A critical aspect of any robust data analysis pipeline is managing absent values. These instances, often represented as NULL, can negatively impact data science models and insights. Ignoring these entries can lead to skewed results and incorrect conclusions. Strategies for null value handling include replacement with mean values, removal of entries