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classificationreport

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pavankethavath / Microsoft-Classifying-Cybersecurity-Incidents-with-ML

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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

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
  • Updated Nov 27, 2024
  • Jupyter Notebook

dharunkumars-hub / Machine-Learning

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Machine learning (ML) is a subset of artificial intelligence (AI) that enables computers to learn from data, identify patterns, and make decisions without explicit programming.

linear-regression supervised-learning pca confusion-matrix unsupervised-learning decision-tree-classifier kmeans-clustering svm-classifier classification-algorithm regression-algorithms random-forest-classifier knn-classifier accuracy-score r2-score gaussiannb mean-squared-error regression-metrics classification-metrics classificationreport
  • Updated Mar 21, 2026
  • Jupyter Notebook

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