A critical aspect of any robust data evaluation pipeline is handling missing values. These situations, often represented as N/A, can severely impact statistical models and reports. Ignoring these records can lead to biased results and faulty conclusions. Strategies for addressing absent data include replacement with median values, removal of entrie