Example Concept: The process of handling missing data typically involves identifying the missing values, understanding the pattern of missingness, and then deciding on an appropriate strategy such as deletion, imputation, or using algorithms that support missing values. Deletion involves removing rows or columns with missing data, while imputation fills in missing values using statistical methods like mean, median, or more complex algorithms like K-Nearest Neighbors. Some machine learning algorithms can handle missing data natively, reducing the need for preprocessing.