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AI Sports Analytics

Welcome to the AI Sports Analytics repository! This project is dedicated to providing the latest insights and visualizations for baseball and basketball, leveraging data from PTT and advanced AI tools.

Table of Contents

Introduction

AI Sports Analytics is an Instagram account and analytical platform that collects data from PTT, performs thorough analysis, and creates detailed visualizations to provide the latest insights in baseball and basketball. Using advanced AI tools, we summarize complex data into easy-to-understand content.

Features

  • Data Collection: Automated data scraping from PTT for baseball and basketball.
  • Analysis: Advanced AI tools to analyze collected data.
  • Visualization: Create informative and visually appealing data visualizations.
  • Summarization: Generate concise and insightful summaries of the analysis.
  • Instagram Integration: Share visualizations and insights directly to our Instagram account.

Installation

To set up the project locally, follow these steps:

  1. Clone the Repository:

    git clone https://github.com/beccalzh/ai-sports-analytics.git
    cd ai-sports-analytics
  2. Install Dependencies:

    Ensure you have Python and pip installed. Then run:

    pip install -r requirements.txt
  3. Set Up Environment Variables:

    Create a .env file in the project root and add the necessary environment variables, such as API keys and database configurations.

  4. Run the Application:

    python main.py

Usage

  1. Data Collection:

    Run the data collection script to fetch the latest data from PTT:

    python scripts/data_collection.py
  2. Data Analysis:

    Perform analysis on the collected data:

    python scripts/data_analysis.py
  3. Generate Visualizations:

    Create visualizations from the analyzed data:

    python scripts/data_visualization.py
  4. Post to Instagram:

    Automatically post the generated visualizations to Instagram:

    python scripts/post_to_instagram.py

Contributing

We welcome contributions to the AI Sports Analytics project! To contribute, please follow these steps:

  1. Fork the repository.
  2. Create a new branch (git checkout -b feature/YourFeature).
  3. Commit your changes (git commit -m 'Add some feature').
  4. Push to the branch (git push origin feature/YourFeature).
  5. Create a new Pull Request.

Please ensure your code follows our coding guidelines and includes appropriate tests.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contact

For any inquiries or feedback, please contact us at:

Thank you for visiting our repository!

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