Skip to content

Worked on the visualizations for the admin user to see review sentiment, vader scores, and mean progression over time for a chosen Mentor.

License

Notifications You must be signed in to change notification settings

sspradling78/underdog-devs-ds-a

 
 

Repository files navigation

banner

Underdog Devs - Data Science Github

A nonprofit that aims to help formerly incarcerated or disadvantaged people find careers in tech.

You can find the deployed project at Underdog Devs.

Contributors

Senior Management Team Data Science Team FT44 Data Science Team FT43
Robert Sharp Mitch Hollberg Christine Wang
Ryan Hamblin Brogan Stich Sirivennela Vempati
Paul St.Germain Brandon Smith Rodrico Sanchez
Austin Wolff Samuel Ulloa
Nicholas Papenburg Ryland Gomez
Scott Reynders Michael Tran
Jonathan McGraw Evgeny Khoroshukhin
Yefim Gorodnitskiy Archana Coimbatore Sivagurunathan
Dagim Bantikassegn Daniel Ho
Eisenhower Lancheros BhavaniLakshmi Annapurna Jagarlamudi
John Baker Kevin Lynner
Matt Grohnke Olatomi Adigun
Mohamed Mosaed
Zachary Rock
Zachary Quintana

Architecture

model_diagram

Key Features

  • Implement a model for connecting viable mentor - mentees pairings based on skills and experience
  • Use sentiment analysis to better understand the significance of a session
  • Created an API interface for MongoDB that the backend can interface with
  • Create mock data in place holder of real data
  • Begin building out test coverage for DS FastAPI
  • Reviewed proposed modifications, including meeting with directors to approve and implement beneficial changes.

Research

Our research into this project stretches far and wide, covering a multitude of libraries. To better consolidate space, you can find our research here

Tech Stack

MongoDB Python Jupyter Notebook NumPy Pandas FastAPI

MIT

code style: prettier

Python

Installation Instructions

  • git clone
  • cd underdog-devs-ds-a
  • python -m venv venv
  • source venv/bin/activate
  • pip install -r requirements.txt
  • chmod +x run.sh
  • ./run.sh

Contributing

When contributing to this repository, please first discuss the change you wish to make via issue, email, or any other method with the owners of this repository before making a change.

Please note we have a code of conduct. Please follow it in all your interactions with the project.

Issue/Bug Request

If you are having an issue with the existing project code, please submit a bug report under the following guidelines:

  • Check first to see if your issue has already been reported.
  • Check to see if the issue has recently been fixed by attempting to reproduce the issue using the latest master branch in the repository.
  • Create a live example of the problem.
  • Submit a detailed bug report including your environment & browser, steps to reproduce the issue, actual and expected outcomes, where you believe the issue is originating from, and any potential solutions you have considered.

Feature Requests

We would love to hear from you about new features which would improve this app and further the aims of our project. Please provide as much detail and information as possible to show us why you think your new feature should be implemented.

Pull Requests

If you have developed a patch, bug fix, or new feature that would improve this app, please submit a pull request. It is best to communicate your ideas with the developers first before investing a great deal of time into a pull request to ensure that it will mesh smoothly with the project.

Remember that this project is licensed under the MIT license, and by submitting a pull request, you agree that your work will be, too.

Pull Request Guidelines

  • Update the README.md with details of changes to the interface.
  • Ensure that your code conforms to our existing code conventions and test coverage.
  • Include the relevant issue number, if applicable.

About

Worked on the visualizations for the admin user to see review sentiment, vader scores, and mean progression over time for a chosen Mentor.

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Jupyter Notebook 98.0%
  • Python 2.0%