exploration.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prosper Loan Data Exploration\n",
    "## By Abhishek Tiwari"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preliminary Wrangling\n",
    "This data set contains information on peer to peer loans facilitated by credit company Prosper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import all packages\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ListingKey</th>\n",
       "      <th>ListingNumber</th>\n",
       "      <th>ListingCreationDate</th>\n",
       "      <th>CreditGrade</th>\n",
       "      <th>Term</th>\n",
       "      <th>LoanStatus</th>\n",
       "      <th>ClosedDate</th>\n",
       "      <th>BorrowerAPR</th>\n",
       "      <th>BorrowerRate</th>\n",
       "      <th>LenderYield</th>\n",
       "      <th>...</th>\n",
       "      <th>LP_ServiceFees</th>\n",
       "      <th>LP_CollectionFees</th>\n",
       "      <th>LP_GrossPrincipalLoss</th>\n",
       "      <th>LP_NetPrincipalLoss</th>\n",
       "      <th>LP_NonPrincipalRecoverypayments</th>\n",
       "      <th>PercentFunded</th>\n",
       "      <th>Recommendations</th>\n",
       "      <th>InvestmentFromFriendsCount</th>\n",
       "      <th>InvestmentFromFriendsAmount</th>\n",
       "      <th>Investors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1021339766868145413AB3B</td>\n",
       "      <td>193129</td>\n",
       "      <td>2007-08-26 19:09:29.263000000</td>\n",
       "      <td>C</td>\n",
       "      <td>36</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2009-08-14 00:00:00</td>\n",
       "      <td>0.16516</td>\n",
       "      <td>0.1580</td>\n",
       "      <td>0.1380</td>\n",
       "      <td>...</td>\n",
       "      <td>-133.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10273602499503308B223C1</td>\n",
       "      <td>1209647</td>\n",
       "      <td>2014-02-27 08:28:07.900000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36</td>\n",
       "      <td>Current</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.12016</td>\n",
       "      <td>0.0920</td>\n",
       "      <td>0.0820</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0EE9337825851032864889A</td>\n",
       "      <td>81716</td>\n",
       "      <td>2007-01-05 15:00:47.090000000</td>\n",
       "      <td>HR</td>\n",
       "      <td>36</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2009-12-17 00:00:00</td>\n",
       "      <td>0.28269</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.2400</td>\n",
       "      <td>...</td>\n",
       "      <td>-24.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0EF5356002482715299901A</td>\n",
       "      <td>658116</td>\n",
       "      <td>2012-10-22 11:02:35.010000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36</td>\n",
       "      <td>Current</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.12528</td>\n",
       "      <td>0.0974</td>\n",
       "      <td>0.0874</td>\n",
       "      <td>...</td>\n",
       "      <td>-108.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0F023589499656230C5E3E2</td>\n",
       "      <td>909464</td>\n",
       "      <td>2013-09-14 18:38:39.097000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36</td>\n",
       "      <td>Current</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.24614</td>\n",
       "      <td>0.2085</td>\n",
       "      <td>0.1985</td>\n",
       "      <td>...</td>\n",
       "      <td>-60.27</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                ListingKey  ListingNumber            ListingCreationDate  \\\n",
       "0  1021339766868145413AB3B         193129  2007-08-26 19:09:29.263000000   \n",
       "1  10273602499503308B223C1        1209647  2014-02-27 08:28:07.900000000   \n",
       "2  0EE9337825851032864889A          81716  2007-01-05 15:00:47.090000000   \n",
       "3  0EF5356002482715299901A         658116  2012-10-22 11:02:35.010000000   \n",
       "4  0F023589499656230C5E3E2         909464  2013-09-14 18:38:39.097000000   \n",
       "\n",
       "  CreditGrade  Term LoanStatus           ClosedDate  BorrowerAPR  \\\n",
       "0           C    36  Completed  2009-08-14 00:00:00      0.16516   \n",
       "1         NaN    36    Current                  NaN      0.12016   \n",
       "2          HR    36  Completed  2009-12-17 00:00:00      0.28269   \n",
       "3         NaN    36    Current                  NaN      0.12528   \n",
       "4         NaN    36    Current                  NaN      0.24614   \n",
       "\n",
       "   BorrowerRate  LenderYield  ...  LP_ServiceFees  LP_CollectionFees  \\\n",
       "0        0.1580       0.1380  ...         -133.18                0.0   \n",
       "1        0.0920       0.0820  ...            0.00                0.0   \n",
       "2        0.2750       0.2400  ...          -24.20                0.0   \n",
       "3        0.0974       0.0874  ...         -108.01                0.0   \n",
       "4        0.2085       0.1985  ...          -60.27                0.0   \n",
       "\n",
       "   LP_GrossPrincipalLoss  LP_NetPrincipalLoss LP_NonPrincipalRecoverypayments  \\\n",
       "0                    0.0                  0.0                             0.0   \n",
       "1                    0.0                  0.0                             0.0   \n",
       "2                    0.0                  0.0                             0.0   \n",
       "3                    0.0                  0.0                             0.0   \n",
       "4                    0.0                  0.0                             0.0   \n",
       "\n",
       "   PercentFunded  Recommendations InvestmentFromFriendsCount  \\\n",
       "0            1.0                0                          0   \n",
       "1            1.0                0                          0   \n",
       "2            1.0                0                          0   \n",
       "3            1.0                0                          0   \n",
       "4            1.0                0                          0   \n",
       "\n",
       "  InvestmentFromFriendsAmount Investors  \n",
       "0                         0.0       258  \n",
       "1                         0.0         1  \n",
       "2                         0.0        41  \n",
       "3                         0.0       158  \n",
       "4                         0.0        20  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('prosperLoanData.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 113937 entries, 0 to 113936\n",
      "Data columns (total 81 columns):\n",
      " #   Column                               Non-Null Count   Dtype  \n",
      "---  ------                               --------------   -----  \n",
      " 0   ListingKey                           113937 non-null  object \n",
      " 1   ListingNumber                        113937 non-null  int64  \n",
      " 2   ListingCreationDate                  113937 non-null  object \n",
      " 3   CreditGrade                          28953 non-null   object \n",
      " 4   Term                                 113937 non-null  int64  \n",
      " 5   LoanStatus                           113937 non-null  object \n",
      " 6   ClosedDate                           55089 non-null   object \n",
      " 7   BorrowerAPR                          113912 non-null  float64\n",
      " 8   BorrowerRate                         113937 non-null  float64\n",
      " 9   LenderYield                          113937 non-null  float64\n",
      " 10  EstimatedEffectiveYield              84853 non-null   float64\n",
      " 11  EstimatedLoss                        84853 non-null   float64\n",
      " 12  EstimatedReturn                      84853 non-null   float64\n",
      " 13  ProsperRating (numeric)              84853 non-null   float64\n",
      " 14  ProsperRating (Alpha)                84853 non-null   object \n",
      " 15  ProsperScore                         84853 non-null   float64\n",
      " 16  ListingCategory (numeric)            113937 non-null  int64  \n",
      " 17  BorrowerState                        108422 non-null  object \n",
      " 18  Occupation                           110349 non-null  object \n",
      " 19  EmploymentStatus                     111682 non-null  object \n",
      " 20  EmploymentStatusDuration             106312 non-null  float64\n",
      " 21  IsBorrowerHomeowner                  113937 non-null  bool   \n",
      " 22  CurrentlyInGroup                     113937 non-null  bool   \n",
      " 23  GroupKey                             13341 non-null   object \n",
      " 24  DateCreditPulled                     113937 non-null  object \n",
      " 25  CreditScoreRangeLower                113346 non-null  float64\n",
      " 26  CreditScoreRangeUpper                113346 non-null  float64\n",
      " 27  FirstRecordedCreditLine              113240 non-null  object \n",
      " 28  CurrentCreditLines                   106333 non-null  float64\n",
      " 29  OpenCreditLines                      106333 non-null  float64\n",
      " 30  TotalCreditLinespast7years           113240 non-null  float64\n",
      " 31  OpenRevolvingAccounts                113937 non-null  int64  \n",
      " 32  OpenRevolvingMonthlyPayment          113937 non-null  float64\n",
      " 33  InquiriesLast6Months                 113240 non-null  float64\n",
      " 34  TotalInquiries                       112778 non-null  float64\n",
      " 35  CurrentDelinquencies                 113240 non-null  float64\n",
      " 36  AmountDelinquent                     106315 non-null  float64\n",
      " 37  DelinquenciesLast7Years              112947 non-null  float64\n",
      " 38  PublicRecordsLast10Years             113240 non-null  float64\n",
      " 39  PublicRecordsLast12Months            106333 non-null  float64\n",
      " 40  RevolvingCreditBalance               106333 non-null  float64\n",
      " 41  BankcardUtilization                  106333 non-null  float64\n",
      " 42  AvailableBankcardCredit              106393 non-null  float64\n",
      " 43  TotalTrades                          106393 non-null  float64\n",
      " 44  TradesNeverDelinquent (percentage)   106393 non-null  float64\n",
      " 45  TradesOpenedLast6Months              106393 non-null  float64\n",
      " 46  DebtToIncomeRatio                    105383 non-null  float64\n",
      " 47  IncomeRange                          113937 non-null  object \n",
      " 48  IncomeVerifiable                     113937 non-null  bool   \n",
      " 49  StatedMonthlyIncome                  113937 non-null  float64\n",
      " 50  LoanKey                              113937 non-null  object \n",
      " 51  TotalProsperLoans                    22085 non-null   float64\n",
      " 52  TotalProsperPaymentsBilled           22085 non-null   float64\n",
      " 53  OnTimeProsperPayments                22085 non-null   float64\n",
      " 54  ProsperPaymentsLessThanOneMonthLate  22085 non-null   float64\n",
      " 55  ProsperPaymentsOneMonthPlusLate      22085 non-null   float64\n",
      " 56  ProsperPrincipalBorrowed             22085 non-null   float64\n",
      " 57  ProsperPrincipalOutstanding          22085 non-null   float64\n",
      " 58  ScorexChangeAtTimeOfListing          18928 non-null   float64\n",
      " 59  LoanCurrentDaysDelinquent            113937 non-null  int64  \n",
      " 60  LoanFirstDefaultedCycleNumber        16952 non-null   float64\n",
      " 61  LoanMonthsSinceOrigination           113937 non-null  int64  \n",
      " 62  LoanNumber                           113937 non-null  int64  \n",
      " 63  LoanOriginalAmount                   113937 non-null  int64  \n",
      " 64  LoanOriginationDate                  113937 non-null  object \n",
      " 65  LoanOriginationQuarter               113937 non-null  object \n",
      " 66  MemberKey                            113937 non-null  object \n",
      " 67  MonthlyLoanPayment                   113937 non-null  float64\n",
      " 68  LP_CustomerPayments                  113937 non-null  float64\n",
      " 69  LP_CustomerPrincipalPayments         113937 non-null  float64\n",
      " 70  LP_InterestandFees                   113937 non-null  float64\n",
      " 71  LP_ServiceFees                       113937 non-null  float64\n",
      " 72  LP_CollectionFees                    113937 non-null  float64\n",
      " 73  LP_GrossPrincipalLoss                113937 non-null  float64\n",
      " 74  LP_NetPrincipalLoss                  113937 non-null  float64\n",
      " 75  LP_NonPrincipalRecoverypayments      113937 non-null  float64\n",
      " 76  PercentFunded                        113937 non-null  float64\n",
      " 77  Recommendations                      113937 non-null  int64  \n",
      " 78  InvestmentFromFriendsCount           113937 non-null  int64  \n",
      " 79  InvestmentFromFriendsAmount          113937 non-null  float64\n",
      " 80  Investors                            113937 non-null  int64  \n",
      "dtypes: bool(3), float64(50), int64(11), object(17)\n",
      "memory usage: 60.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ListingNumber</th>\n",
       "      <th>Term</th>\n",
       "      <th>BorrowerAPR</th>\n",
       "      <th>BorrowerRate</th>\n",
       "      <th>LenderYield</th>\n",
       "      <th>EstimatedEffectiveYield</th>\n",
       "      <th>EstimatedLoss</th>\n",
       "      <th>EstimatedReturn</th>\n",
       "      <th>ProsperRating (numeric)</th>\n",
       "      <th>ProsperScore</th>\n",
       "      <th>...</th>\n",
       "      <th>LP_ServiceFees</th>\n",
       "      <th>LP_CollectionFees</th>\n",
       "      <th>LP_GrossPrincipalLoss</th>\n",
       "      <th>LP_NetPrincipalLoss</th>\n",
       "      <th>LP_NonPrincipalRecoverypayments</th>\n",
       "      <th>PercentFunded</th>\n",
       "      <th>Recommendations</th>\n",
       "      <th>InvestmentFromFriendsCount</th>\n",
       "      <th>InvestmentFromFriendsAmount</th>\n",
       "      <th>Investors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.139370e+05</td>\n",
       "      <td>113937.000000</td>\n",
       "      <td>113912.000000</td>\n",
       "      <td>113937.000000</td>\n",
       "      <td>113937.000000</td>\n",
       "      <td>84853.000000</td>\n",
       "      <td>84853.000000</td>\n",
       "      <td>84853.000000</td>\n",
       "      <td>84853.000000</td>\n",
       "      <td>84853.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>113937.000000</td>\n",
       "      <td>113937.000000</td>\n",
       "      <td>113937.000000</td>\n",
       "      <td>113937.000000</td>\n",
       "      <td>113937.000000</td>\n",
       "      <td>113937.000000</td>\n",
       "      <td>113937.000000</td>\n",
       "      <td>113937.000000</td>\n",
       "      <td>113937.000000</td>\n",
       "      <td>113937.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.278857e+05</td>\n",
       "      <td>40.830248</td>\n",
       "      <td>0.218828</td>\n",
       "      <td>0.192764</td>\n",
       "      <td>0.182701</td>\n",
       "      <td>0.168661</td>\n",
       "      <td>0.080306</td>\n",
       "      <td>0.096068</td>\n",
       "      <td>4.072243</td>\n",
       "      <td>5.950067</td>\n",
       "      <td>...</td>\n",
       "      <td>-54.725641</td>\n",
       "      <td>-14.242698</td>\n",
       "      <td>700.446342</td>\n",
       "      <td>681.420499</td>\n",
       "      <td>25.142686</td>\n",
       "      <td>0.998584</td>\n",
       "      <td>0.048027</td>\n",
       "      <td>0.023460</td>\n",
       "      <td>16.550751</td>\n",
       "      <td>80.475228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.280762e+05</td>\n",
       "      <td>10.436212</td>\n",
       "      <td>0.080364</td>\n",
       "      <td>0.074818</td>\n",
       "      <td>0.074516</td>\n",
       "      <td>0.068467</td>\n",
       "      <td>0.046764</td>\n",
       "      <td>0.030403</td>\n",
       "      <td>1.673227</td>\n",
       "      <td>2.376501</td>\n",
       "      <td>...</td>\n",
       "      <td>60.675425</td>\n",
       "      <td>109.232758</td>\n",
       "      <td>2388.513831</td>\n",
       "      <td>2357.167068</td>\n",
       "      <td>275.657937</td>\n",
       "      <td>0.017919</td>\n",
       "      <td>0.332353</td>\n",
       "      <td>0.232412</td>\n",
       "      <td>294.545422</td>\n",
       "      <td>103.239020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.006530</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.010000</td>\n",
       "      <td>-0.182700</td>\n",
       "      <td>0.004900</td>\n",
       "      <td>-0.182700</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-664.870000</td>\n",
       "      <td>-9274.750000</td>\n",
       "      <td>-94.200000</td>\n",
       "      <td>-954.550000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.009190e+05</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>0.156290</td>\n",
       "      <td>0.134000</td>\n",
       "      <td>0.124200</td>\n",
       "      <td>0.115670</td>\n",
       "      <td>0.042400</td>\n",
       "      <td>0.074080</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-73.180000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.005540e+05</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>0.209760</td>\n",
       "      <td>0.184000</td>\n",
       "      <td>0.173000</td>\n",
       "      <td>0.161500</td>\n",
       "      <td>0.072400</td>\n",
       "      <td>0.091700</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-34.440000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>44.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.926340e+05</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>0.283810</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>0.224300</td>\n",
       "      <td>0.112000</td>\n",
       "      <td>0.116600</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-13.920000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>115.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.255725e+06</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.512290</td>\n",
       "      <td>0.497500</td>\n",
       "      <td>0.492500</td>\n",
       "      <td>0.319900</td>\n",
       "      <td>0.366000</td>\n",
       "      <td>0.283700</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>32.060000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25000.000000</td>\n",
       "      <td>25000.000000</td>\n",
       "      <td>21117.900000</td>\n",
       "      <td>1.012500</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>25000.000000</td>\n",
       "      <td>1189.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ListingNumber           Term    BorrowerAPR   BorrowerRate  \\\n",
       "count   1.139370e+05  113937.000000  113912.000000  113937.000000   \n",
       "mean    6.278857e+05      40.830248       0.218828       0.192764   \n",
       "std     3.280762e+05      10.436212       0.080364       0.074818   \n",
       "min     4.000000e+00      12.000000       0.006530       0.000000   \n",
       "25%     4.009190e+05      36.000000       0.156290       0.134000   \n",
       "50%     6.005540e+05      36.000000       0.209760       0.184000   \n",
       "75%     8.926340e+05      36.000000       0.283810       0.250000   \n",
       "max     1.255725e+06      60.000000       0.512290       0.497500   \n",
       "\n",
       "         LenderYield  EstimatedEffectiveYield  EstimatedLoss  EstimatedReturn  \\\n",
       "count  113937.000000             84853.000000   84853.000000     84853.000000   \n",
       "mean        0.182701                 0.168661       0.080306         0.096068   \n",
       "std         0.074516                 0.068467       0.046764         0.030403   \n",
       "min        -0.010000                -0.182700       0.004900        -0.182700   \n",
       "25%         0.124200                 0.115670       0.042400         0.074080   \n",
       "50%         0.173000                 0.161500       0.072400         0.091700   \n",
       "75%         0.240000                 0.224300       0.112000         0.116600   \n",
       "max         0.492500                 0.319900       0.366000         0.283700   \n",
       "\n",
       "       ProsperRating (numeric)  ProsperScore  ...  LP_ServiceFees  \\\n",
       "count             84853.000000  84853.000000  ...   113937.000000   \n",
       "mean                  4.072243      5.950067  ...      -54.725641   \n",
       "std                   1.673227      2.376501  ...       60.675425   \n",
       "min                   1.000000      1.000000  ...     -664.870000   \n",
       "25%                   3.000000      4.000000  ...      -73.180000   \n",
       "50%                   4.000000      6.000000  ...      -34.440000   \n",
       "75%                   5.000000      8.000000  ...      -13.920000   \n",
       "max                   7.000000     11.000000  ...       32.060000   \n",
       "\n",
       "       LP_CollectionFees  LP_GrossPrincipalLoss  LP_NetPrincipalLoss  \\\n",
       "count      113937.000000          113937.000000        113937.000000   \n",
       "mean          -14.242698             700.446342           681.420499   \n",
       "std           109.232758            2388.513831          2357.167068   \n",
       "min         -9274.750000             -94.200000          -954.550000   \n",
       "25%             0.000000               0.000000             0.000000   \n",
       "50%             0.000000               0.000000             0.000000   \n",
       "75%             0.000000               0.000000             0.000000   \n",
       "max             0.000000           25000.000000         25000.000000   \n",
       "\n",
       "       LP_NonPrincipalRecoverypayments  PercentFunded  Recommendations  \\\n",
       "count                    113937.000000  113937.000000    113937.000000   \n",
       "mean                         25.142686       0.998584         0.048027   \n",
       "std                         275.657937       0.017919         0.332353   \n",
       "min                           0.000000       0.700000         0.000000   \n",
       "25%                           0.000000       1.000000         0.000000   \n",
       "50%                           0.000000       1.000000         0.000000   \n",
       "75%                           0.000000       1.000000         0.000000   \n",
       "max                       21117.900000       1.012500        39.000000   \n",
       "\n",
       "       InvestmentFromFriendsCount  InvestmentFromFriendsAmount      Investors  \n",
       "count               113937.000000                113937.000000  113937.000000  \n",
       "mean                     0.023460                    16.550751      80.475228  \n",
       "std                      0.232412                   294.545422     103.239020  \n",
       "min                      0.000000                     0.000000       1.000000  \n",
       "25%                      0.000000                     0.000000       2.000000  \n",
       "50%                      0.000000                     0.000000      44.000000  \n",
       "75%                      0.000000                     0.000000     115.000000  \n",
       "max                     33.000000                 25000.000000    1189.000000  \n",
       "\n",
       "[8 rows x 61 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ListingKey</th>\n",
       "      <th>ListingNumber</th>\n",
       "      <th>ListingCreationDate</th>\n",
       "      <th>CreditGrade</th>\n",
       "      <th>Term</th>\n",
       "      <th>LoanStatus</th>\n",
       "      <th>ClosedDate</th>\n",
       "      <th>BorrowerAPR</th>\n",
       "      <th>BorrowerRate</th>\n",
       "      <th>LenderYield</th>\n",
       "      <th>...</th>\n",
       "      <th>LP_ServiceFees</th>\n",
       "      <th>LP_CollectionFees</th>\n",
       "      <th>LP_GrossPrincipalLoss</th>\n",
       "      <th>LP_NetPrincipalLoss</th>\n",
       "      <th>LP_NonPrincipalRecoverypayments</th>\n",
       "      <th>PercentFunded</th>\n",
       "      <th>Recommendations</th>\n",
       "      <th>InvestmentFromFriendsCount</th>\n",
       "      <th>InvestmentFromFriendsAmount</th>\n",
       "      <th>Investors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>98768</th>\n",
       "      <td>894E3581237399187154454</td>\n",
       "      <td>809008</td>\n",
       "      <td>2013-06-14 10:45:43.263000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60</td>\n",
       "      <td>Current</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.25102</td>\n",
       "      <td>0.2259</td>\n",
       "      <td>0.2159</td>\n",
       "      <td>...</td>\n",
       "      <td>-45.44</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4485</th>\n",
       "      <td>4A073508738531894FDCC22</td>\n",
       "      <td>496007</td>\n",
       "      <td>2011-02-27 20:51:15.060000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36</td>\n",
       "      <td>Chargedoff</td>\n",
       "      <td>2013-09-07 00:00:00</td>\n",
       "      <td>0.35643</td>\n",
       "      <td>0.3199</td>\n",
       "      <td>0.3099</td>\n",
       "      <td>...</td>\n",
       "      <td>-61.16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1688.55</td>\n",
       "      <td>1688.55</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96076</th>\n",
       "      <td>5A393391388599859232EF8</td>\n",
       "      <td>143660</td>\n",
       "      <td>2007-05-28 12:46:32.880000000</td>\n",
       "      <td>AA</td>\n",
       "      <td>36</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2007-09-18 00:00:00</td>\n",
       "      <td>0.09728</td>\n",
       "      <td>0.0800</td>\n",
       "      <td>0.0750</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.34</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21542</th>\n",
       "      <td>B0B03396381426886880BD3</td>\n",
       "      <td>179068</td>\n",
       "      <td>2007-07-30 21:10:47.243000000</td>\n",
       "      <td>D</td>\n",
       "      <td>36</td>\n",
       "      <td>Chargedoff</td>\n",
       "      <td>2009-03-18 00:00:00</td>\n",
       "      <td>0.18977</td>\n",
       "      <td>0.1825</td>\n",
       "      <td>0.1675</td>\n",
       "      <td>...</td>\n",
       "      <td>-47.30</td>\n",
       "      <td>-30.84</td>\n",
       "      <td>3520.20</td>\n",
       "      <td>3520.18</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40911</th>\n",
       "      <td>31E035263538095871CE8CD</td>\n",
       "      <td>528748</td>\n",
       "      <td>2011-09-22 06:21:34.790000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36</td>\n",
       "      <td>Chargedoff</td>\n",
       "      <td>2012-11-29 00:00:00</td>\n",
       "      <td>0.35643</td>\n",
       "      <td>0.3199</td>\n",
       "      <td>0.3099</td>\n",
       "      <td>...</td>\n",
       "      <td>-48.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5816.11</td>\n",
       "      <td>5816.11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91678</th>\n",
       "      <td>E87733806458267774FC965</td>\n",
       "      <td>90774</td>\n",
       "      <td>2007-01-30 10:50:56.740000000</td>\n",
       "      <td>D</td>\n",
       "      <td>36</td>\n",
       "      <td>Defaulted</td>\n",
       "      <td>2009-04-09 00:00:00</td>\n",
       "      <td>0.18224</td>\n",
       "      <td>0.1750</td>\n",
       "      <td>0.1550</td>\n",
       "      <td>...</td>\n",
       "      <td>-17.08</td>\n",
       "      <td>-5.22</td>\n",
       "      <td>1240.35</td>\n",
       "      <td>1240.38</td>\n",
       "      <td>84.69</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26179</th>\n",
       "      <td>C93D3514784816948B834AC</td>\n",
       "      <td>505956</td>\n",
       "      <td>2011-05-09 10:59:47.500000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60</td>\n",
       "      <td>Current</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.22207</td>\n",
       "      <td>0.1999</td>\n",
       "      <td>0.1899</td>\n",
       "      <td>...</td>\n",
       "      <td>-336.42</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15456</th>\n",
       "      <td>8A063430409324338F8D8E0</td>\n",
       "      <td>396374</td>\n",
       "      <td>2008-09-10 20:38:15.820000000</td>\n",
       "      <td>C</td>\n",
       "      <td>36</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2011-04-05 00:00:00</td>\n",
       "      <td>0.22237</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>0.1900</td>\n",
       "      <td>...</td>\n",
       "      <td>-16.16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6880</th>\n",
       "      <td>CF3F3382518012615057EF1</td>\n",
       "      <td>103305</td>\n",
       "      <td>2007-02-25 10:32:22.533000000</td>\n",
       "      <td>AA</td>\n",
       "      <td>36</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2007-06-21 00:00:00</td>\n",
       "      <td>0.08718</td>\n",
       "      <td>0.0700</td>\n",
       "      <td>0.0650</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.41</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64013</th>\n",
       "      <td>AB8C358115440646657108C</td>\n",
       "      <td>809816</td>\n",
       "      <td>2013-06-15 14:54:42.070000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36</td>\n",
       "      <td>Current</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.13138</td>\n",
       "      <td>0.1034</td>\n",
       "      <td>0.0934</td>\n",
       "      <td>...</td>\n",
       "      <td>-92.12</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>226</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    ListingKey  ListingNumber            ListingCreationDate  \\\n",
       "98768  894E3581237399187154454         809008  2013-06-14 10:45:43.263000000   \n",
       "4485   4A073508738531894FDCC22         496007  2011-02-27 20:51:15.060000000   \n",
       "96076  5A393391388599859232EF8         143660  2007-05-28 12:46:32.880000000   \n",
       "21542  B0B03396381426886880BD3         179068  2007-07-30 21:10:47.243000000   \n",
       "40911  31E035263538095871CE8CD         528748  2011-09-22 06:21:34.790000000   \n",
       "91678  E87733806458267774FC965          90774  2007-01-30 10:50:56.740000000   \n",
       "26179  C93D3514784816948B834AC         505956  2011-05-09 10:59:47.500000000   \n",
       "15456  8A063430409324338F8D8E0         396374  2008-09-10 20:38:15.820000000   \n",
       "6880   CF3F3382518012615057EF1         103305  2007-02-25 10:32:22.533000000   \n",
       "64013  AB8C358115440646657108C         809816  2013-06-15 14:54:42.070000000   \n",
       "\n",
       "      CreditGrade  Term  LoanStatus           ClosedDate  BorrowerAPR  \\\n",
       "98768         NaN    60     Current                  NaN      0.25102   \n",
       "4485          NaN    36  Chargedoff  2013-09-07 00:00:00      0.35643   \n",
       "96076          AA    36   Completed  2007-09-18 00:00:00      0.09728   \n",
       "21542           D    36  Chargedoff  2009-03-18 00:00:00      0.18977   \n",
       "40911         NaN    36  Chargedoff  2012-11-29 00:00:00      0.35643   \n",
       "91678           D    36   Defaulted  2009-04-09 00:00:00      0.18224   \n",
       "26179         NaN    60     Current                  NaN      0.22207   \n",
       "15456           C    36   Completed  2011-04-05 00:00:00      0.22237   \n",
       "6880           AA    36   Completed  2007-06-21 00:00:00      0.08718   \n",
       "64013         NaN    36     Current                  NaN      0.13138   \n",
       "\n",
       "       BorrowerRate  LenderYield  ...  LP_ServiceFees  LP_CollectionFees  \\\n",
       "98768        0.2259       0.2159  ...          -45.44               0.00   \n",
       "4485         0.3199       0.3099  ...          -61.16               0.00   \n",
       "96076        0.0800       0.0750  ...           -1.34               0.00   \n",
       "21542        0.1825       0.1675  ...          -47.30             -30.84   \n",
       "40911        0.3199       0.3099  ...          -48.75               0.00   \n",
       "91678        0.1750       0.1550  ...          -17.08              -5.22   \n",
       "26179        0.1999       0.1899  ...         -336.42               0.00   \n",
       "15456        0.2000       0.1900  ...          -16.16               0.00   \n",
       "6880         0.0700       0.0650  ...           -1.41               0.00   \n",
       "64013        0.1034       0.0934  ...          -92.12               0.00   \n",
       "\n",
       "       LP_GrossPrincipalLoss  LP_NetPrincipalLoss  \\\n",
       "98768                   0.00                 0.00   \n",
       "4485                 1688.55              1688.55   \n",
       "96076                   0.00                 0.00   \n",
       "21542                3520.20              3520.18   \n",
       "40911                5816.11              5816.11   \n",
       "91678                1240.35              1240.38   \n",
       "26179                   0.00                 0.00   \n",
       "15456                   0.00                 0.00   \n",
       "6880                    0.00                 0.00   \n",
       "64013                   0.00                 0.00   \n",
       "\n",
       "      LP_NonPrincipalRecoverypayments  PercentFunded  Recommendations  \\\n",
       "98768                            0.00            1.0                0   \n",
       "4485                             0.00            1.0                0   \n",
       "96076                            0.00            1.0                0   \n",
       "21542                            0.00            1.0                0   \n",
       "40911                            0.00            1.0                0   \n",
       "91678                           84.69            1.0                0   \n",
       "26179                            0.00            1.0                0   \n",
       "15456                            0.00            1.0                0   \n",
       "6880                             0.00            1.0                0   \n",
       "64013                            0.00            1.0                0   \n",
       "\n",
       "      InvestmentFromFriendsCount InvestmentFromFriendsAmount Investors  \n",
       "98768                          0                         0.0         1  \n",
       "4485                           0                         0.0        68  \n",
       "96076                          0                         0.0        10  \n",
       "21542                          0                         0.0       142  \n",
       "40911                          0                         0.0        72  \n",
       "91678                          0                         0.0       129  \n",
       "26179                          0                         0.0       204  \n",
       "15456                          0                         0.0        29  \n",
       "6880                           0                         0.0        39  \n",
       "64013                          0                         0.0       226  \n",
       "\n",
       "[10 rows x 81 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that this data set contains 81 columns. For the purpose of this analysis I’ve took the following columns (variables):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_columns =  [\n",
    "    'Term', 'LoanStatus', 'BorrowerRate', 'ProsperRating (Alpha)', 'ListingCategory (numeric)', 'EmploymentStatus',\n",
    "    'DelinquenciesLast7Years', 'StatedMonthlyIncome', 'TotalProsperLoans', 'LoanOriginalAmount',\n",
    "    'LoanOriginationDate', 'Recommendations', 'Investors'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_df = df[target_columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Term</th>\n",
       "      <th>LoanStatus</th>\n",
       "      <th>BorrowerRate</th>\n",
       "      <th>ProsperRating (Alpha)</th>\n",
       "      <th>ListingCategory (numeric)</th>\n",
       "      <th>EmploymentStatus</th>\n",
       "      <th>DelinquenciesLast7Years</th>\n",
       "      <th>StatedMonthlyIncome</th>\n",
       "      <th>TotalProsperLoans</th>\n",
       "      <th>LoanOriginalAmount</th>\n",
       "      <th>LoanOriginationDate</th>\n",
       "      <th>Recommendations</th>\n",
       "      <th>Investors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27558</th>\n",
       "      <td>36</td>\n",
       "      <td>Completed</td>\n",
       "      <td>0.2607</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5916.666667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3985</td>\n",
       "      <td>2010-01-07 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53775</th>\n",
       "      <td>36</td>\n",
       "      <td>Current</td>\n",
       "      <td>0.2769</td>\n",
       "      <td>E</td>\n",
       "      <td>1</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4166.666667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4000</td>\n",
       "      <td>2013-10-04 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21926</th>\n",
       "      <td>60</td>\n",
       "      <td>Past Due (1-15 days)</td>\n",
       "      <td>0.3304</td>\n",
       "      <td>E</td>\n",
       "      <td>1</td>\n",
       "      <td>Employed</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2916.666667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3000</td>\n",
       "      <td>2012-02-24 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108291</th>\n",
       "      <td>36</td>\n",
       "      <td>Completed</td>\n",
       "      <td>0.2100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3416.666667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1900</td>\n",
       "      <td>2008-09-24 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38346</th>\n",
       "      <td>36</td>\n",
       "      <td>Current</td>\n",
       "      <td>0.1685</td>\n",
       "      <td>C</td>\n",
       "      <td>7</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5500.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10000</td>\n",
       "      <td>2014-01-24 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103204</th>\n",
       "      <td>36</td>\n",
       "      <td>Completed</td>\n",
       "      <td>0.2699</td>\n",
       "      <td>D</td>\n",
       "      <td>2</td>\n",
       "      <td>Employed</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10416.666667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2500</td>\n",
       "      <td>2011-07-26 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10760</th>\n",
       "      <td>36</td>\n",
       "      <td>Chargedoff</td>\n",
       "      <td>0.2994</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5100.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6000</td>\n",
       "      <td>2007-12-28 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8100</th>\n",
       "      <td>36</td>\n",
       "      <td>Current</td>\n",
       "      <td>0.1099</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>Employed</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8500.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15000</td>\n",
       "      <td>2014-01-29 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50612</th>\n",
       "      <td>60</td>\n",
       "      <td>Current</td>\n",
       "      <td>0.1585</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>Employed</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7500.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25000</td>\n",
       "      <td>2013-12-12 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113423</th>\n",
       "      <td>36</td>\n",
       "      <td>Completed</td>\n",
       "      <td>0.0974</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>Employed</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2240.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7000</td>\n",
       "      <td>2012-08-28 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Term            LoanStatus  BorrowerRate ProsperRating (Alpha)  \\\n",
       "27558     36             Completed        0.2607                     C   \n",
       "53775     36               Current        0.2769                     E   \n",
       "21926     60  Past Due (1-15 days)        0.3304                     E   \n",
       "108291    36             Completed        0.2100                   NaN   \n",
       "38346     36               Current        0.1685                     C   \n",
       "103204    36             Completed        0.2699                     D   \n",
       "10760     36            Chargedoff        0.2994                   NaN   \n",
       "8100      36               Current        0.1099                     A   \n",
       "50612     60               Current        0.1585                     C   \n",
       "113423    36             Completed        0.0974                     A   \n",
       "\n",
       "        ListingCategory (numeric) EmploymentStatus  DelinquenciesLast7Years  \\\n",
       "27558                           1        Full-time                      0.0   \n",
       "53775                           1    Self-employed                      0.0   \n",
       "21926                           1         Employed                      8.0   \n",
       "108291                          7        Full-time                      0.0   \n",
       "38346                           7    Self-employed                     25.0   \n",
       "103204                          2         Employed                      0.0   \n",
       "10760                           1        Full-time                      0.0   \n",
       "8100                            1         Employed                      0.0   \n",
       "50612                           1         Employed                      0.0   \n",
       "113423                          2         Employed                      0.0   \n",
       "\n",
       "        StatedMonthlyIncome  TotalProsperLoans  LoanOriginalAmount  \\\n",
       "27558           5916.666667                NaN                3985   \n",
       "53775           4166.666667                NaN                4000   \n",
       "21926           2916.666667                NaN                3000   \n",
       "108291          3416.666667                NaN                1900   \n",
       "38346           5500.000000                NaN               10000   \n",
       "103204         10416.666667                NaN                2500   \n",
       "10760           5100.000000                NaN                6000   \n",
       "8100            8500.000000                NaN               15000   \n",
       "50612           7500.000000                NaN               25000   \n",
       "113423          2240.000000                NaN                7000   \n",
       "\n",
       "        LoanOriginationDate  Recommendations  Investors  \n",
       "27558   2010-01-07 00:00:00                0         86  \n",
       "53775   2013-10-04 00:00:00                0         33  \n",
       "21926   2012-02-24 00:00:00                0         53  \n",
       "108291  2008-09-24 00:00:00                0         78  \n",
       "38346   2014-01-24 00:00:00                0          1  \n",
       "103204  2011-07-26 00:00:00                0         12  \n",
       "10760   2007-12-28 00:00:00                0        106  \n",
       "8100    2014-01-29 00:00:00                0          1  \n",
       "50612   2013-12-12 00:00:00                0          1  \n",
       "113423  2012-08-28 00:00:00                0         67  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_df.sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since Prosper use their own proprietary Prosper Rating only since 2009, we have a lot of missing values in ProsperRating column. Let's drop these missing values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 113937 entries, 0 to 113936\n",
      "Data columns (total 13 columns):\n",
      " #   Column                     Non-Null Count   Dtype  \n",
      "---  ------                     --------------   -----  \n",
      " 0   Term                       113937 non-null  int64  \n",
      " 1   LoanStatus                 113937 non-null  object \n",
      " 2   BorrowerRate               113937 non-null  float64\n",
      " 3   ProsperRating (Alpha)      84853 non-null   object \n",
      " 4   ListingCategory (numeric)  113937 non-null  int64  \n",
      " 5   EmploymentStatus           111682 non-null  object \n",
      " 6   DelinquenciesLast7Years    112947 non-null  float64\n",
      " 7   StatedMonthlyIncome        113937 non-null  float64\n",
      " 8   TotalProsperLoans          22085 non-null   float64\n",
      " 9   LoanOriginalAmount         113937 non-null  int64  \n",
      " 10  LoanOriginationDate        113937 non-null  object \n",
      " 11  Recommendations            113937 non-null  int64  \n",
      " 12  Investors                  113937 non-null  int64  \n",
      "dtypes: float64(4), int64(5), object(4)\n",
      "memory usage: 9.6+ MB\n"
     ]
    }
   ],
   "source": [
    "target_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Term</th>\n",
       "      <th>BorrowerRate</th>\n",
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       "      <th>Investors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>113937.000000</td>\n",
       "      <td>113937.000000</td>\n",
       "      <td>113937.000000</td>\n",
       "      <td>112947.000000</td>\n",
       "      <td>1.139370e+05</td>\n",
       "      <td>22085.000000</td>\n",
       "      <td>113937.00000</td>\n",
       "      <td>113937.000000</td>\n",
       "      <td>113937.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>40.830248</td>\n",
       "      <td>0.192764</td>\n",
       "      <td>2.774209</td>\n",
       "      <td>4.154984</td>\n",
       "      <td>5.608026e+03</td>\n",
       "      <td>1.421100</td>\n",
       "      <td>8337.01385</td>\n",
       "      <td>0.048027</td>\n",
       "      <td>80.475228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.436212</td>\n",
       "      <td>0.074818</td>\n",
       "      <td>3.996797</td>\n",
       "      <td>10.160216</td>\n",
       "      <td>7.478497e+03</td>\n",
       "      <td>0.764042</td>\n",
       "      <td>6245.80058</td>\n",
       "      <td>0.332353</td>\n",
       "      <td>103.239020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1000.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>36.000000</td>\n",
       "      <td>0.134000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.200333e+03</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4000.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>36.000000</td>\n",
       "      <td>0.184000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.666667e+03</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6500.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>44.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>36.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.825000e+03</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>12000.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>115.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.497500</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>1.750003e+06</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>35000.00000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>1189.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Term   BorrowerRate  ListingCategory (numeric)  \\\n",
       "count  113937.000000  113937.000000              113937.000000   \n",
       "mean       40.830248       0.192764                   2.774209   \n",
       "std        10.436212       0.074818                   3.996797   \n",
       "min        12.000000       0.000000                   0.000000   \n",
       "25%        36.000000       0.134000                   1.000000   \n",
       "50%        36.000000       0.184000                   1.000000   \n",
       "75%        36.000000       0.250000                   3.000000   \n",
       "max        60.000000       0.497500                  20.000000   \n",
       "\n",
       "       DelinquenciesLast7Years  StatedMonthlyIncome  TotalProsperLoans  \\\n",
       "count            112947.000000         1.139370e+05       22085.000000   \n",
       "mean                  4.154984         5.608026e+03           1.421100   \n",
       "std                  10.160216         7.478497e+03           0.764042   \n",
       "min                   0.000000         0.000000e+00           0.000000   \n",
       "25%                   0.000000         3.200333e+03           1.000000   \n",
       "50%                   0.000000         4.666667e+03           1.000000   \n",
       "75%                   3.000000         6.825000e+03           2.000000   \n",
       "max                  99.000000         1.750003e+06           8.000000   \n",
       "\n",
       "       LoanOriginalAmount  Recommendations      Investors  \n",
       "count        113937.00000    113937.000000  113937.000000  \n",
       "mean           8337.01385         0.048027      80.475228  \n",
       "std            6245.80058         0.332353     103.239020  \n",
       "min            1000.00000         0.000000       1.000000  \n",
       "25%            4000.00000         0.000000       2.000000  \n",
       "50%            6500.00000         0.000000      44.000000  \n",
       "75%           12000.00000         0.000000     115.000000  \n",
       "max           35000.00000        39.000000    1189.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since Prosper use their own proprietary Prosper Rating only since 2009, we have a lot of missing values in ProsperRating column. Let's drop these missing values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_df = target_df.dropna(subset=['ProsperRating (Alpha)']).reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert LoanOriginationDate to datetime datatype:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_df['LoanOriginationDate'] = pd.to_datetime(target_df['LoanOriginationDate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_df['TotalProsperLoans'] = target_df['TotalProsperLoans'].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 84853 entries, 0 to 84852\n",
      "Data columns (total 14 columns):\n",
      " #   Column                     Non-Null Count  Dtype         \n",
      "---  ------                     --------------  -----         \n",
      " 0   index                      84853 non-null  int64         \n",
      " 1   Term                       84853 non-null  int64         \n",
      " 2   LoanStatus                 84853 non-null  object        \n",
      " 3   BorrowerRate               84853 non-null  float64       \n",
      " 4   ProsperRating (Alpha)      84853 non-null  object        \n",
      " 5   ListingCategory (numeric)  84853 non-null  int64         \n",
      " 6   EmploymentStatus           84853 non-null  object        \n",
      " 7   DelinquenciesLast7Years    84853 non-null  float64       \n",
      " 8   StatedMonthlyIncome        84853 non-null  float64       \n",
      " 9   TotalProsperLoans          84853 non-null  float64       \n",
      " 10  LoanOriginalAmount         84853 non-null  int64         \n",
      " 11  LoanOriginationDate        84853 non-null  datetime64[ns]\n",
      " 12  Recommendations            84853 non-null  int64         \n",
      " 13  Investors                  84853 non-null  int64         \n",
      "dtypes: datetime64[ns](1), float64(4), int64(6), object(3)\n",
      "memory usage: 8.1+ MB\n"
     ]
    }
   ],
   "source": [
    "target_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is/are the main feature(s) of interest in your dataset?\n",
    "> Trying to figure out what features can be used to predict default on credit. Also i would like to check what are major factors connected with prosper credit rating.\n",
    "\n",
    "### What features in the dataset do you think will help support your investigation into your feature(s) of interest?\n",
    "> I think that the borrowers Prosper rating will have the highest impact on chances of default. Also I expect that loan amount will play a major role and maybe the category of credit. Prosper rating will depend on stated income and employment status."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Univariate Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Loan status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# setting color\n",
    "base_color = sns.color_palette()[0]\n",
    "plt.xticks(rotation=90)\n",
    "sns.countplot(data = target_df, x = 'LoanStatus', color = base_color);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observation 1: \n",
    "* Most of the loans in the data set are actually current loans. \n",
    "* Past due loans are split in several groups based on the length of payment delay.\n",
    "* Other big part is completed loans, defaulted loans compromise a minority, however chargedoff loans also comporomise a substanial amount.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Employment Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data = target_df, x = 'EmploymentStatus', color = base_color);\n",
    "plt.xticks(rotation = 90);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observation 2: \n",
    "* The majority of borrowers are employed and all other categories as small part of borrowers. \n",
    "* In small Group full time has highest, after that self empolyed are there and so on."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stated Monthly Income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data=target_df, x='StatedMonthlyIncome', bins=1000);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(**Note**: Distribution of stated monthly income is highly skewed to the right. so, we have to check how many outliers are there)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "245"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "income_std = target_df['StatedMonthlyIncome'].std()\n",
    "income_mean = target_df['StatedMonthlyIncome'].mean()\n",
    "boundary = income_mean + income_std * 3\n",
    "len(target_df[target_df['StatedMonthlyIncome'] >= boundary])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**After Zooming the Graph We Get This**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data=target_df, x='StatedMonthlyIncome', bins=1000);\n",
    "plt.xlim(0, boundary);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observation 3:\n",
    "* With a boundary of mean and 3 times standard deviations distribution of monthly income still has noticeable right skew but now we can see that mode is about 5000.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discuss the distribution(s) of your variable(s) of interest. Were there any unusual points? Did you need to perform any transformations?\n",
    "> Distribution of monthly stated income is very awkward: with a lot of outliers and very large range but still it was right skew. The majority of borrowers are employed and all other categories as small part of borrowers and most of the loans in the data set are actually current loans.\n",
    "\n",
    "### Of the features you investigated, were there any unusual distributions? Did you perform any operations on the data to tidy, adjust, or change the form of the data? If so, why did you do this?\n",
    "> The majority of loans are actually current loans. Since our main goal is to define driving factors of outcome of loan we are not interested in any current loans.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bivariate Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Completed    19664\n",
       "Defaulted     6341\n",
       "Name: LoanStatus, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#I'm just adjusting the form of data\n",
    "condition = (target_df['LoanStatus'] == 'Completed') | (target_df['LoanStatus'] == 'Defaulted') |\\\n",
    "                  (target_df['LoanStatus'] == 'Chargedoff')\n",
    "target_df = target_df[condition]\n",
    "\n",
    "def change_to_defaulted(row):\n",
    "    if row['LoanStatus'] == 'Chargedoff':\n",
    "        return 'Defaulted'\n",
    "    else:\n",
    "        return row['LoanStatus']\n",
    "    \n",
    "target_df['LoanStatus'] = target_df.apply(change_to_defaulted, axis=1)\n",
    "target_df['LoanStatus'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**After transforming dataset we have 19664 completed loans and 6341 defaulted.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Debt Consolidation    12740\n",
       "Other                  7083\n",
       "Home Improvement       2612\n",
       "Business               2366\n",
       "Auto                   1204\n",
       "Name: ListingCategory (numeric), dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categories = {1: 'Debt Consolidation', 2: 'Home Improvement', 3: 'Business', 6: 'Auto', 7: 'Other'}\n",
    "def reduce_categorie(row):\n",
    "    loan_category = row['ListingCategory (numeric)']\n",
    "    if  loan_category in categories:\n",
    "        return categories[loan_category]\n",
    "    else:\n",
    "        return categories[7]\n",
    "    \n",
    "target_df['ListingCategory (numeric)'] = target_df.apply(reduce_categorie, axis=1)\n",
    "target_df['ListingCategory (numeric)'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Variable Listing Category is set up as numeric and most of the values have very `low frequency`, for the easier visualization so we have change it to `categorical and reduce the number of categories`. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Status and Prosper Rating:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d20dad8>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data = target_df, x = 'LoanStatus', hue = 'ProsperRating (Alpha)', palette = 'Blues')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observation 1:\n",
    "* The `most frequent` rating among defaulted loans is actually `D`.\n",
    "* And the `most frequent` rating among Completed is also` D `and second highest is A and so on."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Credit Start with Listing Category:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data = target_df, x = 'LoanStatus', hue = 'ListingCategory (numeric)', palette = 'Blues');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observation 2:\n",
    "* In both of the Graphs the `debt Consolidation` have `most frequency among all of them`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loan Status and Loan Amount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data = target_df, x = 'LoanStatus', y = 'LoanOriginalAmount', color = base_color);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observation 3:\n",
    "* As from Above Graph we can state that `defaulted credits` tend to be `smaller` than `completed credits` onces. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prosper Rating and Employment Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [12, 10])\n",
    "sns.countplot(data = target_df, x = 'ProsperRating (Alpha)', hue = 'EmploymentStatus', palette = 'Blues');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observation 4:\n",
    "* Lower ratings seem to have greater proportions of individuals with employment status Not Employed, Self-employed, Retired and Part-Time. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Talk about some of the relationships you observed in this part of the investigation. How did the feature(s) of interest vary with other features in the dataset?\n",
    "> In Loan status vs Loan amount defaulted credits tend to be smaller than completed credits onces. Employment status of individuals with lower ratings tends to be 'Not employed', 'Self-employed', 'Retired' or 'Part-time'.\n",
    "\n",
    "## Did you observe any interesting relationships between the other features (not the main feature(s) of interest)?\n",
    "> Prosper rating D is the most frequent rating among defaulted credits."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multivariate Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rating, Loan Amount and Loan Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [12, 8])\n",
    "sns.boxplot(data=target_df, x='ProsperRating (Alpha)', y='LoanOriginalAmount', hue='LoanStatus');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observation 1:\n",
    "* Except for the lowest ratings defaulted credits tend to be larger than completed.\n",
    "* Most of the defaulted credits comes from individuals with low Prosper rating."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Relationships between Credit category, Credit rating and outcome of Credit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1170.12x720 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x = 'ProsperRating (Alpha)', hue = 'LoanStatus', col = 'ListingCategory (numeric)',\n",
    "              data = target_df, kind = 'count', palette = 'Blues', col_wrap = 3);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observation 2:\n",
    "* There are 5 graphs in the second one has much up and downs in it other than all of them.\n",
    "* There is no substantial difference for default rates in different categories broken up by ratings."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Amount, Listing Category Loan and Loan Status Interact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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n3PnTpk0bte0SERERkYFddtllJBIJfv7zn/ctW7ZsGZMmTeLOO+8kk8mwd+9ennrqKS688MIB3ulYv/71r8lms7zyyits2bLlmAD91re+lR/96Ed9Yf+FF14AYPz48XR0dAz6uje+8Y3cfvvtQG4ozerVq4e49cWVLYCb2bR8zzdm1gj8FfCSmc0seNm7gbX5x/cDV5pZ3MzmAq8Bljrn2oAOM7vYcocgHwXuK1jnqvzj9wKPa/y3iIiISO0wM37729/yyCOPcOqppzJ//nwWL17MBz/4QRYuXMg555zDZZddxs0338wJJ5wwpPc+44wzuOSSS3j729/OT3/6UxoaGvo9/+Uvf5lUKsXChQtZsGABX/7ylwF405vexPr161m0aBF33nnncV/3mc98hs7OThYuXMjNN9885AOE434m5cqrZrYQuBWIkgv6dznnvm5mtwGLyA0V2Qb8XT5kY2b/L7nhKGngfzjnHsovP58j0xA+BFyXn4awAbgNOJdcz/eVzrktA5Xr/PPPd6Mxf6OU7gMf+ABtbW3cfvvtzJo1a/AVZEy49NJLAXj00UepqyvraDgRCbmgvnjiiSeqWo6w2rBhA2eeeWbf75WehrBcrr76ai6//HLe+973VvxvF3P05wxgZiucc+cf/dpyzoKymlwwPnr5RwZY5ybgpiLLlwMLiizvBd43spJKpWgaQhERkeqrRliW/tTtJCIiIiKh88tf/rLaRRg23YpeRERERKSCFMClYnR9rBSjoUkiIuWnNri8hvr5KoBLxShoiYiIVF5DQwP79+9XCC8T5xz79+8/ZgaWgWgMuIhUlQ7MRETKa/bs2bS2trJ3795qF8VbDQ0NzJ49e/AX5imAi4iIiHgsFosxd+7cahdDCmgIilSMTn1JMeoBFxGRsUYBXCpGQUtEREREAVxEqkwHZiIiMtYogIuIiIiIVJACuFSMxoCLiIiIKIBLBWiIgYiIiMgRCuAiIiIiIhWkAC4iIiIiUkEK4CIiIiIiFaQALiIiIiJSQQrgIiIiIiIVpAAuFaNpCEVEREQUwEVEREREKkoBXERERESkghTARUREREQqSAFcRERERKSCFMBFRERERCpIAVxEREREpIIUwEVEREREKkgBXERERESkghTARUREREQqSAFcRERERKSCFMBFRERERCpIAVxEREREpIIUwEVEREREKkgBXERERESkghTARUREREQqSAFcRERERKSCFMBFpKqcc9UugoiISEUpgIuIiIiIVJACuIhUlXrARURkrFEAF5GqUgAXEZGxRgFcRKpKAVxERMYaBXARERERkQpSABcRERERqSAFcBGpKg1BERGRsaZsAdzMGsxsqZm9aGbrzOxr+eWTzewRM9uU/3dSwTpfMLPNZvaymb21YPl5ZrYm/9wPzczyy+Nmdmd++RIzm1Ou7RERERERGQ3l7AFPAJc5584BFgFvM7OLgc8DjznnXgM8lv8dMzsLuBKYD7wN+Bczi+bf6yfANcBr8j9vyy//BHDQOXca8H3gn8q4PSJSBuoBFxGRsaZsAdzldOZ/jeV/HHAFcGt++a3Au/KPrwDucM4lnHNbgc3AhWY2E2hxzj3nci31r45aJ3ivu4E3B73jIiIiIiK1qKxjwM0samargD3AI865JcAM51wbQP7f6fmXzwJ2FKzeml82K//46OX91nHOpYF2YEp5tkZEREREZOTKGsCdcxnn3CJgNrne7AUDvLxYz7UbYPlA6/R/Y7NrzGy5mS3fu3fvYMUWERERESmbisyC4pw7BDxBbuz27vywEvL/7sm/rBU4qWC12cDO/PLZRZb3W8fM6oAJwIEif/8W59z5zrnzp02bNkpbJSKjQWPARURkrCnnLCjTzGxi/nEj8FfAS8D9wFX5l10F3Jd/fD9wZX5mk7nkLrZcmh+m0mFmF+fHd3/0qHWC93ov8LhTay4SKrpsQ0RExpq6Mr73TODW/EwmEeAu59wDZvYccJeZfQLYDrwPwDm3zszuAtYDaeCzzrlM/r0+A/wSaAQeyv8A/Btwm5ltJtfzfWUZt0dEREREZMTKFsCdc6uBc4ss3w+8+Tjr3ATcVGT5cuCY8ePOuV7yAV5Ewkk94CIiMtboTpgiIiIiIhWkAC4iVaUecBERGWsUwEVEREREKkgBXERERESkghTARUREREQqSAFcRKpKY8BFRGSsUQAXEREREakgBXARqSr1gIuIyFijAC4iIiIiUkEK4CIiIiIiFaQALiIiIiJSQQrgIiIiIiIVpAAuIiIiIlJBCuAiIiIiIhWkAC4iIiIiUkEK4CIiIiIiFaQALiIiIiJSQQrgUjG646GIiIiIAriIiIiI11pbW/nOd75De3t7tYsieQrgUnbOuWoXQWqY9g8RkfK68847eeCBB1i2bFm1iyJ5CuBSMRqCIsUogIuMnu3bt/Opa65h69at1S6K1JCenh4AstlslUsiAQVwEakqBXCR0fPkk0+yaeNGHnvssWoXRUQGoAAuIlWlAC4yenz/Pvm+fTJ2KIBLxajilGK0X4hIqZLJZLWLEGq+DUH5hxtv5B//4cZqF2NY6qpdABk7NAZcislkMtUugoiERDKZJB6PV7sYoZVOp6tdhFG1YuXKahdh2NQDLiJV5VuPjEg1+d7RkUgkql2EUNMZhNqhAC4SAq+++qq3Fad6wEVGn09Duwq3pbe3t4olCb9UKlXtIoyaws6bMO7vCuA15sUXX+TQoUPVLkZZhPELUgv27NnDVVddxS233FLtopSFb6dERWqBTz3hhXWEAvjI+NSRU3g2JIxnRhTAa0gikeCGG27g29/+drWLUhY+NQiV1NHRAcCSJUuqXJLy8KlBkJFrb2/XHNYjEHR0+NTh0d3d3fc4mM9ahiboLfapvi3cF8K4XyiA15Dgi/Hss89WuSTlobG+w+PTKcNifGoQZOS+/vWv87GPfazaxQgtHzs6wh60akFQz/pU3xbuC4UHaWGhAF5DfA+oGus7PL5/bmE8dSjls2LFimoXIdSCnm+fgnhhuApj0KoFPgbwrq6uvsdh3C8UwGuIr2Nhg4bA1+0rt6DC9KlBLRTGilOkVgUdOT4NQQl7T2ctCDo6fOrwKAzgnZ2dVSzJ8CiA1xBfezqDhsD3oRTl4lOPRTGFlaiIjEzQ0eFTexL2ns5a0JsP3j61J4WhO4ztiAJ4DfG9h9inI+9KCipMn3q0CoWx4hSpVUFHh0/tiQL4yPX2+h3A1QMuI+JThVmMT1/8Sgqm3fJ1CEoYK06RWhW0Iz6dcSwM4DpgHx4fh6AEM4Qd/TgsFMBriO8BXPO3Dk9QYfrUA154MHb48OEqlkTEL0Hw9imAB73e9VEF8OHy8SLMsPeA11W7AHKE7wFc00cNj49DUApDd3t7exVLIuIXn8eAT4xrCMpwBe2Ibz3g4+oN58LZA64AXkN86rHoLxcc1QM+PEc+N3+GoBSGbgVwkdHj4xCUnp4e4nVGU11GAXyYUqkggPvVA95U53BOPeAyQj5VmP3ke27VAz48QY9F1vkzT3xh6D506FAVSyLiFx8vwuzu7qaxDhojGbo1BGVY0vn9IpnyLIBHc+2iesBlRHyqMAsFQyd8OvVVScGpw1TKn/0jGIIyJZ6h47B6wEVGSzD0xKchKD09PTREHfGoo7NbAXyonHN9+0My6U9HX1dXF03RDFnCOTRJF2HWEF97wINTXxqCMjy+jt0DmNGUpUMXYYqMGh8DeG9vL/WRLPVRRyKhdmSoCjv30ml/ckZ3VwcNdY7GqKOrK3xDUBTAa4ivPeDBmDOfAmQlHekB9+fUYdADPqMxw+EQnjoUqVVB8PapPUkmk8QiWeIRp3ZkGAr3hUzGn6GM3V3dNEQdDXWOnhAOTSpbADezk8zsj2a2wczWmdkN+eWLzezPZrYq//OOgnW+YGabzexlM3trwfLzzGxN/rkfWn5CZDOLm9md+eVLzGxOubanEnzqsSgUVJgaAz48fbMapP3ZPzo7O4lGYEJ9lu6e3r7bZ4vIyATfJZ++U729PdSbIxaFRK8C+FAVZgufckZvIkF9xFEf0gOzcvaAp4F/cM6dCVwMfNbMzso/933n3KL8z4MA+eeuBOYDbwP+xcyi+df/BLgGeE3+52355Z8ADjrnTgO+D/xTGben7Hz6YhRK54+4fd2+cusL4Fl/Pr9kMkl91IhFctcH+Dr8SqTSgmtufJq2NJlIEIs4YhFHUnXFkBUejPnUDieTSeJRlx+apADexznX5pxbmX/cAWwAZg2wyhXAHc65hHNuK7AZuNDMZgItzrnnXK5G+RXwroJ1bs0/vht4s4X4doE+9VgEstlsX0Pg0xe/koLPLevR55dKpYhFIJavgXy6OYRINQXtiE/1bSabJWIQMT/byXILPjMHfbOS+SCVShGNQJ1BKoRDrioyBjw/NORcYEl+0bVmttrMfmFmk/LLZgE7ClZrzS+blX989PJ+6zjn0kA7MKXI37/GzJab2fK9e/eOyjaVg089FoHCylIVpwRSqRR1EUedesBFZBDZTIao5QKL2pERMCPrUc7IZrNECA7MwrddZQ/gZtYM/Ab4H865w+SGk5wKLALagO8GLy2yuhtg+UDr9F/g3C3OufOdc+dPmzZtiFsgI1FYWfp4gCHDk81mMXIVJ2jfEJHjy2QyRMwRMUcmhEGrpnhU12ayuX0iai6UQzTLGsDNLEYufN/unLsHwDm32zmXcc5lgZ8DF+Zf3gqcVLD6bGBnfvnsIsv7rWNmdcAE4EB5tkaGIxqNFn0spQvxqKrjqq+vJ501UtnctsVisSqXSERqVXCAbrlfqlqWUHNgEX/ak6BpdBgWwjtFl3MWFAP+DdjgnPtewfKZBS97N7A2//h+4Mr8zCZzyV1sudQ51wZ0mNnF+ff8KHBfwTpX5R+/F3jcqSutpkSjUSKR3G6mkDU8wYFLxKMDmFgsRjoL6eyR30Vk5IL61qcOj2g0ShbIYn3bJ6ULOnEsH1V9EY1EyToj6yAaDd9+Uc47Yf4F8BFgjZmtyi/7IvABM1tEbqjINuDvAJxz68zsLmA9uRlUPuucC84pfAb4JdAIPJT/gVzAv83MNpPr+b6yjNtTdj5VmIVidVESySx1dbrx6nAE+0U04s/+EYvFSGWdesBFRllf2PLozFk0Gu6gVW2FBy0+9YBHoxEyLhcmw3hgVrZE5Jx7huJjtB8cYJ2bgJuKLF8OLCiyvBd43wiKWVPCuAOVoq6ujkQypZA1TMGBS7TOnwDe2NhIMgPdaSMWq9PBmcgoCdoR/wI4ZJy/7WQ59R8K6k9dW1dXRzprZIH6EOYL7ck1xNce8IaGeP7fhiqXJJyCAxefDmDGjx8PwL7eCM3jmqpcGhF/+DgEpa4uN2Qt46DOo+2qFF+vxYrX15PMQioD9fF4tYszZArgNcTXXsBYXS44KoAPj88BfE9PlPHN46tcGhF/9A1Z8yho1cfjpLJGKmPUx+urXZzQKcwWPu0X8XicZMZIZo14CPcLBfAa4tMXo5/8qVAF8OGpr89VLLFY+CqY42lubgZyPeDjWyZUuTQi/ui7aNujoRrxeJyUi5DKWl99KKWLRqN9Q5Ji9f505DQ2NZHIGr0Zo7ExfGdS/fmGesDXHnAst5vFQ3iKqBYEDY5PFx8FPeDd6QjjW1qqXBoRfwQB3Kf2pL6+nlQ2QjKrdmS4gv0hOCPtg6amcfSmjUTGaBrXXO3iDJk/LboHfKowi2lsbKx2EULJxx6floLQ3aIALjJqfAzg8XicZDYYaqAzqcMRrfNvKGNjUxO92Qi92Wgo84UCeA3xqcIsRkNQhieoMKMenVIOesCPfiwiI9M3a5JHQxobGhpIZHI9nQ0hHGpQC4J2JO7REJSmpiZ6MlF6MxHGjRtX7eIMmd+JL2R8qjCL0anD4fGxB7wwdAfjwUVk5HzsAc8FcEhmI0xUR86wBAHcp/Zk3Lhx9GYM53JhPGz8+YZ6wKcKs1Bw8YdPX/xK8umUYaBwm8LYcyFSq3ycBaWhoYFE2pHIRnQmdZj6xoB71J40NTXRk3I4FMBlhHyqMIvx6YtfSUHF6dONNQqFceyeSK3ydQiKAzpTpgA+TPX1Ho4Bb2wkkb9fehgDuD+DSqvSYykAACAASURBVD3gU4VZKAiOvvbwl5vvn5sCuMjo8bEHPKgjOpKqL4YrCN4+tSeFoVsBXEbEpwqzGJ+++JUUfG7OuSqXpDzUoyUyenwM4MH1Q87pWqLhCtoRn9rhwtAdxnZEAbyG+HTjhEJBcPSpQaik4HPzdQiKT6dERarNxwBeGK7CGLRqQczDAF54MBbGMyN+Jr6Q8qnCLMbXA4xy8/1z86lBEKm2oL7wqd4oDFq6mH94fOwBLzwYC+OZEX++oR7wtYcz4FODUEnB5+brEBSfGgSRagvqC5/ak8KzZArgw+PjxbmFoVsBXEbE94DqU4NQSb7vFz41CCLV5uMBe2HoVgAfHh+HJoV9v/C7ZQ8ZXwOqr9tVKb5/fr5vn0g1+PS9CnvQqgU+Dk0qPDMSxmuJ/Pmf8IBPXwwZfT71aImIlCrsQasW+D40KYxDGUtKfGY2t5RlIjL6ggrTp4qzkK/bJVJNPh2wF4arMAatWhDUsz519BUOpwnjflHq/8Rviiy7ezQLIgoiIiIyMj62I4VBy6cxzJXkY0dO2AP4gCU2s9cC84EJZvaegqdaAE3GOcp8+mIU41OPTCVpvxCRsSzsQUvKo7A3P4zt5GB78hnA5cBE4K8LlncAnypXocaqMO5AQ6GgJcVovxAZPT5+nwqDlk9DKGRkCjNTGPeLAQO4c+4+4D4ze71z7rkKlUlECvjYoBbKZrPVLoKId3zq0CncFp+2q5KCdsSn9iTs+0Kp53I2m9kXgTmF6zjnPl6OQo1VYd+ZBuPTF78afN0/tF+IyEAUwEfO9wAexv2i1AB+H/A08CiQKV9xxrYw7kBSOT5VnIXUAy4y+nyqL8IetGqBjwG8UBi3q9QA3uSc+19lLYl4K4xfjFrie8WZyeiYXmS0BPWET0G1sO7ztR4st6Cjw6cOj7DvC6WOWn/AzN5R1pKI98L+Zak2nxrUQgrgIqPHp4AVKGw7fNy+SvCxIyfsB2alBvAbyIXwHjM7bGYdZna4nAUbi3wNWIEwfkFqge8NjgK4yOjxvadT7cjwBPWsr/tFGLerpCEozrnx5S6I+F+x+L595eL755ZMJqtdBBFvBEHEp3qjMFyFMWjVAh8PzMK+X5QUwM3sjcWWO+eeGt3ijG0+VZgyeoKeC1/PkKTT6WoXQcQbClpSjO/7RRjzU6kXYX6u4HEDcCGwArhs1Es0hoVxBxoK37evXHwfoqEecJHR42PQKjxI970+LJdMfn/w6fMr3MfDuF2lDkEpvAsmZnYScHNZSiTe8qlBqCQfTykX6u3trXYRRLzhYwAvDFc6YzY8WQ/HgBfuF2HcruHeu7MVWDCaBRF/A1YgjEeotcDHBqdwX+/p6aliSUT8EtSzPtW3hdvi03ZVUtbDWVDCvl+UOgb8R0DwvxYBFgEvlqtQY1UYj+CGwvftK5egYvGp4kwkEn2PFcBFRk9QT/hU32oIysj5fmYkjPtFqWPAlxc8TgP/6Zx7tgzlGdN8CljFhPELUgt87AHv6urqe9zd3V3Fkoj4xcegVVgHplKpKpYkvLIZ//aLsA9NKnUM+K1mVg+cnl/0cvmKNHb59MUoJoxfkFoQNDg+zYLS2dlZ9LGIjIyP8z0Xth1qR4Yn6/wO4GHcrlKHoFwK3ApsAww4ycyu0jSEoyuMO9BQqOIcniCA+3SGpDB0d3R0VLEkUqucc14ddFaK70NQ1I4Mj0/tRyDs+0WpQ1C+C7zFOfcygJmdDvwncF65CjYW+fgFKaRTh8Nz5HPzJ4wEoTsedXR2KoDLsTKZDHV1pTZREvBx1qSwB61akM36d2AW9jHgpc6CEgvCN4BzbiMQK0+Rxq4w7kBDoYpzePp6wPGnQQ0C+NSGDB2H26tcGhF/ZD2c71kBfOR8aj8CYQ/gJV+EaWb/BtyW//1D5G7EI6PIpyPTYtQDPjw+DkEJAvj0xizbNQRFivBpf68kp+nmZAA+7Rdhnx2n1B7wzwDrgOuBG4D1wKfLVaixyvcArp6L4Qk+t3Q6fBXM8QRjwKc1ZOjs7Brk1TIW+V4flovvQ1DCGLSkPMJ+YFbqLCgJ4Hv5HykTXxucoCFQD/jwBJ+bTwcwnZ2dxOuMlnpHIpkimUxSX19f7WKJhJ6PF2GGPWhJeYT9VvQl9YCb2eVm9oKZHTCzw2bWYWaHB1nnJDP7o5ltMLN1ZnZDfvlkM3vEzDbl/51UsM4XzGyzmb1sZm8tWH6ema3JP/dDy18ab2ZxM7szv3yJmc0ZzodQK3yqMPvLNQg+BchKOtID7s/n19PTQ2MdNERd3+8iMnI+9oAXto3+tpOV4dPMQmE/MCt1CMoPgKuAKc65FufceOdcyyDrpIF/cM6dCVwMfNbMzgI+DzzmnHsN8Fj+d/LPXQnMB94G/IuZRfPv9RPgGuA1+Z+35Zd/AjjonDsN+D7wTyVuT03yqcIsZPnZO1RxDo+P8/omEgliEUcs4vp+Fynka31Ybj6OAVcAH7mgHfYpgId9vyg1gO8A1rohfKOdc23OuZX5xx3ABmAWcAW5OcXJ//uu/OMrgDuccwnn3FZgM3Chmc0EWpxzz+X//q+OWid4r7uBN1uI9y6fKsz+ctsVxiPUWhB8bj59fr29vdRHHPX5HvBkMlnlEkmtCXFVXhN8ak/CHrRqQSTidwAP4/5e6iwo/xN40MyeBPq6qpxzJY0Jzw8NORdYAsxwzrXl128zs+n5l80Cni9YrTW/LJV/fPTyYJ0d+fdKm1k7MAXYV+J2SQX5NISikny8tXQymSQWcdTnuwDUAy4ipQhj0KoFEctVtr4G8DB2UJUawG8COoEGYEhXSplZM/Ab4H845w4P8J9f7Ak3wPKB1jm6DNeQG8LCySefPFiRRWpKX4PjWcNjHs5LKyJSiyzidwAP44FZqQF8snPuLUN9czOLkQvftzvn7skv3m1mM/O93zOBPfnlrcBJBavPBnbml88usrxwnVYzqwMmAAeOLodz7hbgFoDzzz8/fP9LIp6JRCJknZG/ORuRSKmj4USkFGEMJFI+wRAUX+vaMO7vpf5PPGpmQwrg+bHY/wZsOGqoyv3kLugk/+99BcuvzM9sMpfcxZZL88NVOszs4vx7fvSodYL3ei/w+FDGqUtl+XTkXQ0+3cksEomQ5cjpKl8bBRk+1Rcjo89PCgV1rE91bWHcC2P0K7UH/LPA/zSzBLkx2Qa4QWZC+QvgI8AaM1uVX/ZF4FvAXWb2CWA78D5yb7bOzO4id5OfNPBZ51wwqOczwC+BRuCh/A/kAv5tZraZXM/3lSVuT01ShSnF+FhxRqNRMs7IuCO/ixRSfSiBwn1B+8XwRMy/izDDrtQb8Ywf6hs7556h+BhtgDcfZ52byI03P3r5cmBBkeW95AO8D3z/YihkDU+wX9hxv07h09DQwIEe46m2eN/vIoV8rw+ldIX7gk8dEZUUybe/+vxqx5D/J8zsVDP7f81sbTkKNJb5+8XIVZ4K4MMTfG6RqD/7R2NjI8ks7O+J9v0uUkgBfGR8+vwK20aftquSgllQ/M0Z4VPqnTBnmtnfm9lSYB25nvMPlLVkY5DvFYsC+PAcGYLiz+fX2NiIcxBcw64ecBE5nsLQqAA5PD7OA14ojGPAB9yTzexTZvY48CS5+bU/CbQ5577mnFtTiQKOJd5WLOb31dflFnxuUY8+v8bGRhyQyUK8PqaDM5FR4mPA0hCUkfPxWqJCYdzvBxsD/v8BzwEfzI/DxszCd5gREr6HkLq6Uq/5lUJ9FadnQ1AA0s5obNLwE5HRYh5ebFfYNvoaIMvNx/0i7AZLRCeSu8jxe2Y2A7gLiJW9VGOUrxVL8IX3/QCjXILPzafPr6mpCYB0FsZr/LcUEcZTylIeGoIychEPb8QT9tlxBtyTnXP7nHM/cc69kdzMJe3AHjPbYGbfrEgJx5Aw7kBDoR7w4QmCt5k/Dc+RAG40jRtX5dKI+Men9iTsQasWqAe89pTcojvnWp1z33HOnQdcASTKV6yxyacezmJ8375yCSpMP4egQENjU5VLI7Uok8kM/iI5RnDmwKczCOoBFx8N2CVpZu8Z4GldhDnKfD8yVcU5PH2nDj2aB7y+vh4AhxGPx6tcGqlFPgXISvIxgMvIaX+oPYONCfjrAZ5zwD2jWJYxz/eAqh7w4Tly6rDKBRlFhaFbAVyKUQ+4FON7R1W5ZLPZfv9K9Q0YwJ1zH6tUQcRfwZG37wcY5eJjgxP0gAPEYrquW46lAD48GzduBPz9/NSTOzw+BvDCfSGM+0XJV8WZ2f8DzAf67pjhnPt6OQo1VvkeUH3fPild4dkQBXApJp1OV7sIodTR0QH49fkVHkz4FCArKZPxO4CHUal3wvwp8H7gOnL3FX8fcEoZyzUm+djTWcj37SuXI2M6q1yQUaRbS8tgfAqQlRTUF6lUqsolGT2F+4JP21VJwefm0/eqMICH8cCi1C7JNzjnPgocdM59DXg9cFL5iiU+UtAanrAf5RejO9vJYHwKCpUUBJHehD8TlRXuC9ovhieRzO0PyWSyyiUZPYWh2+cA3pP/t9vMTgRSwNzyFGnsUkCVYoKKxbnwVTDHowtyZTDq6RyevgDe01vlkoyewn1B+8XwJPIHZAmPDswKhyaF8ZqHUseAP2BmE4FvAyvJzYDyr2UrlYj0CRrUYAyfD3zs1ZfR5VNPXSUF9URXd3eVSzJ6uvPbErUjj2Voerpz/ag9PT2DvDI8xkQAd859I//wN2b2ANDgnGsvX7HGJvWASzFBxRLGU2zHowAug9FQg+HJZHKfW3Axpg86OzsBqIu4vscyNN09uQOXjg5/Pr+wD00ayiwobwDmBOuYGc65X5WpXOIhha7h6esBD2EFczxhnz5Kyk9DDYYum832HbB3d3WSTqepqyu5ma9ZXV1dRA0iOLq6uqpdnNDJZDIkenNDkjq7/AngyWSSukhuSEYYz5iV9M00s9uAU4FVQNDP7wAF8FHkaxAJevZ93b5yC4JIJhu+U2zHE/aLZ6T8wtigVlt7e+7EtLMo5jIcOnSIqVOnVrlUI9fR0UHEckNQDrcfqnZxQufw4cMAODMOtx+ucmlGTyKRoD5qOMI5tr3UQ+PzgbOcElRZ+f7x+r595RKcWkun/OkBD/vYPSk/7RdD19bWBoCrq8dSPbS1tXkRwPfu3UPMstRFYN++fdUuTugcOHAAgGzjZHq799Pb20tDQ8Mga9W+XAA/8jhsSp0FZS1wQjkLIv46Mo+1AvhwBD2BqbQ/p+QLx+spaEmgsI7QEJSh6wvg0Xi/38Nud1sbdRGIRRyHOzq9upCwEg4ePAhAtmkycCSQh11XVxdNdVkao+EcmlRqAJ8KrDezP5jZ/cFPOQs2FvkeRBTAhyc4sk8lk94M11APuBRTuC+E8aKqatuxYweQ6wHHjO3bt1e5RCOXTqfZf+AgsYijLpJrQ/bs2VPlUoXLrl27AMg0Twdg9+7d1SzOqOns7KQxmqGpLhPKi3NLHYKyuJyFkBxfwtXx+L595dLb29vvcVNTUxVLMzoKw5XG+kpAB2Yjs3HjRojUgUVwjZPYuHFTtYs0Yrt37ybrHLEI1Oe7DNva2jjlFN2Mu1RtbW1gEdLjT+j7/dxzz61yqUbucPshxkVzuaL9UPiuDSipB9w592Sxn3IXbqzxNaAGPd9qUIencN5bX069BtthOHp7/dimSjt8+DC33347ra2t1S7KqFEAH5n1G14iG8n1q6WaprDhpZdCf+bxlVdeASAeddRHXb9lUpqdO3diDc24+vFgxs6dO6tdpFGxf/8+JsazTIhnObA/fNcGDBjAzeyZ/L8dZna44KfDzPy5lLZG+D7mUaeUh+dg+5Ep94Or2cMuCOB1Bj26scawPPvss/z85z/ntttuq3ZRRo0C+PDt2rWLQwcPQDQGQHbcNDoOt/PnP/+5yiUbmY0bNxKxXACPGkxvcrmefinZ1m2vkqpvgUgEGlq8GJqUyWQ4eLCdifEsk+qzHDjUHro6Y8AA7pz7y/y/451zLQU/451zLZUp4tjh+6n4sH05akX7oUNk47mv26EQnmYrpi+AR5wC+DAFZ0Z8OSsCmp5yJJYtWwYcuQAz3XJiv+VhtXHjRmY1Z4nk71M3pznJyy9tqG6hQiSdTrNj+6tkGicBkGqYyKbN4T+DsG/fPrLOMTmeZXJDlmw2y/79+6tdrCEpaQiKmU0u8hMrd+HGGt8DuO/bVw7pdJqOw4fJNOUqT1+uXg/mK45F4FC7bqo7HEHw9imoKoAP39KlSyHejMsPQXENLdDQwpIlS6tcsuHLZrO8/NJ65ow70nbMGZ9m1+493nRGlNuOHTvIZDJk8wE82ziJXW07+11bFEbBBcczmzKc0JjptywsSp0FZSWwF9gIbMo/3mpmK83svHIVbqzp9rUnMD8G0aeeukrZtWsXzjky+d4sX8butbW19Z1W7uru8eq22ZUSTLvl04GtAvjw9PT0sHTpMpIts8COLE+2zGLFyhWhbVs2b95M++FOzpp0ZHjmmZNyQxlXrFhRrWKFyksvvQRAdtwUADJNU3Eu/MN4gmE0M5synDAuV1f4GsB/D7zDOTfVOTcFeDtwF/DfgX8pV+HGmjDOY1kaBfDhCsZvZhsnYfFxoR/PGdi1axf1EUd9flqxYJosKV0w7Vant/WGlOpPf/oTiUQv6cnz+i1PTZ5HKpnk6aefrlLJRmbp0lzv/YIpRwL43PFpmuuPPCcD27BhA1ZXT7ZhIgDZ5mnAkWAeVlu2bKEpBhPqHZPqszTFLHQX55YawM93zv0h+MU59zDwRufc80C8LCUbg3ztBQx66MLaC1NNW7duBSDbMJF0vIXNHozdA2jb2UoskptaDPy5YUglBQHclwtzof+9AtQDXrqHH34Y4s1kxve/X162eTo0tPCHhx+uUslGZunSJZwyPsuE+iP7RcRg/sQEy5Y+H/oZXiph3br1pJqmguVOjbhYIzSMZ926dVUu2ci8tGE985pTmOU2bU5zkpc2hOvagFID+AEz+19mdkr+538CB80sCqiWHCWFFxD4UrFks1na8wFBY/aGbt26ddDYgos1kB43jS1bt4R+7F57ezutf27rm1YsGsn10sjQBMH78GF/DtwLQ7cvdWC57dmzh2XLlpGYNK8vZPUxIzF5Hi+sXBm6g9yOjg7WrVvHgknH3mJ84ZQUBw62h34YRbl1dnayZcsrfTfgCaSapvHii6tD+x1LJBJs3baNrjT8+8bcfTFObUmxZeuWUN2SvtQA/kFgNnAvcB9wcn5ZFPjb8hRt7CkM4GG8q1MxBw8eJJPJNap79+6tcmnCxTnHmrXrSDXmThlmxk0nm8mEvtF54YUXcM4xrs4RMXhNS5ply3Q6eagOHcpdvNrZcTi0DenRCrfDl20qt9/97ndks1lS088o+nxq6uk44P77w3Xz6qeffppMJssF048NVIumJokaPPHEE5UvWIisXbs2dw3RUWdGMuNP4NChg6G9h8D69evJZLKkssb2ztxFx6dNSJPJZEPVmVPqjXj2Oeeuc86d65xb5Jy71jm31zmXdM5tLnchx4rC2+v6cqvdIHRHzbFnV7h6YKpt69atHDp4gExLrvLMjJ8BZqEf+7hixQoaY0ZD/qYa8ycl2bz5FZ0hGaKD+c8rm816c8Be2AOuaUsHl0qluP93vyM98SRcfHzR17h4M6kJJ/O7Bx4IVe/g4489xvQmx9zxx+4H42OOBZOTPPboIzpQG8CLL74IkQiZcf17wINA/uKLL1ajWCO2cuVKIgZNdUf+78+YmMYs91xYDHYjnh/k//2dmd1/9E9lijh2bN++g2wsdzrFl4vtggOJ01rS7Nm7V5XlEDz77LMApCeclFtQFyfTPINnnnm2iqUamWw2y9Ilz/PaCYm+s+ULJucusAr7gUUlOec43H6or744ePBglUs0OgpDt8aAD+6Pf/wj7YcOkZx+5oCvS804k86ODh577LEKlWxkDh48yMoXXuCiaT3HjKoJXDQ9wZ69+1i/fn1lCxciK1auJDNuGkTr+i3PNkzA6pt44YUXqlSykVm5cgVzWzJ9c8NDLozPHZ9hxYrl1SvYEA3WAx7cYu07wHeL/MgoyWaz7GzbSXrCbIDQnho62pYtWzDgvGlJehPJ0I1DrKannn6abPN0XH1T37LUxJPZtm1raA/Qli1bxu49e7m44LTy3JY005ocD/xOx/Sl6uzsJJ1Ok23KTS3mYwBXD/jAnHP8x3/egWuaRKZl1oCvzYyfiRs3hf+8485QdII8+eSTZLNZLppx/Ck2XzctRSxCaA4qKq2zs5NNGzeSHj/z2CfNSDafwPIVK0KxPxTq6Ohgw/oNzC9ybcD8SQk2bHgpNBNaDHYnzBX5Cy0/5Zx78uifCpVxTNi1axepZJJs8zQsPo5XX3212kUaFZs2beKEcY7TJ+Z6OTdv1oilUrz66qts2riR5KRT+i1PT5oDwCOPPFKFUo3cPb/5DRPjcMH0Iw1rxOCvTuxm9Zq12j9KFNyQKbhBU9juAHc8haE7nU5XsSS1b8WKFWzbuoXe6fOPvfjyaGb0Tp/Pju2vhuJM0yMPP8zs5iwnNx//IKypzrFoSoLHH3tU+0oRq1evzo//LhLAgUzLTNoPHQpd1li2bBlZ5zinYGrKwDlTUmSz2dDMET/oGHDnXAaYZmb1FSjPmBXMX5lpnEyqYSIbN22qcolGx6aNL3PKuCSzx+VOF23yZLvK7Q9/+AOYkZ5yar/lLt5MpmUmD/3+96HruWhtbWXJ0qVcOrObuqNqnjfOTBCPGvfcc091ChcyQQAPesB9uUNqKnWkUVWoGtivf3031DceU0ccT3ryXIiP465f/7rMJRuZtrY21q1fz+unD37fiNefkORQ++HQBK5KWrVqFUSiZPLzfh8t6BkP2zjwJUuW0FwPp7YcWz+c2pJmXAyef/75KpRs6EqdBWUb8KyZfdnMbgx+yliuMWfLli0AZBsnkmmczPZXX+3XGIXRoUOH2LN3H6eMT1MfhVnNWTZufLnaxap5mUyG3//hD6RbZuFiTcc8n5xyGrt37WL16tVVKN3w3X333UQNLpt17DSK42KON8zo4dFHHvEmTJZT0OOdbZwIFvGmB7wwdCuAH19raytLljxPYuprIRItbaVIlMTUM1ixfHlN93o++uijALx+gOEngXOmJGmKHVlHjnhh1arc+O9IXdHnXXw8Fh8XqnYkm82y5PnnWDAp0W/8dyAagQWTEixd8lworiEpNYDvBB7Iv358wY+Mkk2bNkHjRIjGyDZNIZPJ1HQlWYq1a9cCuemBAE4bn2TtmjWh+GJU0/Llyzmwfz+pqacXfT49aQ5WV89DDz1U4ZIN3/79+/mv/3qAvzyhl4nx4j33bzu5h1Q6xV133VXh0oVPXwCPNWHxJm8OWoKbdh39WPq75557IBIlNf21Q1ovNe0MLBLlN7/5TZlKNnJ/fOxRTp+YZmrj4O1ELAIXTO3lmaef0v5SoLu7m82bNpFunnH8F5mRHDedF15YVbmCjdDmzZs51H6YcyYf//86mCM+DHfFLHUawq+Ru+jyO865rwU/5S3a2PLSyy+TapwMQGZc7rRy2IdrrFmzhrpI7tbBAKdPSNHV3cO2bduqW7Aa9+CDD2KxBtITTyr+gmiMxMQ5PP74H0Nzd9E77riDTDrN5acc/7TyzKYsF09PcO9vf6spCQdx4MABLFIH0XoydY3e9IArgA8ukUjw0O9/T2rinNxdDYfAxRpJTp7HHx5+uCZv6LV79262bHuV86aWPl3i66Yl6elNhKont9w2btyYG/991A14jpZpnsGBA/vZt29fhUo2MsH1CwuKjP8OnJ0P52G41mHQAG5m/93MtgOvAtvN7FUz++/lL9rY0d7ezr69e8nkx3O6eAsWjYX+hitrVr/I3PzwE4DTJ+aC+Jo1a6pYqtrW2dnJM88+S2LyvAFPLaemvoZkMsFTTz1VwdINz8GDB7n/vnt5/YwEM5oG7tV655weEokEd999d4VKF04HDhyA+kYwI1PXyL79fvSAFw67UwAv7rnnnqOnu5vU1NOGtX5qymkkent55plnRrlkIxeM3V00pfT/+7MmpYhFc5+L5Lz8cm6oZ3bc1AFfl8k/H7y+1r344ovMbs4yof741z9NjDtmNWdDMbZ9sHnAvwRcDlzqnJvinJsCvAl4e/45GQVbt24FIJuf0QAzMo0T+5aHUTqdZtOmTZzWcqRBndqQZUJctx0fyJIlS8ik06QmzR3wddnm6RBv5umnn65QyYbvgQceIJFM8denDN5bP2tchvOnJbj3t/cogA3gwIEDZOoagFyv5oED/vWAh/0amHL5/e9/D/FxZFqKz24xmMz4EyDezO//8IdRLtnI/elPf2J6k+OEQQ7UC8WjcNbEJH969pnQXZheLi+//DIWbx70DEm2cTKYhSKAZzIZ1q1dw+kTBm8XTm9JsnbN6pqfynSwHvCPAO9xzm0JFuQf/y3w0XIWbCwJhmRkGyb2LUs3TGJLiAN4a2srqXSGk5uPXEhlBic1pdjyiqaaO56nnnoKq2/KBeyBmJGccBJLly6lp2fw2QKqxTnHIw//gTMmpjlxXGmN6iUnJujs6g7FKcRqOXDwINkggNc10HH4sBfXVgShOx51CuBFJBIJli9fQXLiHLBSL+E6ihnJSXNZuXJlTdUdmUyGVS+sZOHk3kFnVTzaOVOStO3aza5du8pTuJDZ9uqrpAryxHFF66Chhe3bt5e/UCO0detWunt6OX3C4PXC6RNTdPf01vxw11KmITxmoJhzrgcYsLY3s1+Y2R4zW1uwbLGZ/dnMVuV/3lHw3BfMbLOZvWxmby1Yfp6Zrck/90Oz3FfTzOJmdmd++RIzm1PSFteg7du3Y9EYrn5c37Js40QOt7eHZkL5owW997OOmsd1VnOabdterfkj02pwzrF8+QoSLbMG03H/JwAAIABJREFUn9cXSE88mVQqxbp16ypQuuHZvHkz23e08voZpY83nT8pRUs8vHOdV8Khg4f6BXDnXGjrikJB6G6Iqge8mPXr15NOp0gPs/c7kG6ZSTaT6btQvhbs2rWLRDLFKQPM/X08p+RvV1/rgasSnHPs/PNOsvGWkl6frh/Pjh21f+O/YFKKgeaGD5yUf02tT2QxWABvNbM3H73QzC4DBrul4S+BtxVZ/n3n3KL8z4P59zsLuBKYn1/nX/I3AAL4CXAN8Jr8T/CenwAOOudOA74P/NMg5alZe/fuxcWb+4WuIIzv3bu3WsUakS1bthAxOLGp/5flpHEZkqkUO3furFLJatfevXvp6uocdNxeINuUu2g3mMKyFj366KNErf+NdwYTjcCF03p47k9/oqurq4ylCyfnHB2dHdAXwOMAHD58uJrFGhXBgXl91OkgvYhVq1blhig2nzCi98k0zwCL1NStyIOwNGvc0P/fg3am1gNXJbS3t9Pb20M2XtpEddn4+FDcWXn79u0YML1x8P3jhMYMBuzYsaPs5RqJwQL49cDPzOyXZnadmV1rZrcCtwDXDrSic+4poNQrg64A7nDOJZxzW4HNwIVmNhNocc4953KDu34FvKtgnVvzj+8G3hz0jofNnr17Sdf1H6uVzc//HNYAvn//flri1ncBZmBqQ+7L48u0aaPpyFzwk0p6vYs1YvFxNT3d0rp1azmtJcX42NDGZi6akiKZSunOmEUkk0nSqRSuLndvtCCA+9ADHoTumCmAF/PKK6/kpqutG+F98aIxXNOkmjp4D3qvTxxGAB8Xc0xsMPWAk7voHcDVlzZDjos10tvbU/PX3OzcuZMpjRyTKYqpj8LkRmr+wGKwW9GvAxYATwFzgHn5xwvyzw3HtWa2Oj9EJUgas4DCQ5XW/LJZ+cdHL++3jnMuDbQDU4ZZpqo6ePAg7qgAHlw80d7eXo0ijVgmk6Eucmzoiub3ON1k41h9czvXN/dbHt/+PPHtxe/slYmNq+kp6Lq7uhg3xPAN9K1TS2NUa0VnZycALpoPYdFYv+VhFoTuuohTHVHE7j17SNcde3Ou4cjUNbGnhjp42traaIkbTXXDu5ByejypM6vQd9awr34YRPC6Wj/b2NXVxbi60q9zaarL1vw2Fb9FUoH8GPBfjNLf+wnwDcDl//0u8HGgWM+1G2A5gzzXj5ldQ24YCyeffPLQSlwBqVQad/SUc/nfa/2o9HhSqRTRYneqyi9T41q6SPfxzxbU+jX/PT3dnBAdeikbogrgxxPM/e4iueAdNKD6rPy3d+8+svWlDVEbTLa+iT17BhtJWjnZbLZom1Gq3Lq1XiOW30gC+KRJpZ19rYaenh7qI6UH8IZItubrxJIuozaz95jZJjNrN7PDZtZhZkMecOic2+2cyzjnssDPgQvzT7UChXcdmU3u7put+cdHL++3jpnVARM4zpAX59wtzrnznXPnT5s2bajFLrt0On3sFe3538MaVNPpNFEr0gOeXxbW7ao1tT7mqqenl/gwAnhcAfy4+j6TfM+3y99qWp+V/xKJ3uPeWnzIIrHc+9UQxeeROzIVY4mtQ37kbq1P4ZhMJokVOat+PLGII5ks/YZO1VDqPEY3A+90zk1wzrU458Y750q7xLZAfkx34N1AcAn2/cCV+ZlN5pK72HKpc64N6DCzi/Pjuz8K3FewzlX5x+8FHne1vgcdR27Tjip6flNCOqydCRMm0JE8dvfqSOWWtbQMeffx3oQJEwCwVOl3t4yme5g4sYTppqqkpWU8BxJDny7tQG9unfHjS7uQaCxJJHKNShC8g0AWLPdBKCvyCmiIN0B2lDovsmkaGoZ2J81yGp22Lpzt5Wiqq8vVB+ZKHEufzfRbr1Y1NTXRmym9LenNRGhqGjf4C6uo1E98t3NuSHdPMbP/BC4FpppZK/BV4FIzW0Suft0G/B3kxpqb2V3AeiANfNa5vr3nM+RmVGkEHsr/APwbcJuZbSbX833lUMpXSxobG7HkUVNuZXO/NzWNzni/Sps5cyYdSUdP2mgsGNO3pyf3BTrxxBOrVbSaNXdu7uY70Z5Dg88DDpBJ4no7+tarRecseh2PPvRnMtkj4/9LseFgrnd34cKFZSpZePUNS8ufJXMhH65WKBLJbVPWHXksRzQ0NmC9ozM9o2VTNDQ0jMp7jYZYLEYyk+t7Gk4WT2QjNMVio1+wkIkFn0G2tABuLjeso9YDeHNzMzsyJVyBmdedidLc3Dz4C6uo1E98uZndCdwL9HWzOOfuOd4KzrkPFFn8bwO8/ibgpiLLl5O7EPTo5b3A+wYudjiMa2rCevs3npbJVbKNjbXTQzEUQcDe0xPpm6MVYHdPlHh9jMmTJ1eraDVr5syZxOrrSfaUNkNMpDt3tfucOXPKWKqROffcc/nd737Hq51R5rWUPrvBhkMx5s2d03dWQI4Ihm/1XTeSn7HVh3mzg9CdzpoCeBFTp0yhddueUXmvSLKbqbNrpx4+8cQT6U45OlJGywC3Gi/GOdjdW8d8dez0nTW0TP8zYsGF/ImTL+633NK5YUi1flZ6woQJdKRKPzLrSFrNb1OpNVwL0A28Bfjr/M/l5SrUWDNx4gQs3f/LEnwpwhpAZs3KTVazu6f/EeuenigzZ84M7dCacopEIsyfP59YR2l3c6vraMPMmD9/fplLNnznnHMOAOsOlN4zlczA5sMxzn3deeUqVqj1Tc8XXDeS/y75cF1FELozzohGS+/tGivmzZtHXc+hviGKw+Ycdb0HmTdv3ugUbBQEEyS0dQ/9/70jZXQlXU1OslBpQeeWJftfExLpPlD0gn5L9dDUNI54PF6R8g3XzJkz6Uo6OksI4R0poyvlav5Me0kB3Dn3sSI/Hy934caKyZMnU5fu/2WxVE/fc2F08sknE4lE2N7ZvzLd3lXPvFNPq1Kpat/FF12EdR/AkoNPnxQ7/GdOPe20mh4DPmXKFF57xuk8v6ex5Mywcl89yQy84Q1vKG/hQupIALd+/4b0Eph+jgRwDUEpZt68ebh0oqT6YSCW6sGler0J4ME6CuC5TrtoNFrytUSW6v7/2XvzKDvO8tz3+apqz0PPk7qlbg2twZNsyzbYZswABDiHJJcMd52b5Nx7cjknCUngrpOsk6wFrBwCBJKAwRgbjDGyLWzAAwYbmxhZo21JlmSpu9XdUs/zPOzec03f/aN27Z72vKtqV3Xv31pakmrXrvp2d+2q53u/931eVNeY38FZDeqpaayZmE1cDy0tLVn2LC25uqA4CSF/RQj5TsK/+weEEK2sCbc91dXVoHxkXVSDsbgAdzgcaNu1EyPB1SynoECwEAX2799fwpGZm7vvvhsAwAWytAYW42BCs3j3u95lwKiK43c++jGMhRiMhHJ7sJ6ZcqKutgZ33HGHziOzJpuFtiLAZTl3iy6zoopuoZyCkpIDBw4AANjQTFHHUd+vHs8M1NfXw+Gwbwra5MJY4j2tra1aD8tyEEJQ39AIJp5bYy6OD6F5R1P2HUvMzp2KUd5EOHvm9ORWEuAAngTQCODDAE5BsQO0fts1k1BbW6sUTEireeCEj8Dpcls2BxwA2g8cxEh41YtUFeP79pUj4OnYs2cPauvqwC2PZtyPC4wDlOLee+81aGSF88EPfhA2jsXZqexLnItxBl1LNnzkdz5aFmBp2GQztoXSudTfOUU5Ap6K9vZ2eDxesCvFNZxhVybhdLlw8OBBjUZWPCzL4pZbbsH1QP6pEL3LNtTVVKOhoUGHkVmPttZd4OI5OEVTCia2YomVg5aWFjjstpwmaKMhFg6HPRk1Nyu53uH2UUo/ByBMKT0K4GMAbtVvWNuLmsTyD7NmyYgIEctGv1X27duH5RiwwisCQY1SlAV4eggheN973wvbyhQgpS+q45ZGUFlVhUOHDhk4usLw+/247/734K1ZF+QsWRLnZuygFPjwhz9szOC2EFshBUVlC30UTWFZFkeO3Al7cKrwHxKlsAencOcdd5jO+eLw4dsxHmRyyvNVoRS4HnDg8B13lmuLEuzcuRNMbAWgmVfFCB8GlQTTR4oB5drfvWcPRkPZr9nRIIc9u/eYvo4kVwGuKoFlQsgtUJretOkyom1IsmhCWM0DZ4Qo6uq06XhWKtRZ9XRiOWg6wsLv85o6Z9kM3H///aCyCHYlTZc6WYY9OIn777vPMlHCD3zgAwjyQF8g883z8rwD+/buscQDwWxsJfFR1t/pueeee4B4CEyObkkbYWLLQGwF7zJh+trhw4dBAVxfzn1iMB1hEIgDt99+u34DsxhtbW2gsgiSJQ2FiZrfSWst7e37MRq2Z5x7UgqMRuzY195u3MAKJNen9/cIIVUAPgelAU43gK/qNqpthipIibDalYyV46iyuFBVRdRaAb5zp/mXukrNrbfeCrvdAW5lIuXrbHgWVOSVB7FFuPvuu8GyDK7Mp2+PHBQI+gIc7rv/PQaOrIyZUPPYObI1ctr14L777gMhBNxS5jS1dKjvM2OR88GDB2G32dC7nLtrUk9iX9VxqQySxbVswqo2HWxCgJupGDcT+/btQ5inWMjQ3G0+xiDMU0ustOfqgvJ9SukSpfQUpXQPpbSeUvpdvQe3XVC9Ktf6dhIxbnoPy2w0NDSAY1lMJ6wIp2M2tCQKKcqkx2634/bbDyvLzClgVyZBCMGdd95p8MgKx+v14vDhw3hnMX3jj455Gyg1pzAoYwyq6GYZWhbgaaiursahQzfBHihMgNsCo9h/4ADq6uo0HlnxOBwO3HLrLeheyj0PvHtJyf8ur5qt0tbWBkJIMsKdDia6hKrqGst0HFZF9UgwfWrJSCJFpX2rRMAJIRWEkG8QQi4m/vwbIcSaBtUmJCnA13iBUyFumS9FOjiOQ11dDeZjDCQZWIoBjY2NpR6WJbjzzjuB6PK6tCQVLjiDffv2We76uPfe+zAZIliKp06V6FqyobLCX3bJycJqqkliHXYLJUyXI+C58d73vgckvAASD+X1PsJHwITm8N73mHeV6c47j2AsxCRrhzIhU6B72YE777p7S6VgFYvT6URDY1NWAc7FltFugUixitr1OZMTynii1szMHaJVck1B+QGAFQB/mPgTBPC4XoPabthsNjAsC8iJRhqyBFDZVG2CC8Xvr0BEJIhIJPF/a0f1jUIVoZsaJ1AKLrZoKveCXFFviOl8fqejHHbv2WuZvPZSkU5obAUBojYTcrDylmgspBf3338/AIALjOX1PnV/9f1mRF3Z617KnoYyFmIR4mGp1UCj2Ld3D2yx5fQ7UBkkuow9e8wvVFXcbjfq62oxEU4fAZ+MsGior7WEg1yuT7q9lNIvUEoHE3/+CYA1koYsgt3uAJFUAa78vRUEuNfnR0RkEElUtXu93hKPyBqoS21sZGHddsKHQIW4JfLbNqIuEc+kEeAzUVt5GbkglAj41hLgFKKY3gVou7Nr1y7saG7Oale6EW55FPUNjaaODu7fvx8upwPXc8gDV/cp9wzYzO7du4FYYDWwtwEmtgLIkmUKMFVa23ZjMpL+2piK2NC2e6+BIyqcXAV4lBCSXLMihNwPYPPaeJmCsdttAFU63JHE33Z7+oI1q+D1ehGRWETEsgDPB7/fj8qqapBYYN12JqZ4u1rtpgkojTZsNi5ZlLuWkEAQ4mmy2UKZ/NlSApwBeJ7Psvf25t53vxu24ExagbUJWYItOI377n23qa8VjuNw8OAhDAazC/DBFQ411VWor683YGTWYteuXYrPdxonFJJ4lliteVFzczPmYukj4LMx1vT+3yq5CvD/AeAhQsgwIWQYwLcB/HfdRrUNYRl2NZcz8bfZPSxzweFwQJAJeJkk/18mN+rr68Dw69sJqy2orfjAYRgG9bW1WExRwb6U2GbFz1Uytk7qdxJVdDs5ClEoR8AzceTIEcWuNDSX0/5seA5UEnDkyBGdR1Y8Bw8dwmiIBS9l3m8wZMehm242ZlAWQ11NVIM2G2HigXX7WYX6+npEBIpoinlnVCSICtQyz5FcXVCuUkoPA7gNwG2U0jsA/IauI9tmMCwDsqGoaivkwhJCtqJOMISG+npw4noBrjZrUps3WQ273Q4xxQUhyKuvlymMrdCIRxRFsAxgYyiEcgQ8I4cPHwbDMDl3xWRXpkAIsYRf9qFDhyDJwFiGpithgWA6TCzRjKwUqMKapBPgsSA8Xp/livnVbqeL8c0ByoWYopnM6PCTirwUHqV0hVKq/jb/Px3Gs20hhFnjZrB1cjo3shU/k174fD4Qab0IIWIcDqfTskKVtdkgyZuvAYkq28zWmc/UkE3/sDyCIIBjCDgCCOUc8Ix4PB7s3r0HbHg+p/3Z8DxaW9ssIbhUC7lMbcfHw+XOypnw+XxwOJ3rOmyvheHDaGiwRqR4LWrflFTdUtVtVVVVho6pUIoJsW6du34ZXVkbmNsKUTqjcLlcgLRhnU0W4XBYtzjXxtkgpbgEpEQEvCzAC2crfLdEUQTHKD7golB2QcnGgQP7YYst5mRFaYsu4MABa1h81tXVgWEYzMcyNFyJKq/t2LHDqGFZjqqqapB0AlyMoq7Wet221TqycAoBHrZYrVkxAtz6d/syuiNJElgCMInvStnbN3ccDgeotD4KSGQRTqe18+gz3Ti2gojUm3Q/o63wsxMEASxBIgJeFuDZaG9vB+WjaUWWChEioHzEMtFijuNQX1uDhQzFdvOJ16yS71sK6mprwKToJQEArBhFdXW1wSMqHo/HAwBJY4e1RERm3T5mJ2O4iRASROrnJQFgfpNFy7J1FhckSQLHULBEuYzK3r65o9QAbPj6UQqGsW5xbjweQ2WKab8t8ZHKzhfZWRXaifvEFkrrUifsLFH+XSYzyUK7eBCSPb3oIDHFCcNKLkONO3Zgbih1N2BAaTleXVlRLuzPgNfrBSOnqRGQBMtEiteipl8KKVIZRYvVEmUU4JRS8yeLlTE1SkSLgk18V8oP1e1NPB6Hndk8p7cltpUFeHY2CfBN262LLMtgCAVDyq3oc0GN/qruSOlgBOu5J1VX12CqL71ECfAMqi1ajG4UTqczaWu8DkpBJcGSkxc1TVFOlcpI1+9jdqxvs7Elsf6DVEUURWVJOXGlCWVrsSIhlhZafDyeFNtrUUV5PB43ekiWI/n7X6e/yZYQrIoAV4L6W+Hz6I3q9sDwmVvSk4SdqVXcIQClQ3SqehEVkQI2mzUinaXCbreDyKkEuAxQakkBrrrDqYX7a5ET26ziIGeNUW4DUrmDbAXHEEWAy+UUlALgeR6EWT+Tpwxj6UlMLB6HI0UGjT2xrSzAs7MqTNfcH4i1J2Yq6mcg2BoRfb1xuVzKc2JjsfYGSKKWxCq5sYDSByOVY5KKJBNwNmtEOs2H8nO14ndMXUXnUgRyVJ1hlZX2sgA3CYrW3no+4KIogiMUXDkFJW94ngc25nsT1tJpGooA33zjdCa2RaPlBrvZUAU4JesF+FaJGBNspSoYfSGEgLPZUqcZrIVKYFjWUs+UbBFwqRwBz4ooigBJ8TtP3Dus+DxWA1BsipuEutJulUCfdb6NW5xUPuBbAUHgwRKanK1aOXprNPF4fLMAZ1jwFo0Si6IIQRCTYnstDqYswHMlVQScEMaSD9NMbJ27oL7YOBuQZfJFZBkca61osSRJGSdiDEG5W2oWBEEATSfAGWsGc9Qxp0pl5CxWS1QW4CaBYQhWI+DKX1uhFb0oCOAIwFpsZmoGwuEwKLs+wkNZO3g+bsmfoyquU0XAWUZxQikL8Owkf/drH6xbTIBTlKPgucILPGgWZyTKsBBFwVIpB4FAAD5b+omFzyYjEFg2cETWIxwOQ2ZsKV8jnB3hcObiXTMSiSj1DC5u87XsSjxbrPK5ygLcJDAMmxTeZAt1whR4HlMRFs8NKq6VVpmZmoFQKARpw82Tcoogt8oNZi2hkFIo5k5x41S2E0t+LqNJCvC16QQMY8lJWSa2wv1Pb0RRVKLAbGqRlYS1QZZlS9VYLC8vwcelv6Z9dorAclmAZ2J5OQCZTV1oSTkHVlZSt6k3M5meI+o2dR+zUxbgJmFdDvgWEuC8ICAmEUyElOXPcgpK7iwvB0A33DzV/wcCgVIMqSiSN05bOgEuIxgMGjkkS6J+hyhZE/Uk1i7OXYt1YrSlR/1O0TRRThWaEOhWmuAuLy3CmyEC7rXJWAmGtkztgx4sB5ZBudQCXGIcWLbgBEadNHhSCHBP4tlilYlFWYCbhC3rgiIISlFV4qNstSidnswvzEO2u9dto4n/LywslGJIRaGKBQ+X+oHp5iTLRC5KSVJor01BYdgtIcAppcnUEyulS5SKubk5AADN0IQHAGSbZ93+ZkeWZczOzaPKkV5cVztkyJRa5jMZjSRJWFpa2vQMUZFtbszMzho8quJRn30VKa6NCruybXFx0dAxFUpZgJuErfqwicXjyTb09nKOb84IgoCVQADUtkGAJ/4/Pz9fimEVhRq1TxW5AAAvJyOwvGTkkCxJMo1rTd4vJayl0gsyQUjChrDUA7EA09PTAADZkbmjIXV41u1vdqanpxGP82j2pK9rUF8bHh42aFTWYmFhAbIkgdpTXxvU4cHc3JzlVhAWFxfBMYA3xXPEa6NgGes8H8sC3CRIsrympbR1LYI2Eg6HwSS8Od22co5vrkxNKS2YNz5Y5cTNdHIyTXthEzMzMwMAqHGmvuHXOOXkPmXSE4vFFPG9JgIuE25L1FdIkgQGisOF1YRBKVAFdTqRpSLblabWVrlvqKK6xZtdgA8NDRkxJMuRbXIm270QBQFLS9YKeszNzaHKuUYurYEhQJXDOis9ZQFuEqQ1fp2qbZDVBXg8HocgSskIuIuVywI8R0ZGRgAAsrNy/QssB+L0JV+3ElNTU3DbSDJPbyO1TgkrwVD5GslCPB4H2WApJzNs0h3AyoiiCI5RbEtFUdqyK4NaMTQ0BGJ3gdqcmXfk7CAOj2WixaqozhQB99ooKp1lAZ6OiYkJAIDs8Kd8XXb61+1nFSbGx1HnSJ9uV+cQMGWRiWZZgJsEQVhjmJ/42+o5naqQUg3z3Ww5xzdXRkdHAQCyq2LTa4KjAoNDwwaPqHimpqZQ50z/QK1LRMbV6H+Z1EQikc2uF4wN4bD1BbggCErfAKKk5Vk9CKE3ff39EJxVOe0rOKvQ19+v84i04fr166hzp3dMUtnl4XG9t8egUVmL0dFRgDCg6SLgCWGuPmuswtTkBOpd6e8L9S4JExPjBo6ocMoC3CTEY7FkJTtNRLdisVgph1Q0ah4Wl0hBqbDLmJ+zXtFHKejp6QFcFQC7udOb5K7B6Miw5fLpR4aHUO9MP6lscCs3VStG940kEolscr2gLIdI1PoCPBqNwslIcHDlxkzZEEURw0NDkF3VOe0vuaowOjJi+lQlSik6O66i3Ze9pmF/hYjhkVHLuF4YydjYmPIMSdWIB1CEOcMq+1mEYDCI5ZUgGjII8Aa3hOXAiiWCfWUBbgIopYhGI2BCs3CMngMY61lGpUKNZNoStWJ1LglTU1PlZeUsUErR0dkJwV2X8nXJWw9ZltHb22vwyApnfn4e0zOzaK9I74Kz0yPBwRF0dXUZODLrofjDr09Boaw1m2psJBwOwclSyzXUKAWDg4MQBAGStzan/WVPHSRJQr/Jo+ATExNYWg5gf2X2FeD9Fco+5XvGZoaGhyGkST8BoETHnRWWCnioKVS5FOda4XOVBbgJiEajoJSCEWNgIosAw4IwnOUfPkkBnmgPW++SEecFy1gElYqJiQmsBAKQvPUpX1e3d3Z2GjmsolAfkJkeqiwD7PXx6Oy4atSwLEkwFErhD68IcKtPbkPBINwctVxDjVJw7do1AIDkSX2f2Ih631DfZ1bU+9r+DJN1ld1+ESxjrXuhEfA8j6nJyc01RBsQnRUYGLRODr0qqreKO05ZgJsA1Z6Nrl0qsjst2WxlLRMTE/DYSTIHvD6R/2u1og+jefvttwEAon9H6h04B6inBm9fvGjgqIqjs7MTDhbYlcHVAFAiWoODQ5affOrJykoQlLXDMXpOWTEDAM4OSRQtb0W4tLwMn12GP+HnazWHBiO5du0aiN2d1QNchdrdIA4vuru7dR5ZcVy5cgVeO7Ajg8hScbDAHp+IK++8Y8DIrMP4+DhkWYbsyizAZVcV5mZnLFPAPTg4CAeX3kkLAGqdMhyssq/ZKQtwE5DsRrWmtbTMOi3ZpWotfX03sNO9GvFULaUGBgZKNSRLcOHC24DTD+pMv3zI+5tx7do1ywjVC+fPob1CAJfljnOoSoBMKS5dumTMwCxIKBQE5RxgIovKihlWO6RauZNoJBJBPM6j0i4nG2qUBXh6rnZ0gPfUp/ZjSwPvqcOVq1dNu1JCKcXli2/jUMVq/4hsHKricf369fJqyRpUZxjZlblAV42QW6UQs7+vD7s8YsZrgyFAi1dEf3+fcQMrkLIANwHJh8ya1tIS58T8vPW6HaoIgoCBgQHs9q8K8GqHDL8DlspdNpp4PI7Lly+D96WJfieQ/M2QJckSQnVkZARj4xO4sza3oiqvHThz5owBI7MesiwjEg5vai+t/t/KxWhq0XalXUZlQoBbxc/XaGZnZzE3OwvJ25DX+yRvA5YWF03bkGdiYgJzC4u4uTp3B7CbE5P2jo4OHUdmLYaHhwFCIDs3u2itRY2QWyFdg1KKgYF+7PRmT03a5RHR39dn2ommSlmAm4Bk16a1jTVsbsxZpJtTKoaGhiAIInb7Vr8shAC7vWXbqExcvXoV8XgMYuXOjPtJ3gYQzoFz584ZNLLCUcX0nXXZH6osA9xeHcNbb74BUcx+o91uhEIhUEo354Bz1o+Ar23U5OQAr51g1oKtso1AzXmWfHkK8MT+Zs2ZVgMKh6pyF+B7/SJsLHD58mW9hmU5RkZGAGfFum65qZCdfoBhLSHAp6enEY5EsSsHAd7yjsf6AAAgAElEQVTqExGORE3f2K0swE3AwoIS6V6bA07tbqwEli0rQtQo9x7/+vHv9okYGR2zTM6Z0Zw7dw6E5SD5mzLvyDDg/Tvwxptvmr5j4JnTp7DHL6Hakds4j9TxCIUjuHLlis4jsx5qhJty6+0pt0IEXH1Y1ibyO2sckmkjtaWmq6sLhLVBdudmQagiu6pAOLtpXUOuXr2KKifQ6Mr9nmZngXa/gCtXynngKlkdUFQIAzj9lkhBUd17WrPUEQGrtUZmd/wpC3ATMD8/D2J3r8vlozY3KKWWdQzp7e2F106SzVVU9vhFUErR12f+/KxS8Na58xC8TcAGm7lUiBU7EVheNvVNZmFhAddv9OWUfqJyS7UAGwNLRPeNRo1wU25958OtkAM+PT2daCWt3DNqHSKmp6zR0c5oOjo6IXpq03o8p4UwENx16OgwXwScUoqrV97BAX88n7R2AMCBSgEDA4PlPHAo/vCTExNZ009UBEcFhi1g2dff3w8CJb87Gzu9IgjKArxMDszPz0O2uddtk+3K/y0rwHu6sdu7+UaqRsTLeeCbmZ2dxdTkRHr3kw1Iif3eMbEDgOrocltN7s0/HCxwsJLH+XNv6TUsy5KMgLOpI+BWFuBTU1OodSlpSIDSN2B6etr0eZxGE4lEMDg4ADFH+8GNSN56jIwMm06sTk1NYWFxCQdy8P/eyIFKAZRS01ssGsH09DQkScpZgMvOCkxPTZm+8/bQ0BAaPBSOzFk1AJRnSL2HJotRzYpuApwQ8gNCyCwhpGvNtmpCyGuEkL7E31VrXvsHQkg/IeQ6IeTDa7YfIYR0Jl77FiGKpCOEOAghP05sP08IadPrs+jN3Pw8JM61bhtNCPJ5C+aBx2IxDI+MYrd/80zVb6eodZUFeCrUHMas6ScJqN0NuCpx6ZJ5cx/Pnz+PCkd2+8GN3FYjYGx8otyWfgNJ0bShCBMMBxDG0gJ8cmICtY7ViVpduW9ASvoSxWXp+gRkQ/LWgVKKGzduaDyy4rh6VfH/P1CZf9rl3oQfeDltbdXmN5OL1lpkpx+yLJs+X3posB/NrtwnCc0uAUOD5nZc0zMC/kMAH9mw7X8BOE4pbQdwPPF/EEJuAvDHAG5OvOc7hCQtQR4G8CkA7Yk/6jH/G4AlSuk+AN8A8FXdPonOLC0tQbZtWFJOLDFb0YpwZGQEsiynzdVq9fAY6DPXzd8M9PT0gHCOrNZRa+E99bjW3W3KKCGlFBffvoBbq2I5W4qpqBFzNYJeRkEV4Bsj4CAExOawtACfmZlO5n8DQG2ib0C5EHM9PT1KEbvsya0D5kYkT92645iF3t5euLjc/L83ovYYKAd2VgW4nEsOOADq8AEAJifNm+7F8zwmJqbQ7Ml9ctbiETE+MWnqyL5uApxSehrAxtDFJwAcTfz7KIDfXbP9GUppnFI6BKAfwD2EkCYAfkrpW1RRGE9seI96rGcB/KYaHbcSlFLlobkxpzPxfysWVanWYTXO1DfSGqeE+QXrWizqxcDAAERXZV6+vrK7GuFQ0JRRwuXlZQRDYbT5Nl8HT91wYyTIYiTI4suX/XjqxvoUrEaXDAdHLFEcZCRJAb6hCBMAKGuzjC/8RgRBwNJyYF2hrvrvsgBfz/Xr10GcPlCba9NrjtFzYCMLYCMLcPX+crVR01o4B+CqMJ1Y7e+7gV3e1B7P2e4XANDqFdB/47opgxFGMjk5CcJyKa+PVKhC3cwCfGpqCjKleU3OmtwSZFk29Sqq0TngDZTSKQBI/K2uoTUDGFuz33hiW3Pi3xu3r3sPpVQEEABQo9vIdSIej0MUhM0PVIYFGNaSAlxNm0nnelHlkBGJxspOKGuglGJwaAiSM/foN7DaaMGMXb9UB4vaFBOx0RCHqMQgKjHoXbZhNLS+6JQQoM4pl10wNhCJRJTCO7I5EZIydssK8IWFBVBKUb0mAq7+uyzA19N7/QZ4Z2r3EyayCCIJIJIALjidbNS0EcFZjes3zFMIL0kSBgYG0OpNHa3Mdr8AgFaviGA4su2vl5mZGSWqnWMgh9pcAMOaOgVFjeo35OGOU++W173XjJilCDPVlUIzbM/0ns0HJ+RThJCLhJCLZmvsEI1GAQCUsa1/gRAQ1oZYLFaCURXH3NwcWKLke6dCdTkw2++ilMRiMaXBisOb1/vkxP5mrBVQxXNdHjfNtdQ4BExNmvfmWQrC4TAIZ0/5cJUYDuGwNSe1ajMytQMmAHg4CpZYMw1PLyKRCKanJvO2H9yI7K7G7My0aVKWJiYmEIvzaE2xWpYruxLv3e4OW9PT0xBtm1cI0kII4PBaQoDXu3K/PhoS+5YF+CozibQSJP5Wp6rjANZ2HmkBMJnY3pJi+7r3EEI4ABXYnPICAKCUfo9Sehel9K66ujqNPoo28Hyi6CiV7RzLrb5uIaLRKJwcSZv36+Jocr8yCqv2co4se65HTVUKBAKaj6lYsq2EZKPaIZcnaRuIRqMAa0v9ImtDOGLNCLgqstdO2hkC+BykLMDXMDg4qBRguotb7JUSAt4sK2djY8oCeCH53yotifeqx9quTM/MQrbnF8gRObepVxtnZmbg4Ai8ttzTi3w2CjsLU08sjBbgPwfwZ4l//xmAF9ds/+OEs8luKMWWFxJpKkFCyLsT+d1/uuE96rE+CeB1asHkL1Vg01QdqwiLeDx3/2SzYLfbwcvpfxWCTJL7lVFYtZfLT4Cb2f3CZlOEoljgt1KkgN2WRmxuU6LRKGgaj3jK2BCJWHNSq4psn239ZM1nk8oCfA2qYJbd+aWqbUQ2mQBX84/ziXBuxMVReO3E1LnMesPzPIIrAVC7J6/3UbsHM7PmDXbMzs6ixinn5Q9PCFDtpKZOScre7aNACCFPA/gAgFpCyDiALwD4FwA/IYT8NwCjAP4AACil1wghPwHQDUAE8FeUUvWb+BdQHFVcAF5J/AGAxwA8SQjphxL5/mO9PouerHYx3HxlUUIsWVBit9shSAClqdPQBGl1vzIK6nVA826sQUAIY8pumB6P8hCIiAQVadKRMhEVCTze/CI5W51oNAqZpBHgLGfJlDVgtbjUza2/TtysZDq/6lIyODgIwtlB84xwboTa3CA2h6kEuMtG4OWKe97VO0VMbePmTWpX7Y19RbIh291Ymh2CLMtgGLNkJq8yOzuDalv+bibVdgGzJo6A6ybAKaX/Z5qXfjPN/l8C8KUU2y8CuCXF9hgSAr6MuXA4lCiuICttgjfCJyLg6n5lkLzpEeQvpCk1501zVYAzQAGfKyISeL0+jUdlbSKRCGQ2zW2bsSEatWYOuFo86mJp0uHi/9ofgZujCIbMt7pTKgYGByE583NKSgkhEJ2VGBgwh0/y5OQk6p1S0R+r3iliZHz7pqCoaX/Unp8ApzY3ZElZbaquLq6+QA8W5uZwsIBUxmqHjOvz5o3sm++pXWYVCktGwL2JqGVITH15hQTlLuvzlcWVSlJAF/T7pqYU4H6/Ym+l/r7zJSxy8Plz87LdLoQjkc0F2wkoawMfj5tyNSQboVAITo6AZRTHC9Xlws1RhEyYXlUKKKUJq9Li0k9UJFc1BgeHTHG9zM5Mo9qRfwOejdQ4ZczPL1jyuakFSQFuyzcFRRHsCya0B5ZlGQtLy6gsQIBXOWQsLS2b4hpPhfme2tuMVevyzTcMYlJhlY2aGqVAKBBPLbwCPAO3ywmn05ny9e0IxyWimjTPGwWVAUqT+dZmor5ecRldiBV2Dc/HWTQ0NGg5JMsTiUTSFmFSxgZKqSXTUMLhMNwpCqxcnGxZa0WtmZubQyQcLtoBRUV2VyMWi5qi+G5+bi7pjlUMVQ4ZgiiZsijdCFQBLucZAZcTgt2MblpLS0uQZRmVjvwnVZUOClEy7/VgPXW3xcjcO4hmed2cqAJ8mU99eS3zjCmXuUpJUkDLeRYhJfY3owCvqakByzKYj6XIQ8pCVATCPEVjY6MOI7Mu4XB4cxdMlYQwt6JgDYVCcLObH7BuliIciW7biOZa+vv7AQCyS5t7p5Q4Tqlt++LxOILhSMFuSWtRRbwZhaQRzM/PKz1E0t0j0qBGwM34c1OdsKod+Rfoqu8xayFmWYCXmMwPFmsWYariOpBGgAfiDKprCmujvFVRI+CkkAj4mvebCZZlUVdbi/kCIuALCdFejoCvQilFNBIBTRcBTzx0rVi0GAgE4OE2pyB4bRQypZb8TFrT29sLEFK0BaGK7K4CCIPr169rcrxCUUWfFhHwyrIAB3F48q4RoDYXQIgpf26rnbULywEHygK8TBpEMfHQSeF+QQmz+rqFcLmUFrhxKfVNIE5ZuN35LZFtddTfc0o7ykwk9heE/CvEjaCmphYrfP6rOOp7yislq0QiEciynNYrXt1uRkvKbCwvLsBv2/yA9Sca86iNerYzvb3XQV1VQLoi3HxhOFB3NXp7SyvAVYGlhQCv3uZN3mZnZyFyBTxbCQNid5vy56amSBWyQqKKdrN6gZcFeInJJsDNKqwykfR/TvN9EWVStiDcQLLhUt42hOYW4CzHgdL8BbiUeA/L5p++slVJesVzqWsnVAFu1nzHTCwtLaXsnKtu2+4CXJZlXOvuhuDWduVQcNeiu6cHklS4/3axFJNisJFKu+IVbUYhaQTTM7N5WxCqSDY3ZmbMFymemJiA20bgy6MJj4rPRuHiiGm7YZYFeImJRBTbsFTLyjLhLNlYQxXgasOdjQiUmDJnuZSoS+xp83vTQQgIa0uKM7PBsiykArKo1LmbFYuQ9WJxUWn0m16AK9ut1rgmEokgGI6gxrlZgNUkRJlZI1hGMTw8jHAoCMmnbUqW5GtALBopqR+4mh5QSIrBRlgGqHSYN+VAT0RRxML8HGRHYR7xks2DSRN6qI+Pj6HBJRZkUUkI0OCWMD4+rv3ANKD8dCsxarQq1UNV5hyWjPyohaPpmmHKNFvx6fZDnaHLjvxt92Sn37QzfJZlIadoMpUN9dopR8BXSQpwmyvl6+p2M1qJZUJdYq5NIcBUUTY1NWXomMzG1atXAQCST9uiZMmrCPorV65oetx8mJubg8dO4NDoq17tELelAJ+fn1dS1ByF2fvKDh9mZ2dNZ9k3MjyMRlfhqbiNLgEjw0Majkg7ygK8xKjRqlQCnHJOLFksmgWsCoV0vp0VNtFyIkFvxsfHAUJAN0QvHKPnwEYWwEYW4Or9JRyj5za9V7T7MDpmzuYTC/Nz8HD539C9ieVG9Voqs7qsnrbNNMOC2N2mLKTKhDp5TNWG3M4CVU5s6/biAHD58mXA4S26A+ZGqMMLOP24fPkdTY+bD5OTk6jVIP1Epc4hYXLCnBFPPVEnqXKB1wh1eCGJoqnuH4FAAHPzC9jl3SzAn7rhxkiQxUiQxZcv+5MNvDayyytidm7elLUxZQFeYqanpwGGBbWlEOAOL1YCy5bz9U0uKaYR4DUOCbPT2zuitZGenh7AVZksqlRhIosgkgAiCeCC02AimwWp7KrC1OSk6dJQ4vE4RkbH0Jri5pmNFo8IglXrtTKJ7xXDpk1BAdQ8TmulawwPDwMAmtypRVizi8fQoDk6NpYCURRx8dIl8L4dxXfATAHv24HL71wuWR3JxNgoGlzanbvBLWF2ds60dTF6kVxFdRbWvExdfTXTaqraqbXVt/neMBriEJUYRCUGvcu2ZPOujbR6lfea8VlSFuAlZnx8HHD6UxbfqV8kM30hckEV4NVpcvpqnDJm5+ctabGoB/F4HFeuXgXvbSro/aJ/ByileOed0kWxUjE8PAxZlrErxc0zGy4OqPdQU940S8XU1BTg8GUUYZLdiwmLRYuHhoZQ51Z+56lo9koYGRkx3dK4UfT09CAaiUCqaNbl+FJFM+KxGLq6unQ5fiZEUcTUzCwaUqx+FEqjS4JM6bZLWxofH1cm6OlWyLIgOytWj2MSVIvMVBHwXGn1Ke/t7e3VZExaUhbgJWZoeASCPXXOlvqFGBkZMXJIRTM2NgYCoDZFURWgLDULgmiKDmxmoKurCwLPQ6zYUdD7ZU8dCGfHxYsXNR5ZcagNPnYWePPc5eZxo7enPFFLMDExCTFLi2nq8GFmZqakrhb5MtDfh2YXn/b1Fo+EOC9YLhChFefPnwcIgegv7P6QDdHfBDAMLly4oMvxMzE9PQ1ZltHo1m5y1ZA4lpmEpBGMjY2lDeblArV7QBhOOY5J6OrqQqOHpnRIyhW/naLBTXHt2jUNR6YNZQFeQiKRCCbGx9K2FpadlQBhSt6pLF+6urrQ7JXTRrT2+BVB1t3dbeCozMupU6dAWA6Sr7AIOBgGvG8HTp0+Yyrf+DfffAPVTqDRVdjD9VCVgOnZuWSKwnaGUoqx8bGsy8uy0w9JtE4RWjQaxdjYONp86a9b9bUbN24YNSxT8cabbyrFkmn834uGtUPyNuLsG2/oc/wMqN/txjTpR4WgpjJZLXBVLAODQxAcFYUfgBDIrgrT/NwopbjW2YF2X7zoY+3zx9HV2WG6YE5ZgJeQ/v5+UEohedJ4uzKs0iihxJ3K8kGWZVzr6kS7P31Ea6dHgoMjJVnyNBuiKOL1EyfAV+xMthIv6Dg1e7ASWDZNGko4HMbbb7+Nu2qjBaet3lXHgwA4ffq0pmOzIouLi4jHYslVsXSoeZxmimJlYnBwEDKlyWXiVDR7JHDM9hTgs7OzGBochFCxU9fzCBU7MTY6anjahirAWzzaCXCPjaLKqaQ2bRfi8ThmpqcguypTvp5LMT8AiI4KDJTQknItY2NjWF4Jor2y+KDS/goRy4EV062KlAV4CVHzm+R0AhyA4K5Bb2+vZfIfR0ZGEI5Esa8i/ZeGZYC9Ph6dnR0GjsycXLp0CaFgEGL1nqKOI1a0gHAOHD9+XKORFce5c+cgCCLurk8/EctGpYOivVLEqRMnNByZNRkdHQWA7AI88QBW9zc76j2wLUOdAMcAO73SthTg58+fBwBIlS26nkdMHP/cudTCTC+GhoZQ6wJcnLaRyWY3j8FtVLg7OjoKSilkV1XK13Mp5geUgv75uTmEw2E9h5sTqjXmwcrii2nVY5TSbjMVZQFeQrq6ugCnDzRD5yrJW49oJGKZB6oa1W6vyPylaa8QMDgwmGxEtF359a9/DcI5IFYU+YBlOPCVu3Dq1GnE48Uv2RXL6dOnUekA2jNMxHLh7ro4BoeHTRe5MJqkAE8T4VKhnBPE5rDM/aKvrw9+R/Y2021eAX03rptuCVlvzp07Dzi8SjqijlBnBeD0JwW/UQwN9KPZXfgkPR0tHqVw10q1EMWgFqtLrtTprLkiuRUBX8rGTCpXrlxBpRNoKDCFcS2NbhmVjrIAL5OAUoqOzk4I7rqM+0neegCwTLpGZ2cn/I7sX5r2ChEypds6Dzwej+P0mTOIV7Zush8sBKF6D6LRiOEP0Y3E43GcP3cOd9TEwBTpmnakVnk4nz17VoORWZfh4WEQzp5xsg5AKdZzVlpm+f3G9V60evisaUqtPhGhcGRbOVsIgoCLly6C9zfrYj+4Ed7fjEuXLxs2gRdFEaNj49jp1V4k7/Qqhf7bpXB3YGAAhOVAnYU14VGR3TUASm/ZRynF1Xcu46A/rsmlTwhwoCKOK+9cNtUkvizAS8Tc3ByWFhcheTMLcOrwg9iclhGqnR1X0Z7Dl2ZfheLzbJWJhR68/fbbiMdiEKt3a3I8yd8EYnfh5MmTmhyvUC5fvoxYPI4jdcVHtmpdMlp9Ms6eOaPByKzL8PAwJGdFTkJMclZgaNgchVSZEAQBIyOjKT1+N6J6+aq+wNuBnp4exGMx3ewHNyJWNEPgecOeNaOjoxAlCS0e7QvH1WOaIZJrBH19/ZBcVQU7oKhQmxvE5iy5AJ+YmMDC0jIOVmnn5X6oSsDC4pKpJmVlAV4iVE9KyZNZgIMQCK4adPeYz8NyIwsLC5iansH+HNIO3BxFi09GV2enASMzJxcvXgRhbdq1lyaKG8rbFy+VtGbgjTfegIsjOKTRzfOOmhiudXdjaWlJk+NZkcHBIUg5piHIziqEgium/3mNjY1BlCTszEGANScaM20XQQWsLpeLhboj5YnkbQQIMWyZXv1d6hEBb/ZIIGR7FGLKsowbN25AdNUUfzBCILhKb/ygZf63yoFEMefVq1c1O2axlAV4iejt7QUIk9aCcC2SpxajI8OIRqMGjKxw1Ir2XDsftnp4DA1tnwfqRt6+eBGCt0GT9BMV0b8DwZVASR88Fy+cx81VMdg0urvcUcuDUqq0496GBAIBBALLuQvwRJ642e0b8xFgTg6o92wvAf7OO++Aemr0sx/cCGeH7K7BZYOclAYHB8Ey6TugFoOdBRrddFtcL5OTk4hGIxnNHPJBctdieGgIPK99bn6uqKmsTRr6w+9wS/DZzeW+VhbgJWJgYADUVQkwacyy1yB7aiDLsmn8OdOhFso15PilaXTLWFhcMv3EQg9WVlYwMT6uXfQ7geolXqqbTCQSwfTsXDJlQAtavEo0y+yCUi/U7322AkwVqwjw0dFREKK0Ds+FJiePsZFhfQdlEiRJQndPDwRPvaHnFb0N6O3tNaSfwPDwMJrcMjidVEiLm8fQNnBCWV1N1yACDsWVTZKkkk5eujquYp8ve21IPhCi+IF3dpQj4NueoeFhiFksxVTUCnizC/CJiQnYWKAqi6OBitp+2Ew5WUahFpNla6ySL9TuARi2ZF1GVf/pHRr6+toYoMFNTX/964W6mpHOYmwj1OYG4Rym/3mNjY2h3oWcV0qa3BLGJyYtY8laDOPj40r+t0ZRzVyRPLUQeN6Qa2docAA73NqlGGyk2SNhcnLKFK5QetLT0wPC2nK+P2RDveZKVXe2tLSEianprE5qhdBeIWB8YhLLy8uaH7sQygK8BMTjcczNzuZsLSU7lPayZn+gTkxMoMFFc3a+UCNf21qAO4qrWt8EIYDDVzK3CPUa1VKAA0CTS8DI8NbP50zF8PAwCGtTJle5QAhEp3kaaqRjbGwUDRla0G+k0S2BFwTLdPkshlx6ROiBKr7UqKpexGIxTM/MatqAZyM7PBJkSi3TlKpQurquQXTXFF2AqUIdXhCHp2QCvKenBwAy9hIplH2JLtx6X9+5UhbgJWBmZkYxzc9VfDEMiNOLmZkZfQdWJMFgEF4u9y+N16bYAYVCIb2GZFoCgQAAgHIuzY8tso6SzfDn5+cBADUObR+sdU4Jc3Nzmh7TKgwODkFyVeZlRSe5FCtCM1lurYVSismJCdQ7c49m1yWsTbeDFeHg4CDAsFkbL2kNdfhBWE73GpKRkRFQSjWfqK9FFfdmT8Uqhng8jv7+PojZzBzyhHfX4WpHaQwSent7QQjQlqE7bqG0+UQQsiryS01ZgJcAVUjkHNECIHBuzMyYO/ITjYThYHN/4DsT+27HZjwMo99Xj+h8/JzGoINtMTHAC9lsUEoxMDgI0Znf8rLsqkIoGMTiYuqOd6UmGAwiEo2hzpW7AKtP7LsdBPjw8LBSI6RRVDNnCIHkrNRdtK62oNcv17zRLYHd4k4o3d3dkCRJ+1oibwPmZmdKEvTr6elBi0eGQztvgiRODmj2yOjtLQvwbYsqwOU8BDi1ezBj8qXXSCQCVwECfDsWYa6KST0ilKWLeuopkrejAF9YWEAouJKTW9Ja5ERHPLP6Zqsiui6PLnfVDhkMUVwftjqDQ0MQHcZGv1UkZwUGBvUVrUNDQ0pthwZdDtPBMUCjR97SAvzKlSvKpMnboOlxJb8i6I227KOU4npvD/b49HNg2e3jcb2nxxSrg2UBXgJUf15qyz39gNpcWF5eMsVFk45oNJpXBJxjlD/bUYA7HAlrMUn7CBBDJTidTs2PmwuqSNb6MqVQIvvbDbUhRr4CXErs39fXp/mYtEAV4PXO3CPgHAPUuLZ+BDwWiyVqhEojwGVnJZYWF3RdmRwaGsQOjwxWZwXS4hYwOFDapjJ6cunSJaV7JWfX9LiyqxrE5jTc+nV6ehorwRB2+/VbGdntE7G8EjRFSm9ZgJeApaUlENYGsLac3yNzLgg8b2qxKvA87HkIcACws6SkfqOlorJSKcBlRO1/n4wYSx7faGw25ZoWZG3lsiCT5LG3E2oxnuTO02KMcwBOP27cuKHDqIpHjWLnEwEHgDoHj4mJcT2GZBrGx8eVGiFXiQR44ryjo6O6nWOwvx/NOjqgqOz0Spiemd2SaY4rKyvo7u6G4NehUyoh4H1NeOvcOUNdh9T7nR753yq7/cqk3wyFmGUBXgIWFxcBW34RSprY36w5nQAQ54W8m6/YGGxrAU7EmLYHphRUiKGiojQPb49HSauKStoK8IhI4PHmnrK1Veju7gbcVXlN1lUEdw06u67pMKriGRsbQ4UDcHH5Tdgb3TLGx8ZMvRJYLKrwzdUlS2vU8+olwOfn5zG3sKhrlFNFFXJmEFtac/78eciyDLFypy7HFyt3IbC8bOjPrqurC3YW2KVDd1SVVq8IG1M6m8W1lAV4CZidnYVoc+f1HrVg06xOEJRS8IIAO5NvBJxueZ/WVHi9XuUfosaTD1kCZAk+n8b2hjmiCvCIqK0Aj4oEHm9pPlOpkGUZHZ2d4At0OJC8DVhcmC+ZJ3wmhoYGC4qANnskhMIRLCws6DAqczA4OAgQonmPgFyRHX6AYXVrxHLtmjIp3GeAAN+bOId6zq3E2bNnQexuyBo7oKiIFS0AITh79qwux09FV1cndvtE3ZozAUoq226/iM6ODv1OkiNlAV4CpqZnINvyi+apBZtm9cBVrQTzjWi5WBnBYFCPIZkaVagSSdtlWCLx645vNMkIuNYCXGLg8Xg1PabZ6evrQzQSgeQtzOFAdUZ4x6DW4rkiyzJGhoexowAHjGa38p6tXFh340YfqKsqpy7JusAwoK4q3eoHrl27BhsDtOqYZqDisVHs8NItJ8CDwSDeeONNxCvb9LGcAgDOAdHfjF/9x2uGpKFEIhH09fVhf9iRHD0AACAASURBVIX+K+LtfgF9fX0lT00qC3CDicViWJify7sBC7V7AEJM21RAjbLV5uHrCwB1DhFTk9uvEY/brayAEFnjPMjE8Vwu7f3Fc0FNDdDjkbCV0w5S8fbbbwMApIrCcjxlVxWI3Y3z589rOayiGRkZQTQWx25f/svMrYn3mMXHV2sopbh+4zpEV35Ft1ojuKpx/cYNXb5znR0daNM5yrmWfb44rnV1QpL0S2swmhMnTkAUBQi1+3Q9j1CzDwvzc4rbis5cvnwZkiTj5mr9awNuqeYhSpIhnysTZQFuMH19fZBlGVK+y0YMB+quRrdJHzxqUVV9Hr6+AFDnkjA1NbXtxJVaTEu1jnIljleqtJ5YTMlpz7cYNxsORkbMxAXIenD6zBnIntq83JLWQQji/macP3/BVHUWajSykFbTHhtFs1fechFNldHRUQSWlyF560s6Dslbj1AwqPlKw8LCAnqv9+LWauOux1urBQRDYXR1dRl2Tj2hlOKll18GdVcpDig6IlbtAuHsePnll3U9D6DktDs5gv06dMDcyP5KEQ4OOHfunO7nykRZgBuMGrkppMWw4K5FT0+PoVXJuVKoq0G9S0KcF0xdXKoHKysrAADKOTQ9rno89fhGowpwrZsoOFggGt16TgbpmJqawo3r1yFUtRV1HLGqDdFoJBlNNwMdHR3w2Qv3gN7n49HV2QFR1P9BbTSXLl0CAIgFrnpoheTfAQC4ePGipsc9e/YsKAXuqjNOgN9Ww8PGAKdPnzbsnHrS0dGBG9evI153UL/0ExWGQ7ymHSdOnNDVtk+SJLz5xlncXBUzZGXExgA3V/J44+yZkq6MlAW4wZw+cwZwVYLa1xdhOkbPgY0sgI0swNX7SzhGN8/MJF8TopFIyZdNUnHlyhXUuSncG3LAn7rhxkiQxUiQxZcv+/HUjfWfW612Ntrwv9SoufyU09ivm+FAOHvJPE7VWgCnxhFwJ0uTx94OvPrqqwAAoXp3ytdzuV8AipAidhdeeeVV3caaD5Ik4fy5t3BrVbxg7XBbjYBwJLplIppruXDhAuD0g+aZoqg11OEFXJWapy+dPnUKTR6KZh1b0G/ExSkpB6dPndwSK61PP/00iM0JoabdkPPxDTdDphTPPvusbud45513sLC4hHsbjJuY3dsQx8LiUklrZMoC3EDGx8fR1dmJeM3eTa8xkUUQSQCRBHDBaTCRzRFhZTnIgVdeecWI4eZMOBzG5UsXcaRms6XeaIhDVGIQlRj0LtswGlqfcrGvQoTfsXWiE7ly8eJFXTqYAYDgacCFEkU8u7u74bUDVQ5tV2l2+UTMzS9gfn5e0+OaEVEU8dLLv4Tob04rxHK5Xyg7sohX78Wbb75pCueQ7u5uBFaCuL228AftLdU8OAZ48803NRxZ6VlcXMSFt98GX7mr8INIPJxOJz75yU8qzbikwn/O8cpduHz5smbfucXFRbxz5QqO1MZ0D9xu5K46HnPzC5aftPX39+PcuXOI1R0CWGOKdKnDC6F6D37+818kmwhqza9+9Su4bcDtNcYJ8DtqebhtyrlLRVmAG8jLL78MEAKhpsDCCYZDvKoNJ0+dwvLysraDK4Jz585BECUcKWBZkSHAkZoYzp97a1vZEb517pwivjXuYAYAYmULpqemMD5ubMMSSineuXwJhyriYDR+wB6qVPKFzbj6ozWnTp3CwvwchPqDmhyPrzsIWZbw/PPPa3K8Yjh58iQ4RsnLLRQXB9xUJeDUyROmTMcrlNdeew2yJEGo3V/wMYjI4+Mf/zg+/elP42Mf+xhIETanQm07KKWaCZQXX3wRVJbx3iaNex/kwN11cbhtwHPPPWf4ubXkOw8/DGJzgm+4ydDzxptuR5yP44c//KHmx15aWsKpkyfx7voY7BqnLmbCzgLvqovh1MmTJdNTZQFuEJOTk/jpT5+FUNWW9PQuBKHhJgiCgMcee0zD0RXHiROvw+8A2gssnjhSxyMaiyvLr9uAa9euYXBgAEIxka4MiBU7AULws5/9TJfjp2Nqagqzc/M4VKV9FXurT4LbZj5LPa2hlOJHP3oacFVC1Oj6oE4/hKo2vPCzn5U0jUcQBPz6tf/AHTVxeGzFpQLc1xDDzOzclkldk2UZv3jpJcjeesiuwhvwUM6Ol156CQ8++CBefvll0CIm+NRZAdnXgJdefrnoPNl4PI6fvfA8bq/l0eQ2ftLk5IAPNEVx+tQpU/ri58KFCxdw+dIlRBsPK51uDYS6KsDXHcQvfvELjIyMaHrsF154AYIg4MMtxhfZf3hnFLwg4IUXXjD83EBZgBvGtx96CBIF4jvvKeo4sqsKfP1N+MVLLyXbtpaSvr4+nD37Bt7fGCk46nlTlYB6N8XRHz6+pSJaqaCU4tsPPQRid0OoO6DPORxe8DXteOGFFzAxYZzFo5pGdJMOApwhwKHKON56840tvVJy+vRpDAz0I9p4q6YFVnzTYUTCYfz0pz/V7Jj58tZbbyGwEsT7mor//d1Vx8NlA375y19qMLLSc+bMGYyPjSFef6i4A7F2xGIxPPfcc0pBNFvcClu8/iZMTU7i5MmTRR3ntddeQ2AliI/sLJ2T0W+3xABQU6wE5YsgCHjw2w8BTr9mK2P5wu+4HZTh8NBDD2mWSx+JRPDC88/hzloeTR7jn/07PDLurOXx/HPPlsQTvCzADeD48eN48403EGs6XFT0WyW+4w4QmxNf/epXk3Z2peKxx74Ptw346K7ClxU5Bvi9thD6BwZx6tQpDUdnPk6cOIGe7m5Ed9xZUHvxXOGb74QMgkceecSQwqN4PI4fP/M0bqoWsUOnG+lvN8ewuLScLFDcaoiiiEe//31QVyXEFHUixSB7aiBUteGZH/+4ZLngzz/3HKqdwC0a+PzaWeC++hhOnnjd8g5Ksizj8R8eBVwVENMU3ZYKsaoN1F2FHx59ouDgiCiK+PEzT6PVJ+NgZemca2qcMu6pj+OlX/wcgUCgZOMohKeffhpjoyOI7LwHYAzM01gDtbkQbboDFy5cwIkTJzQ55rPPPotgKIyPt5ZOx3y8NYpgKFyS9KSyANeZnp4efOUr/wLZ1wi+4WZtDsrZEW59DwYHB/GVr3ylZFHjzs5OnDt3Hh/bGS56SfneBh4tXhmPff/RLWkvBgBjY2P413/7N1BPre4NFKjdjVjjYZw5c8aQVJSXXnoJS8sB/G5rOKf9oyJZVyyWS+fMQ1Ui2iskHHvyCQiC/s0ajOZnP/sZxsfGEG25CyDa35rjLUcQ5/mSpK/duHEDV65exYdawmA1+mgf2hmFKEqGp1ppzZkzZzA8NKikFujwey8KQhBrPIyx0ZGCRderr76KsfEJfKItbHjx5Ub+c2sU0WgMx44dK+1A8mB0dBRHn3gCQtVuSDqlLeaK0HAIsqcWD3zzW0V3sF5aWsLTPzqGI3U89hrg/Z2OfRUi7qzl8fSPjhmeC26yb/vWYnZ2Fv/wj/8IkXMisveDms5cpcqdiO28G6dPn8bjjz+u2XFzRRRFfPvbD6LSAXxoZ/FFNQwBPrk7hPGJyZLlY+lJOBzGP/zjPyImUIT3ftCQBy3fdBvEyl148MEHdc2d5nnl5nWgUsTBqtxupBGRrCsWi+QgwAkBPtEWxuz8Qkkr1/VgcXERj/3gB5D8OyBV7NTlHNRZAb7+JvzylVfQ3d2tyznS8cwzz8DFAR/YoV36UJNbxu21PF54/rmSt5QulHg8joe+8zCouxpizZ5SDyclYnUbqKcGDz/ySN7pX7FYDD98/AfYWyHhSBHON1rR4pXwnsYYXnjh+ZJZteaDLMv42r/+K2SwiLe+q9TDAQiDaNv9WFkJ4KGHHirqUEePHkU8Hscf7sktaKMnf7g3jFgshqNHjxp63pIIcELIMCGkkxByhRByMbGtmhDyGiGkL/F31Zr9/4EQ0k8IuU4I+fCa7UcSx+knhHyLkFLPr1eZnZ3FZz77WQRWQgjv/a3Cu9llQGi4BXxtO5588kk888wzmh8/E8eOHcP16zfwX/YFNWu6cketgDtqeTz6ve9heHhYm4OaAEEQ8E//9E8YH59AeM8HjPP4JQTRPe+D7KzA5z7/eYyOjupymmPHjmF+YRG/15b7jdTN0XXFYhv949Nxa7WAfRUSHvv+o5ZbRs7EQw89hGgsjuiue3VtrhHfcQdgd+Pf/v3fDVtpGh4exokTr+M3dkRz/j3nirp8bNVJ+7PPPovZmWlEd95jvui3CmEQ3fkuzM/N4cc//nFeb33++ecxv7CIP9oTKnn0W+X39kQBSSxJ4CpfnnvuOXR1diKy8x5Qmzv7GwxAdtcg3ngbXn311YKtQAcGBvDzF1/EB3bESpL7vZEdHhkf2BHDiy++iMHBQcPOW8pv/AcppbdTSu9K/P9/AThOKW0HcDzxfxBCbgLwxwBuBvARAN8hhKiS72EAnwLQnvjzEQPHn5bJyUl8+tN/jamZOYTbfxuyuyr7mwqBEMRb74dQvRuPPPIIHn/8cUPyfXt7e3H06FHc2xDHuzQ0zicE+H8OhuAgAr70z1/cEmkGgiDgC1/4Ai5cuIBY672Q/E3GDoC1I7T3NxCOi/ibv/lbzSvY+/r68NRTT+L+xjhuqs5d0Lk4uq5YzJWjMCME+K8HglgJBPDggw8WOmxTcf78eRw/fhzxxltBXRX6noy1IdryLgwODOjaWGMtjz/+OBws8NFd2ud5tleIuK1GwDNP/wjhcOkjafkwNzeHJ554EmLlrmTnSbMi+RohVLXhyaeeSjYRy8by8jKOPfUkDtfwOa+MGUGtU8ZvtUTxq1+9ioGBgVIPJy0jIyP47ne/B7FyF8RCrYt1gt9xO6inBl/92tfyDoRQSvHNbz4At43ik3vMs3L1f+yJwM3J+OYDDxjWsMlMU+5PAFDj/0cB/O6a7c9QSuOU0iEA/QDuIYQ0AfBTSt+iyk/riTXvKRkjIyP4q0//NeaWAgjt/wgkX6O+J2QYxPa8H0JtO44ePYqHH35Y15zwWCyGf/7i/0alXcaf7tf+gVdhp/i/DwTR1z9g+HKQ1vA8j89//vN48803EWu9VzfXk2xQZwVC+z+CQCSOv/3bz2gmwgVBwL985cvwcTL+S7tx4meXV8In2iL49a9/jbNnzxp2Xj2IRCJKXYCrEnzTYUPOKVa1Qqzchccee0x3r/gbN27g1KlT+FBLBD579odats65qfj93WEEQ2H85Cc/0WLIhvHwww+DFwTEinTGMor4znsginLOqQdPPPEEItEo/miveUSWyn9qjcLNAQ8//J1SDyUloijiS1/+MiTCItZ2n/4t5/OFYRFpey8CKyv4xje+kddbjx8/jo6OTvzB7hC8RdaOaYnPRvHJ3SFc7ejA66+/bsg5SyXAKYD/IIRcIoR8KrGtgVI6BQCJv+sT25sBjK1573hiW3Pi3xu3l4zLly/jL/7yL7EciiK0/yOQPbXGnJgwiLW9B3z9IfzkJz/BF7/4RV2s2iileOCBBzA+MYk/P7hSdOFlOu6q4/Hephh+dOyY0jHSgsTjcXzuc5/HW2+9pYjvQu3FNOpsJ7uqENr/YQQicfzN3/6tJstsTz31FAYGh/Bf968YfiP9eGsUrT4ZX//3f8PKyoqh59aSRx99FPNzc4i03m+cuwEhiLXeB5Ey+NrX/lW3CTulFN/+9oPwO3J3ScrWOTcVe/wS7qmP45mnf5RzdLbUXLlyBa+//jpijbeCOv2lHk5OUIcXsabbcOrUKVy6dCnjvmNjY3jxxZ/hA00xtHiLbztfSNF2Jrw2ik+0hXHx4iWcP3++6PFpzTPPPIMb168jsuvdpkk92Yjsrka86Q6cPHky5wLdcDiMhx/6Nnb7Jby/yHoQra8JQKlR2e2X8J2Hvm1IXUmpBPj9lNI7AfwOgL8ihLwvw76pfqo0w/bNByDkU4SQi4SQi3Nzc/mPNgdeeeUV/M//+XcIUzuCBz8G2V2ty3nSQgjiu96NeMtdOHHiBD7z2c9qXtH78ssv49VXX8V/botoYiWWiT9pD6PJI+GL//ufLPNQVQmHw/i7v/t7nD9/DrHW+woX39C2s50iwj+CQFTAX//136Cnp6fgY/X29uLJJ5/EfQ1x3FlnfKoQxwB/fnAFgcAyvv71rxu2ZKglHR0deOGFF8DX3wTZ12DouandjWjLXejouIqXXnpJl3OcPn0aHR2d+P22kOa53xv5o70RyKKARx99VNfzaIEsy/jmt74FOH3gG28r9XDygm+8BXD68c1vfStjc57vPvIIbETG72uUYlBI0XY2fqs5hno3xcPfechUzlsDAwN4/PHHIVTthlhtzsJcFb7pVsjeOvz717+ekx3o0aNHsbi0jD/dHyq6W7Ie1wRDgD9pD2FhcQlPPPFE0cfLej7dz5ACSulk4u9ZAC8AuAfATCKtBIm/VdU1DmCtLUALgMnE9pYU21Od73uU0rsopXfV1dVp+VEgyzIeffRRfPWrX4XgbUDowEeNK7LbCCHgm25DdO9voLf3Bj713/+HZukGvb29+OYD38At1QJ+f7f+np1ODvibmwPgIyF84fOfA8+XvoI+FwKBAD7z2c+io7MD0T3vL7ppgpad7QBAdlUidOCjCEsMPvPZzxbkjhKLxfClf/4iKu0y/kSHNKRcafVJ+L22CE6ePInjx4+XbByFwPM8vvq1rwFOH+ItR0oyBqF2PyT/Djz88CPQOjARi8Xw8HceQotXxvs1aLyTjTqXjI/sjOC1115DZ2en7ucrhuPHj2NocDDRCyB7hN9UMByizUcwOjKC1157LeUu165dw9k33sDHd0VQkUPaUS4UWrSdCY4B/mhPCMMjo2k/i9FIkoSv/Mu/QGbtiLfeW+rhZIcwiLa9F+FwBA888M2Mu46MjOC5557F+5pi2OsvfsKjxzUBKLaE72uK4ac//YnmNVMbMVyAE0I8hBCf+m8AHwLQBeDnAP4ssdufAXgx8e+fA/hjQoiDELIbSrHlhUSaSpAQ8u6E+8mfrnmPIfA8jy996Us4duwY+Lr9iLR/yPAWsakQq9sQOvA7mFsK4C//8q/Q0dFR1PFWVlbwhc9/Dn6bhL+4KVj0zDVXmjwy/vzgCnp6rxdteWQEwWAQn/nsZ9HfP4DIvt/UppmKxp3tAIA6fAgd+ChijAt///d/n7cI/+53v4ux8Qn8vwcDuqUh5crHdkXRXiHhgW983VIrJceOHcPE+Dgiu+7VtSFTRghBtPU+xOI8vvnNzA/PfHnyyScxPTOLP20Paub7nY3/1BpFjQv4hoEOL/kiCAK+//3HQD01po9upkOsaoPsqcX3H3ssZWDkySefgNeutPnWikKLtrNxVx2PVp+MY089mTGibxQ///nP0d/Xh0jLu0BtzlIPJydkVyViTYdx+vQpXLhwIe1+3/ved2FnZPyBRjUBel0TAPCHeyOwExmPfu97mh0zFaWIgDf8/+ydd3xUZfb/38/c6TPpISSkEnrvRQQBpYlUiYDo0sQVBZTm2iiCZVUU7P27q7vuz+2667p2cBUbvZeQhADpvSeTZOb+/pgEKSF1Zu4M3vfr5cvkzp37nCF3nnue85zzOcBOIcRBYBfwsSzLnwJPAeOFEKeA8XW/I8vyUeCvwDHgU2CZLMv135S7gbdxFmYmA5946kOUlpZy//33O9ULIgdhi70WNN5T0+qwtqO0+xTKHVpWrV7d6iYKsizz9NNPkZeXy/Jexc0qpHIlQ8KquTG6kn/9619tbofsTioqKlh7//2cTj1DeedxijdMaApZb6a8641U66w8+NBDzdaF3r9/Px988AEToirp1QLVE3chaeDXPUqosVXyzDNP+0QqSlpaGu/96U/UBMdjD4hq+g1uRDb6U9WhHzt37nRZLmxqaip//vP7jAyv8qj6hVELt3cuISU11WMKLy3liy++IDs7yxn99rbCuuYiBFVRg8nLzb2sK+2pU6f48cefmBhZ4TJ5WnciBEyNLSctPYNvvvlGUVsKCgp48623sPt38LqOqE1RHd4HTAFse/75BuvPDh8+zHfffc9N0RX4e9iHaA3+epnJ0RXs/O47jhw54rZxPO4xyrKcIstyv7r/esmy/ETd8XxZlm+QZblL3f8LLnjPE7Isd5JluZssy59ccHyPLMu9615bLnvo6VtUVMSy5cs5ePgwlfGjqe7QzysnU9ngR2n3ydSYQti0aVOrOsZ98MEHfPfd98zpVO6SbaPWcEunCuL97Wx55mkyMzMVsaExbDYbDz38MCdPnqQyfgz2AEVrgZuNrDNS3mUiNvSsvf/+JiW5ZFnm9ddeJcQkM9uLlA3amx0kdCxjz569TRaHeQOvvPIKDllg8xL1i+r2vcEUwPPPv9Bm6U+Hw8GWZ57BJDmY29nz98jAul4Cv//d/5GR0WBGoqJ89J//IJsCFV94tRW7XwSyOZiPLqkfeO+99zDpYFxU25uzeYrB7aqJsMi898c/KLqA//3vf09lVZXbewG4BY1ERfRwMjMabqT31ptvEGh07a6Iu5kUU0mgwRm5dxfeE7L1EcrKyli79n7OnUunossE16QZuBOtkfKuE6kNjOH555/n888/b/ZbT506xWuvvkL/0GomKjihajVwT68SHDWVPLZ5k9dtL7/55pscPHCAyrhR1AbFKm1Oi5D1Zsq6TqSiFh56+BEqK688QX777becTDzFzXHl6L0sunV9ZBUhJudE781R8AMHDjiVcSL6Ieu9RN1AI1ERNZTMzAw++uijNl3qww8/5OixY8zrVKpIpEsIWNC1HI2jhi1bnvGqe+HMmTMcP3YMW2gX33OwLkUIbKFdOJWYeH7hnpOTwzfffMMNHSoVT01rCRoBU2LKSU457daOwY2Rm5vLfz/5hOqQru7vBeAm7AGR2P078P6f/3xRFPzEiRMcOnyEm6LLfWJXpB6DBJOjyzl06DAnT550yxiqA94C6iOdScnJlHca6/XNE86j0VLZaQx2/wieeuqpZnWvstlsbHp0I1atnTu7K9/FLMzk4I5upRw7foJ33nlHWWMu4MiRI/zjn/+kOqwHtaHe1SyhucgGPyo6jiYnO4vf/e53DZ5jt9v5v7fepINFZkR79xfVtRSdBmbGlnEy8ZTXaoPLsswbb74JBgvV7Xsqbc5F2AOisPtH8M477za6CGuMrKws3nzjDfoE13BtuHJF08FGB3M6lbF//wE+/vhjxey4lO3bt4MQXtdUpbXUhHQCoTmvmbxz505kWWZUhO9Ev+sZGmZDL6FYGsrf/vY37HY71RF93DOAi+Rsm8IW0Y/ioqKLUpP+8Y9/YNTCdR4oxnY113WwYdA6P4M7UB3wFrBt2zYOHzpEZcdR2AOjm36DN6HRUtF5HLXmEDZs3Nhkde/f/vY30tIzuLN7icfzvq/E0LBqrg2v4s/vv096errS5lBdXc1TTz8Negu2qMFNv8GLsfuFU92uO3/7+985evToZa9/9913nDmXxs0dyzxWVNdSrg23EWGR+cO77yhtSoMcOnSI48eOURXeFzRepn4hBLYOAykpKeaTT1peSiPLMs9ueQbs1SzqpvyCfUwHGz2Cann1lVe8pjg3KSkJTIHIOpPSprgGrRHZFOj8XMC333xDpNVBhFn51uItxSBB32Ab337zP7c2smuIqqoq/vWvf1MTFO82BTVXytk2ht0vHIc1jL/89a/IskxxcTE7tm9nZHilS4skPYVZKzOqfRU7tn/lln4TXvoo9T6OHj3Kp59+ii28r/ennVwJSUdF53HYZcFLL798xe3Z/Px8/vTeHxkUWu12ve+WMqdTBZJw8PrrryttCnv37iXt3Dkqo4cqp2ThQmzRgxFaQ4O1AkeOHEEvOfMlvRVJA8PaVZKUnNLqKK47+ctf/4rQGakJ7aK0KQ1i92t//uHZUifk448/Zs/efcyJLyXUpLwDphGwuFsp9uoqnnvuWa9IRUlKTqHGEKi0GS6l1hhIckoKRUVFHDx4kEGhvhf9rmdQu2ryCwrb1B+hNezfvx+brYoaN+6gulrO9ooIQXVIJzLS00lLS2PPnj3U2u1c2957nxtNcU24jZpau1uaAqoOeDNwOBw8/8ILzq3jDm5qF+2hLSJZZ6Iyoj97du/mxx9/bPCct99+m+pqG3M7t13j2dXdqgINMlNjyvn2228Vy9er5+jRo84tZX/fLqg6j6Sn2tKew4cvr/o+deoUURa7xyQoW0uMnx1Zljl9+rTSplxEYWEhP/7wA1UhXVwT/XbTfGEL60F2VlaLpEtzcnJ49ZWX6RFUy9hI79lmbm92kBBfxk8/7WpR7Ys7cDgc5GRn4fCRrpfNxWH0Jy83l127duGQZQaG+q6j1T+kBiFoVErPHfz0008ISYvdL9x9g7hBzvZK1NYVGP/444/s2rULq17QUSEBB1fQyb8Wi94994XqgDeDffv2cSoxsa5xgnsinZ7aIgKoCesJpgD++N57l7125swZPv30EyZEVtLeBVuJ7uhWNSnaqff7xuuvtflabeHw4SM4zCG+10yjEezWMLKyMi/qaibLMkmnEomxetduSEPEWp0Tff22uLewY8cOHA6Hy+oE3DVf1AbGICQdX375ZbPOl2WZbVu3UltdxeJunusR0FzGR1XRJcDOyy+9SGFhoWJ2aDQa9AYDwuG7jkhDCIcdrU5HdnY2AJEW5bW0W4tFJxNsxONKW7v37KHGGgEaH6pQbATZ4IdsCmTPnj3s27ObXoFVXjcvtASNgF6BNvbu2e3ya189noMbSUxMBJwPJ3dRv0Uky3LdFpEbFRI0Gqr9I0lKSsJutyNJP3/xP/nkEzTATbGu2cKv71ZV/7nauyAPTC/BjVHlvHcykZSUFOLjlWloUVVVhextubxtpW4xcWEVe0lJCaVl5USEe//DNdToQKtxam17E7t27QJTAA5TkEuu57b5QtJR7d+BH378CVmWEU0kc2/f/HHivAAAIABJREFUvp0ffvyRWzuXu2TB7mo0AhZ3L2X9bokXX3yRjRs3KmaLv38AZbW+m6LREKK2Cj8/f3Jzc7HqhU+pXDREsL6WnJxsj46Zl5eHI8BH01qvgF3vR05uLrn5BYzq6P3PjaaItNjZdTqf6upq9HrX7R6oEfBmkJKSgjD6ubfLpQe3iADspmCqbbaLVvt2u50vPv+MviHVLpMQc1e3quHtbUgCRbeWe/fuhbYiH9xYtOMwByNLOmRJR61fOA5zsNvGApDKcggIDCQ8/OftUD8/PwwGPflV3j9dlNQIah3Qrl07pU05j8PhYP+BA1RbI1x3UTfOF3b/SPLzcpvU0S4pKeGlF54n3t/OxGjvdSwjLXZmxFWwY8eOZilAuYvQ0BA0tlK3juHp+UJjKyU0NITs7GxCDL7vaIUY7eRkZXlsvJqaGmxVVche0EHblchaw/kdpxCj9y3MW0qwwfkZ8vLyXHpd73+iegF5eXnUaq+SyvU6ZL0FcOqP1rN//37yCwq5Ntx78jivhL9epk9wNV98/pliLYR79+6NbK9BU57b9MmtxBYzHLs5BLs5hMruk7HFDHfbWMgOdOU59O3T56LIp0ajIS4ujrRy74/2p5U5Q3BK7Yo0REZGBraqKhyWUKVNaRZ2cwjgDDw0xltvvUVxaSmLvDD15FImx1QSaXXw4gvPOxcsCjB82DCkshxEtfsaFHlyvhA1lUhl2QwfNozCgnwCdL6fXhOgd1DgwVSl8nJnnZV8FRTxX4gs6amo+2wBet93wAPrHPD8/HyXXld1wJtBhw4d0FaXKW2GS9HYnJI6kZE/d23cvn07Jp2gf4hvFNJcG24jv6DQra1iG2Pw4MFYrH6Y0veCF6gstBVdzgmoKuWGG2647LX4+E6kV7h3V8YV1C8SOnb0nlbOZ8+eBcBu8g0FjPo0mcakSo8dO8Z//vMRE6IqifVz3QLY1UXb9Wg1sKBLKVnZObzXQO2LJxgzZozTlsJURcZ3NdrCMyDLjBkzBovVj0q777sTlbUCi8XisfH8/PyQJMmtizIlEDXlBAQ65zu77z8aqa1bQxgMrt2p8P1vjAeIiYlBrq6AWu+PDDcXTVUxBoOR0NCfo3L79uymZ2CV13U5vBK9g51V6/v27VNkfD8/P+5eehea0iy0+Y23cfd2RE0Fpoz9DBw4kNGjR1/2epcuXSi2yaSXe/fNcbxQR0hQIEFBrsm1dgVFRUUAyDov6XzZFJIWodVfsWhRlmVefPEFAgxwc0fXyj26o2i7nu5BtVwbbuPP77/v8UI7gNjYWOLiOmLIOwmyj0cFZRlD3kmioqKJj48nKCiI0lrv3yFritIajUfnDkmSiOgQiaaq2GNjegKdrZSouuCeqxbRSlJV9xnMZtfO4aoD3gy6du0KgLbE85O2W5BldKWZdOnaBY3GeQtkZmaSlZNLj0Df2Ua06GTi/Ozs37dXMRsmT55M9x49MKftRth8dJdEljGmfo8GO6tWrWqw8G7s2LFoJYmvM1wTAYix1mKSHJgkB90Da4ixtv2+K7QJDuTrmTDpxiaLBz1JWZnzvpDdXNfhUrSG83Zfyo4dOzhx4iQJcWUub65RX7Rdr1dsdvH1b+lUgQY7b731pkuv21wWLlyAqChEm+ddKj0tRZufjCjPd34eIQgKCqKk2nu+c62lpEYiMMi9efOXEhcbg66q6KrYRQXAXouoKiYmxilaUVLt+25mSY3zM1itVpde1/f/ZTxAv379CAwKQpvv25NmPZrKAkRFIePHjTt/7MCBAwD0CPJ+qbkL6R5YzfHjxxXL69RoNDz04IMYdQJL0pdu0293J/q0PWiLznLP3XcTHd1wh9egoCBGjhrFziwT1S7IOLi9awWxfnZi/ew8PLCE27u2fQv220wjDhluuummthvoQrxpMdASGrK7pqaGt954nWirg5FuaC3trqLteoINDiZFVbB9+w5OnDjh0ms3h9GjR9O9Rw9MmfvB7ltz7XkctZgy9tG5Sxeuv/56AIKDg6mokSmv8c17HZz+b75NIiQkxKPjDh8+HKpK0FS4Nr/4QjxZnKstOovssDNmzBhCggJJLfX9nZHUUi2hIUEEBro2jVB1wJuBJEmMHzcOXUkaXAUyUrr8ZDSSdFGqwYEDB7DqfU/HtWdgDTW1do4dO6aYDbGxsTy2eTNSVRHm5K99antZl3sSQ9Zhpk+fzs0339zouVOnTqW8Bnbnel8k1yHD15kmBg4cQFSUdzVG0umcBVZC9p3dJRy15+2+kC+//JLM7Bxmx5d5feHllbgptgqrHv74xz94fGwhBMvuuQds5RjSldu5awuG9H1gK2PZPfec30Ht2bMnACeLfNfZyqrUUFQFvXr18ui4o0ePRtJq0bkxjdGTxbm6gmSCQ0Lo168f3Xr0JLXM+54XLeV0mZ5u3Xu6/LqqA95MbrzxRnA40OcmKm1K27DXYshP4toRIy5azR06eIBuATafe6h2CaxFQIs697mDwYMHs3r1aqTiNAyp3/nEdqJUdBbjmR8YMmQoK1asaDJSO2DAAKIiO/B5mtnrPt7eXD15lYJp06Yrbcpl1OeUihrX5ku7DdmBXF1JcPDFUTJZlvnbX/5CtNVB3xAfjd7ijLJf36GC77//nnPnznl8/D59+jBz5kz02ceQir1Lr74ppOJ09FlHmDZtGgMGDDh/vGfPnuh1Oo4X+a6ax/FCp+0Xfi5P4OfnxzXDr8FQmOK7uyJ1CFsZ2uJ0xo8bh0ajoVevXmSUCwpsvutqFlRpyCoX5xeZrsR3/1U8THx8PP3798eQe8KnIpyXostPQq6pIiEh4fyx3NxcMrOy6RbgQxG6OsxamRg/BwcPHlTaFKZMmcKCBQvQ553CcO4nr3bCpZIMLMk76Nq1C48+uhGttunIlUajYc7cWzldInGs0HsiXbIMH5810yEinFGjRiltzmWEhYUBoPGRGoH6WoZ6u+vZu3cvKampTIquwEezas4zPqoKScDf//53RcZfunQpsXFxWFJ3+s7CrLYKy5mdREVHc88991z0ksFgoFfvXhwv8l0962OFOkJDghXZQZs7dw5ydSX6bOV2cl2BIWM/Wkk6v5t63XXXAbAr23ej4D/lOG1vSJygragOeAtISEgAW5nvykjJMobc48R36kTfvn3PH66PHncL9M3Vd7cAG8eOHqGmRnn7Fy5cSEJCAvrsY+gz9ittToNoynKwJH1FTEw0W555pkWyWxMmTCAoMICPz3qPoseJIi0pJRJz5t56UVdXbyE2NhYATaVyrdBbglRn56VSjl9//TUmrbMJlq8ToJcZFGrjf1/vwOHGRlpXwmAwsHHDBiRHDaaUr93azMslyA7MyV+jsdvYuGEDRqPxslMGDRrM2VINeT7QsOtSqu1wtNDAoMFDFKnZ6N27NyNGjMCYfdhn01w1lYXo8pO4+eaZtG/fHoDo6Gi6dO7MjzmX3y++wo85Rrp26eyWhZnvfVMU5JprriGiQwcMWUe9Orp5JaTiNERFIXNmz75okklMTESngRirb+V/19PJvxZbdc15vWUlEUKwbNkyJk+ejCHjALosZTTKr4SmogDrqS8ID2vH1ueeIyAgoEXvNxgMJNwymyMFOlJLvcPZ/fismcAAfyZNmqS0KQ1iNpuJjIpCKstR2pRmoSnLRSNJFzngsizz4w/f0zvIhu4qeWr0D62mqLiExERl0grj4+O5//61SCWZGM7tUsSG5mI4twepJIPVq1bRpUuXBs8ZN24cQsA3LlJKuhB3qCZdyN5cPeU1MH78eJdetyXceeedYK/BkKaMrG6bkGWM53ZhMpq47bbbLnppwsSJpJRIpJS49nnh7nsCIKVE4nSJxPgJE11+bVAd8BYhSRJz58xBU56LVJattDktxpB9hOCQkPOV6/WcOXOGcIsDyUfvhvrC0cYah3gSIQRr1qxh9OjRGM/tQusldQOiqgTrqc8JCrCydetzra72nz59OmaTkf+cUb47bGqpxKF8HQm3zHZ5kwRXMnDAAHTlOd4f6QR0ZZl079Ydk+nnv29qaip5+QU+nft9KX2DaxDArl3KOb8TJ0507pjlHPOaeeJStPnJ6LOPMGPGjEYVhsLDwxk8eDDfZptxuDg+5Q7VpAv5OtNERPswBg4c6NLrtoSOHTsya9Ys9LknkEoyFLOjNejyEpGK07nrrl9fFtSZPHkyVouZj138vHD3PQHw0RkTVovZbcpaPupyKcfEiRPx8/dHn6ls0V9L0ZTlIpVkMvuWWy5TNziTepoOJt99sIab7Qi8xwEH52Jt3bp1DBo8GNOZ7xRPWxLVFVhPfY7FILH1ueeIiIho9bWsViszZt7M7hwDmRXKTiH/OWPCbDIyfbr3FV9eyLBhw5Brq5FKvbuXgKiuQFOWy7BhQy86Xt+COcLsm7tkDeGnl7EahMvbS7eUpUuXMmDAQExnv0cqzVLUlkvRlGZjTt1J3779WL58eZPnT5kylfxKOJzvO8WY2RUajhdqmTxl6nlVF6VYsmQJER0iMZ/5zmcKMoWtDFPabvr178+0adMue91isTBj5s3syTWQUe47Lmd6ucS+XAMzb57l8gY89fjOv4aXYDQamTtnDtriNDRluUqb02wMGQewWP0u+4LYbDaysnN8+sGql6CdGa9IQbkQnU7H4489Ro8ePTCnfINGqRQEew2WpC8wyNU8u2ULcXFxbb5kQkICOp2O/yoYBc+s0LA7x8CMmTfj5+enmB3NYciQIRiMRrQFp5U2pVG0hU776tum11NeXg6A0cW63EpjlGQqKpRtA67Vatm8eRMdIiKwJG9HVJUoak89wlaKNWU77duHsXnzpmYVao8YMYKgwAA+TVN+d6y5fJZmRJI0XpHCZjQaeejBB8BWhuHsj0qb0zSyA1Pqt+glDQ8+8MAVFzAJCQmYTEbeT2p+vZGSyDK8n2TBZDIya9Yst42jOuCtYObMmVisfhgyDihtSrPQlOWiLT7HvFvnXraS02g0+LigAQAyKB69aAiTycRvn3yS8PZhWJO+9PzDVXZgSv4aqbKQzZs30b17d5dcNjg4mMk33cTObCN5lcr8u//njAmdTneRoo+3YjAYGDtmDIbC097brEmWMeQl0rlzl/OFo/VUVztt9r5vWNvQCPn8Z1MSPz8/nnn6acwGHdakL6FW4ULX2mosSV9i0ml45umnm92ARKfTceu82zhaoOOEFyklXYm8Kg1fZ5iYNOlG2rVrp7Q5APTt25fbb7sNfd4ptHmnlDanUfQZB5BKMlm58r5Gd1UDAwNZsHARB/P17M/z/t2RA/k6DuXrWLT4Dpc337mQq20+9Qhms5l5t85FW3zOZblabutUJcuY0nZj9fNj5syZl72s0+kIaxdKVqV3FNS1hhoH5FXidQ1Y6gkMDGTLlmewGHVYk77w6MPVcPYntMXnWLlyJcOGDXPptefNm4dG0vLBac9Hu9LLJXZmGZk2ffpletXeyvTp05HtNehc8FB1x3whlWYhKgqZMePydJ6uXbsCcKrY+52q5lJSLcguF+c/m9JERUXx5BOPI1WXYk7eoVy9gOzAnPI1UlUJj23efL6leHOZPn06IUGB/OO0xeu1Cv6dakJoJObPn6+0KRexcOFC+vXrh/nsD2gqvFM9SSpOw5BxgEmTJjn7pDTBrFmziImO4k9Jfti8eMPdZof3kvyIjYlu0GdyJaoD3koSEhIIj4jAfPZHcLT9bnJXpyptfhKa0iyW3nXXFfOYomJiyKn03QdrbqUGWYbIyEilTbkiUVFR/PbJJ5GqyzCn/M8jWvK63ET0OceZPXt2g7l5bSUsLIyZM29mZ7aRtDLPLuD+nmLGZDRy++23e3TcttC9e3d69+mDMftom+cMd8wXhsyDBAQGMm7cuMtei4mJISQ4iKOF3h+9ai7H6j6LkoV3l9KvXz/Wrl2LVJLhTEFQwIM1nN2FVJzGqlUrGTRoUMvfbzBw+/wFnCzSevX9kl2h4dtMI1OmTjsvm+ctaLVaNmzYgL+fFUvKDqhVfpfmQoStDMvpb4mNi2PlypXNeo9Wq2XV6jXkVAivTkV5P8lCXqVg1eo1zUq7aguqA95KDAYDq1auhMoi9FmHlTanYWqrMKftpkfPnkyePPmKp0VFRZNRoaWy1jeTUVJKnF8Sb42A19OnTx9WrlyJVJyGPt29UlOashyMZ39g4KBB3HXXXW4b57bbbsNkNPL3FM/pgicXa9mbq2furfPcuj3oaoQQLFywAGxl6HJPKm3ORUglmUglGdw6d26DGs9CCIYNv4YD+UZyFUo5ciUOGb5MNxEY4H9FWT2luPHGG5k7dy763BPoco57dGxdzgn0OcdISEhg6tSprb7OTTfdRPuwdryfZKXWS4V//pxsQavTee0iPiQkhE2PPorGVorp9DfeI31sr8WS/BVGneDxxx5rcL64EgMGDGD27NlsTzd6ZSrKvlwd29ONzJ49h/79+7t9PN+fSRVk2LBhjB17PYaM/V5XvY4sOwv/5FrWrF7daH70pEmTsNll/p7iO4Uz9VTWwt9O+xHfMc5rtpIbY+rUqdx0000YMg+hLUh1yxiiphJLyg7C2rXj0Y0b3dqcJiAggFvn3ca+PD3HCty/i+KQ4U9JVgID/H0i9/tSBg0a5FS8yDygfJ5vPbIDU9ouQtu1a3TLdeHChWi0Ot475b3Rq+bybaaBxCItv75rqdujXK3hzjvvdDZmOfcTUolnlHOk0iyMZ39k6NCh3H333W26ll6vZ/mKezlXpuGzc97XhGVfro69uXrmL1jYajlWT9C/f3+WL1+GtuisdzR2k2WMqTsRFQVs3LCB6OjoFl9iyZIldIrvyP+d9KfQ5j1BvwKbht8l+tO5Uzx33HGHR8ZUHfA2smbNaiIiOmBO+RpRo2w1/YXoMw4gFadx74oVdO7cudFzu3fvzowZM/kyzeRysXx3848UM0U2WLP2fq98kDbEfffdR9du3TCf+Q5hK3XtxWUZ0+lv0dqreeLxx/D393ft9Rtg9uzZRHaI4HeJ/m7P7fsq3UhSscTd9yxzmzSUOxFCsGLFcqi1YUjbo7Q5gDPqKcrzuefuuxvVUg8LC2PhosXsz9OzK8d3W0sX2QR/SbHSu1cvr1C+aAhJknjkkUeIiorCkrIDYStz63iiuhxLyg46RESwfv16lyzaR40axYgRI/gw1aJYoXZDVNXCH5P86RgXy5w5c5Q2p0lmzpzJpEmTMGQcUFzOVpd9BF1BCkvuuIPhw1uX9qbX61m/YSPVGHjxSAA1XrBDUm2HFw/7UyMMrN+wEb3eM/Ob93wrfBSr1coTjz+GjlpMyTvA4fpuTC1FKjqLIWM/48ePb3bu7x133EFwcBC/P+mP3YVfCHd2qzpdIvFFuompU6fRq1cvl13X3ej1eh7duBGjTsKc8rVLagjOXzvrMFJxGitWLPfY1rrBYGDt/b8hp0Lwz9Puc4rzKjX8NcXCkCGDmTBhgtvGcTfx8fHccsst6HNPeiy6eSWErQxT+l4GDR7M2LFjmzw/ISGBbl278MZxP7eoXLi7u11ZjWDLwUBqhJ7Va9Z4pXJSPRaLhSefeAKjVoMlebv7ni0OO+bk7eg1Mk8++YRLJT3vu+8+hFbPu4neU5D5wWkz+ZW+E7QRQrBq1Sq6deuO+fS3aCoKFLFDKk7DmLaH664bfVm3y5YSFxfHw488QnKxxLsnlb03ZBnePWkhpUTi4UfWXaYA5U68d/bxIeLj43nowQeRSrMxJX+taLc7qSQTS/LXdO7ShTVr1lzUcr4xrFYr9963kjOlGt4+YXWZE+6ublXZFRpePhpAUGCgs4Wvj9GhQwceeOA3aMpyXZYPrinLxZC+l9GjR7ul6LIxBgwYwJQpU/j0nHt2UWQZ3km0IiQDa9asbfZ97a0sXrzYWcR9ZqdysoSy7NTw1UqsbeZcodVqefqZLUR0iGLb4UCX/63d2d2uslbw7MEAsqp0PPnkb4mPj3fZtd1FTEwM69evQ5TnYTjrno6dhnO70ZTl8sjDD7ukR8CFtG/fnsV3LOFgvp6dWcp3qj1ZpOXTNBNTpkyhd+/eSpvTbAwGA0888TgB/n7OxVhtlUfHF1XFWFL+R1xcRx566EGXzL/XXXcd8+fP55tMI5+nKZem9Nk5I99mGVmwYAGjRo3y6NiqA+4irr/+eu677z60RWcxpn6rSMGEpjwPS/JXREZ24NktW1pUHAEwevRoFi9ezHdZBl464ke1l0oFnS2TeHx/EDatH7996mmvb8JyJcaMGePMB8863PYaAnstltRvCAkJZe1aZRzUpUuXEhwUxJvHA1yeirIjw8ChfB13/vrXhIeHu/biCmA0Glm/bh2a6nKMZ35QxAZd1hGkkkzuvXdFizqjBgYG8tzWbQSGtGPLwUASi7w/ilhaI3j2kD+pZToe3bS5VeoeSjFixIjzRZmubuSkLUw9X3R53XXXufTa9cyaNYt+ffvyx1N+ZCvYObe8RvDGcX/C27fnnnvuUcyO1hIaGsoTjz+GVFuBOflrjyhpAc5GbsnbsZj0/PbJJzCZXFcrtnDhQkaNHMn/O2Vhb67nizL35Op5P8nCqJEjWbBggcfHVx1wFzJz5kwWL16MLj8Zw5nvPeqEayrysZ76gtDgQLY+92yr1SHmz5/Pvffey748PVsPBVCpfEbNRSQVa3lyfyA6azAvvfwK3bp1U9qkNrFs2TLatw/HnLqzTa2HDWm7obKYRx5+SLEFidVq5eFH1pFZIXgv0XWFemllEn9KsjJ48CC367J6kl69ejF//nx0+clocxM9OramLAdj+l5GjRrVqELSlWjXrh1bn3+BwHYRPHUggJ2Z3psTnl4usWlvEKnlRjZs2MC1116rtEktZsmSJXTv0cNZN+KiZl7CVoY59Tu6dO3qVqUkSZJ4ZN06tAYTrx/3V0wV5Q+JFgpsEus3bPTJ+hFwzhlrVq9GKslAn7bX/QPKMsbT36KpLGLzpk0tWqg3B41GwyPr1tGtezdeO+ZPconnFvPJxVpeO+ZH9+7deGTdOkXS0VQH3MX86le/Yt68eehzT2JM3emRVaqmPA9r4qcE+1vYtnUrYWFhbbrezTffzMMPP8yJYj1PHwiktNo7tvsP5+t4+mAgQe0ieOnlVzyaq+UuzGYz69Y9grCVYji3u1XXkEoy0eccJyEhQXFN40GDBjFv3m38L9PIj9ltd8psdnjlmD8Wqz8PPfSwV+fstoZf/epX9B8wAPPZH9FU5HtkTKdKzte0bx/Gb37zm1bvlkRERPDa62/Qp19/3jzux1+SzDi8JM+3noP5OjbvC6RGH8jzz7/AmDFjlDapVWi1WmfdiF6LKXVn24M759OPBI9u3IhO597oY1hYGGvv/w3JxRIfpnpebev7LD0/ZBtYuHAhPXv29Pj4rmTy5MlMnToVQ9Zhl++IXIo+6zC6wlSWLl3qtmeL0WjkySd/S0hoGNsOB3hklyS7QsO2IwGEtmvPk799qsXZAq7i6nqaeQFCCO68804WLFiALu8UxpRv3eqEa8pysCZ+RrugQF5++SWXaWFPmDCBzZs3c67SwIa9wR5dmV6KLMN/zhh59pA/kdGxvPjSyy5fiStJnz59mDVrFvrcE0il2S17s6MW89nvCY+I8Jpc+EWLFtGzR3d+n+jXZs3o95MspJdpeGTdeq+WC2stkiSxYf16AgL8sSTvcL80oezAlPI1WruNxzZvbvNuib+/P1u2PMu0adP4+KyJrYf8vWLB7pDhX6ed9kTGxPPGm2/5VKF2Q4SHh3PvihVIpVnoco616Vq63BNIJZksX7bMYw3MxowZw6RJk/go1cyRAs+lG2SWa3gn0Y8+vXu1uXjQW1ixYoVzRyR1J6Kq2C1jSCWZGNL3MnbsWGbPnu2WMeoJDg7mqWe2IOssPHc4kBI3ziEl1YJnDwUi6yw8/cwWgoKC3DZWU6gOuBsQQrBo0SKWLFmCriAZo5sKM6XSLKynPqd9u2BeeulFlzulI0eO5KWXXkbr144n9gWwPd3g8dT2ilrBi4f9+GuyhdGjx/DyK69elY7Y4sWLaRcWhunMdy1SRdFnHIDKYu5fu7ZRCTlPotVqWb9hI0Jr4rVjrVfV2ZOrZ3u6kblz5zJkyBDXGulFBAcHO3M7a8rd3iXVkLYHqSSTNWtWu0w3X6vVsnr1alavXs3xEhPr9gRzUsG88JJqwbMH/fnHaTPXX38DL738Spt3Bb2FSZMmMXTYMEzpe1udiiJspZjS9jBw0CCmTJniYgsb57777iM2NobXj/tTYHO/+2Gzw0tHAzCY/Vi/wb09ETyJXq9n86ZNWMxGLClfu1whR9RUYjn9PyIjI7n//vs9UlMUGxvLk799ioJqPdsOu76OCJz3w9ZDARTW6Hnq6WeIiYlx/SAtQHXA3cjtt9/O3Xffja4wFVPKDpfKzUklmVhOfU6H8DBeevFFt7XS7d69O2++9TYDBw3hnZNW3jxucbvWcz1nyyQ27gniQKGRZcuWsXGj7+buNYXZbHYqUVQWoctuXnRLVBVjyDrChAkTvK6oLCIigjVr15JULPGvVmw5F9g0/O6kH127dPZYUwQl6dWr189dUtPc0yVVm5+MPusIM2bM4MYbb3T59adNm8arr76GOTiC3+4P4D9njB5PSTlRqGXdnmASS82sXbuWdevWubRoTGmEEM7Ftl6HsZWt6o1nf0KvlXigDelHrcVkMrFp82NUo+fVo35uzQevl5dLL9ewbv2Gq2YRVk9YWBjrHnkEUZ7vWoUcWcZ0+hu0cg2bN23y6DO3T58+rN+wgZQSideO+rl0/nDI8OpRP1JLtWzYuNErdsRUB9zNzJkzh3vvvRdt4RlMydsbdcId5mAc5uAmrykVp2NJ+oKoyEhefOEF2rVr50qTLyMgIICnnn6aBQsW8H22kc37gshpQWpBjLUJKv/HAAAgAElEQVS2xXq+32fp2bw3kFpDENu2Pc8tt9zi89JzTTFs2DCGDRuOKesgoqby/PEr3RfGtN0YDHq3FlC1hRtuuIGJEyfyrzPmBiOiV7ovHDK8ccyPWuFs2ODu/FRvYerUqXW5nYfQFqQ0eX5z5wsATXk+5jPf0adPX5YvX95WU69Ily5dePOttxl13Wj+mmxh2yF/Smta9r1tzXzhkOGjVCO/PRCANaQDr73+OlOmTLkq54x27dpxx+LFaIvTkIrPXfZ6Y/eFVHQObdFZFi5c4LagTVPExsZy/28eILFIy99Tmu/ctfS++CbTwM4sI/PnL7hqd9CGDx/+s0JOE016mjtf6LKOIBWnc++999KpUycXWdp8Ro0axfLlK9iXp+cvyY3fHy25J/6cZGZ/np7lK1YwcuRIV5jaZoTsLer4HmLw4MHynj2e70D3r3/9i23btlEbFEtlp7EgWrf2kUqzsJz6nNiYGJ7ftrXVaiet5aeffuLxzZuRa8pZ1rOYXsGu3fpyyPCXZDOfnDXRt09vNj666apMObkSZ86cYdGiRVSFdMUWN+KK50klmZhPfsKSJUu4/fbbPWhhy6ioqGDJHYuxFWXxxJACzNqm55uPzxj5S7KFBx54wC2RWm+mpqaG++5byfGTiZR1n4LD3Pb8RFFThfXER4RYDLz11psEBzfPaW8Lsizz4Ycf8uorL+Ovs7OsZzGdA9wjqVRaI3jjmB+H8nVcf/1Y1q69/6rdKauntraWRYsXcy6niNJeM0HTjNQKhx2/Yx/SIdjKu++8o/jCduvWrfz73/9mVd8SBoS2XgGqIc6WSWzaG0iffgPYsuXZqyb1pCFqa2u5666lpJxNp7TXDGRd63d8NJWFWI79m2uvGc7jjz+u6AL2hRde4IMPPmBRtzLGRratNmZ7uoF3TlqZOXMm9913n4ssbD5CiL2yLA++9LgaAfcQ06dPZ/ny5WgLz2BM/a5VW4eainwsSV/RISJcEecbnFHa1954g5DwaLYcDODzc0aX5YWX1wieO+jPJ2dNzJgxg63bnv9FOd/gjA5NnToVfV5io+2nDRn7CQ4J4ZZbbvGgdS3HbDazbv0GCqrgnylNPxhyKzX8M9XCqFEjvbZNuDvR6XRs3ryJAH8rlpTtUNvGJj11RZdSbSWPP/6YR5xvcKZKzJw5k5defgWdfxhP7AvgMxfOFfUkF2vZsCeY48VGVq1axfr1G6565xuceff3rlgBVSXock826z26vFNQWcyK5csVd77BKcHauXMn3jzu79JW9ZW18PLRAPz8A1m3bv1V7XyD81545JGH0cg1bZM/djgwnf4WP6tFsV4SF7Js2TKGDR3Ku4nWNtWUnCzS8odEK8OHD3Pr7l9rUB1wD5KQkHBeHcWQ1jLJOVFVgvXUFwQH+LH1uecUcb7riYqK4rXX3+CaESN475SFt49b2ty0J71c4tG9QRwvNrJ27VpWrlzpE22C3cG8efPQaAT6rCMNvi6VZiOVZjHv1lu9pvCyMXr27MnUqdP4It3E2dLGH4bvnbIgafWsWHGv4g8ApQgNDeWxzZvR2EoxtbGplz7jIFJJBqtWrqRHjx4utLJ5dO/enbfe/j+GjxjBn05Z+N0Ji8vyfn/I0vPk/gD0/mG8/MqrTJ8+/Rd1zwwePJg+ffpiyj7cdBGew44x6xA9evZk+PDhnjGwCQwGA5s2bUbWGnnlmGv0wWUZ3jlpJadCw/oNGz224FSa+Ph47li8GF3hmSZTUa6EPvsImvI81q5Zo6gySD3OYv4NRERE8OqxgFYpoxTZBK8cCyAiIsIrF2OqA+5hFi5cyPTp09FnHUGbn9y8N9lrsSRvx6yX2Lr1OcVy9y7EbDbz2GOPs2DBAr7NMvL0wUAqa1v38DtRqGXz3kCq9YFse/55j1fmexvt27dnwvjxGPITL8oFr0efdQg/f39uuukmBaxrHUuWLMHfz493E69cWLM/T8f+PD0LFy2+6gqmWkqfPn2466670BaeQZdzvFXXkEoyMWTsZ8KECYreK35+fjz22OPMnz+f/2UaeeZAQIvzwi9Elp27Ka8d86NH7z68/uZbPt+QqzUIIbjjjsXItnJ0OY1HwXV5iWAr447Fi71qkRIZGclvHniQ5GKJvzaR79sc/pdpcOp9L1rEgAEDXGCh7zB79mziOsZjStvd4qZuwlaGMfMA1157LaNHj3aThS3HarWyafNjlNt1vHrUv0VFmXYHvHbMn0qHjs2PPY7VanWfoa1EdcA9jBCCFStW0Kt3b8xnvkNTWdj4G2QZ45nvEZWFbFi/zquaz2g0GhYtWsSjjz5KSqmOZw8GUNXCNM/EIi3PHQ4kLCKKN958iz59+rjHWB9j9uzZyPbayxZpwlaGtugcM2fM8Cl1B39/f5befQ+niiV+aKBBT60D3kvyIzYmmoSEBAUs9D5mz57tLMpN242mool54lJqbZhTvyEyKopVq1Yp7nRpNBoWL17MI488QnKZgc17g1qlEV/rgNeOWfkw1cykSZN47rmtBAQEuMFi36B///707tMHY+7xK8tXyjLGnGN079HD69SSwKkPPn36dD49Z+JYQet3PbMrNPzplJXBgwZ6dV2Mu9BqtaxetRJsZegzD7bovYZzu9BKGq9L0QDo3Lkzq1av5lihlk/ONr9hzifnjBwv1LJy1WpFikmbg+qAK4BWq2XTo4/i72d1Nt9oZPtQm5+ELj+JBfPnM2zYMA9a2XzGjBnD+vUbSC7VtUi/M7lEy3OHAggNC2fr8y/84qOeFxIfH0/Xbt0w5CddlIKgy08C8MnixIkTJ9IxLpb/nrNcllWxJ1dPboXgrqV3/2JTjy5FCMGDDz6An58f5tRvWtRLwHjmBzQ1VWxYv96rFmrjx49n2/MvUCGsPHUgkPyq5j+CHDK8eczKj9kG7rzzTh544AGvyGVWmoRZs6CqBKk4rcHXpeI0qCzmloQExRdiV+Luu+8mskMEb5/0b9VOqkOGt074oTWYeODBh666jrnNpW/fvkyYMAFD9hGErbRZ75FKs9AVpvKr22/32gZ3kyZNYuS11/LPVAuZzeiUmVmh4YPTFkaNGuXVz0qfv0uFEJOEECeFEElCiAeVtqe5hIaGsn7dOqgsQp95uOGTam2Y03bTu08f5s+f71kDW8iYMWOc7euLtDx/2L/JnPDUUoktBwMIDG3Ptudf+MUVWzaHmyZPRlQU/NyiXJYxFCTTr18/r50oG0Oj0ZBwy2zOlWo4cUlRzedpZiI7RHhNfqq3EBQUxP1r1yDK89FnX2GeuASp6By6ghTmz/+VV6Zm9O7dmy3PPUc5Jp46EEihrWmHyyHDW8et/JhjYOnSpdx2221e60x6mpEjRxIcEoLhCqlK+pwTBAYGcd1113nYsuZjNBp5+JF1FFRp+H+nWp6K8tk5I4lFWu69b6XbZXm9nSVLlqDVSOgzmhEFl2WM6fsIDApmzpw57jeulQghWLlqFXqDid+daFwf3CHD7074YTCZWblypeeMbAU+7YALISTgFeBGoCdwqxCip7JWNZ/BgwczevRojFmHG1S8MKTvg1obq1et8rrigYYYN24cDzzwIMcKdfz+5JXzrepbwVoDQ9mmRr6vyOjRoxFCoC1yav2KqhKoLOb6669X2LLWM27cOPz9rHx27ueobHKxlqRiiVkJt/xiI1eNMWrUKK677joMGQeajmo5ajGf/YGY2FivbrvdvXt3tjz7HCUOI88cDGxy1+z9JDPfZRlYvHgxc+fO9YyRPoJWq2XqlClIJRmImoqLXhM1lWhL0rjppslev1vQq1cv5sydy/8yjRzKb76tGeUa/p5iYcSIEUyYMMGNFvoGYWFhTJ8+DX3+qSa7pUolGWhKs5j/q9u9vqA/NDSUu+9ZxskiLXtyL09jrGdXjp6TRVruWbbc6wN7vv60GwokybKcIstyNfBnYLrCNrWIu+++G61WgyHtYm1yTWUR+twTzJgxg/j4eIWsazmTJk3i9tt/xXdZhitOov/vlIWKWomnnn6G8PBwD1voOwQGBtKla1d0JekAaOu2mIcOHaqkWW3CYDAwbfoM9ufpz7ei3p5hwGwy/iJlB5vL8uXL0WklDOca72GgzzoCtjJWrVzpEw7XY48/QUa5hvcSLVc8b3+ejs/Ombj55pu9fidQKcaOHQuyjLYg9aLj2sIzIMs+s2hfuHAhsdFR/PFU87tk/umUFYPJzJo1a9RdkTrmzZuHTqdDn3mo0fMMmQcJCQn1GeGDG2+8kdiYaP552tpgFNzugA9SrXSMi/WJ54mvO+CRwIWtwNLqjvkM4eHhzJg+HV1h6kXRC13OcSRJYsGCBQpa1zpuv/12oqMieTfR77LI1uF8Hd9nG5h3220+tbBQiuHDhqEpz4XaarQl6XSIjPTJ9JMLGTt2LDJwtECHLMPhQiPDrxnxi9Bvbi1hYWHMu/VWdIWn0ZTnNXxSrQ1j1mFGjhzpMwoQgwcPZt682/hfppFdOZdHtYpsgrdP+NMpviNLly5VwELfIC4ujti4OPSFpy86ris8TVRUtM/MtQaDgbuXLSe7QrA9vemCu8P5Og4X6Ji/YKHXRzs9SUhICJMmTsRQkAK1DTex0VQUIJVmccstCej1V44oexOSJLH4jiVklAu+z7rc5u+zDWSWCxbfscQndlO938LGaWi5e9m6SAjxayHEHiHEntzcXA+Y1TKmTZsGsgNdbqLzgL0GQ0EyY8eMUVTvu7UYDAbWrL2f3ErBB6d/dqpsdnjnlB/RUZFevT3uTfTq1QtkGakiH11lPv369lXapDbTsWNHAgP8OVqgI71coqgKr1Rn8DZmz56N2WLBkHGgwdf12UeR7TUsXrzYw5a1jUWLFtG9ezfeSfS7rADvvVMWqtGxYeOjPuMkKMV1o0ahKcv5uXmTvQapNJtRo0b6VGR42LBhDBjQnw/PWKhopCDTIcOfU6xEtA9jxowZHrTQN5gxYwayo/Znv+ISdDnH0el0TJ482cOWtY1Ro0YR37Ejn6VdvGsmy/BZmplO8R29ptV8U/i6A54GRF/wexSQcelJsiy/KcvyYFmWB3tjgUZ0dDQDBg48r3ihLUxFrq12OuY+Sv/+/bnxxhv59JzpvID+/zKM5FYI1qy93+vzzbyFrl27AqAtOotcXUmXLl0UtqjtaDQaBg0ewtEiA0cKnWkSqgPeNBaLhVsSEtAWnUVUFV/8oqMWY+5xRo4c5TPRznq0Wi2rVq2mrBq+TPt5Xkgvl9idY2D2nLleJb/qrQwcONC5WC/LApzqFsgOn/tuCSG4555llNfAR6lXVvDZmWngXKmGO+9aqi7OGqBTp05Oicr8k5c387LXYihI4YYbbsDf318ZA1uJRqNh6rRpnCnVkHpBY7czZRJnSzVMmz7DZxacvu6A7wa6CCE6CiH0wFzg3wrb1CpGX3cdVJUgbKVoizPwDwj0eU3shIQEHDLnCyZ+yjHSKb4j/fv3V9gy3yEoKIig4ODzeuCdO3dW2CLXMGDAAIpt8G2mgYjw9motQDOZNm0aGklCn3PiouPagtPINTZmzbpZIcvaRrdu3Rg6dAifpVvOp619lGrCYNAza9YsZY3zEXr16oVOr0dbkgmAtjQTSauld+/eClvWcrp06cLYsdfzVYaJygZUemUZ/nvOQpfOnZ357yoNMmniRKgsQVNRcNFxbfE5ZHsNEydOVMiytjFu3Dh0Oi3fZPycpvRNhhG9Tucz9Q7g4w64LMu1wHLgM+A48FdZlo8qa1XrqI9SaEsy0JdlMnjQQJ9ZxV2J+Ph4oqMi2ZVjIL9Kw6liibHX36C0WT5HdFQUmtoqwNk57mogLi4OgHNlWmLjOiprjA8REhLCyGuvxVCYclHjFX1eEh0io3x6cXvbbbdTYoPdOXrKawQ/5hiYOm26T6bhKYFer6d7t25I5c40S6k8j65dumA0Nr95iTcxa9Ysqmrhh+zLd0tPFGnJKBfcPGuWzz8n3cnIkSPRaDRoL6kN0Bacxj8gkL4+mtLo5+fHNdeMYG++EVl2Lsj25BsZfs01+Pn5KW1es/FpBxxAluX/yrLcVZblTrIsP6G0Pa0lKiqKoOBgdHmnkKsrnNuJPo4QgrHX38DxIh3v1Wm7jhkzRlmjfJD6okudTk9wcLDC1riGDh06nP/5allUeIobbrgBubrSmWIAiJoKpNJMxo+7waedkT59+hAcFMDBfD2HC3Q4ZHW+aCldu3ZFW1kIsgNtZcH5FDZfpGfPnnTuFM/2DPNlGRRfpRuxWsw+Fe1UgsDAQPr3H4Ch6OzPBx129CVpjL5ulE/IG1+JIUOGUFgFGRUSGRXOWiJfUwjzeQf8akEIQedOnc5HLzp2vDqiguPHj8eg17M310D//v2IiopS2iSfo14nPTgk2KcdrAsJCgo6//OFzrhK0wwfPhydTndeH14qcspTenOjleag0WgYNnwEhwoNfHzGRIC/H927d1faLJ+ic+fOyPYapOJ05Npqn64ZEUIwbfoMzpZqSCr5uXFXcbVgb66BSTdOVmuJmsHQoUOgsghR7VRZk8pzke21PuesXsrgwYMBp5rW0QLfrCVSHXAvIiYmpsGffZno6Gg+/u8nfPnll2zb9rzS5vgk9Vvwei/XdW4JQggG1KVLqE5WyzAYDPTt2xddqbPeXFuSQUBgoM8VXzbEhAkT0Bmt5Nqt3DRlqk9H6JSgvli1fnHm68Wr9bm+uy+QqNyXq8cu43PqHUpRL0kqlWbW/T8LIQT9+vVT0qw2ExERQbuQYFJKtCSXaGkXEuxzEr3apk9R8RQDBw7kww8/pFPnLj6Vx9QU6kO0bdRXqV8t0e96ntu6ldraWlXBoBX079+fvXv3Qq0NXUUOA68ZclXcHwMGDODfH/1HaTN8lvrdJG1JxkW/+ypms5m+ffty5OQewBnBPVKgo11o8FWzS+xuOnfujNFkpro0m9qQTkilWcR17Ohz6icNEd+5M2nH8pCBTr18L91KjYB7Eddeey1fffUVb77xutKmqHgRPXr0ICY29qpTg9BoNKrz3Up69OgB1HVHrSpTdxFUAAgICMBoNKGxlaDTXx01I0OGDCWtTEOhTWB3wLEiI0OGDr8qFpyeQJIkunbtgrayAGQZXVUhPevmD18nPr4T50o1pJVqfHIHUI2Aq6h4OVFRUfzh3XeVNkPFi6iXo9QVpABOzV8VFSEEoe1CSTt3jpCQ0KvCSa3P9T1SoCfCbKe8Rj5/TKV5dOncmcNHjiKqy5GrK6+a+SIyMvJ850VfrC9TI+AqKioqPkZAQAAWq99Vk+ur4jrqW7KHhPh+9BuccrZWi5mkYmeuL+Dz+cueplOnTsj2WrR1aihXiwN+YWNFb2yy2BSqA66ioqLiYwgh6NDBWXCk1ekIDQ1V2CIVbyG4TmEo+AKlIV9Go9EQGxdHRoWWjHIJfz/rVZFa40nqpV6lutoAX4wWN4SvO+BqCoqKioqKDxLZoQOnEhMJCwu7KlINVFxDfQH/1VTIHxfXkW9OHUeu+1m931tGvTqItjQTrU53kQysLxMXF8fChQsRQvikcpzqgKuoqKj4IHfccQc9evSgZ8+eSpui4kVYLJaL/n81EBcXx8fVMlW1Wm5U1U9aTEhICJIkYbfXEBYeiUZzdSQ/aDQaFi5cqLQZrUZ1wFVUVFR8kOjoaObMmaO0GSpehtVqBZwSflcLQ4YMIS42htraWkaOHKm0OT6HJEn4BwRSWJBPOzVdzWtQHXAVFRUVFZWrhEmTJqHRaBg/frzSpriMuLg43nn3D0qb4dMEBwVRWJB/1aSfXA1cHfsQKioqKioqKoSEhHDrrbeqhbkqFxEe3h6AsLAwhS1RqUeNgKuoqKioqKioXMWsXbuW6dOn07t3b6VNUalDdcBVVFRUVFRUVK5igoKCGDp0qNJmqFyAmoKioqKioqKioqKi4kFUB1xFRUVFRUVFRUXFg6gOuIqKioqKioqKiooHUR1wFRUVFRUVFRUVFQ+iOuAqKioqKioqKioqHkR1wFVUVFRUVFRUVFQ8iOqAq6ioqKioqKioqHgQ1QFXUVFRUVFRUVFR8SCqA66ioqKioqKioqLiQVQHXEVFRUVFRUVFRcWDqA64ioqKioqKioqKigdRHXAVFRUVFRUVFRUVD6I64CoqKioqKioqKioeRHXAVVRUVFRUVFRUVDyI6oCrqKioqKioqKioeBDVAVdRUVFRUVFRUVHxIKoDrqKioqKioqKiouJBhCzLStvgUYQQucAZpe3wEkKBPKWNUPE61PtCpSHU+0KlIdT7QqUh1PviZ2JlWW536cFfnAOu8jNCiD2yLA9W2g4V70K9L1QaQr0vVBpCvS9UGkK9L5pGTUFRUVFRUVFRUVFR8SCqA66ioqKioqKioqLiQVQH/JfNm0oboOKVqPeFSkOo94VKQ6j3hUpDqPdFE6g54CoqKioqKioqKioeRI2Aq6ioqKioqKioqHgQ1QG/ShBCRAkh/iWEOCWESBZCvCCE0Ash+gshJl9w3qNCiLVK2vpLQAhhF0IcEEIcFUIcFEKsFkI0+n0TQowRQvznCq893Mj7rEKIN+r+7keFEN8IIYa19TM0hRBioRDi5bqflwoh5jdwTpwQ4kgT14kTQsy74PfBQogXXW+xSkMIIWYKIWQhRPdmnLtSCGH2hF0qrueCeemgEGKfEGJEK6/T4PddxTUIIcou+f38XOuh8d8RQiR4ajxvorFnratRHfCrACGEAP4JfCjLchegK2AFngD6A5MbeXtLx5Jcda2rnEpZlvvLstwLGI/zb7CxDddrbFJ4GygAutSNtxCnBqvHkGX5dVmW/9DKt8cB5x1wWZb3yLJ8r0sMU2kOtwI7gbnNOHcloDrgvkv9vNQPeAj4bWsu0sbvu8ovFB/xH1QHXKVFXA9UybL8ewBZlu3AKmAJ8Awwpy7qMafu/J5CiK+FEClCiPOOjhDidiHErrpz36j/sgghyoQQm4UQPwHXePSTXQXIspwD/BpYLpxIQogtQojdQohDQoi7LjjdXwjxgRDimBDidSGERgjxFGCq+7v86cJrCyE6AcOAdbIsO+rGS5Fl+eO611cLIY7U/bey7licEOK4EOKtuoj550IIU91r99aNfUgI8ee6Y8FCiA/rjv0ohOh76We8cGdFCDGoLsL2A7DsgnPihBDf1kXeLoy+PQWMqvt8qy7cCbjS2HXj/a6h+1il+QghrMC1wB3UOeCX7sQIIV6ui8DdC3QAdgghdtS9dqsQ4nDd/fW0Ah9BpfX4A4Vw5b953c9PXTAnPFt37MLv+9dCiKfrnh2JQohRdccbnOeEEBHCuUt3oO6+GVV37jt1vx8WQqzy7D+F7yCEiBVCfFX3b/qVECKm7vg7QojXhBA76ubE0XVz5HEhxDsXvH+CEOKHujn4b3VzQGPjpQohnqx7zx4hxEAhxGfCueO6tO6cMXV/04ueXXWvXeQ/XOGZ9LQQ4p4LxnxUCLGm7uf7L7iHNtUdixP/v70zD7a6LOP458syIEuQoqUOLpSEmglaliFKwlBTjUuS5YAL1DTaGE2lpYmG02SLqaUM45apiBKKmpgFioAliCirlzZ3K0cwl0CDWJ7+eJ7j/XH6nXvPhXsvS89n5sx5z7svv/O+z+95n9/vlf4k6cbIZ4qk4ZIelVsBHBXxukcfLJK0RNKJ4X+WpLsl/S7i/yT8a661bYKZ5Wcn/wDjgKtK/JdE2MSC3wRgPtAF15L+E+gMHAzMADpHvEnAGeE24NTt3c6d6QOsLfF7HXgPLoyPD78uwBPAgcBQYB3QD+gIPAiMrJVf+J8A3FMj7EhgBdAd3xFpAAbhGueNwMCINw0YHe5/AF3C3Tu+rwG+F+7jgaXhPqtybcV1dV64lwPHhfty4KlwdwO6hvsg4IlwDwXuL9T7nd9NlF16HW/vcd/ZPsBo4Bfhng8cUTIeE4Gzwv080Cfc+wAvAnsCnYCHgZO2d5vy0+R4bwKWAn8C3gSODP/SMQd2B/5M4wsbKnNC8f8+F7gi3J8GHgp3rXnuW8BF4d8R6InPVQ8Wyu+9vftqBxmnyufFwlw7Azgz3GPxnW+Am4GpgIATgX8Bh+GK1ifx3fA+wCNA90jzHeCSkvJvpnHteR44J9xX4fN7z/jfrypcP7XWrnfkB2qvSYOAeYXyVwL7ASPwt6ko2nE/cCyNa1ixfTcV2l7pk8toXNt6A3+Jss8CngV6AV3x09H7RrzStbYtPqkB3zUQfpHX6/8bM1tvZq8Cq3ChcBj+51gkaWn87hfxNwHTW73W/38ovkcAZ0Q/LwT2wAVSgMfNNdibgDuAY7ahvGNw4fwtM1uLmykNibDnzGxpuJ/EJzTwyXWKpNH4BFfJZzKAmT0M7CGpV2kD3b+3mc0Lr8mF4M7ADZJWAHcCh9TZhlpll13HScs4DV+0ie/TWpD2I8BcM1ttZhuBKfjimOy4VExQBgCfAm6VpCbi/wsXrG6U9Dng7Rrx7o7v4lxSa55bBIyRNAE4zMzW4MJQP0nXSPpUlPv/TGWcBprZQOCSQtjRwO3hnsyWa8QMcylyBfCKma0w3xltwMflY/i8+2iMy5nA/nXU5774XgEsNLM1ZrYaWCepd4TVWruK8kPpmmRmS4C9JO0j6XDgdTN7Eb+GRuDKxMXAABrXyueq2je70PYDIs4I4IJo61xc2N4vwmab2Ztmtg4X+Ovph1alU3sXmLQJDcApRQ9J7wL64hd/NesL7k34dSDgFjO7sCT+uvhTJVuJpH54X6/C+/prZjazKs5Q/veGqbn3hDYAh0vqEBPRFlk2ka76Gtgt3J/BhagTgIslHVojn1r1qnXTB24W9QpwOK61WNdE/Yr51Sq77DpO6kTSHviuwgclGa65MnyxLSpnutbKom1rmLQlZrZAUh9ck7mRkjE3s42xnT8MN1E6F79mqqn8F4v/w9J5Do7xVEgAAAjdSURBVEDSsfhcM1nS5WZ2awhen8TN1k7FtbtJ8xTn28o4bGbL+XEzPi6b8J2Gltxo15NvdT2Kv4vyQ1Nzxl3ASOC9NCoFBPzQzK4rRpR0QEk9inUsXoOnmNmfq9J/lB1g/UgN+K7BbKCb4ql0ue32Ffg20iv4dlE9eYyUtFfksbukdr8j3BWRtCdwLb6FaMBM4BxJnSO8v6TuEf0oSQeG/dwX8IfjADZU4hcxs2fwrd1LK5osSQeFrdsjwEmSukX+JwO/b6KeHfBtuDnAt/Etux6Rz6iIMxR41cxKNVRm9gbwpqSK9mNUIbgX8HLcKJyOC3wAa6h9jdZddtJiRgK3mtn+ZnaAmfUFnouwQyR1id2GYYU0xbFaCBwnqU/MOacB80h2CuRvvemIm2+9QMmYh31wLzN7AH8Ad2ALiiid52JdWWVmNwC/AI6IG4EOZjYduBg3hUrKmU/jA9OjaFwj6uExYLCk9wPE2tC/lepVa+0q0tSaNBVv10hcGAe/hsbGdYikfSsySp3MBL5WWBsH1ZGmdK1tC1JjtAtgZibpZGCSpIvxG6sH8Kd5u9O4BVPziXczWylpPDAr/kAbcE3EC23egF2T3aLPO+PapcnAlRF2I75FtjgmhtXASRG2AH8o8TB8sron/K8HlktabGZFoRb8YdsrgKclvY0vqOeb2WL5wzePV8o1syWhPSijI3BbLMDCnyt4I7aKfylpOb4FfWYzbR8D3BR1KWq/JgHTJX0emAO8Ff7LgY2SluE3jUsKaVpadlI/p+HXWpHp+BtppuHj8le2HI/rgd9KetnMPiHpQnwsBTxgZr9u+2on20BlXgIfszNDO/mSpLIx7wn8WlLXiN+ShyNrzXNDgfMlbQDWAmcA++L/84pSsGwnNnHG4fPr+Xifjqk3oZmtlj9ce4ekLuE9HreN3lZqrV3F8kvXpAhrkNQT+LuZvRx+syQdDCwIGXot/txKvTvy3wd+hq+dwu3ZP9tMmqbW2lYlT8JMkiRJkiRJtorYnTzPzJoTbpMCaYKSJEmSJEmSJO1IasCTJEmSJEmSpB1JDXiSJEmSJEmStCMpgCdJkiRJkiRJO5ICeJIkSZIkSZK0IymAJ0mS1IGktSV+Z1fev18jzVBJH683fh116CHpOknPSGqQ9EgcKtFUmu9ubXltgaSTJF3SfMz2QdIDhdP8ysLPlVT3q96SJEnqIR/CTJIkqQNJa82sRwvTTADWmtlPW6kOU/HDci4ys81xwurBZvabJtK0uN5bUa9OcRx9PXHnAyeY2attWac66iF8Daw+QbY6XjfgUTOr5xCPJEmSukgNeJIkyVYiaYKk88I9TtJKScslTY0Dj84GviFpqaQhVfHnSvqxpMcl/UXSkPDvJmla5PMrSQslfVjS+4CPAuMrQqOZPVsRviXdK+nJ0Ix/Jfx+RBy+ImlK+I2OMpeGNr1j+H8p6jFX0g2SJob//pJmR31mS9ov/G+WdKWkOcDlkv4qP/UVSR0kPS0/4bDYX/2B9RXhO/K4WtJ8Sc9KGhn+QyXdX0g3MQ4QQdLzki6TtEDSE5KOkDQzdgXOLqQ5X9KiqPel4XeApD9KmgQsBvpGfn0i/IyIv0zS5Ojjt4Hn5UeyJ0mStAp5EmaSJEnrcAFwoJmtl9Q7ThG9loIGXNKwqjSdzOwoSZ8GvgcMB74KvG5mH5L0QaBycuGhwNI4ubCMsWb2mqTdgEWSppvZBZLONbOBUf7B+DHRg81sQwiioyQ9ROMR4GuAh4Flke9E/Mj6WySNBa6m8eTW/sBwM9sk6Q38aOyfRTuWlWi5B+OCb5G9gWOAAcB9NB5D3RQvmdnRkq7CT08dDHQFGoBrJY0ADgKOwk9wvE/SscCLwAeAMWb21egT4vtQ4KLom1cl7V4o7wlgCI0n+CVJkmwTqQFPkiRpHZYDUySNBuoyxwDuju8n8WO7wYXRqQBm9lTkWw/jJC0DHgP64gJoNcOAI3EBfWn87ocLqvPM7DUz2wDcWUhzNHB7uCdH/SrcWbghuAk/VhxgLPDLkvL3xo/PLnKvmW02s5XAe5pvJuCCOsAKYKGZrTGz1cC6sOceEZ8luMA/gMb+eMHMHivJ83jgrspNg5m9VghbBexTZ92SJEmaJTXgSZIkrcNngGOBE4CLQ6PaHOvjexON87FqxG0ADpfUodpuWX4U9HDgaDN7W9JcXCNcjYBbzOzCqvQn11HXCsUHh956x9PsJUmvSDoeN5UZVZL230CvKr/1BXel7RvZUkFU3ZZKms1V6Tfj/Sjgh2Z2XTFRmAW9RTliy7YV6Rp1T5IkaRVSA54kSbKNSOoA9DWzOcC3gd5AD9yco2cLs/sDcGrkewhwGICZPYObQlwaDxAi6SBJJ+JC7eshfA8APlbIb4OkzuGeDYyUtFek313S/rhpxXGS3i2pE3BKIf184IvhHhX1q8WNwG3AtBqmMn8E3l9HH7wAHCKpi6ReuKa+JcwExkrqASBp30qbm2A2cKqkPSJN0QSlP/BUC+uQJElSkxTAkyRJ6qObpL8VPt8shHUEbpO0Ajd7uMrM3gBmACfHA49D6ixnErCnpOXAd3ATlDcj7MvAe4Gno6wbgH8AvwM6RZrv42YoFa4HlkuaEmYe44FZEfdBYG8z+ztwGbAQeAhYWShzHDAm4p8OfL2Jut+H33iUmZ8APAIMqtxA1MLMXgKmRdun4H1aN2Y2CzebWRD9dBfN3AiZWQPwA2BemPJcWQgejPdLkiRJq5CvIUySJNmBiLeSdDazdfI3n8wG+pvZf9q43B5mtjY04PcAN5nZPS3M48P4zUfNmw1JPwdmmNlOIdBKGgR808xO3951SZJk1yFtwJMkSXYsugFzwmxEwDltLXwHEyQNx+2dZwH3tiSxpAuAcyi3/S5yGW4jvrPQB39DTJIkSauRGvAkSZIkSZIkaUfSBjxJkiRJkiRJ2pEUwJMkSZIkSZKkHUkBPEmSJEmSJEnakRTAkyRJkiRJkqQdSQE8SZIkSZIkSdqRFMCTJEmSJEmSpB35L+YiRpXIEvl8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [12, 8])\n",
    "sns.violinplot(data=target_df, x='ListingCategory (numeric)', y='LoanOriginalAmount', hue='LoanStatus');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observation 3:\n",
    "*  Except for Auto, Business and Home Improvemrnt dont have nearly equal mean amoong all of them.\n",
    "*  Business category tend to have larger amount."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Talk about some of the relationships you observed in this part of the investigation. Were there features that strengthened each other in terms of looking at your feature(s) of interest?\n",
    "> Our initial assumptions were strengthened. Most of the defaulted credits comes from individuals with low Prosper rating and Business category tend to have larger amount.\n",
    "\n",
    "## Were there any interesting or surprising interactions between features?\n",
    "> Interesting find was that defaulted credits for individuals with high Prosper ratings tend to be larger than completed credits."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}