There are no reviews yet. Be the first to send feedback to the community and the maintainers!
Exploring-Explainable-AI-Demystifying-DT-RF-KNN-XGBC
Implemented XAI techniques to enhance transparency in fraud detection models. I employed techniques such as SHAP, LIME on DT, RF, XGBC, and KNN to offer lucid explanations for transactions that were flagged.Hybrid-Feature-Engineering-and-Ensemble-Learning
In this ML project, I proposed a methodology that provided an outperformed performance compared to another existing paper. For the comparison here focused mainly on F1, accuracy, AUC, and ROC score. This methodology provides a 99.96% accuracy score and 90.05% F1 score.ÂEnsemble-majority-voting-Hard
In this project, we implemented an ensemble learning approach using majority voting (hard voting) with five machine learning classifiers: DT, RF, XGBC, ANN, and KNN. The ensemble model achieved an impressive accuracy score of 99.95% and an F1 score of 85.51%.Credit-Card-Fraud-Detection-in-Real-Time
This project delivers a fast and efficient fraud detection methodology, providing predictions in under a second, emphasizing the importance of both high performance and quick response times.Love Open Source and this site? Check out how you can help us