normalization

Not every model cares about scale, but enough of them do that I keep scaling explicit. Linear models, SVMs, neural nets, and distance-based methods all benefit from well-behaved inputs. I prefer putting scalers inside the pipeline so train and inferen

Normalization eliminates redundancy and anomalies. 1NF requires atomic values—no arrays in columns. 2NF eliminates partial dependencies—all non-key columns depend on entire primary key. 3NF removes transitive dependencies—non-key columns don't depend