Working with Missing Data in Machine Learning – Towards Data Science

Missing values are representative of the messiness of real world data. There can be a multitude of reasons why they occur -- ranging from human errors during data entry, incorrect sensor readings, to software bugs in the data processing pipeline. The normal reaction is frustration. Missing data are probably the most widespread source of errors in your code, and the reason for most of the exception-handling. If you try to remove them, you might reduce the amount of data you have available dramatically -- probably the worst that can happen in machine learning.