Quickly go to any section of the Scorecard Building in R 5-Part Series:
ii. Data Collection, Cleaning and Manipulation
iii. Data Transformations: Weight-of-Evidence
iv. Scorecard Evaluation and Analysis
v. Finalizing Scorecard with other Techniques
Part of my job as a Data Scientist is to create, update and maintain a small-to-medium business scorecard. This machine learning generated application allows its users to identify applicants that are more likely to pay back their loan or not. Here, I take the opportunity to showcase the steps I take in building a reliable scorecard, and the analysis associated with evaluating it by using R. I will accomplish this with the use of public data provided by the consumer and commercial lending company, Lending Club (downloaded here).
Here is an overview of the essential steps to take when building this scorecard:
- Data Collection, Cleaning and Manipulation
- Data Transformations: Weight-of-Evidence and Information Value
- Training, Validating and Testing a Model: Logistic Regression
- Scorecard Evaluation and Analysis
- Finalizing Scorecard with other Techniques
See the next post, Scorecard Building – Part II – Data Preparation and Analysis to see how the data is prepared for further scorecard building.