Decision tree and Random forest classifier

LendingLoans.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "\n",
    "# Decision tree and Random Forest portfolio project \n",
    "\n",
    "___\n",
    "\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 they would want to invest in people who showed a profile of having a high probability of paying them 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",
    "**Usual libraries for pandas and plotting.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get the Data\n",
    "\n",
    "**Pandas to read loan_data.csv as a dataframe called loans.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "loans = pd.read_csv('loan_data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**info(), head(), and describe() methods on loans.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": 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": [
    "loans.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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",
       "    </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",
       "</div>"
      ],
      "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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",
       "      <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": 5,
     "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.\n",
    "\n",
    "** Histogram of two FICO distributions on top of each other, one for each credit.policy outcome.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x27263934240>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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2Lb7BAmu+l/zeB/xnn/dPhBBOyb0+C1gNrAVmhBAmhBAmAoeRHbAuSZI0quV7yS8Az/V5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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x27263917588>"
      ]
     },
     "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": [
    "** Similar figure, except this time select by the not.fully.paid column.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x27263f13e10>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GvuWoP7eG3RoaxrPjANoq9zOUq3717UDbVQV9fQcOViW+iwf7PazBGyiwHsiv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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x272639b3748>"
      ]
     },
     "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='Credit.Policy=1')\n",
    "loans[loans['not.fully.paid']==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": [
    "** Countplot using seaborn showing the counts of loans by purpose, with the color hue defined by not.fully.paid. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x27263934ac8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MZt4P/PtAF7MiBmX4jIj1gK8BY4AHm27bAhdQbtv0KPD+pvd/jYgbgA2BKZRH\neh4BLIqIuzNzRjfjbwMuBHYC1gZOz8zvR8S5lMeCQgl750fE5cBCYEvgBcARmXl3RHwZeAmwLnB+\nZn41It4ATKJ8kXeusWO6szJzk4jYDTgfmAMsBu5s3j+L8hjSDYH/zcz3AacCr2x+4e4CfAP4CfBl\nYCtgLeC8zPxm05L4K2AbyhOmDs7MP/Z1uS+PiFi3qWELyjK8CtiP0hp/OrAB8P+Ap4HbM/OjEfEC\nyufaRvlx0DGuh4BXAB8FRkTEzzLzmh6mOxE4iLLuT8nMS7pbbk1r8i7AesAHgMOAfYE/Axv124Ko\nKCLWBy4Dngf8C/D55Rh2S+BLlOXWDpwAbAZsD3yFsnzGRsT3KOv5PZl5ZERsDlxKWc+fAI6irHPX\nUtbx6zLzM/0xf30wLiJuBMZStvU/8NztbXvgY5RtdnPgC5SQ9UrKdjolIvYAJlPWzd8BR2fmU91N\nsNmmjgEOoWzvG1HWs89TfuS+FDicsj5/G/g/ynK9PjNPbfYfGzb//g2YSKd9DHAxcB/wysx8PCL+\ns6nrKrpf7t+krMNbUvYF2wCvAn6Qmaf0sJ98FXAysIiyz/gGcDZ92N66LIuXUrb5xZTt/FLKetPb\nsn47cBwwjLLevWVZ0+libERcAzwfmJaZn2yW6Tcy84cRsS9wSGYe0cM++SFKwP4C3e/HD+a5+6ld\ngXOBp4AFwNubYTrP+6GZ+eflnJceNS1qb25qfwHl++FAyuf7n5Tl+1ZgJPAIZTkeCrysqfkkyjq6\nGLg1M0/uug/MzPu6me4ZzfLZmPJ9e3xm3t7xXdX08w3K8tuSsj517OO3BI6lrJfXZObpwDoR8XXg\nhZT17+2Uz24KMLyZt4mZ+b2ImAzsSdknXZ2Zn+5u/c3Mzk9KbJmIGNbM57828ziRst1OBGZTvufu\nj4jxlODY0WjS8b3+r5T989qU9eaQZt7PoyyjjSjLawzNfjciDgO+kpnjesgP29Nl283MyRGxTdfx\nZubPWrh4nmOwHnY/BpiZmbsDlzTdpgLHZeZ44DrgI033YZSN9nU88yFdTglkzwmejYOAjTJzJ8rK\n/+qIeBPwImAc5cvh0GZDAPhjZu5DCaxHRcQoYHfKzmBfyqND2yg747dm5h7ALZSVtjtTgHdl5t6U\nL9COYDEnM99ACVLjImJTypfkTzPz0k7DHw3MzsxdgL2BSRHREahmNOP9EfCuHqbfH44BHsrM11I2\nsiea+neotlCbAAAK1ElEQVQD/gc4E9ir+XvTZsM6FfjvzNwT+F6X8T1N+UL8ei/B81WUgLsz5YfD\nSyNiNN0vN4D7mmU0kvJ5vQZ4LzCqX5ZAfS+h7HzeCLyR8qXZV5+lfCHvTvnV/cXM/AHlx8p7KdvN\n+pT78b4W2CsiNm6Gu6DZ7j5L+YwANgHeWDF4QgkD+1C+eP+Dnre3zSjB8Nim23so683RzXY6tdNw\nD1N+rPbFE5m5L3A1sH9mvpmyPDpa17ZsxvUa4PURsUPT/afNergrXfYxlC/+q5t6abp9hZ6X+1aU\nH1NvAj5JWQd2brpBz/vJLZppjAM+kpnL3N668QZgBmWfczowml6WdTPMS4F/a/YDv6F8fstjvWac\nuwD7RcQru+upu31yN7113Y9vQPf7qYOAbwF7UPbVY3qY9/42KjP3Bz5NWZ5vpfzo+AAlBO2dmTtT\nwtprOgZqvqfeQVlGu1AaZN7UvH1fZu7SXfDsZEFmvp7yQ2JZP2g79vH3Un68vA7YgRI616N8Xqc0\n/Yym/PB5GXBus48+ivJjBODdlPX9dcDcpltP628NHwQeafaRB1KWxXmUz3wfSqDszWeBs5rvxPMp\n8/4K4KTM3Ivyub6vm/0uy8gPz9p2m27PGe/KzfryG6zh86WUDZ3MvIvypbM1cHHTEvF+oCNg3JmZ\nizLzCcrObcs+jD+AO5rxz8nM05rx35aZ7U0ryJ3Ay5v+/6f5/8/A8MycD3yYsrJ8E1iH8utjXmY+\n3PR7K2UF6c7zM/OB5vX05v8ngI0j4r8pgXs9SrDuztbN+Glq+Q3w4u5q7W0hrKTOy/C3lJ1HNu+9\nhNI6dV3zeb28qW/p58oz872805yRmU83n/lJlB1CT8uto56XAr/IzCWZOY/V92lbfwMOiogrKTum\nntaP7nReZ35FaUnp6vfN9rAE+DswAtgWOKX5HD9O+SUP8IfMXLRCc7Hi7s7Mdkor4wvpeXub2WzD\nc4HfNXXOoWwPYymtL99q5umNlJ17n6bf/D+Xss3RabxQWt3/0QS7uyjrKzyzHva0j7kMeG+U87kz\nMx+l5+X++6YlaC7wt2Z6T1JaFTum0d1+8t7MXJyZj1P2NSvii810fwh8iNLK1tuyhrIeXdG0Sm7H\n8q2zUJbpY80ynUHZljtrg6X7wa775K667ht72k99inJk4SeUlrunepj3/tZR31xKaGynLMu1KSHl\nvyPii5TA33k5vozyPfhUM8xtPLMtJMv2U4DM/DXlR2VXbZ1ed4xvK8pn/0SzPn80M/8J/CMzH2r6\nmUXZh/wf5YffVymNFh21v5vyA+gGytEc6Hn9rWFbYP9m2ldTlvuSzHy0Wa49tSx2LJ/O34nXZOaN\nlB+3p0XEFZR1qaf1v7f80N2229fxtsxgDZ+/obS+dLR2DaOs9O9tfhF9BJjW9PuqiBgaESMpK+7v\ngCX0vmzuo/nlGBGjm8P299EcDmua33cBftv0/6w7+TeHj3fMzLdQDqV9hrLDWL95D8qv5gfo3sMR\nsXXzuuMX7H7A5pn5LuAUyuGXth7m5T7Kr8WOX/zb0rSgdq21hTovw60oO+wlzXt/oOzg39B8XhdS\nvmiXfq50+uXeybI+t/uBHSJiSEQMi4gfUR7h2t1y6xgfzXR3aoYbyTM/KlY3JwF3ZOZhlEO8bcvo\nv7PO68z2PHPaQ+dl3t26cz9wcvM5Ht1Mt2O42jrX9wg9b2+9bQOPAH8BDmzmaTLNl+9yTr87W0fE\niIhYi9Ia2RFQO5ZVt/uY5sdbGzCB0vIDPS/3ZdXQ036yu+GWtb11dSAlPO/V1HNyb/U0RyXOpLQM\nf5Dyxbk86yyUZbpeRAylLNNfUw5LdnzuOzTTes4+uRmms6619rSfOgy4vDlC82tKa113897felqW\nawMHZeY7geMpn1nn5Xg/sHPzPdhGaQHu2Bb6sp3uCNAcyu0IP8Oa5b42z25E6Rjf74CXRcQ6zbBX\nNUecupuHT1IOLb8HuAloa4Y7mHJ0bk/giIjYgp7X3xrupxyZG0/5Pv4mQESMbd7v+M5auv41NW/Q\ndO/8nfjuiDiecgrB6Zl5OKXRo/N3U+dtb3n3Zz2Nt5rBGj6/AGwVEbdTmugXUg5DfKXpdjZwT9Pv\nk8D1wM3AGZn5D+CXwIciYs8exn8NMKcZ1w3A5zJzGvCHiLiDsgO6KjPv7mH4WcAmUR4d+iPgs82v\n/yOB70TEdEpT/Sd7GP7oZl5+wjOtLjOaeb6Vcr7X7ym/vn8HbBsRnS98uBTYsKn/ZuDMzPx7D9Nq\nlUuaem+hHCY8r+ONzJzd/H1LRNxF2ZAfoJzP8pbml+UB3YzzXuDAiOj2IoGmxe6HlFbT2ynnj95F\n98ut63DXAz+nnO9We1n1l2uB45pl/mFK60t3LTzd+U/g+GY5TeGZw7Q/o3x+G/Qy3OmdPud7euiv\ntnb6vr0t1bTqngj8oNl+/x2Y2U81LaIEk7uA72fm/3aZdm/7mC9SDtPd1Py9osu9p/1kd3rd3rrx\nC+ATEfFTSgvWhcvofx5lW72D0hr3BF22zT74ByUE/IyyvH5DaSn+j4j4Mc+0jHW3T+61dbKX/dQM\n4LJm//x6yvJf3nnvT4uBx5v1/EeUlsSlyzEz76WcJjC9qf0hnntaU29e1czrZZRtCuBzNOso8Jzr\nBppl92nKsruDclTi4a79Nb4NfLbZ97yBcsrbQspneydlnb8R+BPLt/72t0sogfoWyvr2R0or9w3N\nutZx14RfAHObdeZMnmn4mQB8rPl+ezfl++lK4NsRcRul1b7jc3vWfrdpWV2e/VlP463Gx2tK0gCL\nckHXNzJz3EDXojVDRBxJOerz8ZUYxxnArMz8Qr8VpjXCoLzavb9ExMcpv1y7el9m/qGb7loFRLmy\n/9Bu3vpYZt5Ru57VRXOI7MZu3srMPLqb7qLc75LSCtHVLVmu4FULub0vv4jYn2duk9aX/r/Dc49u\nPMYz55lKy8WWT0mSJFUzWM/5lCRJ0irI8ClJkqRqDJ+SJEmqxvApSZKkagyfkiRJqsZbLUnSSoqI\n8ZQbRj9FefToDMpDEW7IzC2bfs4AyMwzImI25WEWm1BuLj2xy7AfzMyFEfE+ypOp2pv+P0R5aMaX\ngG2ayV+cmVMj4vmUG11vTnkCyscy88ctnXFJWgG2fEpS/9iJ8kS1l1Ge/f1vvfS7EXB2Zm5PCZ1d\nhz0uIrYFTgX2yMxtgceB0ymP1dwgM19FeZLJrs04zwe+lJk7Up4Adknz+FxJWqUYPiWpf9yaRTvw\nVbp/QEVndy1j2D2AazPz0aafS4G9KI/zjIi4gfIc8Y7nhO9NeYRjx+NghwEv7of5kqR+ZfiUpP7R\n+VngQ4CRQFunbsM695yZT/Qy7GKeu39uA4Y2YfQVlOeDB3B3RDwPWAt4fWZu37SojqM8f12SVimG\nT0nqH7tFxKYRMQR4L/A9YExEjI2IdYB9l2PY64GbgQMiouOxhkcCN0XEAcCVwA+AE4B/Us7z/Cnw\n7wAR8XLgHmBEP8+jJK00w6ck9Y+/Up7x/hvgYeAi4Bzg58CPKRcS9XXYyzLzHuAs4JaIuB94HuXC\npOuBJ4BfN+P8TmbeCxwPjIuIe4BvAu/JzPn9PZOStLJ8trskraTmavczMnN8zWElaXVky6ckSZKq\nseVTkiRJ1djyKUmSpGoMn5IkSarG8ClJkqRqDJ+SJEmqxvApSZKkagyfkiRJqub/AxmvPpmuCznB\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x27263928208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11,7))\n",
    "sns.countplot(x='purpose', data=loans, hue='not.fully.paid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Let's see the trend between FICO score and interest rate.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x27265189240>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ntc387bKnYtSH5z95KY+f8mBMIdGLnr2SUeOKbNcBs1Ilnvyfp+FwOtizZlfX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\nEEI4ggQ0IYQQjiABTQghhCP8f95+jUqsw+/bAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x272640509e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='fico', y='int.rate', data=loans, color='purple')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** lmplots to see if the trend differed between not.fully.paid and credit.policy.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x27265195e80>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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18SCpilWAeDRA19UU53qS+FQFy7axbVAUUBWFM10jNCRCpCpWFeKRALsO92CF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1yRC+cuMBzUWRCg4OeV4xGE+yv/rrkVJdW1hiYWedGeojUVo29LmtZUjBftKU5cmCGltSvHkNLa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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x272656905f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='fico', y='int.rate', data=loans, col='not.fully.paid', hue='credit.policy')"
   ]
  },
  {
   "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": 11,
   "metadata": {
    "collapsed": 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": [
    "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": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['debt_consolidation', 'credit_card', 'all_other',\n",
       "       'home_improvement', 'small_business', 'major_purchase',\n",
       "       'educational'], dtype=object)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_feats = ['purpose']\n",
    "loans['purpose'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now lets 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. Setting this dataframe as final_data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "final_data = pd.get_dummies(loans,columns=cat_feats,drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "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",
    "**sklearn to split your data into a training set and a testing.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X = final_data.drop('not.fully.paid',axis=1)\n",
    "y = final_data['not.fully.paid']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    " 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": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**instance of DecisionTreeClassifier() called dtree and fit it to the training data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dtree = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_split=1e-07, 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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtree.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predictions and Evaluation of Decision Tree\n",
    "**Predictions from the test set and create a classification report and a confusion matrix.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions = dtree.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1978  453]\n",
      " [ 337  106]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.81      0.85      0.83      2315\n",
      "          1       0.24      0.19      0.21       559\n",
      "\n",
      "avg / total       0.70      0.73      0.71      2874\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(predictions,y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the Random Forest model\n",
    "\n",
    "Now its time to train our model!\n",
    "\n",
    "**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
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rfc = RandomForestClassifier(n_estimators=120)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": 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_impurity_split=1e-07, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            n_estimators=120, n_jobs=1, oob_score=False, random_state=None,\n",
       "            verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predictions and Evaluation\n",
    "\n",
    "Let's predict off the y_test values and evaluate our model.\n",
    "\n",
    "** Predict the class of not.fully.paid for the X_test data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "predictions = rfc.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Classification report from the results.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix,accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": 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       0.47      0.02      0.04       443\n",
      "\n",
      "avg / total       0.79      0.85      0.78      2874\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Confusion Matrix for the predictions.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2421   10]\n",
      " [ 434    9]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8455114822546973"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(predictions,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**What performed better the random forest or the decision tree?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Random forest performed better but still feature engineering needed to acquire more accuracy"
   ]
  }
 ],
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