Your question is Feature Selection for Tabular Models. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
You're training a supervised learning model on tabular data and have many candidate predictors, some redundant, noisy, or weakly useful. You want a principled way to reduce the feature set without hurting generalization.
What techniques do you use for feature selection?