Question

A data engineer is preparing a ride-sharing demand forecasting dataset in an AWS Glue ETL job. The raw data contains a single 'pickup_timestamp' column. The team wants the model to learn recurring daily and weekly demand patterns (for example, that 11 PM and 1 AM are close in time, and that Sunday and Monday are adjacent). Which feature engineering approach should the engineer apply to the extracted hour-of-day and day-of-week values?

Accepted Answer

Apply sine and cosine transformations to the hour-of-day and day-of-week values to encode their cyclical nature.

Answer

One-hot encode the hour-of-day and day-of-week values into separate binary columns.

Answer

Keep hour-of-day and day-of-week as raw integer values and apply standard (z-score) scaling.

Answer

Convert the timestamp to Unix epoch seconds and use that single numeric column as the feature.