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": 1,
   "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",
    "%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": 2,
   "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": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9578 entries, 0 to 9577\n",
      "Data columns (total 14 columns):\n",
      "credit.policy        9578 non-null int64\n",
      "purpose              9578 non-null object\n",
      "int.rate             9578 non-null float64\n",
      "installment          9578 non-null float64\n",
      "log.annual.inc       9578 non-null float64\n",
      "dti                  9578 non-null float64\n",
      "fico                 9578 non-null int64\n",
      "days.with.cr.line    9578 non-null float64\n",
      "revol.bal            9578 non-null int64\n",
      "revol.util           9578 non-null float64\n",
      "inq.last.6mths       9578 non-null int64\n",
      "delinq.2yrs          9578 non-null int64\n",
      "pub.rec              9578 non-null int64\n",
      "not.fully.paid       9578 non-null int64\n",
      "dtypes: float64(6), int64(7), object(1)\n",
      "memory usage: 1.0+ MB\n"
     ]
    }
   ],
   "source": [
    "loans.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
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       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.804970</td>\n",
       "      <td>0.122640</td>\n",
       "      <td>319.089413</td>\n",
       "      <td>10.932117</td>\n",
       "      <td>12.606679</td>\n",
       "      <td>710.846314</td>\n",
       "      <td>4560.767197</td>\n",
       "      <td>1.691396e+04</td>\n",
       "      <td>46.799236</td>\n",
       "      <td>1.577469</td>\n",
       "      <td>0.163708</td>\n",
       "      <td>0.062122</td>\n",
       "      <td>0.160054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.396245</td>\n",
       "      <td>0.026847</td>\n",
       "      <td>207.071301</td>\n",
       "      <td>0.614813</td>\n",
       "      <td>6.883970</td>\n",
       "      <td>37.970537</td>\n",
       "      <td>2496.930377</td>\n",
       "      <td>3.375619e+04</td>\n",
       "      <td>29.014417</td>\n",
       "      <td>2.200245</td>\n",
       "      <td>0.546215</td>\n",
       "      <td>0.262126</td>\n",
       "      <td>0.366676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>15.670000</td>\n",
       "      <td>7.547502</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>612.000000</td>\n",
       "      <td>178.958333</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.103900</td>\n",
       "      <td>163.770000</td>\n",
       "      <td>10.558414</td>\n",
       "      <td>7.212500</td>\n",
       "      <td>682.000000</td>\n",
       "      <td>2820.000000</td>\n",
       "      <td>3.187000e+03</td>\n",
       "      <td>22.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.122100</td>\n",
       "      <td>268.950000</td>\n",
       "      <td>10.928884</td>\n",
       "      <td>12.665000</td>\n",
       "      <td>707.000000</td>\n",
       "      <td>4139.958333</td>\n",
       "      <td>8.596000e+03</td>\n",
       "      <td>46.300000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.140700</td>\n",
       "      <td>432.762500</td>\n",
       "      <td>11.291293</td>\n",
       "      <td>17.950000</td>\n",
       "      <td>737.000000</td>\n",
       "      <td>5730.000000</td>\n",
       "      <td>1.824950e+04</td>\n",
       "      <td>70.900000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.216400</td>\n",
       "      <td>940.140000</td>\n",
       "      <td>14.528354</td>\n",
       "      <td>29.960000</td>\n",
       "      <td>827.000000</td>\n",
       "      <td>17639.958330</td>\n",
       "      <td>1.207359e+06</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</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": [
    "loans.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>credit.policy</th>\n",
       "      <th>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",
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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": [
    "loans.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": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'FICO')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "loans[loans['credit.policy']==1]['fico'].hist(alpha=0.5,color='blue',\n",
    "                                              bins=30,label='Credit.Policy=1')\n",
    "loans[loans['credit.policy']==0]['fico'].hist(alpha=0.5,color='red',\n",
    "                                              bins=30,label='Credit.Policy=0')\n",
    "plt.legend()\n",
    "plt.xlabel('FICO')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Create a similar figure, except this time select by the not.fully.paid column.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'FICO')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "loans[loans['not.fully.paid']==1]['fico'].hist(alpha=0.5,color='blue',\n",
    "                                              bins=30,label='not.fully.paid=1')\n",
    "loans[loans['not.fully.paid']==0]['fico'].hist(alpha=0.5,color='red',\n",
    "                                              bins=30,label='not.fully.paid=0')\n",
    "plt.legend()\n",
    "plt.xlabel('FICO')"
   ]
  },
  {
   "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": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1e304f28>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,7))\n",
    "sns.countplot(data=loans, x='purpose', hue='not.fully.paid',palette='Set1')"
   ]
  },
  {
   "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": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x1a1e531da0>"
      ]
     },
     "execution_count": 24,
     "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(data=loans, x='fico', y='int.rate', color='purple')"
   ]
  },
  {
   "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": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1a1da472b0>"
      ]
     },
     "execution_count": 25,
     "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": {},
     "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": 26,
   "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": 27,
   "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": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_data = pd.get_dummies(loans,columns=cat_feats,drop_first=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9578 entries, 0 to 9577\n",
      "Data columns (total 19 columns):\n",
      "credit.policy                 9578 non-null int64\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",
      "purpose_credit_card           9578 non-null uint8\n",
      "purpose_debt_consolidation    9578 non-null uint8\n",
      "purpose_educational           9578 non-null uint8\n",
      "purpose_home_improvement      9578 non-null uint8\n",
      "purpose_major_purchase        9578 non-null uint8\n",
      "purpose_small_business        9578 non-null uint8\n",
      "dtypes: float64(6), int64(7), uint8(6)\n",
      "memory usage: 1.0 MB\n"
     ]
    }
   ],
   "source": [
    "final_data.info()"
   ]
  },
  {
   "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": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = final_data.drop('not.fully.paid',axis=1)\n",
    "y = final_data['not.fully.paid']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=101)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training a Decision Tree Model\n",
    "\n",
    "Let's start by training a single decision tree first!\n",
    "\n",
    "** Import DecisionTreeClassifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtree = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtree.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predictions and Evaluation of Decision Tree\n",
    "**Create predictions from the test set and create a classification report and a confusion matrix.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = dtree.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.86      0.82      0.84      2431\n",
      "          1       0.20      0.25      0.22       443\n",
      "\n",
      "avg / total       0.75      0.73      0.74      2874\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1988  443]\n",
      " [ 334  109]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,predictions))"
   ]
  },
  {
   "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": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predictions and Evaluation\n",
    "\n",
    "Let's predict off the y_test values and evaluate our model.\n",
    "\n",
    "** Predict the class of not.fully.paid for the X_test data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = rfc.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now create a classification report from the results. Do you get anything strange or some sort of warning?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.85      0.99      0.91      2431\n",
      "          1       0.38      0.05      0.08       443\n",
      "\n",
      "avg / total       0.78      0.84      0.79      2874\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Show the Confusion Matrix for the predictions.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2399   32]\n",
      " [ 423   20]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,predictions))"
   ]
  },
  {
   "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": [
    "# High recall for both models, feature engineering is needed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Great Job!"
   ]
  }
 ],
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