In this episode of The Data Science Podcast, Lucas and Luna dive into one of the most common yet overlooked pitfalls in machine learning: data leakage. They break down how a seemingly innocent preprocessing misstep can cause models to appear 20% more accurate in training only to collapse in production. Using the real-world example of a medical diagnosis model that flagged patient IDs as a top predictor, they explain the three main types of leakage—target, time-series, and preprocessing—and share concrete techniques to prevent each. Lucas also introduces the concept of 'target leakage via feature engineering' where future information sneaks into training data, and Luna challenges the audience to audit their own pipelines. By the end, listeners learn to spot leakage symptoms like suspiciously high AUC scores and zero-error features, and walk away with a simple five-minute audit checklist to safeguard their models.

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