Multiple imputation involves creating multiple imputed datasets by filling in the missing values with different plausible values and analyzing each dataset separately. This approach can capture the uncertainty in the imputed values and provide more robust results, but may be computationally expensive and require specialized software.

Multiple imputation involves creating a single imputed dataset by filling in the missing values with a single set of plausible values and analyzing the dataset multiple times. This approach can provide a simple imputation method, but may not capture the uncertainty in the imputed values.

Multiple imputation involves creating multiple imputed datasets by filling in the missing values with the same set of plausible values and analyzing each dataset separately. This approach can provide a simple imputation method, but may not capture the variability in the imputed values.

Multiple imputation is not suitable for categorical features like 'Occupation', as it is designed for numerical features only.