Right order of doing feature selection, PCA and normalization?

Question

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 dimension

Answer 1

Good answers here. One point needs to be highlighted. PCA is a form of dimensionality reduction. It will find a lower dimensional linear subspace that approximates the data well. When the axes of this subspace align with the features that one started with, it will lead to interpretable feature selection as well. Otherwise, feature selection after PCA, will lead to features that are linear combinations of the original set of features and they are difficult to interpret based on the original set of features.