# datawhalechina/competition-baseline

数据挖掘、计算机视觉、自然语言处理、推荐系统竞赛知识、代码、思路

Repository: https://github.com/datawhalechina/competition-baseline
Canonical: https://ross.abutalabs.com/products/competition-baseline
Homepage: http://coggle.club/
Language: Jupyter Notebook
License: GPL-3.0
License Family: copyleft
Topics: kaggle, data-competition, data-science, deep-learning
Last push: 2026-07-22T09:29:05+00:00

## Health v2 (maintenance only)
Score: 74/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 93, release rhythm 35, longevity 100
- inputs: {"age_days": 2465, "days_push": 42, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4757, forks 1082 (observed 2026-08-28T04:08:58.942249+00:00)

## What it is
A curated collection of baseline solutions and code for data science competitions including Kaggle, iFlytek AI challenges, and Tianchi contests. It covers data mining, computer vision, NLP, and recommendation system competitions with simple, beginner-friendly starter code.

## Use cases
- learn kaggle competition basics
- find baseline code for a data competition
- get started with data science competitions
- study nlp competition solutions
- learn computer vision competition approaches
- find starter code for recommendation system contests

## When to choose
- you are a beginner wanting simple, readable competition starter code
- you want baseline approaches for Chinese AI competitions like iFlytek or Tianchi
- you prefer concise solutions over complex winning-solution code

## When to avoid
- you need state-of-the-art winning solutions for maximum leaderboard scores
- you want production-grade, well-tested ML code
- you need English-language documentation

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, deep-learning, nlp, computer-vision, data-science
- domain: data-science, machine-learning, tutorials, education
- platform: python, cross-platform
- tags: kaggle, data-competition, baseline-solutions, jupyter-notebooks, chinese, recommendation-systems

## Member repositories
- datawhalechina/competition-baseline (main) score 74

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:58.942249+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:18:52.396911+00:00, confidence not recorded.
  - readme: https://github.com/datawhalechina/competition-baseline (fetched 2026-08-28T04:08:58.942249+00:00, sha c4c9c4cf6c8e)
- Data as of 2026-08-30T08:39:29.467469+00:00.