102_decision_tree_and_random_forest_project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6739a9f6",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "62153e51",
   "metadata": {},
   "source": [
    "___\n",
    "\n",
    "<a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>\n",
    "___\n",
    "# Random Forest Project \n",
    "\n",
    "For this project we will be exploring publicly available data from [LendingClub.com](www.lendingclub.com). Lending Club connects people who need money (borrowers) with people who have money (investors). Hopefully, as an investor you would want to invest in people who showed a profile of having a high probability of paying you back. We will try to create a model that will help predict this.\n",
    "\n",
    "Lending club had a [very interesting year in 2016](https://en.wikipedia.org/wiki/Lending_Club#2016), so let's check out some of their data and keep the context in mind. This data is from before they even went public.\n",
    "\n",
    "We will use lending data from 2007-2010 and be trying to classify and predict whether or not the borrower paid back their loan in full. You can download the data from [here](https://www.lendingclub.com/info/download-data.action) or just use the csv already provided. It's recommended you use the csv provided as it has been cleaned of NA values.\n",
    "\n",
    "Here are what the columns represent:\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 (takes values \"credit_card\", \"debt_consolidation\", \"educational\", \"major_purchase\", \"small_business\", and \"all_other\").\n",
    "* int.rate: The interest rate of the loan, as a proportion (a rate of 11% would be stored as 0.11). Borrowers judged by LendingClub.com to be more risky are assigned higher interest rates.\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 (amount of debt divided by annual income).\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 (amount unpaid at the end of the credit card billing cycle).\n",
    "* revol.util: The borrower's revolving line utilization rate (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 (bankruptcy filings, tax liens, or judgments)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f64ebe8",
   "metadata": {},
   "source": [
    "# Import Libraries\n",
    "\n",
    "**Import the usual libraries for pandas and plotting. You can import sklearn later on.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8563c0d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52f16a99",
   "metadata": {},
   "source": [
    "## Get the Data\n",
    "\n",
    "**Use pandas to read loan_data.csv as a dataframe called loans.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d1978fe9",
   "metadata": {},
   "outputs": [],
   "source": [
    "loans = pd.read_csv(\"C:\\\\Users\\\\shash\\\\OneDrive\\\\Desktop\\\\Py_DS_ML_Bootcamp-master\\\\Refactored_Py_DS_ML_Bootcamp-master\\\\15-Decision-Trees-and-Random-Forests\\\\loan_data.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b859514c",
   "metadata": {},
   "source": [
    "**Check out the info(), head(), and describe() methods on loans.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8d3c5f8f",
   "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",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   credit.policy      9578 non-null   int64  \n",
      " 1   purpose            9578 non-null   object \n",
      " 2   int.rate           9578 non-null   float64\n",
      " 3   installment        9578 non-null   float64\n",
      " 4   log.annual.inc     9578 non-null   float64\n",
      " 5   dti                9578 non-null   float64\n",
      " 6   fico               9578 non-null   int64  \n",
      " 7   days.with.cr.line  9578 non-null   float64\n",
      " 8   revol.bal          9578 non-null   int64  \n",
      " 9   revol.util         9578 non-null   float64\n",
      " 10  inq.last.6mths     9578 non-null   int64  \n",
      " 11  delinq.2yrs        9578 non-null   int64  \n",
      " 12  pub.rec            9578 non-null   int64  \n",
      " 13  not.fully.paid     9578 non-null   int64  \n",
      "dtypes: float64(6), int64(7), object(1)\n",
      "memory usage: 1.0+ MB\n"
     ]
    }
   ],
   "source": [
    "loans.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8ae6f273",
   "metadata": {},
   "outputs": [
    {
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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",
       "      <th>count</th>\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",
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       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\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",
       "      <th>std</th>\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",
       "      <th>min</th>\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",
       "      <th>25%</th>\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",
       "      <th>50%</th>\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",
       "      <th>75%</th>\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",
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       "    <tr>\n",
       "      <th>max</th>\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",
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      ],
      "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e8ab26d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
       "      <th>0</th>\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",
       "      <th>1</th>\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",
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       "    <tr>\n",
       "      <th>2</th>\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",
       "      <th>3</th>\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",
       "      <th>4</th>\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",
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      ],
      "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb63dd38",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis\n",
    "\n",
    "Let's do some data visualization! We'll use seaborn and pandas built-in plotting capabilities, but feel free to use whatever library you want. Don't worry about the colors matching, just worry about getting the main idea of the plot.\n",
    "\n",
    "**Create a histogram of two FICO distributions on top of each other, one for each credit.policy outcome.**\n",
    "\n",
    "*Note: This is pretty tricky, feel free to reference the solutions. You'll probably need one line of code for each histogram, I also recommend just using pandas built in .hist()*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0c0afde4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'FICO')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "loans[loans['credit.policy']==1]['fico'].hist(bins = 35, color='blue', label='Credit Policy = 1', alpha=0.6)\n",
    "loans[loans['credit.policy']==0]['fico'].hist(bins=35, color='red', label = 'Credit Policy = 0', alpha=0.6)\n",
    "plt.legend()\n",
    "plt.xlabel('FICO')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e458a647",
   "metadata": {},
   "source": [
    "**Create a similar figure, except this time select by the not.fully.paid column.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "132f45da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1d1e1a09160>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "loans[loans['not.fully.paid']==1]['fico'].hist(bins=35, color='blue', label='Not fully paid = 1', alpha=0.6)\n",
    "loans[loans['not.fully.paid']==0]['fico'].hist(bins=35, color='red', label='Not fully paid = 0', alpha=0.6)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58fe93ef",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "10aeb27b",
   "metadata": {},
   "source": [
    "**Create a countplot using seaborn showing the counts of loans by purpose, with the color hue defined by not.fully.paid.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0e337cb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='purpose', ylabel='count'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SJI1gnbxqPoD/BO7JzH9qKd+xZbTDgQVl+Grg6IjYJCKmADsBczPzYeCpiNi3zPM44KpO1VuSJEnDo5NXze8HvAe4OyLmlbK/AY6JiKk0h9eXAu8DyMyFEXEZsIjmivtTyhXzACcDs4BNaa6W94p5SZKkEa5jQTQzb6X9+Z3X9jPNOcA5bcq7gd2HrnaSJEmqzTsrSZIkqQqDqCRJkqowiEqSJKkKg6gkSZKqMIhKkiSpCoOoJEmSqjCISpIkqQqDqCRJkqowiEqSJKkKg6gkSZKqMIhKkiSpCoOoJEmSqjCISpIkqQqDqCRJkqowiEqSJKkKg6gkSZKqMIhKkiSpCoOoJEmSqjCISpIkqQqDqCRJkqowiEqSJKkKg6gkSZKqMIhKkiSpCoOoJEmSqjCISpIkqQqDqCRJkqowiEqSJKkKg6gkSZKqMIhKkiSpCoOoJEmSqjCISpIkqQqDqCRJkqowiEqSJKkKg6gkSZKqMIhKkiSpCoOoJEmSqjCISpIkqQqDqCRJkqowiEqSJKkKg6gkSZKqMIhKkiSpCoOoJEmSqjCISpIkqQqDqCRJkqowiEqSJKkKg6gkSZKqMIhKkiSpCoOoJEmSqjCISpIkqQqDqCRJkqowiEqSJKkKg6gkSZKq6FgQjYhJEXFjRNwTEQsj4rRSvk1E3BAR95W/W7dM89GIuD8iFkfEgS3l0yLi7vLa+RERnaq3JEmShkcne0RfAP46M3cB9gVOiYhdgdOBOZm5EzCnPKe8djSwG3AQcEFEjCnzuhCYCexUHgd1sN6SJEkaBh0Lopn5cGb+sAw/BdwDTAAOBWaX0WYDh5XhQ4FLM/P5zFwC3A9Mj4gdgS0y8/bMTODilmkkSZI0Qg3LOaIRMRnYC7gD2CEzH4YmrALbl9EmAA+0TLaslE0ow73LJUmSNIJ1PIhGxCuArwMfysxf9zdqm7Lsp7zde82MiO6I6F6+fPngKytJkqRh09EgGhEb04TQr2TmFaX4kXK4nfL30VK+DJjUMvlE4KFSPrFN+Woy86LM7MrMrnHjxg3dgkiSJGnIdfKq+QD+E7gnM/+p5aWrgePL8PHAVS3lR0fEJhExheaipLnl8P1TEbFvmedxLdNIkiRphNqog/PeD3gPcHdEzCtlfwOcC1wWEScCPweOBMjMhRFxGbCI5or7UzJzRZnuZGAWsClwXXlIkiRpBOtYEM3MW2l/fifAG/uY5hzgnDbl3cDuQ1c7SZIk1eadlSRJklSFQVSSJElVdPIcUUkd0t01vXYVVurqnlu7CpKkEcoeUUmSJFVhEJUkSVIVBlFJkiRVYRCVJElSFQZRSZIkVWEQlSRJUhUGUUmSJFVhEJUkSVIVBlFJkiRVYRCVJElSFQMKohExZyBlkiRJ0kD1e6/5iBgL/B6wXURsDUR5aQtgfIfrJkmSpFGs3yAKvA/4EE3ovIuXguivgX/tXLUkSZI02vUbRDPzX4B/iYi/yMzPDlOdJEmStAFYU48oAJn52Yj4E2By6zSZeXGH6iVJkqRRbkBBNCK+BLwKmAesKMUJGEQlSZK0VgYURIEuYNfMzE5WRpIkSRuOgf6O6ALgf3WyIpIkSdqwDLRHdDtgUUTMBZ7vKczMt3ekVpIkSRr1BhpEz+xkJSRJkrThGehV89/rdEUkSZK0YRnoVfNP0VwlD/ByYGPgN5m5RacqJkmSpNFtoD2im7c+j4jDgOmdqJAkSZI2DAO9an4VmfnfwAFDWxVJkiRtSAZ6aP4dLU9fRvO7ov6mqCRJktbaQK+af1vL8AvAUuDQIa+NJEmSNhgDPUf0hE5XRJIkSRuWAZ0jGhETI+LKiHg0Ih6JiK9HxMROV06SJEmj10AvVvoicDUwHpgAfKOUSZIkSWtloEF0XGZ+MTNfKI9ZwLgO1kuSJEmj3ECD6GMRcWxEjCmPY4HHO1kxSZIkjW4DDaLvBd4F/AJ4GDgC8AImSZIkrbWB/nzTJ4DjM/MJgIjYBvgUTUCVJEmSBm2gPaJ79oRQgMz8JbBXZ6okSZKkDcFAg+jLImLrnielR3SgvamSJEnSagYaJj8NfD8iLqe5tee7gHM6VitJkiSNegO9s9LFEdENHAAE8I7MXNTRmkmSJGlUG/Dh9RI8DZ+SJEkaEgM9R1SSJEkaUgZRSZIkVWEQlSRJUhUGUUmSJFVhEJUkSVIVBlFJkiRVYRCVJElSFQZRSZIkVWEQlSRJUhUGUUmSJFVhEJUkSVIVBlFJkiRV0bEgGhFfiIhHI2JBS9mZEfFgRMwrj4NbXvtoRNwfEYsj4sCW8mkRcXd57fyIiE7VWZIkScOnkz2is4CD2pR/JjOnlse1ABGxK3A0sFuZ5oKIGFPGvxCYCexUHu3mKUmSpBGmY0E0M28GfjnA0Q8FLs3M5zNzCXA/MD0idgS2yMzbMzOBi4HDOlJhSZIkDasa54ieGhHzy6H7rUvZBOCBlnGWlbIJZbh3uSRJkka44Q6iFwKvAqYCDwOfLuXtzvvMfsrbioiZEdEdEd3Lly9fx6pKkiSpk4Y1iGbmI5m5IjNfBD4PTC8vLQMmtYw6EXiolE9sU97X/C/KzK7M7Bo3btzQVl6SJElDaliDaDnns8fhQM8V9VcDR0fEJhExheaipLmZ+TDwVETsW66WPw64ajjrLEmSpM7YqFMzjohLgDcA20XEMuDjwBsiYirN4fWlwPsAMnNhRFwGLAJeAE7JzBVlVifTXIG/KXBdeUiSJGmE61gQzcxj2hT/Zz/jnwOc06a8G9h9CKsmSZKk9YB3VpIkSVIVBlFJkiRVYRCVJElSFQZRSZIkVWEQlSRJUhUGUUmSJFVhEJUkSVIVBlFJkiRVYRCVJElSFQZRSZIkVWEQlSRJUhUGUUmSJFVhEJUkSVIVBlFJkiRVYRCVJElSFQZRSZIkVWEQlSRJUhUGUUmSJFVhEJUkSVIVBlFJkiRVYRCVJElSFQZRSZIkVWEQlSRJUhUGUUmSJFVhEJUkSVIVBlFJkiRVYRCVJElSFQZRSZIkVWEQlSRJUhUGUUmSJFVhEJUkSVIVBlFJkiRVYRCVJElSFQZRSZIkVWEQlSRJUhUGUUmSJFVhEJUkSVIVBlFJkiRVYRCVJElSFQZRSZIkVWEQlSRJUhUGUUmSJFWxUe0KaHTq7ppeuwordXXPrV0FSZLUhj2ikiRJqsIgKkmSpCoMopIkSarCICpJkqQqDKKSJEmqwiAqSZKkKgyikiRJqsIgKkmSpCoMopIkSarCICpJkqQqOhZEI+ILEfFoRCxoKdsmIm6IiPvK361bXvtoRNwfEYsj4sCW8mkRcXd57fyIiE7VWZIkScOnkz2is4CDepWdDszJzJ2AOeU5EbErcDSwW5nmgogYU6a5EJgJ7FQevecpSZKkEahjQTQzbwZ+2av4UGB2GZ4NHNZSfmlmPp+ZS4D7gekRsSOwRWbenpkJXNwyjSRJkkaw4T5HdIfMfBig/N2+lE8AHmgZb1kpm1CGe5e3FREzI6I7IrqXL18+pBWXJEnS0FpfLlZqd95n9lPeVmZelJldmdk1bty4IaucJEmSht5wB9FHyuF2yt9HS/kyYFLLeBOBh0r5xDblkiRJGuGGO4heDRxfho8HrmopPzoiNomIKTQXJc0th++fioh9y9Xyx7VMI0mSpBFso07NOCIuAd4AbBcRy4CPA+cCl0XEicDPgSMBMnNhRFwGLAJeAE7JzBVlVifTXIG/KXBdeUiSJGmE61gQzcxj+njpjX2Mfw5wTpvybmD3IayaJEmS1gPry8VKkiRJ2sAYRCVJklSFQVSSJElVGEQlSZJUhUFUkiRJVRhEJUmSVIVBVJIkSVUYRCVJklSFQVSSJElVGEQlSZJUhUFUkiRJVRhEJUmSVIVBVJIkSVUYRCVJklSFQVSSJElVGEQlSZJUhUFUkiRJVRhEJUmSVIVBVJIkSVUYRCVJklSFQVSSJElVGEQlSZJUhUFUkiRJVRhEJUmSVIVBVJIkSVUYRCVJklSFQVSSJElVGEQlSZJUhUFUkiRJVRhEJUmSVIVBVJIkSVUYRCVJklSFQVSSJElVGEQlSZJUhUFUkiRJVRhEJUmSVIVBVJIkSVUYRCVJklSFQVSSJElVGEQlSZJUhUFUkiRJVRhEJUmSVIVBVJIkSVVsVLsCahz0sa/VrsJK13/iqNpVkCRJGwB7RCVJklSFQVSSJElVGEQlSZJUhUFUkiRJVRhEJUmSVIVBVJIkSVUYRCVJklSFQVSSJElVGEQlSZJURZU7K0XEUuApYAXwQmZ2RcQ2wNeAycBS4F2Z+UQZ/6PAiWX8D2bmtypUW9IGortreu0qrNTVPbd2FSSpY2r2iP7vzJyamV3l+enAnMzcCZhTnhMRuwJHA7sBBwEXRMSYGhWWJEnS0FmfDs0fCswuw7OBw1rKL83M5zNzCXA/sP50V0iSJGmt1AqiCXw7Iu6KiJmlbIfMfBig/N2+lE8AHmiZdlkpW01EzIyI7ojoXr58eYeqLkmSpKFQ5RxRYL/MfCgitgduiIh7+xk32pRluxEz8yLgIoCurq6240iSJGn9UKVHNDMfKn8fBa6kOdT+SETsCFD+PlpGXwZMapl8IvDQ8NVWkiRJnTDsQTQiNouIzXuGgTcDC4CrgePLaMcDV5Xhq4GjI2KTiJgC7AR4GakkSdIIV+PQ/A7AlRHR8/5fzczrI+JO4LKIOBH4OXAkQGYujIjLgEXAC8ApmbmiQr0lSZI0hIY9iGbmz4DXtil/HHhjH9OcA5zT4apJkiRpGK1PP98kSZKkDUitq+YlSRsg71olqZU9opIkSarCICpJkqQqDKKSJEmqwiAqSZKkKgyikiRJqsIgKkmSpCr8+SZJ6+Sgj32tdhVWuv4TR9WuglSVP4+lkcYeUUmSJFVhEJUkSVIVBlFJkiRVYRCVJElSFQZRSZIkVWEQlSRJUhUGUUmSJFVhEJUkSVIVBlFJkiRVYRCVJElSFQZRSZIkVWEQlSRJUhUGUUmSJFWxUe0KSJIkjQTdXdNrV2Glru65taswJOwRlSRJUhUGUUmSJFVhEJUkSVIVBlFJkiRVYRCVJElSFQZRSZIkVWEQlSRJUhUGUUmSJFVhEJUkSVIVBlFJkiRVYRCVJElSFQZRSZIkVbFR7QpIklTDQR/7Wu0qrHT9J46qXQWpCntEJUmSVIVBVJIkSVV4aF6jnoffJElaP9kjKkmSpCoMopIkSarCICpJkqQqDKKSJEmqwouVJGk95sV2kkYze0QlSZJUhUFUkiRJVRhEJUmSVIXniEqSJI0wo+X8cXtEJUmSVIVBVJIkSVV4aF6SJA250XLoWJ1lj6gkSZKqGDFBNCIOiojFEXF/RJxeuz6SJElaNyMiiEbEGOBfgbcAuwLHRMSudWslSZKkdTEigigwHbg/M3+Wmb8FLgUOrVwnSZIkrYOREkQnAA+0PF9WyiRJkjRCRWbWrsMaRcSRwIGZ+efl+XuA6Zn5F73GmwnMLE9fAywe1oqum+2Ax2pXYpSybTvL9u0s27dzbNvOsn07a6S17yszc1zvwpHy803LgEktzycCD/UeKTMvAi4arkoNpYjozsyu2vUYjWzbzrJ9O8v27RzbtrNs384aLe07Ug7N3wnsFBFTIuLlwNHA1ZXrJEmSpHUwInpEM/OFiDgV+BYwBvhCZi6sXC1JkiStgxERRAEy81rg2tr16KAReUrBCGHbdpbt21m2b+fYtp1l+3bWqGjfEXGxkiRJkkafkXKOqCRJkkYZg6gkSVKHRcSMiPjcEM/zsNY7TUbE2RHxpiF+jzdExDVDOc9WozqIRsSZEfF/+3l9VkQc0aZ8akQc3NnaDV7rxhARb4+I0/sY7+k1zGeriPhAy/PxEXH50NZ2+PQsb+tyDPc67PQHtdMiYmlEbFeG+91++ph+ckT8n5bnQ77DXRulXgsq1+HaiNiqZh1q6L1NdOg9ej77/a7nodoe+9vvjkQR0RUR59euR1/Wl/3Ieu4wmlufA5CZZ2Tmd+pVZ/BGdRBdB1OB9S6ItsrMqzPz3LWcfCtgZRDNzIcyc7VAXlNEDPpCul7LMZUOrsOIGNOpeY9Qk4EhCx2jqX0z8+DMfHIo5zlC2mcyQ7hNrA/Wcb+73snM7sz84EDHX5v9cs35DreIODYi5kbEvIj494gYExEnRMRPIuJ7wH4t467SEdbaARARH4mIuyPixxFxbik7KSLuLGVfj4jfi4g/Ad4O/GN5z1e1zjci3hgRPyrz+kJEbFLKl0bEWRHxw/LazqV8ekR8v0zz/Yh4zXC026gLohHx/0fE4oj4Ds3dlSgr5/qIuCsibulp9OJNpewnEXFI+Z3Ss4Gjyoo9qo/3eUVEfLGsxPkR8c5SfkwpWxAR57WM/3REnFM2oh9ExA6l/Mgy7o8j4uZSNrZl3j+KiP/d5v1X/qcYze+r3l420k/0quOclo3t0PLSucCryvL9Y2tvQl/vXd7vitKO90XEP6zdGlplGY4rbffjiPhS+QD9U0TcCJzX13rrZ3knl7Zc13V4YUR0R8TCiDirZfylEXFGRNwKHBkRB0XEveX5O9a1PYZLRPx3adOF0dyNbDDTRtlmFpR262nbc4HXlfb+y1I2vt32EhFvLuvvhxHxXxHxilK+SvsOxbIWYyLi82V5vx0Rm0bTY/6Dst6vjIitSx1uiojPRMTNEXFPROxdtvv7IuKTLcuw2hdOP222NCK2K9vnvRHxH6X9vhIRb4qI28r8p5fxzyyfh++W8pNK+Rsi4saI+Cpwdz+f1TsiYreW978pIqZFxGbRfBndWcY/tLw+o2wT34iIJRFxakT8VRnnBxGxTRmvr8/jrIg4P5ovrp/FS1+uq2wT5f2/Gc3nfUFEHFXa5u/K9tAdEX8UEd+KiJ9GxPvL/Pvajw3WpFL/xRHx8TLvVXpSI+L/RsSZZfiDEbGobCOXtrRVz363r+UmIj5c2nl+lH1Iu+Uv5ee2vM+nBrtQA9muoo+AEaseZdumbAfzy3rfs5SfGREXRcS3gYv7qMOMiLhqkO17U1n33wNOi+az9v3SPnMjYvMyWV/7kb7206u1Z0SMiya83VkeKwPhUImIXYCjgP0ycyqwAjgWOIsmgP4ZLT2X/cznLTS9nPtk5muBnmW+IjP3LmX3ACdm5vdpflP9w5k5NTN/2jKfscAs4KjM3IPmV5JObnmrxzLzj4ALgZ4jx/cCf5qZewFnAH832HZYK5k5ah7ANOBu4PeALYD7SwPPAXYq4+wDfLcMzwKupwnkO9HcwWksMAP43Bre6zzgn1uebw2MB34OjKNZ6d8FDiuvJ/C2MvwPwN+W4buBCWV4q/L3r4EvluGdyzzHAm8ArinlK+tIsyEeV4ZPAZ4uwxsBW5Th7Up7BE1PxYKWuq983s97zwB+BmxZnv8PMGkd1tVuNLdg3a4836asj2uAMaWsr/XW1/K2LsdarcOeupS/Y4CbgD3L86XAR8rwWOCBst0EcFnPulnfHy3LtymwANi2LFvPuni6n2nfCdxQ2maHsn3s2LpttrT/attL2Q5vBjYr4/1/wBm923cIl3Uy8AIwtTy/jObLYT7w+lJ2ds92UNb3eWX4NJo7uO0IbEKzf9gW2AX4BrBxGe+Cnu2xjzosLcvdU5c9aPY5dwFfKNvPocB/l/HPBH5c1s92ZTsbX9r4N8CUNXxW/xI4q5TvCPykDP8dcGwZ3gr4CbBZWVf3A5vT7Lt+Bby/jPcZ4ENr+DzOAv6rLNOuwP2lvPc28U7g8y3Ptyxtc3LLe81vqcejpbztfqx1W6XXPq3NOpgBPFzWX89239V7OprvizPL8EPAJj3t1TKfz61hud9M87M6UV67BvjTPpZ/G5r9YLS+z1pu431uVzTfhxuV8d8EfL33OgI+C3y8DB8AzGvZHu8CNh3i9r0JuKAMv5xmf7F3eb5FWe8z6ON7hzb76b7aE/gqsH8Z/n3gnqHcz5T5nlq2mXnlsbj8vbhlnA/22n6OaHmtZ1v+NHBSm/m/HriFJjMsAf6tj/nMAo4AXgvc3FL+RpowC83nrid37AN8pwxPAq4s6+9u4N52n+Whfoy2HtHXAVdm5jOZ+WuawDIW+BPgvyJiHvDvNDvnHpdl5ouZeR/NBr8zA/Mm4F97nmTmE8DewE2ZuTwzXwC+QrMDAvgtzQ4Jmg/15DJ8GzArml6Pnl6V/YEvlfneS/Ph+8N+6rIfcEkZ/lJLeQB/FxHzge8AE2jCQ3/6e+85mfmrzHwOWAS8cg3z6s8BwOWZ+Vh5r1+W8v/KzBXR9JL1td76Wt7BarcOAd4VET8EfkQTmFv/i/1a+bszsCQz78vmk/rldajHcPtgRPwY+AHNjmenQUy7P3BJZq7IzEeA79Fs9+202172pWnP28p6PZ5Vt6OvrTaXdbckM+eV4buAV9F8QX2vlM3mpc8pvHTXtruBhZn5cGY+T7N/mESzQ58G3FmW4Y3AHwyiLndn5ovAQpo2yvJek1vGuyozny2fjxuB6aV8bmYuKcN9fVYv46Ue5XfRhCVoAtLppc430ewbf7+8dmNmPpWZy2mC6Dda2mDyGj6P0IToFzNzEX3vY+6mOQJ1XkS8LjN/Vcpb2/uOlno8F825tWuzH2vnhsx8PDOfBa6gab/+zAe+EhHH0gS9dtot95vL40fAD2n2FTvRfvl/DTwH/EdEvAN4Zi2WC9a8XW1Js+4W0AT+3drMo3V7+i6wbURsWV67urRbfwbbvvDS5/01wMOZeWd5/1+X71Do+3un3X66r/Z8E/C5su1eDWzR0uM6VAKYnU3P5NTMfA1NiM8+xn+BclQ6IoImjPfMp900s4BTs+ndPIvm87um+vTn+fJ3BS/9pvwnaPYFuwNvG8B7DIlRcV5GL71X4MuAJ7PpKh/I+H1tNL2121j6W/G/KzsGaFnxmfn+iNgHeCswLyKmrmE+fWlX73fT9CxMy8zfRcRS1m3jfb5luHXjXRt9fdh+U/4Odr0NSR0iYgrNf+17Z+YTETGLVdvsNy3DQ1GHYRURb6DZKf9xZj4TETcxuJ3NYLbNdttL0HxhHdPHNL/po3xd9K7HVgMc/8Ve077IS8swOzM/uo51aZ1/z7x79LVfam2ftusiMx+MiMfLodWjgPe1jP/OzFzcOn7Z/6ypXmv6PLZO31e9fhIR02jO3f77cqi3ddq+2ntt9mNtq9Dm+cowULTO9600/6C8HfhYtJzu0KLdcgfw95n5771H7r38mXl2NKdkvJHm1tWn0vyTPlhrWn89AePwiJhM84/IatVrU9Zuu+vLYNu3db59fR9Am/1IX/vpbO7C2K49X0azz1tTmF4Xc4CrIuIzmfloNKe0/Aj4l4jYliYkH0lztAOaXslpNP84HgpsXMq/DZwREV8t++htSkfN5sDDEbExzWfiwTL+U+W13u6l+Sfy1Zl5P/Aemo6D/mzZMt8ZA1/0dTPaekRvBg6P5hywzWkS/TPAkog4Elae4/balmmOjIiXRcSraHo1FtP3im31bZqNnDLfrYE7gNdHcz7YGOAY1rDiI+JVmXlHZp4BPEbT43IzzYZGRPwhTa/F4r7nwm00Hzp6piu2pDm89btozh/r+U+yv+Ub7HuvrTk0/9FuW95rm9YXS492X+utr+VttbbrcAuaneOvojmP9y19THsvMKVsN9Cs65FgS+CJsoPbmaaHcjBupjn3dkxEjKP5op7LwNobml7Y/SLi1QDRnHDfX29/J/wKeCIiXleeD2QH3WoOcEREbA8rz61bl6MD7RwazTmg29IcFruzzTj9fVYvBT4CbJmZd5eybwF/UXpfiIi9BlqZNXwe+7LKNhER44FnMvPLwKeAPxrg2/e1HxusPyvralOac/BuAx4Bto+IbaO5kOOQUteX0RwCvpGmHbcCXjHA9/kW8N546dznCRGxfbvlL+Nsmc2dAz9Ec5FlJwwkYLRuT2+gOYfw14N4jwG3bxv30pwLund5/82j/wuY2u6n+2nP3vv6qQyx0iv+t8C3S+/9DTRHDc4Ebqfpzf9hyySfp8kLc2kOj/+mzOd6ml7b7tKD23P+5sdoMsYNNO3V41Lgw9Gc/9vzfUTpQT6Bpif8bpp/Sv5tDYvxDzT/JN3GS0doO25U9Yhm5g8j4ms052X8D835FNB8uC6MiL+l+a/jUl76r2QxzZfQDjTnRT0XzcUyPYew/j4z2x0u/CTwr+VQxwqac7KuiIiP0hxKC+DazLxqDdX+x4joOc9wTqnXvcC/lY3nBWBGZj5fvj/aOQ34akScBny9pfwrwDcioru0yb2lnR6P5kT2BcB1tByepjnfbTDvvVYyc2FEnAN8LyJW0Pzn2Ftf662v5W21LuvwRzSHt35GszNtV//nornQ55sR8RhwK7D7QJa9suuB95cd5WKaYDgYVwJ/TLMekuaczl9ExOPAC9Ec8p8FPNFu4sxcHhEzgEvKFxM0O++fDHZB1tHxNNv579Gs5xMGOmFmLirb5LdLYPkdzbnK/zOE9ZsLfJMmXH4iMx9qE9jbflbLa5cD/0LTE9bjE8A/A/NLGF1K38Ggnf72o+3MZ9VtYhHN/u5FmjY7udRzTdrux9bCrTSHnl8NfDUzuwEi4myaL/glLfMeA3w5mkPTAXwmM58cyH4wM78dzYUrt5fxn6Y5L/nVrL78m9P0oo0t7/OX7ee6zv4BmB0Rf0Vz7cIqVS5/zwS+WPYNz9B8RgZjMO27agUyfxvNxVufLUH2WZojN21l5o/72E/31Z4fpNnXz6fJPTcD7x/k8q1R+Z7p/V3zA+CLbcZ9hFU7Aj7a8tq5NBf7tY5/Ic2FRb3ncxurnj42o+W1OcBq/3Bm5uSW4W6af3bJzNtZ9TTAj5Xym2jfiz4kvMWnJK1Hormq+OnMHPQV1NJgRPNLIW/PzMGGzt7zmQF0ZeapaxpX6m1U9YhKkqQ1i4i3A+cA761dF23Y7BFdg4g4geZQcKvbMvOUGvXR4LkOBy8i9mD1XyR4PjP3qVGfkSAi7qD5madW72k5R1MdFhEH0vwsW6slmXl4jfqMNravOsEgKkmSpCpG21XzkiRJGiEMopIkSarCICpJkqQqDKKStJ5Yw494S9KoYxCVpCEUEZMj4t6ImB0R8yPi8nIHqaURsV0Zpyua26sSEWdGxEXR3PLy4oiYERFXRcT1EbE4Ij7eMu+/iogF5fGhUrZZRHwzIn5cyo8q5dMi4nsRcVdEfCsidlytspJUmf99S9LQew1wYmbeFhFfAD6whvGnAftn5rPlx8Gn09yp6xngzoj4Js0dcE6guR1gAHdExPdobk38UGa+FSAitozmftSfBQ4td7M6Cn8zUtJ6yCAqSUPvgXLrPYAv09xisD9XZ+azLc9vyMzHASLiCmB/miB6ZWb+pqX8dTS3bf1URJwHXJOZt0TE7jRB9oZym8kxwMNDs2iSNHQMopI09Hr/QHPS3A++53Sosb1e/80Apm97o/PM/ElETAMOBv6+HOK/EliYmX882IpL0nDyHFFJGnq/HxE9IfAY4FZgKc0heIB3rmH6P4uIbSJiU+Aw4DbgZuCwcr7pZsDhwC0RMR54JjO/DHwK+CNgMTCupw4RsXFE7DZkSydJQ8QeUUkaevcAx0fEvwP3ARcCc4H/jIi/Ae5Yw/S30txi9dXAVzOzGyAiZpX5APxHZv6o3HbxHyPiReB3wMmZ+duIOAI4PyK2pNnX/zOwcAiXUZLWmbf4lKQhFBGTac7V3H0tp58BdGXmqUNZL0laH3loXpIkSVXYIypJkqQq7BGVJElSFQZRSZIkVWEQlSRJUhUGUUmSJFVhEJUkSVIVBlFJkiRV8f8AmVY3HPJBK5YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 792x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11,8))\n",
    "sns.countplot(data=loans, x='purpose', hue='not.fully.paid', palette='Set1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd3c02e2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "01f6af17",
   "metadata": {},
   "source": [
    "**Let's see the trend between FICO score and interest rate. Recreate the following jointplot.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "400a6358",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x1d1e1a55b20>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rr2OVTEq/QEsgFbiSdbsf3I1AS4B7PJ74wlPj4aoauYKNBh9X1TjaEOpJgSB7BnWfF6V0O4Dtvcoe6fH11wF8va9tBQNLpodmNBBl1kWD/LQLSfFF75GX3qZnlmtNWpgL2G00Rg1b1ZgheI5UePdNqCcFAj7CmHcMk0np56xwMuuck7M3v7UWW7mqRl4bQ46BrXYssnzh6x5uCPWkQJA9wh5qDOOc5MRtf7sNdft6KP2uLEw9NFf/aTVeuvOl1JrT6j+thmuyi6uM46kAAaBgdkFKhaiz6KCz6eCY4OC2kWMyU+1YNCexzjaajHkHQz05XK9VIBgoRPAaw8QjcQRaA2lKv2UPL0M8EgelFHJMTpPRyzEZ0VAUp7af4irjWCpAOSbD1+DDtm/1kNf/YXnquKw2Kq0Ks6pmwVnhhLfOC0uhBUVzi6DSqvqlzhvuir6BVE8O92sVDA7hSBhCbSgYE9RW12L7d7aniSK2f2c7HBMdAAVe/trLinWYquIqrnLQNYX94K0/UJ8KXMk2277V5Xk4l7/3XKVVofRa5d69/qjzxpKibyxdq6AboTYUjBm8dV6mKMJb74W/2c9VAfKUgzx4akNPjadf/e6POm8sKfrG0rUKxi4ieI1huBZQbgtMeSa2mIPjX6g1afnnKWafx1rMt43KRH8spcaSH+JYulbB2EUErzFM4ZxCLNu8LE3ltmzzMhTNLULYE8bC+3spBO9fiGgwylUO8nDPTlhK9Wyz/PfL4Z7t7le/+6POG0uKvrF0rYKxC6F09DgqzZkzh+7bt+9Sd2NEEQlEUL+vHt56LyxuC9xz3NAataitrsWWdVsw46szEk6TFDj090O49Zlb4Wv0dXsRdllATV4+GUQi/cq+3B+GSm04UlV7I7XfAgV9/tDsZjtdcPkCvLzn5cHsz1DDvX4h2BjDyDEZNR/WgJBE0KEyRc2HNRi3YBwKLivA/J/MV0jVCy4vAJUp9HY9vHVeWIusKJxTmApcmTIz8+jPg5anzst0rGwVfSNZtTeavB/7y1gL4OFIGEePHR0z2ZRF8BrDtJxqQcfZDuz4bo9syZsr0VLYArVGDWOuEWteXINASwBGlxHxSBzeei/OvHlGEdRmVc1C2+k2psot//J8XNhzQeFEP6tqFohEBixA9DfY8B5ymTJN85SVguHBSH7x6C86rQ7/a+b/wpaaLZe6K0OCCF5jmEBTIBW4gK5syd/dgfU710PSSGg+3Kww5tXb9KnAlWyz/bvb4axwItAS4KoKk4ErWbbt29uQOz0XOqtuwGTd/ZGIZ3rIZco0LYLX8EZsFxj9CMHGGCaTpDrijTCNeb31HHl9nRdak5apcvM3sWX3nhrPgMq6+3OsTObEmTJNC4Y3YrvA6EcErzEMLyuyxW1BLBhj/vFb3Jw2hRYYnAamEtFayJfKD6Ssuz/HyvSQy5RpWjC8EdsFRj9i2nCEkWkROtsFaluJDTc/fjNaj7amvA2dU5ywldpgzDMiZ2JOt9oQwKHnDsFSbGF7Ds4tSq0T9bSUspfbUTinkJ19uSvxJS+TcrYkJeK9pwCTx2Ldn0yO7pkyTQuGNxf7XRCMfIRUfgRxMTVftgvUckzGob8dwiv3vJJqc/NjN2PG2hkAgE+f/pQpspBjMmqra1Py+qK5RVDrE+9B8Ug8UVfrhbU4oUSU1BI+f/1zhDpCiPgi0Jq10Nv1mHDjhH71O3kvspHk8+7d5BWT8dmznzEFKEkfxbGkWBtNjJLPrs8dNmgNtEhfBKIlmL9k/mhRHHKvXwSvEUTL8RY8OvtRxSjh3gP3AgC3jrdA3XysGY9d8ZiizT377wEBwaNXKI93z/570HyoOasAmn9ZPh657JEB6zcvEE1ZPYWbGbr1ZCvzPBte34CXNr6k3M/27K0ZfRcFgiEiq31em+ZuAgBsiW4ZLfu9xD6v0cDFFqF5dVyz2uOtzDatJ1ohqSQYnUZcdsdlqV+fg386yJePz8wDAGbd+h3rB7bfHJHFxt0buX3j3TtvnRftp9rx3r+/l1bnqfGI4CUQDGNE8BqmpDlfFFrgvtJ90Yy7mepY02lJdWDvNhqDBmq9GnO/M1cplbfqsw5EEX+k3/1mwQtEPAPg5LQR6zxJ0Urv8v76LgoEgqFBqA2HIZFABIf/dhhPL30aW9ZuwdNffhqH/3YY5iIzlv+hl0fgH5bDMcEBW6mN6VNoK7VBjsk4suUITrxyAvUf1+PEqydwZMsRWMdZUbm5Mq1N5eZKWEosAAFTKk/UhKvi4im8HOMdXK+9/vjw8c7DMwBOrnewzlM0t2hAfRcFAsHQIEZew5D6ffXMjcCuqS6EPeE09VvYE0b7mXb4Gnx475fv4ZofXpNau3nvl+/BMckBrVmLjjMdaUknFz6wELZSG/b8ck9amz2/3IPVT61G2BNmj6K8kYwqLmbdZCeck53cTMHZZhHmKcncs93cvmXKVjyrahZyp+cyfRdHyaK/QDDqEMFrGMLbCBz2hPHGP7+hmOLKn5UPX4OPuXbjrffCkGNgjqLWvLgGerseeTPzEPF3qQBfS3gW5s3MY06n2cvs6DjfkbKNMuWaIMsyqJwQ/sjxXtmX43Lq64GCSAQVKyuw4fUNaVmWJbWEKaunYOPujWmB6GLehpJaQtHcIsUaF5UpTmw7oZDxJ02IeQyHgDcc+iAQDCYieA1DeOsw0UCUu6aUzM2lWNNxWxBsCzLbETXBlfdcia13b02NVJZuWgp7uR1EIli6aSl2/mBnWp0MGZ4LHkV505EmxENxbL1rq6IPOeU56DzfOaAS/8+e/Uwh45+5biaOv3x8wPzs2j5vQ/PhZsWI1TXFxZ3WHA6eesOhD4KhJxwJ4+cf/xwAQLRk1Bv0ijWvYYj7Sjdz/co52cldU8qUm8tZwW6n1qpTQQhIBLSdP9iJWCiGznOdeP/X7+OaH16D+ffNxzU/vAbv//p9BJoCzDbBtiA6zncwg2THuQ6uBVMmeyYe9QfqmV6JtdW1WR8rE946L9siq87LbdOf6xlohkMfBEOPTqvD/Vfej/uvvB//a+b/QktNy6Xu0qAiRl7DEK1Ri6m3TYVjoiO1ETj/8nxojVqseGQFXv3mq6k36hWPrIBjogOSWsK0NdPS2hRcWQC1Xg3nRCdu/q+b8co3emxG/q+b+T6FtV6oNCrmNGSgiW2+G2gOwFzAVvQZc40DKpXnqQq9dezryXSsTER8Ee5Il0em7QxDZQg7HPogEAw2IngNQ2KhGI4+f1Th+lB+QzlCnaG0NaVQZwjtZ9phL7PjyHNHmE4R3novICGtHSRknGokEuFK2JlrYePsMDgMzKlGg8OQUQ7PsqHKJJVPqgr7Knvvr5+dY4KDeTzHeL491MW2MwwFvJcIc4Hw9ROMHkTwGobUVtcy1Ybrd67nCjb8TX5uqhI5LuOVr72iaHfX+3ehcnOlIp+XMd+IzvOdzEAEAma53q5HTnkO2krbsOrJVWk2UHnT8rgqQCpTLLhvgWL9KpN/oHu2m+mVWDS3aED97JyTOf54k/nHGw6eekRFsPCBhYo9ekQl1rsEowcRvIYhmaa/eNNYUR9bzOGt84LKlD3V1xTAgScPYNUTqxJqQ5MWHz38EZyTnNDoNak1r6SM/v1fv4+Vj69kltvL7XBOcmLCjROYKjeeTL31ZCtz/ar4mmLuFJeklrjy9mxl95nI1O+BbDPQeGu9qH64Ou0zqn64GsVfKhbGtIJRgwhew5BM03m8aaxgW5A7lUZlylUv1uypQc2emrTz+xp8UGnZa17+Rj+zPOwJA0go3cKeMILtQWhMmovK5DOtzzgmOJgmu0AiSOisOhhyDNBZdReVw2cik6y8P8frT5uBxOw2I9AaSPuMhnrqUjD09FQbAoDOrruEvRl8BjV4EUKWAtgEQAXgj5TSX/WqnwLgSQBXALiPUvqbHnU/BPB1ABTAZwDuopSGBrO/w4WkcrD3+lXh3EKs/tNqvHTnS6ny1X9aDedkJ+SYzE1VIsfZddYS/tqRSqfKal3JUmSBHJOZTvSZJOyZbJt4rvZEIgMmBR+NsvLhMHUpGHqSasMkW6JbLmFvBp9Bc5UnhKgAnABwI4AaANUAbqeUHunxM3kAygCsBtCeDF6EkCIAewBMo5QGCSF/B7CdUvpUpnOOJlf5aDCKuuq6lLdh4ZxCSCoJn7/xOSSVhEBLAEaXEXJcxoQlEyCpJZzdfRbxSDxVp9KqMG7hOFCZomZfDeSInBpdSFoJ1gIrTr9xGju+32PN63eVGLdoXOqh3nvdZPyN43HuvXN461/fSpVf/+/XY9yCcZDjMp5a+JQiEG14fQP+cuNfFOX3HrgXzklOZvCwldmYx9q4eyN0Vl3WTvQ8Mjn1j2RlntikPGrol6s8MGqc5S+Jq/xVAE5RSk8DACHkbwBWAUgFL0ppE4AmQshyTt8MhJAoACOAukHs67CCyhQnt51UPNAdkxLTg20n2lKOD45JDjR+1gitWYtnlz/LfAhHA1Gcf+e8IhCNu2EcVDpVmsAiHonD1+iDHJWZ6ya503Kh0qrSlIsqrQreBi/i4TjKFpfhS9/7EgKtARhzjfhw04cXlbCzXDGOv3Kc2cZT44Ehx8A9nnOSM6uH9heRlQ/nAHGppy4FgsFmMINXEYALPb6vAXB1XxpSSmsJIb8BcB5AEMBrlNLXWD9LCLkHwD0AUFpamlUHeQ+fS/1Q4m0y3fDGBnhrvArHB/s4OyJe9p4kX70P8Wicudm25LoSeGu9iqCWNyMPGqOGuW5icBrw/NrnFUHyjjfugNamxbRbpuG5W57rHsk9VJnaXM2Sj1OZMnNw2cbZmG2sxVborDru1GW2U4D9lbaPxulGwcij5/PPoDNc4t4MLYPpsMH6C+7THCUhJAeJUVo5gEIAJkJIFetnKaWPUUrnUErn5Obm9rlzyYfPo7MfxZ8W/wmPzn4UR184CjkmM8uT3n1DAVdV6I0wg1DUH4Upz8R00TDlmbi2UlF/lHm8YHuQ68IeaA3A6DRi/r/Ox/z7Ev+MTiMCLQGEO8LY8b0dacfb8b0diPgiXOd4XqDWW/Vct3de32icZu0s0R9Xe0C4WAiGBz2ff1q19lJ3Z0gZzJFXDYCSHt8Xo+9Tf0sAnKGUNgMAIeQFAPMAPD1QnetPQsOhmoLhjQZ4QSgWiiHkCTH39oQ8Ie6mXt7xwp4wiEQwafkkVO2sSlt3q/3vWmaeL51NB2+tF7nTczHvH+eljH73/mYvvHVeTP3KVKZhrq/ex0x66a3zYvqa6cgZn5O+7telNmTJ0c+9ey7jFCBvRN0faXum6cZMU5eXelQvGL30VhuOdn/DwQxe1QAmEULKAdQCWAtgXR/bngfwJUKIEYlpwxsADKgSoz8JDYcqeDknOXHbc7cp3MyNTiPbfsllROe5ThzZcqR7z1ZX4LCPt8NeZkflQ5WpUVFyOo/nfKG36xELxXDspWNoOdoCKlM0H2mGp8YD+zg7c7R2x5t3wDHBwTT6dU52MqcGp94yFZZCCzMYWoosOPTXQ0zHEJVWxVzTMeWbmNdjyjdddJov2/Uhrkoyw9QlkL0JsUDQV3qrDQFgS83oVRwO2rQhpTQG4LsAdgE4CuDvlNLDhJBvEkK+CQCEkAJCSA2AHwH4X4SQGkKIlVL6EYDnAexHQiYvAXhsIPvXn4SGQ0k8HMfe3+zFe794D3v/cy/i4TgsRRZm8khrkRX2cXZMu3Uatt69Fa98/RVsvWsrpt06DbZSG1RaFaKBKOb9eB7m3zcf8348D9FAFBqjBosfXJx2vOT39Z/Uo/NcZ1ofOs91wt/kZwb3UHsIkVCEadob9oTZU2wnWhHqDDGDoa/Bx3QMqa2u5d6zsCeMhQ8sTLuehQ8sRNgTHvBpvv5MXYqpRoFg4BjUfV6U0u0Atvcqe6TH1w1ITCey2v4UwE8Hq2/9SWg4VPAeclW7qpjJI10VLm7OrntW3gN/s59pK7V261pYSixpubkC7QH4G/1Q69TY//j+7nMB2P/4ftz82M3MEYfGpIG3hmP0y1Ebtp1uQzwcz6pN0tE9HomjtroW3lovrMVWFM4pROe5TqZCMmdCDmLBGHN6sj8KRYDvpJFp6jL5NatOqAIFguwYsw4bRCJMiXZ/LYYGci2DN6XprfOyE07WeSFHZWYbf5MfET9biQgJiHqiePmul7unEzdVwjHBAX+zH1fcfQV2/7zHdN79C0HUhDkFaSu1QVJLWTmDaE1aaHI12W2GLrQgHonj06c/VUwp5k3PYyokLYUWmPJM7OnJwuwViklY040XUy/2R9koEAiUjNl8XkmJ9lMLn8Lfb/k7nlr4FI69dCy1d8lV4cK4RePgqnD1KXANpELRVMBWDlqKLOzyQgu3janAhJzxOcw6tVaNHT/opQ78wQ7EI3HorfpU4ErW7f75bhBKsOdXe9LyfO351R4EmgNQ6VRYumlp2lTa0k1LobVomdN5lkJLymS3t6rQVmpD5UO9pkgfqoSl0MI1LpYhc3Oa0ThljkxDnaEhmU50TnL2W9koEAiUjNmRF29qrj+qwoE8VrI9Szmot+uZox5LoQWhDrbaMNmnxQ8uxtsPvJ2qW/zgYvgaOCO8Wi8klcRUDvoafAmn+Jl5qXL9a3p4aj2IR+L4+LGP04x+9/6fvdDZdIlj9djYnDs9F44JDhCJMFWFtR/V4sgLR7DmhTUpef6HD32YGBVy1t085z2YVTULzgonvHWJYxXNLYJKq+Jeq6fGw51O7M9ndzH14kCa9g6VclEoJEcGvdWGwOhWHI7Z4DWQCfsGOvlfx9kOrruF3q7Hum3r4G/0w5RvQrAtCG+dF/4mP0KBENbvXJ96yJx64xQ6znYASOTI6q1EvOk/b2JPYxWaQSTCVg5WOJnlOeNzEPFG0Hy4GVtu35J2PIvbgtJrS+Gc7FQ8AOOROFNVOG7xOJx7+xw+3/552rGW/nYp1Ho1ewNziRUqrQql1yo3q/Om86zFVq7asb9kUi8OlPPFUG2SFpuxRw4stSEwehWHY3bakKc27M/6w0AeC0gkW7SOsyJvZh7s5XbkzcqDdZwVxjwj2k624dnlz2LL7Vvw7PJn0XayDRqjBq7pLjjGOfDM0mewZe0WPLP0GTjGOeCa7uIqEXU2HXOaT61Vg8YoWznYGWaXe8MouKKAOW3nvtLNnYrlTQF667zcKTaVVsWchlRr+e9ivCk7vU3PnE6k8aHblN4fhkq5KBSSguHKmB15DaTz9kC7eOfPzMcVd12RNrpZtnkZiESYD9ria4oR8UZSSSWTdTu+uwNVO6tgzDMy2xVeVcic5pv3P+elfq4nsWAM3nqOT2GdD826Zpzbew7rXl0Hf5MfpjwTDj57EHkz81B4ZSFz6slby1cV8jY2d5zrSN/T1tVvZ4UT7ivczHuatTqwwQfXFP7G5kwMxTTbQI/2L/V5BIJsGbPBayCTBg50AsKGgw3sTMrb1zMfJBF/hOtt6K33Qo6zlYgRXwSWEgv0dj3kuAy9Qw9LiQUWNz8HGG/6zZhnhOeCBwcfP4iDjx9MO9e0W6Zxp554KetNeSbuxuaeI8meU332cfaM9zVbdWB/psyGapqtv56Mw/U8AkG2jNlpQwBZqwqH6lid5zrZsvdmP3N60jHekZKW966zuC1Q69RsJWKeCRUrKvDcLc/hxaoX8dzq51CxogKGPAO0Zi1zSlGfo0fl73qpAH9XCUOOgeuvqDPruFNPvKlLrUXLbaM2qJkjyd7n7guZFID9mTIbqmm2oVIuCoWkYLgyZkdewxljHscGKtfInp6c7EQ8Fkfl5srU1KHakHDf0Dv06DzfiUU/W4R3fvpOqm7RzxYh6osypxrX71iPYHuQOaVoLjBD79CnpVGRNBJCHSHEo3Es3bQ0tSaWDET+FrY60FfvQyQQYZ5nwf9awG2T/Lp3nb/Bj9wpfTdnBjKPmvszZTZU02wDPdq/1OcRfHFYakMgoThced1KuIpdo0p1KILXMERr1qJyU2VqD1Zy87DWpMW4BeOYDxKVRoXO2s40teGZ3WdQqimFxW2Br96XCjg6iw5hb5i7ednf5IfOpkOoI4Smz5pSEvJQRwg6qw4vf/1lzPjqjFR5Usmot+kR8nUrHi2FFtTsq0He9Dzu1JO/0Q+VXtXdAQKo9CpoLVoUX1eMq79zdUoh+dHmjy662ZflvKHSJo7PW4viKQD7M2U2GqfZRG6wkQFPbZhktKkORfAahuROy4W/yZ+ybUpmTM6dzh9VOCY4kDs5F2feOJMy0s2dlthL1Xa6DYHmQNr+sIUPLETu9Fy2i0WRBRqjBgvuW4Bt396WarP898tBZYor77lSMYoLexJqw6ZDTXhm6TPdQpOHlyF3Ri5WPr4yZfJLVASuKS7klOdArVNj9l2z09avKjdXwjHegau+fRWajzSn2lz17auQU54DIhEs//1yRd9spTam88asqlmQ1FLWa1GOCQ7meRwTHNzPYaDFOzwGY21N7OcSjCRE8LqE8B4W3lovmj5tUuw9clW40HCggfnAaj/Tjo4zHYpEle1n2uGr9zHXiDYs2MDc2EwpRSwUSz20k222fXsbql6vSgWuZPk7P30HVa9VoeFAA7Z/p5fQ5Dvb4ZzshLc2PYnm4gcXo+lQEyJ+jkpyV1XKGLhn35oON0Fj1ODdX7ybtg/u3V+8i5wJOUyhi7PCCaPLmPVG8rbP25jnKb6mmNtmqKbZBnpjvNjPJRhpiOB1icj0sPDWedmS+HnF7AfWjDz4GtkBqviaYkR87OlBzwUP28h2fA4kjcSVxLPKA80BUJlypyGT7h7JsrcfeBv5s/IRbA9y14hY1+O+0g2VRsX1eGQqLmv53o8XW79inedi61dDMc020GtrAx0MBYLBRgSvS0SmhwUv2Pgb2cKHttNt3IdzqDOU0eSWZWRrLjAjGoqyRSMutphEa9GCgDDrNEYNW6ofiHD7ZswzMttEg1HYSm3c62HK7gtMMOePrvWrge6b2M8lGGmMaan8pSTTw8IxwcF27ChgO3loTVoYnAZmnSHHgHg4jkU/W5Qmd170s0WABKYjhiHPAJ1Vx3Sx0JrZJrtGpxF6h55ZZ8hh981WYktI7xk5ynhtnJP5Bre2EraZr63E1i/J93CWiQ903wbaJUYgGGwIpcPbBicb5syZQ/ft23epu9EnWo634NHZjyrenO89cC+ck5zMKcWC2QU49uIxhcFuxaoKAMDxrceZdVSmeHbFs90KQQoc+vsh3PL0LXjvP97D3Hvmpsxvqx+rxg0/vwFyTEbb54kRXVKhSNQk5U9IZZoQk+QaQQjBuIXjAACnXjuFqD+aaqM2JoLUqR2nFGtrE26agLwZeaj/pB6xUCylUFTpVCiYVYDPX/scdft6ZJO+shCTV0wGkQhioVhCVVjnhbWoW1V4fu95yFE55f0oaSSUzitN+SiylIhyTEb9gXpFahwAGesutTFupr715xxizWtY0uebb9AaaJG+iFmn0WlwzQ3XjESpPPf6xbThJeJiqjSD09CtNsw1QqVRwVZig7XEmubObi2xphR4zgpnWp2zwplSxn35t19GXXVXIFATfPm3X4Ysy6hYVoFQZwixUAxhTxgVyyoQbAuCgiIWiOGVe15J9e/mx24GjVN4LngUir5oJAqVSoVgS1ChzlPr1Uw7p9zpucibkYfmQ82K47mmuhBoDaQJNpY9vAzRUBQqtQqfPfuZos3MdTPhb/Ar7imQCEInd5xELJRwFvHUeOBv9mPilyfi0N8OKfo8q2oWiES4Lh8ALqkx7pTVU7h9Gw4uMYKhJ5NUfkt0y0gMXBkRI68hgPfmzCtv+7wNZ946o9jsW3R1ER7/0uPM0ZqrwsV9E+c9AHMm5KBuX53iPAWXF0CWZfx58Z8V56raVYWnv/y0snxnFSABT9+krNvwxgacf/e8YuRVtrAMVKb4y41/YR7v6aXs8xAVYbfh9O3eA/ciFo6h9qNaxbW6prqYfd64eyN0Vh13dAyAWzeQa0Qtx1rw6BXK82x8ZyOeWvTUoJ9fcMnp89uD3Wynm+ZuYtZtiW7By3teHrBODSHc6xdrXoNMpkSVPEspT62H6dwebOMr8zIl1+SJQ8IetkN8xB/hqgq5ir56L1dQ4m/0M5WDEX8EnloP93i8cq6ZL880uN6HYFuQea286/TUeDKuS2aqG0jaPm9jnqfjfMeQnF8gGK6IacNBpj8SZF6yxUBzgK3mM2nQerIVb/zkjW7ZO4A3fvJGahqI+aDjJGgMNAegs+nYajaOka65wAw5KmenNvRFEt6LLOUgp9zsNkOlVmXdpvHTRmYfkn6MvdtYi63QWTn34CIuHwOJzsLpQ97wVUIKBEOBGHkNMv15Q7eX2rlqQ5ZqMB6JI9ASwKJ/W4TSBaVwTXWhbEEZFv3bIgRaAlwlGc/M15Rv4qoNdVYdlj3cS6H48DIYXAYYc41Zqw2txVamQtBaZmUqIe3j7CicU8isK5pbxFXg2cvY91Sfo0+sy/Vos/z3y+Ge7YZzkhO3PXcbFty/APPvm48FDyzAbc/dBuckvuJxoJWIZreZeU8tRZZhq4QUCIYCseY1yGRSFfJGXnJMxqdPf6oQEZQtLMOJV0+kNgQTicCYa8TEpRMRi8RQ+2FtmgVU5UOVKPpSEZwTnEyBQ9niMpx9+6zCzLf4mmJYC60489YZxCPxlAGvSquC+wo3Lnx4AS2HW7qnPqe7UDSnCGa3Gce3Hu+2gZIIXFNdmLRsEo5uOao4/4zbZ0ClUeHE9hOpkZjWrIXaoMaEGyfgzDtnIElSSrQix2VMuGECVFoVosEo6qrr4K33wlJoQeGcQmgMmoyKwoNPH8T2b/fow++X4bKqy0BlmlIuWgotKJpbBJVWBSpTHNlyBC/d+VKqzeo/rca0W6dlVC9mIluFIJUpTmw70S22kQgK5xZi8vLJoDLNWm2YSSF5qdWTAiYDojYkWoKpU6amvh9BJr1CbXip6I/XnaSWMKtqFnKn56Y9mNpOteGNf35DEQgn3DgBwdZgKnABXTZL39uB9TvXo66jDu/98r00J433fvkecibm4MCTB9JUgB89/BFyxucg4okg0BrAzu/3EDj8bik6azrx8l0vK/qwfsd6eOu8ePuBt1OSfBqnePuBt2EtsjLPn7Rt2rJ2i+J4d759J1oOtyik/5Z8C/Jn5ePwc4cVwX3G2hk4tvUYmg8n/BCbDjeh80Inpt06DVSmMOWZFH6RckzG569/ngoOTYeaEGwLYvLyyWg53pIKXMl7+tKdLyFvRh6ckxIvBCyVIi+A8F5KMrUhEsHk5ZNTWxSSD3sAWasNM8nhgUurnhSS/C/OxYx5ezIaTHpF8MqSbN8a+ytBJhKBzqqDISexYZhIhLtG5WvwwdfErvM3+AGAa3NUs6cGNXtq0soDTQGY8k2pwJU81s7v78TarWvZ52nyg8qUbdtU72WX13oRj8SZxwt7w0xLqYLZBag/UM/0XXROcTL9HRs/a0Q8Emf6RZryTGg+3Kxo45riQsvxFmbfWo+3IuKLMPuQOz0XRXPZb7+8fmdqA7DtplqOt2S9lppp/RXAkNhDCRsqwUAhglcW9PetMVuvO9558mbwU4tIGoktYiiygBC2bRPPTsnsNiPQEmCLLPwRZhtjrhGSitOHDLZNPPEFLzN02BtG2BNm13WGuX6IVKbMuqKri7iekFqjltk3tUENTw1bJemp8XADUX/a8BjoXGPJr7M5Xn8QNlSCgUIINrJgqLLk8s5DVIS7SK8xaZjCB41JwxU46J16VG7q1abre41JwxQ4JHON9W6jMWmgs+uYVk/GfCOzb1qzFsGOIJb8akmaKGLJr5akVHas81uLrcy6aCDKfDBGg4lyprIyQ5DW29l2V3q7ntsHa7GV+7n2pw2P/tg5ZWozVPZQwoZKMFCIkVcWDNVbI+883lovJq+YjKpdVWm2SEQi6DzXCb1dj3Xb1qWskYJtQXSe70TBrALYy+1p6z0qrQodn3dg32P7FM4X1/3zdbAUWZjpUjRGDUxF6WtH8XgcWqsWkIGcCTlYv2M9fA0+mAvMoKCgcQp9Tq++tQehM+tA3Im+90TSSCm1Y+/zG51G5M/MZ+bZclW4mCMl52QnQNnS9qQKsXe5Y7wDOeNz0PZ5W5pria3MBvdsNzenmHu2m/u5ume7s27Doz9rqRdrMxR5yIYq35lg9COCVxYMlcs4by+VpdjCVA3OqpoFU74J5945x3SxaD3ZimeXP6s4XtWuKjQfbsaW27eklZsLzdDZdcidlZsKUqZcE2KxGPROPXz1PtQd7PYczJ+Vj5zSHBBC0PxZM5oONaUSYuZNz0NOeQ5CHSG8WNX9wKrcXAmiItCYNAh3hhVrTvocPezj7WmBwz7ejvyZ+ZDUEmasnYGcCTkKb8Ov/OkrePHOHg/GP30FrskuUJkyA0fB5QXsh+nkxLrktFunMZWIAJiimqSjCWtdlCfE6Y8fIZEIpqyego27N6Yd64usvw6FPZSwoRo8wpEwfv7xz/v0s0RLsPK6lWllI0iBCGCQgxchZCmATQBUAP5IKf1Vr/opAJ4EcAWA+yilv+lRZwfwRwAzAFAAd1NKPxjM/l6MoXprJCqCJf+xpFsSryIwuozwNfjSVXvoVu3JMRn7H9+fVrf/8f0ovqYYvnofjE4jLrvjslTdwT8dRKgzhDt335kYPdb5YCmyQKVXQWPUIOKNwN/gV8joLW4L/M3+tGBz029vQueFTkT9UUXdjf95I7y1Xhx/9TjWvLAmYQCca0T1I9VwTnKCypTb74qbK2ApsKTJ4SW1BDkm47O/faaQvc9aNwsmtwnrtq1LjPzc5lRgyJRYsk9BgAKEpJexRDUKabuKoHBOQtpOJMJsczFYwRDIrDbMZBUW9oQRbA9CY9KkXgyS1zPYeciG8jxjjWzUhixGmgJx0IIXIUQF4GEANwKoAVBNCHmZUnqkx4+1Afg+gNWMQ2wCsJNSehshRAvAOFh97SuZ3hoHcv+Mv8kPOSanBYHrf3E9Ir4Irrj7Cuz+eY/R1f0LEWwNQm1QM+vi0TjsZXbM/c5cxagsb2Yezr55VjGSK19WjlBbiJvh+LUfvZZW/tqPXsOGNzYAFHj9f76eVvf6/3wdd7x5ByqWV+C5W57rDoQPVQIEiIfjzH5LagmH/nqIOcqsP1CfClzJ82z/9na4Z7vRerxVsdfNXGCGt46jeKzzoumzJmYQSErbWX2Q1BJbVDMrj61erHDBMdGRteCHK96ZlcdcF82dkQvnRCdTkj9j7QyuCXF/3egFgkvFYP7GXgXgFKX0NKU0AuBvAFb1/AFKaROltBpAtGc5IcQKYAGAx7t+LkIp7RjEvvYZlh9hJv/CTHU8VFoV3rrvrbQH01v3vQWtUZt6yCfLd/98N/Q5euhteux/IjGCmX/ffFzzo2uw/4n90Fv1kGMyU1HnOdftDp8s3/7d7eg40cFfd+P4B/qb/FxbK1+jj7kHjcoUaoOaeU00Tpl9q62uRee5ThidRsz/1/mYf1/in9FpRKgjxDyPt9YLtV7NFAqotCquCKe2upbbB56opvN8J/Ned9Z09kvwk+k8rHvdeqKVK8mvra5lltcfqOeeXyAYrgzmtGERgAs9vq8BcHUf244H0AzgSULIZQA+BvADSqm/9w8SQu4BcA8AlJaWfqEO95eB3j+TKQiwykMdIe7IK+wJZzS55QlQTPlszz9eRmKNQQMQtihCY2B7G/rqfYncXFn0zVvnhbnIzBxJZtoHZy4wMwUgvH1mvnof93Pw1nm5maszyfh90sDJ2yMe9pYFjUHDleTzTJX7I9UXDA96Pv8MOsMl7s3QMpgjL9Y8SF+9qNRIrIP9gVI6G4AfwL+wfpBS+hildA6ldE5ubm7/evoFGWj38aQpbk/UBnXKRLZ3ucakgVrLHsFIWgl6m57ZjudtaHFboDVrsXTT0jSZ+NJNS6Gz8T0PM/kh8s7Dyw7N7VthYt8aa3ST6XpCHSFUP1zdPTL94TWofrga4c4wV7rNk7ZbCi18v0g3uw/WYivMbjNyJuakjRhzJub0W97OzGjtMmbs90BJ9QXDg57PP61ae6m7M6QM5sirBkBJj++LAdRl0baGUvpR1/fPgxO8hgMXUyFmq1DUmrTMUYI+R49lDy/D9u/0WIN5eBmMTiP3rToaiMLoMmLppqWKXFbmYjNWPr6y24tQReCa4oJtsg1xfxwGlwGrnlyV8hyUtBIMDgOsZb0SYpZZoXfqobFqYCuzKaTllnILlm1eplg7yr8iH5IkMa8pf1Y+s437CjdOv3Waea1EQ5jXkz87H95aLwKtgbQ1L7VBDWcFX4Qjl8vM4xXNLYKklpjtCq8sxPI/LMe2b/VYV/pDtxx+wX0LFGtOyYShLHgiocIrC9F5vjNdjVmeUGMCYCori+YWDZhUXzD8yEZtyCKpQBwpqsPBDF7VACYRQsoB1AJYC2BdXxpSShsIIRcIIRWU0uMAbgBw5GLtLhUDvX+m5yghqYyrfrgaJfNLEPFF0h5YEV8EkUAkNbrqHSR1Vh1C7SG8/+v30473/q/fR/6sfKh1vd7EdWrE/DEEm4KIx+KwuC3w1ftgKbTA0+BB28k2NB1twsQlE1MClFNvnILBboDOroN9gh2Wou42kk6C94wXBVcWYP3O9YlytwVamxY6ow4AYHQa067J6DSi9XgrGo80pu0ZO/bKMeR9lgdzXmIEk/RQBIBDzx2CRqeBpJLSxBKrn1oNnUkH3WQdW0Zf4YJ9nB1VO6vSVI1JabvBYUDu9FxEfBHoLDrobDpIainhOcjYc6fSqjBjzQzkjE/I+C1FFhTNSQS7luMtzDWn4muKkVOewzT57X2ennL9qV+ZCmuxNa1NUngxc91MOCY5FMdLlfc4VrJNtklThwvDvX9DxRdVGyYZKarDQQtelNIYIeS7AHYhIZV/glJ6mBDyza76RwghBQD2AbACkAkh/wBgGqXUA+B7AJ7pUhqeBnDXYPX1izLQ+2fMbjNzlBD1RZnGvGtfWgu1Uc0crVGZIhqIMpV2RCJoPdGqUMZZS60gWoKoN4pn7nwmVVe5uRL6iXrojXo8s/SZ9FGhXQ+NUYO6j+uULvXXFqNmT41CBagxaiBHZDy/9nnFNa3btg55U/PwTOUzaW0i/giKv1SM+T+ZrxiVSRoJL23sZaS7MWGk65rigkqvSguSKr0KsXCMqWqcuW4mOms60Xm+UzFibTvdBnupnbnnbsbtM5imwbOqZnGTaAbbg7jw/gWmqhEA8zwz183EiVdPcE12j798XFE3ZfUUZnkmY94pq6dkbQA8lAij37HLoOpjKaXbKaWTKaUTKKW/6Cp7hFL6SNfXDZTSYkqplVJq7/ra01X3Sddc7ixK6WpKaftg9vWLwsuKfLE6FrFgjLmekbQ56v2z0VAUvjofc03HV++Ds8LJXOsIdYSYa0ehjhBi/hhTKh/xRrhZkUOdbHl9oDHAVAH66nxoPdHKvCZJJTHbEBVB/f56pgqw7SQ763DLiRa0nmzFrh/tAo0nll2pTLHrR7tQV13HVRR6atgZrT01Hq4Ssa66jqvoS/pP9v4c5IjM7UMmxSNPucgTENUfqB/QNsOBobJsEww/hMPGMKTjbAeObDmisG266T9vYk4NOic7EWgOMEdr5nwzXJNdzCmzTMo4OcZW03HVb74I4mG+co9Z3uCDPoc93ZlJCUkIW6GY9GNkKfACLQH2Hrn2IFuZV+9Nfd27LtAU4Nbx+u2p8YBIBAvvX6joQyZlJWh258lksstTIfa3zXDYZCyMfscuYmfiMMQ+zo7r//16mAvN0Jq0sBRZcP2/Xw9DrgE3/9fNaSOym//rZjgnOqGz6VD5u17mt7+rhM6eWFdKOk/c8uwtWL99PUxuE1fpZ8o3ZVTuMdVvBeas21jcFq5CMdOxeGo6nYWjdrTpoNKqmGpMXpZni9sCayH7PObCzEpEnqLP6DSm78X7YWIvXiZlJfc8vM8hg8ku71j9bTMcEEa/Yxcx8hqGGJwG1B+oV6wduaa4oMvRdZvi5hoRj8XhqfMgb1oevHXeNMNcSinypuWh41wHOs93ovVYK6hM0XKsBc4KJ0qvK2Uq/WwTbQDAVPvZJttw299uQzQYTYkY1Ho19E491Bo1u814G1ttOCsf3kYvHBWOlKpRZ9GBaAgMLgMqH6pUumUUmmErZh8vd2Yugu1BRcJJ9+VuXPjgAtMiS47JzGMVzS0CpZRZV3hlISSVxK6bU8hV9MkxmblWV3BFAbcPvM+haG5R1iIh92z3gLcZaLIVXwij37ELobSvW6+GP3PmzKH79u271N34wpx992xKEJFEbVDjzt13ovGTRoWAIP/yfBRdWcRNWX/hows4v/u8QsxRurAUradakVOWk1IBtp1rQ97UPBAVQeeFThjshpRyMNAegGOCA20n2xAPx1PBRtJKyJ2ei0BTAPFIHERFUg8fGqfQmDWJjdQ6dcJDsdCCaDiKnLIcqPQq1FXXofFgY5rRrzHXiPbT7dAYNCmpfjQYhXOSE0anEVu/sRVXf+fqtAzQq59YjbqP69BypIe0faoLU1dPRef5TjQeaoQhxwBfQ+JaA20B5E3PQyQYQcQTSV2nxqJBwYwCtH3ehq1fV55n1R9XwTnJiZrqGsRD8ZQaUqVXoXhuMWLhGOqq69LUixqDBgAQ9ofR8HFDqq7gigLoTDrEQrGE2rDeC4s7oQJU6xPvltFgtPt4bgsK5yaOF4/EuabBPG9DXnmmNv1R8/W3DU98QWWadb9HCX1WnRi0Blqk/+KbzYmWYOqUqWlll1A+z71+MfIahvDm8aO+KFNAsG7bOrQc46Ssn56HiIctstj43kbQKE1TDlZurgQFhUqjQrAliBfWvpBWp5qqQqA5oAigEV8EkkZCy7EWxWipaF4Rmj5tUpTrHXqQToK2k20KxaOlyJLmXwgkAviGNzbAU+fBlJVTsPXurak2i362CMH2IDrPdiqO1XCwATq7julsTwlFw/4GxShXY9TAX+9nZpr21fsgqSU0fdqkuA+WAgvOvHmGqxw88tyRrJSDVKZM9WImn0IiEaZCMJNyEMhs9DsQyVQvpgDkiS8KZhfg/Hvns7rWsag2HCipPIvhKJ8fNa8nowneGkigmZ04MdAS4KasbzmRKGcGQ3+UqQ6MBWIIt4WZdb56HzOAhjsS4g+WQjDYHGSWx3wxruIx3MkWkwSaA1BpVXjnp++ktXnnp+8g6o9y1ZP+Rj/zeno65/cuz7Se4qllKxE7znUMqHKwPz6FA6k27I9qr7/H4r20eWo9WV+rUBuOfkTwGoaYC83MjMTWEv7iOS/zscaggXMyWyqfSamVSc3GC6Bcb0HeeRp9CHvZQSoaiDL7rNaruZ6DvKzI0UA047XyypPrKT0/h+R6ir+R4z/JMzSu83L3eWW61/3xKeQGgQzKwf7YmPHo77F4Lwu8zzvTtfan34KRhQhewxBfnQ9EQ7B+53rc+rdbUbWzCkSTcHxYtnlZ2sN02eZlCcGETY8l/7EkrW7JfyxJOEJoJNz025vS6m767U0ZFX0DqjbklJsLzCmj3951pjwT1ydRb2V7NSbNhFnH6o8SsmfCx6++8FVs3L0RU1ZPAZFIvzwZ+6Mc5LWxFnHKuzwUB0pt2B/VXn+PxXtZsJfas75WoTYc/YjgNQyRYzJ0Jh189T5EfBH4GnzQmXRQ6VXQ5eiw6slVuPmPN2PVk6ugy0mU587IRf6sfKzfsR63/vVWrN+5Hvmz8pE3Mw/BtiC0Zi3m/Xge5t83H/N+PA9asxZ6l54ZDG2Tbcifmc+sS3oOKspn5qdUhb3rzMVmVD7UayT5UCWM+UZQmeKGX92QVnfDr26AHJfhrHCmXauzwomCywtgLjIzA7XBZcDy3y9PK1/+++WwFlthK7GxR7OlVix7uFefH16GwrmFoDLFyR0nceKVE6j/uB4nXj2BkztOgsoUlkILKjf1Ot6mSthK2fegaG4RCucUcutue+42LLh/AebfNx8LHliA2567Dc5JTrhnu5nXlFQ19i53z3Zzg0BSOcgaSWYaZWZLf4+VdKq598C9uPPtO3HvgXsx9ZapcF/BvgeZrlWoDUc/Qm04DKmtrkXDJw0KMUDB5QV4auFTChFD1c4q6B161P53rUJ4UHRVEcKdYfzlxr8o2t3x1h3w1nlhyDHA3+iHKd+EYHsQjokO6PP08Jz2QI7JKcWYpJZgHW9F0/4mRLyRlApQa9GidEEpOs91ouN0B1RaVUqqHo/EYR9vZ5Y7JjtACEHtf9ei7URbyrbJMdmBoquLUPthLV6999XU9ax4dAWm/4/p8DX4cP6D82g92ppq45zqRMk1JfA1+pgKQCpTnH33LAghqWullKJ0XimOv3ocTZ82pY6VNysPU1dPRfvpdhx94ahCpTn1lqlwTHTg89c/R6gjlLoPerseE26cADkmc1WALIUgL7HlxbIiZ1IHpmVzlggK5yayOQMYsKSpmRhov8FMisJR7m045GpDZie6FIiXQHUo1IYjiYgvwlUV8tZMKChTeLB+53ruWlC4M4w3/vmNboPbQwmD2+V/WI5wRxhv3PdGSibuq/fho80fYckvluDIC0dw2brLQGUKU74JB58+CJ1VBzkmY8+v9+Dq71yNWCghxvho80e44X/fgF0/2qUw0l3+h+VQ69Q48sIRzL1nLgKtib1r1Y9Uw15qx+4Hd3ebCQPY/eBu2MfZQSnFOw+8kzoelRPfr3x8JUxuE/z1foAm3uT1Dj0AoO3zNvx1xV8VAXzD6xvw0h0vKcrtu+0Ie8LY//j+tD7sf3w/Cq8qhHOyE+WLyxVmukQiUGlVKL2WnVuOVddyvCXrnG8AIKklFM0tUuTiaj3ZmvocIv5EYN3z6z1wTnamVIPZuE/0J3BkUij2J9gQiUBn1cGQY4DOqmPar411R43BVBsmGU6qQxG8hiGZVIVqg9L+yOK2ZFz0T67D9G5HZcq0TKJxikgggsvvuDxNjr5001KojCqUzivFsyueTRvhEQ1B3B9ntpFlmXkeOSZDVsmoWF6B5255rvt4D1WCSuy+RfwJGypWXTQYxYX3Lihl7yYNPBc4YgWOyMRb54WklZjniYVjkGMyPnv2M6Z8O9s9RplEB44JDnz69KdZnSfUEWJ+DqGOELcPPHl7xcoK7nUSiWQtie+PjF6Y7wpYiDWvSwiVKVqOt+DsO2fRcjyxsRZIqA15i/6s9Rm1KYMowm1GxB9httOatEzLJI1BA61Ryxz9xQJsw145LENj0DDbqFRsayatScs34JUJt40hx8DtN6tvHac6oNKpuGIOVrnBYYDOomOeR2fRcSXs9Qfqs/kVAJBZ4NCf88gxmfk5yDGZ24YnOe+PJD+TTH2o2ghGPyJ4XSKSb5OPzn4Uf1r8Jzw6+1EcfeEoqExhzDeyxQXjEmqxnsILtUENnV0H2zi2UMBebofFbYFKp0prp9Kp4G9mS5D9TX74GrOXlnsbspSCN/Al+bzyYHsQoc4Qt9/M8kY/gm1BLLy/l3qxayTHKg97w9zp1mBLkCs799R4kC2ZRAf9OQ9Pxu9v8nPbcCX+HHl/f2XqQ9VGMPoR04aXCN7bZN7MPPgb/Tjw5IE0V/mPHv4IjokOvHz3y4rpv/U714OAINAZSCV8NLvNqP24Fp1nOgECvPL1V5RCj11VzOlEY64RREW4U5S8chBk1cacb+a34Ux16u16EIndN3MeJ6N113mObz/efU/NWny0+SOUXFeSXt51r8uXlKfaK/pWZOHWWYutGdd0eHW8nG9JeTvrPDyS+wGzacPLBp7p/Dqrjn2/M8jUL5Z1fKDaCEY/IngNAawHVqa3SX8z35aI18aYa4TBaki3enqoEvFYnLuGRkFRublSsUak0qtgKbMwDWGNbiOzjT5XD5ubb8DLK/fUe9jHc+qxdNNSheIyGoxCZ9Ux6zRmDfNYGrMGjvEOXHHXFWnrQEmJP6s8b0YeVBoV12QXALOu4LIC7voMwE74mMmCKSmV5/WBRX/a8AxuMxkNE4lkbYrbHyNdYb7bN8KRMH7+8c8H9RxES3D32rsvlc9hGiJ4DTK8xebcGbnst8kCM3/UwxmNWNyJkQBr7ahqZ1VqQ2vvdoQS7Pnlnm41HQX2/HIPVj2+Ct4zXuTPzk+N5CzuhGFtoD6AYGewu7zQgpp9NQg1hxBqDuHc3nNY9+o6+JsScvSDzxyEY6IjVe5rTATwg08nygHg/N7z3W3yTDj47EE4JzrxyZ8/UYyIlvxiCWiM4sSOE1jzwpo0haKrwsW9Hl+jj2nN5JjkYJY7K5wovbYU09dMR874nDST3aRQglXXfqadO6IGwK1zTnIyR2SSWsKMtTOQMyEnzbQ3k1ReUkuYuW4mHJMcCkk+j0yjv2lfndZ9nW4L3HO61YbJTdw9+5BJRHGxrOO8NtmeZywyFGpDYPgoDkXwGmR404N3v383Fj6wULGHiKgIYqEYM2khURPmyMJUaELD/gbuulLhnEJmehF/sx/tp9rTElgCgL/Fj7yZeTj/3nnFuQqvKmSO8OS4DCIRlFxdkqZEXLppKYiWoPSaXgrFrjZhTxiuCa60uoX3L0QkEEHFsoq0EVFSUUjUBNNumYYLey8k9jGdSHwfCUSY1+Nt8EKOc5Jrctb2Aq0Jh/xDfz3ENdll1eVfls8dHVNKmWlZvHVeNH3WxDXmZRnwXsyY9/jLx7NW57FGf7FQDIf/dphpJqzSqvplitsfo19hvivoTVbBixBiopTyV30FCnhTfR1nO1D9cHXaKKH64WoUf6kYkkpirs+457phKjSljYZCgRAaPm7gjspMBSZ0nu+EqciUNloK+UPQG9lZjE0uE3z1PqZyr2pnFXeEBwKmyi1TG55ysGpnFbN8/Y71kDQSvDVehXu8Y4KDPTItsKTUhqw6VrnBYeAa6TorEtNVrDreOqLZbUa4M4y535mreGFRG9TcEVnYE2aq/XIm5DDLc6fnQmfV9WvfGAvePXBMcsCUZxqw82Qi0/rwWN/bNZbpU/AihMwD8EcAZgClhJDLANxLKf32YHZuNJBpITzQGkgbJSQfchQUs++anTbqqNxciZyJOTj71lk8/93n00dDcwqhd+qZozL7BDsA4Py751G3N+G40HykGc4pTmg0GuYILxKIcF3duerABi8IIVm30Rg07JFKEzvohzoTecFY7vFrX1rLvAdETSDHZeZIV9JJzHI5KnPVmN5ab+prRb8bfLjtudu63S1UBIVzCuGc5MSZd84w+337nNu5o7Vge5DdhwzGvIYcA/d42T7seefx1ntB43TAzpOJTGu9IniNXfo68vq/AL4M4GUAoJQeJIQsGLRejSKck5xYt20d4pE4Ai0BmHJNkDRSxgy15/ac47plMEdDu6oQ7gjj+KvHu9eBnEZUP1YN50QnJI0EzzmPYqRiL7On0tInR3/7n9iPlX9cCa1Jm9W6m7nADNDslIPmAjNioRh79JdrYpbrLIm8XKyHWSQQQWdtZ2KE2ZX08vTu03BVuOCp8eDse2e774/LiA9/9yHs5XYc2XIkbZS79zd74Zzi5KokTQUmqNSckVyhBb56X9q9/sqfvwIAiHgj7H77IsiZmKNwIOmZLUBxniJ23/qrAuTBWy+1uC2pPXIDcZ5MCLWhgEWfpw0ppRcISZtfjg98d0YfckxGx5kOxZpB6bWl3IXrbNN3eOsSI4ETL57AiRdPpNXPXDMTerue+cZf9VoVrvjaFYpRBwBorGzlnqXYwiy3ldrQfrqdqQLU5eiYbbQ2LbQmLbON1qplKyENKpgN7IeZpciCYGtQsR4n6SQ4JzoxdfXUNCePpZuWwlnhxLRbp6WvrT2wEK4pLoQ9YWYf1Hp1yri49+dqyjOl+Uj2nOJyTnIy++2Y5MCC+xYo1q8cExKCFpbar2hO0YCqAHkkzYR7X2fSr3EoVIBCbdg3hkJtCCQUhyuvW9mnnx1ML8S+Bq8LXVOHlBCiBfB9AEcHpUejjEzrJqXXljIXrjOpCrlqwwx7rAKtbKm8r97HXHdzTXGBSKR7FNMVXM/sPgPvBS93hKfWqdNVgF11rgpXxlHh+79+P60P7//6fax6fBVXOQg1cP2/X4+3/vWt1MPs+n+/HnJM5q6txVVx9nrcripmYC9bWJZYe2T0O39GPpoONeHYq8cUdQYnf8pOZ9Mxpygjvghz/ar4mmK4KlyYVTULudNzFapCXjmArBV9PNR6dbdysYfaUa1XD+h5MtEfheJYZKjUhtkwmMrEvgavbwLYBKAIQA2A1wCI9a4+wE1A2DVaYkE0bFWh3sVe16KEovBK9huye44bTYeauCMV1rqbxqxBxB+BvdieNopZ9vAyeOu9aKhuwPmp51NTXA3VDfDWe6Gz6dJVgKqECtBb70U83GOgLgHxcBzeem/KRb50fmlK9l73SR289V62crA+cd8+/D8fpgW2D//Ph7jxNzdy12cAoGxxGb70vS+lzvPhpg/hrfMyFYCeGg+MLiPzWqOBKILtQeY18aZbzW4zmo80M18WcsbnZFzToTJFPBKHHJUhR+WUjdhAm9XyNlCr9WqUzS9jthnI82RCmO8KetPX4FVBKV3fs4AQci2A9we+S6MLnkOBpdDCbUNAkDsjN00dKOkkxPwx5F+en1auNqkhh2WotWq457rTlIi6HB3U2oR91KonVqVNja16YhX0Tj2W/2E5tn2rx9TTH5bDUmxBLBTD32/5e/qI8TvbccdbdzAVc7ZSG4iaoPGTRsXa2sTlEzHtlmkK811nRdc+Ik5dplEmK+hyR6yFllRg7X0e13QX83rs4+zQ2/XMOkuRBYZcA/N4llIL8546JjgSllOMfvN+R8wFZsQjcXz69KdMqfqJV08MmHx8qMxvhcmuYKDoUz4vQsh+SukVFyu71AzHfF68h8+sqlmpHE+9af28Fed2n1OMsMpvKMeZN88oysffMB5hXxitx1shR2VEfBHoLDoQNYGzwgmNSQNvnRc0RlOBDWrAVmzDuXfPKXJpuSpc8NZ68dxXnlP0beO7G/H0l59WPGjvePsORLwR/G3l3xR163euT43gepZX7aoCpZRdt7MKbafalGst84ogR2Q0H25Gy9GW1AjPNcUF91w3Lrx7gTn6DHeE8fRSZb+rdlYxyzfu3gidRYdHr3hUUXfv/nsRaA0wc6RteH0DOs51wOg0pgQ6/hZ/Im0JBU7tOoVAcyDVb6PLiLIFZTi145Qyb9itU+Fv8jPPU7Wrivk53Hvg3n6NTlqOt+DR2YxrPXAvdwN1f8h0HjGqYtLnG2032+mmuZsGsy9ZsyW6BS/vefmLHKJ/+bwIIdcAmAcglxDyox5VVgDsJ68gDZVWhVlVs+CscCqSE/KmT7y13qzUhut3rodar0awNai0U/JHAQCtx1qV05A2PV6951XFg2TdtnXQ2diKtWAbW7rtq/NBUkv9Epow6xq8MOYbsebFNd0JLGNxhDvDCLWHEAvF0kZ4yx5ehmBrEERFsOrJVakEkbFQDMHWINe0lyfj99R4oLfr2dfT4OObADf7EfVH8dzX04UhwfYg5Kis6Peiny2Cp9bD3fOXbb/7Kx/nfUaBlgB3A3V/ApiQvQsGiotNG2qR2NulBtBznssD4LaLHZwQshSJtTIVgD9SSn/Vq34KgCcBXAHgPkrpb3rVqwDsA1BLKV1xsfMNV1gJCDNNn2SrNvTV+2DKM/ETWIbYaUzW71zPfWBpTOw9YAaHgRnUDE4DCOmHrRVHaGIuMDNHZOt3rIfWpMX27/QSwXxnO9bvWJ+aruvZZu3La7MWwRhdRqj1amadxqThTvUZcgx4Yd0Lys9h+zpoTVq889N30ure+ek72PD6Bu6eP5WWI8nnmR33Uz7Ok6OrtKoB3SAsZO+Dx1CpDS+GRqfBpIpJABJqw8EiY/CilO4GsJsQ8hSl9Fw2B+4KPA8DuBEJkUc1IeRlSumRHj/WhoRycTXnMD9AQtXIt8MeoWRyDci0dsN7kPGSKgZaElNUvKDHCxwacyI3V+89YK5pLmZQC3WEAApmHQVl2lPJsgxQsOtifDsn3mZontVTNBCFfYKdKXaxllmZUv1YOAYiEeb1xCNxFM0tYgpkeJu7A80BgLJHmfFInCsFl8tlrlR9IOXjPDl6xMfem9bfkZKQvQ8ew0VtOABThX2ir4KNACHkPwFMB6BPFlJKr8/Q5ioApyilpwGAEPI3AKsApIIXpbQJQBMhZHnvxoSQYgDLAfwCwI961490MuYoUoH5oDXkGbiu6eYC9hutKd/EHxG5LczAodarEQ/HsfjBxXj5ay+n6lY+vhIGh4G5sXnV46sAAmZdybUl6KzrTBOTnN59Gs5JTlBK0+qSknznJCcmf2Uy5t4zN81811JgAZUpe1NvgZnZxuAwwFfjQ/G1xWl9MOQb4DnvQfPxZqzfsR6+Bh/MBWYce+UYCi4vgM6qw4XqC2kbm6sfrcaUW6ZApVUxzW/r99dzXzyMLiOKryvG1d+5Os32y1JowbiF45hScJVWhelrp8Mx0ZFmjKvWq1GxsgIbXt+Qdv6LTeXFI3HUVtfCW+uFtdiKwjmFUGlVXPPbts/bMo6UslUOXkz23h8lomBs0tfg9QyA5wCsQEI2fyeA5ou0KQJwocf3NQCuzqJv/x+Af0L6dKUCQsg9AO4BgNLS0kw/OqzINH0SbA/CWmJNe6DHwjGE28IwuAxpasNAewA0TqGz6Zhv6HqbHjkTc5h1tnE2RCNRrNu2LvHgdpsRj8WhMWtgdVvRcaYD8348LyXmAADbJBvm/2S+8liTbQCAxQ8u7hZSqAkWP7gYtsk22E/1kt13pUQJdgThnOjEmTfOpFlX2SbYMGXFlDQ1X/I8cX8cix5chNajrSnhw6IHF8FWxmkz0Qa9Xo+jLxxV9HvqLVPRfrodz1R29235H5Yjb0YeJJXEPF5OeQ6oTJlqv0nLJzHvdeGVhQDATL9iL7NzpeDxSJxpjDvj9hk4/NxhpjGvpJaYQUCOyVzxkKSWmOa3U1ZP4Y6U+qsc5F2rUCJmT8/nn0FnuMS9GVr6qjb8mFJ6JSHkU0rprK6y3ZTShRna/A8AX6aUfr3r+w0ArqKUfo/xs/8GwJdc8yKErACwjFL6bULIIgA/7sua13BUG/LI9IfacrIF5989r5jKmnDTBJx584zi4TNtzTQEmgJoOtoElUqVEDjkJgQOeVPzEA1FEfaGEQ/FU6MOSS9Ba9Gidm+tYuRlLbZCY9DgjfveUIwSvvzbL8Pf4IdKq+oWUkTiMBWYoNar0fhJI16999XU8VY8ugKOSQ78+fo/KwJ11a4qSGoJ53afUyjtyhaW4c83sNuotCqcffts39vsrAIk4OmbGGpDjmrvjrfvgEQkPLXoKUXdxnc2QmvV4rErHlPU3fXuXdjxwx3d960rlcuqP65CoIWvUCy5poQ54jj//nm22jCDSrLwykKc2HZC4a9ocBi45ze6jFwVoGOCg5l6ZaCVg0KJyGTEqQ0HeNqwf2rDHkS7/q/vmuKrA1B8kTY1AEp6fF/c1a4vXAtgJSFkGRLTlFZCyNOU0qo+th/2ZJo+8Tf6meKL9TvXsx2+JzpANATNnzYrHuiGHAMktYSmg02KYJh/eT7bkeK1KkR8EVx+x+Vpo4Slm5aCyhTPr3le8YBZt20dYoFYKnAlj/fqva9izYtrmFOk3nov9Da2dVXGNhy7K26bfpgG+xv8iEfjzLrO851Q6VTMOk+dh5tIlGv0W+flvshwN7lfRCXZfLhZ6WU53s49vxzlrDHW+7hqw4FWDgoloiAb+Nnp0vnfhBAbgH8E8GMkHOZ/eJE21QAmEULKuyyl1qLL2PdiUEp/QiktppSO62r31mgKXEmS0yfjFo2Dq8KVmhrhyaO5kvN6LyKeCPOBHvFEEPVFmcEw6otyz6M1aZltYsEYjE4j5v/rfMy/L/HP6DQi2BbkikaiwSjUhvT3pKQwJBpg9yFjG3+WbfLNqTXB3nXJlCi9y3VWXcocuHedMdeYMr/tXae36dl9cPP7YM43M8U7rSdbU6pGRb8L2f22FlvhrfMyfxeSCkXWsZLT2L3rNCYNt2+8Nl9U8ThQxxOMbi468upSDU6ilL4KoBPA4r4cmFIaI4R8F8AuJKTyT1BKDxNCvtlV/wghpAAJKbwVgEwI+QcA0yilnn5dzSjBnM8xns2gNuT5F0aDUb4CriXAFnnkmbjKvWBbkOs6IUdlrnycpyjkOrfnmdht4jL0dnYeMqPLyLbVcurhrfEyVYXxWJypKIyGojC5TMw2aoMa/gY/s124M8xdI+q80MlWNkZi3JeIknkl7DW0OYVcY95Tu04xj0dUhKtclNQSs9/xCHv06av3oWxB2ZAoHoUSsW8MF6k8y7h3MAx6Lxq8KKVxQshKJNKiZAWldDuA7b3KHunxdQMuMv1IKX0HwDvZnnskE/FHmA85Xs4ujU0DIzGyH+hOI1dtaC40MwMECGDOYwdQo8uIF6vS38R3P7gbt191OywlFubDUaVT4fwH57Hu1XXwN/lhyjPh4LMH4ZzkhDZHixWPrlCsk0kqCUFPME0dWPNxDZxqJzRmDRY/uBhvP/B2qs3iBxdDpVXh/N4e58k34eAzB+GqcEFr0uLCRxeYdb5WX0Jt2DV9e+yVYyizl0Fv14NI6ZueI74IDDkG0BhlJgwtXVgK92w3qnZWJdSBhRYUzikEkQgkSWIaF1/17au44p1Mm9xZakdJLcExwcFUYxodRhRVFTGPBSBrteFAG+byFI9EIgOuQhyNqsbhIpVnMRgGvX1d89pLCNmMhOIwlUmZUrp/wHskgNakxSd//qT7wdi16O+qcCEWi6WpEJuONSHaGYVar8ay3y/D9m/3CBy/X5aQQasIs06tVacHiEILavYl1moigQh7ZNHBHsVFfBFoLBrkz85P65/WooXWqkXpvFI8u+LZtKBrKjJBbVJD0khpqkZJI8FcZobxsFGhULRNsgFRwFZmS2tjK7MhFonBNcGVdp6F9y+EHJNhr7CjbF5ZWl1SvZg/LT9Nbbhs8zJYSiwwu8wwOAyIR7oNeA0OA2wlNliKLEzlYN6MPBz66yGmF6Gt1IbJlZMVaVns4+wZRxy8Te7HXz7OXItyTOSkWJnogKSWFMdKHi9btSEwsIa5mfrAKh/uPo6CwaWvasO3GcX0Ivu8hpyRpDbMRCQQYcqj7RPsaD/VrhgpOSY6UHBFAc68cQbxSDw1SlBpVShfUg5vnRcNBxrQcqQl9bB3TXOh+Jpipldi3qw80CjFi3e82P32ToFDfz+E1U+txksbX1K81a9+ajVUWhU6z3dCjvXwV1QRGJwGvPKNVxRtlv9hOdQ6NVPtx1PTVe1MLH02HmtE3pS8VNBtPNqI/Cn52Pq1rYrzJPegbb2bXcc7j0qnQse5Duht+pRPYbAjCHuZHXJMzkoFWLWrCqZcE9cr0VZmQ111XdpoTWPQAEjkhOut9mv7vI2rzAOQtU/hUHkbZoLXh427N+KphUrV5xhRNY44tSGLL6BA/GJqQ0ppn9a5BNnBm7rQGrXMjan1++q5+aqaDzWj9XirYi3KXGCGHJXx8t0vMx+0zMzMO6tA1ISZqFJtVOPaf7pWMaVJKQUFRbBN6a+oMWtwxd1XKEZxckyGr50vQuGVOyocUKvV6UknN1ciHo0zzxMNRREPseuC7WyvRm+9F7ZxNkS8EWzd2D26qnyoEhSU378Gjudgo4/rGuJv9uPCXqWh8Mx1MyGpJXz69KeKUVTORH4aleTXrDqectBbx0/dkxxZDfaDnSdI8tR4uNcjVI1jl76qDRUQQoaVo/xIIzl18ejsR/GnxX/Co7MfxdEXjqZyNWmNWpQtKMOMNTNQtqAMWqM244Mx7AkzFWZhTzhjIOCVe2u9KbPY+ffNxzU/vAbVD1cj0BRgqxBDMa6qUWPQpIJGqm8/3w2tSctXAfKUcW4Lop4oM+iqNCrmeTR6TUJez6gz5Bi454mH4syXhXgonhLVKNpx1IvmPDPUOjWzDhTMLRC11bWoP1DPTFSZXMdUnMdt7pdykNc3XuaDwYDXb57iUqgaxzZ9XfNi8S0A3xiojow1Mnkb8t7+uGas+eaMasNsTWnNbjNA2TmzeNL2iC8COcIZWTSypf9JtSNLnKIyqriJN311nDdnjkLS1+gDkdj7vEKdIbYVV74BTQebuNejNqrZKsVglOv9SNRsr0Rev731Xqg07P1kgeZAxrWobJWDvDXOUGcIQwVPbeie7Raqxj4wXNSGLFgKRB59VSb2O3hRSkXg+gL0Z+oiHo0z1YHxeJwboMxuMywFFqas2jbRxlQHGpwG+Op9zKDCc5XXWXWpt3dFHzi+i0anESBgilOWVCxJVw4mFYoTnfxr5ZzHnG8GJLZ7vd6mx9GtR5nnsRXZ2EG/KOGvyPR4vG4Vu/yJVVDr1fw63ssFx9neUmRB4ZWFXKVftsrBsCfM7Nutz97K/F0cDDKpFwda1TiQxxsuDGe1YTb0VZnYp+BFCHmTUnrDxcoEfac/qSHUWjWObzuukFrP++E8aG1aLHt4WSpViNqQyHGls+kQCUSg0qnS1HkqnQrxUBz6HH2aFFylVYHGKUz5Jvib/N35tHITyRWN+ey9VLYyGyK+CG7+r5vRerzbc9A5OeGpt+hni1LpQNSGRB6rkCeE/Dn5mPejeSlFH5EI5v1oHmyTbahYXgF/sx8RfwSkhaBieUXKQ/GWZ29BPBxPCUMkrQRdjg4rHlmBV7/ZQ3b/yAqo9Cq4JruYGY4poTj4+EEcfPxg2r2eunoqyq8vZ97TgtkFIIQwFX35l+UzvR8L5xRCpVGh8qFKEEJSApCS+SVwX+nOuP+Kt58rk0cgT7V323O3KWyjkj6FrOtxz3ZnlJXz6gZaij6QqsZMsMQxkrrfqyuCQeRiySj1AIwAXISQHHQrP6wACge5b6Oai01dsP6ICucUMo1ii+YWoea/a/Dev7+X9ub83r+/h5WPJ4bqSXf4JGpDIsPxC+tfYJZrDBp4znvwytdf6Z5GemAhcspzsOeXe9LOs+eXexLGr3EZVKZptkQrHl0BjVkDrVmbFjy1Zi1M+SaEmkIItYcUD+5IRwShjpAicABAuCWMUJuyjVqnBtGQtPMQTULtGAqFoNL2CuBaFQwuA3NPlMVtQdNnTYhH42lt4tE4mj5rQuGVhdDl6FKBX2fRQdJJ8Fzw4NirxxQvGMXXFMMxwQFfnU/Rb0mSYC+3pyXeVGlVKbf3GWtnIGdCjmI/F4/WE+wp6Xv238NtI6ml7n1jPRzniUS4snIAzLr+StuHSsLOO0/Fygp89uxnXLNjwfDiYiOvewH8AxKB6mN0By8PErm6BP0k09RF0v279x/RjLUzYHAZ0kZKaoMaklqCr96H9lPtaWtUAC6qPuOVG5wGrn8g6zzeei9sJTamt+GG1zfgg99+kAoQVKb44Lcf4Ct//gpCwRD2P7lfsdnXMdGRHoyRCMaOiQ5QSpkCh6qdVXjngXfSzvPOA++kpPKsAF71WhWu+6frsOMHPUaSmypBZYpQRwh7/3Nv2vH2/udeLNu8DA0HG9B6VKnuVGlUOPHiCZx48UTa/Zl9x2wE24Jsb8pJDjy7/FlF3+7Zfw+cE504tvUYmg83p1z3vXVeTLt1GohEmKObts/bmJ9r28k2puehq8IFx0QHc99Y/mX53LVZUDDrNr6zsV8JLPuzDtwfeOfZ8PoGpjgmd3ouiuYWDdj5BQPDxZJRbgKwiRDyPUrpQ0PUpzEDbyqEpzDLmZCDLWu3KB5yG3dv7Fe2Yp5gw+K2INDCF4DwcmnxvA2DrUG2TL0jCCIRpgFwPMaWtofaQ4gG2aKRYDvnPO1BxMN8sUIycCXLdvxgB9a+vBZyXGZL/GUZgZYAM7iv27aOeU/VBjVXjs67b60nWhELxtBxpkMRcJqONKHtRBtzlMJbe9Satcw+F15VCEop84G+fgc727av3oeIn52osvN8Z9brucDQSdi5HqGcz8dT4xHBaxjS131eDxFC5gEY17MNpfTPg9SvMQ1vX0umPy6T28RciyLahC0R0/PPxbabggToc9j+geYCM677yXXK80gEGpOGLeaw6Zgy9XXb14FIhOugz2qzfsd6rrehIcfAbrNzPSCzA7jRZWTe04gvAqPLyDxe1WtVCDSzg3vYE8bCBxYqRmRaixYaA/v+mPJNzHKNQcMNkkVfKuKOUiSNxOwDTyka7gzDJ7Ef6BF/hLs262/0s68nj309F5Oi92cduD/wzsN7AbQWj4xE7sNZbdgXNDoNJlVMgqu4by8qfRVs/AXABACfAEh65VAAIngNAsl9LYo/oiJOebEVclyGKd+UlhE47AuD0ERQcU5xptk2ETWBHJER7Aymldd+XAu1Vg1ZLTOVjTRO2Rubd1VBZ9UxH5qhjhDzwRhqD3E37vJGI6HOEGylNmbfwj62dVWoIwRTPttklxtwrTr+SKDBB2sh+7MwFZgQ8UfS1smspVZoTYl9eizhSiwYw5L/WIJAcyAlpDC6jDC6jNxRDC94+up90Nl00Jg0aX3QmDSpPW2s35+kS37vOsd4B3dtlkiE+Xlbiiz9kqIPlYSdd56iuUVcccxIYKSrDbN14eirVH4OEm7vF/eSEnxh3LPdzD+iTE7iLcdbmAkfv1H9DUR8EVx494LiITNxxUQYbUr/QOdUZ8IWyZGuRJS0EsIefoDQWXWwlloVD26Dk/3QNOYaU1+zRni8qS/PeY8yUHvD3JGNwWFAsC0Is9ucZsAb9oYR9oZx2/+7DTqzLmU1FfKGoDVrodGzj2cptECXo8Ntf78NUX80JdhQGxMjORqnsBZbU4bCRE2QOzUXar0ackxO74MvDHu5HWFPGIHmQOo85gIz8qbnpaYce/fBXmbnjlIcExxoPdGadjxTngnuK9xMNWZSucgMHJOdcEx0MM1yHRMcyJ2em/Z5507PRU55DmwlNmx4fYNC/JGJTOvA/VEv8tpkOs+sqlnInZ4r1IYjgL4Gr0MACgDUD2JfBF1Iaon7RzR9zXTkjM9J88CT1BL3TTzQHEAsHGNOPRXPK+Ymt5Q0EtqOtykCXsl1JdypQY1Jg8PPH8bce+amKe3m/8t8VP6uEju+32Ok9LvEhmP7BDtTJm52m1G5qVIhpNCatTA4DKjfV4/nv/p82vEcCxzMY9km2hDpjKBmT016m4cqUb6kHGffPIvn/8fzaW3yZ+cj6o+yNzDnGqAxaRBoCigd9Oeo0Hq8VaGSLJpTBLVODX+jP+1clZsT3pSd5zrT1rUWP7gY7Wfa4ZzoxMonV6LlcEtqVOaa5kL+rHzmi4xjQsJ8d8qqKbAUWhTu8Xq7Pi3YJJ3ziURQsbIiEXB6tAGQUTk4qXISzAXmtN9TKtN+q/aoTBH2hBFsD0Jj0qQcZ7JVIV5Muchbb5bUEormFok1rhFAX4OXC8ARQsh/AwgnCymlfdsyLcga1h9RPBJnOpbPqprFXWcw5ZlSC/89SU4xMdfWGvgZjtdtW8d1kJA0EkquKkmT8i98YCHkqIw9/9FLXv8fe7DqyVXoPNGZrjbs2qScMzEHe37dq82v92DFoyug0qpSgTDZtx3fT3gyvvfLXtsFfvkeHJMcAMD1heQpFykoc1vAyokrQQhht9tVlQpcqfLvbIezIrHfjTXlun7n+lR6l2T52w+8jYIrCgAA3gteRWBrOtyEd3/xblrf3v3Fuyj6UhGcE5049LdDypH73EI8v1aZBfuej++Bc5KTGXCKrmKvreXOyIVrsosZ2Gyltn6p9ngq20zrezwhx1ApFwWXjr4Gr38bzE4I+kZtdS3zgemscIKo2OsPIU8I1hL2+gzXHqrAjGAr27A21Bliu0Q8vgqe8x4c2XIkTfa+9zd74ZjkYMrr/U1+yFEZNXtqULOnJq2OJ/0PdYRAY5QddOu9iIe605eAAPFQHN56L0DZ2wUy+TsCYG8/aPCBgMDoNOKyOy5LqS4P/ukgX1FYnzDmZbXhvUSEO8PolDuZgW3Ni2uY19p6ohURb4QZPG555hbmeVqPtyLiy7LNiVYQQpgBYvWfVzPbXEy1x1PZ3vrXW7n3lBeIhPnu6KevasPdg90RwcXx1vKdv6lMU0a6yaBS/XA17OPtsBZZccOvbsCb//JmKrDd8KsbQAnlijJ4a06WIguu+5frlBZVsTjs4+yYduu0NNn7wgcWQm9jqwP1Nj1UWhV37YZ3/ngwzqyzldqYWZ6txVbIMXaW50z+jgScJJ75ZkhqiX0uzouCzqaD1qxltyllt9FatIh42XL0WCjGPJbepueqVXniFLVBnXUbjUHDDRC8TOAXU+3x+qA1abNWIQ6VcnE4MVLVhtmqDJNczGFjD6X0OkKIFwl1YaoKiXxeI0NDOkrgqRAthRauka7FbYHRZcSZd850uz64jKh+tBru2W501nUm1IZ1CbHC6d2n4ZzsBAiw6qlVaD7UnFpryZ2eCxqnaDralCaWOPbKsZTDBmuq8Y6372COCjUmDTQmDXtdyWnA9f9+Pd7617dS5df/+/UABTRmDVM5CAns8795B3RWHTNQmwrZWwwMTgPkKFtxqbPqEA1GuediSuXNWoCw+7dx90ZmG1uJDf5mP3NfnSHHgP2P70/bxL3/8f0oW1jG/T3hqUH1dj3Xs1Jv0zPbGF1GaIx8QUt/VHu8fhuchqxViKPVfDcTI1Vt2N9cX31KRjlSGC3JKHnEI3F8+vSnzDWv5CI5KycUkQizXflN5Tj/3nm0Hu3hRTjFidJ5pQh5Q6ivrlcID8oWl+HsW2cVD/uiLxWh9Wgrdv3DLsW02PJHlyPYFkTbibaUUMAx2QFjrhGmfBOCLUFIKgn+Rj9M+aaU7L/uo7rE6KNL7RjxRmDMNUKlU0GOyVBpVCk7pXg0Dkopnlv5nOK+rXlpDXTWhH0TjdG07QIqtQrxWFxRTuMUUX8UKp0KBCTVNwqKeDiOqD/KvNalv+uS3+vVKbVhNBSFxqCBHJXx5xuUu0vuePMORHwR1O2rS92fwjmFmLxiMmr+uwZNnzUpArVzshM1e2sUa48l80tQ8qUSnNh+ArFgLKWEVOlVGL9kPM6+cxaSSkrdNzkuY/z14yGpJZx99ywIIfA1JPotyzJK55XizDtnUn6MxlwjqEwxYckEEIng89c/R6gjlDqPzqbDhBsngMqU6xHIUwHKMRmfPvOpwn9y1vrE73dtda1CgHKx440Cn8JRkYwyExcJXl8sGaVgeKDSqjCrahacFU7mH3FaAstCC9xXuqHWq7lrZRve3ADPOY/CvcEzzgMapUzhATeB5a4qWIutzKksg9OAnT/Y2W2zFKfY/bPdWPXEKsSDcXSe60wEJ38EaAIivkgqe3FPheLCBxYid3ou1AY1Gg40KAJrsq73m3tSzFL/cb1iFFU4txAX3rqgGOEVX10MY7kRtXtrlcrBeUWIeqLMa7WX21G/v14R3MctHoeoL8rsnzHXCF+jL+2zjvgjiEfikCSJuYm7alcVcwP1nW/fCSpTBFuCipeVQHMAvjqfQvXpqfXAWmRF59lORRv5SzL8DX7FKApIBA1fvU9RR2XKVe1dTAVocBjS1JAGhyGjepHnvdhff0XByEGMvEYJPKXWrKpZOP7ycfz91r8r2qzfuR7PfeU5xcN0zYtrEOoIYctaZWqCW/92K7P8tr/fBlOeCc9UPqM43vod6/GnRX9StFn7ylqoNCpmH9ZtW8f0+7v9lduh0qrw9JefVtRteHMDzu8+rwgopQtKIcfk1H62tL7tXM8sv/2V26HSqPD0UuV5qnZWARLw9E2Muteq2OU7qyBpJJzbfU7Rv7KFZfjzDco9elW7qhBsC+K51crR5G1/vw3Pf/V5Rfmal9bA6DLiLzf+pc/Xun7nehBCmPe0amcV8x5s3L0RAPDUwqeYdTxhRsvxFjw6+1FFm3sP3AsAzLoNr29gXs/G3Ruhs+qYbTbu3sjs270H7h1pgg0x8uIgRl6jBJ5SK3d6LldGz7ML6k8CS2OukZt00t/EthHSmrRc941gO1vtGPFFEAvFmHWe8x6uaIWnNuSJDkIdfPePpBKRWccT1XS1Yfav3M5tw7PCMrqMbPGFXs3tA+9aA00ByHH+tTLvdY2Hew8yqQozqQAp5ahIOdfjqfHAkGPg1vHOM8KCl4CDCF6jBN4fq6fGA61FyxQ4GPPYD0BDjgFqo5rth5irZ4oYDDkGqNQc5WC+mXl+WZa5QdKUyw64+hw9CGGrAC2FFqZoxVyQQTnIUaWZ8k0A5Rsa98fsGIQvquG1UWlVXK9EVrnOqku4fWTRN1upDbFwjHtPWeVJ5WCmOhaZVIBhT5hdV8hXL/JsrXjiD6E2HH4QLcHda+/uU/bktHZi2nB0UFtdy53CAYCdP9qJq79zddpG4Jt+fROaDjdh5/d7BJXfLUXe9DzIMRltZ9uQU5aTEh60nUt8/8H/90G3i0aXcnHeD+dBY9GgcX+jYt0kf3Y+nv/q892KOQoc+vshrP7zaljLrTj3+rlUKhW1IZEDTG/Xo/lQs0KQkDsjF/lz83F6+2lFYC1bXIZzb51TBNbi64oBADV7ahR1JQtKULOnRrGuVV5ZDr1ej6MvHFVcz9RbpiIUCjH7MH7ZeJzZfobZBgD3eCd3nETL0R4uGlNcmPqVrjYvHu2ukwhcU10o+lIRaj6s6RbbSATOqU4UXF4A5wQnU6Az4/YZOPzcYebUctPhJtR9XKe4nuJrilH3UR2zDQDuVDVPGJFpzevCBxdw/t3zis98/E3j0fRZ01hd8xr104ZAxqlD7vWL4DVKyLTmBbAfMiULS1D7Ya3iAeie40bUG0XDJw2Kh1nu9Fyce+uc4gHjnuuG3q6Hr9EHlbqHCjAWh8agwdY7tyqUeZWbK+Gc4oS31gs5KqfUbJJaglqnxuv/8np3wO3K87Xkl0sSozKbGh0nOlKB1TbZBu9Zb0J1GKMpdSBRE6g0KmgMGoQ6QwBFSuIPAmhNWhiLjN3HKrTANsmGWEcMrikuhDpCaPy0MSGCcVuQPysferseLcdbmH2IdSb2ObHaAGAeT2vW4tRrpxDxRlKqPY1Zg4k3TYSklphtfI0+dNZ1QqVSpc4fi8dgK7TBVeFC2B9Gw8cNqTYFVxZAZ9IhHokzVXstx1twfPtxFF1ZlLoPNftqULGsAjnlOVylXzQYRV11Xeo8hXMLoTFoMnoRxkIxxfHUejVajrdg69e3Kj7zVX9clbEP/VEbDnSm50FEBC8OYtpwlJDJDxFAd5bcHn/8NR/W4JWvvcIWJABsVeFOtsqtamdVIlniZ82KqazyG8uZyjxbqQ3xcBztn7crphRL5pdg9sbZaRueKx9K+Aqa880I+UKJDlMkfr1jgGWcBU0fNyHiiyDij4A0J9zU867MQ7g1jNbjrYrzlC4qRdOBJkiSlFrjajrQhLzZeQh1hBDyhkBBU+cJeRPnNeeb+aMyT682nhAgJT6jE6+ewCv3dGenvvmxm1EwtwDRQDTt84wGomg+1gxbmQ2dNZ1px+us6YSt2IZYKJYKyCCAyWGCuciMWCiG06+fRjwcR8QXga/Bh9Ovn8bEL09E3cd1kKMy5JiccDf57xqUziuFvcyO3IpcxIKx1H3IrciFrdSGU7tOoa46IeNvOtSEYFsQk5dPhhyTcebtM5BUXfcuFMOZt89g/PXjceLVE8xRjxyT00aSzUea4anxYNqt0+CY4MC1P74WsVDid4sQgmt/fC1yynOYiTIz+RRSmeLkjpOpfjd80gBfgw+Tl08GkL1XomD4IYLXKCKTPJn1x59pkT75NauOuRDe6IPOqmPnnrq6iFlecl0J5JjMzue1Yz3Ti/D2V2+H1qbF6deUU3Yl80vgrfMqApStzIZga5B5nrvevyshH+81nWgtsgJ5wOnX2VODnSc7mdsP8mblofHTRmWbyvHw1/hTgSvZ5pV7XsGdu+9EqD2k6Hc0EEWgNYDa/65VHE/n0jGn+TQWDcLtCYf63sdrP9+O1hOtija2YhtCnaFEYs5e96H1VCs7+/IUF6KBKLNN87Fmrq9gqCOkMCFe+MBC1B+oh9FlhL/Jr+h3puPxxBdtn3OyRk9xgcrsxJvC93BkMeJ27Amyh2dSmlyM70lyYT9THavcnG/mqhd5jvf+Rj8CTZy8VJx8XmFPGB0nOpijQn+9nxmgAi0BrhIy4okwg2SoPcQ9T8eJDq6HYcQbYbc52cEV1UR9UWa/o/4oPOc93D6wyr01XoS9YfZ9aAywj3W+A6GOEPc+sF48vHVebptwJzttjq/eh1AH+3ihjhA8tR5mv4NtbOWprz59b1xPvHVebr8zKR4FI4dBDV6EkKWEkOOEkFOEkH9h1E8hhHxACAkTQn7co7yEEPI2IeQoIeQwIeQHg9nP0Q7vj1VlUKFyc2UqGCXfxFUmFaACs05tSkyP9SxftnkZjG5jytOuJ0mFV87EHMz/1/mYf1/iX87EHJhyTSkZf+82vCCpNWszPnx4wZN3Hl6Q9DX6Mp6HF9wzJYlM+gT2tU2gJZD1tfrqffA3sQN1pjbc+8BpE/EnpiONTmPa52p0JjZc834PYkH2NodYMMbtd6A5wD0ej4iP7QkZ8UdSisdsjicYfgzatCEhRAXgYQA3AqgBUE0IeZlSeqTHj7UB+D6A1b2axwD8I6V0PyHEAuBjQsjrvdoK+ghPnhwPxHF+73mse3Ud/E1+mPJMOPjsQTgnOiGpJXbdJCcz7cgtM26BZbyFmU/LVGjCdT+5TjFdpTapodKpmG0M+QZmDjC9TQ+1jp2gkSt7zzNBZ2N7G/Kk4OYCM1+S77ZAZ9cxtxJYijiy90ILJI3EzjycoQ/Jr1l94JVLaikr2bul0AJJyq6NY7wD4c4w22WkzM71FaQyZR7PWeFExBth1mU6Hg/HBAe336PV93CkSuWBhFx+5XWJDFuuYlefZPODpjYkhFwD4N8opV/u+v4nAEAp/SXjZ/8NgI9S+hvOsbYC2EwpfT3TOcey2jATVKY4se1EavGaqBLeefFoHP/v1v+n+PmvvvBVWIos8FzwQI7JKQUcURFIagk7vrtD6en30FIYHAa8/LWXFZL4VY+vwtavbVWYy658fCWIRLD3t3uV0vsfzcMbP3lDIe+v3FQJY6GRKUcvuLIA9dX1ioDnmuGCzqTDq99+VXG85Q8vR111HVNer7VpmecpX5aQ0becaEEs1D0SU+lU0Nl0uPDeBUUfSuaXQNJIqPuoTiF7L1lcwpfXS8DJbUoZPa9N+ZfLYcm1MKXy09ZMw5HnjmTlfzlz3Uyu+KLleAseu/IxRYC45+N74KpwMdV8VKY4suUIXrrzpdTxVv9pNabdOg1UplzFbMrbsEdm5kxqQyCzKEOoDYcvvZSHl0RtWATgQo/vawBcne1BCCHjAMwG8BGn/h4A9wBAaWlp1p0cC1CZItgWTFu8Xv6H5YnU9Iy3U2uxFfFIPG1NI/lQz52Ry/Uv9NWxc3AF24O44u4rlAks20MgEkHJ3F4JLO9fiKg/ioplFenpVe5fiGB7EAanAeXLyhNu+D1k6lFvFHqnHqueXJUy85W0Egw5BngveJl5w2KhGGxlNqzbti4loZfjMqz5iY221jIr1m1b120aLMvQ6xOyd2+tF/FIl6Kv3gdJk5D4m/JNWPPimu7tApE4gq2JfpcsLoGlyJImy0cMzOuBCik1Xc/PbtWTq6DX61H+5V5tJtigt+kRjUSRPzu/u67QAo1ZAxqjKF9SnlZuK7OBSImXkrzL8lJ1ZrcZGpMGklqCrdzWnXmgyAK1MfHY4E41NviQU54Df5MfvkYfJLUEe5kdan3XVLNeneZfmCwnEoHZbU77/PR2fSJw7a+FHJdBZQo5LqN2fy2K5xRz93lNvWUqpqyego27N6apb5MBipWxmUgEIW8IjQcaU/6g+ZfnQ2/RD9Sf4oDT8/ln0BkucW+GlsEMXqyImdUwjxBiBrAFwD9QSj2sn6GUPgbgMSAx8sq2k2OB+gP1KaduoMs66lvb8LUPv8ZNXXFh7wV25uHXqpgL4VWvVXGnmAw5Bq68HgC3jln+WqINa4Owc7ITL65/UXH+qteqoHexbZYopajfV690qrDpIKkldJzuUKjfkmaxgRalos8+zo7mw8rtAmWLyqCxabgbm3nlgfoAtt61Ne0+bL1rK+7aexca9iv34Y1fMh6RQAQNB5R1RrcRp99gnOem8aBRisZPGpXOKU4DGg8q1ZMmlwkaAzslitai5WY46DjXgefXKLM5J70NWT6X9xy4By1HWpTX4zICcTDFSPmX5aPxYCMzqPFGeFO+MgVHtzC2P/yPqcM2gPV8/tnN9jH1/BtMwUYNgJIe3xcDqOtrY0KIBonA9Qyl9IUB7tuYgqdya/+8HbOqZmHj7o346gtfxcbdG1PuCNwMwxkSYsajcVQ+1Evk8VAlQh6Of2FHEP5mjrigkf1WH2wNovNcJ1tpx8tiXJc41uIHF6f1bfGDixHuCLPVb20hhDvYqr1wRxgRT4Rd52EfL9zJV0lmKu8828m8ps6z7HvQcbYDgSaOqpB3ntMd6LzQyXxZ4aodz3UgHo9j4QML0+7pwgcWIh6NM7cR1FbXZhaNcOr8jX5mH3x1/DbeOi8zqLWebEX9frYPaOPBRma/Gw80QjD8GMyRVzWASYSQcgC1ANYCWNeXhoQQAuBxAEcppb8dvC6ODXg+b9ZiK3dvGNfTrohzrCIrQNGd3LJr6unM7jNwTnayR2R2A9cjkJeNV2NKZPA1Oo2KdTeeiMFUYEKgKQC9vXtKUWfRIewJ882JA1Gu5DvQEuAb/XKCbjQQ5ZoQZ3qg63PYI0aNQcNtk/w6m/OAZN+GSCRro+GCywq4Ypvk173rMvWh4HL28XhqQ1+9D/4W9gsT94Wta9+jYHgxaMGLUhojhHwXwC4AKgBPUEoPE0K+2VX/CCGkAMA+AFYAMiHkHwBMAzALwAYAnxFCPuk65L9SSrcPVn9HM+7Z7qwz21oKLUx1nt7JNubVO/TQ2rSwnbCl0m4kp3e0Vi1u/uPNaD3WI+llRSLzssFlwMonVqLlSA9BwlQXtDa28azWpIXWrGWuu0UDUaYBcCwYgynPhJajLdz8YIqAl8c35jW7MysRecfjKe0yOfhLWrZCkWdIm9E0ONN5VFkqFN0WqPXqrI2GL6b0Y9WZ8tkmzZmOx1Mbmt1mEFV2n53FbcFIYLiqDTU6DSZVTOrzz7uK+7ZRXHgbjhF4Pm8pr7teKq6z75zFx098jCs2XpGQyuebsP/J/bji7ivwyjdeUSgKb/6vm0EkwswXdcebd+DC+xfw9gNvpx4wix9cjJLrSiCppMSaSm+F4HQXtnx1S/foigIH/3wQy/6wDBa3BU/Of1JxnqpdVXjjX5UKxSW/XIJ4JI6/rviros3619aj80ynwhi47MYyhNvCqN1bq+hb0bzEKJVVVzy/GPUf1ePVb/Y43iMrUHZTGQDg7M6zipeIcUvHcdWGoVAIF96+kJVCUdJKOPy3w8w6nqWVxqjBkS1HFKrGSTdPwtG/K9vMuH0GVBoVc+1o2lenMc8/c91MqPXqjEo/1u9pLBJjHm/62unQGrXMNpmEHO1n2hPZwH/Q47PbVInxK8bjzA7GPb20a14jXm14kXxdF0N4G451WNOD8UicKY+eVTUL5kIz8qfl49kVz6a98QeaAkxFYaA5ACqz8zGFPeFU4EqWvf3A27j9ldshqaRUAEjW7fj+Dqzfvp75Vm/KN6HzPHsdKBqI4vI7Lk9TKC7dtDSlCGS1IUgo7Xqq3yS1BMiAv86PfY/uw6onVqWC4d7/sxf2cXbEo3GcefdM9z64ruDumOqAIdeQUhuack2Ix+MJhaIaMOYblXUmfbrasEuFqLPq0FnTyVQoxv3xdLVhoQW28Tao9WrEojGULChJUyKaCk0AAcqX9mozwQatWYtYJAYap2mqxpsfuxmSJGHqbVO7M3S7Ewq8pPmuc6oT63d0TxOr9Cpo9BpMvWVqWlbv/Jn5KVVhPBJnKhGpTLlO8KzjaY1aUJni9JunEeoIIeKLQI7KCLYFMeHGCZi8YjKqdlXBW+eFtSjxYkYkAnuZHZYSS7ciNNcIKlNYc63p50maKg9TscZYRwSvMUxtdS1zgdpZkXgTZqoKd1Wxp2MybKjlrSuFPWHIUba/or/Zz5wuk6MyNxGjxqTBJ3/+JC3YfPTwR1j626XQ2/XImZij2GsGCrz8tZeZozi9S49QRwhNnzWl2oQ6QtC79FBpVBg3f1xacF+6aSn0Nj1ajygNgNtOtwEE8NYovRc7z3ei9iOlf+H4ZeOhy9FlpVAsW1wGOSYn9poxVIUsT8iyRWUINAWYvovOKU40fdrEHHn5GnxoPtSs3PhdamGPYL46FWq1OqMSkSWyKLyqEGfeUB5v+trp8NX70Hm+U3lPL3TizJvKNrOqZqHjXAcaP2lU/G7lTsnFmbfYbZJ7ygTDB+FtOIbJpBz0XPAwrX8i/ggqf9dLUfi7SkQDUeRfls+2jupKetkTtUENnV2XWs/oXWfMNeLIliNY9cQq3PzHm7HqiVU4suUIPDUeBNuCWPSzRWnnWfSzRYiFY5i6aiq23r0Vr3z9FWy9eyumrpqKeCwOOS7jun+6Dh/83w/w3i/ewwe//QDX/dN1XIWitz6RpuXaf7o2rc21/3Qt5KiMUFuIqTbklYc7w1z1YrA1yFUHdnyenULRe8ELfz1bnddxmu+HyBNFeGo8zBecuuo6rkKx42QHW7W3v5H7wpRJidhxln28+n31XD/EjnPsNsm0KqwXs47z/DaC4YcYeY1heCpES2Eigy9LFGF0GeGt9aZtJI34ItBZddAatciZlJOYRkrmzJIAvUOPFY+uUKwrGXIMkNQS2x7KacC0W6elb1J+YCFsJbZERuKWQLpy0BuGWqdGzoSctOmgqD+KQFMAar06tcYBdD1of5BI8cJTO/IC0ZoX12StHEwqFFkqyYsZxfanTTZ9u5iqkXV+b703a4XixbIV8JSImY5HVCS7PtR5odKqsm4jGH6I4DWGKZxTyAwcRXOL0HCwgfl2WrqgNO1tG0g8YL5R/Q00HW5C6zHllFnJ/BIQNUlbVyJqAkIIQACdXZfuiqFJrDkxpy1fr4LWqEWgOZA2XbXwgYUonFOI1mOteGH9C93TWL+rhHuOmzvKJFrCvAfWMiuaDzUjd3ou5v3jvFRyxL2/2Yt4NJ4x8DPLixKKPmZeszJbRrUhLxfagKoNeapGu457fsjZn4dIfKWfc5Iz9WKQfCnR2XTQGNmboZOKx6z6UGgBpRzVJ09tWCjUhl+Enr6FPemrhyEPEbzGMCqtCrOqZsFZ4VRkqOVtbPbWsINAoDkASik7N9fO9cykl+t3rgco8GKV0hVj/Y713PPETDF2frBrS9jij53rudJyQglUelWanVOgNQB/vR/mAjOuvOdKhQDEnJ9Y32PJ8vUO9lYCjUGDaDDK7Pcdi+9gBtD8WfloO9uG/Y/v795LBWD/4/sxful4bpuQP8Sss02yccv1Zj06znSkvWDYymzQWdg52r5R/Q3kjM9h92FmPrv8inyoVGwj5sI5haAyha/ep3S+uGUK93garYZZ577SzX0x6zjXgcUPLlaoX22l7PvTew/kcEWn1eH+K++/1N3oM1tqtnyh9iJ4jXFUWhVKr1V6QlpLOJuROeXGXCOajzRnPV2V/FpR15VWo/d5DDkGBFo5+cF4KTVaAlCpVVh4/0KFv2LEH8ErX1cG1jUvroFKp2IG43Xb1yHQHMDHj32sUCLaSm048NQBhWjEWmzlTjV2nu1E/pX5aZu7tTYtWk+2wt/sZ/pC+up9gAZpbdrPtaPx00ZQShHoDKTV1X5ci84TnezyU53oRCc+f/NzXLbusrQsAmq9mtnnjnMdiAaiOPbqMax5YU3CVNlpRPVj1cibmYepX+2l2rsiH3qzHi3HW3Bu7zlFtoKSa0sQ9oSZzhc5E3LQUduh2PzeerQVOqsO+5/cr7jfJdeWcF/MHBMccFY40wK1s8IJW6mN20Yw/BDBa4zD23Ojt+qZ00iSmj29FPaEuSlJMk0jcae4Cizs82slmPPY59Hb2Os2OqsOap0a+5/Yn+YGsf+J/bhxyo3Mh3M0mHDYYK33BJoDMBckZOEpCKDSqxANRJkGwNFglJ/6xG1GPBrv+kAAQgji0Th8NT7o7Xqmx+O6besgx+W0NnJchrfemzC0zdGn1elz9PDWe6HSqdLKVTpVYi2KAiXXJaZ3AUDSSCi5riSVg6x3n7UmLTw1Hsy6cxbURjWkDgkakwaz7pwFT40H7tluqA1qqLSqxM/rtQCQsoGKRxIimngsnioLtge562snXz6J3Em5iTxi9T6cfPkkCmYUwJBjgOesJ00R6jnrga/eB+ckJ4wuY0qhKqkT+jQiEUyqnARzgVmxN4z3MicYfojgNYahMuVu5PTWebnWP0kVYNrep3J7ImcWIweXyqhi5r7Su/QINAaY02zxWJx5/pzxOTA4DVj0s0V456fvpNos+tkiaMwaZsAzOA0ItrKd7ZNKyN4PZ1uZDTRO2es942yQZRmzN85Om1KsfKgS5kJ2YLWNs8FYYGTeB/tkO1cO3/wJezQbi8ag0Wlw5o0zoDJF89FmOKc44Z7rhkqngm+XT+F0UrygGKd3nFaUu69MPLjPvn1WUeea5mL3eZwd9vF2nH79NF66/aX0fn95PDe1ia3Uhmm3TEvLIlD5UCVspTbobOz1NedkJ3P6NmdCDjQGDbONtcTK/d0GwN1PNkzToggYiOA1hmk92crcV5M3Mw9mt5m5SdhWZmOrAEsTD/t92/d1TyN15eZyVjhRcHlB+rSYWQs5IkNj1HT7IdYlNs6e3n0azslOlH+5HOU3lMPXmGjjafTA7DZDkiQce/mYYqqo/Ppy2MvtadNB9nJ7wvpIp8bx7ccVbcoWlTGVkPFQHFSmbNHK/FLQOMWeX+1JW4va86s9WPXkKqx6ahWaDzWnnCpyp+ciHoyj80Qn9vxyT1pA3vPLPXBOdDIl7FU7q6A1admjTIsejfsb0zYVL3xgIXLG5UCOyjj+6vHuzyHXiOpHquGc6MSBJ3tMaZq1+GjzR3BOTFgz8frA7PMkJ0D5bVjTf7nTcxELxdjZCnZVwZhr5N5v1vTtxnc2Qq1VM9cEy68v5/5uA8AbP3kjrc0bP3kDeTPz4KromzWR4NIjgtcYJtNaVNmCMqZnnNas5S7ga81aVKyoSH+r3lwJ2yRbRvsje5E97Y1/2eZlsE2woXReadom4MrNlTC5TYj6o5h9V69Rz+ZKUELx0eaP0u2hNn+EG/73DVCb1cw2hjwDyFmGElJD4D3PFqf4m/yQ1BJzJBf1RxENRNOCyrLfL0M0GEWghe1OwjWEbUisuyx7eBm2f6fHvXt4GeLROPNzWPPiGkgaCRXLKxSjG6IlTAeSSCDCNSH21nu5feaZE/Oux1PjQTwS57ahcbZDC08p6qn1wBQ1MT8HnvluMu8aq02gJQBUgMtwT2A51GrDbD0Le9NXD0MeIniNYXhrVGa3GUQimHrLVOTNzEv7Yz337jm2KKI5gEArOxXH+p3rmZs/HRMdAMCsq9pZxX2rB9hv/Ot3rGcmsIwEIiCEcI/HUkJW7ayCKZdtCGt0GgHCz0O2/du9rufb21H1WhXXkf9ihrkRXyQtuEZ8EYTa2OKPaDAKo8HIHt3srGKOYDKN8C5mVptNG2tRIskpr40pj32/M2YyAPtz2PD6Bu7vdtgTZrbZuHsjeGSaYh8uAWyo1YZf0LPwCyMcNsYwSUdutaHbqaKnwzeRCFwVLoxbNA6uCheIRLiOGKZ8E0Lt2W3c9TZ4M6ah4JXz0o7EI3HmQ0mj1/TrPMF2tpNHqCOEQAtb8cjrm7/JDzkmM/NfaUwaLN20NK186aalICqCsCeMN/75Dbz783fx3i/ew7s/fxdv/PMb0Nl0zM/BMdEBb0N21+pv9sNcZEbl5l7OKZsrYSu3MV1TiuYWwTbRxm3Duh5I3XsLWcfj/T5q7Vrm8VR6FT/vW3uQ+7vN87mM+qPgwZtibz3Zym0jGFzEyGsMwxtdZXqTDHvCXLWhwWHI7u29IIPakNMmUzqSiJ/9UAp7whk3rfLOI2kkeGu9aaMerVkLg8uAWCDGHpVxfBd1Fh2CbUG2CGVCDlN2P/+++dypuWgoipv/62a88o1XUp/Dzf91M1yTXQi1hbK6pwaHAfXV9aASTVuXbD3dipq9NZi5biYck7pl70Vzi6DWq9F+rB06py6tjb/dj5q9Nczrmfc/56FoThH3eACYv4/Hth5jHs/oMkJnYe/fU+vUmLxsMvN3O9NnziPTFLtYJ7s0iOA1xkmOrvr6B9h5rpP7AFZp2XupKKHMfF7xWBzGPCNWPr5SkYrD6GYr84hEEA1EUfm7Smgt2rTEkrzAYS22QmVgKx6tpVbc/PjNaD3ao29TnDDmGRHuDOO1f3xNcbw73roDhlwDM4jrbewtBjqrDlqzlp3/qsiC5sPN2HL7lrRys9sMAnagNuQYsPP7O9M+h3f+7R04JzsTqk9OLjbWxmqDI2HTtfXLW5nTp2q9GmXzyxS/C1qzFs8uf1bZZlcV83pSU42c4wHs30drsZV5vGQyVdb9NrqM3N/ti+UUY5Fpil1waRDBS5AV1mIr+wFcaAGVKXMv1YTKCZCjcpqIYfkflsOQYwBkIB6Op9f9fjniwTjO7z2v2MzqmOiAxqQBKLD1rh7ii02V0NvZ7hYgQKg9hJwJ6b6LFBRhT1iRCmTFYysQ6ggh2Bzkru/lTM6BY5IjbVTmmOSAId/AVDzqXYl9VywHB0uRhVkuqSUQFcGKR1Yo8oOp9CqmkMJT48Hk5ZPhqfWkuYbIcRmO8Q60n2pPs+JS69XIm56H5mPN7O0MDn46kHgkznxZ0efoB9Sp4mLJVFtPtCrud/7MfO7x+jPj0J+AJxhcRPASZEUmP8Smo0247ifXKR6AEW8E277VSzr9rW2o2lUFSilTVr1+53qUfqmX2vB3lVDr1YiH49jz614y9V/vwcrHVzLdLa4ffz0ktYRQZwgGuyGxQVciCLYHIREJr97zatr5X73nVVTtrILOomOmUdFatAg2BeGocMCYa0zlzNI5dOj8vBMqgwrlS8pT5YGOAPx1fqj1arz3y/fSgvt7v3wPjomOdJeILgn7jb+6EaBAOBROy83VeKwRUW+U2TdrsRUqrQrjrx+P2upaSOrE5uGiOYmpuUmVkxTJRyW1BJVGhVgslpbrq/FoI1QavruE0WVkvqxMuWVKxqlBXmJUgK3ok9RS9/F69RsApn5lKqzFVmbdQNGfgCcYXETwEmSFSqvqfpDUeWEpsqBoTsJCR6PXMEdLWpOWLSCo84JIbFdwf6Mf1hIr1m1blxopxSNxhNpDiIfjbJl6IMpWG3ojsE+0o+1UG15Y223au+zhZdCZdOx1Ml8Yar0a1/7TtYppNipTmEpMOL/7vCJQl99Qjs6aTjTsa0hsHj7SDGeFE7JFRtgTZo6Wwt4ws99Upgmz4ROtiq0E+bPzMf8n8xUvEQWXFYDKFCdePaEYJUxZPQXHXz7OVMzZy+zQqDWK89jL7FyJuHOSE0t+uYQ5GiESYU4NyjGZu4GZl/04U78BcOt4gaW/ysFsp9iHmi8qlc9W+v5Fpe5fFEIpvaQdGEjmzJlD9+3bd6m7MarJ9IcPgFlnLjDjLzf9hbk2AgBPf/lp5lpLy/EWReBwX+FGNBDltnl6KbscEvD0TYy6XVXMY214fQOoTNnn2VUFUDDPdcebd+Dc7nOKNZiyhWWgMsVfbmTchwz9NuWb8OjsRxV1G9/ZiKcWPaUov3f/vQABu83ujXhqIaPNgXsRaAkw+7bh9Q0ItgVRV12XWhMsnFOIycsng0gk4yiKRW11LbMPG3dvhM6qy7rfAPta7z1wLzfItBxvybpNfxmAvWF9/mG72U43zd2UfSe7uNTSdw7c6xcjL0FWZHLlcFW4mFMrDQcbmGtRWrMW/iY/c90kGoyyHep3rOfn0mriyNSb/amve9cFWgPsjM0xmWv062vwgcqU6cMX9oSZm4fXbl3LFRcE2viyeznOzjTdeb6TWd52ug0ag4ZZx80UUOeFv5l9rZ5aDzpOdyicPFxTXHBMcGRts8Trg6fGA71dn1W/L5a7jBeIhko5OBL2ho1kRPASZMXF/vBZUyu+Rh9MeaY0AUE8Eoev0QeVWsVcN1k+dzkzOPib/FxJvsnF2VSca4QckZl1GoMm3auxK2eXc4oT5gKOwizfzM2zJcdlZg6wiD8CAExfyCW/XMK+nnwTNAa2MW5yv13vcq1Jy81/xVNjqrQq/taEAjO2buxWISaDcfE1xaAyzfgiw4KXB81abE19rbhWzublpNIvWxXgUCkHL/aiJ/hiiE3KgqxI/uH35GJ/+FqjFs+veR7PLH0GL1a9iGeWPoPn1zwPrVELnU2Ha//pWnzwfz/Ae794Dx/83w9w7T9dC6PTiLnfmdtd/tsPMPc7c2ErtaUMeJP9SAYOtSkxoutZXvlQJSStBJ1dx2xjdBlTXo2vfP0VbL1rK6bdOg2mfBMkncTchKvSqxIOG4wRljm/OwdY8nhX3nMlzG4zcsbnpJ/r7sS5LKUW5nnsE+wpRV9av7vWw1gbd63FVsTCMWabaCDKLA91hkBUhHnveJZNUX/0oqluWCSVgz3Pk1QO8q5VUkvcDccX22jPoj9t+kN/7o+g74iRlyArLiYZZs3x83zmAi0B6Gw6vP/r99NGXu//+n2s/ONKrklroC3A3GuWOz0X5z/oIRjJN+HgMwfhmuKCDBmWEkuapNpSYkEszE5sueH1DZBjMpqONKXJ64+9cgzOiU4E29gy+rA3zLZg2lWFcJQ9pVgyr4Rr2GvKNzFHpuU3lDPvW9n8Mqj17PQvq+avYpbf+uyt0Fl0OL7tuCI317X/eC13FMdL8Gku4L/ISGoJs6pmIXd6rmKdLJN6sWhuEVfpl60KcKiUg2Jv2OAigpcgKzL94fPm+HkJLC1FFvgafUwFHm+9yVvnhSnPxNxrZnAaUL6gPE1ev+KRFSBqgqg/ClOeCdZCK/yNicAWj8W5QSjQEoBKp8Lsr81GsDmYktfP/tpseM55YC2xMqXqgWbO+lWGdbJM5rel15Vi2eZlCHWEUhuySxeUIh6NwzHZkQjmXc7xdZ/Uwdfgg6SVsOQ/lsCQY4CvISGvL7iyABqjJmHQSwgCLQGYck0omV+SymX1pe99CfFIPPU5f+l7XwJRE+aaZDwaB1ERLPmPJYks2l1iDqPLCKLKHASIRKCz6mDIMUBn1aWChnOSk3mtyd+vgVT6DYVycKj3hn1RtSHREqy8bqWi3FXswhN/e+KLdG1QEMFLkDW8P3zeHP/G3RuZzg5EIjA62OswmdabKKVM4QORSGozb/L8r37zVWx4Y0PCAunjeoVoJHdqLvM8BpcB1mIrzrx5himHD7YGmXvabKU27vVoLVrmOpl9nJ0d3IsTG7999T6FtHzc4nHsvFhlNvib/Qh1hPBi1YtpfVNpVfDV+RTyeipTUJmi40yHoq7w6sL0VDJde9DKl5QnXjBCsTQxx6KfLYK/yc99QGcSMfCuNTlSzvZ4yd/JS7Uva6j3hg2WMe+Wmi0X/6FLgFjzEgwYvDn+9tPtKW+6m/94M1Y9sQofP/Yx2k+3I9AaYJrfRvwR5vpH2B9GsL3bI3D+ffNxzQ+vQfXD1fBcYKvS/E1+RPwRptN6sINtvhv2hOE552E60XvOeRD1RZl1ckxmrkWp9WpEfBHmtCFv/UqtU6P+QD1zE3fnuU7m9XjrvIiH4sy+hdpCTAf/2upa1FbXMutCraFUGpXkGt7ld1wOKlOotKpUQtBkm3d++s7/3965h0dV3Xv/u+Z+zeQyyWQyISHcwl1AUhQviMIpASRQa0EIiNWjvZ/2HN+nPe1Tz9v2se05p+e8R8VqrajY1qMe0UoVUKgIeqwUgSIIAnILuZEbycwkmcxtvX/smZ2Z7LU2ZMxtMuvzPHlI1prf3mvv2cyatdZ3fX/QGvgbm9UMbhsOsa+14VBD/493qhUnXj2B38z+DbYs3ILfzP4NTrx6AjQ6tFuDWObWgoFBjLwEAwZvjt9exPbus7slY96DTx1MWuc4+NRBVG1mr89UPVsFbY4WWWOzUDCjQB4NZI3NgsVpQfGNxb35vGKjBL1ZD1+dj6kC1Fv0MOeaZcsko92IHl/PFZ3o47/3rfPWetmmtA/O58Z01HRwY7R6LbcNzPPXefudZ8tXx78eX6OPnQhy78aU3NnVRAy8tdGOmg54vsC2luIdr+1sm1D6jXIGtfMihCwB8AgALYCnKaW/7FM/GcCzAOYA+BGl9FdXGysYefDm+AvnFDItpdxz3fDV+3DjD25UmsjmmzDn3jlMw1W9WY8598xJcqRYumkprEVWzN7YJ+HkY5WwFlhhzjMzU8mbckzoau5KOv+ChxYgf3q+qvs4z9ne7lYx2VWJCbQH0HS0SV4LC7QHYHPboNVruV8IeNOqRMM/D1OKXmiVsk1zpjtZnUPQH1SV1/NQzSGnZbfbUmDp9/F4ri7CBX70MGidFyFEC+BxAIsB1AI4QAjZRik9nvCyNgDfAbAyhVjBCINoCCavnIyNezcqlGRT7pwi5ZpqkLIDu2a5YLAY4Kv1oeHjBknRF1sX+OTVT2DONeP8e+d71W9OCz589EMUX1cMaFQSWDKm0tbvWg9KKXvT8851zKm8u75wF3LH5zId5x2lDvR4e5hGttYiK9elXqPXMGMcZQ7m+ll2aTb8TX7meqEpj21CbMo2AQTM81jcFmaMMcsIUHDrmJ2DzQCiJey1RxXBhpqIQaPTMK81nnSyP8dLJe2JIL0YzJHXFwB8Rik9CwCEkBcBVAGQOyBKaROAJkLIsv7GCkYeNEqZjguTlk/Cif85oRh5zayeiaziLBROL8QfKv+Q9IGVNzkPU1ZOSRIkxPcx1R+s79+0WD1/Wow37RS4HEBnSycIJUmChBVPr5ATUWZ5JO9FWb0YjCDUGUK0J6pwyQ96gwj3hOGa5UrKf6W36aX1K8Ya1QOHHkD72XbmlGLWmCzmxu9wQMpfZnKYktrW3daN9jPt0Jl0STFdLV3w1ngR6g7h+KvHk6TyHz72ISxOC1ttGIzAV+djblkovq6YK9hQEzHklOWgraQtyfXelG1CTlkO95njHQ9AxrnAfx61oZqv4XB7GPIYzM7LA+Biwt+1AOYNdCwh5H4A9wNASUlJ/1spGDB4i+fVb1UzR0p55dIHCS81Pat8/a71/MSSvGmxAqv8+9VOv1ldVnS3dGPbfduS2rDtvm1Y/dpqhLpCqDtchwmLJgAANFoNzvzvGYy9YSz2PbwvyfF+38P7sOyJZdAZdWj6pAk5pdKHMSEETZ80wWRj2yL5GyV3d60pQQBBAK1JC4PFgA/+6wNU3F8hVx169hAW/2IxTA4Tdv1wFyrur0A0EkUkFMGRPxzB/O/NR9OJJmSNyYJGq4HeooevyYfssdkwRU3SNKVBC6Ih0Jq0sLltMOeYuXuvADC3LFxpdMNTqxINwfjF45kKQTWPQBqVUtt0X+6G3qoHjVJptJ8BLvCJn39mozllteEI9TVUZTA7L9ZTcrVSn6uOpZQ+BeApQDLmvcrjCwYB3ijGV68yIuqvuKDeB2O2kTm9BA1Q+WgldnwnYerr0UpEwtLeJVYMpRRLH1+K7d9MGBU+vhRagxY9HT3MNkTDUeRMzEH35e4kF/bKTZUgWsJ0vKcRCluRDR21HTi3+1yv43xsGpLXgdrH2Jnre65ZLkxZkTwyXfbrZcgdnwsapZi8fHJS3dJNS+Ga4ULb6TaFc3zBzAIAQOnp0qQ9cvHz8JzjgYEf3bA6tivJ63ku9RqdZkS7wA8EiZ9/2bbsjPr8G8zOqxbAmIS/iwHUD0GsYJjgLZ5neTiblIvsqiIGtZi/Pf83Rd6uJf9vSXIaFbcNkZ4ILLkWhANhXNx/EWvfWAv/Jckt48gfjqDgmgIU31iclC/L4pa8EI09RqZ60Wg3oqetJ9kVA5IrRtXmKrnjAmJraD/bi+qd1ehq6oL3gldhcps3IQ8Lf7oQex7aI5cv/OlC9Hh7EDwR5I5aWbLy4uuL0ePtYcbkTsjlloOw1xFzJ+aifEU51u9an5Qv60ruFv3N2aU2IlLzCOzx9jDvQ/60/JSTXwrSg8HsvA4AmEgIKQNQB2ANgLVDECsYJniL52oJLAF2dmH3XLdqDGs0otFr0FHToRhd2QptyJ+Sj9L5ypGFvcSOczvOKUZecReLiq9XoPl4syy+qPh6BSLhCIK+IHOE1XWZ7bDR1dYFvVnPFId45kn50BKtq7QGLTovdSISjrBHoHXskam/wY/uy2zXkFSk/756H46+cJQ7smGNlFLJ2aXmtK42cudlGPDWekXnNcoZtM6LUhomhHwLwFuQ5O7PUEo/IYR8LVb/JCGkEMBHALIARAkh3wUwlVLqZcUOVlsFA4PaYvzM6pnIK8+TElgWSZl145tZeXVqMVNXT+1VL8bsj+r/Ws9cJ1v75lruaKR6Z7Xcccnl39yO6reroTfr0d3anXSN3a3dyCnLUbrhI7YP7Zkqttw71yILIfraQ0UjUex/bL9sNUWjFPsf24+qzVUAAdOGiudAorfqua7y9iI781jx/XY8dd6n2z7tVYMWSX/nH5ZGNgF/AJcOXepVkc52SUpLLZVGs/V+2D12tJ1vQ8PhBhizjKj5sEZxvPj+q1B3CPUH6uXjFc0tgs6oY7ZNa9CqutSHA2Epa3S9D1keacQYz+bMq4sEI4pM0/FnbgBycwkGkEHd50Up3Q5ge5+yJxN+b4Q0JXhVsYKRD28xXmvQouQGtqCGV8crDwfCOP7SccWozGBj7+3paumSf+9b529kr9N1t3XD5DAh1BlSTPNFghFEI1HmyCvuDNK3PNAeQM7EHKY9FG+dLBKJILc8l21DNcnBluq7rYCOLZV3TGRL8h0THECUHZM9KRsFUwuS1KCVmypBKUXAH8CJl5Uq0rLbykCPUsV6oN6mRyQUYR4vEoog1B3Csf8+phyFX+tmrleGukNwz2GP0PMm5+HoC0cV5TPWzgAAZt3U1VOZz1V8lDnSc3N9HrUhz9dQjeH2PBSZlAVpx4X3LrCzH7+9PqWMzawsxut2rkOkJ4IXq15U1K3ZtgY6g46b/fj1e1/vHd1Q4NjLx6RRFC+bs1oGaLAzNq/buQ7b7t2mOM+KzdIH0J9/9OfetbrYmuBtD98mdyh9j0VAcPj5w7hm7TWSI3+BFUdeOILZG2b3u22pXM+V6lj3dOVzKwEAu36wS3Gtix5exD5WCs/C+l3rYXFahiz7ch+GLJNyfxkihaLIpCwYPfDWQIKdQea3cKIl6PH2ML+9R8IR3PKTW2SPPp1Z8jYMXA5wc1kFfUEEwuy1lq62LqZjSCQc4TrO+xrZ19PZ3IlomJ1J2d/AduOP54rynvcmOXZ4z3uvmF/qyOYjOLL5SFL9hMUT+r9Olsra2hXqWNcaCUr3tPb9WtS+X5tUp9qGFBSu0RD/fRjNasaRjOi8BGkHT72oN+tx4YMLvfm8YqOHghkFMOeY4avzJW2ADfqDkoOESZckltCZ1PeG2Qr5Fkw2tw09HT1J5wkHwtCb9f3fn5Zv5a5FcdWYbjs3yzNPkq+25qXW5gGNuUId9zwG9nqYWhtSUbjyslALx47hQ7jKC9KOuHpRZ+51YY8rFMfeOBYvLH8BW+/aiheWv4CxN46Fe7YbRrsRO769A1vXbMWf7vsTtq7Zih3f3oFIMILd39+NfT/bh/cefg/7frYPu7+/G9AAREew8KcLk86z8KcLQXTSpllWZmZEgYNPH0zalXjw6YPQmrVwTHQwMyZTQpkO+pFohBsTX/NSlJc5oDVpmapGrUnLjpnogGM853ic82tNWrhmu5jvg6OMHWPz2LjvXVFFESwuCzPOWmTlZj62FdmYGaAdYx3M83gqPNw2FF5byI0ZquzLgqtHrHkJRixq6q4kVZrbjqKKIujNevR09qDxYKOsViucUwij1Yjz757HloVbFOe448U7sHWNMl/RHS/dAVBg14O7etWBFDjy/BEs/tViAOy65b9dDu9Fr2J60lHigMagQe6UXLSfapf3lDkmOdBxpgMf/IfklhH3cTzwmwO47tvXAQBypuQoYnwXfLCX2nvLi+xwTHQg2hnFpaOX8PKqlxXXdOfWO1E0rwjtZxJixjngPe9FoCOAgjkFyrZ91gGLy4LOhk7FPrj88nw0nWhC4HJAPp4x24hAWwDmQjO6L3XLMSaXCR1nOjDh7ybA2+BF+7mENox1IMudhZN/OolgMAiH2yHHtTe0w2AwYMKSCQoVot6sx/l3z6P1fCvyxuXJx2s504K8sXkonFvYq4R02+Ga44LJZlJ9fhJViHaPHZ65HlmhOExqQ7HmxUFMGwpGJFdKMnj6zdOKuglLJzDVYtPvmg5roZU57cOTWjvGOECjlGl/ZPfYAcq2RjJYDGy7q7eqYR9jx9ntZxWKvnFLx6F8eXlyYslNlciZItlI8WKY5UvGwVHGnh7MnZiLs28xYv5uHLKQ1a/zlC4oRfPJZtT/tV6xvjfui+Nw9m3GsSrHwXvJi7O7GHVfHAfnNCcuvn9RoVJ0fcHFVCHOWDsD2eOy0Xa2TRGTPSmbqYScsXYGtAYt8/mZvHIyTr1xiqsoHIrsy5+Hz5tJ+Ur09T8cbs9DMfISjEhaTrZw1V0AmHVqKjeD3YDze8/jnR++I38w3frzW1FyQwnO/fmcYn2o9JZSaHQaNB5uVIyiCmcXQmfVoe4vdYoPYWOWEa/c+Yrier788pdhdVmTFYKQ9lmt2LyCqwIEwK3jlWv0GtTsrVFc05gbxzDVjqmeBxSy5P1qYqp3VoOCqioe+6VefKsaoP1UPL5VDWuBlfn8bNy7Ec8teG44FIVqjJiR1zD5H4qRlyC9uJIyrr8qN3PQDIvTkiTMsDiljcMsZ/Tscdm9iTFZSSINWpx791ySOOTQc4cw56tzmKMei9OCwOUAcz8Xb6+Zv9HPVcZd6f4wr6ksu9/3NJX3getxqaIoTPV97bdysMHHVZF6a9mZuIWicGQiOi/BiEQtaWH89/6q3N64/w3mN3Tm1KDbDgDcDNA6sw6fvvIpjv3uWFLddd++jinJN9gM0Gg1bN/Dt6rZ7S5MQYEXu1bmNQ2wcpCXwPJKbUulbiDvj7Wgf1PIQlE4MhFqQ8GIRE3dxatzX+vmKtl4e6y6Wrv6rUorqiiCe7Yby369LKlu2a+XwZhrhNlpRtWzVbj96dtR9WwVzE4z9Fl69PjZLvWh7hCWPLIk6VhLHlkCoiNwTGSr5rhqw7EOuGawVYDc8pmu/sfMdqGogn1/XDM5MdeonGemi3u/rR4r81qNDiP3PXfNYp/HU+FBTlkO91qFojB9EGteghHLlXI4seqCXUE0fNQgq8jcc90wWAyoO1DHXM/YuHcjnFOdvQrFmE+i0WoEAK56EUDyuYrscF/rRsNHDWi70Iac0hy5bZcvXEZuaa6qw8buH+1WuEQs+c8lCHeHYco3obu5Wz6eOd+MQHMAxEgQ7YnK5RqjBogd2uAwINAWkOtMuSYEO4IomFaASx8nKPBmuqTsywAC7QFmXcAXwKXDCeWzXTDZpRjZC7CP/2SgI4BLRxJirnHB5DCh5WQLOuo7oNVq5bZFIhE4ihxKb8OYCrDpWBP2/WJfrxozz4IDTx3Azf98MzwVHu57LisHY+WeCkk5WHegDlvXblU4dtzxwh0ourao34rCQVYhijUvDmLaUDBiUVN38fI+fbb9M6ZaLP6Nv6/6zDnVibNvn0UkGEHQH4S/0Y+zb5/FpGWTQDQEx//nOLZ/IyHm10txTfU1iIajOPn6SWy7d5tct2LzCtg9dpQtLEP72XaASgknyxaW4fKpy9AYNFjx9Ao5waXOLGVmDgfDuOGfbkDDoQbZvf6Gf7pBylbc4INjkgPdzd3y8Yw5RjR/3Az3PDd8NT653OqywnvOi67mLuTlxEYLsTpoJGeSgmkFCTdR7eZfXXkoEAKNUNCo9BMKhCQjW9onJva3r94Hf5MfDrdDbpu/yQ8t0cJZ7kTSl2kCUEoR9Afhud4DnUUHTbsGeqsenus9CHWGpEtkxMjltPfc8XJvrRembBMKZhTIqW5Mb5vgrfWi6Noi7i1hdVIARrzn4WhFjLwEowY1hWKPtwc7/3GnYnSz+F8Xo/mTZsUalXuOG5FwBM8vfF5xvA17NkBr0OLZG55V1P394b9H7f/WMiXnAFCzp0ZyXY+JRvKm5KFkYQkzLUtZZRmAfkrlY+fh1Z3bfk7Rgce3H5x4VSktn/KlKezyO6cAFDjxSj9ivjQFAW8AZ3cz2rZoHCxOCz558ROl0e/SMvb1LB4Hc66ZuT2C14bpd01H6+lW1O2vU7znxfOL0Xqilbs9g9VJua5x4clrnhxMheJV94Bmg5l6TIOXBoYYCKZMnpJUNgTmvNzrF52XYNTA24h895670X25Gy9/Sblx9yuvfgWvrntV8eGz9s216Gruwiur2bJ3jU7DPN7d797NlI8PhpHtaIsZqjboTDo8d4tyCnn9rvX43WKlsbPa9ox1O9Zhyy3sZ27sLWMV5SkwYqYNWQzBVKKYNhSMftQUinorO8eV3qJnCzlaumApYPvZWZwWREIRZh1P9j4YRrajLWao2qA1aNl1Kgk+eecJdgaFQnGYEGpDwahBTaHIUwda861yWRydWYfs0mwYbAZUPtpH5fZoJQw2A4xZRqbvYVxa3vd4dredXzdUMe4hikmxbUPV7rgkvm8dr9zmtslfjPrW5Y7LFQrFYUKMvASjBrVMzvFszvnT8uGt9SKrOAvu2W7QKGUKOQpnFSIUCMFb65Ud4o12IzQGDfLK84Ao0H6uPWnTs91j5wpDXDNdAIAVm1eg5USLLMxwTnbCNcOFZU8sw5tff1OOWfbEsivGqJ2HVzdgMbNUYtTapuHEzHJBo9P0qw2F1xb2u91FFUXQ6rVY9utlePMbCff718tQNLcIq55fpVjXindEzLpJeciblMd85gSDi1jzEmQ0LSdb8Pp9ryuEHFVPVyHUFcKZt84obJbGf3E8aJTi2ZuUgo3qt6pxeEtCUkeXFUf+cASz75kN10wXPtv+GV6/53X5eFXPVqF4QTGaDjVBo9VI05X5FkTDURRMlZSBtftrFTGuWS7oHXqFkW6gKQC9WQ+tXasw7e1u7Ia50Kwo76rrgmuGC742n6KOdlNEohH46/xyubXICoQBZ7kzWV5fJHXepmwTV3YPgBtDoxTNp5vRdalLviazy4z8ifno8fYwj0ejFE0nm+StBHa3HaZ8EwrKC9Dj6+mV6xfFYhwMY96Y0a/OqEPNBzWIhqLwN8a2H+g0KJlfAqIhwpiXgVjzEgiGCX+Dn5nM0N/gRyQUYaYWcV/rRigQYq6BdLd1wzneiReWv9Db4f14Abpbu9F2qk3uhOKvf/2e17Fx30b46nwK9ZvVZQXREmbMPR/cg4v/e5Gp2oOh/wrF9ovtuLD3ArOuZnsN8zwBf6BfCsXpd01HJBzhqhcDrQGmUtOQZZDUmH1ipq6eiq6mLtR9UKcwBzZmGXHuLWXMtDXToNVpmUa/ZYvK0HqyVXGsLE8WcspyMt6YFxhZ5rxi5CXIaNTk9c2fNOPlO9gKRUuepV8qt3U716G7la143PDOBryw7AWm4jHQHmCrJF/7Cl5dq1RJpmyy28+YwVAHqpn2pmL0y4shWsJUFardn7E3j1W8B0PEiBp5DcNGZe71C8GGIKNRE3lklfAX9rsvdzMTSHa1sm2oulu7ZcVj3+NxrataurgxejNbJelv8Kua6Q5UjK/Bp6roSyUmlTZwTY3V2q2iKmSVdzZ2QjDyENOGgoxGTeThnuXG8ieX442vvSFPIy1/cjncs92IBqM4fOAwVr+6OimB5Jgbx7Dl9XkWaAwaLPzpQux5aI98vIU/XQi7h20iG19zYcUYHUZmTEoGtyMkhmhIv4+XijmwRtfPGI9d+eAIhh0x8hJkPPG1jLG3jIWz3CkvwhMNgSnHhPkPzsdNP7oJ8x+cD1OOCURDUDCrAJOXT8ZLX3oJr1W/hpdWvYTJyydLSrvH+5i+Pr4Urjku5E/Jh3OqE6tfW41Vv1+FNX9cA+dUJwqmFbCNYqe7kD85H3aPPakNdo8d+ZPzsWLzCtz845tx049uws0P3YwVm1eom+zyDHNnurhGtqoxKnU8Q2PueWbzjXld17BjCq8t7Lcxr3uum3se91z2sTwVg+daIUgdseYlEHBQWw/rbOnE7xcr1242vLMB9Qfq0dXc1Zs3LN+C0ptKYbAZcG7POYUwo6iiCA0HG2CwGRD0B+V/86fnQ6PT4LmblG4Q9/7lXjQfb1Z4K069Yyp8l3yAHrJy0Oa2IXtSNhAEoAXazySUj89GqDMEnV6HrstdCPlDcp3epocxywhKKTobOuVyq9sKnU6HzqZORCIR0DCV64iOQG/UIxKOIBKISKq9Qhu0Ji2KK4oldeCJJgQuJ5gG55hQMKUAGp2GaaarNWjReLQRPR09cozRYUThjEK0nWlD3YE6WPIsklIzlqPNU+GB3WNnGvbSKMX5vecRCUbkGK1Bi7ELxiIajjKNhocRsebFQUwbCgQc1NZgOps6mXU9HT3Y/f3dis5mzbY1CLQH5I4r/vqd/7AT63askxVuiTGrX1uNUBdb1djZ3Cl3XPGybfdug6PEgWgkOuiCjXU71yHcHcZLq15iCk1YApS4xyTP3T/u+l56U2nS9bacbMEz1z/DPJ6/0Y8/3fcnZft2rEPexDyU3px8LABoPd3KbZ+z3ImSG0oUMekAT23YVyH4eRhOdWFfROclEHBQs5uioMw6XmfT09HDzeDLEx2EukJcW6ueDnZuMF+9DzTKOY9aVmTSv6zInY2dXJulrha2AMXf4Ef35W5mnbfWy52eU2t3sDPIrAt2BpnHutLxRqoM/mowGoz48bU/VpQPUyqTQUeseQkEHNSUiCa7iZlA0lbIthFSsyXiWRzZCm0wZhmx4KE+qsaHFsDm4pynKDXLJIfH0b8Yjx155XnMuuzSbPb1uG2q1kw8eNZMNrcNueNzmXW543JTOp4gfRCdl0DAIa5EfODwA7h7z9144PADcp6mghkFsDgtSRmTLU4LXNdIVk+Jnc2yJyTrIa5Q4Fo3lj3ZJ+bJZSi6tgj50/KRV54nn2flcyuRV56HgulskYenwoPCOYX9FlLwsiKriRic5U6s3LIyqW7llpVwz3H322PSPdsNQMqZ1XKyBeffPY+Wk5IlVt7EPOZ58iZK1kyrtvQ51xbJtomH2pcSQfowqIINQsgSAI8A0AJ4mlL6yz71JFa/FEAXgI2U0kOxuu8BuA9SKrmjAO6hlAbUzicEG4JUiIajaDjckOR5qNFJ3+t4lkC8Rf/Sm0pxevtp1H9ULws2iuYWYdLySQCQJDywF9lhyDKgYEoBzuw+g6AvKAs2DHYDJvzdBERCEZx8/SRe/2qCPdQzVSivKkekJ8K0TIqGo/C2eBVWT7ZsG/ztfkV5llMa9XibvWg/nVA3wQF7nh2+Zp8k8qj3w+6xwzHOISWTBNB+sR0dFzrkuqySLGSPyU62c+JZQMVizAWSBVTS/YnVGbIMcE5yovVUKwIdAbncmGWEs9wJjU6DxmONCHqDcp3erkfh9EKEekLcLNi8DNlqFlC850Tt+RkAPrdgI82nDYdesEEI0QJ4HMBiALUADhBCtlFKjye8rBLAxNjPPABPAJhHCPEA+A6AqZTSbkLIywDWAHhusNoryEyi4Sg+/v3HCpPWmdUzQTSEmyWXt+i/ce9GvLLmFaUY4NAD0Jl0aDjYoLA/0ug0aPq4SeGhmFWchWgoKndcQMwe6quv497996LhQAM74WMgkFoCyx2cmLf4Mef+fK5fSS8Dfo4FlNUAUDDvj9lpRv3BekW5zqYDIQSNhxoVdZYCC87tZFtKEUKYCSynrp6KMzvOMN9vGqXM52T6muk49uIx5vMzgB2YgMFgCja+AOAzSulZACCEvAigCkBi51UF4HkqDf8+JIRkE0LcCW0zE0JCACwA6gexrYIMpeFwg/zBA0idw5vfeBP50/JhzDKi5sMarNuxTvomXmTDp9s+lTc0z3twHiYsmiB/4z+96zS8tV6mGKDtbBsMNgPe/8X7uP5718vfJ9//xfu4/be3sz0U57oR9LEFCZ2XOuUP33jZ9m9tR+6EXFBQ+cM8XrfjWzuwbuc6nHzjZO/G6nwLDjx5ALkTpPWhmg9qsPaNtfBfkkYdR35/BLkTcrnHAsA9nlrbDj97GFXPVElGyDYD9m/aL7eBdX+u1AZeHa8NAPDeL95LOs97v3gPuRNy5Y4rHvPahtdQMKMAPd4e5nOSMz6H+/wM9f4wntqQGAhW3LhiSNvCYqCzLg9m5+UBcDHh71pIo6srvcZDKf2IEPIrADUAugG8TSl9m3USQsj9AO4HgJKS9JS4CoYPXmfjrfUid2IuCqYWyJmR49/qI6EIssdlI/dsriwjj9dll2Uz1YHmPDO6mrsw56tzsPdnCSOsHy9A0MvuoEKdIVic7ISYoU6OMXB7Nyhlqw3D3WGULyvHS196qbfNj1UiGo5Co5fc0xMNhSs3VSLQHmArCpu7YMoxMY/HUxR2NndCb9Vj1oZZSdOgSx5ZglBXCJGeCPP+8NSLXc1diIaj/VJJ+hp80Jl0zPPw2q2mkvTVs62m1NSTA0ni55/ZaGaqDUcKW2u3DujxBnNcy5qr7LvAxnwNISQH0qisDEARACshpJp1EkrpU5TSuZTSufn5+Z+rwYLMQ039FmgLML/VB9oC6DjfwawL+oNMdSCigCnLJH9gxmP2/mwvTNkmZhvMeWZodBrm8Sx5FnZMthmWXHad3qxP2k8W7g5jx7d3QKvTglDCvB5zjpl5LGu+FVqdlnk83vmt+VYYLAbmXjeDxQBzjpl5f3gJQ6351pQSYvLOw7tWNZVk3Nqrb7maenIgSfz8M+gMQ3LOkcJgjrxqAYxJ+LsYyqk/3msWAThHKW0GAELIqwDmA/j9oLVWMOphLcbH1W991yzcs904vvU499t7/HdFXZ0PBx4/0DslRYEDjx9Adlk2iJZwRyS3/OQWvPsv78ptuOUnt6C7tRvhQJh5POcUJxb8eAFz9ACAWee/xNnf1OgHpRSWPAuu2XCN/JXyyJYj6G7rZh6rx9eDYGew3zGhbs6m66ZOmHJM7NHkZf7xSm8pxX1/vQ/dbd3y9K0pxwRHsQPrdkjTinEBCqUUhdcWou7DOuZ5IqEIvvzSl1F/oF5O/Fk0twh5E/NAo5T5nHjmerjPj2BwGczO6wCAiYSQMgB1kAQXa/u8ZhuAb8XWw+YB6KCUNhBCagBcRwixQJo2vA2AkBEKUoZGKVd8MfUrU5EzLkdWnrmvldRi8W/vfafsrmQwqzUl2AkRQGvSqprIWvOt2P2D3Ukd1MGnDqJqcxWgAbpau/Dez99LijHnmHHomUNJMYeeOSTFEDDrVj67kt3mYjuIlqDimxUK0Yi1wIq3H3xbcaxVv18Fi9PCjnHxY8xRM3Im5GD6V6bLHd6xl47B5rHBaGebDVvyLDi5/aRinaz01lLQEEX9X+sV4gtHsQOXz1xmZlLmbj4vtKHleAs++NUHvc/IllUAAI1OgxlrZyB3Yq7COopVLsQag89gS+WXAvgvSFL5ZyilDxNCvgYAlNInY1L5TQCWQJLK30Mp/SgW+xMAqwGEARwGcB+ltEftfEIqL+DB8yn82sdfQ82+GsWH3Iy1M0ApZSYtnH7XdEQiEZx4+eoTMU5dPRXt59tR96EycWLpraW4sEeZCHLMjWMQ7g6j4aMGRUzJghLU7FMmiSyeXwwaoajbX6eoK5xdiLNvnVV0NmMXjgWNUjx/2/OK+7NhzwY0fdyk8GMsnFWIaDja7xiNToOGQ8rrcc9xIxwI48LeC4r2TVg6AfUH6pkxQV+w3znFNHoN8zylC0qZ13P/ofvhnORkfvmZvHIyPv3jp8wvRQOUZXlEeRt+HlKU7A+PtyGldDuA7X3Knkz4nQL4Jif2XwD8y2C2T5A58BbwvbVetiptYi5KbyrF9LumI3dCrryXqqiiCHqzHk0HmtBe1451O9fJ05Dn9p5D0ydNzOONuWEM2k614firx3vVeXkWfPjYhzDYDPC3+pOOVbO/BpcOXwIAbkzb+bakmM92fyZJzgE0HGmQVJIxY9xPXv0EBquBO6UZb2vf+9NxvkMaBcZHPVYDPviPDzD/wfkpxZiyTcx1stWvrUagPcBsX+HsQtUY3tSu2pQv7z6wYlpPtYIQwlQibty7katQHGqrqcHOpPx5PRIH2hdReBsKMgLeVBHPYDf+Iac365nmrt5aL/Y9tA/7sC+pvKC8gKtYyyrOQo+3R/rADYQR6Aigx9sDe5EdR186iqIZRZJ6LhBG3V/qUDKvBIQQ2Nw2aA1aEA2B1qSFzW2D3S3FjJ0/Vo5pPtaMCYsmyHufSq8vRbAzCH+jH42HGjH9y9OZU5A2tw2E8HNpsaZBrxQTaA+g6WiT/L050B6AzW2T1vEY9yfUHZI2C1cUouSmEll6f+nEJa5fZDyG2QZOebzdrPvAi9Gb9apffnjv91B3Xjxvw4FipG12Fp2XICOIWwL1nd6JexEy17VUiKvP+sY5Shzsda1CK3LG5mDOPXOSZOJLNy2FY6ID5cv7SM43VSJ7YjZAwJSwOyY5MO3Oabj4wUVJXHCKYNqd06RRlB6o+EYFmo83y8KDim9UwDHRgds3347WE61yed7kPCldShio3FSpmGp0THJg9j2zk9pcuakS2eOyAfBjbvznGxXl2eOyYbAZuPfN4rQwr8kxjn1PHSUOmHJNzGtyjHdg1R9WIRqMIugPwmg3gugJrC4r9EY9s91mpxkLHlqgmE60OC3QW9gGybznQPgkDj6i8xKkJWq2TaxyXsbkSDCCpZuWKtaorrRHh6dS1Bq0zA/AcHcYl45eYk4pVu+sZm7OzZuYB1D2JtwNf94A7wVvkrhgwUML4Bvrg8agYdZ1XuyEv86fVL7wpwvhO+9TbqCmsTaU5zHPf/eeu0FB+xVz3/77oNFqmPdHo9XA3+BntjtvQh4zBhSwFdhAQJJiVmxegXBnGIHWAHZ8J6GDerQSGo0GWcVZcF3jSppy1Vv1cJQ4ZB/JuE2X1qCFc7ITOqOO+eXHPdvNVSgKBhfReQnSDp5y8EqL5/GMyYnTOTqTrlctlpAAUWdS/6+h0Wkws3om8qflJ3nandx2krmeklcudUQ8KbjapllWTI+3h+nKsfq11SAawqzzzPNgz0N7ksr3PLRHWju6HMDlzy4nTaUBgK+Oswn3ohcgYMb46zhTbHVeRMNR5v3Jn54PrV7LbHfh7EL2PZ2cB51Jx8xrtm7nOrnjipfv+M4OrNuxDlq9FpeOXFLaTZl1aL/Qjnd++I5cfuvPb8Wljy+heF4x88sPAER6IskKxedXqT47goFBdF6CtKP1dOuALp6zEiBeDRqdBp4KT9IozVpgZa6nWJwWrlSet2m2emc1kMOW5KutA/E6SZ5TRag7JG96Zk2fMuXrBRbumhdvKlZv0XPXm4xZRtX1MOZ2gVwzWk+1ctecWOWdTZ0A4VtKxTuuePk7P3wHq19bDQDMLz8tJ1tGjGAj0xCbEQRpRyqL50NFKBBi5vkK94RBtIRZ1+NjJ5aMhqMw5ZjkUUE8pnJTJddBwpxr5jp28HKNmXPN8sbixPMs+PECREIRZptDXSEEO4PMukg4wjxW4HKAe57A5QDMuWyHC3OumR3THpCTdfaN4eUhs+ZbVZNRssr7liWimuBTMKiIkZcg7eApB0fC4rnJYcLfnv9bkkx8/+P7seQ/l8CYZeTWMUc9Hkk00nS8KUn2/umfPoVruguL/nURdn9/tzxdtehfF8FoNyISjODWn9+qmP7SmXVY/O+Lsev/7JLLF//7Ymj1Wphz2Zuex9wwhtnmRT9fBADsuocX9XsDddXmKmgMGuY1aQ1a1RjWepjBbkDlI5XY8Q8JU4OPVMJgNyBLx35OeGrDvHL++pVatu2hhiWV/7zy9kQGWur+eRnUTcpDjdiknBmkuuY1FFwpxcqZXWcQaA/ICjijw4hxt43jthsAM6ZsYRnOvnNWmoaL5ROjlGL8ovGIhqOo3V8LAHKHBwCFcwpx+o3TaDnRIucac05xYvLKyYiEI6jbXycJJxr9sLvtiIQj8Mz1oO4jZXnpjaUIB8Ko+aAGGq0GXS1dsOZbEQlHUDK/BOf2nEMkGJHbrNFrUHZrGQDgxFbG5u47pgAa4Nzu3ri4YKJsYRlO70hot5bAOVlqN6UU5989L7fB4rQgGomi5KYS1LxXoygfv2i89Py8doJ5vFNvnMIf7/6j3LaVW1Zi6h1Tuc+PmnPLSNikPNLk7SkwPJuUBYLBgKccVCtXQy0BYX/jkmyE6nzIKs5C0dwiOWmhv8Gv6NgAYPLKydi4d2OS+INoCDcmGonCX+9XdAI0Kn0ZZVkjFc8rxuSVk1HvqVdsuiYBAm+NV6m6vM7DLAcArVGLzsZOZZ1O2te1/ZsJ5Y8vhd6sh9agxZQvTend+B1PVOmQElVGQ1G8fk+vLH/lcyuht+hBozRJFHH7b2+HVq8FjVIpPUyfc+n0kvN+344obtuk0WmSjrdyy0potBrQKMX8B+fLnTuNUvn3/j6LgsFFjLwEGU2q35xTGf3Vf1SP5255TjHFtPHdjeio6WDHHKzHcwuUMTz7o/W71oNGKX7/RYY10lvVXGHKhfcusGN4NktvSUke+hOzfvd6jLluDD/B56lW/GaO0sJr/e71+N2i3ynK73nvHoQDYfxusbKu+q1qZtseOPQAADDPs+HPG5j2UBv3bhzy3FwJiJEXByHYEGQ0POVi6+nWlOIaDjdwj9de085c3G+/0M6N4YlQuPZH9T5ujilfvY97PdwYFZulfsfU+VTvd9uZNm4cq7zjYge3jte2trNt/PNw2u2t9Srul2D4EZ2XIKNJVS2WiuLR5mKr/Sz5Fm4MN4+USh4rXp2aiCDLwzkPR7Vnd9tlsQLrPMzrdFpU73fcVZ4VxyrXm/Wwuti5vnjKSoPVwD2PMYtdPlS5uQT9Q3RegoxG7QM4lTheZ2Nz22AvsjOl5TyJuM1tk508EmOW/XoZiuYWYemmpUnlcWcQxxgHU16fXZLNvR7e8VxzXNzzGLOMqHysz3keq4Q5z8y8TmOWUfV+29w2ZuJNg93ALDdlm5DlyWLf0zwzM8ZeZOeex1HiYN7rdMnNFUUUW0Nbk35GmkJwIBFrXoKMZijXvIBk5aDBZoAp26SqNoyLNhoONySJOTQ6DSLBCOoO1CnyS9EoRc0HNYiGoui81AmrywqNXoOS+SWq1xQOhKXj9XEa4ZUHu4JcpR9LNThp2SRodBru/QaAU2+e6rVa0hAUVRRh/OLxyerAmEpy6h1TodFpuPf09I7TimNNWjaJe55JyyaBRinzXg8jV73m5XQ6aUtLy2C2ZTjgXr/ovAQZz0CqDdX8FVONSQW5Y0tQPMY7toE8T09nDxoPNsrKwcI5hTBajQh2BdHwUUNSgk+DRUrXwuuM1ep4HfVAvw8jENF58SpE5yUQjC54e81mrJ2Bk9tODtieJN7oc9LySTj6wlGFhH5m9cwrjrwGec9UOiI6L16F6LwEgtFF3YE6prx+/a71TFn5A4cfSMmHj5edmidTX79rPSxOCzPmgcMxCTunLoN9AkXnxUEINgSCUQZXXs+Rj6fqw8dTDqpJ9dXUhsInUNAfROclEIwy+iuvT9WHj6ccVJPxX0ltOJDtyzT0ev1wN2FIEZ2XQDDK4MnrPRUerHp+VVL5qudXpZw4MZ6duu/xPBUerryeF5M3MU+1TnBlQqHQcDdhSBFrXgLBKISn2htolR3vPLK8vt4Hu8cOz9zeBJ88JSSQVirAoUKsefEqROclEAhSIZW9bjRKua77w7yfaqQiOi8O4mkRCAQpkYq/Y8PhBrnjite9+Y030XC4YdiuQ5CeiM5LIBCkRCr+jrw6YX4r6C+i8xIIBCmRir8jr06Y335+hNpQIBAIrgKeOtA9281VDfKUkOlifjuSEWrDNEYINgSCoSUVX0E1b0OBAiHY4FUMZudFCFkC4BEAWgBPU0p/2aeexOqXAugCsJFSeihWlw3gaQDTAVAAX6WU/kXtfKLzEggEowzReXHQ8So+9xkJ0QJ4HMBiALUADhBCtlFKjye8rBLAxNjPPABPxP4FpE5tJ6X0y4QQAwDLYLU1nRjKfTBD5dY92vb2DPT1DJUTvUCQTgxa5wXgCwA+o5SeBQBCyIsAqgAkdl5VAJ6n0vDvQ0JINiHEDaATwM0ANgIApTQIIDiIbU0LUs09NZDnulK+qv62byivaSgY6OtJ5X1Ix/smEPSXwZxo9gC4mPB3bazsal4zDkAzgGcJIYcJIU8TQqyD2Na0gLevpvV065CdS20PTyrtG8prGgoG+npSeR8EgkxgMDsv1te/vgtsvNfoAMwB8ASldDakkdgPmCch5H5CyEeEkI+am5s/T3tHPEPpup3KHp5U2jfanMQH+npSeR8EmUPi559Gk1mil8G82loAYxL+LgZQf5WvqQVQSyndHyt/BVJnpoBS+hSldC6ldG5+fv6ANHykMpSu26ns4UmlfaPNSXygryeV90GQOSR+/pWUlAx3c4aUwey8DgCYSAgpiwku1gDY1uc12wBsIBLXAeiglDZQShsBXCSElMdedxuS18oykqF03U5lD08q7RttTuIDfT2pvA8CQSYw2FL5pQD+C5JU/hlK6cOEkK8BAKX0yZhUfhOAJZCk8vdQSj+Kxc6CJJU3ADgbq7usdr5MkMoLteHIR6gNBQPIVb+ho/TzT7jKCwQCQRoiOi8OmbXCJxAIBIJRgei8BAKBQJB2iM5LIBAIBGmH6LwEAoFAkHaIzksgEAgEaYfovAQCgUCQdojOSyAQCARph+i8BAKBQJB2iM5LIBAIBGnHqHLYIIQ0A7gw3O0YJpwARl0a1RQQ90Hcgzij4T60UEqXXM0LCSE7r/a1o4FR1XllMoSQjyilc4e7HcONuA/iHsQR92F0I6YNBQKBQJB2iM5LIBAIBGmH6LxGD08NdwNGCOI+iHsQR9yHUYxY8xIIBAJB2iFGXgKBQCBIO0TnJRAIBIK0Q3ReaQQhJJsQ8goh5FNCyAlCyPWEkP9LCKkjhPwt9rM04fX/TAj5jBBykhDyxeFs+0BACClPuM6/EUK8hJDvEkJyCSG7CCGnY//mJMRkyj3ImOcgDiHke4SQTwghxwgh/00IMWXSs5DpiDWvNIIQsgXAe5TSpwkhBgAWAN8F4KeU/qrPa6cC+G8AXwBQBGA3gEmU0sjQtnpwIIRoAdQBmAfgmwDaKKW/JIT8AEAOpfT7GXYP7kEGPQeEEA+A9wFMpZR2E0JeBrAdwFRk4LOQiYiRV5pACMkCcDOAzQBAKQ1SSttVQqoAvEgp7aGUngPwGaT/uKOF2wCcoZRegHStW2LlWwCsjP2eSfeAx2i+BzoAZkKIDtIXuXpk7rOQcYjOK30YB6AZwLOEkMOEkKcJIdZY3bcIIR8TQp5JmCbxALiYEF8bKxstrIH0TRoAXJTSBgCI/VsQK8+kewBk0HNAKa0D8CsANQAaAHRQSt9G5j4LGYfovNIHHYA5AJ6glM4G0AngBwCeADAewCxI/4n/I/Z6wjjGqJgjjk2ZrgDwP1d6KaNstN6DjHoOYp1zFYAySNOAVkJItVoIoyzt70MmIzqv9KEWQC2ldH/s71cAzKGUXqKURiilUQC/Re9USC2AMQnxxZCmVUYDlQAOUUovxf6+RAhxA0Ds36ZYecbcgwx8DhYBOEcpbaaUhgC8CmA+MvNZyEhE55UmUEobAVwkhJTHim4DcDz+HzXGKgDHYr9vA7CGEGIkhJQBmAjgr0PW4MHlLiRPl20DcHfs97sBvJ5QnhH3IAOfgxoA1xFCLIQQAun/wwlk5rOQkeiGuwGCfvFtAH+ITRmdhaQwe5QQMgvSFMh5AA8AAKX0k5gC6ziAMIBvjgZlFSHEAmAxYtcZ45cAXiaE3AvpQ+1OIOPuwb9l0nNAKd1PCHkFwCFI13UYkh2UDRn0LGQyQiovEAgEgrRDTBsKBAKBIO0QnZdAIBAI0g7ReQkEAoEg7RCdl0AgEAjSDtF5CQQCgSDtEJ2XQMCAEPKdmHP/5ZjBq0AgGEEIqbxAwIAQ8imAypiJq0AgGGGIkZdA0AdCyJOQjJC3xXJGbYqVuwghrxFCjsR+5sfK/zGWU+oYIeS7w9h0gSBjEJ2XQNAHSunXIPneLQRwOaHqUQB7KaXXQDJJ/oQQci0kp5N5AK4D8PeEkNlD3GSBIOMQnZdAcPXcCsm9HTET3A4ANwJ4jVLaSSn1QzKIvWkY2ygQZASi8xIIPh+sVBsCgWCQEZ2XQHD1/BnA1wGAEKKNZbfeB2BlzN3cCsnR/b1hbKNAkBGIzksguHr+AcBCQshRAAcBTKOUHgLwHKT0GvsBPE0pPTx8TRQIMgMhlRcIBAJB2iFGXgKBQCBIO0TnJRAIBIK0Q3ReAoFAIEg7ROclEAgEgrRDdF4CgUAgSDtE5yUQCASCtEN0XgKBQCBIO/4/44nGj/QJz7MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(data=loans, x='fico', y='int.rate', color='purple')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "207b0356",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c130135f",
   "metadata": {},
   "source": [
    "**Create the following lmplots to see if the trend differed between not.fully.paid and credit.policy. Check the documentation for lmplot() if you can't figure out how to separate it into columns.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "42378909",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1d1e1dd3430>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 792x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 789.25x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11,8))\n",
    "sns.lmplot(data=loans, x='fico', y='int.rate', hue='credit.policy', col='not.fully.paid', palette='Set1')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "716b0d9e",
   "metadata": {},
   "source": [
    "# Setting up the Data\n",
    "\n",
    "Let's get ready to set up our data for our Random Forest Classification Model!\n",
    "\n",
    "**Check loans.info() again.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7875a98b",
   "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",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   credit.policy      9578 non-null   int64  \n",
      " 1   purpose            9578 non-null   object \n",
      " 2   int.rate           9578 non-null   float64\n",
      " 3   installment        9578 non-null   float64\n",
      " 4   log.annual.inc     9578 non-null   float64\n",
      " 5   dti                9578 non-null   float64\n",
      " 6   fico               9578 non-null   int64  \n",
      " 7   days.with.cr.line  9578 non-null   float64\n",
      " 8   revol.bal          9578 non-null   int64  \n",
      " 9   revol.util         9578 non-null   float64\n",
      " 10  inq.last.6mths     9578 non-null   int64  \n",
      " 11  delinq.2yrs        9578 non-null   int64  \n",
      " 12  pub.rec            9578 non-null   int64  \n",
      " 13  not.fully.paid     9578 non-null   int64  \n",
      "dtypes: float64(6), int64(7), object(1)\n",
      "memory usage: 1.0+ MB\n"
     ]
    }
   ],
   "source": [
    "loans.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbd9a0f2",
   "metadata": {},
   "source": [
    "## Categorical Features\n",
    "\n",
    "Notice that the **purpose** column as categorical\n",
    "\n",
    "That means we need to transform them using dummy variables so sklearn will be able to understand them. Let's do this in one clean step using pd.get_dummies.\n",
    "\n",
    "Let's show you a way of dealing with these columns that can be expanded to multiple categorical features if necessary.\n",
    "\n",
    "**Create a list of 1 element containing the string 'purpose'. Call this list cat_feats.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "84b3e724",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['debt_consolidation', 'credit_card', 'all_other',\n",
       "       'home_improvement', 'small_business', 'major_purchase',\n",
       "       'educational'], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_feats = ['purpose']\n",
    "\n",
    "print()\n",
    "\n",
    "loans['purpose'].unique()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d12953b5",
   "metadata": {},
   "source": [
    "**Now use pd.get_dummies(loans,columns=cat_feats,drop_first=True) to create a fixed larger dataframe that has new feature columns with dummy variables. Set this dataframe as final_data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "40831595",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_data = pd.get_dummies(data=loans, columns=cat_feats, drop_first=True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ec172f0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9578 entries, 0 to 9577\n",
      "Data columns (total 19 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   credit.policy               9578 non-null   int64  \n",
      " 1   int.rate                    9578 non-null   float64\n",
      " 2   installment                 9578 non-null   float64\n",
      " 3   log.annual.inc              9578 non-null   float64\n",
      " 4   dti                         9578 non-null   float64\n",
      " 5   fico                        9578 non-null   int64  \n",
      " 6   days.with.cr.line           9578 non-null   float64\n",
      " 7   revol.bal                   9578 non-null   int64  \n",
      " 8   revol.util                  9578 non-null   float64\n",
      " 9   inq.last.6mths              9578 non-null   int64  \n",
      " 10  delinq.2yrs                 9578 non-null   int64  \n",
      " 11  pub.rec                     9578 non-null   int64  \n",
      " 12  not.fully.paid              9578 non-null   int64  \n",
      " 13  purpose_credit_card         9578 non-null   uint8  \n",
      " 14  purpose_debt_consolidation  9578 non-null   uint8  \n",
      " 15  purpose_educational         9578 non-null   uint8  \n",
      " 16  purpose_home_improvement    9578 non-null   uint8  \n",
      " 17  purpose_major_purchase      9578 non-null   uint8  \n",
      " 18  purpose_small_business      9578 non-null   uint8  \n",
      "dtypes: float64(6), int64(7), uint8(6)\n",
      "memory usage: 1.0 MB\n"
     ]
    }
   ],
   "source": [
    "\n",
    "final_data.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0b73d8fe",
   "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",
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       "\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",
       "      <th>0</th>\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",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\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",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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",
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       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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",
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       "    </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": [
    "final_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60758af3",
   "metadata": {},
   "source": [
    "## Train Test Split\n",
    "\n",
    "Now its time to split our data into a training set and a testing set!\n",
    "\n",
    "**Use sklearn to split your data into a training set and a testing set as we've done in the past.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "38d3c2b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "247b34de",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = final_data.drop('not.fully.paid', axis=1)\n",
    "y = final_data['not.fully.paid']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6d6c9405",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.3, random_state=101)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "014b37af",
   "metadata": {},
   "source": [
    "## Training a Decision Tree Model\n",
    "\n",
    "Let's start by training a single decision tree first!\n",
    "\n",
    "**Import DecisionTreeClassifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f4312190",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48d7c098",
   "metadata": {},
   "source": [
    "**Create an instance of DecisionTreeClassifier() called dtree and fit it to the training data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8b7683e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "dtree = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f5d1b406",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtree.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66195423",
   "metadata": {},
   "source": [
    "## Predictions and Evaluation of Decision Tree\n",
    "**Create predictions from the test set and create a classification report and a confusion matrix.**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c4e6d4df",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = dtree.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "63c533cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1ed424a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1986  445]\n",
      " [ 343  100]]\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.82      0.83      2431\n",
      "           1       0.18      0.23      0.20       443\n",
      "\n",
      "    accuracy                           0.73      2874\n",
      "   macro avg       0.52      0.52      0.52      2874\n",
      "weighted avg       0.75      0.73      0.74      2874\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, predictions))\n",
    "print('\\n')\n",
    "print(classification_report(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "02204473",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.82      0.83      2431\n",
      "           1       0.18      0.23      0.20       443\n",
      "\n",
      "    accuracy                           0.73      2874\n",
      "   macro avg       0.52      0.52      0.52      2874\n",
      "weighted avg       0.75      0.73      0.74      2874\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18b2d3e3",
   "metadata": {},
   "source": [
    "## Training the Random Forest model\n",
    "\n",
    "Now its time to train our model!\n",
    "\n",
    "**Create an instance of the RandomForestClassifier class and fit it to our training data from the previous step.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d4b50787",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2d16f4f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc = RandomForestClassifier(n_estimators=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d3127c12",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "RandomForestClassifier(n_estimators=300)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ab9cae0",
   "metadata": {},
   "source": [
    "## Predictions and Evaluation\n",
    "\n",
    "Let's predict off the y_test values and evaluate our model.\n",
    "\n",
    "**Predict the class of not.fully.paid for the X_test data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "702e929e",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = rfc.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67628133",
   "metadata": {},
   "source": [
    "**Now create a classification report from the results. Do you get anything strange or some sort of warning?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "378725eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      1.00      0.92      2431\n",
      "           1       0.67      0.03      0.05       443\n",
      "\n",
      "    accuracy                           0.85      2874\n",
      "   macro avg       0.76      0.51      0.48      2874\n",
      "weighted avg       0.82      0.85      0.78      2874\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7c51cab5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2425    6]\n",
      " [ 431   12]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45388f6a",
   "metadata": {},
   "source": [
    "**What performed better the random forest or the decision tree?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b23e7b4",
   "metadata": {},
   "outputs": [],
   "source": []
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   "cell_type": "code",
   "execution_count": null,
   "id": "ff3a58a8",
   "metadata": {},
   "outputs": [],
   "source": []
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   "execution_count": null,
   "id": "b358ffbc",
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   "outputs": [],
   "source": []
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   "execution_count": null,
   "id": "b8fee58d",
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   "outputs": [],
   "source": []
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   "cell_type": "code",
   "execution_count": null,
   "id": "38ade6fd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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