Your question is Data Preprocessing and Feature Engineering. 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).
How did you handle data preprocessing and feature engineering for your model?
Describe a practical machine learning project you worked on and explain how you transformed raw data into model-ready features. Cover missing values, categorical and numerical variables, outliers, scaling, leakage prevention, feature selection, and validation. Include the reasoning behind your choices, how preprocessing was fit only on training data, and how you measured whether engineered features improved the model.