EDA Playbook

Skipping EDA before modeling is the single most common source of silent failures in ML pipelines: leakage that makes offline metrics fictional, structural missingness treated as random, a target definition nobody actually agreed on. This playbook is a fixed-order audit that surfaces those problems while they cost hours instead of quarters - and its output is a decision log, not a pile of plots.

Operating procedure

Run the sections in order: schema problems invalidate the target analysis, and the target definition must be pinned before any feature-target relationship means anything.

Step 1: gather inputs

• The dataset, its supposed grain (one row = ?), and the expected row count. If the provider cannot state either, derive them and label the result a guess.
• The target column and its written definition - precisely what event, over what window, measured when. Ambiguity here cascades everywhere; do not proceed on a verbal shrug.
• What information will exist at prediction time. This single fact powers every leakage check later.
• Any known collection changes (new form fields, pipeline migrations) that could create structural breaks.

Step 2: shape and schema audit