kaggle-solutions and Data-Science-Competitions

These two repositories are competitors, as both aim to provide collections of solutions for data science competitions, meaning a user would likely choose one over the other based on their preferred collection and approach.

kaggle-solutions
71
Verified
Data-Science-Competitions
51
Established
Maintenance 20/25
Adoption 10/25
Maturity 16/25
Community 25/25
Maintenance 0/25
Adoption 10/25
Maturity 16/25
Community 25/25
Stars: 6,352
Forks: 2,380
Downloads:
Commits (30d): 17
Language: Astro
License: MIT
Stars: 812
Forks: 218
Downloads:
Commits (30d): 0
Language:
License: Apache-2.0
No Package No Dependents
Stale 6m No Package No Dependents

About kaggle-solutions

faridrashidi/kaggle-solutions

🏅 Collection of Kaggle Solutions and Ideas 🏅

Organizes competition solutions and insights across hundreds of Kaggle competitions in a searchable, categorized archive built with Astro that deploys as static files. Aggregates winning approaches, code notebooks, discussion threads, and learning resources curated by competition category (Computer Vision, NLP, Tabular, Time Series, etc.) to enable systematic study of top performer strategies. Designed for forking with lightweight markdown-based contribution workflow, allowing users to customize content and add personal annotations while keeping infrastructure simple and deployable to modern hosting platforms.

About Data-Science-Competitions

the-black-knight-01/Data-Science-Competitions

Goal of this repo is to provide the solutions of all Data Science Competitions(Kaggle, Data Hack, Machine Hack, Driven Data etc...).

Curated collection of top-ranked competition solutions (1st-29th place finishes) with linked explanations and code implementations across regression, classification, NLP, and computer vision tasks. Organizes winning approaches by problem type and ranking, enabling practitioners to study ensemble techniques, feature engineering strategies, and model architectures used by competition leaders. Community-driven repository accepting pull requests to expand coverage across Kaggle, Machine Hack, and other platforms.

Scores updated daily from GitHub, PyPI, and npm data. How scores work