A substantial aspect of any effective data processing pipeline is handling absent values. These situations, often represented as NULL, can negatively impact statistical models and insights. Ignoring these entries can lead to biased results and erroneous conclusions. Strategies for dealing with missing data include replacement with average values, d… Read More