There is currently a virus that has hurt many people lives and raised unemployment so we would like to make the most effective way to help as many people as possible.
What it does
The front end is a website in which people can make request. The request is then stored in a server hosted by the gcp Someone would access the front end for a donation The donation then goes to the server and based on the location of the people in need it would send to the location most in need.
How we built it
We used python in jupyter notebook to take the csv file from the US Census and John Hopkins to make them in a usable format for sklearn to create a linear regression model. The model was then saved using the pickle library to create a function that can be called upon in the server. The front end was built using html, css and this communicate with a server that runs the function to determine the areas most in need using opensuse linux server.
Challenges we ran into
Learning how to ssh in the server, loss of time When we needed to make the css in a usable format for sklearn to make a linear regresssion model this took time finding all of the functions needed from the pandas library. We could not get the server side side fully functional due to lack of time.
Accomplishments that we're proud of
We learned a lot of new information that will help us in future projects.
What we learned
How to use opensuse linux, pandas, pickle library, and additional function of sklearn
What's next for corona-relief
Better implementation to get the project working and a better prediction of the models.
Try It out
bootstrap, css, gcp, google-fonts, html, john-hopkins-corona-data, kalilinux, matplotlib, opensuse, pandas, pickle, python, sklearn, us-census-bureau