Replace the missing values with zeros to maintain dataset completeness, despite the potential introduction of bias.

Eliminate any rows that contain missing entries to ensure data integrity, at the risk of reducing the dataset size and losing valuable information.

Merge this feature with another column that is fully populated to avoid data loss, though this may dilute the predictive power of the original feature.

Predict the missing values by applying linear regression for an accurate data representation, leveraging the existing data to estimate missing values without introducing significant bias.

Use a combination of imputation for missing values and feature engineering to enhance the 'distance from the nearest school' feature's variance, ensuring both data completeness and improved model performance.