Data Understanding & Preparation

Feature Engineering Playground

Turn raw columns into more useful signals, then measure whether the engineered representation improves a simple model on unseen rows.

1 · Dataset 2 · Preprocessing 3 · Feature engineering 4 · Dimensionality reduction 5 · Class imbalance 6 · Data leakage

Lab concept guide

What to observe while you experiment

Feature engineering changes the representation available to a model. A useful engineered feature exposes task-relevant structure while remaining computable at prediction time and being created inside the training workflow to avoid leakage.

MechanismCreate one transformation from raw columns, compare the new representation, then evaluate the same simple model with and without it on unseen rows.

Failure modeCreating target/future-derived features or judging engineered features only by training improvement.

VerificationCheck the feature formula on a few rows, confirm it uses only prediction-time information, and compare validation scores with a fixed split/model.

Experiment deliberately

Engineer one interaction/date/bin feature. Predict which observations’ representation should change, then compare validation performance against the raw-feature baseline.

Choose transformations

Polynomialsessions² and ad_spend². Interactionssessions × ad_spend. BinningTurn sessions into low / medium / high indicators. Log transformlog1p(page_views) for skewed activity. Date/timeMonth and day-of-week from timestamp. Domain ratiopage_views per session.

Why evaluate features?

More columns are not automatically better. This lab fits the same ridge-stabilised linear regression before and after feature engineering, using the same held-out test rows.

Generating transaction dataset…

Feature Engineering Playground

What to observe while you experiment

Choose transformations

Why evaluate features?

Held-out predictions

Transformation map

Engineered data preview