We built this project because we wanted to help prospective MLH Fellows with their progress toward a better GitHub profile with solid projects and a record of active work. We also wanted to give them some insights into what an average fellow at MLH looks like.

When we were just aspiring to become MLH Fellows, we would look for different sources of information to know what MLH is looking for in their fellows and better ways to prepare. So we tried to address this issue and hopefully support future fellows on their way to success.

However, we make an important notion that your GitHub Profile does not define you as a developer. Our tool is simply to let you see into the data for areas of potential improvement and keep working toward your goals. We do not consider things like:

  • Personal communication skills
  • Spot availability
  • Match in project interests

What it does

Analyzes your GitHub Profile and presents you with a report on how likely you are to become the next MLH Fellow!

Provides you with an extensive analysis on the following features of your profile:

Feature Description
Commits Number of total commits the user made
Contributions Number of repositories where the user made contributions
Followers Number of followers the user has
Forks Number of forks the user has in their repositories
Issues Number of issues the user has raised
Organizations Number of organizations the user is a part of
Repos Number of repositories the user has
Stars Number of stars the user has on their repositories

And gives you a comprehensive score of how similar your GitHub Profile is to an average MLH Fellow's GitHub.

It also shows your statistics in a user-friendly data visualization format for you to gauge the range of your skills and become the next MLH Fellow!

How we built it

Tech Stack Used

We used the following technologies:

  • BentoML along with Heroku to build an API endpoint that calculates the comprehensive score for the user based on a simple query.
  • Flask deployed to Heroku to setup a bridge between the frameworks and collect the input data.
  • React.js served on Firebase to provide user-friendly UI for future MLH fellows to use.

Challenges we ran into

  • We could not collect all the necessary data to follow the initial plan on prediction whether a user will make it to the Fellowship. It was difficult to obtain data of people who applied and did not get in the program since there is no such database.

Accomplishments that we're proud of

  • We are proud of using the Open Source project two of our teammates will be working on: BentoML.
  • We are proud of getting the whole project done under a big difference in time zones.
  • We also feel good about the opportunity to help future MLH Fellows in their path to a good GitHub profile.

What we learned

  • We learned that complex applications using different frameworks can be easily built when there is a proper connection nodes between them.
  • We also learned about the importance of team work because we all contributed major parts of the project, making it a wholesome experience for each other and delivering a better product.

What's next for Fellowship Prediction

  • We hope to develop a Machine Learning model with the data that we already have and some data that we will gather later on to have a proper, data-driven score rank.
  • We also hope to look into the ANOVA analysis for our statistical comparison to determine things like the p-value of somebody getting accepted to the Fellowship based on their data.
  • We would also like to get a domain name for the project for better reach to people.
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