Data Understanding & Preparation

Data Leakage Simulator

Make leakage visible. Run the same prediction problem through an invalid workflow and a leakage-safe workflow so that implausibly strong validation scores become a diagnostic signal rather than a success.

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

Leakage occurs when training or model selection receives information that would not be available at real prediction time. It can enter through preprocessing, target-derived features, future information or split design and often creates implausibly strong validation results.

MechanismCompare the invalid and leakage-safe workflows step by step and identify exactly where information crosses from evaluation/future data into fitting.

Failure modeInterpreting a suspiciously high score as model quality without auditing the data flow that produced it.

VerificationWrite the information available at prediction time, then inspect every feature/transformation/split and verify that no later or held-out information enters training.

Experiment deliberately

Run the leaky workflow and safe workflow on the same task. Point to the exact operation that leaks information and explain why the score changes.

Data Leakage Simulator

What to observe while you experiment

Leaky vs safe evidence

Workflow diagnosis

Split / information flow

Sample rows