Project Apero is a comprehensive web application designed to help individuals assess their risk of stroke and receive personalized recommendations. The system integrates data analysis, machine learning, and a conversational chatbot interface to provide actionable health insights.
- Purpose: Provide users with personalized health risk assessments and actionable recommendations to reduce stroke risk.
- Key Features:
- Personalized Stroke Risk Assessment using machine learning.
- Interactive Data Analysis and Visualization.
- Chatbot Assistance for real-time health advice.
- Technologies: Python, Streamlit, Rasa, Scikit-Learn, Docker.
- Docker
- Git
- Python 3.9+
- Clone the repository:
git clone https://github.com/HlexNC/Project-Arepo.git cd Project-Apero - Build and run Docker services:
docker-compose up --build
- Access the application: Open your web browser and navigate to http://localhost:8501.
The application uses a publicly available stroke prediction dataset.
- Dataset Source: Kaggle - Stroke Prediction Dataset
- Data Handling: The dataset is preprocessed using outlier detection and synthetic data augmentation to improve model robustness.
- Personalized Recommendations: Input your personal health data to receive an estimated stroke probability and risk level.
- Data Analysis: Explore interactive visualizations and key insights from the data.
- Chatbot Assistance: Engage with our Rasa-powered chatbot for quick, real-time health advice.
For detailed documentation, please refer to the project Wiki.
This project is licensed under the GNU General Public License v3. See the LICENSE file for details.
For inquiries or support, please open an issue in the repository.