https://github.com/pavankethavath/microsoft-classifying-cybersecurity-incidents-with-ml

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

classificationreport correlation-analysis dataanalysis decision-tree-classifier exploratory-data-analysis feature-engineering feature-selection gradientboosting hyperparameter-tuning joblib lgbmclassifier logistic-regression machine-learning modelselection pandas randomforestclassifier randomsearchcv shap smote xgboost-classifier

Last synced: 23 Apr 2025