A substantial aspect of any effective data processing pipeline is managing absent values. These occurrences, often represented as NULL, can considerably impact machine learning models and data visualization. Ignoring these entries can lead to skewed results and erroneous conclusions. Strategies for dealing with missing data include substitution wit