Shalaka Thakare 2022-11-27
Importing the Libraries
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library(lubridate)##
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## date, intersect, setdiff, union
library(stringr)
library(pROC)## Type 'citation("pROC")' for a citation.
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library(rpart)
library(ROCR)
library(caret)## Loading required package: lattice
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## Attaching package: 'caret'
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## lift
library(ranger)
library(plotluck)Loading the data…
lcdf <- read_csv('C:/Users/sthaka3/Desktop/Credit_Risk_Project/lcData100K.csv')## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)
## Rows: 100000 Columns: 145
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (21): term, grade, sub_grade, emp_title, emp_length, home_ownership, ve...
## dbl (84): loan_amnt, funded_amnt, funded_amnt_inv, int_rate, installment, a...
## lgl (39): id, member_id, url, desc, next_pymnt_d, annual_inc_joint, dti_joi...
## dttm (1): issue_d
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
# Checking number of rows and columns in the lc dataframe
cat('Number of rows = ', nrow(lcdf))## Number of rows = 100000
cat('\nNumber of columns = ',ncol(lcdf))##
## Number of columns = 145
Exploring the data
head(lcdf)## # A tibble: 6 × 145
## id member_id loan_amnt funded…¹ funde…² term int_r…³ insta…⁴ grade sub_g…⁵
## <lgl> <lgl> <dbl> <dbl> <dbl> <chr> <dbl> <dbl> <chr> <chr>
## 1 NA NA 28000 28000 28000 36 m… 5.32 843. A A1
## 2 NA NA 6150 6150 6125 36 m… 13.3 208. C C3
## 3 NA NA 7200 7200 7200 36 m… 15.0 250. C C5
## 4 NA NA 4750 4750 4750 36 m… 15.3 165. C C4
## 5 NA NA 5000 5000 5000 36 m… 12.7 168. C C2
## 6 NA NA 9600 9600 9600 36 m… 15.0 333. C C3
## # … with 135 more variables: emp_title <chr>, emp_length <chr>,
## # home_ownership <chr>, annual_inc <dbl>, verification_status <chr>,
## # issue_d <dttm>, loan_status <chr>, pymnt_plan <chr>, url <lgl>, desc <lgl>,
## # purpose <chr>, title <chr>, zip_code <chr>, addr_state <chr>, dti <dbl>,
## # delinq_2yrs <dbl>, earliest_cr_line <chr>, inq_last_6mths <dbl>,
## # mths_since_last_delinq <dbl>, mths_since_last_record <dbl>, open_acc <dbl>,
## # pub_rec <dbl>, revol_bal <dbl>, revol_util <dbl>, total_acc <dbl>, …
summary(lcdf)## id member_id loan_amnt funded_amnt funded_amnt_inv
## Mode:logical Mode:logical Min. : 1000 Min. : 1000 Min. : 925
## NA's:100000 NA's:100000 1st Qu.: 7000 1st Qu.: 7000 1st Qu.: 7000
## Median :10000 Median :10000 Median :10000
## Mean :12736 Mean :12736 Mean :12731
## 3rd Qu.:17000 3rd Qu.:17000 3rd Qu.:16975
## Max. :35000 Max. :35000 Max. :35000
##
## term int_rate installment grade
## Length:100000 Min. : 5.32 Min. : 30.12 Length:100000
## Class :character 1st Qu.: 8.90 1st Qu.: 227.60 Class :character
## Mode :character Median :11.99 Median : 347.74 Mode :character
## Mean :12.01 Mean : 421.77
## 3rd Qu.:14.47 3rd Qu.: 557.94
## Max. :28.99 Max. :1407.01
##
## sub_grade emp_title emp_length home_ownership
## Length:100000 Length:100000 Length:100000 Length:100000
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## annual_inc verification_status issue_d
## Min. : 3600 Length:100000 Min. :2013-01-01 00:00:00.00
## 1st Qu.: 43000 Class :character 1st Qu.:2014-04-01 00:00:00.00
## Median : 60600 Mode :character Median :2015-01-01 00:00:00.00
## Mean : 73356 Mean :2014-10-29 08:24:51.83
## 3rd Qu.: 89000 3rd Qu.:2015-07-01 00:00:00.00
## Max. :8500021 Max. :2015-12-01 00:00:00.00
##
## loan_status pymnt_plan url desc
## Length:100000 Length:100000 Mode:logical Mode:logical
## Class :character Class :character NA's:100000 NA's:100000
## Mode :character Mode :character
##
##
##
##
## purpose title zip_code addr_state
## Length:100000 Length:100000 Length:100000 Length:100000
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## dti delinq_2yrs earliest_cr_line inq_last_6mths
## Min. : 0.00 Min. : 0.0000 Length:100000 Min. :0.000
## 1st Qu.:11.63 1st Qu.: 0.0000 Class :character 1st Qu.:0.000
## Median :17.34 Median : 0.0000 Mode :character Median :0.000
## Mean :17.91 Mean : 0.3341 Mean :0.669
## 3rd Qu.:23.81 3rd Qu.: 0.0000 3rd Qu.:1.000
## Max. :71.40 Max. :21.0000 Max. :6.000
##
## mths_since_last_delinq mths_since_last_record open_acc
## Min. : 0.00 Min. : 0.00 Min. : 0.00
## 1st Qu.: 15.00 1st Qu.: 50.00 1st Qu.: 8.00
## Median : 31.00 Median : 69.00 Median :10.00
## Mean : 33.94 Mean : 69.54 Mean :11.42
## 3rd Qu.: 50.00 3rd Qu.: 91.00 3rd Qu.:14.00
## Max. :188.00 Max. :119.00 Max. :79.00
## NA's :49919 NA's :82423
## pub_rec revol_bal revol_util total_acc
## Min. : 0.0000 Min. : 0 Min. : 0.00 Min. : 2.00
## 1st Qu.: 0.0000 1st Qu.: 5896 1st Qu.: 36.20 1st Qu.: 16.00
## Median : 0.0000 Median : 10725 Median : 54.10 Median : 23.00
## Mean : 0.2267 Mean : 15795 Mean : 53.75 Mean : 24.76
## 3rd Qu.: 0.0000 3rd Qu.: 18913 3rd Qu.: 71.80 3rd Qu.: 32.00
## Max. :19.0000 Max. :924579 Max. :153.70 Max. :162.00
## NA's :41
## initial_list_status out_prncp out_prncp_inv total_pymnt total_pymnt_inv
## Length:100000 Min. :0 Min. :0 Min. : 0 Min. : 0
## Class :character 1st Qu.:0 1st Qu.:0 1st Qu.: 6936 1st Qu.: 6935
## Mode :character Median :0 Median :0 Median :11471 Median :11470
## Mean :0 Mean :0 Mean :13790 Mean :13785
## 3rd Qu.:0 3rd Qu.:0 3rd Qu.:18294 3rd Qu.:18290
## Max. :0 Max. :0 Max. :50941 Max. :50941
##
## total_rec_prncp total_rec_int total_rec_late_fee recoveries
## Min. : 0 Min. : 0.0 Min. : 0.000 Min. : 0.0
## 1st Qu.: 6000 1st Qu.: 841.4 1st Qu.: 0.000 1st Qu.: 0.0
## Median :10000 Median : 1491.7 Median : 0.000 Median : 0.0
## Mean :11757 Mean : 1904.2 Mean : 1.248 Mean : 127.6
## 3rd Qu.:16000 3rd Qu.: 2463.6 3rd Qu.: 0.000 3rd Qu.: 0.0
## Max. :35000 Max. :15871.0 Max. :1098.360 Max. :32321.3
##
## collection_recovery_fee last_pymnt_d last_pymnt_amnt next_pymnt_d
## Min. : 0.00 Length:100000 Min. : 0.0 Mode:logical
## 1st Qu.: 0.00 Class :character 1st Qu.: 327.8 NA's:100000
## Median : 0.00 Mode :character Median : 976.2
## Mean : 21.14 Mean : 3486.7
## 3rd Qu.: 0.00 3rd Qu.: 4993.6
## Max. :4653.81 Max. :36058.7
##
## last_credit_pull_d collections_12_mths_ex_med mths_since_last_major_derog
## Length:100000 Min. :0.0000 Min. : 0.00
## Class :character 1st Qu.:0.0000 1st Qu.: 26.00
## Mode :character Median :0.0000 Median : 43.00
## Mean :0.0167 Mean : 43.01
## 3rd Qu.:0.0000 3rd Qu.: 60.00
## Max. :6.0000 Max. :188.00
## NA's :71995
## policy_code application_type annual_inc_joint dti_joint
## Min. :1 Length:100000 Mode:logical Mode:logical
## 1st Qu.:1 Class :character NA's:100000 NA's:100000
## Median :1 Mode :character
## Mean :1
## 3rd Qu.:1
## Max. :1
##
## verification_status_joint acc_now_delinq tot_coll_amt
## Mode:logical Min. :0.00000 Min. : 0.0
## NA's:100000 1st Qu.:0.00000 1st Qu.: 0.0
## Median :0.00000 Median : 0.0
## Mean :0.00577 Mean : 232.8
## 3rd Qu.:0.00000 3rd Qu.: 0.0
## Max. :4.00000 Max. :262740.0
##
## tot_cur_bal open_acc_6m open_act_il open_il_12m
## Min. : 0 Min. : 0.00 Min. : 0.00 Min. :0.00
## 1st Qu.: 25656 1st Qu.: 0.00 1st Qu.: 1.00 1st Qu.:0.00
## Median : 65143 Median : 1.00 Median : 2.00 Median :0.00
## Mean : 128759 Mean : 1.12 Mean : 2.84 Mean :0.73
## 3rd Qu.: 191246 3rd Qu.: 2.00 3rd Qu.: 3.00 3rd Qu.:1.00
## Max. :3292113 Max. :10.00 Max. :34.00 Max. :7.00
## NA's :97313 NA's :97313 NA's :97313
## open_il_24m mths_since_rcnt_il total_bal_il il_util
## Min. : 0.00 Min. : 0.0 Min. : 0 Min. : 0.0
## 1st Qu.: 0.00 1st Qu.: 6.0 1st Qu.: 8864 1st Qu.: 57.0
## Median : 1.00 Median : 13.0 Median : 22032 Median : 74.0
## Mean : 1.57 Mean : 21.9 Mean : 33728 Mean : 70.8
## 3rd Qu.: 2.00 3rd Qu.: 24.0 3rd Qu.: 43170 3rd Qu.: 88.0
## Max. :13.00 Max. :250.0 Max. :561918 Max. :192.0
## NA's :97313 NA's :97393 NA's :97313 NA's :97694
## open_rv_12m open_rv_24m max_bal_bc all_util
## Min. : 0.00 Min. : 0.00 Min. : 0 Min. : 0.00
## 1st Qu.: 0.00 1st Qu.: 1.00 1st Qu.: 2140 1st Qu.: 45.00
## Median : 1.00 Median : 2.00 Median : 4122 Median : 60.00
## Mean : 1.43 Mean : 3.04 Mean : 5481 Mean : 59.23
## 3rd Qu.: 2.00 3rd Qu.: 4.00 3rd Qu.: 7082 3rd Qu.: 74.00
## Max. :11.00 Max. :24.00 Max. :127305 Max. :146.00
## NA's :97313 NA's :97313 NA's :97313 NA's :97313
## total_rev_hi_lim inq_fi total_cu_tl inq_last_12m
## Min. : 0 Min. : 0.00 Min. : 0.00 Min. : 0.00
## 1st Qu.: 12800 1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.: 0.00
## Median : 22000 Median : 0.00 Median : 0.00 Median : 2.00
## Mean : 30545 Mean : 0.92 Mean : 1.45 Mean : 2.21
## 3rd Qu.: 37500 3rd Qu.: 1.00 3rd Qu.: 2.00 3rd Qu.: 3.00
## Max. :1165700 Max. :13.00 Max. :33.00 Max. :18.00
## NA's :97313 NA's :97313 NA's :97313
## acc_open_past_24mths avg_cur_bal bc_open_to_buy bc_util
## Min. : 0.000 Min. : 0 Min. : 0 Min. : 0.00
## 1st Qu.: 2.000 1st Qu.: 2791 1st Qu.: 1203 1st Qu.: 42.30
## Median : 4.000 Median : 6312 Median : 3893 Median : 66.20
## Mean : 4.396 Mean : 12470 Mean : 9046 Mean : 62.45
## 3rd Qu.: 6.000 3rd Qu.: 17267 3rd Qu.: 10602 3rd Qu.: 86.20
## Max. :42.000 Max. :395953 Max. :332178 Max. :188.80
## NA's :2 NA's :964 NA's :1044
## chargeoff_within_12_mths delinq_amnt mo_sin_old_il_acct
## Min. :0.00000 Min. : 0.00 Min. : 0
## 1st Qu.:0.00000 1st Qu.: 0.00 1st Qu.: 96
## Median :0.00000 Median : 0.00 Median :128
## Mean :0.01015 Mean : 12.44 Mean :125
## 3rd Qu.:0.00000 3rd Qu.: 0.00 3rd Qu.:152
## Max. :6.00000 Max. :88216.00 Max. :640
## NA's :3620
## mo_sin_old_rev_tl_op mo_sin_rcnt_rev_tl_op mo_sin_rcnt_tl mort_acc
## Min. : 3.0 Min. : 0.00 Min. : 0.000 Min. : 0.000
## 1st Qu.:115.0 1st Qu.: 4.00 1st Qu.: 3.000 1st Qu.: 0.000
## Median :164.0 Median : 8.00 Median : 6.000 Median : 1.000
## Mean :182.4 Mean : 13.25 Mean : 8.231 Mean : 1.635
## 3rd Qu.:231.0 3rd Qu.: 16.00 3rd Qu.: 10.000 3rd Qu.: 3.000
## Max. :780.0 Max. :293.00 Max. :226.000 Max. :25.000
##
## mths_since_recent_bc mths_since_recent_bc_dlq mths_since_recent_inq
## Min. : 0.0 Min. : 0.00 Min. : 0.000
## 1st Qu.: 6.0 1st Qu.: 21.00 1st Qu.: 2.000
## Median : 14.0 Median : 39.00 Median : 5.000
## Mean : 24.5 Mean : 39.98 Mean : 6.908
## 3rd Qu.: 29.0 3rd Qu.: 59.00 3rd Qu.:10.000
## Max. :616.0 Max. :176.00 Max. :24.000
## NA's :911 NA's :74329 NA's :10612
## mths_since_recent_revol_delinq num_accts_ever_120_pd num_actv_bc_tl
## Min. : 0.00 Min. : 0.0000 Min. : 0.000
## 1st Qu.: 17.00 1st Qu.: 0.0000 1st Qu.: 2.000
## Median : 33.00 Median : 0.0000 Median : 3.000
## Mean : 35.86 Mean : 0.4969 Mean : 3.643
## 3rd Qu.: 53.00 3rd Qu.: 0.0000 3rd Qu.: 5.000
## Max. :176.00 Max. :23.0000 Max. :26.000
## NA's :64746
## num_actv_rev_tl num_bc_sats num_bc_tl num_il_tl
## Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 3.000 1st Qu.: 3.000 1st Qu.: 5.000 1st Qu.: 3.000
## Median : 5.000 Median : 4.000 Median : 7.000 Median : 6.000
## Mean : 5.673 Mean : 4.653 Mean : 8.297 Mean : 8.085
## 3rd Qu.: 7.000 3rd Qu.: 6.000 3rd Qu.:11.000 3rd Qu.: 11.000
## Max. :41.000 Max. :36.000 Max. :61.000 Max. :132.000
##
## num_op_rev_tl num_rev_accts num_rev_tl_bal_gt_0 num_sats
## Min. : 0.00 Min. : 2.00 Min. : 0.000 Min. : 0.00
## 1st Qu.: 5.00 1st Qu.: 9.00 1st Qu.: 3.000 1st Qu.: 8.00
## Median : 7.00 Median :13.00 Median : 5.000 Median :10.00
## Mean : 8.16 Mean :14.76 Mean : 5.633 Mean :11.37
## 3rd Qu.:10.00 3rd Qu.:19.00 3rd Qu.: 7.000 3rd Qu.:14.00
## Max. :67.00 Max. :87.00 Max. :36.000 Max. :79.00
## NA's :1
## num_tl_120dpd_2m num_tl_30dpd num_tl_90g_dpd_24m num_tl_op_past_12m
## Min. :0.000 Min. :0.00000 Min. : 0.00000 Min. : 0.000
## 1st Qu.:0.000 1st Qu.:0.00000 1st Qu.: 0.00000 1st Qu.: 1.000
## Median :0.000 Median :0.00000 Median : 0.00000 Median : 2.000
## Mean :0.001 Mean :0.00383 Mean : 0.09251 Mean : 2.046
## 3rd Qu.:0.000 3rd Qu.:0.00000 3rd Qu.: 0.00000 3rd Qu.: 3.000
## Max. :2.000 Max. :4.00000 Max. :20.00000 Max. :26.000
## NA's :3824
## pct_tl_nvr_dlq percent_bc_gt_75 pub_rec_bankruptcies tax_liens
## Min. : 20.00 Min. : 0.00 Min. :0.0000 Min. : 0.00000
## 1st Qu.: 91.00 1st Qu.: 16.70 1st Qu.:0.0000 1st Qu.: 0.00000
## Median : 97.80 Median : 50.00 Median :0.0000 Median : 0.00000
## Mean : 94.05 Mean : 48.03 Mean :0.1376 Mean : 0.05683
## 3rd Qu.:100.00 3rd Qu.: 75.00 3rd Qu.:0.0000 3rd Qu.: 0.00000
## Max. :100.00 Max. :100.00 Max. :8.0000 Max. :18.00000
## NA's :16 NA's :1034
## tot_hi_cred_lim total_bal_ex_mort total_bc_limit
## Min. : 0 Min. : 0 Min. : 0
## 1st Qu.: 43247 1st Qu.: 18924 1st Qu.: 7000
## Median : 93904 Median : 34085 Median : 13600
## Mean : 159185 Mean : 45925 Mean : 20179
## 3rd Qu.: 229283 3rd Qu.: 57457 3rd Qu.: 26000
## Max. :3647089 Max. :1067947 Max. :332200
##
## total_il_high_credit_limit revol_bal_joint sec_app_earliest_cr_line
## Min. : 0 Mode:logical Mode:logical
## 1st Qu.: 12142 NA's:100000 NA's:100000
## Median : 28353
## Mean : 38086
## 3rd Qu.: 51262
## Max. :1027358
##
## sec_app_inq_last_6mths sec_app_mort_acc sec_app_open_acc sec_app_revol_util
## Mode:logical Mode:logical Mode:logical Mode:logical
## NA's:100000 NA's:100000 NA's:100000 NA's:100000
##
##
##
##
##
## sec_app_open_act_il sec_app_num_rev_accts sec_app_chargeoff_within_12_mths
## Mode:logical Mode:logical Mode:logical
## NA's:100000 NA's:100000 NA's:100000
##
##
##
##
##
## sec_app_collections_12_mths_ex_med sec_app_mths_since_last_major_derog
## Mode:logical Mode:logical
## NA's:100000 NA's:100000
##
##
##
##
##
## hardship_flag hardship_type hardship_reason hardship_status
## Length:100000 Mode:logical Mode:logical Mode:logical
## Class :character NA's:100000 NA's:100000 NA's:100000
## Mode :character
##
##
##
##
## deferral_term hardship_amount hardship_start_date hardship_end_date
## Mode:logical Mode:logical Mode:logical Mode:logical
## NA's:100000 NA's:100000 NA's:100000 NA's:100000
##
##
##
##
##
## payment_plan_start_date hardship_length hardship_dpd hardship_loan_status
## Mode:logical Mode:logical Mode :logical Mode:logical
## NA's:100000 NA's:100000 FALSE:45 NA's:100000
## NA's :99955
##
##
##
##
## orig_projected_additional_accrued_interest hardship_payoff_balance_amount
## Mode:logical Mode:logical
## NA's:100000 NA's:100000
##
##
##
##
##
## hardship_last_payment_amount disbursement_method debt_settlement_flag
## Mode:logical Length:100000 Length:100000
## NA's:100000 Class :character Class :character
## Mode :character Mode :character
##
##
##
##
## debt_settlement_flag_date settlement_status settlement_date settlement_amount
## Mode:logical Mode:logical Mode:logical Mode:logical
## NA's:100000 NA's:100000 NA's:100000 NA's:100000
##
##
##
##
##
## settlement_percentage settlement_term
## Mode:logical Mode :logical
## NA's:100000 FALSE:279
## TRUE :186
## NA's :99535
##
##
##
How many different types of loan status exist in the data?
lcdf %>% group_by(loan_status) %>% tally()## # A tibble: 2 × 2
## loan_status n
## <chr> <int>
## 1 Charged Off 13785
## 2 Fully Paid 86215
Looks like our target variable- loan_status has only 2 values- Charged Off and Fully Paid.
Let’s check the distribution of loan status across all records
loan_status_count <- lcdf %>% group_by(loan_status) %>% count()
pct <- round(loan_status_count$n/sum(loan_status_count$n)*100)
lbls <- paste(loan_status_count$loan_status, pct) # add percents to labels
lbls <- paste(lbls,"%",sep="") # ad % to labels
pie(loan_status_count$n, labels = lbls, main="Percentage of Loans with Loan Status")
Analyzing Home Ownerships
ggplot(lcdf, aes( x = home_ownership)) + geom_bar(colour="black", fill="white") +ggtitle("Number of Loans By Homeownerships") + xlab("Different Types of Homeownership") + ylab("Number of Loans ") + theme(plot.title = element_text(color="#993333", size=14, face="bold.italic"), axis.title.x = element_text(color="#993333", size=14, face="bold"), axis.title.y = element_text(color="#993333", size=14, face="bold")) 
We can see that most of the borrowers have their home on rent or mortgage as compared to those who own their homes.
Let’s now visualize the spread of interest rate to get a better understanding of the given data
summary(lcdf$int_rate)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 5.32 8.90 11.99 12.01 14.47 28.99
ggplot(lcdf, aes( x = int_rate)) + geom_boxplot(color="#993333",outlier.color = "black") +
xlab("Interest Rate ") + theme(plot.title = element_text(color="#993333", size=14, face="bold.italic"), axis.title.x = element_text(color="#993333", size=14, face="bold"), axis.title.y = element_text(color="#993333", size=14, face="bold")) 
25 Percentile of loans have an interest rate of less than 8.9%. Median of the interest rate of all loans in 11.99%. The interest rate can go as high as 28.99 % in some case. This interest seems really active to invest in. Very few investment products give an interest of 12%.
Let’s understand interest rates vary according to loan grade
ggplot(lcdf, aes( y = int_rate, x=grade,color= grade)) + geom_boxplot(outlier.color="black") + xlab("Loan grade ") +
ylab("Interest Rate") + theme(plot.title = element_text(color="#993333", size=14, face="bold.italic"), axis.title.x = element_text(color="#993333", size=14, face="bold"), axis.title.y = element_text(color="#993333", size=14, face="bold")) 
We can see that the median interest rate increases as we go from grade A to grade G, probably due to higher risk.
Let’s summarize the data to check percent of defaults using loan status for all loan grades
lcdf %>% group_by(grade) %>% summarise(TotalLoans=n(), FullyPaid=sum(loan_status=="Fully Paid"), ChargedOff=sum(loan_status=="Charged Off"), Percent_defaults = ChargedOff/TotalLoans*100)## # A tibble: 7 × 5
## grade TotalLoans FullyPaid ChargedOff Percent_defaults
## <chr> <int> <int> <int> <dbl>
## 1 A 22588 21401 1187 5.26
## 2 B 33907 30184 3723 11.0
## 3 C 26645 21907 4738 17.8
## 4 D 12493 9635 2858 22.9
## 5 E 3579 2569 1010 28.2
## 6 F 708 469 239 33.8
## 7 G 80 50 30 37.5
The percent of default loans is higher for lower grade loans. This explains why interest rates are higher for the same.
Now, let’s look at a wider picture to see how number of loans, loan amount, interest rate vary by grade. First, lets do some calculations
# Number of Loans, Sum of Loan Amout, Mean Loan Amount Mean Int Rate by Grade
lcdf %>% group_by(grade) %>% summarise(numberOfLoans=n(), TotLoanAmt=sum(loan_amnt),MeanLoanAmt=mean(loan_amnt),defaults=sum(loan_status=="Charged Off"), defaultRate=defaults/numberOfLoans, Percent_defaults = defaultRate*100,MeanIntRate=mean(int_rate),stdInterest=sd(int_rate), minInt = min(int_rate),maxInt=max(int_rate),avgLoanAMt=mean(loan_amnt), sumPmnt=sum(total_pymnt),avgPmnt=mean(total_pymnt))## # A tibble: 7 × 14
## grade numberO…¹ TotLo…² MeanL…³ defau…⁴ defau…⁵ Perce…⁶ MeanI…⁷ stdIn…⁸ minInt
## <chr> <int> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 A 22588 3.28e8 14505. 1187 0.0526 5.26 7.17 0.967 5.32
## 2 B 33907 4.28e8 12637. 3723 0.110 11.0 10.8 1.44 6
## 3 C 26645 3.20e8 12001. 4738 0.178 17.8 13.8 1.19 6
## 4 D 12493 1.49e8 11894. 2858 0.229 22.9 17.2 1.22 6
## 5 E 3579 4.16e7 11619. 1010 0.282 28.2 19.9 1.38 6
## 6 F 708 6.56e6 9272. 239 0.338 33.8 24.0 0.916 22.0
## 7 G 80 9.46e5 11826. 30 0.375 37.5 26.4 0.849 25.8
## # … with 4 more variables: maxInt <dbl>, avgLoanAMt <dbl>, sumPmnt <dbl>,
## # avgPmnt <dbl>, and abbreviated variable names ¹numberOfLoans, ²TotLoanAmt,
## # ³MeanLoanAmt, ⁴defaults, ⁵defaultRate, ⁶Percent_defaults, ⁷MeanIntRate,
## # ⁸stdInterest
# Loan Amount Distribution
ggplot(lcdf, aes( x = loan_amnt)) + geom_histogram(aes(y=..density..), colour="black", fill="white", bins=15)+ geom_density(alpha=.2, fill="#FF6666") + ggtitle("Distribution of Loan Amount Changing Bins ") + xlab("Loan Amount ") + ylab("Number of Loans ") + theme(plot.title = element_text(color="#993333", size=14, face="bold.italic"), axis.title.x = element_text(color="#993333", size=14, face="bold"), axis.title.y = element_text(color="#993333", size=14, face="bold")) 
The loan amount varies from 0 to 35,000. The number of charged off loans are less in overall number,which is evident in the graph. In an ideal case these would have been normally distributed.There are loans which are higher than 30,000 and still paid.Also, there are loans of less than 10,000 and charged off. This means that loan status has more to do with loan grade rather than the loan amount.
# Loan Amount Distribution by Grade
ggplot(lcdf, aes( x = loan_amnt)) + geom_histogram(aes(fill=grade)) + ggtitle("Distribution of Loan Amount With Grade") + xlab("Loan Amount ") + ylab("Number of Loans ") + theme(plot.title = element_text(color="#993333", size=14, face="bold.italic"), axis.title.x = element_text(color="#993333", size=14, face="bold"), axis.title.y = element_text(color="#993333", size=14, face="bold")) ## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
The loan amount for most loans is approximately $12000.Loan amount is lower for lower grade loans.Let’s dive deeper into the relationship with loan amount and loan grade.
# Let us look at the distribution
ggplot(lcdf, aes( y = loan_amnt, x=grade,color= grade)) + geom_boxplot(outlier.color="black") + xlab("Loan grade ") +
ylab("Loan Amount") + theme(plot.title = element_text(color="#993333", size=14, face="bold.italic"), axis.title.x = element_text(color="#993333", size=14, face="bold"), axis.title.y = element_text(color="#993333", size=14, face="bold")) 
We can see that loans falling under A and B grades have higher loan amounts, while those for C, D, E and F are slightly lesser.Range for loan amounts with Grade G have a broader range.
# Interest Rate with Grade
ggplot(lcdf, aes( x = int_rate)) + geom_histogram(aes(fill=grade)) + ggtitle("Distribution of Interest Rate With Grade") + xlab("Interest Rate ") + ylab("Number of Loans ") + theme(plot.title = element_text(color="#993333", size=14, face="bold.italic"), axis.title.x = element_text(color="#993333", size=14, face="bold"), axis.title.y = element_text(color="#993333", size=14, face="bold")) ## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
We can see that the average interest rate is higher in higher grades of loans. Higher interest rates can mean higher returns for investors, but this also means higher risk. Interest rates vary from 0 to 28 percent. Most loans have an interest rate of 12-14%.
Let’s analyze the data with respect to purpose of loans
table(lcdf$purpose)##
## car credit_card debt_consolidation home_improvement
## 928 24989 57622 5654
## house major_purchase medical moving
## 354 1823 1119 691
## other renewable_energy small_business vacation
## 5091 58 893 678
## wedding
## 100
# Checking number of loans by purpose
lcdf$purpose <- as.character(lcdf$purpose )
lcdf$purpose <- str_trim(lcdf$purpose )
lcdf$purpose <- as.factor(lcdf$purpose )
lcdf$purpose <- fct_collapse(lcdf$purpose, other = c("wedding","renewable_energy", "other"),NULL = "H")
# Get the number of loans by loan purpose
ggplot(data = lcdf, aes(x = purpose)) + geom_bar() + ggtitle("Number of Loans By Purpose") + xlab("Purpose of Loan ") + ylab("Number of Loans ")+ theme(plot.title = element_text(color="#993333", size=14, face="bold.italic"), axis.title.x = element_text(color="#993333", size=14, face="bold"), axis.title.y = element_text(color="#993333", size=14, face="bold")) + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
#Plot of loan amount by purpose
ggplot(lcdf, aes( x = loan_amnt, y=purpose)) + geom_boxplot(aes(fill=purpose)) +
xlab("Loan Amount ") + ylab("Purpose of Each Loan ") + theme(plot.title = element_text(color="#993333", size=14, face="bold.italic"), axis.title.x = element_text(color="#993333", size=14, face="bold"), axis.title.y = element_text(color="#993333", size=14, face="bold")) 
#Bivariate analysis of employment length and purpose.
table(lcdf$purpose, lcdf$emp_length)##
## < 1 year 1 year 10+ years 2 years 3 years 4 years 5 years
## car 104 67 245 90 85 52 70
## credit_card 2260 1726 7366 2323 2078 1485 1463
## debt_consolidation 4489 3838 18435 5136 4588 3402 3500
## home_improvement 302 285 2104 432 386 314 365
## house 41 22 81 43 37 21 31
## major_purchase 149 108 506 191 189 125 118
## medical 87 72 374 109 92 53 63
## moving 148 60 116 82 59 46 45
## other 436 366 1655 449 405 300 290
## small_business 59 64 270 77 87 64 57
## vacation 29 41 242 55 40 30 44
##
## 6 years 7 years 8 years 9 years n/a
## car 42 42 50 26 55
## credit_card 1237 1255 1213 962 1621
## debt_consolidation 2622 3015 2940 2331 3326
## home_improvement 265 275 288 215 423
## house 18 20 19 8 13
## major_purchase 95 95 71 69 107
## medical 46 52 59 38 74
## moving 24 28 24 13 46
## other 270 253 243 176 406
## small_business 52 52 45 36 30
## vacation 41 37 38 34 47
# Percentages
lcdf %>% group_by(purpose) %>% summarise(nLoans=n(), defaults=sum(loan_status=="Charged Off"), Default_per = (defaults/nLoans)*100)## # A tibble: 11 × 4
## purpose nLoans defaults Default_per
## <fct> <int> <int> <dbl>
## 1 car 928 107 11.5
## 2 credit_card 24989 2865 11.5
## 3 debt_consolidation 57622 8319 14.4
## 4 home_improvement 5654 682 12.1
## 5 house 354 63 17.8
## 6 major_purchase 1823 266 14.6
## 7 medical 1119 172 15.4
## 8 moving 691 144 20.8
## 9 other 5249 863 16.4
## 10 small_business 893 203 22.7
## 11 vacation 678 101 14.9
#Does loan-grade vary by purpose? Which pupose the loan grade fall in?
table(lcdf$purpose, lcdf$grade)##
## A B C D E F G
## car 253 306 238 92 27 8 4
## credit_card 8349 9809 5008 1518 266 37 2
## debt_consolidation 11573 19745 16497 7534 1954 292 27
## home_improvement 1457 1777 1496 673 215 33 3
## house 37 74 83 74 48 27 11
## major_purchase 441 553 479 252 70 26 2
## medical 84 270 382 251 97 34 1
## moving 10 96 207 234 108 32 4
## other 327 1050 1749 1385 576 144 18
## small_business 15 100 249 300 159 62 8
## vacation 42 127 257 180 59 13 0
#do those with home-improvement loans own or rent a home?
lcdf %>% group_by(home_ownership) %>% summarise(nLoans=n(), purpose_home_ownership=sum(purpose=="home_improvement"))## # A tibble: 3 × 3
## home_ownership nLoans purpose_home_ownership
## <chr> <int> <int>
## 1 MORTGAGE 46988 4282
## 2 OWN 10437 909
## 3 RENT 42575 463
More than half (58 %) of loans were taken for debt consolidation. This follows the the Pareto principle of 80:20 rule, as the top 3 purposes are more than 80% of loan purposes. While the most number of loans are for the purpose of debt consolidation/credit card, the maximum percent of defaults are found to be for the purpose of small businesses, moving and housing loan. Most credit card and debt consolidation loans fall under grade B, small business loan mostly fall under grade D and moving under grade C. People with 10 + years of experience are the most common borrower of loan for credit card and debt consolidation. Home improvement loans are more common with 10+ years of experience.car loans are more common with people having 2 years of experience. Which might be reflective of the fact that once people are in job for 2 years they would want to keep a car for which they come to the lending club. Other than this, there are several people taking loans for home improvement when their Home ownership status says that they are living on rent. This seems suspicious because rarely will a tenant issue a loan for improvement.
Now that we understand purpose of loans with other factors, let’s check the relationship between employment length and other variables
# Arranging emp_length as factor variables
lcdf$emp_length <- factor(lcdf$emp_length, levels=c("n/a", "< 1 year","1 year","2 years", "3 years" , "4 years", "5 years", "6 years", "7 years" , "8 years", "9 years", "10+ years" ))
# Number of loans in each employment length
ggplot(data = lcdf, aes(x = emp_length)) + geom_bar() + ggtitle("Number of Loans in Each Employement Length ") + xlab("Employement Length ") + ylab("Number of Loans ")+ theme(plot.title = element_text(color="#993333", size=14, face="bold.italic"), axis.title.x = element_text(color="#993333", size=14, face="bold"), axis.title.y = element_text(color="#993333", size=14, face="bold")) 
# Results in a table
table(lcdf$loan_status, lcdf$emp_length)##
## n/a < 1 year 1 year 2 years 3 years 4 years 5 years 6 years
## Charged Off 1296 1204 960 1206 1088 775 841 632
## Fully Paid 4852 6900 5689 7781 6958 5117 5205 4080
##
## 7 years 8 years 9 years 10+ years
## Charged Off 712 698 522 3851
## Fully Paid 4412 4292 3386 27543
# Calculating the proportion of defaults across employment length
lcdf %>% group_by(emp_length) %>% summarise(nLoans=n(), defaults=sum(loan_status=="Charged Off"), defaultPercentage=defaults/nLoans*100, avgIntRate=mean(int_rate), avgLoanAmt=mean(loan_amnt)) ## # A tibble: 12 × 6
## emp_length nLoans defaults defaultPercentage avgIntRate avgLoanAmt
## <fct> <int> <int> <dbl> <dbl> <dbl>
## 1 n/a 6148 1296 21.1 12.6 10152.
## 2 < 1 year 8104 1204 14.9 12.1 12171.
## 3 1 year 6649 960 14.4 12.2 12137.
## 4 2 years 8987 1206 13.4 12.1 12252.
## 5 3 years 8046 1088 13.5 12.0 12433.
## 6 4 years 5892 775 13.2 12.0 12556.
## 7 5 years 6046 841 13.9 12.1 12658.
## 8 6 years 4712 632 13.4 12.2 12589.
## 9 7 years 5124 712 13.9 12.2 12563.
## 10 8 years 4990 698 14.0 11.9 13029.
## 11 9 years 3908 522 13.4 12.0 13077.
## 12 10+ years 31394 3851 12.3 11.8 13741.
# Plot for Distribution of Loan Amount with Employment Length
ggplot(lcdf, aes( x = loan_amnt, y=emp_length)) + geom_boxplot(aes(fill=emp_length)) +
xlab("Loan Amount ") + ylab("Employment Length ")+ggtitle("Distribution of Loan Amount with Employement Length") + theme(plot.title = element_text(color="#993333", size=14, face="bold.italic"), axis.title.x = element_text(color="#993333", size=14, face="bold"), axis.title.y = element_text(color="#993333", size=14, face="bold")) 
Checking for Outliers
#Look at the variable summaries -- focus on a subset of the variables of interest in your analyses & modeling
#lcdf %>% select_if(is.numeric) %>% summary()
# Let us look at the annual income
ggplot(lcdf, aes( x = annual_inc)) + geom_histogram(aes(y=..density..), colour="black", fill="white")+ geom_density(alpha=.2, fill="#FF6666") + ggtitle("Distribution of Number of Loans With Annual Income ") + xlab("Annual Income ") + ylab("Number of Loans ") + theme(plot.title = element_text(color="#993333", size=14, face="bold.italic"), axis.title.x = element_text(color="#993333", size=14, face="bold"), axis.title.y = element_text(color="#993333", size=14, face="bold")) ## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# Let us check how are these very high income associated with loans status
ggplot(lcdf, aes( x = annual_inc, y=loan_status)) + geom_boxplot(aes(fill=loan_status)) + ggtitle("Distribution of Number of Loans With Annual Income By Loan Status - Before Removing Extreme Outliers") + xlab("Annual Income ") + theme(plot.title = element_text(color="#993333", size=14, face="bold.italic"), axis.title.x = element_text(color="#993333", size=14, face="bold"), axis.title.y = element_text(color="#993333", size=14, face="bold")) 
For annual income the data seems to be skewed towards the left. very few borrowers have an income more than 1.5 Milliion. It would be rare occurrence for someone to have an income more than 1.5 million and issue a loan from lending club, so we could consider these data points as outliers. Hence we will remove these 9 observations, which make up a very small percent of all the records in the dataset, therefore the impact would be next to negligible.
The very high income cases are for paid-off loans. We could exclude them, however we do so we might not have a decision tree model which predicts the hypothesis that high income people pay off the loan in most cases.Going with the use case we will discard and keep them in a separate dataframe.We shall observe what difference it makes to out models in the later part. Compared to the 110k data size the number looks really small, hence we will remove these
Removing Outliers….
lcdf <- lcdf %>% filter(annual_inc <= 1500000)
# Let us look at the new distribution of annual income after outlier removal
ggplot(lcdf, aes( x = annual_inc, y=loan_status)) + geom_boxplot(aes(fill=loan_status)) + ggtitle("Distribution of Number of Loans With Annual Income By Loan Status - After Removing Extreme Outliers ") + xlab("Annual Income ") + theme(plot.title = element_text(color="#993333", size=14, face="bold.italic"), axis.title.x = element_text(color="#993333", size=14, face="bold"), axis.title.y = element_text(color="#993333", size=14, face="bold")) 
Now that we have removed the data points over 1.5 million, our data looks much cleaner but outliers still exist.Although, if we would remove the data points above the upper bound(by multiplying the IQR by 1.5), we would have lost essential data.
Let’s calculate annual returns
# Let us look at some columns
lcdf %>% select(loan_status, int_rate, funded_amnt, total_pymnt) %>% head()## # A tibble: 6 × 4
## loan_status int_rate funded_amnt total_pymnt
## <chr> <dbl> <dbl> <dbl>
## 1 Fully Paid 5.32 28000 29437.
## 2 Fully Paid 13.3 6150 7311.
## 3 Fully Paid 15.0 7200 8972.
## 4 Fully Paid 15.3 4750 5611.
## 5 Fully Paid 12.7 5000 6009.
## 6 Fully Paid 15.0 9600 11748.
# We will use the following to calculate annualized return
#annReturn = [(Total Payment - funded amount)/funded amount]*12/36*100
lcdf$annRet <- ((lcdf$total_pymnt -lcdf$funded_amnt)/lcdf$funded_amnt)*(12/36)*100
# Returns for charged off and fully paid loans
lcdf %>% group_by(loan_status) %>% summarise(avgRet=mean(annRet), stdRet=sd(annRet), minRet=min(annRet), maxRet=max(annRet))## # A tibble: 2 × 5
## loan_status avgRet stdRet minRet maxRet
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Charged Off -12.0 9.27 -33.3 13.7
## 2 Fully Paid 5.12 2.42 0 16.5
# Do charged off loans have negative returns -
lcdf %>% select(loan_status, int_rate, funded_amnt, total_pymnt, annRet) %>% filter(annRet < 0) %>% count(loan_status)## # A tibble: 1 × 2
## loan_status n
## <chr> <int>
## 1 Charged Off 12145
We can see that the minimum returns for charged off loans can be as low as 0. This could be because borrowers are paying off their loans much earlier than expected. Maximum returns from fully paid loans could be as high as 16.5%, which cannot be possiblefor high grade loans. While chances of the loan being fully paid are higher for higher grade loans, investors might consider investing in lower grade loans for higher returns.
Let’s further analyze exactly how early or late are the loans being paid?
head(lcdf[, c("last_pymnt_d", "issue_d")])## # A tibble: 6 × 2
## last_pymnt_d issue_d
## <chr> <dttm>
## 1 Jul-2016 2015-05-01 00:00:00
## 2 Jun-2017 2015-07-01 00:00:00
## 3 Nov-2017 2014-11-01 00:00:00
## 4 Aug-2015 2014-03-01 00:00:00
## 5 Nov-2017 2015-04-01 00:00:00
## 6 Mar-2016 2014-01-01 00:00:00
# Bringing them to a consistent format
lcdf$last_pymnt_d<-paste(lcdf$last_pymnt_d, "-01", sep = "")
lcdf$last_pymnt_d<-parse_date_time(lcdf$last_pymnt_d, "myd")## Warning: 64 failed to parse.
#Check their format now
head(lcdf[, c("last_pymnt_d", "issue_d")])## # A tibble: 6 × 2
## last_pymnt_d issue_d
## <dttm> <dttm>
## 1 2016-07-01 00:00:00 2015-05-01 00:00:00
## 2 2017-06-01 00:00:00 2015-07-01 00:00:00
## 3 2017-11-01 00:00:00 2014-11-01 00:00:00
## 4 2015-08-01 00:00:00 2014-03-01 00:00:00
## 5 2017-11-01 00:00:00 2015-04-01 00:00:00
## 6 2016-03-01 00:00:00 2014-01-01 00:00:00
# Creating actual term column - If loan is charged off by default - 3 years
lcdf$actualTerm <- ifelse(lcdf$loan_status=="Fully Paid", as.duration(lcdf$issue_d %--% lcdf$last_pymnt_d)/dyears(1), 3)
# We know using simple interest Total = principle + pnr/100
# Hence r = (Total - principle)/principle * 100/n
# Then, considering this actual term, the actual annual return is
lcdf$actualReturn <- ifelse(lcdf$actualTerm>0, ((lcdf$total_pymnt -lcdf$funded_amnt)/lcdf$funded_amnt)*(1/lcdf$actualTerm)*100, 0)
lcdf %>% select(loan_status, int_rate, funded_amnt, total_pymnt, annRet, actualTerm, issue_d,last_pymnt_d) %>% head()## # A tibble: 6 × 8
## loan_status int_rate funded_amnt total_py…¹ annRet actua…² issue_d
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dttm>
## 1 Fully Paid 5.32 28000 29437. 1.71 1.17 2015-05-01 00:00:00
## 2 Fully Paid 13.3 6150 7311. 6.29 1.92 2015-07-01 00:00:00
## 3 Fully Paid 15.0 7200 8972. 8.20 3.00 2014-11-01 00:00:00
## 4 Fully Paid 15.3 4750 5611. 6.04 1.42 2014-03-01 00:00:00
## 5 Fully Paid 12.7 5000 6009. 6.73 2.59 2015-04-01 00:00:00
## 6 Fully Paid 15.0 9600 11748. 7.46 2.16 2014-01-01 00:00:00
## # … with 1 more variable: last_pymnt_d <dttm>, and abbreviated variable names
## # ¹total_pymnt, ²actualTerm
# Checking the same for charged off loans
lcdf %>% select(loan_status, int_rate, funded_amnt, total_pymnt, annRet, actualTerm, actualReturn) %>% filter(loan_status=="Charged Off") %>% head()## # A tibble: 6 × 7
## loan_status int_rate funded_amnt total_pymnt annRet actualTerm actualReturn
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Charged Off 16.0 8000 5936. -8.60 3 -8.60
## 2 Charged Off 13.7 27500 4704. -27.6 3 -27.6
## 3 Charged Off 16.5 11625 6543. -14.6 3 -14.6
## 4 Charged Off 13.0 5600 6114. 3.06 3 3.06
## 5 Charged Off 16.6 3500 3711. 2.01 3 2.01
## 6 Charged Off 12.0 25000 7314. -23.6 3 -23.6
Let’s find out actual return with respect to actual term.
# For cost-based performance, we may want to see the average interest rate, and the average of proportion of loan amount paid back, grouped by loan_status
lcdf%>% group_by(loan_status) %>% summarise( meanintRate=mean(int_rate), meanRet=mean((total_pymnt-funded_amnt)/funded_amnt),meanRetPer=mean((total_pymnt-funded_amnt)/funded_amnt)*100, sumTotalpymt = sum(total_pymnt), sumFundedamnt = sum(funded_amnt), term=mean(actualTerm) )## # A tibble: 2 × 7
## loan_status meanintRate meanRet meanRetPer sumTotalpymt sumFundedamnt term
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Charged Off 13.9 -0.359 -35.9 107801040. 168999125 3
## 2 Fully Paid 11.7 0.154 15.4 1271091404. 1104497150 2.13
# Checking the same by grade along with loan status
lcdf%>% group_by(loan_status, grade) %>% summarise( intRate=mean(int_rate),meanRet=mean((total_pymnt-funded_amnt)/funded_amnt),
meanRetPer=mean((total_pymnt-funded_amnt)/funded_amnt)*100,sumTotalpymt = sum(total_pymnt), sumFundedamnt = sum(funded_amnt), term=mean(actualTerm) )## `summarise()` has grouped output by 'loan_status'. You can override using the
## `.groups` argument.
## # A tibble: 14 × 8
## # Groups: loan_status [2]
## loan_status grade intRate meanRet meanRetPer sumTotalpymt sumFundedamnt term
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Charged Off A 7.44 -0.347 -34.7 10551097. 16182850 3
## 2 Charged Off B 10.9 -0.346 -34.6 30252911. 46221175 3
## 3 Charged Off C 13.9 -0.359 -35.9 36394725. 57214150 3
## 4 Charged Off D 17.2 -0.371 -37.1 21511774. 34618525 3
## 5 Charged Off E 19.8 -0.381 -38.1 7390448. 12117275 3
## 6 Charged Off F 24.1 -0.357 -35.7 1534358. 2344325 3
## 7 Charged Off G 26.4 -0.432 -43.2 165728. 300825 3
## 8 Fully Paid A 7.16 0.0951 9.51 341332406. 311443175 2.20
## 9 Fully Paid B 10.7 0.142 14.2 436933918. 382261400 2.15
## 10 Fully Paid C 13.8 0.181 18.1 310248814. 262517900 2.09
## 11 Fully Paid D 17.2 0.222 22.2 139263468. 113957300 2.03
## 12 Fully Paid E 20.0 0.257 25.7 36885764. 29451525 2.01
## 13 Fully Paid F 23.9 0.319 31.9 5581142. 4220600 2.09
## 14 Fully Paid G 26.4 0.318 31.8 845893. 645250 1.91
# For Fully Paid loans, is the average value of totRet what you'd expect, considering the average value for intRate?
lcdf %>% group_by(loan_status) %>% summarise(avgInt=mean(int_rate), avgRet=mean(actualReturn),avgTerm=mean(actualTerm))## # A tibble: 2 × 4
## loan_status avgInt avgRet avgTerm
## <chr> <dbl> <dbl> <dbl>
## 1 Charged Off 13.9 -12.0 3
## 2 Fully Paid 11.7 8.02 2.13
ggplot(lcdf, aes( x = actualReturn)) + geom_histogram(aes(fill=grade)) + ggtitle("Distribution of Actual Returns With Grade") + xlab("Actual Return ") + ylab("Number of Loans ") + theme(plot.title = element_text(color="#993333", size=14, face="bold.italic"), axis.title.x = element_text(color="#993333", size=14, face="bold"), axis.title.y = element_text(color="#993333", size=14, face="bold")) ## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
We can see that the actual term is not 3 years for fully paid loans. This could be the reason why returns are lower than expected. Charged off loans are expected to have negative return irrespective of the grade. Higher graded have higher loss / negative mean return rate.
Let’s check distribution of actual term
ggplot(lcdf %>% filter(loan_status=='Fully Paid'), aes( x = actualTerm)) + geom_histogram(aes(y=..density..), colour="black", fill="white", bins=50) +ggtitle("Distribution of Actual Term ") + xlab("Actual Term ") + ylab("Number of Loans ") + theme(plot.title = element_text(color="#993333", size=14, face="bold.italic"), axis.title.x = element_text(color="#993333", size=14, face="bold"), axis.title.y = element_text(color="#993333", size=14, face="bold")) 
ggplot(lcdf %>% filter(loan_status=='Fully Paid'), aes( x = actualTerm, y=grade)) + geom_boxplot(aes(fill=grade)) + ggtitle("Distribution of Actual Term With Loan Grade ")+
xlab("Actual Term ") + ylab("Grade") + theme(plot.title = element_text(color="#993333", size=14, face="bold.italic"), axis.title.x = element_text(color="#993333", size=14, face="bold"), axis.title.y = element_text(color="#993333", size=14, face="bold")) 
Derived attributes
lcdf$propSatisBankcardAccts <- ifelse(lcdf$num_bc_tl>0, lcdf$num_bc_sats/lcdf$num_bc_tl, 0)
# Let us look at the column created
summary(lcdf$propSatisBankcardAccts)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.4286 0.6000 0.6161 0.8000 1.0000
# Plot
ggplot(lcdf, aes( x = propSatisBankcardAccts, y=loan_status)) + geom_boxplot(aes(fill=loan_status)) + ggtitle("Distribution of Proportion of Satisfactory Bank Cards") +
xlab("Proportion of Satisfactory Bank Cards ") + ylab(" Loan Status ") + theme(plot.title = element_text(color="#993333", size=14, face="bold.italic"), axis.title.x = element_text(color="#993333", size=14, face="bold"), axis.title.y = element_text(color="#993333", size=14, face="bold")) 
#Another one - lets calculate the length of borrower's history
# i.e time between earliest_cr_line - open of current credit line. The month the borrowers earliers
# issue_d
# Correcting the date format
lcdf$earliest_cr_line<-paste(lcdf$earliest_cr_line, "-01", sep = "")
lcdf$earliest_cr_line<-parse_date_time(lcdf$earliest_cr_line, "myd")
lcdf$earliest_cr_line %>% head()## [1] "1994-12-01 UTC" "1988-04-01 UTC" "1988-08-01 UTC" "1984-02-01 UTC"
## [5] "2000-07-01 UTC" "2005-01-01 UTC"
lcdf$borrHistory <- as.duration(lcdf$earliest_cr_line %--% lcdf$issue_d ) / dyears(1)
ggplot(lcdf, aes( x = borrHistory, y=loan_status)) + geom_boxplot(aes(fill=loan_status)) +
xlab("Borrower History in Years ") + ylab("Loan Status")+ggtitle("Distribution of Borrower History") + theme(plot.title = element_text(color="#993333", size=14, face="bold.italic"), axis.title.x = element_text(color="#993333", size=14, face="bold"), axis.title.y = element_text(color="#993333", size=14, face="bold")) 
#Another new attribute: ratio of openAccounts to totalAccounts
lcdf$openAccRatio <- ifelse(lcdf$total_acc>0, lcdf$open_acc/lcdf$total_acc, 0)
summary(lcdf$openAccRatio)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.3704 0.4800 0.5014 0.6154 1.0000
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.0000 0.3704 0.4815 0.5017 0.6154 1.0000
ggplot(lcdf, aes( x = openAccRatio)) + geom_boxplot(aes(fill=loan_status)) +
xlab("Proportion of Open Account to Total Accounts ") + ylab(" Loan Status ") + theme(plot.title = element_text(color="#993333", size=14, face="bold.italic"), axis.title.x = element_text(color="#993333", size=14, face="bold"), axis.title.y = element_text(color="#993333", size=14, face="bold")) 
#does LC-assigned loan grade vary by borrHistory?
lcdf %>% group_by(grade) %>% summarise(avgBorrHist=mean(borrHistory))## # A tibble: 7 × 2
## grade avgBorrHist
## <chr> <dbl>
## 1 A 18.2
## 2 B 16.7
## 3 C 15.5
## 4 D 14.9
## 5 E 14.4
## 6 F 13.5
## 7 G 11.4
ggplot(lcdf, aes( x = borrHistory)) + geom_boxplot(aes(fill=grade)) +
xlab("Borrower History ") + ylab(" Loan Status ") + theme(plot.title = element_text(color="#993333", size=14, face="bold.italic"), axis.title.x = element_text(color="#993333", size=14, face="bold"), axis.title.y = element_text(color="#993333", size=14, face="bold")) 
lcdf %>% group_by(grade) %>% summarise(avgBorrHist=mean(borrHistory), minBorrHist=min(borrHistory), maxBorrHist = max(borrHistory), medianBorrHist=median(borrHistory)) ## # A tibble: 7 × 5
## grade avgBorrHist minBorrHist maxBorrHist medianBorrHist
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 A 18.2 3.08 57.6 16.5
## 2 B 16.7 3.00 65.0 15.1
## 3 C 15.5 3.08 63.8 14.2
## 4 D 14.9 3.08 64 13.6
## 5 E 14.4 3.08 58.3 13.1
## 6 F 13.5 3.08 51.4 12.2
## 7 G 11.4 3.16 26.3 10.9
Converting character variables
#glimpse(lcdf)
# there are a few character type variables - grade, sub_grade, verification_status,....
# We can convert all of these to factor
lcdf <- lcdf %>% mutate_if(is.character, as.factor)
#Checking the datatype after conversion
#glimpse(lcdf)Data Leakage Concept of leakage - It is the use of information in the model training process which would not be expected to be available at prediction time, causing the predictive scores (metrics) to overestimate the model’s utility when run in a production environment.Reference - https://en.wikipedia.org/wiki/Leakage_(machine_learning)#:~:text=In%20statistics%20and%20machine%20learning,when%20run%20in%20a%20production
#Identified the variables you want to remove
varsToRemove = c('funded_amnt_inv', 'term', 'emp_title', 'pymnt_plan', 'earliest_cr_line', 'title', 'zip_code', 'addr_state', 'out_prncp', 'out_prncp_inv', 'total_pymnt_inv', 'total_rec_prncp', 'total_rec_int', 'total_rec_late_fee', 'recoveries', 'collection_recovery_fee', 'last_credit_pull_d', 'policy_code', 'disbursement_method', 'debt_settlement_flag', 'settlement_term', 'application_type')
lcdf <- lcdf %>% select(-all_of(varsToRemove))
#Drop all the variables with names starting with "hardship" -- as they can cause leakage, unknown at the time when the loan was given.
#First checking before dropping
lcdf %>% select(starts_with("hardship")) ## # A tibble: 99,994 × 12
## hardship_flag hards…¹ hards…² hards…³ hards…⁴ hards…⁵ hards…⁶ hards…⁷ hards…⁸
## <fct> <lgl> <lgl> <lgl> <lgl> <lgl> <lgl> <lgl> <lgl>
## 1 N NA NA NA NA NA NA NA NA
## 2 N NA NA NA NA NA NA NA NA
## 3 N NA NA NA NA NA NA NA NA
## 4 N NA NA NA NA NA NA NA NA
## 5 N NA NA NA NA NA NA NA NA
## 6 N NA NA NA NA NA NA NA NA
## 7 N NA NA NA NA NA NA NA NA
## 8 N NA NA NA NA NA NA NA NA
## 9 N NA NA NA NA NA NA NA NA
## 10 N NA NA NA NA NA NA NA NA
## # … with 99,984 more rows, 3 more variables: hardship_loan_status <lgl>,
## # hardship_payoff_balance_amount <lgl>, hardship_last_payment_amount <lgl>,
## # and abbreviated variable names ¹hardship_type, ²hardship_reason,
## # ³hardship_status, ⁴hardship_amount, ⁵hardship_start_date,
## # ⁶hardship_end_date, ⁷hardship_length, ⁸hardship_dpd
# Dropping
lcdf <- lcdf %>% select(-starts_with("hardship"))
#similarly, all variable starting with "settlement", these are happening after disbursement
lcdf %>% select(starts_with('settlement'))## # A tibble: 99,994 × 4
## settlement_status settlement_date settlement_amount settlement_percentage
## <lgl> <lgl> <lgl> <lgl>
## 1 NA NA NA NA
## 2 NA NA NA NA
## 3 NA NA NA NA
## 4 NA NA NA NA
## 5 NA NA NA NA
## 6 NA NA NA NA
## 7 NA NA NA NA
## 8 NA NA NA NA
## 9 NA NA NA NA
## 10 NA NA NA NA
## # … with 99,984 more rows
# 4 columns
#Dropping them
lcdf <- lcdf %>% select(-starts_with("settlement"))
# Additional Leakage variables - based on our understanding
varsToRemove2 <- c("last_pymnt_d", "last_pymnt_amnt", "issue_d",'next_pymnt_d', 'deferral_term', 'payment_plan_start_date', 'debt_settlement_flag_date' )
# last_pymnt_d, last_pymnt_amnt, next_pymnt_d, deferral_term, payment_plan_start_date, debt_settlement_flag_date
lcdf <- lcdf %>% select(-all_of(varsToRemove2))Understanding the leakage is very important in the concept of Data Mining where we will be going ahead to predict models based on the training data. The models will be well trained if we use the leakage variable, however when we get unseen set of data the prediction will be poor as they wont be having values of these variables
Missing Values
Potential reasons for missing values in different variables? Are some of the missing values actually ‘zeros’ which are not recorded in the data? Is missing-ness informative in some way? Are there, for example, more/less defaults for cases where values on the attribute are missing
# Dropping columns with all n/a
lcdf %>% select_if(function(x){ all(is.na(x)) } ) # Checking what are those columns ## # A tibble: 99,994 × 19
## id member_id url desc annual_…¹ dti_j…² verif…³ revol…⁴ sec_a…⁵ sec_a…⁶
## <lgl> <lgl> <lgl> <lgl> <lgl> <lgl> <lgl> <lgl> <lgl> <lgl>
## 1 NA NA NA NA NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA
## 8 NA NA NA NA NA NA NA NA NA NA
## 9 NA NA NA NA NA NA NA NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA
## # … with 99,984 more rows, 9 more variables: sec_app_mort_acc <lgl>,
## # sec_app_open_acc <lgl>, sec_app_revol_util <lgl>,
## # sec_app_open_act_il <lgl>, sec_app_num_rev_accts <lgl>,
## # sec_app_chargeoff_within_12_mths <lgl>,
## # sec_app_collections_12_mths_ex_med <lgl>,
## # sec_app_mths_since_last_major_derog <lgl>,
## # orig_projected_additional_accrued_interest <lgl>, and abbreviated …
lcdf <- lcdf %>% select_if(function(x){ ! all(is.na(x)) } ) # Dropping
# Finding names of columns which has atleast 1 missing values
names(lcdf)[colSums(is.na(lcdf)) > 0] ## [1] "mths_since_last_delinq" "mths_since_last_record"
## [3] "revol_util" "mths_since_last_major_derog"
## [5] "open_acc_6m" "open_act_il"
## [7] "open_il_12m" "open_il_24m"
## [9] "mths_since_rcnt_il" "total_bal_il"
## [11] "il_util" "open_rv_12m"
## [13] "open_rv_24m" "max_bal_bc"
## [15] "all_util" "inq_fi"
## [17] "total_cu_tl" "inq_last_12m"
## [19] "avg_cur_bal" "bc_open_to_buy"
## [21] "bc_util" "mo_sin_old_il_acct"
## [23] "mths_since_recent_bc" "mths_since_recent_bc_dlq"
## [25] "mths_since_recent_inq" "mths_since_recent_revol_delinq"
## [27] "num_rev_accts" "num_tl_120dpd_2m"
## [29] "pct_tl_nvr_dlq" "percent_bc_gt_75"
# Finding proportion
options(scipen=999) # To not use scientific notation
colMeans(is.na(lcdf))[colMeans(is.na(lcdf))>0] ## mths_since_last_delinq mths_since_last_record
## 0.4992199532 0.8242294538
## revol_util mths_since_last_major_derog
## 0.0004100246 0.7199531972
## open_acc_6m open_act_il
## 0.9731283877 0.9731283877
## open_il_12m open_il_24m
## 0.9731283877 0.9731283877
## mths_since_rcnt_il total_bal_il
## 0.9739284357 0.9731283877
## il_util open_rv_12m
## 0.9769386163 0.9731283877
## open_rv_24m max_bal_bc
## 0.9731283877 0.9731283877
## all_util inq_fi
## 0.9731283877 0.9731283877
## total_cu_tl inq_last_12m
## 0.9731283877 0.9731283877
## avg_cur_bal bc_open_to_buy
## 0.0000200012 0.0096405784
## bc_util mo_sin_old_il_acct
## 0.0104406264 0.0361921715
## mths_since_recent_bc mths_since_recent_bc_dlq
## 0.0091105466 0.7433145989
## mths_since_recent_inq mths_since_recent_revol_delinq
## 0.1061263676 0.6474888493
## num_rev_accts num_tl_120dpd_2m
## 0.0000100006 0.0382422945
## pct_tl_nvr_dlq percent_bc_gt_75
## 0.0001600096 0.0103406204
# Finding the columns which have more than 60% missing values
names(lcdf)[colMeans(is.na(lcdf))>0.6]## [1] "mths_since_last_record" "mths_since_last_major_derog"
## [3] "open_acc_6m" "open_act_il"
## [5] "open_il_12m" "open_il_24m"
## [7] "mths_since_rcnt_il" "total_bal_il"
## [9] "il_util" "open_rv_12m"
## [11] "open_rv_24m" "max_bal_bc"
## [13] "all_util" "inq_fi"
## [15] "total_cu_tl" "inq_last_12m"
## [17] "mths_since_recent_bc_dlq" "mths_since_recent_revol_delinq"
nm<-names(lcdf)[colMeans(is.na(lcdf))>0.6]
lcdf <- lcdf %>% select(-all_of(nm))
#Impute missing values for remaining variables which have missing values
# - first get the columns with missing values
colMeans(is.na(lcdf))[colMeans(is.na(lcdf))>0]## mths_since_last_delinq revol_util avg_cur_bal
## 0.4992199532 0.0004100246 0.0000200012
## bc_open_to_buy bc_util mo_sin_old_il_acct
## 0.0096405784 0.0104406264 0.0361921715
## mths_since_recent_bc mths_since_recent_inq num_rev_accts
## 0.0091105466 0.1061263676 0.0000100006
## num_tl_120dpd_2m pct_tl_nvr_dlq percent_bc_gt_75
## 0.0382422945 0.0001600096 0.0103406204
nm<- names(lcdf)[colSums(is.na(lcdf))>0]
summary(lcdf[, nm])## mths_since_last_delinq revol_util avg_cur_bal bc_open_to_buy
## Min. : 0.00 Min. : 0.00 Min. : 0 Min. : 0
## 1st Qu.: 15.00 1st Qu.: 36.20 1st Qu.: 2791 1st Qu.: 1203
## Median : 31.00 Median : 54.10 Median : 6311 Median : 3893
## Mean : 33.94 Mean : 53.75 Mean : 12469 Mean : 9046
## 3rd Qu.: 50.00 3rd Qu.: 71.80 3rd Qu.: 17266 3rd Qu.: 10602
## Max. :188.00 Max. :153.70 Max. :395953 Max. :332178
## NA's :49919 NA's :41 NA's :2 NA's :964
## bc_util mo_sin_old_il_acct mths_since_recent_bc mths_since_recent_inq
## Min. : 0.00 Min. : 0 Min. : 0.0 Min. : 0.000
## 1st Qu.: 42.30 1st Qu.: 96 1st Qu.: 6.0 1st Qu.: 2.000
## Median : 66.20 Median :128 Median : 14.0 Median : 5.000
## Mean : 62.45 Mean :125 Mean : 24.5 Mean : 6.908
## 3rd Qu.: 86.20 3rd Qu.:152 3rd Qu.: 29.0 3rd Qu.:10.000
## Max. :188.80 Max. :640 Max. :616.0 Max. :24.000
## NA's :1044 NA's :3619 NA's :911 NA's :10612
## num_rev_accts num_tl_120dpd_2m pct_tl_nvr_dlq percent_bc_gt_75
## Min. : 2.00 Min. :0.000 Min. : 20.00 Min. : 0.00
## 1st Qu.: 9.00 1st Qu.:0.000 1st Qu.: 91.00 1st Qu.: 16.70
## Median :13.00 Median :0.000 Median : 97.80 Median : 50.00
## Mean :14.76 Mean :0.001 Mean : 94.05 Mean : 48.03
## 3rd Qu.:19.00 3rd Qu.:0.000 3rd Qu.:100.00 3rd Qu.: 75.00
## Max. :87.00 Max. :2.000 Max. :100.00 Max. :100.00
## NA's :1 NA's :3824 NA's :16 NA's :1034
# Replacing values - adding median values
lcdf<- lcdf %>% replace_na(list(mths_since_last_delinq=median(lcdf$mths_since_last_delinq, na.rm=TRUE), bc_open_to_buy=median(lcdf$bc_open_to_buy, na.rm=TRUE), mo_sin_old_il_acct=median(lcdf$mo_sin_old_il_acct,na.rm=TRUE), mths_since_recent_bc=median(lcdf$mths_since_recent_bc, na.rm=TRUE), mths_since_recent_inq=5, num_tl_120dpd_2m = median(lcdf$num_tl_120dpd_2m, na.rm=TRUE),percent_bc_gt_75 = median(lcdf$percent_bc_gt_75, na.rm=TRUE), bc_util=median(lcdf$bc_util, na.rm=TRUE) ))
lcdf<- lcdf %>% mutate_if(is.numeric, ~ifelse(is.na(.x), median(.x, na.rm = TRUE), .x))
dim(lcdf) ## [1] 99994 69
Some columns have same percentage of missing values. This could be because they are dependent columns. Information source for one column could also the source of other columns could be the reason. These missing values can be because of the following - 1. Missing Completely at Random 2. Missing at Random 3. Missing Not At Random. We could use various techniques taught in class to impute these missing values. 1. Imputing values 2. Leaving those rows. However approach for each column can be different. We could use various techniques taught in class to impute these missing values. 1. Imputing values 2. Leaving those rows. However approach for each column can be different. If they do not relate well to larger values, than we should not assume that missings are for values higher than the max.We will remove columns with more than 60% missing values, this is taken as a trial and test way - However when it comes to removing columns with NA approach could be different in each case. This could also mean loss of very important variable. We can tune our model based on the results
Next, we can perform a Univariate Analysis to understand exactly Which variables are individually predictive of the outcome
aucAll<- sapply(lcdf %>% mutate_if(is.factor, as.numeric) %>% select_if(is.numeric), auc, response=lcdf$loan_status) ## Setting levels: control = Charged Off, case = Fully Paid
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library(broom)
tidy(aucAll[aucAll > 0.5])## Warning: 'tidy.numeric' is deprecated.
## See help("Deprecated")
## # A tibble: 49 × 2
## names x
## <chr> <dbl>
## 1 loan_amnt 0.521
## 2 funded_amnt 0.521
## 3 int_rate 0.658
## 4 installment 0.507
## 5 grade 0.654
## 6 sub_grade 0.666
## 7 emp_length 0.534
## 8 home_ownership 0.555
## 9 annual_inc 0.577
## 10 loan_status 1
## # … with 39 more rows
tidy(aucAll) %>% arrange(desc(aucAll))## Warning: 'tidy.numeric' is deprecated.
## See help("Deprecated")
## # A tibble: 69 × 2
## names x
## <chr> <dbl>
## 1 loan_status 1
## 2 actualReturn 0.986
## 3 annRet 0.966
## 4 total_pymnt 0.756
## 5 sub_grade 0.666
## 6 actualTerm 0.664
## 7 int_rate 0.658
## 8 grade 0.654
## 9 acc_open_past_24mths 0.583
## 10 annual_inc 0.577
## # … with 59 more rows