Namanya Rinah

I built a multi-modal Monkeypox Detection System combining VGG16 image classification with XGBoost, Random Forest, and Logistic Regression. The VGG16 model achieved 87% training accuracy, and I designed a CSV-based diagnosis records system for the deployed Streamlit application.

For my Glaucoma Diagnosis System, I built an ML model using image-based and symptom-based analysis. I applied data preprocessing, feature engineering, and model evaluation to medical datasets.

I also developed Garage Finder, a cross-platform Flutter app connecting drivers with nearby mechanics, with real-time chat and location-based service discovery. My Automated Note Summarizer used natural language processing to handle PDF, Word, and plain text documents.

During my IT Internship at National Land Information Center (NLIC), I assisted with land-related digital records and government information systems, including data verification and technical troubleshooting. I hold a Bachelor of Science in Computer Science from Makerere University and certificates in machine learning and Splunk fundamentals.