What inspired us to make this application was the pressing need to reduce the student debt crisis, we decided to take the approach of prevention rather than management as a forward thinking, long term solution.

What it does

The app makes suggestions about which colleges to consider based on affordability and, in the future, a number of other factors, including average GPAs of attending students, location, average salaries per degree, and user reviews.

How we built it

We made use of the python libraries numpy and pandas to load data and perform the necessary calculations with the intention of performing more advanced calculations to better tailor suggestions as more information becomes available.

Challenges we ran into

The inaccessibility of detailed relevant data severely limited the scope of the project, as data needed to be obtained manually, which we accomplished by writing a set of scripts to pick the relevant information from web pages. Tying together a webserver python scripts and a website was outside the skill level of our team.

Accomplishments that we're proud of

The python script to perform the calculations for the best match was able to run successfully and output ready to use .json files. the dataset used was formed from information obtained from html DOM elements.

What we learned

We learned new skills with javascript, such as pulling information from elements to form our datasets and loading .json files.

What's next for debtscalibur

finding better sources of information would allow debtscalibur to offer better tailored information.

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