Python Feature Engineering Cookbook

How it works...

In this recipe, we performed MICE using IterativeImputer() from scikit-learn. First, we loaded data using pandas read_csv() and separated it into train and test sets using scikit-learn's train_test_split(). Next, we created a multivariate imputation object using the IterativeImputer() from scikit-learn. We specified that we wanted to estimate missing values using Bayes regression and that we wanted to carry out 10 rounds of imputation over the entire dataset. We fitted IterativeImputer() to the train set so that each variable was modeled based on the remaining variables in the dataset. Next, we transformed the train and test sets with the transform() method in order to replace missing data with their estimates.