This repository curates winning solutions from Chinese data science competitions, offering valuable insights and code for participants and researchers. It serves as a centralized resource for learning advanced techniques and understanding best practices in various domains like computer vision, NLP, and time-series forecasting.

How It Works

The project compiles links to algorithms and source code from top-performing teams in national data competitions. It categorizes solutions by competition type, platform, and deadline, providing a structured overview of successful approaches. The repository aims to democratize access to high-quality machine learning solutions that have proven effective in real-world challenges.

Quick Start & Requirements

• To contribute, clone the repository and use the provided Python script (utility.py) to add new competition solutions.
• No specific software prerequisites are mentioned for browsing the solutions.

Highlighted Details

• Comprehensive coverage of over 30 major Chinese data competitions.
• Includes solutions for diverse tasks: prediction, classification, NLP, computer vision, knowledge graphs, and optimization.
• Links to both algorithmic descriptions and source code for many winning entries.
• Categorization by platform (e.g., Tianchi, Biendata, DataFountain) and organizing body.

Maintenance & Community

The repository is maintained by geekinglcq and welcomes community contributions via pull requests. Specific instructions are provided for adding new solutions using a Python script.

Licensing & Compatibility

The repository states that all code and algorithms belong to their original authors and are for learning purposes only. It notes that any rights infringements should be reported for resolution. The specific license for the curated collection itself is not explicitly stated, but the intent is for educational use.

Limitations & Caveats

Some entries lack direct code links or only provide algorithmic descriptions. The repository's focus is primarily on past competitions, with the last listed competition ending in late 2018, indicating potential staleness for cutting-edge techniques.