PCA works by finding the eigenvectors and eigenvalues of the covariance matrix of the data. The eigenvectors represent the directions of maximum variance (principal components), and the eigenvalues indicate the magnitude of this variance. By projecting the data onto the principal components, PCA reduces the dimensionality while preserving as much variability as possible.
