While working on Kaggle Competition, I ended up with 11,726 columns which are mostly "dummies" (one hot encoding). Is this too many?
I know that we need to find out which features are relevant, but not sure how to do this.
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Your solution will depend on a couple of factors. One is what type of model you are using. If you are using something that automatically calculates feature importances then you could simply look at these (or take a more balanced look using permutation importances).
While you could look at feature importances, with ~11,000 possibilities this is going to be pretty difficult. The main focus should be to cut down these features into something more manageable, do you really need one hot encoding? Without knowing more about the dataset I can't provide much more advice.