Example Concept: Common techniques for handling missing data include imputation, deletion, and using algorithms that support missing values. Imputation involves filling in missing values with mean, median, or mode for numerical data, or using a constant or the most frequent value for categorical data. Deletion can be applied when the amount of missing data is small, either by removing rows with missing values or entire columns if they have too many missing entries. Some algorithms, like decision trees, can inherently handle missing values by using surrogate splits.