This past week and the week before, I mainly worked on completing the frontend and integrating it with the pico and backend. Specifically, I created an APIs to deliver the pico’s readings and return the model’s evaluation and confidence value. I also helped Kat with evaluating the ML model and contributed to the team’s overall data collection. We currently have a KNN model that evaluates signs with 94% accuracy. We are continuing to improve the model and debug the results that it returns when testing the entire pipeline.
This week, we began working on the final presentation where we will deliver our results from this past semester.
Through this project, I learned how to implement a Flask API backend as well as the difference/tradeoffs between ML models, specifically KNN and CNN. I mainly did research online and looked at code from past classes and projects to acquire this new knowledge