Why is feature scaling required before PCA?

PCA requires scaling because it is sensitive to feature magnitude differences.

PCA is based on variance. Features with larger numeric ranges dominate variance computation, biasing principal components. Standardization ensures each feature contributes equally by converting data to zero mean and unit variance.