I would like to thank everyone who took the time to read and engage with my article. Your support and feedback are truly appreciated. You can reproduce the analysis on my GitHub repository: Credit Scoring with Python . is not just about training a machine learning algorithm and evaluating its performance with an AUC or a Gini coefficient.  Many beginners in modeling rush into model training, skipping crucial steps that determine whether a model is truly robust and interpretable. This enthusiasm, which lasts only a few minutes — just long enough for the performance metrics to appear on the screen — often obscures the more in-depth and rigorous work that precedes this stage. In credit risk, the quality of a model depends heavily on the variables it uses. A variable that seems predictive in a training dataset may behave inconsistently across time or across different populations. If we ignore this, we risk building a model that performs well in development but fails in production.…