How do you handle missing data in a dataset when preparing it for machine learning?

Example Concept: Imputation is a common technique for handling missing data, where missing values are replaced with estimated values. Simple imputation methods include replacing missing values with the mean, median, or mode of the column. More advanced methods involve using predictive models, such as k-nearest neighbors or regression, to estimate missing values based on other available data. The choice of imputation method should consider the data distribution and the impact on model performance.