IEEE-CIS works across a variety of AI and machine learning areas, including deep neural networks, fuzzy systems, evolutionary computation, and swarm intelligence. Today they’re partnering with the world’s leading payment service company, Vesta Corporation, seeking the best solutions for fraud prevention industry, and now you are invited to join the challenge.
In this competition, you’ll benchmark machine learning models on a challenging large-scale dataset. The data comes from Vesta's real-world e-commerce transactions and contains a wide range of features from device type to product features. You also have the opportunity to create new features to improve your results.
The data is broken into two files identity and transaction, which are joined by TransactionID. Not all transactions have corresponding identity information.
Categorical Features - Transaction
- ProductCD
- card1 - card6
- addr1, addr2
- P_emaildomain
- R_emaildomain
- M1 - M9
Categorical Features - Identity
- DeviceType
- DeviceInfo
- id_12 - id_38
The TransactionDT feature is a timedelta from a given reference datetime (not an actual timestamp).