13 Loan Risk Analysis_ class imbalance.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loan Risk Analysis\n",
    "PeerLoanKart is an NBFC (Non-banking Financial Company) that facilitates peer- to-peer loan. It connects people who need money (borrowers) with people who have money (investors). As an investor, you would want to invest in people who have a high probability of paying you back. You as an ML expert create a model that will help predict whether a borrower will pay the loan or not.\n",
    "\n",
    "**Objective: Increase profits up to 20% as NPA will be reduced due to loan disbursal for only creditworthy borrowers.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(9578, 14)\n"
     ]
    },
    {
     "data": {
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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",
       "  </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",
       "\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",
       "\n",
       "   delinq.2yrs  pub.rec  not.fully.paid  \n",
       "0            0        0               0  \n",
       "1            0        0               0  \n",
       "2            0        0               0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans=pd.read_csv('loan_borowwer_data.csv')\n",
    "print(loans.shape)\n",
    "loans.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
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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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans.describe()"
   ]
  },
  {
   "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",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(loans.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "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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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize =(10,6))\n",
    "loans[loans['credit.policy']==1]['fico'].hist(alpha=0.5,color='blue',bins=30,label=' Credit.Policy =1')\n",
    "loans[loans['credit.policy']==0]['fico'].hist(alpha=0.5,color='red',bins=30,label=' Credit.Policy =0')\n",
    "plt.legend()\n",
    "plt.xlabel('FICO')"
   ]
  },
  {
   "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": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize =(10,6))\n",
    "loans[loans['not.fully.paid']==1]['fico'].hist(alpha=0.5,color='blue',bins=30,label='not.fully.paid =1')\n",
    "loans[loans['not.fully.paid']==0]['fico'].hist(alpha=0.5,color='red',bins=30,label='not.fully.paid =0')\n",
    "plt.legend()\n",
    "plt.xlabel('FICO')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='purpose', ylabel='count'>"
      ]
     },
     "execution_count": 10,
     "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",
    "sns.countplot( x='purpose',hue= 'not.fully.paid',data =loans,palette ='Set1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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",
       "  </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",
       "\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",
       "\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",
       "\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",
       "\n",
       "   purpose_small_business  \n",
       "0                       0  \n",
       "1                       0  \n",
       "2                       0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data=pd.get_dummies(loans,columns=['purpose'],drop_first=True)\n",
    "final_data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9578 entries, 0 to 9577\n",
      "Data columns (total 19 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   credit.policy               9578 non-null   int64  \n",
      " 1   int.rate                    9578 non-null   float64\n",
      " 2   installment                 9578 non-null   float64\n",
      " 3   log.annual.inc              9578 non-null   float64\n",
      " 4   dti                         9578 non-null   float64\n",
      " 5   fico                        9578 non-null   int64  \n",
      " 6   days.with.cr.line           9578 non-null   float64\n",
      " 7   revol.bal                   9578 non-null   int64  \n",
      " 8   revol.util                  9578 non-null   float64\n",
      " 9   inq.last.6mths              9578 non-null   int64  \n",
      " 10  delinq.2yrs                 9578 non-null   int64  \n",
      " 11  pub.rec                     9578 non-null   int64  \n",
      " 12  not.fully.paid              9578 non-null   int64  \n",
      " 13  purpose_credit_card         9578 non-null   uint8  \n",
      " 14  purpose_debt_consolidation  9578 non-null   uint8  \n",
      " 15  purpose_educational         9578 non-null   uint8  \n",
      " 16  purpose_home_improvement    9578 non-null   uint8  \n",
      " 17  purpose_major_purchase      9578 non-null   uint8  \n",
      " 18  purpose_small_business      9578 non-null   uint8  \n",
      "dtypes: float64(6), int64(7), uint8(6)\n",
      "memory usage: 1.0 MB\n"
     ]
    }
   ],
   "source": [
    "final_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=final_data.drop('not.fully.paid',axis=1)\n",
    "y=final_data['not.fully.paid']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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",
       "      <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>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>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>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>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>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>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>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9573</th>\n",
       "      <td>0</td>\n",
       "      <td>0.1461</td>\n",
       "      <td>344.76</td>\n",
       "      <td>12.180755</td>\n",
       "      <td>10.39</td>\n",
       "      <td>672</td>\n",
       "      <td>10474.000000</td>\n",
       "      <td>215372</td>\n",
       "      <td>82.1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>9574</th>\n",
       "      <td>0</td>\n",
       "      <td>0.1253</td>\n",
       "      <td>257.70</td>\n",
       "      <td>11.141862</td>\n",
       "      <td>0.21</td>\n",
       "      <td>722</td>\n",
       "      <td>4380.000000</td>\n",
       "      <td>184</td>\n",
       "      <td>1.1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>9575</th>\n",
       "      <td>0</td>\n",
       "      <td>0.1071</td>\n",
       "      <td>97.81</td>\n",
       "      <td>10.596635</td>\n",
       "      <td>13.09</td>\n",
       "      <td>687</td>\n",
       "      <td>3450.041667</td>\n",
       "      <td>10036</td>\n",
       "      <td>82.9</td>\n",
       "      <td>8</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>9576</th>\n",
       "      <td>0</td>\n",
       "      <td>0.1600</td>\n",
       "      <td>351.58</td>\n",
       "      <td>10.819778</td>\n",
       "      <td>19.18</td>\n",
       "      <td>692</td>\n",
       "      <td>1800.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9577</th>\n",
       "      <td>0</td>\n",
       "      <td>0.1392</td>\n",
       "      <td>853.43</td>\n",
       "      <td>11.264464</td>\n",
       "      <td>16.28</td>\n",
       "      <td>732</td>\n",
       "      <td>4740.000000</td>\n",
       "      <td>37879</td>\n",
       "      <td>57.0</td>\n",
       "      <td>6</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>9578 rows × 18 columns</p>\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",
       "9573              0    0.1461       344.76       12.180755  10.39   672   \n",
       "9574              0    0.1253       257.70       11.141862   0.21   722   \n",
       "9575              0    0.1071        97.81       10.596635  13.09   687   \n",
       "9576              0    0.1600       351.58       10.819778  19.18   692   \n",
       "9577              0    0.1392       853.43       11.264464  16.28   732   \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",
       "9573       10474.000000     215372        82.1               2            0   \n",
       "9574        4380.000000        184         1.1               5            0   \n",
       "9575        3450.041667      10036        82.9               8            0   \n",
       "9576        1800.000000          0         3.2               5            0   \n",
       "9577        4740.000000      37879        57.0               6            0   \n",
       "\n",
       "      pub.rec  purpose_credit_card  purpose_debt_consolidation  \\\n",
       "0           0                    0                           1   \n",
       "1           0                    1                           0   \n",
       "2           0                    0                           1   \n",
       "3           0                    0                           1   \n",
       "4           0                    1                           0   \n",
       "...       ...                  ...                         ...   \n",
       "9573        0                    0                           0   \n",
       "9574        0                    0                           0   \n",
       "9575        0                    0                           1   \n",
       "9576        0                    0                           0   \n",
       "9577        0                    0                           1   \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",
       "9573                    0                         0                       0   \n",
       "9574                    0                         0                       0   \n",
       "9575                    0                         0                       0   \n",
       "9576                    0                         1                       0   \n",
       "9577                    0                         0                       0   \n",
       "\n",
       "      purpose_small_business  \n",
       "0                          0  \n",
       "1                          0  \n",
       "2                          0  \n",
       "3                          0  \n",
       "4                          0  \n",
       "...                      ...  \n",
       "9573                       0  \n",
       "9574                       0  \n",
       "9575                       0  \n",
       "9576                       0  \n",
       "9577                       0  \n",
       "\n",
       "[9578 rows x 18 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### checking for Imbalanced Classification:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.839946\n",
       "1    0.160054\n",
       "Name: not.fully.paid, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.value_counts()/loans.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 18,
     "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": [
    "y.value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Build a classification model Without Sampling:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "Xtrain,Xtest,ytrain,ytest=train_test_split(X,y,test_size=.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.743910925539318\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[2021,  405],\n",
       "       [ 331,  117]], dtype=int64)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dtm=DecisionTreeClassifier()\n",
    "dtm.fit(Xtrain,ytrain)\n",
    "predicted=dtm.predict(Xtest)\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "print(accuracy_score(ytest,predicted))\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix(ytest,predicted)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.83      0.85      2426\n",
      "           1       0.22      0.26      0.24       448\n",
      "\n",
      "    accuracy                           0.74      2874\n",
      "   macro avg       0.54      0.55      0.54      2874\n",
      "weighted avg       0.76      0.74      0.75      2874\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(ytest,predicted))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### RandomForest:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8455114822546973\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[2418,   13],\n",
       "       [ 431,   12]], dtype=int64)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rfm=RandomForestClassifier()\n",
    "rfm.fit(Xtrain,ytrain)\n",
    "predicted_rfm=rfm.predict(Xtest)\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "print(accuracy_score(ytest,predicted_rfm))\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix(ytest,predicted_rfm)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "****\n",
    "### Applying Oversampling and Undersampling (Data Augmentation ):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({0: 8045, 1: 1533})"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "Counter(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Random Oversampling Minority class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import RandomOverSampler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# over = RandomOverSampler(sampling_strategy='minority')\n",
    "over = RandomOverSampler(sampling_strategy=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_over, y_over = over.fit_resample(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Oversampled: Counter({0: 8045, 1: 6436})\n"
     ]
    }
   ],
   "source": [
    "Counter(y_over)\n",
    "print(f\"Oversampled: {Counter(y_over)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 36,
     "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": [
    "y_over.value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6436.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "8045*.8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Random Underampling Majority class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Combined Random Sampling: Counter({0: 7151, 1: 6436})\n"
     ]
    }
   ],
   "source": [
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "# under = RandomUnderSampler(sampling_strategy='majority')\n",
    "under = RandomUnderSampler(sampling_strategy=0.9)\n",
    "\n",
    "X2, y2 = under.fit_resample(X_over, y_over)\n",
    "print(f\"Combined Random Sampling: {Counter(y2)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 41,
     "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": [
    "y2.value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9000139840581737"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "6436/7151   #=90%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### With Sampling:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "Xtrain2,Xtest2,ytrain2,ytest2=train_test_split(X2,y2,test_size=.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8690213392200147\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[1654,  479],\n",
       "       [  55, 1889]], dtype=int64)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dtm=DecisionTreeClassifier()\n",
    "dtm.fit(Xtrain2,ytrain2)\n",
    "predicted2=dtm.predict(Xtest2)\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "print(accuracy_score(ytest2,predicted2))\n",
    "\n",
    "from sklearn.metrics import classification_report\n",
    "classification_report(ytest2,predicted2)\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix(ytest2,predicted2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.78      0.86      2133\n",
      "           1       0.80      0.97      0.88      1944\n",
      "\n",
      "    accuracy                           0.87      4077\n",
      "   macro avg       0.88      0.87      0.87      4077\n",
      "weighted avg       0.89      0.87      0.87      4077\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(ytest2,predicted2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Random Forest:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9440765268579838\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[1973,  160],\n",
       "       [  68, 1876]], dtype=int64)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rfm=RandomForestClassifier()\n",
    "rfm.fit(Xtrain2,ytrain2)\n",
    "predicted_rfm=rfm.predict(Xtest2)\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "print(accuracy_score(ytest2,predicted_rfm))\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix(ytest2,predicted_rfm)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.92      0.95      2133\n",
      "           1       0.92      0.97      0.94      1944\n",
      "\n",
      "    accuracy                           0.94      4077\n",
      "   macro avg       0.94      0.95      0.94      4077\n",
      "weighted avg       0.95      0.94      0.94      4077\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(ytest2,predicted_rfm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
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}