Credit Card Fraud Detection

Credit card fraud detection by using machine learning techniques in imbalanced datasets has emerged as an important area of study in recent years, considering the increased usage of online transactions. Here, we discuss in detail the detection of credit card transactions by using machine learning techniques and avoiding imbalanced datasets in credit card transactions. Here, we used the Random Forest algorithm to detect credit card transactions and imbalanced datasets were handled by using the Logistic Regression algorithm. Synthesized Minority Over-sampling Technique was used to train the models. Here, we used the SMOTE method, and finally, evaluation of models was carried out by using accuracy, precision, recall, F1-score, and confusion matrix. Our result demonstrated that Synthesized Minority Over-sampling Technique was important in detecting credit card transactions. Also, it was evident that our Random Forest classifier was more accurate compared to our Logistic Regression classifier in detecting credit card transactions.