Portfolio Details
Each project in this portfolio reflects my hands-on experience in solving real-world problems using data science, machine learning, and full-stack development. From predictive models to intelligent web applications, these solutions are built with a focus on scalability, functionality, and impact.
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Project information
- Project Name: Patient Health Prediction
- Category: MultiClass Classification
- Model Algo: Random Forest
- Github Repo: Link
Technologies Used
This project utilizes a range of technologies including Python, Flask, and Scikit-learn for the backend, with a React.js frontend. The model is trained using Random Forest for accurate patient health predictions based on various health parameters.
Details of Project
This project is a full-stack AI-powered medical prediction system developed using Python, Flask, and Scikit-learn on the backend, with a modern React.js frontend. It takes user-inputted symptoms and leverages a trained Random Forest model to perform multi-class classification. The model not only predicts the most probable disease but also provides a comprehensive output that includes the recommended cure, suitable doctor specialization, and an overall risk level (e.g., low, moderate, high).
The system is designed to support early diagnosis and guide users toward appropriate medical action. It features a user-friendly interface, real-time response handling, and secure data processing. This makes it especially useful for general awareness, self-assessment, or as a support tool for digital health platforms.
Project Features
- Real-time health predictions based on user symptoms.
- Multi-class classification for accurate disease identification.
- Risk assessment to guide users on the urgency of medical attention.
- Responsive design for accessibility across devices.
- Secure handling of user data with Flask backend.