ML Micro Projects/Machine Learning with Decision Trees and Random Forests.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning with Decision Trees and Random Forests\n",
    "\n",
    "In this project we will be exploring publicly available data from LendingClub.com. Lending Club is a service that connects borrowers with people who have money (investors). We will try to create a model that will help predict if a person has a high probability of paying the loan back."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Data\n",
    "\n",
    "We will use lending data from 2007-2010 and try to classify and predict whether or not the borrower paid back their loan in full. \n",
    "\n",
    "The data set contains the following features:\n",
    "\n",
    "* credit.policy: 1 if the customer meets the credit underwriting criteria of LendingClub.com, and 0 otherwise.\n",
    "* purpose: The purpose of the loan (takes values \"credit_card\", \"debt_consolidation\", \"educational\", \"major_purchase\", \"small_business\", and \"all_other\").\n",
    "* int.rate: The interest rate of the loan, as a proportion (a rate of 11% would be stored as 0.11). Borrowers judged by LendingClub.com to be more risky are assigned higher interest rates.\n",
    "* installment: The monthly installments owed by the borrower if the loan is funded.\n",
    "* log.annual.inc: The natural log of the self-reported annual income of the borrower.\n",
    "* dti: The debt-to-income ratio of the borrower (amount of debt divided by annual income).\n",
    "* fico: The FICO credit score of the borrower.\n",
    "* days.with.cr.line: The number of days the borrower has had a credit line.\n",
    "* revol.bal: The borrower's revolving balance (amount unpaid at the end of the credit card billing cycle).\n",
    "* revol.util: The borrower's revolving line utilization rate (the amount of the credit line used relative to total credit available).\n",
    "* inq.last.6mths: The borrower's number of inquiries by creditors in the last 6 months.\n",
    "* delinq.2yrs: The number of times the borrower had been 30+ days past due on a payment in the past 2 years.\n",
    "* pub.rec: The borrower's number of derogatory public records (bankruptcy filings, tax liens, or judgments)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reading the data\n",
    "loans = pd.read_csv('../data/loan_data.csv')"
   ]
  },
  {
   "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",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   credit.policy      9578 non-null   int64  \n",
      " 1   purpose            9578 non-null   object \n",
      " 2   int.rate           9578 non-null   float64\n",
      " 3   installment        9578 non-null   float64\n",
      " 4   log.annual.inc     9578 non-null   float64\n",
      " 5   dti                9578 non-null   float64\n",
      " 6   fico               9578 non-null   int64  \n",
      " 7   days.with.cr.line  9578 non-null   float64\n",
      " 8   revol.bal          9578 non-null   int64  \n",
      " 9   revol.util         9578 non-null   float64\n",
      " 10  inq.last.6mths     9578 non-null   int64  \n",
      " 11  delinq.2yrs        9578 non-null   int64  \n",
      " 12  pub.rec            9578 non-null   int64  \n",
      " 13  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": 6,
   "metadata": {},
   "outputs": [
    {
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       "  <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",
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       "      <th>revol.bal</th>\n",
       "      <th>revol.util</th>\n",
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       "  </thead>\n",
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       "      <th>count</th>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
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       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
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       "    <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",
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       "    <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",
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       "    <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",
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      "text/plain": [
       "       credit.policy     int.rate  installment  log.annual.inc          dti  \\\n",
       "count    9578.000000  9578.000000  9578.000000     9578.000000  9578.000000   \n",
       "mean        0.804970     0.122640   319.089413       10.932117    12.606679   \n",
       "std         0.396245     0.026847   207.071301        0.614813     6.883970   \n",
       "min         0.000000     0.060000    15.670000        7.547502     0.000000   \n",
       "25%         1.000000     0.103900   163.770000       10.558414     7.212500   \n",
       "50%         1.000000     0.122100   268.950000       10.928884    12.665000   \n",
       "75%         1.000000     0.140700   432.762500       11.291293    17.950000   \n",
       "max         1.000000     0.216400   940.140000       14.528354    29.960000   \n",
       "\n",
       "              fico  days.with.cr.line     revol.bal   revol.util  \\\n",
       "count  9578.000000        9578.000000  9.578000e+03  9578.000000   \n",
       "mean    710.846314        4560.767197  1.691396e+04    46.799236   \n",
       "std      37.970537        2496.930377  3.375619e+04    29.014417   \n",
       "min     612.000000         178.958333  0.000000e+00     0.000000   \n",
       "25%     682.000000        2820.000000  3.187000e+03    22.600000   \n",
       "50%     707.000000        4139.958333  8.596000e+03    46.300000   \n",
       "75%     737.000000        5730.000000  1.824950e+04    70.900000   \n",
       "max     827.000000       17639.958330  1.207359e+06   119.000000   \n",
       "\n",
       "       inq.last.6mths  delinq.2yrs      pub.rec  not.fully.paid  \n",
       "count     9578.000000  9578.000000  9578.000000     9578.000000  \n",
       "mean         1.577469     0.163708     0.062122        0.160054  \n",
       "std          2.200245     0.546215     0.262126        0.366676  \n",
       "min          0.000000     0.000000     0.000000        0.000000  \n",
       "25%          0.000000     0.000000     0.000000        0.000000  \n",
       "50%          1.000000     0.000000     0.000000        0.000000  \n",
       "75%          2.000000     0.000000     0.000000        0.000000  \n",
       "max         33.000000    13.000000     5.000000        1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "  </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",
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       "    <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",
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       "    <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",
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       "    <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",
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      "text/plain": [
       "   credit.policy             purpose  int.rate  installment  log.annual.inc  \\\n",
       "0              1  debt_consolidation    0.1189       829.10       11.350407   \n",
       "1              1         credit_card    0.1071       228.22       11.082143   \n",
       "2              1  debt_consolidation    0.1357       366.86       10.373491   \n",
       "3              1  debt_consolidation    0.1008       162.34       11.350407   \n",
       "4              1         credit_card    0.1426       102.92       11.299732   \n",
       "\n",
       "     dti  fico  days.with.cr.line  revol.bal  revol.util  inq.last.6mths  \\\n",
       "0  19.48   737        5639.958333      28854        52.1               0   \n",
       "1  14.29   707        2760.000000      33623        76.7               0   \n",
       "2  11.63   682        4710.000000       3511        25.6               1   \n",
       "3   8.10   712        2699.958333      33667        73.2               1   \n",
       "4  14.97   667        4066.000000       4740        39.5               0   \n",
       "\n",
       "   delinq.2yrs  pub.rec  not.fully.paid  \n",
       "0            0        0               0  \n",
       "1            0        0               0  \n",
       "2            0        0               0  \n",
       "3            0        0               0  \n",
       "4            1        0               0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis\n",
    "\n",
    "Using quick visualisations, we can explore the relationship between different variables in the dataset. \n",
    "\n",
    "Let's start with a dual histogram of the FICO score of the borrowers, depending on the credit policy (i.e. if a borrower met the underlying criteria)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'FICO')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEGCAYAAACevtWaAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAfGklEQVR4nO3df3BV1d3v8fcXiA0VH6JoAyW04d4ilQZEiGjHag9Ea7VUwLGow62RysTeRztFb3nE3s7YZ3qraPu0ou1Dm4K90MEGpCCUsU9/oGfUuVUBBaRSFS2WMOGnQI2CBfzeP/YihnDC2UnOyUl2Pq+ZM2fvtdfee52Vk+9ZZ5211zZ3R0REkqVXoQsgIiK5p+AuIpJACu4iIgmk4C4ikkAK7iIiCdSn0AUAOPvss728vLzQxehS3n33XU4//fRCF6NLUt20TnWTWVLrZf369Xvd/ZxM27pEcC8vL2fdunWFLkaXkk6nSaVShS5Gl6S6aZ3qJrOk1ouZvdXaNnXLiIgkkIK7iEgCKbiLiCRQl+hzF5H8OnLkCPX19Rw+fLjQRSmI/v37s2XLlkIXo92Ki4spKyujqKgo9j4K7iI9QH19PWeccQbl5eWYWaGL0+neeecdzjjjjEIXo13cnX379lFfX8/QoUNj76duGZEe4PDhwwwYMKBHBvbuzswYMGBAm791KbiL9BAK7N1Xe/52Cu4iIgmkPneRHuju5S/n9Hj3XTsyVr6dO3cyc+ZM1q5dS0lJCaWlpTz44IOce+65bT7nzTffzMSJE7nuuuuYMWMGd955JyNGjODee+/l29/+dsZ9UqkUDQ0NFBcX069fPx555BGGDx+eMe+2bduYOHEimzdvZt26dSxatIiHHnqozeXM5rHHHuO73/0uW7Zs4YUXXqCysjInx1Vwl04RN5jEDRLS/bg7U6ZMobq6mrq6OgA2btzIrl27moL70aNH6dOn7WFp/vz5TcunCu4AixcvprKyktraWmbNmsWqVauyHr+ysjJnQbeliooKli9fzq233prT46pbRkQ6xVNPPUVRURFf//rXm9LOP/98jh07xqWXXso111zDiBEjOHbsGLNmzeLCCy9k1KhR/PznPweiD4fbb7+d4cOHc/nll7N79+6m46RSKdatW8fs2bM5dOgQo0ePZtq0aacsz2WXXcbWrVtxd2bNmkVFRQUjR45kyZIlJ+VNp9NMnDgRgMbGRqZPn87IkSMZNWoUv/nNb3jkkUeYOXNmU/5f/OIX3HHHHbHq5bzzzmv120NHqOUuIp1i8+bNjB07NuO2F198kc2bNzN06FBqa2vp378/a9eu5f333+eSSy7hC1/4Ai+99BKvvvoqr7zyCrt27WLEiBF87WtfO+E4c+bM4Sc/+QkbNmzIWp7f/va3jBw5kuXLl7NhwwY2btzI3r17ufDCC7nsssta3e973/se/fv35+WXo2+j+/fvp6ioiO9///v84Ac/oKioiF/+8pdNH0qXXnop77zzzknH+eEPf8jll1+etZztpeAuIgU3bty4pjHcf/jDH9i0aRPLli0D4ODBg7z++us8/fTT3HjjjfTu3ZuPf/zjTJgwoV3nmjZtGn379qW8vJyHH36YH/3oR03HLS0t5fOf/zxr165l1KhRGff/05/+1NStBHDmmWcCMGHCBFavXs15553HkSNHGDky6mJ85pln2lXOjlJwF5FO8ZnPfKYpYLfUfDped+fhhx/myiuvPCHPE088kZNyHO9zz7UZM2Zw77338ulPf5rp06c3pReq5a4+dxHpFBMmTOD999+ntra2KW3Tpk0ntWyvvPJK5s2bx5EjRwB47bXXePfdd7nssstYsmQJx44do6GhgaeeeirjeYqKipr2jePSSy9tOu6ePXt4+umnGTduXKv5r7jiCn760582re/fvx+Aiy66iO3bt/Poo49y4403Nm1/5pln2LBhw0mPfAZ2iNlyN7M7gBmAAy8D04FBQB0wAFgPfNXd/2lmHwEWAWOBfcD17r4t90UXkfYqxKgkM2PFihXMnDmT+++/n+LiYsrLy5k8efIJ+WbMmMG2bdsYM2YM7s4555zD448/zpQpU3jyyScZMWIEn/jEJ/jsZz+b8Tw1NTWMGjWKMWPGsHjxYq6++moefPDBVqcfmDJlCn/+8585//zzMTMeeOABBg4cyLZt2zLm/853vsNtt91GRUUFvXv35p577uHaa68FYOrUqWzYsKGpqyaOFStW8I1vfIM9e/bwpS99idGjR/P73/8+9v6tMXc/dQazwcCzwAh3P2RmS4EngKuB5e5eZ2Y/Aza6+zwz+1dglLt/3cxuAKa4+/WnOkdlZaXrZh0nStrNBXI5FDJpdZNLrdXNli1bOO+88zq/QF1EZ80tM3HiRO644w6qqqpyfuxMf0MzW+/uGfuY4nbL9AH6mlkf4KNAAzABON6BthCYHJYnhXXC9irTdc8ikmAHDhzg3HPPpW/fvnkJ7O2RtVvG3XeY2Q+BvwOHgD8QdcMccPejIVs9MDgsDwa2h32PmtlBoq6bvc2Pa2Y1QA1AaWkp6XS6wy8mSRobGxNVJxW9DsXKl07vy5onaXWTS63VTf/+/TP+qNdTHDt2LK+vv3fv3qxfvx4gb+c5fPhwm973WYO7mZ1J1BofChwAHgO+2L7ifcjda4FaiLpl9DX7REnreojbLTMtpW6ZjjhVt0x3nfI2F7rzlL/HFRcXc8EFF8TOH6db5nLgb+6+x92PAMuBS4CS0E0DUAbsCMs7gCEAYXt/oh9WRUSkk8QJ7n8HLjazj4a+8yrgFeAp4LqQpxpYGZZXhXXC9ic926+2IiKSU1mDu7s/T/TD6ItEwyB7EXWn3AXcaWZbifrUF4RdFgADQvqdwOw8lFtERE4h1jh3d78HuKdF8pvASSP93f0w8JWOF01E8ua338zt8b48N1Y2Tfl7srfffpvrr7+ebdu2UV5eztKlS9s0Tr41ukJVRDrF8Sl/U6kUb7zxBuvXr+e+++5j165dTXmOHj16iiO0bv78+YwYMQKIpvw9lcWLF7Nx40aqq6uZNWtWrONXVlbmJbBDNNlZVVUVr7/+OlVVVcyZMycnx1VwF5FOoSl/M1u5ciXV1dHPlNXV1Tz++OOx9stGE4eJSKfQlL8nOj5x2K5duxg0aBAAAwcOPOGbTEcouItIwWnK34iZ5exG5gruItIpNOXviY633EtLS2loaGDQoEE0NDTwsY99LCflUZ+7iHQKTfmbecrfa665hoULo+m4Fi5cyKRJk2KX/VTUchfpiWIOXcwlTfmb2ezZs5k6dSoLFizgk5/8JEuXLo2976lknfK3M2jK35Mlbf4UTfnbOTTlb2aa8ldERNqsW075KyIip1ZSUsJrr71W6GKcQC13kR6iK3TBSvu052+n4C7SAxQXF7Nv3z4F+G7I3dm3bx/FxcVt2k/dMiI9QFlZGfX19ezZs6fQRSmIw4cPtzk4diXFxcWUlZW1aR8Fd5EeoKioqOkK0J4onU636S5GSaBuGRGRBFJwFxFJoKzB3cyGm9mGZo9/mNlMMzvLzP5oZq+H5zNDfjOzh8xsq5ltMrMx+X8ZIiLSXJzb7L3q7qPdfTQwFngPWEF0+7w17j4MWMOHt9O7ChgWHjXAvDyUW0RETqGtP6hWAW+4+1tmNglIhfSFQJrovqqTgEXhptjPmVmJmQ1y94YclVkkljhTHsSZ7kCkO2prcL8B+HVYLm0WsHcCpWF5MLC92T71Ie2E4G5mNUQte0pLS0mn020sSrI1NjYmqk4qeh2KlS+d3pc1T9y6iXPOOOfrTpL2vsmVnlgvsYO7mZ0GXAPc3XKbu7uZtenqCHevBWohmjhME0GdKGmTY8WdOGxaKncTh8U5Z5zzdSdJe9/kSk+sl7aMlrkKeNHdj98DapeZDQIIz8dvaLgDGNJsv7KQJiIinaQtwf1GPuySAVgFVIflamBls/SbwqiZi4GD6m8XEelcsbplzOx04Arg1mbJc4ClZnYL8BYwNaQ/AVwNbCUaWTMdERHpVLGCu7u/CwxokbaPaPRMy7wO3JaT0omISLvoClURkQRScBcRSSAFdxGRBFJwFxFJIAV3EZEEUnAXEUkgBXcRkQRScBcRSSAFdxGRBFJwFxFJIAV3EZEEUnAXEUkgBXcRkQRScBcRSSAFdxGRBFJwFxFJoLh3YioB5gMVgANfA14FlgDlwDZgqrvvNzMD5hLdjek94GZ3fzHXBZeOiXvD6vuuTdYNpEV6irgt97nAf7n7p4HzgS3AbGCNuw8D1oR1iG6kPSw8aoB5OS2xiIhklTW4m1l/4DJgAYC7/9PdDwCTgIUh20JgclieBCzyyHNAiZkNynG5RUTkFCy65ekpMpiNBmqBV4ha7euBbwI73L0k5DFgv7uXmNlqYI67Pxu2rQHucvd1LY5bQ9Syp7S0dGxdXV0OX1b319jYSL9+/fJ2/B0HDsXKN7ikb5c7X9y6iXPOXL2+riLf75vuKqn1Mn78+PXuXplpW5w+9z7AGOAb7v68mc3lwy4YILoptpmd+lOiBXevJfrQoLKy0lOpVFt2T7x0Ok0+6yRun/u0VG763HN5vrh1E+ecuXp9XUW+3zfdVU+slzjBvR6od/fnw/oyouC+y8wGuXtD6HbZHbbvAIY0278spIkkmn6klq4ka5+7u+8EtpvZ8JBURdRFswqoDmnVwMqwvAq4ySIXAwfdvSG3xRYRkVOJNRQS+Aaw2MxOA94EphN9MCw1s1uAt4CpIe8TRMMgtxINhZye0xKLiEhWsYK7u28AMnXaV2XI68BtHSuWiIh0hK5QFRFJIAV3EZEEUnAXEUkgBXcRkQRScBcRSSAFdxGRBFJwFxFJIAV3EZEEUnAXEUkgBXcRkQRScBcRSSAFdxGRBFJwFxFJIAV3EZEEUnAXEUmgWMHdzLaZ2ctmtsHM1oW0s8zsj2b2eng+M6SbmT1kZlvNbJOZjcnnCxARkZO1peU+3t1HN7vT9mxgjbsPA9bw4U2zrwKGhUcNMC9XhRURkXg60i0zCVgYlhcCk5ulL/LIc0BJuIG2iIh0Eovuipclk9nfgP2AAz9391ozO+DuJWG7AfvdvcTMVgNz3P3ZsG0NcJe7r2txzBqilj2lpaVj6+rqcviyur/Gxkb69euXt+PvOHAoVr7BJX273Pni1k2cc+bq9cU9X67P2VK+3zfdVVLrZfz48eub9aacIO4Nsj/n7jvM7GPAH83sr803urubWfZPiRP3qQVqASorKz2VSrVl98RLp9Pks07uXv5yrHzTUiO73Pni1k2cc+bq9cU9X67P2VK+3zfdVU+sl1jdMu6+IzzvBlYA44Bdx7tbwvPukH0HMKTZ7mUhTUREOknW4G5mp5vZGceXgS8Am4FVQHXIVg2sDMurgJvCqJmLgYPu3pDzkouISKvidMuUAiuibnX6AI+6+3+Z2VpgqZndArwFTA35nwCuBrYC7wHTc15qERE5pazB3d3fBM7PkL4PqMqQ7sBtOSmdiLQqUx9/Ra9DJ6Xfd23++vil69IVqiIiCaTgLiKSQAruIiIJpOAuIpJACu4iIgmk4C4ikkAK7iIiCaTgLiKSQAruIiIJpOAuIpJACu4iIgmk4C4ikkAK7iIiCRT3TkwirYp7ByIR6TxquYuIJJCCu4hIAsUO7mbW28xeMrPVYX2omT1vZlvNbImZnRbSPxLWt4bt5Xkqu4iItKItLfdvAluard8P/NjdPwXsB24J6bcA+0P6j0M+ERHpRLGCu5mVAV8C5od1AyYAy0KWhcDksDwprBO2V4X8IiLSSSy65WmWTGbLgPuAM4BvATcDz4XWOWY2BPidu1eY2Wbgi+5eH7a9AVzk7ntbHLMGqAEoLS0dW1dXl7MXlQSNjY3069cvb8ffceBQrHyDS/rm7Fi5Ol/cuolTrjjniyuXddre8/XlCIcoysv5urN8/z8Vyvjx49e7e2WmbVmHQprZRGC3u683s1SuCuXutUAtQGVlpadSOTt0IqTTafJZJ3GHL05LZb+5ci6HQm5+O3ueK88iVt3EKVec1xdXLuu0veer6FXP5g/K8nK+7izf/09dUZxx7pcA15jZ1UAx8C/AXKDEzPq4+1GgDNgR8u8AhgD1ZtYH6A/sy3nJRUSkVVmDu7vfDdwNEFru33L3aWb2GHAdUAdUAyvDLqvC+p/D9ic9Tt+PSEw7DhzShVMiWXRknPtdwJ1mthUYACwI6QuAASH9TmB2x4ooIiJt1abpB9w9DaTD8pvAuAx5DgNfyUHZRESknXSFqohIAim4i4gkkIK7iEgCKbiLiCSQgruISALpZh3So8UdL3/ftbrKU7oXtdxFRBJIwV1EJIEU3EVEEkjBXUQkgfSDqkgMmqhMuhu13EVEEkjBXUQkgdQtI6ek7giR7kktdxGRBFLLXaQL0jcm6aisLXczKzazF8xso5n9xcz+PaQPNbPnzWyrmS0xs9NC+kfC+tawvTzPr0FERFqI03J/H5jg7o1mVgQ8a2a/I7qF3o/dvc7MfgbcAswLz/vd/VNmdgNwP3B9nsovGajVJyJZW+4eaQyrReHhwARgWUhfCEwOy5PCOmF7lZlZrgosIiLZxepzN7PewHrgU8BPgTeAA+5+NGSpBwaH5cHAdgB3P2pmB4luoL23xTFrgBqA0tJS0ul0h15I0jQ2Nra7Tip6HcptYbqYvhyhold9oYvRbun0vqx52vs3zFQ3cc6XdB35f+quYgV3dz8GjDazEmAF8OmOntjda4FagMrKSk+lUh09ZKKk02naWydJ75ap6FXP5g/KCl2MdpuWyj59cHv/hpnqJs75kq4j/0/dVZtGy7j7ATN7CvgsUGJmfULrvQzYEbLtAIYA9WbWB+gPqOkgkgBxPnQ0933XEGe0zDmhxY6Z9QWuALYATwHXhWzVwMqwvCqsE7Y/6e6ewzKLiEgWcVrug4CFod+9F7DU3Veb2StAnZn9H+AlYEHIvwD4lZltBd4GbshDuUVE5BSyBnd33wRckCH9TWBchvTDwFdyUjoREWkXTT8gIpJAmn5ApJMlfTSTdA1quYuIJJCCu4hIAqlbpqv77Tez5/ny3PyXQ0S6FbXcRUQSSMFdRCSBFNxFRBJIwV1EJIEU3EVEEkjBXUQkgRTcRUQSSMFdRCSBdBGTSMLpBhs9k1ruIiIJlLXlbmZDgEVAKeBArbvPNbOzgCVAObANmOru+83MgLnA1cB7wM3u/mJ+ii+dbXL9A7HyPV72b3kuieSSZqpMnjgt96PA/3L3EcDFwG1mNgKYDaxx92HAmrAOcBUwLDxqgHk5L7WIiJxS1uDu7g3HW97u/g7R/VMHA5OAhSHbQmByWJ4ELPLIc0Q30h6U64KLiEjr2vSDqpmVE91y73mg1N0bwqadRN02EAX+7c12qw9pDc3SMLMaopY9paWlpNPpNhY92RobG6M6+aAye+YWdVfR61BeygSwd+CEWPkqetXnrQx9OZLX43dnXaFu0ul9BT1/Jk3/Tz1I7OBuZv2A3wAz3f0fUdd6xN3dzLwtJ3b3WqAWoLKy0lOpVFt2T7x0Ok0qlYo35W/qqyes5rP/dPLOR2PlezaPfe4VverZ/EFZ3o7fnXWFupmW6nojb5r+n3qQWKNlzKyIKLAvdvflIXnX8e6W8Lw7pO8AhjTbvSykiYhIJ8ka3MPolwXAFnf/UbNNq4DqsFwNrGyWfpNFLgYONuu+ERGRThCnW+YS4KvAy2a2IaR9G5gDLDWzW4C3gKlh2xNEwyC3Eg2FnJ7LAouISHZZg7u7PwtYK5urMuR34LYOlktERDpA0w+ISJelqRPaT9MPiIgkkIK7iEgCKbiLiCSQ+tylS8s0UdnegRNOuJhKk5SJnEwtdxGRBFJwFxFJIAV3EZEEUnAXEUkgBXcRkQTSaBkRyam4U07rytL8UstdRCSBFNxFRBJIwV1EJIHU595DZLrSsyVd6al6kuRQcJe8UJCUbPJ5r1+JEdzN7BFgIrDb3StC2lnAEqAc2AZMdff94ZZ8c4nuxPQecLO7v5ifondz2W58/UFlvJtji4hkEKfl/n+BnwCLmqXNBta4+xwzmx3W7wKuAoaFx0XAvPAs+dTiQ2By/dsFKoiIdBVZf1B196eBltFiErAwLC8EJjdLX+SR54ASMxuUo7KKiEhMFt3yNEsms3JgdbNumQPuXhKWDdjv7iVmthqYE+67ipmtAe5y93UZjlkD1ACUlpaOraury80r6goObu/wIRo5nX682659333/aIfP3xkOnDYwa56Sf+48Ke1o0b/Q58g/2nScuDKdr6Vcni/X+nKEQxQVuhidanBJ36x5Ghsb6devXyeUpnONHz9+vbtXZtrW4R9U3d3NLPsnxMn71QK1AJWVlZ5KpTpalK4jB33l6Q8qSfU66TMxlud3do9umWdj/KDafN724/YOnMDZO59s03HiynS+lnJ5vlyr6FXP5g/KCl2MTjUtlf1K13Q6TaJiTAztDe67zGyQuzeEbpfdIX0HMKRZvrKQJnKSOCNqRKR92nsR0yqgOixXAyubpd9kkYuBg+7e0MEyiohIG8UZCvlrIAWcbWb1wD3AHGCpmd0CvAVMDdmfIBoGuZVoKOT0PJRZRESyyBrc3f3GVjZVZcjrwG0dLZSIiHSMrlAVkW4tzpWuFb0O5exY0D2mK1Zwl25PUx2InEyzQoqIJJCCu4hIAqlbRnoEjamXnkbBXaSN1Mcv3YG6ZUREEkgtdxHpEXrazUEU3EXyIFd9/OrekfZSt4yISAIpuIuIJJCCu4hIAim4i4gkkIK7iEgCKbiLiCSQhkKKdGFxh1RqyKS0lJfgbmZfBOYCvYH57j4nH+cpiBzc/Fqkq9LUCsmR8+BuZr2BnwJXAPXAWjNb5e6v5PpcIhI5HpT3DpzA5J2PFrg0XVMuP7hyebVrvm78kY+W+zhgq7u/CWBmdcAkID/BPU5L+stz23zY1v54k+vfblq+aOhZbT6uSHfXFWfY1LeJk1l029McHtDsOuCL7j4jrH8VuMjdb2+RrwaoCavDgVdzWpDu72xgb6EL0UWpblqnusksqfXySXc/J9OGgv2g6u61QG2hzt/Vmdk6d68sdDm6ItVN61Q3mfXEesnHUMgdwJBm62UhTUREOkk+gvtaYJiZDTWz04AbgFV5OI+IiLQi590y7n7UzG4Hfk80FPIRd/9Lrs/TA6jLqnWqm9apbjLrcfWS8x9URUSk8DT9gIhIAim4i4gkkIJ7gZhZiZktM7O/mtkWM/usmX3XzHaY2YbwuLpZ/rvNbKuZvWpmVxay7PlkZsObvf4NZvYPM5tpZmeZ2R/N7PXwfGbIb2b2UKibTWY2ptCvIV9OUTc9/n0DYGZ3mNlfzGyzmf3azIrDwI7nQx0sCYM8MLOPhPWtYXt5gYufe+6uRwEewEJgRlg+DSgBvgt8K0PeEcBG4CPAUOANoHehX0Mn1FFvYCfwSeABYHZInw3cH5avBn4HGHAx8Hyhy12Auunx7xtgMPA3oG9YXwrcHJ5vCGk/A/5nWP5X4Gdh+QZgSaFfQ64farkXgJn1By4DFgC4+z/d/cApdpkE1Ln7++7+N2Ar0TQPSVcFvOHubxHVwcKQvhCYHJYnAYs88hxQYmaDOr2kna953bSmp71v+gB9zawP8FGgAZgALAvbW75vjr+flgFVZmadV9T8U3AvjKHAHuCXZvaSmc03s9PDtttD98Ijx7seiFol25vtXx/Sku4G4NdhudTdG8LyTqA0LKtuIj36fePuO4AfAn8nCuoHgfXAAXc/GrI1f/1NdRO2HwQGdGaZ803BvTD6AGOAee5+AfAuUVfDPOC/A6OJ3qD/UagCFlroG70GeKzlNo++S/fYMbwZ6qbHv2/CB9okoobTx4HTgS8WtFAFpuBeGPVAvbs/H9aXAWPcfZe7H3P3D4Bf8OFX6J44pcNVwIvuvius7zre3RKed4f0Hl83et8AcDnwN3ff4+5HgOXAJUTddMcv1mz++pvqJmzvD+zr3CLnl4J7Abj7TmC7mQ0PSVXAKy36iqcAm8PyKuCG8Av/UGAY8EKnFbgwbuTEbodVQHVYrgZWNku/KYyauRg42Kz7JqlOqBu9b4CoO+ZiM/to6DuvIppm/CngupCn5fvm+PvpOuDJ8I0wMXSFaoGY2WhgPtFImTeB6cBDRF+tHdgG3Ho8UJnZ/wa+BhwFZrr77zq90J0k/P7wd+C/ufvBkDaAaOTDJ4C3gKnu/nb4R/4J0Vfw94Dp7r6uMCXPv1bq5lfofYOZ/TtwPdFrfQmYQdS3XgecFdL+h7u/b2bFwK+AC4C3iUbUvFmQgueJgruISAKpW0ZEJIEU3EVEEkjBXUQkgRTcRUQSSMFdRCSBFNylRzKzYy1mWCw3s5SZrW6W5yozW2dmr4RpIv6j2baaMKPnX83sBTP7XGFeiUhmOb/Nnkg3ccjdRzdPaD7tq5lVEI2f/5K7/9XMegM1YdtE4Fbgc+6+N0wz/LiZjQsXqIkUnFruIpn9G/B9d/8rQLi8f17Ydhcwy933hm0vEs0weFtBSiqSgYK79FR9m3XJrMiwvYJoVsFMPpNh27qQLtIlqFtGeqqTumVEkkQtd5HM/gKMbWXbKxm2jQ37iHQJCu4imf0A+LaZnQtgZr3M7Oth2wPA/WEys+OTwN0M/GcByimSkbplRDJw901mNhP4tZl9lGjGxdVh2yozGwz8PzNz4B2i2QaTPtWwdCOaFVJEJIHULSMikkAK7iIiCaTgLiKSQAruIiIJpOAuIpJACu4iIgmk4C4ikkD/H5sgf5Hf1eLMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "loans[loans['credit.policy']==1]['fico'].hist(bins=30,alpha=0.6,label='Credit.Policy=1')\n",
    "loans[loans['credit.policy']==0]['fico'].hist(bins=30,alpha=0.6,label='Credit.Policy=0')\n",
    "plt.legend()\n",
    "plt.xlabel('FICO')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking out a similar histogram, based on the 'not.fully.paid' column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'FICO')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "loans[loans['not.fully.paid']==1]['fico'].hist(bins=30,alpha=0.6,label='not.fully.paid=1',color='red')\n",
    "loans[loans['not.fully.paid']==0]['fico'].hist(bins=30,alpha=0.6,label='not.fully.paid=0')\n",
    "plt.legend()\n",
    "plt.xlabel('FICO')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize the counts of of loan purposes, based on whether a borrower fully paid the loan back or not. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,5))\n",
    "sns.countplot(x=loans['purpose'],hue=loans['not.fully.paid'])\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see the trend between FICO score and interest rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x14721ad70>"
      ]
     },
     "execution_count": 12,
     "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=loans)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Building"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The _purpose_ column is categorical.\n",
    "\n",
    "That means we need to transform them using dummy variables so sklearn will be able to understand them. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating a list of all categorical features.\n",
    "cat_feats = ['purpose']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_data = pd.get_dummies(loans,columns=cat_feats,drop_first=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = final_data.drop('not.fully.paid',axis=1)\n",
    "y = final_data['not.fully.paid']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training a Decision Tree Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtree= DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtree.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predictions and Evaluation of Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = dtree.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.83      0.84      2411\n",
      "           1       0.23      0.27      0.25       463\n",
      "\n",
      "    accuracy                           0.74      2874\n",
      "   macro avg       0.54      0.55      0.54      2874\n",
      "weighted avg       0.75      0.74      0.74      2874\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1994  417]\n",
      " [ 340  123]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training the Random Forest model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predictions and Evaluation of Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = rfc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.99      0.91      2411\n",
      "           1       0.27      0.02      0.04       463\n",
      "\n",
      "    accuracy                           0.83      2874\n",
      "   macro avg       0.56      0.50      0.47      2874\n",
      "weighted avg       0.75      0.83      0.77      2874\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2387   24]\n",
      " [ 454    9]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the random forest performed somewhat better than the decision tree model."
   ]
  }
 ],
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