Exercise

Imputing missing data

Imputation is a common technique to deal with missing data. In a Machine Learning interview, you might be required to perform modeling on a dataset that requires imputing missing values. Careful selection of an adequate imputation method could pay off quite well down the road. In this exercise, you will impute the missing values in the bands dataset using mean and linear regression as your imputation models.

The naniar and simputation packages for missing data imputation have been loaded in your workspace, as well as dplyr and tidyr for data manipulation.

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Apply mean imputation to all variables with missing values in the bands dataset. Store the result in the imp_mean variable.