Learn how accidental leakage can make a model look much better in testing than it will perform in real use.
The core idea
A test set should approximate unseen examples. Data leakage occurs when information from the target or future leaks into training, preprocessing, or feature selection. The result is an overly optimistic evaluation.
A practical way to approach it
Split data before fitting preprocessing steps. Fit scalers, encoders, and feature selectors on the training set only, then apply them to validation and test data. For time-dependent data, use chronological splits rather than random shuffling when that better matches deployment.
Try it in a small project
For a sales forecast, train on earlier months and test on later months. Check whether any feature includes information that would not be available at prediction time.
Common mistakes to avoid
Do not repeatedly tune against the final test set. Keep it for final evaluation and document how the data was split.
Key takeaway
Keep the first version small, make your assumptions explicit, and verify the result with a repeatable test. For production use, check the current official documentation for the tools and versions you choose.