{"adoption": {"forks": 612, "observed_at": "2026-08-28T04:06:56.707550+00:00", "stars": 2496}, "canonical_url": "https://ross.abutalabs.com/products/an-introduction-to-statistical-learning", "card": {"archived": false, "artifact_type": "learning-resource", "description": "This repository contains the exercises and its solution contained in the book \"An Introduction to Statistical Learning\" in python.", "domain": ["machine-learning", "data-science", "tutorials", "education"], "enriched": true, "function": ["machine-learning", "data-science"], "health_score": 20, "homepage": null, "language": "Jupyter Notebook", "license": null, "license_family": "other", "maturity": "maintenance", "member_repos": ["hardikkamboj/An-Introduction-to-Statistical-Learning"], "name": "hardikkamboj/An-Introduction-to-Statistical-Learning", "platform": ["python"], "pushed_at": "2024-09-23T08:44:58+00:00", "repo": "hardikkamboj/An-Introduction-to-Statistical-Learning", "stars": 2496, "tags": ["jupyter-notebooks", "islr", "statistical-learning", "exercise-solutions", "textbook-companion"], "topics": ["datascience", "machine-learning", "statistical-learning", "python"], "urls": [], "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"], "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.", "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"], "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"]}, "data_as_of": "2026-08-30T08:39:29.467469+00:00", "members": [{"path": "/products/an-introduction-to-statistical-learning", "repo": "hardikkamboj/An-Introduction-to-Statistical-Learning", "role": "main", "score": 32}], "provenance": {"archived": {"kind": "observed", "observed_at": "2026-08-28T04:06:56.707550+00:00", "source": "github"}, "artifact_type": {"confidence": null, "enriched_at": "2026-08-30T02:27:11.561446+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "bafda5cd0f94c8611495e451b1723ac5dd6a02bcfed98818793dc5859d8eac23", "fetched_at": "2026-08-28T04:06:56.707550+00:00", "kind": "readme", "missing": false, "url": "https://github.com/hardikkamboj/An-Introduction-to-Statistical-Learning"}], "taxonomy_version": 1}, "description": {"kind": "observed", "observed_at": "2026-08-28T04:06:56.707550+00:00", "source": "github"}, "domain": {"confidence": null, "enriched_at": "2026-08-30T02:27:11.561446+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "bafda5cd0f94c8611495e451b1723ac5dd6a02bcfed98818793dc5859d8eac23", "fetched_at": "2026-08-28T04:06:56.707550+00:00", "kind": "readme", "missing": false, "url": "https://github.com/hardikkamboj/An-Introduction-to-Statistical-Learning"}], "taxonomy_version": 1}, "enriched": {"inputs": [], "kind": "computed", "method": "enrichment_status"}, "function": {"confidence": null, "enriched_at": "2026-08-30T02:27:11.561446+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "bafda5cd0f94c8611495e451b1723ac5dd6a02bcfed98818793dc5859d8eac23", "fetched_at": "2026-08-28T04:06:56.707550+00:00", "kind": "readme", "missing": false, "url": "https://github.com/hardikkamboj/An-Introduction-to-Statistical-Learning"}], "taxonomy_version": 1}, "health_score": {"inputs": ["days_since_push", "days_since_release", "archived"], "kind": "computed", "method": "health_v1"}, "homepage": {"kind": "observed", "observed_at": "2026-08-28T04:06:56.707550+00:00", "source": "github"}, "language": {"kind": "observed", "observed_at": "2026-08-28T04:06:56.707550+00:00", "source": "github"}, "license": {"kind": "observed", "observed_at": "2026-08-28T04:06:56.707550+00:00", "source": "github"}, "license_family": {"inputs": ["license"], "kind": "computed", "method": "license_family"}, "maturity": {"confidence": null, "enriched_at": "2026-08-30T02:27:11.561446+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "bafda5cd0f94c8611495e451b1723ac5dd6a02bcfed98818793dc5859d8eac23", "fetched_at": "2026-08-28T04:06:56.707550+00:00", "kind": "readme", "missing": false, "url": "https://github.com/hardikkamboj/An-Introduction-to-Statistical-Learning"}], "taxonomy_version": 1}, "member_repos": {"kind": "observed", "observed_at": "2026-08-28T04:06:56.707550+00:00", "source": "github"}, "name": {"kind": "observed", "observed_at": "2026-08-28T04:06:56.707550+00:00", "source": "github"}, "platform": {"confidence": null, "enriched_at": "2026-08-30T02:27:11.561446+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "bafda5cd0f94c8611495e451b1723ac5dd6a02bcfed98818793dc5859d8eac23", "fetched_at": "2026-08-28T04:06:56.707550+00:00", "kind": "readme", "missing": false, "url": "https://github.com/hardikkamboj/An-Introduction-to-Statistical-Learning"}], "taxonomy_version": 1}, "pushed_at": {"kind": "observed", "observed_at": "2026-08-28T04:06:56.707550+00:00", "source": "github"}, "repo": {"kind": "observed", "observed_at": "2026-08-28T04:06:56.707550+00:00", "source": "github"}, "stars": {"kind": "observed", "observed_at": "2026-08-28T04:06:56.707550+00:00", "source": "github"}, "tags": {"confidence": null, "enriched_at": "2026-08-30T02:27:11.561446+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "bafda5cd0f94c8611495e451b1723ac5dd6a02bcfed98818793dc5859d8eac23", "fetched_at": "2026-08-28T04:06:56.707550+00:00", "kind": "readme", "missing": false, "url": "https://github.com/hardikkamboj/An-Introduction-to-Statistical-Learning"}], "taxonomy_version": 1}, "topics": {"kind": "observed", "observed_at": "2026-08-28T04:06:56.707550+00:00", "source": "github"}, "urls": {"kind": "observed", "observed_at": "2026-08-28T04:06:56.707550+00:00", "source": "github"}, "use_cases": {"confidence": null, "enriched_at": "2026-08-30T02:27:11.561446+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "bafda5cd0f94c8611495e451b1723ac5dd6a02bcfed98818793dc5859d8eac23", "fetched_at": "2026-08-28T04:06:56.707550+00:00", "kind": "readme", "missing": false, "url": "https://github.com/hardikkamboj/An-Introduction-to-Statistical-Learning"}], "taxonomy_version": 1}, "what_it_is": {"confidence": null, "enriched_at": "2026-08-30T02:27:11.561446+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "bafda5cd0f94c8611495e451b1723ac5dd6a02bcfed98818793dc5859d8eac23", "fetched_at": "2026-08-28T04:06:56.707550+00:00", "kind": "readme", "missing": false, "url": "https://github.com/hardikkamboj/An-Introduction-to-Statistical-Learning"}], "taxonomy_version": 1}, "when_to_avoid": {"confidence": null, "enriched_at": "2026-08-30T02:27:11.561446+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "bafda5cd0f94c8611495e451b1723ac5dd6a02bcfed98818793dc5859d8eac23", "fetched_at": "2026-08-28T04:06:56.707550+00:00", "kind": "readme", "missing": false, "url": "https://github.com/hardikkamboj/An-Introduction-to-Statistical-Learning"}], "taxonomy_version": 1}, "when_to_choose": {"confidence": null, "enriched_at": "2026-08-30T02:27:11.561446+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "bafda5cd0f94c8611495e451b1723ac5dd6a02bcfed98818793dc5859d8eac23", "fetched_at": "2026-08-28T04:06:56.707550+00:00", "kind": "readme", "missing": false, "url": "https://github.com/hardikkamboj/An-Introduction-to-Statistical-Learning"}], "taxonomy_version": 1}}, "score": {"components": {"activity": 0, "longevity": 100, "rhythm": 35}, "computed_at": "2026-09-02T17:46:02.011165+00:00", "flags": ["no_releases", "no_license"], "formula": "round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)", "inputs": {"age_days": 2272, "days_push": 709, "days_rel": null, "gap_med": null, "n_releases_24m": 0}, "score": 32, "version": 2}, "staleness": {"enrichment_outdated": false, "low_confidence": false, "scrape_days": 9, "stale_scrape": false}}