Determine useful features for machine learning model
I suggest taking a look at this page for some more ideas:

Feature Selection

That being said a couple of ideas that come to mind quickly, is to:


use a tree based method (like Random Forest) and look at your feature importances. Scikit Learn has a handy class for doing just that see the link above.
Use some sort of regularization/penalty like L1 or L2 regularization. That will force non-useful features to have parameters close to zero.
Recursively remove variables and see what the resulting output is and cross-validate. Again sklearn has a method for this.


Generally, these methods will be "expensive" as you are fitting multiple models to get you where you need to go.