DT_RF_LoanSample.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('loan_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>credit.policy</th>\n",
       "      <th>purpose</th>\n",
       "      <th>int.rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>log.annual.inc</th>\n",
       "      <th>dti</th>\n",
       "      <th>fico</th>\n",
       "      <th>days.with.cr.line</th>\n",
       "      <th>revol.bal</th>\n",
       "      <th>revol.util</th>\n",
       "      <th>inq.last.6mths</th>\n",
       "      <th>delinq.2yrs</th>\n",
       "      <th>pub.rec</th>\n",
       "      <th>not.fully.paid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>0.1189</td>\n",
       "      <td>829.10</td>\n",
       "      <td>11.350407</td>\n",
       "      <td>19.48</td>\n",
       "      <td>737</td>\n",
       "      <td>5639.958333</td>\n",
       "      <td>28854</td>\n",
       "      <td>52.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>0.1071</td>\n",
       "      <td>228.22</td>\n",
       "      <td>11.082143</td>\n",
       "      <td>14.29</td>\n",
       "      <td>707</td>\n",
       "      <td>2760.000000</td>\n",
       "      <td>33623</td>\n",
       "      <td>76.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>0.1357</td>\n",
       "      <td>366.86</td>\n",
       "      <td>10.373491</td>\n",
       "      <td>11.63</td>\n",
       "      <td>682</td>\n",
       "      <td>4710.000000</td>\n",
       "      <td>3511</td>\n",
       "      <td>25.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>0.1008</td>\n",
       "      <td>162.34</td>\n",
       "      <td>11.350407</td>\n",
       "      <td>8.10</td>\n",
       "      <td>712</td>\n",
       "      <td>2699.958333</td>\n",
       "      <td>33667</td>\n",
       "      <td>73.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>0.1426</td>\n",
       "      <td>102.92</td>\n",
       "      <td>11.299732</td>\n",
       "      <td>14.97</td>\n",
       "      <td>667</td>\n",
       "      <td>4066.000000</td>\n",
       "      <td>4740</td>\n",
       "      <td>39.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   credit.policy             purpose  int.rate  installment  log.annual.inc  \\\n",
       "0              1  debt_consolidation    0.1189       829.10       11.350407   \n",
       "1              1         credit_card    0.1071       228.22       11.082143   \n",
       "2              1  debt_consolidation    0.1357       366.86       10.373491   \n",
       "3              1  debt_consolidation    0.1008       162.34       11.350407   \n",
       "4              1         credit_card    0.1426       102.92       11.299732   \n",
       "\n",
       "     dti  fico  days.with.cr.line  revol.bal  revol.util  inq.last.6mths  \\\n",
       "0  19.48   737        5639.958333      28854        52.1               0   \n",
       "1  14.29   707        2760.000000      33623        76.7               0   \n",
       "2  11.63   682        4710.000000       3511        25.6               1   \n",
       "3   8.10   712        2699.958333      33667        73.2               1   \n",
       "4  14.97   667        4066.000000       4740        39.5               0   \n",
       "\n",
       "   delinq.2yrs  pub.rec  not.fully.paid  \n",
       "0            0        0               0  \n",
       "1            0        0               0  \n",
       "2            0        0               0  \n",
       "3            0        0               0  \n",
       "4            1        0               0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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",
       "      <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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x297f0aaac18>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 8))\n",
    "\n",
    "data[data['credit.policy'] == 1]['fico'].hist(bins = 35, label = 'CP = 1', \n",
    "                                              alpha = 0.6, color = 'blue')\n",
    "data[data['credit.policy'] == 0]['fico'].hist(bins = 35, label = 'CP = 0', \n",
    "                                              alpha = 0.6, color = 'red')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x297f1600b00>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 8))\n",
    "\n",
    "data[data['not.fully.paid'] == 1]['fico'].hist(bins = 35, color = 'blue', \n",
    "                                               label = 'Not Fully Paid = 1', alpha = 0.6)\n",
    "\n",
    "data[data['not.fully.paid'] == 0]['fico'].hist(bins = 35, color = 'orange', \n",
    "                                               label = 'Not Fully Paid = 0', alpha = 0.6)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x297f25ca828>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (11, 7))\n",
    "\n",
    "sns.countplot(x = 'purpose', hue = 'not.fully.paid', data=data, palette=\"Set1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x297f2449e80>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='fico', y = 'int.rate', data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x297f388feb8>"
      ]
     },
     "execution_count": 36,
     "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 789.25x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11,7))\n",
    "\n",
    "sns.lmplot(y='int.rate', x='fico', data=data, hue='credit.policy', \n",
    "           col='not.fully.paid', palette='Set1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_feats = ['purpose']\n",
    "\n",
    "final_data = pd.get_dummies(data, columns=cat_feats, drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>credit.policy</th>\n",
       "      <th>int.rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>log.annual.inc</th>\n",
       "      <th>dti</th>\n",
       "      <th>fico</th>\n",
       "      <th>days.with.cr.line</th>\n",
       "      <th>revol.bal</th>\n",
       "      <th>revol.util</th>\n",
       "      <th>inq.last.6mths</th>\n",
       "      <th>delinq.2yrs</th>\n",
       "      <th>pub.rec</th>\n",
       "      <th>not.fully.paid</th>\n",
       "      <th>purpose_credit_card</th>\n",
       "      <th>purpose_debt_consolidation</th>\n",
       "      <th>purpose_educational</th>\n",
       "      <th>purpose_home_improvement</th>\n",
       "      <th>purpose_major_purchase</th>\n",
       "      <th>purpose_small_business</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.1189</td>\n",
       "      <td>829.10</td>\n",
       "      <td>11.350407</td>\n",
       "      <td>19.48</td>\n",
       "      <td>737</td>\n",
       "      <td>5639.958333</td>\n",
       "      <td>28854</td>\n",
       "      <td>52.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.1071</td>\n",
       "      <td>228.22</td>\n",
       "      <td>11.082143</td>\n",
       "      <td>14.29</td>\n",
       "      <td>707</td>\n",
       "      <td>2760.000000</td>\n",
       "      <td>33623</td>\n",
       "      <td>76.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.1357</td>\n",
       "      <td>366.86</td>\n",
       "      <td>10.373491</td>\n",
       "      <td>11.63</td>\n",
       "      <td>682</td>\n",
       "      <td>4710.000000</td>\n",
       "      <td>3511</td>\n",
       "      <td>25.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.1008</td>\n",
       "      <td>162.34</td>\n",
       "      <td>11.350407</td>\n",
       "      <td>8.10</td>\n",
       "      <td>712</td>\n",
       "      <td>2699.958333</td>\n",
       "      <td>33667</td>\n",
       "      <td>73.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.1426</td>\n",
       "      <td>102.92</td>\n",
       "      <td>11.299732</td>\n",
       "      <td>14.97</td>\n",
       "      <td>667</td>\n",
       "      <td>4066.000000</td>\n",
       "      <td>4740</td>\n",
       "      <td>39.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   credit.policy  int.rate  installment  log.annual.inc    dti  fico  \\\n",
       "0              1    0.1189       829.10       11.350407  19.48   737   \n",
       "1              1    0.1071       228.22       11.082143  14.29   707   \n",
       "2              1    0.1357       366.86       10.373491  11.63   682   \n",
       "3              1    0.1008       162.34       11.350407   8.10   712   \n",
       "4              1    0.1426       102.92       11.299732  14.97   667   \n",
       "\n",
       "   days.with.cr.line  revol.bal  revol.util  inq.last.6mths  delinq.2yrs  \\\n",
       "0        5639.958333      28854        52.1               0            0   \n",
       "1        2760.000000      33623        76.7               0            0   \n",
       "2        4710.000000       3511        25.6               1            0   \n",
       "3        2699.958333      33667        73.2               1            0   \n",
       "4        4066.000000       4740        39.5               0            1   \n",
       "\n",
       "   pub.rec  not.fully.paid  purpose_credit_card  purpose_debt_consolidation  \\\n",
       "0        0               0                    0                           1   \n",
       "1        0               0                    1                           0   \n",
       "2        0               0                    0                           1   \n",
       "3        0               0                    0                           1   \n",
       "4        0               0                    1                           0   \n",
       "\n",
       "   purpose_educational  purpose_home_improvement  purpose_major_purchase  \\\n",
       "0                    0                         0                       0   \n",
       "1                    0                         0                       0   \n",
       "2                    0                         0                       0   \n",
       "3                    0                         0                       0   \n",
       "4                    0                         0                       0   \n",
       "\n",
       "   purpose_small_business  \n",
       "0                       0  \n",
       "1                       0  \n",
       "2                       0  \n",
       "3                       0  \n",
       "4                       0  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['purpose'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['credit.policy', 'int.rate', 'installment', 'log.annual.inc', 'dti',\n",
       "       'fico', 'days.with.cr.line', 'revol.bal', 'revol.util',\n",
       "       'inq.last.6mths', 'delinq.2yrs', 'pub.rec', 'not.fully.paid',\n",
       "       'purpose_credit_card', 'purpose_debt_consolidation',\n",
       "       'purpose_educational', 'purpose_home_improvement',\n",
       "       'purpose_major_purchase', 'purpose_small_business'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "Name: not.fully.paid, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = final_data.drop('not.fully.paid', axis =1)\n",
    "y = final_data['not.fully.paid']\n",
    "\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dtree = DecisionTreeClassifier()\n",
    "dtree.fit(X_train, y_train)\n",
    "pred_d = dtree.predict(X_test) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1984  413]\n",
      " [ 370  107]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.83      0.84      2397\n",
      "           1       0.21      0.22      0.21       477\n",
      "\n",
      "    accuracy                           0.73      2874\n",
      "   macro avg       0.52      0.53      0.52      2874\n",
      "weighted avg       0.74      0.73      0.73      2874\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "\n",
    "print(confusion_matrix(y_test, pred_d))\n",
    "print(classification_report(y_test, pred_d))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rfc = RandomForestClassifier(n_estimators=300)\n",
    "rfc.fit(X_train, y_train)\n",
    "pred_r = rfc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2391    6]\n",
      " [ 470    7]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      1.00      0.91      2397\n",
      "           1       0.54      0.01      0.03       477\n",
      "\n",
      "    accuracy                           0.83      2874\n",
      "   macro avg       0.69      0.51      0.47      2874\n",
      "weighted avg       0.79      0.83      0.76      2874\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, pred_r))\n",
    "print(classification_report(y_test, pred_r))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accuracy for DT is 73% and for RF is 83%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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