This project is to predict fraudulent credit card transactions with the help of machine learning models. In this project, you will analyze customer-level data that has been collected and analyzed during a research collaboration of Worldline and the Machine Learning Group. We balanced the data with various approaches such as Undersampling, Oversampling, SMOTE, and Adasy. With every data balancing technique, we built several models such as Logistic, XGBoost, Decision Tree, and Random Forest. the SMOTE and Adasyn models performed well. Among those models, the simple model Logistic regression has an ROC score of 0.99 in the train set and 0.97 on the test set. we can conclude that the Logistic regression model with SMOTE is the best model for its simplicity and less resource requirement.