A substantial aspect of any robust data analysis pipeline is handling missing values. These situations, often represented as NULL, can severely impact data science models and reports. Ignoring these values can lead to skewed results and erroneous conclusions. Strategies for dealing with missing data include replacement with mean values, removal of