# susanli2016/Machine-Learning-with-Python

Python code for common Machine Learning Algorithms

Repository: https://github.com/susanli2016/Machine-Learning-with-Python
Canonical: https://ross.abutalabs.com/products/machine-learning-with-python
Language: Jupyter Notebook
License Family: other
Topics: linear-regression, polynomial-regression, logistic-regression, decision-trees, random-forest, svm, svr, knn-classification, naive-bayes-classifier, kmeans-clustering, hierarchical-clustering, pca, lda, xgboost-algorithm
Last push: 2025-06-05T01:08:21+00:00

## Health v2 (maintenance only)
Score: 44/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 25, release rhythm 35, longevity 100
- inputs: {"age_days": 3386, "days_push": 455, "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 4592, forks 4759 (observed 2026-08-28T04:08:54.735523+00:00)

## What it is
A collection of Jupyter Notebooks demonstrating common machine learning algorithms in Python, covering regression, classification, clustering, and dimensionality reduction techniques. It serves as a hands-on educational resource with example code for algorithms like SVM, random forests, XGBoost, and PCA.

## Use cases
- learn machine learning algorithms with python code examples
- understand how decision trees and random forests work
- see examples of logistic regression and svm in jupyter notebooks
- study clustering algorithms like kmeans and hierarchical clustering
- get started with xgboost and pca implementations
- find reference code for common scikit-learn models

## When to choose
- you are learning machine learning and want runnable notebook examples
- you need reference implementations of classic ml algorithms
- you prefer studying code over theory-heavy textbooks

## When to avoid
- you need production-ready or maintained ml code
- you want a library or framework rather than example notebooks
- you need a license for reuse in your own projects

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, data-science
- domain: machine-learning, data-science, tutorials
- platform: python
- tags: jupyter-notebooks, scikit-learn, algorithms, educational, classification, clustering, dimensionality-reduction

## Member repositories
- susanli2016/Machine-Learning-with-Python (main) score 44

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:54.735523+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 2026-08-29T18:19:50.052410+00:00, confidence not recorded.
  - readme: https://github.com/susanli2016/Machine-Learning-with-Python (fetched 2026-08-28T04:08:54.735523+00:00, sha 5f187ee5bbdb)
- Data as of 2026-08-30T08:39:29.467469+00:00.