Python Feature Engineering Cookbook

How it works...

In this recipe, we performed different imputation techniques on different variable groups from the Credit Approval Data Set by utilizing Feature-engine within a single scikit-learn pipeline.

After loading and dividing the dataset, we created four lists of features. The first list contained numerical variables to impute with an arbitrary value. The second list contained numerical variables to impute by the median. The third list contained categorical variables to impute with a frequent category. Finally, the fourth list contained categorical variables to impute with the Missing string.

Next, we assembled the different Feature-engine imputers within a single scikit-learn pipeline. With ArbitraryNumberImputer(), we imputed missing ...