How-to Guide to Handling Missing Data in AI/ML Datasets

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Artificial Intelligence and Machine Learning are the noble pursuits that depend largely on the data they are fed. With this data, systems figure out the future path and learn to handle complex scenarios. All of the applications of Machine Learning and Artificial Intelligence makes sense only when the supplied data is complete and rich.

But, in the real world, the data is not perfect, just like everything else. But, there are steps to fix the data when it is incomplete, incoherent, and unsuitable. Today, we discuss the methods to treat missing data when a comprehensive data is required for ML and AI applications.

Whether to ignore the missing values or to treat them effectively, depends on some factors to be considered such as the percentage of the missing values in the dataset, the variables these values affect, and whether the missing values belong to a dependent or an independent variable, etc. 

The performance of your predictive analytics depends on the accuracy and the integrity and the completeness of the data. Therefore, it becomes necessary to treat missing data when the need arises.

Treatment by Deletion

The best avoidable method to get over the missing data is to delete the record. This can be done either …

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