MANAGING ABSENT VALUES

A critical aspect of any robust data evaluation pipeline is handling missing values. These occurrences, often represented as NaN, can negatively impact statistical models and data visualization. Ignoring these entries can lead to biased results and erroneous conclusions. Strategies for null value handling include substitution with average values, r