r1shabhraj / soccer-analysis-and-visualization-using-vaep-and-xgboost

This project is a comprehensive Jupyter notebook (google colab notebook) designed for soccer analytics. It utilizes advanced libraries and frameworks to process and analyze soccer data, providing valuable insights into game strategies, player performance, and team dynamics.

Soccer Analysis Notebook ⚽️📊

This project is a comprehensive Jupyter notebook (google colab) designed for soccer analytics. It utilizes advanced libraries and frameworks to process and analyze soccer data, providing valuable insights into game strategies, player performance, and team dynamics.

Key Features:

Data Processing: Efficiently handles large datasets with player and match statistics. Visualization: Generates insightful plots to illustrate the flow and events of soccer matches. Machine Learning: Implements predictive models to forecast game outcomes and player impacts. Interactivity: Offers interactive widgets for a dynamic analysis experience.

Getting Started: To begin exploring the soccer analytics notebook, simply click on the Google Colab link below and follow the instructions provided within the notebook.

Contributions: We welcome contributions from the community. Whether it’s improving the code, adding new features, or reporting issues, your input is valuable! Feel free to adjust the content as needed for your specific project details and GitHub repository structure.