What is the intuition behind eigenvectors in PCA?

Eigenvectors represent directions where data varies the most.

In PCA, eigenvectors of the covariance matrix define orthogonal directions in feature space. Each eigenvector points to a direction of maximal variance, and eigenvalues quantify how much variance exists along that direction. The first principal component captures the strongest pattern of variation in the dataset.