{"id":22445466,"url":"https://github.com/anas436/cancer-detection-using-support-vector-machines-with-python","last_synced_at":"2026-04-15T05:32:07.639Z","repository":{"id":110394379,"uuid":"532991339","full_name":"Anas436/Cancer-Detection-using-Support-Vector-Machines-with-Python","owner":"Anas436","description":null,"archived":false,"fork":false,"pushed_at":"2022-09-05T17:14:06.000Z","size":38,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-03-27T10:48:08.847Z","etag":null,"topics":["confusion-matrix","f1-score","itertools","jaccard-score","jupyterlab","matplotlib","model-selection","numpy","pandas","pylab","python3","scipy","sklearn"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Anas436.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2022-09-05T17:10:00.000Z","updated_at":"2022-09-05T17:17:54.000Z","dependencies_parsed_at":null,"dependency_job_id":"713c1ed1-5306-4dcd-a3cf-dbd3d3381195","html_url":"https://github.com/Anas436/Cancer-Detection-using-Support-Vector-Machines-with-Python","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Anas436/Cancer-Detection-using-Support-Vector-Machines-with-Python","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Anas436%2FCancer-Detection-using-Support-Vector-Machines-with-Python","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Anas436%2FCancer-Detection-using-Support-Vector-Machines-with-Python/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Anas436%2FCancer-Detection-using-Support-Vector-Machines-with-Python/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Anas436%2FCancer-Detection-using-Support-Vector-Machines-with-Python/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Anas436","download_url":"https://codeload.github.com/Anas436/Cancer-Detection-using-Support-Vector-Machines-with-Python/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Anas436%2FCancer-Detection-using-Support-Vector-Machines-with-Python/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31828531,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-14T18:05:02.291Z","status":"online","status_checked_at":"2026-04-15T02:00:06.175Z","response_time":63,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["confusion-matrix","f1-score","itertools","jaccard-score","jupyterlab","matplotlib","model-selection","numpy","pandas","pylab","python3","scipy","sklearn"],"created_at":"2024-12-06T03:14:34.696Z","updated_at":"2026-04-15T05:32:07.602Z","avatar_url":"https://github.com/Anas436.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Cancer-Detection-using-Support-Vector-Machines-with-Python\n\n\n\n## Objectives\n\nAfter completing this lab you will be able to:\n\n*   Use scikit-learn to Support Vector Machine to classify\n\n\nIn this notebook, you will use SVM (Support Vector Machines) to build and train a model using human cell records, and classify cells to whether the samples are benign or malignant.\n\nSVM works by mapping data to a high-dimensional feature space so that data points can be categorized, even when the data are not otherwise linearly separable. A separator between the categories is found, then the data is transformed in such a way that the separator could be drawn as a hyperplane. Following this, characteristics of new data can be used to predict the group to which a new record should belong.\n\n\n\u003ch1\u003eTable of contents\u003c/h1\u003e\n\n\u003cdiv class=\"alert alert-block alert-info\" style=\"margin-top: 20px\"\u003e\n    \u003col\u003e\n        \u003cli\u003e\u003ca href=\"https://#load_dataset\"\u003eLoad the Cancer data\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"https://#modeling\"\u003eModeling\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"https://#evaluation\"\u003eEvaluation\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"https://#practice\"\u003ePractice\u003c/a\u003e\u003c/li\u003e\n    \u003c/ol\u003e\n\u003c/div\u003e\n\u003cbr\u003e\n\u003chr\u003e\n\n\u003ch2 id=\"load_dataset\"\u003eLoad the Cancer data\u003c/h2\u003e\nThe example is based on a dataset that is publicly available from the UCI Machine Learning Repository (Asuncion and Newman, 2007). The dataset consists of several hundred human cell sample records, each of which contains the values of a set of cell characteristics. The fields in each record are:\n\n| Field name  | Description                 |\n| ----------- | --------------------------- |\n| ID          | Clump thickness             |\n| Clump       | Clump thickness             |\n| UnifSize    | Uniformity of cell size     |\n| UnifShape   | Uniformity of cell shape    |\n| MargAdh     | Marginal adhesion           |\n| SingEpiSize | Single epithelial cell size |\n| BareNuc     | Bare nuclei                 |\n| BlandChrom  | Bland chromatin             |\n| NormNucl    | Normal nucleoli             |\n| Mit         | Mitoses                     |\n| Class       | Benign or malignant         |\n\n\u003cbr\u003e\n\u003cbr\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fanas436%2Fcancer-detection-using-support-vector-machines-with-python","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fanas436%2Fcancer-detection-using-support-vector-machines-with-python","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fanas436%2Fcancer-detection-using-support-vector-machines-with-python/lists"}