Example Concept: Imputation is a common strategy for handling missing data, where missing values are filled in using statistical methods or machine learning algorithms. Techniques such as mean, median, or mode imputation are simple and quick but may introduce bias. More advanced methods include k-nearest neighbors (KNN) imputation, which uses the nearest data points to predict missing values, or multiple imputation, which creates several complete datasets by imputing missing values multiple times and then averages the results to account for uncertainty.