Cardiovascular diseases are a leading cause of death globally, and early detection can significantly improve patient outcomes. This project aims to predict the mortality of patients with heart failure using machine learning techniques.
The dataset consists of 12 features related to heart failure patients, including clinical measurements and demographics. The primary objective is to analyze this data and develop a predictive model.
- Data Exploration: Analyzing the dataset for missing values, outliers and understanding the distribution of features.
- Preprocessing: Normalizing data and addressing any class imbalances.
- Model Development: Training multiple machine learning models to predict patient mortality.
- Evaluation: Utilizing various metrics to assess model performance and explainability through SHAP values.
- Python 3.x
- Libraries: NumPy, Pandas, Seaborn, Matplotlib, Scikit-learn, SHAP
Clone this repository and run the notebook to see the analysis in action. Adjust model parameters and explore different techniques for enhancing prediction accuracy.
This project is licensed under the MIT License.