Question

A data engineer is preparing a customer churn dataset for training a logistic regression model in Amazon SageMaker. The dataset contains a 'contract_type' column with three unordered values: 'month-to-month', 'one-year', and 'two-year'. The engineer wants to convert this nominal categorical feature into a numeric representation that does not impose any artificial ordinal relationship between the categories, using a low-code approach. Which transformation should the engineer apply?

Accepted Answer

Apply one-hot encoding in AWS Glue DataBrew to create a separate binary column for each contract type.

Answer

Apply ordinal (label) encoding to map 'month-to-month' to 0, 'one-year' to 1, and 'two-year' to 2.

Answer

Apply min-max scaling to the 'contract_type' column to normalize the values between 0 and 1.

Answer

Hash the 'contract_type' values into a fixed 256-dimension feature vector using feature hashing.