Your question is Feature Selection Approach. 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).
Given a dataset, how would you approach feature selection?
Explain a practical, leakage-safe process for selecting features before training a supervised machine learning model. Address numeric and categorical variables, missing values, correlated features, high-dimensional data, class imbalance when relevant, and how you would validate that selected features improve generalization rather than merely fitting the training sample. Include the methods you would compare, the evaluation metrics you would use, and how you would communicate the final selection.