A critical aspect of any robust data evaluation pipeline is addressing missing values. These situations, often represented as NaN, can considerably impact machine learning models and reports. Ignoring these entries can lead check here to inaccurate results and faulty conclusions. Strategies for dealing with missing data include imputation with medi