrsAFko6Gdu3bawbFr6c0xd4a2Bz4LPDXNXNNoAszm0OTAVxEjKrMwEVEjKaDgcfavql2kGk4o3n0nFMpx3QcCDwduBjA9k8kPaxupAldT8tnNocpA7iIGFXLageIiIihuAa4s3aIqUhaB9jVdlt71K3qbtv39Kp6Srof7V3N0pmZzWGYNzbW1v+XiIiJSdoM+AjwSNvPl7Qt8Azbc6aMcETEXNRUIH0SsJTxe+Bad9Eu6ZvAi2zfM+UXVybpY8AtlBYNbwXeDPzA9qFVgw0gab/JPj/qVagzAxcRXXUipQ9M743lx8BpzKE+MBERc9RXmkcXXA8sk3QWcEfvpO2jqiWa2HuAAyjtGd5Aadfwz1UTDdDBmc0ZlwFcRHTVJrYt6RAA2ysk3Vs7VEREDJftJZLWBR7fnPqR7T/WzDSJXzSP+cCDKmeZlO0/Af/UPFrL9r2SNpW0bhdmNochA7iI6Ko7JD2UZn2+pIXArXUjRUTEsEn6K0qfz+spBaseJWk/2+fVzDWI7cNrZ5iKpKuYZK+b7e1mMc50XU93ZjZnXAZwEdFVBwNnAVtLWgZsCryibqSIiJgFnwSeZ/tHAJIeD3wReGrVVANIWsqAwZHtXSrEmcgezfOBzfPJzfOraW+xmM7MbA5DiphERGc1FbK2odyBbfMSmoiImCGSrlx1VmjQuTaQ1D+oXB/YE1hh+92VIk1I0jLbz5zqXNSXGbiI6CRJewHfsH2NpPcBO0o6wvaltbNFRMRQfV/S8YyfKVpeMc+EbK+aa5mkc6uEmdoGkhbZvgBA0l8CG1TONFBHZjaHJgO4iOiq99s+XdIiYDfgE8CxwIK6sSIiYsjeRFnu9zbKCozzgH+smmgCkjbuO5wPPA14eKU4UzkA+LykjSiDo1uBv60baULv7Pv4f2c2K2WZdRnARURX9SpOvhA41vaZkg6rmCciImaB7buBo5pH2y1n5UzRCkrxjQOqpZlEM1u4vaQNgXm2xxUGawrFtKK/WsdmNmdcBnAR0VU/l/Q54LnAYknrUe5uRkTECJP0TOAw4NH0XcvafkytTJPYltIQexFlIHc+8P2qiaZg+7YJPnUQpfpndR2b2ZxxGcBFRFcJ2B34hO1bJD0CeFflTBERMXzHA++gzG61vf/nEuA24OjmeG/K3r29qiVac/NqB+jTmZnNYcgALiI6yfadkq4DdpO0G3C+7W/VzhUREUN3q+2za4eYpm1sb993vFTSFdXSrJ02la7v3MzmTMoALiI6SdJBwOuAf21OnSLpONvHVIwVERFDImnH5sOlkj5Oef2/u/f5llYhvkzSQtsXAUhaACyrnGlNtWkGbpRmNldbBnAR0VUHAAts3wEgaTHwPSADuIiI0fTJVY6f1vfxGNDGEvILgH0l3dgcbwFcK+kqYKyNvesm0aaB5yjNbK62DOAioqvmMX7vw7206+5gRETMINvPmc7XtalaImWvdidI2gz4CPBI28+XtC3wDNvHA9h+S9WA443SzOZqywAuIrrqBOBiSWdQBm4voWxsj4iIua011RJt31A7w2o4kfLeemhz/GPgNNr53jpKM5urLSW3I6KTbB8F7A/cDPwW2N/2P9RNFRERLZDVGGtmE9sG/gRgewXtrfK5O7AVsHPz2Ap4AbAH8KKKuWZFZuAiouvmUd5s8oYdERHQrmqJXXKHpIfS/PwkLQRunfxb6ujYzOaMywxcRHSSpL+nLJF5CLAJcIKk99VNFRERLZAbemvmYOAsYGtJy4CTgLfWjRSDZAYuIrpqb2AH23cBSPoocClwRNVUERFR25wpZjGTbF8qaWdgG8og+Ee2/1g5VgyQGbiI6KrrgfX7jtcDrqsTJSIiZoukzSQdL+ns5nhbSQf0Pt+yaomdIWkv4P62rwFeCpzW13svWiQDuIjoqruBaySdKOkE4Grg95KOlnT0FN8bERHddSLwTeCRzfGPgbdXSzM63m/7dkmLgN0o2xSOrZwpBsgALiK66gzgvcBS4BxK2eOzgeXNIyIiRlOXqiV2Se9n+ELgWNtnAutWzBMTyB64iOgcSesAu9rep3aWiIiYdZ2pltgxP5f0OeC5wGJJ65HJnlbKf0pEdI7te4FNJeXOYETE3JNqicMhytLU3W3fAmwMvKtupBhk3thYWmVERPc0dwl3pLyJ39E73zT4joiIESbpfqRa4oyTtD3wrObwfNtX1MwTg2UGLiK66hfA1yivYw/qe0RExAhLtcThkHQQ8AXgYc3jFEmZ2Wyh7IGLiE6yfXjtDBERUcX7bZ/eVy3xE5RqiQvqxuq8A4AFtu8AkLQY+B5wTNVUcR8ZwEVEJ0laSrOBvZ/tXSrEiYiI2XOfaomSDquYZ1TMY3w1z3ubc9EyGcBFRFe9s+/j9YE9gRWVskRExOxJtcThOAG4WNIZlIHbS4Dj60aKQVLEJCJGhqRzbe9cO0dERAyPpAcAuwNX2f6JpEcAf2H7W5WjdV6zl3BRc3i+7ctq5onBMgMXEZ0kaeO+w/nA04CHV4oTERGzxPadkq4DdpO0G2WgkcHbzJlHaZKe5ZMtlenmiOiq5cD3m8eFlL5AB1RNFBERQ5dqicMh6e+BJcBDgE2AEyS9r26qGCQzcBHRVdsCb6Ys9RgDzqcM5iIiYrSlWuJw7A3sYPsuAEkfBS4FjqiaKu4jM3AR0VVLgCcCR1PetJ8InFw1UUREzIZUSxyO6ylFwXrWA66rEyUmkxm4iOiqbWxv33e8VNIV1dJERMRsSbXE4bgbuEbStykrW3YFLpB0NIDtt9UMFytlBi4iuuoySQt7B5IWAMsq5omIiFlg+yhgf+Bm4LfA/rb/oW6qkXAG8F5gKXAOcChwNmXP+fJ6sWJVmYGLiK5aAOwr6cbmeAvgWklXAWO2t6sXLSIiZkGqJc4QSesAu9rep3aWmFpm4CKiq3YHtgJ2bh5bAS8A9gBeVDFXREQMUaolzjzb9wKbSlq3dpaYWmbgIqKTbN9QO0NERFSRaonDcT2wTNJZwB29k82S1WiRDOAiIiIiokuup1RLvKs5TrXEmfGL5jEfeFDlLDGJeWNjY7UzRERERERMi6SvADsB46olAr+BVEuM0ZcZuIiIiIjokjOaR885lXKMFElLKQPicWzvUiFOTCIDuIiIiIjohFRLHKp39n28PrAnsKJSlphEllBGRERERGdI+ibwItv31M4y6iSda3vn2jlivMzARURERESXXE+qJc44SRv3Hc4HngY8vFKcmEQGcBERERHRJamWOBwSc+cIAAABWklEQVTLWbkHbgVloHxAtTQxoQzgIiIiIqIzbB9eO8OI2hZ4M7CIMpA7H/h+1UQxUAZwEREREdEZqZY4NEuA24Cjm+O9gZOBvaolioEygIuIiIiILkm1xOHYxvb2fcdLJV1RLU1MKAO4iIiIiOgM28tXObVM0rlVwoyWyyQttH0RgKQFwLLKmWKADOAiIiIiojNSLXFoFgD7SrqxOd4CuFbSVcCY7e3qRYt+GcBFRERERJekWuJw7F47QExPBnARERER0SWpljgEtm+onSGmJwO4iIiIiOiSVEuMOS0DuIiIiIjoklRLjDltfu0AERERERGr4TJJC3sHqZYYc828sbH79EGMiIiIiGglSdcC2wDjqiUCfyLVEmMOyBLKiIiIiOiSVEuMOS0zcBERERERER2RPXAREREREREdkQFcRERERERER2QAFxERERER0REZwEVERERERHTE/wep5iAvNaM7TwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate feature importances\n",
    "importances = gboosting.feature_importances_\n",
    "\n",
    "# Plot feature importances\n",
    "indices = np.argsort(importances)[::-1]               \n",
    "names = [dat1.columns[i] for i in indices]\n",
    "plt.figure(figsize=(15,6))\n",
    "plt.title(\"Feature Importance\", fontsize=25)\n",
    "plt.bar(range(X.shape[1]), importances[indices])\n",
    "plt.xticks(range(X.shape[1]), names, rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that gradient boosting completely ignored some of the features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion:\n",
    "- The gradient boosting model and random forests model give us the highest accuracy 84%, whereas decision trees gives 73% accuracy.\n",
    "- In terms of precision, all three models performed similarly for class 0.\n",
    "- In terms of recall and f1-score, random forests and gradient boosting performed much better than decision tree for class 0.\n",
    "- For class 1, none of the models performed well. However, decision trees did a better job in terms of recall.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}