Test For Credit Card Fraud using machine learning

MCA

The most popular mode of payment for both offline and online purchases may now be credit cards, thanks to recent developments in electronic commerce systems and communication technology; as a result, there is a lot more fraud involved with such transactions. Businesses and people lose money of fraudulent credit card transactions every year, and con artists are continuously looking for new methods and instruments with which to perpetrate fraud. Researchers have a challenging challenge when attempting to identify credit card theft since thieves are resourceful and quick-witted. Because of the grave imbalance in the dateset used for credit card fraud detection, it is challenging for the algorithm to identify fraud.Therefore, procedures that are effective and efficient for spotting credit card transaction fraud are needed. This study suggests Gradient Boosting Classifier, a machine learning technology, as a clever technique for transactions. With training accuracy of 100% and test accuracy of 91%, approach performed better than existing machine learning algorithms.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.