Скачать или смотреть Intelligent Blood Group Identification through Image Processing and Deep Learning

Online payment systems have become a crucial component of modern financial services, but they are increasingly vulnerable to fraudulent activities. Detecting fraudulent transactions is a challenging task due to the high class imbalance between genuine and fraudulent transactions. This project presents a machine learning-based approach to accurately detect online payment fraud by leveraging balanced algorithms to address the skewed distribution of data. Various resampling techniques such as Synthetic Minority Over-sampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN) are employed to balance the dataset. Multiple machine learning classifiers including Random Forest, Decision Tree, Logistic Regression, and XGBoost are trained and evaluated on the balanced data. The models are assessed using performance metrics such as accuracy, precision, recall, F1-score, and Area Under the Curve (AUC-ROC) to ensure robustness. Experimental results demonstrate that balancing the data significantly improves the ability of the classifiers to detect fraudulent activities. This study highlights the effectiveness of combining balanced sampling strategies with machine learning algorithms to build reliable fraud detection systems in online payment environments. For Project Contact: +91-9866393564