Overview • Usage • Suggestions • Contribution

This repository is a curated collection of solutions, ideas, and insights from top performers across hundreds of Kaggle competitions. Whether you're a beginner looking to learn from the best or an experienced competitor seeking inspiration, this resource provides:

• Winning Solutions: Detailed approaches from competition winners and top finishers
• Discussion Threads: Links to the most valuable community discussions
• Code Notebooks: Top-rated kernels and implementations
• Learning Resources: Videos, tutorials, and educational content
• Competition Insights: Analysis of evaluation metrics, datasets, and problem-solving strategies

The repository is regularly updated as competitions conclude, making it a living archive of competitive machine learning knowledge.

Visit the live website at farid.one/kaggle-solutions to:

• Browse competitions by category (Computer Vision, NLP, Tabular, Time Series, etc.)
• Search for specific competitions or techniques
• Access curated solutions and discussion links
• Watch tutorial videos and presentations

Fork this repository to create your personal version:

1. Click the Fork button at the top of this repository
2. Your forked version will be available at https://<YOUR_USER_NAME>.github.io/kaggle-solutions
3. Add your own notes, solutions, and insights in markdown format
4. Customize the content to match your learning journey

This Jekyll-based site automatically deploys via GitHub Pages, giving you a personal knowledge base for Kaggle competitions.

To maximize your learning from past competitions, follow this comprehensive approach for each competition you study:

• Competition Description: Understand the business problem and objectives
• Evaluation Metric: Study how solutions are scored (AUC, RMSE, Log Loss, etc.)
• Dataset Characteristics: Analyze data types, size, features, and any special considerations
• Timeline & Rules: Review competition duration and specific constraints

• Leaderboard Analysis: Check profiles of top finishers to understand their approach patterns
• Solution Discussions: Read post-competition solution threads (often titled "1st place solution", "Our approach", etc.)
• Code Notebooks: Study the most upvoted and awarded kernels for implementation details
• Ensemble Strategies: Note how winners combined multiple models

• Feature Engineering: What creative features did winners develop?
• Model Selection: Which algorithms performed best and why?
• Validation Strategy: How did top performers set up cross-validation?
• Post-Processing: What techniques were applied to final predictions?

• Try implementing winning solutions on your own
• Experiment with variations and test your understanding
• Document your learnings and insights in your forked repository

Contributions are welcome and encouraged! Help make this the most comprehensive Kaggle solutions resource.

Found a missing solution? If you discover a competition solution, discussion, or resource not listed here:

1. Fork this repository
2. Add the solution link to the appropriate competition page
3. Ensure the link is valid and points to valuable content
4. Submit a pull request with a clear description

What to contribute:

• Winner's solution write-ups and code repositories
• Insightful discussion threads from competition forums
• High-quality notebooks and kernels
• Tutorial videos or blog posts analyzing competitions
• Additional competition metadata or corrections

Quality Guidelines:

• Verify links are working and point to relevant content
• Follow the existing markdown format and structure
• Provide context when adding new resources
• Check for duplicates before submitting

If you have questions, suggestions, or encounter any issues, please open an issue on GitHub.

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

Thanks to the Kaggle community and all competition participants who share their solutions and insights, making machine learning knowledge accessible to everyone.