Heart Disease Prediction With Ensemble Learning
تفاصيل العمل
Implemented and compared multiple ensemble machine learning models (Bagging, AdaBoost, Gradient Boosting, XGBoost, and Stacking) to predict the presence of heart disease. The project included preprocessing, feature scaling, and model evaluation using metrics such as accuracy, precision, recall, F1-score, ROC-AUC, and confusion matrix. Results highlighted the trade-offs between accuracy and training time across different algorithms.
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