| name | eda-workflow |
| description | Run a disciplined exploratory pass before anyone models: profile the data (shape, types, missingness, cardinality, distributions), make and document cleaning decisions, visualize distributions and relationships, spot leakage candidates and target-definition problems, generate hypotheses, and communicate findings with their uncertainty. |
EDA Workflow
Define and check the target first
A fuzzy or leaking target makes every downstream number meaningless. Confirm the target is correctly defined and available at prediction time before exploring around it.
Profile, don't just summarize
Shape, dtypes, missingness pattern (and why it's missing), cardinality, duplicates, and the distribution of every column. Plot distributions — the mean of a bimodal column describes nobody. Summary statistics lie by omission.
Clean as a documented decision
Missingness handling, outlier treatment, type coercions, dedup — each is a modeling choice with consequences. Record what you did and why, so the next person can disagree.
Read plots adversarially
Look for outliers, the bin hiding the bimodality, and the confounder behind a Simpson's-paradox reversal. A relationship plot is an argument; stress-test it before you believe it.
Spot leakage candidates
Flag columns absent at prediction time, target-derived fields, and IDs that encode the answer. A suspiciously strong early signal is a leakage alarm. Hand the list to feature-and-modeling-engineer to enforce inside the split.
Generate hypotheses, don't rule on them
Exploration produces candidate effects. Frame each as a hypothesis with its uncertainty and route the "is it statistically real" question to applied-statistics — never p-hack an exploratory pattern into a confirmatory claim.
Output
An EDA report: the profile, the cleaning decisions, the visual findings, the leakage candidates, the hypotheses generated, and the uncertainty (sample size, confounders, the caveat) on every claim. Hand features to feature-and-modeling-engineer and significance to applied-statistics.
