A substantial aspect of any effective data evaluation pipeline is handling null values. These instances, often represented as NULL, can negatively impact statistical models and insights. Ignoring these entries can lead to skewed results and erroneous conclusions. Strategies for addressing absent data include imputation with mean values, removal of