# intro-stat-learning/ISLP_labs

Up-to-date version of labs for ISLP

Repository: https://github.com/intro-stat-learning/ISLP_labs
Canonical: https://ross.abutalabs.com/products/islp_labs
Language: Jupyter Notebook
License: BSD-2-Clause
License Family: permissive
Last push: 2026-05-18T18:48:10+00:00

## Health v2 (maintenance only)
Score: 66/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 83, release rhythm 35, longevity 81
- inputs: {"age_days": 1134, "days_push": 107, "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 1377, forks 814 (observed 2026-08-28T04:04:33.150921+00:00)

## What it is
Up-to-date Jupyter notebook labs accompanying the textbook 'An Introduction to Statistical Learning with Python' (ISLP), maintained by the book's authors. It provides runnable notebooks with a reproducible uv-based environment setup.

## Use cases
- work through ISLP textbook labs in Python
- learn statistical learning with hands-on notebooks
- reproduce ISLP lab results in a fresh environment
- practice regression and classification with scikit-learn
- set up a teaching environment for an intro stats learning course

## When to choose
- you are studying the ISLP book and want the official, current labs
- you need reproducible notebook environments for teaching or self-study
- you want Python versions of the classic ISL labs

## When to avoid
- you need production machine-learning code rather than educational notebooks
- you want labs for a different textbook
- you need a maintained software library rather than course materials

## Facets
- artifact type: learning-resource
- maturity: active
- function: data-science, machine-learning, developer-tools
- domain: data-science, machine-learning, education, tutorials
- platform: python, cross-platform
- tags: jupyter-notebooks, statistics, islp, textbook-labs, regression, classification

## Member repositories
- intro-stat-learning/ISLP_labs (main) score 66

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:33.150921+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 2026-08-30T04:40:26.603400+00:00, confidence not recorded.
  - readme: https://github.com/intro-stat-learning/ISLP_labs (fetched 2026-08-28T04:04:33.150921+00:00, sha 3064a31af1e1)
- Data as of 2026-08-30T08:39:29.467469+00:00.