02-Decision Trees and Random Forest Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "\n",
    "<a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>\n",
    "___\n",
    "# Random Forest Project \n",
    "\n",
    "For this project we will be exploring publicly available data from [LendingClub.com](www.lendingclub.com). Lending Club connects people who need money (borrowers) with people who have money (investors). Hopefully, as an investor you would want to invest in people who showed a profile of having a high probability of paying you back. We will try to create a model that will help predict this.\n",
    "\n",
    "Lending club had a [very interesting year in 2016](https://en.wikipedia.org/wiki/Lending_Club#2016), so let's check out some of their data and keep the context in mind. This data is from before they even went public.\n",
    "\n",
    "We will use lending data from 2007-2010 and be trying to classify and predict whether or not the borrower paid back their loan in full. You can download the data from [here](https://www.lendingclub.com/info/download-data.action) or just use the csv already provided. It's recommended you use the csv provided as it has been cleaned of NA values.\n",
    "\n",
    "Here are what the columns represent:\n",
    "* credit.policy: 1 if the customer meets the credit underwriting criteria of LendingClub.com, and 0 otherwise.\n",
    "* purpose: The purpose of the loan (takes values \"credit_card\", \"debt_consolidation\", \"educational\", \"major_purchase\", \"small_business\", and \"all_other\").\n",
    "* int.rate: The interest rate of the loan, as a proportion (a rate of 11% would be stored as 0.11). Borrowers judged by LendingClub.com to be more risky are assigned higher interest rates.\n",
    "* installment: The monthly installments owed by the borrower if the loan is funded.\n",
    "* log.annual.inc: The natural log of the self-reported annual income of the borrower.\n",
    "* dti: The debt-to-income ratio of the borrower (amount of debt divided by annual income).\n",
    "* fico: The FICO credit score of the borrower.\n",
    "* days.with.cr.line: The number of days the borrower has had a credit line.\n",
    "* revol.bal: The borrower's revolving balance (amount unpaid at the end of the credit card billing cycle).\n",
    "* revol.util: The borrower's revolving line utilization rate (the amount of the credit line used relative to total credit available).\n",
    "* inq.last.6mths: The borrower's number of inquiries by creditors in the last 6 months.\n",
    "* delinq.2yrs: The number of times the borrower had been 30+ days past due on a payment in the past 2 years.\n",
    "* pub.rec: The borrower's number of derogatory public records (bankruptcy filings, tax liens, or judgments)."
   ]
  },
  {
   "cell_type": "markdown",
   "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": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\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": 73,
   "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": 74,
   "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>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",
       "</div>"
      ],
      "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": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\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",
       "    </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",
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       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.216400</td>\n",
       "      <td>940.140000</td>\n",
       "      <td>14.528354</td>\n",
       "      <td>29.960000</td>\n",
       "      <td>827.000000</td>\n",
       "      <td>17639.958330</td>\n",
       "      <td>1.207359e+06</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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",
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       "      <th>not.fully.paid</th>\n",
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       "  </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",
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       "    <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",
       "</div>"
      ],
      "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "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": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'fico')"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "loans[loans['credit.policy']==1]['fico'].hist(bins=30,color='blue')\n",
    "loans[loans['credit.policy']==0]['fico'].hist(bins=30,color='red')\n",
    "plt.legend()\n",
    "plt.xlabel('fico')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x119b1f518>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+/JnAT4FrQgi9wEPATTHGJIRwGTDCc5PqJ9uuTJLUNWbzYReAQmFfslmX99Xe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\nklJm4JIkSUqZgUuSJCll/z8k9pgdvHIZDwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119b0cc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Create a similar figure, except this time select by the not.fully.paid column.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'fico')"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlYAAAFzCAYAAAD8LEcHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAcmElEQVR4nO3df5Bd5X3f8fc3CCzD2pYQ9pawtCK1SuO4NWZ3sOy0mV3klB/tVHgGxrit0VAlShpCjCljSPuHnZnOGE/VUjPO4KrgRnhS1oSaQYNJUirYOswE7F3AmB9mkHEsFmTAIOQuBNs43/5xH7nL6q72Xu1zdX/s+zVz557znGfPfs8+nMtH59xzTmQmkiRJWr5f6HYBkiRJg8JgJUmSVInBSpIkqRKDlSRJUiUGK0mSpEoMVpIkSZWs6nYBACeddFKuX7++22WogldffZUTTjih22WoMsd1MDmug8lx7byZmZkfZuY7my3riWC1fv16pqenu12GKpiammJ8fLzbZagyx3UwOa6DyXHtvIj4/mLLPBUoSZJUicFKkiSpEoOVJElSJT3xHStJkqRu+OlPf8rs7Cyvv/76IctWr17NyMgIxx57bMvrM1hJkqQVa3Z2lre97W2sX7+eiPh5e2by0ksvMTs7y2mnndby+jwVKEmSVqzXX3+ddevWvSlUAUQE69ata3ok63AMVpIkaUVbGKqWaj8cg5UkSVIlBitJkqRKDFaSJGlFy8y22g+npWAVEZ+MiMci4tGIuCUiVkfEaRHxQEQ8FRFfiYjjSt+3lPk9Zfn6tquSJEk6ClavXs1LL710SIg6eFXg6tWr21rfkrdbiIhTgN8D3pOZfx0RtwIXA+cD12XmZER8EdgK3FDe92fmuyPiYuBzwEfbqkqSJOkoGBkZYXZ2lhdffPGQZQfvY9WOVu9jtQp4a0T8FDge2AecDfyLsnwn8BkawWpzmQa4DfhCREQeyfE0SZKkDjr22GPbuk/VUpY8FZiZzwLbgb00AtUBYAZ4JTPfKN1mgVPK9CnAM+Vn3yj911WrWNLiIpb/kiQdsVZOBa6lcRTqNOAV4E+A85p0PXhEqtkn8yFHqyJiG7ANYHh4mKmpqdYqVk+bm5tzLLtp+/blr6PJ+Dmug8lxHUyOa3e1cirww8D3MvNFgIj4KvAhYE1ErCpHpUaA50r/WeBUYDYiVgHvAF5euNLM3AHsABgbG8vx8fFlbop6wdTUFI5lF01MLH8dTc7aO66DyXEdTI5rd7VyVeBeYGNEHB+NW5BuAh4H7gUuLH22AHeU6V1lnrL8Hr9fJUmSVoJWvmP1AI0voT8IfLv8zA7gauDKiNhD4ztUN5UfuQlYV9qvBK7pQN2SJEk9p6WrAjPz08CnFzQ/DZzVpO/rwEXLL03qMzW++O3BXUnqa955XZIkqRKDlSRJUiUGK0mSpEoMVpIkSZUYrCRJkioxWEmSJFVisJIkSarEYCVJklSJwUqSJKkSg5UkSVIlBitJkqRKDFaSJEmVGKwkSZIqMVhJkiRVYrCSJEmqxGAlSZJUicFKkiSpEoOVJElSJQYrSZKkSgxWkiRJlRisJEmSKjFYSZIkVWKwkiRJqsRgJUmSVInBSpIkqRKDlSRJUiUGK0mSpEoMVpIkSZUYrCRJkioxWEmSJFWyZLCKiNMj4uF5rx9FxBURcWJE3B0RT5X3taV/RMT1EbEnIh6JiDM7vxmSekrE8l+S1IeWDFaZ+WRmnpGZZwCjwGvA7cA1wO7M3ADsLvMA5wEbymsbcEMnCpckSeo17Z4K3AR8NzO/D2wGdpb2ncAFZXozcHM23A+siYiTq1QrSZLUwyIzW+8c8SXgwcz8QkS8kplr5i3bn5lrI+JO4NrMvK+07wauzszpBevaRuOIFsPDw6OTk5MVNkfdNjc3x9DQULfL6I6ZmeWvY3S0J2toe1x74W+hJa3o/XWAOa6dNzExMZOZY82WtRysIuI44DngVzLz+cMEq68Bn10QrD6VmYt+0o6NjeX09PRii9VHpqamGB8f73YZ3VHje0Ft/EPnaNbQ9rj2wt9CS1rR++sAc1w7LyIWDVbtnAo8j8bRqufL/PMHT/GV9xdK+yxw6ryfG6ERyCRJkgZaO8HqY8At8+Z3AVvK9Bbgjnntl5SrAzcCBzJz37IrlSRJ6nGrWukUEccDvw781rzma4FbI2IrsBe4qLTfBZwP7KFxBeGl1aqVJEnqYS0Fq8x8DVi3oO0lGlcJLuybwGVVqpMkSeoj3nldkiSpEoOVJElSJQYrSZKkSgxWkiRJlRisJEmSKjFYSZIkVWKwkiRJqsRgJUmSVInBSpIkqRKDlSRJUiUGK0mSpEoMVpIkSZUYrCRJkioxWEmSJFVisJIkSarEYCVJklSJwUqSJKkSg5UkSVIlBitJkqRKDFaSJEmVGKwkSZIqMVhJkiRVYrCSJEmqxGAlSZJUicFKkiSpEoOVJElSJQYrSZKkSgxWkiRJlRisJEmSKjFYSZIkVdJSsIqINRFxW0R8JyKeiIgPRsSJEXF3RDxV3teWvhER10fEnoh4JCLO7OwmSJIk9YZWj1h9HvizzPz7wPuAJ4BrgN2ZuQHYXeYBzgM2lNc24IaqFUuSJPWoJYNVRLwd+DXgJoDM/ElmvgJsBnaWbjuBC8r0ZuDmbLgfWBMRJ1evXJIkqcdEZh6+Q8QZwA7gcRpHq2aATwDPZuaaef32Z+baiLgTuDYz7yvtu4GrM3N6wXq30TiixfDw8Ojk5GS9rVLXzM3NMTQ01O0yumNmZvnrGB3tyRraHtde+FvUMkjbssCK3l8HmOPaeRMTEzOZOdZsWSvBagy4H/jVzHwgIj4P/Ai4fJFg9TXgswuC1acyc9FPp7GxsZyenl5ssfrI1NQU4+Pj3S6jOyKWv44l9sdu1dD2uPbC36KWQdqWBVb0/jrAHNfOi4hFg1Ur37GaBWYz84EyfxtwJvD8wVN85f2Fef1PnffzI8BzR1K4JElSP1kyWGXmD4BnIuL00rSJxmnBXcCW0rYFuKNM7wIuKVcHbgQOZOa+umVLkiT1nlUt9rsc+OOIOA54GriURii7NSK2AnuBi0rfu4DzgT3Aa6WvJEnSwGspWGXmw0Czc4mbmvRN4LJl1iVJktR3vPO6JElSJQYrSZKkSgxWkiRJlRisJEmSKjFYSZIkVWKwkiRJqsRgJUmSVInBSpIkqRKDlSRJUiUGK0mSpEoMVpIkSZUYrCRJkioxWEmSJFVisJIkSarEYCVJklSJwUqSJKkSg5UkSVIlBitJkqRKDFaSJEmVGKwkSZIqMVhJkiRVYrCSJEmqxGAlSZJUicFKkiSpEoOVJElSJQYrSZKkSgxWkiRJlRisJEmSKjFYSZIkVdJSsIqIv4qIb0fEwxExXdpOjIi7I+Kp8r62tEdEXB8ReyLikYg4s5MbIEmS1CvaOWI1kZlnZOZYmb8G2J2ZG4DdZR7gPGBDeW0DbqhVrCRJUi9bzqnAzcDOMr0TuGBe+83ZcD+wJiJOXsbvkSRJ6gutBqsE/ldEzETEttI2nJn7AMr7u0r7KcAz8352trRJko5UxPJfkjouMnPpThG/mJnPRcS7gLuBy4FdmblmXp/9mbk2Ir4GfDYz7yvtu4FPZebMgnVuo3GqkOHh4dHJyclqG6XumZubY2hoqNtldMfMzNJ9ljI62pM1tD2uvfC3qKVXtqUDdazo/XWAOa6dNzExMTPvq1Fv0lKwetMPRHwGmAN+ExjPzH3lVN9UZp4eEf+1TN9S+j95sN9i6xwbG8vp6em26lBvmpqaYnx8vNtldEeNIwJt7o9Hq4a2x7UX/ha19Mq2dKCOFb2/DjDHtfMiYtFgteSpwIg4ISLednAa+CfAo8AuYEvptgW4o0zvAi4pVwduBA4cLlRJkiQNilUt9BkGbo/Gv5ZWAf8jM/8sIr4J3BoRW4G9wEWl/13A+cAe4DXg0upVS5Ik9aAlg1VmPg28r0n7S8CmJu0JXFalOkmSpD7indclSZIqMVhJkiRVYrCSJEmqxGAlSZJUicFKkiSpEoOVJElSJQYrSZKkSgxWkiRJlRisJEmSKjFYSZIkVWKwkiRJqsRgJUmSVInBSpIkqRKDlSRJUiUGK0mSpEpWdbsASfNEdLsCSdIyeMRKkiSpEoOVJElSJQYrSZKkSgxWkiRJlRisJEmSKjFYSZIkVWKwkiRJqsRgJUmSVInBSpIkqRKDlSRJUiUGK0mSpEoMVpIkSZUYrCRJkipZ1e0CJEl9JGL568hc/jqkHtXyEauIOCYiHoqIO8v8aRHxQEQ8FRFfiYjjSvtbyvyesnx9Z0rXQIhY/kuSpB7RzqnATwBPzJv/HHBdZm4A9gNbS/tWYH9mvhu4rvSTJEkaeC0Fq4gYAf4pcGOZD+Bs4LbSZSdwQZneXOYpyzeV/pIkSQOt1SNW/wX4FPA3ZX4d8EpmvlHmZ4FTyvQpwDMAZfmB0l+SJGmgRS7xJcKI+GfA+Zn5OxExDlwFXAr8ZTndR0ScCtyVmf8gIh4DzsnM2bLsu8BZmfnSgvVuA7YBDA8Pj05OTtbdMnXF3NwcQ0NDrf/AzMzyf+no6PLXUUONbekFTf6ejusy1diWDtTR9rh2qA7VdUTjqrZMTEzMZOZYs2WtBKvPAh8H3gBWA28HbgfOAf5WZr4RER8EPpOZ50TEn5fpv4yIVcAPgHfmYX7R2NhYTk9PH8m2qcdMTU0xPj7e+g8M0hVGg3LGu8nf03Fdphrb0oE62h7XDtWhuo5oXNWWiFg0WC15KjAzfz8zRzJzPXAxcE9m/kvgXuDC0m0LcEeZ3lXmKcvvOVyokiRJGhTLuUHo1cCVEbGHxneobirtNwHrSvuVwDXLK1GSJKk/tHWD0MycAqbK9NPAWU36vA5cVKE2SZKkvuIjbSS9WbObsM7MeNNWSWqBwUqSJKkSg5UkSVIlBitJkqRKDFaSJEmVGKwkSZIqMVhJkiRVYrCSJEmqxGAlSZJUicFKkiSpEoOVJElSJQYrSZKkStp6CLMkHTU1njmYufx1SFIbPGIlSZJUicFKkiSpEoOVJElSJQYrSZKkSgxWkiRJlRisJEmSKvF2C+p/XpavxdT4b0OS2uARK0mSpEoMVpIkSZUYrCRJkioxWEmSJFVisJIkSarEYCVJklSJwUqSJKkSg5UkSVIl3iBUAm8kKUmqwiNWkiRJlSwZrCJidUR8IyK+FRGPRcQflPbTIuKBiHgqIr4SEceV9reU+T1l+frOboIkSVJvaOWI1Y+BszPzfcAZwLkRsRH4HHBdZm4A9gNbS/+twP7MfDdwXeknSZI08JYMVtkwV2aPLa8EzgZuK+07gQvK9OYyT1m+KcIvsEiSpMEXmbl0p4hjgBng3cAfAv8RuL8clSIiTgX+NDPfGxGPAudm5mxZ9l3gA5n5wwXr3AZsAxgeHh6dnJyst1Xqmrm5OYaGhlr/gZmZzhWjauZGRhiane12Gf1rdHT566ixryyoo+39tUN1qK4jGle1ZWJiYiYzx5ota+mqwMz8GXBGRKwBbgd+uVm38t7s6NQh6S0zdwA7AMbGxnJ8fLyVUtTjpqamaGssJyY6Vovqmdq+nfGrrup2Gf2rhX/ALqnGvrKgjrb31w7VobqOaFxVTVtXBWbmK8AUsBFYExEHg9kI8FyZngVOBSjL3wG8XKNYSZKkXtbKVYHvLEeqiIi3Ah8GngDuBS4s3bYAd5TpXWWesvyebOV8oyRJUp9r5VTgycDO8j2rXwBuzcw7I+JxYDIi/gPwEHBT6X8T8OWI2EPjSNXFHahbkiSp5ywZrDLzEeD9TdqfBs5q0v46cFGV6iRJkvqId16XJEmqxGAlSZJUicFKkiSpEoOVJElSJQYrSZKkSgxWkiRJlRisJEmSKjFYSZIkVWKwkiRJqqSVR9pIkpYjotsVSDpKPGIlSZJUicFKkiSpEoOVJElSJQYrSZKkSgxWkiRJlRisJEmSKvF2C5KklafGLTAyl78ODRyPWEmSJFVisJIkSarEYCVJklSJwUqSVoqIN79mZg5tW+ol6bAMVpIkSZUYrCRJkioxWEmSJFVisJIkSarEYCVJklSJwUqSJKkSg5UkSVIlBitJkqRKDFaSJEmVLBmsIuLUiLg3Ip6IiMci4hOl/cSIuDsinirva0t7RMT1EbEnIh6JiDM7vRGSJEm9oJUjVm8A/zYzfxnYCFwWEe8BrgF2Z+YGYHeZBzgP2FBe24AbqlctSZLUg5YMVpm5LzMfLNP/F3gCOAXYDOws3XYCF5TpzcDN2XA/sCYiTq5euSRJUo+JzGy9c8R64OvAe4G9mblm3rL9mbk2Iu4Ers3M+0r7buDqzJxesK5tNI5oMTw8PDo5ObnMTVEvmJubY2hoqPUfmJnpXDGqZm5khKHZ2W6Xocq6Nq6jo0f/dy5U47OnF7ajibY/h9W2iYmJmcwca7ZsVasriYgh4H8CV2Tmj2Lxp5w3W3BIesvMHcAOgLGxsRwfH2+1FPWwqakp2hrLiYmO1aJ6prZvZ/yqq7pdhirr2ri28Q/6jqnx2dML29FE25/DqqqlqwIj4lgaoeqPM/Orpfn5g6f4yvsLpX0WOHXej48Az9UpV5IkqXe1clVgADcBT2Tmf563aBewpUxvAe6Y135JuTpwI3AgM/dVrFmSJKkntXIq8FeBjwPfjoiHS9u/A64Fbo2IrcBe4KKy7C7gfGAP8BpwadWKJUmSetSSwap8CX2xL1RtatI/gcuWWZckSVLf8c7rkiRJlRisJEmSKjFYSZIkVWKwkiRJqsRgJUmSVInBSpIkqRKDlSRJUiUtPytQkiRVtvhzd1vXo88sXKk8YiVJklSJwUqSJKkSg5UkSVIlBitJkqRKDFaSJEmVGKwkSZIq8XYLK5WX+EqSVJ1HrCRJkirxiJUkqb/UOOIudYhHrCRJkioxWEmSJFVisJIkSarEYCVJklSJX16XJB1dfvlcA8wjVpIkSZUYrCRJkioxWEmSJFVisJIkSarEYCVJklSJwUqSJKkSg5UkSVIlBitJkqRKlgxWEfGliHghIh6d13ZiRNwdEU+V97WlPSLi+ojYExGPRMSZnSxekiSpl7RyxOqPgHMXtF0D7M7MDcDuMg9wHrChvLYBN9QpU5IkqfctGawy8+vAywuaNwM7y/RO4IJ57Tdnw/3Amog4uVaxkiRJvSwyc+lOEeuBOzPzvWX+lcxcM2/5/sxcGxF3Atdm5n2lfTdwdWZON1nnNhpHtRgeHh6dnJyssDlq2czM8tcxOnpI09zcHENDQ0e3DnXc3MgIQ7Oz3S5DlTmuy9TkM7BtHfgsbvtzWG2bmJiYycyxZstqP4S52ZM1mya3zNwB7AAYGxvL8fHxyqXosCYmlr+OJqF8amqKtsayRh3quKnt2xm/6qpul6HKHNdlauHAxJI68Fnc9uewqjrSqwKfP3iKr7y/UNpngVPn9RsBnjvy8iRJkvrHkQarXcCWMr0FuGNe+yXl6sCNwIHM3LfMGtWrIg59zcw0b1/sJUnSAFnyVGBE3AKMAydFxCzwaeBa4NaI2ArsBS4q3e8Czgf2AK8Bl3agZkmSpJ60ZLDKzI8tsmhTk74JXLbcoiRJkvqRd16XJEmqxGAlSZJUicFKkiSpktr3sZIkaWUYpCuba2xLjft6DQCPWEmSJFVisJIkSarEYCVJklSJwUqSJKkSg5UkSVIlBitJkqRKDFaSJEmVGKwkSZIqMVhJkiRVYrCSJEmqxGAlSVI/i3jza2bm0LalXqrGYCVJklSJwUqSJKkSg5UkSVIlBitJkqRKDFaSJEmVGKwkSZIqMVhJkiRVsqrbBahzDndrkjx6ZUiStGJ4xEqSJKkSg5UkSVIlBitJkqRKDFaSJEmVGKwkSZIqMVhJkiRV0pFgFRHnRsSTEbEnIq7pxO+QJEnqNdXvYxURxwB/CPw6MAt8MyJ2ZebjtX/XIDvcPagkSeo5Nf7Hlf1/l8VOHLE6C9iTmU9n5k+ASWBzB36PJEkaJBHLf3VZJ+68fgrwzLz5WeADHfg97emBP3Y7+j+zS5K08kRWPuwWERcB52Tmb5T5jwNnZeblC/ptA7aV2dOBJ6sWom45Cfhht4tQdY7rYHJcB5Pj2nl/JzPf2WxBJ45YzQKnzpsfAZ5b2CkzdwA7OvD71UURMZ2ZY92uQ3U5roPJcR1Mjmt3deI7Vt8ENkTEaRFxHHAxsKsDv0eSJKmnVD9ilZlvRMTvAn8OHAN8KTMfq/17JEmSek0nTgWSmXcBd3Vi3ep5nt4dTI7rYHJcB5Pj2kXVv7wuSZK0UvlIG0mSpEoMVmpLRKyJiNsi4jsR8UREfDAiPhMRz0bEw+V1/rz+v18ebfRkRJzTzdrVXEScPm/sHo6IH0XEFRFxYkTcHRFPlfe1pX9ExPVlXB+JiDO7vQ061GHG1f21z0XEJyPisYh4NCJuiYjV5YKxB8r++pVy8RgR8ZYyv6csX9/d6gefpwLVlojYCfxFZt5YdtzjgSuAuczcvqDve4BbaNyN/xeB/w38vcz82VEuWy0qj6R6lsZNfS8DXs7Ma8szP9dm5tXlf8SXA+eXfp/PzO7fBFiLWjCul+L+2rci4hTgPuA9mfnXEXErje80nw98NTMnI+KLwLcy84aI+B3gH2bmb0fExcBHMvOj3duCwecRK7UsIt4O/BpwE0Bm/iQzXznMj2wGJjPzx5n5PWAPjQ9t9a5NwHcz8/s0xm9nad8JXFCmNwM3Z8P9wJqIOPnol6o2zB/Xxbi/9o9VwFsjYhWNf9zuA84GbivLF+6vB/fj24BNEX32KJI+Y7BSO34JeBH47xHxUETcGBEnlGW/W04LfengKSOaP97olKNYr9p3MY2jFgDDmbkPoLy/q7Q7rv1n/riC+2vfysxnge3AXhqB6gAwA7ySmW+UbvPH7ufjWpYfANYdzZpXGoOV2rEKOBO4ITPfD7wKXAPcAPxd4AwaO/p/Kv2b/avIc889qpza/efAnyzVtUmb49qjmoyr+2sfK0F4M3AajVO2JwDnNel6cOwc16PMYKV2zAKzmflAmb8NODMzn8/Mn2Xm3wD/jf9/+qClxxupZ5wHPJiZz5f55w+e4ivvL5R2x7W/vGlc3V/73oeB72Xmi5n5U+CrwIdonJI/eG/K+WP383Ety98BvHx0S15ZDFZqWWb+AHgmIk4vTZuAxxd8v+YjwKNlehdwcbkq5TRgA/CNo1aw2vUx3ny6aBewpUxvAe6Y135JuTpwI3Dg4ClD9aQ3jav7a9/bC2yMiOPLd6U2AY8D9wIXlj4L99eD+/GFwD3pVWsd5VWBaktEnAHcCBwHPE3jCqPraZxWSOCvgN86+D/aiPj3wL8G3gCuyMw/7ULZWkJEHE/jexi/lJkHSts64Fbgb9P4ML8oM18uH+ZfAM4FXgMuzczp7lSuw1lkXL+M+2tfi4g/AD5KY5weAn6DxnepJoETS9u/yswfR8Rq4MvA+2kcqbo4M5/uSuErhMFKkiSpEk8FSpIkVWKwkiRJqsRgJUmSVInBSpIkqRKDlSRJUiUGK0l9KyJ+LyKeiIj95UHRktRV3m5BUt+KiO8A55WHBktS13nESlJfiogv0ngw+K6I+GREfKG0D0fE7RHxrfL6UGm/MiIeLa8rulm7pMFlsJLUlzLzt2k8D20C2D9v0fXA/8nM99F4aPhjETFK4ykBHwA2Ar8ZEe8/yiVLWgEMVpIGzdnADQDlYcMHgH8E3J6Zr2bmHI0H1/7jLtYoaUAZrCStBNHtAiStDAYrSYNmN/BvACLimIh4O/B14IKIOD4iTgA+AvxFF2uUNKAMVpIGzSeAiYj4NjAD/EpmPgj8EfAN4AHgxsx8qHslShpU3m5BkiSpEo9YSZIkVWKwkiRJqsRgJUmSVInBSpIkqRKDlSRJUiUGK0mSpEoMVpIkSZUYrCRJkir5f/Py684MniqAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "loans[loans['not.fully.paid']==1]['fico'].hist(bins=30,color='blue')\n",
    "loans[loans['not.fully.paid']==0]['fico'].hist(bins=30,color='red')\n",
    "plt.legend()\n",
    "plt.xlabel('fico')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11a2cdd30>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nyqTpMXBJkua07kym5mk5AJyYQ3OBgUuS5rBKM92n03l27eqvqr0z3Utzg4FL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\nJClhBi5JkqSEGbgkSZISZuCSJElK2P8PnV2+VcEOYNIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119b0c780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "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": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2b5bb182710>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11,7))\n",
    "sns.countplot(x='purpose', data=loans, hue='not.fully.paid', palette='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11a508cf8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RBlBJkiQVZQCVJElSUQZQSZIkFWUAlSRJUlEGUEmSJBVlAJUkSVJRBlBJkiQVZQCVJElS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\nqP8fAV85Cib/YDIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a52c390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "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": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x2b5bb1549e8>"
      ]
     },
     "execution_count": 78,
     "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x11a87d7f0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8nrRuSpmxyos3yzFqXoqnqYjXMFEPz0lPVDnYxioZrdxVLDlZzKzN0oWZV/mE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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a87d748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "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": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2b5b96220f0>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 792x504 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 789.5x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11,7))\n",
    "sns.lmplot(y='int.rate',x='fico',data=loans,hue='credit.policy',\n",
    "           col='not.fully.paid',palette='Set1')"
   ]
  },
  {
   "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": 80,
   "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": 81,
   "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": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_data=pd.get_dummies(loans, columns=cat_feats,drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "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>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",
       "      <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",
       "    </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",
       "      <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",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.1357</td>\n",
       "      <td>366.86</td>\n",
       "      <td>10.373491</td>\n",
       "      <td>11.63</td>\n",
       "      <td>682</td>\n",
       "      <td>4710.000000</td>\n",
       "      <td>3511</td>\n",
       "      <td>25.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>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",
       "      <td>0</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>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": 117,
     "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": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "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=0.30, random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "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": 119,
   "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": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtree=DecisionTreeClassifier()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtree.fit(X_train,y_train)\n",
    "pred=dtree.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "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      0.23      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": [
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "print(classification_report (y_test, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.85      0.81      0.83      2431\n",
      "          1       0.16      0.20      0.18       443\n",
      "\n",
      "avg / total       0.74      0.72      0.73      2874\n",
      "\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2000  431]\n",
      " [ 342  101]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, pred))"
   ]
  },
  {
   "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": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rfc=RandomForestClassifier(n_estimators=100)\n",
    "rfc.fit(X_train, y_train)\n",
    "rfc_pred=rfc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "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_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=600, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "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": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.99      0.92      2431\n",
      "           1       0.42      0.02      0.04       443\n",
      "\n",
      "    accuracy                           0.84      2874\n",
      "   macro avg       0.63      0.51      0.48      2874\n",
      "weighted avg       0.78      0.84      0.78      2874\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, rfc_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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       0.56      0.01      0.02       443\n",
      "\n",
      "avg / total       0.80      0.85      0.78      2874\n",
      "\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Show the Confusion Matrix for the predictions.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2417   14]\n",
      " [ 433   10]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, rfc_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**What performed better the random forest or the decision tree?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Great Job!"
   ]
  }
 ],
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