Right order of doing feature selection, PCA and normalization? : <p>I know that feature selection helps me remove features that may have low contribution. I know that PCA helps reduce possibly correlated features into one, reducing the dimensions. I know that normalization transforms features to the same scale.</p>

<p>But is there a recommended order to do these three steps? Logically I would think that I should weed out bad features by feature selection first, followed by normalizing them, and finally use PCA to reduce dimensions and make the features as independent from each other as possible.</p>

<p>Is this logic correct?</p>

<p>Bonus question - are there any more things to do (preprocess or transform) 
 to the features before feeding them into the estimator?</p>
