Credit card fraud is a significant issue that affects cardholders and financial institutions worldwide. This project leverages machine learning techniques to predict and flag potentially fraudulent credit card transactions. By using a dataset of historical transactions, we train a model to identify patterns and anomalies that may indicate fraudulent activity.
- Data Preprocessing: Exploratory data analysis, data cleaning, and feature engineering.
- Machine Learning: Utilizing various algorithms (e.g., Random Forest, XGBoost, Logistic Regression) for classification.
- Model Evaluation: Assessing the model's performance using metrics like accuracy, precision, recall, F1-score, and ROC-AUC.
- Real-time Detection: Implementing the model in a real-time credit card transaction processing system.
- Visualization: Visualizing the results and important insights.
- Documentation: Providing detailed documentation and usage examples.