Exploratory Data Analysis (EDA)

Summary

The systematic process of understanding a dataset's shape, distributions, missingness and relationships before modeling — the step that most determines whether your model will actually work.

• 1Build a repeatable EDA checklist: shape, dtypes, missingness, distributions, correlations, outliers.
• 2Practice summarizing findings in plain language for a non-technical audience.
• 3Run a full EDA pass on a new dataset before ever training a model on it.