{"adoption": {"forks": 464, "observed_at": "2026-08-28T04:04:33.834499+00:00", "stars": 1380}, "canonical_url": "https://ross.abutalabs.com/products/data-science-competition", "card": {"archived": false, "artifact_type": "learning-resource", "description": "该仓库用于记录作者本人参加的各大数据科学竞赛的获奖方案源码以及一些新比赛的原创baseline. 主要涵盖：kaggle, 阿里天池，华为云大赛校园赛，百度aistudio，和鲸社区，datafountain等", "domain": ["data-science", "machine-learning", "computer-vision", "tutorials"], "enriched": true, "function": ["machine-learning", "data-science", "computer-vision", "nlp"], "health_score": 20, "homepage": null, "language": "Python", "license": null, "license_family": "other", "maturity": "maintenance", "member_repos": ["DLLXW/data-science-competition"], "name": "DLLXW/data-science-competition", "platform": ["python"], "pushed_at": "2023-04-21T15:26:20+00:00", "repo": "DLLXW/data-science-competition", "stars": 1380, "tags": ["kaggle", "tianchi", "competition-solutions", "baselines", "data-mining", "time-series-forecasting", "image-segmentation", "object-detection", "natural-language-processing"], "topics": [], "urls": [], "use_cases": ["learn from winning kaggle competition solutions", "find baselines for data science competitions", "study image segmentation competition code", "learn time series forecasting approaches for tianchi contests", "reference nlp text classification competition solutions", "prepare for datafountain or heywhale competitions"], "what_it_is": "A curated collection of award-winning solutions and original baselines from data science competitions on platforms like Kaggle, Alibaba Tianchi, Huawei Cloud, Baidu AIStudio, Heywhale, and DataFountain. It covers computer vision, data mining, time-series forecasting, and NLP tasks with full source code.", "when_to_avoid": ["you need a maintained, installable library with an API", "you want production-ready or licensed code for reuse", "you need up-to-date solutions for recent competitions"], "when_to_choose": ["you want real-world competition-winning code to study", "you need a starting baseline for a similar competition task", "you are learning practical ML across CV, NLP, and tabular data"]}, "data_as_of": "2026-08-30T08:39:29.467469+00:00", "members": [{"path": "/products/data-science-competition", "repo": "DLLXW/data-science-competition", "role": "main", "score": 32}], "provenance": {"archived": {"kind": "observed", "observed_at": "2026-08-28T04:04:33.834499+00:00", "source": "github"}, "artifact_type": {"confidence": null, "enriched_at": "2026-08-30T04:40:13.555686+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "ad8db85019c8e4dd616f6da0da43ba656da58bdc24370f986703f41d33a6d84d", "fetched_at": "2026-08-28T04:04:33.834499+00:00", "kind": "readme", "missing": false, "url": "https://github.com/DLLXW/data-science-competition"}], "taxonomy_version": 1}, "description": {"kind": "observed", "observed_at": "2026-08-28T04:04:33.834499+00:00", "source": "github"}, "domain": {"confidence": null, "enriched_at": "2026-08-30T04:40:13.555686+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "ad8db85019c8e4dd616f6da0da43ba656da58bdc24370f986703f41d33a6d84d", "fetched_at": "2026-08-28T04:04:33.834499+00:00", "kind": "readme", "missing": false, "url": "https://github.com/DLLXW/data-science-competition"}], "taxonomy_version": 1}, "enriched": {"inputs": [], "kind": "computed", "method": "enrichment_status"}, "function": {"confidence": null, "enriched_at": "2026-08-30T04:40:13.555686+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "ad8db85019c8e4dd616f6da0da43ba656da58bdc24370f986703f41d33a6d84d", "fetched_at": "2026-08-28T04:04:33.834499+00:00", "kind": "readme", "missing": false, "url": "https://github.com/DLLXW/data-science-competition"}], "taxonomy_version": 1}, "health_score": {"inputs": ["days_since_push", "days_since_release", "archived"], "kind": "computed", "method": "health_v1"}, "homepage": {"kind": "observed", "observed_at": "2026-08-28T04:04:33.834499+00:00", "source": "github"}, "language": {"kind": "observed", "observed_at": "2026-08-28T04:04:33.834499+00:00", "source": "github"}, "license": {"kind": "observed", "observed_at": "2026-08-28T04:04:33.834499+00:00", "source": "github"}, "license_family": {"inputs": ["license"], "kind": "computed", "method": "license_family"}, "maturity": {"confidence": null, "enriched_at": "2026-08-30T04:40:13.555686+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "ad8db85019c8e4dd616f6da0da43ba656da58bdc24370f986703f41d33a6d84d", "fetched_at": "2026-08-28T04:04:33.834499+00:00", "kind": "readme", "missing": false, "url": "https://github.com/DLLXW/data-science-competition"}], "taxonomy_version": 1}, "member_repos": {"kind": "observed", "observed_at": "2026-08-28T04:04:33.834499+00:00", "source": "github"}, "name": {"kind": "observed", "observed_at": "2026-08-28T04:04:33.834499+00:00", "source": "github"}, "platform": {"confidence": null, "enriched_at": "2026-08-30T04:40:13.555686+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "ad8db85019c8e4dd616f6da0da43ba656da58bdc24370f986703f41d33a6d84d", "fetched_at": "2026-08-28T04:04:33.834499+00:00", "kind": "readme", "missing": false, "url": "https://github.com/DLLXW/data-science-competition"}], "taxonomy_version": 1}, "pushed_at": {"kind": "observed", "observed_at": "2026-08-28T04:04:33.834499+00:00", "source": "github"}, "repo": {"kind": "observed", "observed_at": "2026-08-28T04:04:33.834499+00:00", "source": "github"}, "stars": {"kind": "observed", "observed_at": "2026-08-28T04:04:33.834499+00:00", "source": "github"}, "tags": {"confidence": null, "enriched_at": "2026-08-30T04:40:13.555686+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "ad8db85019c8e4dd616f6da0da43ba656da58bdc24370f986703f41d33a6d84d", "fetched_at": "2026-08-28T04:04:33.834499+00:00", "kind": "readme", "missing": false, "url": "https://github.com/DLLXW/data-science-competition"}], "taxonomy_version": 1}, "topics": {"kind": "observed", "observed_at": "2026-08-28T04:04:33.834499+00:00", "source": "github"}, "urls": {"kind": "observed", "observed_at": "2026-08-28T04:04:33.834499+00:00", "source": "github"}, "use_cases": {"confidence": null, "enriched_at": "2026-08-30T04:40:13.555686+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "ad8db85019c8e4dd616f6da0da43ba656da58bdc24370f986703f41d33a6d84d", "fetched_at": "2026-08-28T04:04:33.834499+00:00", "kind": "readme", "missing": false, "url": "https://github.com/DLLXW/data-science-competition"}], "taxonomy_version": 1}, "what_it_is": {"confidence": null, "enriched_at": "2026-08-30T04:40:13.555686+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "ad8db85019c8e4dd616f6da0da43ba656da58bdc24370f986703f41d33a6d84d", "fetched_at": "2026-08-28T04:04:33.834499+00:00", "kind": "readme", "missing": false, "url": "https://github.com/DLLXW/data-science-competition"}], "taxonomy_version": 1}, "when_to_avoid": {"confidence": null, "enriched_at": "2026-08-30T04:40:13.555686+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "ad8db85019c8e4dd616f6da0da43ba656da58bdc24370f986703f41d33a6d84d", "fetched_at": "2026-08-28T04:04:33.834499+00:00", "kind": "readme", "missing": false, "url": "https://github.com/DLLXW/data-science-competition"}], "taxonomy_version": 1}, "when_to_choose": {"confidence": null, "enriched_at": "2026-08-30T04:40:13.555686+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "ad8db85019c8e4dd616f6da0da43ba656da58bdc24370f986703f41d33a6d84d", "fetched_at": "2026-08-28T04:04:33.834499+00:00", "kind": "readme", "missing": false, "url": "https://github.com/DLLXW/data-science-competition"}], "taxonomy_version": 1}}, "score": {"components": {"activity": 0, "longevity": 100, "rhythm": 35}, "computed_at": "2026-09-03T02:39:23.370411+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": 2149, "days_push": 1230, "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}}