2016 Skunkworks Project: Using Machine Learning to Learn from athlete-related data

Getting Started With the Environment:

• Clone the repository..
• Ensure that you have npm and pip installed.
• To install pip, run sudo easy_install pip.
• Install virtualenv. This is so that we can have mulitple versions of Python/Python libraries. Virtualenv lets you have many different installations for python, useful if you use it for other projects. Run sudo easy_install virtualenv.
• Set up the backend by creating a virtual environment and then installing the backend requirements with pip:

virtualenv venv
source venv/bin/activate
pip install -r requirements.txt

(NOTE: requirements.txt may not be completely up-to-date with the latest python library dependencies. If you get an error saying a certain module couldn't be found, run pip install x where x is the missing module.

For the front end, use npm to install webpack and the dependencies as listed in package.json: npm install -g webpack and then npm install.

To bundle up the JS files, run webpack. This will create a bundled front-end file static/bundle.js. Run webpack --watch to make changes to the front-end without having to restart the backend.

Run the backend: Activate the virtualenv with source venv/bin/activate. Then run python app.py and go to the localhost the app is listening on.

Development:

• Make changes to the backend by editing app.py.
• Make changes to the front-end by editing the JS files and recompiling them with webpack --watch.

Screenshots:

• Front-end UI: Query for athletes:

• Results page rendered after parsing JSON response from backend: