Welcome to the Scikit-Learn - NHL Examples repository! This repository is dedicated to showcasing the capabilities of Scikit-Learn, a powerful machine learning library in Python, through the exciting lens of NHL (National Hockey League) statistics.
Each example in this repository uses real NHL data to demonstrate different features and techniques of Scikit-Learn, making these concepts both practical and engaging. Whether you're a sports enthusiast, a data scientist, or just curious about machine learning, these examples are tailored to provide valuable insights.
For more information about Scikit-Learn, check out their official website.
Additionally, accompanying articles for most examples are posted on my LinkedIn as part of my weekly series, Syntax Sunday.
This Syntax Sunday post shows you how to create an Hockey Stats and Analysis Expert GPT using OpenAI's ChatGPT.
Note: This project is for educational purposes. While we strive for accuracy, assumptions are made in these examples. Therefore, please use the results with discretion.
- Points Regression - Top 100 NHL Players 2023/24 Season
- This example focuses on analyzing and visualizing the top 100 players of the 2023/24 NHL season based on their points (as of Jan 6, 2023). Learn about data processing, visualization, and basic statistical analysis with Scikit-Learn.
- Game Classification - Game Win/Loss for 2023/24 Season
- In this example, we will analyze historical NHL game data and build machine learning models (Classification) to predict the outcome of games for the 2023/24 season. The models will classify each game as either a win or a loss based on various features.
The NHL statistics data used in these examples are sourced from Official NHL Statistics. You can access and download the data to follow along with the examples.
The information in this repository is provided "as is" without any representations or warranties, express or implied. I make no representations or warranties concerning the accuracy or completeness of the data or information used here.
Your feedback and contributions are welcome! If you have suggestions, questions, or would like to contribute to the repository, please feel free to open an issue or submit a pull request.
Follow me on LinkedIn for updates on this and other projects. Join the conversation and be a part of the #SyntaxSunday series!