15-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": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sns.set()"
   ]
  },
  {
   "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": [
    "df = pd.read_csv('data/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": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
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       "  <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",
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       "      <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",
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       "  </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": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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",
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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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "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": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0], dtype=int64)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['credit.policy'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(rc={'figure.figsize':(11.7,8.27)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1388c786d88>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "for credit_policy_value in df['credit.policy'].unique():\n",
    "    temp_df = df[df['credit.policy'] == credit_policy_value]\n",
    "    label = \"credit.policy={}\".format(credit_policy_value)\n",
    "    sns.distplot(temp_df['fico'], ax=ax, kde=False, label=label)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1388ca25b48>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "for credit_policy_value in df['not.fully.paid'].unique():\n",
    "    temp_df = df[df['not.fully.paid'] == credit_policy_value]\n",
    "    label = \"not.fully.paid={}\".format(credit_policy_value)\n",
    "    sns.distplot(temp_df['fico'], ax=ax, kde=False, label=label)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Create a similar figure, except this time select by the not.fully.paid column.**"
   ]
  },
  {
   "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": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1388ccf2948>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='purpose', data=df, hue=\"not.fully.paid\")"
   ]
  },
  {
   "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": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x1388ce8e848>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x=\"fico\",y=\"int.rate\", data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "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": 11,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x11ac4cb70>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ad7e048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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74HKhzjoH1q0h8ac/9d9+ggWzS1n/9jEylo3fpfLTv7mA4M53eTvmxlYUXJbF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rs+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\nJCAQQgghhBCihElAIIQQQgghRAn7/22Ddex5GWNPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ac1b6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "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": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "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": []
  },
  {
   "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": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "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": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "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": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": 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": 18,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "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": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            presort=False, random_state=None, splitter='best')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 21,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "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": 24,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1980  451]\n",
      " [ 355   88]]\n"
     ]
    }
   ],
   "source": []
  },
  {
   "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": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "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": 28,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "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": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "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": 31,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2427    4]\n",
      " [ 438    5]]\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**What performed better the random forest or the decision tree?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Great Job!"
   ]
  }
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