Feature Scaling (Normalization / Standardization)

Summary

Rescaling numeric features to comparable ranges — normalization maps to a 0-1 range, standardization centers around a mean of 0 with unit variance — required for many algorithms (like KNN, SVM, gradient descent-based models) to work correctly.

• 1Apply both normalization and standardization to the same numeric column and compare.
• 2Learn which model types actually require scaled features (distance-based, gradient-based) and which don't (tree-based).
• 3Practice fitting a scaler on training data only, then applying it to test data — never the reverse.