Steps to Deal with Missing Data
Imputation vs. Removing Data. When dealing with missing data, data scientists can use two primary methods to solve the error: imputation or the removal of data.
Deletion. There are two primary methods for deleting data when dealing with missing data: listwise and dropping variables.
Imputation. When data is missing, it may make sense to delete data, as mentioned above. However, that may not be the most effective option.
Multiple Imputation. Multiple imputation is considered a good approach for data sets with a large amount of missing data.
K Nearest Neighbors. In this method, data scientists choose a distance measure for k neighbors, and the average is used to impute an estimate.
Regards,
Rachel Gomez
