Example Concept: Common strategies for handling missing values include deletion, imputation, and using algorithms that support missing values. Deletion involves removing rows or columns with missing data, which is suitable when the proportion of missing data is small. Imputation involves filling in missing values with statistical measures like mean, median, or using more sophisticated techniques like K-Nearest Neighbors (KNN) or regression models. Some machine learning algorithms, like decision trees, can handle missing values internally without the need for imputation.