Pca, Principal Components, Dimensionality Reduction, Eigenvectors, Eigenvalue.
the axis. This is essentially due to the fact that in PCA we don’t take all the components otherwise our whole objective will fail instead we choose top n components which in this case would be accounting for 80% of the variance in the original data. Variance-Covariance matrix Now one question you