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I have performed PCA on my data set and found out that 95% of variability has been covered by the first three principal components. I wanted to perform a logistic regression on the data set. What do I do now?

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Train the model as usual with those three components. We have to do the same thing on test data too. Your classification job can be done.

Note: By training a model with PCA components. You don't get feature importance. If that is fine, you can proceed with those three components. Else, those components can be useful for just visualization purpose.

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  • $\begingroup$ any online resources to help me what I need to do step by step? $\endgroup$ – cgo Apr 7 at 0:57
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    $\begingroup$ I found this as most appropriate for your problem. This Explains step by step process of implementing PCA and finally used logistic regression for classification. Hope this helps. $\endgroup$ – Venkatesh Gandi Apr 7 at 4:49

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