Databytes on Instagram: "High dimensional data gets compressed with PCA by using the covariance matrix eigenvalues and eigenvectors to find directions of maximum variability. Principal components are chosen by explained variance ratio so fewer dimensions still preserve most information in real data science workflows. [pca dimensionality reduction, covariance matrix, eigenvalues eigenvectors, principal components, explained variance ratio, feature compression, high dimensional data, orthogonal di