# datawhalechina/team-learning-data-mining

主要存储Datawhale组队学习中“数据挖掘/机器学习”方向的资料。

Repository: https://github.com/datawhalechina/team-learning-data-mining
Canonical: https://ross.abutalabs.com/products/team-learning-data-mining
Language: Jupyter Notebook
License Family: other
Last push: 2022-03-16T02:39:44+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2227, "days_push": 1631, "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 1858, forks 820 (observed 2026-08-28T04:05:45.141053+00:00)

## What it is
A collection of open-source study materials for Datawhale's group learning programs in the data mining and machine learning direction. It contains Jupyter Notebook-based curricula covering math foundations, machine learning algorithms, data analysis, and applied data mining competitions.

## Use cases
- learn machine learning fundamentals from scratch
- study linear algebra and probability statistics for data science
- practice data analysis with pandas on real datasets
- work through hands-on data mining competitions like rent price prediction
- learn ensemble learning and anomaly detection techniques
- follow a structured group study curriculum for data mining
- practice financial risk control modeling

## When to choose
- you want free, structured, notebook-based tutorials for data mining and ML
- you prefer learning through hands-on practice with real competition datasets
- you are organizing or joining a group study program on machine learning
- you read Chinese and want beginner-friendly ML curricula

## When to avoid
- you need production-ready machine learning code or libraries
- you require an actively maintained resource with recent updates
- you need English-language materials
- you want a formal course with certification

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, data-science, nlp
- domain: machine-learning, data-science, tutorials, education
- platform: python, cross-platform
- tags: jupyter-notebooks, team-learning, chinese-language, datawhale, hands-on-tutorials, anomaly-detection, ensemble-learning, data-analysis

## Member repositories
- datawhalechina/team-learning-data-mining (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:45.141053+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 2026-08-30T03:16:02.379898+00:00, confidence not recorded.
  - readme: https://github.com/datawhalechina/team-learning-data-mining (fetched 2026-08-28T04:05:45.141053+00:00, sha a05a0a235c91)
- Data as of 2026-08-30T08:39:29.467469+00:00.