loan_repayment.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "loan=pd.read_csv(\"lending_club_loan_two.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 396030 entries, 0 to 396029\n",
      "Data columns (total 27 columns):\n",
      "loan_amnt               396030 non-null float64\n",
      "term                    396030 non-null object\n",
      "int_rate                396030 non-null float64\n",
      "installment             396030 non-null float64\n",
      "grade                   396030 non-null object\n",
      "sub_grade               396030 non-null object\n",
      "emp_title               373103 non-null object\n",
      "emp_length              377729 non-null object\n",
      "home_ownership          396030 non-null object\n",
      "annual_inc              396030 non-null float64\n",
      "verification_status     396030 non-null object\n",
      "issue_d                 396030 non-null object\n",
      "loan_status             396030 non-null object\n",
      "purpose                 396030 non-null object\n",
      "title                   394275 non-null object\n",
      "dti                     396030 non-null float64\n",
      "earliest_cr_line        396030 non-null object\n",
      "open_acc                396030 non-null float64\n",
      "pub_rec                 396030 non-null float64\n",
      "revol_bal               396030 non-null float64\n",
      "revol_util              395754 non-null float64\n",
      "total_acc               396030 non-null float64\n",
      "initial_list_status     396030 non-null object\n",
      "application_type        396030 non-null object\n",
      "mort_acc                358235 non-null float64\n",
      "pub_rec_bankruptcies    395495 non-null float64\n",
      "address                 396030 non-null object\n",
      "dtypes: float64(12), object(15)\n",
      "memory usage: 81.6+ MB\n"
     ]
    }
   ],
   "source": [
    "loan.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "scrolled": true
   },
   "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>loan_amnt</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>emp_title</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>...</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>initial_list_status</th>\n",
       "      <th>application_type</th>\n",
       "      <th>mort_acc</th>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "      <th>address</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>11.44</td>\n",
       "      <td>329.48</td>\n",
       "      <td>B</td>\n",
       "      <td>B4</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>117000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36369.0</td>\n",
       "      <td>41.8</td>\n",
       "      <td>25.0</td>\n",
       "      <td>w</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0174 Michelle Gateway\\nMendozaberg, OK 22690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>11.99</td>\n",
       "      <td>265.68</td>\n",
       "      <td>B</td>\n",
       "      <td>B5</td>\n",
       "      <td>Credit analyst</td>\n",
       "      <td>4 years</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>65000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20131.0</td>\n",
       "      <td>53.3</td>\n",
       "      <td>27.0</td>\n",
       "      <td>f</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1076 Carney Fort Apt. 347\\nLoganmouth, SD 05113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15600.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>10.49</td>\n",
       "      <td>506.97</td>\n",
       "      <td>B</td>\n",
       "      <td>B3</td>\n",
       "      <td>Statistician</td>\n",
       "      <td>&lt; 1 year</td>\n",
       "      <td>RENT</td>\n",
       "      <td>43057.0</td>\n",
       "      <td>...</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11987.0</td>\n",
       "      <td>92.2</td>\n",
       "      <td>26.0</td>\n",
       "      <td>f</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87025 Mark Dale Apt. 269\\nNew Sabrina, WV 05113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7200.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>6.49</td>\n",
       "      <td>220.65</td>\n",
       "      <td>A</td>\n",
       "      <td>A2</td>\n",
       "      <td>Client Advocate</td>\n",
       "      <td>6 years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>54000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5472.0</td>\n",
       "      <td>21.5</td>\n",
       "      <td>13.0</td>\n",
       "      <td>f</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>823 Reid Ford\\nDelacruzside, MA 00813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24375.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>17.27</td>\n",
       "      <td>609.33</td>\n",
       "      <td>C</td>\n",
       "      <td>C5</td>\n",
       "      <td>Destiny Management Inc.</td>\n",
       "      <td>9 years</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>55000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24584.0</td>\n",
       "      <td>69.8</td>\n",
       "      <td>43.0</td>\n",
       "      <td>f</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>679 Luna Roads\\nGreggshire, VA 11650</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   loan_amnt        term  int_rate  installment grade sub_grade  \\\n",
       "0    10000.0   36 months     11.44       329.48     B        B4   \n",
       "1     8000.0   36 months     11.99       265.68     B        B5   \n",
       "2    15600.0   36 months     10.49       506.97     B        B3   \n",
       "3     7200.0   36 months      6.49       220.65     A        A2   \n",
       "4    24375.0   60 months     17.27       609.33     C        C5   \n",
       "\n",
       "                 emp_title emp_length home_ownership  annual_inc  ...  \\\n",
       "0                Marketing  10+ years           RENT    117000.0  ...   \n",
       "1          Credit analyst     4 years       MORTGAGE     65000.0  ...   \n",
       "2             Statistician   < 1 year           RENT     43057.0  ...   \n",
       "3          Client Advocate    6 years           RENT     54000.0  ...   \n",
       "4  Destiny Management Inc.    9 years       MORTGAGE     55000.0  ...   \n",
       "\n",
       "  open_acc pub_rec revol_bal revol_util total_acc  initial_list_status  \\\n",
       "0     16.0     0.0   36369.0       41.8      25.0                    w   \n",
       "1     17.0     0.0   20131.0       53.3      27.0                    f   \n",
       "2     13.0     0.0   11987.0       92.2      26.0                    f   \n",
       "3      6.0     0.0    5472.0       21.5      13.0                    f   \n",
       "4     13.0     0.0   24584.0       69.8      43.0                    f   \n",
       "\n",
       "  application_type  mort_acc  pub_rec_bankruptcies  \\\n",
       "0       INDIVIDUAL       0.0                   0.0   \n",
       "1       INDIVIDUAL       3.0                   0.0   \n",
       "2       INDIVIDUAL       0.0                   0.0   \n",
       "3       INDIVIDUAL       0.0                   0.0   \n",
       "4       INDIVIDUAL       1.0                   0.0   \n",
       "\n",
       "                                           address  \n",
       "0     0174 Michelle Gateway\\nMendozaberg, OK 22690  \n",
       "1  1076 Carney Fort Apt. 347\\nLoganmouth, SD 05113  \n",
       "2  87025 Mark Dale Apt. 269\\nNew Sabrina, WV 05113  \n",
       "3            823 Reid Ford\\nDelacruzside, MA 00813  \n",
       "4             679 Luna Roads\\nGreggshire, VA 11650  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DATA ANALYSIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22c732f44e0>"
      ]
     },
     "execution_count": 192,
     "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": [
    "sns.countplot(x='loan_status',data=loan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22c0e507518>"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,5))\n",
    "sns.distplot(loan['loan_amnt'],kde=False,bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "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>loan_amnt</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>dti</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>mort_acc</th>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>loan_amnt</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.168921</td>\n",
       "      <td>0.953929</td>\n",
       "      <td>0.336887</td>\n",
       "      <td>0.016636</td>\n",
       "      <td>0.198556</td>\n",
       "      <td>-0.077779</td>\n",
       "      <td>0.328320</td>\n",
       "      <td>0.099911</td>\n",
       "      <td>0.223886</td>\n",
       "      <td>0.222315</td>\n",
       "      <td>-0.106539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>int_rate</th>\n",
       "      <td>0.168921</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.162758</td>\n",
       "      <td>-0.056771</td>\n",
       "      <td>0.079038</td>\n",
       "      <td>0.011649</td>\n",
       "      <td>0.060986</td>\n",
       "      <td>-0.011280</td>\n",
       "      <td>0.293659</td>\n",
       "      <td>-0.036404</td>\n",
       "      <td>-0.082583</td>\n",
       "      <td>0.057450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>installment</th>\n",
       "      <td>0.953929</td>\n",
       "      <td>0.162758</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.330381</td>\n",
       "      <td>0.015786</td>\n",
       "      <td>0.188973</td>\n",
       "      <td>-0.067892</td>\n",
       "      <td>0.316455</td>\n",
       "      <td>0.123915</td>\n",
       "      <td>0.202430</td>\n",
       "      <td>0.193694</td>\n",
       "      <td>-0.098628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>annual_inc</th>\n",
       "      <td>0.336887</td>\n",
       "      <td>-0.056771</td>\n",
       "      <td>0.330381</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.081685</td>\n",
       "      <td>0.136150</td>\n",
       "      <td>-0.013720</td>\n",
       "      <td>0.299773</td>\n",
       "      <td>0.027871</td>\n",
       "      <td>0.193023</td>\n",
       "      <td>0.236320</td>\n",
       "      <td>-0.050162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dti</th>\n",
       "      <td>0.016636</td>\n",
       "      <td>0.079038</td>\n",
       "      <td>0.015786</td>\n",
       "      <td>-0.081685</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.136181</td>\n",
       "      <td>-0.017639</td>\n",
       "      <td>0.063571</td>\n",
       "      <td>0.088375</td>\n",
       "      <td>0.102128</td>\n",
       "      <td>-0.025439</td>\n",
       "      <td>-0.014558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>open_acc</th>\n",
       "      <td>0.198556</td>\n",
       "      <td>0.011649</td>\n",
       "      <td>0.188973</td>\n",
       "      <td>0.136150</td>\n",
       "      <td>0.136181</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.018392</td>\n",
       "      <td>0.221192</td>\n",
       "      <td>-0.131420</td>\n",
       "      <td>0.680728</td>\n",
       "      <td>0.109205</td>\n",
       "      <td>-0.027732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pub_rec</th>\n",
       "      <td>-0.077779</td>\n",
       "      <td>0.060986</td>\n",
       "      <td>-0.067892</td>\n",
       "      <td>-0.013720</td>\n",
       "      <td>-0.017639</td>\n",
       "      <td>-0.018392</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.101664</td>\n",
       "      <td>-0.075910</td>\n",
       "      <td>0.019723</td>\n",
       "      <td>0.011552</td>\n",
       "      <td>0.699408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>revol_bal</th>\n",
       "      <td>0.328320</td>\n",
       "      <td>-0.011280</td>\n",
       "      <td>0.316455</td>\n",
       "      <td>0.299773</td>\n",
       "      <td>0.063571</td>\n",
       "      <td>0.221192</td>\n",
       "      <td>-0.101664</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.226346</td>\n",
       "      <td>0.191616</td>\n",
       "      <td>0.194925</td>\n",
       "      <td>-0.124532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>revol_util</th>\n",
       "      <td>0.099911</td>\n",
       "      <td>0.293659</td>\n",
       "      <td>0.123915</td>\n",
       "      <td>0.027871</td>\n",
       "      <td>0.088375</td>\n",
       "      <td>-0.131420</td>\n",
       "      <td>-0.075910</td>\n",
       "      <td>0.226346</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.104273</td>\n",
       "      <td>0.007514</td>\n",
       "      <td>-0.086751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_acc</th>\n",
       "      <td>0.223886</td>\n",
       "      <td>-0.036404</td>\n",
       "      <td>0.202430</td>\n",
       "      <td>0.193023</td>\n",
       "      <td>0.102128</td>\n",
       "      <td>0.680728</td>\n",
       "      <td>0.019723</td>\n",
       "      <td>0.191616</td>\n",
       "      <td>-0.104273</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.381072</td>\n",
       "      <td>0.042035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mort_acc</th>\n",
       "      <td>0.222315</td>\n",
       "      <td>-0.082583</td>\n",
       "      <td>0.193694</td>\n",
       "      <td>0.236320</td>\n",
       "      <td>-0.025439</td>\n",
       "      <td>0.109205</td>\n",
       "      <td>0.011552</td>\n",
       "      <td>0.194925</td>\n",
       "      <td>0.007514</td>\n",
       "      <td>0.381072</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.027239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "      <td>-0.106539</td>\n",
       "      <td>0.057450</td>\n",
       "      <td>-0.098628</td>\n",
       "      <td>-0.050162</td>\n",
       "      <td>-0.014558</td>\n",
       "      <td>-0.027732</td>\n",
       "      <td>0.699408</td>\n",
       "      <td>-0.124532</td>\n",
       "      <td>-0.086751</td>\n",
       "      <td>0.042035</td>\n",
       "      <td>0.027239</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      loan_amnt  int_rate  installment  annual_inc       dti  \\\n",
       "loan_amnt              1.000000  0.168921     0.953929    0.336887  0.016636   \n",
       "int_rate               0.168921  1.000000     0.162758   -0.056771  0.079038   \n",
       "installment            0.953929  0.162758     1.000000    0.330381  0.015786   \n",
       "annual_inc             0.336887 -0.056771     0.330381    1.000000 -0.081685   \n",
       "dti                    0.016636  0.079038     0.015786   -0.081685  1.000000   \n",
       "open_acc               0.198556  0.011649     0.188973    0.136150  0.136181   \n",
       "pub_rec               -0.077779  0.060986    -0.067892   -0.013720 -0.017639   \n",
       "revol_bal              0.328320 -0.011280     0.316455    0.299773  0.063571   \n",
       "revol_util             0.099911  0.293659     0.123915    0.027871  0.088375   \n",
       "total_acc              0.223886 -0.036404     0.202430    0.193023  0.102128   \n",
       "mort_acc               0.222315 -0.082583     0.193694    0.236320 -0.025439   \n",
       "pub_rec_bankruptcies  -0.106539  0.057450    -0.098628   -0.050162 -0.014558   \n",
       "\n",
       "                      open_acc   pub_rec  revol_bal  revol_util  total_acc  \\\n",
       "loan_amnt             0.198556 -0.077779   0.328320    0.099911   0.223886   \n",
       "int_rate              0.011649  0.060986  -0.011280    0.293659  -0.036404   \n",
       "installment           0.188973 -0.067892   0.316455    0.123915   0.202430   \n",
       "annual_inc            0.136150 -0.013720   0.299773    0.027871   0.193023   \n",
       "dti                   0.136181 -0.017639   0.063571    0.088375   0.102128   \n",
       "open_acc              1.000000 -0.018392   0.221192   -0.131420   0.680728   \n",
       "pub_rec              -0.018392  1.000000  -0.101664   -0.075910   0.019723   \n",
       "revol_bal             0.221192 -0.101664   1.000000    0.226346   0.191616   \n",
       "revol_util           -0.131420 -0.075910   0.226346    1.000000  -0.104273   \n",
       "total_acc             0.680728  0.019723   0.191616   -0.104273   1.000000   \n",
       "mort_acc              0.109205  0.011552   0.194925    0.007514   0.381072   \n",
       "pub_rec_bankruptcies -0.027732  0.699408  -0.124532   -0.086751   0.042035   \n",
       "\n",
       "                      mort_acc  pub_rec_bankruptcies  \n",
       "loan_amnt             0.222315             -0.106539  \n",
       "int_rate             -0.082583              0.057450  \n",
       "installment           0.193694             -0.098628  \n",
       "annual_inc            0.236320             -0.050162  \n",
       "dti                  -0.025439             -0.014558  \n",
       "open_acc              0.109205             -0.027732  \n",
       "pub_rec               0.011552              0.699408  \n",
       "revol_bal             0.194925             -0.124532  \n",
       "revol_util            0.007514             -0.086751  \n",
       "total_acc             0.381072              0.042035  \n",
       "mort_acc              1.000000              0.027239  \n",
       "pub_rec_bankruptcies  0.027239              1.000000  "
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22c0e728cc0>"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Here we find loan_amnt is highly Correlated with installment\n",
    "plt.figure(figsize=(20,15))\n",
    "sns.heatmap(loan.corr(),cmap='coolwarm',annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22c00205d68>"
      ]
     },
     "execution_count": 198,
     "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": [
    "sns.scatterplot(x='installment',y='loan_amnt',data=loan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22c002696a0>"
      ]
     },
     "execution_count": 199,
     "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": [
    "sns.boxplot(y='loan_amnt',x='loan_status',data=loan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">loan_amnt</th>\n",
       "      <th colspan=\"2\" halign=\"left\">int_rate</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"2\" halign=\"left\">mort_acc</th>\n",
       "      <th colspan=\"8\" halign=\"left\">pub_rec_bankruptcies</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>...</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan_status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Charged Off</th>\n",
       "      <td>77673.0</td>\n",
       "      <td>15126.300967</td>\n",
       "      <td>8505.090557</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>8525.0</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>77673.0</td>\n",
       "      <td>15.882587</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>77586.0</td>\n",
       "      <td>0.128412</td>\n",
       "      <td>0.368853</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fully Paid</th>\n",
       "      <td>318357.0</td>\n",
       "      <td>13866.878771</td>\n",
       "      <td>8302.319699</td>\n",
       "      <td>500.0</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>19225.0</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>318357.0</td>\n",
       "      <td>13.092105</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>317909.0</td>\n",
       "      <td>0.119997</td>\n",
       "      <td>0.352992</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 96 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            loan_amnt                                                      \\\n",
       "                count          mean          std     min     25%      50%   \n",
       "loan_status                                                                 \n",
       "Charged Off   77673.0  15126.300967  8505.090557  1000.0  8525.0  14000.0   \n",
       "Fully Paid   318357.0  13866.878771  8302.319699   500.0  7500.0  12000.0   \n",
       "\n",
       "                               int_rate             ... mort_acc        \\\n",
       "                 75%      max     count       mean  ...      75%   max   \n",
       "loan_status                                         ...                  \n",
       "Charged Off  20000.0  40000.0   77673.0  15.882587  ...      2.0  23.0   \n",
       "Fully Paid   19225.0  40000.0  318357.0  13.092105  ...      3.0  34.0   \n",
       "\n",
       "            pub_rec_bankruptcies                                               \n",
       "                           count      mean       std  min  25%  50%  75%  max  \n",
       "loan_status                                                                    \n",
       "Charged Off              77586.0  0.128412  0.368853  0.0  0.0  0.0  0.0  8.0  \n",
       "Fully Paid              317909.0  0.119997  0.352992  0.0  0.0  0.0  0.0  8.0  \n",
       "\n",
       "[2 rows x 96 columns]"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan.groupby(['loan_status']).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['A', 'B', 'C', 'D', 'E', 'F', 'G']"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(loan['grade'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['A1',\n",
       " 'A2',\n",
       " 'A3',\n",
       " 'A4',\n",
       " 'A5',\n",
       " 'B1',\n",
       " 'B2',\n",
       " 'B3',\n",
       " 'B4',\n",
       " 'B5',\n",
       " 'C1',\n",
       " 'C2',\n",
       " 'C3',\n",
       " 'C4',\n",
       " 'C5',\n",
       " 'D1',\n",
       " 'D2',\n",
       " 'D3',\n",
       " 'D4',\n",
       " 'D5',\n",
       " 'E1',\n",
       " 'E2',\n",
       " 'E3',\n",
       " 'E4',\n",
       " 'E5',\n",
       " 'F1',\n",
       " 'F2',\n",
       " 'F3',\n",
       " 'F4',\n",
       " 'F5',\n",
       " 'G1',\n",
       " 'G2',\n",
       " 'G3',\n",
       " 'G4',\n",
       " 'G5']"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub=sorted(loan['sub_grade'].unique())\n",
    "sub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22c002d90b8>"
      ]
     },
     "execution_count": 202,
     "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": [
    "sns.countplot(x='grade',data=loan,hue='loan_status')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22c002d9b70>"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,7))\n",
    "sns.countplot(x='sub_grade',data=loan,order=sub,hue='loan_status')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22c4b0a0080>"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f_g=loan[(loan['grade']=='G') | (loan['grade']=='F')]\n",
    "plt.figure(figsize=(14,7))\n",
    "ord=sorted(f_g['sub_grade'].unique())\n",
    "sns.countplot(x='sub_grade',data=f_g,hue='loan_status',order=ord)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "loan['loan_repaid']=loan['loan_status'].map({'Fully Paid':1,'Charged Off':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22c0072b400>"
      ]
     },
     "execution_count": 208,
     "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": [
    "loan.corr()['loan_repaid'].sort_values().drop('loan_repaid').plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DATA PREPROCESSING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_amnt               0.000000\n",
       "term                    0.000000\n",
       "int_rate                0.000000\n",
       "installment             0.000000\n",
       "grade                   0.000000\n",
       "sub_grade               0.000000\n",
       "emp_title               5.789208\n",
       "emp_length              4.621115\n",
       "home_ownership          0.000000\n",
       "annual_inc              0.000000\n",
       "verification_status     0.000000\n",
       "issue_d                 0.000000\n",
       "loan_status             0.000000\n",
       "purpose                 0.000000\n",
       "title                   0.443148\n",
       "dti                     0.000000\n",
       "earliest_cr_line        0.000000\n",
       "open_acc                0.000000\n",
       "pub_rec                 0.000000\n",
       "revol_bal               0.000000\n",
       "revol_util              0.069692\n",
       "total_acc               0.000000\n",
       "initial_list_status     0.000000\n",
       "application_type        0.000000\n",
       "mort_acc                9.543469\n",
       "pub_rec_bankruptcies    0.135091\n",
       "address                 0.000000\n",
       "loan_repaid             0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to check how many percentage of data  is nuLL\n",
    "100*loan.isnull().sum()/len(loan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Teacher                                4389\n",
       "Manager                                4250\n",
       "Registered Nurse                       1856\n",
       "RN                                     1846\n",
       "Supervisor                             1830\n",
       "Sales                                  1638\n",
       "Project Manager                        1505\n",
       "Owner                                  1410\n",
       "Driver                                 1339\n",
       "Office Manager                         1218\n",
       "manager                                1145\n",
       "Director                               1089\n",
       "General Manager                        1074\n",
       "Engineer                                995\n",
       "teacher                                 962\n",
       "driver                                  882\n",
       "Vice President                          857\n",
       "Operations Manager                      763\n",
       "Administrative Assistant                756\n",
       "Accountant                              748\n",
       "President                               742\n",
       "owner                                   697\n",
       "Account Manager                         692\n",
       "Police Officer                          686\n",
       "supervisor                              673\n",
       "Attorney                                667\n",
       "Sales Manager                           665\n",
       "sales                                   645\n",
       "Executive Assistant                     642\n",
       "Analyst                                 623\n",
       "                                       ... \n",
       "North County Cat Hospital                 1\n",
       "Administrative Branch Manager             1\n",
       "Senior Operations Managrer                1\n",
       "Geoservices a schlumberger company        1\n",
       "Hall Construction                         1\n",
       "Telemarketing Manager                     1\n",
       "TM Paralegal                              1\n",
       "dallas grice SNT                          1\n",
       "Assistant S-3                             1\n",
       "Copy Central                              1\n",
       "alpine bistro&bakery                      1\n",
       "Pratt & Whittany                          1\n",
       "Progressive GaitWays                      1\n",
       "SM Beauty, Inc                            1\n",
       "GF                                        1\n",
       "Executive Assistance                      1\n",
       "Pequea Machine.inc                        1\n",
       "BASIS Employment Group                    1\n",
       "Server and Storage Specialist             1\n",
       "Miami V A Medical Center (Hospital)       1\n",
       "Kentucky Corrections                      1\n",
       "Orsay Restaurant                          1\n",
       "triad family services                     1\n",
       "Carprnter                                 1\n",
       "Manager of Clinical Practice              1\n",
       "lead specialist                           1\n",
       "Tripp Scott,PA                            1\n",
       "Oscar Winski Company                      1\n",
       " Maintenance                              1\n",
       "Call center lead                          1\n",
       "Name: emp_title, Length: 173105, dtype: int64"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan['emp_title'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Since there are 173105 unique values in emp_title with 5% Null values...\n",
    "#So we drop the column\n",
    "loan = loan.drop('emp_title',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "emp_length_order = [ '< 1 year',\n",
    "                      '1 year',\n",
    "                     '2 years',\n",
    "                     '3 years',\n",
    "                     '4 years',\n",
    "                     '5 years',\n",
    "                     '6 years',\n",
    "                     '7 years',\n",
    "                     '8 years',\n",
    "                     '9 years',\n",
    "                     '10+ years']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22c01be4b00>"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4))\n",
    "sns.countplot(x='emp_length',data=loan,order=emp_length_order,hue='loan_status')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "loan_not_paid=loan[loan['loan_status']==\"Charged Off\"].groupby(\"emp_length\").count()['loan_status']\n",
    "loan_paid=loan[loan['loan_status']==\"Fully Paid\"].groupby(\"emp_length\").count()['loan_status']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "emp_length\n",
       "1 year       0.248649\n",
       "10+ years    0.225770\n",
       "2 years      0.239560\n",
       "3 years      0.242593\n",
       "4 years      0.238213\n",
       "5 years      0.237911\n",
       "6 years      0.233341\n",
       "7 years      0.241887\n",
       "8 years      0.249625\n",
       "9 years      0.250735\n",
       "< 1 year     0.260830\n",
       "Name: loan_status, dtype: float64"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emp_len=loan_not_paid/loan_paid\n",
    "emp_len"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Since the data is almost same throughout the emp_length..\n",
    "#So we drop the column\n",
    "loan=loan.drop(\"emp_length\",axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The title column is simply a string subcategory/description of the purpose column. So we drop the title column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "loan=loan.drop('title',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0     139777\n",
       "1.0      60416\n",
       "2.0      49948\n",
       "3.0      38049\n",
       "4.0      27887\n",
       "5.0      18194\n",
       "6.0      11069\n",
       "7.0       6052\n",
       "8.0       3121\n",
       "9.0       1656\n",
       "10.0       865\n",
       "11.0       479\n",
       "12.0       264\n",
       "13.0       146\n",
       "14.0       107\n",
       "15.0        61\n",
       "16.0        37\n",
       "17.0        22\n",
       "18.0        18\n",
       "19.0        15\n",
       "20.0        13\n",
       "24.0        10\n",
       "22.0         7\n",
       "21.0         4\n",
       "25.0         4\n",
       "27.0         3\n",
       "23.0         2\n",
       "32.0         2\n",
       "26.0         2\n",
       "31.0         2\n",
       "30.0         1\n",
       "28.0         1\n",
       "34.0         1\n",
       "Name: mort_acc, dtype: int64"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan['mort_acc'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To deal with missing data, we find the column with highest correlation\n",
    "mort_acc annd use it to fill null values.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int_rate               -0.082583\n",
       "dti                    -0.025439\n",
       "revol_util              0.007514\n",
       "pub_rec                 0.011552\n",
       "pub_rec_bankruptcies    0.027239\n",
       "loan_repaid             0.073111\n",
       "open_acc                0.109205\n",
       "installment             0.193694\n",
       "revol_bal               0.194925\n",
       "loan_amnt               0.222315\n",
       "annual_inc              0.236320\n",
       "total_acc               0.381072\n",
       "mort_acc                1.000000\n",
       "Name: mort_acc, dtype: float64"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan.corr()['mort_acc'].sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Total_acc has highest correlation with mort_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_avg=loan.groupby('total_acc').mean()['mort_acc']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fill_mort_acc(total_acc,mort_acc):\n",
    "    '''\n",
    "    Accepts the total_acc and mort_acc values for the row.\n",
    "    Checks if the mort_acc is NaN , if so, it returns the avg mort_acc value\n",
    "    for the corresponding total_acc value for that row.\n",
    "    \n",
    "    total_acc_avg here should be a Series or dictionary containing the mapping of the\n",
    "    groupby averages of mort_acc per total_acc values.\n",
    "    '''\n",
    "    if np.isnan(mort_acc):\n",
    "        return total_avg[total_acc]\n",
    "    else:\n",
    "        return mort_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "loan['mort_acc']=loan.apply(lambda x: fill_mort_acc(x['total_acc'],x['mort_acc']),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_amnt                 0\n",
       "term                      0\n",
       "int_rate                  0\n",
       "installment               0\n",
       "grade                     0\n",
       "sub_grade                 0\n",
       "home_ownership            0\n",
       "annual_inc                0\n",
       "verification_status       0\n",
       "issue_d                   0\n",
       "loan_status               0\n",
       "purpose                   0\n",
       "dti                       0\n",
       "earliest_cr_line          0\n",
       "open_acc                  0\n",
       "pub_rec                   0\n",
       "revol_bal                 0\n",
       "revol_util              276\n",
       "total_acc                 0\n",
       "initial_list_status       0\n",
       "application_type          0\n",
       "mort_acc                  0\n",
       "pub_rec_bankruptcies    535\n",
       "address                   0\n",
       "loan_repaid               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "loan = loan.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_amnt               0\n",
       "term                    0\n",
       "int_rate                0\n",
       "installment             0\n",
       "grade                   0\n",
       "sub_grade               0\n",
       "home_ownership          0\n",
       "annual_inc              0\n",
       "verification_status     0\n",
       "issue_d                 0\n",
       "loan_status             0\n",
       "purpose                 0\n",
       "dti                     0\n",
       "earliest_cr_line        0\n",
       "open_acc                0\n",
       "pub_rec                 0\n",
       "revol_bal               0\n",
       "revol_util              0\n",
       "total_acc               0\n",
       "initial_list_status     0\n",
       "application_type        0\n",
       "mort_acc                0\n",
       "pub_rec_bankruptcies    0\n",
       "address                 0\n",
       "loan_repaid             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dummy Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['term', 'grade', 'sub_grade', 'home_ownership', 'verification_status',\n",
       "       'issue_d', 'loan_status', 'purpose', 'earliest_cr_line',\n",
       "       'initial_list_status', 'application_type', 'address'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan.select_dtypes(['object']).columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 36 months    301247\n",
       " 60 months     93972\n",
       "Name: term, dtype: int64"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan['term'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "loan['term'] = loan['term'].apply(lambda term: int(term[:3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "loan = loan.drop('grade',axis=1)#Since,grade is part of sub_grade."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [],
   "source": [
    "subgrade_dummies = pd.get_dummies(loan['sub_grade'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [],
   "source": [
    "loan = pd.concat([loan.drop('sub_grade',axis=1),subgrade_dummies],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['home_ownership', 'verification_status', 'issue_d', 'loan_status',\n",
       "       'purpose', 'earliest_cr_line', 'initial_list_status',\n",
       "       'application_type', 'address'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan.select_dtypes(['object']).columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [],
   "source": [
    "dummies = pd.get_dummies(loan[['verification_status', 'application_type','initial_list_status','purpose' ]],drop_first=True)\n",
    "loan = loan.drop(['verification_status', 'application_type','initial_list_status','purpose'],axis=1)\n",
    "loan = pd.concat([loan,dummies],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MORTGAGE    198022\n",
       "RENT        159395\n",
       "OWN          37660\n",
       "OTHER          110\n",
       "NONE            29\n",
       "ANY              3\n",
       "Name: home_ownership, dtype: int64"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan['home_ownership'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "loan['home_ownership']=loan['home_ownership'].replace(['NONE', 'ANY'], 'OTHER')\n",
    "\n",
    "dummies = pd.get_dummies(loan['home_ownership'],drop_first=True)\n",
    "loan = loan.drop('home_ownership',axis=1)\n",
    "loan = pd.concat([loan,dummies],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "loan['zip_code'] = loan['address'].apply(lambda address:address[-5:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [],
   "source": [
    "dummies = pd.get_dummies(loan['zip_code'],drop_first=True)\n",
    "loan = loan.drop(['zip_code','address'],axis=1)\n",
    "loan = pd.concat([loan,dummies],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [],
   "source": [
    "loan = loan.drop('issue_d',axis=1) #due to data  leak"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "loan['earliest_cr_year'] = loan['earliest_cr_line'].apply(lambda date:int(date[-4:]))\n",
    "loan = loan.drop('earliest_cr_line',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['loan_status'], dtype='object')"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan.select_dtypes(['object']).columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "loan=loan.drop('loan_status',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = loan.drop('loan_repaid',axis=1).values\n",
    "y = loan['loan_repaid'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.20, random_state=101)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Normalizing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler=MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train=scaler.fit_transform(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test = scaler.transform(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense,Activation,Dropout\n",
    "from tensorflow.keras.constraints import max_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "# input layer\n",
    "model.add(Dense(78,  activation='relu'))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(Dense(57,  activation='relu'))\n",
    "model.add(Dropout(0.2))\n",
    "# hidden layer\n",
    "model.add(Dense(39,  activation='relu'))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "# hidden layer\n",
    "model.add(Dense(17, activation='relu'))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "# output layer\n",
    "model.add(Dense(units=1,activation='sigmoid'))\n",
    "\n",
    "# Compile model\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 316175 samples, validate on 79044 samples\n",
      "Epoch 1/35\n",
      "316175/316175 [==============================] - 23s 74us/sample - loss: 0.3082 - val_loss: 0.2844\n",
      "Epoch 2/35\n",
      "316175/316175 [==============================] - 18s 57us/sample - loss: 0.3071 - val_loss: 0.2836\n",
      "Epoch 3/35\n",
      "316175/316175 [==============================] - 24s 75us/sample - loss: 0.3067 - val_loss: 0.2839\n",
      "Epoch 4/35\n",
      "316175/316175 [==============================] - 22s 69us/sample - loss: 0.3066 - val_loss: 0.2834\n",
      "Epoch 5/35\n",
      "316175/316175 [==============================] - 23s 73us/sample - loss: 0.3058 - val_loss: 0.2832 0s - loss:\n",
      "Epoch 6/35\n",
      "316175/316175 [==============================] - 13s 42us/sample - loss: 0.3065 - val_loss: 0.2830\n",
      "Epoch 7/35\n",
      "316175/316175 [==============================] - 13s 42us/sample - loss: 0.3058 - val_loss: 0.2832\n",
      "Epoch 8/35\n",
      "316175/316175 [==============================] - 21s 66us/sample - loss: 0.3050 - val_loss: 0.2831\n",
      "Epoch 9/35\n",
      "316175/316175 [==============================] - 32s 102us/sample - loss: 0.3055 - val_loss: 0.2829\n",
      "Epoch 10/35\n",
      "316175/316175 [==============================] - 34s 106us/sample - loss: 0.3043 - val_loss: 0.2840\n",
      "Epoch 11/35\n",
      "316175/316175 [==============================] - 28s 90us/sample - loss: 0.3048 - val_loss: 0.2835oss: 0 - ETA: - ETA: 0s - l\n",
      "Epoch 12/35\n",
      "316175/316175 [==============================] - 24s 76us/sample - loss: 0.3038 - val_loss: 0.2829\n",
      "Epoch 13/35\n",
      "316175/316175 [==============================] - 19s 61us/sample - loss: 0.3043 - val_loss: 0.2825 - ETA: 1s - loss: 0. - E\n",
      "Epoch 14/35\n",
      "316175/316175 [==============================] - 19s 61us/sample - loss: 0.3036 - val_loss: 0.2830\n",
      "Epoch 15/35\n",
      "316175/316175 [==============================] - 19s 59us/sample - loss: 0.3030 - val_loss: 0.2832\n",
      "Epoch 16/35\n",
      "316175/316175 [==============================] - 20s 62us/sample - loss: 0.3041 - val_loss: 0.2837\n",
      "Epoch 17/35\n",
      "316175/316175 [==============================] - 26s 83us/sample - loss: 0.3036 - val_loss: 0.2833\n",
      "Epoch 18/35\n",
      "316175/316175 [==============================] - 20s 63us/sample - loss: 0.3027 - val_loss: 0.2832\n",
      "Epoch 19/35\n",
      "316175/316175 [==============================] - 25s 79us/sample - loss: 0.3035 - val_loss: 0.2840\n",
      "Epoch 20/35\n",
      "316175/316175 [==============================] - 21s 67us/sample - loss: 0.3033 - val_loss: 0.2837\n",
      "Epoch 21/35\n",
      "316175/316175 [==============================] - 33s 104us/sample - loss: 0.3027 - val_loss: 0.2858\n",
      "Epoch 22/35\n",
      "316175/316175 [==============================] - 19s 61us/sample - loss: 0.3029 - val_loss: 0.2839\n",
      "Epoch 23/35\n",
      "316175/316175 [==============================] - 20s 64us/sample - loss: 0.3023 - val_loss: 0.2840\n",
      "Epoch 24/35\n",
      "316175/316175 [==============================] - 20s 64us/sample - loss: 0.3017 - val_loss: 0.2839\n",
      "Epoch 25/35\n",
      "316175/316175 [==============================] - 24s 75us/sample - loss: 0.3006 - val_loss: 0.2830\n",
      "Epoch 26/35\n",
      "316175/316175 [==============================] - 21s 67us/sample - loss: 0.3015 - val_loss: 0.2835- ETA: 3s - los - ETA: 1s - loss: 0.30 - ETA: 1s -  - ETA: 0s - loss\n",
      "Epoch 27/35\n",
      "316175/316175 [==============================] - 22s 70us/sample - loss: 0.3018 - val_loss: 0.2837\n",
      "Epoch 28/35\n",
      "316175/316175 [==============================] - 21s 66us/sample - loss: 0.3012 - val_loss: 0.2829\n",
      "Epoch 29/35\n",
      "316175/316175 [==============================] - 22s 68us/sample - loss: 0.3009 - val_loss: 0.2852\n",
      "Epoch 30/35\n",
      "316175/316175 [==============================] - 12s 39us/sample - loss: 0.3013 - val_loss: 0.2837\n",
      "Epoch 31/35\n",
      "316175/316175 [==============================] - 13s 40us/sample - loss: 0.3014 - val_loss: 0.2849\n",
      "Epoch 32/35\n",
      "316175/316175 [==============================] - 17s 55us/sample - loss: 0.3009 - val_loss: 0.2839\n",
      "Epoch 33/35\n",
      "316175/316175 [==============================] - 18s 57us/sample - loss: 0.3014 - val_loss: 0.2839\n",
      "Epoch 34/35\n",
      "316175/316175 [==============================] - 13s 40us/sample - loss: 0.3004 - val_loss: 0.2841\n",
      "Epoch 35/35\n",
      "316175/316175 [==============================] - 13s 41us/sample - loss: 0.3010 - val_loss: 0.2831\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x22c67d71e48>"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x=x_train, \n",
    "          y=y_train, \n",
    "          epochs=35,\n",
    "          batch_size=355,\n",
    "          validation_data=(x_test, y_test), \n",
    "          )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict_classes(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.44      0.61     15658\n",
      "           1       0.88      1.00      0.93     63386\n",
      "\n",
      "   micro avg       0.89      0.89      0.89     79044\n",
      "   macro avg       0.93      0.72      0.77     79044\n",
      "weighted avg       0.90      0.89      0.87     79044\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 6867  8791]\n",
      " [  114 63272]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,predictions))"
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}