Abstract
The escalating use of the Internet has led to a surge in online shopping and e-commerce, resulting in a corresponding increase in credit card fraud incidents. Therefore, this research focuses on employing machine learning techniques, which offer enhanced precision and efficiency compared to manual detection, to identify fraudulent activities. To establish the association between credit card transaction attributes and the presence of fraudsters, this study initially gathers data from Kaggle, subsequently normalizing the collected data. Furthermore, the data exhibits severe imbalance, leading to overfitting concerns. To ascertain feature correlations, a correlation heatmap is constructed. Moreover, this investigation selects three models for analysis. Finally, the performance of each model is evaluated using a confusion matrix and derived metrics. The findings reveal that both the decision tree and random forest models exhibit optimal performance, achieving 100% across all indicators. The most influential factors in determining credit card fraud involve the ratio to median purchase price and the geographical proximity of the transaction location to the cardholder's residence.
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