Online card transactions have increased daily as a result of the development of technologies like e-commerce and financial technology (FinTech) apps. As a result, there has been an increase in credit card fraud that impacts banks, merchants, and card issuers. Thus, it is critical to creating systems that guarantee the confidentiality and accuracy of credit card transactions. In this study, we use imbalanced real-world datasets produced by European credit cardholders to create a machine learning (ML) based framework for detecting credit card fraud. In order to address the class imbalance problem, we resampled the dataset using the Synthetic Minority over-sampling Technique (SMOTE).