This article explores the impact of missing data by breaking down the three main mechanisms—MCAR, MAR, and MNAR. It explains how these mechanisms influence your analysis and the risks of dropping data units with missing values. By understanding these concepts you can minimize bias and uncertainty, ensuring reliable predictions and insights.

The article explores Functional Data Analysis as an alternative to data aggregation and bucketing, emphasizing its value in preserving data detail by representing information as curves rather than single points. It further demonstrates how FDA can be integrated into machine learning, such as Neural Networks, for better insight and classification accuracy.