# JWarmenhoven/ISLR-python

An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani, 2013): Python code

Repository: https://github.com/JWarmenhoven/ISLR-python
Canonical: https://ross.abutalabs.com/products/islr-python
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: machine-learning, predictive-modeling, islr, statistical-learning, islr-python
Last push: 2022-10-27T09:23:52+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": 4098, "days_push": 1406, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4407, forks 2378 (observed 2026-08-28T04:08:48.424393+00:00)

## What it is
A collection of Jupyter Notebooks reproducing the tables, figures, and lab sections of the book 'An Introduction to Statistical Learning' in Python instead of R. It serves as a free companion resource for learning statistical and machine learning concepts hands-on.

## Use cases
- learn machine learning concepts from ISLR using Python instead of R
- follow along with ISLR book labs in Jupyter notebooks
- study linear regression, classification, resampling, and regularization with worked code
- find Python implementations of ISLR chapter exercises and figures
- self-study statistical learning with scikit-learn examples

## When to choose
- you are reading ISLR and prefer Python over R
- you want notebook-based walkthroughs of classic statistical learning methods
- you need reproducible examples covering the full ISLR curriculum

## When to avoid
- you need a production machine learning library
- you want the second edition of ISLR with Python labs (use ISLP instead)
- you need maintained, up-to-date code for current package versions

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, data-science
- domain: machine-learning, data-science, tutorials
- platform: python
- tags: islr, jupyter-notebooks, statistical-learning, textbook-companion, predictive-modeling, education

## Member repositories
- JWarmenhoven/ISLR-python (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:48.424393+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 2026-08-29T18:21:04.816977+00:00, confidence not recorded.
  - readme: https://github.com/JWarmenhoven/ISLR-python (fetched 2026-08-28T04:08:48.424393+00:00, sha 02a1ed4881b8)
- Data as of 2026-08-30T08:39:29.467469+00:00.