Create features that express useful information without leaking the answer into the model.
The core idea
Feature engineering transforms raw fields into signals a model can use. Useful features can represent counts, time intervals, categories, ratios, or domain-specific patterns. The value depends on the problem and what is available at prediction time.
A practical way to approach it
Explore distributions and missing values, establish a simple baseline, and create one feature at a time. Validate the transformation on training data, use pipelines to avoid inconsistent preprocessing, and compare results on held-out data.
Try it in a small project
For a delivery-time model, derive distance bands, weekday, and order-size features if those values are available before delivery. Check that no feature accidentally encodes the actual delivery duration.
Common mistakes to avoid
More features can increase noise and complexity. Keep transformations explainable, test edge cases, and watch for leakage and fairness issues.
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.