What it does

Our device and data analytics both collect data and help to decipher the hard-to-read csv file.

How I built it

We assembled an Arduino with a multitude of sensors and then programmed it to hash the inputs into a uniform csv file. Next, we utilized Jupyter Notebook, Python and a very useful Python Library--Pandas--to organize any new set of raw-data into comprehensible, dynamic, analytic results.

Challenges we ran into

Our soldering iron was nearly nonoperational, often needing 10 minutes to heat up followed by pain-staking determination to accomplish even trivial tasks. Our RGB graph absolutely refused to accept tuples as input so we had to carefully convert into hexadecimal as well as completely reformat our while loops involved.

Accomplishments that we're proud of

Overcoming the RGB graph was certainly a stark challenge and thus provided an immense amount of satisfaction upon completion. By setting the graph color to a custom, corresponding RGB color value, our RGB graph is much more useful and readable.

What we learned

I learned that large, multi-faceted problems are better taken on by a supportive team. By splitting large goals into smaller, bite-sized pieces, our team managed to get a lot done in the 24 hours allotted.

What's next for Water-Sled Data Analytics and Construction

Minor improvements and above all else, implementation, await 'Water-Sled Data Analytics and Construction.'


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