Decision Trees and Random Forest Project - Solutions.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\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": {
    "collapsed": true
   },
   "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": 4,
   "metadata": {},
   "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": {},
   "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, 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": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x119963f28>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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+/JnAT4FrQgi9wEPATTHGJIRwGTDCc5PqJ9uuTJLUNWbzYReAQmFfslmX99Xe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\nklJm4JIkSUqZgUuSJCll/z8k9pgdvHIZDwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119996940>"
      ]
     },
     "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": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11c47a7f0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nyqTpMXBJkua07kym5mk5AJyYQ3OBgUuS5rBKM92n03l27eqvqr0z3Utzg4FL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\nJClhBi5JkqSEGbgkSZISZuCSJElK2P8PnV2+VcEOYNIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c61eeb8>"
      ]
     },
     "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": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x119996828>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RBlBJkiQVZQCVJElSUQZQSZIkFWUAlSRJUlEGUEmSJBVlAJUkSVJRBlBJkiQVZQCVJElS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\nqP8fAV85Cib/YDIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119963b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11,7))\n",
    "sns.countplot(x='purpose',hue='not.fully.paid',data=loans,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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x119963320>"
      ]
     },
     "execution_count": 9,
     "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 0x119963518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='fico',y='int.rate',data=loans,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": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x11d34b668>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d3094e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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F4+ZlbDrUwfMTZtMyaTqTfvx9Jpb6Ud/ZiaPhOHM8MRZePxOl67x6xTxWFHn7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tBGVDcpy/MQ1U3WBdr4xJwaensza0y2uvYUb9efJqwZtPaGWV17ikWxXaz38gHlpCICnFNpUrb2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\naFtKyyAYhcOirSx95n6OzLue1vIa6vo7WLpvA+0f/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fhcvB4nFZqJ1+NkysXn0tDThDMW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\nJgGBEEIIIYQQJUwCAiGEEEIIIUrY/wdD70EJLdXmfwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d158080>"
      ]
     },
     "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": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9578 entries, 0 to 9577\n",
      "Data columns (total 14 columns):\n",
      "credit.policy        9578 non-null int64\n",
      "purpose              9578 non-null object\n",
      "int.rate             9578 non-null float64\n",
      "installment          9578 non-null float64\n",
      "log.annual.inc       9578 non-null float64\n",
      "dti                  9578 non-null float64\n",
      "fico                 9578 non-null int64\n",
      "days.with.cr.line    9578 non-null float64\n",
      "revol.bal            9578 non-null int64\n",
      "revol.util           9578 non-null float64\n",
      "inq.last.6mths       9578 non-null int64\n",
      "delinq.2yrs          9578 non-null int64\n",
      "pub.rec              9578 non-null int64\n",
      "not.fully.paid       9578 non-null int64\n",
      "dtypes: float64(6), int64(7), object(1)\n",
      "memory usage: 1.0+ MB\n"
     ]
    }
   ],
   "source": [
    "loans.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categorical Features\n",
    "\n",
    "Notice that the **purpose** column as categorical\n",
    "\n",
    "That means we need to transform them using dummy variables so sklearn will be able to understand them. Let's do this in one clean step using pd.get_dummies.\n",
    "\n",
    "Let's show you a way of dealing with these columns that can be expanded to multiple categorical features if necessary.\n",
    "\n",
    "**Create a list of 1 element containing the string 'purpose'. Call this list cat_feats.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "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": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_data = pd.get_dummies(loans,columns=cat_feats,drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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 float64\n",
      "purpose_debt_consolidation    9578 non-null float64\n",
      "purpose_educational           9578 non-null float64\n",
      "purpose_home_improvement      9578 non-null float64\n",
      "purpose_major_purchase        9578 non-null float64\n",
      "purpose_small_business        9578 non-null float64\n",
      "dtypes: float64(12), int64(7)\n",
      "memory usage: 1.4 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": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "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": 22,
   "metadata": {
    "collapsed": 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": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dtree = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "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": 24,
     "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": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions = dtree.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.85      0.82      0.84      2431\n",
      "          1       0.19      0.23      0.20       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": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1995  436]\n",
      " [ 343  100]]\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": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rfc = RandomForestClassifier(n_estimators=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=600, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 31,
     "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": 32,
   "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": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.85      1.00      0.92      2431\n",
      "          1       0.57      0.03      0.05       443\n",
      "\n",
      "avg / total       0.81      0.85      0.78      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": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2422    9]\n",
      " [ 431   12]]\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": [
    "# Depends what metric you are trying to optimize for. \n",
    "# Notice the recall for each class for the models.\n",
    "# Neither did very well, more feature engineering is needed."
   ]
  }
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