classification_models_for_coronary_heart_disease

• Conducted research and developed a system under Dr Jixin Ma on the comparison of numerous classification models to predict coronary heart disease using past medical data from the UCI Machine Learning Repository.
• Executed the project using tools such as Pandas, NumPy, Matplotlib, Seaborn, and Scikit-Learn, and evaluated the classification models beyond accuracy using ROC Curve and AUC Score, Confusion Matrix, and Classification Report.
• Achievement - Implemented supervised machine learning algorithms such as Logistic Regression, K-Nearest Neighbour and Random Forest to conduct the research and achieved an accuracy of 89% by hyperparameter tuning and cross-validation.