https://www.kaggle.com/c/ieee-fraud-detection
Can we predict the probability of an online transaction being fraudulent?
TransactionDT: timedelta from a given reference datetime (not an actual timestamp)
TransactionAMT: transaction payment amount in USD
ProductCD: product code, the product for each transaction
card1 - card6: payment card information, such as card type, card category, issue bank, country, etc.
addr: address
dist: distance
P_ and (R__) emaildomain: purchaser and recipient email domain
C1-C14: counting, such as how many addresses are found to be associated with the payment card, etc. The actual meaning is masked.
D1-D15: timedelta, such as days between previous transaction, etc.
M1-M9: match, such as names on card and address, etc.
Vxxx: Vesta engineered rich features, including ranking, counting, and other entity relations.
Variables in this table are identity information – network connection information (IP, ISP, Proxy, etc) and digital signature (UA/browser/os/version, etc) associated with transactions. They're collected by Vesta’s fraud protection system and digital security partners. (The field names are masked and pairwise dictionary will not be provided for privacy protection and contract agreement)
id_01 - id_38
DeviceType
DeviceInfo
