Your question is Handling Missing Data in ML. 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 preparing data for a supervised learning task, and several features have missing values. You need a practical way to handle them without distorting model performance.
How do you handle missing data in a dataset?