Online Payment Fraud Detection to identify fraudulent and non-fraudulent payments.
Online payment is the most popular transaction method in the world today. However, with an increase in online payments also comes a rise in payment fraud. The objective of this notebook is to train machine learning models for identifying fraudulent and non-fraudulent payments. The dataset is collected from Kaggle, which contains historical information about fraudulent transactions that can be used to detect fraud in online payments.
The dataset consists of 10 variables:
- step: represents a unit of time where 1 step equals 1 hour
- type: type of online transaction
- amount: the amount of the transaction
- nameOrig: customer starting the transaction
- oldbalanceOrg: balance before the transaction
- newbalanceOrig: balance after the transaction
- nameDest: recipient of the transaction
- oldbalanceDest: initial balance of recipient before the transaction
- newbalanceDest: the new balance of the recipient after the transaction
- isFraud: fraud transaction
- For Fraudulent Transaction:
- For Fraudulent Transaction:
