11 - ML - Decision Trees and Random Forest Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest Project \n",
    "\n",
    "For this project we will be exploring publicly available data from [LendingClub.com](www.lendingclub.com). Lending Club connects people who need money (borrowers) with people who have money (investors). Hopefully, as an investor you would want to invest in people who showed a profile of having a high probability of paying you back. We will try to create a model that will help predict this.\n",
    "\n",
    "Lending club had a [very interesting year in 2016](https://en.wikipedia.org/wiki/Lending_Club#2016), so let's check out some of their data and keep the context in mind. This data is from before they even went public.\n",
    "\n",
    "We will use lending data from 2007-2010 and be trying to classify and predict whether or not the borrower paid back their loan in full. You can download the data from [here](https://www.lendingclub.com/info/download-data.action) or just use the csv already provided. It's recommended you use the csv provided as it has been cleaned of NA values.\n",
    "\n",
    "Here are what the columns represent:\n",
    "* credit.policy: 1 if the customer meets the credit underwriting criteria of LendingClub.com, and 0 otherwise.\n",
    "* purpose: The purpose of the loan (takes values \"credit_card\", \"debt_consolidation\", \"educational\", \"major_purchase\", \"small_business\", and \"all_other\").\n",
    "* int.rate: The interest rate of the loan, as a proportion (a rate of 11% would be stored as 0.11). Borrowers judged by LendingClub.com to be more risky are assigned higher interest rates.\n",
    "* installment: The monthly installments owed by the borrower if the loan is funded.\n",
    "* log.annual.inc: The natural log of the self-reported annual income of the borrower.\n",
    "* dti: The debt-to-income ratio of the borrower (amount of debt divided by annual income).\n",
    "* fico: The FICO credit score of the borrower.\n",
    "* days.with.cr.line: The number of days the borrower has had a credit line.\n",
    "* revol.bal: The borrower's revolving balance (amount unpaid at the end of the credit card billing cycle).\n",
    "* revol.util: The borrower's revolving line utilization rate (the amount of the credit line used relative to total credit available).\n",
    "* inq.last.6mths: The borrower's number of inquiries by creditors in the last 6 months.\n",
    "* delinq.2yrs: The number of times the borrower had been 30+ days past due on a payment in the past 2 years.\n",
    "* pub.rec: The borrower's number of derogatory public records (bankruptcy filings, tax liens, or judgments)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Libraries\n",
    "\n",
    "**Import the usual libraries for pandas and plotting. You can import sklearn later on.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set(style='darkgrid')\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get the Data\n",
    "\n",
    "**Use pandas to read loan_data.csv as a dataframe called loans.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "loans = pd.read_csv('loan_data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Check out the info(), head(), and describe() methods on loans.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9578 entries, 0 to 9577\n",
      "Data columns (total 14 columns):\n",
      "credit.policy        9578 non-null int64\n",
      "purpose              9578 non-null object\n",
      "int.rate             9578 non-null float64\n",
      "installment          9578 non-null float64\n",
      "log.annual.inc       9578 non-null float64\n",
      "dti                  9578 non-null float64\n",
      "fico                 9578 non-null int64\n",
      "days.with.cr.line    9578 non-null float64\n",
      "revol.bal            9578 non-null int64\n",
      "revol.util           9578 non-null float64\n",
      "inq.last.6mths       9578 non-null int64\n",
      "delinq.2yrs          9578 non-null int64\n",
      "pub.rec              9578 non-null int64\n",
      "not.fully.paid       9578 non-null int64\n",
      "dtypes: float64(6), int64(7), object(1)\n",
      "memory usage: 1.0+ MB\n"
     ]
    }
   ],
   "source": [
    "loans.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>credit.policy</th>\n",
       "      <th>purpose</th>\n",
       "      <th>int.rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>log.annual.inc</th>\n",
       "      <th>dti</th>\n",
       "      <th>fico</th>\n",
       "      <th>days.with.cr.line</th>\n",
       "      <th>revol.bal</th>\n",
       "      <th>revol.util</th>\n",
       "      <th>inq.last.6mths</th>\n",
       "      <th>delinq.2yrs</th>\n",
       "      <th>pub.rec</th>\n",
       "      <th>not.fully.paid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>0.1189</td>\n",
       "      <td>829.10</td>\n",
       "      <td>11.350407</td>\n",
       "      <td>19.48</td>\n",
       "      <td>737</td>\n",
       "      <td>5639.958333</td>\n",
       "      <td>28854</td>\n",
       "      <td>52.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>0.1071</td>\n",
       "      <td>228.22</td>\n",
       "      <td>11.082143</td>\n",
       "      <td>14.29</td>\n",
       "      <td>707</td>\n",
       "      <td>2760.000000</td>\n",
       "      <td>33623</td>\n",
       "      <td>76.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>0.1357</td>\n",
       "      <td>366.86</td>\n",
       "      <td>10.373491</td>\n",
       "      <td>11.63</td>\n",
       "      <td>682</td>\n",
       "      <td>4710.000000</td>\n",
       "      <td>3511</td>\n",
       "      <td>25.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>0.1008</td>\n",
       "      <td>162.34</td>\n",
       "      <td>11.350407</td>\n",
       "      <td>8.10</td>\n",
       "      <td>712</td>\n",
       "      <td>2699.958333</td>\n",
       "      <td>33667</td>\n",
       "      <td>73.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>0.1426</td>\n",
       "      <td>102.92</td>\n",
       "      <td>11.299732</td>\n",
       "      <td>14.97</td>\n",
       "      <td>667</td>\n",
       "      <td>4066.000000</td>\n",
       "      <td>4740</td>\n",
       "      <td>39.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   credit.policy             purpose  int.rate  installment  log.annual.inc  \\\n",
       "0              1  debt_consolidation    0.1189       829.10       11.350407   \n",
       "1              1         credit_card    0.1071       228.22       11.082143   \n",
       "2              1  debt_consolidation    0.1357       366.86       10.373491   \n",
       "3              1  debt_consolidation    0.1008       162.34       11.350407   \n",
       "4              1         credit_card    0.1426       102.92       11.299732   \n",
       "\n",
       "     dti  fico  days.with.cr.line  revol.bal  revol.util  inq.last.6mths  \\\n",
       "0  19.48   737        5639.958333      28854        52.1               0   \n",
       "1  14.29   707        2760.000000      33623        76.7               0   \n",
       "2  11.63   682        4710.000000       3511        25.6               1   \n",
       "3   8.10   712        2699.958333      33667        73.2               1   \n",
       "4  14.97   667        4066.000000       4740        39.5               0   \n",
       "\n",
       "   delinq.2yrs  pub.rec  not.fully.paid  \n",
       "0            0        0               0  \n",
       "1            0        0               0  \n",
       "2            0        0               0  \n",
       "3            0        0               0  \n",
       "4            1        0               0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>credit.policy</th>\n",
       "      <th>int.rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>log.annual.inc</th>\n",
       "      <th>dti</th>\n",
       "      <th>fico</th>\n",
       "      <th>days.with.cr.line</th>\n",
       "      <th>revol.bal</th>\n",
       "      <th>revol.util</th>\n",
       "      <th>inq.last.6mths</th>\n",
       "      <th>delinq.2yrs</th>\n",
       "      <th>pub.rec</th>\n",
       "      <th>not.fully.paid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9.578000e+03</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.804970</td>\n",
       "      <td>0.122640</td>\n",
       "      <td>319.089413</td>\n",
       "      <td>10.932117</td>\n",
       "      <td>12.606679</td>\n",
       "      <td>710.846314</td>\n",
       "      <td>4560.767197</td>\n",
       "      <td>1.691396e+04</td>\n",
       "      <td>46.799236</td>\n",
       "      <td>1.577469</td>\n",
       "      <td>0.163708</td>\n",
       "      <td>0.062122</td>\n",
       "      <td>0.160054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.396245</td>\n",
       "      <td>0.026847</td>\n",
       "      <td>207.071301</td>\n",
       "      <td>0.614813</td>\n",
       "      <td>6.883970</td>\n",
       "      <td>37.970537</td>\n",
       "      <td>2496.930377</td>\n",
       "      <td>3.375619e+04</td>\n",
       "      <td>29.014417</td>\n",
       "      <td>2.200245</td>\n",
       "      <td>0.546215</td>\n",
       "      <td>0.262126</td>\n",
       "      <td>0.366676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>15.670000</td>\n",
       "      <td>7.547502</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>612.000000</td>\n",
       "      <td>178.958333</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.103900</td>\n",
       "      <td>163.770000</td>\n",
       "      <td>10.558414</td>\n",
       "      <td>7.212500</td>\n",
       "      <td>682.000000</td>\n",
       "      <td>2820.000000</td>\n",
       "      <td>3.187000e+03</td>\n",
       "      <td>22.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.122100</td>\n",
       "      <td>268.950000</td>\n",
       "      <td>10.928884</td>\n",
       "      <td>12.665000</td>\n",
       "      <td>707.000000</td>\n",
       "      <td>4139.958333</td>\n",
       "      <td>8.596000e+03</td>\n",
       "      <td>46.300000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.140700</td>\n",
       "      <td>432.762500</td>\n",
       "      <td>11.291293</td>\n",
       "      <td>17.950000</td>\n",
       "      <td>737.000000</td>\n",
       "      <td>5730.000000</td>\n",
       "      <td>1.824950e+04</td>\n",
       "      <td>70.900000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.216400</td>\n",
       "      <td>940.140000</td>\n",
       "      <td>14.528354</td>\n",
       "      <td>29.960000</td>\n",
       "      <td>827.000000</td>\n",
       "      <td>17639.958330</td>\n",
       "      <td>1.207359e+06</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "       credit.policy     int.rate  installment  log.annual.inc          dti  \\\n",
       "count    9578.000000  9578.000000  9578.000000     9578.000000  9578.000000   \n",
       "mean        0.804970     0.122640   319.089413       10.932117    12.606679   \n",
       "std         0.396245     0.026847   207.071301        0.614813     6.883970   \n",
       "min         0.000000     0.060000    15.670000        7.547502     0.000000   \n",
       "25%         1.000000     0.103900   163.770000       10.558414     7.212500   \n",
       "50%         1.000000     0.122100   268.950000       10.928884    12.665000   \n",
       "75%         1.000000     0.140700   432.762500       11.291293    17.950000   \n",
       "max         1.000000     0.216400   940.140000       14.528354    29.960000   \n",
       "\n",
       "              fico  days.with.cr.line     revol.bal   revol.util  \\\n",
       "count  9578.000000        9578.000000  9.578000e+03  9578.000000   \n",
       "mean    710.846314        4560.767197  1.691396e+04    46.799236   \n",
       "std      37.970537        2496.930377  3.375619e+04    29.014417   \n",
       "min     612.000000         178.958333  0.000000e+00     0.000000   \n",
       "25%     682.000000        2820.000000  3.187000e+03    22.600000   \n",
       "50%     707.000000        4139.958333  8.596000e+03    46.300000   \n",
       "75%     737.000000        5730.000000  1.824950e+04    70.900000   \n",
       "max     827.000000       17639.958330  1.207359e+06   119.000000   \n",
       "\n",
       "       inq.last.6mths  delinq.2yrs      pub.rec  not.fully.paid  \n",
       "count     9578.000000  9578.000000  9578.000000     9578.000000  \n",
       "mean         1.577469     0.163708     0.062122        0.160054  \n",
       "std          2.200245     0.546215     0.262126        0.366676  \n",
       "min          0.000000     0.000000     0.000000        0.000000  \n",
       "25%          0.000000     0.000000     0.000000        0.000000  \n",
       "50%          1.000000     0.000000     0.000000        0.000000  \n",
       "75%          2.000000     0.000000     0.000000        0.000000  \n",
       "max         33.000000    13.000000     5.000000        1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis\n",
    "\n",
    "Let's do some data visualization! We'll use seaborn and pandas built-in plotting capabilities, but feel free to use whatever library you want. Don't worry about the colors matching, just worry about getting the main idea of the plot.\n",
    "\n",
    "**Create a histogram of two FICO distributions on top of each other, one for each credit.policy outcome.**\n",
    "\n",
    "*Note: This is pretty tricky, feel free to reference the solutions. You'll probably need one line of code for each histogram, I also recommend just using pandas built in .hist()*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x122d64310>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "cp = loans['credit.policy']\n",
    "col = ['blue', 'green']\n",
    "\n",
    "for i in range(2):\n",
    "    loans[cp==i]['fico'].plot.hist(color = col[i], bins=30,  label='Credit Policy = '+str(i), alpha=0.75)\n",
    "\n",
    "plt.title('Histogram of Fico wrt Policy')\n",
    "plt.xlabel('Fico')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create a similar figure, except this time select by the not.fully.paid column.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a26000d10>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "cp = loans['not.fully.paid']\n",
    "col = ['blue', 'green']\n",
    "\n",
    "for i in range(2):\n",
    "    loans[cp==i]['fico'].plot.hist(color = col[i], bins=30,  label='Not fully paid = '+str(i), alpha=0.75)\n",
    "\n",
    "plt.title('Histogram of Fico wrt not.fully.paid ')\n",
    "plt.xlabel('Fico')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create a countplot using seaborn showing the counts of loans by purpose, with the color hue defined by not.fully.paid. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a26000ed0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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oH4mNImIIsDcwJTOnArOr4AewHzClWXWWJEmqo2a2sB0JjABOj4jOsrOAU4HbgKHAFZn5Q4CImERpjRsBXEPpJgXYBzg3IkYDdwHfbmKdJUmSaqeZFx18Gvh0D2+f2c3w1wObd1N+D+UqUkmSpBWSTzqQJEmqOQObJElSzRnYJEmSas7AJkmSVHMGNkmSpJozsEmSJNWcgU2SJKnmDGySJEk1Z2CTJEmqOQObJElSzRnYJEmSas7AJkmSVHMGNkmSpJozsEmSJNWcgU2SJKnmDGySJEk1Z2CTJEmqOQObJElSzRnYJEmSas7AJkmSVHMGNkmSpJozsEmSJNWcgU2SJKnmDGySJEk1Z2CTJEmqOQObJElSzRnYJEmSas7AJkmSVHMGNkmSpJozsEmSJNXcSs0ceUQcB3y4evmzzPx8ROwCnA6MBC7LzGOqYbcAzgNGA7cAh2Xm/IhYF7gYWBNIYJ/MnNXMekuSJNVJ01rYqmC2K/BmYAtgy4j4CHABsAfwBmDriHh39ZGLgcMzczzQBhxclZ8JnJmZGwN3Asc2q86SJEl11Mwu0WnAZzNzbmbOA+4HxgMPZuZDmTmfEtImRsR6wMjM/E312Yuq8qHAjsDljeVNrLMkSVLtNK1LNDP/3Pl3RLye0jV6BiXIdZoGvBYY10P5GsBzVbhrLJckSVphNPUcNoCI2BT4GfA5YD6lla1TG7CQ0tLX0YdyqvI+GzNmVD9rPLjGjl251VWQJEk11+yLDrYHrgCOyMxLI2In4DUNg6wNPAY82kP5k8AqETEkMxdUwzzWnzrMmDGLhQu7Zr6iDmFp+vSZra6CJElqsfb2tl4bmZp50cG/AD8G9s7MS6viO8pbsVFEDAH2BqZk5lRgdhXwAParyucBtwJ7VuX7A1OaVWdJkqQ6amYL25HACOD0iOgsOwuYRGl1GwFcw6ILCvYBzo2I0cBdwLer8o8D34+IY4BHgI80sc6SJEm108yLDj4NfLqHtzfvZvh7gG26KZ8KTBjQykk1MGr0cEYOH9aSab84Zy6znpvTkmlLkvqvT4EtItbJzH90KdskM+9rTrWk5d/I4cPY4dCTWjLtW88+llkY2CRpWdFrYIuI1as/r4mICZSrNwGGAlcCGzevapIkSYLFt7D9EHhX9feMhvL5LDr3TJIkSU3Ua2DLzN0AIuKCzPzY4FRJkiRJjfp0Dltmfqx6fNTqLOoWJTPvalbFJEmSVPT1ooMTKE8qeJJFTx7oADZsUr0kSZJU6ettPfYHNsrMfj1lQJIkSUuvr086+LthTZIkqTX62sJ2fUR8FfgJ8GJnoeewSZIkNV9fA9uk6v+JDWWewyZJkjQI+nqV6AbNrogkSZK619erRD/TXXlmnj6w1ZEkSVJXfe0S3azh72HATsD1A18dSZIkddXXLtEDGl9HxDjg/KbUSJIkSS/T19t6vEx1i4/1B7YqkiRJ6s6SnMPWBmxFeeqBJEmSmmxJzmHrAB6hPKpKkiRJTdavc9iqB8APzcy/NLVWkiRJeklfu0Q3ojzlYBzQHhFPAe/LzPubWTlJkiT1/aKD7wBfzczVMnMV4GTgu82rliRJkjr1NbCtlZnf73yRmRcCY5tTJUmSJDXqa2BbKSJW73wREWtQLj6QJElSk/X1KtEzgN9ExGWUoLYX8M2m1UqSJEkv6WsL2zWUoDYM2ARYB7iqWZWSJEnSIn0NbBcB383Mo4B9gS8BFzSrUpIkSVqkr4Ftjcz8NkBmzs7MycBrmlctSZIkderPRQfjOl9ExFqUR1RJkiSpyfp60cHpwN0RcS3lXLZd8NFUkiRJg6JPLWyZeQElpP0BuBPYLTMvaWbFJEmSVPS1hY3M/CPwxybWRZIkSd3oc2BbUhExGrid8uzRhyPiQuDtwPPVICdk5lURsQul63UkcFlmHlN9fgvgPGA0cAtwWGbOb3a9JUmS6qKvFx0skYjYFvgVML6heCtgx8zcovp3VUSMpNwmZA/gDcDWEfHuaviLgcMzczzlQoeDm1lnSZKkumlqYKOEq08AjwFExKuAdYELIuKPEXFCRLQD2wAPZuZDVevZxcDEiFgPGJmZv6nGdxEwscl1liRJqpWmdolm5kEAEdFZtDZwA/Bx4Fngp8CBwCxgWsNHpwGvBcb1UN5nY8aMWoKaD56xY1dudRW0gnLdk6RlR9PPYWuUmX8DPtD5OiLOAPYHLuflD5NvAxZSWgC7K++zGTNmsXBh98+pr8MP1vTpM1tdBbVIq9c/1z1Jqo/29rZeG5ma3SX6MhGxWUR8sKGoDZgHPMrLn5ywNqUbtadySZKkFcagBjZKQJscEatFxFDgEMpD5O8AIiI2ioghwN7AlMycCsyOiO2rz+8HTBnkOkuSJLXUoAa26l5upwK3AfcBd2fmDzNzNjAJuKIqf4DSTQqwD/DNiHgAGAV8ezDrLEmS1GqDcg5bZq7f8PeZwJndDHM9sHk35fdQriKVJElaIQ12l6gkSZL6ycAmSZJUcwY2SZKkmjOwSZIk1ZyBTZIkqeYMbJIkSTVnYJMkSao5A5skSVLNGdgkSZJqzsAmSZJUcwY2SZKkmjOwSZIk1ZyBTZIkqeYMbJIkSTVnYJMkSao5A5skSVLNGdgkSZJqzsAmSZJUcwY2SZKkmjOwSZIk1ZyBTZIkqeYMbJIkSTVnYJMkSao5A5skSVLNrdTqCmjZNWr0cEYOH9ay6b84Zy6znpvTsulLkjRYDGxaYiOHD2OHQ09q2fRvPftYZmFgkyQt/+wSlSRJqjkDmyRJUs0Z2CRJkmquqeewRcRo4HbgfZn5cETsApwOjAQuy8xjquG2AM4DRgO3AIdl5vyIWBe4GFgTSGCfzJzVzDpLkiTVTdNa2CJiW+BXwPjq9UjgAmAP4A3A1hHx7mrwi4HDM3M80AYcXJWfCZyZmRsDdwLHNqu+kiRJddXMLtGDgU8Aj1WvtwEezMyHMnM+JaRNjIj1gJGZ+ZtquIuq8qHAjsDljeVNrK8kSVItNa1LNDMPAoiIzqJxwLSGQaYBr+2lfA3guSrcNZZLkiStUAbzPmztQEfD6zZgYT/Kqcr7ZcyYUf39yKAaO3blVldhmebyW3IuO0ladgxmYHsUeE3D67Up3aU9lT8JrBIRQzJzQTXMY/TTjBmzWLiwa+4r6vCDNX36zFZXYYm5/JZOq5ffsrzsJGl5097e1msj02De1uMOICJio4gYAuwNTMnMqcDsiNi+Gm6/qnwecCuwZ1W+PzBlEOsrSZJUC4MW2DJzNjAJuAK4D3iARRcU7AN8MyIeAEYB367KPw4cEhH3ATsAxwxWfSVJkuqi6V2imbl+w9/XA5t3M8w9lKtIu5ZPBSY0sXqSJEm155MOJEmSas7AJkmSVHMGNkmSpJozsEmSJNWcgU2SJKnmDGySJEk1Z2CTJEmqOQObJElSzRnYJEmSas7AJkmSVHMGNkmSpJozsEmSJNWcgU2SJKnmDGySJEk1Z2CTJEmqOQObJElSzRnYJEmSas7AJkmSVHMGNkmSpJozsEmSJNWcgU2SJKnmDGySJEk1Z2CTJEmqOQObJElSzRnYJEmSas7AJkmSVHMrtboCK7K5C+YxduzKLZv+7HlzmfnMnJZNX5Ik9Y2BrYWGDRnKrpce3bLpX7fXqczEwCZJUt3ZJSpJklRzBjZJkqSaa0mXaETcCKwJzKuKDgVeBxwDDAUmZ+Z3q2F3AU4HRgKXZeYxg19jSZKk1hn0wBYRbcB4YL3MnF+VrQNcCmwJzAFur0LdQ8AFwE7A34GfRcS7M3PKYNdbkiSpVVrRwhbV/9dFxBjgXGAmcENm/hMgIi4HPgTcDDyYmQ9V5RcDEwEDm7QCGzV6OCOHD2vZ9F+cM5dZz3nBjqTB04rAthpwPfBJSvfnTcBlwLSGYaYB2wDjuil/7aDUUlJtjRw+jB0OPall07/17GOZ5RXWkgbRoAe2zPw18OvO1xFxPuUctZMbBmsDFlIuiujoprzPxowZtcR1XRG08j5wA2FZr38rueyWjstP0mBqxTlsbweGZ+b1VVEb8DDwmobB1gYeAx7tobzPZsyYxcKFHd2+5w4Xpk+fucSfrcPyW5r6t1qrl5/Lbuksy8tPUv20t7f12sjUii7RVYETI+JtlC7RjwL7AhdHxFjgeeCDwCHAH4GIiI0oFyDsTbkIQZIkaYUx6Pdhy8yfAj8D/gD8HrggM28DvgTcCNwNXJKZv83M2cAk4ArgPuAB4PLBrrMkSVIrteQ+bJl5LHBsl7JLgEu6GfZ6YPNBqpokLfe8ylZa9vgsUUlawXiVrbTs8dFUkiRJNWdgkyRJqjm7RKUV0NwF81p6a4zZ8+Yy8xm7xLRs8hxAtYKBTVoBDRsylF0vPbpl079ur1OZ6TlMWkZ5DqBawS5RSZKkmjOwSZIk1ZyBTZIkqeYMbJIkSTVnYJMkSao5A5skSVLNGdgkSZJqzvuwSZKkQeFNh5ecgU2SJA0Kbzq85OwSlSRJqjkDmyRJUs0Z2CRJkmrOwCZJklRzBjZJkqSaM7BJkiTVnLf1kCQNqrkL5jF27Motm/7seXOZ+cyyeWsHrbgMbJKkQTVsyFB2vfTolk3/ur1OZeYyei8urbgMbFpmeZQuSVpRGNi0zPIoXZK0ovCiA0mSpJqzhU2S+snueEmDzcAmSf1kd7ykwWaXqCRJUs3ZwiZJklYIy/LpDMtEYIuIvYFjgKHA5Mz8bourJElSSyzLoaPVluXTGWof2CJiHeAUYEtgDnB7RNyYmfe1tmaSJA2+ZTl0aMktC+ew7QLckJn/zMzngcuBD7W4TpIkSYOm9i1swDhgWsPracA2ffjcEID29rZeB1p7zCpLXLGBsNarV23p9Be3fBbH5bfsLj+X3dJx+S0dl9/SWZaXn8uu+2XXUD6ku/fbOjo6mlSlgRERXwJGZOax1euDgS0z87DFfPTtwK3Nrp8kSdIA2gH4VdfCZaGF7VFK5TutDTzWh8/9rvrcNGBBE+olSZI0UIYAr6Hkl1dYFlrY1qEkzW2A54HbgUMy87ctrZgkSdIgqf1FB5n5D+BLwI3A3cAlhjVJkrQiqX0LmyRJ0oqu9i1skiRJKzoDmyRJUs0Z2CRJkmrOwCZJklRzBjZJkqSaM7BJGnQRMSEibmpxHQ6LiMU9MWW5EhHvi4jPtLoey7uIuCYixrW6Hj2JiIcjYv1W16OvIuL4iDh+AMe3SkRcVf09LiKuGahxV+Nsyv5tuQxsEXFRREzq5f1u72USEQdHxEeaVrEl1PjlR8R5EbFVN8P0Os/VMBdGxHrV33Xfobxinhs3skGsR23vexMR60fEw9Xfi/3+exnPCRGxQ/X3TRExYaDqWGeZeVZmntXqegyyrYDRSzOCxh/PxW0fETEpIi5amulV4zkxIv51acczWDLzPZnZlyfyqDVWA94MkJmPZeZ7WlyfPlkWHk01mLYHbmp1JXqTmQctxcffAZxQjWeZWEFh0TxXR4Rvbm1tlks7UW5MPdjGVke2rwMSmAjsDXwW6AB+DxyembMi4nHgx8C2wOPABcCngNcCkzLz5ojYCPgeMAZ4AfhkZv6hp4l3ho7MPL6P47+JcvPuHYERwBGZeV0VSMYAGwGfB6YD36qGeQo4FHgV8IPM3Kya9vuBgzJzj4j4AvBhymNpfg4cBaxX1ecBYFPgLspTXiZRfmw+kJn3R8TWwDer8T8FHJqZD1V1/S3l8XxjgU8CU4HDqulPzcwLF/sN1URmfnmwp1kduHwJmAtsAPwfMAv4N6ANeA9lnd0PeHU13EcyM6sDqQnAI8BkYGfKOv0/mXlaNe6vUr7zezPzoz3UYRLwPmBNyiOLrqZsHzsBx2fmhGq4iyi/XTcB11LWhReB9wPfpTxbex5wUmZeVo3+yxHxZsq6s39m3hEROwGnVGWrAv+ZmT+JiL0p6/YC4CFg38yc3d26m5n9PsjtYRs4EjikmpenKeszEdGRmW0Ny2dCZk6KiF2Ab1AaoqZS9iUA51O243HAL4GDgG8D46oGgP8EbsrM9SNirWr4dYH5wBcz89pqX7EO8HrKtnleZp4SEaN7GH9TLBeBLSLaKF/U+yjPGR0C3BQR+wNHUL7A3wOfyMzZ1WfOoTzu6ingYzvPHNgAABC1SURBVMB44F+Bd0bEtMz8eQ/TWp3yBW0MzAE+k5k3RMT7gJOraf2NsuN8otpw/wfYjbJR75+Zv6+6JT4KLAR+m5mHRkQ73WzcXaZ/E3A8cHN381wNc0o1jtWr9/YEDqCsUNdUrSm/Z/E7lC9SfvjeAPwJ2Dsz5/bhu/gv4AOUFf7s6u9/Un549qQ8D/ZEYChl4z84M2dExK6UH5/ZlB+qrvP8GaqNLDM/0Esd9gaOqebnd8DBlB3e+ZSd0Djgosz8crXBfxRYg7IzPAe4GBgF/Ka3eR1MEbESJYy8EVgL+CNwdD/H8Yp1lLJebgWcFxGdy/TAiDidsqw+nZlXVzuys4F/oayzR2fmL6sd2VspO7gzMvN7/ajSupT1dyplWR9GCRbbVuvDd4HjgM9V8zwlMw+LiBspgWWHiPgoZRu/Gfg+JeD9ISI2Aa4Coo916cv4AUZn5lsiYgtgSmeLNTAjM98fEcOA/wdMzMzfRcRE4IeZuXVELIyIN2bmvcBewMURsTuwJbA11fYH7EN5HN+bKNvtPdU4H8vM7SLiOOCQiDgKOA94f2Y+EhG7AecCu1R1GlYNPwn4ESUULwCmAMdFxHjgXZTt9CRKEHg98NnM/FFEvBE4g7ItrAmcuoQtkhtFxC2U/dFPKevtelQ/krAoPFPCwgWU9RzgzMw8t0sguQq4l3Lw9kS1rP9ZLcvu9itfr+ZzIfDjzDwhInamBKYOShj4SGY+1U3dt6Xst2YAT1bLZquIuJDyHb6fEhhejIgTgcMp63CnwyjbzJuA4ZTfpXspj1kcD6yXmc8uZvm9Hdi8qudNLNqf9iSA3TPz4Yj4HOX7ewPlO7y+oZfivsz8WEQcTglHE6u6H5SZD0TEOykHHj+h7DfemplPRsTXgI0jYm26X3cvXsz8vLyy3W8DX6rG9eaq7NdUga2HcQwHfgDslpl3R8SplP36U8DdmTmx2jbvA95CORi7KTM/EC/vGj4DuCEzT4+IDYFfVaEWyne4A2W/+Ndq//TuHsbfFMtLl+gHKV/sppSVbiNKODoYeFtmbkHZ2I5s+MzNVflVwLcy85eUI6gv9xTWKicBf8nMN1COrE6JiDUpP2b/lplvAm4DvtPwmRmZuQ1wFvDFiBhC2WltRVlRh0V5Zmrjxr0N8MGIeG8/5pmqlWHjar7HUwLZvpn5X5Tw9p7MnNEwnt6m+TbKDugNlB/X3XpZLp0+RGmp3Kwa3wGUgPbHzAzgH5RAt1tmvplyNHVatcF9H/hQZm5JOTrs6lOUH63ewto6lNC3a2ZuSgmy7wU+QvnhfGtVtyMiYo3qY68F3pyZX6R8bxdV68ZtfZjfwfI2YG5mbkf5rlelHOH3SU/raGb+N3AnZSf9p2rwZzOzc6fW2bLxLeCC6rv5V+DsiFi5em9EZm7Sz7AGcE9mPpSZC4H7q3m6umH9PIdyINFpSvX/VOCGhr9Xi4hRlB3+hRFxN3AJMCoixvSjPj2Ov2GYcwEy825gGmW7Abij+n888HRm/q4a7n8pgWUVyg/ZXhExktJCcjUlXG1LOYC6i7JP2LQa1+OZ+Ydq+TwKXN+lTuMprZP/V83zacCGDXW9tvp/c2BeZm5FaUldt2H8W1GW/ReAXYF9WXQgcBBwcmZuTWmd/1pvC68XG1D2V2+hhI/eujbfBqxe7RveS/mB7Gpz4PTMfCPwDLBPRIyl+/3KesC7M3Nzyn5pk4gYQTmgO6ya/1/Q84/svZn598x8gfLj3/U72JvynZ5KCW+junz+nZT9yYJqHD9g0TqdfQhrAD/JzCeqg+VLq3H25snMfLj6eydKy+7CzHw8MzdtOOj+cfX/nykHrFC+/zdGxLGUAN85P1cDt0XEV4ErqvW/t3W3P7obz5eAazJzVmY+D/zvYsaxGfCPql5k5tGZeUZm/hD4RUQcQQljY3jld9TonZQDezLzb5TtetvqvRszc25mPkkJzKsswfiXynLRwkZpKboyM+cB06tuljbK0eJvIgJgGGVlAHgxM39Q/f0/lKOHvtqJqqm1+oHbrmq5+G3DRnIOL2/96Nxx3gv8e2YuiIjbKa0/PwG+kZn/qI5oLsrMBcALEdG5cf9fH+eZzPxLRHwWOCjKjG8H/LWX+eltmvdm5qMAEXE/5Qi5L8vnR5k5h9ICuUXVQtb5g7Yt5Qfjxup7GUJZ+TejhLH7q+G+TwnH/bUdcFtnvTNzv843IuIdEXEk5eh9GCXUA9yVmfOrvydQwh2Unev5S1CHAZeZt0TEjIj4BCWQv57+7Ri2ofd1tFF3O/JdKEfVJ1avh1LCAiz6bvtrfsPfnS0dqzaUtdGwj+rSutv4WSjr0ewqaAMQEa+l95aIl1nM+Lsrb294/WJDWVdtVf1+QAmC9wA/z9KlNASYnJmnV3VetRrnGpQutp6mTTXOv3XOczWutRren139/yvgUxFxCaWV57eUsNcYUP+RmfMjojGgfhbYPSKOpmyfS/pD9H+ZOb2q448o29g9PQx7bxksfg5cQ2ld7erJXNTVfS9lv9TTfuUfwIsRcRulde+oarn/H3BVRPyYEoh+0UN9evsO/oXS8vMdyrJ8nFeestF1fWhcp7s7KO1Od+tcRzWuTkMb/m4c77xqWOClA/pHuoy3cVy3UkL9TZRweglAZn46Is6nhOiLqxbRntbd/upuPJ8GVmkYZn41XOd8tGXpeu2c767zuQqwMqU18kOU/d0vKfv+xuXWVW/f1+yG8g6gLSI+2c/xL5XlpYWt68rb+eX+KDO3qHZo21Bai6B0C3Rqo3zZfdV1xdiY3r9kWPRFN9bz34D/qF5fG+XcgcWNp1F380xEbAlcV43rckoL4oCtoL2Mp1PX5bM+JRh17kSGAL9q+F62phx9dzs/S6Dr9MdW/75BaTGaSgnoTzVMr3EH18GiZdLBy9eVlolywvUPKF3UFwK3UOalr/qzbnW3Ix8CvLPhe9uW0k0Off/h6Yt/jXLaAZQW8j6dW1e1VDwYEfsCRMS7KMtooO1VjX8rSrD5U5f3ExgT5dwyIuLDwNTM/GeWk9D/TgnKnd1GNwD7RcSoKN3eP6b8APTFA8DqUV0wQjm145Juhvs9Jbj8HNiERef2LC6g/ojyg3cfpcVjSXUNHJ3b6CsCR9W6uimltSKAu6of8Ebd7Ze63a9UB2LbAsdSWj9+HRHjM/OblOD4F+CrEbEk87c1pbflm5SD7w/QECoqNwAfjYghEfEqSjdff88X3T3KBVcjKAeTUyj7rw0jYkS1vXTXEgllG9gzItqqVvabKaH9FarxjKe0qk8B9gCGRMRKEfEg8FRmngr8NyWYLs2626i78TwLvL9hvht7VZ4CNo1y+k1na20Ca0Y5FQLK+XaHUbrCz64aaEYAW1C+o/l0v/+7ATiwWh4bUlplf91L3Xsaf1MsL4Htl8CHI2J4RKwG7F6VfyAi1qy+2O9RzkWB0lXS+UV/rPo89PwlNrqFqgWmCmvXUloY3trQF34IvWyUVfP9fcCfspxMex2la6U/G3dP87wTpW/+LMp5L+9j0QrU3fwNxA6l0S2UbtWh1fiupZys2ekOSqvk+Or1scDXKedkrRURm1fl3V2t25fv53eU72Lt6vU3KTuedwFfq7qooqpTdxvWLyndAgD/TtkI62AXygHIhZRuoHfQvx1Db+toX5brDcDHAaqd4r2UE5MH0nPAqcDNEfEApbXtmH58fh9Ky/Ifq/HsmUtwAvRibBgRd1GOqPesWqZfUrUs7wl8J8q5SodXrzv9D+UigJur4a8GrqB8P/dSLmr4fl8qUk1rIvCNap4/SvVj08UXgFdn5vcp29t6lNaHxXkX5RSRn1DO1elsxeuv90TEqtUP716UbewZStgcG+V0iN2r8f8rZRn9jHKANYvSkrU43e5Xopx/dDNwS2YeSdnvRkTcAaycmZMp+4glOe/oOqA9Iu6j9N48QOn+bXQ2pTv7HuAPlC7//l7pPp3S2ngP8NPM/Hlm/pmyjP5M6S68tYfPnkk5X+4eynL/ZGbO7G7AzPwnpUfhz5Ru8pUp2/hwSoj7RUTcSTln9bSlWXe7TLe78XyLcm717yjfX+PB6RcoraW/pgQ1spybvi/w39W2sAmli3wy5XzNP1V/3075jp4AHolyvmqjT1HOY/8TJTgelJnTeql+T+NviraOjtretaBfIuJkyo7xccpGfhnlR6jzooO7gY9VzeFPU1qetqQceR6Q5QKBvYCvAJ/PzMt7mM6qlPNYxlN+6I7IzFujXPV1IqWrbSpwYGZOi+pqoSwngE6gurInIv6T8qP5AmWlO5hyxPsNSjflUMq5Byd2+dxN1d839TDPvwCuZNGP6R+A9szcNyImU8572o3yYz2hmv9ep1nN90WUIHhRH76LUyhHPu2U7oI9O+tcvf9+SnfnEMrObN8sJwfvWA0/n7ID3KhxninnXd0MzMnMd/Qy/Q9RdthDKBv1YZQrkE6mtAb9ndLldDTl/LUJmTmp+uw6lB+MMZRzuyZm5lLdBmEgRMRmLGo9mQs8TJmXt2e5uukiFvP99LKOHklZRvtT1v/O9Wt9Fl09NY4SUtaltGh8PjOnRMOVlgM6wzXUuO21uCr9EhH/Qll3Vqa0GH+ZcuVg537peHjpatn1WfSdf4YSOGdTfvDfSglx+zYM/9IVez1MexIlVK5GCeCXZObJ1XvHUgLm3yk/1E9QLjo4j9J6NRu4KjNPipdfdPCKixWquvS0X/ka5fyyFyj7kP+kHNieTtnXzKL8MD/Y32Xbwzy3U0JWZPcXMvRnXJNo2D9pxbbcBDZJ6qo6MOrulgn9vvfSshrYNHiq1sIHgTsz89/7+Jk96fl80skY2FQxsHVjIHfyGnhRrrLr6byCL2dmdxdpLPdcLmqF3gJH40UgkpaOgU2SJKnmlpeLDiRJkpZbBjZJkqSaM7BJkiTVnIFNkiSp5paXR1NJUreqewqeRrn33MaU+9dNAo6iPH7t69VwF3W+ru6feAflhtZfpNxc9YeU+5CtSnmc3Peqzx1CueHmAsq9xA7PzP8XEW+n3OtrCOWO/Kdm5hVRHhJ9GuVeYEMo90r8VGY+18zlIGnZZgubpBXBVsAZWR58fyHl5siLc29mvqHhzvSrU27oOgE4MSI2i/L8388D78jygPFLgB9XT1c5gfKQ8i0pT1TpfGj3Fyg3bN2y+sxjlLuyS1KPDGySVgT3ZGbn43suoDwLccxiPtP1cT/fzcyOzHyU8si1XSmPVLqs8+Hm1ZMm1gHWpzyL87sR8QPKU1W+WI3nfZTHpf0hIu6mPFd4EySpFwY2SSuCxgeQdz5K6Sle/gDyYV0+M6uXcbRTukA7uzsbtQFDM/NsYDPK4+J2A/5YPU9zCPDphgeVb8OSPTRb0grEwCZpRbBFRLyp+vsQykOap1O6SqmelbrTYsaxfzXsupTWtSmUlra9ImJs9d4BwAzgLxFxO/DmqtXtEMq5b2sDPwcOj4hh1XMnz6U8rF6SeuRFB5JWBI8Dp1QPN38S2I/SQvaDiEjgYeCGxYxjg4j4PTCScpFAAhkR3wRuaHjo9/syc2FEfB74VkScTGmFO6F62PpJwNcpFxsMAe4GPjuwsytpeeOjqSQt16qrRL+TmW9cinE8DHwoM+8coGpJUr/YJSpJklRztrBJkiTVnC1skiRJNWdgkyRJqjkDmyRJUs0Z2CRJkmrOwCZJklRzBjZJkqSa+//QFQSVlQeDewAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "sns.countplot('purpose', hue='not.fully.paid', data = loans, palette='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Let's see the trend between FICO score and interest rate. Recreate the following jointplot.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x1a262ea850>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='fico', y='int.rate', data=loans, kind='reg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create the following lmplots to see if the trend differed between not.fully.paid and credit.policy. Check the documentation for lmplot() if you can't figure out how to separate it into columns.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1a26a592d0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x360 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 782.55x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.lmplot(x='fico', y='int.rate', hue='credit.policy', col='not.fully.paid', data=loans, palette='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setting up the Data\n",
    "\n",
    "Let's get ready to set up our data for our Random Forest Classification Model!\n",
    "\n",
    "**Check loans.info() again.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9578 entries, 0 to 9577\n",
      "Data columns (total 14 columns):\n",
      "credit.policy        9578 non-null int64\n",
      "purpose              9578 non-null object\n",
      "int.rate             9578 non-null float64\n",
      "installment          9578 non-null float64\n",
      "log.annual.inc       9578 non-null float64\n",
      "dti                  9578 non-null float64\n",
      "fico                 9578 non-null int64\n",
      "days.with.cr.line    9578 non-null float64\n",
      "revol.bal            9578 non-null int64\n",
      "revol.util           9578 non-null float64\n",
      "inq.last.6mths       9578 non-null int64\n",
      "delinq.2yrs          9578 non-null int64\n",
      "pub.rec              9578 non-null int64\n",
      "not.fully.paid       9578 non-null int64\n",
      "dtypes: float64(6), int64(7), object(1)\n",
      "memory usage: 1.0+ MB\n"
     ]
    }
   ],
   "source": [
    "loans.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categorical Features\n",
    "\n",
    "Notice that the **purpose** column as categorical\n",
    "\n",
    "That means we need to transform them using dummy variables so sklearn will be able to understand them. Let's do this in one clean step using pd.get_dummies.\n",
    "\n",
    "Let's show you a way of dealing with these columns that can be expanded to multiple categorical features if necessary.\n",
    "\n",
    "**Create a list of 1 element containing the string 'purpose'. Call this list cat_feats.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_feats = ['purpose']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now use pd.get_dummies(loans,columns=cat_feats,drop_first=True) to create a fixed larger dataframe that has new feature columns with dummy variables. Set this dataframe as final_data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_data = pd.get_dummies(loans, columns=cat_feats, drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>credit.policy</th>\n",
       "      <th>int.rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>log.annual.inc</th>\n",
       "      <th>dti</th>\n",
       "      <th>fico</th>\n",
       "      <th>days.with.cr.line</th>\n",
       "      <th>revol.bal</th>\n",
       "      <th>revol.util</th>\n",
       "      <th>inq.last.6mths</th>\n",
       "      <th>delinq.2yrs</th>\n",
       "      <th>pub.rec</th>\n",
       "      <th>not.fully.paid</th>\n",
       "      <th>purpose_credit_card</th>\n",
       "      <th>purpose_debt_consolidation</th>\n",
       "      <th>purpose_educational</th>\n",
       "      <th>purpose_home_improvement</th>\n",
       "      <th>purpose_major_purchase</th>\n",
       "      <th>purpose_small_business</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.1189</td>\n",
       "      <td>829.10</td>\n",
       "      <td>11.350407</td>\n",
       "      <td>19.48</td>\n",
       "      <td>737</td>\n",
       "      <td>5639.958333</td>\n",
       "      <td>28854</td>\n",
       "      <td>52.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.1071</td>\n",
       "      <td>228.22</td>\n",
       "      <td>11.082143</td>\n",
       "      <td>14.29</td>\n",
       "      <td>707</td>\n",
       "      <td>2760.000000</td>\n",
       "      <td>33623</td>\n",
       "      <td>76.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.1357</td>\n",
       "      <td>366.86</td>\n",
       "      <td>10.373491</td>\n",
       "      <td>11.63</td>\n",
       "      <td>682</td>\n",
       "      <td>4710.000000</td>\n",
       "      <td>3511</td>\n",
       "      <td>25.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.1008</td>\n",
       "      <td>162.34</td>\n",
       "      <td>11.350407</td>\n",
       "      <td>8.10</td>\n",
       "      <td>712</td>\n",
       "      <td>2699.958333</td>\n",
       "      <td>33667</td>\n",
       "      <td>73.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.1426</td>\n",
       "      <td>102.92</td>\n",
       "      <td>11.299732</td>\n",
       "      <td>14.97</td>\n",
       "      <td>667</td>\n",
       "      <td>4066.000000</td>\n",
       "      <td>4740</td>\n",
       "      <td>39.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   credit.policy  int.rate  installment  log.annual.inc    dti  fico  \\\n",
       "0              1    0.1189       829.10       11.350407  19.48   737   \n",
       "1              1    0.1071       228.22       11.082143  14.29   707   \n",
       "2              1    0.1357       366.86       10.373491  11.63   682   \n",
       "3              1    0.1008       162.34       11.350407   8.10   712   \n",
       "4              1    0.1426       102.92       11.299732  14.97   667   \n",
       "\n",
       "   days.with.cr.line  revol.bal  revol.util  inq.last.6mths  delinq.2yrs  \\\n",
       "0        5639.958333      28854        52.1               0            0   \n",
       "1        2760.000000      33623        76.7               0            0   \n",
       "2        4710.000000       3511        25.6               1            0   \n",
       "3        2699.958333      33667        73.2               1            0   \n",
       "4        4066.000000       4740        39.5               0            1   \n",
       "\n",
       "   pub.rec  not.fully.paid  purpose_credit_card  purpose_debt_consolidation  \\\n",
       "0        0               0                    0                           1   \n",
       "1        0               0                    1                           0   \n",
       "2        0               0                    0                           1   \n",
       "3        0               0                    0                           1   \n",
       "4        0               0                    1                           0   \n",
       "\n",
       "   purpose_educational  purpose_home_improvement  purpose_major_purchase  \\\n",
       "0                    0                         0                       0   \n",
       "1                    0                         0                       0   \n",
       "2                    0                         0                       0   \n",
       "3                    0                         0                       0   \n",
       "4                    0                         0                       0   \n",
       "\n",
       "   purpose_small_business  \n",
       "0                       0  \n",
       "1                       0  \n",
       "2                       0  \n",
       "3                       0  \n",
       "4                       0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Test Split\n",
    "\n",
    "Now its time to split our data into a training set and a testing set!\n",
    "\n",
    "**Use sklearn to split your data into a training set and a testing set as we've done in the past.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = final_data.drop('not.fully.paid', axis=1)\n",
    "y = final_data['not.fully.paid']\n",
    "(X_train, X_test, y_train, y_test) = train_test_split(X, y, test_size=0.3, random_state=101)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training a Decision Tree Model\n",
    "\n",
    "Let's start by training a single decision tree first!\n",
    "\n",
    "** Import DecisionTreeClassifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create an instance of DecisionTreeClassifier() called dtree and fit it to the training data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtree = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "                       max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort=False,\n",
       "                       random_state=None, splitter='best')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtree.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predictions and Evaluation of Decision Tree\n",
    "**Create predictions from the test set and create a classification report and a confusion matrix.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_hat = dtree.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Actual True</th>\n",
       "      <th>Actual False</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Predicted True</th>\n",
       "      <td>1991</td>\n",
       "      <td>440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Predicted False</th>\n",
       "      <td>338</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Actual True  Actual False\n",
       "Predicted True          1991           440\n",
       "Predicted False          338           105"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(confusion_matrix(y_test, y_hat), index=['Predicted True', 'Predicted False'],\n",
    "                                               columns = ['Actual True', 'Actual False'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.82      0.84      2431\n",
      "           1       0.19      0.24      0.21       443\n",
      "\n",
      "    accuracy                           0.73      2874\n",
      "   macro avg       0.52      0.53      0.52      2874\n",
      "weighted avg       0.75      0.73      0.74      2874\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_hat))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the Random Forest model\n",
    "\n",
    "Now its time to train our model!\n",
    "\n",
    "**Create an instance of the RandomForestClassifier class and fit it to our training data from the previous step.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc = RandomForestClassifier(n_estimators = 250)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "                       max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=250,\n",
       "                       n_jobs=None, oob_score=False, random_state=None,\n",
       "                       verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predictions and Evaluation\n",
    "\n",
    "Let's predict off the y_test values and evaluate our model.\n",
    "\n",
    "** Predict the class of not.fully.paid for the X_test data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc_yhat = rfc.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now create a classification report from the results. Do you get anything strange or some sort of warning?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      1.00      0.92      2431\n",
      "           1       0.50      0.01      0.03       443\n",
      "\n",
      "    accuracy                           0.85      2874\n",
      "   macro avg       0.67      0.51      0.47      2874\n",
      "weighted avg       0.79      0.85      0.78      2874\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, rfc_yhat))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Show the Confusion Matrix for the predictions.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(confusion_matrix(y_test, rfc_yhat), index=['Predicted True', 'Predicted False'],\n",
    "                                               columns = ['Actual True', 'Actual False'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**What performed better the random forest or the decision tree?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Both from confusion matrix and classification report, it is apparent that Random Forest outperformed the decision tree in terms of accuracy, f1 score, precision. However, decision tree did a better job on recall for the class 1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Great Job!"
   ]
  }
 ],
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