My Projects
A collection of data science projects showcasing machine learning, deep learning, data visualization, and analytics across various domains and industries.
This project implements a machine learning model to predict the risk of type-2 diabetes based on clinical and lifestyle-related health features. Trained on publicly available medical datasets, the model achieves an accuracy of 85%, offering a reliable tool to support early detection and preventive healthcare strategies.
Technologies Used:
Key Results:
- 85% accuracy on test dataset
- Identified top diabetic risk factors
- Potential application in healthcare settings
CornGuardAMS is a transfer learning-based model designed to classify corn leaf diseases with high precision. Leveraging pre-trained convolutional neural networks, it achieves over 90% accuracy in identifying multiple disease types, aiding early detection and crop protection.
Technologies Used:
Key Results:
- 90% directional accuracy
- Utilized transfer learning for efficiency
- Classified 3 disease types
Emotify is an interactive system that analyzes facial expressions in real time to recommend music that matches the user's mood. Using emotion detection via webcam, it maps expressions like joy, sadness, or surprise to curated tracks, offering a personalized and adaptive listening experience.
Technologies Used:
Key Results:
- User-friendly
- Low latency
- Mobile-responsive design
Interested in Collaborating?
I'm always looking for new opportunities to work on exciting data science projects. Whether it's research collaboration, internships, or full-time opportunities, I'd love to hear from you.