🧳 Projects
Kidney Disease Classification
- Description: Developed a deep learning-based solution for classifying kidney disease using medical image data. Leveraged VGG16 architecture and modern MLOps tools to ensure model traceability, version control, and reproducibility.
- Technologies: Python, Pandas, Tensorflow, Keras, Matplotlib, MLflow, DVC, AWS.
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Link: View Code on GitHub Live Demo - Highlights:
- Achieved ~80% accuracy using a VGG16-based convolutional neural network.
- Integrated MLflow for experiment tracking and DVC for data and model version control.
- Followed object-oriented programming (OOP) principles to ensure modular, reusable, and maintainable code
- Deployed workflows to AWS for scalable execution and storage.
- Screenshot:
Schizophrenia Detection with Machine Learning
- Description: Designed a machine learning pipeline to detect schizophrenia from clinical data, aiming to support early diagnosis and enhance treatment planning. Focused on model performance, production-readiness, and reproducibility.
- Technologies: Python, Scikit-learn, Pandas, Seaborn, Matplotlib, Cookiecutter, Jupyter Notebooks, NumPy.
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Link: View Code on GitHub Live Demo - Highlights:
- Achieved a 75% F1 score, outperforming baseline models by 15% and providing a robust tool for early-stage schizophrenia detection.
- Deployed the model into a production environment, enabling integration with clinical workflows for real-world evaluation.
- Cut deployment setup time by 40% using Cookiecutter templates, streamlining collaboration and ensuring consistent project structure.
- Screenshot:
