Credit Line Increase Model Card

Basic Information

• Person or organization developing model: Patrick Hall, [email protected]
• Model date: August, 2021
• Model version: 1.0
• License: MIT
• Model implementation code: DNSC_6301_Example_Project.ipynb

Intended Use

• Primary intended uses: This model is an example probability of default classifier, with an example use case for determining eligibility for a credit line increase.
• Primary intended users: Students in GWU DNSC 6301 bootcamp.
• Out-of-scope use cases: Any use beyond an educational example is out-of-scope.

Training Data

• Data dictionary:

Name	Modeling Role	Measurement Level	Description
ID	ID	int	unique row indentifier
LIMIT_BAL	input	float	amount of previously awarded credit
SEX	demographic information	int	1 = male; 2 = female
RACE	demographic information	int	1 = hispanic; 2 = black; 3 = white; 4 = asian
EDUCATION	demographic information	int	1 = graduate school; 2 = university; 3 = high school; 4 = others
MARRIAGE	demographic information	int	1 = married; 2 = single; 3 = others
AGE	demographic information	int	age in years
PAY_0, PAY_2 - PAY_6	inputs	int	history of past payment; PAY_0 = the repayment status in September, 2005; PAY_2 = the repayment status in August, 2005; ...; PAY_6 = the repayment status in April, 2005. The measurement scale for the repayment status is: -1 = pay duly; 1 = payment delay for one month; 2 = payment delay for two months; ...; 8 = payment delay for eight months; 9 = payment delay for nine months and above
BILL_AMT1 - BILL_AMT6	inputs	float	amount of bill statement; BILL_AMNT1 = amount of bill statement in September, 2005; BILL_AMT2 = amount of bill statement in August, 2005; ...; BILL_AMT6 = amount of bill statement in April, 2005
PAY_AMT1 - PAY_AMT6	inputs	float	amount of previous payment; PAY_AMT1 = amount paid in September, 2005; PAY_AMT2 = amount paid in August, 2005; ...; PAY_AMT6 = amount paid in April, 2005
DELINQ_NEXT	target	int	whether a customer's next payment is delinquent (late), 1 = late; 0 = on-time

• Source of training data: GWU Blackboard, email [email protected] for more information
• How training data was divided into training and validation data: 50% training, 25% validation, 25% test
• Number of rows in training and validation data:
  • Training rows: 15,000
  • Validation rows: 7,500

Test Data

• Source of test data: GWU Blackboard, email [email protected] for more information
• Number of rows in test data: 7,500
• State any differences in columns between training and test data: None

Model details

• Columns used as inputs in the final model: 'LIMIT_BAL', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6'
• Column(s) used as target(s) in the final model: 'DELINQ_NEXT'
• Type of model: Decision Tree
• Software used to implement the model: Python, scikit-learn
• Version of the modeling software: 0.22.2.post1
• Hyperparameters or other settings of your model:

DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',
                       max_depth=6, max_features=None, max_leaf_nodes=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, presort='deprecated',
                       random_state=12345, splitter='best')

Quantitative Analysis

• Models were assessed primarily with AUC and AIR. See details below:

Train AUC	Validation AUC	Test AUC
0.3456	0.7891	0.7687*

Group	Validation AIR
Black vs. White	0.8345
Hispanic vs. White	0.8765
Asian vs. White	1.098
Female vs. Male	1.245

(*Test AUC taken from https://github.com/jphall663/GWU_rml/blob/master/assignments/model_eval_2023_06_21_12_52_47.csv)

Correlation Heatmap