A substantial aspect of any reliable data evaluation pipeline is managing null values. These occurrences, often represented as N/A, can negatively impact statistical models and insights. Ignoring these values can lead to biased results and faulty conclusions. Strategies for addressing absent data include substitution with average values, discard… Read More