Inspiration
After observing Waterloo's hackathons, yet never participating in one, we were finally ready to give it a shot over the reading week instead of studying for midterms.
What it does
The project was given a dataset that needed to be preprocessed before ML, and we had to pick out the useful and important information.
How we built it
Using Google Colab, the workshop's videos and hours of our time, we used a multitude of Python libraries to achieve our goal.
Challenges we ran into
For our first hackathon, we ran into efficiency issues. Plenty of time and effort was sunk into ideas that were ultimately meaningless, and some code we wrote could not handle the vast amount of data given to us.
Accomplishments that we're proud of
After processing the data, our proudest moment was seeing the scores of over 90% on our first try.
What we learned
Think more about our steps and approaches before starting anything and read through and understand concepts first and foremost.
What's next for Infinite Investment Systems Challenge
Win.
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