The Performance Analysis of Machine Learning Algorithms for Credit Card Fraud Detection

This paper studies the performance analysis of machine learning (ML) and data mining techniques for anomaly detection in credit cards. As usage digital money or plastic grows developing nations, so does risk fraud. To counter these scams, we need a sophisticated fraud method that not only identifies but also detects it before occurs efficiently. We have introduced notion card its many variants this research. Numerous ML approaches are studied including Principal Component Analysis (PCA) Fuzzy C-Means methodologies, as well Logistic Regression (LR), Decision Tree (DT), Naive Bayes (NB) algorithms. The existing proposed models been thoroughly reviewed, strategies compared using quantitative metrics accuracy rate characteristics curves. discusses shortcomings proposes an efficient technique to analyze detection.