ARCHIVES

1. Dal Pozzolo, O. Caelen, R. A. Johnson, and G. Bontempi, “Calibrating Probability with Undersampling for Unbalanced Classification,” IEEE Symposium on Computational Intelligence and Data Mining, 2015.
2. N. Patil and P. Pawar, “Credit Card Fraud Detection Using Machine Learning,” International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), vol. 9, no. 3, 2020.
3. H. He and E. A. Garcia, “Learning from Imbalanced Data,” IEEE Transactions on Knowledge and Data Engineering, vol. 21, no. 9, 2009.
4. European Credit Card Dataset, Kaggle. [Online]. Available: https://www.kaggle.com/mlg-ulb/creditcardfraud
5. J. West and M. Bhattacharya, “Intelligent Financial Fraud Detection: A Comprehensive Review,” Computers & Security, vol. 57, pp. 47–66, 2016.
6. F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” Journal of Machine Learning Research, 2011.
7. N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “SMOTE: Synthetic Minority Over-sampling Technique,” Journal of Artificial Intelligence Research, 2002.
8. T. Chen and C. Guestrin, “XGBoost: A Scalable Tree Boosting System,” Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016.
9. Streamlit Documentation. [Online]. Available: https://docs.streamlit.io/
10. L. Breiman, “Random Forests,” Machine Learning, 2001.