Heart Disease Prediction System
تفاصيل العمل
• Implemented full data cleaning, encoding, and feature scaling on the UCI Heart Disease dataset. • Reduced dimensionality and selected key features using PCA and Recursive Feature Elimination (RFE). • Trained and compared four supervised models: Logistic Regression, Random Forest, Decision Tree, and SVM. • Experimented with unsupervised approaches — K-Means and Hierarchical Clustering — for pattern discovery. • Optimized model performance using GridSearchCV and RandomizedSearchCV hyperparameter tuning. • Evaluated all models using Accuracy, Precision, Recall, F1-Score, and ROC/AUC curves. • Deployed the best-performing model as an interactive real-time prediction app via Streamlit.
مهارات العمل
بطاقة العمل
طلب عمل مماثل
