One of the datasets used, this one contains information about universities. we used a sample 100..
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
What's next for debtscalibur
finding better sources of information would allow debtscalibur to offer better tailored information.