# aswintechguy/Machine-Learning-Projects

This repository contains mini projects in machine learning with notebook files

Repository: https://github.com/aswintechguy/Machine-Learning-Projects
Canonical: https://ross.abutalabs.com/products/aswintechguy-machine-learning-projects
Language: Jupyter Notebook
License Family: other
Topics: machine-learning, python, jupyter-notebook, classfication, regression, notebook-files
Last push: 2025-03-14T09:40:47+00:00

## Health v2 (maintenance only)
Score: 37/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 11, release rhythm 35, longevity 100
- inputs: {"age_days": 2269, "days_push": 537, "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 1335, forks 680 (observed 2026-08-28T04:04:25.256417+00:00)

## What it is
A collection of mini machine learning projects implemented as Jupyter notebooks, covering classification and regression tasks. It includes video tutorials for guided learning.

## Use cases
- learn machine learning through hands-on projects
- find beginner ml notebook examples
- practice classification and regression in python
- follow along with ml project video tutorials
- get starter code for jupyter notebook ml projects

## When to choose
- you are a beginner wanting guided ml practice
- you prefer notebook-based learning with video walkthroughs

## When to avoid
- you need production-ready ml code
- you require a maintained library with a license

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, data-science
- domain: machine-learning, data-science, tutorials
- platform: python
- tags: jupyter-notebook, mini-projects, classification, regression, beginner-friendly, video-tutorials

## Member repositories
- aswintechguy/Machine-Learning-Projects (main) score 37

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:25.256417+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 2026-08-30T04:44:21.539990+00:00, confidence not recorded.
  - readme: https://github.com/aswintechguy/Machine-Learning-Projects (fetched 2026-08-28T04:04:25.256417+00:00, sha 5e2be0c7eb41)
- Data as of 2026-08-30T08:39:29.467469+00:00.