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four eigenvalues corresponding to 4 features. We use each eigenvalues (lambda) to find the eigenvectors. So, in total there will be 4 eigenvectors. The eigenvector is a column vector and each eigenvector will have one column and the same number of rows as the covariance matrix. In this example,