Skip to content
Hackathon project to predict winners of future hackathons and generate fake hackathons projects
Jupyter Notebook JavaScript Python HTML
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
data
generate-text
get-text
predictions
.gitignore
LICENSE
README.md
data-interest.txt
resources.txt

README.md

this-hackathon-project-isnt-real

About

This is a hackathon project for Hack UTD VI.

At hackathons, you submit what is called a devpost at the end. it is basically a short blog post about what you did, why you did it, what technologies you used, etc. This project scraped a bunch of old devposts and used some machine learning to predict if a future project will be a winner, and also generate new fake hackathon projects.

Authors

Running

Running is just running the site.py file in generate-text directory with Python 3. That will start a local server on port 80 in dev mode. You will need some Python dependencies which can be installed with pip3, such as beautifulsoup4, flask, wtforms, numpy, scikit-learn, and matplotlib. You can then navigate to localhost for the randomly generated posts, and localhost/predict to enter the title of a devpost to predict if that project will win.

Brief Technical Description

Like all hackathon projects, everything is thrown together quickly and is a bit of a mess. We didn't always follow best practices with git and such, so there are definitely messes left around. Also, the sites are not generated as you refresh, rather pull from a pre-compiled list of data we created using gpt2.

Devpost

Inspiration

N/A

What it does

Generates a devpost for a hackathon project, and predicts if a project is a winner based on its devpost.

How I built it

First, we explored text generation by forcing a bot to read all of Twilight, and then rewrite its own version. Meanwhile, Jake the data fairy scraped all the most popular devposts from devpost.com, using the Python and BeautifulSoup. Once the bot could write with the same suspenseful elegance of Stephanie Meyers, we decided it was ready to take on the devposts. Our text generating/sass department used gpt-2-simple and ignored how little sense the results made.

Challenges I ran into

Creating the model to classify a project was rough. After extensive data analysis, we came to the conclusion that there is a minimal correlation between any quantitative data that can be pulled from a devpost and the project's success. So that is a fat our bad. The prediction algorithm will classify a project with the same accuracy as that of a slightly more insightful coin. Someone kept moving things around in the git repo and Jake forgot his glasses. Also our bot got waaaaay too political.

Accomplishments that I'm proud of

  • Successfully implemented k-nearest neighbors
  • Generated adequately coherent text
  • Had some good yucks

What I learned

  • There is NOT enough coconut water to go around
  • The quality of a devpost is entirely subjective ;)
  • Jeb Bush was in prison for 30 years

What's next for This Hackathon Project is Not Real

  • We plan to continue by creating random manifesto generators and exploring writing our own novels by training our bot on individual author’s styles.
You can’t perform that action at this time.