What is the mathematical intuition behind PCA in Scikit-Learn?

PCA finds orthogonal directions maximizing variance using eigen decomposition or SVD.

PCA computes covariance matrix of data, then extracts eigenvectors (principal components) corresponding to largest eigenvalues. These directions represent maximum variance. Data is projected onto these orthogonal axes, reducing dimensionality while preserving structure.