Inspiration
Our inspiration was the difficulty of parking in San Francisco and hoping to improve on the public resources for access to parking.
What it does
Our project aims to improve on SFMTA's existing parking map which lists the publicly owned lots in the city. We wanted our map to display lots and allow filtering based radius of one's destination, price point, and crime safety.
How we built it
We used React Vite for the front end and Django for the back end and server. We used a Jupyter notebook to perform the machine leaning model training.
Challenges we ran into
We ran into issues with Cross-Origin Resource Sharing and general debugging.
Accomplishments that we're proud of
We're proud of our concept and believe it's something that would be highly useful in a city like SF.
What we learned
We learned a lot about full-stack development and gained valuable experience working with new frameworks. We also learned a lot about working with data and machine learning techniques to implement them in a real use case.
What's next for Parktacular
We hope to display the price and hours of operation in the UI as well as integrate the machine learning crime safety prediction in our website. Maybe we could offer a filter that shows metered street parking as well, there was a dataset for SF that we found.
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