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faridrashidi/kaggle-solutions · source

Snapshot 0 · 2026-11-04T12:47:25Z · Astro · MIT

GitHub description at capture

🏅 Collection of Kaggle Solutions and Ideas 🏅

README at capture

<p align="center">
  <a href="https://kaggle.farid.one/">
    <img src="public/images/logo.png" height="150">
  </a>
</p>
<p align="center">
  <h1 align="center">Kaggle Solutions</h1>
</p>

<p align="center">
  <h3 align="center" style=" border-bottom: 0px;">The Most Comprehensive Collection of Kaggle Competition Solutions and Ideas</h3>
</p>

<p align="center">
  <a href="#overview"><strong>Overview</strong></a> •
  <a href="#usage"><strong>Usage</strong></a> •
  <a href="#suggestions"><strong>Suggestions</strong></a> •
  <a href="#contribution"><strong>Contribution</strong></a>
</p>

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<hr />

## Overview

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.

## Usage

### Browsing Solutions

Visit the live website at **[kaggle.farid.one](https://kaggle.farid.one/)** 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

### Creating Your Own Copy

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 Astro-based site builds to static files, so it can deploy cleanly to modern hosts such as Cloudflare Pages, Netlify, or Vercel while keeping the content workflow lightweight.

### Running Locally

```bash
npm install
npm run dev
```

The competition archive still reads from `data/competitions.yml`, and the helper scripts in `scripts/` can keep updating that file without changing the frontend stack.

## Suggestions

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

### Understanding the Competition

- **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

### Learning from Top Performers

- **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

### Key Areas to Focus On

- **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?

### Applying Knowledge

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

## Contribution

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

### How to Contribute

**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

### Questions or Issues?

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

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### License

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

### Acknowledgments

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