You are developing an ML model to predict house prices. While preparing the data, you discover that an important predictor variable, distance from the closest school, is often missing and does not…

Deleting rows with missing values would result in losing important instances, which the problem explicitly states is undesirable ('Every instance (row) in your data is important').

Applying feature crossing creates new features from existing ones but does not directly address or impute the missing values in the original important predictor variable.

Since 'distance from the closest school' is an important predictor, every instance is crucial, and the feature does not have high variance, predicting the missing values using a regression model (like linear regression with other features) is a sophisticated imputation method. This approach leverages the relationships between features to provide more accurate estimates than simple replacements, thus retaining valuable rows without introducing significant bias.

Replacing the missing values with zeros can introduce significant bias, especially if zero is not a semantically meaningful value for 'distance from closest school,' and can distort the feature's distribution, leading to incorrect model learning.