- 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
- 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.
- 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
- 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