OxyAnalytics

An expected results performance evaluation web app designed for the Occidental College Men's Soccer Team

This documentation is for a Windows system; adjust accordingly

Oxy Analytics Machine Learning Code (Prediction Model):

1. Clone GitHub repo and open xG Model folder in development environment that supports Python (e.g. VSCode, PyCharm, IntelliJ, etc.)

2. Python Module Installation (via Command Prompt):

• Numpy (scientific computing): pip install numpy
• Pandas (data manipulation): pip install pandas
• Scikit-Learn (ML model construction): pip install -U scikit-learn
• Seaborn (data visualization): pip install seaborn
• Matplotlib (data visualization): pip install matplotlib
• Pygsheets (Google Sheets connectivity): pip install pygsheets
  • My Google credentials will not be included in the repo, so follow this step-by-step guide for authorizing your own pygsheets: https://pygsheets.readthedocs.io/en/latest/authorization.html

3. Code Execution:

• Read in desired test and training data sets via Google Spreadsheet or local .csv file.
  • Download project training set events.csv from https://www.kaggle.com/gabrielmanfredi/expected-goals-player-analysis?select=events.csv
• If updating existing Google Spreadsheet with prediction results: establish connection to specific spreadsheet via pygsheets.
• If operating locally: include additional code to export output as .csv file for future use, or simply execute program without use of pygsheets.
• Run dataMain.py to execute MoBot xG prediction model.
• To create data visualizations: input spreadsheet data into dataViz.py , adjust to specifications, and execute.

Oxy Analytics Web Development Code (Flask Web App):

1. Clone GitHub repo and open Flask App folder in development environment

2. Module/Library Installation:

• Flask (Python web framework): pip install Flask
• Pandas (if not already installed): pip install pandas
• Pygsheets (if not already installed): pip install pygsheets
  • My Google credentials will not be included in the repo, so follow this step-by-step guide for authorizing your own pygsheets: https://pygsheets.readthedocs.io/en/latest/authorization.html
• Gunicorn (Python WSGI HTTP Server): pip install gunicorn

3. Running Web App Locally:

• Alter HTML/CSS/JavaScript as desired (or leave it alone)
• Type app.py on the command line and click Enter.
• Example:

  C:\Users\..\..\FlaskApp>app.py
   * Serving Flask app "app" (lazy loading)
   * Environment: production
     WARNING: This is a development server. Do not use it in a production deployment.
     Use a production WSGI server instead.
   * Debug mode: on
   * Restarting with windowsapi reloader
   * Debugger is active!
   * Debugger PIN: xxx-xxx-xxx
   * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)
  

4. Deploying Web App via Heroku (refer to links below):

• https://www.geeksforgeeks.org/deploy-python-flask-app-on-heroku/

• https://stackabuse.com/deploying-a-flask-application-to-heroku/

• Note: other hosting options can be used, but this specific project makes use of Heroku.

  • Heroku Alternatives: https://blog.back4app.com/heroku-alternatives/