# Mryangkaitong/python-Machine-learning

机器学习算法项目

Repository: https://github.com/Mryangkaitong/python-Machine-learning
Canonical: https://ross.abutalabs.com/products/python-machine-learning
Language: Jupyter Notebook
License Family: other
Last push: 2021-10-15T06:02:57+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": 3066, "days_push": 1783, "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 1043, forks 341 (observed 2026-08-28T04:03:20.953757+00:00)

## What it is
A collection of Jupyter Notebook projects implementing machine learning algorithms, including small practice projects and competition code written by the author. It serves as a learning resource with accompanying blog posts on Zhihu, CSDN, and a WeChat public account.

## Use cases
- learn machine learning algorithms through hands-on examples
- find example code for ML competition projects
- study Python implementations of classic ML algorithms
- practice data science with notebook-based tutorials
- reference small end-to-end machine learning projects

## When to choose
- you want readable notebook-style examples of ML algorithms
- you are a beginner studying machine learning with Python
- you want reference code for data competition problems

## When to avoid
- you need a production-ready ML library or framework
- you require maintained software with a license and active releases
- you need English-language documentation

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, data-science
- domain: machine-learning, data-science, tutorials
- platform: python
- tags: jupyter-notebooks, practice-projects, kaggle-competitions, chinese-language, educational

## Member repositories
- Mryangkaitong/python-Machine-learning (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:20.953757+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 2026-08-30T07:02:16.980515+00:00, confidence not recorded.
  - readme: https://github.com/Mryangkaitong/python-Machine-learning (fetched 2026-08-28T04:03:20.953757+00:00, sha 1c5b09106aa7)
- Data as of 2026-08-30T08:39:29.467469+00:00.