A critical aspect of any reliable data evaluation pipeline is managing null values. These occurrences, often represented as N/A, can negatively impact data science models and insights. Ignoring these values can lead to inaccurate results and erroneous conclusions. Strategies for dealing with missing data include replacement with median values, dele