Abstract:
Payment cards offer a simple and convenient method for making purchases. Generally credit card
fraud activities can happen in both online and offline. But in today’s world online fraud transaction
activities are increasing day by day. It is therefore crucial to implement mechanisms that can detect
the credit card fraud. Features of credit card frauds play important role when machine learning is
used for credit card fraud detection, and they must be chosen properly.So in order to find the online
fraud transactions various methods have been used in existing system.In this proposed project de-
signed a model to detect the fraud activity in credit card transactions. This system can provide most
of the important features required to detect illegal and illicit transactions.As technology changes
constantly it is becoming difficult to track the behavior and pattern of criminal transactions.
The algorithms such as : Random Forest and Extreme Gradient Boosting “XGBoost”. This algo-
rithms is based unsupervised learning algorithm. After classification of data set a confusion matrix
is obtained. The performance of the algorithm is evaluated based on the confusion matrix.
