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
machine-learning exploratory-data-analysis pandas feature-selection logistic-regression feature-engineering hyperparameter-tuning decision-tree-classifier gradientboosting smote dataanalysis correlation-analysis joblib shap modelselection xgboost-classifier randomforestclassifier lgbmclassifier randomsearchcv classificationreport
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Updated
Nov 27, 2024 - Jupyter Notebook