Part_I_exploration_template.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part I - Propser Loan Data\n",
    "## by (Samson Amwata)\n",
    "\n",
    "## Introduction\n",
    "This dataset contains loan information of about 113,937 rows with 81 variables on each\n",
    "loan, including loan amount, borrower rate (or interest rate), current loan status, borrower income, and many others.\n",
    "\n",
    "I am looking forward to investigate the following questions\n",
    "- What factors affect a loan’s outcome status?\n",
    "- What affects the borrower’s APR or interest rate?\n",
    "- Are there differences between loans depending on how large the original loan amount was?\n",
    "\n",
    "\n",
    "\n",
    "## Preliminary Wrangling\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import all packages and set plots to be embedded inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Load dataset\n",
    "df = pd.read_csv(\"prosperLoanData.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(113937, 81)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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: 68.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ListingKey', 'ListingNumber', 'ListingCreationDate', 'CreditGrade',\n",
       "       'Term', 'LoanStatus', 'ClosedDate', 'BorrowerAPR', 'BorrowerRate',\n",
       "       'LenderYield', 'EstimatedEffectiveYield', 'EstimatedLoss',\n",
       "       'EstimatedReturn', 'ProsperRating (numeric)', 'ProsperRating (Alpha)',\n",
       "       'ProsperScore', 'ListingCategory (numeric)', 'BorrowerState',\n",
       "       'Occupation', 'EmploymentStatus', 'EmploymentStatusDuration',\n",
       "       'IsBorrowerHomeowner', 'CurrentlyInGroup', 'GroupKey',\n",
       "       'DateCreditPulled', 'CreditScoreRangeLower', 'CreditScoreRangeUpper',\n",
       "       'FirstRecordedCreditLine', 'CurrentCreditLines', 'OpenCreditLines',\n",
       "       'TotalCreditLinespast7years', 'OpenRevolvingAccounts',\n",
       "       'OpenRevolvingMonthlyPayment', 'InquiriesLast6Months', 'TotalInquiries',\n",
       "       'CurrentDelinquencies', 'AmountDelinquent', 'DelinquenciesLast7Years',\n",
       "       'PublicRecordsLast10Years', 'PublicRecordsLast12Months',\n",
       "       'RevolvingCreditBalance', 'BankcardUtilization',\n",
       "       'AvailableBankcardCredit', 'TotalTrades',\n",
       "       'TradesNeverDelinquent (percentage)', 'TradesOpenedLast6Months',\n",
       "       'DebtToIncomeRatio', 'IncomeRange', 'IncomeVerifiable',\n",
       "       'StatedMonthlyIncome', 'LoanKey', 'TotalProsperLoans',\n",
       "       'TotalProsperPaymentsBilled', 'OnTimeProsperPayments',\n",
       "       'ProsperPaymentsLessThanOneMonthLate',\n",
       "       'ProsperPaymentsOneMonthPlusLate', 'ProsperPrincipalBorrowed',\n",
       "       'ProsperPrincipalOutstanding', 'ScorexChangeAtTimeOfListing',\n",
       "       'LoanCurrentDaysDelinquent', 'LoanFirstDefaultedCycleNumber',\n",
       "       'LoanMonthsSinceOrigination', 'LoanNumber', 'LoanOriginalAmount',\n",
       "       'LoanOriginationDate', 'LoanOriginationQuarter', 'MemberKey',\n",
       "       'MonthlyLoanPayment', 'LP_CustomerPayments',\n",
       "       'LP_CustomerPrincipalPayments', 'LP_InterestandFees', 'LP_ServiceFees',\n",
       "       'LP_CollectionFees', 'LP_GrossPrincipalLoss', 'LP_NetPrincipalLoss',\n",
       "       'LP_NonPrincipalRecoverypayments', 'PercentFunded', 'Recommendations',\n",
       "       'InvestmentFromFriendsCount', 'InvestmentFromFriendsAmount',\n",
       "       'Investors'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is the structure of your dataset?\n",
    "\n",
    "> We have 113937 rows and 81 columns or features. From describe function it seems most of the features are numeric, some are categorical and others are time/periodical features \n",
    "\n",
    "### What is/are the main feature(s) of interest in your dataset?\n",
    "\n",
    "> I am interested in borrowers features and features related to loan or those that affect loan performance. \n",
    "\n",
    "\n",
    "### What features in the dataset do you think will help support your investigation into your feature(s) of interest?\n",
    "\n",
    "> In this analysis I will be focusing on the following columns that I suspect has effect on the loan performance  \n",
    "\n",
    "``` \n",
    "[ListingNumber, CreditGrade',Term, LoanStatus, ClosedDate, BorrowerAPR, BorrowerRate, BorrowerState, Occupation, EmploymentStatus, InvestmentFromFriendsCount, InvestmentFromFriendsAmount,LoanOriginalAmount,ProsperRating (Alpha),IncomeRange', StatedMonthlyIncome, LoanCurrentDaysDelinquent, LoanOriginalAmount, LoanOriginationDate,  MonthlyLoanPayment and Investors ]\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ListingNumber</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>BorrowerState</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>EmploymentStatus</th>\n",
       "      <th>...</th>\n",
       "      <th>ProsperScore</th>\n",
       "      <th>IncomeRange</th>\n",
       "      <th>StatedMonthlyIncome</th>\n",
       "      <th>LoanCurrentDaysDelinquent</th>\n",
       "      <th>DebtToIncomeRatio</th>\n",
       "      <th>LoanOriginalAmount</th>\n",
       "      <th>LoanOriginationDate</th>\n",
       "      <th>MonthlyLoanPayment</th>\n",
       "      <th>Investors</th>\n",
       "      <th>IsBorrowerHomeowner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>193129</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>CO</td>\n",
       "      <td>Other</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$25,000-49,999</td>\n",
       "      <td>3083.333333</td>\n",
       "      <td>0</td>\n",
       "      <td>0.17</td>\n",
       "      <td>9425</td>\n",
       "      <td>2007-09-12 00:00:00</td>\n",
       "      <td>330.43</td>\n",
       "      <td>258</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1209647</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>CO</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Employed</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>$50,000-74,999</td>\n",
       "      <td>6125.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.18</td>\n",
       "      <td>10000</td>\n",
       "      <td>2014-03-03 00:00:00</td>\n",
       "      <td>318.93</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>81716</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>GA</td>\n",
       "      <td>Other</td>\n",
       "      <td>Not available</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Not displayed</td>\n",
       "      <td>2083.333333</td>\n",
       "      <td>0</td>\n",
       "      <td>0.06</td>\n",
       "      <td>3001</td>\n",
       "      <td>2007-01-17 00:00:00</td>\n",
       "      <td>123.32</td>\n",
       "      <td>41</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>658116</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>GA</td>\n",
       "      <td>Skilled Labor</td>\n",
       "      <td>Employed</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>$25,000-49,999</td>\n",
       "      <td>2875.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.15</td>\n",
       "      <td>10000</td>\n",
       "      <td>2012-11-01 00:00:00</td>\n",
       "      <td>321.45</td>\n",
       "      <td>158</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>909464</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>MN</td>\n",
       "      <td>Executive</td>\n",
       "      <td>Employed</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$100,000+</td>\n",
       "      <td>9583.333333</td>\n",
       "      <td>0</td>\n",
       "      <td>0.26</td>\n",
       "      <td>15000</td>\n",
       "      <td>2013-09-20 00:00:00</td>\n",
       "      <td>563.97</td>\n",
       "      <td>20</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ListingNumber CreditGrade  Term LoanStatus           ClosedDate  \\\n",
       "0         193129           C    36  Completed  2009-08-14 00:00:00   \n",
       "1        1209647         NaN    36    Current                  NaN   \n",
       "2          81716          HR    36  Completed  2009-12-17 00:00:00   \n",
       "3         658116         NaN    36    Current                  NaN   \n",
       "4         909464         NaN    36    Current                  NaN   \n",
       "\n",
       "   BorrowerAPR  BorrowerRate BorrowerState     Occupation EmploymentStatus  \\\n",
       "0      0.16516        0.1580            CO          Other    Self-employed   \n",
       "1      0.12016        0.0920            CO   Professional         Employed   \n",
       "2      0.28269        0.2750            GA          Other    Not available   \n",
       "3      0.12528        0.0974            GA  Skilled Labor         Employed   \n",
       "4      0.24614        0.2085            MN      Executive         Employed   \n",
       "\n",
       "   ...  ProsperScore     IncomeRange  StatedMonthlyIncome  \\\n",
       "0  ...           NaN  $25,000-49,999          3083.333333   \n",
       "1  ...           7.0  $50,000-74,999          6125.000000   \n",
       "2  ...           NaN   Not displayed          2083.333333   \n",
       "3  ...           9.0  $25,000-49,999          2875.000000   \n",
       "4  ...           4.0       $100,000+          9583.333333   \n",
       "\n",
       "  LoanCurrentDaysDelinquent  DebtToIncomeRatio LoanOriginalAmount  \\\n",
       "0                         0               0.17               9425   \n",
       "1                         0               0.18              10000   \n",
       "2                         0               0.06               3001   \n",
       "3                         0               0.15              10000   \n",
       "4                         0               0.26              15000   \n",
       "\n",
       "   LoanOriginationDate  MonthlyLoanPayment  Investors  IsBorrowerHomeowner  \n",
       "0  2007-09-12 00:00:00              330.43        258                 True  \n",
       "1  2014-03-03 00:00:00              318.93          1                False  \n",
       "2  2007-01-17 00:00:00              123.32         41                False  \n",
       "3  2012-11-01 00:00:00              321.45        158                 True  \n",
       "4  2013-09-20 00:00:00              563.97         20                 True  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns_sub = ['ListingNumber', 'CreditGrade', 'Term', 'LoanStatus', 'ClosedDate', 'BorrowerAPR', \n",
    "               'BorrowerRate', 'BorrowerState', 'Occupation', 'EmploymentStatus', 'InvestmentFromFriendsCount', \n",
    "               'InvestmentFromFriendsAmount', 'LoanOriginalAmount', 'ProsperRating (Alpha)','ProsperScore','IncomeRange', \n",
    "               'StatedMonthlyIncome', 'LoanCurrentDaysDelinquent', 'DebtToIncomeRatio','LoanOriginalAmount', 'LoanOriginationDate',  \n",
    "               'MonthlyLoanPayment', 'Investors','IsBorrowerHomeowner' ]\n",
    "loan_df1 = df[columns_sub]\n",
    "loan_df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ListingNumber</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>BorrowerState</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>EmploymentStatus</th>\n",
       "      <th>...</th>\n",
       "      <th>ProsperScore</th>\n",
       "      <th>IncomeRange</th>\n",
       "      <th>StatedMonthlyIncome</th>\n",
       "      <th>LoanCurrentDaysDelinquent</th>\n",
       "      <th>DebtToIncomeRatio</th>\n",
       "      <th>LoanOriginalAmount</th>\n",
       "      <th>LoanOriginationDate</th>\n",
       "      <th>MonthlyLoanPayment</th>\n",
       "      <th>Investors</th>\n",
       "      <th>IsBorrowerHomeowner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>193129</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>CO</td>\n",
       "      <td>Other</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$25,000-49,999</td>\n",
       "      <td>3083.333333</td>\n",
       "      <td>0</td>\n",
       "      <td>0.17</td>\n",
       "      <td>9425</td>\n",
       "      <td>2007-09-12 00:00:00</td>\n",
       "      <td>330.43</td>\n",
       "      <td>258</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1209647</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>CO</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Employed</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>$50,000-74,999</td>\n",
       "      <td>6125.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.18</td>\n",
       "      <td>10000</td>\n",
       "      <td>2014-03-03 00:00:00</td>\n",
       "      <td>318.93</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>81716</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>GA</td>\n",
       "      <td>Other</td>\n",
       "      <td>Not available</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Not displayed</td>\n",
       "      <td>2083.333333</td>\n",
       "      <td>0</td>\n",
       "      <td>0.06</td>\n",
       "      <td>3001</td>\n",
       "      <td>2007-01-17 00:00:00</td>\n",
       "      <td>123.32</td>\n",
       "      <td>41</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>658116</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>GA</td>\n",
       "      <td>Skilled Labor</td>\n",
       "      <td>Employed</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>$25,000-49,999</td>\n",
       "      <td>2875.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.15</td>\n",
       "      <td>10000</td>\n",
       "      <td>2012-11-01 00:00:00</td>\n",
       "      <td>321.45</td>\n",
       "      <td>158</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>909464</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>MN</td>\n",
       "      <td>Executive</td>\n",
       "      <td>Employed</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$100,000+</td>\n",
       "      <td>9583.333333</td>\n",
       "      <td>0</td>\n",
       "      <td>0.26</td>\n",
       "      <td>15000</td>\n",
       "      <td>2013-09-20 00:00:00</td>\n",
       "      <td>563.97</td>\n",
       "      <td>20</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ListingNumber CreditGrade  Term LoanStatus           ClosedDate  \\\n",
       "0         193129           C    36  Completed  2009-08-14 00:00:00   \n",
       "1        1209647         NaN    36    Current                  NaN   \n",
       "2          81716          HR    36  Completed  2009-12-17 00:00:00   \n",
       "3         658116         NaN    36    Current                  NaN   \n",
       "4         909464         NaN    36    Current                  NaN   \n",
       "\n",
       "   BorrowerAPR  BorrowerRate BorrowerState     Occupation EmploymentStatus  \\\n",
       "0      0.16516        0.1580            CO          Other    Self-employed   \n",
       "1      0.12016        0.0920            CO   Professional         Employed   \n",
       "2      0.28269        0.2750            GA          Other    Not available   \n",
       "3      0.12528        0.0974            GA  Skilled Labor         Employed   \n",
       "4      0.24614        0.2085            MN      Executive         Employed   \n",
       "\n",
       "   ...  ProsperScore     IncomeRange  StatedMonthlyIncome  \\\n",
       "0  ...           NaN  $25,000-49,999          3083.333333   \n",
       "1  ...           7.0  $50,000-74,999          6125.000000   \n",
       "2  ...           NaN   Not displayed          2083.333333   \n",
       "3  ...           9.0  $25,000-49,999          2875.000000   \n",
       "4  ...           4.0       $100,000+          9583.333333   \n",
       "\n",
       "  LoanCurrentDaysDelinquent  DebtToIncomeRatio LoanOriginalAmount  \\\n",
       "0                         0               0.17               9425   \n",
       "1                         0               0.18              10000   \n",
       "2                         0               0.06               3001   \n",
       "3                         0               0.15              10000   \n",
       "4                         0               0.26              15000   \n",
       "\n",
       "   LoanOriginationDate  MonthlyLoanPayment  Investors  IsBorrowerHomeowner  \n",
       "0  2007-09-12 00:00:00              330.43        258                 True  \n",
       "1  2014-03-03 00:00:00              318.93          1                False  \n",
       "2  2007-01-17 00:00:00              123.32         41                False  \n",
       "3  2012-11-01 00:00:00              321.45        158                 True  \n",
       "4  2013-09-20 00:00:00              563.97         20                 True  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Rename ProsperRating (Alpha) column by removing the (Alpha)\n",
    "loan_df2=loan_df1.copy()\n",
    "loan_df2.rename(columns = {'ProsperRating (Alpha)':'ProsperRating'}, inplace = True)\n",
    "loan_df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 113937 entries, 0 to 113936\n",
      "Data columns (total 24 columns):\n",
      " #   Column                       Non-Null Count   Dtype  \n",
      "---  ------                       --------------   -----  \n",
      " 0   ListingNumber                113937 non-null  int64  \n",
      " 1   CreditGrade                  28953 non-null   object \n",
      " 2   Term                         113937 non-null  int64  \n",
      " 3   LoanStatus                   113937 non-null  object \n",
      " 4   ClosedDate                   55089 non-null   object \n",
      " 5   BorrowerAPR                  113912 non-null  float64\n",
      " 6   BorrowerRate                 113937 non-null  float64\n",
      " 7   BorrowerState                108422 non-null  object \n",
      " 8   Occupation                   110349 non-null  object \n",
      " 9   EmploymentStatus             111682 non-null  object \n",
      " 10  InvestmentFromFriendsCount   113937 non-null  int64  \n",
      " 11  InvestmentFromFriendsAmount  113937 non-null  float64\n",
      " 12  LoanOriginalAmount           113937 non-null  int64  \n",
      " 13  ProsperRating                84853 non-null   object \n",
      " 14  ProsperScore                 84853 non-null   float64\n",
      " 15  IncomeRange                  113937 non-null  object \n",
      " 16  StatedMonthlyIncome          113937 non-null  float64\n",
      " 17  LoanCurrentDaysDelinquent    113937 non-null  int64  \n",
      " 18  DebtToIncomeRatio            105383 non-null  float64\n",
      " 19  LoanOriginalAmount           113937 non-null  int64  \n",
      " 20  LoanOriginationDate          113937 non-null  object \n",
      " 21  MonthlyLoanPayment           113937 non-null  float64\n",
      " 22  Investors                    113937 non-null  int64  \n",
      " 23  IsBorrowerHomeowner          113937 non-null  bool   \n",
      "dtypes: bool(1), float64(7), int64(7), object(9)\n",
      "memory usage: 20.1+ MB\n"
     ]
    }
   ],
   "source": [
    "loan_df2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data cleaning\n",
    "\n",
    "#### Definition of data cleaning issues\n",
    "1. Remove duplicates rows\n",
    "2. Convert the date columns to date data type\n",
    "3. Rename ProsperRating (Alpha) column to `ProsperRating`\n",
    "4. Replace `NaT` dates to Null\n",
    "5. Add new columns Month and Year pulled from loanOriginateDate column\n",
    "6. We have `Not employed` and `Not displayed` in the list of ranges. We can replace this with `$0`\n",
    "7. Replace True and False values in `IsBorrowerHomeowner` to have Yes or No\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for duplicates\n",
    "loan_df2.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop the duplicate rows\n",
    "loan_df3=loan_df2.copy()\n",
    "loan_df3.drop_duplicates(keep=False, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check to confirm if we still have duplicate rows\n",
    "loan_df3.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Convert ClosedDate and LoanOriginationDate column to datetime data type\n",
    "loan_df4 =loan_df3.copy()\n",
    "loan_df4[\"ClosedDate\"]=pd.to_datetime(loan_df3[\"ClosedDate\"])\n",
    "loan_df4[\"LoanOriginationDate\"]=pd.to_datetime(loan_df3[\"LoanOriginationDate\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2009-08-14\n",
       "1          NaT\n",
       "2   2009-12-17\n",
       "3          NaT\n",
       "4          NaT\n",
       "Name: ClosedDate, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#After converting now check the column\n",
    "loan_df4.ClosedDate[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2007-09-12\n",
       "1   2014-03-03\n",
       "2   2007-01-17\n",
       "3   2012-11-01\n",
       "4   2013-09-20\n",
       "Name: LoanOriginationDate, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#After converting now check the column\n",
    "loan_df4.LoanOriginationDate[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2009-08-14\n",
       "1          NaT\n",
       "2   2009-12-17\n",
       "3          NaT\n",
       "4          NaT\n",
       "5          NaT\n",
       "6          NaT\n",
       "7          NaT\n",
       "8          NaT\n",
       "9          NaT\n",
       "Name: ClosedDate_test, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Replace NaT with null\n",
    "loan_df4=loan_df4.copy()\n",
    "loan_df4['ClosedDate_test'] = loan_df4['ClosedDate'].replace(\"NaT\", np.nan)\n",
    "loan_df4.ClosedDate_test[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define\n",
    "Add new columns Month and Year pulled from loanOriginateDate column\n",
    "#check for duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Add month and year columns\n",
    "loan_df5=loan_df4.copy()\n",
    "loan_df5['loan_year'] = pd.DatetimeIndex(loan_df5['LoanOriginationDate']).year\n",
    "loan_df5['loan_month'] = pd.DatetimeIndex(loan_df5['LoanOriginationDate']).month"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2007\n",
       "1    2014\n",
       "2    2007\n",
       "3    2012\n",
       "4    2013\n",
       "Name: loan_year, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_df5.loan_year[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     9\n",
       "1     3\n",
       "2     1\n",
       "3    11\n",
       "4     9\n",
       "Name: loan_month, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_df5.loan_month[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define\n",
    "Create a column with Howmowner being Yes or No\n",
    "\n",
    "### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "loan_df6=loan_df5.copy()\n",
    "loan_df6[\"HomeOwner\"]= loan_df6.IsBorrowerHomeowner.map(lambda IsBorrowerHomeowner:'Yes' if IsBorrowerHomeowner==True else 'No')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Yes    57478\n",
       "No     56459\n",
       "Name: HomeOwner, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_df6.HomeOwner.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define\n",
    "We have `Not employed` and `Not displayed` in the list of ranges. We can put this as `$0` so that we have a clean chart\n",
    "\n",
    "### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Replace Not employed and Not displayed with $0\n",
    "loan_df7=loan_df6.copy()\n",
    "loan_df7[\"IncomeRange\"]=loan_df7[\"IncomeRange\"].replace([\"Not employed\", \"Not displayed\"], \"$0\").ffill()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "$25,000-49,999    32192\n",
       "$50,000-74,999    31050\n",
       "$100,000+         17337\n",
       "$75,000-99,999    16916\n",
       "$0                 9168\n",
       "$1-24,999          7274\n",
       "Name: IncomeRange, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_df7.IncomeRange.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Function to define the chart labels\n",
    "def plotlabel(xlab,ylab,title):\n",
    "    plt.xlabel(xlab)\n",
    "    plt.ylabel(ylab)\n",
    "    plt.title(title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Function to create a histogram\n",
    "def histplot(df,Var):\n",
    "    plt.figure(figsize = [8, 5])\n",
    "    bins = np.arange(0, loan_df.BorrowerRate.max()+0.06, 0.01)\n",
    "    base_color = sns.color_palette()[0]\n",
    "    plt.hist(data = df, x = Var, bins = bins, color=base_color);\n",
    "    plt.title(f'''Distribution of {Var}'''.title())\n",
    "    plt.xlabel(Var.title(), fontsize=10)\n",
    "    plt.ylabel('Count'.title())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Univariate Exploration\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Yes    57478\n",
       "No     56459\n",
       "Name: HomeOwner, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#make a copy of the final table from data cleaning to use in analysis\n",
    "loan_df=loan_df7.copy()\n",
    "#Check the value counts for home Owners\n",
    "loan_df['HomeOwner'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the count to check whether the borrower is home owner\n",
    "type_counts = loan_df['HomeOwner'].value_counts()\n",
    "plt.pie(type_counts, labels = type_counts.index, startangle = 90,\n",
    "        counterclock = False, wedgeprops = {'width' : 0.4}, autopct='%1.1f%%');\n",
    "plt.axis('square');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the chart we can find out that the borrowers with Homes were more with 1% which is not very much high. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BorrowerRate\n",
    "The **BorrowerRate** - This is defined as the interest rate on the annual cost of a loan to  a borrower which is expressed as a percentage. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "histplot(loan_df,'BorrowerRate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "#This is for testing the function\n",
    "# def histplot(df,Var):\n",
    "#     plt.figure(figsize = [8, 5])\n",
    "#     bins = np.arange(0, loan_df.BorrowerRate.max()+0.06, 0.01)\n",
    "#     base_color = sns.color_palette()[0]\n",
    "#     plt.hist(data = df, x = Var, bins = bins, color=base_color);\n",
    "#     plt.title(f'''Distribution of {Var}'''.title(), fontsize=14, weight='bold')\n",
    "#     plt.xlabel(Var.title(), fontsize=10, weight='bold')\n",
    "#     plt.ylabel('Count'.title(),fontsize=14, weight='bold')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BorrowerAPR\n",
    "The **BorrowerAPR** - This is defined as the annual cost of the loan which contains other fees like discounts and other deductions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    113912.000000\n",
       "mean          0.218828\n",
       "std           0.080364\n",
       "min           0.006530\n",
       "25%           0.156290\n",
       "50%           0.209760\n",
       "75%           0.283810\n",
       "max           0.512290\n",
       "Name: BorrowerAPR, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_df.BorrowerAPR.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "histplot(loan_df,'BorrowerAPR')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution of Borrower rate and Borrower APR looks multimodal. There is a peak centered at 0.2 and there is a very high increase between 0.35 and 0.36."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### StatedMonthlyIncome\n",
    "\n",
    "**StatedMonthlyIncome** - This is the monthly income declared by the borrower "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1.139370e+05\n",
       "mean     5.608026e+03\n",
       "std      7.478497e+03\n",
       "min      0.000000e+00\n",
       "25%      3.200333e+03\n",
       "50%      4.666667e+03\n",
       "75%      6.825000e+03\n",
       "max      1.750003e+06\n",
       "Name: StatedMonthlyIncome, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_df.StatedMonthlyIncome.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def stated_monthly_income():\n",
    "  # define parameters\n",
    "    bins = np.arange(0, 50000, 500)\n",
    "    plt.figure(figsize=[10, 5])\n",
    "    base_color = sns.color_palette()[0]\n",
    "    plt.hist(data = loan_df, x = 'StatedMonthlyIncome', bins=bins, color=base_color)\n",
    "    plotlabel('StatedMonthlyIncome','Count', 'Distribution of stated monthly income');\n",
    "stated_monthly_income()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the graph it seems that most borrowers income is less than 4k with the mean of 5k and the monthly income is skewed to the right. let us now see the other features assosciated with borrowers with more than 10k monthly income "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ListingNumber</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>BorrowerState</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>EmploymentStatus</th>\n",
       "      <th>...</th>\n",
       "      <th>DebtToIncomeRatio</th>\n",
       "      <th>LoanOriginalAmount</th>\n",
       "      <th>LoanOriginationDate</th>\n",
       "      <th>MonthlyLoanPayment</th>\n",
       "      <th>Investors</th>\n",
       "      <th>IsBorrowerHomeowner</th>\n",
       "      <th>ClosedDate_test</th>\n",
       "      <th>loan_year</th>\n",
       "      <th>loan_month</th>\n",
       "      <th>HomeOwner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8066</th>\n",
       "      <td>500651</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2012-01-26</td>\n",
       "      <td>0.35643</td>\n",
       "      <td>0.3199</td>\n",
       "      <td>IL</td>\n",
       "      <td>Police Officer/Correction Officer</td>\n",
       "      <td>Employed</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2000</td>\n",
       "      <td>2011-04-26</td>\n",
       "      <td>87.10</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>2012-01-26</td>\n",
       "      <td>2011</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8870</th>\n",
       "      <td>66246</td>\n",
       "      <td>A</td>\n",
       "      <td>36</td>\n",
       "      <td>Chargedoff</td>\n",
       "      <td>2009-08-11</td>\n",
       "      <td>0.12700</td>\n",
       "      <td>0.1200</td>\n",
       "      <td>CT</td>\n",
       "      <td>Other</td>\n",
       "      <td>Not available</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01</td>\n",
       "      <td>12500</td>\n",
       "      <td>2006-12-12</td>\n",
       "      <td>415.18</td>\n",
       "      <td>105</td>\n",
       "      <td>True</td>\n",
       "      <td>2009-08-11</td>\n",
       "      <td>2006</td>\n",
       "      <td>12</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17411</th>\n",
       "      <td>501462</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2013-04-11</td>\n",
       "      <td>0.35643</td>\n",
       "      <td>0.3199</td>\n",
       "      <td>HI</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Employed</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7500</td>\n",
       "      <td>2011-05-06</td>\n",
       "      <td>326.62</td>\n",
       "      <td>128</td>\n",
       "      <td>True</td>\n",
       "      <td>2013-04-11</td>\n",
       "      <td>2011</td>\n",
       "      <td>5</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35218</th>\n",
       "      <td>461028</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2013-06-11</td>\n",
       "      <td>0.07389</td>\n",
       "      <td>0.0705</td>\n",
       "      <td>VA</td>\n",
       "      <td>Attorney</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1000</td>\n",
       "      <td>2010-06-11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>58</td>\n",
       "      <td>True</td>\n",
       "      <td>2013-06-11</td>\n",
       "      <td>2010</td>\n",
       "      <td>6</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45028</th>\n",
       "      <td>589104</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36</td>\n",
       "      <td>Current</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0.33973</td>\n",
       "      <td>0.2999</td>\n",
       "      <td>KS</td>\n",
       "      <td>Nurse (RN)</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4000</td>\n",
       "      <td>2012-05-17</td>\n",
       "      <td>169.78</td>\n",
       "      <td>15</td>\n",
       "      <td>False</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2012</td>\n",
       "      <td>5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53014</th>\n",
       "      <td>694401</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2014-01-23</td>\n",
       "      <td>0.25785</td>\n",
       "      <td>0.1795</td>\n",
       "      <td>CA</td>\n",
       "      <td>Other</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4000</td>\n",
       "      <td>2013-01-23</td>\n",
       "      <td>366.62</td>\n",
       "      <td>93</td>\n",
       "      <td>True</td>\n",
       "      <td>2014-01-23</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53167</th>\n",
       "      <td>560783</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36</td>\n",
       "      <td>Current</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0.35797</td>\n",
       "      <td>0.3177</td>\n",
       "      <td>OR</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4000</td>\n",
       "      <td>2012-02-24</td>\n",
       "      <td>173.71</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2012</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56867</th>\n",
       "      <td>120075</td>\n",
       "      <td>AA</td>\n",
       "      <td>36</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2007-05-11</td>\n",
       "      <td>0.11144</td>\n",
       "      <td>0.1045</td>\n",
       "      <td>NC</td>\n",
       "      <td>Other</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01</td>\n",
       "      <td>25000</td>\n",
       "      <td>2007-04-18</td>\n",
       "      <td>811.97</td>\n",
       "      <td>434</td>\n",
       "      <td>True</td>\n",
       "      <td>2007-05-11</td>\n",
       "      <td>2007</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57133</th>\n",
       "      <td>499194</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36</td>\n",
       "      <td>Current</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0.35643</td>\n",
       "      <td>0.3199</td>\n",
       "      <td>CT</td>\n",
       "      <td>Other</td>\n",
       "      <td>Employed</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4000</td>\n",
       "      <td>2011-04-05</td>\n",
       "      <td>174.20</td>\n",
       "      <td>72</td>\n",
       "      <td>True</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2011</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59298</th>\n",
       "      <td>558458</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36</td>\n",
       "      <td>Current</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0.24246</td>\n",
       "      <td>0.2049</td>\n",
       "      <td>CT</td>\n",
       "      <td>Other</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15000</td>\n",
       "      <td>2012-03-20</td>\n",
       "      <td>561.21</td>\n",
       "      <td>204</td>\n",
       "      <td>False</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2012</td>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63925</th>\n",
       "      <td>25554</td>\n",
       "      <td>E</td>\n",
       "      <td>36</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2009-03-03</td>\n",
       "      <td>0.25948</td>\n",
       "      <td>0.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Retail Management</td>\n",
       "      <td>Not available</td>\n",
       "      <td>...</td>\n",
       "      <td>0.02</td>\n",
       "      <td>2000</td>\n",
       "      <td>2006-07-24</td>\n",
       "      <td>79.52</td>\n",
       "      <td>16</td>\n",
       "      <td>True</td>\n",
       "      <td>2009-03-03</td>\n",
       "      <td>2006</td>\n",
       "      <td>7</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78431</th>\n",
       "      <td>185978</td>\n",
       "      <td>C</td>\n",
       "      <td>36</td>\n",
       "      <td>Chargedoff</td>\n",
       "      <td>2009-06-21</td>\n",
       "      <td>0.20253</td>\n",
       "      <td>0.1952</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Executive</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5000</td>\n",
       "      <td>2007-08-20</td>\n",
       "      <td>184.60</td>\n",
       "      <td>61</td>\n",
       "      <td>False</td>\n",
       "      <td>2009-06-21</td>\n",
       "      <td>2007</td>\n",
       "      <td>8</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80075</th>\n",
       "      <td>1111360</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36</td>\n",
       "      <td>Current</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0.20217</td>\n",
       "      <td>0.1655</td>\n",
       "      <td>FL</td>\n",
       "      <td>Attorney</td>\n",
       "      <td>Employed</td>\n",
       "      <td>...</td>\n",
       "      <td>0.05</td>\n",
       "      <td>25000</td>\n",
       "      <td>2014-01-21</td>\n",
       "      <td>885.73</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82831</th>\n",
       "      <td>637992</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36</td>\n",
       "      <td>Current</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0.31012</td>\n",
       "      <td>0.2710</td>\n",
       "      <td>WA</td>\n",
       "      <td>Other</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4000</td>\n",
       "      <td>2012-09-21</td>\n",
       "      <td>163.52</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2012</td>\n",
       "      <td>9</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83697</th>\n",
       "      <td>93861</td>\n",
       "      <td>AA</td>\n",
       "      <td>36</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2007-04-03</td>\n",
       "      <td>0.08684</td>\n",
       "      <td>0.0800</td>\n",
       "      <td>NC</td>\n",
       "      <td>Other</td>\n",
       "      <td>Not available</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5000</td>\n",
       "      <td>2007-02-15</td>\n",
       "      <td>156.68</td>\n",
       "      <td>92</td>\n",
       "      <td>True</td>\n",
       "      <td>2007-04-03</td>\n",
       "      <td>2007</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111265</th>\n",
       "      <td>814064</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2014-02-18</td>\n",
       "      <td>0.20593</td>\n",
       "      <td>0.1819</td>\n",
       "      <td>NY</td>\n",
       "      <td>Executive</td>\n",
       "      <td>Employed</td>\n",
       "      <td>...</td>\n",
       "      <td>0.03</td>\n",
       "      <td>12765</td>\n",
       "      <td>2013-06-24</td>\n",
       "      <td>325.47</td>\n",
       "      <td>114</td>\n",
       "      <td>False</td>\n",
       "      <td>2014-02-18</td>\n",
       "      <td>2013</td>\n",
       "      <td>6</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113270</th>\n",
       "      <td>715718</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36</td>\n",
       "      <td>Current</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0.22712</td>\n",
       "      <td>0.1899</td>\n",
       "      <td>CA</td>\n",
       "      <td>Other</td>\n",
       "      <td>Employed</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2000</td>\n",
       "      <td>2013-02-26</td>\n",
       "      <td>73.30</td>\n",
       "      <td>26</td>\n",
       "      <td>False</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2013</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ListingNumber CreditGrade  Term  LoanStatus ClosedDate  BorrowerAPR  \\\n",
       "8066           500651         NaN    36   Completed 2012-01-26      0.35643   \n",
       "8870            66246           A    36  Chargedoff 2009-08-11      0.12700   \n",
       "17411          501462         NaN    36   Completed 2013-04-11      0.35643   \n",
       "35218          461028         NaN    36   Completed 2013-06-11      0.07389   \n",
       "45028          589104         NaN    36     Current        NaT      0.33973   \n",
       "53014          694401         NaN    12   Completed 2014-01-23      0.25785   \n",
       "53167          560783         NaN    36     Current        NaT      0.35797   \n",
       "56867          120075          AA    36   Completed 2007-05-11      0.11144   \n",
       "57133          499194         NaN    36     Current        NaT      0.35643   \n",
       "59298          558458         NaN    36     Current        NaT      0.24246   \n",
       "63925           25554           E    36   Completed 2009-03-03      0.25948   \n",
       "78431          185978           C    36  Chargedoff 2009-06-21      0.20253   \n",
       "80075         1111360         NaN    36     Current        NaT      0.20217   \n",
       "82831          637992         NaN    36     Current        NaT      0.31012   \n",
       "83697           93861          AA    36   Completed 2007-04-03      0.08684   \n",
       "111265         814064         NaN    60   Completed 2014-02-18      0.20593   \n",
       "113270         715718         NaN    36     Current        NaT      0.22712   \n",
       "\n",
       "        BorrowerRate BorrowerState                         Occupation  \\\n",
       "8066          0.3199            IL  Police Officer/Correction Officer   \n",
       "8870          0.1200            CT                              Other   \n",
       "17411         0.3199            HI                       Professional   \n",
       "35218         0.0705            VA                           Attorney   \n",
       "45028         0.2999            KS                         Nurse (RN)   \n",
       "53014         0.1795            CA                              Other   \n",
       "53167         0.3177            OR                       Professional   \n",
       "56867         0.1045            NC                              Other   \n",
       "57133         0.3199            CT                              Other   \n",
       "59298         0.2049            CT                              Other   \n",
       "63925         0.2500           NaN                  Retail Management   \n",
       "78431         0.1952           NaN                          Executive   \n",
       "80075         0.1655            FL                           Attorney   \n",
       "82831         0.2710            WA                              Other   \n",
       "83697         0.0800            NC                              Other   \n",
       "111265        0.1819            NY                          Executive   \n",
       "113270        0.1899            CA                              Other   \n",
       "\n",
       "       EmploymentStatus  ...  DebtToIncomeRatio  LoanOriginalAmount  \\\n",
       "8066           Employed  ...               0.00                2000   \n",
       "8870      Not available  ...               0.01               12500   \n",
       "17411          Employed  ...               0.00                7500   \n",
       "35218         Full-time  ...                NaN                1000   \n",
       "45028     Self-employed  ...                NaN                4000   \n",
       "53014     Self-employed  ...                NaN                4000   \n",
       "53167     Self-employed  ...                NaN                4000   \n",
       "56867     Self-employed  ...               0.01               25000   \n",
       "57133          Employed  ...               0.00                4000   \n",
       "59298     Self-employed  ...                NaN               15000   \n",
       "63925     Not available  ...               0.02                2000   \n",
       "78431         Full-time  ...               0.00                5000   \n",
       "80075          Employed  ...               0.05               25000   \n",
       "82831     Self-employed  ...                NaN                4000   \n",
       "83697     Not available  ...               0.00                5000   \n",
       "111265         Employed  ...               0.03               12765   \n",
       "113270         Employed  ...               0.00                2000   \n",
       "\n",
       "        LoanOriginationDate MonthlyLoanPayment  Investors IsBorrowerHomeowner  \\\n",
       "8066             2011-04-26              87.10         10                True   \n",
       "8870             2006-12-12             415.18        105                True   \n",
       "17411            2011-05-06             326.62        128                True   \n",
       "35218            2010-06-11               0.00         58                True   \n",
       "45028            2012-05-17             169.78         15               False   \n",
       "53014            2013-01-23             366.62         93                True   \n",
       "53167            2012-02-24             173.71          2                True   \n",
       "56867            2007-04-18             811.97        434                True   \n",
       "57133            2011-04-05             174.20         72                True   \n",
       "59298            2012-03-20             561.21        204               False   \n",
       "63925            2006-07-24              79.52         16                True   \n",
       "78431            2007-08-20             184.60         61               False   \n",
       "80075            2014-01-21             885.73          1                True   \n",
       "82831            2012-09-21             163.52          6                True   \n",
       "83697            2007-02-15             156.68         92                True   \n",
       "111265           2013-06-24             325.47        114               False   \n",
       "113270           2013-02-26              73.30         26               False   \n",
       "\n",
       "        ClosedDate_test  loan_year  loan_month  HomeOwner  \n",
       "8066         2012-01-26       2011           4        Yes  \n",
       "8870         2009-08-11       2006          12        Yes  \n",
       "17411        2013-04-11       2011           5        Yes  \n",
       "35218        2013-06-11       2010           6        Yes  \n",
       "45028               NaT       2012           5         No  \n",
       "53014        2014-01-23       2013           1        Yes  \n",
       "53167               NaT       2012           2        Yes  \n",
       "56867        2007-05-11       2007           4        Yes  \n",
       "57133               NaT       2011           4        Yes  \n",
       "59298               NaT       2012           3         No  \n",
       "63925        2009-03-03       2006           7        Yes  \n",
       "78431        2009-06-21       2007           8         No  \n",
       "80075               NaT       2014           1        Yes  \n",
       "82831               NaT       2012           9        Yes  \n",
       "83697        2007-04-03       2007           2        Yes  \n",
       "111265       2014-02-18       2013           6         No  \n",
       "113270              NaT       2013           2         No  \n",
       "\n",
       "[17 rows x 28 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "more_than_10k = loan_df.query(\"StatedMonthlyIncome >1e5\")\n",
    "more_than_10k"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems that most users who indicated they earn more than 10k dollars a month are self employed and took less than 5k dollars of loan.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Debt to Income Ratio\n",
    "\n",
    "**Debt to Income Ratio** - This compares the amount the borrowers owe each month to the amount the borrower earn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "histplot(loan_df,'DebtToIncomeRatio')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the graph it shows that most borrowers debt to income ratio is between 18% t0 20%. There was a spike in debt ratio at 35%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LoanOriginationDate\n",
    "This is defined as"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [20, 5])\n",
    "#Plot the count of loan on each month \n",
    "plt.subplot(1, 2, 1)\n",
    "base_color = sns.color_palette()[0]\n",
    "sns.countplot(data=loan_df, x='loan_month', color=base_color)\n",
    "plotlabel('Loan Month','Count', 'Number of loans per month');\n",
    "\n",
    "\n",
    "# Plot the count of loans on each year\n",
    "plt.subplot(1, 2, 2)\n",
    "base_color = sns.color_palette()[0]\n",
    "sns.countplot(data=loan_df, x='loan_year', color=base_color)\n",
    "plotlabel('Loan year','Count', 'Number of loans per year');\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the charts the vizualization loans by month there was a high number of loans in January October and December. It is also noted that the loans by year dropped off in 2009 and there was high number of loans in 2013"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loan Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Current                   56576\n",
       "Completed                 38074\n",
       "Chargedoff                11992\n",
       "Defaulted                  5018\n",
       "Past Due (1-15 days)        806\n",
       "Past Due (31-60 days)       363\n",
       "Past Due (61-90 days)       313\n",
       "Past Due (91-120 days)      304\n",
       "Past Due (16-30 days)       265\n",
       "FinalPaymentInProgress      205\n",
       "Past Due (>120 days)         16\n",
       "Cancelled                     5\n",
       "Name: LoanStatus, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_df.LoanStatus.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the count of loans on each year\n",
    "plt.figure(figsize = [10, 5])\n",
    "type_counts = loan_df['LoanStatus'].value_counts()\n",
    "order = ['Current', 'Completed', 'FinalPaymentInProgress', 'Past Due (1-15 days)', 'Past Due (16-30 days)'\n",
    "        ,'Past Due (31-60 days)','Past Due (61-90 days)','Past Due (91-120 days)','Past Due (>120 days)', 'Chargedoff', 'Defaulted']\n",
    "base_color = sns.color_palette()[0]\n",
    "sns.countplot(data=loan_df, y='LoanStatus', color=base_color, order=order)\n",
    "plotlabel('Count','Loan status','Number of loans per loan status');\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the chart it shows that most of the loans are Current and quite good number of loans have been completed or paid of fully. Only 5 loans have been cancelled and 16 loans are past due for more than 3 months\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Income Range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "$25,000-49,999    32192\n",
       "$50,000-74,999    31050\n",
       "$100,000+         17337\n",
       "$75,000-99,999    16916\n",
       "$0                 9168\n",
       "$1-24,999          7274\n",
       "Name: IncomeRange, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_df.IncomeRange.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create categories\n",
    "categories =  [\"$0\", \"$1-24,999\", \"$25,000-49,999\", \"$50,000-74,999\", \"$75,000-99,999\", \"$100,000+\"]\n",
    "var = pd.api.types.CategoricalDtype(ordered = True, categories = categories)\n",
    "loan_df['IncomeRange'] = loan_df['IncomeRange'].astype(var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "$25,000-49,999    32192\n",
       "$50,000-74,999    31050\n",
       "$100,000+         17337\n",
       "$75,000-99,999    16916\n",
       "$0                 9168\n",
       "$1-24,999          7274\n",
       "Name: IncomeRange, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_df.IncomeRange.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the count of loans on each year\n",
    "plt.figure(figsize = [10, 5])\n",
    "type_counts = loan_df['IncomeRange'].value_counts()\n",
    "order = type_counts.index\n",
    "base_color = sns.color_palette()[0]\n",
    "sns.countplot(data=loan_df, y='IncomeRange', color=base_color, order=categories)\n",
    "plotlabel('Count','Income range','Number of loans per income range');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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",
    "\n",
    "> The distribution of Borrower rate and Borrower APR looks multimodal. There is a peak centered at 0.2 and there is a very high increase between 0.35 and 0.36\n",
    ">  Most borrowers debt to income ratio is between 18% t0 20%.\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",
    "\n",
    "> I noticed that there was a high number of loans in January October and December months of the year which also was noted that the loans by year dropped off from 2008 to 2009 and there was high number of loans in 2013. \n",
    "> I also noted that most of the borrowers have monthly income of less than 4k dollars where those that have more than 10k monthly icome was borrowing less than 4k dollars.\n",
    "> There is no need of further transformation of data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bivariate Exploration\n",
    "\n",
    "\n",
    "\n",
    "### Borrowers Rate Vs Period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ListingNumber</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>BorrowerState</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>EmploymentStatus</th>\n",
       "      <th>...</th>\n",
       "      <th>DebtToIncomeRatio</th>\n",
       "      <th>LoanOriginalAmount</th>\n",
       "      <th>LoanOriginationDate</th>\n",
       "      <th>MonthlyLoanPayment</th>\n",
       "      <th>Investors</th>\n",
       "      <th>IsBorrowerHomeowner</th>\n",
       "      <th>ClosedDate_test</th>\n",
       "      <th>loan_year</th>\n",
       "      <th>loan_month</th>\n",
       "      <th>HomeOwner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>193129</td>\n",
       "      <td>C</td>\n",
       "      <td>36</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2009-08-14</td>\n",
       "      <td>0.16516</td>\n",
       "      <td>0.1580</td>\n",
       "      <td>CO</td>\n",
       "      <td>Other</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>...</td>\n",
       "      <td>0.17</td>\n",
       "      <td>9425</td>\n",
       "      <td>2007-09-12</td>\n",
       "      <td>330.43</td>\n",
       "      <td>258</td>\n",
       "      <td>True</td>\n",
       "      <td>2009-08-14</td>\n",
       "      <td>2007</td>\n",
       "      <td>9</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1209647</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36</td>\n",
       "      <td>Current</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0.12016</td>\n",
       "      <td>0.0920</td>\n",
       "      <td>CO</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Employed</td>\n",
       "      <td>...</td>\n",
       "      <td>0.18</td>\n",
       "      <td>10000</td>\n",
       "      <td>2014-03-03</td>\n",
       "      <td>318.93</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2014</td>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>81716</td>\n",
       "      <td>HR</td>\n",
       "      <td>36</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2009-12-17</td>\n",
       "      <td>0.28269</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>GA</td>\n",
       "      <td>Other</td>\n",
       "      <td>Not available</td>\n",
       "      <td>...</td>\n",
       "      <td>0.06</td>\n",
       "      <td>3001</td>\n",
       "      <td>2007-01-17</td>\n",
       "      <td>123.32</td>\n",
       "      <td>41</td>\n",
       "      <td>False</td>\n",
       "      <td>2009-12-17</td>\n",
       "      <td>2007</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>658116</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36</td>\n",
       "      <td>Current</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0.12528</td>\n",
       "      <td>0.0974</td>\n",
       "      <td>GA</td>\n",
       "      <td>Skilled Labor</td>\n",
       "      <td>Employed</td>\n",
       "      <td>...</td>\n",
       "      <td>0.15</td>\n",
       "      <td>10000</td>\n",
       "      <td>2012-11-01</td>\n",
       "      <td>321.45</td>\n",
       "      <td>158</td>\n",
       "      <td>True</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2012</td>\n",
       "      <td>11</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>909464</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36</td>\n",
       "      <td>Current</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0.24614</td>\n",
       "      <td>0.2085</td>\n",
       "      <td>MN</td>\n",
       "      <td>Executive</td>\n",
       "      <td>Employed</td>\n",
       "      <td>...</td>\n",
       "      <td>0.26</td>\n",
       "      <td>15000</td>\n",
       "      <td>2013-09-20</td>\n",
       "      <td>563.97</td>\n",
       "      <td>20</td>\n",
       "      <td>True</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2013</td>\n",
       "      <td>9</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ListingNumber CreditGrade  Term LoanStatus ClosedDate  BorrowerAPR  \\\n",
       "0         193129           C    36  Completed 2009-08-14      0.16516   \n",
       "1        1209647         NaN    36    Current        NaT      0.12016   \n",
       "2          81716          HR    36  Completed 2009-12-17      0.28269   \n",
       "3         658116         NaN    36    Current        NaT      0.12528   \n",
       "4         909464         NaN    36    Current        NaT      0.24614   \n",
       "\n",
       "   BorrowerRate BorrowerState     Occupation EmploymentStatus  ...  \\\n",
       "0        0.1580            CO          Other    Self-employed  ...   \n",
       "1        0.0920            CO   Professional         Employed  ...   \n",
       "2        0.2750            GA          Other    Not available  ...   \n",
       "3        0.0974            GA  Skilled Labor         Employed  ...   \n",
       "4        0.2085            MN      Executive         Employed  ...   \n",
       "\n",
       "   DebtToIncomeRatio  LoanOriginalAmount  LoanOriginationDate  \\\n",
       "0               0.17                9425           2007-09-12   \n",
       "1               0.18               10000           2014-03-03   \n",
       "2               0.06                3001           2007-01-17   \n",
       "3               0.15               10000           2012-11-01   \n",
       "4               0.26               15000           2013-09-20   \n",
       "\n",
       "  MonthlyLoanPayment  Investors IsBorrowerHomeowner  ClosedDate_test  \\\n",
       "0             330.43        258                True       2009-08-14   \n",
       "1             318.93          1               False              NaT   \n",
       "2             123.32         41               False       2009-12-17   \n",
       "3             321.45        158                True              NaT   \n",
       "4             563.97         20                True              NaT   \n",
       "\n",
       "   loan_year  loan_month  HomeOwner  \n",
       "0       2007           9        Yes  \n",
       "1       2014           3         No  \n",
       "2       2007           1         No  \n",
       "3       2012          11        Yes  \n",
       "4       2013           9        Yes  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot a line graph over time\n",
    "plt.figure(figsize = [10, 5])\n",
    "sns.lineplot(data=loan_df, x=\"loan_year\", y=\"BorrowerRate\")\n",
    "plotlabel('Year','Borrowers rate','Borrowers rate over time');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Borrower's rate is defined as the interest rate on the annual cost of a loan to a borrower which is expressed as a percentage.\n",
    "As you can see from the chart this rate increased between 2007 an 2011 qhich is seen to reduce steadily from 2012 to 2014"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data = loan_df.groupby('IncomeRange')['BorrowerRate'].mean()\n",
    "order = [\"$0\", \"$1-24,999\", \"$25,000-49,999\", \"$50,000-74,999\", \"$75,000-99,999\", \"$100,000+\"]\n",
    "plot_data.index\n",
    "plot_data.plot(kind ='bar',color=base_color,figsize=(8,5), legend=False, sort_columns=order)\n",
    "plotlabel('Income range','Borrowers rate','Borrowers rate per income range');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot a box plot between income range and the year\n",
    "plt.figure(figsize = [10, 6])\n",
    "order = [\"$0\", \"$1-24,999\", \"$25,000-49,999\", \"$50,000-74,999\", \"$75,000-99,999\", \"$100,000+\"]\n",
    "sns.boxplot(data = loan_df, x=\"BorrowerRate\", y=\"IncomeRange\", palette = 'Blues_r', order=order)\n",
    "plotlabel('BorrowerRate','IncomeRange','Borrowers rate Vs Income range');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that borrowers rate has negative correlation with Income range where the Borrowers rate reduces with increased income rate. This means that the borrowers with high income range is having a lower interest rate which I think it is sales technique to allow high income earners to borrow more"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot a box plot between income range and the year\n",
    "plt.figure(figsize = [10, 6])\n",
    "sns.boxplot(data = loan_df, x=\"BorrowerRate\", y=\"EmploymentStatus\", palette = 'Blues_r')\n",
    "plotlabel('BorrowerRate','EmploymentStatus','Borrowers rate Vs EmploymentStatus');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Same as Income range, borrowers rate have negative correlation to employment status where those who are employed get lower interest compared to those who are not empoyed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot a box plot between income range and the year\n",
    "plt.figure(figsize = [12, 6])\n",
    "sns.boxplot(data = loan_df, x=\"loan_year\", y=\"BorrowerRate\", palette = 'Blues_r')\n",
    "plotlabel('Lona year','BorrowerRate','Borrowers rate over time');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that borrowers in 2011 got the highest interest rate compared to borrowers in 2005 who got the lowest interest rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def mycountplot(df,xVar):\n",
    "#     plt.figure(figsize = [12, 6])\n",
    "#     sns.countplot(data=df, x=xVar, color=base_color);\n",
    "#     plt.title(f'''Distribution of {xVar}'''.title(), fontsize=14, weight='bold')\n",
    "#     plt.xlabel(xVar.title(), fontsize=10, weight='bold')\n",
    "#     plt.ylabel('Frequency'.title(),fontsize=14, weight='bold')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kURh4YGCS9wDntNcvoXexIEmSNKEGLQKOB06gd+OfAJ8DThtWKEmSNHyDnh1wL/D29pAkSVuBgcYEJDkiyReTfHdzbyUsSZLmh0EPB7wDeB5wTbttryRJmnCDnh1wK/BVCwBJkrYeg/YE/BHw8SSX0bu8LwBV5RgBSZIm1KBFwMnA94HtgQcPL44kSRqVQYuA3arq2UNNIkmSRmrQMQH/nMQiQJKkrcigRcAJwCeT3OMpgpIkbR0GvVjQTsMOou5aeuLHhrLd1accPpTtStLWYtCegJ9J8qtJ3pTkq8MIJEmSRmPQKwbuleT1Sa4ErqXXg/DioSaTJElDNWsRkOTVST4DXAbsDrwKWFtVb6mqa0YRUJIkDcfGxgS8C7gc+O2qWgmQxKsGSpK0FdhYEfAw4AXA25PsCVwAbDv0VJIkaehmPRxQVd+uqtOr6hnAIcBdwB1Jrk/ytpEklCRJQzHw2QFVtaaq/qqqDgSOpO8eApIkafLMejggyfNmme3AQElqvN6FJtHGxgQ8Z5Z5BXxoC2aRJEkjNGsRUFWvGFUQSZI0WoPeRZAkhwOPpXc7YQCq6s+HEUqSJA3foFcM/HvgRcDvA6F32uDDh5hLkiQN2aBnBzy1qo4G7qyqtwBPAfYeXixJkjRsgxYB97TnHyZ5GPATYN/hRJIkSaMw6JiAjybZBfhL4Gp6Zwa8e1ihJEnS8A1UBFTVX7TJC5N8FNi+qu4aXixJkjRsm3J2wFOBpVPrJKGqzh5SLkmSNGQDFQFJzgF+FfgScH9rLsAiQJKkCTVoT8ByYL+q8jbCkiRtJQY9O+CrwC9vqZ0m2SXJB5N8rd2R8ClJdktySZIb2/OufcuflGRVkhuSHNrXfmCSa9q8U5NkS2WUJGlrN2gRsAdwXZJPJbl46rEZ+/1b4JNV9WjgAOB64ETg0qpaBlzaXpNkP2AFvasVHgaclmRB287pwHHAsvY4bDMySZLUKYMeDvizLbXDJAuBZwAvB6iqHwM/TnIk8My22FnAZ4E30Ltt8XlVdS9wU5JVwEFJVgMLq+rytt2zgaOAT2yprNJMvFucpK3FoKcIXrYF9/krwDrgfUkOAK4CXgvsWVVr2/7WJnloW34xcEXf+mta20/a9PT2B0hyHL0eA/bZZ58t904kSZpgsx4OSPKv7Xl9krv7HuuT3D3HfW4DPBE4vaqeAPyA1vW/oRgztNUs7Q9srDqjqpZX1fJFixZtal5JkrZKsxYBVfX09rxTVS3se+xUVQvnuM81wJqq+nx7/UF6RcHtSfYCaM939C3ff5+CJcBtrX3JDO2SJGkAg95FcLcZHtvOZYdV9S3g1iSPak2HANcBFwPHtLZjgIva9MXAiiTbJdmX3gDAK9uhg/VJDm5nBRzdt44kSdqIQQcGXk3vr/E76XXD7wKsTXIH8OqqumoT9/v7wPuTPBj4BvAKegXJBUmOBW6hd7tiquraJBfQKxTuA06oqqkLFh0PnAnsQG9AoIMCJUka0KBFwCeBf6qqTwEkeTa90/EuAE4DnrwpO62qL9G7ANF0h2xg+ZOBk2doXwnsvyn7liRJPYNeJ2D5VAEAUFWfBp5RVVcA2w0lmSRJGqpBewK+m+QNwHnt9YuAO9tFe346lGSSJGmoBu0J+G16o+8/TG/w3T6tbQHwwqEkkyRJQzXoxYK+TW8w30xWbbk4kiRpVGYtApK8o6pel+QjzHAhnqp67tCSSZKkodpYT8A57fmvhh1EkiSN1qxFQFVd1Qb/vbqqXjqiTJIkaQQ2OjCwXZhnUbuwjyRJ2koMeorgauDfklxM74Y/AFTV24cRSpIkDd+gRcBt7fEgYKfhxZEkSaMy6CmCb0myY2+yfrDRFSRJ0ry30TEBSV6T5BbgZuCWJDcnec3wo0mSpGGatQhI8sfAEcAzq2r3qtod+A3gv7R5kiRpQm2sJ+BlwPOq6htTDW36hcDRwwwmSZKGa5BTBH80Q9s9eOMgSZIm2saKgDVJDpnemOQ3gbXDiSRJkkZhY2cH/AFwUZJ/Ba6id/+AJwFPA44ccjZJkjREs/YEVNW1wP7A54ClwK+06f3bPEmSNKE2ep2ANibgvSPIIkmSRmijAwMBkjwvyY1J7kpyd5L1Se4edjhJkjQ8g142+P8Az6mq64cZRpIkjc6gRcDtFgCTYemJHxvKdlefcvhQtivNZFg/x+DPstRv0CJgZZLzgQ8D9041VtWHhhFKkiQN36BFwELgh8Cz+9oKsAiQJGlCDXoXwVcMO4gkSRqtgYqAJNsDxwKPBbafaq+qVw4plyRJGrKBThEEzgF+GTgUuAxYAqwfVihJkjR8gxYBj6iqPwF+UFVnAYcDvza8WJIkadgGLQJ+0p6/l2R/YGd6lxGWJEkTatCzA85IsivwJ8DFwI5tWpIkTahBzw54d5u8jN5NhCRNEC8iJWkmg947YOckf5NkZXv8VZKdhx1OkiQNz6BjAt4L3A28sD3WA+8bVihJkjR8g44J+NWq+m99r9+S5EtDyCNJkkZk0J6Ae5I8fepFkqcB92zOjpMsSPLFJB9tr3dLckm7ZfElbSDi1LInJVmV5IYkh/a1H5jkmjbv1CTZnEySJHXJoEXA7wLvSrI6yWrgncDvbOa+Xwv035nwRODSqloGXNpek2Q/YAW9qxUeBpyWZEFb53TgOGBZexy2mZkkSeqMgYqAqvpyVR0APA54XFU9AfjNue40yRJ6Fxx6d1/zkcBZbfos4Ki+9vOq6t6quglYBRyUZC9gYVVdXlUFnN23jiRJ2ohBewIAqKq7q+ru9vK/b8Z+3wH8EfDTvrY9q2pt289a4KGtfTFwa99ya1rb4jY9vf0Bkhw3dWbDunXrNiO2JElbj00qAqaZ0/H3JEcAd1TVVZuxn5ql/YGNVWdU1fKqWr5o0aIBdytJ0tZt0LMDZjLjF+4AngY8N8lv0bsj4cIk/wDcnmSvqlrbuvrvaMuvAfbuW38JcFtrXzJDuyRJGsCsPQFJ1ie5e4bHeuBhc9lhVZ1UVUuqaim9AX+fqaqX0rsc8TFtsWOAi9r0xcCKJNsl2ZfeAMAr2yGD9UkObmcFHN23jiRJ2ohZewKqaqdRBQFOAS5IcixwC/CCluHaJBcA1wH3ASdU1f1tneOBM4EdgE+0hyRJGsDmHA7YbFX1WeCzbfo7wCEbWO5k4OQZ2lcC+w8voSRJW6/NGRgoSZImmEWAJEkdZREgSVJHWQRIktRRYx0YON8tPfFjQ9v26lMOH9q2JUkahD0BkiR1lEWAJEkdZREgSVJHWQRIktRRFgGSJHWURYAkSR1lESBJUkdZBEiS1FEWAZIkdZRFgCRJHWURIElSR1kESJLUURYBkiR1lEWAJEkdZREgSVJHWQRIktRRFgGSJHWURYAkSR1lESBJUkdZBEiS1FEWAZIkdZRFgCRJHWURIElSR1kESJLUURYBkiR1lEWAJEkdZREgSVJHWQRIktRRFgGSJHWURYAkSR018iIgyd5J/r8k1ye5NslrW/tuSS5JcmN73rVvnZOSrEpyQ5JD+9oPTHJNm3dqkoz6/UiSNKnG0RNwH/CHVfUY4GDghCT7AScCl1bVMuDS9po2bwXwWOAw4LQkC9q2TgeOA5a1x2GjfCOSJE2ykRcBVbW2qq5u0+uB64HFwJHAWW2xs4Cj2vSRwHlVdW9V3QSsAg5KshewsKour6oCzu5bR5IkbcRYxwQkWQo8Afg8sGdVrYVeoQA8tC22GLi1b7U1rW1xm57ePtN+jkuyMsnKdevWbdH3IEnSpBpbEZBkR+BC4HVVdfdsi87QVrO0P7Cx6oyqWl5VyxctWrTpYSVJ2gqNpQhIsi29AuD9VfWh1nx76+KnPd/R2tcAe/etvgS4rbUvmaFdkiQNYBxnBwR4D3B9Vb29b9bFwDFt+hjgor72FUm2S7IvvQGAV7ZDBuuTHNy2eXTfOpIkaSO2GcM+nwa8DLgmyZda2xuBU4ALkhwL3AK8AKCqrk1yAXAdvTMLTqiq+9t6xwNnAjsAn2gPSZI0gJEXAVX1r8x8PB/gkA2sczJw8gztK4H9t1w6SZK6wysGSpLUURYBkiR1lEWAJEkdZREgSVJHWQRIktRRFgGSJHWURYAkSR1lESBJUkdZBEiS1FEWAZIkdZRFgCRJHWURIElSR1kESJLUURYBkiR1lEWAJEkdZREgSVJHWQRIktRRFgGSJHWURYAkSR1lESBJUkdZBEiS1FEWAZIkdZRFgCRJHWURIElSR1kESJLUURYBkiR1lEWAJEkdZREgSVJHWQRIktRRFgGSJHWURYAkSR1lESBJUkdZBEiS1FEWAZIkddTEFwFJDktyQ5JVSU4cdx5JkibFRBcBSRYA7wL+C7Af8OIk+403lSRJk2GiiwDgIGBVVX2jqn4MnAccOeZMkiRNhFTVuDPMWZLnA4dV1ava65cBT66q35u23HHAce3lo4AbhhBnD+DbQ9juMJl5+CYtL5h5FCYtL5h5FIaV9+FVtWimGdsMYWejlBnaHlDVVNUZwBlDDZKsrKrlw9zHlmbm4Zu0vGDmUZi0vGDmURhH3kk/HLAG2Lvv9RLgtjFlkSRpokx6EfAFYFmSfZM8GFgBXDzmTJIkTYSJPhxQVfcl+T3gU8AC4L1Vde2Y4gz1cMOQmHn4Ji0vmHkUJi0vmHkURp53ogcGSpKkuZv0wwGSJGmOLAIkSeooiwBJkjrKIkCSpI6yCJiDJHtMe/3SJKcmOS7JTBcwGrsk/zXJbm16UZKzk1yT5PwkS8adb7okb0/ytHHn2BRJdkvyp0lelZ43Jflokr9Msuu4821Ikt9I8s4kFyW5MMkpSR4x7lwbkuTQJKcnubhlPj3JYePONRdJ/nTcGTakfc7HJlk6rf2VY4q0Qe3/2wuTvKBNH9J+J78mycR8zyX5zMj36dkBmy7J1VX1xDb9x8CvA/8IHAGsqarXjzPfTJJcV1X7tenzgSuADwDPAl5SVf95nPmmS7IOuBlYBJwPnFtVXxxvqtkl+ThwDbAQeEybvgD4z8ABVTXv7muR5BRgT+BS4CjgJuDrwGuAt1XVB8aX7oGSvAN4JHA2vYuFQe8iYUcDN1bVa8cUbU6S3FJV+4w7x3RJ3gY8HbgaeA7wjqr6uzbvZ7//5oskpwEPBR4M3A1sB3wE+C3g9vn4c5HkK9Ob6P1s3wBQVY8bSQ6LgE2X5ItV9YQ2fTXw61X1gyTbAldX1a+NN+EDJbmhqh7Vpq+qqgP75n2pqh4/tnAzmPqMkyyjdxGoFfSuBXEuvYLg62MNOIOpz7H1Bq2pqsXT540v3cySXDP185pkG+Cyqnpa67n4l6raf7wJf1GSr1fVI2doD/D1qlo2hlizSnL3hmYBO1TVvLteS5JrgCe0a7HsQu+PnBuq6vX9v//mi6mf4/Y7+FvAXlX14/Yz/cV5+jv5YnoFy1uBe+j9PPwLveKLqrp5FDkmpptkntkhyROSHAgsqKofAFTVT4D7xxttgz6b5M+T7NCmj4JeVzBw11iTzawAqurGqvqLqnos8EJge+DjY022YQ9qX557AztOdaMm2Z3eXyjz0U+nDhMBD6NXaFFVdzLzvTnG7UdJDpqh/UnAj0YdZkDfA5ZV1cJpj52AtWPOtiHbVNV9AFX1PXq9AQuTfID5+bM8lfUnwBfaXWVp72Fe/k6uqucCF9K7QNABVbUa+ElV3TyqAgAm/IqBY7QWeHub/m6Svapqbftlf98Yc83m94A38fM7KL4+yQ/odZm9bGypNuwBX0BV9RXgK8BJo48zkP8FfK1NvxJ4d5IC9gPeMrZUs3sb8MUkNwCPBo6H3rgR4MvjDLYBLwdOT7ITPz8csDe9v6hePqZMG3M28HDg9hnm/eOIswzqP5L8p6q6DKCq7geOTfJW4L+NN9qMvpVkx6r6flX9bHxIkl8GfjzGXLOqqn9K8mngL5K8ijEUWB4O2IKSLAC2q6ofjjvLbJLsTK/S/864s2zI1H/ocefYVO1nIK0bdRvg8cA3q2q+/sVH6wn4FWBV+6tv3mu/3BfTKxbXVNW3xhxpq9J6DKmqe2aYt7iqvjn6VJsuyS8Bv1RVd4w7y8YkOQB4SlX9/Uj3axEwN+0Y5EH0fhEVvbsXXlnz+AOdtMyTlhfMPE5JHl1VX9v4kvOHmYdv0vLCaDNbBMxBkmcDpwE3AlMV8RLgEcBrqurT48q2IZOWedLygpnHbb6OtJ+NmYdv0vLCaDM7JmBu/hZ4VhvI8TNJ9qU3aO0x4wi1EZOWedLygpmHLsmpG5oF7DLCKAMz8/BNWl6YP5ktAuZmG34+KKnfN4FtR5xlUJOWedLygplH4RXAHwL3zjDvxSPOMigzD9+k5YV5ktkiYG7eC3whyXnAra1tb3rnsr9nbKlmN2mZJy0vmHkUvgB8tar+ffqMJH82+jgDMfPwTVpemCeZHRMwR0n2A55L3whl4OKqum6swWYxaZknLS+YedjamQw/mu9n4PQz8/BNWl6YP5ktAiRJ6iivGDgHSXZO7yYrX0vynfa4vrXtMu58M5m0zJOWF8w8CpOWF8w8CpOWF+ZPZouAubkAuBN4ZlXtXlW7A79B7/Kg8+qGK30mLfOk5QUzj8KG8t7J/MwLZh6FScsL8ySzhwPmIH0349mUeeM0aZknLS+YeRQmLS+YeRQmLS/Mn8z2BMzNzUn+KMmeUw1J9kzyBn4+wnq+mbTMk5YXzDwKk5YXzDwKk5YX5klmi4C5eRGwO3BZkjuTfBf4LLAbvTvdzUeTlnnS8oKZR2HS8oKZR2HS8sI8yezhgDlK8mh6l1e9ov9GN0kOq6pPji/Zhk1a5knLC2YehUnLC2YehUnLC/Mkc1X52MQH8Af0bsn7YWA1cGTfvKvHnW9ryDxpec1sXjObdxIze8XAuXk1cGBVfT/JUuCDSZZW1d/Su9jKfDRpmSctL5h5FCYtL5h5FCYtL8yTzBYBc7OgWtdNVa1O8kx6/4APZ/7+wE1a5knLC2YehUnLC2YehUnLC/MkswMD5+ZbSR4/9aL9Qx4B7AH82rhCbcSkZZ60vGDmUZi0vGDmUZi0vDBPMjswcA6SLAHuq6pvzTDvaVX1b2OINatJyzxpecHMozBpecHMozBpeWH+ZLYIkCSpozwcIElSR1kESJLUURYBkn5Bku9vfClJWwOLAEkTLT3+LpPmwP84kjYqyeOTXJHkK0n+Kcmurf3VSb6Q5MtJLkzykNZ+ZpJTk/x7km8keX5rPyfJkX3bfX+S5yZZkOQv27a+kuR32vwdk1ya5Ook10ytm2RpevdePw24Gth71J+JtDWwCJA0iLOBN1TV44BrgDe39g9V1ZOq6gDgeuDYvnX2Ap5O79znU1rbu4FXACTZGXgq8PG23l1V9STgScCrk+wL/Aj4r1X1RHr3Wv/rJFMXUnkUcHZVPaGqbh7Gm5a2dl4xUNKs2pf1LlV1WWs6C/hAm94/yVuBXYAdgU/1rfrhqvopcF3a7VKr6rIk70ryUOB5wIVVdV+SZwOPm+oxAHYGlgFrgLcleQbwU2AxMHXr1Zur6oohvGWpMywCJG2OM4GjqurLSV4OPLNv3r190/2XQT0HeAmwAnhl3/zfr6r+IoK2zUX0rrH+kySrge3b7B9skXcgdZiHAyTNqqruAu5M8uut6WXAVK/ATsDaJNvS+2IfxJnA69q2r21tnwKOb9shySOT/BK9HoE7WgHwG8DDN/PtSOpjT4Ck6R6SZE3f67cDxwB/3wb+fYN2XB/4E+DzwM30xgrstLGNV9XtSa6ndwvVKe8GlgJXt2P+64CjgPcDH0myEvgS8LW5vilJD+RlgyWNVCskrgGe2HoZJI2JhwMkjUySZ9H7a/7vLACk8bMnQJKkjrInQJKkjrIIkCSpoywCJEnqKIsASZI6yiJAkqSOsgiQJKmj/n/UnXRSoF61+gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot a graph to show Loan amount borrowed over time\n",
    "loan_df.groupby('loan_year')['LoanOriginalAmount'].mean().plot(kind='bar',color=base_color,figsize=(8,5), legend=False)\n",
    "plotlabel('Loanyear','LoanOriginalAmount','Loan amount per year');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the charts it shows that the amount of loans borrowered by year dropped off in 2009 and there was high number of loan amount in 2013 and increased steadily after 2009"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data = loan_df.groupby('EmploymentStatus')['MonthlyLoanPayment'].sum()\n",
    "plot_data.index\n",
    "plot_data.plot(kind ='bar',color=base_color,figsize=(8,5), legend=False)\n",
    "plotlabel('EmploymentStatus','LoanOriginalAmount','Loan amount per employment status');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that those who are employed borrowed more amount followed by borrowers with Full-time jobs. This can be true because of job security. Borrowers with job security have high borrowers rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data = loan_df.groupby('ProsperRating')['MonthlyLoanPayment'].sum()\n",
    "prosper_order = [\"AA\", \"A\", \"B\", \"C\", \"D\", \"E\", \"HR\"]\n",
    "loan_df.ProsperRating = pd.Categorical(loan_df.ProsperRating, categories=prosper_order, ordered=True)\n",
    "plot_data.plot(kind ='bar',color=base_color,figsize=(8,5), legend=False, sort_columns=prosper_order)\n",
    "plotlabel('ProsperRating','LoanOriginalAmount','Loan amount by prosper rating');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "C     18345\n",
       "B     15581\n",
       "A     14551\n",
       "D     14274\n",
       "E      9795\n",
       "HR     6935\n",
       "AA     5372\n",
       "Name: ProsperRating, dtype: int64"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_df.ProsperRating.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the borrowers with prosper rating C borrowed more amount compared to even the borrowers with prosper rating of A. Hower the borrowes with the least prosper rating `HR` has the least total amount borrowed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 113937 entries, 0 to 113936\n",
      "Data columns (total 28 columns):\n",
      " #   Column                       Non-Null Count   Dtype         \n",
      "---  ------                       --------------   -----         \n",
      " 0   ListingNumber                113937 non-null  int64         \n",
      " 1   CreditGrade                  28953 non-null   object        \n",
      " 2   Term                         113937 non-null  int64         \n",
      " 3   LoanStatus                   113937 non-null  object        \n",
      " 4   ClosedDate                   55089 non-null   datetime64[ns]\n",
      " 5   BorrowerAPR                  113912 non-null  float64       \n",
      " 6   BorrowerRate                 113937 non-null  float64       \n",
      " 7   BorrowerState                108422 non-null  object        \n",
      " 8   Occupation                   110349 non-null  object        \n",
      " 9   EmploymentStatus             111682 non-null  object        \n",
      " 10  InvestmentFromFriendsCount   113937 non-null  int64         \n",
      " 11  InvestmentFromFriendsAmount  113937 non-null  float64       \n",
      " 12  LoanOriginalAmount           113937 non-null  int64         \n",
      " 13  ProsperRating                84853 non-null   object        \n",
      " 14  ProsperScore                 84853 non-null   float64       \n",
      " 15  IncomeRange                  113937 non-null  category      \n",
      " 16  StatedMonthlyIncome          113937 non-null  float64       \n",
      " 17  LoanCurrentDaysDelinquent    113937 non-null  int64         \n",
      " 18  DebtToIncomeRatio            105383 non-null  float64       \n",
      " 19  LoanOriginalAmount           113937 non-null  int64         \n",
      " 20  LoanOriginationDate          113937 non-null  datetime64[ns]\n",
      " 21  MonthlyLoanPayment           113937 non-null  float64       \n",
      " 22  Investors                    113937 non-null  int64         \n",
      " 23  IsBorrowerHomeowner          113937 non-null  bool          \n",
      " 24  ClosedDate_test              55089 non-null   datetime64[ns]\n",
      " 25  loan_year                    113937 non-null  int64         \n",
      " 26  loan_month                   113937 non-null  int64         \n",
      " 27  HomeOwner                    113937 non-null  object        \n",
      "dtypes: bool(1), category(1), datetime64[ns](3), float64(7), int64(9), object(7)\n",
      "memory usage: 27.7+ MB\n"
     ]
    }
   ],
   "source": [
    "loan_df.info()"
   ]
  },
  {
   "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",
    "\n",
    "> From the start I choose Borrowers Rate as I think it has effect on the potential amount the borrower can be entitled to. I noticed that the Borrower Rate has effect on the amount where the amount borrowed increased with higher ratings across all terms\n",
    "\n",
    "### Did you observe any interesting relationships between the other features (not the main feature(s) of interest)?\n",
    "\n",
    "> Borrowers with more monthly income and borrowers rate was borrowing more money and there fore the loan amount is positivey correlated with stated monthly income and borrowers rate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multivariate Exploration\n",
    "\n",
    "### Borrowers Rate Vs Employment status over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZDNFolGg0SiAQIJlMAnDEEUcwefJkAC688EKeeeYZPvrRj1bGeOaZZzj77LMJh8MAnHPOOTz99NNcd911JBIJXnrpJVpaWjjkkENIJBIAHH744YwePRqAKVOmcMopp1Tm8fjjjwPw6KOPsnLlysrzpFIp0uk0AB/84Afx+/34/X7q6+tpaWnZ4drGjRvHm2++yWOPPcZjjz3GiSeeyD333MOJJ564w7G33HILDzzwAACbN2/mrbfeqsx1KObNm8dFF13Ehz/8YT784Q8P+byhUEGhoiiKoiiKouznbMuio2kT2a5ODN/Qm5a/E/x+PwCaplV+7rlt2zaw446Z/W97ia6BXXnlldxxxx00NzdzxRVX7PC8/Z+79/O6rsuSJUsIBoODzhu8jXR6zhnouNNPP53TTz+dhoYGFi9evENQ+MQTT/Doo4+yZMkSQqEQxx133IBtIwzD6FM62/uYv/71rzz11FM8+OCDfPOb3+T111/HMPZMOKfWFCqKoiiKoijKfiyfTrHljdcppFNousGTf7qdtcte2NvTGpYXXniB9evX47oud999N0cffXSfx4855hgWL15MLpcjm83ywAMP8P73vx+As88+m//7v//jxRdf5NRTTx3W855yyinceuutlduDZTt7RKPRSiYRYPny5WzduhXwAsxXXnmFiRMnAmCaJpZlAdDd3U11dTWhUIg33niD5557rjJG7+MaGhpobW2lo6ODYrHIww8/XBl78+bNHH/88Xzve98jmUySyWSGda07ozKFiqIoiqIoirIfch2HruYtdLc04w9H6GjazDN//j2lfI4phx3xjs6lZ7OUHqeddtqw2lIcddRR3Hzzzbz66quVTWd6O/TQQ7nssss44gjvuq688koOOeQQAHw+H8cffzxVVVXouj6sed9yyy1cc801zJs3D9u2OeaYYyob4AwkkUjwvve9jzlz5nD66adz4okn8olPfIJisQh4ZbCf/vSnAbjqqquYN28ehx56KLfddhu/+tWvmDdvHtOnT+c973lPZczexy1atIivfvWrHHnkkUyaNIkZM2YA4DgO//Ef/0F3dzdSSm644YbKLqd7gthZKvbdYsGCBXLp0qV7exqKoiiKoiiKskcUczlaN67DLhYJhMKsevYJVjzyNyI1Cd537kUkxk9g1JRpe/Q5V61axcyZM/fomOCVVv7gBz+oZMWGy3VdDj30UO655x4OOuigPTy7/dNA75UQYpmUcsFAx6tMoaIoiqIoiqLsJ6TrkupopXNLE4YvgG4YPLnodra88ToT587nyHMuQNPErgd6l1i5ciVnnHEGZ599tgoI3wYVFCqKoiiKoijKfsAqFenYtJFcOkUwEqVz6xaevvN28ukUh3/oI0w76miEENil4t6e6rAcd9xxHHfccbt17qxZs1i3bt2endABSAWFiqIoiqIoirKPy3R10r55A5rQCcXirF7yDMv+tphgNMYp/3kdteMn7u0pKvuxEd19VAhxmhDiTSHEGiHEzQM8fpYQ4hUhxAohxFIhxNG7OlcIUSOE+KcQ4q3y39UjeQ2KoiiKoiiKsrc4tk3bpg20rFuLLxBCM3SeuesPvPjQfYyeOp0PXHtTn4BQui7FXA7DH9iLs1b2NyOWKRRC6MDPgZOBJuBFIcSDUsqVvQ77F/CglFIKIeYB/wvM2MW5NwP/klJ+txws3gx8fqSuQ1EURVEURVH2hkImQ+vGdTi2TbiqimTLNp5adDuZjnbmn3oGs485AaFtz/G4jkMulaJ69CiqR43dizNX9jcjWT56BLBGSrkOQAhxF3AWUAkKpZS9m2uEATmEc88Cjisf93vgCVRQqCiKoiiKorxLuK5Dd2sLXVu34A+F8AdDrF32PC/85V7MQJCTrryGhslT+5xjl4oU8znqGycRTdTupZkr+6uRLB8dC2zudbupfF8fQoizhRBvAH8FrhjCuQ1Sym0A5b/r9/C8FUVRFEVRFGWvsAoFmtespmvbVkKxOAjBkvv+zJJ7/0zt+EY+eO1NOwSEhWwG27IYO33Wuz4g/Pa3v83s2bOZN28e8+fP5/nnnx/02Msuu4x7770XgKeffprZs2czf/588vn8OzLXSCQyouP3vr63ayQzhQPthbtDU0Qp5QPAA0KIY4BvAicN9dydPrkQVwFXAUyYMGE4pyqKoiiKoijKO0pKSaajnfYtmzAMk3C8ilR7G08tup1k81bmHH8y8046Ha1XuaiUknyqm0A0St2ESRg+3168gpG3ZMkSHn74YZYvX47f76e9vZ1SqTSkcxctWsRNN93E5ZdfPsKz3D+NZKawCRjf6/Y4YOtgB0spnwKmCCFqd3FuixBiNED579ZBxvu1lHKBlHJBXV3d7l+FoiiKoiiKoowg27Jo3bCOtk0bCIQi+IIhNr66gr/f+gPyqW6Ov+wq5p/ywT4Boes45LqTRGvraZg89V0fEAJs27aN2tpa/H4/ALW1tYwZM4Zly5Zx7LHHcthhh3Hqqaeybdu2Puf99re/5X//93/5xje+wUUXXbTDuH/605844ogjmD9/Pv/5n/+J4ziAl+n7/Oc/z2GHHcZJJ53ECy+8wHHHHcfkyZN58MEHAbjjjjs466yzOO2005g+fTpf//rXdxhfSsnnPvc55syZw9y5c7n77rsBuPjii/nLX/5SOe6iiy7iwQcfxHEcPve5z3H44Yczb948/ud//qcyzqc//WlmzZrFBz/4QVpbBwyDdstIBoUvAgcJISYJIXzABcCDvQ8QQkwVQojyz4cCPqBjF+c+CFxa/vlS4C8oiqIoiqIoyn4ol+pmyxuvU0inCFdVI6XkxYfu5+k77yBeP4oPXHsTY6fP6nOOXSqRz6Spm9BI7fgJaJq+l2b/zjrllFPYvHkz06ZN41Of+hRPPvkklmVx7bXXcu+997Js2TKuuOIKvvSlL/U578orr+TMM8/k+9//PosWLerz2KpVq7j77rt59tlnWbFiBbquV47JZrMcd9xxLFu2jGg0ype//GX++c9/8sADD/DVr361MsYLL7zAokWLWLFiBffccw9Lly7t8xz3338/K1as4OWXX+bRRx/lc5/7HNu2bePKK6/k9ttvB6C7u5t///vffOADH+B3v/sd8XicF198kRdffJHf/OY3rF+/ngceeIA333yTV199ld/85jf8+9//3mOv7YiVj0opbSHEp4F/ADpwm5TydSHE1eXHfwV8BLhECGEBeeB8KaUEBjy3PPR3gf8VQnwc2AScO1LXoCiKoiiKoigjwXUcupq30N3SjD8cwTB9ZJNdPH3nHbRv3siM9x3LIad9CN3o+3G9mM0ikYyZNoNAeGTXrO3M1x96nZVbU3t0zFljYnztQ7MHfTwSibBs2TKefvppHn/8cc4//3y+/OUv89prr3HyyScD4DgOo0ePHvJz/utf/2LZsmUcfvjhAOTzeerrvS1LfD4fp512GgBz587F7/djmiZz585lw4YNlTFOPvlkEokEAOeccw7PPPMMCxYsqDz+zDPPcOGFF6LrOg0NDRx77LG8+OKLnHnmmVxzzTW0trZy//3385GPfATDMHjkkUd45ZVXKusFu7u7eeutt3jqqacq44wZM4YTTjhhyNe5KyPavF5K+Tfgb/3u+1Wvn/8b+O+hnlu+vwM4cc/OVFEURVEURVHeGcVcjtaN67CLRUKxKoSmseWNlTz7v39Cui7HXHQ5E+Yc3OccKSX5dAp/KERd42RMn38vzX7v0nWd4447juOOO465c+fy85//nNmzZ7NkyZIhnb9582Y+9KEPAXD11VcjpeTSSy/l//2//7fDsaZpUi5qRNO0StmqpmnYtl05rueYwW57Oa+BXXzxxSxatIi77rqL2267rXL8z372M0499dQ+x/7tb3/bYew9ZUSDQkVRFEVRFEVRPNJ1SXW00rmlCdMfIBSL4zoOK/7xMK8/8SjVo8dwzMcuJ1rbdz8M13HIpbuJ1TWQGDtunygX3VlGb6S8+eabaJrGQQcdBMCKFSuYOXMmjzzyCEuWLOGoo47CsixWr17N7NkDz2/8+PGsWLGicnvlypWcddZZ3HDDDdTX19PZ2Uk6nWbixIlDntc///lPOjs7CQaDLF68uBLc9TjmmGP4n//5Hy699FI6Ozt56qmn+P73vw94O4geccQRjBo1qjLnU089lV/+8peccMIJmKbJ6tWrGTt2bGWcSy65hNbWVh5//HE+9rGPDeclHJQKChVFURRlhGW7i2S6isQSAfxhE00bmW96FUXZd1mlIh2bNpJLpwhGomi6Tj6d4pm7/kDLujVMPfw9LPjQORhm3w1jbKtEMZulbkIj0UTdiGWK9geZTIZrr72WZDKJYRhMnTqVX//611x11VVcd911dHd3Y9s2119//aBBYX+zZs3iW9/6Fqeccgqu62KaJj//+c+HFRQeffTRXHzxxaxZs4aPfexjfUpHAc4++2yWLFnCwQcfjBCC733ve4waNQqAhoYGZs6cyYc//OHK8VdeeSUbNmzg0EMPRUpJXV0dixcv5uyzz+axxx5j7ty5TJs2jWOPPXbIc9wVsbN05rvFggULZP8Fn4qiKIryTkl15OloyqDpAqELotUBwlV+fAH13ayiHAgyXZ20b96AJnT84TAAzeve4pk//wGrWODID5/L5EOP2OG8Yj6HdBzqJ08lGIm+09PewapVq5g5c+bensY+5Y477mDp0qXceuutu3V+Lpdj7ty5LF++nHg8vsfmNdB7JYRYJqVcMNDx6r9GiqIoivIO0E2NQNjEdSXpzgLd7Xn8QYNYIkAg6kPXR3JDcEVR9gbHtunc2kSqrY1gNIpuGEjX5fUn/8XL//wb0do6Tvr4p6ga1XdjFCklhUwaMxCgfuo0TH9gL12BMpIeffRRrrjiCm688cY9GhDuDhUUKoqiKMo7SNMEgbAJgF1yaG/KIkSWcLWfSHUAX0A/oMvDFOXdopDJ0LpxHY5tE66qQghBMZvl2XsWsfXNlUycdwjvOef8HQI+13XJl/sPJsaNR9P3/vpBZXCXXXYZl1122W6de9JJJ7Fp06Y9O6HdpIJCRVEURdlLDJ+O4dORriTXXSTdUcDnN4jVBghGfeimyh4qyv7GdR2SLc0kt23FHwrhD4YAaN+0gaf//Hvy6RSHn/lRpr3nfTt8AdSzfjAxYQKx2gb1BZHyjlFBoaIoiqLsZUIT+ENe9tCxXDq2ZWCbIBT3Ea0O4A8aCLU5jaLs86xCgbZN6ylks4RicYSmIaXkzX8/xfK/P0gwFufUqz9DYtyEHc4t5XM4js3og6YTjMb2wuyVA5kKChVFURRlH6KbGkHTh5SSYsYilyyhm4JYIkgo5sPwqVIyRdnXSCnJdLTTvmUThmESjlcBUCoUeO7+u9j06grGzZzDUed+rJI57C2XTuHzBxg1ZRpmQK0fVN55KihUFEVRlH2QEAJf0PvPtGO7JFtydDVnCUZ9RBMB/CHV2kJR9gW2ZdHRtIlsVyfBaKyyBrBr2xaeWnQ7ma5ODjn9TGa9//gdykFd1yWf6iaSSJAYOwHdUB/Nlb1D/eYpiqIoyj5ONzT0iFeGZhVsWtan0AxBNBEgHFOtLRRlb8mlumnbuB6kJFxVXbl/zdLnePEv9+ELBjn5ymuonzRlh3MdyyKfyZAYN554vVo/OFS6rjN37tzK7QsuuICbb775bY/b2NjI0qVLqa2tfdtjDWThwoVEIhFuuummERn/7VL/FVEURVGU/YQQAjNgYAbAdVzS7UVSrXn8IZNoIkAwYqKp1haKMuJcx6GreQvdLc34w5FKw3m7VOKFB+9l3bIXGDVlGkdfcDGBAfoLlgp57FKJUVMPqpSaKkMTDAZZsWLF3p7Gu476L4eiKIqi7Ic0XSMQNghGfbiOS/vmDE1vdNG1LUspb+/t6SnKu1Yxl2PL6lWk2toIxasrAWF3Wwv/94sfs275i8w94VROuOLqAQPCfCYNCMbOmKUCwj2osbGRL37xixx11FEsWLCA5cuXc+qppzJlyhR+9atfAfDEE09wzDHHcPbZZzNr1iyuvvpqXNfdYawf/ehHzJkzhzlz5vCTn/wEgK985Sv89Kc/rRzzpS99iVtuuQWA73//+xx++OHMmzePr33ta5Vjvv3tbzN9+nROOukk3nzzzRG8+rdPZQoVRVEUZT/Xu7VFJlkg1VHADOhea4uID91Q3wErytslXZdURyudW5ow/QFCse3Nxje8spzn7rsb3TQ44bL/ZMy0GQOen0unCFdVUzt+ArphvpPTf9fI5/PMnz+/cvsLX/gC559/PgDjx49nyZIl3HDDDVx22WU8++yzFAoFZs+ezdVXXw3ACy+8wMqVK5k4cSKnnXYa999/Px/96Ecr4y1btozbb7+d559/HiklRx55JMceeywf//jHOeecc/jMZz6D67rcddddvPDCCzzyyCO89dZbvPDCC0gpOfPMM3nqqacIh8PcddddvPTSS9i2zaGHHsphhx32jr5Ww6GCQkVRFEV5l+jd2sK2HDq2ZABBJO4jXB3AHzLUuiVF2Q1WqUjHpo3k0imCkWhlMxnHtln218Wsfu4Z6iZO4ugLLx0w++fYNoVMiuox46iqH4XQ3gVf1Pz9Zmh+dc+OOWounP7dnR6ys/LRM888E4C5c+eSyWSIRqNEo1ECgQDJZBKAI444gsmTJwNw4YUX8swzz/QJCp955hnOPvtswuEwAOeccw5PP/001113HYlEgpdeeomWlhYOOeQQEokEjzzyCI888giHHHIIAJlMhrfeeot0Os3ZZ59NKBTqM7d9lQoKFUVRFOVdyDB1DFNHSkk+Y5FJFtF92vbWFqZqbaEoQ5Hp6qR98wY0ofcJ+DJdHTx95+/paNrEzKOP45DTPlQJFnuzigWsUpGGyQf12YxG2fP8fj8AmqZVfu65bdteWX3/L8b635ZSDjr+lVdeyR133EFzczNXXHFF5fgvfOEL/Od//mefY3/yk5/sV1/CqaBQURRFUd7F+re26GrO0bXNa20RSwS97OG7uLVFwXIoWA4BU8dvaPvVhzRl73Jsm84tTaTa2whGo33aRTStep1/37MIKV2O+Y8rmDB73oBj5NNpdNNg7LSZ+AboT7hf20VGb1/1wgsvsH79eiZOnMjdd9/NVVdd1efxY445hssuu4ybb74ZKSUPPPAAf/zjHwE4++yz+epXv4plWdx5550AnHrqqXzlK1/hoosuIhKJsGXLFkzT7DOObds89NBDOwSO+xIVFCqKoijKAUI3NILl1halgk3zhm50QyOWCBCK+zF9757soe24bE3mWdeeBUAAuiaIBU2qgiaRgEmwHCiqfo9Kf4VMhtaN63Bsm3BVVeXLBNdxWPHIX1n51GNUjxnHMR+7jGhixxYG3vrBbsLxamonTFTrB/eg/msKTzvtNL773aEHqEcddRQ333wzr776amXTmd4OPfRQLrvsMo444gjAyw72lIb6fD6OP/54qqqq0MtZ4VNOOYVVq1Zx1FFHARCJRPjTn/7EoYceyvnnn8/8+fOZOHEi73//+9/OZY84sbMU6bvFggUL5NKlS/f2NBRFUZQDVKojT7IlRyC8730wdB2XUsFBuhAIG0QTQQJhY79tbSGlpDNbYnVLmqLtUhX0oZeDPseVFG2Hku3iSInAy6RGAgZVQZNowCTo0wmaeuUc5cDiug7JlmaS27biD4UwfNtLEHOpbp758+9p3bCOg454LwvOOBvd3PHftGPb5DMpqkeNpXrU6HfH+sGyVatWMXPmzL09jd32xBNP8IMf/ICHH354t853XZdDDz2Ue+65h4MOOmgPz27PGui9EkIsk1IuGOh4lSlUFEVRlAOY19rC+9BqFR3aNqUROkSrA4Sr/PgC+89HhWzRZm1bho5MkYjfJNIvCNc1QchnEPJtv8+VkpLtsjVZwHZzIAEBYZ9OPOQjHjAI+g2Cpo65nwbKytCUCnnaN22gkM0SisX7BHPNa1fzzF1/wCqWeO95/8HkQwb8XI1VLGIVCzRMmkqkuuadmrryDli5ciVnnHEGZ5999j4fEO6O/ef/6RVFURRFGVGmX8f067iuJN1ZINWWxwwaxGsDBKI+9H00KLIcl6auHBvac/gNjdpIYMjnakIQMHUCvTbekVJiOZL2dJGtyXzlfr+hEQ+ZVAV8hPy6Wqf4LiGlJNPRTnvTJgzT7LOZjHRdXnviUV559O/E6uo56cpPU9UwasBxCpkMQhOMmTYTf+hdtn7wXeK4447juOOO261zZ82axbp16/bshPYhKihUFEVRFKUPTROVUle75NDelEEIQbjaT6TKjy+4b7S2kFLSli7yVmsG23WpDvn6lH0+t66DP7+4iXjAZFJtuPJnbFUQYycBrhACnyHw9evvaDsuqZxNe7qEWy4/7VmnWB3yES5nFAOmChT3F7Zl0dG0iWxXJ8ForM/uoYVshmfv/hPb3nqDxvmHceSHz8PstaNlDykluVQ3oWiM2omTMAYoKVWUfZ0KChVFURRFGZTh0zF8XmuLXHeJTGcB02cQrQ0QivrQzb2TPUwXLNa0ZkjmLGIBE1+vjTyyRZvfPL2Of73RyvjqIN0Fiwdf3ortevsoGJpgQk2Ixl6B4qREmFhw5x/mDV3D0DXCve5zXEm+5JDMZSuBoiYE4fI6xVjQJGCqdYr7olyqm7aN60HKHVpFtG1cz9N//j2FTJojPnwuBx3x3gEDfddxyKVSVI1qoHr0WDTt3bNZk3JgUUGhoiiKoii7JITAH9re2qJzW5aOrRnCcT/RmgD+4DvT2qJoO2zqyNHUlSNoGtRG+mZuXm1K8uN/vUVHpsh5C8ZzweHjMXUN23HZksyzvj1b+bN8UxePvdFaOTcR9vXJKDbWhhkTD+40mNvVOsVNndvXKUb8RmX300B5Qxu1TvGd5zoOXc1b6G5pxh+OYJjb3zwpJW88+yTL//4g4Xg1p37yehJjxw84jl0qUsznqG+cNOAOpIqyP1FBoaIoiqIow9K7tUUxa5FLltBNQaw2SCjmwxiB1hauK2lJFVjTlkFKqAn70Xplbkq2yx+WbOAvL29lTDzAf39kHjNGxSqPG7rGxESYiYkwx03fPm5XrsT69iwbegWLL21O4pSzij5DY2JNqG+wmAgT9g/+EWo46xQDhk4sZFAV8MpP/abW5zxlzyrmcrRuXIddLBKKV/fJ/pUKeZbc+2c2v/4K42fN5aiPXjhob8FCNgPA2Omz8IfCAx6jKPuTEQ0KhRCnAT8FdOC3Usrv9nv8IuDz5ZsZ4JNSypeFENOBu3sdOhn4qpTyJ0KIhcAngLbyY1+UUv5tBC9DURRFUZQBCCHwBQ0Ieq0tki05upqzBCI+YokA/rC5R3oAducs3mpJky5ZxAO+HbJra1oz/OjR1WzuzPGBuaO5/L2NQw6sqkM+qif4OHTC9vJBy3HZ1JnbHix2ZFmyroNHVrZUjqmP+vsEipNqwzTEAn0C1d4GW6do9VqnKKVE4pW3xkM+qoKmWqe4h0jXJdXRSueWJkx/gFAs3ufxzq1NPLXoDrLJTg79wFnMPPq4AV9vKSX5VDf+SJT6iZMwfL4djlFGlhCCG2+8kR/+8IcA/OAHPyCTybBw4cJBz1m8eDHTpk1j1qxZIzavBx98kJUrV3LzzTezcOFCIpEIN91006DHX3bZZZxxxhl89KMf7XP/222bsbtGLCgUQujAz4GTgSbgRSHEg1LKlb0OWw8cK6XsEkKcDvwaOFJK+SYwv9c4W4AHep33YynlD0Zq7oqiKIqyJ0kpcV0H19URAOUPm++mD/marhEoZw/tkkPrxhRCF0QTAcKx3WttUbAc1rdn2dadJ+wzqA333VXUcSX3LtvMn1/cTDxo8vUPzebQidWDjDZ0pq4xpS7ClLpI5b6e/oeV8tMO7+8XN3RSTioSNHUaE33XKjYmwjsNUE1dwxxgnWKuaNOVLfVZpxgJGMTL6xSD5UykWqe4a1apSMemjeTSKYKRaJ/NZKSUrHlxCS8+dD+BUJiTP/Fp6hsnDziO6zjk0ylidQ3UjFXrB/cWv9/P/fffzxe+8AVqa4dWtrt48WLOOOOMEQ0KzzzzTM4888wRG3+kjWSm8AhgjZRyHYAQ4i7gLKASFEop/93r+OeAcQOMcyKwVkq5cQTnqiiKoih7nGNb5JJJmtdsJNmS9rJqsrzArIfoCQ41L1YUAoEADQSi/Jjw7hdieyApBEJovX7ue6wmAKGVn2rHx4XAO7/n+SrzENvnVb6/PEIlmPWesmesfo+X7zb9AldKupvTdG7tJhAyiSaCBCM+NL2c9ep5jl7XAF5QtK07z9rWDLomqA37dwigtybz/Oifq3mzJc0xB9Vy9bFTiAb6bhTjSklXrtT71cZneG0khruWTwhBIuInEfGzoHF7/7mC5eyQVXxydRt/f62552VkdDzQZ53ipESYuuiO19RjKOsUBd5SxYjfCxTjap3igDJdnbRv2oCm6X1aTYC3JvD5xfew/qWljD5oOu8772ICkciA49ilEsV8jroJjURr696BmSuDMQyDq666ih//+Md8+9vf7vPYxo0bueKKK2hra6Ouro7bb7+dpqYmHnzwQZ588km+9a1vcd999zFlypTKOQ899BDf+ta3KJVKJBIJFi1aRF1dHZMnT2bFihVUVVUBMHXqVJ599lleeOGFHY5vaGjgjjvuYOnSpdx666195vSb3/yGX//615RKJaZOncof//hHQuWWJY8++ig//elPaWlp4Uc/+hFnnHFGn3Oz2SzXXnstr776KrZts3DhQs4666wReFVHNigcC2zudbsJOHInx38c+PsA918A/LnffZ8WQlwCLAU+K6XsejsTVRRFUZQ9qZTPkepoJ93urXQQuk4gHPWCwn6klHgf772/pPc/PY96MSSy/LNEurLXw7LXGNvP2T5cz8+y1+G9ji2XK24PVMt/i/Lflbvl9oCw55DKaRKkF9B5zycrgWbv2122S9NKFwSEoj4CERPD1Mrj9jyfJFO0aeouULIlkYCJFIIuTaMnmJVIHttU5K43Mpia4FOHxjlqrInbtonuckQqNIHjSroLNuOqQ4ypi4MZwNZM0hak8hapglW5Yl14ZZ1+Y/iZt4CpM60hyrSGaJ/3tDVd7LOpzbr2LM+u7agcE/brNCb67n46IRHCbwycfdrZOsW2dJEtO1mnGPBpg477buXYFp1btpDuaCMQiaIbff/tdbc289Sdd9Dd2sK8k05jzvGnoGkDB9PFXBYpJWMOmjFo0Ki8s6655hrmzZvHf/3Xf/W5/9Of/jSXXHIJl156KbfddhvXXXcdixcv5swzzxywVBPg6KOP5rnnnkMIwW9/+1u+973v8cMf/pCzzjqLBx54gMsvv5znn3+exsZGGhoaBj1+MOeccw6f+MQnAPjyl7/M7373O6699loANmzYwJNPPsnatWs5/vjjWbNmTZ9zv/3tb3PCCSdw2223kUwmOeKIIzjppJMIh/f8OtaRDAoH+n9VOcB9CCGOxwsKj+53vw84E/hCr7t/CXyzPNY3gR8CVwww5lXAVQATJkwY/uwVRVEUZRhc16GQyZBs2UYhk0HXDQKRKJqmkekuAM6A5+2YnXt3ky5YJZtst9fuIhzz4Y+YWK6kqStHZ9YhFIwQNbQdAtrOvM1vX0nzaluJuXU+rpwXpdqvIR0b6VAJkgu2TcF2mVQTpMq0KXS04rouUkrCfj91sTi+6iiO7sMWOrmSQ1fOIl2wsV23Eiz6dA2foeHTh7eeTwhBQyxAQyzAeyYnKvfnSjYbO3J9gsVHV7VQsFwANAFjq4LbM4rlYLEm7Bvw+Xe2TrF7h3WKGvGQeUCsUyxkMrRuXIdj24TiVTtc4/oVy3j+gbvRTR8nXnE1o6dOH3AcKSX5dAp/KERd42RM3449Cg9k//3Cf/NG5xt7dMwZNTP4/BGf3+VxsViMSy65hFtuuYVgMFi5f8mSJdx///0AXHzxxTsEjQNpamri/PPPZ9u2bZRKJSZNmgTA+eefzze+8Q0uv/xy7rrrLs4///ydHj+Y1157jS9/+cskk0kymQynnnpq5bHzzjsPTdM46KCDmDx5Mm+80ff1fOSRR3jwwQf5wQ+8VXOFQoFNmzYxc+bMXV7XcI1kUNgE9N7Ddxywtf9BQoh5wG+B06WUHf0ePh1YLqWsrOzu/bMQ4jfAgKswpZS/xlujyIIFCwYMRhVFURTl7bJLJbLdnSSbm3FtGzMQ2KFMTdlOaFTWF7q2pLM1R/cGi24c/GGT6qi/V0np9vOea8px+ytJbBcunxfnxMbwgAFNqmDh8/mZNzZC0LfjxxzHtsl2ddHd1ooQAk3XCUXjVMdi+BIhpGFSdKBQskkVbFJ5m66c5WVjhZex85cDRWOYZZohn8HM0TFmjt6+K6orJc3dhco6xQ3tWd5oTvPUW+2VY2IBY4dNbcZVhwYtE+1Zp9jnug+AdYqu65BsaSa5bSv+UAh/v51DHcti2V8Xs/r5Z6lrnMz7L7iE0CD/VnvWD0Zr60iMHd9nHaKyb7j++us59NBDufzyywc9Zihfelx77bXceOONnHnmmTzxxBOVDWuOOuoo1qxZQ1tbG4sXL+bLX/7yTo8fzGWXXcbixYs5+OCDueOOO3jiiScGnV//21JK7rvvPqZPH/iLiz1pJIPCF4GDhBCT8DaKuQD4WO8DhBATgPuBi6WUqwcY40L6lY4KIUZLKbeVb54NvLanJ64oiqIoOyOlpJjLkmprJZvsAiHwB0Po4R3/s5pL2Wx6PYcmXGJ1An/o3ZmdGS4pIWM7tOSKWLZLQGjQbZHO2PijPsyggaZrZEout7+S5LkteaZWm3zy0BpGRQYI9qQkVShRE/IzMREaNGDTDQPdMOjJ+biOQyGXIZPs9O4QEAhHCMXjxMMRzEQUoekUbZe85ZAt2pXy02LBqoxrauWsoqENugPpQDQhGFMVZExVkPdN3b5pRqZob2+TUd7U5m+vNlNyvKyioQnGVQd7BYoRJtWGiQfNAZ9nqOsUQRD26946xZBJwNw/1imWCnnaN22gkM0SisUR/UpB053tPL3oDjq3NjHrmBOYf8oHBw30bKtEMZslMX4Csdp69e91EEPJ6I2kmpoazjvvPH73u99xxRVe0eB73/te7rrrLi6++GIWLVrE0Ud7RYjRaJR0Oj3gON3d3YwdOxaA3//+95X7hRCcffbZ3HjjjcycOZNEIrHT4weTTqcZPXo0lmWxaNGiyrkA99xzD5deeinr169n3bp1TJ8+neeee67y+KmnnsrPfvYzfvaznyGE4KWXXuKQQw4Zzss0ZCMWFEopbSHEp4F/4LWkuE1K+boQ4ury478CvgokgF+U/8HZUsoFAEKIEN7Opf/Zb+jvCSHm41V2bBjgcUVRFEUZEa7jkEt1k2zZhlXIoxsmwWhspx8aO7YWWflMpnJb0yEU070/cW37zzEdwzwwPnwWbZeWVJFMwSLo0wn02iBGui6FZIlCssQbeYc/rM2RLrmcOyPGhw6KDJjFKjkO2ZLD+OoQDbHAsD7Ea7qOTw/iC3glaN7uqSW6tm5FSi8AMwMhQvE4gXCEaCTA+BovA2U5LgXLIW855UDRCxjdStlrOau4GxvbRPwGc8bGmTN2e+sEx5VsTeb77ID6clM3j7/ZVjmmJuTrs/vppNowY6uCA75uw1qnaHqBYlXQJOTbd9YpSinJdLTT3rQJwzQHzNJvXvkq/75nEQLBsRdfyfhZcwYdr5jPIR2H0dNmEIxEBz1O2Td89rOf7bOxyy233MIVV1zB97///cpGMwAXXHABn/jEJ7jlllu49957+2w0s3DhQs4991zGjh3Le97zHtavX1957Pzzz+fwww/njjvuGNLxA/nmN7/JkUceycSJE5k7d26f4HT69Okce+yxtLS08Ktf/YpAoO8Oy1/5yle4/vrrmTdvHlJKGhsbR6xVhei7OP3dacGCBXLp0qV7exqKoijKfsoqFEh3dZBqbUFKiS8QHHJ/MiklHdtytG/KUypALuWQS7nkUg6FjNvnWF9QEIrphOM9gaIXNAbCGmI/LenrzXahI1OkI1PCpwv8g7RqKDqSezfkeLK5yOiAxuWTQ0wZFcAMGuj9ApFcycZxJVPqI8QGyZK9XY5lYZWKuI6DlGCYBsFYjFCsCjMQwPT5K5kpKSVF2wsWc0WHZKFEKm9TsLavKTU0rbJecU+Uanbnre1ZxXKwuLkzh13ulWHqggk1oT6b2kyqjRAZRpsQy3Ep2i6W41bWKZq6RqwcKEb8RjnAfOcy4bZl0dG0iWxXJ8FobIfMn+s4vPSPh1n19OPUjB3PMR+7jEhNYsCxetYP+oJB6hsnY/oDAx53oFu1atWIrGdT9ryB3ishxLKeBFx/I9q8XlEURVH2V9J1KWQzdLc2k0ul0TQNfyg87LVFQggCYZ2qemOH3UcdR5LvFST2/GnZUMIubf/SVtMgGN0xsxiKaZj+fbusD7xS0XTeojldxHUlkYAx6IY669I2t63O0FZwOXlMgA9PDGIIKHZbFJIWhl/HHzPRfRrpkk3Q1Jk2KjrkZvW7QzdNdHN7wOmtN0uT6ewEKRGaRjAaIxiL4wt6WcdAyEdVCMbgZSBtx6VQDhYzBZvucgmq48rKa+HT9XJWUQwrsIoHTQ4eX8XB46sq91mOS1PX9qziho4sSzd08eiq1soxtRE/k2pDldLTSYkwo6sCA5a/7mqdYk+gqAtBNOitU4wGTKIBY0QyirlUN20b14OUhKt27E2Z607y9F1/oG3DOqa952gO+8BZfd7D3lzXJZ/qJpKopXbcBLV+UDkgqaBQURRFUXpxbItsMkmyZRtOqYTh9xOK7bxEdHfpuiBSbRDp95lWSolVkL0CRS9ozCYd2jdb9C7yMf2CUL/MYiimE4xqaPtAdjFvOTR3F8iXXEI+HX2QElnblTy8Oc/fmwrU+DVunBNlenz7h3gj4H1Qd22HTJtN3rKpqwsxqT40ogHhQDRd9zYxKW96KF0Xq1gg19RdOcYXDBGKxQlEIpiBIIZpEtE1In6D2oi3orEnq1i0XPKWXSk/7crZ9DQe2d12GaauVTKDvXVlS33WKa5vz7JsY1e55BX8hkZjItynBLUxESI0wKY9O1unuKWrgOXkCJgah0yo3mPvkes4dDVvobulGX84gmHumLHf9tabPHP3H3Asi/edfzGT5h826HiOZVHIpsvrBxvU+kHlgKWCQkVRFEUBirkc6Y520h3e+ix/KLzD7oXvFCEEvqDAF9SoaujXkN2V5NPuDgFj++YSVlH2GgOCUa1PVrHnZzMwvEzU7rBdb11aV9bCbwiiOylV3Jqz+d3qLJuzDu+t93H+pDBBY+D5OUJQFJKxtWFCQqNrSxbDrxOu8uEPmeh7YUMUoWmY/kCl5FBKiWNZpNpaSDZv9Uot/X5CsTjBaAzTH8Dwe83re9b0xTEZVV4+6LiSou2QL3kb23SXg0XL2V5uvLvtMqrDPqrDPg6duP2biJLtsqkz55WgloPFZ9e084/XmyvHjIoFtgeJ5b8bov4dnrv/OsVUweL1rd0cPK5q2Lu19lfM5WjdsA67VCQUr97huV3X5bXHHuGVx/5BvL6BYz52OfH6hkHHK+VzOI7N6INmEIzGBj1OUQ4EKihUFEVRDliu61BIp+lq2UYxm/E2jolEd9i5cF+iaYJw3Ft32J9VdHcoRc2lXDq3Wri9li8aPtEnSOwJGoMxHV1/e8GilJDMW7SmiiDYaamoKyWPbi2weGOeoCH41IwI8xODr9XMl9flNdaGCPm2X79ju3S35hHkCcR8hKI+TL++17I+QggMn6/PulPHtskmu0i1tYIQaJpGIBYjFI3jC4YwA340zbum7Rk4g0Rke2+8ku1SsB0KlkN3ziKVt0nmy+0yAEFPVnF47TJ8hsbU+ghT67c3ZpdS0p7pm1Xc0J7luXUdlabTIZ9OY6LvpjYTavpmbmMBk85ckdUtaWaMiu1W9lq6LqmOVjqamvAFAoRi8R2OKWQyPHv3H9m25k0mHbKAIz98LsZO+grm0ilMv5+xU6ZhBtT6QUVRQaGiKIpywLFKRa9XXWszjmPj8wcJx3dcl7S/Mf0a8TqNeF3f/7xLV1LIun0yi7mUQ1ezRfO6Up9jAxFth8xiKKbjD+06u5grOTR35ylYkrBP32kA0F5wuOOtLKtTNgfXmFw8JUzMN3AgI4FMwSYSMBgdD2D2C1x1Q0M3NKSEQsYinyqhGxrhKj+BsIlu7P0gv6cVRg/XdSnlcuSSSUCCEJVWGP5gBDPgRzf6Zol7Wl7EAib10UB5nF4b25RsugsW3bm+7TIMTavsgjrUdhlCCOqifuqifo6YVFO5v2A5bOzI9QkWH3ujtRKwawJGx4M01oaZUhvm1NmjqAn5aUkXCJg6k+sigz3lgKxSkY5NG8mlUwQj0QHX+7VuWMczf/49hVyWI885n6kL3jPo72pl/WBNDYlxE/u8J4pyIFP/EhRFUZQDgpSSYjZLqr2VTFcnQtMI7MbGMfsjoQmCUZ1gVCcxtu9jtuVtdpPtV47a3Wrh2NuP0w0I9itFDcd0gjEdqUFbqkAybxEwdKKBwV9TKSX/bi1x9/osAJdNDXNUvW/QD/GOK8mVbBIRP/VRPzuLaYQA3XBJtW2ja+tmOrdtItvVSiheQ+24CdRNnEDdxEaC0b3fakDTNHyBvq0wHKtE19Zt5VYYEjMQJBiNE4xGK7uc7jiOIOjTCfp0qsM+et7ennYZBcslXfA2tUkVLBxHIoQX9Jm6FywOp11GwNSZPirK9FHbX0NXSlpTRdZ3ZCu7oK5tzfDsmnaWrOvgO2fPpSbkZ0NHlqCpM7oqOKTnynR10r5pA5qmD9hqQkrJqqcf56V/PEy4qobTPnk9NWPGDTqeY9vk02kSY8cRbxil1g8qSi8qKFQURVHe1Rzb9noLNm/DKuYxfN7aLvWB0GOYgmjCIJrol12UkmJO7lCKmmqzad3Qt5WG5gczLPBHNOyIiy8s8UUERrBvdjFVcvnj2iwvd1pMixlcdlCY2p0EkCXboeRIxlWHiAV3/MgipSTX3UmyeTPJ5iaSLZtJtTUjXS9rFYjGiSZGkUt2sGrDm6x6xjsvGK2iesw4asdPoG7CRBLjxuPbS+tHe3glp/4+JY+OZZHpbCfV2oxEVFphBKPeLqe9W2H017NbaDQAddG+G9v0tMtIFaxyf0ULAbjSO2+47TI0IRgVDzAqHuCoydtbPjy3roPv/G0VP/rnaj5/2gyqgz5WNacImF4AOxjHtujcsoV0RxuBSHTAbF4pn+Pf995J08rXGD97Hkd99MJKgD2QUqGAXSoyaupBAwaYyv5D13Xmzp1bub148WIaGxsHPPaOO+5g6dKl3HrrrSxcuJBIJMJNN93U55hkMsmdd97Jpz71KQC2bt3Kddddx7333jti17AvUkGhoiiK8q5UKuTJdHbQ3dYCrsQXCr0rSkTfKV4rDUEgrFEzum8Zo21JOjtLNLcUKWRcREFQyrpktjq49vZ+fELzgkVfWNCpSZ5JF+nA5fyJQY6fMHDrgx65koOuweS6MP5y+adVzJeDv6ZKIGgVcgDopo94/VgmHfJeqkaNp2rUeALh7dksq1gg1baVZPNWki2b6dzaxNY3X6s8HqmpJTFuPLXjJ5IYN4GaMWN3uibtnTBQK4xCOk26sxNB+T2KRAnF4vhCIXyB4E4z3703tundLsNxZTmr6JAut8tIFyzscrsMibexjd/Qh9Uu4z2TE1z5/kn85un13PHv9Xz86MnEAiavbkly2MQawv4dP4YWMhlaN6zFcRxC8aoBn6tjy2aeXnQH2e4uDvvgh5nxvmN3Oqd8Jo1umIydMWungaOyfwgGg6xYsWKPjZdMJvnFL35RCQrHjBlzwAWEoIJCRVEU5V1Eui75TJrulhby6RS6oRMIR9H24Y1j9jdF26UlXSTjWERG61Tr24MWKSVOCaysSykjKWUlhbRLW4eD34ITKGeHXoMNqwuYEQ1fOWjs+dkICrKWTcyvE7JTtKxaSVfzZrqbm8h0tZWfSRCpqaNh8oxKABipqats1NKbKLhgSXy6SW1dI4n6SaABAkrFHF0tW0k2b6a7dQvNa9aw8ZWXyicK4nUN1I6fQM3YCSTGTaB69Ji9ugZN03V8wRC+Xq0w7FKRji2b6dn9xRfasRXGruiaIOw3CPv7bmxTtL3y00LJa5fRXW6XQblhhiaEt1ZRH3xjmw/NG8O2ZIHFK7YyKh7kg3NH47iSV5uSHDKxutLD0HUdki3NJLdtxR8K4Q+FdxhLSslbL/ybpQ/dTyAS5ZSrrqVu4qRBr0u6Lrl0inC8itoJE3dYo6m8ezQ2NrJ06VJqa2tZunQpN910E0888cSQzr355ptZu3Yt8+fP5+STT+aaa67hjDPO4LXXXuOOO+5g8eLFOI7Da6+9xmc/+1lKpRJ//OMf8fv9/O1vf6Ompoa1a9dyzTXX0NbWRigU4je/+Q0zZswY2Yvew1RQqCiKouz3bMsim+yku6UZ2yph+oOEq6r29rTeVRwXOrNF2tIlTF0QDez4AVsIgeEHw68TrIE3uy3ueCtPZ9jl9LEBTqnx4ebByriUspJSRpJtcUiVQLoZXHsbrrMNZDNuqQUpvc1SDH+IqoZxjJ4+j+pR44k3jKu0fxh8whKRdtEKLlIXCAlIFwRIBAIISh/BcCOjp01CzhCgQy6foqttK93tW0h1bGXT66+xdtkLgBeUVY0aQ2LseBLjJpAYN554/ai9ti51wFYYtkWqrZVky1ZAoJsmwVicUDSGLxCstMIYCr+h4zd04kGThvKGn64ryzugumSLXquM7ry3sU3PqKamVTbF0YTgyvdPpiVd4NdPraU+6ufwxhqvVcWWbuaNq8K1irRt3EAxl/VKuwf4EscqFnn+gf9lw8vLGDNtBu897z8IhAfftMaxbQqZFFWjx1LdMHqf3lFYGZ58Ps/8+fMBmDRpEg888MDbGu+73/0ur732WiX7uGHDhj6Pv/baa7z00ksUCgWmTp3Kf//3f/PSSy9xww038Ic//IHrr7+eq666il/96lccdNBBPP/883zqU5/isccee1vzeqepoFBRFEXZbxVz2V69BYXXW3CADIOy+6SEdMGmOVXAlZKI39jpZi8AlitZvDHPo1sL1AU0Pj8vxuRo+SNHDNyES76zhVz7Flx7C8XUFux82ntcaPiCDZiRuTiyAaGNRmhxMmlBYb2gu0MjtNkmFMsP3EpDSkTBRUu7SAQy4AUDPU0bKtfVc1tKb/NPV4INYRElUjeN8fXTQILrSLLpTrq6tpFKbiPVvY31Ly3jrRf+DYBumFSPGkti3Hhqxo4nMX4C8br6vRKECCEwTF+fhu6ObZPvTpJpb0MK0DV90FYYQ6H1alhf02tdoOW45MslqN46RS9gdKSkJuTjc6fM4OYHXuF7/3iD754zjyl1EToyBV5+YwPVpU5Mn2/QtX7JlmaevvN2Um2tHHzKB5hz7Ek7fX2tYgGrVKRh8kGEq1TJ+Ehp/s53KK56Y4+O6Z85g1Ff/OJOj9nT5aO7cvzxxxONRolGo8TjcT70oQ8BMHfuXF555RUymQz//ve/OffccyvnFIvFd2x+e4oKChVFUZT9ius45FLddLe0UMxnMQyTYCSmMgEjoGA5tKSKZEsOQVPHGMLGI5syXiP6bXmHY0f5+cjEICLXRde6LeTat5Jr30Khq9XL2gFmpAp/7TgSdWOZ0DiZmlGjK2V+A7XSyHY7dG7r10pDQCCsEYpqhAMa4YDwAsaojl+y88yYENtjxXJsJHsFjwKIhOqI1NeCnItrS+ySQy7VSaprK+nUNrq7t7HmxedwnnsaAMP0U9MwlsSo8dSMGU9i3HgiNQmErnmlq5oAzdsVdqQN3AojX26F4V1gIBQhGIvjD4fxBQK7VWbZs7FN/3YZa9rSbOkqUBvx89UPzuKme1/hGw+v5Ptnz8KfbmVTWztydC3j4wNv9LPupaU8/8D/Yvp9nPjxTzJqyrSdzqOQSaMZBmOnzdzrmwcp7xzDMHDLzVgLhcJOj928eXMlsLv66qs57bTTdnq837+9pFrTtMptTdOwbRvXdamqqnpHA9WRoIJCRVEUZb9gFQtkOjvpbmvBdR38gaDaRXCE2K6kI1OkI1PCZ2hEB9gQpD9HSv6vqcAjG7potNv4aKiTyMZm1i3bilPyPqRppo9gYgx1s99DqHYswZoxFDQ/VWEfo6IB+sf1u2qlUdkVtdsh3+X9vaXVxt2+1w26AfEqg6qETnXCIF6lo+m7EYyVg0fNJ/D5NHyRBuKjG5DOwTiOxHFccqk2spkWUsmtdLdv5c3lz+Au9SbjD4SoqRtHTf1YEvXjqKkbRygSB0OALry/DYEwhBc0CvoGkHtot1yvFUYAX6BXyalVItm8Ddd1EIAZCBCMVXmtMPzejqi78/yaJphaF6Vou3RlSyQifr52xiz+676XWfiXV/ji4RFq62rZlioR9BWojW4vCXYsixcfvp81LyyhftIUjr7gkgGb1veQrks+nSIYi1E3cZJaP/gO2FVG753U2NjIsmXLOP3007nvvvt2euz48eP7BHAdHR2k0+ndfu5YLMakSZO45557OPfcc5FS8sorr3DwwQfv9ph7gwoKFUVRlH2WlJJCNkO6rZVsdxdCaPgPkN6Ce4OU0J23aEkXQUIkYLKzUEC6DoWuVpq3NfHGxo1EM81caXVXHrer6olNmEGodiyh2jH4Y7WVjG5PH73RVUGqguYuS1L7M0xBLGEQi+loMQcxRiJNgQSKBUku45LNOmTTLskOm3Vv2kARTYNYtRcgVtcYxKt1dGP3Ai4hQBgamgEmOv7gGOKJUTSMm1d+fWwKhU4y3VtJdmylq3ULK5c/We5DCMFwlJqGcdTUjydRN5aauvEEAiGvtrVnSuWfpQYYmpfN7Akg9d4B5O5lHwdshWHbZDs7SLW1AF62MRiNEYjG8AeDmP7AkDPzmiaYMSrGy5uTpLIFap0kn5xl8pNXivzytSI3HRkhFjDZ0JHDb+pEAybpjnaeWnQ7Xdu2MPvYEzn45A/s9N+8Y9vkMymqR42hetQYVTVwAPra177Gxz/+cb7zne9w5JFHDuvcRCLB+973PubMmcPpp5/ONddcM+znX7RoEZ/85Cf51re+hWVZXHDBBftdUCiklHt7DiNuwYIFcunSpXt7GoqiKMoQObZNLtlFsrUZq1jE9PkwA8H9trdgprtApqOAb4Bee/uKXMmhpbtA3nYImcYOPeqklFi5NPn2LeTavVLQfOc2ZLnDfU4P4k+MZcyYcV4WMDEa3Ry4pUPe8rJn46qDhHy7GeC7EpFz0bIS2RMo7YRVcunqdEh22HR1OKS7vTkIAbEqL0isSuhU1RgYuxkk9icluLaL45RXMOoC3XDJZdro7thCV9sWOps3k6rsqgrhWDWJhvHUjBrn/d0wFtMX8IJDt2dQvNsDfYbTAF3zvvbX+2Ufd7N01XUc7FIRy7JAeoFeIBIlGIvhD4Ux/YFd7sqa6k7zzAuvIJwS0VgVj2/M8buXk5zYGObyeXEs16VouUS7NrBs8d0ITeO9517EuJmzdzquVSxSKuSpb5xMpLpmyNek7J5Vq1Yxc+bMvT0NZQgGeq+EEMuklAsGOn7f/a+ToiiKcsAp5XOkOztItbWClPhDYXxx1VdsJFmOpC1dJJkr4Tc0on6v7M61S+Q6tpEvrwPMtW/BzmcAEJqOWT2KjTVzWEkdsbqxnDtrFDU7aUQPXhyTKdhEAgaj4wHM3SnjBChJ9JQNLkifYChpRtOnUT9Ko36Ud322JUl22pVAcePaIhvWeMfG4nql3LSqxsD07X4mUTc1erp2uBJcG3xmPXWj6mkYeyj+oAHCJtW1jWTbFjpaNtPZ0sSmt16pjBOtriPRMM5bo9jglaD23kymN9kTNFoSSt4mOgMlACT0LV3Vvd1XK9nHXgHkwK0wSnRt3eKNLcEXDBGMxwiEI94upz5f5dhURysdTU1MrQ+zptPEdiUnNIZpydo8vCZDQ1jn9EkhNi35B6+//Bw1Y8dzzEWXEalO7PT1LWQyCE0wdvos/CG1flBR3g4VFCqKoih71fbegs1ek2ndIBBRvQVHmishmS/R2l1ECDCK3WS3bKGtnAUsJFsrmShfpJpIQyPB2jGEasfymlPFnRuK2K7ko40hjh216zVnjivJlWwSET91UT+7tceKIxEZF1GQSEPAbgZr4JWf1jaY1DZ4EZtjS5JdPZlEm6YNJTaVN7OJxLRKgFid0PH5d+93UxOgmRpUgkRJIWcjXYlpjGL0hDFMmP4efD4D28mTbNtKR7MXJLZsXsuGN7weikII4okGahrGkyhnFKtqR6Mbhvc+6HhB3k70BHM4gOWWM5ESOcBpsnf2sRxAGrrh9UAsB4+2UyLd1kayZRsC0E0fwVgcu1ggn04TjETRdJ2DDIs3W1JE/Sbnz4rRlnN48OWtJJY8Bh1bqJl7BJOOPZ3gTtYLSynJpboJRqPUTZw8pF6MiqLsnAoKFUVRlL3CLpXIdneSbG7GsSx8QbVxzDslmUyxYcM6sm1bsbu2ke/Yimt5W6hrpp9Q7RjqZr+3shbQCHhtPrKWy6J1OV5sLzAponPFtAgNwV2Xf5Zsh5IjGVcdIrY7JbRSIorlNhNSgI8hZQeHQzcEiTqDRJ03P8eRpJIOXR02yQ6bLRtLbF7vBYnhiEZVwgsQqxMG/sDuBokCrVdg67qSQsYi73r9GYOhcUyZ3cj0Q3QMU6OQS9PZ0kRHcxMdLZvZsvZ11r3+ojeWplNVN7pPRjGeaBi03YTo2XVVA3a6ctTbBRaJl3ks9Ms+lk/VAR0DDBN0gVN0yac7EaYgFI9XylVjQZNJiTDr2rNUhXx8LNHOyufuoeQ6JN7/EaYddgjJQolNnVkaE+EdvmxwHYd8OkW8oYHq0WOH1U5DUZTBqaBQURRFecdIKcu9BdvIdHSA8HoL7qwJtfL2uI5Nqr2ZZHMTnVs30dm8mVK6y3tQCAJVdVQ1ziJYO5ZQ7Vj8scSAWb/Xu0r8fk2WlCU5a0KQ08YF0IcQmOUsB13A5LowfmM3gidbomVcRNFFmuXSxmFwrBKu66DpBpquI8TQ5qDrwtuMJuF9VHJdL0hMdnglp81bSmzZ6B0bDGtU1+jlQNEgGNrNIFETaL3WWLqOSy5VQkov9jL8JomGqTRMmI5heMdlU13lQHEzHS2b2fDGCt565TnvGgyT6voxJBrGV4LFaHXtkF+DHpX1h0PJPrqAA7or0KUPihKZLSIjOiKkI3watdEARcvmlX/9g47lzxCtqeeBmpNoa63i61mburBJR8YrZx5Ttb0s1C556wfrJk4imqgd1jUoirJzKihUFEVRRlxPb8FkyzZK+TyGaRKMxffbjWP2VVJKCukkXc1NJJs3k2zeTKptG255Mxg9ECGQGEPN1EMI1o4hWDMafZC1aT2KjuS+DTmeaC4yOqhzzcwwEyO7/vggJWRKFtGAyeh4kGHHg1Ii8uXsoCaQwyjZlNLFLhVxHQczECIQjmIX81jFUrk/orelp9DEkINFTRNU1XglpI14QWIm5dJVLjdtbbbZutnL8AWCwgsQy+WmwbC2ey0ddI2eRJiUIB2XbLdTCRLNgI4vGGHMpFmMP2guQgikdEl3tdNRDhQ7W5pY8+rzvPnSMwCYvgA1DWOpKQeJiYbxhGPVe+TfYqV01btVuV+6EjIuMuUgfYK8yLHywTvpWPcW8enzmXbSmTQUBAufbuP7z3Ww8P11xIImTV15/IZOIuKnmM0ikYw+aIb6EklRRoAKChVFUZQRYxUKpLs6SLW2IF0XXzCkSkT3ILtUpLtlC8mWzSSbm+hq3kwp520Go+kG8foxjJ59OE6kAbN6NLF4NdowMm3r0ja3rc7QVnA5eUyAD08MYg7hfNuV5Es29dEAiYhv+JWetkRLOQhLDis76LoOdtErgw1EogTCcXTT632nVXtjuLaNa9s4joVVLGCXCljFYp+dPIWmoen6ToNFTRPEqnRiVToTp/iRUpIpt7/o6rDpaLVpbvKCRJ+/J+voZRPDkeEHib3bX4A3XcdyyRSKlcf9QQMzaBCKJohW1zFp5qGV16W7o5XOls2VYPHN5c/glhs6+oNhL0jsVXoaigzeE3C4hCbA711vy8Z1PPv3P1Eq5jn8lHPRp84l7diMiZrccESC//fvdn7yYgefP6qWWMBkXVsGN5+hpqaK+omTKhvYKIqyZ6mgUFEURdmjpOtSyGbobmsl151E01RvwT1BSpdMZ1s5A+hlAtOd2zeDCVfVUjdhKlWjxlM1ahy+eD1tWZtMwSLm0zGGsXGP7Ur+ujnP35oK1Pg1bpwTZXp8aJt5FG0Hx5VMSISIDKHpfR/92kz0zg56+6JIbzOU8r4o0pVecGSXcCwLzTAIRqvxByPopoHQNIQA14VizgbpBU+a4cPv9+MPRyuDu46Da1s4jo1VLJT/FJHSRZSzXkLT0HUdMUCwKIQgGtOJxnTGT/KCxFymnEks73DastULEk2foKpGr5SnRmK7FyT23tlUSigVHQo5LyssdIE/YOALGOimoLpuNNV1o5ky5wjAa/uSbN9GR0sTnc1esPj6xsd79VCMkRg1ztvMpmEcNaPGEwiGhzXH3qR0Wbn0SV5+5v+IxGs47pyPU101Gsdy2ZSyKBZLzKr2cdUh1fxyeRe/XdHFVQfHMEpZtlg1NI5vVAGhAkBTUxPXXHMNK1euxHVdzjjjDL7//e+zcuVKtm7dygc+8AEAFi5cSCQS4aabbtrLM94/qKBQURRF2SMc2yKbTJJs2YZTKmH4/YRUiehuK+YyleAv2bKZ7uYt2OXNYEx/kKpR42iYMovqUeOJjxqHL+CtvbJd6MgU2dJRwKcLooHh7cy4NWdz2+osm7IO7633cf6kMMEh9O2TQLZoEzA0JtSG8O2iXlSWNyuRPUFe0UVPOUgHpAlIAfb244XwsnPCEOiGQNO8YBBcItVhYola/OEIuqF5ZaH9souuI7EtB6voUMzblPJ2OZ6W6JqGZugYAQMD+gWLXmbRsi2cUrESMFJJLEqErqNrfYNFIQThqE44qjOu0bvWfM4l2eFUSk7bmr0LNEzKO5saVNXoROP6sDK6Pa+P0W9n01LBppD1AlFdF/hCBqbfwDA1dMPwykdHjYeDjwLAtkp0lXc87QkWm9aurDxHOFZdyST2rFM0/YFdzq2Yz7HkH3ezZd1KJhw0j/eccm7lPMOnMdYIsbkli8iWOD7qp3VqhPvWZEgYNh9771ScYIzXt2aYP8HAb6gvlw5kUkrOOeccPvnJT/KXv/wFx3G46qqr+NKXvsTs2bNZunRpJSh8uxzHQT+AvsxUQaGiKIrythRzOdKd7aTbvQbc/mAIf1D1DBsOx7ZJt2+jq1cWMJ/yNoMRQiNaO4oxMw6uZAHDVYkdMlVSQjpv0Zwu4rqSSMDYxZ6SfblS8q+tBR7YmCdoCD45I8IhiaFlZqSETNEiFjCpj/jRXEmp4ICUA/ZX966rvGZOA18J9KJAxH1ofh0hvKBPCEATaJqGVv5ywXFsSoUCIInX1RKKV+EL7Dow0XSBT/cyZ+G4v5xhdLFLLqW8RTHnUMhaCMT2LJwh0AwTzTAx6NUvsxwsOraNbVs4xQJWqYBVnlfPejqha+i6gdC8awqFdUJhnTETvNe1kHPp6rTLO5w6tLcUANB1L0js6ZUYqxp+kKgJgWaK7UGiKylkLfJpL0g0TA1f0MD06961ahqG6aNuTCN1Yxor45SKeTpbtvdP7GjezKbV23soxqrrqBm1fSOb6roxfXoodjRv5umH/0g+k2LB8Wcxbf77dviiyO83GDs6zPq2DKGiw3lRjbYak8XrLSY3Whw/26Q7X2LVtjRzx8bRd6ufifJu8NhjjxEIBLj88ssB0HWdH//4x0ycOBHTNJFS8swzz/CFL3wBgJUrV3LcccexadMmrr/+eq677joA/vSnP3HLLbdQKpU48sgj+cUvfoGu60QiEW688Ub+8Y9/8MMf/pCjjz56r13rO21Eg0IhxGnAT/GWHf9WSvndfo9fBHy+fDMDfFJK+XL5sQ1AGq+Dji2lXFC+vwa4G2gENgDnSSm7RvI6FEVRlL5c16GQTpNsbaaQSaPrJsFIFKF6C+6SlJJ8qqtPGWiqbVtlfVcgEqOqYTwT5x1J1ahxxOvG7HIzmLzl0NxdIF9yCfl0dHN4H5o7Cg63v5Vldcrm4BqTi6eEifoE0nW9fVkoZ/MkvTJkHstxKFouDVUBqiM+dFPD0IUX8JkCXd+euesJBHtuuzkHuiwICIiLnWaVbauEVSyi6Trx+jqCkTi6sfsfY3oya4apEQiXdxjtySYWytnEgu1dv+jJJmpohgCxPVg0CUIk5g0qJU45s1gJFosFrEKe3sFiz3pFf1Bn9Dgfo8d572+x4JLstOkqZxPXvmEDRTQN4tVegFiVMIhX6+i72Am0vx13NpXk0yVyKe+24dfwB3qCRL0ShPv8QUZNmMqoCVMr5xby2UrJaWfLZlo2rWHDquXl11Ujnmgg0TAOfyjCG8ufIhCKcvL5n6J29IRB5xc0DcZVh1i/tYNYNMz1R48n+ewGbnliLQ2OYPbkatozRVa3pJkxKqoqEA5Qr7/+Oocddlif+2KxGI2NjVx++eWsXr2aW2+9FfDKR9944w0ef/xx0uk006dP55Of/CRr1qzh7rvv5tlnn8U0TT71qU+xaNEiLrnkErLZLHPmzOEb3/jG3ri8vWrEgkIhhA78HDgZaAJeFEI8KKVc2euw9cCxUsouIcTpwK+BI3s9fryUsr3f0DcD/5JSflcIcXP59udRFEVRRpxVKpLt6qK7tRnHtvEFgoTj1Xt7Wvs06bq0bVxL81trSbdvIdnSRCmfBUAzTKrqx9I4/6hyFnA8gZ4AYwhsV9KWLtKVtfAbgmhg8P+se83KZZ8gz3Ukz3VY3LM5D8DFE4O8J2EiXIlblAhdIHTNCyj08kYnulbJ4OUsB58mmVUfIxYaepmqtCVulwVZG3w6YpAm9FK6WIUijmNjBoIkxo7HFw6hDbOlwlD1ySZWedlE23JwLEkxb1HM2Vg5G2TfbGJlJx0h0A0TfRfBol0ob3DTL1jUdZ26Bp360SZCCEpFl2Tn9l6J61YXgSJCg3iVXi451YnXGBhDKPHtf60963yl9H5PcykLKb1ejD6/Xs4kauiG3icICwTDjJk0gzGTZlTuy2W6e7XGaKJp7esUCznGTJrBe0+7AP8u1iO6roNpF5g4tp52Ecbn9/Ol90/hpn+9yTeXrOPH2hTGRgN0dBfZ5MKEMSow3Nue/t/VtG/O7NExa8dHeP950wZ9XEo54Ps+2P0f/OAHvfXDfj/19fW0tLTwr3/9i2XLlnH44YcDkM/nqa+vB7x/gx/5yEf20NXsX0YyU3gEsEZKuQ5ACHEXcBZQCQqllP/udfxzwLghjHsWcFz5598DT6CCQkVRlBEjpaSYzZJqbyXT1YnQNAJq45ghaV2/lqUPP0Dn1iYAwtV11DVOo2rUeKpHjSeSqN+t5ttSQle2RGuqiEQS1DSEC3bBGfQcIdge5BmCjO3yhw1ZXmorMaPa5Mp5ceojBkII77idFJ9KJN0Fi0jIYFJteMjrvKSUyJwDnTZoAjFII3vXdSgW8uBKQvE44aqaIZWI7mlCgOnTMX0MOZuomxqifxZvsGDRlThuOVi0StjFotc6o5CvnFpVBTUJHU0PYjsaqa6eNYkOG9cW2bDGm2c0rld2N62qMTCHkS32fjf6tr9wHZdsslg+oKf9hYFp7hgkAoQicUKROOOmzC6PISkWcvgDoV0Gb7ZtYeXzxEeNIlxVgz+ZZ2syT1XQx8L3T+XGf73JV17cwI9PmEaVo7N1VRv+ziK146LoUR9id/pfKvul2bNnc9999/W5L5VKsXnz5gHX//n9/srPuq5j2zZSSi699FL+3//7fzscHwgEDqh1hL2NZFA4Ftjc63YTfbOA/X0c+Huv2xJ4RAghgf+RUv66fH+DlHIbgJRymxCifg/OWVEURSlzbLvSW9Aq5DFMn9o4ZogyXR289PeH2PjqCkLxKg794LlEE5MJV0UHPccrz/Q2XnHLf+P2rdYUQLZk05YuYrmSoN/A8Ble1kfr+WC/PZMnNK8dgND6BnnLtuX57cvd5C2Xi2bHOG1KpFIuuCu265IuWoyKBhhTHRry+i5puchOG4ou+LXtDdF7j22VsEpFNE0nlqgjFHt7JaIjYafZxJxFMW/jFL1NbDRNoBv9sol9BhPoWjlYDASh59fDLWcWHRu7VPT+FAvgFIjFIBaDiZM0XAzS3dDdJUl2OmxaX2LjWi/TF41pXq/E8uY1vmH0eRyw/UXJJZMvt7/ot7OpMcCXAkKIIe1WWioWkK5D7cTGylrkMVVBSrZLZ7bE6Iifrx09hZufWM03/r2e7xx7EGF/kPWdWQwg7NPRoz6MKj8iYAz4e6WMjJ1l9EbKiSeeyM0338wf/vAHLrnkEhzH4bOf/SyXXXYZDQ0NPP/880Ma46yzzuKGG26gvr6ezs5O0uk0EydOfAeuYN81kv9PO9C/ygGXmwshjscLCnuv5nyflHJrOej7pxDiDSnlU0N+ciGuAq4CmDBh8Bp2RVEUpS+rUCDd2U53WwvSlfhDIVUiOkRWscjrTzzKymceRwjBvBNPY9YxJ1DIuySbc15bhEEI3Vt/pxsapuH9rRnlNXlCYEmX5lSBTiAyJkzAHP5/wnOWy59e6+bJTTkmxk2++N5axseGXvZZsG0Klsvk2giJiH/XJ1BuG5FxIGmDLhDBvgGElBKrWMCxbUx/kOrRYwlEIiNWIrqnDZhNtCW23Sub2LPT6c6yib31BItmOVjs0RMs2paXWSwVMI0C8bjF+EaB6xikuyWplCDZJdmyscTm9V6QGI5qlQCxOmHgDwwvSOzd/sKV9NnZVNMFvp4g0ScwhphpyecymKafmvET+mxOI4CJiTAlR5Ip2sxIhLnpyEa+8+/1/PCFDdx81CQCQZO1mQIzRkXw5yyKqSLC1NCrAuhRX5/1k8q7hxCCBx54gE996lN885vfxHVdPvCBD/Cd73yHbDbLd7/7XebPn1/ZaGYgs2bN4lvf+hannHIKrutimiY///nPD/igUMjBtgV7uwMLcRSwUEp5avn2FwCklP+v33HzgAeA06WUqwcZayGQkVL+QAjxJnBcOUs4GnhCSjl9Z3NZsGCBXLp06du+JkVRlHcr6brkM2lSbS3kulPeJhjhMJraOGZIpOuy7qWlrPjHw+TTKRrnH8Yhp32IcLwKAKtoU8ja24M8rZzZK2fxNCEGzXDYjkt7pkhTMo+haYR9+k5LOwezqr3Ir5Z30ZF3OHNalHOmRzGGkVVJFy0MTTC5LkJ4iP0HZclFdlhgyR2ygwOViJqBwG5c2b6vJ5vo7XRqU8xbOJYLwvuQu9Ns4lC4EtuxkLaNbXmZRatQwCoVSae8TGJ3UpJKSpxyhXEorFV2N62uMQiEdv/fuislri1xHe8zpaFrmCEdn99A93k7m/Y93qWYyRCsilNVP3rQ/58pOS6rm9O4EkI+nXvfaOG2V7Zw7owGLp83llzJQRMwfVQMUxdIx8UtuuC6aCEToyaAFjR3HoArw7Jq1Spmzpy5t6ehDMFA75UQYlnP5p39jWSm8EXgICHEJGALcAHwsX4TmwDcD1zcOyAUQoQBTUqZLv98CtCzDdCDwKXAd8t//2UEr0FRFOVdzbYssslOultbsEtFTH+AcFXV3p7WfqV1wzpv3eCWzdSOn8gx/3EFdRMa+xxj+r3+cMMhpaQ7b7GpM0fJcYn6TfTdCBpKjuSeVSn+vjZDfVjna++v5aCaoWX5AFwk3fkS1SEfExNhTH3XwYN0JbLbhpQDZt/soLeLaAFNN4gl6gjGYhjG8HopDofres3Y9+YXHNuziTrBiAkEK9nEUsGmmHeGn03sTRMYmg9MH2avdjDSlSQcC9e2cKwSxXyeZHueznaL7i5Jy1aLrZu8bF8gKCq7m1YndIIhbcil4jtrfyE0QTjuIxDyNs9xbJtSPkd8VIPXWmUn4/p0jakNEd7YlqZou3xkej3bMkXueaOF0WE/p02pJV20Wd+eZWp9BE3X0MvBrVtysLZkvKxr3I8e96EN89+gohxIRuxfh5TSFkJ8GvgHXkuK26SUrwshri4//ivgq0AC+EX5/3h6Wk80AA+U7zOAO6WU/1ce+rvA/wohPg5sAs4dqWtQFEV5tyrmsqQ72kl3tAECfyisegsOU6ark5f+7yE2vvISoVic9533HzQefOgeacuRL9k0deXpzpcImgZVgaH1C+xvQ7LEL5d30ZS2OakxzIWzYwSGsSlHz/rBMVVBRseCQ+qVJwsusqMELhD0Aou+JaIBqseM2+MlolJKbMfGKjeaLxSLFEoFbNsGAQFfgEgoTMDnx2f69vraWM0Q+Ayv5DJSNUA2MWfhFN5eNlH0ChYJhgnGqqlqgImuxHEsnFKJrvYc7duydLQVaW+x2NbkBYk+P5VS05pak1BkGEFir/YXrpRkOosUshb+IGi6JDFuAoFIZEhjBQydqfVeYKgHDD516HhacyVuXb6J+rCPQ0fF6M5bbO7MMiERrgSZmk8Hn450JU6ygN1ZQAvo6NUB9LCpNqdRlH5GrHx0X6LKRxVFUbxyvVx3N92tLRRzWXTDwB8Mqd6Cw2QVi7z+5L9Y9fTjIGDWMScw+5gTMHxDz74NxnZcWtIFtiULmLpG2Ld73906ruThNRnueyNF1Kdx1SHVHNwwvN0785aN5bhMrg1TFd71tUlHIpM2ZB0wNYQhvBLRfB6kJBiLEamuwQwE33aJqOM4WI7X3qFQKpIvFrBKFhLpNXgQ3ro2XdfRNb0SMNq2hZTe46FAkHAwhN/nxzTMvR4kDsS1vZ1OS0Uvm2j1ZBMBXRfDyyYO5fkcl2RHjvZtGdqbc3S0FikVvSc0fRCvEsSrBdW1JtG4OaxdGgvpLBKNhkkTqWqIoQ8zKEvmSqxuzRAP+Cg6Dp97bDXN2SI/OGE6jfEgybzF+Oogo+KD/55Ly8UtefWzRtyHHvMjgsY++d7vq1T56P5juOWjKihUFEV5lyvlc2S6Okm1t+G6Dj5/ENP/9gOYA410XdavWMpL//gr+VQ3jQcfxiGnnUG46u1vwiOlpCtXYlNnHsd1iexmqShAc8bmV8u7eKurxHvGBrl8XhUR39A/gEskqYJFwNCYUh/Z5YY2lTYTXTYgwOeVCFrFAkLXiFYnCMbju1Ui6rpuJftXskrkiwVKpRK2ayOkACHRdMMLALUd2yTsbM6WbWHbFiDQNI1wMEQoEMLv82GOYDnr27E9m+hQyjteNtH2ymPFrnY63a3nk2RSJdq2ZmnblqWjOUc+Vw6qjO1BYrxaEIkKdNNE040+pbpSupTyOQLhGJGaOhxLIJFEawIEY36Gs1loa7rAxo4c8aCPznyJG/71JpqAH504nSq/j+5Cial1EWrCO8+sSymRBQfpSoShYdQE0CM+hKm+INsVFRTuP/alNYWKoijKXuLYFrlUilRrC8V8Fl33soKqt+Du6b1uMDFuAsd87DLqJk7aI2Nnizabu3JkijZh08DczeyglJJ/bchx5+vd6Bp8+rBqjho3vJJgR0pSeYvaqI/x1SGMXawflLZEdlmQc5E+ge2UcLKlconoWALhyJDW8m3P5JVLP0tFCsUilm1VjhGahqHpmKaJX3t7X2oIIfCZPnzlHS9d1yVXyJPOZpBIDN0gEgoT9Afx+3wY+r7xcanv2kSorE3sySbmvIyiwAsg3242UQhBNO4nGvczeWYNANl0ifbmHO3bcl6g+Jb3HumGoKoGYnGLeFwSiQk0TSClS7i6ztvBWAg0w8sqp9oL5Lot4vUBfIGhvb710QBFW9LSXaA25GPh0VP43OOr+foza/nv46YRC5isa8viMzQiO1k/KMT2HpnScbFac1gtObSIiVEdQAuq1hbKgWff+H85RVEU5W2TrksxlyPd2UamsxOkxBdU7STejmyyi+V/f7CybvC95/0Hk/bQusGS49CcLNCaLuA39N1eNwjQlXf49YouXmktMrfOz1WHVFMTHN4XAEXbIVeymZAIUx/17zTrJuX2NhMuLhZFZFESjESJjB6LGRy8RHR76adNoVSgWCxStEpUKpcE6JqX/Qu93XWunRZidRZCOjJhQo0JA7wumqbh71X+67gO6VyW7nQ3EvAbPsKhEEF/EJ/Ph67tO1+uVNYmBr21ia4Ex3KwytnEUs/aRLy+lW83mxiO+ghHfUw8qAqAfNaivTlH27Yc7c1Z1r/ltcDQdUF1rY9J06upKweEPYQu8IcNHMulvSlDMOojmghgDKGkdGxVkJLtkMxbTKkOcfN7JvGNZ9fyvec38OX3Tibo01nTmmHm6Bj+IYwndA09rHm/00WHUlPay7pW+9GjfjT/vvNeK8pIUuWjiqIo+zmrWCCXStLd2opdKmGYJr5AUK0VfBvskrducOVTjwPldYPH7pl1g64r6cwW2dyVR0qIBAy0t7HKbsmWHLe/nMRy4WOzY5zUGB72GqlM0QYkU+ujRHaRtZElF9ll42RLWJQQuiBSXUsoHuvTa65/6WehWKRYKuK4Ts9IXumn5q3926Pruoou4pU0Ym0ODAGORJQ/7sigBgkfMmEia8qB4i7KBm3HwbItpOsAAr/PRyQYIeD3Nq3Z11u3OLaLY7neTqc5m1KxVzbR8MpO99TaxELepqMcJDY3pcmmLGpHhZj3ngaqa4M7niAlVtHBdb2S0lDMv8sNjRxX8lartyNp2Gfw0Ftt/PKlzZw5tY6rDx1PruSgazCtwWtVMVzSkbhFB6RECxroVX70sE+1tmDfKB8VQnDjjTfywx/+EIAf/OAHZDIZFi5cOOg5ixcvZtq0acyaNesdmiUsXLiQSCTCTTfdNCLjb9iwgTPOOIPXXnttwMdV+aiiKMoBwHUdCuk03W2tFDIpEBr+YEjtIPo2eesGl/HSPx4urxs81Os3uAfWDQJkCjabOrLkbJuIz8R4G8FEpuRyxytJlmzJM6Xa5JOHVjM6Mry1cBJJd6FExGcwqS6K3xg8KyJdiZuycdpy2NLGCPuprh2LPxTGlS6WbZMrpka89HOnXIlYl0e8kgJLIqeFkXMioAlIWogOCzpKiE4LralQfg2AmFHJJMqED+IG9AoADF2vNGT3Sl0dOlNdSCkRQCAQJBIM4fcF8Jn73qY1XnZQ87KJ1eVsYsnBssrZxGy/bKKpoeu7l00MBA3GTooxdlIM121g/RtdrFrexmOL1zPhoDizF9QTCvf6PRUCM2CAK0l3Fsh1l4jVBvGHzUG/KtHL/TLfbE6TLzl86KA6tmWLLF7dyuiIn7Om1fdtVTHMyxC6QA95H5HdkoO1LYstsuXWFn6Efw9/iaEMi9/v5/777+cLX/gCtbW1Qzpn8eLFnHHGGe9oULi/UUGhoijKfqSYy5FNljeNcRxMf4BgNK4+oOwBbRvXs/ThB+ho2rTH1w0WLJtt3QXaM0WCpk5V4O0FRq+0Fvj1S12kii7nzojxoYMi6MP85Gu7LpmiRV3Uz7jq8E7Pd3IWpW0Z7KKNURUkFEtga9CVy1Ds7qyUfgoB2p4q/RyujhLashSi00LW+XAPi0FVr+Cj1oes9QFhLxAsutBZQnRYiE4LsbWIWJ8HQGpAtVkOFL2sIhEdhEAIgWkYmEZ5TZqU2LZNe1cHLhJN6P12Nt33drfUBGh+HdOvE4oAdcFKNrFYsCmV1yaWt2rdvjZxmL9jmiaYMquGCVPjvLGinTWvdbJlXYpp8xJMm1eL0TtDqwn8IRPXlnRuyxIIG0QTQUzfwF9U+HSt0qqiaLt8fN5YWrNFfr2iiYawj/eMrSKZs9jclWVCTXi3c/F9WlukSthdRYRPw6gOoEd9qrXFXmAYBldddRU//vGP+fa3v93nsY0bN3LFFVfQ1tZGXV0dt99+O01NTTz44IM8+eSTfOtb3+K+++5jypQplXPa2tq4+uqr2bRpEwA/+clPeN/73sfChQtZv34927ZtY/Xq1fzoRz/iueee4+9//ztjx47loYcewjRNGhsbOf/883n8ca+y5M4772Tq1Kl95rVixQquvvpqcrkcU6ZM4bbbbqOzs5Nzzz2X5cuXA/DWW29xwQUXsGzZMpYtW8aNN95IJpOhtraWO+64g9GjR7Ns2TKuuOIKQqEQRx999J59XffoaIqiKMoe59iW10qirQUrn0fTdbVpzB6UTXbx0v89xIaXlxOMxXnvuRcxaf5hb6v81nUlOcshlbfoyBYp2i6GEFQFfYi3USpasF3uWpnin+uzjI0afPbIBJOqhr8WsWA7FCyHxkSY2uiOW/i7rovjOOSzeXLNSeysg4gG8cWD6LpGIZdCaDqGphPwB/Zu0FNwEK+k0dblkQEN96gq5ITArrNcfg1GB5CjA16QKCXkHOgJEjssxNo8YnUOAGmKSiax9/pEIQSmaWKaXgDqSpeiVSSbzwKgazqhYJBwMIzPNPfZnU17ZxPpn03MeWWnritBgqaDZgw9m2j6dOYe0cDkmdW89mIrq15qZ/2bSWYvqGfi1HifYFMzBIGIiV1waN+cJlwdIFI1cElp0NQ5qD7CG81pIgGDm46cxM1PrOa/n9vA946fxtTqEG2pEgFDpyE2vJYs/Qmt1+Y0dnlzmtYcetSHUeVHBNTmNO+ka665hnnz5vFf//Vffe7/9Kc/zSWXXMKll17KbbfdxnXXXcfixYs588wzOeOMM/joRz+6w1if+cxnuOGGGzj66KPZtGkTp556KqtWrQJg7dq1PP7446xcuZKjjjqK++67j+9973ucffbZ/PWvf+XDH/4wALFYjBdeeIE//OEPXH/99Tz88MN9nuOSSy7hZz/7Gcceeyxf/epX+frXv85PfvIT4vE4K1asYP78+dx+++1cdtllWJbFtddey1/+8hfq6uq4++67+dKXvsRtt93G5ZdfXhnnc5/73B59TXcZFAohpgG/BBqklHOEEPOAM6WU39qjM1EURVEqpOtSyGXJdLST6eoABL5AkFC8am9P7V3DWzf4GCuffgwkzD3hFGYdc+Jut+uwHZdsyaErVySZtbClREcQ9OmEtRx6oRPXDSN1P67uA90HDD3wXNNZ4pfLO2nJOpw+JcJ5M2P4dmONU6ZooWmCGaNjhH06tuN4LSRsm2KpRLFYopDLYmdL6CWdUCRGdHwM0+fft9bOuRKxNod4JQ22xJ0RRs6O7HJ94KCEgLABYQM5IegFiq6ElO2VnXZaiI4SYlUGrWd9YkirZBJ71idqpjbozqbgZTkiwTDBQAC/6R9Wr793Ut9sonctju1il9cmetlEm3JjyCFlE8NRH0eeMI6ps3O88lwLy57ayprXO5l3ZAP1Y8J9jjUCOobUyCaL5FMlYrUBAhHfDl+pRAIGk+vCrGnzehh+7egp3PDomyx8Zg0/OnE6tUE/Gztz+AyN6tDub+bUmygH0FJK3JxFMVVEmBp6tdfaQhsku/lu8/gdv6Z147o9Omb9xMkcf9lVuzwuFotxySWXcMsttxAMbl+rumTJEu6//34ALr744h2CxoE8+uijrFy5snI7lUqRTqcBOP300zFNk7lz5+I4DqeddhoAc+fOZcOGDZVzLrzwwsrfN9xwQ5/xu7u7SSaTHHvssQBceumlnHvuuQBceeWV3H777fzoRz/i7rvv5oUXXuDNN9/ktdde4+STTwa8zblGjx69wzgXX3wxf//733d5fUM1lEzhb4DPAf8DIKV8RQhxJ6CCQkVRlD3MKhbIJpOk2lpwLBvdNAhGYmrTmD1Iui7rX17Oiv97iFyqm4nzDuGQ0z5EpLpm2GMVbYdMwaIza9FdsBCAoWkEfUafPoPCzmPkmpGaARJvkw8EGAEcM4Jr9g8Wt59ru5IH3kzzl9VpaoI6X3xfLbNqhx+42q5Dd7ZA2KdRHzLIppJ0WVa59FPiuhKnVEKTECREMF6PPxLaN8vj2ktoS7sRSRtZXy4VjY9ABk4TUGUiq0yYUl5/aEvo8rKJA65PjBtegJgwkTU+tCqj786mjkMqm6Yr3Y2Q4PP5iITCBPwB/Pv4pjU92UR//2xiyakEiUPJJiYaQhx3ZiNN61K89mIrT/9tI6MnRJh7ZAPReK/fbSHwhwxcW9LVnMMX9NYb+vrtCFoT9jHRCbGpM0dV0Mc33j+Fzz62moVPr+UHJ0wnFjBZ25pl5hiN8G62fBmIEAIR2N7awm4vYLfl0cIGRlUALWSq7OEIuv766zn00EO5/PLLBz1mKFUMruuyZMmSPsFlD3/5S0JN0zB7rRfWNA3btgd8nuFUTnzkIx/h61//OieccAKHHXYYiUSCrVu3Mnv2bJYsWdLn2GQyOaJVGUP5lxGSUr7QbxL2YAcriqIow+M6DoWMt2lMPp1CCIE/FMYfCu/6ZGVY2jZtYNnDD9C+eSM1Y8dz9IWXUt84ecjnSynJlRzSBZuOTIGC7SKBgKERD5g7LQ2Vmonri/W+B+Ha6KUker6tHCgCQkMaQRwzxOZikFtfddiQcjlmfJCL51YR2kUmTEqJ6zg4ruO1f7BKFEtFMkWL2oiPiPBRKtromobfZ+I6DlYhjy4gGqnBZwcwfAZiJ33e9pqCg1iRRtuQRwY1nPdWwfghlIruSYaAOh+ybvfWJ+oJEz3ir8zZdmy6UklcKRECAr4AkVAYv8+P3/Ttc+sRe+uTTYwOnE0sZG10Q8P0a33bUgjB+ClxxkyMsub1Tt5Y0c4/713L5Fk1zDykFn+vXXArJaVFr6Q0UuUnUhVAM7aPVx8LULRd2tJFJsSDfOm9k/jKU2v49pJ1fOP9Uwn6dN5qzTBz1NBaVQyX0DX0kDeuW3Iobcl4rS3ifvS4D21f/Pf0Ng0lozeSampqOO+88/jd737HFVdcAcB73/te7rrrLi6++GIWLVpUWXcXjUYr2b/+TjnlFG699dZKOWZPOedw3H333dx88/9n78/j5Urv+k78/Zy99nvrrrqSrqSWem+pV3e77faGMcbGMSFgcGJCEgieIcCMQ5YJyQQ64EzIYBjihPkxBGI8xCEkZkKMCbvXxmu3u1ut3hftd99qP9vzfH9/nLqbdCVdSfeqr7rr83rVq7ZzTj2n6lTV8znfz/fz+Sf87u/+Lg8++OC65yqVCv39/Xz5y1/mLW95C7/927+9Uu0LgoB3v/vd/NiP/Ri/+Zu/CcDNN9/M7OwsX/3qV3nwwQdJkoQXXniB22+/nUqlwiOPPMJDDz3Epz71qcsa46WwmSN0Til1kOX/KqW+D5jc0lH00EMPPbzOICLEnQ6tpXnqs7MYMXh+QKEnD90WZH2Dn+XEk4+RK5V58Pv+Bjfcfd+mKrCpNnQSzVI7Zr6ZkBqDrRS+a1G5imxBUIjlIpZ7zr+xQXTKH70S8qmTNjnb8I9vqvHAQII082i3gDgFtOWilYPBXpF/pkmC1tl5W0GwsEjEoFEcHO6jlFutpiVRRBR2sF2PSnUUJ3EgBpW3UJcIrb/mMIJ6sY061gAtmFsLyG1XIRXdamzUn9jS3Wpilyiu7U/0uv2JVQ9nwMWpepCzu86mKfNL85nHC4p8Lkc+yBP4Pq6z85xNz8W51cQ0MTQWQsJGjO1aOOdU+WzH4uY7B9l3Yx/PfGuGl59Z4NRLS9x69xAHb61irXWB9W0cz6LdSGg3EsoDAbmSh1JZbX1Pf55EG5baCXeNlPnJ+/bxK988yb977BT/633jaKN4eabJTaMlnG2s4K0zp1kKSRdCrMDO5KUFd2dW369T/IN/8A/4d//u363c//jHP84P//AP84u/+IsrRjMAH/zgB/nRH/1RPv7xj/PpT396ndHMxz/+8ZUexTRNeetb38qv/dqvXdY4oijigQcewBjD7/zO75z3/Cc/+ckVo5kbbrhhZVwAH/rQh/j//r//j+/4ju8AMvXApz/9af6X/+V/oVarkaYpH/nIR7j99tv5xCc+sWI08+53v/uyxngpXDKnUCl1A/DrwJuAReA48CERObmlI9lG9HIKe+ihh52CNEno1Jaozc0QdzrYtoOXz+9oydj1jDSOefpLf8EzX/ocIsJtb3kHt7/t2y/ZNxhrTSvULLRiap0Eg+Aoi8C1rihGwm5P4zbPnFMp3BizoeJXn/N4umZz30DK/3RTTNkRtEmRNCZNEtIkJkkFQUBZGMdHuXmUmwfHBdsH5dCKUxw7mywHro0xQhJ2MFoTFIsU+qrYqYtZilA2O7M6OBNlrqK1FBn1MPdUoLwDx3kpbNCfSC1dzU/M213J6Wp/ojiKJE1WZGqWZVHI5ckHeXzP27GmNRshjjSNuQ5RmOJ6NvYFCH1tIeTo16eZOduiUPY4fP8wY/tK55Fh0UIcahzfpjIY4HUri6kRXphpkCSGgu/wyacm+N1np/jbh8f4/ltHaYQppZzDDYOXH1VxNZBEY2IDCpxyN9oiuP6iLXZCTuFOw/79+3n00Uc3HY9xLj72sY9Rq9X4+Z//+S0d13bkFIqIfLtSqgBYItJQSm2NR3cPPfTQw+sAy6YxjblZWksLLJvG9KqC2wcxhhNPfovH/+SztGtLl+wbFBHCRNMMU+ZaMa0om4T7jnXV4fJOvERl4VtoI4S2j7E3JqQi8IUpm0+87CHAj9zQ5o39HdKOZm75BK5kkQ+W62J7qjuhNKBTlK6jkiWku61QQ3++yEB/HypOCdsaLJfiwDD5Sh+W2KTzHUwcZZPTndb71NGoJ+pYJ0Mkb6Mf6ofd/rWVim4lLtqfGGcVxfkE6/T6/kSr6uJ3+xNN2aIdhiumNbZtd01rcvieh2PvXLLs+TbV3UXiTkp9PiRqJbi+s04CClCpBjz0neNMn2ly9OvTfO3PzzA4mufIG0foH1zt+VK2wi846MQwd6ZJvuxRqgY4jsWhwSLPTtUJE80P3bGLqVbEbz01wUjB423jVZZaCaftq4uquFwo18Z2s2qwbsSkS5k5jVPNzGnUTql693BN8T3f8z28/PLLfO5zn3u1h7KpSuG3ROSecx57TETu3daRbSF6lcIeeujh1cB605gE2/Pw/KBnGrPNOLdv8L73fc+GfYPLsRG1Tsx8MyZOTdbT5dgXDXG/XFSnv8StT/zzlfuJWybyBgn9AUJvgLZTZVZV+e3pPfxFfYx9BcMPjdcY9A2WsrAshXUZRCjVhig1DBRc8iSYOMayoVwpEeR9lJ1DxwE68lFBDhX4YLmZM8hOgBHUCy3UsSYYQW4pZlJR5zolg5eLyKwY2KxUFSMDgNhkxLLbn5j2WySBYIxBEDzHo5jPk/NzeJ6HvVM+03MgAmErpjEXoo3B8x3UBk66xgjHn1vkmW/NEoeafTdWuP2+YXIF97wNJpHBGKFcDciVPUJteG6yTuDaKOCffvFFXlho83+8/UZuGyiy1IkZr+avOqriaiCpwUQ6O9lTdHH6A6zczo626FUKrx9cbqXwgqRQKXULcDvwf5K5jy6jDPwjEbl9S0Z8DdAjhT300MO1woppzMwMnWYDpcDPF7CdnXsG/7WCVm0pyxt84jGCUpm73/1d3HD3G9aR8EQbWlHKYjum1s5iI65GFroZqLhBYfZRckvP4yRNvGiWIJ4nlyyQSxaxMCvLGhSxU6HjDRB6VTruQHa7ex25FURdeKIfpQajDdXAwrMhyOUoVsp4gYdSFiYS0oUE0SnKTlGsmQPYLrh5cHPgBNl92wV1DU9iTHelovUUGfMxd5eh9Dr/7mzQn8hCgtLZZ5f1J3pI1UX328RlMJ4gKALPo5grEvg+3g50NjVG6DRiGgsRiOAFNhtpOpNY89wTc7x0bAGl4KYjA9x0ZBDnnOqaGCEOU2zbojKcJ1LCC1NNSoFDK9H81F88TzNO+eV33sxIIaAeJtw4XKBvi6IqrhQigsQaSSUzp+kPsMs7M9qiRwqvH2wlKfxu4K8C7wc+s+apBvCfReQrWzHga4EeKeyhhx62E8umMc3FeRpzq6Yxrv/qnYF+PSGNY5750ud4+kt/kfUNPvQObn/7at9gmGiaUcJCK6YeZrJQz7LwXXtdbMTVwhiDMRqjDUmaoNOENEnRYrCjRezOHMorrFT+QmPxX0/5vDTf4d5gih8YOsMYswTJPLl4gVwyj58srSNuBovQ7V8hjKGXkcWOO8C8lEhVjsFynkpfmXypgNvNyhMtpA2DaYNyQW3Es7QGSUGnGRFZhuODkwMvl/Uq2m4Wm7GVMs62Rj1exzodIgUbc08Zdve+PxfE2v7EblXx3P5EqTqYfoe4otBlC+UqgiBHMZfH9zKSuFP62YwWmvWI1mKYWf+f41S6jFYj5tg3ZjhzvE6Qd7j9vmH23Vg5bz90akhCTVBwSXM2J5baVAKP6VbIT/3F8xQ9h19+583kXZtWlHLLrtKWRlVcDURLVj00gpV3sPt87IK3YSX11UCPFF4/2DJSuGblB0XkqxddaIejRwp76KGH7UDPNObVhYhkfYN//AdZ3+Dhu7j7PX+FfKVKO8nyA+eaEWGSyUJ9x8Z3rIvGRmwGFyN/qpsroZTCsiwsy0IpCyucx+nMIG4WM/JCw+E/HC8yH1u8Z7TD+8c6bGRIqExKkCySS+YJukQxF6/e9tP6+rFZLjo3hC4Mo/MjpPlhEneIWA+TBsOYfPnypWk6BdO9LE8ZFGAH4OUzwrhMFJ3LrLjorlT06SaIILcWkVteR1LRrURqYDHNDGy60lPV0gCIAsoOut8mrSjSPhsqDvl8gUIuh+8FuI7zqpNEnRqaiyHteoxtWzjBxpWy+ek2T35tisXZkL6BgMMPjDA8dn6ETxpqtDa0bZjXhv6ixzNzTX76iy9yc7XA//G2QxgDiRhuGy3j7TBXUBNrpPv7lUVb+Cj/1TWn6ZHC6wfbQQoD4EfIpKQrp+1E5IeverTXCD1S2EMPPWwVxBjCVpPG/NyqaUwuh+O+uvKj1xvmTp3g0eW+wbE93PXev0pxbB+L7ZjFVkIqWWxE4Ni4VxivsEz+tNakaUqaJuhLkL8LYZkUxnaB/3Y2z59NBwz5hh850ORg8fKjf0WENE5Iow45GhwopwzYDZz2DHZ7Brs1g92axk7W53IZKyANhkiDYdJcRhRXLyMrpPXSA6BbUUzB6NXKolJZNdHLZ1JU2wXLyxxRz8VkhPWtGqqhkd1dqWhxa6o1URxTb9ZZatZod9oMVYcYHRh+/Z2wuUR/oqk46D6LtN9GVT1ygwUK+SKe676qzqZJrGnMh4StBMfLYijOhYhw5pU6x745Q7uZsGtficP3D1OqnGPkZIQoTJlpxnRcxUA1x5dOL/Cvv3aCt4/3848e2E870biWte1RFVcKMYJEGtGC5VuZvLTovSrRFj1SeP1gO9xHfxt4Dng38HPAh4Bnr3KcPfTQQw/XFZIwpFVbojYzhdEptuuRK50vW+phe9GuLfH4H3+W4088SlAscfh930f+0BEmwxRmGtjKIufZ2BvqIzfGhcifkcw+HpGs2mdZ2I6No65ssnyq4/Ibpyuc7Ti8fSjkA3ta+JfZMmS0QSdxdscLyPf3s2+4Qs61aXeXERFM25DWwZIOrp7BCc+5dGYIas9i6fb67dt50tzIKnEMlm9n1+J03R8V4DicN41YJothHdoLq48rlfUpegVIPKxjGjWRIkUb/dZ+GLt8qagxhma7Ra1Zo9aoU2uuXsIoPG953/MZH93DvrG9jA3twrZ3Xr/WlsO3YCxAxjbOT7TmY6yTMd4rAB3Eq1Prs9F9Ngx65HaVyfcX8F3/mr5frmdT3VUgDlPqcyFhM8ENbOw1JEgpxd6DFcb2lXjx2ALPPznHn336ZQ7eVuXWu4ey/kQAS+HnXXZ5Dqemm8y2Gzw02sfUHWN88tgEu4o+f/OOMephyon51jWPqtgMlKVQuey7JqkhmW6TTLexSx5On48KdrY5zVbDtm0OHz5MmqbceuutfPKTnySfz29q3SeeeIKJiQne+973bvj8r/zKr/DhD394ZXvvfe97+U//6T/R19e3VcPfsdhMpfBxEblbKXVURI4opVzgT0Tk267NEK8evUphDz30cCUwWtOu12jMzdJpNnumMa8i1vUNGmHorgcp3/kmbM8ncCx8x76kLPRyyN+lKn+Xg0QLf3GixmfPOBRtw9/ZV+OOSto11Njca6RpikkTLMsmKJVIbI9SzmNXX4C7pvplEiGtGSQRlKcu7hEjgpW2cMIZ7BWyOI0TzuKE2bVlonWraLfcJYtD6wljbpjUH8okpBu+FhCnqBMW6mRGAGVfBxmPwM91DW6C1crimmiFKI4ysncO8Ws0G9ln14Xv+fSVypSLZSrLl1KFnB8wOTvFyYnTnJ46Q5wmuI7LnpEx9o2Ns2dk7LrK+9tyGMn6ERc27k80OYXud1ADHv5YidyuMkE+uGZVVwGiVkJ9PiSNDX7O3rC/LmynPPOtGY4/v4TrWdx69xAHb61irVlWG+HEVJM41pT6fH79pWn+9MQ8f/8N+3jXgQFqnYThks+eav6aRVVcKURWq4fKUSvVw+02p9kJlcJisUizmcWyfOhDH+Lee+/lp37qpy65Xpqm/Mf/+B959NFH1wXer8XVZg7uJGxHpTDpXi8ppe4ApoD9VzPIHnrooYedisw0pk1jYZ7G/CwYwQ0CCpXKqz20q4Z03Qp3imHBZpBqw0vfeoyn/uyzRI0apRtuZeSN76JUHbhgbMS1qvxdCrMdw5cnUr4ymdBKXe6rJnxoX4eSrVGp7sovzXmTTwGwLARIY4MRwQlyFAYGUG5AJ00ZKgYMFv2VioaIoJsG3QRlgxVs4jNWCuMWid0ilM6P7EAEK6mvEMSMMGbk0WueID/3TZSsl76mXj9pMIxeK0vNDZO0xpGXh1AdCxnRmFtSyNlgcmBSTHuORrtDLYyodSJqYUQ9TKh1OoRJsrJ9S1mUiiUqxTLjo3u6xC8jgL63cf4jwL6xcfaNjaONZnJ2mpMTpzg1eZrjZ09iWza7h3exb2ycvaO7L7qd1yQsBf0u0u/CwXw3P3F9f6Izn6AmWuinWjSYola2UAMe7kiBYKxMMFTEdreHjCggKLh4eZewEdNYCDFR5lS6tjoW5B3ueWiMg7dVOfr1aY5+bZqXn1nk8P3DjO0roZTCthT7hou8Mtek3Uj4m7uqTDcjPv7oSYbyHncOl5iuh/iuxXBpZxsdKaVQQbd6qA3pbEg628EqODh9AVbefV1UD9/ylrdw9OhR/uAP/oCPfvSjxHHMwMAAn/rUpxgZGeHhhx9mYmKCEydOMDg4yCOPPEKn0+GRRx7hp3/6p/mBH/iBlW19/OMfZ2Jigne84x0MDg7y+c9/foUkNptNvvM7v5OHHnqIr33ta9x55538nb/zd/jZn/1ZZmZm+NSnPsX9999Pq9XiJ3/yJ3nqqadI05SHH36Y7/7u734V36HNYzOVwr8L/B5wGPgtoAj8cxH5f7Z9dFuEXqWwhx56uBTSJKFdW6I2M00SRdiOjZd7bZjGiBGkpaHWNQpRgGeBp1CeBbbqXtgRcthYa9qR5uTLr/DSX3yWzvQZgsFR9r3tvfTvXSUvxhi01hizSv7SJGH1f217Kn8XgzbCsQXNl86mPLOosYAjgzZv2+1yS/9Gk2YDosEYlOjsfhqTdtqZyUzeI8h5uDYkcUSqDaOVgJK/TGQFkyjShoUYCxUolLK7zHCbP0sx2PEiTmcDeWo4gx3Ooc0QS+mHCc0DOOoMTvBfWMqHzNqjzFFlwRRYTBxqsVlneBq4LpVcQCXwqAQ+lZxPOQgo5fNYfh6cQtcV1b3ijEUjhpn5WU5MnOLk5GnanTZKKXYNjbJv117Gd+0lH+QuvaHXC0K9YmDDQpwZ2cTdWAwL6HdxRvJ4o0VyY6XMFGUbfk+MEdr1iOZiBCi84HynUhFh+kyTo1+fprEUMzia58gbR+gfzD7PMNEcn2vjWxadMOFnnz7NYpzyS++8md2lHPUw5sbh4qseVXG5EBEkMZk5jaVwhvI4fVt7kmMnVQrTNOV7v/d7+c7v/E4++MEP0tfXh1KK3/iN3+DZZ5/ll37pl3j44Yf5gz/4Ax555BFyuRy/9Vu/dVmVwrWk8NChQzz++OPcfvvtvOENb+DOO+/kN3/zN/nMZz7DJz7xCX7/93+ff/pP/ym33XYbP/iDP8jS0hL3338/jz/+OIXCJvu1txBbWilU2T9oXUQWgS8BG5xK7KGHHnq4PrFiGjM3S2tpEVRmGvNaqApCNkEgEmQxgUTAt1CWyh7XQNMgorsLAwrEzcginspMDJxrQxbDJKUZpsy1YhbnF5j9xueov3AUN1/kwDv/Kv03HUYQWq3mRcmf4zjXhPydi1pk+MvJlEcmUxYjoeIpvmu/y0O7HPr8i43HynIAbUjTGJ3EWHaeYGwPbqGI5ThoYDFM8CzFwaE8OVvAaCRN0Eshuh2h8hpLpWCSrAKpw8wAZiMolb2mZdH9cDNSdbkfsbLQ/gDaHyBideJhjKHRCqkfj6jNhNTsFvXcl1mSDqG5E1rZcjYpVRYZZYE7WGSAGv2eUMn5OPnqOSY4/RivL+uJSyOImutjM2w3y1d08xlZXInNuIj5j7IYHRxhdHCEBw7fx9zSPCcnTnNi4hRfffIbfPXJbzAyMMy+XXvZN7aXYr54mW/QawyBDWM20u3/lOX+xG4shszHpM/X0M/U6AB4CmsoR/6eYXLjW9d/bVmKYl9AvujTrEW0lkIsW+H69go5VEoxurfE8O4ix59b5JlvzfK53z/Ovhsr3H7fMLmCy3h/jpPzbYoFj39y+x7+2RMn+ZkvvsTHvu1m+vIuL8+1uXXUIr9Doio2A6UUyrPBszGxRjfiLSeFa7H0By8TT7S2dJveWIG+v3Lwost0Oh3uuusuIKsU/siP/AjPP/88P/ADP8Dk5CRxHHPgwIGV5d///veTy139CZ4DBw5w+PBhAG6//Xbe+c53opTi8OHDnDhxAoA//dM/5TOf+Qwf+9jHAAjDkFOnTr3qRHozuOiRLiJGKfUTwH+5RuPpoYceeth2JGFIa2mR2uw0Rqc4rkeu/NoyjZHYILUUOhpcG5Vbb9CAw3m2/yICBugYaEk2516WJzoW+ApchXIzEoOtrlieZIzQTjT1TsJ8KyJKDWkcUX/qa8w9/hXEGAYO30/fbfegHJd6fYn15M991T8vEeGFJcOXJhKemNMYgVv7Lb7/kMfhARt7E++NYNBRhDEaxw8ojuzGCXKoboXaGKEWJgwUPMYH8jiWlRnJxAl6PkUkh+ovbvxeiHSdQfVKNTK7TkEnqxeTQBquJ1lrsUwglQV0b1sWYZzJO2vtNrV2Z+XS6ITIcnaFAznXpZzPMZ6vUsnnVi5Fz8aL5tb0MK6a4Dhzr2AnS+uGYSwPvWKAc87FH8IkAlHjnIxFL4vMcHMZWbScrLJor5/+KKUY6h9kqH+Qe2+7i6VGLasgTpziG8ce4xvHHmOwr5rJUHftpVJ6bZw4uiooBUUHKTqwL5twm25/osxHMBdjTXdo/MEJFm7N4d1ZpVAo4Lsezhb0ZVuOojwQkC97KzEWjmvhrHFvsizFwduqjB+q8NwTc7x0bIEzx+vcdHiAm44MMtaXY6LWYaQY8E+O7OVnHz/Jv/jii/zLNx/Czzu8ONPk1h0YVbEZvIb+zs5DLpfjiSeeWPfYT/7kT/JTP/VTvP/97+cLX/gCDz/88MpzF6vSvfvd72Z6epr77ruP3/iN37jo6/r+KsG2LGvlvmVZpGkmpRcRfu/3fo+bb775Mvfq1cdmvpV/ppT6h8DvsnJ+D0Rk4cKrZFBKfSfwb8imD78hIr9wzvMfAv637t0m8GMi8qRSai/w/wKjZFOUXxeRf9Nd52HgR4HZ7nr/VET+xyb2o4ceengdY9k0pj43Q9hoYtkWfr6A9RpzIBQtSD2Fhs5IW27zky+l1ArZW7fNdWQRhG4fmYC4GVHEy8Kxs8rixmQx1YZWrJlvdlhohCRaI2mKjabxynPMfesR0naTwt6DDN3zEH6lH8t6dTO5NkI7Eb42nfLliYSptpB34B27Hd465jKc39zk0ZgUHYcI4Bcr+KUKtr++hylODa0oZW81x0g5QCmVOQ8uhEg7RXkW1sUmq0p1yc8mjwExWYA9uksmDVonNBp1avUatVaDerPFUqdNvRMSpauVSEspyp5PNS1wIB2h4gaU97uUR4OsR+8CH2GaHyPNj208fB2tN8FZJozhDPn6i9hpc93yxg7Oi9hIvQFSf4DUG0CsNRUTZWUk0c2tyVnMCKOybPrLffSX+7j7liPUmw1OTp7i5MRpHnvmCR575gn6SpUVglit9O+4Y/RVQ7c/UfW7cAgkMfBoHe/ZDnpumum7c0guq+iXcgWCIIfvelflbOq4Fn3DefIVn+ZCJ3Mq9W1sd/W74Xo2h+8f4YZb+zn2jRmefXyO488vcft9wwyOBsw1Yw6V83zk9j384lOn+aVHT/JTt+1GFR1emm1y08jOjKp4tXGpit61RK1WY/fu3QB88pOfvOBypVKJRmM1qudP/uRPNnz+So1m3v3ud/Nv/+2/5d/+23+LUorHH3+cu++++4q2da2xmX+K5TzCH1/zmHAJKalSygZ+FXgXcAb4plLqMyLyzJrFjgNvE5FFpdR7gF8HHgBS4B+IyLeUUiXgMaXUn61Z9/8SkY9tYuw99NDD6xgbm8bkKLwGraXFCNLWsJRmSdWBtWUT1QuRxeXXJRbo6FU5p8p6jLQthEpTT2PmmiGLYQcjGiXguxYWEM/PMvHYlwhnJwmqw+x+23dRGN27JePeapxsZL2Cj86kxAb2lyx+6BaXe4ccvE2a9+hliahlk+sfwi2UsDaomrQjjRbDzaMlyjk3M5JpxKSLYab2zG+dpE1ECOOIWqNOvVlb5/TZaDfXyHQh5wdUShX2D4xRKRSpFApU/ALFkwr7pQQskBsVsjcFkqwqGbczwrkRlOr2P66pQiorO4Zsn6Swl6Sw8fGg0la3yjhzTl/jNMHSMSy9PpYi9fpIc7tIc6MkwSipP0IaDJJ4g4i9RlpmOZkTqpNdyq7D4QOHOHzoVlphh5MTpzk5eZqjzx/jyeefopQvsm9sL/vGxhnqH+wRxLVwLeSNFcyIh/VYncIXW5g39qGHbWqtBguNWtbi7HkUcwUCP8B3vSvq5fZ8m/5dRZJOSm0+i7HwAgdrjSKiUPJ44J17ODjV5ujXp3jsSxP0DQSM3d5HUyneMFTib904wm+9OM2nTszyoT2DLDUTjgsc3FXecVEVPazi4Ycf5gMf+AC7d+/mjW98I8ePH99wuXe84x38wi/8Anfdddd5RjMAH/7wh3nPe97Drl27+PznP3/Z4/jn//yf85GPfIQjR44gIuzfv5/PfvazV7RP1xqXNJq54g0r9SDwsIi8u3v/pwFE5F9dYPl+4JiI7N7guf8O/DsR+bNupbB5OaSwZzTTQw+vL6RxTLte65rGhNiO85oxjdkIEprz+gavJbTWpEajjSZKYhphRKMV0urEpF3HU9e28GwXy7bAsYiSJlNH/5La8WewgzzD9z5E36E7VmSTOwWxFh6bSfnSRMqJhsGz4A0jDm8dcxgvbb66oZMInSbYfkBQqeLm8hvuq4jQCBMCz+HgYBHftTCxIV3oIKFG+Rvb8W9qDEbTaDZWYx3WRDzEy9mHgG1ZK7EO5WJ5XcyD564x3hCBMyHW4w1UW2P25ZC7Spmr6Hk7ZtbIV7MqJJJm/Y9mrZS1K23dCIouYVx7uYChjghW2lyN2ehM4XSmcDuT2e14vdhJu2WS3K6MNAYjJF1JauINIs4a6ZntdgljjtDAqdk5TkxNMDk3gxFDPsgxvisjiKMDw6/Z35wrQi3B+soSqpZibi0gh0srn12apiRpt09YQeAFFPMFfM/Hd73LJtoiELYSGnMdtDZ4gXPe90ZEOP1ynWPfnKbTSunflWfw5jJ9/QH/4cUp/vjMIn/3plG+fbhCo5Owa1eRQ+MVrOuEGUqiwbHx95a2dLs7wWimh81hOyIprhS7gdNr7p8hqwJeCD8C/NG5Dyql9gN3A19f8/BPKKV+CHiUrKK4eNWj7aGHHq5rrDONqS0B4OfyeJW+V3Vc2wlJDLKUZrJO11rXN7gdWEv+4jQhSmLiNCU1miQ1tGNDJzaIAkfZeEGO/DkTMZMmzD7zTWae/yYihqFD9zFy0xuwPR9pCuJoxFGIpTJHQwsu3wHl6jHdNnx5IuGrUyntFEbziu8/5PHAiEPe3fx4BCEJW9iuT2nXOE5wYZt7rQ2NKGWo5LOnP4eFIq1F6MUo4z6bqA6KCGEUbkj8mq3maq8fkA9ylItlDuzeR6VUWcn2K+TzWJcy66mnWI/VUNMx0ueg3zgAwxdxalQW2BabnnaYLnnEdKM7JCOUOl7tg9QppJ2Nq5AKDBaxP0ac2wPYmTy0+9EpHXbJ4iRuZ2rlOlg6hhN9Yd2mtFPKqovdKuMyWbT8YW4qFLnp4CjxviFO19ucXKjx4smXeO74C/iux/jobvbt3sfY0K5rGv6+I1FxMe8aRH2rhvVsC5mNMQ/2Q8HGcZyVPkMRIdUp80vzZBxRkc/lyAd5At/H3UQ/sVKQK7r4BZdOI6Y5H2JE8IPVkwhKKcYPVdi9v8SLx+Z57ol5FqfaDO4v8YO3DTHbSfjNF6YYClzurhaZnGwiHc343jJB0d3xOYY99HC52E5SuNH3ZcOypFLqHWSk8KFzHi+SxWF8RETq3Yf/f8DPd7f188AvsSpxXbvuh4EPA4yPj1/ZHvTQQw87HkkY0lxcoD43g04TXM8nVyq/piVcogVppFBf7hvc2snmhcifGOnm/IFBSLWikxg6iUEQbGWT850Ne29EhKUzLzB59Msk7QaV3YfYdeQt+MW+5QVQRlAxEMrqH0hXiiqOwjgg9rK0sPvkVu63EY7Oa740kfDcosFScPegzVvHXG7su3w5rjEpSdghKPcT9A9mVdILIE4N7Thl/0CBgaKHxIZkvo3EBnVOHhtkn1G91VhH+urNOrVGjThdrbTZlk25WGKg0s8Ne/avEL9ysbS+6rdZJAb1TBP1fAtshbmnjBzKb338hWWvxkxcaqZyQUOdZSfWJHMsXSNjFSBxB0i8ETr93dfqVvWyXsY11cX2JE5nkqD2LPbMl1FrpjLaKWZEMTfKkD/Ckf4h2sODnAyLHK9FnJg4yYunj+PaNnsHBtg3OsbukT24ftA1vbl+nC23BI5C7u/DDPuoR2tYfzKLeaAPdq+eLFFK4TourpPFrogIURLT6rSBzNSjkMuTD/L4nodjOxf8bloKCmWPXMGlWY9oLUZYlsL1V2MsbMfilruG2H9TP8ceneHkC0ssnmnxoZsrLBZS/q+nz/Jz9+xnfylguhlinxKq/QGlgRzuNgfF99DDtcRF5aMq+5btEZHTF1zowutuSj6qlDoC/DfgPSLywprHXeCzwJ+IyC9f4DX2A58VkTsuNpaefLSHHl5b0GlKp1GnNjtN3GqhrNemacy5EOnmDS518wb9K+8bFBGMMauyzzgh1ueTP8tS2JaNpRSJEaIkq2Ytm4w4loXrKKyLELT2whRnn/gi7fkJgsoQu+96G8XhTfYNLpvcCKhzCkJig9hdsuhcOVlcigyPTGRxErVY6PcVbxlzeNOoQ+WicRIXhk4ijE7ID47iFcsXXbYVZcY9B4eLFFwLvRSjazG4EJqEWqO2Svq61b9mu3VO1S+/Lsh9+XYhV9iaEyQiqNMh6vE6qmMwB3LInaUspuB6gk5XZas6ySqNaZRdTLq63LJU1XK6EtVuZIdJcDrTKzLU5Sqj05nECedQrB6kxs4TBrt4xbmZ59IxXg6LdLTCVordfSX2VfvYW+3Dz5W6pjf57PVst0sYr7P39nLRSLH+chG1lGJuLiBHShv2LZ8LYwxJmqJ19nnZtk0hyJPP5S/pbKpTQ2sporkU4TgWzgbH7/R0i8e/OkVrLsItOPyRE3LCE/7VGw5Q8RzCRDNeDrAFiv0BxT4f6wol3duJnny0h8uVj24mvP4xEbn3cgeilHKAF4B3AmeBbwJ/Q0SeXrPMOPA54IdE5CtrHlfAJ4EFEfnIOdvdJSKT3dt/H3hARD54sbH0SGEPPVz/EBGidovmwgKNhVXTGNffvgymnYSVvsFUMqfPy6zMGDFEcUwYxxclf7a1SjSNCLE2tGNNM0xJjUEBrm3jbmISlHSaTB77SxZPPIPj5xm9401UD9y+NTmCIhkxNqCEdTqU9ZVFBUplZHENOTIiPL+YxUkcndMIcGvV5q1jDocHMhJ8RcNCSMM2luNSGB7D9i5cjTMi1DsJBc+i6qY05xdZmJqj1mxQb2e9f8naqp9tUymsEr/yGhK4XFXZFtQSrMfqqJkY6Xcw91Zg8PoK9d4UzJq4DkkhibK8xyQ8v89xxdl1jSzVJJnxzTmS1Iw0ziAIpxjjWW7kWW6kThELw14/5Iayxd5qH15hBO32dbfvZmTRyYHrZ1EaltPNX9x5JOSKoAX1RB3rxTZSdTFv6oPi5VVPjTHEaYJJNSg25WyaxJrmQkTYjLE9G8db/5tU78Q899wis8/XCZsJZxzDM1X4B2/cj0323d0/UMgq+ZaiNBCQK3o76mPpkcIetoMU/irwWyLyzcsdjFLqvcCvkPnW/QcR+ZdKqf8ZQER+TSn1G8D3Aie7q6Qicp9S6iHgy8BTsHLa7Z+KyP9QSv02cBfZFOAE8D8tk8QLoUcKe+jh+sWqacwUaRxh2a9t05hzcV7foHN5sw6tNa2oQ73dwojGUtZ55G/d8kYIU00rSunEGi2CrSwcW23akt3olNkXvsXMs99AxDB4492M3Ho/tnuNCLyRC5LFpsAji5ovzWhmQqHowptGXR4acxi6yp5MYzRp2MYvVwj6h9fJRUWEOOzQbjZoN+s06zXq9Rpp2KLTWh+tkM/l6Suumrss9/sVcvlrK4tODOpYE/VCC1yFHC4hB7dBKno9QKRbZUyy2A4dZWQxDbPba+dSClDOqgTWssCkXcKYmd3Y7Snmmi1eagc8n+5igT5AGOcst6oTHArq5At9pP4waTBE4g+T+sNor7+bE+l24zS6LqnL+YvbeXJgO3G6g/WNGgDm/grsvfKgca01SZqguyexLuZsGocpjfmQqJPiBjb2moiXxVbCxEKbzlTIyWcWkMQwWbJ495t2o1yF41jsq+ZRAlEnxQscyoMBXrAzJME9UtjDdpDCZ4CbyQhYi+55ZRE5shUDvhbokcIeeri+YIwmbDZpzM/SrmUTBT+Xx3av0wnPFUBMt2+wpsFSqMuUMcZJQiNs0wrbKAHPdS9IpGNt1hFByGShjp1J3TY9ZhFqZ15g4ugjJO36+X2DrxJEhOMt4QszKd9cNCQCh/KKtw/Y3FfJ9nNdZfEKTG50GmOSmPzgKG6xlGUKilBfnGfixEvMTpwmXVP1s2ybcrlCtVyh5HSln319VEoV3C0I9r4qiKBOdlBPNCA0yA055M4yXKGU9nWBdbLUOCOLG8lSoUvqurJU20Z0SmNpgpMzUxxf7DAfZ5WtXdYCt5rnuI3nGWQJAGN5Wf5iboQkGMlIoz9EEoygvWo3fzFYk8HorxJGe2eQlQuimWbupAsJ5lAeubu8KTnppXApZ1OUIm6n1OdC0kTj+vZKjMVsPWKuFREoi68+No09GaKUYu/NFSr7ilSKHrv7cqAgjQ1prClUPIr9wTqC+WqgRwp72A730fdsxcB66KGHHi6FOOzQXFygMTeLTlNcz3vNm8acC5E1eYOGy8obXJaI1tpNoiTGtiyCDezcl2WhYaxpRCmJ1oDCtW3yno26AgOX9uI0E098gdbcBEFlkPG3fd/m+wa3CZEWvr6g+cKs5lRb8C1406DN24ds9q4Nme+a3NgRWZVxGSojieIs9y9aWQ7fOWQxCVtYtkNpbBzbD0jiiKnTJ5g4+RKteg3bdhga20u5fwC8PKVyhVt2VXFbGmkn3ZiJHUK4lrpS0dk4k/O9pR8Grl+pqIhApDMXS9dCbddE3XYAZ+NZ1cVkqWkHJVAuVDl8oMrhGxS1OOXUfI2T8yU+16jyOd5Ef2BxKB9xszvFruRk5pS6eBTLrEaJiHK6ZHGUNBgm8Ye6lcZRUr/arST6qxVG28vGvVMMb4oO5p0DqKMNrOdbyFyMeVM/lK9ubJt1Ni2P+qSh0FqISOIsxmKo5JNoQz1MecuDY/zu05NErzSxn6sxe6LJ4E1lnJsVI5UAx7NwXEXYTOk0G5SqAbmy/7osrG83bNvm8OHDpGnKgQMH+O3f/m36LpI//Pu///vcdNNN3HbbbQD8zM/8DG9961v59m//9it6/S984Qt87GMfu27yBzeLS37TRORkV855o4h8Qik1BBS3f2g99NDD6wFrTWOiVgvLsvHz+de8acxGkKjbNxh3+wa9TVapzpGIurZD3l8ffSAiRKmhGaW0ohQtgkLh2RYF78orsFnf4FdYPPE0jp9jz73fvnV9g1eIiY7hC7Oar85rOhp25xQfGnd444BNbqPKg1JZk8O5WOOImpnc6O7yGTE0thCnIV65hF8dpF5fYOLky8xOnMYYQ6mvys133c/I7n1YtkMtTBjIu4y5LmouAgVWfodUv2ODOtZAvdgGV2HeUEFuyF23vWsigkQaCWOUZ1CuQlIHSbIPWjkKnCs3arosLMtIna58eu1XU2Q1n9GkkMZU/A6HcwUO7xqgGUacWqxxcqHGowuGb7KXUnCIfYMD7BscYNRPcMM1+YtrojUsE62+jHIyohiMkAar1cU0GCH1Bs6pLgarhje2m1UfrxVshdxdRg97WF9fwvrTOeS+CrL/yuWka7EZZ9NcMYedunSaEY7tMFIOSHSHME75wG2j/Iqc5T9NNPnr5Jk8usjCiSa33zfMgRsqoBRuzka0UJ8LadcSKkMBXm4HkO7XEHK5HE888QQAf+tv/S1+9Vd/lX/2z/7ZBZf//d//fd73vvetkMKf+7mf23A5rfXrOjrmkkepUupngfvIJKSfAFzgPwJv3t6h9dBDD69VrJrGzNOYnwPA9QMKr+FMwYtBUkFqKTQ1uJuPmNhYIrqeZBgROolmqZUQ6RRb2biORXCVk+GVvsHnvoFozdDN9zJy6wPXrm/wHKRGeHzJ8PmZlBeagqPg3n6Ltw85HCqqK5v8ryGL6xotRNBxinRSvCDPzOQME0e/STts4dgOY6P7GRs/SKmvirEVrTQlTRL2FnyqiSDteMOYiVcFIqjjHdSTDYgMciifhYpfh1JR0RqJInSjg2m2EWljuwLLfbgCuD52sYIkARLZWd+nBbiv0uehFDgesHE1tqhTbhtLuM2kdNoNTk9PcGJ6imfOTnLszAR512W8WmFf9Q5G97wZa7nypxR2vNg1uZlan8VYfxZLhyuvIVikwVCWxdjtXcwI5CipP5gRRTcAO8iuV+So7vadNNgdYN49hPXVRayvLWFmIuSeyupnuUVQSuG53ko8izGGMI7QuoVxhCQ0ULcoezlmEkOSKn781jH+RXiSf9No8c9uHyZ5pcW3PneWsy/WuPONI5QqPspW+AUHnRjmzjbJlTxKAwHOqywpfS3iwQcf5OjRowC8/PLL/PiP/zizs7Pk83n+/b//9ywsLPCZz3yGL37xi3z0ox/l937v9/j5n/953ve+9/F93/d97N+/nx/+4R/mT//0T/mJn/gJqtUqP/uzP0sURRw8eJBPfOITFItF/viP/5iPfOQjDA4Ocs8997zKe7092Mypi+8hC4//FoCITCiltlag3EMPPbwuoNOE1tIStZkpkijEcTxyxRLqdWIacy7O7Rskd+nKxTqJaJpgK7WhRFSL0IlTFtoJqTa4tn1VFcGVMYtQO/sik09+mbhdpzx2kLE73/qq9Q3OR8KX5lK+PKuppzDoKb53t8ObB23KlxEyfzlIopBap8F8p8bcyWlEhL7yAPvHb2FkYAwbG4wQTbfQIlQDl2olwG2l4FibCqG/JlhMsB6toeYTZMDFvK0K1R1SubwEBCBJMhLY6WCaTUwrzkhuoLBLHlZQRJ1z1t8kCbo+n/X+oSBXAr8IsYty3IzjuDZqp0QM2M5KL2DOL3FT/xg33QJRHHNm6hQnJ07z4swUz03P4bsu4wNV9vWXGSvlwPLQwT6iYByq1qpbqmVjpbWMKLYnuoQxuxTrL2Lp9srLZ4RxMKso+sMkwTBpsNzPOAheN07DyWfXtg2WtzX9iwUb820DmdnRM03UXIJ5cx9Utu8YtSwL31sl6SZv6IQRzVoNwoTZUFMqFPj7tw7z8JMT/PLMHB99y3706TZTL9T4s0+/zMHbq9x61xBeYGO7FrZrEXcSZk/FlPoD8hUfayecEHoNQGvNX/zFX/AjP/IjAHz4wx/m137t17jxxhv5+te/zt/7e3+Pz33uc7z//e9fIYEbIQgCHnnkEebm5vhrf+2v8ed//ucUCgX+9b/+1/zyL/8y//gf/2N+9Ed/lM997nMcOnSIH/iBH7iWu3nNsJlvbSwiopQSAKVUYZvH1EMPPbzGkMQRjfk5ajNTIJlpjFfpf7WH9aphXd+gZlN9gxtKRL3zq3LaCM0oZamTue/5to3vbQ0JyfoGv0hr7ixBZZAb3va9lIbHt2TblwMjwjN1w+dnNEdrmUH14YrF24ds7qhYVxwncSmEUcjU/FlmG/NESYTreOwdO8juXfsp5FezCMPEkIhQqXgMFn18x0a0yQyDdoIcMzKopxqol9vgWZj7K8iBnS0VFWMgjtFRhLRamHY7qwyKgMliIZxqASt/cUJnuS50DavEGCSKkFoDEUHZPiooQRhkBNGxUc429iFeBXzP4+D4IQ6OHyJNU87MTHBy4hQnps7y4tQ0ruOwd3iMfSO72D0wgItZzWRM2hhxiPw9RP4eqNKtLjqgLCzdWHFJXY3VmCI//zXstLUyBkGh/cEuURzuksauJDUYQbxSJkX1i+CXrix30VLIkRIy7GF9tSsnvffaHa+WZVHI5yjkc6SxJphtcmq6hudb/E8HPH7phTb/6qmT/Iu79nDznlGmn6/z0tMLnHxxiVvvHuLgrVUsW+EGDhihsRjSrsWUh3L4BfcKurd3Fv7oj/6IqampLd3m6Ogo73nPxe1MOp0Od911FydOnODee+/lXe96F81mk6985St84AMfWFkuiqKLbGUVyyTva1/7Gs888wxvfnMmhozjmAcffJDnnnuOAwcOcOONNwLwgz/4g/z6r//6lezejsZmZgr/RSn1/wB9SqkfBX4Y+PfbO6weeujhtYC406Y+N0tjfg6lFEG++LrsFVyLrG8whdhsqm9wMxJRgNQYGmFKrZNgRPAdm2CLXCyTsMXUU3/Jwomnsb0ce+55J9Ub7rjmfYONRHhkTvOlOc1sJJQceM+ozduGHAb87ZleiQgLjQUm5ydYai0BUO0b5qZd+xka2IW1ZqIbp4YoNRR9hz3VgJy7+v7vCCMZEdQrHdTRBsRrpKLeDhjbOZA0RaII0+lgWi1MGGblQQXKdsD1UJZCCVg5dUkyuBGUZaFyOcjlVl7ThEugU8Qo8Ioot4CyPfBdLMe6dn2IlwHHcdg/Ns7+sXG00UzOTnFy4jQnJ0/zysQpbMtm98gY+3btZe/oXnzPzyqlptvLqOMuWexAGmLEJvZ3E/u7oY810RoOlm6tI4pOZxK3PUl+/lHstLFuXKk3QJoboVV9A83ht0JlDwR9V2ZqM+pjvnMwI4bfqGGmI+S+CrjX7th1PJvR3RX8os+J03V2uxb/8w0W/+alBh976jQ/frBA6QaHyt4KjZdjjn5tmleeWeTwAyPsGi+iLIWfdzGpsDDZIig4lAZyuN7r+z/xSrDcU1ir1Xjf+97Hr/7qr/K3//bfpq+vb6XX8HJQKGT1LhHhXe96F7/zO7+z7vknnnhix33vtwObMZr5mFLqXUCdrK/wZ0Tkz7Z9ZD300MN1CREharVYmpmkXathO87rWiK6jMvpG9ysRBSyOImMDMYowHfty4qRuBiMTpl9sZs3qDVDN93LyG3Xtm9QRHi5JXx+JuWxRUMqcFNR8T27Xe7pszadnXi5COMOU4vTzCxOE6cxru2yb+wQu3cfJJ9bL5hJtCFKNIFns38gT97fIfLQtViIsR6toxYSZNDF3FuF/p0hFRURiGMkjtGtFqbZRFINCFg2ynOx8gWUynxZJBFIL0wGJU2R06cxx49jjh9HJidR4+PYR45g3Xoryt/4+FWOg10sroxJoggJpxEDUrcxfgll57ACD+W5WW7oDpso2pbNnpHd7BnZzYN33s/0/AwnJ09zcuI0pyZPo5RibGiUfWPjjI/uIRds0A20bHqjl81vwpWIDaMVsTdG7I1BhcyExrZB2Vg6xAmn1lQZp/CarzBw/JNUzvx3Gru+g8bIO5HKfsj1Xb7ENGdj3l7NpKRPN7Poijf1X/PjuL8SYGzFxFyLm2yPH9pb5BOnmvznMzF/a59DWzUp3aKo7stx+pmYr/7ZaQZGcxx5YITqUB7LUQRFlzTUzJ1uUOgLKPb5WDtFsnwZuFRFb7tRqVT4+Mc/znd/93fzYz/2Yxw4cID/+l//Kx/4wAcQEY4ePcqdd95JqVSi0WhccntvfOMb+fEf/3FeeuklDh06RLvd5syZM9xyyy0cP36cl19+mYMHD55HGl8r2IzRzA8DXxaRf3QNxtNDDz1cpxBj6DQbLE5MEHWauJ7/ujWOWYusb1BDPb1k32CqNe1NSEQBolRTDxMaYYqlFDnPxtoiMVLWN/gSk0e/RNzq9g0eeQt+6dpJfkMtfG0+i5M40xECC946ZPO2IZvdVxkyfyEYY5hvzDO9MLVSFezLV7hh10F2jR/CttdPPlNjCGOD71qMDxTI+87Ok4NFBvVkHfVKBwIL88YKsu/VlYpKmmZS0DBE2m10a1WSiG1jOS5WcK57LphYMp6YV1i5jAwaY9DTM+hXXkFeeQU5cQLOnEGlWTaglEowOgrf+Abmy18Gx8G6+WasI0ewDh/GGhracIxKKVQQQHcckqaYqIG0F0mbCuw8ys1jBQEq8FHeDjEOWgPLstg1NMquoVEeOHwfc4vznJg8xcmJU3zlia/zFb7OyMAw+8bG2bdrL8V892THckTFRjNEMee4pUarhNFA7I0Se6NQuSs7xpRF0HiB8pnfp//Uf6E88Yc0R76N+q53Y/pugKAKzmWQOkshd6yRk/7ZHHJ3GTmUv6bHdLXok2phvhnx1oLHbGz47FSbYd/mr+wp0o5TCv0OR97hMXU85OzzHT7/308wsj/gtnsHKZVzuIGLIxbtWkSnEVMeDAiK3s77DdnhuPvuu7nzzjv5z//5P/OpT32KH/uxH+OjH/0oSZLwwQ9+kDvvvJMPfvCD/OiP/igf//jH+fSnP33BbQ0NDfFbv/Vb/PW//tdXpKcf/ehHuemmm/j1X/91vuu7vovBwUEeeughjh07dq128ZphM+H1Pwc8BOwDHgO+TEYSn9j20W0ReuH1PfSwfTBa01pcYHF6kjSK8HJ53AuciX894XL6BqMkphl2NhU0H6aaWjumFWtsZeG76opyBS+E9uIME09+kdbsGYLyAGN3vZ3SyLXrGzzTzuIkvjavCQ3szSnePmzzQNUm2KYz6e2ozdTCFDNL06Q6xXd9hivDDBT6KVWH8Qsl1oaNaZM5ujqWYqQcUArcndeOZwT1SjuTiiaC3FRA7iheU7kddA1hoghJkqwK2GojcbQygVeuh3KdDb8bIoIRQccGY0wW5ZC04fRJ1MnswsmTqGVS6XnY+/fjHTqEf+gQ3k034Q0NYVsWaRxTP3qU5je/iXnqKdTsbPb6u3ZhHTmCffgw6uBB1CYk1yKCxBESRkgKom2UX8TK5bECHxW4O0MufAGICIv1JU5OnOLk5GkW60sADPZVM4I4Nk6lWL74Ri6ENAFJQGvQUZbFGDfBpHids5TPfob83NcRy6U5/Fbqu96Drt4Cuf7VyI7NItRYX6+hJiNkb4B5Q+WaSqFF4OxSm0aYkndsfu3oHF+ZDfk740XeNJynnWqGij6lwCGNDSefazHxUohSMHrIY/eNAcVCnpyXw8bBJODlHMqDOTz/6iWlvfD6Hi43vP6SpHDNRnLAjwL/ENgtIteNCLpHCnvoYeuRJgnNhXmWpicxWhPkC9juzpCjvdqQuNs3GHX7BjcgMxtJRD3HveDkuJ0YljoxUaJxLAt/i80vkrDF1LGvsHD8GLaXY/SOBxk4cPiayH4TI3xr0fCF2ZQXu3ESb6hmcRI3FLbHnEUbzVxtjunFKertOgpFtVxltH8XRS+PUkLQP4SzJu/RGKHdJYNDJZ9Kztt5ZBBgLsZ6rIZaTJEhD3NvGfquzXdTtF41hGk20e02GNON98iqgMrJpg/GGIwIRrLr5exMkExSGqY405PYM6exJk4hJ09gpqezF1IKZ88e/EOHCG66Ce/QIbzx8fPcRs8bnwhRklA/fpzWY4/BsWOol17KxpjPY91+eyYzvf12VGlzk2nRKSYMkTBFUgWWj5UvooIcdjG3I41q1qLWrHd7EE8xtzgPQF+pwv4uQewv913dd1AMRA1oTkMa4cSzVCb+iML0FwFoDb6R+q7vIhk4DIVqZk6z6W0L6rlWdvIjb2Pe1AcDG8d7bAeMEU4utEi14NgW//qxWV5civhfbyhzY9ElRNjdlyNws+Oy09QcP9Zi7kyEFyj23OpT3WOjlMKxHTzl4yiP/oEilWoB6yoiOHqksIctJ4VKqf+dLJOwCDwOPEJWKZzckhFfA/RIYQ89bB2SMKSxMEttZgaAoNAzj1nGSt9gS4OjUBtUZTaSiDoX6K1ZjpVYbCck2uDaFt4WVyCMTpl78XGmn/0GRqcM3XhXljfoXcbE7AoxGxm+OKv5yzlNI4UhX/H2IZs3D9oUtziPbBnNTpPpxSlmlmbQRhN4OUb7RxjuH8G1XXQUYnkBQf/AynFtDLSTTKY7XPKo5LydaSkfatSTDazjHSRnIXeVkfFgW2V1kiRZL2C7nbmCdiVXRgxiO9lFCWZlqrE657AtG9e2cSwHRynshXn0qVfQr7xCeuoV0rMns4oTYFerWfXvxhvxb7wR/+BBrNzVBZprY2hHEbW5OaJjx7COHUM98ww0GqAU6oYbsA8fxjpyBLVnz6aIkYhg4ghpR5ncVdtYQQFVKGQE0d/4xM9OQbPd4tTkaU5OnGJ6fhZBKBWK7Ns1zr6xvQz1D17d+KNmRg6TDnZaozz5JxQn/xzLRLT776U29l7ioXuh0M1H3CzmYqyvLEGokTtLyE2FayYnTVLDyfkWSikSgYe/Pk091vz0bVX6RdAIuwcL6367a3MJrzzZpLGYUuxzuOFIgdKgTapTjNGkieDaHsMjfVSqJQLPu6B65ELokcIetoMUfgtIgT8Evgh8TUTCi660w9AjhT30cPWI2i1qM9M0FxewbRsvX7jsP6nXKsQI0tRQS7OJiHd+dSuTiLZphZ1LSkS1EVpdMqiNxrMd3C2WTp7fN3gDY0feuu19g0aEozXDF2Y0T9ezOIm7+rI4iVvL2xMnkeqUudosU4tTNDtNlFIMlgcZ6R+lUqiglMJojY4jvFIFv1jJepcEOonGiDBU9OnLezg70QzCCOqlNuqpBqSC3FxAbt96qehyLETaCUmbDdJ2C50kGc2zLJSb9aIptZbw2biWjWs7WJaFrVR23DebJCdfJj7+MvHxl4hPvIS0Mxmo8n28gwcz8te9OAMDW7ov5yJOU5qdDrVmE3PyJPYzz6Cefho5eTJboL9/hSBat9xyQbOacyFaY9oddEdDLOAE2IUiVimPVQh2tAFXJwo5PXmaExOnmZydwoghH+QY37WX/WPjjAwMX/l/QNKG1hyEday0RWn285TO/hF22iQs30Zt7L2EIw9CYRi8/Oa2GRmsbyyhzkbImI95oA/8a/P+RqnmxFwb11YsRoaHvz5N4Fj8zD2DWO0EnRjGqnm8NU6jIsLs6YjjT7WIOoaBMY8bDhfIlbKThEmS0OnE2K6iUPYolgoU8wUCz8e7gPHYWvRIYQ/bIh/thtU/1L18PzAtIg9d/XCvDXqksIcergwiQthssDg1Sdio43geXi6/o890X0uICNLRsJiCIZOKrqkgXY5EFDIyuOwkqruxEtvhrtlZmuHsE2v7Bt9GaWTflr/OWtSW4yRmU+bjLH/6LYM2bx1yqF4iluNKICI0Ow2mFqeYrc1ijCHv5xmtjjJUGcZdY26RxgmIJugfxAly0CWDWoRqwaOa93B3qgRwtisVXUqREQ9zbwXKV+5+aozpyjoNOo5JwyiLhmi3kCiLhbAtC9v18IMcnuPi2A62pbCV1SV+6/tnJYmJT51YIX/x8ZfQc5nSAKVwdu3F23cQ/+YbCW6/CW987yVloJeCaI3EceYgajSgUJ6HlctddNtGhDCOqbfbtMMQajXc559Hjh3DPPMMRBG47opZjX3kCOoyCKuOIkwzxESABitfxC4VsYp5LH/nyu+jOObM9FlOTJzi7MwEWmt8z2d8dA/7xsYZGxrFvpLPLA2hvQDtBZQJKc79JeUzn8WJF4gKB6iPvZf26NugOJLlHV4KIqgX26gn6pmx0oP9MHRt5KSdKOXEQpuca3O8HvMvH51lf9nlp+8ZJo4SaKcM5Hw8317nNKq1cPaFNqef72C0MHYwx/htedxuf6RODUmscQMLt6CwrKyPPJ/LkQ/yBL6Pu8H/ynaSwltuuaU3D9jhEBGee+65La8U3gG8BXgbcB9wmkw++jNbMuprgB4p7KGHy4MxmnatxuLkBGkU4vgBXrD9csLrCSt9g6EBf33f4OVIRAGS5YzBdozAtpHB9X2DAaN3vGlb+wZFhBeawhdmUr61ZNACt5SyquBd2xQnkeqEmaUZphamaEdtLGUxVBlipDpKKVdaT1ZE0FGIcn1y/QNYtkOYaFIj9OVdBov+ziWDHY16ooF1soPkLczdZdhzcanoMtlbJn5izIqYUxAkTrBSjR1HWO0Q2wiebWHZDo4XYHsejm1fdDIoxpDOTK5WAI+/RHLmJJiuDLS/infgEO6+g7h7DuGOH8AZyGMXLj9ncO1rShRlJFDr7C2wLOxKH3ZfBbtY7I5rlnRuLhuL42Ll8xc1lkm0ph2GLLVaaK2xjME9fhzz1FOYo0eRZbOa3buxDh/OCOINN2ya0Jo0xbQiTCdFosx0xy6XsEt5VCG3YyfdSZpydmaCkxOnOD11liRNcB2XvaO72bdrnN0jY7iXm5GaJhAuQmsWdExx4RuUz34GtzNFkttFbdd7aY1+W5Z1uBlyuBBj/eUStDVyuITcem3kpPVOwunFNkXP5dGZNh8/Os8DIzl+/MgA7SglpyzKojBGcM9xrI1Dw4mnW0wdD3Fcxb7b8uw6mOtK1YU0NhgDhYqLn3dJTUraddm1LItCLk8+yON7Hq7jbhspPH78OKVSiYGBgR17jL7eISLMz8/TaDQ4cODAuueulhT+IfAlMtfRb4pIskVjvmbokcIeetgcdJrQXFxgaWoSrVP8II/jXbum/esBkgrSSKHe7Rtc43YXJTHNTotWFF5SIgrnxEqg8FxryzIG06hD1FggbCwS1ReIGgs0Z89idMrgobsYvW37+gbbqfC1Bc0XZjQToZCz4c0DWZzErm2IkxAR6u06UwtTzNfnMGIoBkVGqqMMVYY2JORGG3Qc4pbKBMU+Qi0k2lDJOwwWfXxnh/bJGkG92EI91QSzKhU19poq3xrCJ8iKO61t2TiOg+u4OApcbZAwQrXb0GqtVvgcB8v1NuXEqeu1FfIXn3iJ+MTLSKcNgPIDvP0H8Q4cyi77D2JV+pE4m3dYReuyyaAYs1oBXEcAK9iVClaxmFUDfX9j0yatMc0myfw8enoak6Qo28pyEC9glLViTtPp0Ox0AMG1HezZ2RWCaF56Ket/zOex7rgjM6u54w5UobDhNjd6DdOJ0c0QCckqsoUCdn8Jq5C/4NhebWitmZyb6hrVnCaKI2zLZvfIGPt27WXv6B78y/kPMSmES9CcAx2TX3qSytn/jtc8TupVqe96N82x78iyDv3SxYlebFDfrGGdDpFRD/PGPgi2/3s934yYqUcUfYfPnqjzn1+s8VcOlPiBG/tohAmDBZ+SbdGuxSDg+ta6/WgupbxytMnSTEKuaHPgSIGBXZlcVERIIo1tK4r9AU6QxeAYY0jSFK0zkmjbNgU3oFgqUzm0cezKlSJJEs6cOUMYXledZK87BEHAnj17cM/57dgK+agH3NS9+/z1Rgx7pLCHHi6OJI5ozM9Rm5kCAT9fwL7cM72vcYgRpNWNmFjTN3i5ElFYjpVIaMUJtrLxXHVFGYMihrhVz8hffZGosbByW8edleWUZeOX+sn1DTF86/0EpeoVvQeXwql21iv49QVNZGB/PouTeEO/jb8NvXhxGjOzOMP04hSduINt2Qz3DTPSP0oxd+FqQhonYDRB/wDGDQhTTcl3GSp75Nydd9wvV/eYjnAfb2I1DOmwQ3g4QIrZJFcpC9d18BwP13Uy8xbHxrZsLMtGJQkqjtHNJrpWz2IhulCej/JclLo4YTdxRLIsAz3+IvGJl9Dzc9mTloW7e3wdCXRGd69UocVIFjqPwiqqTZHBLPohRsIQMTr7hiiFVS7jVCpY5XI3JzC4ooqFiGBaLfTiIsnUFNLpZL2RuTzWBXoGtTG0w5Bau02UJFiWhe84qDDEPPMM+uhRzLFjq2Y1Bw9mBPHIEdTY2KbHaeIU3Yow7RQSwHVx+krY5UK2vzuwF9EYw/T8DCcnT3Ny4jTtsI1SirGhXezfPc74rr0EF8hdPQ9iIKpDcwaSDkHzeSpn/oCg9gzaKdEY/XYaY+/B9B+EoAwXOnZFUC+3Ud+qg2dhHuyDke2NTBJgqtZhqZVQ8G0+8ewinzvT4kdu6+ftu4s0ooQ9fTnKvku7GdNpxFiWwvEs6P4PiAiLUzEvH23RaWgqQy4HjxQo9mcTfK0NaWzwcw75iofjrie7xhg67Tb5fIH999y4rfvbw/WFq60Uvg34f4ETZEfrXuBviciXtnic24YeKeyhh40Rd9rU52ZpzM+hlMLPF3pOoucgs8bvSkVTyaSilrpsiaiIEKaGxXZMJ0lxLBvf2VzGoE5jom7FL2x0yV99gai51O2XyuD4efxSP36pn6BcxS9V8Uv9eIXyJSf8V4rECN9cyOIkXmkJngVv6Ld5+7DNgcL2VAWXmktMLU6x0JhHRCjny4z0jzJYGcS2Lnz8ighpFGI5Lk5lgEQUgWczWgrI+a8OGRQRtDEYo1fknZzzt2yFEDwTYp+OoGDj3j+Ed0MJ23ay/j7LXleRFq2RKMocQRsN0no9C1UTyWST3qWrgGIM6dTEuj7A5OypzIoVsKuD6yqA7vgBLP/8yvM6MlhS2PmNyeAKAYwi0Gk2VmVhlYrYfX3YawngNhEi026T1mqkk5PoZivLXg9yFySdUZLQDEPqrRaCZJECjpNVM0+cyAjiU08hp05lK1SrKwTRuvlm1CYraKIF3YkxjQiJsngPu1jA6ith53Ob3s61hIgwtzjPiYlTnJg4RbOdGTztGhrNoi527SXY4HjZEGscS/3WK5Qn/pD8/KMYO6Ax/HYaY9+FHrgFggpc6Pu/lGD95SI0NXJ7EbmtuC57dKshAmeW2rQiTeBY/NLjcxxbCPlHdw9xe9WnHWv2DeTJ+w46NXQaMWEryXpz15jRGCNMHQ858XSLNBZG9vnsv6OAn8uW0bFGGyFX9sgXvXPkqBGO53GgRwp7WIOrJYWPAX9DRJ7v3r8J+B0RuXfLR7pN6JHCHnpYhYgQtVoszUzSrtWwHQc/l9+RZ55fbUhskKVu36BroRx12RJRI1nQ+VIrIdLLZPD85UWENGwRdqWea2WfSae5uqBS+IU+/HI/fqlKUKqu3HYuJgcVsDQYG7Yi6346XI2TaGkYDRRvG7J504BNYRviJKIkYnpxmunFKaIkwrEdhvtGGO0fIR9cWqK3LBdVuSISlAh8h5FyQN53tuLtuCSSNCVJ4/MIn1IWrmPjOm5m2uK42LaNbdtYAvGxRTqPziBGyN8zTOGekfOiTkyXTOlmE1OvYzprqsSuh/K8S36/dW1pTQXw5UwGGmbbUUHuHBnoIexK30W3dzEyKCJZlEUUQZogIihlYRUL6wlgLveq/S6ZKELXaqQzM+jFxYyj+n4mTT1nTMYYOsvmNFGUnWBzXezlKuniInpZZvrssxDH4HlYt9yy2otY3Vz1XoxgYo1pdKuISFZF7C/jFAvg+1dt0rPVEBHmawucOHuKExMnabS6BHFwhH3dLMTcZghi3Ib2LIR13PYE5ak/pjDzCCiL5tCbqY+9j3ToCAR9sNEJusSgHqtjneggw15WNcxt33uljXBqoUWaCsZS/Pw3ZpjtpPzs/cPsKjjEqbB/ML8iVU8TTbsWE3VSHNfCXvM/kcaGU8+1OftSB6Vg78159tyUx3YUiBBHBtuCfF+Al89+03qksIeNcLWk8KiIHLnUYzsZPVLYQw/Zmf9Os8HixARRp4nr+Xi5TVp9v84gWpB6Cg0NtkJcLlsiupwxuNBOSLXBtW08W2F0StxcWlfxW75t0lVlvuV43Wpfl/x1b3vFPqyLVMPOhdeB8oJNecHCixSCoB3QjpC62bV2IV177UDqZtdrSaQW4eiS4QuzWZyErVbjJG4pWVck4bsYRISFxgJTi1MsNhYAqBQqjPaPMlAe3LQdfpok6DRF8hWCYpGRckApcK9JjJkxhk7UwXNc+kqVrLpn2xtW+NYiPt2g8aUz6MUIb3+Z4lt241T8FVMV0wkx9Tq60UDSBFCZ9NH3UJc4Nk0Ukpw8vlIBjI+/hO6GlmPZuHvWykBvxBnZtWlythEZxGTh7qRZfIUCrHweq78fp1xGBTmsXLDjyMwypCu7TWdmukY1BlwvM6o5Z8xJmtIKQ2rtFqk2OLaN5zgrn4ckCeb55zOC+NRTyFwmv1V79qw3q9nE+52Ra8F0EnQjgtSAlVUR7b4yVuBnJHHr35IrhoiwUFvMKohnT1JvNVAoRgaHObB7H+O79pIPLpFPmIbQWoDOAk40R3nqTylOfQ4kpV19A7U97yMZvh9y/RuSQ/VKG/VYHRyV9Rnu2j45aZIaTsy3sJSikRoe/voMloKHHxihYCsE2DeQx12TYZhEKa2lOHMc9dY7lXaamuNPNZk7G+PlLA7cUWB43O/G6ghJovE8m0KfjzFpjxT2cB6ulhR+gsxs/be7D30IcETk72zpKLcRPVLYw+sZRmtaiwssTk+SRhFeLo+7yYyt1xtW+gZrKaBIbUMnDjctEYXs7HAzSplfqtOpzyPtGklzcaX6FzdrrC0XuflSt+LXj1/uVv5K/ThB4YpJlhNBecGivGARdCwEoV0SWmWDZRR2Ck6SXduJwknB1hu/llFC4sAiwtnUMCOGjiUMFBR7KgovYB2plC0o7IRxyPTiFNOL08RpjOu4jPSNMtI/Qs7ffKC1iBCHIQmKXP8gu6olyjnvWmVaE8YRxmgGylXKxdKmSKxuxDQfOUv0cg2r7FF88yjeiIdutdD1OqbVYoVZOS6W71+UQIgxpJNnswrg8cwIJpk4vSoDHRhaVwH0xg9ckRxRjCCxIDpFeSmWm4JiRYJpV7sEMJff0QTwUhCtMY3GqlFNqlG2nRHENYYOIkKYJDTaLZph1r/pOQ7Omv0WEWRyEvPUU+ijR5GXXso+l2IxM6s5fBjr9ts3ZVYjIqDBRBrdiJAoMxxRro3dV8IuFjPznR3UKy4iLNaXVghirVkHYHRgmH2797F/bC/54CInLtM4cyxtzmLFS5RnPkdp8k+xdIdO3xFqY99FNPpmyA+Ac84xXUuwvrKEqqWYWwvI4dK2yUnDVHN8tkXgWpxppvz8N2cYKzj8s/uGQQTPsRmv5tf9LgkQt1Pa9Yg0ETx/fdxRbS7hlSebNBZTin0ON9xZoK8bvaETjdaC4ypK1Rw3vOHmbdmvHq5PXC0p9IEfJ8soVGROpP+3iEQXXXEHoUcKe3g9Ik0SmgvzLE1PYrQmyBewd6iD3asNEYFIkMUEEiGyUlpRO5OIAp6zsUQ0q77WaC3N01iaY2lhjubiPHFzAROvOrMtG71kFb/+bq9fRv5sZ2s+EzuB0mJGBPPNbKydgqFeNdT7DfpS83xDRg5ThZ1k21towXRdiCOooBizLIYsRV4rLLlA1qK1vvK4tiK5UplcU4lcLmMYY1hozDO1MMVSawmA/mI/o9VR+ktVrMvsiUy1od1q4RWKjI2N0F8Iutbu249Ua8IopJgrMNhfXZeJeCGINrQfn6H16DQIBDcGOMNJ1l+nAGVlMlD3EhXqxYV1FcD45CtZviCgcvnzZaDlyhXvp6QpOoqRToJSCisPdr+PU+3HLlewCvlMBrqDiMhWQozJjGoWFjKjmihCLAvrHKOarP84otZqkqQay7Iy2fm5uXKtFubppzOp6bFj0Gxm1d9DhzKCeOQIateuTZ0sEi2Y2GBaKaYdZzEctsIq5rDLZexcbkdVEUWEpUaNE2dPcmLiFEuNGgAjA0PsH9vHvrG9FHIXIMc6hWgJGjOopEFp9kuUJ/4IO6kRFg9R3/0+OmPvgMIQOGtOiKaC+lYN65UOMuhmmYaF7TlZ0Y5STsy3KHgOT86H/PLjc9w1FPD37xqkHWsqOZddfbnzPg8RiFoJ7XqUFaj9VUWGiDBzOuL4Uy3ijmFgzOOGwwVyJQcQOo0I23W48Y23UOj3se1ei0gPV0EKVeZMcFRE7tiuwV0L9EhhD68nJGFIY2GW2kwWDh0Uij3zmItAkqxv0LRTIlLqUes8iWiaxLRr87SW5mnV5mktLWTXtYV1Ri+2n1uRei5X/PxyFS9f2hajF0tDsUsEC/XMtCYKukSwakiuIHGilQpfmdd8cVYzFQoFG948mMVJjATdfRBQyyRyudK45nptJdLpksyNDHUEYcFp8bwzwctMEZGQUz7jwSh7CqO4vn9BKeuFYBDanQhJE0Z3jTI0WL1mWYMiQifsYFkWw/2D5HP5i2f6pSkShoSvLNJ+dAHTFuyq4N+gsArOJQ1hTBgSn8zyAJMT2bVeymS22Dbunn2rFcADh3CGR6+4R28lDL4rcRYNKAerWMDd3Y83UsbewdEJ2w0RQdpt0qWlzKimnTlvrjWqERHiNKXR6dBotzEieI6zYaafGIO88spKL6KcOQOAGhjIjGqWzWo28X6LFiQVTGgwrRhJkqyK69nYlTJ2obDjqohLyxXEiVMs1pcAGK4OZSY1Y+MU8xsQxGXH0sY0KmlSmPsqlbOfxYlmiXN7qO/+Llq73w2lEXBWfxzVyQ7qmzWwwDzQB7u3J6qn1o45s9Sh5Lv8+ekGn3xuie8YL/JDN/fTjFIGSx5DpY1fW4wQtmJatQSlujEW3R9DnQpnX2xz6rk2YmDsUI7xW/NAimXZ9O/dg2VZ9I3kKVQ8rB45fF3jaiuFnwJ+WkRObcfgrgV6pLCH1wOidovazDTNxQVs28bLFzbdc/V6xHLfYLoU09YR9bhF2K4TN2qEjaUu+ctIYNRqrKynlCJX7idXrmIX+iCo4JerFPsH8LzNSxuvFMpAsaYoLdgUl7KKXewJ9aqhUTVE+UvHDG2E460sTuKbi5rYwA0FxduHHO6rWnhXW2Hrmtw4SVaJVLFmpjnP6c4k82kNhWK3GuAms5u9aRVXb3zcihJSB7R7zrUjpK7QUYbQRAQll5F9u8iVtv/zWEacxCRpQn+pj75y5TwXVIGskhSGK7EQuh4RvSLoBYXKKXK3+bjDG5d0RWuSyTOrgfDLMtDuf7g9OLxaATxwI97efSj3ylwpV7IAk3hl8Mp1sItFKJZQ4mD5Hs5oGafioXqTzPNgwhBdq5PMTGMWFxGlsDx/xTzHGEM7iqi323TiGKtrTnOh32xZWFg1q3nuuVWzmltvxTpyBPvwYVR//yXHJSYjiBIZdDvNZKaSgqOw8zmschk7yEGwc6qItUatKzE9xUJ9EYCh/sEVglgqnBM/IwJxExrTEDcoLD5K+exn8dpnSP1BarveQ2vve5HyHnC78tRGivWVRdRiirm5gBwpwTbE6Mw1ImYaESXf4T++sMgfn2zyN2/u4zvGSzSihL19ecr5CxN9kxo6zYRO8/wYizg0nHi6xdTxEMdT7LnJZ+xQnt233oDRhqidYjsZOcxX/GumnOhhZ+FqSeHngDcA3wBay4+LyPu3cpDbiR4p7OG1ChEhbDZYnJokbNRxPA/vEtWJ1zvSJKF+ZpqF42eYm52kVpumU1+kU1tEp/HKco7rk++rUqgMUOgbWLm2cmUakaYVa2xl4bubi5W4KggU6orygkVx0cI2itSRlYpgWJArchSNtPDNxSxk/kQ7i5N4YzWLkxjPb/1EvxW2mFqYZGZpFm1SAi9gtH+U4b4RvLUE5hwpq3OJ6wtJWcVREFjgWxBYSPca315/P7DAs66op8gYQzts49suQ6U+PKVAa0wUZYYwy0HrSdKNWVCIsknOKuITmTTUP+jj7/fWuXPqxfmVHsD4+Eskp17J3DoBlS+sqwB6Bw5iF8uX/4HQJYBJsppbKKAcB7tYQJVK2EEAQYBSNiZMUZbCHsj1yOBlQOIY3Wh0jWrmETGoNUY1cZrSCjvUWm20yUyp3DXmNBttb9msRh89CgtZdVjt3btKEA8cuGRVWKRLEGOD6QgmTiBJwBIsz8EqFrNexB0kAa4165zsEsT5Wrbfg30D7N89zv6xcUqF0voV4ha0ZiGskas9ReXsH+I3XkC7Feqj76Ix/n6ksh+8AmhBPVHHerGNVF3Mm/qguLX7LcDUUoelTkLes/k3T87zrZkOH7lrkLsHA9qxZv9A/pIROTo1tGsxYTvBcRT2mpzC5lLKK0ebLM0kBAWLu961nz239KOUQqeGuKOxPUX/SIF8aX2MRQ+vfWxFTuF5EJEvbuKFvxP4N4AN/IaI/MI5z38I+N+6d5vAj4nIkxdbVylVBX4X2E+Wnfj9IrJ4sXH0SGEPrzUYo2nXaixOTpBGIY4f4AXbI3m5XhG1WtRmp6nPzqxez0zTXJxfqa4ABIXyOtK3fO3lCut6N9qJYakTEyUax7I2jJXYUgjkmhkRLC1aOKlC20Kjz1AfMLRLV0YEASY7WZzEV+Y1bQ1jQRYy/8aqTX6L4yS01szWZplanKLZaaCUYrA8yEj/KJVC5apOYESpIdGGooKyQF+pH9/Oo0IDkcmiRCJ9zn2D2uBvTyAjj+cQSPEUeIAL4gjGEUQlGB0Thm1MklJ1fYp+sHpyoNsDiG2juhesrBcomUkInwkxHcEddQhuCYCQ+OQr60igqXX/1hwHd88+/AM34nb7AZ3h0SsLa18hgHGXoNI1SSlglUtZn1kQYK0hAKJlhQw6gznsco8MXg0kTTOCOD9POj0NWoPtYOXz4DiEcUyt3aYdZn2gvptFlFxweyLIxMQKQZSXX84+21JpvVlN/uJO0ytGNYlBOoJJNBInmU7YFuwglx0j+XzWi7gDTjzWW40uQTzJXFc2PVCpdgniPsrFNQQxDaE1B+1F/MbzVCb/B7nFJzF2jsbIO6nv/W5M9Sbwi3C6g/WNrKfR3F+BvVurOBCB0wttOonGshT/8tEZJpop//v9w+wpOCRaODCYx3Mu3faRxJpOPSLq6HUxFiLC7Jk2p56NaNc1/aN5jnzbHob3ZSePdGKIOile4NA/micoXrxXuYfXDl6VnkKllA28ALwLOAN8E/jrIvLMmmXeBDwrIotKqfcAD4vIAxdbVyn1fwILIvILSql/AvSLyP/GRdAjhT28VqDThObiAktTk2id4gd5nB0YXHytYIyhtbiwQvrqs9PUutdRa0XYgGU7FPoGCPIVgko/peoQ5f4h8pUqzkUkdsuxEovthEQbXNvC284JsYDfUZTnLcqLFm6sMJbQrGQVwVZFrtjdMzHCk904iecaWZzEPX0W7xh2uLGotnRCICI0O02mFqeYq82ijSbv5xnpH2W4b3hTxisXQ6wNsdYUPIeibfBdh/LQMO5mss5EIBYINdJOUB0NHQ3h8sWgIgOxoBKFuoArqygBFyzfwvYtlGehfIXlKdTyxbdW7ptQCJ8NSWczd05lvURy6lvEx18inTq7cqLCGR5dUwE8hLtn3xX16YkYJE6yCqUxXaKqsIslrFIxy9sLgsy4ZqP1tcGEukcGtxFiDKbZzAjizEwW29El6ca2aYchS+0WSaqxbQv/ElEj0DWrOXYMffRoZlbTbmfbPHQo60M8fBg1eumTCqIFkxiIBB0JpCmiY7DIqsXFEna5tGOqiI1WkxMTpzg5cYrZxSzmo1rpZ//YPvbvHqeyXElPY+gsQHser3mc8uQfk5/7GmI5NIfeSn3vX0UPHoYkwPpqDbWQYA7lkbvLWyonTbVweqFFaiA0wsNfnyYxwr94YISSY6EUjJ8TVXEhCJCGKc1aTLomxiKJYyzbJo7LHPvSWTqNhNEbyhx+xx76hvPdt0OThAYvb9M/UsAvXLhC3cNrA69KT6FS6kEykvfu7v2fBhCRf3WB5fuBYyKy+2LrKqWeB94uIpNKqV3AF0Tkon67PVLYw/WOJI5ozM9Rm5nKiEO+gL0D/oivFZIooj43Q31mlfTVZ2eoz81g9KrRS1AsUh4aoTw0TGVohKDcj2XlMJGN5Tj4OW9zsQBGaHXJoDYaz3Zwt6G/ZBluuBwhYeOHClFCq5zJQ5t9JjNYuQJMh4ZjNcNTNcMLTUNsoOrB24YcHhq0qbhbu0+pTplZmmFqcYp22MJSFoOVIUaro5RypauebCRaiHWK7zpUcy5WGuHnCxSrA+urWwBpmlVhtM5iBLTO5JdJgomT1X65c2HbmSmQbaEsO6v+JaASkDibfDnaJo+PnVpZDENsMLEgkWQBTheA6Jj4hT8kfuFPQTRWodSVga46glrn9kdtAiIGSdKsAmg0y972dqmEVSxmMkXfz9xLL7WtHhl8VSAimFYbvbRIOjWFbrUzWV+QI7HtzJym0+5m1bu4mzAPE63Xm9WcPQuAGhpaIYjWTTdd8qTDcvakRAYTSVZxThNAZ5mTgY9VyoyGdkIVsdlucXLiFMcnTjK7kBHE/nIf+8fG2b97H32lSuZYGi5BcwanPUF56o8pznwZxNAafID63u8hGboH9byF9Xwb6XMwb+qH8tb97yap4fh8C0cppkPNz31jmmrg8DNvGEaJ4LvnR1VcDCIQd1LaSyHaCKBxPJddNx9AJ4aXvjXDs1+ZJAk1++4Y4Pa3jlGoZG6sSaRJY42fd+kfzeNfpK+xh+sbr0pPoVLq+4DvFJG/273/N4EHROQnLrD8PwRuEZG/e7F1lVJLItK3Zr1FEblod3WPFPZwvSLutKnPzdKYn0MphZ8vvGadREWETr22jvQtVwDbtaWV5ZRlUawOUFlD/spDw5SHhvHzBYwxhGHE0uwS8VKIpWy84MIGDmuhjdAIU2qdGC2C79g429Rv4cQZESwtWOTaWZZgpyjUBwyNPoO+gv/kSAvPNTIieKxumI2y3/cRX3F7xeJIxeK2snWeFf7VQESot+tMLU4xX5vDiKEQFBmtjjJUGbpkruNmkGoh0hrPtujPu3ho0k5IoVwmly9kksjlSxwjabqa5bcCtUr0rC7pu4z3IUoTUm2o5ouUbAepLZEuzqMX5rqXedKFeXSthmnFoHyUX8LySyivBK4PySm8vaOrfYCDI5c9gc76wJJM2qfTFQK43P+1QgAvM27gfDLor/Q49nDtYTod0lqNdHoaU6uDAuN6dCyLertNnCYoLm5Ocy5kfj6rID71VGZWkyTg++vNavr6Lr4Ns6YPMepKTnUKJs6ON5WdjLBLpZVK9KuJZrvFycnTnDh7kpmFWQD6ShX2797H/rFx+oslCBvQmsZuT1Ge/nOKU5/DMiHt/rup7fluYutBrMdTMCD3VZD9WycnDZOU43NtAtfmucWIX/zWLLdWff7h3UOEyYWjKi4GMULYTmjMtrAcmz13HFzpG4w7Kc99bYoXvzkNwKF7h7nlwV34+ex3OglT0kQIii79I3m83Ovn5PPrBa9KT6FS6gPAu88hdveLyE9usOw7gP8beEhE5i+27mZJoVLqw8CHAcbHx+89efLkRfezhx52CkSEqNViaWaSdq2G7Tj4ufwVW8nvNOg0pTE/u0r6ZlbJXxqvxp+6frBK+oaHKQ+NUBkaplgd3LBKmmpNu92mtlAnrSV42DiBu6km+sSYjAy2YwS2jQxaKZQXLcrzFrlmZlDTyWeuofWqIb1MJbCIMBnKSjXwxaYhlcwz5ZaSxR0VizvKFsPB1h87SRozvTTD9MIUnbiDbdkM9Q0x2r+LYu7yq10AGAGTTb4wmjRNiaMYx2jKriJASLv5j8Vy36oM1bKynj27S/gs64oD6sUYaDaQpUVYXMAsLpAuzuHUatj1GmZpAVOvnbeeVapgVwew+wdwqoPY1cHsfjW7b5Uql/0dXiGASZoRwOXXyudXTUA8LyOAV7jDog2mo1GOwhnokcGdCBPH6FoNPTNDurCIiCGxbNoK6p0QI4LrOLi2venjQKII89xzmKeeysxqFrMeVrVvH9bhw9hHjqD27bvoMZsdn2QEMRTQklWtdYJCZ6ZFnpf1IgYByvNgE9Xq7UKr0856ECdOMT2fRTZVSpWsgji2l37fQTVnsDqzlGY/T2niT7DTJmH5FpaGvpdk8kHUApgbcsg9Fdii/utmmHJqoUXBc/nyZJN///Qib99d4Edu7acZa4ZLPoMl/9IbOgdRKyRJhOLQLixL4eVWpaHteszTXz7LiaPzuL7NLQ+Ocui+YZyuYU3cSUlTQ6HiUxnK4QU9cvhawVWRwu4GRsiqhQDfEJGZTayzKfmoUuoI8N+A94jIC5datycf7eG1iiwIvcHixARRp4nr+Xi5i5sDXC+Iww6nnnqS4088yszxl1n7u1Po6+9W+jLStyz/zJXKm5rgRHFMs9mi2WhB2+ClNpZnb2piG6WaepjQCFMsFJ5rYW+x9ElpKC2tyRIURRQI9aq+oizBjhaerS9XAzULXRXkWJBVAw9XLG4sWrjbQGpFhKXWEtMLU8w35hERSrkSo9VRBitD58UwrIM2mWmFMdlFa0hS0Mmq1NNkx4XBEKeCpRSlnJc58dk2SRrh+XmK5cpFzTcuug9hCEsLyOIC1BaRxYWMAC4tIksLsLSYjWUtPG+F6DnVQez+gS7hy247/QPZhPcqYbrVznUEMJdbJYDLFcAtOEZ7ZPD6hCQJutkknZ0lnZ3FpCmhQMMYOkZ3f8dc7Ms4ASEiyNmzq2Y1r7ySaRHLZew77sikprfdhspdvEImWjCxQcJMbpptWyM6RnWNjbAs7FwOVSxiL5PEV6EVoh22OTlxOiOIczMIQqVYZt/YOPuHh6haMVZnnuLslyhP/BFOvECYP8Ci8xHSmYNQtjFv7ofK1lRCl9oxE0sdir7Lp1+q8d+P1/n+Gyu8b1+ZZpywtz9POXd5rxWHEY7nseeOG6jNtGkuRriehbuG4NVmOzz1hTNMvlQjV3K5/S1j7Ds8iGV1czU7Gp0aSlWf8lAe13ttKpVeT7jaSuH3A78IfIHsK/0W4B+JyKcvsZ5DZhbzTuAsmVnM3xCRp9csMw58DvghEfnKZtZVSv0iML/GaKYqIv/4YmPpkcIedjKM1rQWF1icniSNIrxcHte//LOCOw06TZl88TleefxRzjx7DJOmlAaHGL/jTvpGdlEZHqE8OITjXf6+GmMIo5h6o0EYRViR4IXdKpHLJSfNYaqptRNacYKtbDxXYW3h+WtloFDrRkjULCyjSLzVCIkot3nnUBHhTEd4qmY4VtO83BK0ZCkKt5aXq4E2A/72TeijJGJmcZqpxWmiJMSxHYb7hhnpH6UQ5EFL1stm9CrhS9P1l43QrephKVA2Boi1BhTlwCXv2yiVEZgkSSiUSuTyhQt+vqI11JZWyJ0sLmQEcGlxpfJH2Dl/DOUKqq8K/f2oShVTqZCUKhRGRunfNY5TvPp+yPPGmqaYOF59bxRYQbCGAAYo39tyhcCKTNTuykRLPTJ4vUK0XjWqmZ4hCju005SmQKoUjm3jXSTa4oLbbTQwTz+dSU2ffnrVrOamm7I+xMOHsUZHLzG2rsw0zKqIylMoa7k/Men2JGZQrotVKGDn86vVxGvYl9gJO5nEdOIUU7PTCEKpUGL/6Bj7+4sMqA6l+a9RnvwfuJ1Jmva3sRT+PUR85O4ScqjIFUsT1mC2ETLbiCh6Dv/3sQW+OtXmJ44M8IbhHJ1Ys38wT87bPIFeJoUH7rkRgKiTsjjZImwl+DkH2139bZk91eDo58+wMNGiPBhw+O172HUoc4deJodGG8qDOUoDwUpFsYfrD1dLCp8E3rVcHVRKDQF/LiJ3buKF3wv8ClmsxH8QkX+plPqfAUTk15RSvwF8L7Cs7UyXB7rRut3HB4D/AowDp4APiMjCxcbRI4U97ESkSUJzYZ6l6UmM1gT5Avar3H9xtRAR5k+f5JXHH+Xk0ceJ2i38QoH9R+7hwN33MbBn/Kr+7JclovVmkzTVuNrC6aisf8y9uIOmiBCmhsV2TCdJcSwb39nCjEGBfCNzDi0tWdg6yxJs9GdEsFPcPBFspcIza6qBte78aW9OZSSwYnOwoLat3xGy92uxscDUwiQLzUxaVglKjBb7GPCLWOka8nculOoSvS7pU9ZF913I4iVAKAUuBc9BdecrSRKDQLnch5smKxW9jPB1yV6X+FGvrYsbASBfgL5+VF9/l/hVUX390FfNwr5LlSwyAjBiCJME13YYLJQJrjD8fd2+aY2kaUZY9Wqvo/J87HJptQLoeSvj2A70yOBrG5lRTQu9uEg8OUnYaFCPItrKAtfBd1ycKzi+RGvk5Zczgnj0KDI5CYAaHs4qiEeOYN1440WrfbqjMXWD8tWG3ElSjUmTdRV6K5/PLkGQfT+uUTUxjMJuD+IpJuemMjVEvsC+oUEOlFz2Rs9SmfxDnMYi8+lPE+vbkV0p5v4hyF1dLJQAk0sdap0Ez7H5hcdmOF6P+en7hjlQci8rqgLOJ4XQ7d1vxCxOtUljg5+3sezVGIuzLyzx1BfO0FyIGNhT5Mg79jC4p7jyfNTWiAjloYByf24dsezh+sDVksKnROTwmvsW8OTax3Y6diopnO/MA+BYDq7l4loujtWzA36tIwlDGguz1GYyFXZQKF735jGNhTmOP/4oxx9/jMb8LLbjsue2Ozhw932M3XjLVe/fskS01W6DUng4qJaBRDIyeBFyZEToJJqlVkKkl8ngFv2RCQQt1XUO7WYJWkKzSwRb5c0RQSPCqfZqNfCVliBA3obbypkk9PayTZ+3hb8N5/TvYQykCWGnw3R7nulOjdhoXGUx4hUZ8UrkbHeV7KnMuGWFvV0BBIhTgyAULEMpamE36qjaEqpeg8UF7GYdu9nMZJ1rqgsAOE5G7taQvNXbVaj0ozZZdQ+TGCNCNV+mFOQ2bcYjxiA6zcxtUt3dq1Uox8niH/ygO8ntSkCv0Xd+hQyulYn2wqpf8zDtNmmtRjQxSWN+nnrYIbFtbD/A97wrNpsys7OZUc2yWU2aZtmWt922alZTLp+3nm5pdNNgXYAYrkXWq6jXxalkeZp5VKGA7fvgutv+HQrjiFNdienE7CQiQjHIsb+/xE3eHIcW/ph08Q7q6YdQbgNzj4XZOw7OlZNDETi10CZKNCnw8DdmaCeGhx8YoeJaWAr2DRRwNnFCZyNSuAxjhNZSxNJUGxFZF0VhtOH40Xme+fIEYSth7KY+Dr99N+WBTD4sRojaKSioDOUoVgPsnkPxdYOrJYW/CBwBfqf70A+QZRdeNBtwJ2GnksJHzj5yvkOegGu7BE6Ab/nknBy+468Qxh55vH4RtVvd8PQFbNvGyxc27Rq3ExG1W5x86gmOP/4osyePg1KMHDjEDXffx947juAFV+fQtlYiGiUxtmXhWg60DLQNOAp1kUb/5YzBhXbmGunaNt4WVUb8tlpxDvVihVFCs09oVDXNTWYJNhLh6brhqZrm6bqh2S247c+vVgMPFNTl9ziKdCWcJiN7sqaip9Pz+vcgI6ULSZupuMlSmskr+70CI/k+qn7x6t1KjcFqNXEaNexGDatRQ9Vq2M0afquO06ijOu31uwFQLKGqA6hl4tdfzap93cofWyDrTI0mShIKfkA1X8I9xy1VxGSVvjSLt0DWZ04o20HlAizfR+VyWK6bOS46TpYB+Cr9TkvaJYOuwhnIZ9ESPTL4uoSJInStRuvsBLXpaeqdDuI4+Pk83lX0wkoUYZ59NutFfOopWFoCQO3fj334MNadd6L27l2RQOtW2iWGl28EJUYyZ+G1/ba+j8rnM9mp62ZxGFe8NxdHFEecmjzDiYmTTMxMZS7LnscNJeG21inKS9+JUCaofoXw8C3owVvAvbL/wFQLJ+dbGIGlWPPwN2YouhY/e/8wNpBzbfZuIqriYqRwGTo11Oc71Gc7WPZ6M5o01rzwzWme/9oUaWI4cOcgtz80Rq6UHTOmSw6VBX3DeYp9/krVsYedi60wmvlrwENk9OVLIvLftnaI24udSAofnXqUJ2efZH9lP9Wgimev/jBro9GiSU1KKina6A3Jo2d7+I5/Hnl0bRdHOT3yuAMgIoTNBotTk4SNOo7n4eXy1+1notOUs889zfHHH+Xs889gtKYyMsoNd9/H/jvvpdB30XSYTWGtRFRrg+c62JaNtA00uxlsF+kb1EZoRilLnQRtDL5tb+qs6qXgRmSh8gvW/7+9/w6TLEvv+87vOdeFS2/KV3e1qTbT3dNuetAzQ7AHwwFAUCBAu+BqQe5CWgjaBSVQ4vMIJPdZzorkQyyX0kIUSGJBAlzRARQEYDkkhgCBwcCMQ3tT7U11dfmqtGGvO+fsH/dGZmRWZpmsyMrMrvfzPFEZleHuvZkZEb8473kPUVwsIbFmLcFrVDdZ5/ig4zixbDixbDnVLUYDGz58YlTz8JjHg6Oa0c3WDrSuaNTSn793HWFvjXXz91DQy1Mu9Ja41Fsms4ZQ++yrjrGvNk7Fu85SZudQSYzXaq6EvuLUXD3fbhaNJgZ3J4xwY2MwNo4dHcONjuPGxsnrdUx9lMahI0SNLXYxva7NdvSyDK1gMqpR0/6GoQ+t0VFUlLFVyvA3GPp22Qc7a8LgdA1vRMKgWOWyjGx5mdaZsyycPUsvTVCeT6XRwL+J6QvOOdzp0ysB0Z08WXxANTaGvv9+9H33oY8fx9YmsV2ua8TwWmzeH6Hvz8tVePV6ERQrlW1rYpOkCacvnOXDs6c4e+l8ERB9zV15nTuS49yhThAdeJ7WXT9APvMohDfeMC7LLSfnOvie4oNmxt99/hJ3j0X85JMzJJlhvBqw/xpLVVxPKFx5vMSwdKlLdynBjzyCaHUUNulmvPGN87z/4mW0Vtz71Cz3f3r/SsMaayxJL8fzNOOzNWrjEVqec3atmw6FA3c0TdHk5fpvtAvsxlD4w1/5YV6+/PLK/8fCMaaqU0xVp5iuTjNVKb+W3xuPxtHryrSuKzwCob4yPIZeiK99CY/bxFpDd3mZxfPnyJMYP6oQVm5uvsFOcdZy+dRJPnj5eT569WXSuEdlZJRjnyzmCU4cODSU3531JaJRWIyyuNhCyxRljuHm8wbzclmJpW6GYzhrDHppuYTEgqbaKf7+uo2iNLQ1ce21BJdSx+tNw2vLljealq4p/jzvqiseGvN4aExzRwU0dm3Yy81q0Mvzsrxzg6fdNXP3VsPe1RhnmY9bXOgu0cyKUcHJqMH+2jgT4QZNXPK8KOFsLZehr3lF8NPrFoJ3WmMao5iRsfI0StoYJa6N4E1MUp2eJGisfaPkHORJih/4NMbG8Yf0Zs45igXsrS3m9VlDmufkzjAW1hirVAn6c5cqleKr7xehrzztBS63uMSAL2FQXB9nDMnCAkvnzrHw0UfkeY5XBkR9k/PbXatVLHdx4kRRZtpqFReMj6PuOg5H78G77z6Ynhnaew/nHC7LcVla/uErlO+hGw10tYaOyiY2Q/wgJ81STl84w4enP+Ds5YsY56i5kGO2waPq19k/ldO+88+S7n8aohv7kKuX5Xx4uUs19Hj2Yo9/+No8n9lf4798aJJ2atg3GjHV2Lw8/kZCYV/SzVi40CXZoBlNezHh9d8/y0dvLBBWfR74zAHufnwGr5yOYXJL2jN4gWJiX42alKrvSlsKhUqp7wB+ClgA/hbwL4BpQFN0C/2N7dnc4duNofBs+yy/cfI3SEzCfDzPXG+O+d48873ifGziNdf3lMdkZXJNaFw5X36t+RuPQBlrVkLjjYTHil8pRh0lPF43k2e0FxdYunAeY3KiSg1/CK3qd8Ly5YucfOkFTr78PJ3FBbwg5OgnHuHYY0+y/57jQyl93ahENCxL7lzqcK38mvMGU2NXFpxXQBR4N7WshM5hpAyCtVbRiCau2pXOoflVpqjl1vH+wGjg6V7x/Drmw0MNx0NVwwOVjIYbftjbjHWWXp7RMwnLaY/LvWVyZ6l4AfuqY+x3AbVOeyXoXRH8up0r7tPU6leEvnzl/Bi2Vi9GJctjkhlD4PuMV32CDeZzOmvJ05RKvU6tMXrD5arOFIHPWbParKJ/WBWoIESHASYISHDUanVmpmap1GpF+LuhR9tdXG6xSTlncKaG15AwKG6cNYbO/DyLZ86wfP48pCmh7+PX6uibfA1zzuEuXMC+/Tb2nXewb78NzWZx4egY6u7j6LuPo+4+PtSQCANNbPLyeUGBjiqoRn3oayemWcqZsx9y8uQHnF1awChL3Rke4DXuaeSM3vkM2ZHvgmjkuruVtuKM0ws96qHPv/uwyS+/t8wP3jXKn75r7JpLVWwlFELx8+o2UxYvdDCZJar5a8pCFy90ePVrZ7j0YYvaWMhD33mIo5+YXPm5FeEwJwh9xvdXqY7c2m6y4uq2GgqfB/46MAb8HMU6gt9WSt0P/KJz7rHt2uBh242hEIo5hROVK8vtnHN08+5KQNwoNC7ECxi3di2tildZM7o4ONo4XZ2+okx1/WMaV4w8bhgeB0JkPzxWvEpxkvBIlia05udYvnQBHES1+oYLrO92cbvFh6+8yMmXX2D+zEcopdh/z3GOPfYpjjz48NCWysiNodPt0hooEe2PDDnjcG0DPQve5vMG16wxqBRRoLe8rIQy0FguFpVvlGsJplF/CQlDOjg1xJZz9coGLQtp0Rzmtbbiza5H7BQejruDjIeilIejlMO+Kd4DKFV241TX1ZXzRmTW0MsTumlMnHSL8zanx2qzG+UcB5ZaHDtzln1nTuO3m6h1a/JZP1gT9q4MfqPgX3sUIXeONLeEnmK0Fm7a3CfPc1xuaYyPU9lkNN1Zh7NFF88r1hCkbEJRqUAQoqKwmNfneUUjGt8H50jSBOcc0+NTjNQbe/75ScKg2C5ZltGem2Pu7Fniy5dRcVKU8VcqqPDm18l0zmHPn8e8+jbu3XdwJ9+B1mBIvHcgJM4ONyQ6isZQ6WoTm+1YOzFrtjj7rfc52b7IGW+OHEedDsf9Sxw5eBcT930vujZ5XeFwsZNwfjmmHvr8/JuL/N7ZDj/6iUk+e6B21aUqthoK+6x1tBdjli72AEdUW/u+7uLJZV792lmWLnYZm63yyOcPs+/Y6vrCeWZIe4aw6jO5v76mmY3YOVsNhS875x4tz7/pnHtg4LKXJBTevK+f/fqaMs6rLvy8jnWWpWRpJSgOBsd+kFxOlq+43WCZ6vrQOFWZYrxyZZnqoKuFR4XCDbwBDXVYNMzxIip+ESA/buEx7XVpzl2mNT+HUoqoVt9znUTzLOXMGyc4+dLznHv3LZy1TBw4xLHHnuTOTz5ObXTsph/DWEue56RZRhzH9HrxSolof8TRWYfrlfMG2XjeoHOOxFiWuymd1OApTRRscVkJC42mYmShWEJCW0UWWFpjOctjKUmYgy3bpOdmZc5eZh3vpgEnkpATaci5vHgxnvQMD1VyHqrmPFC1VG/m18A5VJai4x46jlFJDx33UHGPJEvoOkNHQ8fXdMKAVrVCOvBpvjaGkVaL0WaTkWaL0VbxdaTVQlVrRchrlKFvtH9+jHx0DBdVbmrNrdxBlhs8rRirBVT8jUc5++Winu8xMjpeLJacl11Qr2jm4hUdO4OgKPHsl3X6xaL2V3sOyfKcNE0YqTeYGpscWlnqTlkNgxp/piphUGwb5xxJkrA8N8fi+QtkS4v4vR6B1hBGxfzam6gYcdZhmwaTWvTyJez77+I+eAf3/rvF8jIAI6OoMiDqu++FmX3DXzP0WmsnBiFEWxjtcg71dpv81WXOBJc4VXmDj5KcDJ8qMcdGfQ7f8x0cOHQP2rv689LFZsx8O6Ea+vy/XrzMW4sJ/90TM9w7FpIbx7Hp+hVVGDcbCvtMVjajmeuhPU1UW91W5xyn31jgtd87S3c5ZfaOER75/GEmDtRXrpOnhjQ2VOoB4/tqVOp7e+mtvW6rofBF59zj689v9P/dbreGwgudC7TTNp28Qy/rkbsc5RROFT8TrfRKx1Ff+1cNaxtJTcpCvLBpaJzvzdPL1y7k7CmPicoE09XpNWWqg8GxHmy+eHTfjYTHyItWgmPkRVeGx7Jxzm4Jj845kk6HpUvn6S4v4/k+UbW26xpOXI2zlosn3+PkS8/z0YlXyJKE2tg4xx59gmOPPsn4/gNbv2/nyPOcLM9JkpRekpAPvNj6noc/sKCycw6XWGiWo28blIo65+hmlqVeSpIZfK2vc1kJV8zVc2WZprHU2prRJY+RVoRnNUYbmlGbZrVNL4jXBpiyhPOy8XgtCTkRB7wVe6RO4eO4t2J4qJrzcNVwILBXZilry2DXQyXxynldhrx+6NNJEfiKy2JMntKuN2iOjtIaHaE5UnxtjYxgBz50iNKURq9HI8moZ4a6g5ryiPwQF1WxldWTiyrYqLJS1jlsxkFmDEopxioBldBbPR6uXFjeFV1RnTGkWUq1WqNWbxQjfUFQrtcXocOgCH3laN9W/rass8RxjO/7zE5MU73Jbrg7baWBTKiLdQYlDIpbyJQNwBYvX6a7tIRbWsLrdtEAflDMxd3KOojWYZYNLnfocPU1gblLuPffKYLi+++sC4n3ou46jr7n+LaERNhk7cRqtVhWplq9sbUTFzL0Nxaga8nuaHHR/QGn5y7xntlHSkhFW47uP8wdR+/l4OyhDadmOAfnlrs04xylNP/9sxdZTAx/86l9TEUenoajk2uXqhhWKOzrN6PpLCUE65rRmNzywUuXeeMb50l7OUcemOChP3qIxkRlze2zxFAdCRnfVyOq7u0P6PaqrYZCA3Qo3iJVgX6/cAVUnHN7Jurv1lC4Xm5zMpuR2YzUpMR5TDfv0st7dLNuUS46UM6ptV4JjFsJjQDdrLsaFuO5NWWq/eC4UZnq+rmN6/+/WZnqehuFx9wW4Xij8FjxKsW8x4HwOHgMtjM8OmvptVssnjtH0msThBFh9ca7iu2kpQvn+eCl5/jwlRfpLi8RRBFHH36UY48+yb5jd2/pzXduDHmWk2QpSZwQp6tNR7RW+J6Ht8mbBZc5XDOH1BVNZNa9ye0vK7HYzciMJfA0oadZCXv9xiy27MTZH9Uz+WpnTgeVLGI0bjASNwisj1WWVqVLs9alU0uK9fYGpBbejj1O9HxOdD2WkpyRtMdR2+Yh1eY4bY6aDmE6EO6SeDXYlf/XabLpcXNAd3SM5akpmuPjRfBr1GhXK8QDTR6Uc1TQ1JRPNQiphlWqYY1qEBHcQHXBdrEO0rL732igqfrFNq8tPVfFKF8YFFW3WjE6NUNldKwY6Rvy6F2SJuQmZ2pskrHG6J5e+sVl5cighEGxSyRJQrvdZml+nqzTQXc66FarWAanLOW+kb9pZ8pgaB16g+7LRUi8XIbEdzYJiUXJKbP7tyckOoqlaba6dmJqUc8to0/HuFmNva9JsPRt5k6/wLtxnbe5m5SQ0Pc5euAIdx66k4Oz+9dUkFnrOL3QJTWWdu74m394kVArvvTpfQTqyqUqhh0K++JOxuKFDkk3J6r5K41mALI45+0/vMg7z13EGsfdj83wwGcPrBkdTOOcPLXUxyPGZqqEFQmHt9LQuo/uVXslFF5LbnNSk64Ex17eo5N26JkecR4XI3H9N2GAp73VsLTF0GidZTlZXg2L6+c3xnMblqmOhqObNsS5njLV9a4IjzZfCcnrw2PFK0Yc++Gx6lfXBscbDI/WGDqLCyxePE+eJITV2tDm1t0K3eZyMU/wpedZPH8WpTUHjz/Ascee4PADD+EH199EwFpLludkWUacpOWbb0MxXW41AF5zJHmTeYOuXGPP5oZ2krLcjjG5IcTiG1eUc+Zmw3llwJqF1UMTMtqtM9qpEeY+Fkc36tDS8/TMJVTaXR3Fi3vEvZhWOybtxRDHNLIuI2mXkayHbzd5PIpOm7ZSxZYjc65SWfP/vFKhXavRjiLaoU/XU3SUo2czzMDzr6c0VT+k6oXU/JCqH1HzQypesKW/3aErR/n6Xy2OLHeAo17xqdcrPjnCuwAAVHRJREFU6CBAByEq8MHzi7JPz1sZnUyTGM/zGZ2ZwQ+H/zdkjCFOYmqVGtMTU4Q32UFxJ60JgzNVvLqEQbG7WGvp9XosLy/TbrdxcYzf68HyMi5NQWtUVLmuRjXOOPKl8rXkGhnBOQfzl9eOJC4vFRc2Rspy0+0NibCFtROdQ73fQ724DKHGPhlBZZ5o4SXqZ/8D55abvK7u5y3uIXUeoR9w5MAR7jx4lEOzB/A8j8xYTs13wcGZbsbfee4yRxoBf/1TM2SZZbwesH+sWKpiu0JhsStlM5rzHYyxRLVgzTIUvXbKG18/z8mXL+MFmvs+vZ/jT+3DD72V26c9gzWWxnjE6Extzcij2D4SCj8mofBaMpuRmWwlNHayoiy1l/fomR62P0+nHxo9byhz+zKTrYwq9r/O9eauu0x1/fIbN1Kmut5geMxtvrJsx/rwWPEqPDr7KL7e/NUnzzLaC/MsXTyPNYZKrY63R95kZknC6ddf5eRLz3Ph/XdwzjF1+GgxT/CRx6lcx/pvRRmoKcpA04QkTkjzrBwAUniexve86xqFcc7hcodLc1wvhXYO1uFcjrN50Ubc5JgsJ84M7djgcPi+xqfsxKnLuWnWoJPkivJLHfcIMk01OEy1fhdBZQbnLPnie2RnnsOcfhay3qbb2PUjWkGVOKpBpUJUq1KrFguSFyGvMlCKOVCWGQSg1Gqjlzyll6d0TUIvT4lNtuZxIu0X4a8MfVUvpOqHhDs9v9a5cp0+t3Ho9jwIAqzvk6KxnsdoLWK0UVlTDrwRay1p3KXSGGVkYnJLZWZX33RHnPRQSjMzMUW9euPPHbtFEQZzVOhJGBR7RpZldDodFhcXyfMclWb4aYKdn8cmSfG8EoRFaNqscuQGguGa2zkH83MDI4nvwvJicWFjBHXXvSsdTtm3fSERBtZOHHgOvWLtxLZDf2MR2gb3iQbuuIbuPMH8a4yd+3XC+Rf4gDt5tfJZ3s2mSY0l8AOO7D/EnYfuYHZyHx8tJgSe4tX5hJ9+eY4nZ6v85Uem6JZLVUw2om0NhSv7ayztxYSlS11AEdXWfijcnO9x4vfOcvbtJaK6zyc+d5Bjn5xe6WZahMMcaxwjk1VGpysrwVFsDwmFt0kovJbB0JiatChNLUNjN+8WgWmgjbunvSIwKv+mG8KsL1Ndmdt4lTLVyItWRxgrA11VB/5/vWWq6y3Gi3x6/6cJNligO4tjWguXWb50CYBKvbEnmsdYY7jw/jt88NLznH79NUyWUp+Y5NijT3LssScYm9l31dsbY8qmHClxmpIkKa78IEHr1QB4rd8DmxtckuPiFOIc20lwcYZNcxQOlANlIU1QSYJKetg4Juv0yDptdBwTpAneJnPw1nfLVNEI/sEnCQ5/Cm/qHgCy5VOkC2/Q65zE+BZbqWCjKgtBjVOqxjtqhLepsxTUyaIqh0cCHqzDJ6o5M8Hmz4nOOWKTrQl9vTylm6fkA7+/CkXVD9eEvpofUfVCvFtVyuj6/5R/184NnOyVS2FoBUEIgV90F/XKOX2eBu3hFCSZwzjLSMVnvBYWDSeuweQZeZoyMjVDNDIy9OUfsiwjzVLGRkaZHJ3YtFR5t3OZwaYGHXp40xIGxd7knCOOY5rNJs1y2Qkf8LMMs7yMWV4uQpNSZfOotesGOuPIF4tuzTcSDNdvAwtzuPffLULie++shsR6Y+1I4r4D2xoSV9dOzMoGWuXaiZU6wUkf72yOnQlwn5mAIIfuPP7Cm4ye+wqNy1/HOHhr5I/xWvQUHy4nJFmK7/scnDlIpb6P/TMH+J1zPf7l20t83x0j/NC947TTjKOTNSLstofCPpNZli/3aM738ANNuG6+4PyZNq9+7QxzZ9o0JiIefuYQh+6bWNNbIO0arHOMTlcYnayuWSNRDI+EQgmF1+ScW5nTmJqU1KQrYbGbdYlNvBoay+fPfilmoAM8de2Swavpl6lutgzHfDzPUrJ0xe1Gw9ErQuNgkNysTHWjUJh0OyxfukB7cRHP8whr9V0/F8k5x+L5s3zw4nN8+OpLxK0mYaXKHY88xrHHnmTmjmMb/lzsSjfQYhQwThJMGbYUCs/38K9RBmrzHJvkkGTQS7DtFBtnkJXhKMvQS/Oo5jx6cQG9OI9euIxaWoI4LgLiZvcdBCslmG6lWUploERzhGr1Dur+QSqMo1AkfsJyI6ZV75EFxTZ0DbwZ+7zW83i957Noip/nocDwUNXwcDXnnoph/aoXxlp6JqWbJyuhr2eKAOgGtjvQ3trQ54fUvJDIC9Yeu35Ac+tC2uoPcuByrrx8K5Qq262XIalfWuv5EATge0Xo0xq0X1y+iSS35NZSD33GawGRf33BK4l7eJ7HyPTs0EuurbXEcY8wjJiZmKISbbycxW63GgZ9/JkquhZIGBQfC3me0+12WVpaIkkStNaEYYhKU0yvh11cwjSb9N9YqEpRauryMhjqrQfDQVeExPffgaWBkLgyknhvERK3+XV/cO1EfyEgPFMBDdnDPupQDeUpyNv4S+8ycu4/MHLxa2ib0Bp7lLem/xTvphOcuniOJE3Q2mN6cj9vpuN85VLEX3xgmmcO1emlhsMjAfV65ZaEwr40zlm+1KOznBBWvDWjfs45zr+3zGu/e4bmXMzkgTqPfNdhZo6OrF7HOpJeUY47Nl2lMVlZM2dR3DwJhRIKb5pzbk0TnJWRxrIJTmziK9Y0XD+P72ZlJmMhXlhTlrp+9HGzMtXB4Dhdnabm1/iRh34EX/vE7RaLF84Tt1v4QUBYre360rPO0iInX36Bky89z/KlC2jP49D9n+DYo09w6P5PrFkj0TlXNIPJc5I0JU4S0jRbucwvA+DGHc8cLs+xaQZJjusl2G6C62aQ2aIst9NCLS2gl+bQSwvoxTnU3By6ubR6Pyjc+Dhuaho7PompVIn9iMQPcZUaul4rwl8Z+thgtEdZaPQqjHZq1LsVNIrUz2nWuzTrPdIwxzo4neqiQUzP4/3Ew6KoKseD1ZyHqoZPVHIm/aKpQWrz1ZJPkxVB0KSk6+YQVrRPTQdUvYCqDqh5xflAXeeo1MBcR6AYfVtZo1CDpyg6F2jQrF63fx1VXt6/LwbWN1Tl9VGr1x3S729qLKkx1EOfiVp43WHQWksW94gadUYmplDXaLd+o+IkxlnL5HjRSGa3/71uxGUGmxh0VIbBerAn90OI65EkCa1Wi+XlZay1BEFAEATgHK7Xw3Q6mMVFbLdbfiDmY3o+OvS53qfZ61WExPmi3PSDd4uRxKWF4sKVkDgwkrjdHw53IHxLoXuadDYmO5CA7xXrQnqGID7DyPzXGL34Vby8RTxyH4t3/Fk+rD3G22fPc+biGfI8xaA5bcZ4+OgdfPLIYUxmOLZvjONP3b+927+BuJOxcL5D2ruyGY2zjg9PzPP675+l18rYf/cYjzxziLHZ1cZ91jqSbrEG8dhslcZEtFJyKm6OhEIJhduuHxr7jXASkxSNcLIOcR4T53FxxTI0KqXWNn4ZQmiEokx1o9HGwSBpnGGmOsOvfNcv0r50mTyJ8aMK4SYLZ+8WadzjoxOvcPKl57l48n1wjpk7jnHssSe54+FHiWrFukCmDIBplhMnMfFgGahSeL6Pt64M1BlbhL8shTQrg1+C7WYoqyA1qKX5IvQtzRcjgAtz6PnLqIFuoy4MsZPT2KkZzOQUZmqGfHKabGSS3NcYC7mxpMbiK0Xg6WLEcGVkbKCEuexgWY8rjPbqNOI6ntPkOqdZadOstImDhLZTvJGEvJaEvJ6GNG3xwnHUz3g4SvlEmHDA65HYjJ7N6dqUnsnp2XSDRi8R1aAc9Qur1IIqlTBCa68MYwOB64owRnlZ+cKlVHl2773RT40jMzlR4DNVC6kE1/+urF8u2piaojIyNtS9z01OEsc06g2mxicJ/L0xx3eQTQ0ulTAobk/rm9NorYmiaKXs22UZttfDtFrklxfJ59NimSLfQ4fRto2iu35I7M9JXJwvLqjVi+6mdx9H33Uv7D+4PSHRgH9S4V/Q2BFHetxgdbl2onOQddHpPOO9P2Ri+Xfxs0WS2lGWj/5pPpz8Y5xc6LK4eIFTF88QkaGUZmJshn2zd/L093wns6OVG3oeHwZnHd1mwuKF7obNaExmefeFi7z1zQtkieGOh6d46I8cpDa2WlVijSXp5WitmdhXozYerbkPceMkFEoo3HHW2ZU5jalNSUxCN1sdaUxNuvreeV1oDHSwpi3zzW5HM2ny/nuv8nT9MWqNUfzr6Iy2U6wxnHvnLU6+9Bxn3nwdk2eMTM1w7LFinmBjYoosz8nznDhJiOOEPC9HuZS7ck1AY7D9RXqTBNtOsHEKSQ4GaPaKEb/FhdVRv7Lkc7Dc046OYSanySemSSenyManiMenSav11WsNNC5RxhaVjBrQqmges1l5I4pqEjHaqTHSqeBbjdGW1khKcyylXbN8mMCJtuJEW3GyV+TICS/nkXrMXWHMlBdjTEwv6RFn8ZpjGgVF4KtGNWpR8bUaVQn9LSxO/DGTG0dsDBXPY7weUguuPYd0UFo2exmdmSUYYjlnv5GM1h6zk9PUKje2FMya17n1L3lug2+6jc678r42eoCBK254/+VZY4swOFtD13bP2qtC7IT1zWk8zyOKojV/F/lyl/TMEqRdbLeDs8UHnCoIi2VutulPaDUkvov74B1YGAiJgyOJQw6J+rIieK94bczutdipgW3Kc2yvCa1LjLafZbL7+0T5ZdJghrOT38sHs99PJ5rhf37uPQ4wz/FgiSMH7uLOz30eB+wfq3BwvMpIdGufe6yxtBZili710EoRrmtGk/Zy3vzWed57vujjcM+Tszzw9IE18xKtsSRdg+crJvbXqI1u3wcEH3cSCiUU7nrW2TXLbSR5UnRPLec1ZiYrBmRc0UFUabVmYfsbDY0fnniFh2cfIdqF85Ccc8yfPsXJl1/gw1dfJOl0iOp17nzkcY4+/Cgj+w+SZTm9JC7LQIu/YU9rvLIM1JUd0FyW4+IY24txvQQyh8sMen4RvbBYjPotzKP68/2S1QDl/IBsYopsYpp0fJJ0fIp0YopsfBLXX8bCgcKhrUE5h8ahVxfSLOauBWHxdaVxyeoSBasPBlFXMbqgGV3QBJnCakd73NKctJyrWl5vW15byjnV6hHYmDEVcziMmfZiItvD2tW24EqpYqSvDHxFAKxRDat7thHJTemvG2g3vthaR5IbfE8zUQ2ohH75c9zkvgY+wIHi77dYrqVKbXwCz/PX3lrd2BTJwdsmWUpmcsZHxhmvj2zytz6wFk//9ivfUmtLba/4qgZuUH4uMXhHauD2qIFK3n457+D1y8v7lw1U/aJAVwMJg0Kss1FzmjAMi/JSwHRT8osxquJBlmLiHq7dxnQ65fORRkUhesjrna7ZxoV53AcDcxLXhMR7VrubDiEkqh4Eb2l0R5EftOR3urXPMwB5jOvMU1/4FhPNr1HNzpCqEc5Gn+OF8R/g/3Z6lNlQ8dcen+LwY/fjnKMV5yTGMFoJODpZY6oR4d3CYJVnhubleNNmNN3lhBN/cI5Tr80TVDzuf3o/9z6xb03DGZNb0l6OH3qM76tRG5UPc2+UhEIJhXuesaYoT7UpmcmI87goT82LZTdyVyx471Tx+6yVXhll3GiNxt0YClsLc5x86QVOvvw8rbnLaN/n4H0PcuihTzJ28CipMVhrcRSDah4K7VyxRlKWYeO4CIDdtHgz3OqiLy/AwgLe4gJqYQ5vcQ69vIRyq+kgr48UYW9iinR8mmxikmxiBtMYRWmF1sXjaGuLxeLXd630/SL4hWFxfrBr5TWEMYzOe4wsaKKk+Pl1Rh2Xx1Neoc0HrQ7z3S7K9BhTMaMqxlMDjV68YCD0rX6Ngsrt90Jh+2W4xfzLvnLteJwG5xUBp38EjXPEucHzFJO1kEY12Lg0p59xBktn+/eR52RJzMj0DPWx8eI664/94H83uI8rH6hcczDuUa1WmZ2ZKf5W199mIKxtdNlt9zsgxB5njKHb7bK4uEgcx2itqVQquE5OPt9DVfyVESJnLSQJptvFtlrYuPxQ0/PRQYC6zjnQW+EWF9aGxPm54oJqbe1I4oFDWwuJtiwnPa+xDUd2v8Vt9HYlT6G3RHX5ecaXfod6/C65qvKK9zl+tP1nSPUI33fQ44/f2WBqvA7VOrH26aLxopAjs2PsG6ve0tLSNM5ZvNCl10qvaEYDsHypy6u/e5YL7y9THQn4xHce4s6HptaMDOaZIY0NYcVnYn+NipThXzcJhRIKP/aMNUVgLOc1JnlSBMayPLW/VmF/9GD5vVM8vO+TOx4Kk26HU6+9zMmXnufyqZMATB69k9n7PsHknXfjRxEuz9GAdhadFuEv78WYJMWmBrW4hDe3gJpfwltcwFucx1+cw0tWm+5YzyMbL8o8s4kpssnplfJP1V9c11GGPnvlOnVaQRiV4S8oRvo8rwiAN/hE7KcwuqAZmVfYXsaS7nCp0uFC2OGc6ZDkPUI3ME8RsLpCvVJlslqjVlkd9duLc8q2zLlipK9c1WNdM+CV0Oc8ipMuwpnz+lcamMuBo5cWk/inRyLGKgG+d+MvqEmv+B2bPHiYqHZjJZ2bcc7R6/VQSjE9Pc3o6N5sJCOEuDlJktBut1laWsIYgxeDauabduh1eY5LEkyng221imUglCpCYri9S7y4pYWB7qbvwvzl4oKbDIl6DoJ3i+tn91rs9CZXNDkkTbzFlxmZ/y2m0zfICPkdnuYfJN/NWxzjc9Mef3IWjpfLFFvj6GQGW6kyNTPO7L4JRsdHirUkw7A4bWOTnbidsXChQxbnhFX/ii6jl041ee1rZ1k432F0usLDzxzmwD1ja8uL0yIcRvWAiX1FOBRXJ6FQQuFtb3C5jcxmnHnjBDONffg7sBi9yXM+euO1YmH5d9/CGkNtcoqZe+5n6s57iKo1XJIWL269mDzLcZ0eem4Rb2GBYHGJcGmRYGmeoLmIsgOjfrUGeVnyWYS+KfLJaczI2GrJprXlqQyAg10tfb8c8euP+m1S7nkdlIUwVoTdovyn1+vQTju0bJcl1WFJd8lZDZ+p0yy7KjEVapUaBxo17h6rM1Gt7vqlQYZioMRTubVVmjAw0qfBev1RP4XTXHdDG4MjTg3gmB6JGK+GWwqDzjribptKrcH4/gN4Q/o7yrKMJEkYGxtjamoKfxvLwYQQe8Ngc5rl84vY5YRotEZwjecHl2W4OMa02thOG2dMUeK9zfMRAdzS4tqRxLl+SKyuLoFx171w8PA1g5eKIXhbo1uKfL8lv2uDctI+a4g7y/QuvMbR+PeYSV5Bk/ORvoN/kn6RX8k/w9GxOt9/tMZn9oX4FB3Ge72YLE6pBzA9UmG0UlSN6EoFXS06hOt6vQjX/dMQnp+ddXSaCUubNKNxznH27UVe+92ztBcTpo80eOSZw0wdbqy5nywxZImhOhIwPlsjqkk43IyEQgmFYp3Tb7yG5wdrlm7YLtZa4iTl3PvvceqV57n0zpuYNMGPqowcPMrIzGGisIoxFm+5SWVpkWhpmWhpkXBpkXBxHi/urtyf05p8vAh7/QDYP+/6I5/9Ji/9ADjI94sFyvvhrx/8vLKT5g1Spgh/UaxQnZxe3KWddGiaDou6zaLqkKrV+X4+IW1V5VQesWCrtF2FyVqN+8YiHh73OFhRH8+RoXXz+tS6H4srG5c6T62GPl2We2o2HJF1OKxzGOvK8+WPvCwQXTf1D08pxqsBk42QYIvtvU2ek3R7jM7OFMtNDCGw99/0BUHA7OwstSGNOgohPl6yLGPp9BwLpy9hAvB8nyi49rwyBxAnOzIfEQZD4rtlSCyaqlCprp2TuFlItOCfUvhnNbZelpNWN3+8dpxx8dIcE/Yix/RzjC4/T5RcIKHCV9zT/NP0i1wIj/F9R6p8z+EKY2HxmGlu6GUGTyv2jURMVjwCZ4tRV5OveQzlB6h+WKzXixHGIESHAeoGG/gZY2lfpRmNNZaTr8zx+tfPkXRyDt03zsN/9DAjU2urvdI4x6SW2ljE2GyVsCIfLK4noVBCoVhnWKHQWkfuLMY4cuvIckucZnQ6XbrtLosXzrL8/ht0z57ExF2U9qiPzzAejTCdGCrNZhH8lhYJFhdQA+vjmWqNfGKafHKq+DoxXZR9jo6vjtyZq5R79hu8BOHqQuVbKPfsGwx/XtfS6/Toph1atsOiKgJgRyWr13ceKTUumyqnXZV5W2XJVUnxmQ4VD41pHhrT3D+iqWxhtGpX2qDEc83FZVmn9SkDoL4i9BkczjqMczhXBD1j3cq0uXKZZxwODfha4fsaX2t8Db7nEXgKT6tiCRJdLkWi1E238k57PZxzTB46vLIEys2K4xhjDFNTU4yPj98eo8JCiC1zzpFd7NC53KJje7R6bXAUax9e55SCjecjKvC8bZ+PCOCWl3Dvv7MymsjlgZB4rAyJ99wLB4+sCYl6AYJ3NFjI7nHY2c3fw881eyzFObOzEfVsgfriS4wuPUuj9Sra5bzHnfyT7Iv8pnuaT+0f5U/eUePOkeI9kbGWTlKs/TvVCJkZqdCIVuYiFPtgTDEam2W4PF8pb1EO8DS6VitOjQa6UimmqoT9UdqNX4vyzLB8uUd7Icbzr2xGk6eGd569yNt/eAGTWY49OsODnztAtbE2hKa9nDyzNCYixmZqBNFt2GBuExIKJRSKda4VCgfDnrGQW1sEvtyQ5o40tyS5IY17kCTkSYzptDC9HqbbIr58lt7F08S9NgDjzuNAJ+XgxXmidmvlcZzW5KMTKyN9+eRq+HOVan9jBso91/29hkEx2hcG4Adlqef1NXlZzzhHO4d24qADQU9TjSFKU7TpktJhSbdZUB2WVXelqQ9Okboqi67KOVulp6tYr0YURIyGilFfMRqUJx+mI8VstEdHA9eFvv7T5xXz+nQZAD2FVcXIncHhAOP6d+MGQt7AaJ5WBFrha03gF2s5+roIeZ7SaE1R1oPaUunnlna7LBcNq3UmDhzAD25+GZc8z4njmHq9zszMDOEuXhpGCLG7OOfIL3bJlxOoanpxj6XWMkmaFiNNYXRDHzCtmY/YbBalpnBL5iNCGRI/eHdlGQwuXywuqFRWQqK6+zjq4GFU7hG+rdFNRb6vLCfdIPNkSU4CqLE6zTgnIGeENpXWB4wuPMtosxg97FHh/2c+y7/Mv4A3dhfff2edT82EeEoVc7wzQ5pb6qHH/rEKY7UQfa2RWWtXw2KWQdncrl/D4lUrRVis19HV6mpJajmPMe3lLF7s0GtlGzajiTsZb37jPO+/dBntKY5/ah/3fcf+NeHPOUfay7HGMTJZYXS6esX93I4kFEooFOu8/+orWOVhlSY3ljgvFlRPM0tiLJkp10ICMBaXZ+g0w6U9iHvYuIOJY1QSEy4t4S/O01m8zELSZlFbnFKM9hIOLrY4uNgm9ALyiZmi3HNyYPRvbKIIco6iNMNZMP26wjIu9Ms91yztUDZ6uUa5Z24dzRyamStOuaOZFf+PM0cj0Uxkitlcs88ZJlQPpbssqTYLusOiapOp1VFIR0Sua2RBFT+sU6vUGatUGA09Rn1F3WfTFwtnHcbkOJOXfU90UbqjFVp7u2PNoXXz+q4Y6StLPHMPjHZYrTAUo3tGuWK+yuD1i6vjewrf0ysBr/9V69URPK8c1dttYdkYQ9rtMDI1Q2Nq+qZH8gYbyczOztJoNHbdPgshdj9nHdmFDqaV4pUNRtIspd3rsNxqYq3B8wLCq4xMbXrfaTmvv9XGtFvFB7O3aD4igGsuFyOJ7/dHEteFxLuOE049hd+cwNUgu8/i1hVvZInBCzRH7pqlm+ZcbsXMtVOUc4zohKh3ifryq+Xo4Wtol/G6u4t/nn+BZ4On+a4j43zxcI16cGVp6f6RCpONkHALI6rOOcjzlVFGrBn4hFWjKhG6WkPVqmS6wtKiITeaaLSCH60dCW4vxpz4vbOcfnORsOrz4GcPcNdjM2ua1hThsOjePjpVZWSqgn8Lu63uNhIKJRSKdX77a98kzhXK91AUb8h9l6PzHM/k6DTG9rqYbpes18O2lvDmFwgXFwmWl4iWlggWFmi6jLOTI1wYa5B7mii37DMe09EE0dT+YuRvcgpbrRXlgdYUoc/atQu3rSn3LIOf74H2i8sGJGZtuGv1z+duIPwVl3UNVIGjaI7hcQzNUWBGxXiqy4Jus6jaLOg2XbXa8dNTPpWwRq1aZ7Rap16tU4tq+N71l9s657Amx+XFGJlSCj+q4lWqKKUwefEpojU51piVsDg4cqZQoL0iP6oiON50gNhg6YYiCzqMA6sdVkFWNnTpl3c6VZR+KhgIdhrfYyXw9Us2tWKgfHPvBp40ibG5YeLgIaqNkZu/vzQlTVPGx8eZnJyURjJCiJvirCM938a2s5VgCOWHT0lMs92k0+sCjjCs4G9hnVrnHCTr1kcEQN2S+YhQhsTBkcRLFwDwDj5C5fH/E8qrkE7NY4+Pr5S+DobCvjQ3LHRSLjRjcuOo+5ZqvkzY/IDRpecYXXqOKL1Ilwq/mn+WX3XfxcH99/L9d45wsF7cr7GWbmow1m1aWnpT+zoQGF2eAdDtOZrLYLVPZayCV6uiKpViHqMfsDiX8tofnOfyqRb18ZCHvvMQRx6cXPN+wTlH0jU45xibqTIyWbmi4+ntYMdCoVLqe4H/iWJg+586535q3eX3A/8MeBz4G865v19+/z7g3wxc9S7g/+6c+2ml1JeA/zNQtnLirzvnvnK17ZBQKNb7zV//KqG1hHlWjPz1upi4h5qbRy3MoRcXCBb7jV4W0Vm2ctvlRp0z+6a5UA9IlMNTmvGx/YwduY/G1IGV0cVN1/QLozL8+aB9nNb00FcNd4Pnk00WIJ/UcL/ncVwXAfCQUzRMSu66K8FvQbVp6l5ZyAgaRa0Mf7VqnXpUp1apEfpbWxDWGoPNs5XAq8MKQbWKF0ZoL7gi4K69scNhccbinC3mK1hThEeTY7J87SeKKxRoXQZGjVIK5RTGuLJixWFt0ZAFys6duliqwejivOeVp0AT+JpAa4Ly+/1g1y/Z3I2jecNWvHh28KMKkwcO4d9kaae1lm63S6VSYXZ2lkpl96wPKoTY25xxpOda2J7Bq10Z0PI8pxt3WWwtk2UZnucRhuEV6xdf/+OZYj5ir7d2PqLvof3tn48I/ZD4XjGa+NE5Knd8P/7sA2TnnidpP4s6dgx75G70nXdy9J79V9zeOsdyN+X8co9uaol8Rd11CbqXqC2/yujSc9Rbr+G5jFfsXfxr8wXOjT7NH7tzisemi3WANyotHa0GeNs0L9waR2s5ZXk+RVlDoPOV1+Lig2THXCvkzddTmkuGsemIhz+3j/33TIC/OrrrrCPp5qAU47NV6hMR3hYbr+1FOxIKlVIe8A7wReAM8BzwF5xzbwxcZxa4A/hBYLEfCje4n7PAp51zp8pQ2N7oupuRUCgG5Zcv8wc//Y8I5y7hLy/jL84TLCwQtJor13GAGR0vSjzHp+mOjXHRM8z1Fuj1lgHFyOQBJmaOMja+H60HXog8D+sHdP2QJgFNp2laTdOoMvStLedsZZBt8GeogLrPwJy84vyUpziK5oDRzOSa8UzhJTntvMOCKub8Leg2S7qzZsmHil8pgl+lRr1SlH5Ww+pNBRxrHNZkOGtQFN3I/GoNP6oUL45DfHGwFqyz5CbHZQ6b59isGGGkHG3E5uQmB8+Bp/FCjfbB8xR+6BOEHkEQoD2PwNfFHD3FTTdg+TgxxpB0uoxMTTIyPXvT5aK9sjlNf81BaSQjhBg2ZyzpmTYuM+hNOk4650iylFa7RavbwlkIwutvTrPpY6+fj5iXXTr94JbMRwRwzSbeW22C5BAuXqD37X+EXT4DjRFGPvsZak8/TeWBB1BXjJQ62rHhYitmsZPieYqGNgTpIlHzJCPLz1Nfep5aeoG2K+Yefs3/PPcfvY9nDo9S8Yt9G1Zp6fXIc0dzKafTNvi+Iig7pzpri59FnnP2o4y33zX0ejA9pbj/uGZ8toJXraAqVXSlgvM80kyho4CJAyPUx0L0bRAOdyoUPg18yTn3PeX//xqAc+7vbnDdL7FJ0FNKfTfwN51zn73WdTcjoVAMOvNX/gqt//AbANggJCvn95nxabLxacz4DPnEBLlyNOc+YuHiSdpLRalGWJ/An7yTZOIYrXCkDH1eGfqglasi6OVFQ5H1NDASXBn0BhuxjARFc5YxDdVYE8WKsKeIegovtrSzLovlqN+CLub+xayWfgbaL0b+KsXIX71SoxbV8bZQNrOecw6TD8wL1LoMgVW8METp638M54pPK61zZeArOm06Z8tGLq4o77QOVQY3TykCX+OHHkHo4Vd9/NDD8z28QOH5RdgDt1KWWoxe5uRZiklzsizB5lkRIle3pnhM7aE9XXzVQypX3UOyJMHkKeP7D1MbHb2p++o3khkZGWF6eppgB9YEFULcPlxuSU+3cLlFV69e0mmsKZrTtJvESYxWmugGm9Nsuh0r8xFbmHb7ls5HVMsQvq0hg8Q7Sf7ub8FbJ3BJgh4bo/4d37FpQIzznLlWyqVWjHNQDzWR6eC3LlBrnaCx9ByN5mv4ZLxs7+LX7OfJ9n2WL9w5y2y9qCbZ7tLSQWlqWV7MiXuOIFT4/trHMMbx4fsJ777RI00dhw77HL/Ppx6tXarLWkfqfPxalfGDdeqzY/iVaHVNxo/Za9dOhcI/C3yvc+4/L///wxSjfT++wXW/xOah8BeAF51zPzNw3f8j0ASeB/5b59ziBrf7UeBHAY4ePfrEqVOnhrNjYs/rvfoqf/D//d9Qk/uxY5M0jWLZ2KJsM3ckzUsEiydpND/Cczkdf4T3Ru7ltdq9LIaTV9yfr7gi3I34lCGvDH7l+Y0asWhDEfoGwl8QQ5zFq8FPtVnw2rTord5OaWpRrQh/ldpKCAz8G59Uv5liXqApPn1bNy/QC0K0HxRz7gYCXnG+DJAOrujWAmAdHhBAMS9PFaWagdbFqF6gUKGHH/p4kYfv62L+pwdqSJ/kOWuLwGjNSnjMs4w8TTBZRp6l2Dzf4JYa7Sm09lGeRmu954NjUS7axQ9DJg4eIgijm7qvbreL7/vMzs5Srw9n6QohhLgWl1mS002wbtMRw/XSLKPT67DUWsZYg7/F5jQbbs/AfETbamG7/TWHt3E+YlYsW+EtKrKDltnPjtB76SU63/wmvRdeKALi+Dj1T396w4CYG8tSN+X8ckxqLJGvqeocL14oRg+XXiBaeoHR7DwtV+HL5rO83niGTx57gAemGiit15SW1kLNgbHqtpSWOudIYsfiQrEERRQVr89rDkdqee/tmA/eibEO7rw74vgDVaLKQDMaY8iTjLSX42EYHYFarfxQ2POKpTX66zFWKqudUof0e3Ir7VQo/HPA96wLhU855/7yBtf9EhuEQqVUCJwDPuGcu1h+bx8wR/FO828BB5xzP3K1bZGRQjHo7//m2/zy779Fy2q6ToNzTKdz3N9+l+Odd6mbLokOOT1yD5cmj2PGDjIa6DXhbnCUr+pd3xyzwfAX9dTK+SxNy4YvHRZ0i3mvwzIdDKufZFXC6sqIX70MgZWbLP3cTJ7nxTw+W6yTp4IQFdaKRWkDv+gauo5SZbjTxTIJvtYEuphM7AHaFU1jtC7mMXq+RkceOvBQvkb5CvxiXiDe7hmdc84V8xqNweUGY3JMnmOylDzNiq9ZRrFORX/CQtGFtBhpHBx53B37tJ41lqTboTY+ztjs/pv6pDxJErIsW1lzcBij00IIcSNsakhPN4vn4RtYgmC1OU2LTreD0hAE4Q01WLvmY1wxH7Fc23fY8xEdcMrhpuDww6uNZmyS0HvxRTrf+tY1A6JzjmaccX45phVnhJ5HLQA/axO0z1NtvUGw8DyT7VcJytHD39bPUD/8OT59dN9KWe6tKC211tHrGpYWDc5CFKkrynbjnuXt13ucOpnge3DP/VXuOl65coQxd6RpMfo4NuETBcVa0CvdUsvKIgCUwqtVVwPjBstr7DZ7tnxUKfUDwP/VOffdmzzGncC/d849dLVtkVAoBv2T3/+AX/+9E0zR5UDzfUbn3sbrLoDSVPYdY/KO+5k6dBd6iy8EOi8XeV83+kdmWVRtFnWHedViwW+zSIeE1SY2gR+UzV5WR/9qUQ3vBsoyB5ly5K4/krfydWW1oOLJFJNjbTFp2/MDwmqNqFYljCIC31/ptqmUwiv6uuCV4dCjKPXEuDU9YJQCQg8deqhAo3xdBD/vyifrvW6lTNXkWGuL4Jim5Flalq1mxWLJ63ZbqX5o1OgyQN5KWZIW5aL7DlAbG9/y/Rhj6PV6VKtVZmdniaKtjzQKIcTNsqkhPdUE78aCYV9ucrq9sjlNnuHpm2tOs5kN5yMqNZT1ETfqPjroRgJiN8251IqZb6dFv4PIJ7AJXm+esP0hlcWXCBefZ9qcp+Wq/Af3NJcm/iifvPshxusNUAprHZ0039bSUmMc7aah1TQoBeEGayK3moY3X+ty4WxGVFHc94kqR49FV3xwm+eOLHWEkWJ8wt/wvpxzRVDsr8lozcr3/bExqp/85ND2bVh2KhT6FI1mvkDRKOY54H/vnHt9g+t+iY1D4S8Bv+mc+2cD3zvgnDtfnv8rFCWpP3S1bZFQKAa9/a0/4Kv//F/QWzgHQG3qABN3PMD44eP4UfW672ez8OdljqbqrZR+zvtF+WfHxau3VXo1+A2M/gX+xl0eHeV8O1uunb5JwOsv4+DKhdF1WZqpPYp18Lyig6a2Oc4aNOD5PpV6nUqtRhhFeBuUszjrihr8fDX4rTw1+roIfZGHDlaDH7fZfLxrsdaW8xxXvw6WqposxZqc1SNb/BS10mWZqleGx5svV+2Xi3p+wOShQwTR1rqBDq452G8kIz9zIcRuYJOc5FQLHRSvUVvRb07T7rZptptFc5og2LY50i5NsXGMbbcxrdbK97cyH/FaoXCQjePVEtMXX9w0IA4uaWGsoxZ6BMrhp038zgWqzTfJ5l/kUPdlQorOpS9WvpOZO76TO/ftw+kA5xxxZki2sbQ0zxzN5Sub0QxamMt449UeC3M59RHNAw/XOHDoynLQPHNkuSOqKMbGfaLo2tvp8hyXJtSfempo+zQsO7kkxfcBP00xmPALzrm/o5T6MQDn3M8qpfZTzAscpXiv2wYedM41lVI14DRwl3NueeA+/wXwKMX73w+B/6IfEjcjoVAM+u2f/8e88fU/YOzofUwfe4ioMX7V6+ucNaGvHwT9DLqkLOo28wPhr+k62IF5dNWoumb0rxrVCPwKCoVl3UjeQMDr66/b52ldBrsi4GmvnIunQSldfIWVpRO0YqVttDU5eZYX96ZUMQpYqRNEEToIikBZjvYVXV+4ctSvX+4ZlqN+3sdz1G8nOWuvCI8my8iyFJtl5GmKybMNbln8PmjtoTyvCJKb/Fz65aLVsXHGZ/eht1jimWUZSZIwNjbG1NSUrDkohNh1bC8nOd0sKlZuck06ay3duMdyu0kv6aGVIgyjLVfyXEsxHzHBxPHqfETnirV7w+Ca8xFvJBQOsnG8dgQxTVcCYv0znyG6/36sUjR7GWeXeiS5IfQ8KoGHl/fw4jnC9kfY+VeoLz3HAXuOlqvy++rTxPue4d47HkZFxejhYGnpvpEKU0MuLU0Ty9JiThw7wg2a0TjnuHgu443XurSblokpjwcfqTE1c2Xoz1JLnjuqVc3ohE+4QdBcuV8JhbuXhEIxKEtivvKvfpOgEhAONNRYH/7654NMkZKzqDrMey3m/DaLus2S65C51UYkgR9SjWpUwxqVqEYlrBVLPmi9ZlF2WA14vlesgbdyUsXomt4k4F0vYwwmT3GmeNSgEhHVa/hhBd8LiheWjco9fV2EvtBH9+f5+WrIS0vYYi1BGVHakv48R2tssSyHNeVo4+ocxzzLwBnWl+UopbHWrJSLbmktSmvp9XoEQcC+ffuoVq9/dF0IIW41281IPmqhq97QGpX1m9Mst5vkxuB7HmGwtfV9r9fKfMRuF9tur52PGIRX7NtWQ+GglYDYH0FMU7zxcWrf8R3Un36a6P776GSOC82YpW6KpzX10Edj8JNl/M451PK79OZe4nj8IpHKeN3dxQeNz7L/2DNUx/fhdLimtHSyETLbqNCoDKe01DlH3LMsLZqiGU1FX1Eqaq3j9Icpb7/eJe459h0IeOCRKqNjVwbvNLWY3FGre4yOeRuOQkoo3MUkFIpBtpvxtX/2H6nbkFoWrIz+BZnCYllWXea9Npf9YgRwmTY9m6zcXitNJaqtBMB6tUGjWqcahHha4WuF9sqA12+uooquo8WcvOG/aBTz2DJsbkApfN8jiOpFOajy0Z63Oi/aAwIfHRbNXvojfsNs8OKcK4JpeeoHQShKWovy17XPPUUY1niehx5SmeTtzNrV0Li6LEdGpTFCuMXF4+M4xhiz0khG1hwUQuwFeTslO9NCV/2hBUMoA0cSs9xu0el1AAjD4Tan2fSx+/MR221sq7UyH1F5PioMyTN706Fw0LUCorvnXua7GZeaxfulWuTja43OOvi9OXT7NIsXX2N/61mOcpa2q/BS8BTewWcYO/hJbNDAwbaVllrr6HYMy4sGB0ThlZVOee44+W7Mu2/F5LnjyB0h9z9UpVpbO3rpXDHf0BhHfcRjdNTHD1bvS0LhLiahUAya/5dv0D0xR4eEOa/F5aBY7mGJDm3TXSnhBKhXaoxU64xUG4zWGozVG9SiKr7Wxdp5OxRaivUCM0yWF6238QgrNYJqBS8IilI+X6MrXrGUQ1CWe/rDLfe01q4Jf4PCMCSKIiqVCkG5Tb7v43leUdK67rZ5npNl2ZrTZsFxMDxKcNx+/TUH6/U6MzMzhOHGc1+FEGK3ypsJ2bkOuuZvy7SHojlNj+XWMkmebltzmo04gHXzEbPc4HkedzxwZINF62/O1QJi5dPfQfvoXVxspaS5pRp4RIGHshlesozfPs/SwgeY+Rd5NHueisp4l2NcHnuaiWNfwDSK0cP1paWTjZBoCKWl/WY0zaZBawjDKz8QTxPLO2/GfPheDAruuqfCPQ9UrigZdc6RJkXPh5FRj8aIh+8rCYW7mYRCMegrv/xlXnrjFTK3GmKqYVSEvtoIY/UiAI5U60NfU2crnHM4Ay7LydMMVFESGlaqhI0aQb2KX4vwIm9bmrysH/UbfM7wfX8l/EVRtBL8fN8fyuMPPq4Ex1uv30hGa72y5qAcSyHEXpUvxWQXOuhasG3z4Z1zpFlKq9um2W7hnCXww21rTrPpNjTbqLjLgZoDBXpkdOjhEMD2emub1AwERPfEp7i87yid1BL6HtXAK6bGZB38eI546QznL77Jvd1vcrc6S8dVeKfyJJVDfxQ3+zgmaGAddNIc6xwT9eGVlmaZo7mU0+0Y/EATBFfeX7djeOtEjzOnUoJAce8DFY7dW8FbtxZif71EKMJhvebQJpVQuBtJKBSDnn32WU58+wXGGiNMjo4zWmsQ+rfuyXojzpXr3lj67UWxxmDyHIcFT+E3qlTGG0SNYkRQ+d7QR/3yPF8p9+xTShGGIZVKhTAMrxj122k3Gxz74VHCzlppmpKmKRMTE0xOTu6Kn7UQQtysfDEmu9jdthHDQdZaeknMcmuZ7i1oTjMojRP8MOSOh+4gO3eO7PSZbQ2HUAbEfpOafkCcmCD41FP0Hn6c+X134PleMe9Qq2L0MF6C9nneu3ia6tJzfM4+R0VlnNJ30pt8Co58kby2H6vDbSktTRLL8kJOkhTNaDz/yt+J5aWcN1/tcelCRrWmue8TVY7cceWSIc46ktSBMYzWc/Z919M3tW3bQUKhhEKxzrO/+h/xw4DgFpfBOVuGPwOw9m/P4rAqx2LAV/hRSDQ+SqXRKLqEDuFJvD/ql+fFenrrR/36I379UT/P84Y26reT1s9vzPOcNE3XBEdgzfG4XYNjv5FMFEXMzs5S2eL8QyGE2K2yuR75XBddv3IJgm17zDyj3S2b0+QG39/e5jT9UHjs8XsBsGlahMMzZ4DtDYewcUDUExPw2JMsP/goydG7qVcDAs8D59BZG793mVOXLzF3+Q2eTL7OcX2GLhXO1R7HO/hH6U0/gQkapMbRywxaK/aNREw1opsqLXXO0esWzWiMsUTRlc1oAOYuZbzxSpelRcPImMeDD1eZPbDBMhZpjo0z7v5BCYW7joRCsd52hsKVUT9DmfvcautRBfgKAgUeGJuRmxy8Yr3AqDFCtTFCEEZ4N1Fqsj4E9Sml1gS/wVG/271pyODxGhxxHAyPH+cRR+cccRzjnFtZc/B2/50QQnw8OefI53rk871bGgz7jx0nMa1um1anDUAYhENf1md9KOyzaUp+/jzp6dPA9odDGAiI3/wmvZdewqUpanyc5OHH6T78BN4991KtFO/HlEnwkiWWFy/y6vnzHGj/IV9Uf0hFZVzw7yCfeor2gS+S1w9gVDjU0lJrHZ22YXnJoGDTBevPnUl587Ue3bZlasbnwUdqTEyt/vxMlmN6Egp3JQmFYr1hhMJiQXdWyj3XrDvhq5VTsaYfOAXW5uRpv5OpotKoU2mMElaqeMGNvTDZck27fsnnoP6oX7/kc7Dcc6+Glt1gMDT2T/0yy36IHAzhcGVw7J92k34jmdHRUaampm7p3BchhNgJzjnyS13yxQRd35mKGGMM3bjLUnOZNM9QWhMF4VBeIzYLhX02TckvXCD96KNipG50bNvDIRQBsfvCC3S/9S16L72ISzPc2Djxw4+TP/oklfvuK7bDWbysg2lf5Nnzy/TmX+O77e9xXJ8lpsLSyKMk+z9PZ+oJcr9OnDuS3FINNQdGK4zVwi2XlhrjaDUNrabB0xBs0IzGGsepDxLefqNHmjgOHA544OEajRFPQuFuJqFQrHe9odC5weAHKyWfDtAUI35B0dUTXYz+oVfXFDR50RzG2hyFIqhWqY6MElarBGF0zfX/Brt05nm+ZqRKa00URSvz/QabvOy20HE7WR8a+8FxsFR1fXAE1izFcauCY79U1Pd99u3bR61W2/bHFEKI3cJZR3axi1lO8Bo7+2FYkiYr5aXOWXw/IAy2/sH1tUJhn0tTsh0Ih7A2IHZffBGyDDs6TvLI4+gnn8K75zhojTIJurfIW5cWOHHxPJ+Iv8mf0N+mojIWgyOkk59msRw9TAnopsMpLb2eZjR55njv7R7vvxNjDRy9K+Ke4wGBMxIKdyMJhWK9wVB4RZOXdXP9Vkb9AlWsb9QPfhvUm1tjybMEm+egwPMjqqMjRLWrzwvcbGkHpRRBEGxY7jnsUhNx62wWHAfnOm53cEyShCzLVtYclEYyQojbkbOO9Hwb287w6jtfJTHYnKaXxGUZ4403p7neUNjn0pTs4sUiHFp7S8MhrAbE9je+Qfzyy5BlmNFx7KNPoJ78NNx1T/G+KmsxvzDHb5/rUFl+mT+jvsZxfZZURXRGH6U5/Xk6M0+QeXU6mRtKaWkSW5YWc9KrNKOJY8s7b/Q49X6C1nD33T5f/G++8+YPzJBJKJRQKNZ59lf/Iz4+fli+AHgKfNaO+mnWjPptxFlHnmXkeQo4PK+cF1hvEESVNfMCr7a0g+d5a9b1Gyz3lFG/29NGwbE/0tgPkOvLhoE1S3FsFByNMfR6ParVKrOzs0RRdKt2SQghdiVnHem5NraT49V3zweuWZ7R6XZZai+T53mxDNR1Nqe50VDYt9PhEIqA2Hn+eZpf/wbpq6+gsgw7No577Enc40/BXfegXErWWeR3Tzd5d+4CX7C/vzJ62I6O0Jn4NIv7v5ustp+e9UmNIfI9Do5trbR0pRnNgsHYzZvRtFuGN1/tUK/C9/xVCYW7joRCsd6zv/Xb+EFIUAmvGfwGFYvGX31e4PrwN2hwTb/1c/2EuFHrg6O19opSVWMMSqmVDyH6aw42Gg2ZXyqEECVnHOnZFi4x6OruCYZQNqdJE1qdFu1OG8e1m9NsNRSuPOYuCIcAttdl8dvP0fzGN+HEK6g8x42N4x77FPbxT8Gdx9B5m1fPL/Bb51OO9l7gP/W+yr36LJmK6I49yuLUM3SnnyDWdbq5u6nSUmsdnZZhedmAg6hy5XxDk+Xk3ZR7/tRnhnkohkJCoYRCsc6zv/s7BOH1LSa70bzASmMELwzRno917opRv/VLOwxzQXchbsRgcLTWrnwYIYQQYi2XW9IzLVzu0JXd+WHtSnOaVpMkS9Da27A5zc2Gwr6VcHjqI3C26Fa6Q68hvVabuW/+Ib1vfYvgrRNFQByfwD36JPaJp1BHDnF+eYl//1HM5aVz/Bn9Nf4T79tEZPQqh2mOf5rFfV8krR6gbYv3b2PVgP2jFRoVnxspLe03o2k3DVpDGK0ef2k0s4tJKBTrXS0UrswLNAZrDeiAsF4jiCr4UQWt9cqC7v0mL7ttQXchhBBC3DiXW9LTTZxx6Mru/gAtSVPa3TbNThNjHYHvE/hFJ/NhhcK+IhxeIj11asfDYW4ti/PLzH3zWfRLzxEOBsTHnsQ++gTxvmm+eqbN71zK+Yz5Nn/R/yp3qbMYFdEe+yRLU5+nPfU4HVUntW6ltHS0FuLfQGlpllqWlwy9riEINH6gJBTuZhIKxXqDodAaS5okpGmMsbb41K1ep1JvUKnWqDUaK6N+/cXcZdRPCCGE+HhymSE53Sq6cUa7OxjCQHOadpNe3CuWRXYQVqpDC4V9LsuKkcMPdz4cOudoJznnzy+QvPQi0WsvErz52tqA+MgjPB9N8G/POrzuR/wf/N/hT3jfJiQjrhxmuRw97FVmaZsQzytKSycbIZUb2K8ktiwu5GSpw9cWl0oo3JUkFIr1nvvdr9Hr9VCqmGPVGBtlZHKasbFxKvU6QRDgeZ6M+gkhhBC3IZsa0o+aoBU63DvvBbI8o9PrsrS4SKVW4+ijd2/L4+ymcAjQy3IuNWPm55YJ3niV2msvot8YCIiPPsGFe+7nl+0Mzy9a/hP9DX4k/G2OunMYHdEeeZSl6WdoTTxKU9UxTjNeu7HS0n4zmsW5BGVy7v6TEgp3HQmFYr2Lpz5EeYr66DjVemPTpSKEEEIIcXuySRkMvb0VDAFsmmM9Re2O8W19nJVweOpU0ZBmh8NhZgwLnYzzyz1st0v9rRMErzyPGgiIyUOf5Ov77+Ofc5Sj5kP+s/C3+aJ6lmBw9HDmC7SifSQqvOHSUpNmZN2Eic99+hbs8Y2RUCihUAghhBBC3CAb5ySnWuhQo4K9s0SUywz4HtGRkVv0eBnZpUukH364K8KhdY7lbsr55R7d1BLlCdU3X0O/9NxqQBwb58y9D/Nrkw/yreo+/nzwDX4k/Cr7bTl6OPooS1PfydL4Y7Sooz2f2dGIqWuUlro8x6UJ9aeeuoV7fH0kFEooFEIIIYQQW2B7OclHTXTkofy9EQxvdShcfdyBcGhMEQ6vo9P7Nm4R7dhwqR2z0E7xPEU9T/FOvIIaCIjZ6DivHPkEvzT5SWpTCT9e+yqfdc/hu8HRw8+zHM6SqtpVS0slFO5iEgqFEEIIIcRWmU5GerqJrvoob/cHw50KhauPv27ksDGyw+EQ4jxnrpVyuZVgnaMe+fhJjHr15TUBsdMY4/f2P8y3Dt7Hdx54n/80+BpT+fmB0cM/wvzIY/T8GmEQcWCswthAaamEwl1MQqEQQgghhLgZeSslO9NG13yUt7s7kO90KFzZjjwnu3iR7NQpnDG7Ihzm1rLUSTm/HJMYS8XXVAIfet0iIL74LOqNEyiTs1gb42sHHqF1dIw/d+hVHjUv4A2MHl6efoamvw+CGjOjFaZHQiKQULhbSSgUQgghhBA3K19OyM610fUApXdvMNwtobDP5TnZ5ctkH36Iy/NdEQ6dc7SSnHNLPdpxTuBpaqFXLDnW66JefQn14nPwxgm0yblUHef5g/dz1z0x3zXzEmMDo4eLE5/j0ugnybwRRqsh+yqa2T/ymR3dv41IKJRQKIQQQgghhiBfisnOd3Z1MNxtobBvMBzaLMPb8TmHhW6ac7kVc7mdooF65OP1O42WATF97lnCt07gWcOl6jgXjh7kkXvmOT5yAq/fuXTsKS6OfZYsnOWJP/snd3SfNiKhUEKhEEIIIYQYkmwhJr/YQTeCYmRpl9mtobBvt4bDNDcsdFIuNGOMdVQDj9AfWI6k1yV76SWWv/Utpk6+SWANC9VR9F0N7j96hvHRC1gvYm7ks+z7K7+2czuyiauFwm3tFauU+l7gfwI84J86535q3eX3A/8MeBz4G865vz9w2YdACzBA3t8BpdQk8G+AO4EPgT/vnFvczv0QQgghhBCiL5isgHXkc91ixHAXBsPdTPk+4YEDBDMzZHNzZCc/xLSaOx4OQ99j/1iVmZGIZq9Y73C5lxB6HpXAQ1VrBJ/5LNOf+Sy20+H9bz5H9vyz3P3G21x4XfNB/V7G7lRU74t3bB+2attCoVLKA/4h8EXgDPCcUurLzrk3Bq62APxXwA9ucjefd87NrfveTwJfdc79lFLqJ8v//3dD3XghhBBCCCGuwp8qg+FCjK77Egy3QPk+4f79BNPTq+Gw3cLb4TmHntZM1CMm6iHtOOdCM2apm+J7mlrgo7VC1+vc8cVn4IvPcG6uydu//4eMn3iRR994h9c6Df7Ujm391mznSOFTwHvOuQ8AlFK/BPwAsBIKnXOXgEtKqT9xA/f7A8Az5fn/BfhdJBQKIYQQQohbSCmFP1MF58gXE7zGzpc/7lUr4XBmpigr3SUjh6BoVALuqQQrS1pcasU4V8w77C9DcXB6lIN/+ou0v/8L/Pu3LzGZNHdwm7dmO0PhIeD0wP/PAJ++gds74D8qpRzw/3HO/Vz5/X3OufMAzrnzSqnZoWytEEIIIYQQN6AIhjWcdZhmileXYHgzlOetDYcf7pZwCBXf5/CEz/7RCkvdlHPLMe08pxp4REEx77ARaL7vvkmyXnVHt3UrtjMUbjSGfiNdbT7rnDtXhr7fUkq95Zz7/et+cKV+FPhRgKNHj97AwwohhBBCCHF9lFYE++pFMGxnEgyHYDAc5nNzpCdP7ppw6Hua6ZEKU42IZpxxbjlmqZsQeB610Lv2HexSehvv+wxwZOD/h4Fz13tj59y58usl4NcoylEBLiqlDgCUXy9tcvufc8496Zx7cmZmZgubL4QQQgghxLUprQj3N9C1ANPNd3pzPjaU5xHs20ftU5+ict99uCQmX5jHpulObxpKKcaqIQ/sH+WBg2OM1XyWexntZG/+/LczFD4H3KuUOqaUCoEfAr58PTdUStWVUiP988B3AyfKi78M/KXy/F8C/u1Qt1oIIYQQQogbpDxFeLCOjjxsb28Gg91qbTi8H9Jk14RDgHroc+dUg0cOj3FwtEo93HujxdtWPuqcy5VSPw78JsWSFL/gnHtdKfVj5eU/q5TaDzwPjAJWKfUTwIPANPBrZRcnH/jXzrnfKO/6p4D/VSn1nwEfAX9uu/ZBCCGEEEKI66U8TXioQXq6hY0NurJ3ywl3oyIczuJPT5HPzZN+eJK83UY3Gugw3OnNK5a0GK/iats57rY9tnWdQufcV4CvrPvezw6cv0BRVrpeE/jkJvc5D3xhiJsphBBCCCHEUChfEx4ZIfmoiY1zdGVb327fltaEw/l50pO7KxzuRXsvxgohhBBCCLGLKV8THRkBBTYxO705H1vK8whmZ6k9+SSV+++DLCWfn8cmyU5v2p4joVAIIYQQQoghU4FHeGQUnMOmEgy300o4/NSnqDxwP+SZhMMbJKFQCCGEEEKIbaBDj/DICOQOl0kw3G5K69Vw+OADYI2Ew+skoVAIIYQQQohtoiOf8OgINnO43O705twWlNYEMzPUnnhiNRwuSDi8GgmFQgghhBBCbCNd8YkON7CxkWB4C60Jhw/0Rw7nJBxuQEKhEEIIIYQQ20zXAoLDDWyc44wEw1tp7cjhgxIONyChUAghhBBCiFvAb4QEhxrYrsEZt9Obc9tZEw4/8QkJhwNk4RQhhBBCCCFuEX8kggOQXWijawFKq53epNuO0ppgehp/cpJ8YaFY53B+Dt0YQUfRTm/ejpBQKIQQQgghxC3kj0fgHNnFLrrmSzDcIRuGw7k59MjtFw4lFAohhBBCCHGL+RMVnHXkl7roRoBSEgx3yvpwmJ38EDM/h6o30JXKTm/eLSGhUAghhBBCiB0QTFXBOfK5HrouwXCnDYZDs7RE+sEHt004lFAohBBCCCHEDvGnqmAc+VJSlJJKMNxxSmv8yUm8iQnM4uJtEQ4lFAohhBBCCLFDlFL4szVwkC/FeI1wpzdJlJRSt004lFAohBBCCCHEDuoHQ2cdppXi1YOd3iQxYE04XCkrnUfVauhqdac3byhknUIhhBBCCCF2mNKKYH8d3QgwnWynN0dsQCmFPzFB9fHHqTzyMEorzPw8ttfb6U27aRIKhRBCCCGE2AWUVoT7G+iaj+nmO705YhMbhcN8fm5Ph0MJhUIIIYQQQuwSylOEBxvoyMPGEgx3s8FwWH3kEZSnMYsLO71ZWyJzCoUQQgghhNhFlKcJDzVIT7ewvRxdlbfsu1k/HHrj45ilJWy7s9ObdMNkpFAIIYQQQohdRvma8PAIeAqbyIjhXtAPh+GRwzu9KTdMQqEQQgghhBC7kArKYAjY1Ozw1oiPMwmFQgghhBBC7FI69AiPjIJxEgzFtpFQKIQQQgghxC6mQ4/w6Agud7jM7vTmiI8hCYVCCCGEEELscjryiY6MYDODyyUYiuGSUCiEEEIIIcQeoKs+0eERbGxwRoKhGJ5tDYVKqe9VSr2tlHpPKfWTG1x+v1LqW0qpRCn1Vwe+f0Qp9TWl1JtKqdeVUv/1wGVfUkqdVUq9XJ6+bzv3QQghhBBCiN1C1wKCww1sL5dgKIZm2xY9UUp5wD8EvgicAZ5TSn3ZOffGwNUWgP8K+MF1N8+B/9Y596JSagR4QSn1WwO3/X875/7+dm27EEIIIYQQu5XfCOFgg+xcB11TKK12epPEHredI4VPAe855z5wzqXALwE/MHgF59wl59xzQLbu++edcy+W51vAm8ChbdxWIYQQQggh9gx/NCLYX8N2M5x1O705Yo/bzlB4CDg98P8zbCHYKaXuBB4D/nDg2z+ulHpVKfULSqmJm9pKIYQQQggh9iB/vEKwr47t5jgnwVBs3XaGwo3GsW/ot1Up1QB+BfgJ51yz/PY/Bu4GHgXOA//DJrf9UaXU80qp5y9fvnwjDyuEEEIIIcSe4E9U8Ker2E4mwVBs2XaGwjPAkYH/HwbOXe+NlVIBRSD8V865X+1/3zl30TlnnHMW+CcUZapXcM79nHPuSefckzMzM1vaASGEEEIIIXY7f6qCPyXBUGzddobC54B7lVLHlFIh8EPAl6/nhkopBfw88KZz7n9cd9mBgf/+KeDEkLZXCCGEEEKIPUcphT9dxZ+oYDv5Tm+O2IO2rfuocy5XSv048JuAB/yCc+51pdSPlZf/rFJqP/A8MApYpdRPAA8CjwA/DLymlHq5vMu/7pz7CvD3lFKPUpSifgj8F9u1D0IIIYQQQuwFSin8mRrOQj7fwxvzdnqTxB6ybaEQoAxxX1n3vZ8dOH+Boqx0va+z8ZxEnHM/PMxtFEIIIYQQ4uNAaUWwr4az9gY7eYjb3baGQiGEEEIIIcSto7QiPNDAZbKwvbh+EgqFEEIIIYT4GFFaoSIpHxXXbzsbzQghhBBCCCGE2OUkFAohhBBCCCHEbUxCoRBCCCGEEELcxiQUCiGEEEIIIcRtTEKhEEIIIYQQQtzGJBQKIYQQQgghxG1MQqEQQgghhBBC3MYkFAohhBBCCCHEbUxCoRBCCCGEEELcxiQUCiGEEEIIIcRtTDnndnobtp1S6jJwaqe3YwPTwNxOb8RtSo79zpFjv3Pk2O8cOfY7S47/zpFjv3Pk2O+c3Xrs73DOzWx0wW0RCncrpdTzzrknd3o7bkdy7HeOHPudI8d+58ix31ly/HeOHPudI8d+5+zFYy/lo0IIIYQQQghxG5NQKIQQQgghhBC3MQmFO+vndnoDbmNy7HeOHPudI8d+58ix31ly/HeOHPudI8d+5+y5Yy9zCoUQQgghhBDiNiYjhUIIIYQQQghxG5NQOERKqSNKqa8ppd5USr2ulPqvy+9PKqV+Syn1bvl1YuA2f00p9Z5S6m2l1PcMfD9USv2cUuodpdRbSqk/sxP7tFcM69grpUaUUi8PnOaUUj+9Q7u1Jwz59/4vKKVeU0q9qpT6DaXU9E7s014x5GP/vyuP++tKqb+3E/uzl9zosVdKTZXXbyulfmbdfT1R/t6/p5T6B0optRP7tJcM+fj/HaXUaaVUeyf2Za8Z1rFXStWUUr9evsd5XSn1Uzu1T3vFkH/vf0Mp9Up5Pz+rlPJ2Yp/2imEe+4H7/LJS6sSt3I+rcs7JaUgn4ADweHl+BHgHeBD4e8BPlt//SeD/WZ5/EHgFiIBjwPuAV172/wD+dnleA9M7vX+7+TTMY7/ufl8AvnOn9283n4Z17AEfuNT/XS9v/6Wd3r/dfBrisZ8CPgJmyuv9L8AXdnr/dvNpC8e+DnwO+DHgZ9bd17PA04AC/gPwx3d6/3b7acjH/zvK+2vv9H7thdOwjj1QAz5fng+BP5Df/Vtz7MvLRsuvCvgV4Id2ev9282mYx768/E8D/xo4sdP71j/JSOEQOefOO+deLM+3gDeBQ8APULzJovz6g+X5HwB+yTmXOOdOAu8BT5WX/Qjwd8v7ss653bgA5q4x5GMPgFLqXmCW4oVKbGKIx16Vp3o5UjIKnLtV+7EXDfHY3wW845y7XF7vtwGpTriKGz32zrmOc+7rQDx4P0qpAxRvzr7lincK/5zVn5fYxLCOf3nZt51z52/Fdn8cDOvYO+e6zrmvledT4EXg8K3Yh71qyL/3zfKsTxHKpcnIVQzz2CulGsB/A/zt7d/y6yehcJsope4EHgP+ENjXf8Epv86WVzsEnB642RngkFJqvPz/31JKvaiU+mWl1L5bsuEfAzdz7Nfd1V8A/k35Rk1ch5s59s65DPgvgdcowuCDwM/fmi3f+27y9/494H6l1J1KKZ/iRe3Irdnyve86j/1mDlH8HPo2ei4SV3GTx1/chGEd+/J9z/cDXx3+Vn48DePYK6V+k6JCpwX8b9uzpR8/Qzj2fwv4H4Dudm3jVkgo3AblJwC/AvzEwCcxG151g+85ik9tDgPfcM49DnwL+PtD39CPoSEc+0E/BPzisLbt4+5mj71SKqAIhY8BB4FXgb829A39GLrZY++cW6Q49v+GYmT8QyAf9nZ+HN3Asd/0Ljb4nnwQdZ2GcPzFFg3r2JcfRP0i8A+ccx8Ma/s+zoZ17J1z30NRFhkB3zWkzftYu9ljr5R6FLjHOfdrw962myWhcMjKN7a/Avwr59yvlt++WJYI9UuFLpXfP8PaT+MPU4yQzFN8etD/hfll4PFt3vQ9b0jHvn9fnwR859wL277hHwNDOvaPAjjn3i9HZ/9X4DPbv/V727B+751z/84592nn3NPA28C7t2L797IbPPabOcPakrk1z0Vic0M6/mILhnzsfw541zn300Pf0I+hYf/eO+di4MsUZZDiKoZ07J8GnlBKfQh8HTiulPrd7dniGyOhcIjKeVA/D7zpnPsfBy76MvCXyvN/Cfi3A9//IaVUpJQ6BtwLPFu+If53wDPl9b4AvLHNm7+nDevYD9zuLyCjhNdliMf+LPCgUmqmvN4XKWr2xSaG+XuvlJotv04A/xfgn27/HuxdWzj2GyrLjVpKqe8o7/MvXus2YnjHX9y4YR57pdTfBsaAnxjyZn4sDevYK6UaA0HGB74PeGv4W/zxMcTn/H/snDvonLuTohHNO865Z4a/xVvgdkG3m4/LieKH6yjK3l4uT99H0dnvqxSfvH8VmBy4zd+g6AD4NgNdt4A7gN8v7+urwNGd3r/dfBrmsS8v+wC4f6f3ay+chvx7/2MUQfBVig9GpnZ6/3bzacjH/hcpPnx6A+lCt13H/kNgAWhTjBA+WH7/SeBE+XP5GUDt9P7t9tOQj//fK/9vy69f2un9282nYR17ilFxVz7n9+/nP9/p/dvNpyEe+33Ac+X9vA78zxTVUTu+j7v1NMznnIHL72QXdR9V5UYJIYQQQgghhLgNSfmoEEIIIYQQQtzGJBQKIYQQQgghxG1MQqEQQgghhBBC3MYkFAohhBBCCCHEbUxCoRBCCCGEEELcxiQUCiGEEFukCl9XSv3xge/9eaXUb+zkdgkhhBA3QpakEEIIIW6CUuoh4JeBxwCPYv2q73XOvb+F+/Kcc2a4WyiEEEJcnYRCIYQQ4iYppf4e0AHq5dc7gIcBn2Ix9H+rlLoT+BfldQB+3Dn3TaXUM8DfBM4DjzrnHry1Wy+EEOJ2J6FQCCGEuElKqTrwIpAC/x543Tn3L5VS48CzFKOIDrDOuVgpdS/wi865J8tQ+OvAQ865kzux/UIIIW5v/k5vgBBCCLHXOec6Sql/A7SBPw98v1Lqr5YXV4CjwDngZ5RSjwIGOD5wF89KIBRCCLFTJBQKIYQQw2HLkwL+jHPu7cELlVJfAi4Cn6Ro9BYPXNy5RdsohBBCXEG6jwohhBDD9ZvAX1ZKKQCl1GPl98eA8845C/wwRVMaIYQQYsdJKBRCCCGG628BAfCqUupE+X+AfwT8JaXUtylKR2V0UAghxK4gjWaEEEIIIYQQ4jYmI4VCCCGEEEIIcRuTUCiEEEIIIYQQtzEJhUIIIYQQQghxG5NQKIQQQgghhBC3MQmFQgghhBBCCHEbk1AohBBCCCGEELcxCYVCCCGEEEIIcRuTUCiEEEIIIYQQt7H/P9cNKkIyWhNOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot a line graph over time\n",
    "plt.figure(figsize = [15, 5])\n",
    "sns.lineplot(data=loan_df, x=\"loan_year\", y=\"BorrowerRate\", hue=\"EmploymentStatus\")\n",
    "plotlabel('Year','Borrower rate','Borrowers rate by employment status');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the graph we find out that those borrowers with Full time job was getting lowest interest rate over time and those who are self employed and Not employed had high interest rate.\n",
    "Also we can see that the interest rate for those not employed increased significantly from 2012 while those employed and with full time jpob decreased signficantly at the same period.\n",
    "This can be because of job security and repayment confidence.\n",
    "\n",
    "### Borrowers rate Vs HomeOwners"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot borrower bar graph over time\n",
    "plt.figure(figsize = [15, 5])\n",
    "sns.barplot(data=loan_df, x=\"loan_year\", y=\"BorrowerRate\", hue=\"HomeOwner\")\n",
    "plotlabel('Year','Borrower rate','Borrowers rate by home owners')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This also shows that the intereste rate for borrowers also depended on whether the borrower had a home or not. It shows that those with homes had lower interest rate compared to those without homes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot a box plot between borrowers rate and the year grouped by if the borrower is a home owner\n",
    "plt.figure(figsize = [10, 6])\n",
    "sns.boxplot(data = loan_df, x=\"loan_year\", y=\"BorrowerRate\", hue=\"HomeOwner\", palette = 'Blues_r')\n",
    "plotlabel('Year','Borrower rate','Borrowers rate Vs home owners')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I wanted to check the relationship between Interest rate among borrowers with home and those without. From the chart we can notice that the homeowners got lower interest  rate in general throughout the years.\n",
    "We also have outliers on 2005 2006 2007 2008 and 2014. we might consider removing them if we decide to do predictive analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Plot a box plot between borrowers rate and the year grouped by if the borrower is a home owner\n",
    "# loan_df['LoanOriginalAmount']=loan_df['LoanOriginalAmount'].astype(int)\n",
    "# plt.figure(figsize = [20, 6])\n",
    "# sns.boxplot(data = loan_df, x=\"ProsperRating\", y=\"LoanOriginalAmount\", hue=\"LoanStatus\", palette = 'Blues_r')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "C     18345\n",
       "B     15581\n",
       "A     14551\n",
       "D     14274\n",
       "E      9795\n",
       "HR     6935\n",
       "AA     5372\n",
       "Name: ProsperRating, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data= loan_df[loan_df['ProsperRating'].notna()]\n",
    "data.ProsperRating.value_counts()\n",
    "#data['ProsperRating']=data['ProsperRating'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [10, 5])\n",
    "sns.barplot(data=loan_df, x=\"ProsperRating\", y=\"StatedMonthlyIncome\", hue=\"Term\")\n",
    "plotlabel('Prosper ratings','Stated monthly income','BLoan amount given by Prosper rating');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the graph we can see that there is a relationship between the ratings and the amount of loan given. The better the rating the higher the loan amount. Also in most of the terms it seems that most borrowers preferred short terms where short term borrowed more than those with long term"
   ]
  },
  {
   "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",
    "\n",
    "> From the start I choose `Borrowers Rate` as I think it has effect on the potential amount the borrower can be entitled to. \n",
    "I noticed that the Borrower Rate has effect on the amount where the amount borrowed increased with higher ratings across all terms \n",
    "\n",
    "### Were there any interesting or surprising interactions between features?\n",
    "\n",
    "> From the analysis I was able to find out that those borrowers with Full time job was getting lowest interest rate over time and those who are self employed and Not employed had high interest rate. \n",
    "\n",
    "> Also those employed and those with full time job got lowest interests over time\n",
    "\n",
    "> Employed and self employed borrowed high amounts\n",
    "\n",
    "> Loan amount borrowed decred from 2007 to 2009 but increased steadily from 2009 to 2014"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions\n",
    "From the analysis I can conclude that the following features have effect on the amount of the loan borrowed\n",
    "1. Borrowers Rate - The higher the ratings the higher the amount\n",
    "2. Prosper ratings - The better the prosper ratings does not mean the higher the amount borrowed but it seems that middle ratings borrowers borrowed more amount\n",
    "3. Employment status - Employed borrowers got better interest rate compared with those who are not employed\n",
    "4. Stated income range - Borrowers with high income range borrowed less amount compared with those borrowers with less income range\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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