Lessons
- Credit Risk Modelling - Case Studies
- Classification vs. Regression Models
- Case Study - German Credit - Steps to Build a Predictive Model
- Import Credit Data Set in R
- German Credit Data : Data Preprocessing and Feature Selection in R
- Credit Modelling: Training and Test Data Sets
- Build the Predictive Model
- Logistic Regression Model in R
- Measure Model Performance in R Using ROCR Package
- Create a Confusion Matrix in R
- Credit Risk Modelling - Case Study- Lending Club Data
- Explore Loan Data in R - Loan Grade and Interest Rate
- Credit Risk Modelling - Required R Packages
- Loan Data - Training and Test Data Sets
- Data Cleaning in R - Part 1
- Data Cleaning in R - Part 2
- Data Cleaning in R - Part 3
- Data Cleaning in R - Part 5
- Remove Dimensions By Fitting Logistic Regression
- Create a Function and Prepare Test Data in R
- Building Credit Risk Model
- Credit Risk - Logistic Regression Model in R
- Support Vector Machine (SVM) Model in R
- Random Forest Model in R
- Extreme Gradient Boosting in R
- Predictive Modelling: Averaging Results from Multiple Models
- Predictive Modelling: Comparing Model Results
- How Insurance Companies Calculate Risk
Data Cleaning in R - Part 5
Numeric Features
Let’s look at all numeric features we have left.
> str(data_train[getNumericColumns(data_train)])
'data.frame': 41909 obs. of 54 variables:
$ funded_amnt : int 10000 35000 14400 7250 10000 10000 25000 8400 6950 16000 ...
$ annual_inc : num 52000 85000 85000 72000 45000 ...
$ dti : num 15 24.98 28.11 23.93 8.03 ...
$ delinq_2yrs : int 0 0 0 1 0 0 0 0 0 0 ...
$ earliest_cr_line : num 5630 2647 5873 11382 9436 ...
$ inq_last_6mths : int 1 1 0 0 0 1 1 1 0 1 ...
$ mths_since_last_delinq : num 44 31 72 20 31 31 31 60 55 25 ...
$ pub_rec : int 2 0 0 1 1 0 0 0 4 0 ...
$ revol_bal : int 1077 10167 37582 12220 471 10139 47954 11059 7096 9891 ...
$ revol_util : num 19.53 20.75 10.75 13.67 5.32 ...
$ collections_12_mths_ex_med: int 0 0 0 0 0 0 0 1 0 0 ...
$ acc_now_delinq : int 0 0 0 0 0 0 0 0 0 0 ...
$ tot_coll_amt : int 622 0 0 0 0 0 0 0 0 2017 ...
$ open_acc_6m : num 2 0 0 1 0 1 2 1 0 0 ...
$ open_act_il : num 1 3 3 2 1 2 2 1 0 2 ...
$ open_il_12m : num 4 0 1 1 0 1 2 1 0 2 ...
$ mths_since_rcnt_il : num 2 14 12 3 23 6 6 10 91 9 ...
$ total_bal_il : num 14809 73863 22387 40343 11499 ...
$ il_util : num 99 83 47 92 72 73 61 91 76 67 ...
$ open_rv_12m : num 0 0 0 1 1 1 1 2 0 3 ...
$ max_bal_bc : num 1007 5109 12211 3694 325 ...
$ all_util : num 88 71 66 84 54 55 60 86 59 50 ...
$ inq_fi : num 3 5 0 0 2 2 0 1 0 1 ...
$ total_cu_tl : num 0 1 0 0 2 0 1 1 0 1 ...
$ inq_last_12m : num 2 2 0 1 0 3 4 3 1 4 ...
$ avg_cur_bal : int 3972 17960 11885 30540 1710 4752 47914 22436 1419 2506 ...
$ bc_open_to_buy : num 1623 4833 3393 997 4329 ...
$ chargeoff_within_12_mths : int 0 0 0 0 0 0 0 0 0 0 ...
$ delinq_amnt : int 0 0 0 0 0 0 0 0 0 0 ...
$ mo_sin_old_il_acct : num 101 87 145 132 135 65 258 129 153 113 ...
$ mo_sin_rcnt_rev_tl_op : int 25 22 26 8 10 10 4 2 17 7 ...
$ mo_sin_rcnt_tl : int 2 14 12 3 10 6 4 2 17 7 ...
$ mort_acc : int 0 1 6 4 2 0 4 2 8 0 ...
$ mths_since_recent_bc : num 25 22 32 59 10 10 4 89 31 7 ...
$ mths_since_recent_inq : num 4 5 20 9 23 6 4 2 11 0 ...
$ num_accts_ever_120_pd : int 2 0 0 3 0 0 0 2 1 2 ...
$ num_actv_bc_tl : int 2 3 5 3 2 4 3 2 3 4 ...
$ num_bc_tl : int 3 4 9 4 5 4 7 7 7 5 ...
$ num_il_tl : int 7 9 7 8 4 4 8 2 3 7 ...
$ num_tl_90g_dpd_24m : int 0 0 0 1 0 0 0 0 0 0 ...
$ pct_tl_nvr_dlq : num 83.3 100 93.9 83.3 100 100 100 86.4 91.3 93.3 ...
$ percent_bc_gt_75 : num 50 33.3 100 100 0 0 0 100 33.3 25 ...
$ pub_rec_bankruptcies : int 0 0 0 0 1 0 0 0 3 0 ...
$ tax_liens : int 0 0 0 1 0 0 0 0 1 0 ...
$ is_ny : num 0 1 0 0 0 0 0 0 0 0 ...
$ is_pa : num 0 0 0 0 0 0 0 0 0 0 ...
$ is_nj : num 0 0 0 0 0 0 0 0 0 0 ...
$ is_oh : num 0 0 0 0 0 0 0 0 0 0 ...
$ is_fl : num 0 0 0 0 1 0 0 0 1 0 ...
$ is_co : num 0 0 0 0 0 0 0 0 0 0 ...
$ is_ga : num 1 0 0 0 0 0 0 1 0 0 ...
$ is_va : num 0 0 0 0 0 0 0 0 0 0 ...
$ is_az : num 0 0 0 0 0 0 0 0 0 0 ...
$ is_ca : num 0 0 0 0 0 0 0 0 0 0 ...
>
We will transform annual_inc
, revol_bal
, avg_cur_bal
, bc_open_to_buy
by dividing them by funded_amnt
(amount of loan).
data_train$annual_inc = data_train$annual_inc/data_train$funded_amnt
data_train$revol_bal = data_train$revol_bal/data_train$funded_amnt
data_train$avg_cur_bal = data_train$avg_cur_bal/data_train$funded_amnt
data_train$bc_open_to_buy = data_train$bc_open_to_buy/data_train$funded_amnt
We can now remove the funded amount attribute.
data_train$funded_amnt = NULL
Character Features
Let’s look at all character features we have left.
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