This is my learning through Kaggle competition (https://www.kaggle.com/c/ieee-fraud-detection).

https://www.kaggle.com/kabure/extensive-eda-and-modeling-xgb-hyperopt

In this competition you are predicting the probability that an online transaction is fraudulent, as denoted by the binary target isFraud.

The data is broken into two files identity and transaction, which are joined by TransactionID. Not all transactions have corresponding identity information.

• ProductCD
• card1 - card6
• addr1, addr2
• P_emaildomain
• R_emaildomain
• M1 - M9

• DeviceType
• DeviceInfo
• id_12 - id_38

The TransactionDT feature is a timedelta from a given reference datetime (not an actual timestamp).

train_{transaction, identity}.csv - the training set test_{transaction, identity}.csv - the test set (you must predict the isFraud value for these observations) sample_submission.csv - a sample submission file in the correct format