Example Concept: Common techniques for handling missing data include deletion methods (e.g., listwise or pairwise deletion), imputation methods (e.g., mean, median, mode, or more advanced methods like k-nearest neighbors, or multiple imputation), and model-based approaches (e.g., using algorithms that can handle missing values natively like XGBoost). Each method has its pros and cons, and the choice should be guided by the data's missingness mechanism (MCAR, MAR, or MNAR) and the impact on model performance.