Developed a machine learning model to detect fraudulent online payment transactions, leveraging various algorithms and data preprocessing techniques to achieve high accuracy. The project involved collecting and cleaning transaction data, encoding categorical variables, and normalizing numerical features. Implemented and optimized multiple algorithms, including Logistic Regression, Decision Trees, Random Forest, and Neural Networks. Evaluated model performance using metrics like Accuracy, Precision, Recall, F1 Score, and ROC-AUC Curve. The project resulted in a robust fraud detection system that significantly improves the identification of fraudulent activities in online payments.