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

https://github.com/pramodyasahan/model-selection

This repository explores and compares different regression models for predicting continuous outcomes. This repository includes implementations and evaluations of five key regression models. The primary goal is to demonstrate how each model works, evaluate their performance using R-squared values, and guide users in selecting the best model.

machine-learning modelselection numpy pandas python regression scikit-learn

Last synced: 08 Mar 2025

https://github.com/sircornflake/bms

Tools to perform model selection alongside estimation under Linear, Logistic, Negative binomial, Quantile, and Skew-Normal regression. Under the spike-and-slab method, a probability for each possible model is estimated with the posterior mean, credibility interval, and standard deviation of coefficients and parameters under the most probable model.

bayesian-data-analysis bayesian-inference bayesian-methods bayesian-statistics model-selection modelselection variable-selection

Last synced: 14 Jul 2026

https://github.com/zoeyportfolio/premature-mortality

box-cox-transformation linear-regression linearmodel modelselection modelvalidation r testing training

Last synced: 22 Jun 2026

https://github.com/udhaya2823/microsoft---classifying-cybersecurity-incidents-with-machine_learning

🚨Microsoft: Classifying Cybersecurity Incidents with Machine Learning🔐 This project leverages the power of Machine Learning to classify cybersecurity incidents 🚨, improving the efficiency of Security Operation Centers (SOCs) at Microsoft. We train a model to predict incident grades, helping analysts prioritize threats with precision🎯.

classification data feature-engineering iqr-method machine-learning matplotlib model-evaluation modelselection predictive-modeling python sklearn

Last synced: 17 Apr 2026