A one-page preprocessing reference, every line explained: cleaning, stratified splits, scaling in the right order, encoding, engineered features, TF-IDF, selection and PCA.
Feature engineering is where most of the score actually comes from, and where most of the quiet mistakes live. Scale before you split and you have leaked the test set. Label-encode a nominal category and you have told the model that Tuesday is greater than Monday. Keep a column that would not exist at prediction time and validation looks wonderful right up until production. This cheatsheet lays out the pipeline in the order it has to happen, each line annotated with what it does and what it costs. Keep it open while you work, or save the image and pin it nearby.
Hope this helps.
[…] Feature Engineering Cheatsheet […]