Relational data can be a goldmine for classical Machine Learning applications — yet extracting useful features from multiple tables, time windows, and primary-foreign key relationships is notoriously difficult. In this code tutorial, we’ll use the H&M Fashion dataset to demonstrate how getML FastProp automates feature engineering for both classification (churn prediction) and regression (sales prediction) with minimal manual effort, outperforming both Relational Deep Learning and a skilled human data scientist according to the RelBench leaderboard.

This code tutorial is perfect for data scientists looking to leverage their relational and time-series data data effectively for any kind of predictive analytics applications.