# hardikkamboj/An-Introduction-to-Statistical-Learning

This repository contains the exercises and its solution contained in the book "An Introduction to Statistical Learning" in python.

Repository: https://github.com/hardikkamboj/An-Introduction-to-Statistical-Learning
Canonical: https://ross.abutalabs.com/products/an-introduction-to-statistical-learning
Language: Jupyter Notebook
License Family: other
Topics: datascience, machine-learning, statistical-learning, python
Last push: 2024-09-23T08:44:58+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": 2272, "days_push": 709, "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 2496, forks 612 (observed 2026-08-28T04:06:56.707550+00:00)

## What it is
A collection of Jupyter Notebook solutions to the conceptual and applied exercises from the book 'An Introduction to Statistical Learning', implemented in Python instead of the book's original R. It covers chapters on regression, classification, resampling, regularization, tree methods, SVMs, and unsupervised learning.

## Use cases
- solve ISLR exercises in python
- learn statistical learning concepts with python notebooks
- python alternative to R labs in An Introduction to Statistical Learning
- study machine learning fundamentals with worked solutions
- practice linear regression classification and SVM exercises
- self-study companion for ISLR book

## When to choose
- you are reading ISLR and prefer Python over R
- you want worked notebook solutions with commentary
- you want a free self-study resource for statistical learning basics

## When to avoid
- you need guaranteed-correct solutions - the author notes errors are possible
- you need production machine learning code or a library
- you need the newer Python edition of ISLR with official labs

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

## Member repositories
- hardikkamboj/An-Introduction-to-Statistical-Learning (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:56.707550+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 2026-08-30T02:27:11.561446+00:00, confidence not recorded.
  - readme: https://github.com/hardikkamboj/An-Introduction-to-Statistical-Learning (fetched 2026-08-28T04:06:56.707550+00:00, sha bafda5cd0f94)
- Data as of 2026-08-30T08:39:29.467469+00:00.