feature-engineering

Design, transform and debug model features so the model learns the signal that matters. Use when someone asks what features to build; how to encode categorical variables or high-cardinality IDs; how to handle dates, times, text, locations or event histories; how to create ratios, lags, rolling windows or per-customer aggregates without leaking the future; how to log-transform or scale skewed columns, impute missing values or pick defaults; whether target encoding is safe; or when a model underperforms, looks strangely complex, or misbehaves on certain inputs and a feature may be the cause. Not for auditing whether a dataset is usable at all, and not for reducing many existing columns to fewer.