NBA-Machine-Learning-Sports-Betting
by kyleskom (personal account)
NBA sports betting using machine learning (description from the project’s GitHub page)
Repository facts
- Stars
- 1,746
- Forks
- 569
- Open issues and pull requests
- 5
- Primary language
- Python
- License
- No license file detected
- Owner
- kyleskom · Personal account
- Created
- Nov 29, 2019
- Last push
- Sep 12, 2026
Topics
Add this badge to your README
Shows this repository’s rank in AI on GitHub and links back to this page. Ranks are by stars and change when the list is refreshed. The badge is not an endorsement or a rating. About the badges
Markdown (for a README)
[](https://unitedstatesofamericasuperintelligence.com/github/kyleskom--nba-machine-learning-sports-betting/)
HTML
<a href="https://unitedstatesofamericasuperintelligence.com/github/kyleskom--nba-machine-learning-sports-betting/"><img src="https://unitedstatesofamericasuperintelligence.com/badges/github/kyleskom--nba-machine-learning-sports-betting.svg" alt="Ranked #4,079 in AI on GitHub" height="20"></a>
Related projects
Projects that share this one’s GitHub topics.
Rank 8,757: roclark/sportsipy
A free sports API written for python
577 starsPythonMITLast push Jan 31, 2025
Rank 4,043: ScottfreeLLC/AlphaPy
Python AutoML for Trading Systems and Sports Betting
1,766 starsPythonApache-2.0Last push Oct 5, 2026
Rank 7,714: HunterMcGushion/hyperparameter_hunter
Easy hyperparameter optimization and automatic result saving across machine learning algorithms and libraries
704 starsPythonMITLast push Jan 20, 2021
Rank 8,732: freud14/poutyne
A simplified framework and utilities for PyTorch
579 starsPythonLGPL-3.0Last push Jun 7, 2026
Rank 275: donnemartin/data-science-ipython-notebooks
Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy,…
29,370 starsPythonOther licenseLast push Mar 20, 2024
Rank 708: wandb/wandb
The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.
11,301 starsPythonMITLast push Oct 17, 2026