Building Robust Credit Scoring Models (Part 3)

Critical data preparation steps for building a robust credit scoring model are detailed, with a specific focus on handling outliers and missing values. The process emphasizes splitting the dataset into training, testing, and out-of-time (OOT) samples before applying any preprocessing to preserve the model's ability to generalize. Using a Kaggle credit scoring dataset as an example, an artificial time variable is created to facilitate a proper temporal split for model validation. This methodology ensures that all transformations are learned from the training data and then applied consistently to the test and OOT sets.