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Exercise

Complete feature engineering pipeline

The recipes package is designed to encode multiple feature engineering steps into one object, making it easier to maintain data transformations in a machine learning workflow.

In this exercise, you will train a feature engineering pipeline to prepare the telecommunications data for modeling.

The telecom_df tibble, as well as your telecom_training and telecom_test datasets from the previous exercises, have been loaded into your workspace.

Instructions

100 XP
  • Create a recipe that predicts canceled_service using all predictor variables in the training data.
  • Remove correlated predictor variables using a 0.8 threshold value.
  • Normalize all numeric predictors.
  • Create dummy variables for all nominal predictors.
  • Train your recipe on the training data and apply it to the test data.