What are the different imputation techniques for handling missing data?

What are the different imputation techniques for handling missing data?

There are several imputation techniques, including mean, median, mode, k-nearest neighbors, and regression imputation. Would you like to know more about a specific technique?

Can you explain how k-nearest neighbors imputation works?

Sure! K-nearest neighbors (KNN) imputation replaces missing values with the average of the k closest data points. It considers the similarity between data points based on other features.

What are the pros and cons of using mean imputation?

Mean imputation is simple and quick, but it can reduce variability and may not be suitable for skewed distributions. It also doesn't account for relationships between variables.

Are there any advanced techniques I should consider?

Yes, advanced techniques like multiple imputation and machine learning-based methods can provide better estimates by considering the uncertainty of missing data.