Example Concept: Imputation is a popular method for handling missing data, where missing values are filled in using statistical techniques such as mean, median, or mode substitution, or more sophisticated methods like k-nearest neighbors (KNN) or multiple imputation. These techniques help maintain the dataset's size and can improve model performance by providing a complete dataset for training. However, it's important to assess the impact of imputation on your model's bias and variance.