End-to-end feature engineering and classical machine learning pipeline for real-world tabular data, including preprocessing, feature selection, and scientific model selection with cross-validation.

End-to-end explainable AI pipeline for medical classification using Random Forest and XGBoost with SHAP and LIME for global and local interpretability. Designed for transparent, trustworthy machine learning in healthcare and research applications.

EEG-based brain signal classification using classical machine learning with feature engineering and comparative model evaluation for BCI and NeuroAI research.