# Importing packages for analysis
import numpy as np
import pandas as pd
from datetime import datetime as dt
import matplotlib.pyplot as plt
import seaborn as sns#Importing the dataset for analysis
loan = pd.read_csv('loan.csv')
loan.head(5)D:\HP\Anaconda3\envs\AI\lib\site-packages\IPython\core\interactiveshell.py:3146: DtypeWarning: Columns (47) have mixed types.Specify dtype option on import or set low_memory=False.
interactivity=interactivity, compiler=compiler, result=result)
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| id | member_id | loan_amnt | funded_amnt | funded_amnt_inv | term | int_rate | installment | grade | sub_grade | ... | num_tl_90g_dpd_24m | num_tl_op_past_12m | pct_tl_nvr_dlq | percent_bc_gt_75 | pub_rec_bankruptcies | tax_liens | tot_hi_cred_lim | total_bal_ex_mort | total_bc_limit | total_il_high_credit_limit | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1077501 | 1296599 | 5000 | 5000 | 4975.0 | 36 months | 10.65% | 162.87 | B | B2 | ... | NaN | NaN | NaN | NaN | 0.0 | 0.0 | NaN | NaN | NaN | NaN |
| 1 | 1077430 | 1314167 | 2500 | 2500 | 2500.0 | 60 months | 15.27% | 59.83 | C | C4 | ... | NaN | NaN | NaN | NaN | 0.0 | 0.0 | NaN | NaN | NaN | NaN |
| 2 | 1077175 | 1313524 | 2400 | 2400 | 2400.0 | 36 months | 15.96% | 84.33 | C | C5 | ... | NaN | NaN | NaN | NaN | 0.0 | 0.0 | NaN | NaN | NaN | NaN |
| 3 | 1076863 | 1277178 | 10000 | 10000 | 10000.0 | 36 months | 13.49% | 339.31 | C | C1 | ... | NaN | NaN | NaN | NaN | 0.0 | 0.0 | NaN | NaN | NaN | NaN |
| 4 | 1075358 | 1311748 | 3000 | 3000 | 3000.0 | 60 months | 12.69% | 67.79 | B | B5 | ... | NaN | NaN | NaN | NaN | 0.0 | 0.0 | NaN | NaN | NaN | NaN |
5 rows × 111 columns
# Size of the dataframe
loan.shape(39717, 111)
# Datatypes of all the columns
pd.set_option('display.max_columns', 200)
pd.set_option('display.max_rows', 200)
loan.dtypesid int64
member_id int64
loan_amnt int64
funded_amnt int64
funded_amnt_inv float64
term object
int_rate object
installment float64
grade object
sub_grade object
emp_title object
emp_length object
home_ownership object
annual_inc float64
verification_status object
issue_d object
loan_status object
pymnt_plan object
url object
desc object
purpose object
title object
zip_code object
addr_state object
dti float64
delinq_2yrs int64
earliest_cr_line object
inq_last_6mths int64
mths_since_last_delinq float64
mths_since_last_record float64
open_acc int64
pub_rec int64
revol_bal int64
revol_util object
total_acc int64
initial_list_status object
out_prncp float64
out_prncp_inv float64
total_pymnt float64
total_pymnt_inv float64
total_rec_prncp float64
total_rec_int float64
total_rec_late_fee float64
recoveries float64
collection_recovery_fee float64
last_pymnt_d object
last_pymnt_amnt float64
next_pymnt_d object
last_credit_pull_d object
collections_12_mths_ex_med float64
mths_since_last_major_derog float64
policy_code int64
application_type object
annual_inc_joint float64
dti_joint float64
verification_status_joint float64
acc_now_delinq int64
tot_coll_amt float64
tot_cur_bal float64
open_acc_6m float64
open_il_6m float64
open_il_12m float64
open_il_24m float64
mths_since_rcnt_il float64
total_bal_il float64
il_util float64
open_rv_12m float64
open_rv_24m float64
max_bal_bc float64
all_util float64
total_rev_hi_lim float64
inq_fi float64
total_cu_tl float64
inq_last_12m float64
acc_open_past_24mths float64
avg_cur_bal float64
bc_open_to_buy float64
bc_util float64
chargeoff_within_12_mths float64
delinq_amnt int64
mo_sin_old_il_acct float64
mo_sin_old_rev_tl_op float64
mo_sin_rcnt_rev_tl_op float64
mo_sin_rcnt_tl float64
mort_acc float64
mths_since_recent_bc float64
mths_since_recent_bc_dlq float64
mths_since_recent_inq float64
mths_since_recent_revol_delinq float64
num_accts_ever_120_pd float64
num_actv_bc_tl float64
num_actv_rev_tl float64
num_bc_sats float64
num_bc_tl float64
num_il_tl float64
num_op_rev_tl float64
num_rev_accts float64
num_rev_tl_bal_gt_0 float64
num_sats float64
num_tl_120dpd_2m float64
num_tl_30dpd float64
num_tl_90g_dpd_24m float64
num_tl_op_past_12m float64
pct_tl_nvr_dlq float64
percent_bc_gt_75 float64
pub_rec_bankruptcies float64
tax_liens float64
tot_hi_cred_lim float64
total_bal_ex_mort float64
total_bc_limit float64
total_il_high_credit_limit float64
dtype: object
#Summary of data
loan.describe(include = 'all')
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| id | member_id | loan_amnt | funded_amnt | funded_amnt_inv | term | int_rate | installment | grade | sub_grade | emp_title | emp_length | home_ownership | annual_inc | verification_status | issue_d | loan_status | pymnt_plan | url | desc | purpose | title | zip_code | addr_state | dti | delinq_2yrs | earliest_cr_line | inq_last_6mths | mths_since_last_delinq | mths_since_last_record | open_acc | pub_rec | revol_bal | revol_util | total_acc | initial_list_status | out_prncp | out_prncp_inv | total_pymnt | total_pymnt_inv | total_rec_prncp | total_rec_int | total_rec_late_fee | recoveries | collection_recovery_fee | last_pymnt_d | last_pymnt_amnt | next_pymnt_d | last_credit_pull_d | collections_12_mths_ex_med | mths_since_last_major_derog | policy_code | application_type | annual_inc_joint | dti_joint | verification_status_joint | acc_now_delinq | tot_coll_amt | tot_cur_bal | open_acc_6m | open_il_6m | open_il_12m | open_il_24m | mths_since_rcnt_il | total_bal_il | il_util | open_rv_12m | open_rv_24m | max_bal_bc | all_util | total_rev_hi_lim | inq_fi | total_cu_tl | inq_last_12m | acc_open_past_24mths | avg_cur_bal | bc_open_to_buy | bc_util | chargeoff_within_12_mths | delinq_amnt | mo_sin_old_il_acct | mo_sin_old_rev_tl_op | mo_sin_rcnt_rev_tl_op | mo_sin_rcnt_tl | mort_acc | mths_since_recent_bc | mths_since_recent_bc_dlq | mths_since_recent_inq | mths_since_recent_revol_delinq | num_accts_ever_120_pd | num_actv_bc_tl | num_actv_rev_tl | num_bc_sats | num_bc_tl | num_il_tl | num_op_rev_tl | num_rev_accts | num_rev_tl_bal_gt_0 | num_sats | num_tl_120dpd_2m | num_tl_30dpd | num_tl_90g_dpd_24m | num_tl_op_past_12m | pct_tl_nvr_dlq | percent_bc_gt_75 | pub_rec_bankruptcies | tax_liens | tot_hi_cred_lim | total_bal_ex_mort | total_bc_limit | total_il_high_credit_limit | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 3.971700e+04 | 3.971700e+04 | 39717.000000 | 39717.000000 | 39717.000000 | 39717 | 39717 | 39717.000000 | 39717 | 39717 | 37258 | 38642 | 39717 | 3.971700e+04 | 39717 | 39717 | 39717 | 39717 | 39717 | 26777 | 39717 | 39706 | 39717 | 39717 | 39717.000000 | 39717.000000 | 39717 | 39717.000000 | 14035.000000 | 2786.000000 | 39717.000000 | 39717.000000 | 39717.000000 | 39667 | 39717.000000 | 39717 | 39717.000000 | 39717.000000 | 39717.000000 | 39717.000000 | 39717.000000 | 39717.000000 | 39717.000000 | 39717.000000 | 39717.000000 | 39646 | 39717.000000 | 1140 | 39715 | 39661.0 | 0.0 | 39717.0 | 39717 | 0.0 | 0.0 | 0.0 | 39717.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 39661.0 | 39717.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 39020.000000 | 39678.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| unique | NaN | NaN | NaN | NaN | NaN | 2 | 371 | NaN | 7 | 35 | 28820 | 11 | 5 | NaN | 3 | 55 | 3 | 1 | 39717 | 26527 | 14 | 19615 | 823 | 50 | NaN | NaN | 526 | NaN | NaN | NaN | NaN | NaN | NaN | 1089 | NaN | 1 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 101 | NaN | 2 | 106 | NaN | NaN | NaN | 1 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| top | NaN | NaN | NaN | NaN | NaN | 36 months | 10.99% | NaN | B | B3 | US Army | 10+ years | RENT | NaN | Not Verified | Dec-11 | Fully Paid | n | https://lendingclub.com/browse/loanDetail.acti... | debt_consolidation | Debt Consolidation | 100xx | CA | NaN | NaN | Nov-98 | NaN | NaN | NaN | NaN | NaN | NaN | 0% | NaN | f | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | May-16 | NaN | Jun-16 | May-16 | NaN | NaN | NaN | INDIVIDUAL | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | |
| freq | NaN | NaN | NaN | NaN | NaN | 29096 | 956 | NaN | 12020 | 2917 | 134 | 8879 | 18899 | NaN | 16921 | 2260 | 32950 | 39717 | 1 | 210 | 18641 | 2184 | 597 | 7099 | NaN | NaN | 370 | NaN | NaN | NaN | NaN | NaN | NaN | 977 | NaN | 39717 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1256 | NaN | 1125 | 10308 | NaN | NaN | NaN | 39717 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| mean | 6.831319e+05 | 8.504636e+05 | 11219.443815 | 10947.713196 | 10397.448868 | NaN | NaN | 324.561922 | NaN | NaN | NaN | NaN | NaN | 6.896893e+04 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 13.315130 | 0.146512 | NaN | 0.869200 | 35.900962 | 69.698134 | 9.294408 | 0.055065 | 13382.528086 | NaN | 22.088828 | NaN | 51.227887 | 50.989768 | 12153.596544 | 11567.149118 | 9793.348813 | 2263.663172 | 1.363015 | 95.221624 | 12.406112 | NaN | 2678.826162 | NaN | NaN | 0.0 | NaN | 1.0 | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.043260 | 0.0 | NaN | NaN | NaN | NaN |
| std | 2.106941e+05 | 2.656783e+05 | 7456.670694 | 7187.238670 | 7128.450439 | NaN | NaN | 208.874874 | NaN | NaN | NaN | NaN | NaN | 6.379377e+04 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 6.678594 | 0.491812 | NaN | 1.070219 | 22.020060 | 43.822529 | 4.400282 | 0.237200 | 15885.016641 | NaN | 11.401709 | NaN | 375.172839 | 373.824457 | 9042.040766 | 8942.672613 | 7065.522127 | 2608.111964 | 7.289979 | 688.744771 | 148.671593 | NaN | 4447.136012 | NaN | NaN | 0.0 | NaN | 0.0 | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.204324 | 0.0 | NaN | NaN | NaN | NaN |
| min | 5.473400e+04 | 7.069900e+04 | 500.000000 | 500.000000 | 0.000000 | NaN | NaN | 15.690000 | NaN | NaN | NaN | NaN | NaN | 4.000000e+03 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.000000 | 0.000000 | NaN | 0.000000 | 0.000000 | 0.000000 | 2.000000 | 0.000000 | 0.000000 | NaN | 2.000000 | NaN | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | NaN | 0.000000 | NaN | NaN | 0.0 | NaN | 1.0 | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.000000 | 0.0 | NaN | NaN | NaN | NaN |
| 25% | 5.162210e+05 | 6.667800e+05 | 5500.000000 | 5400.000000 | 5000.000000 | NaN | NaN | 167.020000 | NaN | NaN | NaN | NaN | NaN | 4.040400e+04 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 8.170000 | 0.000000 | NaN | 0.000000 | 18.000000 | 22.000000 | 6.000000 | 0.000000 | 3703.000000 | NaN | 13.000000 | NaN | 0.000000 | 0.000000 | 5576.930000 | 5112.310000 | 4600.000000 | 662.180000 | 0.000000 | 0.000000 | 0.000000 | NaN | 218.680000 | NaN | NaN | 0.0 | NaN | 1.0 | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.000000 | 0.0 | NaN | NaN | NaN | NaN |
| 50% | 6.656650e+05 | 8.508120e+05 | 10000.000000 | 9600.000000 | 8975.000000 | NaN | NaN | 280.220000 | NaN | NaN | NaN | NaN | NaN | 5.900000e+04 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 13.400000 | 0.000000 | NaN | 1.000000 | 34.000000 | 90.000000 | 9.000000 | 0.000000 | 8850.000000 | NaN | 20.000000 | NaN | 0.000000 | 0.000000 | 9899.640319 | 9287.150000 | 8000.000000 | 1348.910000 | 0.000000 | 0.000000 | 0.000000 | NaN | 546.140000 | NaN | NaN | 0.0 | NaN | 1.0 | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.000000 | 0.0 | NaN | NaN | NaN | NaN |
| 75% | 8.377550e+05 | 1.047339e+06 | 15000.000000 | 15000.000000 | 14400.000000 | NaN | NaN | 430.780000 | NaN | NaN | NaN | NaN | NaN | 8.230000e+04 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 18.600000 | 0.000000 | NaN | 1.000000 | 52.000000 | 104.000000 | 12.000000 | 0.000000 | 17058.000000 | NaN | 29.000000 | NaN | 0.000000 | 0.000000 | 16534.433040 | 15798.810000 | 13653.260000 | 2833.400000 | 0.000000 | 0.000000 | 0.000000 | NaN | 3293.160000 | NaN | NaN | 0.0 | NaN | 1.0 | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.000000 | 0.0 | NaN | NaN | NaN | NaN |
| max | 1.077501e+06 | 1.314167e+06 | 35000.000000 | 35000.000000 | 35000.000000 | NaN | NaN | 1305.190000 | NaN | NaN | NaN | NaN | NaN | 6.000000e+06 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 29.990000 | 11.000000 | NaN | 8.000000 | 120.000000 | 129.000000 | 44.000000 | 4.000000 | 149588.000000 | NaN | 90.000000 | NaN | 6311.470000 | 6307.370000 | 58563.679930 | 58563.680000 | 35000.020000 | 23563.680000 | 180.200000 | 29623.350000 | 7002.190000 | NaN | 36115.200000 | NaN | NaN | 0.0 | NaN | 1.0 | NaN | NaN | NaN | NaN | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2.000000 | 0.0 | NaN | NaN | NaN | NaN |
# Lets check for duplicate records in our data.
loan.drop_duplicates()
loan.shape
# seeems like there are none.(39717, 111)
# As we are interested to figure out if a borrower is defaulter or not, the loan_status of interest is FULLY PAID and CHARGED OFF
# Lets remove all loans with statuses = "CURRENT" as its uncertain as to know if they will repay the loan or not
loan = loan.loc[loan['loan_status']!='Current']
loan.shape(38577, 111)
# Percentage of null values in columns
total = pd.DataFrame(loan.isnull().sum().sort_values(ascending=False), columns=['Total'])
percentage = pd.DataFrame(round(100*(loan.isnull().sum()/loan.shape[0]),2).sort_values(ascending=False),columns=['Percentage'])
pd.concat([total, percentage], axis = 1)
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| Total | Percentage | |
|---|---|---|
| total_il_high_credit_limit | 38577 | 100.00 |
| il_util | 38577 | 100.00 |
| bc_util | 38577 | 100.00 |
| bc_open_to_buy | 38577 | 100.00 |
| avg_cur_bal | 38577 | 100.00 |
| acc_open_past_24mths | 38577 | 100.00 |
| inq_last_12m | 38577 | 100.00 |
| total_cu_tl | 38577 | 100.00 |
| inq_fi | 38577 | 100.00 |
| total_rev_hi_lim | 38577 | 100.00 |
| all_util | 38577 | 100.00 |
| max_bal_bc | 38577 | 100.00 |
| open_rv_24m | 38577 | 100.00 |
| open_rv_12m | 38577 | 100.00 |
| total_bal_il | 38577 | 100.00 |
| mo_sin_old_rev_tl_op | 38577 | 100.00 |
| mths_since_rcnt_il | 38577 | 100.00 |
| open_il_24m | 38577 | 100.00 |
| open_il_12m | 38577 | 100.00 |
| open_il_6m | 38577 | 100.00 |
| open_acc_6m | 38577 | 100.00 |
| tot_cur_bal | 38577 | 100.00 |
| tot_coll_amt | 38577 | 100.00 |
| total_bc_limit | 38577 | 100.00 |
| dti_joint | 38577 | 100.00 |
| annual_inc_joint | 38577 | 100.00 |
| mths_since_last_major_derog | 38577 | 100.00 |
| next_pymnt_d | 38577 | 100.00 |
| mo_sin_old_il_acct | 38577 | 100.00 |
| verification_status_joint | 38577 | 100.00 |
| mo_sin_rcnt_rev_tl_op | 38577 | 100.00 |
| num_il_tl | 38577 | 100.00 |
| total_bal_ex_mort | 38577 | 100.00 |
| tot_hi_cred_lim | 38577 | 100.00 |
| percent_bc_gt_75 | 38577 | 100.00 |
| pct_tl_nvr_dlq | 38577 | 100.00 |
| num_tl_90g_dpd_24m | 38577 | 100.00 |
| num_tl_30dpd | 38577 | 100.00 |
| num_tl_120dpd_2m | 38577 | 100.00 |
| num_sats | 38577 | 100.00 |
| num_rev_tl_bal_gt_0 | 38577 | 100.00 |
| num_rev_accts | 38577 | 100.00 |
| num_op_rev_tl | 38577 | 100.00 |
| num_tl_op_past_12m | 38577 | 100.00 |
| num_bc_tl | 38577 | 100.00 |
| num_bc_sats | 38577 | 100.00 |
| num_actv_rev_tl | 38577 | 100.00 |
| num_actv_bc_tl | 38577 | 100.00 |
| num_accts_ever_120_pd | 38577 | 100.00 |
| mths_since_recent_revol_delinq | 38577 | 100.00 |
| mths_since_recent_inq | 38577 | 100.00 |
| mths_since_recent_bc_dlq | 38577 | 100.00 |
| mths_since_recent_bc | 38577 | 100.00 |
| mort_acc | 38577 | 100.00 |
| mo_sin_rcnt_tl | 38577 | 100.00 |
| mths_since_last_record | 35837 | 92.90 |
| mths_since_last_delinq | 24905 | 64.56 |
| desc | 12527 | 32.47 |
| emp_title | 2386 | 6.19 |
| emp_length | 1033 | 2.68 |
| pub_rec_bankruptcies | 697 | 1.81 |
| last_pymnt_d | 71 | 0.18 |
| collections_12_mths_ex_med | 56 | 0.15 |
| chargeoff_within_12_mths | 56 | 0.15 |
| revol_util | 50 | 0.13 |
| tax_liens | 39 | 0.10 |
| title | 11 | 0.03 |
| last_credit_pull_d | 2 | 0.01 |
| purpose | 0 | 0.00 |
| verification_status | 0 | 0.00 |
| url | 0 | 0.00 |
| pymnt_plan | 0 | 0.00 |
| loan_status | 0 | 0.00 |
| issue_d | 0 | 0.00 |
| loan_amnt | 0 | 0.00 |
| annual_inc | 0 | 0.00 |
| home_ownership | 0 | 0.00 |
| sub_grade | 0 | 0.00 |
| grade | 0 | 0.00 |
| installment | 0 | 0.00 |
| int_rate | 0 | 0.00 |
| term | 0 | 0.00 |
| funded_amnt_inv | 0 | 0.00 |
| funded_amnt | 0 | 0.00 |
| addr_state | 0 | 0.00 |
| member_id | 0 | 0.00 |
| zip_code | 0 | 0.00 |
| total_rec_prncp | 0 | 0.00 |
| dti | 0 | 0.00 |
| total_pymnt_inv | 0 | 0.00 |
| acc_now_delinq | 0 | 0.00 |
| application_type | 0 | 0.00 |
| policy_code | 0 | 0.00 |
| last_pymnt_amnt | 0 | 0.00 |
| collection_recovery_fee | 0 | 0.00 |
| recoveries | 0 | 0.00 |
| total_rec_late_fee | 0 | 0.00 |
| total_rec_int | 0 | 0.00 |
| delinq_amnt | 0 | 0.00 |
| total_pymnt | 0 | 0.00 |
| delinq_2yrs | 0 | 0.00 |
| out_prncp_inv | 0 | 0.00 |
| out_prncp | 0 | 0.00 |
| initial_list_status | 0 | 0.00 |
| total_acc | 0 | 0.00 |
| revol_bal | 0 | 0.00 |
| pub_rec | 0 | 0.00 |
| open_acc | 0 | 0.00 |
| inq_last_6mths | 0 | 0.00 |
| earliest_cr_line | 0 | 0.00 |
| id | 0 | 0.00 |
# Dropping all columns with only null values
loan=loan.dropna(axis=1,how='all')
loan.shape(38577, 56)
loan.emp_length.unique()array(['10+ years', '< 1 year', '3 years', '8 years', '9 years',
'4 years', '5 years', '1 year', '6 years', '2 years', '7 years',
nan], dtype=object)
loan.collections_12_mths_ex_med.unique()array([ 0., nan])
loan.chargeoff_within_12_mths.unique()array([ 0., nan])
loan.pub_rec_bankruptcies.unique()array([ 0., 1., 2., nan])
loan.tax_liens.unique()array([ 0., nan])
The columns collections_12_mths_ex_med, chargeoff_within_12_mths and tax_liens has either value of 0 or nan. The range of values that these categorical columns can take is not of any significant impact to the analysis.
# Removing columns that is not of interest for our analysis along with columns that has many null values
drop_columns = ['desc','title','url','mths_since_last_record','mths_since_last_delinq','collections_12_mths_ex_med',
'last_pymnt_d','revol_util','collections_12_mths_ex_med','chargeoff_within_12_mths','tax_liens',
'pymnt_plan','zip_code','initial_list_status','policy_code','application_type','acc_now_delinq','delinq_amnt',]
loan = loan.drop(drop_columns, axis=1)
loan.shape(38577, 39)
# Percentage of null values in columns
total = pd.DataFrame(loan.isnull().sum().sort_values(ascending=False), columns=['Total'])
percentage = pd.DataFrame(round(100*(loan.isnull().sum()/loan.shape[0]),2).sort_values(ascending=False),columns=['Percentage'])
pd.concat([total, percentage], axis = 1)
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| Total | Percentage | |
|---|---|---|
| emp_title | 2386 | 6.19 |
| emp_length | 1033 | 2.68 |
| pub_rec_bankruptcies | 697 | 1.81 |
| last_credit_pull_d | 2 | 0.01 |
| funded_amnt | 0 | 0.00 |
| funded_amnt_inv | 0 | 0.00 |
| term | 0 | 0.00 |
| int_rate | 0 | 0.00 |
| installment | 0 | 0.00 |
| grade | 0 | 0.00 |
| addr_state | 0 | 0.00 |
| loan_amnt | 0 | 0.00 |
| member_id | 0 | 0.00 |
| home_ownership | 0 | 0.00 |
| annual_inc | 0 | 0.00 |
| verification_status | 0 | 0.00 |
| issue_d | 0 | 0.00 |
| loan_status | 0 | 0.00 |
| purpose | 0 | 0.00 |
| sub_grade | 0 | 0.00 |
| dti | 0 | 0.00 |
| delinq_2yrs | 0 | 0.00 |
| earliest_cr_line | 0 | 0.00 |
| last_pymnt_amnt | 0 | 0.00 |
| collection_recovery_fee | 0 | 0.00 |
| recoveries | 0 | 0.00 |
| total_rec_late_fee | 0 | 0.00 |
| total_rec_int | 0 | 0.00 |
| total_rec_prncp | 0 | 0.00 |
| total_pymnt_inv | 0 | 0.00 |
| total_pymnt | 0 | 0.00 |
| out_prncp_inv | 0 | 0.00 |
| out_prncp | 0 | 0.00 |
| total_acc | 0 | 0.00 |
| revol_bal | 0 | 0.00 |
| pub_rec | 0 | 0.00 |
| open_acc | 0 | 0.00 |
| inq_last_6mths | 0 | 0.00 |
| id | 0 | 0.00 |
The columns emp_title, emp_length and pub_rec_bankruptcies have 6.19%, 2.68% and 1.81% missing value respectively. These columns have information about the customer/borrower like their job title and their employment length in years.Lets treat the missing value as it is for our analysis as we do not want to add bias to data by imputing
#loan=loan[~loan.emp_title.isnull()]
#loan=loan[~loan.emp_length.isnull()]
#loan=loan[~loan.pub_rec_bankruptcies.isnull()]loan.shape(38577, 39)
# Percentage of null values in columns
total = pd.DataFrame(loan.isnull().sum().sort_values(ascending=False), columns=['Total'])
percentage = pd.DataFrame(round(100*(loan.isnull().sum()/loan.shape[0]),2).sort_values(ascending=False),columns=['Percentage'])
pd.concat([total, percentage], axis = 1)
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| Total | Percentage | |
|---|---|---|
| emp_title | 2386 | 6.19 |
| emp_length | 1033 | 2.68 |
| pub_rec_bankruptcies | 697 | 1.81 |
| last_credit_pull_d | 2 | 0.01 |
| funded_amnt | 0 | 0.00 |
| funded_amnt_inv | 0 | 0.00 |
| term | 0 | 0.00 |
| int_rate | 0 | 0.00 |
| installment | 0 | 0.00 |
| grade | 0 | 0.00 |
| addr_state | 0 | 0.00 |
| loan_amnt | 0 | 0.00 |
| member_id | 0 | 0.00 |
| home_ownership | 0 | 0.00 |
| annual_inc | 0 | 0.00 |
| verification_status | 0 | 0.00 |
| issue_d | 0 | 0.00 |
| loan_status | 0 | 0.00 |
| purpose | 0 | 0.00 |
| sub_grade | 0 | 0.00 |
| dti | 0 | 0.00 |
| delinq_2yrs | 0 | 0.00 |
| earliest_cr_line | 0 | 0.00 |
| last_pymnt_amnt | 0 | 0.00 |
| collection_recovery_fee | 0 | 0.00 |
| recoveries | 0 | 0.00 |
| total_rec_late_fee | 0 | 0.00 |
| total_rec_int | 0 | 0.00 |
| total_rec_prncp | 0 | 0.00 |
| total_pymnt_inv | 0 | 0.00 |
| total_pymnt | 0 | 0.00 |
| out_prncp_inv | 0 | 0.00 |
| out_prncp | 0 | 0.00 |
| total_acc | 0 | 0.00 |
| revol_bal | 0 | 0.00 |
| pub_rec | 0 | 0.00 |
| open_acc | 0 | 0.00 |
| inq_last_6mths | 0 | 0.00 |
| id | 0 | 0.00 |
#Standardise columns
loan.int_rate = loan.int_rate.apply(lambda x: x[:-1]).astype('float64')#Lets have two columns 'Charged Off' and 'Fully Paid'
# Based on Loan status CHARGED_OFF column will have the value 1 if Loan status = 'Charged Off', else 0
# and FULLY_PAID column will have the value 1 if Loan status = 'Fully Paid', else 0
loan['fully_paid'] = loan['loan_status'].apply(lambda x: 1 if x=='Fully Paid' else 0)
loan['charged_off'] = loan['loan_status'].apply(lambda x: 1 if x=='Charged Off' else 0)
loan
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| id | member_id | loan_amnt | funded_amnt | funded_amnt_inv | term | int_rate | installment | grade | sub_grade | emp_title | emp_length | home_ownership | annual_inc | verification_status | issue_d | loan_status | purpose | addr_state | dti | delinq_2yrs | earliest_cr_line | inq_last_6mths | open_acc | pub_rec | revol_bal | total_acc | out_prncp | out_prncp_inv | total_pymnt | total_pymnt_inv | total_rec_prncp | total_rec_int | total_rec_late_fee | recoveries | collection_recovery_fee | last_pymnt_amnt | last_credit_pull_d | pub_rec_bankruptcies | fully_paid | charged_off | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1077501 | 1296599 | 5000 | 5000 | 4975.0 | 36 months | 10.65 | 162.87 | B | B2 | NaN | 10+ years | RENT | 24000.0 | Verified | Dec-11 | Fully Paid | credit_card | AZ | 27.65 | 0 | Jan-85 | 1 | 3 | 0 | 13648 | 9 | 0.0 | 0.0 | 5863.155187 | 5833.84 | 5000.00 | 863.16 | 0.00 | 0.00 | 0.00 | 171.62 | May-16 | 0.0 | 1 | 0 |
| 1 | 1077430 | 1314167 | 2500 | 2500 | 2500.0 | 60 months | 15.27 | 59.83 | C | C4 | Ryder | < 1 year | RENT | 30000.0 | Source Verified | Dec-11 | Charged Off | car | GA | 1.00 | 0 | Apr-99 | 5 | 3 | 0 | 1687 | 4 | 0.0 | 0.0 | 1008.710000 | 1008.71 | 456.46 | 435.17 | 0.00 | 117.08 | 1.11 | 119.66 | Sep-13 | 0.0 | 0 | 1 |
| 2 | 1077175 | 1313524 | 2400 | 2400 | 2400.0 | 36 months | 15.96 | 84.33 | C | C5 | NaN | 10+ years | RENT | 12252.0 | Not Verified | Dec-11 | Fully Paid | small_business | IL | 8.72 | 0 | Nov-01 | 2 | 2 | 0 | 2956 | 10 | 0.0 | 0.0 | 3005.666844 | 3005.67 | 2400.00 | 605.67 | 0.00 | 0.00 | 0.00 | 649.91 | May-16 | 0.0 | 1 | 0 |
| 3 | 1076863 | 1277178 | 10000 | 10000 | 10000.0 | 36 months | 13.49 | 339.31 | C | C1 | AIR RESOURCES BOARD | 10+ years | RENT | 49200.0 | Source Verified | Dec-11 | Fully Paid | other | CA | 20.00 | 0 | Feb-96 | 1 | 10 | 0 | 5598 | 37 | 0.0 | 0.0 | 12231.890000 | 12231.89 | 10000.00 | 2214.92 | 16.97 | 0.00 | 0.00 | 357.48 | Apr-16 | 0.0 | 1 | 0 |
| 5 | 1075269 | 1311441 | 5000 | 5000 | 5000.0 | 36 months | 7.90 | 156.46 | A | A4 | Veolia Transportaton | 3 years | RENT | 36000.0 | Source Verified | Dec-11 | Fully Paid | wedding | AZ | 11.20 | 0 | Nov-04 | 3 | 9 | 0 | 7963 | 12 | 0.0 | 0.0 | 5632.210000 | 5632.21 | 5000.00 | 632.21 | 0.00 | 0.00 | 0.00 | 161.03 | Jan-16 | 0.0 | 1 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 39712 | 92187 | 92174 | 2500 | 2500 | 1075.0 | 36 months | 8.07 | 78.42 | A | A4 | FiSite Research | 4 years | MORTGAGE | 110000.0 | Not Verified | Jul-07 | Fully Paid | home_improvement | CO | 11.33 | 0 | Nov-90 | 0 | 13 | 0 | 7274 | 40 | 0.0 | 0.0 | 2822.969293 | 1213.88 | 2500.00 | 322.97 | 0.00 | 0.00 | 0.00 | 80.90 | Jun-10 | NaN | 1 | 0 |
| 39713 | 90665 | 90607 | 8500 | 8500 | 875.0 | 36 months | 10.28 | 275.38 | C | C1 | Squarewave Solutions, Ltd. | 3 years | RENT | 18000.0 | Not Verified | Jul-07 | Fully Paid | credit_card | NC | 6.40 | 1 | Dec-86 | 1 | 6 | 0 | 8847 | 9 | 0.0 | 0.0 | 9913.491822 | 1020.51 | 8500.00 | 1413.49 | 0.00 | 0.00 | 0.00 | 281.94 | Jul-10 | NaN | 1 | 0 |
| 39714 | 90395 | 90390 | 5000 | 5000 | 1325.0 | 36 months | 8.07 | 156.84 | A | A4 | NaN | < 1 year | MORTGAGE | 100000.0 | Not Verified | Jul-07 | Fully Paid | debt_consolidation | MA | 2.30 | 0 | Oct-98 | 0 | 11 | 0 | 9698 | 20 | 0.0 | 0.0 | 5272.161128 | 1397.12 | 5000.00 | 272.16 | 0.00 | 0.00 | 0.00 | 0.00 | Jun-07 | NaN | 1 | 0 |
| 39715 | 90376 | 89243 | 5000 | 5000 | 650.0 | 36 months | 7.43 | 155.38 | A | A2 | NaN | < 1 year | MORTGAGE | 200000.0 | Not Verified | Jul-07 | Fully Paid | other | MD | 3.72 | 0 | Nov-88 | 0 | 17 | 0 | 85607 | 26 | 0.0 | 0.0 | 5174.198551 | 672.66 | 5000.00 | 174.20 | 0.00 | 0.00 | 0.00 | 0.00 | Jun-07 | NaN | 1 | 0 |
| 39716 | 87023 | 86999 | 7500 | 7500 | 800.0 | 36 months | 13.75 | 255.43 | E | E2 | Evergreen Center | < 1 year | OWN | 22000.0 | Not Verified | Jun-07 | Fully Paid | debt_consolidation | MA | 14.29 | 1 | Oct-03 | 0 | 7 | 0 | 4175 | 8 | 0.0 | 0.0 | 9195.263334 | 980.83 | 7500.00 | 1695.26 | 0.00 | 0.00 | 0.00 | 256.59 | Jun-10 | NaN | 1 | 0 |
38577 rows × 41 columns
#emp_lenght is a categorical variable and the values are as such seems to be fine and self explanatory
#As their value doesnt affect the analysis we need not modify it and lets make use of it as it is
loan.emp_length.value_counts()10+ years 8488
< 1 year 4508
2 years 4291
3 years 4012
4 years 3342
5 years 3194
1 year 3169
6 years 2168
7 years 1711
8 years 1435
9 years 1226
Name: emp_length, dtype: int64
#Converting the dtype of issue date to datetime
loan.issue_d = pd.to_datetime(loan.issue_d, format='%b-%y')
loan.issue_d 0 2011-12-01
1 2011-12-01
2 2011-12-01
3 2011-12-01
5 2011-12-01
...
39712 2007-07-01
39713 2007-07-01
39714 2007-07-01
39715 2007-07-01
39716 2007-06-01
Name: issue_d, Length: 38577, dtype: datetime64[ns]
#split the available date into month and year column
loan['issue_d_month'] = loan['issue_d'].dt.month
loan['issue_d_year'] = loan['issue_d'].dt.year#Master Data
loan
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| id | member_id | loan_amnt | funded_amnt | funded_amnt_inv | term | int_rate | installment | grade | sub_grade | emp_title | emp_length | home_ownership | annual_inc | verification_status | issue_d | loan_status | purpose | addr_state | dti | delinq_2yrs | earliest_cr_line | inq_last_6mths | open_acc | pub_rec | revol_bal | total_acc | out_prncp | out_prncp_inv | total_pymnt | total_pymnt_inv | total_rec_prncp | total_rec_int | total_rec_late_fee | recoveries | collection_recovery_fee | last_pymnt_amnt | last_credit_pull_d | pub_rec_bankruptcies | fully_paid | charged_off | issue_d_month | issue_d_year | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1077501 | 1296599 | 5000 | 5000 | 4975.0 | 36 months | 10.65 | 162.87 | B | B2 | NaN | 10+ years | RENT | 24000.0 | Verified | 2011-12-01 | Fully Paid | credit_card | AZ | 27.65 | 0 | Jan-85 | 1 | 3 | 0 | 13648 | 9 | 0.0 | 0.0 | 5863.155187 | 5833.84 | 5000.00 | 863.16 | 0.00 | 0.00 | 0.00 | 171.62 | May-16 | 0.0 | 1 | 0 | 12 | 2011 |
| 1 | 1077430 | 1314167 | 2500 | 2500 | 2500.0 | 60 months | 15.27 | 59.83 | C | C4 | Ryder | < 1 year | RENT | 30000.0 | Source Verified | 2011-12-01 | Charged Off | car | GA | 1.00 | 0 | Apr-99 | 5 | 3 | 0 | 1687 | 4 | 0.0 | 0.0 | 1008.710000 | 1008.71 | 456.46 | 435.17 | 0.00 | 117.08 | 1.11 | 119.66 | Sep-13 | 0.0 | 0 | 1 | 12 | 2011 |
| 2 | 1077175 | 1313524 | 2400 | 2400 | 2400.0 | 36 months | 15.96 | 84.33 | C | C5 | NaN | 10+ years | RENT | 12252.0 | Not Verified | 2011-12-01 | Fully Paid | small_business | IL | 8.72 | 0 | Nov-01 | 2 | 2 | 0 | 2956 | 10 | 0.0 | 0.0 | 3005.666844 | 3005.67 | 2400.00 | 605.67 | 0.00 | 0.00 | 0.00 | 649.91 | May-16 | 0.0 | 1 | 0 | 12 | 2011 |
| 3 | 1076863 | 1277178 | 10000 | 10000 | 10000.0 | 36 months | 13.49 | 339.31 | C | C1 | AIR RESOURCES BOARD | 10+ years | RENT | 49200.0 | Source Verified | 2011-12-01 | Fully Paid | other | CA | 20.00 | 0 | Feb-96 | 1 | 10 | 0 | 5598 | 37 | 0.0 | 0.0 | 12231.890000 | 12231.89 | 10000.00 | 2214.92 | 16.97 | 0.00 | 0.00 | 357.48 | Apr-16 | 0.0 | 1 | 0 | 12 | 2011 |
| 5 | 1075269 | 1311441 | 5000 | 5000 | 5000.0 | 36 months | 7.90 | 156.46 | A | A4 | Veolia Transportaton | 3 years | RENT | 36000.0 | Source Verified | 2011-12-01 | Fully Paid | wedding | AZ | 11.20 | 0 | Nov-04 | 3 | 9 | 0 | 7963 | 12 | 0.0 | 0.0 | 5632.210000 | 5632.21 | 5000.00 | 632.21 | 0.00 | 0.00 | 0.00 | 161.03 | Jan-16 | 0.0 | 1 | 0 | 12 | 2011 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 39712 | 92187 | 92174 | 2500 | 2500 | 1075.0 | 36 months | 8.07 | 78.42 | A | A4 | FiSite Research | 4 years | MORTGAGE | 110000.0 | Not Verified | 2007-07-01 | Fully Paid | home_improvement | CO | 11.33 | 0 | Nov-90 | 0 | 13 | 0 | 7274 | 40 | 0.0 | 0.0 | 2822.969293 | 1213.88 | 2500.00 | 322.97 | 0.00 | 0.00 | 0.00 | 80.90 | Jun-10 | NaN | 1 | 0 | 7 | 2007 |
| 39713 | 90665 | 90607 | 8500 | 8500 | 875.0 | 36 months | 10.28 | 275.38 | C | C1 | Squarewave Solutions, Ltd. | 3 years | RENT | 18000.0 | Not Verified | 2007-07-01 | Fully Paid | credit_card | NC | 6.40 | 1 | Dec-86 | 1 | 6 | 0 | 8847 | 9 | 0.0 | 0.0 | 9913.491822 | 1020.51 | 8500.00 | 1413.49 | 0.00 | 0.00 | 0.00 | 281.94 | Jul-10 | NaN | 1 | 0 | 7 | 2007 |
| 39714 | 90395 | 90390 | 5000 | 5000 | 1325.0 | 36 months | 8.07 | 156.84 | A | A4 | NaN | < 1 year | MORTGAGE | 100000.0 | Not Verified | 2007-07-01 | Fully Paid | debt_consolidation | MA | 2.30 | 0 | Oct-98 | 0 | 11 | 0 | 9698 | 20 | 0.0 | 0.0 | 5272.161128 | 1397.12 | 5000.00 | 272.16 | 0.00 | 0.00 | 0.00 | 0.00 | Jun-07 | NaN | 1 | 0 | 7 | 2007 |
| 39715 | 90376 | 89243 | 5000 | 5000 | 650.0 | 36 months | 7.43 | 155.38 | A | A2 | NaN | < 1 year | MORTGAGE | 200000.0 | Not Verified | 2007-07-01 | Fully Paid | other | MD | 3.72 | 0 | Nov-88 | 0 | 17 | 0 | 85607 | 26 | 0.0 | 0.0 | 5174.198551 | 672.66 | 5000.00 | 174.20 | 0.00 | 0.00 | 0.00 | 0.00 | Jun-07 | NaN | 1 | 0 | 7 | 2007 |
| 39716 | 87023 | 86999 | 7500 | 7500 | 800.0 | 36 months | 13.75 | 255.43 | E | E2 | Evergreen Center | < 1 year | OWN | 22000.0 | Not Verified | 2007-06-01 | Fully Paid | debt_consolidation | MA | 14.29 | 1 | Oct-03 | 0 | 7 | 0 | 4175 | 8 | 0.0 | 0.0 | 9195.263334 | 980.83 | 7500.00 | 1695.26 | 0.00 | 0.00 | 0.00 | 256.59 | Jun-10 | NaN | 1 | 0 | 6 | 2007 |
38577 rows × 43 columns
loan.int_rate.describe()count 38577.000000
mean 11.932219
std 3.691327
min 5.420000
25% 8.940000
50% 11.710000
75% 14.380000
max 24.400000
Name: int_rate, dtype: float64
# Binning the interest rates into different slots
def interest_rate_slot(loan,cut_points,label_names):
column_index = loan.columns.get_loc('int_rate') + 1
loan.insert(loc=column_index,column='interest_rate_bins',value=pd.cut(loan['int_rate'],cut_points,labels=label_names, include_lowest=True))
return loan
cut_points = [5,10,15,25]
label_names = ["Low","Medium","High"]
loan = interest_rate_slot(loan,cut_points,label_names)
loan.head()
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| id | member_id | loan_amnt | funded_amnt | funded_amnt_inv | term | int_rate | interest_rate_bins | installment | grade | sub_grade | emp_title | emp_length | home_ownership | annual_inc | verification_status | issue_d | loan_status | purpose | addr_state | dti | delinq_2yrs | earliest_cr_line | inq_last_6mths | open_acc | pub_rec | revol_bal | total_acc | out_prncp | out_prncp_inv | total_pymnt | total_pymnt_inv | total_rec_prncp | total_rec_int | total_rec_late_fee | recoveries | collection_recovery_fee | last_pymnt_amnt | last_credit_pull_d | pub_rec_bankruptcies | fully_paid | charged_off | issue_d_month | issue_d_year | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1077501 | 1296599 | 5000 | 5000 | 4975.0 | 36 months | 10.65 | Medium | 162.87 | B | B2 | NaN | 10+ years | RENT | 24000.0 | Verified | 2011-12-01 | Fully Paid | credit_card | AZ | 27.65 | 0 | Jan-85 | 1 | 3 | 0 | 13648 | 9 | 0.0 | 0.0 | 5863.155187 | 5833.84 | 5000.00 | 863.16 | 0.00 | 0.00 | 0.00 | 171.62 | May-16 | 0.0 | 1 | 0 | 12 | 2011 |
| 1 | 1077430 | 1314167 | 2500 | 2500 | 2500.0 | 60 months | 15.27 | High | 59.83 | C | C4 | Ryder | < 1 year | RENT | 30000.0 | Source Verified | 2011-12-01 | Charged Off | car | GA | 1.00 | 0 | Apr-99 | 5 | 3 | 0 | 1687 | 4 | 0.0 | 0.0 | 1008.710000 | 1008.71 | 456.46 | 435.17 | 0.00 | 117.08 | 1.11 | 119.66 | Sep-13 | 0.0 | 0 | 1 | 12 | 2011 |
| 2 | 1077175 | 1313524 | 2400 | 2400 | 2400.0 | 36 months | 15.96 | High | 84.33 | C | C5 | NaN | 10+ years | RENT | 12252.0 | Not Verified | 2011-12-01 | Fully Paid | small_business | IL | 8.72 | 0 | Nov-01 | 2 | 2 | 0 | 2956 | 10 | 0.0 | 0.0 | 3005.666844 | 3005.67 | 2400.00 | 605.67 | 0.00 | 0.00 | 0.00 | 649.91 | May-16 | 0.0 | 1 | 0 | 12 | 2011 |
| 3 | 1076863 | 1277178 | 10000 | 10000 | 10000.0 | 36 months | 13.49 | Medium | 339.31 | C | C1 | AIR RESOURCES BOARD | 10+ years | RENT | 49200.0 | Source Verified | 2011-12-01 | Fully Paid | other | CA | 20.00 | 0 | Feb-96 | 1 | 10 | 0 | 5598 | 37 | 0.0 | 0.0 | 12231.890000 | 12231.89 | 10000.00 | 2214.92 | 16.97 | 0.00 | 0.00 | 357.48 | Apr-16 | 0.0 | 1 | 0 | 12 | 2011 |
| 5 | 1075269 | 1311441 | 5000 | 5000 | 5000.0 | 36 months | 7.90 | Low | 156.46 | A | A4 | Veolia Transportaton | 3 years | RENT | 36000.0 | Source Verified | 2011-12-01 | Fully Paid | wedding | AZ | 11.20 | 0 | Nov-04 | 3 | 9 | 0 | 7963 | 12 | 0.0 | 0.0 | 5632.210000 | 5632.21 | 5000.00 | 632.21 | 0.00 | 0.00 | 0.00 | 161.03 | Jan-16 | 0.0 | 1 | 0 | 12 | 2011 |
# Binning the dti values
def dti_slot(loan,cut_points,label_names):
column_index = loan.columns.get_loc('dti') + 1
loan.insert(loc=column_index,column='dti_bins',value=pd.cut(loan['dti'],cut_points,labels=label_names, include_lowest=True))
return loan
cut_points = [0,5,10,15,20,25,30]
label_names = ["Less Than 5","Btwn 5 & 10","Btwn 10 & 15","Btwn 15 & 20","Btwn 20 & 25","Btwn 25 & 30"]
loan = dti_slot(loan,cut_points,label_names)
loan.head()
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| id | member_id | loan_amnt | funded_amnt | funded_amnt_inv | term | int_rate | interest_rate_bins | installment | grade | sub_grade | emp_title | emp_length | home_ownership | annual_inc | verification_status | issue_d | loan_status | purpose | addr_state | dti | dti_bins | delinq_2yrs | earliest_cr_line | inq_last_6mths | open_acc | pub_rec | revol_bal | total_acc | out_prncp | out_prncp_inv | total_pymnt | total_pymnt_inv | total_rec_prncp | total_rec_int | total_rec_late_fee | recoveries | collection_recovery_fee | last_pymnt_amnt | last_credit_pull_d | pub_rec_bankruptcies | fully_paid | charged_off | issue_d_month | issue_d_year | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1077501 | 1296599 | 5000 | 5000 | 4975.0 | 36 months | 10.65 | Medium | 162.87 | B | B2 | NaN | 10+ years | RENT | 24000.0 | Verified | 2011-12-01 | Fully Paid | credit_card | AZ | 27.65 | Btwn 25 & 30 | 0 | Jan-85 | 1 | 3 | 0 | 13648 | 9 | 0.0 | 0.0 | 5863.155187 | 5833.84 | 5000.00 | 863.16 | 0.00 | 0.00 | 0.00 | 171.62 | May-16 | 0.0 | 1 | 0 | 12 | 2011 |
| 1 | 1077430 | 1314167 | 2500 | 2500 | 2500.0 | 60 months | 15.27 | High | 59.83 | C | C4 | Ryder | < 1 year | RENT | 30000.0 | Source Verified | 2011-12-01 | Charged Off | car | GA | 1.00 | Less Than 5 | 0 | Apr-99 | 5 | 3 | 0 | 1687 | 4 | 0.0 | 0.0 | 1008.710000 | 1008.71 | 456.46 | 435.17 | 0.00 | 117.08 | 1.11 | 119.66 | Sep-13 | 0.0 | 0 | 1 | 12 | 2011 |
| 2 | 1077175 | 1313524 | 2400 | 2400 | 2400.0 | 36 months | 15.96 | High | 84.33 | C | C5 | NaN | 10+ years | RENT | 12252.0 | Not Verified | 2011-12-01 | Fully Paid | small_business | IL | 8.72 | Btwn 5 & 10 | 0 | Nov-01 | 2 | 2 | 0 | 2956 | 10 | 0.0 | 0.0 | 3005.666844 | 3005.67 | 2400.00 | 605.67 | 0.00 | 0.00 | 0.00 | 649.91 | May-16 | 0.0 | 1 | 0 | 12 | 2011 |
| 3 | 1076863 | 1277178 | 10000 | 10000 | 10000.0 | 36 months | 13.49 | Medium | 339.31 | C | C1 | AIR RESOURCES BOARD | 10+ years | RENT | 49200.0 | Source Verified | 2011-12-01 | Fully Paid | other | CA | 20.00 | Btwn 15 & 20 | 0 | Feb-96 | 1 | 10 | 0 | 5598 | 37 | 0.0 | 0.0 | 12231.890000 | 12231.89 | 10000.00 | 2214.92 | 16.97 | 0.00 | 0.00 | 357.48 | Apr-16 | 0.0 | 1 | 0 | 12 | 2011 |
| 5 | 1075269 | 1311441 | 5000 | 5000 | 5000.0 | 36 months | 7.90 | Low | 156.46 | A | A4 | Veolia Transportaton | 3 years | RENT | 36000.0 | Source Verified | 2011-12-01 | Fully Paid | wedding | AZ | 11.20 | Btwn 10 & 15 | 0 | Nov-04 | 3 | 9 | 0 | 7963 | 12 | 0.0 | 0.0 | 5632.210000 | 5632.21 | 5000.00 | 632.21 | 0.00 | 0.00 | 0.00 | 161.03 | Jan-16 | 0.0 | 1 | 0 | 12 | 2011 |
loan
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| id | member_id | loan_amnt | funded_amnt | funded_amnt_inv | term | int_rate | interest_rate_bins | installment | grade | sub_grade | emp_title | emp_length | home_ownership | annual_inc | verification_status | issue_d | loan_status | purpose | addr_state | dti | dti_bins | delinq_2yrs | earliest_cr_line | inq_last_6mths | open_acc | pub_rec | revol_bal | total_acc | out_prncp | out_prncp_inv | total_pymnt | total_pymnt_inv | total_rec_prncp | total_rec_int | total_rec_late_fee | recoveries | collection_recovery_fee | last_pymnt_amnt | last_credit_pull_d | pub_rec_bankruptcies | fully_paid | charged_off | issue_d_month | issue_d_year | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1077501 | 1296599 | 5000 | 5000 | 4975.0 | 36 months | 10.65 | Medium | 162.87 | B | B2 | NaN | 10+ years | RENT | 24000.0 | Verified | 2011-12-01 | Fully Paid | credit_card | AZ | 27.65 | Btwn 25 & 30 | 0 | Jan-85 | 1 | 3 | 0 | 13648 | 9 | 0.0 | 0.0 | 5863.155187 | 5833.84 | 5000.00 | 863.16 | 0.00 | 0.00 | 0.00 | 171.62 | May-16 | 0.0 | 1 | 0 | 12 | 2011 |
| 1 | 1077430 | 1314167 | 2500 | 2500 | 2500.0 | 60 months | 15.27 | High | 59.83 | C | C4 | Ryder | < 1 year | RENT | 30000.0 | Source Verified | 2011-12-01 | Charged Off | car | GA | 1.00 | Less Than 5 | 0 | Apr-99 | 5 | 3 | 0 | 1687 | 4 | 0.0 | 0.0 | 1008.710000 | 1008.71 | 456.46 | 435.17 | 0.00 | 117.08 | 1.11 | 119.66 | Sep-13 | 0.0 | 0 | 1 | 12 | 2011 |
| 2 | 1077175 | 1313524 | 2400 | 2400 | 2400.0 | 36 months | 15.96 | High | 84.33 | C | C5 | NaN | 10+ years | RENT | 12252.0 | Not Verified | 2011-12-01 | Fully Paid | small_business | IL | 8.72 | Btwn 5 & 10 | 0 | Nov-01 | 2 | 2 | 0 | 2956 | 10 | 0.0 | 0.0 | 3005.666844 | 3005.67 | 2400.00 | 605.67 | 0.00 | 0.00 | 0.00 | 649.91 | May-16 | 0.0 | 1 | 0 | 12 | 2011 |
| 3 | 1076863 | 1277178 | 10000 | 10000 | 10000.0 | 36 months | 13.49 | Medium | 339.31 | C | C1 | AIR RESOURCES BOARD | 10+ years | RENT | 49200.0 | Source Verified | 2011-12-01 | Fully Paid | other | CA | 20.00 | Btwn 15 & 20 | 0 | Feb-96 | 1 | 10 | 0 | 5598 | 37 | 0.0 | 0.0 | 12231.890000 | 12231.89 | 10000.00 | 2214.92 | 16.97 | 0.00 | 0.00 | 357.48 | Apr-16 | 0.0 | 1 | 0 | 12 | 2011 |
| 5 | 1075269 | 1311441 | 5000 | 5000 | 5000.0 | 36 months | 7.90 | Low | 156.46 | A | A4 | Veolia Transportaton | 3 years | RENT | 36000.0 | Source Verified | 2011-12-01 | Fully Paid | wedding | AZ | 11.20 | Btwn 10 & 15 | 0 | Nov-04 | 3 | 9 | 0 | 7963 | 12 | 0.0 | 0.0 | 5632.210000 | 5632.21 | 5000.00 | 632.21 | 0.00 | 0.00 | 0.00 | 161.03 | Jan-16 | 0.0 | 1 | 0 | 12 | 2011 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 39712 | 92187 | 92174 | 2500 | 2500 | 1075.0 | 36 months | 8.07 | Low | 78.42 | A | A4 | FiSite Research | 4 years | MORTGAGE | 110000.0 | Not Verified | 2007-07-01 | Fully Paid | home_improvement | CO | 11.33 | Btwn 10 & 15 | 0 | Nov-90 | 0 | 13 | 0 | 7274 | 40 | 0.0 | 0.0 | 2822.969293 | 1213.88 | 2500.00 | 322.97 | 0.00 | 0.00 | 0.00 | 80.90 | Jun-10 | NaN | 1 | 0 | 7 | 2007 |
| 39713 | 90665 | 90607 | 8500 | 8500 | 875.0 | 36 months | 10.28 | Medium | 275.38 | C | C1 | Squarewave Solutions, Ltd. | 3 years | RENT | 18000.0 | Not Verified | 2007-07-01 | Fully Paid | credit_card | NC | 6.40 | Btwn 5 & 10 | 1 | Dec-86 | 1 | 6 | 0 | 8847 | 9 | 0.0 | 0.0 | 9913.491822 | 1020.51 | 8500.00 | 1413.49 | 0.00 | 0.00 | 0.00 | 281.94 | Jul-10 | NaN | 1 | 0 | 7 | 2007 |
| 39714 | 90395 | 90390 | 5000 | 5000 | 1325.0 | 36 months | 8.07 | Low | 156.84 | A | A4 | NaN | < 1 year | MORTGAGE | 100000.0 | Not Verified | 2007-07-01 | Fully Paid | debt_consolidation | MA | 2.30 | Less Than 5 | 0 | Oct-98 | 0 | 11 | 0 | 9698 | 20 | 0.0 | 0.0 | 5272.161128 | 1397.12 | 5000.00 | 272.16 | 0.00 | 0.00 | 0.00 | 0.00 | Jun-07 | NaN | 1 | 0 | 7 | 2007 |
| 39715 | 90376 | 89243 | 5000 | 5000 | 650.0 | 36 months | 7.43 | Low | 155.38 | A | A2 | NaN | < 1 year | MORTGAGE | 200000.0 | Not Verified | 2007-07-01 | Fully Paid | other | MD | 3.72 | Less Than 5 | 0 | Nov-88 | 0 | 17 | 0 | 85607 | 26 | 0.0 | 0.0 | 5174.198551 | 672.66 | 5000.00 | 174.20 | 0.00 | 0.00 | 0.00 | 0.00 | Jun-07 | NaN | 1 | 0 | 7 | 2007 |
| 39716 | 87023 | 86999 | 7500 | 7500 | 800.0 | 36 months | 13.75 | Medium | 255.43 | E | E2 | Evergreen Center | < 1 year | OWN | 22000.0 | Not Verified | 2007-06-01 | Fully Paid | debt_consolidation | MA | 14.29 | Btwn 10 & 15 | 1 | Oct-03 | 0 | 7 | 0 | 4175 | 8 | 0.0 | 0.0 | 9195.263334 | 980.83 | 7500.00 | 1695.26 | 0.00 | 0.00 | 0.00 | 256.59 | Jun-10 | NaN | 1 | 0 | 6 | 2007 |
38577 rows × 45 columns
Lets us observe some general trend in Loans based on factors such as loan amounts, interest rates, annual income and dti
#charged_off = loan.loc[loan['loan_status']=='Charged Off']
plt.figure(figsize=(12, 10), dpi=80, facecolor='w', edgecolor='k')
# subplot 1: loan_amnt
plt.subplot(3, 2, 1)
sns.distplot(loan['loan_amnt'], color='red')
plt.xlim([0, 35000])
# subplot 2: int_rate
plt.subplot(3, 2, 2)
sns.distplot(loan['int_rate'], color='blue')
# subplot 3: annual_inc
plt.subplot(3, 2, 3)
plt.xscale('log')
sns.boxplot('annual_inc', data=loan)
# subplot 4: dti
plt.subplot(3, 2, 4)
sns.distplot(loan['dti'], color='green', bins=10)
plt.xlim([0, 30])
plt.tight_layout()
plt.show()D:\HP\Anaconda3\envs\AI\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
warnings.warn(msg, FutureWarning)
D:\HP\Anaconda3\envs\AI\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
warnings.warn(msg, FutureWarning)
D:\HP\Anaconda3\envs\AI\lib\site-packages\seaborn\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
FutureWarning
D:\HP\Anaconda3\envs\AI\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
warnings.warn(msg, FutureWarning)
- We can see that most of the loans are in the range of 2000 to 16000.
- Most Loan interest rates are in between 5% to 10% and 10% to 15%, the trend decreases after 15.
- The frequency distribution of dti seems to be symmetric in nature centered around 15.
- There are quite a few top outlier values in annual income of borrowers.
Some more Insights based on factors like loan terms, grades, employess lengths, home ownerships, loan verification status, loan purpose and derogatory public records`
plt.figure(figsize=(12, 12), dpi=80, facecolor='w', edgecolor='k')
# subplot 1: Terms
plt.subplot(4, 2, 1)
sns.countplot(x='term', palette='coolwarm', data=loan)
# subplot 2: Grade
plt.subplot(4, 2, 2)
sns.countplot(x='grade', palette='BrBG', data=loan)
# subplot 3: Emp_length
plt.subplot(4, 2, 3)
sns.countplot(y='emp_length', palette='BrBG', data=loan)
# subplot 4: home_ownership
plt.subplot(4, 2, 4)
sns.countplot(x='home_ownership', palette='coolwarm', data=loan)
# subplot 5: verification_status
plt.subplot(4, 2, 5)
sns.countplot(x='verification_status', palette='coolwarm', data=loan)
# subplot 6: loan_status
plt.subplot(4, 2, 6)
sns.countplot(x='loan_status', palette='BrBG', data=loan)
# subplot 7: purpose
plt.subplot(4, 2, 7)
sns.countplot(y='purpose', palette='BrBG', data=loan)
plt.tight_layout()
plt.show()
- Most of the approved loans seems to be having loan term of 36 months.
- B, A and C Grade loans seems to be more compared to others
- We can say that most loans are borrowed by people with either 0-3 years of exp or greater than 10 years.
- Rented homes and mortgage seems to be predominant over people who own a house.
- Most of the accepted loans are not verified.
- About 1/6th of all accepted loans are 'Charged off'. The rest are fully paid.
- Debt consolidation and Credit card loan appears to be the primary purpose for loan application .
print("%.2f" % (loan.loc[loan['loan_status'] == 'Charged Off'].loan_status.count() * 100/len(loan)))14.59
Approximately 14.6% of loans in the dataset are defaulted.
loan_purpose = pd.DataFrame(loan.groupby('purpose')['charged_off','fully_paid'].sum().reset_index())
loan_purposeD:\HP\Anaconda3\envs\AI\lib\site-packages\ipykernel_launcher.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.
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| purpose | charged_off | fully_paid | |
|---|---|---|---|
| 0 | car | 160 | 1339 |
| 1 | credit_card | 542 | 4485 |
| 2 | debt_consolidation | 2767 | 15288 |
| 3 | educational | 56 | 269 |
| 4 | home_improvement | 347 | 2528 |
| 5 | house | 59 | 308 |
| 6 | major_purchase | 222 | 1928 |
| 7 | medical | 106 | 575 |
| 8 | moving | 92 | 484 |
| 9 | other | 633 | 3232 |
| 10 | renewable_energy | 19 | 83 |
| 11 | small_business | 475 | 1279 |
| 12 | vacation | 53 | 322 |
| 13 | wedding | 96 | 830 |
#bar chart
loan_purpose.plot.bar(x='purpose', y=['charged_off','fully_paid'],figsize=[10,5])
plt.ylabel('Loan Count')
plt.xlabel('Loan Purpose')
plt.title("Loan count based on purpose")Text(0.5, 1.0, 'Loan count based on purpose')
# Calculate percentage
r = [0,1,2,3,4,5,6,7,8,9,10,11,12,13]
totals = [i+j for i,j in zip(loan_purpose['charged_off'], loan_purpose['fully_paid'])]
charged_off = [i / j * 100 for i,j in zip(loan_purpose['charged_off'], totals)]
fully_paid = [i / j * 100 for i,j in zip(loan_purpose['fully_paid'], totals)]
names = list(loan_purpose['purpose'])
# plot
plt.figure(figsize=(8, 15), dpi=80, facecolor='w', edgecolor='k')
# subplot 1: stacked bar
plt.subplot(3, 1, 1)
barWidth = 0.85
# Create charged_off Bars
plt.bar(r, charged_off, color='#b5ffb9', edgecolor='white', width=barWidth)
# Create fully paid Bars
plt.bar(r, fully_paid, bottom=[i for i in charged_off], color='#a3acff', edgecolor='white', width=barWidth)
# Custom x axis
plt.xticks(r, names, rotation='vertical')
plt.legend(['charged_off','fully_paid'],frameon=True, fontsize='small', shadow='True', title='Loan Status', bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
plt.title("Percentage of Defaulted Loans out of Total loans for different types of loans")Text(0.5, 1.0, 'Percentage of Defaulted Loans out of Total loans for different types of loans')
- Small business have highest % of charged off loans.
- Debt_consolidation has highest no of charged off loans.
loan_term = pd.DataFrame(loan.groupby('term')['charged_off','fully_paid'].sum().reset_index())
loan_termD:\HP\Anaconda3\envs\AI\lib\site-packages\ipykernel_launcher.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.
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| term | charged_off | fully_paid | |
|---|---|---|---|
| 0 | 36 months | 3227 | 25869 |
| 1 | 60 months | 2400 | 7081 |
#bar chart
loan_term.plot.bar(x='term', y=['charged_off','fully_paid'],figsize=[8,5])
plt.ylabel('Loan Count')
plt.xlabel('Loan Term')
plt.title("Loan Count based on Term")Text(0.5, 1.0, 'Loan Count based on Term')
#Calculate percentage
r = [0,1]
totals = [i+j for i,j in zip(loan_term['charged_off'], loan_term['fully_paid'])]
charged_off = [i / j * 100 for i,j in zip(loan_term['charged_off'], totals)]
fully_paid = [i / j * 100 for i,j in zip(loan_term['fully_paid'], totals)]
names = list(loan_term['term'])
# plot
plt.figure(figsize=(12, 10), dpi=80, facecolor='w', edgecolor='k')
#stacked bar
plt.subplot(2, 2, 1)
barWidth = 0.85
# Create charged_off Bars
plt.bar(r, charged_off, color='#b5ffb9', edgecolor='white', width=barWidth)
# Create fully paid Bars
plt.bar(r, fully_paid, bottom=[i for i in charged_off], color='#a3acff', edgecolor='white', width=barWidth)
# Custom x axis
plt.xticks(r, names)
plt.legend(['charged_off','fully_paid'],frameon=True, fontsize='small', shadow='True', title='Loan Status', bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
plt.title("Percentage of Defaulted Loans out of Total loans for different loan terms")Text(0.5, 1.0, 'Percentage of Defaulted Loans out of Total loans for different loan terms')
- Loans whose term is over 60 months have deafault rate that is twice that of deafault rate of loans whose term is 36 months.
- We can also observe that more deafulters are observed in loans that spans over 36 months as compared to loans that spans over 60months
loan_issue_yr = pd.DataFrame(loan.groupby('issue_d_year')['charged_off','fully_paid'].sum().reset_index())
loan_issue_yrD:\HP\Anaconda3\envs\AI\lib\site-packages\ipykernel_launcher.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.
"""Entry point for launching an IPython kernel.
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| issue_d_year | charged_off | fully_paid | |
|---|---|---|---|
| 0 | 2007 | 45 | 206 |
| 1 | 2008 | 247 | 1315 |
| 2 | 2009 | 594 | 4122 |
| 3 | 2010 | 1485 | 10047 |
| 4 | 2011 | 3256 | 17260 |
#bar chart
loan_issue_yr.plot.bar(x='issue_d_year', y=['charged_off','fully_paid'],figsize=[8,5])
plt.ylabel('Loan Count')
plt.xlabel('Loan Issued Date(Year)')
plt.title("Year wise loans count")Text(0.5, 1.0, 'Year wise loans count')
#percentage
r = list(loan_issue_yr.index) #r = [0,1,2]
totals = [i+j for i,j in zip(loan_issue_yr['charged_off'], loan_issue_yr['fully_paid'])]
charged_off = [i / j * 100 for i,j in zip(loan_issue_yr['charged_off'], totals)]
fully_paid = [i / j * 100 for i,j in zip(loan_issue_yr['fully_paid'], totals)]
names = list(loan_issue_yr['issue_d_year'])
# plot
plt.figure(figsize=(12, 10), dpi=80, facecolor='w', edgecolor='k')
#stacked bar
plt.subplot(2, 2, 1)
barWidth = 0.85
# Create charged_off Bars
plt.bar(r, charged_off, color='#636363', edgecolor='white', width=barWidth)
# Create fully paid Bars
plt.bar(r, fully_paid, bottom=[i for i in charged_off], color='#a3acff', edgecolor='white', width=barWidth)
# Custom x axis
plt.xticks(r, names, rotation='vertical')
plt.legend(['charged_off','fully_paid'],frameon=True, fontsize='small', shadow='True', title='Loan Status', bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
plt.title("Year wise Loan Default Rate")Text(0.5, 1.0, 'Year wise Loan Default Rate')
- Its evident that no of Defaulters are increasing every year
- Its also interesting to note that though defaulters are increasing the default rate was maximum on 2007 followed by 2011
loan_ir = pd.DataFrame(loan.groupby('interest_rate_bins')['charged_off','fully_paid'].sum().reset_index())
loan_irD:\HP\Anaconda3\envs\AI\lib\site-packages\ipykernel_launcher.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.
"""Entry point for launching an IPython kernel.
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| interest_rate_bins | charged_off | fully_paid | |
|---|---|---|---|
| 0 | Low | 830 | 11486 |
| 1 | Medium | 2707 | 15558 |
| 2 | High | 2090 | 5906 |
#bar chart
loan_ir.plot.bar(x='interest_rate_bins', y=['charged_off','fully_paid'],figsize=[8,5])
plt.ylabel('Loan Count')
plt.xlabel('Interest Rate Type')
plt.title("Loan Count based on Interest Rate")Text(0.5, 1.0, 'Loan Count based on Interest Rate')
#percentage
r = [0,1,2]
totals = [i+j for i,j in zip(loan_ir['charged_off'], loan_ir['fully_paid'])]
charged_off = [i / j * 100 for i,j in zip(loan_ir['charged_off'], totals)]
fully_paid = [i / j * 100 for i,j in zip(loan_ir['fully_paid'], totals)]
names = list(loan_ir['interest_rate_bins'])
# plot
plt.figure(figsize=(12, 10), dpi=80, facecolor='w', edgecolor='k')
#stacked bar
plt.subplot(2, 2, 1)
barWidth = 0.85
# Create charged_off Bars
plt.bar(r, charged_off, color='#636363', edgecolor='white', width=barWidth)
# Create fully paid Bars
plt.bar(r, fully_paid, bottom=[i for i in charged_off], color='#a3acff', edgecolor='white', width=barWidth)
# Custom x axis
plt.xticks(r, names, rotation='vertical')
plt.legend(['charged_off','fully_paid'],frameon=True, fontsize='small', shadow='True', title='Loan Status', bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
plt.title("Percentage of Defaulted loans out of Total loans for different Rate of Interest ranges")Text(0.5, 1.0, 'Percentage of Defaulted loans out of Total loans for different Rate of Interest ranges')
- We can observe that the default rate increases as the rate of interest increases.
- The defaulters are more likely observed in loans where interest rate is between 10% and 15%
loan_grade = pd.DataFrame(loan.groupby('grade')['charged_off','fully_paid'].sum().reset_index())
loan_gradeD:\HP\Anaconda3\envs\AI\lib\site-packages\ipykernel_launcher.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.
"""Entry point for launching an IPython kernel.
<style scoped>
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| grade | charged_off | fully_paid | |
|---|---|---|---|
| 0 | A | 602 | 9443 |
| 1 | B | 1425 | 10250 |
| 2 | C | 1347 | 6487 |
| 3 | D | 1118 | 3967 |
| 4 | E | 715 | 1948 |
| 5 | F | 319 | 657 |
| 6 | G | 101 | 198 |
#bar chart
loan_grade.plot.bar(x='grade', y=['charged_off','fully_paid'],figsize=[8,5])
plt.ylabel('Loan Count')
plt.xlabel('Loan Grades')
plt.title("Loan Count based on Loan Grade")Text(0.5, 1.0, 'Loan Count based on Loan Grade')
#Calculate percentage
r = [0,1,2,3,4,5,6]
totals = [i+j for i,j in zip(loan_grade['charged_off'], loan_grade['fully_paid'])]
charged_off = [i / j * 100 for i,j in zip(loan_grade['charged_off'], totals)]
fully_paid = [i / j * 100 for i,j in zip(loan_grade['fully_paid'], totals)]
names = list(loan_grade['grade'])
# plot
plt.figure(figsize=(15, 10), dpi=80, facecolor='w', edgecolor='k')
#stacked bar
plt.subplot(2, 2, 1)
barWidth = 0.85
# Create charged_off Bars
plt.bar(r, charged_off, color='#b5ffb9', edgecolor='white', width=barWidth)
# Create fully paid Bars
plt.bar(r, fully_paid, bottom=[i for i in charged_off], color='#a3acff', edgecolor='white', width=barWidth)
# Custom x axis
plt.xticks(r, names, rotation='vertical')
plt.legend(['charged_off','fully_paid'],frameon=True, fontsize='small', shadow='True', title='Loan Status', bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
plt.title("Percentage of Defaulted Loans out of Total loans for different Loan Grades")Text(0.5, 1.0, 'Percentage of Defaulted Loans out of Total loans for different Loan Grades')
- Default rate in loan increases from grade A to G.
- Loan applications with grades B, C and D have the highest no of Defaulters
loan_subgrade = pd.DataFrame(loan.groupby(['sub_grade'])['charged_off','fully_paid'].sum())
loan_subgradeD:\HP\Anaconda3\envs\AI\lib\site-packages\ipykernel_launcher.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.
"""Entry point for launching an IPython kernel.
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| charged_off | fully_paid | |
|---|---|---|
| sub_grade | ||
| A1 | 30 | 1109 |
| A2 | 74 | 1434 |
| A3 | 103 | 1707 |
| A4 | 178 | 2695 |
| A5 | 217 | 2498 |
| B1 | 171 | 1626 |
| B2 | 228 | 1773 |
| B3 | 341 | 2484 |
| B4 | 329 | 2108 |
| B5 | 356 | 2259 |
| C1 | 336 | 1719 |
| C2 | 321 | 1610 |
| C3 | 270 | 1218 |
| C4 | 212 | 994 |
| C5 | 208 | 946 |
| D1 | 167 | 764 |
| D2 | 271 | 1015 |
| D3 | 256 | 860 |
| D4 | 215 | 703 |
| D5 | 209 | 625 |
| E1 | 198 | 524 |
| E2 | 163 | 451 |
| E3 | 119 | 397 |
| E4 | 126 | 298 |
| E5 | 109 | 278 |
| F1 | 91 | 214 |
| F2 | 70 | 163 |
| F3 | 51 | 123 |
| F4 | 53 | 98 |
| F5 | 54 | 59 |
| G1 | 31 | 63 |
| G2 | 28 | 49 |
| G3 | 19 | 26 |
| G4 | 13 | 41 |
| G5 | 10 | 19 |
#bar plot
loan_subgrade.plot.bar(figsize=(12,6))
plt.xlabel('Loan Sub Grade')
plt.ylabel('Loan Count')
plt.title("Loans Count based on Sub Grades")
plt.tight_layout()
plt.show()
emp_exp = pd.DataFrame(loan.groupby('emp_length')['charged_off','fully_paid'].sum().reset_index())
emp_expD:\HP\Anaconda3\envs\AI\lib\site-packages\ipykernel_launcher.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.
"""Entry point for launching an IPython kernel.
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| emp_length | charged_off | fully_paid | |
|---|---|---|---|
| 0 | 1 year | 456 | 2713 |
| 1 | 10+ years | 1331 | 7157 |
| 2 | 2 years | 567 | 3724 |
| 3 | 3 years | 555 | 3457 |
| 4 | 4 years | 462 | 2880 |
| 5 | 5 years | 458 | 2736 |
| 6 | 6 years | 307 | 1861 |
| 7 | 7 years | 263 | 1448 |
| 8 | 8 years | 203 | 1232 |
| 9 | 9 years | 158 | 1068 |
| 10 | < 1 year | 639 | 3869 |
#bar chart
emp_exp.plot.bar(x='emp_length', y=['charged_off','fully_paid'],figsize=[8,5])
plt.ylabel('Loan Count')
plt.xlabel('Borrower Experience')
plt.title("Loan Count based on Borrower Experience")Text(0.5, 1.0, 'Loan Count based on Borrower Experience')
#Percentage
r = [0,1,2,3,4,5,6,7,8,9,10]
totals = [i+j for i,j in zip(emp_exp['charged_off'], emp_exp['fully_paid'])]
charged_off = [i / j * 100 for i,j in zip(emp_exp['charged_off'], totals)]
fully_paid = [i / j * 100 for i,j in zip(emp_exp['fully_paid'], totals)]
names = list(emp_exp['emp_length'])
# plot
plt.figure(figsize=(8, 15), dpi=80, facecolor='w', edgecolor='k')
#stacked bar
plt.subplot(3, 1, 1)
barWidth = 0.85
# Create charged_off Bars
plt.bar(r, charged_off, color='#b5ffb9', edgecolor='white', width=barWidth)
# Create fully paid Bars
plt.bar(r, fully_paid, bottom=[i for i in charged_off], color='#a3acff', edgecolor='white', width=barWidth)
# Custom x axis
plt.xticks(r, names, rotation='vertical')
plt.legend(['charged_off','fully_paid'],frameon=True, fontsize='small', shadow='True', title='Loan Status', bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
plt.title("Percentage of Defaulted Loans out of Total loans for different Employment Lengths")Text(0.5, 1.0, 'Percentage of Defaulted Loans out of Total loans for different Employment Lengths')
- Here we can see that more defaulters can be observed in the bins where borrowers experience are 10+ years and 1 year or lesser
- As far as default rate is concerned no significant trend is seen as all of them are more or less closer to each other
loan_home_ownership = pd.DataFrame(loan.groupby('home_ownership')['charged_off','fully_paid'].sum().reset_index())
loan_home_ownershipD:\HP\Anaconda3\envs\AI\lib\site-packages\ipykernel_launcher.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.
"""Entry point for launching an IPython kernel.
<style scoped>
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| home_ownership | charged_off | fully_paid | |
|---|---|---|---|
| 0 | MORTGAGE | 2327 | 14694 |
| 1 | NONE | 0 | 3 |
| 2 | OTHER | 18 | 80 |
| 3 | OWN | 443 | 2532 |
| 4 | RENT | 2839 | 15641 |
#bar chart
loan_home_ownership.plot.bar(x='home_ownership', y=['charged_off','fully_paid'],figsize=[8,5])
plt.ylabel('Loan Count')
plt.xlabel('Home Ownership')
plt.title("Home Ownership based Loan count")Text(0.5, 1.0, 'Home Ownership based Loan count')
# From raw value to percentage
r = [0,1,2,3,4]
totals = [i+j for i,j in zip(loan_home_ownership['charged_off'], loan_home_ownership['fully_paid'])]
charged_off = [i / j * 100 for i,j in zip(loan_home_ownership['charged_off'], totals)]
fully_paid = [i / j * 100 for i,j in zip(loan_home_ownership['fully_paid'], totals)]
names = list(loan_home_ownership['home_ownership'])
# plot
plt.figure(figsize=(12, 10), dpi=80, facecolor='w', edgecolor='k')
#stacked bar
plt.subplot(2, 2, 1)
barWidth = 0.85
# Create charged_off Bars
plt.bar(r, charged_off, color='#b5ffb9', edgecolor='white', width=barWidth)
# Create fully paid Bars
plt.bar(r, fully_paid, bottom=[i for i in charged_off], color='#a3acff', edgecolor='white', width=barWidth)
# Custom x axis
plt.xticks(r, names, rotation='vertical')
plt.legend(['charged_off','fully_paid'],frameon=True, fontsize='small', shadow='True', title='Loan Status', bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
plt.title("Default rate for different Home Ownership types", fontsize='medium')Text(0.5, 1.0, 'Default rate for different Home Ownership types')
- Default rate in home ownership type 'Other' is slightly high over others.
- It appears that most defaulters are the borrowers with ownership types house-mortgage and house-rent.
loan_verification_status = pd.DataFrame(loan.groupby('verification_status')['charged_off','fully_paid'].sum().reset_index())
loan_verification_statusD:\HP\Anaconda3\envs\AI\lib\site-packages\ipykernel_launcher.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.
"""Entry point for launching an IPython kernel.
<style scoped>
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| verification_status | charged_off | fully_paid | |
|---|---|---|---|
| 0 | Not Verified | 2142 | 14552 |
| 1 | Source Verified | 1434 | 8243 |
| 2 | Verified | 2051 | 10155 |
#bar chart
loan_verification_status.plot.bar(x='verification_status', y=['charged_off','fully_paid'],figsize=[8,5])
plt.ylabel('Loans Count')
plt.xlabel('Verification Status')
plt.title("Verification Status wise Loans count")Text(0.5, 1.0, 'Verification Status wise Loans count')
#Calculate percentage
r = [0,1,2]
totals = [i+j for i,j in zip(loan_verification_status['charged_off'], loan_verification_status['fully_paid'])]
charged_off = [i / j * 100 for i,j in zip(loan_verification_status['charged_off'], totals)]
fully_paid = [i / j * 100 for i,j in zip(loan_verification_status['fully_paid'], totals)]
names = list(loan_verification_status['verification_status'])
# plot
plt.figure(figsize=(12, 10), dpi=80, facecolor='w', edgecolor='k')
#stacked bar
plt.subplot(2, 2, 1)
barWidth = 0.85
# Create charged_off Bars
plt.bar(r, charged_off, color='#b5ffb9', edgecolor='white', width=barWidth)
# Create fully paid Bars
plt.bar(r, fully_paid, bottom=[i for i in charged_off], color='#a3acff', edgecolor='white', width=barWidth)
# Custom x axis
plt.xticks(r, names, rotation='vertical')
plt.legend(['charged_off','fully_paid'],frameon=True, fontsize='small', shadow='True', title='Loan Status', bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
plt.title("Default rate for different Verification Statuses", fontsize='medium')Text(0.5, 1.0, 'Default rate for different Verification Statuses')
- Default Rate is slightly more for ‘Verified’ loans followed by ‘Source verified’ ones and ‘Not verified’ ones
loan_addr_state = pd.DataFrame(loan.groupby('addr_state')['charged_off','fully_paid'].sum().reset_index())
loan_addr_stateD:\HP\Anaconda3\envs\AI\lib\site-packages\ipykernel_launcher.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.
"""Entry point for launching an IPython kernel.
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}
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| addr_state | charged_off | fully_paid | |
|---|---|---|---|
| 0 | AK | 15 | 63 |
| 1 | AL | 54 | 381 |
| 2 | AR | 27 | 208 |
| 3 | AZ | 123 | 726 |
| 4 | CA | 1125 | 5824 |
| 5 | CO | 98 | 668 |
| 6 | CT | 94 | 632 |
| 7 | DC | 15 | 196 |
| 8 | DE | 12 | 101 |
| 9 | FL | 504 | 2277 |
| 10 | GA | 215 | 1144 |
| 11 | HI | 28 | 138 |
| 12 | IA | 0 | 5 |
| 13 | ID | 1 | 5 |
| 14 | IL | 197 | 1281 |
| 15 | IN | 0 | 9 |
| 16 | KS | 31 | 224 |
| 17 | KY | 45 | 266 |
| 18 | LA | 53 | 374 |
| 19 | MA | 159 | 1138 |
| 20 | MD | 162 | 861 |
| 21 | ME | 0 | 3 |
| 22 | MI | 103 | 601 |
| 23 | MN | 81 | 524 |
| 24 | MO | 114 | 556 |
| 25 | MS | 2 | 17 |
| 26 | MT | 11 | 72 |
| 27 | NC | 114 | 636 |
| 28 | NE | 3 | 2 |
| 29 | NH | 25 | 141 |
| 30 | NJ | 278 | 1512 |
| 31 | NM | 30 | 153 |
| 32 | NV | 108 | 371 |
| 33 | NY | 495 | 3203 |
| 34 | OH | 155 | 1023 |
| 35 | OK | 40 | 247 |
| 36 | OR | 71 | 364 |
| 37 | PA | 180 | 1288 |
| 38 | RI | 25 | 169 |
| 39 | SC | 66 | 393 |
| 40 | SD | 12 | 50 |
| 41 | TN | 2 | 15 |
| 42 | TX | 316 | 2343 |
| 43 | UT | 40 | 212 |
| 44 | VA | 177 | 1192 |
| 45 | VT | 6 | 47 |
| 46 | WA | 127 | 691 |
| 47 | WI | 63 | 377 |
| 48 | WV | 21 | 151 |
| 49 | WY | 4 | 76 |
#bar chart
loan_addr_state.plot.bar(x='addr_state', y=['charged_off','fully_paid'],figsize=[20,8])
plt.ylabel('Loans Count')
plt.xlabel('States')
plt.title("State wise Loans count")Text(0.5, 1.0, 'State wise Loans count')
# Calculate percentage
r = list(loan_addr_state.index)
totals = [i+j for i,j in zip(loan_addr_state['charged_off'], loan_addr_state['fully_paid'])]
charged_off = [i / j * 100 for i,j in zip(loan_addr_state['charged_off'], totals)]
fully_paid = [i / j * 100 for i,j in zip(loan_addr_state['fully_paid'], totals)]
names = list(loan_addr_state['addr_state'])
# plot
plt.figure(figsize=(20, 20), dpi=80, facecolor='w', edgecolor='k')
#stacked bar
plt.subplot(3, 1, 1)
barWidth = 0.85
# Create charged_off Bars
plt.bar(r, charged_off, color='#b5ffb9', edgecolor='white', width=barWidth)
# Create fully paid Bars
plt.bar(r, fully_paid, bottom=[i for i in charged_off], color='#a3acff', edgecolor='white', width=barWidth)
# Custom x axis
plt.xticks(r, names, rotation='vertical')
plt.legend(['charged_off','fully_paid'],frameon=True, fontsize='small', shadow='True', title='Loan Status', bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
plt.title("Percentage of Defaulted Loans out of Total loans across different States")Text(0.5, 1.0, 'Percentage of Defaulted Loans out of Total loans across different States')
- Default Rate is maximum in the state NE .
- CA has maximum no of Defaulters followed by NY, FL and TX.
loan_dti_bins = pd.DataFrame(loan.groupby('dti_bins')['charged_off','fully_paid'].sum().reset_index())
loan_dti_binsD:\HP\Anaconda3\envs\AI\lib\site-packages\ipykernel_launcher.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.
"""Entry point for launching an IPython kernel.
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| dti_bins | charged_off | fully_paid | |
|---|---|---|---|
| 0 | Less Than 5 | 626 | 4436 |
| 1 | Btwn 5 & 10 | 1005 | 6868 |
| 2 | Btwn 10 & 15 | 1402 | 8228 |
| 3 | Btwn 15 & 20 | 1389 | 7422 |
| 4 | Btwn 20 & 25 | 1118 | 5460 |
| 5 | Btwn 25 & 30 | 87 | 536 |
#bar chart
loan_dti_bins.plot.bar(x='dti_bins', y=['charged_off','fully_paid'],figsize=[10,5])
plt.ylabel('Loans Count')
plt.xlabel('DTI Bins')
plt.title("Loans across different DTI Bins")Text(0.5, 1.0, 'Loans across different DTI Bins')
# Compute percentage
r = list(loan_dti_bins.index)
totals = [i+j for i,j in zip(loan_dti_bins['charged_off'], loan_dti_bins['fully_paid'])]
charged_off = [i / j * 100 for i,j in zip(loan_dti_bins['charged_off'], totals)]
fully_paid = [i / j * 100 for i,j in zip(loan_dti_bins['fully_paid'], totals)]
names = list(loan_dti_bins['dti_bins'])
# plot
plt.figure(figsize=(15, 10), dpi=80, facecolor='w', edgecolor='k')
#stacked bar
plt.subplot(2, 2, 1)
barWidth = 0.85
# Create charged_off Bars
plt.bar(r, charged_off, color='#b5ffb9', edgecolor='white', width=barWidth)
# Create fully paid Bars
plt.bar(r, fully_paid, bottom=[i for i in charged_off], color='#a3acff', edgecolor='white', width=barWidth)
# Custom x axis
plt.xticks(r, names, rotation='vertical')
plt.legend(['charged_off','fully_paid'],frameon=True, fontsize='small', shadow='True', title='Loan Status', bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
plt.title("Percentage of Defaulted Loans out of Total loans for different ranges of DTI")Text(0.5, 1.0, 'Percentage of Defaulted Loans out of Total loans for different ranges of DTI')
loan_pub_rec = pd.DataFrame(loan.groupby('pub_rec')['charged_off','fully_paid'].sum().reset_index())
loan_pub_recD:\HP\Anaconda3\envs\AI\lib\site-packages\ipykernel_launcher.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.
"""Entry point for launching an IPython kernel.
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| pub_rec | charged_off | fully_paid | |
|---|---|---|---|
| 0 | 0 | 5160 | 31347 |
| 1 | 1 | 457 | 1556 |
| 2 | 2 | 10 | 38 |
| 3 | 3 | 0 | 7 |
| 4 | 4 | 0 | 2 |
#bar chart
loan_pub_rec.plot.bar(x='pub_rec', y=['charged_off','fully_paid'],figsize=[10,5])
plt.ylabel('Loans Count')
plt.xlabel('No of Public Records')
plt.title("Loans count based on Public Records")Text(0.5, 1.0, 'Loans count based on Public Records')
# Compute percentage
r = list(loan_pub_rec.index)
totals = [i+j for i,j in zip(loan_pub_rec['charged_off'], loan_pub_rec['fully_paid'])]
charged_off = [i / j * 100 for i,j in zip(loan_pub_rec['charged_off'], totals)]
fully_paid = [i / j * 100 for i,j in zip(loan_pub_rec['fully_paid'], totals)]
names = list(loan_pub_rec['pub_rec'])
# plot
plt.figure(figsize=(12, 10), dpi=80, facecolor='w', edgecolor='k')
#stacked bar
plt.subplot(2, 2, 1)
barWidth = 0.85
# Create charged_off Bars
plt.bar(r, charged_off, color='#b5ffb9', edgecolor='white', width=barWidth)
# Create fully paid Bars
plt.bar(r, fully_paid, bottom=[i for i in charged_off], color='#a3acff', edgecolor='white', width=barWidth)
# Custom x axis
plt.xticks(r, names, rotation='vertical')
plt.legend(['charged_off','fully_paid'],frameon=True, fontsize='small', shadow='True', title='Loan Status', bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
plt.title("Percentage of Defaulted Loans out of Total loans for different Pub_rec Values", fontsize='medium')Text(0.5, 1.0, 'Percentage of Defaulted Loans out of Total loans for different Pub_rec Values')
- Default Rate is highest for pub_rec value 1 and 2 compared to 0.
#Creating bins for Annual Income
cut_points = [0,20000,40000,60000,80000,100000,120000,140000,160000]
label_names = ["Less Than 20000","Btwn 20000 & 40000","Btwn 40000 & 60000","Btwn 60000 & 80000","Btwn 80000 & 100000","Btwn 100000 & 120000","Btwn 120000 & 140000","Above 140000"]
column_index = loan.columns.get_loc('annual_inc') + 1
loan.insert(loc=column_index,column='annual_inc_range',value=pd.cut(loan['annual_inc'],cut_points,labels=label_names, include_lowest=True))
loan
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| id | member_id | loan_amnt | funded_amnt | funded_amnt_inv | term | int_rate | interest_rate_bins | installment | grade | sub_grade | emp_title | emp_length | home_ownership | annual_inc | annual_inc_range | verification_status | issue_d | loan_status | purpose | addr_state | dti | dti_bins | delinq_2yrs | earliest_cr_line | inq_last_6mths | open_acc | pub_rec | revol_bal | total_acc | out_prncp | out_prncp_inv | total_pymnt | total_pymnt_inv | total_rec_prncp | total_rec_int | total_rec_late_fee | recoveries | collection_recovery_fee | last_pymnt_amnt | last_credit_pull_d | pub_rec_bankruptcies | fully_paid | charged_off | issue_d_month | issue_d_year | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1077501 | 1296599 | 5000 | 5000 | 4975.0 | 36 months | 10.65 | Medium | 162.87 | B | B2 | NaN | 10+ years | RENT | 24000.0 | Btwn 20000 & 40000 | Verified | 2011-12-01 | Fully Paid | credit_card | AZ | 27.65 | Btwn 25 & 30 | 0 | Jan-85 | 1 | 3 | 0 | 13648 | 9 | 0.0 | 0.0 | 5863.155187 | 5833.84 | 5000.00 | 863.16 | 0.00 | 0.00 | 0.00 | 171.62 | May-16 | 0.0 | 1 | 0 | 12 | 2011 |
| 1 | 1077430 | 1314167 | 2500 | 2500 | 2500.0 | 60 months | 15.27 | High | 59.83 | C | C4 | Ryder | < 1 year | RENT | 30000.0 | Btwn 20000 & 40000 | Source Verified | 2011-12-01 | Charged Off | car | GA | 1.00 | Less Than 5 | 0 | Apr-99 | 5 | 3 | 0 | 1687 | 4 | 0.0 | 0.0 | 1008.710000 | 1008.71 | 456.46 | 435.17 | 0.00 | 117.08 | 1.11 | 119.66 | Sep-13 | 0.0 | 0 | 1 | 12 | 2011 |
| 2 | 1077175 | 1313524 | 2400 | 2400 | 2400.0 | 36 months | 15.96 | High | 84.33 | C | C5 | NaN | 10+ years | RENT | 12252.0 | Less Than 20000 | Not Verified | 2011-12-01 | Fully Paid | small_business | IL | 8.72 | Btwn 5 & 10 | 0 | Nov-01 | 2 | 2 | 0 | 2956 | 10 | 0.0 | 0.0 | 3005.666844 | 3005.67 | 2400.00 | 605.67 | 0.00 | 0.00 | 0.00 | 649.91 | May-16 | 0.0 | 1 | 0 | 12 | 2011 |
| 3 | 1076863 | 1277178 | 10000 | 10000 | 10000.0 | 36 months | 13.49 | Medium | 339.31 | C | C1 | AIR RESOURCES BOARD | 10+ years | RENT | 49200.0 | Btwn 40000 & 60000 | Source Verified | 2011-12-01 | Fully Paid | other | CA | 20.00 | Btwn 15 & 20 | 0 | Feb-96 | 1 | 10 | 0 | 5598 | 37 | 0.0 | 0.0 | 12231.890000 | 12231.89 | 10000.00 | 2214.92 | 16.97 | 0.00 | 0.00 | 357.48 | Apr-16 | 0.0 | 1 | 0 | 12 | 2011 |
| 5 | 1075269 | 1311441 | 5000 | 5000 | 5000.0 | 36 months | 7.90 | Low | 156.46 | A | A4 | Veolia Transportaton | 3 years | RENT | 36000.0 | Btwn 20000 & 40000 | Source Verified | 2011-12-01 | Fully Paid | wedding | AZ | 11.20 | Btwn 10 & 15 | 0 | Nov-04 | 3 | 9 | 0 | 7963 | 12 | 0.0 | 0.0 | 5632.210000 | 5632.21 | 5000.00 | 632.21 | 0.00 | 0.00 | 0.00 | 161.03 | Jan-16 | 0.0 | 1 | 0 | 12 | 2011 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 39712 | 92187 | 92174 | 2500 | 2500 | 1075.0 | 36 months | 8.07 | Low | 78.42 | A | A4 | FiSite Research | 4 years | MORTGAGE | 110000.0 | Btwn 100000 & 120000 | Not Verified | 2007-07-01 | Fully Paid | home_improvement | CO | 11.33 | Btwn 10 & 15 | 0 | Nov-90 | 0 | 13 | 0 | 7274 | 40 | 0.0 | 0.0 | 2822.969293 | 1213.88 | 2500.00 | 322.97 | 0.00 | 0.00 | 0.00 | 80.90 | Jun-10 | NaN | 1 | 0 | 7 | 2007 |
| 39713 | 90665 | 90607 | 8500 | 8500 | 875.0 | 36 months | 10.28 | Medium | 275.38 | C | C1 | Squarewave Solutions, Ltd. | 3 years | RENT | 18000.0 | Less Than 20000 | Not Verified | 2007-07-01 | Fully Paid | credit_card | NC | 6.40 | Btwn 5 & 10 | 1 | Dec-86 | 1 | 6 | 0 | 8847 | 9 | 0.0 | 0.0 | 9913.491822 | 1020.51 | 8500.00 | 1413.49 | 0.00 | 0.00 | 0.00 | 281.94 | Jul-10 | NaN | 1 | 0 | 7 | 2007 |
| 39714 | 90395 | 90390 | 5000 | 5000 | 1325.0 | 36 months | 8.07 | Low | 156.84 | A | A4 | NaN | < 1 year | MORTGAGE | 100000.0 | Btwn 80000 & 100000 | Not Verified | 2007-07-01 | Fully Paid | debt_consolidation | MA | 2.30 | Less Than 5 | 0 | Oct-98 | 0 | 11 | 0 | 9698 | 20 | 0.0 | 0.0 | 5272.161128 | 1397.12 | 5000.00 | 272.16 | 0.00 | 0.00 | 0.00 | 0.00 | Jun-07 | NaN | 1 | 0 | 7 | 2007 |
| 39715 | 90376 | 89243 | 5000 | 5000 | 650.0 | 36 months | 7.43 | Low | 155.38 | A | A2 | NaN | < 1 year | MORTGAGE | 200000.0 | NaN | Not Verified | 2007-07-01 | Fully Paid | other | MD | 3.72 | Less Than 5 | 0 | Nov-88 | 0 | 17 | 0 | 85607 | 26 | 0.0 | 0.0 | 5174.198551 | 672.66 | 5000.00 | 174.20 | 0.00 | 0.00 | 0.00 | 0.00 | Jun-07 | NaN | 1 | 0 | 7 | 2007 |
| 39716 | 87023 | 86999 | 7500 | 7500 | 800.0 | 36 months | 13.75 | Medium | 255.43 | E | E2 | Evergreen Center | < 1 year | OWN | 22000.0 | Btwn 20000 & 40000 | Not Verified | 2007-06-01 | Fully Paid | debt_consolidation | MA | 14.29 | Btwn 10 & 15 | 1 | Oct-03 | 0 | 7 | 0 | 4175 | 8 | 0.0 | 0.0 | 9195.263334 | 980.83 | 7500.00 | 1695.26 | 0.00 | 0.00 | 0.00 | 256.59 | Jun-10 | NaN | 1 | 0 | 6 | 2007 |
38577 rows × 46 columns
loan_inc = pd.DataFrame(loan.groupby('annual_inc_range')['charged_off','fully_paid'].sum().reset_index())
loan_incD:\HP\Anaconda3\envs\AI\lib\site-packages\ipykernel_launcher.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.
"""Entry point for launching an IPython kernel.
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| annual_inc_range | charged_off | fully_paid | |
|---|---|---|---|
| 0 | Less Than 20000 | 237 | 943 |
| 1 | Btwn 20000 & 40000 | 1514 | 7004 |
| 2 | Btwn 40000 & 60000 | 1729 | 9534 |
| 3 | Btwn 60000 & 80000 | 1024 | 6597 |
| 4 | Btwn 80000 & 100000 | 531 | 3983 |
| 5 | Btwn 100000 & 120000 | 244 | 2084 |
| 6 | Btwn 120000 & 140000 | 137 | 1081 |
| 7 | Above 140000 | 84 | 626 |
#bar chart
loan_inc.plot.bar(x='annual_inc_range', y=['charged_off','fully_paid'],figsize=[10,5])
plt.ylabel('Loans Count')
plt.xlabel('Annual Income Range')
plt.title("Loans count based on Annual Income")Text(0.5, 1.0, 'Loans count based on Annual Income')
# Compute Default Rate
r = list(loan_inc.index)
totals = [i+j for i,j in zip(loan_inc['charged_off'], loan_inc['fully_paid'])]
charged_off = [i / j * 100 for i,j in zip(loan_inc['charged_off'], totals)]
fully_paid = [i / j * 100 for i,j in zip(loan_inc['fully_paid'], totals)]
names = list(loan_inc['annual_inc_range'])
# plot
plt.figure(figsize=(12, 10), dpi=80, facecolor='w', edgecolor='k')
#stacked bar
plt.subplot(2, 2, 1)
barWidth = 0.85
# Create charged_off Bars
plt.bar(r, charged_off, color='#b5ffb9', edgecolor='white', width=barWidth)
# Create fully paid Bars
plt.bar(r, fully_paid, bottom=[i for i in charged_off], color='#a3acff', edgecolor='white', width=barWidth)
# Custom x axis
plt.xticks(r, names, rotation='vertical')
plt.legend(['charged_off','fully_paid'],frameon=True, fontsize='small', shadow='True', title='Loan Status', bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
plt.title("Percentage of Defaulted Loans out of Total loans based on Annual Income")Text(0.5, 1.0, 'Percentage of Defaulted Loans out of Total loans based on Annual Income')
- It seems that default rate decreases with increase in annual income of the borrower.
- Most Defaulters are the Borrowers whose annual income is between 0-20K, 20k-40k and 40K-60K
# adjust figure size
plt.figure(figsize=(10, 6), dpi=80, facecolor='w', edgecolor='k', frameon='True')
sns.set_style("whitegrid")
#box_plot
plt.subplot(1, 2, 1)
sns.boxplot(x='loan_status', y='loan_amnt', palette='BrBG', data=loan)
plt.title("Distribution of loan amount across Loan Statuses")Text(0.5, 1.0, 'Distribution of loan amount across Loan Statuses')
#Distribution of Annual Income across Statuses
plt.figure(figsize=(10, 6), dpi=80, facecolor='w', edgecolor='k', frameon='True')
sns.set_style("whitegrid")
# subplot 1: box_plot
plt.subplot(1, 2, 1)
sns.boxplot(loan['loan_status'], loan['annual_inc'][loan['annual_inc'] < loan['annual_inc'].quantile(0.96)], palette='BrBG')
plt.title("Distribution of Annual Income across Loan Statuses")D:\HP\Anaconda3\envs\AI\lib\site-packages\seaborn\_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
FutureWarning
Text(0.5, 1.0, 'Distribution of Annual Income across Loan Statuses')
#Variation of loan amount with annual income
plt.figure(figsize=(20, 20))
charged_off_loan = loan.loc[(loan['loan_status']=='Charged Off')]#[['loan_amnt','annual_inc']]
sns.pairplot(charged_off_loan, vars = ['loan_amnt','annual_inc'], size = 5)
plt.show()D:\HP\Anaconda3\envs\AI\lib\site-packages\seaborn\axisgrid.py:1912: UserWarning: The `size` parameter has been renamed to `height`; please update your code.
warnings.warn(msg, UserWarning)
<Figure size 1440x1440 with 0 Axes>
cor = charged_off_loan[['loan_amnt','funded_amnt', 'int_rate', 'installment','annual_inc','dti','open_acc','pub_rec']].corr()
round(cor, 2)
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| loan_amnt | funded_amnt | int_rate | installment | annual_inc | dti | open_acc | pub_rec | |
|---|---|---|---|---|---|---|---|---|
| loan_amnt | 1.00 | 0.98 | 0.35 | 0.93 | 0.35 | 0.06 | 0.18 | -0.05 |
| funded_amnt | 0.98 | 1.00 | 0.35 | 0.95 | 0.35 | 0.06 | 0.18 | -0.05 |
| int_rate | 0.35 | 0.35 | 1.00 | 0.33 | 0.13 | 0.04 | 0.04 | 0.08 |
| installment | 0.93 | 0.95 | 0.33 | 1.00 | 0.36 | 0.04 | 0.18 | -0.04 |
| annual_inc | 0.35 | 0.35 | 0.13 | 0.36 | 1.00 | -0.09 | 0.21 | -0.01 |
| dti | 0.06 | 0.06 | 0.04 | 0.04 | -0.09 | 1.00 | 0.30 | 0.01 |
| open_acc | 0.18 | 0.18 | 0.04 | 0.18 | 0.21 | 0.30 | 1.00 | 0.05 |
| pub_rec | -0.05 | -0.05 | 0.08 | -0.04 | -0.01 | 0.01 | 0.05 | 1.00 |
plt.figure(figsize=(15,10))
# heatmap
sns.heatmap(cor, cmap="YlGnBu", annot=True)
plt.show()
#Loan amount count over the years
plt.figure(figsize=(10,5))
loan.groupby('issue_d_year').loan_amnt.count().plot(kind='line', fontsize=7)
plt.show()
#Average Loan amount over the years
plt.figure(figsize=(10,5))
loan.groupby('issue_d_year').loan_amnt.mean().plot(kind='line', fontsize=7)
plt.show()
#Distribution of loan amount over Grade
loan.boxplot(column='loan_amnt', by='grade')
plt.show()
- It seems that apparently lower grade is given to Large loan amounts.
- Its very evident from graph that as median loan amount increases corresponding grades given are decreasing
#Distribution of annual income over Grade
loan.loc[loan['annual_inc']<260000].boxplot(column='annual_inc', by='grade')
plt.show()
sns.barplot(x='verification_status', y='loan_amnt', hue="loan_status", data=loan, estimator=np.mean)<AxesSubplot:xlabel='verification_status', ylabel='loan_amnt'>
- Higher loan amounts are Verified more often.
- Higher loan amounts are riskier and are also verified more often.
#Interest vs Term
loan.boxplot(column='int_rate', by='term')
plt.show()
- Seems like large amounts are having longer term and hece the intrest associated with them is also high.
#Loan Status vs Int rate
loan.boxplot(column='int_rate', by='loan_status',figsize=(7,5))<AxesSubplot:title={'center':'int_rate'}, xlabel='loan_status'>
- Seems like Loans at a higher intrest rate are more likely to be defaulted.
sns.jointplot('dti', 'int_rate', loan)D:\HP\Anaconda3\envs\AI\lib\site-packages\seaborn\_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y, data. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
FutureWarning
<seaborn.axisgrid.JointGrid at 0x27531ae6a08>
sns.jointplot('int_rate', 'loan_amnt', loan)D:\HP\Anaconda3\envs\AI\lib\site-packages\seaborn\_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y, data. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
FutureWarning
<seaborn.axisgrid.JointGrid at 0x27532542f08>
