Loan_Defaulter_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import libraries necessary for this project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xgboost\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import auc\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sumit Ranjan\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3020: DtypeWarning: Columns (19,47,55,112,123,124,125,128,129,130,133,139,140,141) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "data= pd.read_csv(\"C:\\\\Users\\\\Sumit Ranjan\\\\Desktop\\\\pgdm\\\\semester 5\\\\loan.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction To The Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>id</th>\n",
       "      <th>member_id</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>funded_amnt</th>\n",
       "      <th>funded_amnt_inv</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>...</th>\n",
       "      <th>hardship_payoff_balance_amount</th>\n",
       "      <th>hardship_last_payment_amount</th>\n",
       "      <th>disbursement_method</th>\n",
       "      <th>debt_settlement_flag</th>\n",
       "      <th>debt_settlement_flag_date</th>\n",
       "      <th>settlement_status</th>\n",
       "      <th>settlement_date</th>\n",
       "      <th>settlement_amount</th>\n",
       "      <th>settlement_percentage</th>\n",
       "      <th>settlement_term</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2500</td>\n",
       "      <td>2500</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>13.56</td>\n",
       "      <td>84.92</td>\n",
       "      <td>C</td>\n",
       "      <td>C1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30000</td>\n",
       "      <td>30000</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>18.94</td>\n",
       "      <td>777.23</td>\n",
       "      <td>D</td>\n",
       "      <td>D2</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5000</td>\n",
       "      <td>5000</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>17.97</td>\n",
       "      <td>180.69</td>\n",
       "      <td>D</td>\n",
       "      <td>D1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4000</td>\n",
       "      <td>4000</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>18.94</td>\n",
       "      <td>146.51</td>\n",
       "      <td>D</td>\n",
       "      <td>D2</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30000</td>\n",
       "      <td>30000</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>16.14</td>\n",
       "      <td>731.78</td>\n",
       "      <td>C</td>\n",
       "      <td>C4</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 145 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  member_id  loan_amnt  funded_amnt  funded_amnt_inv        term  \\\n",
       "0 NaN        NaN       2500         2500           2500.0   36 months   \n",
       "1 NaN        NaN      30000        30000          30000.0   60 months   \n",
       "2 NaN        NaN       5000         5000           5000.0   36 months   \n",
       "3 NaN        NaN       4000         4000           4000.0   36 months   \n",
       "4 NaN        NaN      30000        30000          30000.0   60 months   \n",
       "\n",
       "   int_rate  installment grade sub_grade       ...        \\\n",
       "0     13.56        84.92     C        C1       ...         \n",
       "1     18.94       777.23     D        D2       ...         \n",
       "2     17.97       180.69     D        D1       ...         \n",
       "3     18.94       146.51     D        D2       ...         \n",
       "4     16.14       731.78     C        C4       ...         \n",
       "\n",
       "  hardship_payoff_balance_amount hardship_last_payment_amount  \\\n",
       "0                            NaN                          NaN   \n",
       "1                            NaN                          NaN   \n",
       "2                            NaN                          NaN   \n",
       "3                            NaN                          NaN   \n",
       "4                            NaN                          NaN   \n",
       "\n",
       "  disbursement_method  debt_settlement_flag debt_settlement_flag_date  \\\n",
       "0                Cash                     N                       NaN   \n",
       "1                Cash                     N                       NaN   \n",
       "2                Cash                     N                       NaN   \n",
       "3                Cash                     N                       NaN   \n",
       "4                Cash                     N                       NaN   \n",
       "\n",
       "  settlement_status settlement_date settlement_amount  settlement_percentage  \\\n",
       "0               NaN             NaN               NaN                    NaN   \n",
       "1               NaN             NaN               NaN                    NaN   \n",
       "2               NaN             NaN               NaN                    NaN   \n",
       "3               NaN             NaN               NaN                    NaN   \n",
       "4               NaN             NaN               NaN                    NaN   \n",
       "\n",
       "  settlement_term  \n",
       "0             NaN  \n",
       "1             NaN  \n",
       "2             NaN  \n",
       "3             NaN  \n",
       "4             NaN  \n",
       "\n",
       "[5 rows x 145 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data = data[['member_id', 'loan_amnt', 'funded_amnt', 'addr_state', 'funded_amnt_inv', 'grade', 'term', 'emp_length', 'int_rate', 'annual_inc', 'dti', 'delinq_2yrs', 'inq_last_6mths', 'mths_since_last_delinq', 'mths_since_last_record', 'open_acc', 'pub_rec', 'revol_bal', 'revol_util', 'total_acc', 'total_rec_int', 'total_rec_late_fee', 'recoveries', 'collection_recovery_fee', 'collections_12_mths_ex_med', 'mths_since_last_major_derog', 'acc_now_delinq', 'tot_coll_amt', 'tot_cur_bal', 'total_rev_hi_lim', 'loan_status']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>member_id</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>funded_amnt</th>\n",
       "      <th>addr_state</th>\n",
       "      <th>funded_amnt_inv</th>\n",
       "      <th>grade</th>\n",
       "      <th>term</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>...</th>\n",
       "      <th>total_rec_late_fee</th>\n",
       "      <th>recoveries</th>\n",
       "      <th>collection_recovery_fee</th>\n",
       "      <th>collections_12_mths_ex_med</th>\n",
       "      <th>mths_since_last_major_derog</th>\n",
       "      <th>acc_now_delinq</th>\n",
       "      <th>tot_coll_amt</th>\n",
       "      <th>tot_cur_bal</th>\n",
       "      <th>total_rev_hi_lim</th>\n",
       "      <th>loan_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>2500</td>\n",
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       "      <td>NY</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>C</td>\n",
       "      <td>36 months</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>13.56</td>\n",
       "      <td>55000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16901.0</td>\n",
       "      <td>42000.0</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>30000</td>\n",
       "      <td>30000</td>\n",
       "      <td>LA</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>D</td>\n",
       "      <td>60 months</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>18.94</td>\n",
       "      <td>90000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1208.0</td>\n",
       "      <td>321915.0</td>\n",
       "      <td>50800.0</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5000</td>\n",
       "      <td>5000</td>\n",
       "      <td>MI</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>D</td>\n",
       "      <td>36 months</td>\n",
       "      <td>6 years</td>\n",
       "      <td>17.97</td>\n",
       "      <td>59280.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>110299.0</td>\n",
       "      <td>24100.0</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4000</td>\n",
       "      <td>4000</td>\n",
       "      <td>WA</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>D</td>\n",
       "      <td>36 months</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>18.94</td>\n",
       "      <td>92000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>686.0</td>\n",
       "      <td>305049.0</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>30000</td>\n",
       "      <td>30000</td>\n",
       "      <td>MD</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>C</td>\n",
       "      <td>60 months</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>16.14</td>\n",
       "      <td>57250.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>116007.0</td>\n",
       "      <td>23100.0</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   member_id  loan_amnt  funded_amnt addr_state  funded_amnt_inv grade  \\\n",
       "0        NaN       2500         2500         NY           2500.0     C   \n",
       "1        NaN      30000        30000         LA          30000.0     D   \n",
       "2        NaN       5000         5000         MI           5000.0     D   \n",
       "3        NaN       4000         4000         WA           4000.0     D   \n",
       "4        NaN      30000        30000         MD          30000.0     C   \n",
       "\n",
       "         term emp_length  int_rate  annual_inc     ...       \\\n",
       "0   36 months  10+ years     13.56     55000.0     ...        \n",
       "1   60 months  10+ years     18.94     90000.0     ...        \n",
       "2   36 months    6 years     17.97     59280.0     ...        \n",
       "3   36 months  10+ years     18.94     92000.0     ...        \n",
       "4   60 months  10+ years     16.14     57250.0     ...        \n",
       "\n",
       "   total_rec_late_fee  recoveries  collection_recovery_fee  \\\n",
       "0                 0.0         0.0                      0.0   \n",
       "1                 0.0         0.0                      0.0   \n",
       "2                 0.0         0.0                      0.0   \n",
       "3                 0.0         0.0                      0.0   \n",
       "4                 0.0         0.0                      0.0   \n",
       "\n",
       "   collections_12_mths_ex_med  mths_since_last_major_derog  acc_now_delinq  \\\n",
       "0                         0.0                          NaN             0.0   \n",
       "1                         0.0                          NaN             0.0   \n",
       "2                         0.0                          NaN             0.0   \n",
       "3                         0.0                          NaN             0.0   \n",
       "4                         0.0                          NaN             0.0   \n",
       "\n",
       "   tot_coll_amt  tot_cur_bal  total_rev_hi_lim  loan_status  \n",
       "0           0.0      16901.0           42000.0      Current  \n",
       "1        1208.0     321915.0           50800.0      Current  \n",
       "2           0.0     110299.0           24100.0      Current  \n",
       "3         686.0     305049.0            7000.0      Current  \n",
       "4           0.0     116007.0           23100.0      Current  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sumit Ranjan\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:5890: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._update_inplace(new_data)\n",
      "C:\\Users\\Sumit Ranjan\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "new_data['term'].replace(to_replace=' months', value='', regex=True, inplace=True)\n",
    "new_data['term'] = pd.to_numeric(new_data['term'], errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sumit Ranjan\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "new_data['emp_length'].replace('n/a', '0', inplace=True)\n",
    "new_data['emp_length'].replace(to_replace='\\+ years', value='', regex=True, inplace=True)\n",
    "new_data['emp_length'].replace(to_replace=' years', value='', regex=True, inplace=True)\n",
    "new_data['emp_length'].replace(to_replace='< 1 year', value='0', regex=True, inplace=True)\n",
    "new_data['emp_length'].replace(to_replace=' year', value='', regex=True, inplace=True)\n",
    "new_data['emp_length'] = pd.to_numeric(new_data['emp_length'], errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sumit Ranjan\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "new_data['grade'].replace(to_replace='A', value='0', regex=True, inplace=True)\n",
    "new_data['grade'].replace(to_replace='B', value='1', regex=True, inplace=True)\n",
    "new_data['grade'].replace(to_replace='C', value='2', regex=True, inplace=True)\n",
    "new_data['grade'].replace(to_replace='D', value='3', regex=True, inplace=True)\n",
    "new_data['grade'].replace(to_replace='E', value='4', regex=True, inplace=True)\n",
    "new_data['grade'].replace(to_replace='F', value='5', regex=True, inplace=True)\n",
    "new_data['grade'].replace(to_replace='G', value='6', regex=True, inplace=True)\n",
    "new_data['grade'] = pd.to_numeric(new_data['grade'], errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "member_id                      2260668\n",
       "mths_since_last_record         1901512\n",
       "mths_since_last_major_derog    1679893\n",
       "mths_since_last_delinq         1158502\n",
       "emp_length                      146907\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.isnull().sum().sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sumit Ranjan\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:5434: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._update_inplace(new_data)\n"
     ]
    }
   ],
   "source": [
    "cols = ['term', 'loan_amnt', 'funded_amnt', 'int_rate', 'grade', 'annual_inc', 'dti', 'mths_since_last_delinq', 'mths_since_last_record', 'open_acc', 'revol_bal', 'revol_util', 'total_acc', 'total_rec_int', 'mths_since_last_major_derog', 'tot_coll_amt', 'tot_cur_bal', 'total_rev_hi_lim', 'emp_length']\n",
    "for col in cols:\n",
    "    new_data[col].fillna(new_data[col].median(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['acc_now_delinq', 'total_rec_late_fee', 'recoveries', 'collection_recovery_fee', 'collections_12_mths_ex_med']\n",
    "for col in cols:\n",
    "    new_data[col].fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Loan status columns into two categories i.e. default and fully paid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sumit Ranjan\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "C:\\Users\\Sumit Ranjan\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "default=['Charged Off','Late (31-120 days)','Default','Late (16-30 days)','In Grace Period']\n",
    "paid=['Fully Paid']\n",
    "new_data['loan_status']=new_data['loan_status'].apply(lambda x: 'Default' if x in default else x)\n",
    "new_data['loan_status']=new_data['loan_status'].apply(lambda x: 'Fully Paid' if x in paid else x)\n",
    "new_data=new_data[(new_data['loan_status']=='Default') | (new_data['loan_status']=='Fully Paid')]\n",
    "target_variable=new_data['loan_status'].apply(lambda x: 1 if x=='Default' else 0)\n",
    "new_data.drop('loan_status',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_cols = ['member_id', 'emp_length', 'loan_amnt', 'funded_amnt', 'funded_amnt_inv', 'grade', 'int_rate', 'annual_inc', 'dti', 'mths_since_last_delinq', 'mths_since_last_record', 'open_acc', 'revol_bal', 'revol_util', 'total_acc', 'total_rec_int', 'total_rec_late_fee', 'mths_since_last_major_derog', 'tot_cur_bal', 'total_rev_hi_lim', 'tot_coll_amt', 'recoveries', 'collection_recovery_fee', 'term', 'acc_now_delinq', 'collections_12_mths_ex_med']\n",
    "new_data_2=new_data[selected_cols]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating new variable for better training of data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loan to income ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sumit Ranjan\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "new_data_2['loan_to_income_ratio'] = new_data_2['annual_inc']/new_data_2['funded_amnt_inv']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>member_id</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>funded_amnt</th>\n",
       "      <th>funded_amnt_inv</th>\n",
       "      <th>grade</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>dti</th>\n",
       "      <th>mths_since_last_delinq</th>\n",
       "      <th>...</th>\n",
       "      <th>mths_since_last_major_derog</th>\n",
       "      <th>tot_cur_bal</th>\n",
       "      <th>total_rev_hi_lim</th>\n",
       "      <th>tot_coll_amt</th>\n",
       "      <th>recoveries</th>\n",
       "      <th>collection_recovery_fee</th>\n",
       "      <th>term</th>\n",
       "      <th>acc_now_delinq</th>\n",
       "      <th>collections_12_mths_ex_med</th>\n",
       "      <th>loan_to_income_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>30000</td>\n",
       "      <td>30000</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>22.35</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>30.46</td>\n",
       "      <td>51.0</td>\n",
       "      <td>...</td>\n",
       "      <td>44.0</td>\n",
       "      <td>472330.0</td>\n",
       "      <td>42200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40000</td>\n",
       "      <td>40000</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>16.14</td>\n",
       "      <td>45000.0</td>\n",
       "      <td>50.53</td>\n",
       "      <td>31.0</td>\n",
       "      <td>...</td>\n",
       "      <td>44.0</td>\n",
       "      <td>271068.0</td>\n",
       "      <td>54200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>20000</td>\n",
       "      <td>20000</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.56</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>18.92</td>\n",
       "      <td>48.0</td>\n",
       "      <td>...</td>\n",
       "      <td>44.0</td>\n",
       "      <td>515779.0</td>\n",
       "      <td>85100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4500</td>\n",
       "      <td>4500</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>1</td>\n",
       "      <td>11.31</td>\n",
       "      <td>38500.0</td>\n",
       "      <td>4.64</td>\n",
       "      <td>25.0</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>29137.0</td>\n",
       "      <td>29200.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8425</td>\n",
       "      <td>8425</td>\n",
       "      <td>8425.0</td>\n",
       "      <td>4</td>\n",
       "      <td>27.27</td>\n",
       "      <td>450000.0</td>\n",
       "      <td>12.37</td>\n",
       "      <td>31.0</td>\n",
       "      <td>...</td>\n",
       "      <td>44.0</td>\n",
       "      <td>690320.0</td>\n",
       "      <td>56000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53.412463</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     member_id  emp_length  loan_amnt  funded_amnt  funded_amnt_inv  grade  \\\n",
       "100        NaN         5.0      30000        30000          30000.0      3   \n",
       "152        NaN         0.0      40000        40000          40000.0      2   \n",
       "170        NaN        10.0      20000        20000          20000.0      0   \n",
       "186        NaN        10.0       4500         4500           4500.0      1   \n",
       "215        NaN         3.0       8425         8425           8425.0      4   \n",
       "\n",
       "     int_rate  annual_inc    dti  mths_since_last_delinq  \\\n",
       "100     22.35    100000.0  30.46                    51.0   \n",
       "152     16.14     45000.0  50.53                    31.0   \n",
       "170      7.56    100000.0  18.92                    48.0   \n",
       "186     11.31     38500.0   4.64                    25.0   \n",
       "215     27.27    450000.0  12.37                    31.0   \n",
       "\n",
       "             ...           mths_since_last_major_derog  tot_cur_bal  \\\n",
       "100          ...                                  44.0     472330.0   \n",
       "152          ...                                  44.0     271068.0   \n",
       "170          ...                                  44.0     515779.0   \n",
       "186          ...                                  25.0      29137.0   \n",
       "215          ...                                  44.0     690320.0   \n",
       "\n",
       "     total_rev_hi_lim  tot_coll_amt  recoveries  collection_recovery_fee  \\\n",
       "100           42200.0           0.0         0.0                      0.0   \n",
       "152           54200.0           0.0         0.0                      0.0   \n",
       "170           85100.0           0.0         0.0                      0.0   \n",
       "186           29200.0         306.0         0.0                      0.0   \n",
       "215           56000.0           0.0         0.0                      0.0   \n",
       "\n",
       "     term  acc_now_delinq  collections_12_mths_ex_med  loan_to_income_ratio  \n",
       "100    36             0.0                         0.0              3.333333  \n",
       "152    60             0.0                         0.0              1.125000  \n",
       "170    36             0.0                         0.0              5.000000  \n",
       "186    36             0.0                         0.0              8.555556  \n",
       "215    36             0.0                         0.0             53.412463  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sumit Ranjan\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "new_data_2['avl_lines'] = new_data_2['total_acc'] - new_data_2['open_acc']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Total Interest paid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sumit Ranjan\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "new_data_2['int_paid'] = new_data_2['total_rec_int'] + new_data_2['total_rec_late_fee']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Total repayment "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data_2['total_repayment'] =  ((new_data_2['recoveries']/new_data_2['funded_amnt_inv']) * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1338224, 30)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data_2.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Splitting the data as train and test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(401468, 30)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(np.array(new_data_2),target_variable,test_size=.30,random_state=1) \n",
    "X_train.shape\n",
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "eval_set=[(X_test, y_test)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1], dtype=int64), array([344825,  56643], dtype=int64))"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import xgboost\n",
    "from numpy import loadtxt\n",
    "from xgboost import XGBClassifier\n",
    "# fit model no training data\n",
    "model = XGBClassifier()\n",
    " \n",
    "from sklearn import tree\n",
    "model.fit(X_train,y_train)\n",
    "df_test_output= model.predict(X_test)\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "np.unique(y_test, return_counts=True)\n",
    "np.unique(df_test_output, return_counts=True)\n",
    " \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9146332958043979"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, df_test_output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Accuracy calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 91.4633295804%\n"
     ]
    }
   ],
   "source": [
    "accuracy = accuracy_score(np.array(y_test).flatten(),df_test_output)\n",
    "print(\"Accuracy: %.10f%%\" % (accuracy * 100.0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix :\n",
      "[[311613   1060]\n",
      " [ 33212  55583]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      1.00      0.95    312673\n",
      "           1       0.98      0.63      0.76     88795\n",
      "\n",
      "   micro avg       0.91      0.91      0.91    401468\n",
      "   macro avg       0.94      0.81      0.86    401468\n",
      "weighted avg       0.92      0.91      0.91    401468\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix \n",
    "from sklearn.metrics import accuracy_score \n",
    "from sklearn.metrics import classification_report \n",
    "actual = y_test\n",
    "predicted = df_test_output\n",
    "results = confusion_matrix(actual, predicted) \n",
    "print ('Confusion Matrix :')\n",
    "print(results) \n",
    " \n",
    "print (classification_report(actual, predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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