/fraud_detection

Primary LanguageJupyter Notebook

fraud_detection

data

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

Transaction table:

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.

Categorical Features: ProductCD

card1 - card6

addr1, addr2

Pemaildomain Remaildomain

M1 - M9

Identity table

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)

Categorical Features:

DeviceType

DeviceInfo

id12 - id38

eda-for-columns-v-and-id.ipynb

EDA for Columns V and ID

model.ipynb

feature engineering, train model and prediction