• Sanmati Marabad


Due to the rapid growth of the E-Commerce industry, the use of credit cards for online purchases has increased dramatically. In recent years, credit card fraud is becoming a major complication for banks as it has become very difficult for detecting fraud in the credit card system. To overcome this hardship Machine learning plays an eminent role in detecting the credit card fraud in the transactions. Modeling prior credit card transactions with data from ones that turned out to be fraudulent is part of the Card Fraud Detection Problem. In Machine learning the machine is trained at first to predict the output so, to predict the various bank transactions various machine learning algorithms are used.  The SMOTE approach was employed to oversample the dataset because it was severely unbalanced. This paper  the examines and overview the performance of K-nearest neighbors, Decision Tree, Logistic regression and Random forest, XGBoost  for credit card fraud detection. The assignment is implemented in Python and uses five distinct machine learning classification techniques. The performance of the algorithm is evaluated by accuracy score, confusion matrix, f1-score, precision and recall score and auc-roc curve as well.

Keywords: Fraud Detection, Machine Learning, Logistic regression, KNN, Decision tree, random forest, SMOTE,XGboost


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How to Cite
Marabad, S. (2021). CREDIT CARD FRAUD DETECTION USING MACHINE LEARNING. Asian Journal For Convergence In Technology (AJCT) ISSN -2350-1146, 7(2), 121-127. Retrieved from