03-Keras-Project.ipynb

{
 "cells": [
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   "cell_type": "markdown",
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   "source": [
    "<a href=\"https://www.pieriandata.com\"><img src=\"../Pierian_Data_Logo.PNG\"></a>\n",
    "<strong><center>Copyright by Pierian Data Inc.</center></strong> \n",
    "<strong><center>Created by Jose Marcial Portilla.</center></strong>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Keras API Project Exercise\n",
    "\n",
    "## The Data\n",
    "\n",
    "We will be using a subset of the LendingClub DataSet obtained from Kaggle: https://www.kaggle.com/wordsforthewise/lending-club\n",
    "\n",
    "## NOTE: Do not download the full zip from the link! We provide a special version of this file that has some extra feature engineering for you to do. You won't be able to follow along with the original file!\n",
    "\n",
    "LendingClub is a US peer-to-peer lending company, headquartered in San Francisco, California.[3] It was the first peer-to-peer lender to register its offerings as securities with the Securities and Exchange Commission (SEC), and to offer loan trading on a secondary market. LendingClub is the world's largest peer-to-peer lending platform.\n",
    "\n",
    "### Our Goal\n",
    "\n",
    "Given historical data on loans given out with information on whether or not the borrower defaulted (charge-off), can we build a model thatcan predict wether or nor a borrower will pay back their loan? This way in the future when we get a new potential customer we can assess whether or not they are likely to pay back the loan. Keep in mind classification metrics when evaluating the performance of your model!\n",
    "\n",
    "The \"loan_status\" column contains our label.\n",
    "\n",
    "### Data Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "-----\n",
    "There are many LendingClub data sets on Kaggle. Here is the information on this particular data set:\n",
    "\n",
    "<table border=\"1\" class=\"dataframe\">\n",
    "  <thead>\n",
    "    <tr style=\"text-align: right;\">\n",
    "      <th></th>\n",
    "      <th>LoanStatNew</th>\n",
    "      <th>Description</th>\n",
    "    </tr>\n",
    "  </thead>\n",
    "  <tbody>\n",
    "    <tr>\n",
    "      <th>0</th>\n",
    "      <td>loan_amnt</td>\n",
    "      <td>The listed amount of the loan applied for by the borrower. If at some point in time, the credit department reduces the loan amount, then it will be reflected in this value.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>1</th>\n",
    "      <td>term</td>\n",
    "      <td>The number of payments on the loan. Values are in months and can be either 36 or 60.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>2</th>\n",
    "      <td>int_rate</td>\n",
    "      <td>Interest Rate on the loan</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>3</th>\n",
    "      <td>installment</td>\n",
    "      <td>The monthly payment owed by the borrower if the loan originates.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>4</th>\n",
    "      <td>grade</td>\n",
    "      <td>LC assigned loan grade</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>5</th>\n",
    "      <td>sub_grade</td>\n",
    "      <td>LC assigned loan subgrade</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>6</th>\n",
    "      <td>emp_title</td>\n",
    "      <td>The job title supplied by the Borrower when applying for the loan.*</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>7</th>\n",
    "      <td>emp_length</td>\n",
    "      <td>Employment length in years. Possible values are between 0 and 10 where 0 means less than one year and 10 means ten or more years.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>8</th>\n",
    "      <td>home_ownership</td>\n",
    "      <td>The home ownership status provided by the borrower during registration or obtained from the credit report. Our values are: RENT, OWN, MORTGAGE, OTHER</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>9</th>\n",
    "      <td>annual_inc</td>\n",
    "      <td>The self-reported annual income provided by the borrower during registration.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>10</th>\n",
    "      <td>verification_status</td>\n",
    "      <td>Indicates if income was verified by LC, not verified, or if the income source was verified</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>11</th>\n",
    "      <td>issue_d</td>\n",
    "      <td>The month which the loan was funded</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>12</th>\n",
    "      <td>loan_status</td>\n",
    "      <td>Current status of the loan</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>13</th>\n",
    "      <td>purpose</td>\n",
    "      <td>A category provided by the borrower for the loan request.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>14</th>\n",
    "      <td>title</td>\n",
    "      <td>The loan title provided by the borrower</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>15</th>\n",
    "      <td>zip_code</td>\n",
    "      <td>The first 3 numbers of the zip code provided by the borrower in the loan application.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>16</th>\n",
    "      <td>addr_state</td>\n",
    "      <td>The state provided by the borrower in the loan application</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>17</th>\n",
    "      <td>dti</td>\n",
    "      <td>A ratio calculated using the borrower’s total monthly debt payments on the total debt obligations, excluding mortgage and the requested LC loan, divided by the borrower’s self-reported monthly income.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>18</th>\n",
    "      <td>earliest_cr_line</td>\n",
    "      <td>The month the borrower's earliest reported credit line was opened</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>19</th>\n",
    "      <td>open_acc</td>\n",
    "      <td>The number of open credit lines in the borrower's credit file.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>20</th>\n",
    "      <td>pub_rec</td>\n",
    "      <td>Number of derogatory public records</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>21</th>\n",
    "      <td>revol_bal</td>\n",
    "      <td>Total credit revolving balance</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>22</th>\n",
    "      <td>revol_util</td>\n",
    "      <td>Revolving line utilization rate, or the amount of credit the borrower is using relative to all available revolving credit.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>23</th>\n",
    "      <td>total_acc</td>\n",
    "      <td>The total number of credit lines currently in the borrower's credit file</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>24</th>\n",
    "      <td>initial_list_status</td>\n",
    "      <td>The initial listing status of the loan. Possible values are – W, F</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>25</th>\n",
    "      <td>application_type</td>\n",
    "      <td>Indicates whether the loan is an individual application or a joint application with two co-borrowers</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>26</th>\n",
    "      <td>mort_acc</td>\n",
    "      <td>Number of mortgage accounts.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>27</th>\n",
    "      <td>pub_rec_bankruptcies</td>\n",
    "      <td>Number of public record bankruptcies</td>\n",
    "    </tr>\n",
    "  </tbody>\n",
    "</table>\n",
    "\n",
    "---\n",
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Starter Code\n",
    "\n",
    "#### Note: We also provide feature information on the data as a .csv file for easy lookup throughout the notebook:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_info = pd.read_csv('../DATA/lending_club_info.csv',index_col='LoanStatNew')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Revolving line utilization rate, or the amount of credit the borrower is using relative to all available revolving credit.\n"
     ]
    }
   ],
   "source": [
    "print(data_info.loc['revol_util']['Description'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def feat_info(col_name):\n",
    "    print(data_info.loc[col_name]['Description'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of mortgage accounts.\n"
     ]
    }
   ],
   "source": [
    "feat_info('mort_acc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the data and other imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# might be needed depending on your version of Jupyter\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../DATA/lending_club_loan_two.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 396030 entries, 0 to 396029\n",
      "Data columns (total 27 columns):\n",
      "loan_amnt               396030 non-null float64\n",
      "term                    396030 non-null object\n",
      "int_rate                396030 non-null float64\n",
      "installment             396030 non-null float64\n",
      "grade                   396030 non-null object\n",
      "sub_grade               396030 non-null object\n",
      "emp_title               373103 non-null object\n",
      "emp_length              377729 non-null object\n",
      "home_ownership          396030 non-null object\n",
      "annual_inc              396030 non-null float64\n",
      "verification_status     396030 non-null object\n",
      "issue_d                 396030 non-null object\n",
      "loan_status             396030 non-null object\n",
      "purpose                 396030 non-null object\n",
      "title                   394275 non-null object\n",
      "dti                     396030 non-null float64\n",
      "earliest_cr_line        396030 non-null object\n",
      "open_acc                396030 non-null float64\n",
      "pub_rec                 396030 non-null float64\n",
      "revol_bal               396030 non-null float64\n",
      "revol_util              395754 non-null float64\n",
      "total_acc               396030 non-null float64\n",
      "initial_list_status     396030 non-null object\n",
      "application_type        396030 non-null object\n",
      "mort_acc                358235 non-null float64\n",
      "pub_rec_bankruptcies    395495 non-null float64\n",
      "address                 396030 non-null object\n",
      "dtypes: float64(12), object(15)\n",
      "memory usage: 81.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project Tasks\n",
    "\n",
    "**Complete the tasks below! Keep in mind is usually more than one way to complete the task! Enjoy**\n",
    "\n",
    "-----\n",
    "------\n",
    "\n",
    "# Section 1: Exploratory Data Analysis\n",
    "\n",
    "**OVERALL GOAL: Get an understanding for which variables are important, view summary statistics, and visualize the data**\n",
    "\n",
    "\n",
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Since we will be attempting to predict loan_status, create a countplot as shown below.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='loan_status', ylabel='count'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CODE HERE\n",
    "sns.countplot(data=df,x=\"loan_status\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x207932022c8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Create a histogram of the loan_amnt column.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='loan_amnt', ylabel='Count'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CODE HERE\n",
    "plt.figure(figsize=(12,6),dpi=200)\n",
    "\n",
    "sns.histplot(data=df,x=\"loan_amnt\",bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 45000)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Let's explore correlation between the continuous feature variables. Calculate the correlation between all continuous numeric variables using .corr() method.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>loan_amnt</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>dti</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>mort_acc</th>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>loan_amnt</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.168921</td>\n",
       "      <td>0.953929</td>\n",
       "      <td>0.336887</td>\n",
       "      <td>0.016636</td>\n",
       "      <td>0.198556</td>\n",
       "      <td>-0.077779</td>\n",
       "      <td>0.328320</td>\n",
       "      <td>0.099911</td>\n",
       "      <td>0.223886</td>\n",
       "      <td>0.222315</td>\n",
       "      <td>-0.106539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>int_rate</th>\n",
       "      <td>0.168921</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.162758</td>\n",
       "      <td>-0.056771</td>\n",
       "      <td>0.079038</td>\n",
       "      <td>0.011649</td>\n",
       "      <td>0.060986</td>\n",
       "      <td>-0.011280</td>\n",
       "      <td>0.293659</td>\n",
       "      <td>-0.036404</td>\n",
       "      <td>-0.082583</td>\n",
       "      <td>0.057450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>installment</th>\n",
       "      <td>0.953929</td>\n",
       "      <td>0.162758</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.330381</td>\n",
       "      <td>0.015786</td>\n",
       "      <td>0.188973</td>\n",
       "      <td>-0.067892</td>\n",
       "      <td>0.316455</td>\n",
       "      <td>0.123915</td>\n",
       "      <td>0.202430</td>\n",
       "      <td>0.193694</td>\n",
       "      <td>-0.098628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>annual_inc</th>\n",
       "      <td>0.336887</td>\n",
       "      <td>-0.056771</td>\n",
       "      <td>0.330381</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.081685</td>\n",
       "      <td>0.136150</td>\n",
       "      <td>-0.013720</td>\n",
       "      <td>0.299773</td>\n",
       "      <td>0.027871</td>\n",
       "      <td>0.193023</td>\n",
       "      <td>0.236320</td>\n",
       "      <td>-0.050162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dti</th>\n",
       "      <td>0.016636</td>\n",
       "      <td>0.079038</td>\n",
       "      <td>0.015786</td>\n",
       "      <td>-0.081685</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.136181</td>\n",
       "      <td>-0.017639</td>\n",
       "      <td>0.063571</td>\n",
       "      <td>0.088375</td>\n",
       "      <td>0.102128</td>\n",
       "      <td>-0.025439</td>\n",
       "      <td>-0.014558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>open_acc</th>\n",
       "      <td>0.198556</td>\n",
       "      <td>0.011649</td>\n",
       "      <td>0.188973</td>\n",
       "      <td>0.136150</td>\n",
       "      <td>0.136181</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.018392</td>\n",
       "      <td>0.221192</td>\n",
       "      <td>-0.131420</td>\n",
       "      <td>0.680728</td>\n",
       "      <td>0.109205</td>\n",
       "      <td>-0.027732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pub_rec</th>\n",
       "      <td>-0.077779</td>\n",
       "      <td>0.060986</td>\n",
       "      <td>-0.067892</td>\n",
       "      <td>-0.013720</td>\n",
       "      <td>-0.017639</td>\n",
       "      <td>-0.018392</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.101664</td>\n",
       "      <td>-0.075910</td>\n",
       "      <td>0.019723</td>\n",
       "      <td>0.011552</td>\n",
       "      <td>0.699408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>revol_bal</th>\n",
       "      <td>0.328320</td>\n",
       "      <td>-0.011280</td>\n",
       "      <td>0.316455</td>\n",
       "      <td>0.299773</td>\n",
       "      <td>0.063571</td>\n",
       "      <td>0.221192</td>\n",
       "      <td>-0.101664</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.226346</td>\n",
       "      <td>0.191616</td>\n",
       "      <td>0.194925</td>\n",
       "      <td>-0.124532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>revol_util</th>\n",
       "      <td>0.099911</td>\n",
       "      <td>0.293659</td>\n",
       "      <td>0.123915</td>\n",
       "      <td>0.027871</td>\n",
       "      <td>0.088375</td>\n",
       "      <td>-0.131420</td>\n",
       "      <td>-0.075910</td>\n",
       "      <td>0.226346</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.104273</td>\n",
       "      <td>0.007514</td>\n",
       "      <td>-0.086751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_acc</th>\n",
       "      <td>0.223886</td>\n",
       "      <td>-0.036404</td>\n",
       "      <td>0.202430</td>\n",
       "      <td>0.193023</td>\n",
       "      <td>0.102128</td>\n",
       "      <td>0.680728</td>\n",
       "      <td>0.019723</td>\n",
       "      <td>0.191616</td>\n",
       "      <td>-0.104273</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.381072</td>\n",
       "      <td>0.042035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mort_acc</th>\n",
       "      <td>0.222315</td>\n",
       "      <td>-0.082583</td>\n",
       "      <td>0.193694</td>\n",
       "      <td>0.236320</td>\n",
       "      <td>-0.025439</td>\n",
       "      <td>0.109205</td>\n",
       "      <td>0.011552</td>\n",
       "      <td>0.194925</td>\n",
       "      <td>0.007514</td>\n",
       "      <td>0.381072</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.027239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "      <td>-0.106539</td>\n",
       "      <td>0.057450</td>\n",
       "      <td>-0.098628</td>\n",
       "      <td>-0.050162</td>\n",
       "      <td>-0.014558</td>\n",
       "      <td>-0.027732</td>\n",
       "      <td>0.699408</td>\n",
       "      <td>-0.124532</td>\n",
       "      <td>-0.086751</td>\n",
       "      <td>0.042035</td>\n",
       "      <td>0.027239</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      loan_amnt  int_rate  installment  annual_inc       dti  \\\n",
       "loan_amnt              1.000000  0.168921     0.953929    0.336887  0.016636   \n",
       "int_rate               0.168921  1.000000     0.162758   -0.056771  0.079038   \n",
       "installment            0.953929  0.162758     1.000000    0.330381  0.015786   \n",
       "annual_inc             0.336887 -0.056771     0.330381    1.000000 -0.081685   \n",
       "dti                    0.016636  0.079038     0.015786   -0.081685  1.000000   \n",
       "open_acc               0.198556  0.011649     0.188973    0.136150  0.136181   \n",
       "pub_rec               -0.077779  0.060986    -0.067892   -0.013720 -0.017639   \n",
       "revol_bal              0.328320 -0.011280     0.316455    0.299773  0.063571   \n",
       "revol_util             0.099911  0.293659     0.123915    0.027871  0.088375   \n",
       "total_acc              0.223886 -0.036404     0.202430    0.193023  0.102128   \n",
       "mort_acc               0.222315 -0.082583     0.193694    0.236320 -0.025439   \n",
       "pub_rec_bankruptcies  -0.106539  0.057450    -0.098628   -0.050162 -0.014558   \n",
       "\n",
       "                      open_acc   pub_rec  revol_bal  revol_util  total_acc  \\\n",
       "loan_amnt             0.198556 -0.077779   0.328320    0.099911   0.223886   \n",
       "int_rate              0.011649  0.060986  -0.011280    0.293659  -0.036404   \n",
       "installment           0.188973 -0.067892   0.316455    0.123915   0.202430   \n",
       "annual_inc            0.136150 -0.013720   0.299773    0.027871   0.193023   \n",
       "dti                   0.136181 -0.017639   0.063571    0.088375   0.102128   \n",
       "open_acc              1.000000 -0.018392   0.221192   -0.131420   0.680728   \n",
       "pub_rec              -0.018392  1.000000  -0.101664   -0.075910   0.019723   \n",
       "revol_bal             0.221192 -0.101664   1.000000    0.226346   0.191616   \n",
       "revol_util           -0.131420 -0.075910   0.226346    1.000000  -0.104273   \n",
       "total_acc             0.680728  0.019723   0.191616   -0.104273   1.000000   \n",
       "mort_acc              0.109205  0.011552   0.194925    0.007514   0.381072   \n",
       "pub_rec_bankruptcies -0.027732  0.699408  -0.124532   -0.086751   0.042035   \n",
       "\n",
       "                      mort_acc  pub_rec_bankruptcies  \n",
       "loan_amnt             0.222315             -0.106539  \n",
       "int_rate             -0.082583              0.057450  \n",
       "installment           0.193694             -0.098628  \n",
       "annual_inc            0.236320             -0.050162  \n",
       "dti                  -0.025439             -0.014558  \n",
       "open_acc              0.109205             -0.027732  \n",
       "pub_rec               0.011552              0.699408  \n",
       "revol_bal             0.194925             -0.124532  \n",
       "revol_util            0.007514             -0.086751  \n",
       "total_acc             0.381072              0.042035  \n",
       "mort_acc              1.000000              0.027239  \n",
       "pub_rec_bankruptcies  0.027239              1.000000  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CODE HERE\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>dti</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>mort_acc</th>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>loan_amnt</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.168921</td>\n",
       "      <td>0.953929</td>\n",
       "      <td>0.336887</td>\n",
       "      <td>0.016636</td>\n",
       "      <td>0.198556</td>\n",
       "      <td>-0.077779</td>\n",
       "      <td>0.328320</td>\n",
       "      <td>0.099911</td>\n",
       "      <td>0.223886</td>\n",
       "      <td>0.222315</td>\n",
       "      <td>-0.106539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>int_rate</th>\n",
       "      <td>0.168921</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.162758</td>\n",
       "      <td>-0.056771</td>\n",
       "      <td>0.079038</td>\n",
       "      <td>0.011649</td>\n",
       "      <td>0.060986</td>\n",
       "      <td>-0.011280</td>\n",
       "      <td>0.293659</td>\n",
       "      <td>-0.036404</td>\n",
       "      <td>-0.082583</td>\n",
       "      <td>0.057450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>installment</th>\n",
       "      <td>0.953929</td>\n",
       "      <td>0.162758</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.330381</td>\n",
       "      <td>0.015786</td>\n",
       "      <td>0.188973</td>\n",
       "      <td>-0.067892</td>\n",
       "      <td>0.316455</td>\n",
       "      <td>0.123915</td>\n",
       "      <td>0.202430</td>\n",
       "      <td>0.193694</td>\n",
       "      <td>-0.098628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>annual_inc</th>\n",
       "      <td>0.336887</td>\n",
       "      <td>-0.056771</td>\n",
       "      <td>0.330381</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.081685</td>\n",
       "      <td>0.136150</td>\n",
       "      <td>-0.013720</td>\n",
       "      <td>0.299773</td>\n",
       "      <td>0.027871</td>\n",
       "      <td>0.193023</td>\n",
       "      <td>0.236320</td>\n",
       "      <td>-0.050162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dti</th>\n",
       "      <td>0.016636</td>\n",
       "      <td>0.079038</td>\n",
       "      <td>0.015786</td>\n",
       "      <td>-0.081685</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.136181</td>\n",
       "      <td>-0.017639</td>\n",
       "      <td>0.063571</td>\n",
       "      <td>0.088375</td>\n",
       "      <td>0.102128</td>\n",
       "      <td>-0.025439</td>\n",
       "      <td>-0.014558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>open_acc</th>\n",
       "      <td>0.198556</td>\n",
       "      <td>0.011649</td>\n",
       "      <td>0.188973</td>\n",
       "      <td>0.136150</td>\n",
       "      <td>0.136181</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.018392</td>\n",
       "      <td>0.221192</td>\n",
       "      <td>-0.131420</td>\n",
       "      <td>0.680728</td>\n",
       "      <td>0.109205</td>\n",
       "      <td>-0.027732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pub_rec</th>\n",
       "      <td>-0.077779</td>\n",
       "      <td>0.060986</td>\n",
       "      <td>-0.067892</td>\n",
       "      <td>-0.013720</td>\n",
       "      <td>-0.017639</td>\n",
       "      <td>-0.018392</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.101664</td>\n",
       "      <td>-0.075910</td>\n",
       "      <td>0.019723</td>\n",
       "      <td>0.011552</td>\n",
       "      <td>0.699408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>revol_bal</th>\n",
       "      <td>0.328320</td>\n",
       "      <td>-0.011280</td>\n",
       "      <td>0.316455</td>\n",
       "      <td>0.299773</td>\n",
       "      <td>0.063571</td>\n",
       "      <td>0.221192</td>\n",
       "      <td>-0.101664</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.226346</td>\n",
       "      <td>0.191616</td>\n",
       "      <td>0.194925</td>\n",
       "      <td>-0.124532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>revol_util</th>\n",
       "      <td>0.099911</td>\n",
       "      <td>0.293659</td>\n",
       "      <td>0.123915</td>\n",
       "      <td>0.027871</td>\n",
       "      <td>0.088375</td>\n",
       "      <td>-0.131420</td>\n",
       "      <td>-0.075910</td>\n",
       "      <td>0.226346</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.104273</td>\n",
       "      <td>0.007514</td>\n",
       "      <td>-0.086751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_acc</th>\n",
       "      <td>0.223886</td>\n",
       "      <td>-0.036404</td>\n",
       "      <td>0.202430</td>\n",
       "      <td>0.193023</td>\n",
       "      <td>0.102128</td>\n",
       "      <td>0.680728</td>\n",
       "      <td>0.019723</td>\n",
       "      <td>0.191616</td>\n",
       "      <td>-0.104273</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.381072</td>\n",
       "      <td>0.042035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mort_acc</th>\n",
       "      <td>0.222315</td>\n",
       "      <td>-0.082583</td>\n",
       "      <td>0.193694</td>\n",
       "      <td>0.236320</td>\n",
       "      <td>-0.025439</td>\n",
       "      <td>0.109205</td>\n",
       "      <td>0.011552</td>\n",
       "      <td>0.194925</td>\n",
       "      <td>0.007514</td>\n",
       "      <td>0.381072</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.027239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "      <td>-0.106539</td>\n",
       "      <td>0.057450</td>\n",
       "      <td>-0.098628</td>\n",
       "      <td>-0.050162</td>\n",
       "      <td>-0.014558</td>\n",
       "      <td>-0.027732</td>\n",
       "      <td>0.699408</td>\n",
       "      <td>-0.124532</td>\n",
       "      <td>-0.086751</td>\n",
       "      <td>0.042035</td>\n",
       "      <td>0.027239</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      loan_amnt  int_rate  installment  annual_inc       dti  \\\n",
       "loan_amnt              1.000000  0.168921     0.953929    0.336887  0.016636   \n",
       "int_rate               0.168921  1.000000     0.162758   -0.056771  0.079038   \n",
       "installment            0.953929  0.162758     1.000000    0.330381  0.015786   \n",
       "annual_inc             0.336887 -0.056771     0.330381    1.000000 -0.081685   \n",
       "dti                    0.016636  0.079038     0.015786   -0.081685  1.000000   \n",
       "open_acc               0.198556  0.011649     0.188973    0.136150  0.136181   \n",
       "pub_rec               -0.077779  0.060986    -0.067892   -0.013720 -0.017639   \n",
       "revol_bal              0.328320 -0.011280     0.316455    0.299773  0.063571   \n",
       "revol_util             0.099911  0.293659     0.123915    0.027871  0.088375   \n",
       "total_acc              0.223886 -0.036404     0.202430    0.193023  0.102128   \n",
       "mort_acc               0.222315 -0.082583     0.193694    0.236320 -0.025439   \n",
       "pub_rec_bankruptcies  -0.106539  0.057450    -0.098628   -0.050162 -0.014558   \n",
       "\n",
       "                      open_acc   pub_rec  revol_bal  revol_util  total_acc  \\\n",
       "loan_amnt             0.198556 -0.077779   0.328320    0.099911   0.223886   \n",
       "int_rate              0.011649  0.060986  -0.011280    0.293659  -0.036404   \n",
       "installment           0.188973 -0.067892   0.316455    0.123915   0.202430   \n",
       "annual_inc            0.136150 -0.013720   0.299773    0.027871   0.193023   \n",
       "dti                   0.136181 -0.017639   0.063571    0.088375   0.102128   \n",
       "open_acc              1.000000 -0.018392   0.221192   -0.131420   0.680728   \n",
       "pub_rec              -0.018392  1.000000  -0.101664   -0.075910   0.019723   \n",
       "revol_bal             0.221192 -0.101664   1.000000    0.226346   0.191616   \n",
       "revol_util           -0.131420 -0.075910   0.226346    1.000000  -0.104273   \n",
       "total_acc             0.680728  0.019723   0.191616   -0.104273   1.000000   \n",
       "mort_acc              0.109205  0.011552   0.194925    0.007514   0.381072   \n",
       "pub_rec_bankruptcies -0.027732  0.699408  -0.124532   -0.086751   0.042035   \n",
       "\n",
       "                      mort_acc  pub_rec_bankruptcies  \n",
       "loan_amnt             0.222315             -0.106539  \n",
       "int_rate             -0.082583              0.057450  \n",
       "installment           0.193694             -0.098628  \n",
       "annual_inc            0.236320             -0.050162  \n",
       "dti                  -0.025439             -0.014558  \n",
       "open_acc              0.109205             -0.027732  \n",
       "pub_rec               0.011552              0.699408  \n",
       "revol_bal             0.194925             -0.124532  \n",
       "revol_util            0.007514             -0.086751  \n",
       "total_acc             0.381072              0.042035  \n",
       "mort_acc              1.000000              0.027239  \n",
       "pub_rec_bankruptcies  0.027239              1.000000  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Visualize this using a heatmap. Depending on your version of matplotlib, you may need to manually adjust the heatmap.**\n",
    "\n",
    "* [Heatmap info](https://seaborn.pydata.org/generated/seaborn.heatmap.html#seaborn.heatmap)\n",
    "* [Help with resizing](https://stackoverflow.com/questions/56942670/matplotlib-seaborn-first-and-last-row-cut-in-half-of-heatmap-plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2400x1200 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CODE HERE\n",
    "plt.figure(figsize=(12,6),dpi=200)\n",
    "\n",
    "sns.heatmap(df.corr(),annot=True,cmap=\"viridis\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 0)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OaZp22Pz9AaDGTbbfSCm1Uyl1DBiMqYP6r9Wa6XTpGBCjadoxTdOMwIk828wE1tzmcwGgadpXmqa10jSt1cghBefD/L85GR5NlcrOVPZwxEavo0eH+uzaX7R3Bpfk9AEAPz8jkZE6Ll9WZGXBli02dGhvOfcyJUVhNPenv/+hHL17m0Yw09JMUwj+LXP8uJ7q1Yu34308Mprqbi74ujhiq9fRu3F9toaetShT3S338nDXerWISDBdQhvy9S/0nLOYnnMWszzoEF9t38cPe48US85TJyLxqeaGl68zNjZ6uvZqTPB2y99r8LZQevQ3nR927tmQI/vO3da2A3qX7OjrybPRVPVyprK7qV33bOfHjkOWdV6vugevDuvBy5/8SZKV54rmt3rxNsZ2e5ux3d4maO1hAh9tD4Bfy5pcSc0gMcayA5sYk0JG+jX8WtYEIPDR9gStO5yzvHlXfy6GRRN/OfdSa+ylRJp2rg9A+Yrl8GtVk0tnoq36Ogq05Sa3aMv1c9uyr4sjep1pHMPH2YGa7i5EJpXsIEHoqcv4+rrg7e2EjY2O7l392RNU/HdNKYp/zpvaro+57d7Xxo8dRyzruH5VD6YO6cFLn1m2XRu9jg/H9uevoH/YfOCMZM5n9ZebeL7ddJ5vN509qw/Q48lOAPi1qc3V1KuFznX9f2e04ldpuJNJgDe69joY8ABaapqWpZQ6D9iZl+V9a6EBuNls7aXAAE3TjiilhgEBeZb9ux1jvm0ayX1NWVruNQEDd8EdFybNgH2HITkFAgbBuOEw6IHSy2Mwany8eAufTHsYvU7Hmq3HOXcpgWcf7UBoeAy7DoTjX9uL2ZMfxKGSHZ1a1mbEox14atK3AHh7OOLl7sChfy6WWGa9HiaMv8aUVypiNEDv3lnUrGlk8ZJy1K9noGNHA4cP61n0dXmUgiZNsnlhgqmJRETo+PgTO5QyXTp84olMi7sXFAeDUWPWmi0sGjoQnU6x8sAJwmITGBfYnhORMWwNPcuTbZvRvnY1so0GUjKuM/X3DcWaqTBGg5GFs9cw6/Oh6HQ6/v7jIBHhsQx5vjtnTkQRvD2U9SsPMmXWwyxePZG01AxmT/klZ/1v175ERfvy2Njqad/Nn2mjv825g0GX+xrx+tjlJfZaDEaNj5ZtZd6Uh9EpxeodxzkXmcDIgR04eS6anYfOMv7xLlS0s+XdcX0BiE5I4+VP/yz2bPs2HqN1j0YsDpnF9YxMPp6wNGfZgq2vM7bb2wB89vL3TPpsGOXsyrF/83FCNh3PKRfwUGu2rbC83d3qxduYNG8YX+56CxRs/HEP5/6JtGp2g1Fj1uotLBo2EJ1SrDxYSFtul68t/2Zqyy2q+/Jcl9ZkGw0YNY23V20h+eo1q+a7FaNRY978jXzw7qPodIp1G45xPiKe4U934tTpaPYEh1G/njdvvzkQe4fytG9Xh+FDOjF85DclltFg1Pjwh618NvFh9DrFqt3HORuVwKgHO3DyfDQ7jpxlwiNdqGBny3ujTW03JjGNl+b/Sc/W9WlR1xenSnb07WAa75mxZAOnL8bd7Cnvycz71h+h9f3NWHLiI65fzWTOqNy7eywMfofn200HYMSsx+n2WHvKVyzHd2FzWb9kG9/NWkm9ljV54+eJODhXol2fZjw9fSAjW75WrJmLg+GG3biyQRV13odSKl3TNHul1EBgFNAHcAX2A22Bx4A6mqaNV0p1A7YANc2rr9E0rZF5O5MBe03T3rrB88RjerNVErAWiNQ0bZh5nusaTdN+U0rVyLfNvMvSNU2zNz8+COhrXv8z4KCmaUtu9jqN0fXK3ISYThNGlXaEIvntkzmlHaHIenz+cmlHKLJqqxNvXeguktTU5daF7jIua0vuKoQ1XBjlf+tCdxmvkMxbF7rLpFWzLe0I//fcvj9U2hGKbEPG8rui53jmko/V+jl1q0SV+Gu6k1HJlUB74Aime6VM0TQtWin1PbBaKbUfOAz81yP768BeIALTVAFr3YDqJ2CRUmoCMCj/PFghhBBCiP93Zf0uYEXuwP47qmm+PP+y+Svv8nhMHdvCNMpT7qMblPl3+efA54U8PizP9+fzbTPvMvs83/8G/Gb+fjdyGy0hhBBC3MPK+hSCe/E+sEIIIYQQogwr9Tc2KaUWAB3zPTz3VnNUhRBCCCHEf1PWR2BLvQOradrY0s4ghBBCCHEvMWpluwMrUwiEEEIIIUSZUuojsEIIIYQQomTJFAIhhBBCCFGmGMr4RXjpwAohhBBC3GNkDqwQQgghhBAlSEZghRBCCCHuMTIHVgghhBBClCkGrWxfhC/b6YUQQgghxD1HRmCFEEIIIe4xxjI+hikd2BvoNGFUaUcosl3zviztCEXSefyk0o5QZNV3hZV2hCLLrO9b2hGKxOVwYmlHKDJD/WqlHaFIqv8WU9oRiuxSX6/SjlBkVX6/UNoRikRLTS/tCEVmaF6/tCOUWWV9DmzZ7n4LIYQQQoh7jozACiGEEELcY8r6m7ikAyuEEEIIcY8xyhQCIYQQQgghSo6MwAohhBBC3GMMZXwMUzqwQgghhBD3mLI+B7ZspxdCCCGEEPccGYEVQgghhLjHyAcZCCGEEEKIMsWgyV0IhBBCCCGEKDEyAiuEEEIIcY+RuxAIIYQQQogyxSh3IRBCCCGEEKLkyAisEEIIIcQ9RqYQiBxtm9Zg4vBu6HWK1ZuPs/zPfRbLm/n78sLQbtSu7sGbn65h694zALRoWJUJQwNyylX3ceXNuX+xIySsJOMXMO092BYEri6wemmpRrHQtpmpnnXmev7uD8t6burvywvDcut5W/CZnGVe7g68Ovo+PN0c0IDJ764gOi61WHKOfvsRWgc25HpGJnMmLif82MUCZeo0qcpLnw6hvF05Qjaf4IvXfwVg8KQ+9BrckZSEdAC+nb2KkC0n6DawNQ+P6ZGzfs0GPoy/733Onrh0x3lbt6nF2HE90ekVa/86wk8/BFkst7XV88pr/ahX35vUlAzenvkHMdEpBPZoyKOPt8spV6uWJ6NHfkN4WCwB3fwZ/FRHdDrF3uAwvvpy6x3n/FfLjnUZ80ofdDod61cc4JfFOwrknTxrEHUb+JCacpXZL/9MTFQyDk4VmD7nCeo18mXjn4dYOHsNAOXtbJn20eNUruqK0WAkePsplsz922p5AVq1rcXzE+9Hp1OsW32Yn7/bUyDzlNf7U7d+ZVJTMpj1xgpiolMAqFnbk4lT+lCxUnk0o8bYZ78hK9PA8JEB9OjVBAcHO/r3/MCqef81elo/Wnetz/VrWcx59VfC/4kqUKZOQ19emv0I5e1sCNl+ii9mrQagU6/GPDWuB1VrezDxkQWcOR4JgINzRabNG0y9RlXYuPIAn7+9yuq5O9avzisDAtDrdKzYe5xvtoRYLH+6SwsGtm2EwWgk8UoGb/z8N5eT0gB48YFOdPavCcCXm/ay4fBpq+fLa/SbD9G6mz/XM7KYM/lHwgvZp+s0qsJLHz1BeTtbQrae5IsZKwEY8lJv2vdshFHTSIlPZ87kH0iMNR3XGrerzag3HsLGRk9qUjpTHltgtcxjZj9O656NTce4sUsIO3qhYOam1Zi0YLjpGLfxGJ+/9hMAtRpVZfzHT1GuvC2GbAPzX/6e0wfP0653U4ZOHYDRqGHINvDl1J85sffO/xbm7Ht68763/Ab7np9533vdtO95eTvxzY+juRSRAMDJE5HM/XAdFSqW45OFT+es7+7pyOYNx/h87sY7zlpSyvpdCKQDayU6pZg8IpAX3vmN2IQ0vpk9mJ37wzgfmZhTJjo+jXcWrufJfq0s1j144iLDpiwHwKGSHb9+9gx7j5wvyfiFGtAbnhwIr75b2kly6XSKSSMCmfj2b8QmpvH17MHs2h/G+Uu59RwTn8asBet5on+rAutPH9ebZSv2EnI0ggp2thiNWrHkbN29IT61PBjR4S38WtRg3HuP8+IDHxYoN+69x5n38o+EHjjHzO+fp1X3Buzf8g8Af3y1hd+/2GxRfuuKELauMP0RruHnwxtLR1ml86rTKSa8cD9TJv9IXFwqC78YTtDuM0RExOeU6d2nKenp13h68Bd0696A50Z2452Zf7B50wk2bzoBQM2aHsycNYjwsFgcHSswcnR3xoxcQkrKVV55tS/NW9Tg0MHzVsk7dmo/po5cQnxMKvN+HE3wtpNcOBuXU+b+gS1JT83gmb6f0LVXY56ZeD+zp/xMZmY2yxZspnodT2rU8bLY7m/f7uJoyDlsbPS89/VwWnWqy/5dZ/I//X/OPH5Sb16Z+D3xsanM/3oEQbtOc+F8bh336tuM9LRrDHtsIQGBDXj2+e7MemMlOr3i1Tce5P23/+RsWCwOjhUwZBsBCN59hj9/38/Sn563Ss78Wnepj08Nd0bc9xF+Tasy7q0BvPjowgLlxr01gHlvrCD08AVmLhpOqy712L/jNBGno3l7/HImzBhoUT7zehbL5/5N9breVK/rVWB7d0qnFNMGdmfklyuITknjp4lPsvVEOGdjco8VJyNjefzTH7iWlc2j7ZvwUt/OvLx8LZ39a+JfxZNHPv6OcjZ6ljz/KLtOnufK9Uyr5wRoHeCPT00PRgS8i1/z6oybNYgXB3xaoNy4dwYxb+ovhB6MYObSkbQK8GP/tlB+/2oLyz9eB0D/YZ158oX7mT/tVyo52jHu7UFMH/olcVHJOLnZWy9zj0b41PbkmVbT8GtVi3FzBjOx5+wC5cZ/9BTzXlzOyZCzvP3LBFr1aMT+TccZMeNhvv9gNfs3Had1j0Y8+9YgpvT/iMM7QgleNwOAmg18mbp4FM+1e+OOsup0ivGTe/PKC+Z975sRBO3Mt+/1M+97jy4koEfuvgcQFZnE6GFfW2wz42qmxWMLFo9g1/ZTd5RTFE2JjR8rpfbcRpmJSqmKVnq+YUopH2ts63Y0qOPNpehkomJTyDYY2bTnFJ1b17EoEx2XSviFeIzajTtN3dvVJejQea5nZhd35Ftq3RScHUo7hSX/vPWcbWTz7lN0blV4PWv56rlGFVf0eh0hRyMAyLiWVWz13K5XEzb/uheA0IPnsXesgIuno0UZF09HKjrYEXrgHACbf91L+15Nb/s5uj7Uiu1/7LdKXj8/HyIjk7h8OZnsbCNbt/xDh451Lcp06FiPv9cfA2D79pO0aFmjwHa6BzZg62ZTB7xyZWcuXUokJeUqAAcOnKdzl/pWyVu/URUuX0ggOjKJ7GwD29cfo303f4sy7QP82bTqEAA7N56gWdtaAFzPyOLEoQiyrlv+7q9fy+JoiOl3kZ1tIOxkFO5eTlbJC1Df34eoS4lER5nqeNvmE3ToXM+iTIfO9fh77VEAdmw7SfOWphHAVm1qcTY8lrNhsQCkpWbknHydPBFJonmkvji0C2zA5j8OAhB65KKpLXtYHhhcPByoaF+e0MOmEbjNfxykfWBDAC6ejSPyXDz5Xc/I4sSBCDKvF88+2LiaNxcSkrmUaDomrzt0im4Na1uUCQm/xLUs0/MfvXAZLyfT66rt5cr+8EsYjBoZmdmcioqjk1+NYskJ0O6+Rmw2n5iGHorA3qECLh75jhce5uPFQdPxa/OKENrf1xiAq+nXc8rZVSwH5mNfQP+W7F5/lLioZICcKzrW0L5PMzb/FGzKvP8s9o4Vcc23v7h6OVHRwY6TIWdNmX8KpkOfZqaFGlR0sAOgkmNFEqJNGa9dyfNaKpXHGkMM9Rvk2/c23WDfW2fe97aepHmrmre9fd8qLji7VOLY4YIj0HczIzqrfZWGEntWTdM63EaxicBtd2CVUvqbLB4GlFgH1sPVnpiEtJyf4xLS8HAt+tluj45+bNwdas1o/1c8XO2JzVPPsYlpeNzmqELVyq6kX7nGu5P7s+SDIYwd0gWdrnguobh5OxFv/qMBEH85GffKzhZl3Cs7Fyjj5p37B6DfM11ZuHkqL378FPZOFQo8R9f+Ldi20jodWHcPB+LyTKWIi0vDPV8nxd3DgVhzGaNB40r6dRzz5Qro1oAt5hHkyMgkqlVzw8vbCZ1e0bFTPTzzdeL/KzcvR+JiUnJ+jo9JxS3ftvOWMRqMprzOt3d4qeRgR9uufhwODrdKXjDXcWxuHcfHFqxjt5zjV+QAACAASURBVDxljAaNK1dMdexb1Q00mP3xEyxcPIJHn2xvtVy34ublSHx0nnYanYK7l2Vdu3s5Eh+dYlHGzcs6v+v/ytPJnujk3GNFTEo6Xk43PlYMbNOIXaGmExhTh7UmdrY2OFeyo02dqng5W2/0Mj83r3zHi+hk3L0tO4Pu3k7EX85Tx5dTcMvTYRw6uQ/L9rxBtwdb5ozGVqnlgb1TRd7/aSzzVr9E4MCCV6X+c+bKLsTlucIYF5WEW75jnFtlZ+KjkvKVcQHgi6k/8eyMQSw/9j7PzhzEkpkrcsp1eKA5i4JnMvOnCXwyfukdZ3X3cCAuJs++V8jxzS1Pmbz7HoB3ZWc+X/oscxYMoVHTqgW2361nI7ZvPnHHOUuaQdNZ7as0lOQIbLr5/wCl1Dal1G9KqVCl1PfKZAKmDudWpdQNJ8oppdKVUjOVUnuB9kqpN5RSIUqp40qpr8zbGgS0Ar5XSh1WSlVQSrVUSm1XSh1QSm1QSlW28gss8NBNBloL5eZciVrV3O+K6QN3K8V/r2e9XtHUvwrzl23n2Ve/w8fTiT4BDa2c0EQV2h4sgxbadTYX+evbnTzT7k3G9phNYmwKz735sEWx+s1rcC0jk4hTl62UuJAo+er1ZnkB/Px9uHY9i/PnTJfx09OvMffj9bz+xgDmzhtCTHQKBoPRKtkKy3I79Zu/TGF0eh2vvv8of/4QRHRk0i3L367C28TtldHrdTRsUpXZM/7gxTHf0rFrfZoXMgJeHP5rbqsMnd2BQn//NwjVt4UfDap6sWTrAQCCTl9g58lzLB//GB881YcjEVEYimm6Edzm8aLwF5Tj24/W8nSHmWz98wD9hnYGTG25buMqvDF8EdOf/pInxt+Hb00PK2UuJE6BzDd+XX2HB/DltF8Y0vgVvpz+Cy/OG5pTZs9fh3iu3RvMeGoBT7/24J1nvY2/Gzcqk5iQzuCHPmPMsK/5Yt5GXnvrISpWLGdRLqBHA7ZuLHsd2LKutObANgcaAlHAbqCjpmnzlFIvAd00TSt4vSlXJeC4pmlvACil/tE0bab5++VAX03TflNKjQMma5q2XyllC3wGPKhpWpxS6jFgFvBM3g0rpUYCIwFqtRyEV6123K64hDS83HLP6DzcHIhPKtrlmsD29dixL8xqf+T/H8UmpuGZp549XR2IT7y9eo5LSOf0uViiYk2jGDtCwmhY1wc4bpVsfYd1odfgjgCcPhKBu0/uaIR7ZWcS8oxQAcRdTi5YxjximByfO3K07rvdzFg+xmLdrgNasv2PA1bJDaYRCY88lyw9PBxIyJMBTKOynh6OxMelodMrKtmXJzU1I2d5t+650wf+FRQURlCQ6Q0YD/RthtFonbYdH5OKR57RJ3cvRxLj0gotEx+Tik6vo5J9edJSMvJvqoAX3niQqIgE/vgu6JZliyIuNhWPPKPE7p4F6zjeXCanjiuVJy01g/jYVI4djiDVnH9fUBh16ntz6MB5q2b8V98n29Hr0TYAnD52CXdvZ8B06drd24mEWMs3PsZFp1iMGBZWpqTFpKTjnWcOlJeTPbEpVwqUa1e3Gs/1aMPwhb+SZTDkPL5o8z4WbTa9QfT9wb25EGe9kxmAvkM60usJ00j66SMXLI8F3s4kxOSr48spuFfOU8eVnUiItTymAGz78yAzFj/Hd5+sJz46hdSkK1zPyOR6RibH94VT09+HyHNxBda7Hf1GBNDr6S6mzIfO4eHrmrPMw8eFxHzHuPioJNx9XPKVMY0093iifc4bunb+sZ+Jc58mv+NBZ6hc0xNHV3tSb/M4X5i4uFQ88lwRcC/k+BZvLpN/3wPIyjL9f+ZUNJcjk6hSzY3ToabBg1p1PNHrdZw5Ff2f85UWY+HDEmVGad1DYZ+maZc0TTMCh4EaRVjXAPye5+duSqm9SqljQHdMHeP86gONgI1KqcPAdKBK/kKapn2laVorTdNaFaXzCnAyPJoqlZ2p7OGIjV5Hjw712bW/aJcfZfrArYWGmevZ0xEbGx2BHW+/nk+GR+NQqTzOjqbLQi0bVeP8pQSrZVuzdAfjes5mXM/ZBK07QuAjbQHwa1GDK2kZJOX7g54Um0pG+nX8WtQAIPCRtgSvN83ByjtftkOfpkSE5r7rWylF577NrTb/FSD0VBS+VVzw9nbCxkZHt+4N2LPH8s1LQXvOcF8v05y7rl39OWSei2fKBF0D/Ni6xbID62y+ZG9vb0f/AS1Z+9cRq+Q9dSISn+puePm6YGOjp2uvxgRvs9x3greF0qN/cwA692zIkX1nb7ndoeN6UMnBji8+WGuVnBaZQ6PwreKKd2VnbGx0BAQ2JGiX5Tvbg3ad5r4+TQDoEuDPYXMHdf++s9Ss7Un58jbo9IomzaoTUci8UmtZ80Mw4wbMY9yAeQRtOkHggBYA+DWtypW0ayTlO1lIiksj48p1/MyXVwMHtCA438lMSTt+MZrq7i74upqOyb2b12fbCcs24OfrwRuDAhm/eBWJ6bknNzqlcKpomp9Zr7I7dSu7s+d0BNa0ZvluxvX5iHF9PiLo7+MEDmxtytS8uul4ke/uKElx5uNF8+oABA5sTfDfppNvnxruOeXa9WjEpXDTXOngv4/RqHUtdHod5e1sqd+sOhfDYv5z5tXfbGNs15mM7TqToL8OE2i++4hfq1pcSc0gMcayA5sYk2LK3Mo0/zzw8XYErT0MQEJ0Ck06muahNuviR5Q5c+U8I8R1mlTDxlZ/R51XgFMn8+17PQrZ93ae5r7e5n2vW+6+5+RcMWeqmbePM75VXbic58pMt54Ny+zoa1mfQlBaI7DX83xvKGKOa5qmGQCUUnbAQqCVpmkXlVJvAXaFrKOAE5qmFdvEMYNR4+PFW/hk2sPodTrWbD3OuUsJPPtoB0LDY9h1IBz/2l7MnvwgDpXs6NSyNiMe7cBTk74FwNvDES93Bw79U/BWS6Vl0gzYdxiSUyBgEIwbDoMeKN1MBqPGJ99s4eP89fyYuZ73h+NX24vZL5vquWPL2jz7aAeeeulbjEaNBct3MPeNR1AKTp2NYdXmo8WSM2TzCVoHNmRx0Ftcy8jkkxe/y1k2f+NrjDO/W3f+qz+Zb6NlS8iWfwjZYjoQjnj9IWo19AUNYi4mMG/KjznrN2pXh/jLyURfsF7n22jQ+Gzu37z/4ePodDrWrTtCxPl4hg3vwqlTlwnac4a1aw/z2tT+LPt+NGmp13hn5h856zdpWo24uDQuX0622O7Y8T2pXdv0DvPly3ZxKc/dIu4sr5GF765h1udD0el1/P3HASLCYxnyfCBn/okkeFso61ceYMq7g1i85kXSUjKYPeXnnPW/XTeJivblsbHV0767P9NGLeXqles8MTKAC2djmf+z6R39q38KZv0K64x0Gw0a8z9Zz+yPn0Cn17FhzWEizsUz9NmunA6NImjXGdatOcyrrz/I0p+fJy01g1lvmt4FnZ52jd9/2sv8b0agaRr7gsLYZx7Zfvb57nTv2Yjydrb8sHIC61YfZnm+W4rdiZDtp2jd1Y/FG1/mWkYWn0z9NWfZ/D8mMG7APNP3b/1hvo2WLSE7ThGyw/SO7A49GjLm9f44uVZixpfDOHvyMtOfXQzA0s2v5PweOvRoyLRnvuGCuSNzpwxGjXdXbOGLkQPRK8XKfScIj0lg7P3tOXEphm0nzjKpbxcqlrdlztOmA9vl5DQmLF6FjV7Ht2MfBSD9eiav/bC+WKcQhGz9h9bd/Fm8fZrpePHyTznL5q+dzLg+H5m+n/5b7m20tp0kZNtJAIa/0pcqtTzRjBqxkUl8Ns30O7oYHsv+7aF8vv5ljEaNDT8HE3HaOiOF+zYeo3XPxiw+MIvrGZl8PG5pzrIF299gbNeZAHw2+TsmLRhOOTtb9m86TsgmU6d77gvLGD37cfQ2OjKvZzH3xWUAdOrXkh6Ptyc7y0DmtUxmj/jqjrMaDRrzP17P7E9use+98SBLfzHve+Y7EDRuVo2hz3bFYDBiNBqZ+8E60tKu5Wy7a/cGTJv8042eWhQjdTtzwqzyREqla5pmr5QKwHRpv6/58fnAfk3TlppHUftrmnbuVtsxf+8MnMI0gqsHgoHfNE17Sym1GvhY07StSqlywD/AEE3TgsxTCuppmnbD06YOj84p5RlcRbdr3pelHaFIOo8fVdoRisxx161H8e42mfV9SztCkdgmXi3tCEVmcCjsvPnuZRNffHctKC6X+lr/VlvFrcrvZetd6Vpq2WsXBr/qpR2hyDbumX5XXLtfeKqb1fo5z9ffWuKv6W67D+xXwDql1GVN07rdqrCmaclKqUXAMeA8kPcu1UuBL5RSGUB7YBAwTynlhOl1fwqUzXF/IYQQQog7YJQPMrg9/46aapq2DdiW5/Fxeb7/DNObrW65nTw/T8c0pzV/ud+xnCt7GOhS9ORCCCGEEOJuUrY/CFcIIYQQQhSZAZ3Vvm5FKdVLKXVKKRWmlHq1kOXVlFJblVKHlFJHlVJ9brXNu20KQQ7zfV7L53t4iKZpx0ojjxBCCCHE/wtjCd09wPyhUwuAnsAlIEQptUrTtLy3KpkO/KJp2udKqQbAWm5xh6q7tgOraVrb0s4ghBBCCCHuSBsgTNO0swBKqZ+ABzG9uf5fGvDv/SOdMH1OwE3dtR1YIYQQQghRPAwl90EGvkDee4ReAvIPUr4F/K2UGo/pA6t63GqjMgdWCCGEEOIeY9R0VvtSSo1USu3P8zUyz1Pd4oOQAXgCWKppWhWgD7BcKXXTPqqMwAohhBBCiP9M07SvMN0KtTCXgKp5fq5CwSkCI4Be5m0FmT+oyh244SebyAisEEIIIcQ9xoCy2tcthAB1lVI1zR8s9TiwKl+ZC0AggFLKH9OnqsbdbKMyAiuEEEIIcY8pqbsQaJqWrZQaB2zA9KmpizVNO6GUmonpk1hXAZOARUqpFzFNLxim3eKjYqUDK4QQQgghio2maWsx3Ror72Nv5Pn+H6BjUbYpHVghhBBCiHuMoYRGYIuLdGCFEEIIIe4xxpK7jVaxKNvdbyGEEEIIcc+REdgb+O2TOaUdocg6j59U2hGKZOdnX5Z2hCJr9NmY0o5QZNXWJJZ2hCJJbuJa2hGKzGXjmdKOUCQXhtcr7QhF5n4sq7QjFFlC16q3LnQXUcbSTlB0ziuPlHaEMkumEAghhBBCiDLFqMkUAiGEEEIIIUqMjMAKIYQQQtxjDGV8DFM6sEIIIYQQ9xiZQiCEEEIIIUQJkhFYIYQQQoh7jLGMj2FKB1YIIYQQ4h5jkCkEQgghhBBClBwZgRVCCCGEuMeU9TdxSQdWCCGEEOIeY5RP4hJCCCGEEGWJgbI9Alu2u99CCCGEEOKeIyOwQgghhBD3GJkDK4QQQgghyhSZAyty7NunZ/58OwxGeKBPFk8+mWmxPDpa8cGHdqSkKBwcNKZNvYaHhwZAYA97atY0AuDlqTFrVkaJZG7brAYTh3dDp1Os3nyc7/7YZ7G8qb8vLwzrRu3qHrz56Rq2BZ/JWebl7sCro+/D080BDZj87gqi41JLJPeNTHsPtgWBqwusXlqqUXJ0qlud1x4IQK/T8dv+43y9I8Ri+WNtmvBE26YYNSNXrmfx1h+bCI9LpHEVL2YM6GEupViwJYjN/4QXW86WHeow5pUH0OkU61ce4JfFOy2W29rqmTzrYer6+5CacpXZU34hJioZB6cKTJ/zOPUa+rJx1SEWzv4rZx0bGz3Pv/YATVrXRDNqLP1sE7s3/2P17O2a1ODFIQHodDpWbTvG8tWWddysvi8vDgmgdlUPXp//F1tDctvx2Mc606FZTQCW/BHMpr2nrZ4vr9GzHqV1YEOuZ2QyZ8Iywo9dLFCmTpNqvDTvacrb2RKy+QRfTPslZ1n/EQH0eyYAQ7aBfZuOs/jtlehtdEz8eAi1m1RFr9ex+de9/DJvg9Wzd6pXnVf7BaBXOn4POc7X2y3r+dG2TXiifVOMRiNXM7N4a8UmwmMTaV+nGi/26oStjZ6sbANz1u1kb3jB121tbVrWZPzIQHQ6xV9/H+WHX/daLG/SsArjRwZSq6YHM99fxfbdpt99nVqevPR8TypWLI/RaGT5z8Fs3Rla7Hnza9e4BpMGm9r1n9uPsewvy/p+8v4W9O/aGIPRSHJqBm9/s4HohLRSyflSnv1v2ZpC9r+nAqhT1YPXF/zFlnz7X0fz/re4mPe/MR8Ops19TbmWkcmcUYsIOxJRoEydZjWY/OWzlLcrx76/j/D5y98DUKtRVcbPHUYF+/LERMTz/ogvuJp2DRtbPS/MG07dFjXQjBqfT/meo6XQVu5Ft+zAKqX2aJrWoagbVkoNAE5rmnbTv1ZKqbeAdE3TPlJKLQXWaJr2W1Gfrwi5hgF/a5oWZc3tGgwwd64dH354FQ8PjdFjKtKhQzY1ahhzynzxRXnuuy+LXvdnc/CgnkWLyjN16jUAypWDrxddtWakW9LpFJNGBDLx7d+ITUzj69mD2bU/jPOXEnPKxMSnMWvBep7o36rA+tPH9WbZir2EHI2ggp0tRqNWkvELNaA3PDkQXn23tJOY6JRier/uPLtkBTGpafw85km2ngwnPC63jtccCeXnfUcB6OZXiyl9ujLq25WciUngkYU/YDBquDtUYuW4p9gWehZDMdSzTqcYO7UfU0ctJT4mlXk/jCZ4WygXzsbllLn/oZakp2bwTL9P6dqrMc9MvI/ZU34hMzObZQs2U72OFzXqeFps9/HnupKSeIVn+89FKYWDUwXrZ1eKyUO7M+G934lNTGPJzMHsPBDO+ag87Tghjbe/3MCTfSzbcYdmNalfw5Onpy3H1lbP59MeZc/R81zNyMz/NFbROrAhPjU9GdHuTfxa1mTcB0/wYu8PCpQb98ETzJv8PaH7zzHzh3G06t6Q/VtO0KRjPdr1asrz3d4hKzMbJ3cHADr3b4lteRueD3iH8hVs+XLHm2xbGULsxcQC2/6vdEox7cHuPPfNCmJS0vh5nLktx+Y+x1+HQ/llr7kt+9diygNdGbVkJUlXMxj77Z/EpV2hjpcbXz0zkO6zF1ktW6F5dYqJY3owafovxMWn8eUnT7M7OIyIiwk5ZWLjUpn9yVoeH9jaYt1r17KY9fFaIqOScHO1Z9Hcpwk5eI70K9eLNbNFfqWY8nR3xn1gatffvjWYnYfCOZenXZ+KiGPoW99zPTObh7s3YfxjXZi28K+bbLV4cr48tDvj3zflXDpzMDsPWuaMSUjj7a82MDjf/texqWn/G2Le/76Y+ihBR85z5Zr197/W9zXBt7Y3w5tOwa91bcZ/OpQXus0sUG7Cp0OZO34JJ/eF886KSbTq2YT9G48yccEzLJr2E8d2neK+IZ0ZNLEPy95eQe/hAQCMbjsdJw8HZq2YzPgub6Fppf/38FaM/+9v4vovnVezAUCD/7hucRoG+Fh7o6GhOnx8jfj4aNjaQvfu2ezeY3l+cD5CR8sWBgCaNzcUWF7S/Ot4cyk6majYFLKzjWzefYrOrepYlImOSyX8QnyBnbFGFVf0eh0hR01nsBnXsriemV1i2W+kdVNwdijtFLkaV/HmQmIyl5JSyDIYWXf0FN39a1uUuXI992BdoZwtYKrra1nZOZ3V8jZ6NIrvgFi/URUuX0wgOjKJ7GwD29cfo32Av0WZ9t382LTqMAA7N56gWZtaAFzPyOLEoQtkXS/4+79/QAt+WrwDAE3TSE22/klag9reXIpJJiouhWyDkY3BoXRpaVnHl+NTCbtYsB3X9HXjUOglDEaNa9ezOXMhnvZNalg947/a9WrK5l+DAQg9cA57x4q4eDpalHHxdKSivR2h+88BsPnXYNr3bgrAA0O78MtnG8gy72sp8abRNk3TsKtYDp1eRzm7cmRlZXM17ZpVszeu6s3FhGQuJZra8tojp+jW4OZt+d82GxoVR1zaFQDCYhIob6PHVq+3ar78/OtVJjIqmcvRpuPblh0n6dQu3/EtNpWz5+Mw5msXl6KSiIxKAiAhMZ2k5Ks4OVUs1rz5Naxl2a7/3htKlxaW9X0g9GLOcfdY2GU8Xe1LNCMUbf/LX88F9r+L8bQrpv2vfd8WbPpxNwChIeFUcqqIq5eTRRlXLycqOtpxcp/pStemH3fToV8LAKrUrcyxXacAOLTlBJ0eNHXGq/n5cGibaZwuJS6N9JQr1GtRs1heg7UZNGW1r9Jwyw6sUird/H+AUmqbUuo3pVSoUup7pZQyL3tPKfWPUuqoUuojpVQHoD/woVLqsFKqtlLqOaVUiFLqiFLqd6XUTY8GSqnzSql3lVJBSqn9SqkWSqkNSqlwpdToPOVeNm/3qFJqhvmxGkqpk0qpRUqpE0qpv5VSFZRSg4BWwPfmXFYbDoqP1+HpmTva6uFuJD7O8pdau7aR7TtMndadO224elWRkmJalpkJo0ZX5PmxFdm1q2Q6th6u9sTmudwUm5iGh9vtHQCrVnYl/co13p3cnyUfDGHskC7odGX7bK44eDnaE52SW8fRqel4OhWs4yfaNmX9S8OZdH9n3l2zLefxJlW8WTXhaf4cP4QZf24ultFXADdPR+KiU3J+jo9Nwc3L4YZljAYjV9Kv4+h84924koMdAEPHBjL/pzFM+/AxnF0rWT27h4s9sYl523E6Hi63dxZzJiKO9k1rUL6cDU72drRsUAUv1+I7A3Kr7Ex8ZFLOz/GXk3Cv7GxRxr2yM/GXk3PLRCXjZi7jW9uTRm3r8Mm6KXyw8kXqNasOwK7VB7l2NZMfjr7HsoOzWPH5JtKtfLLg5WjP5TxtOSYlHS/HQtpyu6ase3k4L/XuzLurthVYfl+jupyMiiPLYLBqvvzc3eyJjc/NGxefhrtb0X+3fvW8sbXVE3U56daFrcjDxZ6YIrTr/l0bE3T0fAkks+RZxJx5nbkQR/smefY//yp4/Yff0e1wr+xC3KXc0ff4qETcfFwsyrj5uFjun5GJuFc2lYn45xLtH2gOQOeHWuPh6wrA2WMXad+3OTq9Dq/q7tRtVgOPKq7F8hqEpaLO4G0OTMQ0sloL6KiUcgUeAhpqmtYEeEfTtD3AKuBlTdOaaZoWDqzQNK21pmlNgZPAiNt4vouaprUHdgJLgUFAO2AmgFLqPqAu0AZoBrRUSnUxr1sXWKBpWkMgGXjYPDVhPzDYnMtqE00Lu1qg8vXnxoy+ztEjep4bWZEjR/W4uxv5dxDi55+u8OUXV5k+LYP5C8oTGVn8nUFVyOWD273qodcrmvpXYf6y7Tz76nf4eDrRJ6ChlROWffnbAFBoJf+49wi9Pl7Cxxt2Miqgbc7jRy9F03/eMh79/Eee69qGcjbFM2pVWM78MVUhhW52mUyv1+Hh7cSJQxcY9/jnnDx6kecm9brTqAUUWse3OVq973gEew6fY9Gbj/P22Ac4fuYy2UbjrVf8j26nORRWz/8W0tvosXeuyIu9P+DrmSt4bdGzANRvXgOjwcjgpq8yrPXrDBzdA+/q7sUevrCrAj8GH6H3h0v4ZN1ORndva7GstqcbL/buxIyVm6ybrRCF1mMRr2K4ulRi2qS+vPfJ2ts+NlrL7R47AHp18Me/hhfL1+4v3lCFKbS53l5l7T0ewZ4j5/j6DdP+dyzsMgZDMe1/d3iM+/j5b+g3sgfzd86ggkMFsjNNJ2Ablu0gPjKJ+TvfYsz7g/lnbxiG7OI9ObMWo6az2ldpKOpQ3z5N0y4BKKUOAzWAYOAa8LVS6i9gzQ3WbaSUegdwBuyB23mHwSrz/8cAe03T0oA0pdQ1pZQzcJ/565C5nD2mjusF4JymaYfNjx8wZ70ppdRIYCTA++858dRTt3/JyMPDSGysbc7PcfE63Nwt9w53d42ZM02X9TIyYMcOG+ztc5cB+PhoNGtmICxMj69v8V6Sj01MwzPP2a6nqwPxiem3tW5cQjqnz8USFWsakdsREkbDuj7A8eKIWmZFp6Tj7ZRbx96O9sSmXrlh+bXHTvHGg4Hwu+XjZ+MSycjMoq6XOyciY6yeMz4mFQ/v3Mtp7p5OJMam5SuTgoe3E/Gxqej0OirZlyct5cbngKnJV7mWkcmeLScB2PH3ce5/qKXVs8cmpuPpmrcd2xOXdHvtGGDpqn0sXWV68+KM5/twMTr5FmsUTd/hXen1VEcATh+OwN03d9THvbILCfmeLy7KclTW3ceZBPPId3xUErv/Mh3uTh+KQDNqOLnZEzCwDfu3nMCQbSQlPo1/QsKp27Qa0RHxVnsdMSnpVM7Tlr2cbtGWj57i9YcC4VdzeUd75g3px9RfNnAxMeWG61lLXHwanu65eT3cHYhPuP12UbFCOd5/axDfLN/JP6cuF0fEm4pNTLe4GuDpak9ccsH8rRtUY3i/Nox+9xeySqHjVFjO+EJy3kje/W/mmD5cjLHe/tdvZCC9h3UF4PSBc3hUcQNMbyBz93ElMd+oenxkouX+6euas39ePH2ZqQ9+CIBvHS/a3m+a1mM0GPny1R9y1vlk03Qiw61/jC4OZf02WkXtNuedwW4AbDRNy8Y0Avo7pnmv62+w7lJgnKZpjYEZgF0Rns+Y77mNmDrfCphtHk1tpmlaHU3TvrlR1ls9maZpX2ma1krTtFZF6bwC+PkZiYzUcfmyIisLtmyxoUN7yw5oSori38Gd738oR+/eWQCkpZmmEPxb5vhxPdWrF98o0L9Cw6KpUtmZyp6O2NjoCOxYn137b+9d7ifDo3GoVB5nR9MsjJaNqnE+z+UZYXI8Mprqbi74ujhiq9fRu0l9toaetShT3S23s9K1fi0iEkwHTF8XR/TmaRk+zg7UdHchMql4/vCfOhGJTzU3vHydsbHR07VXY4K3W76TNnhbKD36NwOgc8+GHNl37pbbDd5+iiatawDQvG1tLoTHWj37ybPRVPV2prKHIzZ6HT3b+bHz4Nlbr4jpDSiO9qZDUZ2q7tSp6s6+Y+etmm/Nku2MC3yXcYHvd9gXYwAAIABJREFUErTuCIGPtAPAr2VNrqRlkBRreeeOpNhUMtKv4dfSNI8u8JF2BK8/AkDQuiM061QfAN9antjY6klJSCcuMpGm5sfLVyyHX4uaXAyz7h/R45eiqZanLfdpWp+t/1jWc7W8bdmvFhHxprbsYFeez4cP4NMNuzgUYdX3z95Q6OnLVPF1wdvLCRsbHd27+LN7b9htrWtjo+Od6Q+xYctxtpnnPf6PvfuOr+n+4zj++t6IBImITIk9Y48KYiWEGrVXKVq7RihqtDVqVLXooFSrrVmtn2opSocVWnvP2Ft2ZJHIuOf3x43ITYKk7k2kPs/H4z7ce8/3nvu+X2d8z/d8z0lOO3s1iBIuRXBzNCzXL9f3YM8x4/quWNKJd/u3YNznv3I3JmfuXJNeZuvf7n+7/pV05IAJ179NS7YzvOFUhjecyt7NR2nRy3Ag6eFZjvvRcUQEG29PI4KjuB8Tj4enYQxvi16N2Lf5KAB2ToZGulKK1yZ0ZPN3OwCwKpAfq4L5AajTrCrJyXpuBOTMMv6ie+bBlkopG6CgpmlblFL7gYdbiBgg7WAWWyBQKWUJ9AZuP+t3Y+jFnamUWq1pWqxSyh1IfMpn0ucyCQsLGDUyngkTC6JPhjZtEilTRs/SZfmpVDGZRo2SOX7cgm++tUIpqFEjibdGGdrY16/r+PQza5QynNLo1SvB6O4F5pKs1/jsux18OqkrFjodm3ee5uqtcAa92pCAy8H8ffgyHuVcmD2+I7aFrGn0UjkG9WhIn7Er0Os1Fq3azfyp3VEKzl8JZuP2k2bP/DRvT4eDxyEyCny6gV9/6PZK7uVJ1mvM2rSDb/p1QacU64+e4VJIOH6+Xpy5HczOgCu81qAWXuVKkqRPJiruAe+tM5ycqFPKncFNPUnSJ6PXNGZu3EHkfdNemPOQPlnPl7M3M2vxG+h0Ov7ccJTrl0PoO7w5F8/cYb9/AL+vP8qEWV1Zumk0MdFxzJ7w6NZOK7aMpaCNFfksLfBqVplJQ1dw40ooSz//g/GzujF0vDWRd+/x6dT1Js+erNeYt2In8yd0RadTbPY/zdXb4Qzu2pCAq0HsOXqFymVd+Hh0B2wLWtO4dlkGd/XitXdWki+fjq+nvArAvbgEpi3earZxxgCHtp3G07caSw/MID4ugc/eWpk6beH29/DzNdw+Y+HEHxm74I3U22gd2n4GgD9/3MuYz/uy2H8KSQlJfDLK8PlNS/0ZO78vX/lPQSnFn2v2ce2sKTaxjyTrNWZt3MGSAV3Q6RTrD5/hckg4fi29OHMrmJ3nrvBaw1p4lS9JUnIy0XEPeG+tYVl+rWFNSjgUYWjz+qnDCgZ/9wsR98zX6ErWa3y+eBvzZnZHp1Ns+esU126EM6BPYwIuBrH3wCU8Krgyc3JnbG2saFivPP17N6bf8KU0a+JBzWrFKVzYmtYtqgHw0WdbuXTF9AdgT8o/d9VOFow3LNebdp/myu1whnRuyLlrQew5doVRPZtSwMqS2SPaARAUEcO4z3/NsYwPc85baZzz6u1whnRpyLmrhpyVy7gwZ3QHbAtZ06RWWQZ38aLXu4b1b8nkR+vf+2Zc/w7+cQLPVjVYdnIuD+Ie8MnQb1Onfbl3BsMbTgXgi9ErGPf1YPJb5+fwXyc59GfKXTW6N6D9YMNtDf/ZeJg/VxluM1jEqTCzNoxD0zTC79xlzqCvzZLfHPL6XQjU08aqKKViNU2zUUr5AOM0TWuX8v5CDONJ/wB+xdCjqoB5mqatUEo1Ar7B0BPaDcOp/gnAdQxDAmw1Tev3uNtoKaWuAXU1TQtLufVVXU3T/FK+O+20t4BBKXFjgT4Yelw3a5pWLaX8OAxDEKYppboCHwJxgNfjxsHeue32/N8DI53uo9/O7QjZsueLvLOiP1Tti2G5HSHbSm423a2UckJkdfunF3rO2P918emFniM3+lfM7QjZ5njqaX0Tz584x7x1q3Vl/n4Tkyuy/kRuR8i2P2JXPBctx177h5isnfNjgyU5/puyclrdJuXfXcCuNO/7pSlWL5PP/YPxbbQWpzzSl5uW5nm/NM9Lp3m+HMMQhMymzQfmZxK9Wpoy89I8/5kMIwyFEEIIIURekbcOD4UQQgghxDOTPyUrhBBCCCHylBftLgRCCCGEEELkKumBFUIIIYR4weT1uxBIA1YIIYQQ4gUjQwiEEEIIIYTIQdIDK4QQQgjxgsnrPbDSgBVCCCGEeMHk9QasDCEQQgghhBB5ivTACiGEEEK8YPJ6D6w0YIUQQgghXjB5/TZaMoRACCGEEELkKdIDK4QQQgjxgpEhBP9RLRaPz+0I2Vbq70u5HSFbqn0xLLcjZNvpkYtzO0K2tV3sndsRsiV/6cK5HSH77PJWZl1SbifIPuuge7kdIdvii9rldoRsSSyY9xo0uiJ5q46fJ3m9AStDCIQQQgghRJ4iPbBCCCGEEC+YvN4DKw1YIYQQQogXTF5vwMoQAiGEEEIIkadID6wQQgghxAtGy+M9sNKAFUIIIYR4wcgfMhBCCCGEECIHSQ+sEEIIIcQLJq9fxCUNWCGEEEKIF0xeHwMrQwiEEEIIIUSeIj2wQgghhBAvGBlCIIQQQggh8hQZQiCEEEIIIUQOkh5YIYQQQogXjAwhEKkaVyjFu219sNDpWHfkNN/uPmQ0/VXPGvSqXxO9pudeQiLTNmzjcmhE6vRidrZsGvU6i3bsZ9k/R8yadejM7nj6VuVBXAKfjF7F5VM3M5QpX6MEYz/vi5V1fg5tP8NXU34CoPfbbWnduxFR4bEArJi9kUM7ztCsiyddh7VI/XyZKm6MfPljrpy5ZdLsjSuU4t1XUur5cCb1XC9NPT94VM/Vi7swvdPDfIpFO/ax/exlk2b7NyZ9BLv2QVF72LQ8d7MMm90Tz5bVDcvFiGVcOnkjQ5nyNUvy9qL+huXir1MsfncNAGWrlWDkp33Ib2VJclIyC8ev5sLRazTrVp8eb7UGIO5ePF+8vZqrJl4mAOrVLs2owb7odIrf/jrJ6p8PGk2vWaU4Iwc1p2xpJ6bP24T/3gtG0wsWyM+qRQPYs/8iny/ZbvJ8Dw2d3AFP70o8iEvkk3fWcvnsnQxlyld1Z+xH3bGytuSQ/3m++mAjAI1bV6fPyJaUKOfE6G4LuXj6ttHnnIoV4estY1n9xTZ+XrrbbL8BoFGlUkzsaFgPfzlwmu92Gq+H3b1q0KthTZL1eu4nJDJ93TauBEc8Zm6mU9erPEPHtcHCQrF1w1HWLv/baLqlpQXjZ3ShQuViREfF8eE7PxEcGEmd+mUZMLIl+SwtSEpM5pv5f3Li0FUAfFpVo+eApmiaRkRoDB9P+YXoyPtmyd+gemnG9vVBp9OxcdcpVm42rtdaldwZ08eH8iWcmLLoN3Ycupg6bcSrTWhUqwwASzfsZ9sB42XcXBpWLcW4HoZlYf3fp1n+h3Hm3i3q0LlRNZL1eu7GxjF9xZ8ERsSkTi9knZ+fp73BzuOX+HjNzhzJPHRGVzybVzFs68as5vLpjNuk8tVLMPaz3ob1cMdZvpr6s+H3jG1D69e8Hu0DP97MoR1ncyS3KWlabid4NjKEwER0SjG5fXPeXLmB9gtW0LZ6Jco5FTUqs/lkAJ0WrqLLotUs3XOYCW28jaZPbOvNnovXzJ7Vs3lV3Mo6MbDhNBaM/wG/j3pmWs7vo54sGP8jAxtOw62sE3WbV0mdtmHJDvxazsav5WwO7TgDwM5fDqW+N2/kCoJvRpi88Zpazys20H7+CtrWyKSeTwTQ6YtVdFmYUs9tDfV8MTic7l/+QJeFqxmyYj3TOrbAQpf7R6Cd2sCSubmdAjxbVMOtnDMD6k5i/phV+H3SO9NyI+f1YcGYVQyoOwm3cs7UbVENgIHTu7J6ziZGeM9g1exfGTStGwBBN8IY324uw5pM54d5v/HW531Nnl2nU4x5syXjp6/jdb+l+DapTKkSDkZlgsOi+XD+VrbtPpfpPAb1bszx0xkP5EzJ07sSbqUdGdhyLgum/ILf9M6ZlvOb3pkFU35hYMu5uJV2pG7TSgBcvxjMTL+VnE5pWKU35L12HN593mz5H9IpxaTOzRn+7QY6zl1Bm9qVKOtivB5uORpAl09W0f2z1SzbeZjx7b0fMzcT5tIpRrzzCpNHfc/gboto1qo6Jcs4GZVp1akOsdFx9O+0gF9W72PgqJYAREXeZ+roHxj66pfMfX89E2Z0MczTQsewcW2Y8OZyhvVczJWLwXToUc88+ZVi/BvNGT13PT0nLudlLw/KuBnXa3B4DDOX/MGf+wKM3m9UswyVSjvTd9IqBkz7gT5t61LIOr9ZcqbPPLFXc0Z+sYGu01bQ2rMSZYoZZz5/I4Q+H/7AqzO/Z9uRi7zVtYnR9GEdGnLkoukPah/Hs3kV3Mo4MbDxTBZM/B9+s3tkWs5vdg8WTFjDwMYzcSvjRN1mlVOnbfhmF36t5uDXak6ebLz+F+SJBqxSarlSqtsTpn+rlKryuOk5oXpxV26ER3LrbhSJyXq2njpP88rljMrce5CQ+rxAfkvg0eGPb+Vy3IqI4lJIuNmzNmhdg+0/HQAg4Og1bAoXwN65sFEZe+fCFLS1JuCIYUe5/acDeLWumeXv8O5cF/8Nh00XOkX14q7ciEhTzyezXs/xiUkk6w3PrfJZoPF8HH561oQitrmdArza1mL7mv0ABBy+gk3hghR1sTMqU9TFjoK21pw7dAWA7Wv207BtLcNEDQraWgNQqHBBwoMiATh38DKxUYbeqoBDV3AsZm/y7JUrFON20F0Cg6NIStKzfU8AjeuVNyoTFBLNleuhaPqM/+8Vy7lgX6Qgh45fM3m2tBr4VmX7esPZlYATN7CxLYC9k/F/vr2TLQVtrAg4buj93r7+CF4tqgJw83IIt6+GZTpvrxZVCLoZwfVLwWb8BQbVS6Zs7yKiSErWs/X4eZpVzfr2zlwqVXXnzs0Igm7fJSkpmV1/nsbLx8OojJe3B39tPg7Anu1nqVXP0GN5+XwQEWGGXsHrl0PInz8flpYWKAUohbW1JQCFClkRHhqDOVQp58qt4EjuhBrq9a/9ATR9ybheA8OiuXQzDH267rMy7g4cC7hFsl4j/kESF2+G0aBGabPkTKtaGVduhURyO8yQ+Y/D5/GpaZz58IVbxCcmAXDqaiDOaTZ4lUs641C4IPvPXjd71ocavFyd7esMZ2ieuA+0sSbg6DUAtq87iFerGjmWMSfoUSZ75IY80YB9Gk3TBmmalquHQC6FbQiKerRRC4qOxbmwTYZyverX5Pex/Xm7VRM+/G0XAAUs8zGwSV2+3Lk/R7I6uNoRdicy9XVYYCSOxYoYlXEsViRDGQfXR42Z9gO8+XL7e4z5tA82dgUyfId3hzrsWm/6Bmym9Wz3lHrevCv1/RrFXdk46nV+HdmX6b9uT23QCnAoZk/o7UeneEPv3MUh3XLhUKwIYXfupitjaJB+9d4aBk3vxqpTHzNoRjeWzfglw3e06tuYw9tPmzy7o4MNIWGPlovQ8BicHDIuF5lRCkb092Hxcn+T50rPwaUwYUFRqa/DgqNwdDHecTpmUsYhXZn0rApY0n2wD6sXbjNt4MdwtrMhKPJRfQdHxuKSyXrYs2FNtrzTn7HtmjB7wy6z53JwLkxocLr6TXeA4OhkS2hwNAD6ZD33Yh9QuEhBozKNfatw+XwgiYnJJCfp+WL2Zr7633B++GMcJcs68cevR82S39nehuA0p9ZDImJxss/a0e3FG6F41SiNVf582NlY81Ll4rg4mP/I2KmIDUF302S+G4tzkceve50aVeOfM4aOEaVgTLemfP6zeYe7pJfpPtDV+GDd0dWOsMAn7AP7NeHLvyYyZt5rme4D8wJNUyZ7PI1SqrVS6rxS6pJS6p3HlOmhlDqrlDqjlPrhafPMVgNWKbVBKXUkZeZDUt6LVUrNUkqdUErtV0q5pLy/XCm1QCm1Vyl15WEPqlLKRym1Oc08Fyql+qU8n6qUOqSUOq2UWqKUylKzXim1SylV9yl5XJRS61PeP6GUapid3/7UDJm9mckAkx8PnKD1p8v49I89vOlTHwA/Xy9W7j3G/YREU0Z6rMyqVUuXNfPfY/jntxV7GNDgfUa0mE1ESBSD3+9qVKxS7dLExyVw/XygiRKnyZVZsCzWM8DJW0F0WLCSHot/ZLB3PfLnszB5xrwqs7rNsFw8Ydlp19+HryetpW/1iXw9eS1jFrxhVK5G40q06tOY76b9bLrQD3Nl8l5Wx3d1blOb/UeuGjWAzSXzOk5fJguF0uk76mXWL/+b+PsJTyxnKpnXd8aMa/aeoO1Hy/jstz0MaVE/k0+ZONe/rN+02UuVdWLgqJbM/3ATABb5dLTr5smI3l/xWqt5XL0YzKv9m2SYh0lkYR18nAOnr7P3xFW+ndqTmSNe4dSlQJKT9SYOmFGmy8Jjetvb1vegSikXVv5pOAvRw7sm/5y+RvDdWDMmzCjzZeDpZVL3gSv/ZkCjGYx4eY5hHzgl86FAwkApZQEsAtoAVYBe6c+aK6UqAO8CjTRNqwqMftp8s3sR1wBN0yKUUgWAQ0qpn4FCwH5N0yYppeYAg4EPUsoXAxoDHsBGYN1T5r9Q07QZKT9mFdAO2JTNjI/LswDw1zStc0plZjhETGmUDwFwbdMd+zpeWf7SoOhYXO0eHe26FrYhJObeY8tvOXWeqR18AahRvBgvV63A260aY2tthabBg6QkfjhwIsvf/zTt+jWlde9GAFw4cR1Ht0c9a47FihCepscHIDQwMmOZlJ6NyDQ7+q3f/8P0VcOMPuvd6SX8N5jnIrSgqEzqOfop9dzRF9K1ma6ERhCXkEgFF0fO3Db/KdfnVfuBPrR+vSkAF45dxcn90dg1Jzd7ItItF2F37uLoZp+ujKGXokUvr9QLuvZsOMzo+a+nlitTxZ3R819nSo8FxNx9/P/XvxUaHouz46PlwsnBlrCIrO0Uq3q4UaNKcTq1qUWBApZY5rMgLj6Rr1eapleoXW8vWqeMmbxw6pZRT4+jix3hIdFG5UODojIp8+TGdaWaJWjcqhoDx7ehUOECaHqNhIRENn2/zyS/Ib3gqFhc05wGdiny5PVw6/HzTO7ia5YsaYUFR+Pkkq7u0h2YhIZE4+RSmLCQaHQWOgrZWBETFWco71yYqfN6MnfqLwTeMpxpKFfRFSD1tf9fZ3i1X2Oz5A+JiMWl6KN6dS5qQ1hk1ht3yzceZPlGw6nxGcPacjM48imfeHYhkbG4pukldra3ITQy47JQz6MkA9vUY9AnP5GYlAxA9bLFqF3Bne7eNShgnR9LCx33HyTyxfq/M3z+WbV7owmtXzPs0y+cuPHY/dtDoenOTD52H/jDPqYvH2LyvDkhB+9CUA+4pGnaFQCl1BqgI5D2zPlgYJGmaXcBNE0LedpMs9uAHaWUenioUQKoACQAD3tUjwAt05TfoGmaHjj7sCf0KZoppSYABYGiwBmy34B9XJ7mwOsAmqYlA1HpP6hp2hJgCUCVyZ9l69zy6dtBlHKwx92+MCHRsbSpXokJP201KlPKoQjXww0bFO+KZVOf9/12bWqZEc0bcP9BokkbrwCbl+9m83LDDtnTtyrtB3jjv+EIHnVKcy8mjrvpdqJ3Q6KJi32AR53SBBy9hm/3+mz6znCK1d65cGr5hm1rcj3g0VXUSimatKvN+M6fmTT/QxnquUYlJqx9Qj1XelTP7vaFCYqKIVmv4VbEljKO9ty+m2ExeKFs+m4Xm77bBUC9ltVpP7gZu345iEfdstyLjiMi3UY9IjjKsFzULUvA4Sv49mzAxiU7AAgPiqJGo4qc/OcCtZp6cOeyYfvj5F6UKSuHM3fYUm5fNs/BQsDFQIoXs6eYsx2hETH4NvFgxiebn/5BYOanv6U+b928Kh7lXU3WeAXYvHofm1cbGpKePh6079MQ/99O4FGzJPdi47mbbjzl3dAY4u49wKNmSQJO3MC380tsWvXPE79j/GtfpT7vPbIF8fcSzNZ4BTh9M4hSjva4Fy1McFQsbWpVYuJq4/WwpGMRboQZ1r2mlcumPjen82fv4F6iKC5uRQgPicHn5Wp8NMm432S//3latqvFuVO3aOJbJfVOA4VsrJk5vzfLFm7j7IlHF/OFhcRQsqwTdkUKEhV5nzoNynHzWubjkJ/VuStBlHAtQjGnwoRGxNKygQdTvtySpc/qlMKmkBXRsfGUL+FI+ZKOHPj6mllypnXmWhAlnO1xcyhMSGQsrepW4r3vjJeFSiWcmNTHF78F67kbE5f6/uSlv6c+b+9VhSqlXMzSeAXYvGIPm1fsAQwXcbXv3xT/X4+m7APjH7MPjH+0D+xWj03LDNsFo31g6xpmOduYE0x5F4K0HYAplqS0qQDcgbRXyN4C0p+SqZgyn38AC2Capmm/8wRZbsAqpXyAFoCXpmn3lVK7AGsgUXt0jiM53TwfpJ1Fyr9JGA9dsE6ZvzXwJVBX07SbSqlpD6dl05PymE2yXmPW5h1880YXdDrF+iNnuBQSjp+vF2duB7Mz4Aqv1a+FV7mSJOmTiYp7wHs//5ET0TI4tP0Mnr5VWbpvGvFxCXw25vvUaQv/ehe/lrMNz99Zk3IbLcMtRB7ebWDglM6UreoOGgTfDGfBhB9TP1+tQXnCAiMJumGei9GS9RqzNu3gm35d0CnF+qOZ1HODdPW8zlDPdUq5M7ipJ0n6ZPSaxsyNO4i8H2+WnNnx9nQ4eBwio8CnG/j1h26v5HyOg3+dwrNldZYemcWDuAQ+9VueOm2R/1RGeM8A4Itx3/P2ov7kt7bk8LbTHNpmGNM6/62VDJ3dE4t8OhIeJDJ/zEoAek9oh23RQvjNNdzVIDkpmVG+s0yaPVmv8fmSbcyb1g2dTseW7ae4djOcAa814vylIP45eBmP8q588G4nbG2saOhZjgG9GvHGyGUmzfE0h3YF4OldiaXbJhjWvXd/Sp228Ne38Os43/D8/fWM/aiHYd3bfZ5D/oY7CzRsWZVhUzpiV7QQ05f058q5QCYP/C5HfwMY6vvD9Tv4anAXLJRi/aEzXA4OZ0QrL87cDGbX2Sv0alSLBhVKkpScTHTcAyatMf/2Tp+sZ9GcLXy4sC86Cx1//nqM61dCeX1oMy6cvcP+3ef5/dejTJjZhWUbRhETFceH7xkauB1erYdbiaK8Nsib1wYZ7pjw7ohVRITFsHrJLuZ9O4CkpGRCAqOYN229WfIn6zXmrdzJgvFd0ekUm3af5urtcIZ0aci5q0HsOXaFymVcmDO6A7aFrGlSqyyDu3jR692V5MunY8nkVwG4F5fA+4u35sgY/2S9xsdrdrDoLcO+b+M/Z7gSGM7Q9l6cvR7M7pNXGN21KQWtLJkzxLBhC4qIYcyXG82e7XEO7TiLZ/OqLP17KvHxCXw2dnXqtIV/TMCv1RzD8/fWMvbT3oZbBu46m3q3gYGTOqbsAzWCb0aw4J3/5crveJ6k7QDMROYjTYzlw9Ap6gMUB/YopappmvbYI1+V1fE1SqmOwCBN09orpTyA40BrYLOmaTYpZboB7TRN66eUWp4ybV3KtFhN02yUUiWAPUAlDA3U48B0YANwHiiNofW9H1inadq09PPKJNsuYJymaYcffk8medZgGFrwecoQgkKapkVnNj/Ifg/s86DUd5dyO0K2XB9Q/umFnjOnRy7O7QjZ1raK+W9fZEr3m1TM7QjZVuiceXrkzOVWB9fcjpBtbjvN34NrapFV7J5e6DmSWDD3byuYXS6/ZX5LuefZ1lsLnouKrrFpqsnaOSfbz3jsb1JKeWHoUW2V8vpdAE3TZqcp8xWGNtrylNfbgXc0TTuUcY4G2bmI63cgn1LqJDATQwMz2zRNuwmsBU4Cq4FjKe9HAt8ApzA0Zh8b+l96C8MQhVMYhhZUNfH8hRBCCCHyhBy8C8EhoIJSqoxSKj/QE8N1UWltAJoBKKUcMQwpuPKkmWb59LqmaQ8wXEGWnk2aMutIuVBL07R+6T6fttwEYEIm3zEZmJzJ+/3Sv5duus9jvidtnmAMg4aFEEIIIUQO0DQtSSnlB/yB4Qz7Uk3TziilZgCHNU3bmDLtZaXUWQzDP8drmvbEsYjyp2SFEEIIIV4wOXgXAjRN2wJsSffe1DTPNWBsyiNL8lQDVim1HiiT7u2JmqblztVQQgghhBB5kCnvQpAb8lQDVtM0uVuwEEIIIcQzyspf0Hqe/Sf+lKwQQgghhHhx5KkeWCGEEEII8ezyeg+sNGCFEEIIIV4weXwIrAwhEEIIIYQQeYv0wAohhBBCvGBkCIEQQgghhMhb8vgYAhlCIIQQQggh8hTpgRVCCCGEeMHIEAIhhBBCCJGnyF/i+o8quSkityNkW0Il99yOkC3F/onD8m5cbsfIlraLvXM7QrZtOeuf2xGyxefNirkdIfvy2J6g+K+BJLnY5XaMbEmys87tCNlWMDgxtyNkm/Wl0NyOkD0FC+R2ApFLpAErck1ea7wK8V+R1xqvImfkucareCYyhEAIIYQQQuQtebwBK3chEEIIIYQQeYr0wAohhBBCvGDy2ND9DKQBK4QQQgjxosnjDVgZQiCEEEIIIfIU6YEVQgghhHjByF0IhBBCCCFE3iJDCIQQQgghhMg50gMrhBBCCPGCkSEEQgghhBAib5EhBEIIIYQQQuQc6YEVQgghhHjhyBACIYQQQgiRl+TxIQTSgH1GLzUsz7CJr6DTKX5ff4S1S/cYTbe0tGDcrK5UqOxGdNR9Zk9YS/CdSGztCjD5k55UrOrOXxuP8eXs3wAoUDA/85YNSv28o0thdvx2gq/nbjVZZs96ZRnh1xKdhWLLbydY88O+DJknvtueipVciY6KY+aMDQQHReHboio9ejZILVe2rDNDh3zH5Ush+DSrTO8+jdDpFAf2X2LJ1ztNltfUdQyQL58Fw999hRqeZdD0Gsu/2MaomEDBAAAgAElEQVQ/28+aLDPAsNk98WxZnQdxCXwyYhmXTt7IUKZ8zZK8vag/Vtb5OfTXKRa/uwaAstVKMPLTPuS3siQ5KZmF41dz4eg1mnWrT4+3WgMQdy+eL95ezdUzt0ya+2kmfQS79kFRe9i0PEe/+onq1S7NqMG+6HSK3/46yeqfDxpNr1mlOCMHNadsaSemz9uE/94LRtMLFsjPqkUD2LP/Ip8v2W62nEOndMDT24MHcYl8MnEtl8/ezlCmfFV3xn7cAytrSw75B/DVzI0ANG5dnT6jWlKinDOjuy7k4mnD/71FPh2jZ3WjXFV3LPLp2L7+KGtNtA7WrV+W4W+9jE6n2Lr5OP/7PuP2YsLkDlSo5Ep0dByzpq4nOCgKgDLlnBk9vg0FC1mh6TVGDF5KYkJy6mdnfNQdV7ciDHn9G5NkBfD0LMsIvxbodDq2bDnOmh/3Z8g78Z12VKxYjOjolO1bcBQWFjrGjWtL+QouWFjo+OvP0/z4o+G3du3mSdu2NdE0uHo1lDkfbyYxMTmzrzcpz7pl8Bvmi4VOx2+/n+DH/x0wml6jenFGDPWlXFlnZny4kd17zps9U1pDp3bE06cyD+IT+GT8/7h8JpNluZo7Y+f2xMrKkkO7zvHVjF8BGPhOO+r7ViEpMYnA6+F8OuF/3IuJx9ndniV/TeDWlRAAAo7fYOHkn02Td3IHPL0rGda9d9Zy+eydjHmrujP2o+4p6955vvogzbo3siUlyjkxuttCLp42/FZnd3uWbH2bW1dDH+V9f71J8oon+0+PgVVKTVNKjVNK9VNKuaV5/1ulVJVnnb9OpxjxXnsmD1/JkM5f4NO6BiXLOhmVadX5JWKj4xjQ/nPWf7+PAaNfBiAhIYmVi7bzzad/GJWPu5/AiFe/TH2EBEaatGGl0ylGvdWKdyf+jwFvLKF58yqUKuVoVKZN25rExsbzeu+v+HndIQYPaQbA9m1neHPQd7w56Ds+mrWRoKBILl8KoXDhAgwZ2pxxY39gYP9vsLcvRO06pU2W19R1DNBzsDdREfcY1GE+Qzp/wakj10yS9yHPFtVwK+fMgLqTmD9mFX6f9M603Mh5fVgwZhUD6k7CrZwzdVtUA2Dg9K6snrOJEd4zWDX7VwZN6wZA0I0wxreby7Am0/lh3m+89Xlfk+bOik5tYMncHP/aJ9LpFGPebMn46et43W8pvk0qU6qEg1GZ4LBoPpy/lW27z2U6j0G9G3P89E2z5vT09sCtlCMDW8xhwZSf8ZvROdNyftM7s2DyzwxsMQe3Uo7UbVoJgOsXg5k5YhWnD101Kt+kTQ0s8+djeLvPGNVpAW171sfZ3f6Z8+p0ipFjW/PeuDUM6vM1zVpUpWRp4+1F63a1iI2Jp1/Pxfzyv4MMGtbc8FkLxTtTOjB/3lYG913C2yO/JzlJn/q5xk0rEReX8MwZ0+cd9dbLvPvOWgb0f7h9M14O2rSpSWxMPK/3/Yqf1x1k8BAfALy9PbC0tGDwoO8YNnQZ7drXwsXFDkdHGzp3rsuwocsZNPBbdDpF8+bPvPvI0m95y68l70z6iX6Dv8XXpwqlSqZbpkOi+XjeFrbvMO3Bd1Z4+njgVtqJgc0/YsF76/Cb2TXTcn4zu7LgvXUMbP4RbqWdqOvtAcCxvy8wtPU8hrf9lNvXwnh1uG/qZwKvh+PX7jP82n1mssarp3cl3Eo7MrDlXBZM+QW/6U9Y96b8wsCWc3ErnW7d81uZYd0DCLwRjl/H+fh1nJ+3Gq+aCR+54D/dgE2jH5DagNU0bZCmac+8xleqVpzAm+EE3b5LUlIy/r+fwsunslEZr2YebNt4HIA9f52hVr2yADyIS+TMsRskPkh67PzdShalSFEbTh+9/qxRU3l4uHH79l0CAyNJStKzc8dZGjaqYFSmYaOK/Pn7KQD8/c9R56XSGebT3LcKO1Ma1sWKFeHWrQiiou4DcOTINZqkrPTPylx13KpTHdYs3Q2ApmlER943Sd7UTG1rsX2Noecn4PAVbAoXpKiLnVGZoi52FLS15tyhKwBsX7Ofhm1rGSZqUNDWGoBChQsSHhQJwLmDl4lNqeeAQ1dwLPbsjZTs8qwJRWxz/GufqHKFYtwOuktgcBRJSXq27wmgcb3yRmWCQqK5cj0UTZ9xa1uxnAv2RQpy6Pg1s+Zs0KIK2zccBQw9NTa2BbB3Mq5MeydbCtpYE3Dc0GO/fcNRvFpWBeDm5RBup/T0pKVpYF0wPzoLHfmtLUlMTOZ+bPwz561U2Y07tyIIumPYXuzadpaGjSsalWnYuAJ/bj0JwO5d56idsr2o61mWK5dDuHLJ0JMWEx2HPqXurQtY0rVnfVav+OeZM6aVcft2joYN0+VtVIE//zwNgL9/AHVSDra1lFw6ncLKypKkRD337z8AwMJCh5VVPnQ6hbWVJWHhsSbNnelvqVSMO3ciCQwyLNM7/M/RqKHxtjo4OJorV0PRaznfgmjQoirb1x8GUpblwtaPX5aPGfZh29cfTl2Wj/59AX2y4YAm4Nh1HF2Nt48mz+tble3rjxi+78ST1j2rR+ve+iN4tUi77oWZNWOO05TpHrngP9eAVUpNUkqdV0ptAx62ouoCq5VSx5VSBZRSu5RSdZ/1uxycCxOacqoMICwkCgcX28eW0SfruRf7gMJFCmZp/j5tauD/x6lnjWnE0cmW0NDo1NehoTE4pluJHZ1sCUkpo0/WDJntChhna1aFHSlH/bdv36VkSQdcXO3QWSgaNa6Is3Nhk+Q1Rx0XSmkYvjHCl4VrhjFp7qsUKVrIJHlTMxWzJ/R2ROrr0Dt3cShWJF2ZIoTduZuujKFB+tV7axg0vRurTn3MoBndWDbjlwzf0apvYw5vP23S3HmVo4MNIWExqa9Dw2NwcrDJ0meVghH9fVi83N9c8VI5uNgRFhiZ+josKBLHdAc2ji52hKVd5oMicXB58s79799PEn8/gR/2Tmal/3v88t1uYqPinjmvo5MtoSGP6jUsNDrD9sLByZbQkDTbi3uG7YV7iaKgwexPevLldwPp8dqj4Uf9Bnmzbs0BHsQnPnNGo7yONqlZAELDMtm+OdoS8jCvPiVv4QLs9g8gPi6Rn9aN4ocfh7N27QFiYuIJC4vlp7UH+HHNCH5aN4rYew84cjhjL5ypOTo+2g5DyrY6i8t0TnBwTb8sR2VohDq62hEWZFzGIZOG6svd63FoV0Dqa9cSRVm4aQxzfhxGVc8ypsnrUth4vQqOwtHFeD/lmEkZB5en78tcixdl4YZRzPn+TarWLW2SvOLp/lMNWKXUS0BPoDbQBfBMmXQY6K1pWi1N0559q576fRnfS38grDIppGXxaNm7VXV2bTVtAzYzGTJnWujRU4/KbsQ/SORaSk9QbGw88z/9nSlTOzF/QV+Cg6JITtZnNpdsM0cdW1jocHK148yxG/j1XMy5kzcZ/HbrZ42aLlPG99JnelLudv19+HrSWvpWn8jXk9cyZsEbRuVqNK5Eqz6N+W6aaU6v5XWZLbNZ7ZTq3KY2+49cNWoAm0vWlotMPviUH1OpRgn0eo3ejT6gX7PZdBnQFNcSRZ8h6eOzZG05NozLrVqjBLNn/MqY4Sto1LQStV8qTbnyLrgVL8o/u80wXjML24JM6xfw8ChGsl6jR/cv6NN7Md171KNYsSLY2FjTsFEFer/2JT26f0EBa0tapPTKmdOzLNM5ISvb3czKpD/d3HO4L8lJyez81XBm4m5oNK83/gC/9p+xZNZGJn7Wm4I2VibIm0mULOxLnlbpd0Oied1nNn6dFrBk9mYmftKLgoWePW9O0DTTPXLDf+0iribAek3T7gMopTZm58NKqSHAEIAq7m0p4VDnieXDgqNxSnM06ehsR0RITLoyUTi52hEWEo3OQkchGytistAzUqaiKxb5dFw6l3GQ+bMIC43ByenREaWTky3h6XbcoaExODsVJiw0Bp2FopCNFdHRjzI3a/5o+MBD+/ZdYt++SwC80q4Wer1pGrDmqOPoyPvExyWwd4dhLOTuP0/TqvNLz5y1/UAfWr/eFIALx67i5P6oAeHkZk9EmiN7gLA7d3F0s09XxtBb0aKXV+oFXXs2HGb0/NdTy5Wp4s7o+a8zpccCYu7ee+bc/wWh4bE4Oz7qaXNysCUsImuneat6uFGjSnE6talFgQKWWOazIC4+ka9X7jZJtna9vWj9an0ALpy8iWOannhH1yKEp+kxBAhN15OVWZn0fNrX5vDu8yQn6YmKuMfZo9eoUK04QTcjnvi5pwkNicHJ+VG9OjoVJjzMuF7DQqJxck6zvShkRUx0HGEhMZw6fp3olHXx4L7LlK/oSlxcAhUrubLqpxFYWOgoYl+IeV/0YdzI758pK6Rs39Kc/XFytM2QNzQ0BmfnwoSFxaDTGfJGR8fh61uVQ4eukJysJzLyPqdP36JiRVc0ICgwiqiU37Fnz3mqVC3Otm1nnjnvk4SGGbbDqb/FyZbwLC7T5tKub8MnLMt2hAenW5YDI3F0TV/m0XawRZe61GtemXf7fJ36XmJCMokJhmFSl07fJvBGOO5lnLh4KvsXq7br7UXrHvUMeU/dMl6vXOyevu652BEe8uQD28TEZBJThqBdOvMwr2PqRV7PtefogOjf+E/1wKb41/8lmqYt0TStrqZpdZ/WeAU4f+Y2biUdcHEvQr58Fni3rs5+/wCjMvt3BdCig2FcY5OWVTlxMGunnnzamKf3NeD8HdyL2+Pqake+fDqaNa/C3r0Xjcrs23uRl1tXB8DbuzLH0ozBVQq8fTzYme6igSIpp+xtbKzp0Okltvx2wiR5zVXH+/3PU8OzNAC165fjxuWQZ8666btdjPCewQjvGez77Ti+KXds8KhblnvRcUQEGzdgI4KjiIt9gEddw5hd354N2LfFMJY3PCiKGo0MY/dqNfXgTko+J/eiTFk5nLnDlnL7cvAzZ/6vCLgYSPFi9hRzNizXvk08+OfgpSx9duanv9F90Ne8OmQJXy7bxR87z5is8QqwefU+/Dp8jl+Hz9m37Qy+nQzbFo9aJbkXE8fdUOMd5N3QGOLuPcCjVkkAfDvVYf+2Jw/ZDw2MpKZXOQCsCljiUaskN688+zJ9PuAO7iWK4lrMUK8+Laqw7x/juzfs++ciL7epAUBTn8ocP3oNgMMHr1CmnIth7KiFokbtkly/FsbmDUfp2WkBfbsvYszwldy6GW6SxitAQMAd3N3Tbt8qs3dfJtu3lw0XS3p7e3AsZXxmSEg0tWuXAsDa2pIqld25eTOckOBoKldxw8rK0N9Tp05pbtww/1jIgPOBRr+luXdl9u7L2jJtLptX7U29uGrfX2fw7WwYiWdYluOfvix3rsv+lIb/S00r0f3NZkwfssxoKIld0ULodIaeUNcSRXEr7UjgjfB/l3f1vtSLq/ZtO4NvSkeFR82S3It9Qt6aD/O+xP7tTz5QsbPPJO8zHjiKrFFZPZ2dFyil6gDLgfoYepePAl8D3sCnmqbtTCm3Cxinadrhx82rdc0pWaoYz8YVeHNCW3Q6HX9uOMqab/3pO7w5F8/cYb9/AJb58zFhVlfKeRQjJjqO2RPWEnTbMO5xxZaxFLSxIp+lBbEx8UwauoIbVwyn5Zf9NoYpI1Zx61rWN5SJ9gWeXgioV79c6m1mtm49wQ/f76Vf/6acPx/Ivr0XscxvwbvvdaB8BRdiouP5YMYGAlPGOtWsVZJBQ5oxcvgKo3lOmtKRcuVcAFi18u8MDdzMWN7N2mgOc9SxczE7xs/qho2tNZF37/Hp1PVGY20f62ZQljIDjJjzGi/5VuVBXAKf+i3n4nHDjnKR/1RGeM8AoEKtUry9qD/5rS05vO00X078EYCq9cszdHZPLPLpSHiQyMJxq7l04gaj579Oo/Z1CEnZQCYnJTPKd9YTc2w5a9qxnW9Ph4PHITIKHIqCX3/o9orp5u/z5uB/9bkGL5Vh5MDmhtsnbT/Fqp/2M+C1Rpy/FMQ/By/jUd6VD97thK2NFQkJyURE3uONkcuM5tG6eVU8yrtm+zZahc5mvLDqcYa/34m6TSsRH5fAZ+/8lHorrIUbR+PX4XMAKlQrbnQbrcUptx5q2LIqw6Z2xK6oDbHRcVw5d4fJA77DumB+xn7Ug5LlnVFK8efPh/n528f/vyc9ZUxtWvUalGPYWy3R6XT88dsJflj5D28MbMqFgED2/WPYXrwzpSPlUrYXs6atJ+iOYXvh+3I1evZtiKZpHNx3mW8X7zCat4urHTPn9MjSbbT0+bPW31KvfjlGDG+BzkKxdetJfli9l379mnD+QiD79l7C0tKCd99rT/nyrsTExPHBzF8JDIzE2tqSCRNfoVQpRxSK3/84ydqU21a98UYTfJpVJjlZz6VLwXwyb0uWbqOlz/dsfUT1PcsyYpjh1nBb/zjF6h/30f/1xpy/EMTe/ZeoVNGVme93wcbWsEzfjbhH/yHf/evvs76U9eUYYPj0zoZlOT6Rzyb8L7WXdOHmMfi1+wyACtWLM3ZOT6ys83HI/zyLpxmu0v9uxztY5s9HdKThLNLD22U1al2dvqNbkZysR5+s5/vP/+TAk/Yn+Syynvf9jtRtkrLuvftTai/pwl/fwq/jfEPeau6M/Shl3dt93njdm9IRu6KFUta9QCYP/I5GL1ej71svk5ycjD5Z4/sFf3FgZ+Z3Onlo64WPn4u/IFDq27kmawBeHzQ+x3/Tf6oBC4aLuIDXgevALeAscBX4EIgDvICtmKgB+zzJagP2eZHVBuxzJRsN2OeFqRuw5vZvG7C5KTsN2OdBdhqwz4usNmCfJ8/agM1p2W3APhey0YB9XjwvDdjS35iuAXttcM43YP9rY2DRNG0WkFmXVNqrXXxyJo0QQgghhDC1/1wDVgghhBBCPEWeO89sTBqwQgghhBAvmlz6AwSmkrcG6AghhBBCiBee9MAKIYQQQrxoZAiBEEIIIYTIU/J4A1aGEAghhBBCiDxFemCFEEIIIV40ebwHVhqwQgghhBAvGrkLgRBCCCGEEDlHemCFEEIIIV4wSoYQCCGEEEKIPCWPN2BlCIEQQgghhMhTpAErhBBCCCHyFBlCIIQQQgjxgpExsP9Rd2va53aEbLM/HpHbEbIlskbR3I6QbflLF87tCNnm82bF3I6QLbu+/ia3I2TbK0065XaEbLlbqUBuR8g2+2X7cjtCthX0d83tCNkSM6t4bkfItgIXQnM7gsgl0oAVQgghhHjR5PH7wEoDVgghhBDiRZPHhxDIRVxCCCGEECJPkR5YIYQQQogXTR7vgZUGrBBCCCHEC0buQiCEEEIIIfKWPN6AlTGwQgghhBAiT5EGrBBCCCHEi0Yz4eMplFKtlVLnlVKXlFLvPKFcN6WUppSq+7R5yhACIYQQQogXTE6NgVVKWQCLgJbALeCQUmqjpmln05WzBUYBB7IyX+mBFUIIIYQQ5lIPuKRp2hVN0xKANUDHTMrNBOYA8VmZqTRghRBCCCFeNJoy3ePJ3IGbaV7fSnkvlVKqNlBC07TNWY0vDVghhBBCiBeNCcfAKqWGKKUOp3kMSfNNmbVwUwcwKKV0wGfA29mJL2NghRBCCCHEv6Zp2hJgyWMm3wJKpHldHLiT5rUtUA3YpZQCcAU2KqU6aJp2+HHfKQ1YM2lQvTRj+/ig0+nY6H+KlZsPGU3v1boOHb2rk5SsJzImjg++/YOg8JgcyfZSowoMm9gWnU7H778cYe3S3UbTLS0tGDerGxWquBEddZ/Z4/9H8J1IbO0KMPmTXlSs5s5fvx7jy9mGnn4ra0smzetJsRJF0Sfr2e9/nmXz/zRb/gY1SjOmb0rd7jrFqk3GdVurkjtj+vpQroQTUxb+xs5DF1OnjXi1CQ1rlQFg2Yb9bDtwwWw5H6pXuzSjBvui0yl+++skq38+aDS9ZpXijBzUnLKlnZg+bxP+e40zFSyQn1WLBrBn/0U+X7Ld7HnzauYnmfQR7NoHRe1h0/LczTJ0Sgc8vT14EJfIJxPXcvns7Qxlyld1Z+zHPbCytuSQfwBfzdwIQOPW1ekzqiUlyjkzuutCLp6+BYBFPh2jZ3WjXFV3LPLp2L7+KGu/3mny7F7VSjOulw86pWPDnlOs2Gq87vV+uQ4dm1QnOVnP3dg4ZiwzbNcqlnDinb6+FLLOj16vsfS3A/x1yPzrHsDw+f2p16YOD+4/YG7/RVw6djVDmf4f9KJF36bY2tvQoXDf1PfbvdmSDsNbo0/WExcbz2dvfs2Nc7fMmvfe6TBCfgwAvYZdk+IUbVsmQ5mYQ0GEb7wMCqyK21JsSA0AQn+6wL1ToaCHglUccOpViZQGgVnVe6kMfsN8sdDp+O33E/yw1vganBrViuM31JdyZZyZMXsj/n+fT50254PuVPFw49SZW7z7/s9mzTl0akc8fSrzID6BT8b/j8tnMln3qrkzdm5PrKwsObTrHF/N+BWAge+0o75vFZISkwi8Hs6nE/7HvZh4w7o3uwflqrljYaFj+/ojrF28w6y/w1Ry8A8ZHAIqKKXKALeBnsBrDydqmhYFOKbmUmoXMO5JjVeQIQRmoVOK8a83Z/S89fR8ZzkvN/CgjFtRozIXrofyxvur6TN5FTsOXcCvZ9OcyaZTjHivPZOHrWRIpwX4tKlOybJORmVadXmJ2Og4BrT7jPWr9jJgdCsAEhKSWLloO9988nuG+a5b8TeDO85nRI8vqVq7JHUbVzBPfqUY90ZzxsxZT68Jhrotna5ug8NjmPn1H/y5N8Do/Ya1ylCptDOvT1rFwGk/0PuVuhQskN8sOVPz6hRj3mzJ+OnreN1vKb5NKlOqhINx3rBoPpy/lW27z2U6j0G9G3P89M1Mp5lDXsz8NJ3awJK5uZ0CPL09cCvlyMAWc1gw5Wf8ZnTOtJzf9M4smPwzA1vMwa2UI3WbVgLg+sVgZo5YxelDxo2wJm1qYJk/H8PbfcaoTgto27M+zu72Js2uU4qJvZsz6rP1dJ+ynFb1PShTzHjdC7geSt+Zq+k1bRXbD19gVDfDdi0+IZH3v/2dV6euZOTnv/B2Tx9sCliZNF9m6rWpjXv5YvSrOJLP3/yaUV8OzrTc/k2HGVn/3Qzv7/jhb4bUfJuhdcazdu6vDP3kDbPm1fQaIavP4T66DqVnNiL6YCAP7sQalUkIvkfElquUeKcepWc0wqmnYdmIuxRJ3KVISk1rSKkZDYm/FkXc+btmzQuG7cVbI1oycfJPvDHkW5r7VKFUSePtRUhoNB99soVtO89m+PyadQeZNTfLwx7/NU8fD9xKOzGw+UcseG8dfjO7ZlrOb2ZXFry3joHNP8KttBN1vT0AOPb3BYa2nsfwtp9y+1oYrw73BaBJ25pY5rdgeJtPGNXhc9r2amDydc9scug2WpqmJQF+wB/AOWCtpmlnlFIzlFId/m18acCaQZVyrtwKieROaBRJyXr+2h9A0zrljMocOXeTBwlJAJy+HIizvU2OZKtUrTiBN8IJun2XpKRk/H8/hVezykZlvHwqs23jMQD2/HWGWvXLAvAgLpEzx66T+CDJqPyD+EROpuxQk5KSuXTuDo4udmbJX6WcK7eC09XtS8Z1GxgWzaWbYWia8VpVxt2BYwG3SNZrxD9I4uKNMLxqlDZLzocqVyjG7aC7BAZHkZSkZ/ueABrXK29UJigkmivXQ9H0GbcCFcu5YF+kIIeOXzNrzrTyYuan8awJRWxzOwU0aFGF7RuOAhBw/AY2tgWwdzIOZu9kS0EbawKO3wBg+4ajeLWsCsDNyyHcvhqaYb6aBtYF86Oz0JHf2pLExGTux2bpQt4sq1rWlZshkdwOM6x7fx4MwLt2uu3a+TTbtSuBuKRs124ER3IzJBKAsMh7RMTEYW9bwKT5MuPV0ZNtq/wBOHfgIjZFClHUtUiGcucOXCQiKDLD+/dj4lKfWxeyyrBNMbX4q1FYOhckv1NBVD4dheu5cu94iFGZqN23KdKsBBaFLAHIVzjlQECBlpiMlqRHS9SjJWtYFDbvATqAR6Vi3A6MJDDIsL3Y4X+ORl7GHRhBwdFcuRqaaf0dPX6duLgEs+ds0KIq29cbOvQCjt/AprD149e9Y9cB2L7+cOq6d/TvC+iT9YbPH7uOo6thH6dpGtYFrcy67v0XaJq2RdO0ipqmldM0bVbKe1M1TduYSVmfp/W+gjRgzcLZ3obgNMMBQiJicbJ//N6zQ9Pq7Dt5LQeSgYNLYUKDo1JfhwVH4+Bc+LFl9Ml67sU+oHCRglmafyFba+p7e3B8/2XThU7Dyd6GkIis121aF6+H4lWzNFb582FnY81LVYrjUtS8rRpHBxtCwh7lDQ2PwckhawcrSsGI/j4sXu5vrniZyouZ8woHFzvCAh81lMKCIjMc7Dm62BEWFGVUxuEpB4R//36S+PsJ/LB3Miv93+OX73YTGxX3xM9kl3MRG4LTrnt3Y3F+wlFBx8bV2Xv6Wob3q5ZxxdJCx63QjA1GU3N0K0rIzfDU12G3wnF0L/qET2TUYXgrVlz8gkEf9+HLt5aaOqKRpLvx5LO3Tn2dz96axLsPjMokBN8jIfg+N2Yf5MaHB7h3OgyAAuWKUNCjKFfe9ufKOH8KVXXAys38HSNODraEhkanvg4Ny/r2Iic5uKZf96JSG6EPObraERZkXMbBNeO693L3ehzaZTjD9/fWk8Tff8AP+6ey8u/J/PLNLpOve+aiNNM9coPJGrBKqbFKqdMpj9FKqdJKqQCl1Aql1Eml1DqlVMGUsi8ppfyVUkeUUn8opYqlvL9LKfWxUuqgUuqCUqrJE76vtFJqj1LqaMqjYZppE5RSp5RSJ5RSH6W8V14ptS3lvaNKqXKPm7c5aI/pY2/dsDKVy7jw/ZanHmyYRKaXAqY7Ks5KmczoLHS883EPfv1hH0G3zXPqKvPhXFlbew6evs7e41f55v2ezBzxCqcvBpKk15s0X3qZ1yXohTYAACAASURBVGXWPtu5TW32H7lq1JjMCXkxc16R2fKbYf37F/8BlWqUQK/X6N3oA/o1m02XAU1xLZG9htpTZZb9MetemwaVqVzahZW/G2/XHOwKMWNQa6Yv+zPLy9SzyGz8Z3a/d+OXf/BGhZF8+85qXpuU+Wlnc8rwE/QaCSH3KTG+LsUGVyd4xRmS7yeSEHyfhMB7lJ3blLJzm3I/IIL7FyJyIGDGt3Li/za7Ml8W0q97mS7kRnoO9yU5KZmdvxrOpFSqWdKw7nnNoJ/3h3QZ5G36dc9ccvAvcZmDSS7iUkq9BPQH6mNYnA8A/kAlYKCmaf8opZYCw5VS84EvgI6apoUqpV4FZgEDHmbSNK2eUqot8D7Q4jFfGwK01DQtXilVAfgRqKuUagN0AuprmnZfKfVwSVoNfKRp2nqllDWZNN5TbvswBKB0/W44V/T6V/URcjcWF4dHPRPORW0IuxuboZxn1ZL061CPYbPWkpiU/K++K7vCgqNxStOb4+hSmIjQmEzLhAVHo7PQUcjGipgsHFG+NbUjd66Hs+H7fSbP/VBIRCzORY3rNjSTun2c5RsPsnyj4YKk6cPbcjOT04amFBoei7Pjo7xODraERWQtb1UPN2pUKU6nNrUoUMASy3wWxMUn8vXK3U//8DPIi5mfZ+16e9H61foAXDh5E8dij05hO7oWITwk2qh8aLqeoczKpOfTvjaHd58nOUlPVMQ9zh69RoVqxQm6aboGTMjdWKMzFs72NoRGZlwu6lUuyYBX6jFkjvF2rZB1fua/1Ykv1//D6SuBJsuVXofhrWg7yLDbOH/4Es4lHDiTMs2xuAPhd/5dnexa8w9vfTmYuf0XmShpRvnsrUm6+3/27jwsquqP4/j7DCAoIIJs7rvgvi+4omJqmvozl9zKJS0VLS3LTCszM7Uyl9SyrLRNLTU1zS23FBUXzA0XcEf2HQGBub8/BtlRyRmI/L6eh0fm3nPvfDjee+fMuWfOZN5+To1KwryMZa4yVtXtUOY6LJxKYeFiTUrIXe5ejMSquh06K8PLunUDR5ICYihV27SNqbDwOJycMu/iOTk++vXC1HoNb/OAc8+OiJAc596daBxdc5bJvBvi1a85LTvX4a1hX2Qs8+zdhOP7/Q3nXkQ8509co1aDSkY990TejNUD2w7YqGlagqZp8cAGoD1wU9O0Q+llvk8v54ZhuoRdSik/YAaGKRXu25D+7wmg6gOe0wJYqZQ6A6wH6qYv9wK+0TTtLoCmaZHpX09WQdO0jenLku6vz0rTtC81TWuuaVrzf9p4BbgQGEwllzKUcyyNuZmOrq3dOXAqMFuZ2lWcmDbCi6kLfyMqrvBuN1w8d5vyVcriUsEec3MzOnZvwJF92T/sdGSfP169mwDQvms9Th8LzGtX2bzg7YW1rRUr5m8zSe77LgQGU8m1DOWcMuv24MmH5wPDh1BK2xhuz9Ws5EjNSo4cO3PNhGnB//IdKpazp5yzHebmOrq0d+fQsSuPtO3sT39nwItfMGjslyz7Zh879p4rlIZgccz8b7b1Bx+8e3+Gd+/P8Nl9ji59mwLg3rgyCXGJROV4AxkVFkdiQjLujSsD0KVvU47szv3hl6zC7kTTyMNwU8mypAXujStzMzD0gdsU1Pmrhuta+fTr2lMt3Tngl/3cc6vsxPTnvZiyJPt1zdxMxwLv3vx++Dx7jl/OuWuj2rxsBy83ncrLTadyaJMvXsM7AlCnVS0SYu7mOdY1PxVqumb83qpnU25fNl3DG8CqamlSQu6SEnYXLVVP7LFgrBs5Zytj08SZRH9D4ygt7h4pIQlYOJXEwqEkiZei0NL0aKl67l6MokQ5a5PmBbh48Q4Vy9vj6mK4XnTuWIfDRx7temFqW9ccxrvXQrx7LcRn1zm6/K85cP/cS3r4ufe/5hzZbXj706yDGwNe6sSssd+QnJSSsU1YUDSN2hjG/FqWLIF74ypGP/dMRnpggbzvOkLuP0tLL3tO07T8Woj3B/yk8eB8k4EQoBGGhvj9t60qj+c1/TwiWaTpNT5evZfFbzyLTim2HDjL1dsRjO3XhgtXgzl4KpCJz3WglJUFH3r3AiA4Io6pn/1m8mz6ND3LPtzKnOUvoDPTsXPTCa4HhDJ8fBcun7/NkX3+/LHxBG982J9VWycTF5PI3DfWZmz/3fbXKGVjibmFGR6d6/D2S99yNyGZwWM9uREYytK14wHY8vMR/thwwuj50/QaH3+3l0VvPItOp9i631C3Y55tg//VYA6eDKROdRfmvdob21JWtGtSnTHPejBk2mrMzXV8MXMQAAmJ93hv+XbS8vgQkrHzfvblbj5+rz86nY5te85w7WYEo4a05eKVYA4dC8C9pisfvNUXWxtL2rSowajBbXlh4jcmzfVfy/wwr82CY34QHQOe/cF7JPTvWfg5fPf506KjO6v2vElS4j0WTlufsW7p5lfx7v2Z4fd3N2abRst3v+FNZpuu9Rj3Th/sHGyYtXIkgReCmDHqa7Z8f5gpHw1kxbYpKKXY+etxrl0MNmr2NL3Ggh/2smTys5jpFJv/OktgUAQv9WnDhWvBHDgdyKQBHShpacFH4wzXtZDIOKYs+Y2uLdxoWqsCdtZW9Gpr+FDMrFU7uHQz9wfSjOnYtpO0eroJ311eQvLde3w8KrP3dMXJBbzcdCoAL84bRufB7bAsVYIfb6xg+9d7WDNrPX28e9CkSwPSUtKIi4pn/oilJs2rzHQ4DXHn1mcnQa9Rum0FLCvYEL7pClZVS2PT2JlS9cqScC6CazMPgU7hOKA2ZjYlsGnuwl3/SK6/6wMKStV3xKax88Of9DGl6TUWLdvFgjkD0ekU23ee4dr1cEYOb8fFy8EcPnIFt9qufDCzHza2lni0qsmI4e0Y+dLXACz+eAiVK5alZEkL1q8Zz/zPtuN7IvdUZ4/Ld+8FWni6s2rvNJKSUliY5XVt6dbJePdaaPh95q9Mmf8cllbm+O6/mDHWdfx7/8OihDlzVhvm5/f3u8HSGb+yZc0hpswfxIo/Xjece7/4cs3ftG90jKWoxq4aizLGpyqVUk2Bb4HWZA4hGA6cBNpomuajlFoJ+GMYPnAeGJ6+3AKonT6lwj7S5/5SSjkCxzVNq5rPcy4Ebmma9olSaiSwStM0pZTqDrwDeN0fQpDeC3sEwxCCTUopS8Asr17Y+1o9/2mx+6+19ytetyyiGxaTcUJZlIgrnKEeT7J9X6ws6ggF1rN936KOUCBhHVwfXuhfxv4b0w1NMhWr/cWrnuPmVHx4oX+ZkpdM+ybIFLYHflyonWr5cZu90GjtnIszJxf632SUIQSapp3E0IA9hqHx+hUQhWG+rxeUUn8DDsByTdPuAf2BeUqp04Af0Cav/T7EsvR9HwFqAwnpWf4ANgPH04covJ5efjgwKT3LYQzf9CCEEEIIIYoZo30Tl6ZpnwKf3n+slKoK6DVNezmPsn5Arpn7NU3zzPJ7OA8YA6tp2mWgYZZFb2VZ9xHwUR7lOz/s7xBCCCGE+M8rdveZs5N5YIUQQgghRLFitB7YnDRNu4ZhtoHHopTqBszLsfiqpml5fwejEEIIIYR4oOL+IS6TNWCNRdO0HRi+P1cIIYQQQhhDMW/AyhACIYQQQghRrPzre2CFEEIIIYSRFfMeWGnACiGEEEI8YYr7GFgZQiCEEEIIIYoV6YEVQgghhHjSFPMeWGnACiGEEEI8YWQIgRBCCCGEEIVIemCFEEIIIZ40xbwHVhqwQgghhBBPmmLegJUhBEIIIYQQoliRHth82G/zL+oIBZbmVrmoIxSI/a7LRR2h4OxKF3WCgtOK19vsnu37FnWEAvv94KaijlAgPWq0LuoIBaYc7Is6QoGlTCxema30cUUdocBsvk8o6gjFVnH/EJc0YIUQQgghnjTFvAErQwiEEEIIIUSxIj2wQgghhBBPmmLeAysNWCGEEEKIJ0xxHwMrQwiEEEIIIUSxIj2wQgghhBBPmmLeAysNWCGEEEKIJ4wMIRBCCCGEEKIQSQ+sEEIIIcSTppj3wEoDVgghhBDiSVPMG7AyhEAIIYQQQhQr0gMrhBBCCPGEUUUd4DFJA1YIIYQQ4kkjQwiEEEIIIYQoPNIDawTjPhxEC68GJCfe45OJ33Ll7xu5ytRsVJnXlozE0soC391nWD59LQBvfTWGijVcAbCxK0l8TCITOs3GzNyMVz8bTs2GVTAz17FnrQ9rF/1hlLzNW1Vn/Kvd0OkU27f4sfb7w9nWW1iY8cbM3tRyK0dsTCJz3tlASHAMANVqOPPqG09TytoSTa8x4cWvSbmXxsixnnh1b4itrRW9u843Ss6cXp4zkBZd6hnqedJqAs7czFWmZsPKTFn8vKGe95xjxdvrMtb1Hu3JM6M8SUtN49jus6yavREzcx2vfjqcGg0rYWamY8/6o6xbvMM4eWf0pkVHN5ITU/hk2joCzgflzluvAlM+GmDIu/8iKz7YDEC77g0YNrErlWo48Wr/pVw+ezvbdk7lyvDFtin8sGQ3v646YJS8AC/P7E2Lju6GzG+uI+D87VxlatarwJR5A9Mz+7NidpbMk7pSqYYzrz67lMtnbwEY6nhOf2rUq2A4ljeeZN0Xe5/IvAXx9kewzwcc7GHLt4X+9I9k3IJhtOzWiKTEZD55aSVX/K7nKjPi3f54DWmLTRlr+rqMLZxcJrgmA1SrW4FJnwyjlG1J9HqNSV3nkJKcatTszdrUZNwbT6PTKf7YeJJ13xzMtt7CwozXP+hHrTrliY1JZO6b6wgJiqZJ6xqMmtQVcwszUlPS+GrhDk77XjVqtmw529Zi3JtPo9Pp+GPDCdbluA5ZWJjx+pz+1KpbntiYu8ydupaQoGhs7Uoy45PB1K5fgV2/nWLZ3K0AWFpZ8PbHz1GukgP6ND1H9l/km0U7TZb/vrgzEdz+8TLoNRw6lMO5Z9Vs64N+ukz8hSgA9PfSSI1Nof6yDibPVRhkHlgTUEq9p5R6vahzPIoWXvUpX92FUS1nsGjKGrwXDM2z3MQFQ1k8ZQ2jWs6gfHUXmnepD8DcF1cyodNsJnSazV9bT3Lo95MAtO/TDAtLC8Z1mMXELnN4+oUOuFQq+9h5dTrFxNd6MP21n3hx6Ao6edWjclXHbGW692pMfFwSIwYtY8Pao7w4vrNhWzPFtHf6sGjBNsYM+4LXvNeQlqoH4Mihy0wcs+qx8+WnRZd6lK/mzOjW77L49R/xnj84z3Le8wez+PUfGN36XcpXc6Z553oANGxbm9bdGzG+0we83HE2vy7fDUD73s2wsDRnvOcHTHpqLk8Pb49zJYfHz9vRjfJVHRnddQGLZ27Ae9b/8s47638snrmB0V0XUL6qI807uAFw/XIIs71XczafF6Cx03tx/MDFx86ZPbM75as4MtprPotn/or3+w/IPONXRnvNp3yVHJknrMmVuX2PhliUMGd8r4VM6ruYp59rhXMF+ycub0H17QFfLij0p31kLbo1pEJNF0Y2nMoi72+Y+NmIPMsd2XaKSR3fK7xcJrom68x0vLF8NItf/4GX2r3HG30+Ji0lzajZdTrFhLd6MWPCGsb2W4pn9wZUru6UrUy3/zUlPjaJUb0XsfH7w4x6pSsAsVEJvPvKD4wb8Dkfz9zA1DnPGjVbrpzTn2HGuNWM7bsYzx555OzXjPjYREb1WsjGNYcZ9Wo3AO7dS2X153tY+UnuDplfvvuLMX0WMWHgMuo1qUzzdrVM9jcAaHqN22suUm1yI2rPaUX00VCSbidkK1N+cC1qv9+S2u+3xNGrInbNnPLZWzGkGfGnCPwrG7D/lFLKrLCf06NHY/as8wHA/8RVbOxK4uBil62Mg4sdpWxLcuF4IAB71vnQ5unGufbVoU9z9m3wNTzQNKxKlUBnpqOElQUpKWkkxCU+dl63OuUJuhVJcFA0qal69u05R5v2tbOVadO+Nju3/Q3AgX0XaNKsGgDNW1YnMCCUwCuhAMTFJqLXG47cC+duExkR/9j58tO6eyP2rD8CpNdz6VLYO5fOVsbeuTSlbKzwP25okOxZfwSPHo0A6PlCB9Yt2UHKPUNvSUx4HABatnouQUpKKnfjkh4/b5d67Nl4wpD39A1sbEti72SbPa+TLaVsLPH3M/QO7dl4Ag8vQ4P7ZkAot6+G57lvD6+6BN+M5PqVkMfOmS2zV132bDK8WPv7PSizVWbmTSfx6Jo1c1iu/WoauY7lu/FGqONilregWjSCMrYPL1dUPHo2ZfePhwDw9w3A2q4UDq52ucr5+wYQmX4Hp1Bymeia3KxTXa6ev8XVc4ae+riohIzrn7G41a/InZuRBN+OIjU1jf07zuDh6Z797/Osw+4tfgAc3H2exi2rAxBwMZjIMMN17XpAKCVKmGNhYZqXRLf6FblzIyIz5x9n8OhUJ3fOzacMOXedo3ErQ87kxBTOnbqeq+c6OSmFv9PfTKampnHlQhCOLrmPJ2O6GxhLCedSWDqXRGeuo0xLZ2JP5b4m3Bd9JIQyrV1Mmkk8ukJpwCqlqiql/JVS3yml/lZK/aKUKqWUuqaUckwv01wptS/LZo2UUn8qpS4rpcY8YN+eSqm9SqkfgTPpy4YppY4ppfyUUl/cb9gqpborpU4qpU4rpfYY428rW64MYbejMh6HBUVRtlyZXGXCgx5cpr5HLaLCYgkKNDQOD24+SdLde/x4bgFr/D7i1893Eh9997HzOjrZEhYam/E4PDQOxxwv+mWzlNGnaSQkJFPariQVKpUFDeZ+Ophlq0YzcIjHY+d5VGXLlSE8Sz2H34nCMUcdOpYrQ/id6MwyQdEZ9VyhhjP1W9Vk4fY3mL9xMrUbVwHgry3p9fz3R6w+OYcNy3cbpZ7LupQmPMuLdnhIDI4u2RvcjnmUKZujTE6WJS0YMMaTH5bufuyMOZV1sctef8HRuV5AHF3ssmcOjqbsQ15k/vrjb0MdH57B6v3T2fD1AeJjHv/NWHHL+1/jWN6BsFuRGY/DgyIpW+7x7148LlNdkyvUcEHTYM66V1j65wz6T+xm/OzOtoRluybEUjbHG/WsZfRpehLikyldplS2Mu286hLgf4cUI/cQZ2RwKU1YyENyZimTX878WNta0aqjO35HAowXOg8pUclYOFhmPLZwsCQlKjnPsvfCE7kXnoRNncK/G2MyxbwHtjDHwLoBozVNO6SUWgWMf0j5hkBrwBo4pZT6XdO03IMIDVoC9TVNu6qUqgMMAtpqmpailFoGDFVKbQdWAh3Sy+W60iqlxgJjAepat6OSVZ2cRXJRKvdEFJqm5SiTe7ucZTz7tcjsfQXcmlZFn6ZnaP03sClTik+2TuXU/gsEX8+7V+5R5Z330cqYmemo17AS3i9+TXJSCvMXD+PyxTucOnHtsTI9irym+3iU3PcLmZmbYVOmFJN7zKd2kyq8tfJFRraYiVuT9HpuNA2bMtZ8/NtrnDrgb4R6fry8+Rk+6Sk2fvsXSXfvPUa6vD3KcZpXmYdldmtYCb1eY2jbD7ApXZKPfxrPqcOXCb4Z+cDtHqa45f3PeYT6Lwqmuiabmeuo16omk7p+SHLiPT7aMJkrftfxO+hvnOA8avYHl6lSw4lRrzzF2+O+M1qunPI+rbQCl8mLzkzHtHkD+e1HH4KzvBEpNHleNCD6aCh2zZ1RuuI++VSm4j4GtjAbsDc1TTuU/vv3wKSHlP9N07REIFEptRdDI3VTPmWPaZp2fyBbF6AZ4Jt+opcEQjE0hg/cL6dpWq5XI03TvgS+BOjuODbf/9pnRnnSfXh7AC75XcMpy/g4p/L2uW6XhQdF41g+/zI6Mx1tezZlYpcPMpZ1erYlJ/acIy01jZjwOM4dDaBW4yqP3bAKC43FKcs7ZUdnWyLSb6dn5E0vEx4Wh85MYW1tSVxsIuGhsZzxu05sem/UMZ8r1HRzNVkDttfIjnQf1haAS37XccxSz47l7IkIjs5WPiwoe6+sY/kyRKTXc3hQFId+N9zOunTqOppew66sDZ79WnL8z3OkpeqJCY/jvG8AtRpV/kf13GuoB90HtjQ8x5lbOGa5neroYkdElp5vgLDgmDzKZP+/yMmtUSXadavP6Kk9sC5dEk2vce9eClu+9ylw3ozMg1oZMv99M3v9uZZ5eOY8yuTk+UwTjh+4aKjjyATOn7xGrfoV/1GDsLjl/a95ZmwXeoz0BODSias4VczsB3As70BkcBE0OCica3J4UDRnDl8iNtIwVMp391lqNqps1AZseEgsTtmuCaUzhgXkLBMeGovOTIe1jSVx6ddkR+fSzPx0MB/P3MCdW6b7vwgPicXJ5RFyutgRHpI754O88k4fgq5HsOkfXtMKwsLekpTIzB7XlMhkLMqUyLNs9LEQKgxzM3km8egKcwxszgahBqRmyWD1COXzk3XUtQK+0zStcfqPm6Zp76UvN8r7jS2r9mUM8vfZ5keXgYZb6e7NqpEQm0hkSPaLZWRIDInxSbinjyXtMtADn+1+GeubdKzDzSvB2W6Hht6KpFF7w8liWaoE7s2rcety8GNnv+gfRIWKDriWK4O5uQ7PLvXw+etStjI+f13iqacbAtDBsw5+6Q3U48cCqVbDGUtLc3RmioaNq3A9n3GaxrD1m/14d/kQ7y4f4rP9NF0GtAbS6zkukagcDZGo0Njs9TygNUf+OG34m7afpnE7Q31WqO6MuYUZMRHxhN2OpFG7LPXctBo3/+HY0q0/+ODdZxHefRbhs/scXf7XzJC3UWUS4pOIynGBjwqLIzEhGfdGlQ15/9eMI3vOPfA5pg5ZwYjO8xjReR6bvvuLtSv2/uPGa0bm3p/h3fszQ+a+TQ2ZG1c21HF+mRunZ+7blCO7zz/wOcLuRNPIowZgGALh3rgyN9Nvy/7X8/7XbPlyD+M9ZjLeYyaHt5zAa4jhDaZ7ixrcjb1bqGNds+UqhGvyiT/PUa1eRSxLGsZHN2hTmxsX7xj177h47jblKzvgUr4M5uZmdOzWgCP7szeQj+z3x+sZw3jd9l51M2YasLa14v0lw/hm8W7O++WedcHoOauUxaWCvSFn9wYc2Zcj5z5/vHo3MeTsWo/TxwIfut8XvL2wtrVixfxtJsmdU6lqttwLvcu9sET0qXqij4VSuoljrnJJdxJIS0ilVM0HD/Eqdor5EAJVGLd8lFJVgatAG03TfJRSKwF/oAfwiaZp25VSC4EmmqZ5KqXeA/qSZQgB0DqvIQRKKU/gdU3TeqU/rgv8hmEIQWj6UAFb4C5wkixDCPLqhb3vQT2wOU2YN5hmneuTnHiPTyd9y+X0qWQ+3zszY/qVWo2r8NqSEZSwKsHxPWdZNu2njO1fWzKCCycC2fZt5jQkVtaWvLZ4BJXdyoGCXT8d5pelD55SJM2t8iPlbelRg3GTnkJnpmPHVj9+XH2IF17syCX/IHz+uoxFCTOmzexDjdquxMUmMufdjQQHGS7kXZ6qz3PPt0XTNI75XOGrZX8C8OL4znTuWp+yjoYe3e1b/FjzkOmdzK/knvLoQcbPfY7mneuSlHiPha+s5vJpw0V66Z7peHf5EIBajSozZfELGdNo3Z8ax9zCjMmfDad6/Uqk3kvlq1kbOP3XRaxKWTJl0XAq1y6HUoqdP/vw67Jd+Yewe/QL2Ph3+9C8vZsh71vrM6bCWvrbK3j3WWTIW78CUz5Kn+LpwEWWv/8bAG261mPczD7YOVgTH5tI4IU7zBj9dbb9D53oRVLCvYdPo1WAc3z8u31p3iE987T1GVNLLd38Kt69P0vPXDHbtFTZMr/TBzsHm/TMQcwY9TVWpUow5aOBVK7pbKjjX4/z61f7HzlTccj7+8H8bg79c6/NgmN+EB0DZR3AeyT072mcffeo0doo+5nw6fM075o+XdVLX3H5lKExtcxnNuM9ZgIw+oNBdBroQdlyZYi4E80f3+7n+w83Fvi5VMmcfRwPyGWCazJA5wGtGPRKDzRNw3f3Wb6e9euDg1R0feTM97VoV4uXpvZAp9Ox87eT/PzVAYaP68zl87c5sv8iFiXMeWNOP2q4lSMuNpG5b64n+HYUg1/syKDR7bl9IyJjX9NfXk1MVMIDni0Hvb4AOWvz0htPozPTsXPTCX5euZ/h47sYcu7zN+T8sD813MsRF5PI3DfWZgwJ+G77a5SyscTcwoz4uCTefulb7iYk8/2uN7gRGErKPcPY3S0/H+GPDScemMN2RcQD1z9M7Olwgn4yTKNl3748Ls9UJXhjICWr2mLXxDDjQPCmQLQUPeUG1Hys57pvfZvl/4pxCE0mLDRaA/DU55ML/W8qzAbsNuAA0Aa4DAzHcKv/ayAEOAo0z9KALQ/UACoD8zVNW5nPvj3J0oBNXzYIeAtD724KMEHTtCNKqR7Ah+nLQzVN65pf5oI0YP8tHrUB+29R0Absv0IBGrD/Gv+CcYn/daZowJqSsRqwhakgDdh/jX/QgC1SBWjA/ls8bgO2KPxrGrDjjdiAXVb4DdjCHAOr1zTt5RzLDgK1cxZMv+X/SDRN2wfsy7FsLbA2j7Lbge2Pum8hhBBCCPHvI9/EJYQQQgjxhJFZCB6BpmnXgPqPsw+lVANgTY7FyZqmtXqc/QohhBBCPHGkAVs4NE07A+T+qhQhhBBCCPFEKTYNWCGEEEIIYSTSAyuEEEIIIYqT4j4GtjC/yEAIIYQQQojHJj2wQgghhBBPmmLeAysNWCGEEEKIJ4wq5l9yI0MIhBBCCCFEsSI9sEIIIYQQT5ri3QErDVghhBBCiCeNzEIghBBCCCFEIZIGrBBCCCHEk0Yz4s9DKKW6K6UuKqWuKKWm5bF+ilLqvFLqb6XUHqVUlYftU4YQ5OPGS3WKOkKBVfklpKgjFMiNkbWLOkKB6VKLOkHBuR65W9QRCiTKrWRRRyiwHjVaF3WEAtkecKSoIxRYyxkvF3WEAjNLLuoE/31p7S8XdYSCSyvqAAaFNYRAPWVIdQAAIABJREFUKWUGfA50BW4BvkqpzZqmnc9S7BTQXNO0u0qpccB8YNCD9is9sEIIIYQQwlRaAlc0TQvUNO0e8DPQJ2sBTdP2app2v7flCFDxYTuVBqwQQgghxJOm8IYQVABuZnl8K31ZfkYD2x+2UxlCIIQQQgjxhDHmEAKl1FhgbJZFX2qa9uX91XlskuezK6WGAc2Bjg97TmnACiGEEEKIfyy9sfplPqtvAZWyPK4IBOUspJTyAt4GOmqa9tAR5DKEQAghhBDiSVN4Qwh8gVpKqWpKqRLAc8DmrAWUUk2AL4DemqaFPkp86YEVQgghhHjCFNYsBJqmpSqlvIEdgBmwStO0c0qp94HjmqZtBhYANsB6pRTADU3Tej9ov9KAFUIIIYQQJqNp2jZgW45l72T53aug+5QGrBBCCCHEk0Yr3t8lKw1YIYQQQognTGENITAV+RCXEEIIIYQoVqQHVgghhBDiSVPMe2ClASuEEEII8YRR+qJO8HhkCIEQQgghhChWpAdWCCGEEOJJI0MIxH3talXhrZ6emOl0/HL8LF8d8M22flDLhgxu1Qi9pichOYX3Nu0mICySBhVdmNX3/hRois//9GHP+QCTZn357Wdo0dGN5KQUPpm2noDzub7VjZr1KjBl7gAsrczx3X+RFXO2GP7O7g0Y5u1FpRpOvDrgcy6fvQ2AbZlSvL14KLXrV2TXxhMsn7051z6NoV3tKkx7xhMzpeNX37N8tT97PQ9s1ZDBHo3Q6/XcvZfCext2ExAaiUfNykzu3g4LczNSUtP4ZPtBjgbcNEnG/LR1q8KbfQzHyIajZ/l6b/bsAzwaMrhNI9LSs8/6ZTeBIZGFkq15q+qMf+UpdDrF9q1+rP3eJ9t6Cwsz3pjRm1pursTGJjLnnY2EBMcAUK2GM69O7UEpa0s0vcaEMatIuZeWse37Hw3AtXwZxj6/0iTZPepX5fXBnuiUjk0Hz/Dd9uz1OvSppvRp34C0ND1R8Ym8/80OgiPiqF3JiWnDu2BtVQK9XmPV70fZ5XvJJBkfZtyCYbTs1oikxGQ+eWklV/yu5yoz4t3+eA1pi00Za/q6jM1jL0Xn7Y9gnw842MOWb4s6jYFHvfTjQpfPcdE1/bjQ64mKSz8uIuNwdbBlwYTe6JTC3EzHuj/9+HX/34WSuXWDqkwZbsi8ed8ZVm/NnrmxWwUmD/OkZiUnZn7+O3/6Xs5YN2FQe9o2rgbAqk1H2H20cI7l4ph5/GcjaNmjCcl3k1kwajlXTl3NVWbk7EF4De+Arb0Nve1eyFj+7Ks96TG6M2mpacSExfLxiysIvRFeKLmNqbjPQlAsGrBKqWtAc03Tch0hSqmqwFZN0+oXYH/fpm/zi5EiolOKGc905sVvNhASG8facUPYeyGAgLDMxsfW0/6sPWa4CHZyr84bT3fkpe82cjkkggHLfiRNr+Foa81G72Hs8w8kTW+ao6tFBzfKV3Vk9FMf496oEt7v9WXywGW5ynm/15fF72zA3+8G768cSfMOtTl+4BLXLwUze+IaJs3ql638veQU1izaSZVarlSp5WKS7DqleLtPZ8Z8vYGQmDjWeqfXc2hmPf/u58+6o+n1XKc6b/TsyEvfbCTqbiITvvuNsLgEarqU5ctR/eg81zQNqnyz/68zY7/cQHBMHD+/MoS95wOyNVC3nfRnvY8hu2fd6kx9piPjvtpo+mw6xcQp3Xlz8o+Eh8ay9KtR+Px1mRvXMk+57r0aEx+XxIjnluPZpS4vjuvMnHc3ojNTTJvZm3kfbCbwSii2pUuSlpo5uKpdBzcSE++ZLrtSvDm0MxM++ZWQqDhWzxzKAb8Art7JrFf/62H8su8Hku+l8qxnQyb178D0L34n6V4K7371BzdDo3EsY833M4fic/Y68YkP/Rpuo2rRrSEVarowsuFU3FvUYOJnI3jFc1aucke2nWLzF7tYdXpBoeZ7FH17wJB+MO3Dok5ikHFcfJp+XMzI47i4EcYvH2Q5LgYYjovwmARGzf2ZlNQ0SlpasHbW8+z3CyA8JsHkmae+0JmJ834lNDKOb98fysGTAVwNyswcEhHH7C93MPTp5tm2bduoGm5VnRn+9hosLMxYMX0gPqevkZBkunOvuGZu2aMxFWq5MsLtFeq0qsWkz0czqc2MXOWObD3Jb5/v4NuLi7Itv+J3jQkt3yI58R69Xu7KmHlDmTN4Ua7thWmZbAysMnhixtg2qOjKjchobkXFkJKmZ/vfF+lcp0a2MgnJmSdlyRIW3O+/T0pJzWisWpqboZm4X791l7rs2XQSAP/TN7EpXRJ7J9tsZeydbCllY4m/3w0A9mw6iUeXegDcDAzj9tXc7zaTE1M4d+I695JTTZa9QSVXbkZEcyvSUM/bTl+kU90H1/P9+vQPCiMszvACdCUkAktzMyzMzEyWNVf2yq7cSM+emqZnu99FOtV7tGPE1NzqlCfoViTBQdGkpurZt/s8bdrVzlamTbta7NxuaFwf2HeBJs2qAtC8RXUCA0IJvGL4+uq42ET06cezVUkLnn2uFT98d8hk2etVd+VmaDS3ww31uvOYPx2bZK/XExdvknzPcFyeDbyDi70NADdCorkZGg1AeHQCkXGJ2NuWNFnW/Hj0bMruHw115O8bgLVdKRxc7XKV8/cNIDK91/vfpkUjKGP78HKFpV61PI6Lxg84LgIyj4vUND0pqYY7CCXMzdAZvtrS5OrWcOVWSDRBYYbMu47406FZ9sx3wmO5cjMcfY5J6KtVKMsp/1uk6TWSklO5fDOc1g2rSuY8ePRuwe41BwC4cPQyNmWscXAtk6vchaOXiQyOzrX89L5zJKe/Kb9w5DJOFcqaNrCpaJrxfoqAURuYSqmqSqkLSqllwElguFLKRyl1Uim1Xillo5TqoZRal2UbT6XUlvTfByulziilziql5hXgqc2VUt8ppf5WSv2ilCqVvr93lFK+6fv7UinTXYVcStsQHBOX8Tg4Nh5nO5tc5Qa3asQfU0byWrf2fLh1X8byhhVd2TzpeX6bOJxZv+0xWe8rQFmX0oRnOSnDg2NwdCmdrYyjS2nCs7xQhgfHUDZHmaLgUtqGO1nqOSQmHpfSedRz60ZsnzqSKT3a8+HmfbnWP1W/FheCwkhJS8u1zlSc7WwIjs6SPToelzyOkefaNGLbtJFM6dWeuZv2FUo2RydbwkIzs4WHxeKY401NWSdbwkJjAdCnaSQkJFPariQVKjmABnM/eY5lX49m4JDWGduMeLEjv/x8lOSkFJNldy5jQ0hkZvbQqHicH9CS6tOuAYfPXsu1vF41VyzMdNwKy/2CZWqO5R0Iu5XZYxUeFEnZcg6FnuO/xNnehpCoHMeF/QOOi/YNOHzmWsZjF3sbfnpvOL/PH8N3f/iavPcV0jNnPZYj43F6QOasLt8Iw6NhVSxLmGNnY0WzOhVxKWv6dxTFMbNjBXtCb0ZkPA6/FYFjhX92vvUY1Yljf/gZK1qhUprxfoqCKXpI3YDVQFdgNOClaVpT4DgwBdgFtFZKWaeXHwSsVUqVB+YBnYHGQAulVN8CPOeXmqY1BGKB8enLl2qa1iJ9eEFJoNeDdqKUGquUOq6UOh51yudBRfPYNo+Febwr+enoabp/+g2f7jjIS56tMpb/fSuY3otXM3D5T4zp2JIS5qbrGcyrHZ8zap5t/X/DeJk8Y+VRz0dO02PBNyzcfpCXO7fKtq6Gc1km92jHrI27TZUyT3kfIrmz/3z4NE9/9A0Lfz/IWK9WeWxlfHn+d+fIlt9xY2auo17DSsx9/zcmj/+Oth3caNKsKjVqulC+ogOHDlw0Vez0YLkX5XcXo0frOtSp6sLqP45nW17Wzpr3X+zOrG92Fk1nwiPUv3h8+dVpj9Z1qFPFhdU7Mo+LkKh4Br+3hr7TV9GrTT0cSpcyfcDHOA6Onr3O4dNX+eqd55g9oSdnrtwhLa0Q5kkqhpnzvpYV/HzrMrQdtZvVYP3Hpvm8h3gwUzRgr2uadgRoDdQFDiml/IAXgCqapqUCfwDPKKXMgZ7Ab0ALYJ+maWHpZX4AOjzic97UNO3+PcrvgXbpv3dSSh1VSp3B0DCu96CdaJr2paZpzTVNa27fxOOR/2CA4Jh4XO0y3zm6lrYhNDb/d+zbzlykS45b3wCBYZEk3kuhlotjgZ7/YXoNac3STZNYumkSEaGxOGa5XeLoakdEes/afWHBMThmuYWZV5miEBITT7ks9exi95B6/vsinbPcpncpbcPi4c8wfd0ObkYW7q3YkJh4XLP0DLqUeXD27X7Zs5tSWGgcTs6Z2RydShMRHp+tTHhoLE7Ohl54nZnC2tqSuNhEwkPjOON3ndiYRJKTUznmE0DN2q7UqV+B2m6urFk/gYXLnqdipbJ8vGSY0bOHRsXj4pCZ3dnehrDo+FzlWtapzKieLZmyZFPG7WEAa6sSLHqlL8s2HuJs4B2j58vPM2O7sMxnNst8ZhN5Jxqnipk9QI7lHYgMjiq0LP9FoVHxuNgX4LhYmv24uC88JoGAoHCa1Kpg0rxg6L3Mdiw72BCeR+b8fLv5GMNnfM+keb+iUNwMMf3dhOKSufe4p1hxYh4rTswjIigK50qZt/0dK5YlIqhg51uTLg0Y8lY/3uk7n5R7phs2Z1KaEX+KgCkasPdfkRWwS9O0xuk/dTVNG52+bi0wEEOj0lfTtDjy7qB6VDmrT1NKWQHLgP6apjUAVgJWj/EcD3T2djBVytpTwb40FmY6ejR0Y69/YLYyVcpmNho7ulXneoThRK1gXxozneHPL1/GlmqO9tyOMm7jauuPR/Duuxjvvovx2X2OLn2bAuDeqBIJcUlEhcVlKx8VFkdiQjLujSoB0KVvU47sOW/UTP/E2VvBVM5Sz083cmPv+ez1XDlrPbtX53q4oZ5trSxZPrIvn+34i1PXc8+6YGpnbwZTxdGeCg6lMTfT0aOxG/vO5cjumJm9Q53q3AgvnNvZF/2DqFDJAddydpib6/D0qovPoeyfBvY5dJmnejQ0ZPOsg9/JawAcPxZItRouWFqaozNTNGxSmevXwtm66STP9V3M8AGfM3n8am7djOD1id8bPfv5q8FUcilDeUdDvT7V0p0Dftnr1a2yE9Of92LKkt+IikvMWG5upmOBd29+P3yePccv59y1SW35cg/jPWYy3mMmh7ecwGtIWwDcW9Tgbuzdf+1Y1+Li/LU8jovTOY6LSk5MH577uHC2t8HSwvAZZ9tSljSqUYFrhfCG4kJgMJVcy1DOyZC5a2t3DpwMfPiGGD5MVdrG8BJXs5IjNSs7cjTLkAhTKS6ZNy/fycvN3uTlZm9y6DdfvIYb+sfqtKpFQszdPMe65qdG46q8uvxF3uk7n+iwou/Y+aeK+xACU85CcAT4XClVU9O0K+njUitqmnYJ2Ad8DYzB0JgFOAosUko5AlHAYGDJIz5XZaWUh6ZpPunb/UVmYzVcKWUD9AeMNutATml6jTlb/mTliH7olGLjyXNcCY3Au4sH526HsNc/kCGtG+NRozKp+jRiEpOZ/ssOAJpWqcCYDi1I1aeh1zRmb/6T6LtJpoqK7/6LtOjozqpdU0lKTGHh9PUZ65ZumoR338WG39/blD6NlgW+By7im34ruI1XPcbN7I2dgzWzvhhB4IU7zHhxFQDf7nmTUjaWmFuY0carHm+P+pobAaFGy56m15iz+U++HNUPnU6x8fg5AkIj8O7qwblbIey9EMiQNo3xqFmZ1LQ0YhOTmb7OUM9D2jSiUtkyvNy5VcawgjFfbyAyIfFBT2nU7B9u/JMVY/phphQbfc8REBLBhG4enLsZwr7zgQxu25jWtTKzv/3zjkLJpk/TWPrpDuZ+OhidTseO309z/Wo4L4zuwCX/O/gcusz2rX5Mm9mHb38eR1xsEnPeM8yOEB+XxK9rj7L0q1FomsYxnwCO+VwplNxgqNcFP+xlyeRnMdMpNv91lsCgCF7q04YL14I5cDqQSQM6UNLSgo/GGUYRhUTGMWXJb3Rt4UbTWhWws7aiV1vDDZpZq3Zw6WZYoeUHOLbjNC26NeKbMwtITrzHJy99lbFumc9sxnvMBGD0B4PoNNADy1Il+P7SZ/zx7X6+/9D0s1Q8itdmwTE/iI4Bz/7gPRL69yy6PGl6jQU/7mXJq+nHxaF8jgsrCz56OctxsfQ3qpVz4NWBHdE0w/Ca73ceJ+C26adJStNrfLx6L4unPotOp9hy4CxXb0cwtl8bLlwN5uCpQOpUc2H+q72xtbaifePqjOnnweC3VmNuruPLGYMASEi8x7vLt5v0sxTFOfOxbado1aMJ311aRPLde3w8ennGuhUn5vFyszcBePGjoXQe3BbLUiX48foytn/9J2ve/4Wx84ZR0saKmWsnAxB6M5x3+v77Zgb5r1PGHGeVc0orpVRnDONaLdOLzNA0bXP6uqXACMBZ07S76cuGAG9h6I3dpmnaG+nLr/HgabS2AQeANsBlYLimaXeVUh8AzwHXgJsYhje89yjTaNV9e2GxG4BW5ZeQoo5QIDf+Z5qptkxJVwzvFLkeuVvUEQokyq3wZwF4XGV/Ll4f4tgecKSoIxRYyxkvF3WEAjMr3NnYnkilfzxa1BEKbFfa2sKZ1uIh2vddYLR2zsFNUwv9bzJqD6ymadeA+lke/4lhbGteZb0B7xzLfgR+zKNs1Yc8Z9181s0Ack3upmnaiPz2J4QQQgjxX1fcv8jgiZmnVQghhBBC/DcUi2/iAlBKlQX25LGqi6ZpEXksF0IIIYQQeSnmPbDFpgGb3khtXNQ5hBBCCCGKOxlCIIQQQgghRCEqNj2wQgghhBDCSAphyjJTkgasEEIIIcSTpni3X2UIgRBCCCGEKF6kB1YIIYQQ4glT3D/EJQ1YIYQQQognjRG/ibUoyBACIYQQQghRrEgPrBBCCCHEE0aGEAghhBBCiOKlmDdgZQiBEEIIIYQoVqQHNh8uvveKOkKB3erlUtQRCsTxTEpRRygwq+CEoo5QYKl2VkUdoUDsv/Ep6ggFphzsizpCgbSc8XJRRyiwYx+sKOoIBdajRuuijlAgqnqloo5QcKVtizpBsaWK+Ye4pAErhBBCCPGk0Rd1gMcjDVghhBBCiCdMce+BlTGwQgghhBCiWJEeWCGEEEKIJ03x7oCVBqwQQgghxBNHhhAIIYQQQghReKQHVgghhBDiCSPfxCWEEEIIIYoXGUIghBBCCCFE4ZEeWCGEEEKIJ4ySLzIQQgghhBDFigwhEEIIIYQQovBID6wQQgghxJOmeHfASgNWCCGEEOJJo4r5EAJpwJpIi+bV8B7XBTOdjt//OM1Pa49mW9+wQUUmvNyFGtWdef/DzRw4eLFIcrZ1q8KbfT0x0+nYcPQsX//pm2398x2a0q9VfdL0eiITEnln7U7uRMUBMLlnO9rXqQbAF7uPssPvUqFkbtmsGhPHdkGnU/y+829+XJ+jbutVZOLYLlSv5sT78zaz/5AhV83qzkwZ35VSpSzR6/WsWXuEvQf9TZKxuUdNXn69B2Zmiu2bTrLu27+yrbewMGPq+/2oVaccsTGJfDhtPSF3omnaqjqjJnbF3MKM1JQ0Vi7ayWnfqwB4dqvPc6M6oGkakWFxzJu5gdjou0bL3KJFdSZ4e6HT6di2zY+ffzqSK/Ob03pRu3Y5YmMTmf3+JkJCYjAz0/H6609Ts5YLZmY6du08y08/+QDwbP8WPP10IzQNrl4NY/68raSkpBktc07jF42kZY+mJN9NZsHIz7ly6mquMiM/GIzX8A7Y2tvQu/TwjOW9XupK7/Hd0afpSYxPYuFLX3Djwi2jZxz34SBaeDUgOfEen0z8lit/38hVpmajyry2ZCSWVhb47j7D8ulrAXjrqzFUrOEKgI1dSeJjEpnQaTYA1epWYNInwyhlWxK9XmNS1zmkJKcaNbtHvaq8PtgTnU7HpoNn+G579uvF0K5N6dO+AWl6PVFxibz/zQ6CI+NwdbBlwYTe6JTC3EzHuj/9+HX/30bN9k+8/RHs8wEHe9jybVGnydu4BcNo2a0RSYnJfPLSSq74Xc9VZsS7/fEa0habMtb0dRlrkhzN2tZi3Js90Znp+GPDcdZ9fSDbegsLM17/sD+16lYgNvouc6f+TEhQNACDRnegW7/m6NP0LP9oKycOX6FiVUfeWvBcxvauFe1Z8/keNn1/mGHjOtP92RbERCUA8O3infgefPzXl3EfDaZF1/Rzb/yqfM69Kry2bCSWViXw3XWG5dN+AqB6/UpM/HQYJawsSEvVs/T1H7h08ir9J3aj04BWAJiZm1GpdjkG1ZxMfHTCY+cVDyYNWBPQ6RSveHdl6rS1hIXHsWLJCxz2ucL1GxEZZUJCY5n38TYG9W9ZdDmV4u1+nRn7xQaCY+L4+dUh7D0XQGBIZEaZC7dDee6zH0lKSWWgR0Om9GrP1DXbaF+nGnUqOjPg0+8pYW7GN+MH8teFayQk3zNtZp3i1XFevDZjHWHhcXyx8HkOHbnC9ZuZdRsaFsvchdt4rl+LbNsmJaUw59Nt3A6KoqyDDSsXPY/vyavEJyQbPeOEaT15a/xqwkNiWbJmLEf2X+TG1bCMMt36NiU+NpGRfRfT8an6jJ7UlQ/fWk9M9F3eefVHIsPjqFLDmQ+XDmdoj0/QmekY93oPxgz4nNjou4ye1JXeA1vy/Zf7jJZ50itP8cbUnwkLi2XZ8hH4HL7M9euZ9dqjRyPi45J4fvgKOnWqw5ixnnww+zc6dnTHwsKMMS9+jaWlOau+GcOff54nLS2N//2vOaNGruTevVRmvtOXzp3rsmPHGaNkzqlljyZUqFmOEbUnUqdVLSYtG8Mkj+m5yh3Zcpzflm7n20tLsi3/88e/2PrFLgA8nmnOy5+8wPSn5xg1Ywuv+pSv7sKoljNwb1YN7wVDebXb3FzlJi4YyuIpa7hwPJDZP0+ieZf6HN9zlrkvrswoM+b9/iTEJgKgM9PxxvLRzB+/iqvnbmFrb02akd8o6JTizaGdmfDpr4RExbF6xlAO+AVw9U7m9cL/Rhi/fPADyfdSedazIZMGdGD6F78THpPAqLk/k5KaRklLC9bOep79fgGExxTti3zfHjCkH0z7sEhj5KtFt4ZUqOnCyIZTcW9Rg4mfjeAVz1m5yh3ZdorNX+xi1ekFJsmh0ykmvP0M08d+Q3hwLIt/HseRvRe4EZjlmtavOfGxSYzq+Skduzdg1ORuzJ26lsrVnejYoyEv9V2Eg3Np5q4cyYu9FnLrWjgTBizN2P/3e97k8J7zGfvbuOYQv373V64s/1SLrg0oX8OZUc2m4968Ot6fDOPVrrn/4yd+MozFr67mgm8gs9e/QnOv+hzffZbRs/rzw/wtHN99lhZdG/DirP688cwCflmyg1+W7ACgVfdG/G+cV/FpvBbzHth//Ye4lFLXlFKO/2C7EUqp8lkef6WUqvs4+3xU7m7lCAqK5k5wDKmpev7cf4G2bWplKxMSEkvg1TD0RXgANajsyo2IaG5FxpCapmf7qYt0qlcjWxnfgFskpRh6cf6+cQcXO1sAarg4cDzgFml6jcR7qVwMCqOde1WTZ65Tuxy3s9btgQu0a10zW5ng0FgCr+Wu21tBUdwOigIgIjKeqOi72NmVMnpGt3oVCLoZSfDtKFJT09i38ywenu7Zynh0dGfXVj8ADu45T+OWhp7sgIvBRIYberivB4RSooQ5FhZmKAUohZWVBQDW1pZEhMUZLbO7e3lu347izp1oUlP17P3zAm3a1M5Wpk3bWuzceRaA/fv9adq0KmAYRmVV0gKdTmFpaUFqip67dw1vCszMdFhamqPTKawsLQiPiDda5pw8+rRg95r9AFw4ehmbMtY4uJbJVe7C0ctEBkfnWn43LjHjdytrSzQTnJsePRqzZ52hd9r/xFVs7Eri4GKXrYyDix2lbEty4XggAHvW+dDm6ca59tWhT3P2bTD0gDbrVJer529x9ZyhxzguKgG93rj561Vz5WZoNLfDDdeLncf86dg4+/XixMWbJN8zXC/OBtzBxd4GgNQ0PSmphgZ1CXMzdEoZNds/1aIRlLEt6hT58+jZlN0/HgLA3zcAa7tSOLja5Srn7xtAZHCMyXK4NajInRuRBN8yXNP2b/8bj051smftVIfdm08CcHDXORq3qpGxfP/2v0lJSSPkdhR3bkTi1qBitm0bt6rBnZuRhN7JfV4ai8fTjdnzc/q5dzwQG7tS+Zx7VlzwTT/3fvahTc8mhpWaRinbkgBYly5JRB7XEM9nW7Lv12Mm+xuMTm/EnyJgkh5YpQwvt5qmFeUsYyOAs0AQgKZpLxbWEzs62hIaFpvxOCwsjjru5Qrr6R+Zs50NwdGZjaCQmHgaVnbNt3y/lvX5y99wS/ZiUBjjnvJg9f6TWJUwp2XNSgSEROS7rbE4lrUhNDwzc1h4HHXcyj9gi7y513bFwsKMoDtRxowHQFnn0oSFZL6YhIfE4F4/+wXb0cmWsBDDMaJP05MQn0zpMqWyDQlo16UuARfvZNxyXzJ3KyvWjicpKYWgGxEsnfe70TI7OtoQFprlmA2Po06d8jnK2BKaXkav10hISKZ06ZIc2O9P2za1WP/LJCwtzVm+bA9xcUnExcH6dUf56ecJJCencvz4VU4cz31L32h/Q3kHQrP0xIffisCxgkOejdX89B7fjWcn98K8hDlvdMnd0/W4ypYrQ9jtzGMuLCiKsuXKEJnleClbrgzhQbnLZFXfoxZRYbEEBYYCUKGGC5oGc9a9gp2jLfs2+mb0ChmLs70NIVGZ515oVDz1q+d/XevTvgGHz1zLeOxib8Nnr/yPSk5lWPTLgSLvfS0OHMs7EHYrs4c7PCiSsuUcTNpYzUtZ59KEBWe9psXi1rBSvmUM17QkSpcpRVkXO/yz3KoPD4mhrHPpbNt27NGQfduzDynpPbg1Xr2bcOncbVZ+vI342KT50N1UAAAgAElEQVTH+xvKlSHsdmZdFvTcWzF9LXN+fZUxsweglGJK9+x3TixLlqB5l/p8PvXHx8opHp3RemCVUlWVUheUUsuAk8BwpZSPUuqkUmq9UspGKdVDKbUuyzaeSqkt6b8PVkqdUUqdVUrNK8Bzns3y+HWl1HtKqf5Ac+AHpZSfUqqkUmqfUqr5Q/Y3Vil1XCl1POjW0QcVfXCuPJb9G3vq88yZz8cSezV1p24lF77ZewIAn0s3OHjhKmsmDmL+sKc5fT2INCP3+ORF5dlzU7DndbC35u3XevHRwm0m+X/JK2LO58nr78ja41eluhOjJ3Vl0YdbADAz19GrfwsmDF3BkG4fc/VyCINGtjdp6Jw9kPl1mrm7lyNNrzFwwBKGDV3OgIEtKVeuDDY2VrRpW4uhQ5YxcMASSlpZ4OVVz3iZc8i7Tgu2j83LdvBCrYl8Ne0Hhrz9rJGSZXrY/7uhTO7tcpbx7Ncio/cVDMdHvVY1mffy17zWcz5tn25M4/buOXdjdPn1UvdoXYc6VVxYveN4xrKQqHgGv7eGvtNX0atNPRxKG//ux3/OIxwLhRLjMY7bh10Pzc3NaO3pzsGdmUOLtq47ysinP2F8/6VEhsUx5vWn/3H2zHwPvz48qEyvUZ58MX0tw+u/wRdvr2Xy4hHZyrXq3ohzR68Un+EDGD7EZayfomDsIQRuwGqgKzAa8NI0rSlwHJgC7AJaK6Ws08sPAtam3+qfB3QGGgMtlFJ9/2kITdN+SX/OoZqmNdY0LfFh26Rv96Wmac01TWtevmKrf/r0hIXH4eyU+Q7TycmWiEjT3Tr9p0Ji4nHNcv/Mxc6G0Dx6RVrXqswYr5ZMWvUbKWmZ4+pW7jnGgE9/YOwXG1AoboQZvzczp7DwOJwdMzM7OdoW6LZ0qZIlmPdef75ec5DzF++YIiLhIbE4Zbk15ehiR0R49tv9YaGxOLkYjhGdmQ5rG0viYgyHqaNzad75+DkWvLOBO7cMdVqjtqFn/P7j/bvOUTdHD8hjZQ6LwylLr4iToy0R4dnrNSwsDuf0MjqdwtraktjYRLp0qYevbyBpaXqio+9y9uwtatd2pWmzqgTfiSEmJpG0ND0HD16kbr3sPdGPq/f4bqw4uYAVJxcQcScS50plM9Y5VixLRFDkA7bO376fD9G2r3HGpz8zypPP987k870ziQiOxqmCfcY6p/L2uXrTwoOicSyffxmdmY62PZtyYKNvtm3OHL5EbGQ8yYn38N19lpqNKhsl/32hUfG42Geee872NoRF5z73WtapzKieLZmydFPGsIGswmMSCAgKp0mtCkbN91/xzNguLPOZzTKf2UTeicapokPGOsfyDkQG/5+98w6Pquj++OdsEgi9d5AmCU1FFAREiiAIKvYC9oYIWH4W7A17fV/BrmBB7A1QKSrNV3rvJSCdBEIgBQJp5/fH3JBN2DTd3bsL83me+2Tv3Ll3vzuZO/fcmTNnAt/OFiQxIZladb3btMokeY3YmDwpR/OYNi2a1OR0EuOTj2kPk7xGKM88J4a4tbs4sC/v2XNgn3GBUVWmfL+Q2Lb/rN246LaevD37Sd6e/ST7dh+gVoO8sjT3Vf7RmcRd+33ceyZP74Gd+WuS4yLx0yJi2jfNd273yzow8/t/3vHlCqr+21zA3wbsVlWdB3QCWgN/icgy4EagsapmAVOAi0QkErgAmAB0AGaq6l4nz3igm5+1BY1163fToEE16tatQmSkh3O7t2LO3Di3ZR3Dqu3xNK5ZjQbVKxMZ4aHf6bHMXL05X56WDWrx5BW9uGvsRJLS8t4DPCJUKR8NQEy9mrSoV5M5G46dHetv1m3YTcMG1ahbxynbbq34a37JyjYy0sNzj1/K1OmrmPm/wEV9WL9mFw0aVadO/apERkbQo09b5s3KH+1g3qz1nHeh8Ws8p1fro5EGKlSM5tk3r+Xjt35nzfLtR/Mn7knlpGa1qFLV9Fq179Sc7VsS/aZ53bpd+epsz3NbMWfuxnx55s7ZSJ8+bQHo3r0lS5ea//eePSmcfnpjAKKjo2jdqgHbt+9jT0IKrVrXp2xZ46nUvn0Ttm3zn2YwPaZD2j/IkPYP8tdPC+l9fXcAWp3VgoPJh0rlPtDg5Dz3mbMuaM/Ojf55wZk0dibDej7LsJ7PMvfXZfS6qjMALc9oysGU9HxDmABJCcmkpx2m5RnmAdnrqs7Mnbzs6PHTu7die1w8iV7+gounr6Zpm4aULVcGT4SHU7rEsM3PL2hrtsTTqE5V6tc07UWfji2ZvTx/exHbqBaPXt+b+0ZPYL+XT3HtahUpG2XqQaXyZTmteQO2uGCIhQOTPviDoZ2fYGjnJ5gzaTG9B50NQMsOzTmUcijo7gMA61ftpH7jGtRpUI3IyAi69zuVeTMLtGkz19J7QHsAzjmvDcsXbHbS19G936lERUVQp0E16jeuwfqVedE9evhwH6ju1UnRpVdrtsQl/CPdkz6awbBuIxnWbSRzf11Kr2uce+/MZkXfe2c2A6DXNZ2Z+6u59/btTubUs2MBaNet5VH3HYDylctx6tmxR/NagoO/fWBzX6EE+E1VB/rI8zUwDEgCFqpqqvgeFy4JWeQ3wqP/4XX8Sk6OMuqt33jlhavweITJU1eyZWsiN9/QlfUb4pkzL47YmLo8+9RlVKxUls6dTubm67ty8+AxQdWZnaO88MN03ht8GREi/LhgNZsS9jGsb2dW70hg5urN3H9hN8qXjeL1Gy4AYPeBVO4eO5HICA+fDrsKgLQjGTzyxZSguBBk5yj/ffd3Xnv2Sjwe4dffVrJl2z5uua4r6zbGM2d+HC1b1OXZxy+lUsWydOl4Mjdf25Wbho6l5zktOa1tQypXjub83sYQe+k/k4nzaoj8QU52Dm+/8isvvHU9nggP0yYsZevmvdwwpCcb1uxi3uz1TJmwhBHPXsbHP91NanI6Lzz6HQADru5I/UbVGXRbdwbdZoyxR4aNIykxlfEfzOS1j24hKyubPbuTee3pH/2nOUcZPfo3Xn75GjwRwuTJK9i6JZGbbjqH9Rt2M3dOHL/+upxHHr2Iz8YNITU1neeenQDATz8tZsRDFzBm7G0IwpSpK9jszE6ePWs9771/C9nZOcTFJfDLz4Fr4Bf8uoSz+p/OpxtHc+RQBq/d8vbRY+8teZUh7R8E4LaXr+PcgV0pW74MX2x7j8lj/mDcM99y8fB+nN7rFLIzs0ndn8YrN73lf42/raRD77aMXfg8R9IzeOPuT44ee3vGE0dDYo1+cDz3j76JMtFlWPTHKhb+ftRTih6XdmDmD/kniqQlH+KHd39j1G+Poqos/H0VC37zb7SH7Bzl1S9mMPrey4nwCBP/WsXmXfu44+IurN0Sz+zlm7n7ym6Ui47ipSEXApCQlMp9b02gab3q3HtVd1TNUPPn0xaxaad/X2b+Cfc/AwuWwYFk6HEFDL8ZrrjAbVV5LJi6nA59T+Pjla+a0E93fHT02Dtzn2Vo5ycAuPW5q+l5VWfKli/D5xv+y5RPZvH5C35sH7JzeOeFSTz/3k14IoRpPy5h66Y9XD+sFxtX72TezHVM+WExI168grG/3EdqcjovjvgKMJNRZ09dxfsT7iEnK4e3n590dIJh2ego2nc+mVEjf8r3fbfe15dmLeuBQsLO/YwaOeFf/4YF01bS4bxTGLvkBXPvDfv46LG3Zz/JsG4jARh9/+fc/84tlImOYtHvq1jo3Edv3vspQ14cSESkh4zDmbx572dHzz/7gtNZPGM1Rw4FNgqP3wlF38ZSIP7ypxGRJsDPqtpWRGoBi4FzVTVORMoDDVV1g4hEAJuAhcC3qvqNiNQD5gFnAPuBqcBoVZ0gIluAM1X1mNZORKKA3RjXhTRgFjBFVZ92fGvfUNUZTt6ZwAOquqioa+bSs8/LYfefTTyljNsSSkX1dZluSyg10fHh49+US1aVkHivKzGemUvcllBqIqpXKz5TCJF4SeD9Y/3Ngufec1tCqenXvJPbEkqFNPOfW1LQ2PHPemfdZMr+j0IiDEff05/ym50zdekzQf9NAQmjpap7MVEAvhSRFRjjtKVzLBv4Gejn/EVVdwOPADOA5cASVS32lUtVM4GRwHznWt5jGp8A7+VO4vLLD7NYLBaLxWKxuI7fXAhUdQvQ1mt/Osa31Vfe4cDwAmlfAMfEn1DVJsV87yhglI/074HvvZJ6lPSaFovFYrFYLMczdilZi8VisVgsFkt4YQ3YwCMiNYA/fBzqpaqBj55vsVgsFovFYgkZwsKAdYzUY9dStFgsFovFYrGUnjDvgQ3IJC6LxWKxWCwWSwgTxIUMROR8EVkvInEi8rCP42VF5Gvn+HwnslWRWAPWYrFYLBaLxRIQnPCpb2OiT7UGBopI6wLZbgX2q+rJwH8wq7MWiTVgLRaLxWKxWE40cvy4FU1HIE5VN6tqBvAVcHGBPBcDnzqfvwN6FbfIlTVgLRaLxWKxWE4wRNV/m8hgEVnktQ32+qoGwHav/R1OGr7yqGoWkAzUKEp/WEzislgsFovFYrGEJqr6AfBBIYd99aQWdJwtSZ58WAPWYrFYLBaL5UQjeFEIdgDe6xQ3BHYVkmeHiEQCVYCkoi5qXQgsFovFYrFYTjRy1H9b0SwEWohIUxEpA1wDTCyQZyJwo/P5CmC6atEWtu2BtVgsFovFYrEEBFXNEpHhwFQgAhirqqtFZCSwSFUnAmOAcSISh+l5vaa461oD1mKxWCwWi+VEI4gLGajqr8CvBdKe9Pp8GLiyNNe0BmwhpJ4U5baEUtPw+21uSygV+7o3Kj5TiHG4ehW3JZSa8gmZbksoFeVn1XVbQqnJvKua2xJKRcQRtxWUnn7NO7ktodRM3jTPbQmlouWY092WUGqaTCjrtoTwxa7EZbFYLBaLxWKxBA/bA2uxWCwWi8VyohHmPbDWgLVYLBaLxWI50Sg+ekBIY10ILBaLxWKxWCxhhe2BtVgsFovFYjnR0By3FfwrrAFrsVgsFovFcqIR5j6w1oXAYrFYLBaLxRJW2B5Yi8VisVgslhONMJ/EZQ1Yi8VisVgslhMN60JgsVgsFovFYrEED9sDa7FYLBaLxXKiEeY9sNaAtVgsFovFYjnRCHMD1roQWCwWi8VisVjCCtsDa7FYLBaLxXKikWMXMrA4dG7ThAcG9sDj8fDTnyv5dPLCfMevPa89F59zCtk5OexPTWfkx1OJT0olplEtHr6uFxWiy5Cjythf5vPbwg0B1TrkqUvp0LMVR9Izef2BL9m0escxeU5u25D7XhtI2egoFs5Yy3vP/AjA9ff1o/N5bclRJTkxjdcf+IKkPSkAnNKpOXc8eSmRkRGk7E9jxNVvB/R3dDqlCfdfa8p8wqyVfPZL/jIf1Lc9A7qbMj+Qks6zY6YSvy81oJp8abzveqNx4syVfPZzfo3tYhvwf9f14ORGtXji7V+YvnDj0WPDrj6Hs9s1BWDsT/P4fX5g64UvOpzZlOF39iLC4+GXKcv58uv5+Y6fekpDhg3pRfNmtRn5wkRm/7k+6BoBDq5KZM+X6yBHqXJOQ6r3b3pMntSF8eybuAkEyjasRL3BpwKw99sNHFy5F3KgfOsa1BoYi4gEVO8ZXU7mzhH98XiEKT8u4ZuP/8x3PCoqggeeu4wWreqTkpzOiw99Q8KuA5zeqTm33H0ekVERZGVm89F/prJ84d8B1ZpLuNdlgDtfvY6OfU/jcPoRXr/jQ+KWbT0mz01PXUHvQWdTsWoFLqkz2AWVvnnsJZg5F6pXg0mfuK0mj3OaNeaxPj2IEA/fLlvFB3Pz14tr2p/KtWecRo7mcCgjk8d//Z1NiUlEejw8f8F5tK5bm0iP8NPKtbw/Z2Eh3/LvOLNTc+68vy8ej4cpE5by9Wd/5TseFRXBg09fQouW9UhNTuf5x74jYXfy0eO16lTmo6+HMu7DWXw3fi61alfmwacvoXqNCuSo8uuPS/jp6wUB0R4wrAuBBcAjwkPXnsvd//2RK5/4hL4dW9K0XvV8edZt28v1z41n4NPj+GPxBu6+shsAhzMyeWrMFK5+6jPu+s8P3H91DyqWKxswrR16tKJ+01rc2uMFRj36DcOfv8JnvuHPXcGoR7/h1h4vUL9pLc7s0RKA7z+YztB+rzK8/2vMn76aQff0BaBC5WiGP3sFz9z2EUP6vMzzQz8N2G8AU+YjbjiXe17/kasf+YS+nVrStH7+Ml+/dS83Pj2eax8fx/RFG7jr6m4B1eRL44M3nsu9r/7INQ99Qp/Ox2pM2JfKsx9MZdrcdfnSzz6tKbFNanP9Y+O45ekvuK7/mVSILhNM+Xg8wj3Dz+Phx77lpts/oleP1jQ+qUa+PAl7Unj5tV/5Y/qaoGrzRnOUPePX0uDe9jR59mxSFuzmyK60fHkyEg6S9OvfNHq4I01Gnk2ta2IBSI87QHrcARo/3YXGI7tweEsy6ev3B1SvxyMMe+RCHh82jsGXvUWP80/hpGa18uXpe2l70lIOc8uAN/nx8znccs95AKTsP8hT94znzivf5rUnfuDB5y8PqNajmsO8LgN06HsqDU6uw82nPsibwz/mrv/e5DPfvF+Xcnf3p4OqrSRc0g8+eNVtFfnxiPDU+edy+1c/0f/9T7mwTSzNa+avF5NWreOiD8dx8Ufj+XDuIh7p3R2A81u1oExEBBd9OI5Lx3zB1aefQoMqlf2v0SMMH9GPx+75gtuvfocefdtwUtOa+fKcP+B00lLTufnyt/jhy3ncOrx3vuND/q8vC+fGHd3Pzs7hgzencdvV73LPLWMZcGWHY65pCSx+NWBFpKqIDC0mTxMRGVSCazURkVX+UxdY2jSty/Y9B9iZmExWdg7TFqyje7vm+fIsXr+dIxlZAKzatJs61SoCsC3hANv3HAAgMfkgSanpVKtULmBaO/Vpyx8/mLfcdUu3UrFSOarVyt9oVKtVmfKVolm3xPRO/PHDQjr3OQWAQ2lHjuaLLl/m6FtcjwFn8NeUFezdZX5L8r78BoS/adOsLjsSDrBrr1Pm89fRrX2BMl+XV+Yr43ZTu3rFgGoqSOvm+TX+Nm8d3c7Ir3F3Ygpx2xPJKfA23LRBDZau20F2jnL4SBYbtyfS6dQmQVQPLWPrsWvXAXbHJ5OVlcP0WWs5u0uLfHkSElLY/PfeY/QHk8N/JxNVuzxlapVHIj1U7liXg8v25MuTPHsnVXs2IqJCFACRlZ2XRAHNzEazctDMHDRbiagcWOMqtm1Ddm9PIn7nfrKyspk1dSWdnRfEXDr3aMXvk5YB8Ofva2jXsRkAm9bHk7TXjCJs3bSHMmUiiYqKCKheCP+6DND5gvb8/oXpeVu3cBMVqpSnet0qx+Rbt3ATSfHJx6S7TYfToGolt1Xk59T6ddmadIDtB5LJzMnhlzXr6R2Tv14czMg4+rlcVBRg6ocqlCsTRYQI0VGRZGbnkHbkCP4mtk0Ddu3YT/yuA2Rl5TBr2mq6dIvNl6dz91h++2UFALOnr+H0DnkjOF26xxK/cz9bN+89mpa0L4249fEApB/KYNvfidSs5X/jO6Co+m9zAX/3wFYFijRggSZAsQZsuFG7WkUS9ucNTe/Zn0btaoW3NBefcwpzVm45Jr1N07pERXrYsfdAIGQCUKNOFRJ35V0/Mf4ANQs04jXrViHRa/gkcXcyNerk5bnxgf58NudJel58BuPemAxAw2a1qFilPC9/NYxRk+6j12VnBuw3ANSqVpGEJK8yT0qjVhFlPqD7KcxdsSWgmgpSu5Qavdm4bS+dT21C2TKRVKkYzRmtGlKnRnCfXjVrVmLP3pSj+3v3plKzRnBfAkpC1v7DRFaLProfWS2azP35H4QZCQfJSDjEthcXsO2F+RxclQhAueZVKd+yOpvvn8XmB2ZRoU0NytYP7G+sUbsSe70MpMSEFGrUrlxonpzsHA6mHaFy1fL58nTt3ZpN63aTmZkdUL0Q/nUZoGb96uzdkXR0P3FXEjUKjJRZSkedShWJT82rF/EpadSpdOz9c+0Zp/H70JsZ0escnp06E4Cp6zaSnpHJX/cMZubw2xg7fzHJh/1vwNasVYm9CXn32949KdSoVanQPDnZysG0w1SuUo7o6CiuuuFsxn00q9Dr16lXhZNj67LOhyteSJOj/ttcwN8G7EtAcxFZJiKvOtsqEVkpIld75TnHyfN/Tk/rnyKyxNm6lOSLijpPREY437lcRF5y0k4Wkd+dtCUi0tzHNQeLyCIRWbR33dx/XRhayFtJv06taNW4Dp9NXZQvvUaVCoy89Xye+XhaQF9ofPn2FdTq0/3PK8unr/3KDV1GMmPCYi668RwAPBEeWpzSkCdv/pDHb3ifgXf1oUHTWj4u5B98a/RdcOd3aUWrJnUY9+sin8cDhg+NhdWLgsxftZU5y//moyev4dlhF7AybjfZ2cF1ui9FEYccx9SPHCVjzyEaPXgm9W4/hYRPV5N9KJOMhENk7D5Is1e70ezVbhxal8ShDUk+r+k/bSW5B4vO07h5LW65pw+jnpvof4G+CPO6DPyr32Dxje824tgyHb94Ob3f+ZhXp//J0K5nAab3Nltz6DrqQ859eww3n9WeRlWP7REPhMhjFBaS5/rBPfjhy3kcTs/0eenoclE8+dKVvPvGVA4dzPCZJ1RRzfHb5gb+nsT1MNBWVduJyOXAEOA0oCawUERmO3keUNULAUSkPHCeqh4WkRbAl0BJuu72+DpPRPoBlwBnqeohEcl9vR4PvKSqP4pIND6Md1X9APgA4Mzb3ihVq7Znfxp1vHojaleryN4Dxw6hd2x1Erdc0JHBr3xDZlZer0mF6DK8efclvPPjX6zavLs0X10iLrz+bM4f2BmADcu3UbN+1aPHatatyr6ElHz59+5Opma9vIakZr0q7Ntz7JDazAlLeGbs7Xz+nykkxieTsv8gR9IzOJKewaoFm2jaqj47/957zHn+YE9SGnWqe5V5dd9l3qH1Sdx8UUeGvJC/zIOBL42JPjQWxicTF/DJRDMxYOSd/dmeELieeV/sTUylttewWK1aldiXFFjXkH9CZLVosvYfPrqftf8wkVXLHpMnulkVJNJDVK3yRNWpQGbCIQ6tTyK6WRU80aY5rHBKTQ5vSqZ8TOB65hITUqjlNepRs07lo24BBfMk7knBE+GhQsWypCanm/y1K/PEGwN57Ykf2L0jsP66uYRrXb5ocC/63dwDgA2L/6ZWw7z/a8361UmKD075Ha/Ep6ZRt1JevahbuSJ70g4Wmv+X1et55vxeAFzUJpY/N20lKyeHpEPpLNmxi7b16rD9gH/dNxL3pFLLawSxVm0f95uTJ3FPKp4IoULFaFKT02nZtgHnnNuK24b3pmKlaHJylIyMLCZ+u5CICA9PvnwV06eu4q+Z6wp+rSXABHISV1fgS1XNVtUEYBbQwUe+KOBDEVkJfAu0LuH1CzuvN/Cxqh4CUNUkEakENFDVH520w7nH/cWaLfE0qlOV+jUrExnhoU/HlsxevjlfnthGtXj0+t7cN3oC+1PTj6ZHRnh4ddgAfpm7hj8Wbyx4ab/w87i/GN7/NYb3f42501bR6zLzr2h5emMOpqazf29+A3b/3hTS047Q8vTGAPS6rAPzphmX5PpN8hzVO/Vuy45Nxtdw3rSVtO3QDE+Eh7LRUcS2a8z2uISA/B6ANX8XKPOzWvLn0vxlHnNSLR65uTcP/Dd/mQeLtZvjaVS3KvVqGY3ndWrJ7CWbiz8RMzmickUzLH5yo5qcfFJN5vtwOwkk69bvpkGDatStW4XISA/ndm/FHK+JDKFCdJPKZCYcInPvITQrh5QF8VQ4rXa+PBVPr036OtOzmp2aQWbCQaJqlSOqejnSN+xHs3PQrBwOrd9PmXoVAqp3/eqd1D+pOnXqVyUyMoLufU9h3qz8D8B5s9bR+6J2AJzTu/XRSAMVKkUzcvR1fDzqd9Ys2xZQnd6Ea12e9MEfDO38BEM7P8GcSYvpPehsAFp2aM6hlEMh6esaTqzcFU+T6tVoWKUyUR4PF7SO5Y8N+etF42p5HSY9WjRjy37z8rIrJZVOTRoBUC4qknb167F5n/9HP9av2UmDRtWpW78qkZEeuvdpw9w/80fBmDt7PeddYKKSdDu3NcsWmfvt/sGfcMMlo7jhklH8+NV8vvrkf0z81swhue+Ji9j2916+/2Ke3zUHhTB3IQhkGK2SxqD5PyAB01PrAQ4Xnb3Y84RjRwcCGw8HyM5RXv1iBqPvvZwIjzDxr1Vs3rWPOy7uwtot8cxevpm7r+xGuegoXhpyIQAJSanc99YEzusQS/sWDahSIZoLu7QB4JmPp7Jhe2B6LhfOWEOHnq0YO+sxDqdn8J8Hvzp67K1fH2B4/9fM58e/ywujNXMtC2euBeDmhy6kYbPaZub3zv2MfuxbALZv2sOiWet4d8qD5OQoU7+ex9YN8QH5DeCU+bgZjHrwcjweYdLsVWzeuY/Bl5oy/3PpZu6+phvlykbx4jBT5vFJqTzw3wkB0+RL42uf5df49859DL6sC2v/NhpbNa3DK/cOoFKFaM5p14zbL+vMwEc+IzLSwwePG8+bg+kZPPXuZLKD3FDk5Cij3vqNV164Co9HmDx1JVu2JnLzDV1ZvyGeOfPiiI2py7NPXUbFSmXp3Olkbr6+KzcPHhNUnRLhodagluz47xLIUSqf3YCyDSqS+FMc0U0qU7Fdbcq3qcHB1fvY8sRf4BFqXhlDRMUyVDyzDofWJbH1qbkgUL5tTSq2q138l/4LcrJzeOelX3j+3RvweDxMm7CErZv2cv2d57JxzU7mzVrPlB+XMOL5yxg78R5SU9J58SFznw24+izqn1SdQYO7M2iwmc396JDPSN5feK+XPwj3ugywYOpyOvQ9jY9XvsqR9Axev+Ojo8femfssQzs/AcCtz11Nz6s6U7Z8GT7f8F+mfDKLz1/4Meh6C+s6S+sAACAASURBVHL/M7BgGRxIhh5XwPCb4YoL3NWUrcrIqdMZM/AyIjzCd8tXE5e4j7u7dWbV7gSmb9zMdWe2o0vTk8jKySY5/QgPTZwKwPhFy3nxoj78MvgGBPh+xWrW70n0u8acbOWtVyfzwqhr8XiEqZOWsXXzXm4Y3IMNa3cx788NTJm4lIeeuZSPvx9Oako6Lzz2fZHXbHNaI87rfxqbNybw7ucm1NrYd6azcE7oveAXSpi7z4g//X9EpAawRFUbi8hlwB1Af6A6sAg4C2gAvKGq3Z1z/gPsUNXXReRmYKyqiog0AX5W1baFfFdh550PPAn0znUhcHph52FcCH4SkbJARFG9sKV1IQgFav2+3W0JpWJf90ZuSyg1EoZxn8sn+PbdClUqPhFmEyGAzLuquS2hVOw/pWrxmUKMqj8sc1tCqZm8Kbx65lqOudNtCaWmyYTgxvX2B9MWPBnwTrWScH612/xm50zZ/1HQf5NfXQhUdR/wlxP+qjOwAlgOTAdGqGq8k5blTKb6P+Ad4EbHwIwBStqN4PM8VZ0CTAQWicgy4AEn//XA3SKyApgD1P3XP9hisVgsFoslHMnJ8d/mAn53IVDVgiGyHixwPBPoVSDPqV6fH3HybQF89r46xzf6Os859hIm2kHB/OcWrd5isVgsFovlBCDMXQjsSlwWi8VisVgslrAikJO4/IKI9AVeLpD8t6pe6oYei8VisVgslnBHXRr69xchb8Cq6lRgqts6LBaLxWKxWI4brAuBxWKxWCwWi8USPEK+B9ZisVgsFovF4mdcWoDAX1gD1mKxWCwWi+VEQ8PbB9a6EFgsFovFYrFYwgrbA2uxWCwWi8VygqHWhcBisVgsFovFElZYFwKLxWKxWCwWiyV42B5Yi8VisVgslhMM60JgsVgsFovFYgkvwtyFQDTMV2IIR0RksKp+4LaOkhJuesFqDgbhphes5mAQbnrBag4G4aYXwlPziYT1gXWHwW4LKCXhphes5mAQbnrBag4G4aYXrOZgEG56ITw1nzBYA9ZisVgsFovFElZYA9ZisVgsFovFElZYA9Ydws2nJtz0gtUcDMJNL1jNwSDc9ILVHAzCTS+Ep+YTBjuJy2KxWCwWi8USVtgeWIvFYrFYLBZLWGENWIvFYrFYLBZLWGENWIvFYrFYLBZLWGEN2CAhIk1Lkmb554hI2ZKkWSwWiyWwiEgFEfF47XtEpLybmizHF9aADR7f+0j7LugqSoGI1BGRMSIy2dlvLSK3uq2rCOaWMC1kEJFOIlLJa7+SiJzlpiZfiEhL5297X5vb+gpDRJqKSLTXfjkRaeKeopIhIr+JSFWv/WoiMtVNTYUhIpcVtbmtryAiUr2ozW19hRGGdfkPwNtgLQ/87pKWEiMir4hIZRGJEpE/RCRRRK5zW5flWCLdFnC84zz42wBVCjTmlYFo32eFDJ8AHwOPOfsbgK+BMW4J8oWI1AUaAOVE5HRAnEOVyd+AhiLvAt4G4EEfaaHAfZhVaV73cUyBc4Mrp8R8C3Tx2s920jq4I6fE1FTVA7k7qrpfRGq7KagILirimAI/BEtICVmM0SU+jinQLLhySky41eVoVU3L3VHVtDDpge2jqiNE5FJgB3AlMAP43F1ZloJYAzbwxAIXAlXJ39CnAre7oqjk1FTVb0TkEQBVzRKRbLdF+aAvcBPQEHjDKz0VeNQNQaVA1CuWnarmiEjI3ZeqmrukYj9VPex9zLtXKASJVNWM3B1VzRCRMm4KKiE5InKSqm4DEJHGGOMq5FDVm93WUBpUNVxdt8KtLh8UkfaqugRARM4A0l3WVBKinL/9gS9VNUnE17uOxW1C7kF5vKGqE4AJItJZVUN6ONsHB0WkBs6DU0Q6AcnuSjoWVf0U+FRELldVX64aocxmEbkb0+sKMBTY7KKe4pjDsb3DvtJChb0iMkBVJwKIyMVAosuaSsJjwP9EZJaz340wWJddRC7AjDgdfalR1ZHuKToWEWmpqusKc33JNbhCkHCry/cC34rILme/HnC1i3pKyiQRWYcxtoeKSC3gcDHnWFzALmQQJJyb4HagCV4vDqp6i1uaisNp4EcDbYFVQC3gSlVd7qqwQnAmbF3OsWUcUg9Qb5xh4VGYIXjF+I3dq6p7XBVWAC83jc+Ba70OVQbeU9WWrggrBhFpDowH6jtJO4AbVDXOPVUlQ0RqAp0wQ91zVTWUjRVE5D2My05P4CPgCmCBqoaU37yIfKCqg0Vkho/Dqqoh6Q4TjnVZRKIwo5ACrFPVTJcllQgRqQakqGq2iFQAKqlqvNu6LPmxk7j8y/nAeiAOeLjAsQnvv/9+r/379/dPSko6d8eOHWdeeOGF851j7TCTjVYDKwj8W6pgjKY45/t89kTs3LmzTE5OTuXDhw8nb9269e/WrVu3AdY5eucBy4BFQEfnlIud6+Wmdw2Q/sLKeQJw8ZAhQxrt3LlzcHZ29lPPPfdc8wLnvowxxlcRvN6AQuuFqu5R1QWqmqiqCapaR1XLFTi/MrATeCvAOouqF32B14CGPXr0eD8uLm729u3b//fll1+OjYiIeMTJ8yqmfqwAfsS4zYB5oUjH1ItlwHsB0O6zjFV1k6p22rx58+2ZmZlJqtpRVdsVOHcKcAD4OQC6SqTTi7LA16oaFx8fP69z587nquqkO+64o35SUtIiII389aA88Aum3FcDLwVWfpH6u6jqR6tWraqpqk/Ex8e/DDTyOh7Ie69E5QrEqeppmDrZT1V7quo0VW2kqvX37t2b64IUS159XQakYHoUAZ7G3I+5x/oH47fk1uWkpKTXs7KyUlW1vKp+ArR2skQBnwIrgbXAI7iIiAwDKsTExNwXExPzR4sWLZaLyFAXJZWojuTk5GzaunXrinbt2o0DmDx58pX79u37H6ZcF5Pn71+J/HUkEfhvYH+CJR+qajf/bBFOA9NMVcuo6nJVbZ17HFjmNJblnbQ7VfVr53OMqrZwPtdX1d2qWjWAWvur6mRVFVXtpKrzfeVbvnz5QVXt7OSbrKr9gCVqGvx+Xtea6Xyu6ORFVU9V1XXBLGdglZOnifP9n6nqFV7nXqCqv6lqpKpWUNVFqlo5gOVcknpRa+jQoR9VrVp1DDD2xRdfnDNr1qzNBa7xpqp+oapvBVhrofUCuD932759+7Zbb711lMfjuX/dunVrH3744Q+cfH2cskVVX3a23P/HKj/o+yd14gWgam6dOHjw4FePP/74dwXO76WqF6nqz27WBWcbqqrvAe8+/PDDUydOnJiiqgwePLhB375916rqkAL1oLyadgXnmn9q3r0Z7Lo8X1WbnHXWWSsOHDjwbXJy8jXARud4IO+9Eper8/kaVf3aactaO/nLqmrTbdu2HXGuV/D68ara2Nl/WlUfCHYZe9Xlys5+tQcffPBrVZ3inDtIVb/yqhdb1NT7QNbpQjfnmUdMTEy3mJiY9jExMauApS7pKc299/WLL744fuLEicmqyubNmzs1btw4t/1qq6o7C/mOxaraza3yPhE32wPrPzpi3uw2AxnAV5geyVx+FpFywCFnfx5m0hGY2f0bnc+7gD2Y4fpAcTHwGWbIeh6mp6xe7kERqTto0KC+FSpUiBSRwyJy+m+//TZ748aNd2J6fBTTKwhQxdEMpnco1yelAoGZdFJUOc8RkVOALZhewJwC57YGZgFZmNn+yzFv5YGkuHox4Z133tl34MCBqcAvO3bs+CE2NjbD6/gZQB1gWoB1QtH1oiJQ8eSTTz77yJEjdceMGZORk5NT/5VXXqnatWvXc5w80zBlC/nrd6Apqoz7qZnNvwVYUb58+cM7duwoOOLwB2bCn5s6c7kY04t21osvvnhB165dywPy/vvv75w6dWoGx/riHcLMkMa55hICV+4laeMOzJ8//5lffvnlwiFDhrwLfOkcC+S9V5pypUWLFn9mZmb2EZFyK1euHLJixYrZItJGRBpv3rw5k7wRpVx6AZuArX7SWxQlqcspYCJTHDhwoCN57axi2t1IoJxzfkoQNBeGR0Rk/fr1s4EkVQVwa9JZaepI84cffvjGrl27VgCkadOm87Zu3Zo7eXk1xr+7YHzxFkBt4M8A6bf4wE7i8h8NgO1e+zsA73ie9wCPisgRIPOdd96JTkhIyHn66acLXqcj5ibfFGStDYDdzn7f3bt337V169YInFn9b775ZpVHH320PmaS0VpgKmZI2UP+0C6XAi9ibuYLgqQ9t5y7AjeJyN/AkW+++abRL7/80vOTTz7Jjbe7HHjK+U25vnprAqCxpHoByqvqQ177b5EXrsWDCVt1PeYhGmgKrReq+gxAr1695p100kl/qeoDAHFxcVPj4+N9hZe5BTNkm0tTYCnmgfo4/m3oiyrjCBEpq6pHADIyMiJycnKiCl4gSBRXF7zzZIqIbtu2LbtatWo1xEyDLvhCVpDcSCdv+ktwIdpyyadfVZ91Pn6fmZl5cbdu3X7/4osvPnPSAnnvlaZciYuL671nz57oGjVqNFq5cuVl8+bNS8H4+afWrFlznpPXm2vIM8RzGQ7cgHGVuh/Y75dfUvK6PCwnJ+f+Z555piHQxzn+HcYI240p4/8Dkvyk658wFfhGRN6rVKlS7Zo1azbC+Ea7QWnqSIaIRHnde1WAI06eyzHt2JEC5w7EtHd2UlEQsQas/ygspqD5oFrJK/06TAPYvUD+esA44EaKf1j9G4rT+imwZufOnWNUtaeTfA4wQlV/wPhJ/h9mcYarMHFhezv5fnS2bsCzXunB0N7PO7FXr16vNWvW7A+vpGmYmIlzgL0Yv+MsAkuRZY3pteqvqr9i6sWZ5NWLocCv5G94A0lxWsnIyKjr8XiOvlxVqVIla8uWLQV9dh/DlOt4Z383cBKwD9Oj/BNmprq/eoeK0v058IeIfAzoDz/80KdFixbB6M32RbHl65VnFPBjdnZ2RPPmzR/FvAw+julh80UkxsgaReCiWBSp3wmnNhTo+ssvv7RftmxZ9mOPPfaNmrBrgbz3SlOuue3bkyeddNLIQYMGdRw0aNDcUaNG5b6EjSlwbhlgAPn9Sd/FtG3q/H0d88LmD0pal9OBXS+//PK8ESNGPI55ZnTExIatD1TDvCT+jntRTR4C7gDuPHLkSAWPx5MGjHBJS2nqyFPAlKysrMhGjRq9i6m3N2HarJfJe2Hw5hpMR4MliFgXAv+xg/wTFhqSN7QOgIic+uqrrz6RnJz8UpcuXd5zQs7kUhkzGeNxzPCrvxlGnrP5ruK0AjsaNGhQRkQuEJERzz///N0zZ86sKyJPYhrL3ODk33LskBvAbKA5UNOfP4IiyllVtzrHzlXVrWXKlMnKzs4uWMefx0xCOw/TYG0ksBRXL+4Bfu7Tp8+R9evXf1qnTp22IrLXOdYZ86KzBdPbfQP+n6RTqnqRkpLy5datW88WkadF5KnHHntsbJkyZbx70m7ExD2+lrwHxBGM8QpmEsQmIMaPv6GoOvEK5n/eCmjTtm3b5Y8//vgEP353aSi2jcjNo6rj69Sp80idOnUOb9myZTNwiap+W8S1P8DU5UBOIilO/2eYh/zoli1brtu3b19DzAt5LoG690pcrs7nSKDK0qVLm4wfPz5m/PjxN4rIkyLy5Nq1a7sVOLcfxi0jwSstAWMo5gAf4rv9+6eUuC4Dz44YMeI64BIn7yDMhMRMjBvaX5gXYldQ1RxVfVdVr2jSpMmdERER+1XVrTjipbn3fmvbtu2V9evXT9u9e/enwJlqIj38iGmDC46OnoapU4sDI91SKG474R5HW6SqblbVpprnJN4m9zgwtkuXLqt3796d2rFjx+8xK1yNdY6XUdU/VPXeIGm9QPNP1lngK19cXNyeAQMG/Coi21etWrXxiiuu2AKMUdW1qtrDyddLjfM6qnqy5k3iaq/G2V1KoetflTPmzXkSsEFVOXDgwNe33nqr90SyCFWt4Xw+Vc3Eokg/aPrH9cLZTlczwaBFEde5SQM/iatE9SIlJWX1bbfd9obH47knMTHxf2omf6Gq56vqGlWtVeCcWpo3MaaZUy+qB7mMc7dPNP/EvtythwZ+EldJdA7Lzs5+DzMh8RpV/aYE9eA5Vf1eVT1u6geWFyxnr7RA3nslKlfNP4nrG+D+iy+++JXdu3fvqly58pMtW7ZcEh8fn6L5J3F9pao3F7hWPa/P/6d5E6eC9Vu824mL1EyIQ1UfUtWP1dy/FdTci6cGuE4UumH8Qr8D1kRFRW1r0qRJBlBwgmqwtmLLdfny5SPVTOJqP3Xq1Ifj4+OnAe179OjRLS0tbYOqXl7ItV9S1WfcKucTeXNdwHG29VfVDWqMkcectJGqOgBYo6q/qwmVtMzZJjp5rlPVTK/0ZaraLoA6RVXfdnSuVNUzvY4ty/3ctWvXjaq6auvWrUdU9a0aNWpUBKapalc1RutyNTPVz3DOeUhVVzvXmOvkC1o5Dxw4cDMg3bp1W6+qO1T14P79+7McTahqtJpGfY2qzgtwGRepd9OmTW879aJ9UlLS/IyMjH1paWnr09LS1u/Zs2eWj2vcpIE3YEtUL5z0VU6+tzTvJSVOVbdrXh3ONRgud/4Hy1V1iZqHblDqxDfffPMAsLBjx46Htm/frmlpabpv374crzqBmpn7e1U13ak3fYNZF9RpIzSvjn67a9eu1PT09GVqDP7cc7eoapKqpjk6W6tqQzWs9Sr329zQD3zyyiuv3OxoO5iRkZG8Y8eOJK/fFch7r0TlqqaOLihQro+p6qbs7OwNgwYNWuyVXl5V96lqlQLfNU7N/bFCTRteT4PwW3Lr8ujRozNWr16ds3TpUp05c2aW5hliFZ3fuNop5wcDWA+K3YD/Ab3q169/oEWLFntatGiR3bx585SYmJhbXdJUZB2pWLHiGFX9dvv27YdWrVqV0qZNm7nAjDfeeGPzoUOHsjX/87m213U3q2pLN8v6RN3sQgZBQkTGAK+raqAnDfkNEVmgqh1FZB5wGWYYeJWqtnBZmk+89C5R1fZOAOq5qnqq29oKEq7B1MMNEVmE8U/7FjOcegNwsqo+5qqwYhCR6RjfuwWYWfsAqDEUQwoRWYlxF8kNWr/N2W+MeXFv66K8EuMEr18Qwu1bWNVlEVmsqmeIyEpVPcVJ+1NVzynuXIulJNhJXMHjU2CuiMRjfAIFY6iEnHHlxSQRqYoJUL8E81D60F1JRfKNiLwPVBWR2zETK0JSr6oOdv72LCqfiJynqr8FR9XxiarGiUiEGv+7j0VkjtuaSsAzbgsoBReWJJOIVFNVf83W/9d4Gd4AEZjQhSG7ah+EXV0+LCIeYKOIDMcs/lDbZU3F4izAMF5NyLLcF5uBqvqOu8osBbE9sEFCROKA+zCreRyNMKBm4lHI4TQ8nVR1jrNfFohW1WR3lRWNiJyHmSUqwNRwN/5ye5Pd1hGuiMhsTCSMj4B4TESEm9SsxhS2iMhcVe3sto7SEGp1WUQae+1mAQmqmuV1PNQM7rCqyyLSARNysSomWkNl4FVVDcQkZb8hIsu0wGp9IrJUVU93S5PFN9aADRIiMj3choXD8SEJICKV8RpdUFU3YyH+K2zD+e9wjJQETDik/8MsvPGOhvD68SUhHOtFuGkOUYP7uKnLIjJaVe9yW0dBRGQFcJo6xpGIRAArVLWNu8osBbEuBMFjnYh8gZklfzQIspq4qqHKNBG5HPhBw+BNR0TuwAwBpmN6uQUzRNjMTV3/kpAv91DGa4TjMD6G5UXke1W9PLiq/EI41otw0+wrdqhrHId1+Wy3BRTCNJwFGDB1dggmPJklxLAGbPAohzFcvYMgK3nxVEOR+zCB07NE5DB5fruViz7NNR4A2qhqottCLGFDOL/cWAJLuBncti77hxHAYOBOzDNvGiE6l+JExxqwQUJVb3ZbQ2nR/KuHHYOItFHV1cHSUwI2YdaGP57Y4raA45xwM1JyCanewRISjprDiXCty6HGXar6JvBeboKI3EPglmi2/EOsARsknGUWb8WsoBKdm66q/lqC0A3GASHjI4ZZ7nGOiMwnv5vG3e5J8o2IXFbU8VzXElUtMp/l+EVE6mJWeVJgoarGex0OmWUrRaR6Uce9fNB7BUGOP7EGd2AJ1fK9kWON1Zt8pFlcxhqwwWMcsA7oi/HTvBYzQzOcCbUG6H1gOgUiPYQoFxVxLNRdS44nQq0OAyAitwFPYuqzAKNFZKSqjgVQ1VVu6ivAYkydLWy9+WYQOpMprcEdHETkSi2w/HGBtJAyCEVkIGY53qYiMtHrUGXylsK2hBA2CkGQyJ2BKyIrVPVUEYnChHkKq8gE3oTgLN05qtrFbR2W8EFE+qjqNLd1FERE1gNdVHWfs18DmKOqse4qC39E5G+KMLhVNSx9SUOtLvt6PoTaM8MbJ8pDU+BF4GGvQ6mYKARZPk+0uIbtgQ0emc7fAyLSFhPHr4l7co5LZojIYI6N9BASPT+FISIXcKxrSUgHVA8XRORs4GnMqlCR5E1EzO0VDJkHfgF2YB6cuaQC213SUmJEZADQzdmdqao/u6nHF6ra1G0NpaHAggv5DuG1GE6o1GUR6Qf0BxqIyCivQ5Ux8XZDEifKw1agcwH3nfXWeA1NrAEbPD5wVvR4HJgIVASecFfSvybDbQEFGOT8fcQrLaTDaDmhWsoDPTEByq/ALB9q8Q9jMDEzFwPZLmspFhG5z/m4E5gvIhMwdfhiQrxeiMhLmOVvxztJ94jI2ar6SBGnuUo4GNyUcKWzEGIXsAgYgLnvcknF3IshjYjcCjxFIe47ltDBuhCECCJyo6p+6rYOb0TkD1XtVVya5Z/j5VKS+7ciJu5un2JPthSLiMxX1bPc1lFSROSpoo6rasguMesEgG+nqjnOfgSwNFSXy/ZhcA8EFoWywR0uOP/7z1T1Wre1lBbrvhM+2B7Y0OEeICQMWCdiQnmgptNrnOsrVhmo75qwQijpjP4QJd35e0hE6mMmC4TVEGeIM0NEXsVMivN2K1ninqTCCWUDtYRUBXJddqq4KaQE9Ce/wf0psJT8Izghg4h0AkYDrTCrcUUAB0MxLreqZotIDREpo6qhNlJXHGHpvnMiYg3Y0CGUZpDeAdyLMVYXk6ctBXjbLVFFEM4z+n8WkarAq8ASjN6P3JV0XJHb+3qmV5oCIT15UkRm4MPvMcQnfb4ILHW0C2ZoPiSNQS/CyeB+C7gG+BZTn28ATnZVUdFsBf5yZvQfzE1U1Tfck1QifLrv5Lr3hIH+EwbrQhAihOLsTBG5S1VHu63jREFEygLRqprsthaLu4jIGV670cDlQJaqjnBJUokQkXqYYXkB5heIXRtSOGGTXgLyGdyq+pWrwgpBRBap6pm57kZOWshGXinMHSbURxnC2Y3nRMMasCFCbpgtt3UURES6YKIlHO2tV9XPXBPkA6+JLz4J5TdmEbnBV3qolXG4IiJ1gBeA+qraT0RaA51VdYzL0kqNiMxS1e5u6ygMp6ftS2Ciqh4sLn8oEGYG92ygN2aEJh7YDdykqqe5KqwYRKQSJlpCmttaLMcX1oUgdPjLbQEFEZFxQHNgGXkzuBUINeOqyCVvQ5wOXp+jMcHTlxB6ZRyufAJ8DDzm7G8AvsZEJwhZCgTb92CGjOu6JKekvA5cDbwkIgsw5fyzqh52V5ZvwtDgvh5TF4ZjZvM3AkJ2pT4nXOQ4oLqznwjcEGLLjx9DmLrvnJDYHtgg4QwPX86xvZkhG+9TRNYCrdVWkqAhIlWAcao6wG0txwMislBVO3iPcIjIMlVt57a2ovAKtg8mduYWYKSq/s81USXEmYF+LnA7cH4oTjICEJHuGIP7AkyIslA3uO9R1TeLSwsVRGQO8JiqznD2ewAvhKrLQy7h6r5zImJ7YIPHBCAZMynqSDF5Q4VVmF6f3W4LKQlO9IRbOXZRgFtcE1V6DgEt3BZxHHHQCYOjcHQmdzj4GLcGhgJdMdr/xMTWDGlEpBxmUuXVQHtCJLKKL1R1FjCrgME9FhNtJRS5kWOXX73JR1qoUCHXeAVQ1ZkiUsFNQSVBVRcXSPpLRGa5IsZSJNaADR4NVfV8t0WUkprAGmc40DsEUaj2Do4D1gF9gZHAtcBaVxUVg4hMIq+nzYMxXL5xT9Fxx32YhUOai8hfQC3MYhGhzqeYqB+5KxkNxNTvK11TVAwi8jUm6sMUTLSSmbkhqkKVcDC4nclmg4CmjttDLpUxYfdClc0i8gSm3gJcB/ztop4S4cN95wxC333nhMS6EAQJEfkAGK2qK93WUlKcIbZjcHouQo7cYWKvRQGigKmh7LtUoIyzgK2qusMtPccjIhIJxGIm6qxX1cxiTnEdEVlecHKOr7RQQkTOB35T1ZBf8QyOMbi/IUQNbhFpjIkN/SLwsNehVGBFqC5z6sQQfwY4G3PvzQaeVtUDrgorBi/3HcG0yX8TJu47Jxq2BzZ4dAVucm6OIxRYxzoUCVVDtQhyDZMDzgSCeIzPccgShmUcVjhuJfmG4kXkvVD1c/RiqYh0UtV5ACJyFiE40bMAs4FHROQkVR0sIi2A2BBdnhXM5L5BoW5wq+pWTEzVzk5UjdyJn2tD1Xh1aI6ZaObB2Bq9MK4aIfvMExEPcJ2qhvq9ZsH2wAYN5y36GJzGKaQQkf+palcRSSX/bMxcozskfcRE5Dbge+AUzOzzisATqvq+m7qKwllF7GWgNqZ8Q7qMww0R+QbTU/W5kzQQqKaqITsUD0cnUMYC25ykkzDuMDmE6Iuv06O5GDPTvK0zPD83VCfMiUh5jItJWBjcInIl8BowE9NOnAM8qKrfuamrMJwlWR/AzKU42rMdis88b0Rkrqp2dluHpXisARtkRKQ2+ScYbSsiu6UUiEhTVf27uLRQQkTigItUNaR9dcOVcByKh8JfeHMJRSPAK9C+d8SHkC3rMDS4lwPnqeoeZ78W8HsIl+//VLWr2zpKi4g8VhzWWQAAEnxJREFUA6wAfrAReEIb60IQJERkACZOYn1gD9AY06PSxk1dxxnfYyZiePMdxgk/VEmwxmtACceh+JA0UEtAhmME5kZ8aE5oR1xprqpXO5OkUNV0EQmlJb0L4sk1Xh32YYbnQ5WnROQj4A/yTwIO5aW9wfTKVwCyROQwdlQsZLEGbPB4FuiEeWM+XUR6YoYzLf8SEWmJeRGo4gzJ51IZr97uEGWR0xP0E+HVyIcLZwE3iMg2jGHVGFgrIisJ0aH4cMQx/N7DTIhqJCLjMZN3bnJTVzGEm8E9WUSmYhZfABM54VcX9RTHzUBLIIo8FwIFQrptU9VjFsYJ8RebExZrwAaPTFXdJyIeEfGo6gwRedltUccJscCFQFVMSJxcUjGxHUOZypjYr3280kK+kQ8jzgeqYfwFwUw0CulZ0OGIqqqI3IOpx50wvVb3qGqiu8p8E6YGtwLvYyYkCvABpqxDldNU9RS3RZQWERmpqk967XswocCudU+VxRfWBzZIiMjvwCXAS0ANjBtBh1BflSScEJHOqjrXbR2W0MExqm7DvBAI5h78UFVHuyrsOERE3gY+UdWFbmspCSKymPwG97xQNbgBRGSJqrYvkLYiVEcRRORD4D+qusZtLaVBRD7BhNt7UcwKmt8CS1T1aVeFWY7BGrBBwlmBJNef5lqgCjBeVUM5EHVYISKvAM8B6ZieldOAe1X18yJPdBFnIsbtHLvEcDitHhayiMgKoHPuWvfOfTg3VB/64YyIrAFiMCGfDhLioQLDxeAWkTsxoeCaAZu8DlUC/lLV61wRVgxOJI3mmDiqYRE6Eo72zo8HVgI9gcmq+h93VVl8EcoO4OGIYFbOicPMYjz6tuw8QGsC58+bN69/VlbWQz4manQDlmCCJwdytaDzgfWOzod9HC+LWRc8DphPXizVJhjjcJmzveekV/JKWwYkAv/1o95Cy9Wbs88++2JV/SslJWXb999/X79SpUoxwIMYQ3YupkGaRN5SkWUwsSBXAsuBHn7UXFL9EzAvM7/ffffda5OSknqlpKT0c/Ln+l09Dewkr3z7u6DTmzMwZRZXQOeVwGqMv9uZXvmb4LveBEOvbNu27dRcvUeOHHnFy5/tVczKbSuAHzEuKGB89j51zlkLPOInvYUSGxt7fmxs7PrY2Ni42NjYY+7J2NjY/8TGxi5ztg2xsbFuuUEUVeb9MAbLuTt37pyVmZlZIzs7u3mB8+8D1jjn/oHxSQ6mxqN06dKl34oVKxZs2bIlc+zYsYkej2el88LTDpiHqauLgI7OKQ+SV4dXAdlAdR+X/icU1SZ/ceqpp14+ffr0xIyMjMqHDh3KfuWVV+4EzvAyXk8C0jBhq8DEX52Bqb+rgXv8pLOkmmnXrt2AAwcOTM3IyCjraB6CcfE6DxP9YaXzN3ehmUA/R4pERNqLSHvg9KZNm1Zq3rz5yKZNm56BWW64sLbQ4iaqajf/bf1VdbKqiqp2UtX5uceAqzA9E58OGDDg14YNG27LyMhIL3B+E1U9VVU/U9UrAqQxQlU3qWozVS2jqstVtXWBPENV9T3n8zWq+rWXvlUl+I7FqtotGOXqvS1btuyQqnYWkQ937ty5UFX7ActVdaGqdnfy3aKqzzqfh6nqx87n2o5uT5DrxTKvfAtUtbOTb7Kq9nPSn1bVBwKg6x+VcxE6W6lqrKrOVNUzvfKXtN74XS9w3/Llyw9dd911H4nI0zNnzkx55JFH3neO91HVSOfzy86Gqg5S1a+cz+VVdYvzGwJS7jExMRExMTGbYmJimsXExJSJiYlZHhMTU/Ce9M5/V0xMzNgg1Id/U0c6qWo9VU0rkN7TKVNU9U7Na1uCrjE9PX3ZpEmTLi1btmzj1NTUGX/++eeNQGNVnaZ5dbq/mvpc8PyLVHW6n/T+mzY5d/teVb/VvHainqq2dz5XUtUNPq7plubTVbW+87mtqu4s5Dv8/RwpcsMY/DOAGRUqVFhSrly5hU2aNElz0vz1v7abHzfbA+tfLgY+wzjbz8P06NRzjj2G8Xm9ccKECf137NhxRmZmZpkC52/B9BgEcjnDjpg35s1ABvCVo9ubi8lbE/w7zAoqJZ2F2QITlP/Pf600v57CyjWXenXr1k0XkY9V9cyqVau+mZKScg3GbSMWM3kH4Dfgcudza0wvEBif5APk7zkMhv6fRaS/s18Z01OsTv5LAqDln+rMpSidazE9MsGiWL2q+mWLFi0SPv/88xWquj8zM/OFF154IffwNMxoB875DXNPw4TRiQTKYe6TlAD+jo5A3Pr16zevX7++sHvSm4HkzUQPNiWpIzjHdvtIn4GZtJibp6GPPMHQWC86Ojr6wgsv/PHw4cNbK1as+EHXrl27qBkVU/JGaaoAu3x8hz//B/+2Tb7EOXe1V/7dmNE8MJNZ1wIN/KT332peSl6ZrsZEiilb4NxAPEeKRM3LVU9V7ZmWlta+UaNGV5YpU2aLkxayy5GfyFgD1r80ALZ77e8gr9HwFcPPDYrS6CtPFpCMmXgGZk3upcAs8mZ2ezMQ437gT+fqEmmuU6fOUqAzcGb58uW3litXrhamEV0FDHDyXYkZXgPjNnAxxlBpihkab4T/KUr/PcDPHTt2/HvGjBkni0iqiKRw7G8cjnm5GYuZVR8ISlo3dhSTxxfF1Zt/Qon0litXbpOqjlLVN3v37v2XjzwAtwCTnc/fYXw4d2NWwnoNSPKTZl+U5Hfw/+3de4xcZR3G8efXi224hHJrCiEtNOkpSOV+KdiIAUWCoUSKgKBCCvECMRYUSKwhFKNBTFO8haAxIWlqIkpIMSagKFa5tiIsAkspELrdVjAWSBuqdbd9/OM9w8xOZ3an3Zl9d9vvJznZOWfObJ49c/acd8783vdI0uzZs2cobcs/dTDPYFrO2oJrVd3m7TTc/XiRUnnJBqX3vr6EZD+lr88fGOG8jY7J+0u6VdKSQX7/0ZJOVioHa5fhnkcqFigdF+qHL+vEeaQlETEpIq7s6em5vr+///CIuC0ibhv6lRhpNGDbq9FVyso/4MMR8UhEXBMR10j63fjx43Pcg3uwjEOt80+lWquTlWrZfqnqlYqKK9T+q0O7k/k4SZffcsstn+rp6TlCqZfxQkk3KNVbHah0xUBKjcFepTq3uyU9qeoVuXZqmt9pzMHDLrvssq/MmTPneaXhwC6qXUfSPUq1hScpvQdLO5Bx0Jy7uU69VvabPdGuvIuV3vcV5fwZSvWNRyo1Fr+h1IGmU3Znm14h6Tdr167NceyQ9uz9b+TzSt92/GB4cRoa7n7xVUk3Kn2YvVHSL+rWu0jpZhjt+lAznLxLJC1Tqn9t5AClhvYitfdbhHb87x2vdBvtLzdYrxPnkVatVLqwsUPp29D3ywmjDA3Y4btB1aLzTRp4Be+ocpls36w0bt8JSp2KfjZp0qT/aeT1qknGJutMUPoa7R2lT8mVK8fPKvWILWped2K5/rNtyNnSdq3NvHHjxtOVrpjMmzhx4mnr1q2bqHSSfEWpIXuq0kGx0pO3X+kEdZLSAWuKpHVtyN5y/oi4TtKqZcuW3b1t27bjlEZPuK3ub3xb1YPpz1XtVDJiOWv0auDXvo3WqTfUfpMz79VKHxquUvXkeqXS+9CnVFryhDpTWlKbcai/oyLHiX13t/lQPqH0oWG+2nfjgHbuF1erOg7zr7Xr/1u734PhHJPPlHSXUvnZIknfUvq2RkqdER9Q+mDW7nGlh5O5sv6Dkr6ogSMrSO09j+yJo2xfPn369HsnTJiw2fZS2526aIDhyF2Eu5dNn/bAzgOrh1i/voNDZbrPnevENcH2G7aPcbX4/vi6dW7wwOL7+8vHhzsV78upeH+j7UNqXnen7SW5tmtXV9d/+vr65rrauejC8rmp5c9xTh3kFpbz+9nev3z8Sdt/6dA2b5pfqSfuZKXOXGuWL19+eUT8qi7/ETW/60ZXOxjl2n/XlM/Xb+fK9GcP7MQ11H6TK+8Ftl8u89Wuf6tT575w2j9edupc2YltrqIoJhRF8UZRFMfUdOKq/59UURSzi6J4syiK6FSWNm7zylR/jDvZqfPPrFGQsdl+0W374+Xj85w6E1Vec5Dtd1w9brRjGs4xuXa63dVOXOF0rLu7Q9t4OJmnlOsvaPK7O3UeaWlSutD0kaIoji6KohOdT5na9V7lDrCXTWH7p04H6H/YPk2pgH5LV1fXDqWvcLYsXbp0e29v784dO3bYdq/TgUe2Ty/n37e92fZLHcp5oVOv1NdtLy6X3WF7fvl4slOP1tecDv4zy+ULykxdtv/u1BO39ve+YfvYkdiuNc990Iv//PPPf3T79u2vlOv9pHydbH+9/HtfdTo4VpYfbXut0wnrUdszRmq/qDzX3d29zU6jEWzatOks2y+uX79+e13+5eXrXrD9kAc2aEd8O5fLX2ywnT/jtP9ut/227Ufc2n6TK+9rtjeU6z7v6sn2AKf9/yWnxuvNHdreH0xFUVxYFMWr5WgEi8tldxRFMb9mnduLoriz01natM3vKveFnR54jHu03Dcq2/yhjBmb7RfznBqtXU4jGJxa85pr3JkPkHt6TK6dbne1ATvPyQs127r+g2auzN92Osc9XzNNrfm9nTqPtDRJennatGk7Zs6c2T9r1izPnDmzb8qUKRty5WFqPnEjA+w1IuIxpXKA1ar5atL2/KYvyiwiHlS6Z/gipfEQ35U00XanxnoFADQREQ3HJvau47YjMxqw2GtExDmNltteNdJZ9kSZ/yBJD9vOUR8NAJAUEVOVyrskSbZ7MsZBAzRgAQAAJEXEfKWRXo5U6sQ5Q1K37eOzBsMuGIUAY15EPF7+3BoRW2qmypiqAAC04juS5kp61fYxSjdgeCJvJDQyIXcAYLhszyt/Hpg7CwBgTOuzvTkixkXEONuPRcT3c4fCrmjAAgAAJO9FxAFKt7FdERH/UmducINhogYWAABAUkTsL+m/SncSu0qpY+0K27lu/44maMACAACUImKa0h3YLGmN7bcyR0IDdOICAADQB7f3Xi3pEkmXSno6IhbmTYVGuAILAAAgKSLWSjq7UjIQEYdKetL27LzJUI8rsAAAAEmv0i3gK7ZK2pApCwbBKAQAAGCfFhE3lQ83SnomIlYq1cBerFRSgFGGBiwAANjXVcYRf72cKlZmyIIWUAMLAADQgoj4se2v5c4BamABAABa9dHcAZDQgAUAAMCYQgMWAAAAYwoNWAAAgNZE7gBIaMACAAC05oe5AyChAQsAACApIv4QEVNq5g+OiEcq87bvyxIMu6ABCwAAkBxm+73KjO13JU3NmAdN0IAFAABIdkbE9MpMRMxQuiMXRhnuxAUAAJAslvR4RKwq5z8m6UsZ86AJ7sQFAABQiojDJM1VGnHgKdv/zhwJDVBCAAAAICkiQtIFkk6x/VtJ+0XEGZljoQGuwAIAAEiKiHsk7ZR0ru3jIuJgSb+3fXrmaKhDDSwAAEBypu1TIuI5KY1CEBEfyh0Ku6KEAAAAIOmLiPEqRx6IiMOVrshilKEBCwAAkPxI0oOSpkbEdyU9Lul7eSOhEWpgAQAAShFxrKTzlEYh+KPt7syR0AANWAAAsM+LiHGSXrA9J3cWDI0SAgAAsM+zvVNSV+2duDB6MQoBAABAcoSklyJitaT3Kwttz88XCY3QgAUAAEiW5A6A1lADCwAA0IKIeMr2WblzgBpYAACAVk3OHQAJDVgAAIDW8LX1KEEDFgAAAGMKDVgAAIDWRO4ASBiFAAAAoBQR0ySdoVQusMb2WzVPfyFPKtTjCiwAAICkiLhO0mpJl0i6VNLTEbGw8rztF3Nlw0AMowUAACApItZKOtv25nL+UElP2p6dNxnqcQUWAAAg6ZW0tWZ+q6QNmbJgENTAAgCAfVpE3FQ+3CjpmYhYqVQDe7FSSQFGGRqwAABgX3dg+fP1cqpYmSELWkANLAAAAMYUrsACAABIiojH1OBuW7bPzRAHg6ABCwAAkHyz5vFkSQsk9WfKgkFQQgAAANBERKyyfU7uHBiIK7AAAACSIuKQmtlxkk6TNC1THAyCBiwAAEDyrKo1sP2S3pR0bbY0aIoGLAAAQPJhSddLmqfUkP2rpL9lTYSGqIEFAACQFBH3S9oiaUW56HOSDrb92Xyp0AgNWAAAAEkR0WX7xKGWIb9xuQMAAACMEs9FxNzKTEScKemJjHnQBFdgAQAAJEVEt6TZknrKRdMldUvaKcm2T8iVDQPRgAUAAJAUETMGe972+pHKgsHRgAUAAMCYQg0sAAAAxhQasAAAABhTaMACAABgTKEBCwAAgDGFBiwAAADGlP8Dl8dvk30wQqkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: You should have noticed almost perfect correlation with the \"installment\" feature. Explore this feature further. Print out their descriptions and perform a scatterplot between them. Does this relationship make sense to you? Do you think there is duplicate information here?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The monthly payment owed by the borrower if the loan originates.\n"
     ]
    }
   ],
   "source": [
    "# CODE HERE\n",
    "feat_info(\"installment\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The monthly payment owed by the borrower if the loan originates.\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The listed amount of the loan applied for by the borrower. If at some point in time, the credit department reduces the loan amount, then it will be reflected in this value.\n"
     ]
    }
   ],
   "source": [
    "feat_info(\"loan_amnt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20798026f48>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Create a boxplot showing the relationship between the loan_status and the Loan Amount.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='loan_status', ylabel='loan_amnt'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CODE HERE\n",
    "sns.boxplot(data=df,x=\"loan_status\",y=\"loan_amnt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20798056c48>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Calculate the summary statistics for the loan amount, grouped by the loan_status.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan_status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Charged Off</th>\n",
       "      <td>77673.0</td>\n",
       "      <td>15126.300967</td>\n",
       "      <td>8505.090557</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>8525.0</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>40000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fully Paid</th>\n",
       "      <td>318357.0</td>\n",
       "      <td>13866.878771</td>\n",
       "      <td>8302.319699</td>\n",
       "      <td>500.0</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>19225.0</td>\n",
       "      <td>40000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                count          mean          std     min     25%      50%  \\\n",
       "loan_status                                                                 \n",
       "Charged Off   77673.0  15126.300967  8505.090557  1000.0  8525.0  14000.0   \n",
       "Fully Paid   318357.0  13866.878771  8302.319699   500.0  7500.0  12000.0   \n",
       "\n",
       "                 75%      max  \n",
       "loan_status                    \n",
       "Charged Off  20000.0  40000.0  \n",
       "Fully Paid   19225.0  40000.0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CODE HERE\n",
    "df.groupby(\"loan_status\").describe()[\"loan_amnt\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan_status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Charged Off</th>\n",
       "      <td>77673.0</td>\n",
       "      <td>15126.300967</td>\n",
       "      <td>8505.090557</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>8525.0</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>40000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fully Paid</th>\n",
       "      <td>318357.0</td>\n",
       "      <td>13866.878771</td>\n",
       "      <td>8302.319699</td>\n",
       "      <td>500.0</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>19225.0</td>\n",
       "      <td>40000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                count          mean          std     min     25%      50%  \\\n",
       "loan_status                                                                 \n",
       "Charged Off   77673.0  15126.300967  8505.090557  1000.0  8525.0  14000.0   \n",
       "Fully Paid   318357.0  13866.878771  8302.319699   500.0  7500.0  12000.0   \n",
       "\n",
       "                 75%      max  \n",
       "loan_status                    \n",
       "Charged Off  20000.0  40000.0  \n",
       "Fully Paid   19225.0  40000.0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Let's explore the Grade and SubGrade columns that LendingClub attributes to the loans. What are the unique possible grades and subgrades?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['A', 'B', 'C', 'D', 'E', 'F', 'G']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CODE HERE\n",
    "sorted(df[\"grade\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['A', 'B', 'C', 'D', 'E', 'F', 'G']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['A1',\n",
       " 'A2',\n",
       " 'A3',\n",
       " 'A4',\n",
       " 'A5',\n",
       " 'B1',\n",
       " 'B2',\n",
       " 'B3',\n",
       " 'B4',\n",
       " 'B5',\n",
       " 'C1',\n",
       " 'C2',\n",
       " 'C3',\n",
       " 'C4',\n",
       " 'C5',\n",
       " 'D1',\n",
       " 'D2',\n",
       " 'D3',\n",
       " 'D4',\n",
       " 'D5',\n",
       " 'E1',\n",
       " 'E2',\n",
       " 'E3',\n",
       " 'E4',\n",
       " 'E5',\n",
       " 'F1',\n",
       " 'F2',\n",
       " 'F3',\n",
       " 'F4',\n",
       " 'F5',\n",
       " 'G1',\n",
       " 'G2',\n",
       " 'G3',\n",
       " 'G4',\n",
       " 'G5']"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(df[\"sub_grade\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['A1',\n",
       " 'A2',\n",
       " 'A3',\n",
       " 'A4',\n",
       " 'A5',\n",
       " 'B1',\n",
       " 'B2',\n",
       " 'B3',\n",
       " 'B4',\n",
       " 'B5',\n",
       " 'C1',\n",
       " 'C2',\n",
       " 'C3',\n",
       " 'C4',\n",
       " 'C5',\n",
       " 'D1',\n",
       " 'D2',\n",
       " 'D3',\n",
       " 'D4',\n",
       " 'D5',\n",
       " 'E1',\n",
       " 'E2',\n",
       " 'E3',\n",
       " 'E4',\n",
       " 'E5',\n",
       " 'F1',\n",
       " 'F2',\n",
       " 'F3',\n",
       " 'F4',\n",
       " 'F5',\n",
       " 'G1',\n",
       " 'G2',\n",
       " 'G3',\n",
       " 'G4',\n",
       " 'G5']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Create a countplot per grade. Set the hue to the loan_status label.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='grade', ylabel='count'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CODE HERE\n",
    "sns.countplot(data=df,x=\"grade\",hue=\"loan_status\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2078f679ac8>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Display a count plot per subgrade. You may need to resize for this plot and [reorder](https://seaborn.pydata.org/generated/seaborn.countplot.html#seaborn.countplot) the x axis. Feel free to edit the color palette. Explore both all loans made per subgrade as well being separated based on the loan_status. After creating this plot, go ahead and create a similar plot, but set hue=\"loan_status\"**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sub_grade', ylabel='count'>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#CODE HERE\n",
    "plt.figure(figsize=(12,4))\n",
    "subgrade_order = sorted(df['sub_grade'].unique())\n",
    "sns.countplot(x='sub_grade',data=df,order = subgrade_order,palette='coolwarm' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20798504288>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sub_grade', ylabel='count'>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CODE HERE\n",
    "plt.figure(figsize=(12,4))\n",
    "subgrade_order = sorted(df['sub_grade'].unique())\n",
    "sns.countplot(x='sub_grade',data=df,order = subgrade_order,palette='coolwarm',hue=\"loan_status\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20798359608>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9d955Z9G6Bx10EHfddRdA20WeAEcffTQzZ85k/frcX3dXrlzJmjVrGD9+PHPmzOGNN97g9ddf50c/+lHBuP/8z//M5z//edatWwfkLhy9+eab+cxnPlPhkdemIfcBlyRJUuNUctvAjhQRzJkzhwsuuIArrriC/v37t92GcOXKlQX3GThwINOnT2fUqFE0NTUxduzYovGvvfZaTj/9dK6++mqOO+443v3udwNw1FFH8dxzz/HhD38YyK3R/v73v8/o0aM59dRTaW5u5n3ve1/bxaBbOvHEE1m5ciUHH3wwEcGOO+7I97//ffbYY4929khp0Xq1aW8xZsyYtHDhws5uhjpIR69nLhWvM2N1JNeAS1LP89xzzzFs2LDObkbdbNiwge23356IYPbs2dx2223cc0/Xufldof6PiEUppTGF6jsDLkmSpC5t0aJFzJgxg5QSAwcOZObMre7N0a2YgEuSJKlLO+yww3jqqac6uxkdxoswJUmSeoDetqy4q6il350Bl7qBrrqeXJLUNfTv359169axyy671P0e1vqrlBLr1q2jf//+Ve1nAi5JktTNDRkyhBUrVrB27drObkqv079//7YvGaqUCbgkSVI3169fP4YOHdrZzVCFXAMuSZIkNZAz4JJq5j3FJUmqnjPgkiRJUgOZgEuSJEkNZAIuSZIkNZAJuCRJktRAJuCSJElSA5mAS5IkSQ1kAi5JkiQ1kPcBV8N572hJktSbOQMuSZIkNZAJuCRJktRALkFRRVw2IkmS1DGcAZckSZIayARckiRJaiCXoEjqElzmJEnqLZwBlyRJkhrIBFySJElqIBNwSZIkqYFMwCVJkqQGMgGXJEmSGsgEXJIkSWogE3BJkiSpgUzAJUmSpAbyi3gk9Th+qY8kqStzBlySJElqoLol4BExMyLWRMQzeWWXRsTKiGjJHpPytn0xIpZFxPMRcXRe+TFZ2bKI+EJe+dCI+GVELI2I2yNi23odiyRJktRR6jkDfjNwTIHyr6aUmrPHXICIGA58AhiR7fOtiOgTEX2A64BjgeHAaVldgC9nsfYB/gicXcdjkSRJkjpE3RLwlNJ84JUKq58EzE4p/SWl9DtgGTAueyxLKf02pfQmMBs4KSICmAjcme1/CzC5Qw9AkiRJqoPOWAM+IyKezpao7JyV7QW8mFdnRVZWrHwX4NWU0qYtyguKiHMiYmFELFy7dm1HHYckSZJUtUYn4N8G3g80A6uAq7PyKFA31VBeUErphpTSmJTSmF133bW6FkuSJEkdqKG3IUwprW59HhE3AvdlL1cA782rOgR4KXteqPxlYGBE9M1mwfPrS5IkSV1WQxPwiNgjpbQqezkFaL1Dyr3ADyLiGmBPYB/gCXIz3ftExFBgJbkLNf8upZQiYh5wMrl14dOAexp3JN2D90KWJEnqeuqWgEfEbcDhwKCIWAFcAhweEc3klossB/4BIKX0bETcAfwK2AScl1LanMWZAfwU6APMTCk9m73F54HZEfF/gSeB79brWCRJkqSOUrcEPKV0WoHioklySulLwJcKlM8F5hYo/y25u6RIkiRJ3YbfhClJkiQ1kAm4JEmS1EAm4JIkSVIDmYBLkiRJDWQCLkmSJDVQQ+8DLkndjffTlyR1NGfAJUmSpAaqKAGPiAcrKZMkSZJUWsklKBHRH3gXuW+z3JncV8MD7ETuK+MlSZIkVaHcGvB/AC4gl2wv4q8J+J+A6+rYLkmSJKlHKpmAp5S+BnwtIj6bUvpGg9okSZIk9VgV3QUlpfSNiDgYaMrfJ6V0a53aJUmSJPVIFSXgEfHvwPuBFmBzVpwAE3BJkiSpCpXeB3wMMDyllOrZGEmSJKmnq/Q+4M8Au9ezIZIkSVJvUOkM+CDgVxHxBPCX1sKU0ol1aZUkSZLUQ1WagF9az0ZIkiRJvUWld0F5uN4NkSRJknqDSu+C8jq5u54AbAv0A/6cUtqpXg2TJEmSeqJKZ8B3zH8dEZOBcXVpkSRJktSDVXoXlHdIKd0NTOzgtkiSJEk9XqVLUD6a93IbcvcF957gkiRJUpUqvQvKCXnPNwHLgZM6vDWSJElSD1fpGvCz6t0QSeoN5i5YX3TbpLEDGtgSSVJnqWgNeEQMiYg5EbEmIlZHxF0RMaTejZMkSZJ6mkovwvwecC+wJ7AX8KOsTJIkSVIVKk3Ad00pfS+ltCl73AzsWsd2SZIkST1SpQn4yxFxekT0yR6nA+vq2TBJkiSpJ6o0Af8k8HHgD8Aq4GTACzMlSZKkKlV6G8LLgWkppT8CRMR7gK+QS8wlSZIkVajSGfD9WpNvgJTSK8D+9WmSJEmS1HNVmoBvExE7t77IZsArnT2XJEmSlKk0ib4aeCwi7iT3FfQfB75Ut1ZJkiRJPVSl34R5a0QsBCYCAXw0pfSrurZMkiRJ6oEqXkaSJdwm3ZIkSVI7VLoGvGoRMTP76vpn8sreExEPRMTS7OfOWXlExNcjYllEPB0Ro/P2mZbVXxoR0/LKD4iIJdk+X4+IqNexSJIkSR2lbgk4cDNwzBZlXwAeTCntAzyYvQY4Ftgne5wDfBvaLva8BDgQGAdckncx6Lezuq37bflekiRJUpdTtwQ8pTQfeGWL4pOAW7LntwCT88pvTTm/AAZGxB7A0cADKaVXstsgPgAck23bKaX0eEopAbfmxZIkSZK6rEbfSnC3lNIqgJTSqogYnJXvBbyYV29FVlaqfEWB8oIi4hxys+Xsvffe7TwESeoa5i5YX3TbpLEDGtgSSVI1usq9vAut3041lBeUUroBuAFgzJgxRet1BaX+QQX/UZUkSeru6rkGvJDV2fIRsp9rsvIVwHvz6g0BXipTPqRAuSRJktSlNToBvxdovZPJNOCevPK/z+6GchDwWrZU5afAURGxc3bx5VHAT7Ntr0fEQdndT/4+L5YkSZLUZdVtCUpE3AYcDgyKiBXk7mZyBXBHRJwN/B44Jas+F5gELAM2AGcBpJReiYjLgQVZvctSSq0Xdn6a3J1Wtgf+M3tIkiRJXVrdEvCU0mlFNh1RoG4CzisSZyYws0D5QmBkre3z4iVJkiR1hkYvQZEkSZJ6NRNwSZIkqYG6ym0IuzWXs0iSJKlSzoBLkiRJDWQCLkmSJDWQS1AkSX4LryQ1kDPgkiRJUgOZgEuSJEkNZAIuSZIkNZAJuCRJktRAJuCSJElSA5mAS5IkSQ3kbQglSR3KWxpKUmnOgEuSJEkNZAIuSZIkNZAJuCRJktRAJuCSJElSA5mAS5IkSQ1kAi5JkiQ1kAm4JEmS1EAm4JIkSVIDmYBLkiRJDWQCLkmSJDWQCbgkSZLUQH07uwGSJBUzd8H6ktsnjR3QoJZIUsdxBlySJElqIBNwSZIkqYFMwCVJkqQGMgGXJEmSGsgEXJIkSWogE3BJkiSpgUzAJUmSpAYyAZckSZIayARckiRJaiATcEmSJKmBOiUBj4jlEbEkIloiYmFW9p6IeCAilmY/d87KIyK+HhHLIuLpiBidF2daVn9pREzrjGORJEmSqtGZM+AfSSk1p5TGZK+/ADyYUtoHeDB7DXAssE/2OAf4NuQSduAS4EBgHHBJa9IuSZIkdVV9O7sBeU4CDs+e3wI8BHw+K781pZSAX0TEwIjYI6v7QErpFYCIeAA4Britsc2WJHUHcxesL7l90tgBDWqJpN6us2bAE3B/RCyKiHOyst1SSqsAsp+Ds/K9gBfz9l2RlRUrlyRJkrqszpoBPySl9FJEDAYeiIj/LlE3CpSlEuVbB8gl+ecA7L333tW2VZIkSeownTIDnlJ6Kfu5BphDbg336mxpCdnPNVn1FcB783YfArxUorzQ+92QUhqTUhqz6667duShSJIkSVVp+Ax4ROwAbJNSej17fhRwGXAvMA24Ivt5T7bLvcCMiJhN7oLL11JKqyLip8D/y7vw8ijgiw08FEnd1LoHZ5fcvssRn2hQS9RduZ5cUnt0xhKU3YA5EdH6/j9IKf0kIhYAd0TE2cDvgVOy+nOBScAyYANwFkBK6ZWIuBxYkNW7rPWCTEk9T6mk2YRZktSdNDwBTyn9FvhQgfJ1wBEFyhNwXpFYM4GZHd1GSR2jNyTNzqZLkqrlN2FKkiRJDWQCLkmSJDVQV/oiHkldQG9YNtJVuZxFknoHZ8AlSZKkBnIGXJJ6IGfTJanrMgGXJKmTlbqvuPcUl3oel6BIkiRJDeQMuNQJXB6g3swLfSX1dibgkqSyTJolqeOYgEvdnLPpkiR1LybgUoVMdCV1B17QKXV9JuCSpG7LpTGSuiPvgiJJkiQ1kAm4JEmS1EAm4JIkSVIDuQZckiQ69kLrnnLRthd0SvXhDLgkSZLUQM6Aq0frKbNQkiSp5zABlySpC3MiQep5XIIiSZIkNZAz4JIk9RKdOZte6oJO8KJO9S7OgEuSJEkN5Ay4uhzXO0qSpJ7MBFySJFXNyRKpdi5BkSRJkhrIGXB1iFIzIc6CSJI6khd0qrtzBlySJElqIGfAJUlSp+usv6Q6m67OYAIuSZJ6FJdFqqszAe/F/ICSJElqPBNwSZKkIqqZrHI5iyrlRZiSJElSAzkDLkmS1AB+eZFamYB3M67bliSp5yu3nOXAP91Xcrs5QddmAi5JktTDlUroXZveeCbgdeafmyRJUldWba5iMt9+3T4Bj4hjgK8BfYCbUkpXtDemSbMkSVL1OjKH6sn5WLdOwCOiD3Ad8LfACmBBRNybUvpV57ZMkiRJ5ZSaTT+wA2NB6XXz1Sbz7b0mr1sn4MA4YFlK6bcAETEbOAkwAZckSVJNyibz7YwfKaV2hug8EXEycExK6VPZ6zOAA1NKM7aodw5wTvbyg8DzZUIPAl7uoGZ2ZKyOjmcsY9U7nrF6RqyOjmcsY9U7nrGMVe94lcR6X0pp10IbuvsMeBQo2+p/FCmlG4AbKg4asTClNKY9DatHrI6OZyxj1TuesXpGrI6OZyxj1TuesYxV73jtjdXdvwlzBfDevNdDgJc6qS2SJElSWd09AV8A7BMRQyNiW+ATwL2d3CZJkiSpqG69BCWltCkiZgA/JXcbwpkppWc7IHTFy1UaHKuj4xnLWPWOZ6yeEauj4xnLWPWOZyxj1Tteu2J164swJUmSpO6muy9BkSRJkroVE3BJkiSpgUzAgYiYEhEpIvbNK/tJRLwaEcW/NqmCWBHRHBGPR8SzEfF0RJzajljvi4hFEdGSxTu3PceYle8UESsj4pvtiRURm7N2tURERRfC5u3zVEQsjoiD87ZV1f/FYtXa/yXiVX0OSh1ntr3ic1Cmz6o6BxGxe0TMjojfRMSvImJuRHygHWO/ULxxNfZ/oVgTahn/xY4z21bV+C/RZ9X2/ea843gqIv5XRGyTbdslIuZFxPoqx0ShWH+b9dmS7OfEdsQal3eMT0XElPa0La/O3tmxXtiOtjVFxBt57ftOFW1rfXwhK58REcsi9xk3qFycMrFmRcTzEfFMRMyMiH7tiPXd7Lifjog7I2JArbHytn8jIkp/20j5dt0cEb/LK2+uIVZTteO+gnhVjf0ysaoe+8Vi5W2raNyXaVfV4z6LtVtE/CAifpv1zeOR+3e96nNQIlbV/V8iVi2fPQVj5W2vuP9LtKum/m+TUur1D+AO4BHg0ryyI4ATgPvaEwv4ALBP9nxPYBUwsMZY2wLbZc8HAMuBPWs9xqz8a8APgG+2s7/W19Dv6/OeHw08XGv/F4tVa/+XiFf1OSh1nNWegzJ9VvE5IHcP/ceBc/PKmoHDahn7JeJNqLb/y8Sqtu+LHmcNfV+qz6oa/1ucx8HAfwH/mr3eATgUOLeGMbFlrP1b+wgYCaxsR6x3AX2z53sAa1pf1xIvr/wu4D+AC9vRtibgmVrPwRbl+2fxlgOD2hlrUjZuArgN+HQ7Yu2U9/wa4Au1xsq2jQH+vdKxW6JdNwMnt7fvqx33FcSrauyXiVX12K+g/ysa92XaVcu4L/Q59j7gszV89pSKVe1nT6lYVfV/qVjV9n+ZdlXd//mPXj8Dns0iHAKcTe42hgCklB4EXm9vrJTSr1NKS7PnL5EbOAW/FamCWG+mlP6SVdmOCv+CUewYI+IAYDfg/krilIrVAXYC/tj6opb+LxSr1v4vEa+mc1AoFt960nMAAAmpSURBVNR2DorFqtJHgLdSSm3/Y08ptaSUHqmx74vFe7iG/i8Vq9q+L3qcNfR90VgV7l9QSmkNuW/qnRERkVL6c0rpUWBjB8R6Mut3gGeB/hGxXY2xNqSUNmWb+1PgS8+qiQcQEZOB32Zta1esjpL12fIOijU3ZYAnyH1XRa2x/gSQHe/2VNn/+SKiD3AV8M+1xuho7Rn3ReLVPPYLxGrX2N9SreO+g0wE3tzic+yFlNI3ajgHpWJV2/+lYlXb/0VjQdX9XzJWe/T6BByYDPwkpfRr4JWIGF2vWBExjtwM6m9qjRUR742Ip4EXgS/nDfCqYkXuT7dXA5+r9OBKtYvcL9fCiPhFNrgrsX32Z5v/Bm4CLq+yLVXFqrL/i8ar4RwUjFXjOSh1nNWcg5HAoiret5yy8aro/6Kxauj7grFq7PtSx1jL+G+TUvotuc/jwdXuW0WsjwFP5v0npupYEXFgRDwLLCE3I7Sp1P6l4kXEDsDngX+tJkaxtgFDI+LJiHg4Ig6rIETr71Lro+LlgdXGitzSkzOAn7QnVkR8D/gDsC9QSQJQLNYM4N6U0qqKjq5Mu4AvRW5pzFcrTHLzY82pog21xqtm7BeMVePY3ypWO8Z9sWOsdtyPABZX+d7tjVVJ/5eMVWX/F41VQ/+XO8Zq+79Nt74PeAc5Dbg2ez47e13r4CwaKyL2IPfnvmkppbdrjZVSehHYLyL2BO6OiDtTSqtriHUwMDel9GKVE0jFjnHvlNJLEfE3wM8iYklKqVyi9UZKqRkgIj4M3BoRI7PZomqVjFVD/xeNV8M5KBgL+AzVn4NSx1nLOWiIGvq/oBrHfyG19H0pHdH3HTmT+45YETEC+DJwVHtipZR+CYyIiGHALRHxnymlamcsW+P9K/DVlNL6dpyD1h1XkTsH6yL3l427I2JE66xxEW2/Sx2gXKxvAfMr/GtJ0VgppbMiN3v9DeBU4HvVxsp+d04BDq+gLZW064vk/lOwLbn7In8euKzGWLUqGq+GsV8wVo1jv1CsWsd9oVi1jPt3iIjryC07eTOlNLaaBlUSq9bPni1jteezJz8W8DDt+NzZItahtKf/U41rV3rCA9gFeAN4gdx6vxeB3/PX+6MfTuVrkIvGIrdUYDFwSke0K6/e9yiz9q5ErFnZz+XAy8CfgCs6qF03l2tXVm/9Fq9XA4PzXlfT/0VjVdv/lbStynNQMFaN56DSdpU8B+TWec8vsb3ivi8Xr4bxX7JtVfZ9wVg19n2l7So7/gucx78B1uX/LgFnUuUa8EKxyC17+DVwSHtjbbFtHjCm1njkriNZnj1eBV4BZnRQ2x6qtm0Fti+nnWvAs22XAHcD27Q3Vl6dCZX8fhaKBRxHLmFu7fu3gWUd1K6KPjfK9FdF476SeNWM/SqPs+zYL9H/VY37KttVybg/gq2vQxoELK/2HJSLVeVnT9l2Vdr/pWLV8LlTTbvK9v876ldasSc+gH8Art+i7GH+eoFWRR8mZWJNAB4ELuiIdgHbZ693zgb2qPYcY/a60l+2Uu1qvThuELAUGF5BvPyLqvYllwj1ySurpv8LxiI3K1NV/5eJN6SGc1DyOKs8B8XatXM154BcAvRLYHpe2VhgQrV9Xy5eDeO/VKxq+77kcVbZ96XaVdX43+I87kpuHfqWFyfWMibeEQsYCDwFfKyGcb9lrKH89UKo9wEvUSZBreQ4s22XUt1FmFu2bdfW3ylyiflK4D2VxiuyfXm54ysXC/gU8FjruK01Vjb2/kfe868AX6m1XdXWKXOMe+S161rK/Ee23HtWOu4r6LOqxn6ZWFWP/QrHWNlxX6ZdtYz71s+xT+eV7U1tCXjRWDV89pSKVVX/V3KMlfZ/mXZV3f/viF3NwOxpD3L/Wzlmi7L/CXyb3P+S1pKb8V0BHF1jrN8BbwEteY/mGmM9BzydDeqngXPac4x5ryv9ZSsWax65dVlPZT/PrrD/N+f1yVPAcXnbqu3/grGA06vt/zLx/raGc1D0OGs4B8XadXC154DcXUnuILcm+1ngx8A+1fZ9mXj/p8b+LxRrerV9X+o4q+37ErEm1ND3refx2Wy/C8mbISX34f4KsD47B6X+M1U0FvC/gT9v0f9b/cWkwlhnZOUt5P6iMbm9x5lX71LK/0NYqm0fyytfDJxQ5e9SC1niSO4zbQWwidw/9De1I9ambKy0ll9cSyxya91/no2vZ8j9BWenWtu1RZ1KE/Bix/izvHZ9HxhQQaxiyXzF475cvGrHfplYVY/9Svq2knFfpl1Vj/tsvz3ILSH9HbmLg+cBp9ZyDorFqqX/S8Sq5bOn6DFW2/8l2lVT/7c+/Cp6SZIkqYG8C4okSZLUQCbgkiRJUgOZgEuSJEkNZAIuSZIkNZAJuCRJktRAJuCSJElSA5mAS1IvERGXRsSFnfj+6zvrvSWpKzEBlyTVLCL6dHYbJKm7MQGXpG4sInaIiB9HxFMR8UxEnBoRyyNiULZ9TEQ8lLfLhyLiZxGxNCKml4i7TUR8KyKejYj7ImJuRJycbVseERdHxKPAKRExPSIWZG24KyLeldUbGhGPZ9su3yL+57LypyPiXzu8YySpCzMBl6Tu7RjgpZTSh1JKI4GflKm/H3Ac8GHg4ojYs0i9jwJNwCjgU1n9fBtTSoemlGYDP0wpjU0pfQh4Djg7q/M14NsppbHAH1p3jIijgH2AcUAzcEBEjK/oaCWpBzABl6TubQlwZER8OSIOSym9Vqb+PSmlN1JKLwPzyCXBhRwK/EdK6e2U0h+yuvluz3s+MiIeiYglwFRgRFZ+CHBb9vzf8+oflT2eBBYD+5JLyCWpV+jb2Q2QJNUupfTriDgAmAT8W0TcD2zirxMs/bfcpczrVlHmrf+c9/xmYHJK6amIOBM4vEz8AP4tpXR9mfeQpB7JGXBJ6sayJSQbUkrfB74CjAaWAwdkVT62xS4nRUT/iNiFXKK8oEjoR4GPZWvBd+OdSfWWdgRWRUQ/cjPgrX4OfCJ7nl/+U+CTETEgO4a9ImJwifiS1KM4Ay5J3dso4KqIeBt4C/g0sD3w3Yi4CPjlFvWfAH4M7A1cnlJ6qUjcu4AjgGeAX2dxii1v+T/Z9hfILYnZMSs/H/hBRJyfxQMgpXR/RAwDHo8IgPXA6cCaCo9Zkrq1SKnYXx8lSb1ZRAxIKa3PZsufAA7J1oNLktrBGXBJUjH3RcRAYFtys+Um35LUAZwBl6ReLCJG8c47lAD8JaV0YGe0R5J6AxNwSZIkqYG8C4okSZLUQCbgkiRJUgOZgEuSJEkNZAIuSZIkNdD/Bwy1plfnwpKbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: It looks like F and G subgrades don't get paid back that often. Isloate those and recreate the countplot just for those subgrades.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sub_grade', ylabel='count'>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CODE HERE\n",
    "f_and_g = df[(df['grade']=='G') | (df['grade']=='F')]\n",
    "\n",
    "plt.figure(figsize=(12,4))\n",
    "subgrade_order = sorted(f_and_g['sub_grade'].unique())\n",
    "sns.countplot(x='sub_grade',data=f_and_g,order = subgrade_order,hue='loan_status')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20795ef7a88>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Create a new column called 'loan_repaid' which will contain a 1 if the loan status was \"Fully Paid\" and a 0 if it was \"Charged Off\".**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CODE HERE\n",
    "df[\"loan_repaid\"]=df[\"loan_status\"].map({\"Fully Paid\":1,\"Charged Off\":0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Fully Paid', 'Charged Off'], dtype=object)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>loan_repaid</th>\n",
       "      <th>loan_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>Charged Off</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396025</th>\n",
       "      <td>1</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396026</th>\n",
       "      <td>1</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396027</th>\n",
       "      <td>1</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396028</th>\n",
       "      <td>1</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396029</th>\n",
       "      <td>1</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>396030 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        loan_repaid  loan_status\n",
       "0                 1   Fully Paid\n",
       "1                 1   Fully Paid\n",
       "2                 1   Fully Paid\n",
       "3                 1   Fully Paid\n",
       "4                 0  Charged Off\n",
       "...             ...          ...\n",
       "396025            1   Fully Paid\n",
       "396026            1   Fully Paid\n",
       "396027            1   Fully Paid\n",
       "396028            1   Fully Paid\n",
       "396029            1   Fully Paid\n",
       "\n",
       "[396030 rows x 2 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"loan_repaid\",\"loan_status\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>loan_repaid</th>\n",
       "      <th>loan_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>Charged Off</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396025</th>\n",
       "      <td>1</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396026</th>\n",
       "      <td>1</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396027</th>\n",
       "      <td>1</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396028</th>\n",
       "      <td>1</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396029</th>\n",
       "      <td>1</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>396030 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        loan_repaid  loan_status\n",
       "0                 1   Fully Paid\n",
       "1                 1   Fully Paid\n",
       "2                 1   Fully Paid\n",
       "3                 1   Fully Paid\n",
       "4                 0  Charged Off\n",
       "...             ...          ...\n",
       "396025            1   Fully Paid\n",
       "396026            1   Fully Paid\n",
       "396027            1   Fully Paid\n",
       "396028            1   Fully Paid\n",
       "396029            1   Fully Paid\n",
       "\n",
       "[396030 rows x 2 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**CHALLENGE TASK: (Note this is hard, but can be done in one line!) Create a bar plot showing the correlation of the numeric features to the new loan_repaid column. [Helpful Link](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.bar.html)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#CODE HERE\n",
    "df.corr()[\"loan_repaid\"].sort_values()[:-1].plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20795034cc8>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "---\n",
    "# Section 2: Data PreProcessing\n",
    "\n",
    "**Section Goals: Remove or fill any missing data. Remove unnecessary or repetitive features. Convert categorical string features to dummy variables.**\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>loan_amnt</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>emp_title</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>...</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>initial_list_status</th>\n",
       "      <th>application_type</th>\n",
       "      <th>mort_acc</th>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "      <th>address</th>\n",
       "      <th>loan_repaid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>11.44</td>\n",
       "      <td>329.48</td>\n",
       "      <td>B</td>\n",
       "      <td>B4</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>117000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36369.0</td>\n",
       "      <td>41.8</td>\n",
       "      <td>25.0</td>\n",
       "      <td>w</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0174 Michelle Gateway\\r\\nMendozaberg, OK 22690</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>11.99</td>\n",
       "      <td>265.68</td>\n",
       "      <td>B</td>\n",
       "      <td>B5</td>\n",
       "      <td>Credit analyst</td>\n",
       "      <td>4 years</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>65000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20131.0</td>\n",
       "      <td>53.3</td>\n",
       "      <td>27.0</td>\n",
       "      <td>f</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1076 Carney Fort Apt. 347\\r\\nLoganmouth, SD 05113</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15600.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>10.49</td>\n",
       "      <td>506.97</td>\n",
       "      <td>B</td>\n",
       "      <td>B3</td>\n",
       "      <td>Statistician</td>\n",
       "      <td>&lt; 1 year</td>\n",
       "      <td>RENT</td>\n",
       "      <td>43057.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11987.0</td>\n",
       "      <td>92.2</td>\n",
       "      <td>26.0</td>\n",
       "      <td>f</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87025 Mark Dale Apt. 269\\r\\nNew Sabrina, WV 05113</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7200.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>6.49</td>\n",
       "      <td>220.65</td>\n",
       "      <td>A</td>\n",
       "      <td>A2</td>\n",
       "      <td>Client Advocate</td>\n",
       "      <td>6 years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>54000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5472.0</td>\n",
       "      <td>21.5</td>\n",
       "      <td>13.0</td>\n",
       "      <td>f</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>823 Reid Ford\\r\\nDelacruzside, MA 00813</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24375.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>17.27</td>\n",
       "      <td>609.33</td>\n",
       "      <td>C</td>\n",
       "      <td>C5</td>\n",
       "      <td>Destiny Management Inc.</td>\n",
       "      <td>9 years</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>55000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24584.0</td>\n",
       "      <td>69.8</td>\n",
       "      <td>43.0</td>\n",
       "      <td>f</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>679 Luna Roads\\r\\nGreggshire, VA 11650</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   loan_amnt        term  int_rate  installment grade sub_grade  \\\n",
       "0    10000.0   36 months     11.44       329.48     B        B4   \n",
       "1     8000.0   36 months     11.99       265.68     B        B5   \n",
       "2    15600.0   36 months     10.49       506.97     B        B3   \n",
       "3     7200.0   36 months      6.49       220.65     A        A2   \n",
       "4    24375.0   60 months     17.27       609.33     C        C5   \n",
       "\n",
       "                 emp_title emp_length home_ownership  annual_inc  ... pub_rec  \\\n",
       "0                Marketing  10+ years           RENT    117000.0  ...     0.0   \n",
       "1          Credit analyst     4 years       MORTGAGE     65000.0  ...     0.0   \n",
       "2             Statistician   < 1 year           RENT     43057.0  ...     0.0   \n",
       "3          Client Advocate    6 years           RENT     54000.0  ...     0.0   \n",
       "4  Destiny Management Inc.    9 years       MORTGAGE     55000.0  ...     0.0   \n",
       "\n",
       "  revol_bal revol_util total_acc initial_list_status  application_type  \\\n",
       "0   36369.0       41.8      25.0                   w        INDIVIDUAL   \n",
       "1   20131.0       53.3      27.0                   f        INDIVIDUAL   \n",
       "2   11987.0       92.2      26.0                   f        INDIVIDUAL   \n",
       "3    5472.0       21.5      13.0                   f        INDIVIDUAL   \n",
       "4   24584.0       69.8      43.0                   f        INDIVIDUAL   \n",
       "\n",
       "  mort_acc  pub_rec_bankruptcies  \\\n",
       "0      0.0                   0.0   \n",
       "1      3.0                   0.0   \n",
       "2      0.0                   0.0   \n",
       "3      0.0                   0.0   \n",
       "4      1.0                   0.0   \n",
       "\n",
       "                                             address  loan_repaid  \n",
       "0     0174 Michelle Gateway\\r\\nMendozaberg, OK 22690            1  \n",
       "1  1076 Carney Fort Apt. 347\\r\\nLoganmouth, SD 05113            1  \n",
       "2  87025 Mark Dale Apt. 269\\r\\nNew Sabrina, WV 05113            1  \n",
       "3            823 Reid Ford\\r\\nDelacruzside, MA 00813            1  \n",
       "4             679 Luna Roads\\r\\nGreggshire, VA 11650            0  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Missing Data\n",
    "\n",
    "**Let's explore this missing data columns. We use a variety of factors to decide whether or not they would be useful, to see if we should keep, discard, or fill in the missing data.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: What is the length of the dataframe?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "396030"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CODE HERE\n",
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "396030"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Create a Series that displays the total count of missing values per column.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_amnt                   0\n",
       "term                        0\n",
       "int_rate                    0\n",
       "installment                 0\n",
       "grade                       0\n",
       "sub_grade                   0\n",
       "emp_title               22927\n",
       "emp_length              18301\n",
       "home_ownership              0\n",
       "annual_inc                  0\n",
       "verification_status         0\n",
       "issue_d                     0\n",
       "loan_status                 0\n",
       "purpose                     0\n",
       "title                    1755\n",
       "dti                         0\n",
       "earliest_cr_line            0\n",
       "open_acc                    0\n",
       "pub_rec                     0\n",
       "revol_bal                   0\n",
       "revol_util                276\n",
       "total_acc                   0\n",
       "initial_list_status         0\n",
       "application_type            0\n",
       "mort_acc                37795\n",
       "pub_rec_bankruptcies      535\n",
       "address                     0\n",
       "loan_repaid                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CODE HERE\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_amnt                   0\n",
       "term                        0\n",
       "int_rate                    0\n",
       "installment                 0\n",
       "grade                       0\n",
       "sub_grade                   0\n",
       "emp_title               22927\n",
       "emp_length              18301\n",
       "home_ownership              0\n",
       "annual_inc                  0\n",
       "verification_status         0\n",
       "issue_d                     0\n",
       "loan_status                 0\n",
       "purpose                     0\n",
       "title                    1755\n",
       "dti                         0\n",
       "earliest_cr_line            0\n",
       "open_acc                    0\n",
       "pub_rec                     0\n",
       "revol_bal                   0\n",
       "revol_util                276\n",
       "total_acc                   0\n",
       "initial_list_status         0\n",
       "application_type            0\n",
       "mort_acc                37795\n",
       "pub_rec_bankruptcies      535\n",
       "address                     0\n",
       "loan_repaid                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Convert this Series to be in term of percentage of the total DataFrame**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_amnt               0.000000\n",
       "term                    0.000000\n",
       "int_rate                0.000000\n",
       "installment             0.000000\n",
       "grade                   0.000000\n",
       "sub_grade               0.000000\n",
       "emp_title               5.789208\n",
       "emp_length              4.621115\n",
       "home_ownership          0.000000\n",
       "annual_inc              0.000000\n",
       "verification_status     0.000000\n",
       "issue_d                 0.000000\n",
       "loan_status             0.000000\n",
       "purpose                 0.000000\n",
       "title                   0.443148\n",
       "dti                     0.000000\n",
       "earliest_cr_line        0.000000\n",
       "open_acc                0.000000\n",
       "pub_rec                 0.000000\n",
       "revol_bal               0.000000\n",
       "revol_util              0.069692\n",
       "total_acc               0.000000\n",
       "initial_list_status     0.000000\n",
       "application_type        0.000000\n",
       "mort_acc                9.543469\n",
       "pub_rec_bankruptcies    0.135091\n",
       "address                 0.000000\n",
       "loan_repaid             0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CODE HERE\n",
    "df.isnull().sum()/len(df)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_amnt               0.000000\n",
       "term                    0.000000\n",
       "int_rate                0.000000\n",
       "installment             0.000000\n",
       "grade                   0.000000\n",
       "sub_grade               0.000000\n",
       "emp_title               5.789208\n",
       "emp_length              4.621115\n",
       "home_ownership          0.000000\n",
       "annual_inc              0.000000\n",
       "verification_status     0.000000\n",
       "issue_d                 0.000000\n",
       "loan_status             0.000000\n",
       "purpose                 0.000000\n",
       "title                   0.443148\n",
       "dti                     0.000000\n",
       "earliest_cr_line        0.000000\n",
       "open_acc                0.000000\n",
       "pub_rec                 0.000000\n",
       "revol_bal               0.000000\n",
       "revol_util              0.069692\n",
       "total_acc               0.000000\n",
       "initial_list_status     0.000000\n",
       "application_type        0.000000\n",
       "mort_acc                9.543469\n",
       "pub_rec_bankruptcies    0.135091\n",
       "address                 0.000000\n",
       "loan_repaid             0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Let's examine emp_title and emp_length to see whether it will be okay to drop them. Print out their feature information using the feat_info() function from the top of this notebook.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job title supplied by the Borrower when applying for the loan.*\n"
     ]
    }
   ],
   "source": [
    "# CODE HERE\n",
    "feat_info(\"emp_title\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job title supplied by the Borrower when applying for the loan.*\n",
      "\n",
      "\n",
      "Employment length in years. Possible values are between 0 and 10 where 0 means less than one year and 10 means ten or more years. \n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: How many unique employment job titles are there?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "173105"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CODE HERE\n",
    "len(df[\"emp_title\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "173105"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Teacher                           4389\n",
       "Manager                           4250\n",
       "Registered Nurse                  1856\n",
       "RN                                1846\n",
       "Supervisor                        1830\n",
       "                                  ... \n",
       "mechanic/lead                        1\n",
       "SUPV. MECHINE SHOP                   1\n",
       "Mcccd                                1\n",
       "Dr. Dennis Norkiewicz DDS            1\n",
       "bernie little distributing llc       1\n",
       "Name: emp_title, Length: 173105, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Realistically there are too many unique job titles to try to convert this to a dummy variable feature. Let's remove that emp_title column.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CODE HERE\n",
    "df=df.drop(\"emp_title\",axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Create a count plot of the emp_length feature column. Challenge: Sort the order of the values.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1 year',\n",
       " '10+ years',\n",
       " '2 years',\n",
       " '3 years',\n",
       " '4 years',\n",
       " '5 years',\n",
       " '6 years',\n",
       " '7 years',\n",
       " '8 years',\n",
       " '9 years',\n",
       " '< 1 year']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CODE HERE\n",
    "sorted(df[\"emp_length\"].dropna().unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1 year',\n",
       " '10+ years',\n",
       " '2 years',\n",
       " '3 years',\n",
       " '4 years',\n",
       " '5 years',\n",
       " '6 years',\n",
       " '7 years',\n",
       " '8 years',\n",
       " '9 years',\n",
       " '< 1 year']"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "emp_length_order = [ '< 1 year',\n",
    "                      '1 year',\n",
    "                     '2 years',\n",
    "                     '3 years',\n",
    "                     '4 years',\n",
    "                     '5 years',\n",
    "                     '6 years',\n",
    "                     '7 years',\n",
    "                     '8 years',\n",
    "                     '9 years',\n",
    "                     '10+ years']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='emp_length', ylabel='count'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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teT5wG0Dbfl8r/5P4qH3Giz9EkhVJ1iZZu2HDhkl2SZIkSdpy056YJ3k+cGdVXTHddY9WVadU1ZKqWjJv3rxhN0eSJEmz2Nwh1PlM4AVJngs8CtgeeBewY5K5bVR8AXB7K387sDuwPslcYAfgroH4iMF9xotLkiRJvTTtI+ZVdVxVLaiqhXQnb15YVS8FLgIObcWWA+e25fPaOm37hVVVLX54u2rLHsAi4DLgcmBRu8rLNq2O86aha5IkSdIWG8aI+XjeDJyZ5G3AlcCpLX4q8MEk64C76RJtquq6JGcB1wMPAEdX1YMASV4LrAbmACur6rpp7YkkSZK0mYaamFfVxcDFbflmuiuqjC7zA+CwcfZ/O/D2MeKfAT6zFZsqSZIkTSnv/ClJkiT1QJ+mskhD86mVBw+7CZPy/Fd+dthNkCRJk+SIuSRJktQDJuaSJElSD5iYS5IkST1gYi5JkiT1gIm5JEmS1AMm5pIkSVIPeLnETdhw8oeG3YRJm/dHLxt2E9RDJ374oGE3YVLe+Purh90ESZK2KkfMJUmSpB4wMZckSZJ6wMRckiRJ6gETc0mSJKkHTMwlSZKkHjAxlyRJknrAxFySJEnqAa9jLkkz1PPOed+wmzBpn37hq4fdBEmaNibmeoj/ePeyYTdh0p74unOH3QRJkqTN4lQWSZIkqQdMzCVJkqQeMDGXJEmSesA55pJmjYPP/cNhN2FSPrvsH4fdBEnSFHLEXJIkSeoBE3NJkiSpB6Y9MU+ye5KLklyf5Lokb2jxnZOsSXJT+7lTiyfJSUnWJbk6yTMGjrW8lb8pyfKB+N5Jrmn7nJQk091PSZIkaXMMY8T8AeBPqmoxsB9wdJLFwLHABVW1CLigrQMcDCxqjxXAydAl8sDxwL7APsDxI8l8K/Oqgf2WTkO/JEmSpC027Sd/VtUdwB1t+TtJbgDmA8uA/Vux04CLgTe3+OlVVcAlSXZMslsru6aq7gZIsgZYmuRiYPuquqTFTwcOAT47Dd2TJA3RC85++N9c7LxDN+8mb4edc+0UtWT6fOyFew67CVIvDHWOeZKFwK8ClwK7tqQd4BvArm15PnDbwG7rW2xj8fVjxMeqf0WStUnWbtiwYXKdkSRJkiZhaJdLTLIdcA5wTFV9e3AaeFVVkprqNlTVKcApAEuWLJny+iRJ0uSddc63ht2ESXvRC3cZdhPUQ0MZMU/yCLqk/Iyq+ngLf7NNUaH9vLPFbwd2H9h9QYttLL5gjLgkSZLUW8O4KkuAU4EbquodA5vOA0aurLIcOHcgfkS7Ost+wH1tystq4MAkO7WTPg8EVrdt306yX6vriIFjSZIkSb00jKkszwReDlyT5KoW+zPg74CzkhwF3Aq8qG37DPBcYB3wfeAVAFV1d5K/Bi5v5d46ciIo8BpgFbAt3UmfnvgpSZKkXhvGVVn+FRjvuuIHjFG+gKPHOdZKYOUY8bWAp3hLkiTpYcM7f0qSJEk9YGIuSZIk9cDQLpcoSZKkifnqe7857CZM2lNes+umC81yjphLkiRJPWBiLkmSJPWAibkkSZLUAybmkiRJUg+YmEuSJEk9YGIuSZIk9YCJuSRJktQDJuaSJElSD5iYS5IkST1gYi5JkiT1gIm5JEmS1AMm5pIkSVIPmJhLkiRJPWBiLkmSJPWAibkkSZLUAybmkiRJUg+YmEuSJEk9YGIuSZIk9YCJuSRJktQDJuaSJElSD5iYS5IkST0wYxPzJEuT3JhkXZJjh90eSZIkaWNmZGKeZA7wHuBgYDHwkiSLh9sqSZIkaXwzMjEH9gHWVdXNVfVD4Exg2ZDbJEmSJI0rVTXsNmx1SQ4FllbVH7T1lwP7VtVrR5VbAaxoq08GbpzWhnZ2Ab41hHqHyT7PDvZ5drDPM99s6y/Y59limH1+QlXNGx2cO4yW9EVVnQKcMsw2JFlbVUuG2YbpZp9nB/s8O9jnmW+29Rfs82zRxz7P1KkstwO7D6wvaDFJkiSpl2ZqYn45sCjJHkm2AQ4HzhtymyRJkqRxzcipLFX1QJLXAquBOcDKqrpuyM0az1Cn0gyJfZ4d7PPsYJ9nvtnWX7DPs0Xv+jwjT/6UJEmSHm5m6lQWSZIk6WHFxFySJEnqARPzKZTksCTXJflxkl5djmeykqxMcmeSa4fdlqmUZPckFyW5vr2Wbxh2m6ZakkcluSzJV1qf/2rYbZoOSeYkuTLJp4bdlumQ5GtJrklyVZK1w27PdEiyY5Kzk3w1yQ1Jfm3YbZpKSZ7cXt+Rx7eTHDPsdk21JG9sf7uuTfKRJI8adpumWpI3tP5eN1Ne4/HyjCQ7J1mT5Kb2c6dhtXEqmJhvBUm2SfKYMTZdC/we8IVpbk+STPVruwpYOsV1PESS6T5h+QHgT6pqMbAfcHSSxdNR8RD6OuJ+4NlV9XRgL2Bpkv2mo+Ih9hngDcAN01nhkPsL8FtVtdd0Xsd3yH1+F/C5qnoK8HSm6fUeVp+r6sb2+u4F7A18H/jEdNQ9rD4nmQ+8HlhSVXvSXQDi8Gmqe1h93hN4Fd1dz58OPD/Jk6ap7qns8yrGzjOOBS6oqkXABW19dLtWJdl/Cts2piRzJnsME/NJSPLLSf6B7o6hvzR6e1XdUFUbvZtoktOTHDKwfkaSZW307u+TXJ7k6iSvbtu3S3JBki+30a5lLb4wyY1JTqf7QLD7mBVuJVX1BeDujfTrsUluSfKItr79yHqSJyb5XJIrkvy/JE9pZX4nyaVt1PJfkuza4m9J8sEkXwQ+OJX9Gq2q7qiqL7fl79D9I58/XX1N8tQ2en1Vex8smoY+V1V9t60+oj1+5izx1q8vD6wvGllPsneSz7c+r06yW4u/qr2fv5LknCSPbvFVSf4xyaXA/07ymwMjfFcmeexU9znJAuB5wPvH2T6j+jsRM+19nWQH4FnAqQBV9cOquncm93mUA4D/qKpbZ0Gf5wLbpksaHw3856g+z7Tf518GLq2q71fVA8Dn6QYFB/v8sHudN5JnLANOa8unAYds7rGTvDLJOwfWX5XkxLb8soH+vC8t2U5ycpK1GfVNcrpvH09o76HDkrw+3bfsVyc5c3PbRlX52IwH8BjgFcC/tsdRwGM3sc/FdJ/ex9r2m8An2/IOwC10f1RWAH/R4o8E1gJ7tG3bt/guwDogwELgx8B+0/hcLASu3cj2DwCHtOUVwD+05QuARW15X+DCtrwTP71S0B8MlH8LcAWw7ZBf+4XA10ee/+noK/Bu4KVteZvpeg7oRpmuAr4LnDBOmYuAvdry3wCvo0vi/w2Y1+IvprtcKcDjBvZ9G/C6trwK+BQwp63/M/DMtrwdMHca+ns23Yji/sCnZnp/W123AF9u77cV45SZMe9rum9/LmvP/5V0H8IeM5P7PKpfK4HXzvTXudX1Brq/XRuAM8YpM2N+n+kS838HHkf3QeRLwLtnwuvMGHkGcO/AcgbXB+KrgP03ctztgP8AHtHW/w34lfZc/vNA/L3AEW155/ZzDl1e97S2/jXgTQPH/k/gkW15x83t87C/Rn04ugO4GviDqvrqZA9WVZ9P8t4k84AXAudUdx32A4GnJTm0Fd0BWASsB/4mybPoEvH5wK6tzK1Vdclk27QVvR94E/BJug8zr0qyHfDrwMeSjJR7ZPu5APhoG6HYhi5xGHFeVf3/6Wj0WFq7zwGOqapvj1Fkqvr6JeDP043ofryqbtp6vRpfVT0I7JVkR+ATSfasqtHnE7wfeEWSP6b7B7YP8GRgT2BN6/Mcut8ZgD2TvA3Yke6P4uqBY32s1QnwReAdSc6g6/P6rd2/QUmeD9xZVVdk4199zoj+DviNqro9yePp2v/V6kaoBs2k9/Vc4Bl0CdWlSd5F9xX4/xpVbib1GeimWwIvAI4bp8iM6XO6+cbL6Aay7qVr/8uq6kOjis6Y3+equiHJCcD5wPfoBlUeHKPojHmdR1RVJeky9OQg4IS26ReB30jyXeD+qtp31H7fTXIh3bSfG+gS8WvS3QNnb+Dy9nxsC9zZdntRkhV0f0t2AxbT5YMAHx04/NXAGUk+Sfdcb3anfGzep7cD2wtwPfCXwBMmsM/FjDNi3ra/GXgjcCmwuMXOAQ4ao+yRrf6RT3Nfo/tEuZCNjF5P0XOxyTqBr9CNQl7W1rcH7tjI8/SCtrw/cHFbfgvwp0N8zR9B90f4j4fRV+CJdHMmb6Kb+z3d/f/LsZ5/4FF0ozTLgLNa7FeAL41znFuAp7flI4FVbXkVcOiosr/Sfi9uBZ4yxf37W7oPvF8DvkE3D/dDM7W/47R13N+xmfK+Bn4e+NrA+v8APj2T+zxQ7zLg/E2UmRF9Bg4DTh1YPwJ47xjlZvLv898Ar5kJrzNjj5jfCOzWlncDbhxjv1VsZMS8ldkXOJcumX9Ni70O+Nsxyu5BN0Nhp4HjH9mWvwbsMlB2DvBbwDvopr9u1rcmzjHfTFV1flW9mO6P+n3AuW3u1cJJHHYVcEw7/vUtthr4o4H5YL+U7gTTHehG936U5LeAJ0yi3ulwOvBhuq/QqG60+ZYkh8FPTlR9eiu7A3B7W14+3Q0dS7qPzKcCN1TVOzZRfKv3Ncl/A26uqpPo/oA8bUv7MlFJ5rWRcpJsCzwHeMi3Q1X1A7r36cm0PtP9wZyXdrWLdPMXn9q2PRa4o72nX7qR+p9YVddU1QnA5cBTtkrHxlFVx1XVgqpaSHeS2IVV9bIxys2I/rY6H5M297X9XTmQ7tyUscyI93VVfQO4LcmTW+gAugGWscyIPg94CfCRTZSZKX3+OrBfkke3v98HMMZJvjPp97nV+/j28xfp5pd/eJyiM+F1Pm+gPctbnZutqi6lOx/v9/np78cFwKEDz+fOSZ5A9wHme8B96ebaHzzWMdNdeGP3qrqI7sPZDnTfsEyYifkWqqq7qupd1Z3t/meM8bVRkt9Nsh74NeDTSVaPLtOO9U26PxwfGAi/n+6fxpfTXSrofXRfn5wBLElyDd1IwKSn02yJJB+h+/rqyUnWJzlqnKJn0M1TG/yn8FLgqCRfAa6jG7GA7lP4x5JcAXxrShq++Z4JvBx4dn56Qs9zxyk7FX19EXBtkqvovmI9fUs7shl2Ay5KcjXdP5Y1VTXeJQTPoJtSdT50J9QBhwIntD5fRfc1KXRTBi6l+6p3Y+/bY9Jd9utq4EfAZyfXna1qpvR3V+BfW5svoxs5/tw4ZWfK+xq60bAz2nO9F93I4lhmTJ/bB6/nAB/fRNEZ0eeWbJ1Nd/7ENXR5zni3XZ8pv88A5yS5nm5+9NE16sTmAQ+b13kjecbfAc9JchPw2219S50FfLGq7oGfDI7+BXB+ew3X0I3Of4Xu3JSv0n2w+eI4x5sDfKjlaFcCJ23ktRjTyKR+DVG6s7uvAZ5RVfcNuz1bU7o58suq6uXDbstUm019HZHkT4Edqmr0PN0Zabb1F2bt+9o+zwL+PivdfStOrKoLht2WEZ78OWRJfptuqsSJMzApfzfd1z3jjTDPGLOpryOSfIJuXuGzh92W6TDb+guz9n1tn2cBf59nt3TTNS8DvtKnpBwcMZckSZJ6wTnmkiRJUg+YmEuSJEk9YGIuSZIk9YCJuSRJktQDJuaSpM2W7lbXW/uYew3eJyDJW9ol7SRpVjAxlyT1xV54KTdJs5iJuSTNIEleluSydpfa9yWZk+S7Sf4+yXVJ/iXJPkkuTnJzkhe0/Y5Mcm6L35Tk+M2o838muTzJ1Un+qsUWJrkhyT+1es9Psm3b9t9b2atau65Nsg3wVuDFLf7idvjFA219/VZ+uiSpV0zMJWmGSPLLwIuBZ1bVXsCDdLfbfgxwYVU9FfgO8Da627T/Ll0yPGIf4IXA04DDkiyZQJ0HAovavnsBeyd5Vtu8CHhPq/fedmyADwCvHmjjyO3Q/xL4aFXtVVUfbWWfAhzUjn98kkdM/BmRpIcX7/wpSTPHAcDewOVJALYF7gR+CHyulbkGuL+qfpTkGmDhwP5rquougCQfB34DWLuJOg9sjyvb+nZ0CfnXgVuq6qoWvwJY2O6499iq+lKLfxh4/kaO/+mquh+4P8mdwK7A+k20SZIelkzMJWnmCHBaVR33M8HkT+unt3n+MXA/QFX9OMng/4HRt4KeyK2hA/xtVb1vVJ0LR+ppHqT7oLC5Rh/D/1uSZiynskjSzHEBcGiSxwMk2TnJEzZj/+e0fbYFDgG+OIF9VgOvTLJdq3P+SP1jqap7ge8k2beFDh/Y/B3gsZvRXkmaUUzMJWmGqKrrgb8Azk9yNbAG2G0zDnEZcA5wNXBOVW1qGgtVdT7ddJQvtakxZ7Pp5Poo4J+SXEU3//2+Fr+I7mTPwZM/JWnWyE+/3ZQkzVZJjgSWVNVrp6Gu7arqu235WGC3qnrDVNcrSX3nXD1J0nR7XpLj6P4H3QocOdzmSFI/OGIuSRpTksfRzVsf7YCRq7dIkrYeE3NJkiSpBzz5U5IkSeoBE3NJkiSpB0zMJUmSpB4wMZckSZJ64L8A7c+oVurNMiMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4))\n",
    "\n",
    "sns.countplot(x='emp_length',data=df,order=emp_length_order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2079cee4f48>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Plot out the countplot with a hue separating Fully Paid vs Charged Off**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='emp_length', ylabel='count'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CODE HERE\n",
    "plt.figure(figsize=(12,4))\n",
    "sns.countplot(x='emp_length',data=df,order=emp_length_order,hue='loan_status')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20797fc6f48>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**CHALLENGE TASK: This still doesn't really inform us if there is a strong relationship between employment length and being charged off, what we want is the percentage of charge offs per category. Essentially informing us what percent of people per employment category didn't pay back their loan. There are a multitude of ways to create this Series. Once you've created it, see if visualize it with a [bar plot](https://pandas.pydata.org/pandas-docs/version/0.23.4/generated/pandas.DataFrame.plot.html). This may be tricky, refer to solutions if you get stuck on creating this Series.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "emp_co = df[df['loan_status']==\"Charged Off\"].groupby(\"emp_length\").count()['loan_status']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "emp_fp = df[df['loan_status']==\"Fully Paid\"].groupby(\"emp_length\").count()['loan_status']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "emp_len = emp_co/emp_fp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "emp_length\n",
       "1 year       0.248649\n",
       "10+ years    0.225770\n",
       "2 years      0.239560\n",
       "3 years      0.242593\n",
       "4 years      0.238213\n",
       "5 years      0.237911\n",
       "6 years      0.233341\n",
       "7 years      0.241887\n",
       "8 years      0.249625\n",
       "9 years      0.250735\n",
       "< 1 year     0.260830\n",
       "Name: loan_status, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emp_len"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "emp_length\n",
       "1 year       0.248649\n",
       "10+ years    0.225770\n",
       "2 years      0.239560\n",
       "3 years      0.242593\n",
       "4 years      0.238213\n",
       "5 years      0.237911\n",
       "6 years      0.233341\n",
       "7 years      0.241887\n",
       "8 years      0.249625\n",
       "9 years      0.250735\n",
       "< 1 year     0.260830\n",
       "Name: loan_status, dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='emp_length'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "emp_len.plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20798297d88>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Charge off rates are extremely similar across all employment lengths. Go ahead and drop the emp_length column.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CODE HERE\n",
    "df=df.drop(\"emp_length\",axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Revisit the DataFrame to see what feature columns still have missing data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_amnt                   0\n",
       "term                        0\n",
       "int_rate                    0\n",
       "installment                 0\n",
       "grade                       0\n",
       "sub_grade                   0\n",
       "home_ownership              0\n",
       "annual_inc                  0\n",
       "verification_status         0\n",
       "issue_d                     0\n",
       "loan_status                 0\n",
       "purpose                     0\n",
       "title                    1755\n",
       "dti                         0\n",
       "earliest_cr_line            0\n",
       "open_acc                    0\n",
       "pub_rec                     0\n",
       "revol_bal                   0\n",
       "revol_util                276\n",
       "total_acc                   0\n",
       "initial_list_status         0\n",
       "application_type            0\n",
       "mort_acc                37795\n",
       "pub_rec_bankruptcies      535\n",
       "address                     0\n",
       "loan_repaid                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_amnt                   0\n",
       "term                        0\n",
       "int_rate                    0\n",
       "installment                 0\n",
       "grade                       0\n",
       "sub_grade                   0\n",
       "home_ownership              0\n",
       "annual_inc                  0\n",
       "verification_status         0\n",
       "issue_d                     0\n",
       "loan_status                 0\n",
       "purpose                     0\n",
       "title                    1755\n",
       "dti                         0\n",
       "earliest_cr_line            0\n",
       "open_acc                    0\n",
       "pub_rec                     0\n",
       "revol_bal                   0\n",
       "revol_util                276\n",
       "total_acc                   0\n",
       "initial_list_status         0\n",
       "application_type            0\n",
       "mort_acc                37795\n",
       "pub_rec_bankruptcies      535\n",
       "address                     0\n",
       "loan_repaid                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Review the title column vs the purpose column. Is this repeated information?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CODE HERE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0              vacation\n",
       "1    debt_consolidation\n",
       "2           credit_card\n",
       "3           credit_card\n",
       "4           credit_card\n",
       "5    debt_consolidation\n",
       "6      home_improvement\n",
       "7           credit_card\n",
       "8    debt_consolidation\n",
       "9    debt_consolidation\n",
       "Name: purpose, dtype: object"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                   Vacation\n",
       "1         Debt consolidation\n",
       "2    Credit card refinancing\n",
       "3    Credit card refinancing\n",
       "4      Credit Card Refinance\n",
       "5         Debt consolidation\n",
       "6           Home improvement\n",
       "7       No More Credit Cards\n",
       "8         Debt consolidation\n",
       "9         Debt Consolidation\n",
       "Name: title, dtype: object"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['title'].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: The title column is simply a string subcategory/description of the purpose column. Go ahead and drop the title column.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CODE HERE\n",
    "df=df.drop(\"title\",axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "**NOTE: This is one of the hardest parts of the project! Refer to the solutions video if you need guidance, feel free to fill or drop the missing values of the mort_acc however you see fit! Here we're going with a very specific approach.**\n",
    "\n",
    "\n",
    "---\n",
    "**TASK: Find out what the mort_acc feature represents**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of mortgage accounts.\n"
     ]
    }
   ],
   "source": [
    "# CODE HERE\n",
    "feat_info(\"mort_acc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of mortgage accounts.\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Create a value_counts of the mort_acc column.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0     139777\n",
       "1.0      60416\n",
       "2.0      49948\n",
       "3.0      38049\n",
       "4.0      27887\n",
       "5.0      18194\n",
       "6.0      11069\n",
       "7.0       6052\n",
       "8.0       3121\n",
       "9.0       1656\n",
       "10.0       865\n",
       "11.0       479\n",
       "12.0       264\n",
       "13.0       146\n",
       "14.0       107\n",
       "15.0        61\n",
       "16.0        37\n",
       "17.0        22\n",
       "18.0        18\n",
       "19.0        15\n",
       "20.0        13\n",
       "24.0        10\n",
       "22.0         7\n",
       "21.0         4\n",
       "25.0         4\n",
       "27.0         3\n",
       "23.0         2\n",
       "32.0         2\n",
       "26.0         2\n",
       "31.0         2\n",
       "30.0         1\n",
       "28.0         1\n",
       "34.0         1\n",
       "Name: mort_acc, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CODE HERE\n",
    "df[\"mort_acc\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0     139777\n",
       "1.0      60416\n",
       "2.0      49948\n",
       "3.0      38049\n",
       "4.0      27887\n",
       "5.0      18194\n",
       "6.0      11069\n",
       "7.0       6052\n",
       "8.0       3121\n",
       "9.0       1656\n",
       "10.0       865\n",
       "11.0       479\n",
       "12.0       264\n",
       "13.0       146\n",
       "14.0       107\n",
       "15.0        61\n",
       "16.0        37\n",
       "17.0        22\n",
       "18.0        18\n",
       "19.0        15\n",
       "20.0        13\n",
       "24.0        10\n",
       "22.0         7\n",
       "21.0         4\n",
       "25.0         4\n",
       "27.0         3\n",
       "23.0         2\n",
       "32.0         2\n",
       "26.0         2\n",
       "31.0         2\n",
       "30.0         1\n",
       "28.0         1\n",
       "34.0         1\n",
       "Name: mort_acc, dtype: int64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: There are many ways we could deal with this missing data. We could attempt to build a simple model to fill it in, such as a linear model, we could just fill it in based on the mean of the other columns, or you could even bin the columns into categories and then set NaN as its own category. There is no 100% correct approach! Let's review the other columsn to see which most highly correlates to mort_acc**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int_rate               -0.082583\n",
       "dti                    -0.025439\n",
       "revol_util              0.007514\n",
       "pub_rec                 0.011552\n",
       "pub_rec_bankruptcies    0.027239\n",
       "loan_repaid             0.073111\n",
       "open_acc                0.109205\n",
       "installment             0.193694\n",
       "revol_bal               0.194925\n",
       "loan_amnt               0.222315\n",
       "annual_inc              0.236320\n",
       "total_acc               0.381072\n",
       "mort_acc                1.000000\n",
       "Name: mort_acc, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()[\"mort_acc\"].sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation with the mort_acc column\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "int_rate               -0.082583\n",
       "dti                    -0.025439\n",
       "revol_util              0.007514\n",
       "pub_rec                 0.011552\n",
       "pub_rec_bankruptcies    0.027239\n",
       "loan_repaid             0.073111\n",
       "open_acc                0.109205\n",
       "installment             0.193694\n",
       "revol_bal               0.194925\n",
       "loan_amnt               0.222315\n",
       "annual_inc              0.236320\n",
       "total_acc               0.381072\n",
       "mort_acc                1.000000\n",
       "Name: mort_acc, dtype: float64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Looks like the total_acc feature correlates with the mort_acc , this makes sense! Let's try this fillna() approach. We will group the dataframe by the total_acc and calculate the mean value for the mort_acc per total_acc entry. To get the result below:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "total_acc\n",
       "2.0      0.000000\n",
       "3.0      0.052023\n",
       "4.0      0.066743\n",
       "5.0      0.103289\n",
       "6.0      0.151293\n",
       "           ...   \n",
       "124.0    1.000000\n",
       "129.0    1.000000\n",
       "135.0    3.000000\n",
       "150.0    2.000000\n",
       "151.0    0.000000\n",
       "Name: mort_acc, Length: 118, dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"total_acc\").mean()[\"mort_acc\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean of mort_acc column per total_acc\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "total_acc\n",
       "2.0      0.000000\n",
       "3.0      0.052023\n",
       "4.0      0.066743\n",
       "5.0      0.103289\n",
       "6.0      0.151293\n",
       "           ...   \n",
       "124.0    1.000000\n",
       "129.0    1.000000\n",
       "135.0    3.000000\n",
       "150.0    2.000000\n",
       "151.0    0.000000\n",
       "Name: mort_acc, Length: 118, dtype: float64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**CHALLENGE TASK: Let's fill in the missing mort_acc values based on their total_acc value. If the mort_acc is missing, then we will fill in that missing value with the mean value corresponding to its total_acc value from the Series we created above. This involves using an .apply() method with two columns. Check out the link below for more info, or review the solutions video/notebook.**\n",
    "\n",
    "[Helpful Link](https://stackoverflow.com/questions/13331698/how-to-apply-a-function-to-two-columns-of-pandas-dataframe) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CODE HERE\n",
    "total_acc_avg = df.groupby('total_acc').mean()['mort_acc']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_acc_avg[2.0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fill_mort_acc(total_acc,mort_acc):\n",
    "    '''\n",
    "    Accepts the total_acc and mort_acc values for the row.\n",
    "    Checks if the mort_acc is NaN , if so, it returns the avg mort_acc value\n",
    "    for the corresponding total_acc value for that row.\n",
    "    \n",
    "    total_acc_avg here should be a Series or dictionary containing the mapping of the\n",
    "    groupby averages of mort_acc per total_acc values.\n",
    "    '''\n",
    "    if np.isnan(mort_acc):\n",
    "        return total_acc_avg[total_acc]\n",
    "    else:\n",
    "        return mort_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['mort_acc'] = df.apply(lambda x: fill_mort_acc(x['total_acc'], x['mort_acc']), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_amnt                 0\n",
       "term                      0\n",
       "int_rate                  0\n",
       "installment               0\n",
       "grade                     0\n",
       "sub_grade                 0\n",
       "home_ownership            0\n",
       "annual_inc                0\n",
       "verification_status       0\n",
       "issue_d                   0\n",
       "loan_status               0\n",
       "purpose                   0\n",
       "dti                       0\n",
       "earliest_cr_line          0\n",
       "open_acc                  0\n",
       "pub_rec                   0\n",
       "revol_bal                 0\n",
       "revol_util              276\n",
       "total_acc                 0\n",
       "initial_list_status       0\n",
       "application_type          0\n",
       "mort_acc                  0\n",
       "pub_rec_bankruptcies    535\n",
       "address                   0\n",
       "loan_repaid               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_amnt                 0\n",
       "term                      0\n",
       "int_rate                  0\n",
       "installment               0\n",
       "grade                     0\n",
       "sub_grade                 0\n",
       "home_ownership            0\n",
       "annual_inc                0\n",
       "verification_status       0\n",
       "issue_d                   0\n",
       "loan_status               0\n",
       "purpose                   0\n",
       "dti                       0\n",
       "earliest_cr_line          0\n",
       "open_acc                  0\n",
       "pub_rec                   0\n",
       "revol_bal                 0\n",
       "revol_util              276\n",
       "total_acc                 0\n",
       "initial_list_status       0\n",
       "application_type          0\n",
       "mort_acc                  0\n",
       "pub_rec_bankruptcies    535\n",
       "address                   0\n",
       "loan_repaid               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: revol_util and the pub_rec_bankruptcies have missing data points, but they account for less than 0.5% of the total data. Go ahead and remove the rows that are missing those values in those columns with dropna().**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CODE HERE\n",
    "df=df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_amnt               0\n",
       "term                    0\n",
       "int_rate                0\n",
       "installment             0\n",
       "grade                   0\n",
       "sub_grade               0\n",
       "home_ownership          0\n",
       "annual_inc              0\n",
       "verification_status     0\n",
       "issue_d                 0\n",
       "loan_status             0\n",
       "purpose                 0\n",
       "dti                     0\n",
       "earliest_cr_line        0\n",
       "open_acc                0\n",
       "pub_rec                 0\n",
       "revol_bal               0\n",
       "revol_util              0\n",
       "total_acc               0\n",
       "initial_list_status     0\n",
       "application_type        0\n",
       "mort_acc                0\n",
       "pub_rec_bankruptcies    0\n",
       "address                 0\n",
       "loan_repaid             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_amnt               0\n",
       "term                    0\n",
       "int_rate                0\n",
       "installment             0\n",
       "grade                   0\n",
       "sub_grade               0\n",
       "home_ownership          0\n",
       "annual_inc              0\n",
       "verification_status     0\n",
       "issue_d                 0\n",
       "loan_status             0\n",
       "purpose                 0\n",
       "dti                     0\n",
       "earliest_cr_line        0\n",
       "open_acc                0\n",
       "pub_rec                 0\n",
       "revol_bal               0\n",
       "revol_util              0\n",
       "total_acc               0\n",
       "initial_list_status     0\n",
       "application_type        0\n",
       "mort_acc                0\n",
       "pub_rec_bankruptcies    0\n",
       "address                 0\n",
       "loan_repaid             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categorical Variables and Dummy Variables\n",
    "\n",
    "**We're done working with the missing data! Now we just need to deal with the string values due to the categorical columns.**\n",
    "\n",
    "**TASK: List all the columns that are currently non-numeric. [Helpful Link](https://stackoverflow.com/questions/22470690/get-list-of-pandas-dataframe-columns-based-on-data-type)**\n",
    "\n",
    "[Another very useful method call](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.select_dtypes.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['term', 'grade', 'sub_grade', 'home_ownership', 'verification_status',\n",
       "       'issue_d', 'loan_status', 'purpose', 'earliest_cr_line',\n",
       "       'initial_list_status', 'application_type', 'address'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CODE HERE\n",
    "df.select_dtypes(include=\"object\").columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['term', 'grade', 'sub_grade', 'home_ownership', 'verification_status',\n",
       "       'issue_d', 'loan_status', 'purpose', 'earliest_cr_line',\n",
       "       'initial_list_status', 'application_type', 'address'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "**Let's now go through all the string features to see what we should do with them.**\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "### term feature\n",
    "\n",
    "**TASK: Convert the term feature into either a 36 or 60 integer numeric data type using .apply() or .map().**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 36 months    301247\n",
       " 60 months     93972\n",
       "Name: term, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CODE HERE\n",
    "df[\"term\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['term'] = df['term'].apply(lambda term: int(term[:3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         36\n",
       "1         36\n",
       "2         36\n",
       "3         36\n",
       "4         60\n",
       "          ..\n",
       "396025    60\n",
       "396026    36\n",
       "396027    36\n",
       "396028    60\n",
       "396029    36\n",
       "Name: term, Length: 395219, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"term\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### grade feature\n",
    "\n",
    "**TASK: We already know grade is part of sub_grade, so just drop the grade feature.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CODE HERE\n",
    "df = df.drop('grade',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Convert the subgrade into dummy variables. Then concatenate these new columns to the original dataframe. Remember to drop the original subgrade column and to add drop_first=True to your get_dummies call.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CODE HERE\n",
    "subgrade_dummies = pd.get_dummies(df['sub_grade'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([df.drop('sub_grade',axis=1),subgrade_dummies],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['loan_amnt', 'term', 'int_rate', 'installment', 'home_ownership',\n",
       "       'annual_inc', 'verification_status', 'issue_d', 'loan_status',\n",
       "       'purpose', 'dti', 'earliest_cr_line', 'open_acc', 'pub_rec',\n",
       "       'revol_bal', 'revol_util', 'total_acc', 'initial_list_status',\n",
       "       'application_type', 'mort_acc', 'pub_rec_bankruptcies', 'address',\n",
       "       'loan_repaid', 'A2', 'A3', 'A4', 'A5', 'B1', 'B2', 'B3', 'B4', 'B5',\n",
       "       'C1', 'C2', 'C3', 'C4', 'C5', 'D1', 'D2', 'D3', 'D4', 'D5', 'E1', 'E2',\n",
       "       'E3', 'E4', 'E5', 'F1', 'F2', 'F3', 'F4', 'F5', 'G1', 'G2', 'G3', 'G4',\n",
       "       'G5'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['loan_amnt', 'term', 'int_rate', 'installment', 'home_ownership',\n",
       "       'annual_inc', 'verification_status', 'issue_d', 'loan_status',\n",
       "       'purpose', 'dti', 'earliest_cr_line', 'open_acc', 'pub_rec',\n",
       "       'revol_bal', 'revol_util', 'total_acc', 'initial_list_status',\n",
       "       'application_type', 'mort_acc', 'pub_rec_bankruptcies', 'address',\n",
       "       'loan_repaid', 'A2', 'A3', 'A4', 'A5', 'B1', 'B2', 'B3', 'B4', 'B5',\n",
       "       'C1', 'C2', 'C3', 'C4', 'C5', 'D1', 'D2', 'D3', 'D4', 'D5', 'E1', 'E2',\n",
       "       'E3', 'E4', 'E5', 'F1', 'F2', 'F3', 'F4', 'F5', 'G1', 'G2', 'G3', 'G4',\n",
       "       'G5'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['home_ownership', 'verification_status', 'issue_d', 'loan_status',\n",
       "       'purpose', 'earliest_cr_line', 'initial_list_status',\n",
       "       'application_type', 'address'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select_dtypes(['object']).columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### verification_status, application_type,initial_list_status,purpose \n",
    "**TASK: Convert these columns: ['verification_status', 'application_type','initial_list_status','purpose'] into dummy variables and concatenate them with the original dataframe. Remember to set drop_first=True and to drop the original columns.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CODE HERE\n",
    "dummies = pd.get_dummies(df[['verification_status', 'application_type','initial_list_status','purpose' ]],drop_first=True)\n",
    "df = df.drop(['verification_status', 'application_type','initial_list_status','purpose'],axis=1)\n",
    "df = pd.concat([df,dummies],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### home_ownership\n",
    "**TASK:Review the value_counts for the home_ownership column.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MORTGAGE    198022\n",
       "RENT        159395\n",
       "OWN          37660\n",
       "OTHER          110\n",
       "NONE            29\n",
       "ANY              3\n",
       "Name: home_ownership, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#CODE HERE\n",
    "df['home_ownership'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MORTGAGE    198022\n",
       "RENT        159395\n",
       "OWN          37660\n",
       "OTHER          110\n",
       "NONE            29\n",
       "ANY              3\n",
       "Name: home_ownership, dtype: int64"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Convert these to dummy variables, but [replace](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.replace.html) NONE and ANY with OTHER, so that we end up with just 4 categories, MORTGAGE, RENT, OWN, OTHER. Then concatenate them with the original dataframe. Remember to set drop_first=True and to drop the original columns.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#CODE HERE\n",
    "df['home_ownership']=df['home_ownership'].replace(['NONE', 'ANY'], 'OTHER')\n",
    "\n",
    "dummies = pd.get_dummies(df['home_ownership'],drop_first=True)\n",
    "df = df.drop('home_ownership',axis=1)\n",
    "df = pd.concat([df,dummies],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### address\n",
    "**TASK: Let's feature engineer a zip code column from the address in the data set. Create a column called 'zip_code' that extracts the zip code from the address column.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#CODE HERE\n",
    "df['zip_code'] = df['address'].apply(lambda address:address[-5:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Now make this zip_code column into dummy variables using pandas. Concatenate the result and drop the original zip_code column along with dropping the address column.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "dummies = pd.get_dummies(df['zip_code'],drop_first=True)\n",
    "df = df.drop(['zip_code','address'],axis=1)\n",
    "df = pd.concat([df,dummies],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### issue_d \n",
    "\n",
    "**TASK: This would be data leakage, we wouldn't know beforehand whether or not a loan would be issued when using our model, so in theory we wouldn't have an issue_date, drop this feature.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#CODE HERE\n",
    "df = df.drop('issue_d',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### earliest_cr_line\n",
    "**TASK: This appears to be a historical time stamp feature. Extract the year from this feature using a .apply function, then convert it to a numeric feature. Set this new data to a feature column called 'earliest_cr_year'.Then drop the earliest_cr_line feature.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#CODE HERE\n",
    "df['earliest_cr_year'] = df['earliest_cr_line'].apply(lambda date:int(date[-4:]))\n",
    "df = df.drop('earliest_cr_line',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['loan_status'], dtype='object')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select_dtypes(['object']).columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Test Split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Import train_test_split from sklearn.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: drop the load_status column we created earlier, since its a duplicate of the loan_repaid column. We'll use the loan_repaid column since its already in 0s and 1s.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CODE HERE\n",
    "df = df.drop('loan_status',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Set X and y variables to the .values of the features and label.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#CODE HERE\n",
    "X = df.drop('loan_repaid',axis=1).values\n",
    "y = df['loan_repaid'].values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "----\n",
    "\n",
    "# OPTIONAL\n",
    "\n",
    "## Grabbing a Sample for Training Time\n",
    "\n",
    "### OPTIONAL: Use .sample() to grab a sample of the 490k+ entries to save time on training. Highly recommended for lower RAM computers or if you are not using GPU.\n",
    "\n",
    "----\n",
    "----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "395219\n"
     ]
    }
   ],
   "source": [
    "# df = df.sample(frac=0.1,random_state=101)\n",
    "print(len(df))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Perform a train/test split with test_size=0.2 and a random_state of 101.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "#CODE HERE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=101)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normalizing the Data\n",
    "\n",
    "**TASK: Use a MinMaxScaler to normalize the feature data X_train and X_test. Recall we don't want data leakge from the test set so we only fit on the X_train data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CODE HERE\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = scaler.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating the Model\n",
    "\n",
    "**TASK: Run the cell below to import the necessary Keras functions.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense,Dropout"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Build a sequential model to will be trained on the data. You have unlimited options here, but here is what the solution uses: a model that goes 78 --> 39 --> 19--> 1 output neuron. OPTIONAL: Explore adding [Dropout layers](https://keras.io/layers/core/) [1](https://en.wikipedia.org/wiki/Dropout_(neural_networks)) [2](https://towardsdatascience.com/machine-learning-part-20-dropout-keras-layers-explained-8c9f6dc4c9ab)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CODE HERE\n",
    "model = Sequential()\n",
    "\n",
    "# Choose whatever number of layers/neurons you want.\n",
    "\n",
    "# https://stats.stackexchange.com/questions/181/how-to-choose-the-number-of-hidden-layers-and-nodes-in-a-feedforward-neural-netw\n",
    "\n",
    "# Remember to compile()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# input layer\n",
    "model.add(Dense(78,  activation='relu'))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "# hidden layer\n",
    "model.add(Dense(39, activation='relu'))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "# hidden layer\n",
    "model.add(Dense(19, activation='relu'))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "# output layer\n",
    "model.add(Dense(units=1,activation='sigmoid'))\n",
    "\n",
    "# Compile model\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Fit the model to the training data for at least 25 epochs. Also add in the validation data for later plotting. Optional: add in a batch_size of 256.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/25\n",
      "1236/1236 [==============================] - 2s 1ms/step - loss: 0.3122 - val_loss: 0.2652\n",
      "Epoch 2/25\n",
      "1236/1236 [==============================] - 2s 1ms/step - loss: 0.2674 - val_loss: 0.2631\n",
      "Epoch 3/25\n",
      "1236/1236 [==============================] - 2s 1ms/step - loss: 0.2637 - val_loss: 0.2626\n",
      "Epoch 4/25\n",
      "1236/1236 [==============================] - 2s 1ms/step - loss: 0.2624 - val_loss: 0.2633\n",
      "Epoch 5/25\n",
      "1236/1236 [==============================] - 2s 1ms/step - loss: 0.2614 - val_loss: 0.2616\n",
      "Epoch 6/25\n",
      "1236/1236 [==============================] - 2s 1ms/step - loss: 0.2607 - val_loss: 0.2614\n",
      "Epoch 7/25\n",
      "1236/1236 [==============================] - 2s 1ms/step - loss: 0.2602 - val_loss: 0.2622\n",
      "Epoch 8/25\n",
      "1236/1236 [==============================] - 2s 1ms/step - loss: 0.2597 - val_loss: 0.2614\n",
      "Epoch 9/25\n",
      "1236/1236 [==============================] - 2s 1ms/step - loss: 0.2596 - val_loss: 0.2612\n",
      "Epoch 10/25\n",
      "1236/1236 [==============================] - 2s 1ms/step - loss: 0.2593 - val_loss: 0.2616\n",
      "Epoch 11/25\n",
      "1236/1236 [==============================] - 2s 1ms/step - loss: 0.2588 - val_loss: 0.2618\n",
      "Epoch 12/25\n",
      "1236/1236 [==============================] - 2s 1ms/step - loss: 0.2589 - val_loss: 0.2613\n",
      "Epoch 13/25\n",
      "1236/1236 [==============================] - 2s 1ms/step - loss: 0.2585 - val_loss: 0.2610\n",
      "Epoch 14/25\n",
      "1236/1236 [==============================] - 2s 1ms/step - loss: 0.2580 - val_loss: 0.2613\n",
      "Epoch 15/25\n",
      "1236/1236 [==============================] - 2s 1ms/step - loss: 0.2580 - val_loss: 0.2611\n",
      "Epoch 16/25\n",
      "1236/1236 [==============================] - 2s 1ms/step - loss: 0.2578 - val_loss: 0.2610\n",
      "Epoch 17/25\n",
      "1236/1236 [==============================] - 2s 2ms/step - loss: 0.2576 - val_loss: 0.2609\n",
      "Epoch 18/25\n",
      "1236/1236 [==============================] - 2s 1ms/step - loss: 0.2573 - val_loss: 0.2609\n",
      "Epoch 19/25\n",
      "1236/1236 [==============================] - 2s 1ms/step - loss: 0.2573 - val_loss: 0.2609\n",
      "Epoch 20/25\n",
      "1236/1236 [==============================] - 2s 1ms/step - loss: 0.2569 - val_loss: 0.2622\n",
      "Epoch 21/25\n",
      "1236/1236 [==============================] - 2s 1ms/step - loss: 0.2566 - val_loss: 0.2611\n",
      "Epoch 22/25\n",
      "1236/1236 [==============================] - 2s 1ms/step - loss: 0.2569 - val_loss: 0.2609\n",
      "Epoch 23/25\n",
      "1236/1236 [==============================] - 2s 1ms/step - loss: 0.2566 - val_loss: 0.2615\n",
      "Epoch 24/25\n",
      "1236/1236 [==============================] - 2s 1ms/step - loss: 0.2563 - val_loss: 0.2609\n",
      "Epoch 25/25\n",
      "1236/1236 [==============================] - 2s 1ms/step - loss: 0.2562 - val_loss: 0.2608\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x11cc2d32700>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CODE HERE\n",
    "model.fit(x=X_train, \n",
    "          y=y_train, \n",
    "          epochs=25,\n",
    "          batch_size=256,\n",
    "          validation_data=(X_test, y_test), \n",
    "          )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 316175 samples, validate on 79044 samples\n",
      "Epoch 1/25\n",
      "316175/316175 [==============================] - 4s 13us/sample - loss: 0.2959 - val_loss: 0.2652\n",
      "Epoch 2/25\n",
      "316175/316175 [==============================] - 3s 10us/sample - loss: 0.2652 - val_loss: 0.2643\n",
      "Epoch 3/25\n",
      "316175/316175 [==============================] - 3s 10us/sample - loss: 0.2628 - val_loss: 0.2626\n",
      "Epoch 4/25\n",
      "316175/316175 [==============================] - 3s 10us/sample - loss: 0.2613 - val_loss: 0.2621\n",
      "Epoch 5/25\n",
      "316175/316175 [==============================] - 3s 10us/sample - loss: 0.2609 - val_loss: 0.2621\n",
      "Epoch 6/25\n",
      "316175/316175 [==============================] - 3s 10us/sample - loss: 0.2603 - val_loss: 0.2618\n",
      "Epoch 7/25\n",
      "316175/316175 [==============================] - 3s 10us/sample - loss: 0.2600 - val_loss: 0.2616\n",
      "Epoch 8/25\n",
      "316175/316175 [==============================] - 3s 10us/sample - loss: 0.2595 - val_loss: 0.2616\n",
      "Epoch 9/25\n",
      "316175/316175 [==============================] - 3s 10us/sample - loss: 0.2593 - val_loss: 0.2620\n",
      "Epoch 10/25\n",
      "316175/316175 [==============================] - 3s 10us/sample - loss: 0.2589 - val_loss: 0.2609\n",
      "Epoch 11/25\n",
      "316175/316175 [==============================] - 3s 10us/sample - loss: 0.2588 - val_loss: 0.2613\n",
      "Epoch 12/25\n",
      "316175/316175 [==============================] - 3s 10us/sample - loss: 0.2584 - val_loss: 0.2607\n",
      "Epoch 13/25\n",
      "316175/316175 [==============================] - 3s 10us/sample - loss: 0.2581 - val_loss: 0.2613\n",
      "Epoch 14/25\n",
      "316175/316175 [==============================] - 3s 10us/sample - loss: 0.2580 - val_loss: 0.2605\n",
      "Epoch 15/25\n",
      "316175/316175 [==============================] - 3s 10us/sample - loss: 0.2580 - val_loss: 0.2607\n",
      "Epoch 16/25\n",
      "316175/316175 [==============================] - 3s 10us/sample - loss: 0.2574 - val_loss: 0.2609\n",
      "Epoch 17/25\n",
      "316175/316175 [==============================] - 3s 10us/sample - loss: 0.2575 - val_loss: 0.2606\n",
      "Epoch 18/25\n",
      "316175/316175 [==============================] - 3s 10us/sample - loss: 0.2573 - val_loss: 0.2614\n",
      "Epoch 19/25\n",
      "316175/316175 [==============================] - 3s 10us/sample - loss: 0.2572 - val_loss: 0.2611\n",
      "Epoch 20/25\n",
      "316175/316175 [==============================] - 3s 10us/sample - loss: 0.2567 - val_loss: 0.2606\n",
      "Epoch 21/25\n",
      "316175/316175 [==============================] - 3s 10us/sample - loss: 0.2569 - val_loss: 0.2606\n",
      "Epoch 22/25\n",
      "316175/316175 [==============================] - 3s 10us/sample - loss: 0.2565 - val_loss: 0.2608\n",
      "Epoch 23/25\n",
      "316175/316175 [==============================] - 3s 10us/sample - loss: 0.2564 - val_loss: 0.2612\n",
      "Epoch 24/25\n",
      "316175/316175 [==============================] - 3s 10us/sample - loss: 0.2561 - val_loss: 0.2609\n",
      "Epoch 25/25\n",
      "316175/316175 [==============================] - 3s 11us/sample - loss: 0.2560 - val_loss: 0.2612\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x20a2a8474c8>"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: OPTIONAL: Save your model.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CODE HERE\n",
    "from tensorflow.keras.models import load_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('full_data_project_model.h5')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Section 3: Evaluating Model Performance.\n",
    "\n",
    "**TASK: Plot out the validation loss versus the training loss.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CODE HERE\n",
    "losses = pd.DataFrame(model.history.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "losses.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20a2cf62f48>"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Create predictions from the X_test set and display a classification report and confusion matrix for the X_test set.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CODE HERE\n",
    "from sklearn.metrics import classification_report,confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions=(model.predict(X_test) > 0.5).astype(\"int32\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.45      0.61     15658\n",
      "           1       0.88      1.00      0.93     63386\n",
      "\n",
      "    accuracy                           0.89     79044\n",
      "   macro avg       0.92      0.72      0.77     79044\n",
      "weighted avg       0.90      0.89      0.87     79044\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.44      0.61     15658\n",
      "           1       0.88      1.00      0.93     63386\n",
      "\n",
      "    accuracy                           0.89     79044\n",
      "   macro avg       0.93      0.72      0.77     79044\n",
      "weighted avg       0.90      0.89      0.87     79044\n",
      "\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 6996,  8662],\n",
       "       [  249, 63137]], dtype=int64)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,predictions)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 6850,  8808],\n",
       "       [  100, 63286]], dtype=int64)"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Given the customer below, would you offer this person a loan?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_amnt           25000.00\n",
       "term                   60.00\n",
       "int_rate               18.24\n",
       "installment           638.11\n",
       "annual_inc          61665.00\n",
       "                      ...   \n",
       "48052                   0.00\n",
       "70466                   0.00\n",
       "86630                   0.00\n",
       "93700                   0.00\n",
       "earliest_cr_year     1996.00\n",
       "Name: 305323, Length: 78, dtype: float64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import random\n",
    "random.seed(101)\n",
    "random_ind = random.randint(0,len(df))\n",
    "\n",
    "new_customer = df.drop('loan_repaid',axis=1).iloc[random_ind]\n",
    "new_customer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.5000e+04, 6.0000e+01, 1.8240e+01, 6.3811e+02, 6.1665e+04,\n",
       "        3.0110e+01, 1.6000e+01, 0.0000e+00, 2.7778e+04, 6.5500e+01,\n",
       "        4.6000e+01, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00, 1.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 0.0000e+00, 1.0000e+00, 1.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 0.0000e+00, 1.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 1.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 1.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 1.9960e+03]])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CODE HERE\n",
    "new_customer.values.reshape(-1,78)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1]])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(model.predict(new_customer.values.reshape(-1,78)) > 0.5).astype(\"int32\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Now check, did this person actually end up paying back their loan?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CODE HERE\n",
    "df.iloc[random_ind]['loan_repaid']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GREAT JOB!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}