Pca, Principal Components, Dimensionality Reduction, Eigenvectors, Eigenvalue.
magnitude. Can you figure out the reason? Remember, eigenvectors belong to the variance-covariance matrix. Now when we standardize the variables, we essentially bring the variance of all the variables to 1. So this can be the reason that despite taking random data, the eigenvector looked as such.