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This is Camel. I engineered a machine learning recommendation engine with TF-IDF vectorization and category-aware similarity scoring, featuring real-time preference learning from user interactions, achieving average accuracy of 92%. I also implemented secure JWT authentication, account personalization, and calendar integration to create the perfect solution to finding campus events - one that can currently serve 10,000+ university students. I automated a data pipeline using Selenium, Apify and Gemini API for scraping and enriching 200+ events daily.

Hackathon project for no name hacks: an AI platform that transforms individual grocery shopping into collaborative community experiences. Built an intelligent circle formation algorithm using Haversine distance calculations and life-stage classification via OpenAI to automatically group neighbors with similar shopping patterns. Implemented weekend cash challenges with real money rewards, group discount engines, and dynamic circle rebalancing as users' life stages evolve. Features comprehensive transaction analysis, geocoding integration with OpenCage API, and a mobile-responsive interface. Created for Loblaw's corporate hackathon addressing social commerce and healthy spending habits.

I was working as a contract developer and needed a good way to log my time and know how much I was making at the same time. A MacOS application, it uses local memory to track and store your payouts even after closing out of the app - no more spreadsheets! Low CPU usage: less than the native stopwatch app! Set your own pay rate. Swap between USD and CAD currencies using an API. Great motivator to keep working

Used YOLOv5 and PyTorch to train a custom model on a dataset of 120 images, each labeled with one of six common signs from American Sign Language (ASL). The model achieved high accuracy in sign recognition with comprehensive training metrics available.