Why is PCA sensitive to feature scaling and normalization?

PCA is variance-based, so unscaled features with large ranges dominate results.

PCA relies on covariance matrix, where variance magnitude directly influences principal components. If features are not standardized, high-magnitude variables dominate eigenvectors, leading to biased projections. Standardization ensures equal contribution from all features.