Example Concept: Missing data can be addressed through techniques such as mean/mode imputation, which replaces missing values with the mean or mode of the available data, or more advanced methods like multiple imputation, which involves creating multiple complete datasets by predicting missing values based on other variables. Additionally, algorithms like k-nearest neighbors (KNN) or using models that handle missing data natively, such as decision trees, can be effective. The choice of technique depends on the missing data mechanism (MCAR, MAR, MNAR) and the impact on the dataset's statistical properties.