mshuning / gwu_dnsc_6301_project

Example project for DNSC 6301

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