1. Business Objective : Classify the likelihood of
customers being default or current after taking loan
Data:
Data Cleaning:
• Feature reduction based on 60% & above missing data
• Missing value treatment using MICE(PMM, poly reg)
• Outlier treatment
Evaluation:
Process Flow:
Model Development:
Classification Metrics (Test Dataset):
# Row 42,535
# Column 111
# Default (1) 5660 (13%)
# Current (0) 36875 (87%)
Loans issued: 2007-2011
Business
Need
Data
Cleaning
Data Prep.
(ABT)
Modeling
Evaluation
MICE
Feature
Selection
SMOTEClassification
Tree
Boosting
algorithm
Probabilistic
method
Descriptive Analytics:
Univariate Analysis
Binning
and
Standardiz
ation
Imputation
using MICE
and Outlier
correction
Data
import
and
merging
Data Preparation:
Feature
Selection
• Variance Inflation
factor (VIF)
• Chi-Sq Tests
• Weight of
Evidence (WOE)
Feature
Transformation
• One hot
encoding
• Label Encoder
Range
Normaliz
ation
Analytical
Base
Table
(ABT)
Outlier
Treatment
Synthetic Minority Over-Sampling Technique ROC performance (Multiple models - Test Dataset)
Boosting and simple classifiers are used to train and
performances were compared on validation data
Stochastic Gradient Boosting – Tuning Parms:
• n_estimators = 100
• Learning_rate=0.2
• Subsample = 0.8
• max_depth= 10
• Min_samples_split=30
• warm_start=True
• Execution time = 7.1 mins
Parameter Relative Importance (%)
SMOTE Output: Balanced Classification (50:50)
Categorical: WOE, IV, Chi-Sq test
Numerical: VIF (Multi-collinearity)
Applicants’ Credit Risk Scoring – Jigar, Preethi
0.4%
0.5%
1.5%
1.7%
1.8%
2.0%
2.2%
2.2%
2.3%
2.7%
3.1%
4.0%
4.6%
5.2%
6.3%
6.5%
6.8%
7.1%
7.4%
10.3%
21.3%
delinq_2yrs
out_prncpal_amt
loan_grade
home_ownership
Purpose_loan
inquiries_last_6mths
employment_leng
Region_home
term_loan
verification_status
open_credit_lines
credit_window_time
total_crdt_lines_used
applied_issued_dif_amt
annual_inc
int_rate
revol_bal
Debt-to-Income Ratio
revol_util
mon_installment
last_pymnt_amnt
Misclassification
Rate
Precision (for '1') Recall (for "1") AUC Score
10%
0.61
0.57
0.92
16%
0.41
0.49
0.85
12%
0.56
0.36
0.84
25%
0.30
0.65
0.82
Gradient Boosting ADA Boosting
Random Forest Logistic Regression
Tools : R, Python
1.42
1.44
1.46
1.48
1.5
1.52
1.54
1.56
1.58
1.6
1.62
0
0.2
0.4
0.6
0.8
1
1.2
1
29
57
85
113
141
169
197
225
253
281
309
337
365
393
421
449
477
505
533
561
589
617
645
673
Error
N_estimators = 700
Deviance Plot
Training Data Error Test Data Error
Lowest Test Error
Train - Test Error Gap
Precision (for 1) 0.59
Recall (for 1) 0.56
Validation data Results: