**Heart Disease Prediction: A Comprehensive ML Pipeline** I'm excited to share my latest project - a robust machine learning system for heart disease prediction that implements industry best practices and compares multiple algorithms to find the optimal solution. **Technical Highlights:** • **Multi-Model Comparison**: Evaluated 5 classification algorithms (Random Forest, Gradient Boosting, Logistic Regression, SVM, KNN) • **Advanced Pipeline Architecture**: Proper preprocessing with separate handling for numerical, ordinal, and categorical features • **Data Leakage Prevention**: Correct train-test splitting and pipeline implementation • **Class Balancing**: SMOTE technique for handling imbalanced datasets • **Robust Validation**: 5-fold stratified cross-validation for reliable performance metrics **Key Features:** • Comprehensive data preprocessing (imputation, scaling, encoding) • Multiple evaluation metrics (Accuracy, Precision, Recall, F1-Score) • Feature importance analysis • Automated model selection based on performance • Detailed classification reports and confusion matrices **Results:** The pipeline automatically selects the best-performing model through rigorous cross-validation, achieving strong predictive performance on heart disease classification. **Best Practices Implemented:** - Scikit-learn pipelines for reproducibility - Stratified sampling for imbalanced data - Proper evaluation methodology - Modular, extensible code structure This project demonstrates my commitment to building production-ready ML solutions that follow industry standards. The complete code and documentation are available on my GitHub. What's your experience with healthcare ML applications? I'd love to connect with others working in this space! #MachineLearning #Healthcare #DataScience #HeartDisease #Python #ScikitLearn #PredictiveAnalytics #MLOps #HealthTech