PCA works by calculating the covariance matrix of the data and then determining its eigenvalues and eigenvectors. The eigenvectors represent the principal components, and the eigenvalues indicate the amount of variance captured by each principal component. The data is then projected onto these principal components to generate a simplified dataset.
