Fraud detection-Kaggle Competition

In this competition we are predicting the probability that an online transaction is fraudulent, as denoted by the binary target isFraud. The competition score was based on roc-auc score

The data contains:

• 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 : adress
• 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.

The used libiraries:

• 1- Pandas
• 2- Numpy
• 3- Sklearn