# DLLXW/data-science-competition

该仓库用于记录作者本人参加的各大数据科学竞赛的获奖方案源码以及一些新比赛的原创baseline. 主要涵盖：kaggle, 阿里天池，华为云大赛校园赛，百度aistudio，和鲸社区，datafountain等

Repository: https://github.com/DLLXW/data-science-competition
Canonical: https://ross.abutalabs.com/products/data-science-competition
Language: Python
License Family: other
Last push: 2023-04-21T15:26:20+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2149, "days_push": 1230, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1380, forks 464 (observed 2026-08-28T04:04:33.834499+00:00)

## What it is
A curated collection of award-winning solutions and original baselines from data science competitions on platforms like Kaggle, Alibaba Tianchi, Huawei Cloud, Baidu AIStudio, Heywhale, and DataFountain. It covers computer vision, data mining, time-series forecasting, and NLP tasks with full source code.

## Use cases
- learn from winning kaggle competition solutions
- find baselines for data science competitions
- study image segmentation competition code
- learn time series forecasting approaches for tianchi contests
- reference nlp text classification competition solutions
- prepare for datafountain or heywhale competitions

## When to choose
- you want real-world competition-winning code to study
- you need a starting baseline for a similar competition task
- you are learning practical ML across CV, NLP, and tabular data

## When to avoid
- you need a maintained, installable library with an API
- you want production-ready or licensed code for reuse
- you need up-to-date solutions for recent competitions

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, data-science, computer-vision, nlp
- domain: data-science, machine-learning, computer-vision, tutorials
- platform: python
- tags: kaggle, tianchi, competition-solutions, baselines, data-mining, time-series-forecasting, image-segmentation, object-detection, natural-language-processing

## Member repositories
- DLLXW/data-science-competition (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:33.834499+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 2026-08-30T04:40:13.555686+00:00, confidence not recorded.
  - readme: https://github.com/DLLXW/data-science-competition (fetched 2026-08-28T04:04:33.834499+00:00, sha ad8db85019c8)
- Data as of 2026-08-30T08:39:29.467469+00:00.