A significant aspect of any reliable data analysis pipeline is addressing null values. These occurrences, often represented as N/A, can negatively impact statistical models and data visualization. Ignoring these records can lead to skewed results and erroneous conclusions. Strategies for dealing with missing data include substitution with mean valu