From Raw Data to Risk Classes

1. 1Collect and preprocess raw data using Python libraries like Pandas and NumPy
2. 2Apply categorization techniques such as binning and labeling to create risk classes
3. 3Use machine learning algorithms like decision trees and random forests to validate the risk classes
4. 4Evaluate the performance of the risk classes using metrics like accuracy and ROC-AUC
5. 5Refine the categorization model by iterating on the preprocessing and algorithm selection steps

Data scientists and credit risk analysts can benefit from this guide to improve their credit scoring models

💡 Categorization is a crucial step in credit scoring, and using the right techniques can improve model performance

Categorize raw data into risk classes for credit scoring with Python!