What is a common method to handle missing data in a dataset?

Imputation involves replacing missing values with statistically derived estimates such as mean, median, mode, or values predicted by machine learning models. This approach ensures the dataset remains robust without significant loss of information. For example, imputing the mean for missing sales figures can maintain the overall trend without introducing bias. Advanced imputation techniques, such as regression or k-nearest neighbors, provide more accurate estimations based on the relationships within the data. Proper handling of missing data is crucial to preserve dataset integrity and improve the reliability of subsequent analyses. Why Other Options Are Incorrect: • A: Discarding the entire dataset is impractical and results in data loss. • B: Using random values introduces noise and reduces reliability. • D: Ignoring missing data can lead to biased or incomplete analysis. • E: Replacing with zeros distorts the dataset, particularly in numerical or continuous data.