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lgbmclassifier

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A machine learning pipeline for classifying cybersecurity incidents as True Positive(TP), Benign Positive(BP), or False Positive(FP) using the Microsoft GUIDE dataset. Features advanced preprocessing, XGBoost optimization, SMOTE, SHAP analysis, and deployment-ready models. Tools: Python, scikit-learn, XGBoost, LightGBM, SHAP and imbalanced-learn

  • Updated Nov 27, 2024
  • Jupyter Notebook

This project is about to detecting the text generated by different LLM given prompt. The instance is labeled by Human and Machine, and this project utilised both traditional machine learning method and deep learning method to classify the instance.

  • Updated Jul 12, 2023
  • Jupyter Notebook

This project detects credit card fraud using a Kaggle dataset of over 1.8 million transactions. To handle the highly imbalanced data, it uses SMOTE to balance fraudulent and legitimate transactions. The project then trains several models, with the LightGBM classifier achieving the best performance at 99.5% accuracy.

  • Updated Sep 13, 2025
  • Jupyter Notebook

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