This project aims to develop an intelligent fraud detection system that identifies suspicious credit card transactions using machine learning classification techniques. The system helps financial institutions minimize losses by detecting fraudulent activities in real time.
Collect publicly available credit card transaction datasets Understand fraud patterns and class imbalance issues Perform data preprocessing, normalization, and feature scaling Apply oversampling or undersampling techniques to balance data Implement classification models such as Logistic Regression, Random Forest, and SVM Train and test models using historical transaction data Evaluate performance using precision, recall, F1-score, and ROC curve Compare models to identify the most effective fraud detector Develop a prediction module for new transactions Create visual dashboards to show fraud detection results Implement secure data storage practices Perform system testing with unseen data Document system design, algorithms, and limitations
