How do you handle missing data? What imputation techniques do you recommend?

There are various options for dealing with missing data:

  • Rows with missing data should be deleted.

  • Imputation of the mean, median, and mode

  • Creating a one-of-a-kind value

  • Predicting the values that are lacking

  • Using a missing value-supporting method, such as random forests

Delete rows with missing data is the ideal technique since it assures that no bias or variance is added or removed, resulting in a robust and accurate model. This is only suggested if there is a large amount of data to begin with and a low percentage of missing values.