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Daily Dose of Data Science | Avi Chawla | Substack

Seven categorical data encoding techniques are explained: one-hot encoding creates binary vectors for each category; dummy encoding drops one feature to avoid multicollinearity; effect encoding uses -1 for reference categories; label encoding assigns unique integers without inherent order; ordinal encoding maintains category hierarchy; count encoding uses frequency values; and binary encoding converts ordinal values to binary code for high-cardinality features. Each technique varies in dimensionality impact and use cases.

7 Categorical Data Encoding Techniques

Drag-and-drop UI to build AI agent workflows [open-source]!

P.S. For those wanting to develop “Industry ML” expertise: