First Place Toyota Track 🥇
Inspiration
The data sources we were given were certainly comprehensive, but "comprehensive" does not necessarily mean "informative," and even "informative" does not mean "remembered." The difference the first and the last is exactly what we have made.
What it does
FUELICIENT gives a clean UI for Toyota fuel-efficiency data exploration, coupled with a system for retaining previous analyses on a per-account basis.
How we built it
We used Python as the general backbone of our project, Streamlit for the GUI, and Pinata to store the fuel efficiency data. User authentication used Firebase, which is also were our settings were stored.
Challenges we ran into
Python virtual environment configuration across separate devices required some workarounds. Authentication and databases were difficult.
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