ML_MastercardFraud | Machine Learning's Mastery in Credit Card Fraud Detection

In an era dominated by digital transactions, the use of credit card payment methods is rapidly expanding, fueled by technological advancements and the surge in online transactions. This phenomenon caused credit card fraud issues to continuously escalate; which poses a substantial financial threat in transactions, prompting the need for robust detection mechanisms. Fraud is deigned as the wrongful or deceptive behavior with the aim of financial or personal gain, or to to cause harm to another individual without necessarily leading to direct legal consequences. This paper focuses on the need to investigate the utilization of machine learning algorithms to mitigate losses experienced by retailers, particularly small businesses. The aim of this research is to analyze diverse features within the examined dataset to effectively construct a resilient model for fraud detection. The evaluated algorithms include Random Forest, Gradient Boosting, Naive Bayes, k-Nearest Neighbor, Decision Trees and Logistic Regression. Through carefully partitioning the dataset into training and testing subsets, these models are trained to recognize complex patterns indicative of fraudulent activities. After extensive testing, the Random Forest model was the machine learning model that outshone the rest; achieving consistent outstanding performance metrics. The evaluation also encompasses detailed analysis of the training and prediction process and a comprehensive classification report, emphasizing the models' robustness in fortifying fraud detection capabilities. This study concludes that the tested machine learning algorithms, particularly Random Forest, are a promising and effective tool for credit card fraud detection. Not only do they help lessen the financial losses, but also serves as a valuable asset for reinforcing consumer trust in the reliability of electronic payment systems.