Describe a good EDA workflow for a new dataset.

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Answer

$(1) Shape: rows \times columns, dtypes, missing values.$
$(2) Univariate: histograms / boxplots / value counts per column.$
$(3) Target: distribution of the outcome, class balance.$
$(4) Bivariate: correlations, scatter plots, feature-target relationship.$
$(5) Missingness: patterns (MCAR / MAR / MNAR)?$
$(6) Duplicates and near-duplicates.$
$(7) Time / group structure.$
$(8) Sanity checks: min/max make sense? plausibility of extremes?$
$Report findings, then decide on cleaning + feature engineering.$

Check yourself — multiple choice

Skip EDA
Shape → univariate → target → bivariate → missing patterns → duplicates → time/group → sanity checks
Only feature engineering
Random exploration

EDA workflow: shape / univariate / target / bivariate / missing / dupes / time / sanity.

#eda

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