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https://github.com/heyitsjoealongi/fantasy-football-qbwr-model
Fantasy Football: Quarterback / Wide Receiver - Gaussian Process Regression (GPR) Machine Learning Model
https://github.com/heyitsjoealongi/fantasy-football-qbwr-model
machine-learning matplotlib model numpy python scikit-learn
Last synced: about 1 month ago
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Fantasy Football: Quarterback / Wide Receiver - Gaussian Process Regression (GPR) Machine Learning Model
- Host: GitHub
- URL: https://github.com/heyitsjoealongi/fantasy-football-qbwr-model
- Owner: heyitsjoealongi
- Created: 2023-10-29T01:59:16.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2023-10-29T21:11:49.000Z (about 1 year ago)
- Last Synced: 2024-10-06T14:40:54.061Z (3 months ago)
- Topics: machine-learning, matplotlib, model, numpy, python, scikit-learn
- Language: Python
- Homepage:
- Size: 5.86 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.MD
Awesome Lists containing this project
README
# Fantasy Football QBWR Model
Forecasting the throw and catch relationship of QB (Quarterback) to WR (Wide Receiver) over four seasons with a base level of passes and percentage of catches through Machine Learning with Pandas, Scikit Learn, Numpy, and Matplotlib.
## Develop Python Application Locally
1. Install Requirements File:
```zsh
pip install -r requirements.txt
```
2. Test Requirements File:
```zsh
python -m pip check
```
3. Start Python Application:
```zsh
python model.py
```
#### Acknowledgements:
- [Forecasting of CO2 level on Mona Loa dataset using Gaussian process regression (GPR)](https://scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpr_co2.html)