project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Decision Trees and Random Forests in Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this karnel we will be exploring publicly available data from LendingClub.com. \n",
    "\n",
    "Lending Club connects people who need money (borrowers) with people who have money (investors). \n",
    "\n",
    "Hopefully, as an investor you would want to invest in people who showed a profile of having a high probability of paying you back. \n",
    "\n",
    "We will try to create a model that will help predict this."
   ]
  },
  {
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df= pd.read_csv('loan_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9578 entries, 0 to 9577\n",
      "Data columns (total 14 columns):\n",
      "credit.policy        9578 non-null int64\n",
      "purpose              9578 non-null object\n",
      "int.rate             9578 non-null float64\n",
      "installment          9578 non-null float64\n",
      "log.annual.inc       9578 non-null float64\n",
      "dti                  9578 non-null float64\n",
      "fico                 9578 non-null int64\n",
      "days.with.cr.line    9578 non-null float64\n",
      "revol.bal            9578 non-null int64\n",
      "revol.util           9578 non-null float64\n",
      "inq.last.6mths       9578 non-null int64\n",
      "delinq.2yrs          9578 non-null int64\n",
      "pub.rec              9578 non-null int64\n",
      "not.fully.paid       9578 non-null int64\n",
      "dtypes: float64(6), int64(7), object(1)\n",
      "memory usage: 1.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DATA ANALYSIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'FICO')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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11ocRsTml1NDVtl22JqW0kbYT2H8E/LTYZhHwaeCTEbGTtnOuHio2eQgYVZR/Erij/Lci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\nRK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "df[df['credit.policy']==1]['fico'].hist(alpha=0.5,color='blue',\n",
    "                                              bins=30,label='Credit.Policy=1')\n",
    "df[df['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": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'FICO')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lTcV04BtFeTMwvN3LhwGv7bvPlNJiYDFAfX19amhoOLgjUE1pbGykU325aFGu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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "df[df['not.fully.paid']==1]['fico'].hist(alpha=0.5,color='blue',\n",
    "                                              bins=30,label='not.fully.paid=1')\n",
    "df[df['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": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e696c51c18>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SvnlLt32BW8r9GcCBEbFORGwJbAVcD9wAbBURW0bEC2lOhprRrbolSZI0/Lp59v3OwPuA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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 792x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11,7))\n",
    "sns.countplot(x='purpose',hue='not.fully.paid',data=df,palette='Set1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Kaustubh\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x1e696d831d0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OczmA/y+lbLWI3AigD8ByVU1Lrywi3QC6ASAUCmV8Mi8Lb3NxLMC9fZQX0Yen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\nREQ1ktQiIqIa/wMVs9Cj9twqLAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='fico',y='int.rate',data=df,color='purple'  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1e69709f860>"
      ]
     },
     "execution_count": 10,
     "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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6NBoAampINzdDPO41DACdyaATCYKf/SyZHTvQ69d3He+66P37URMnkHhqHU3L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y6Mtbd76VVKWS5aGqqur3B1jMNpItn40MMUvfh816lLxAjQ7W2PofXEPfHvLQRNnkz+Mfa7vD5i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\njBkzarRo1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r8u2ZxZzJdDuoOUqKZOUHXz4pBJuHwH0UNOCyL2vmFxNy/72hPli9rFkMvD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O9b305QgUaTqWdXb06YTlv7AXaJz9q9dsna31LBPO1OGF9uOua3dsM2VwDtsbI7QlJElTogkI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\n+VX+BzkP7+jmM7/fyb8/f4CeIW+udtFi+r/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6Otw5DRPdlCltPBOHHcDg/2LnAjvD1gnG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\niIiIiEhO/x+UAD6MfRil7QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 789.5x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11,7))\n",
    "sns.lmplot(y='int.rate',x='fico',data=df,hue='credit.policy',\n",
    "           col='not.fully.paid',palette='Set1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cat_feat=['purpose']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data= pd.get_dummies(df,columns=cat_feat ,drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X=data.drop('not.fully.paid' ,axis=1)\n",
    "y=data['not.fully.paid']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dtree=DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtree.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predit=dtree.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix,classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1994  417]\n",
      " [ 338  125]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,predit))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.86      0.83      0.84      2411\n",
      "          1       0.23      0.27      0.25       463\n",
      "\n",
      "avg / total       0.75      0.74      0.75      2874\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,predit))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training the Random Forest model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rfc = RandomForestClassifier(n_estimators=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=600, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "prediction= rfc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2406    5]\n",
      " [ 454    9]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.84      1.00      0.91      2411\n",
      "          1       0.64      0.02      0.04       463\n",
      "\n",
      "avg / total       0.81      0.84      0.77      2874\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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