Inspiration
The inspiration for the project was based on the idea of diversifying the Charlotte community. We would have to find a way to get people from poorly rated areas and give them and opportunity to work in upper class areas so they feel they have an opportunity to excel outside of their setting.
What it does
Our application takes several quality of life datasets and finds correlations between them to see what factors are actually contributing to low income areas.
How I built it
Our team built it using SciKit Learn, several Charlotte Quality of Life datasets and the python programming language.
Challenges I ran into
The majority of the night was spent trying to research similar projects and finding the right kinds of datasets that by the time we were finished, we could only do a correlation search.
Accomplishments that I'm proud of
I'm proud of the datasets we managed to find and the solutions we had presented to the judges. Our solutions tended to find that the city could buy out foreclosed homes and replace them with affordable housing units.
What I learned
I learned that a majority of the time I spend looking at a data driven challenge is to really try to find datasets that help solve the prompt. Next time, it'll be better to try to get as many as possible and decide on a dataset quickly.
What's next for HackathonCLT
Next year, I hope to compete again. The winners used similar tactics to what I planned on using if we had enough time, so I at least know I'm on the right track.
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