Kaggle Competition Development

Comprehensive skills for Kaggle competition development, including workflow patterns, API usage, Google Colab integration, and machine learning best practices.

Quick Start

# Setup Kaggle API
uv sync --extra kaggle

# Download competition data
uv run kaggle competitions download -c competition-name

# Submit predictions
uv run kaggle competitions submit -c competition-name -f submission.csv -m "Message"

Available Resources

Setup and Tools

• •kaggle-api-setup.md - Kaggle API installation and authentication guide
• •colab-workflow.md - Google Colab + Claude Code development workflow
• •claude-friendly-outputs.md - Creating outputs Claude can review locally
• •data-analysis-workflow.md - Complete data analysis workflow with Claude + Colab

Core Competition Skills

• •kaggle-workflow.md - Efficient competition workflow patterns
• •competition-strategy.md - Strategic competition management
• •ml-modeling.md - Machine learning modeling techniques
• •data-analysis.md - Data analysis and preprocessing

Detailed Knowledge Base

• •kaggle-competition.md - Competition-specific information template
• •data-understanding.md - Dataset analysis and feature documentation
• •competition-insights.md - Community insights and discussions
• •solution-strategy.md - Solution approach and methodology
• •feature-engineering.md - Feature engineering patterns
• •model-zoo.md - Model configurations and strategies
• •evaluation-metrics.md - Validation and evaluation frameworks

Competition Setup

1. •Assess problem type (tabular, CV, NLP) and evaluation metric
2. •Set up validation strategy matching competition timeline and data structure
3. •Establish baseline using simple models (mean/mode prediction, basic tree model)
4. •Configure experiment tracking and reproducibility (random seeds, version control)

Development Workflow Options

Standard Setup (Local execution)

cp -r kaggle-template/ my-competition/
cd my-competition/
uv sync --extra kaggle

Google Colab Setup (Cloud execution with GPU)

For competitions requiring large datasets or GPU/TPU resources:

• •Develop code locally with Claude Code
• •Store data in Google Drive
• •Execute training on Google Colab
• •See colab-workflow.md for complete setup guide