What are the best practices for handling missing data in datasets?

What are the best practices for handling missing data in datasets?

Handling missing data is crucial for accurate analysis. Common practices include imputation, deletion, and using algorithms that support missing values.

Can you explain imputation and when to use it?

Imputation involves filling in missing values with estimates, like the mean or median. It's useful when you want to retain data without losing valuable information.

What are the risks of deleting rows with missing data?

Deleting rows can lead to biased results, especially if the missing data is not random. It may also reduce the dataset size significantly.

Are there specific algorithms that handle missing data better?

Yes, some algorithms like decision trees and k-nearest neighbors can handle missing data more effectively without requiring imputation.