Credit Card Fraud Detection

In this python machine learning project, I built a binary classifier using the 6 algorithms to detect credit card fraud transactions. Through this project, I applied techniques to address the class imbalance issues and achieved an accuracy of more than 90%. The random forest model yields a very good performance as indicated by the model accuracy which was found to be 99.990035%. To address the issue of class imbalance problem, we used the oversampling technique, this was done by the SMOTE package imported from the imblearn module. The ROC AUC of our models approaches towards 1. So, we can conclude that our classifier does a very good job in predicting whether a transaction is genuine or fraudulent.