{"id":20862835,"url":"https://github.com/quickheaven/scs-3251-statistics-for-data-science","last_synced_at":"2026-04-10T11:01:19.676Z","repository":{"id":181623272,"uuid":"659481966","full_name":"quickheaven/scs-3251-statistics-for-data-science","owner":"quickheaven","description":"Repository dedicated to Term Project of UofT Statistics for Data Science Course","archived":false,"fork":false,"pushed_at":"2023-09-02T00:25:14.000Z","size":6778,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-12-29T06:21:29.255Z","etag":null,"topics":["data-science","jupyter-notebook","logistic-regression","numpy","pandas","python"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/quickheaven.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","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":"2023-06-27T23:59:22.000Z","updated_at":"2023-07-16T15:47:32.000Z","dependencies_parsed_at":null,"dependency_job_id":"c918c310-237b-4e4f-844c-cf417c8ae974","html_url":"https://github.com/quickheaven/scs-3251-statistics-for-data-science","commit_stats":null,"previous_names":["quickheaven/scs-3251-statistics-for-data-science"],"tags_count":3,"template":false,"template_full_name":null,"purl":"pkg:github/quickheaven/scs-3251-statistics-for-data-science","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/quickheaven%2Fscs-3251-statistics-for-data-science","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/quickheaven%2Fscs-3251-statistics-for-data-science/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/quickheaven%2Fscs-3251-statistics-for-data-science/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/quickheaven%2Fscs-3251-statistics-for-data-science/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/quickheaven","download_url":"https://codeload.github.com/quickheaven/scs-3251-statistics-for-data-science/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/quickheaven%2Fscs-3251-statistics-for-data-science/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31639524,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-10T07:40:12.752Z","status":"ssl_error","status_checked_at":"2026-04-10T07:40:11.664Z","response_time":98,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"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":["data-science","jupyter-notebook","logistic-regression","numpy","pandas","python"],"created_at":"2024-11-18T05:26:06.152Z","updated_at":"2026-04-10T11:01:19.657Z","avatar_url":"https://github.com/quickheaven.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Spam Email Classification\nSCS 3251 Statistics for Data Science Project\n\nJupyter Notebooks:\n* [Project Spambase](https://nbviewer.org/github/quickheaven/scs-3251-statistics-for-data-science/blob/5721d97fecd5b56f98a38e33a36ee0b459aff6af/project_spambase_nb.ipynb)\n* [Spambase Dataset Loader](https://nbviewer.org/github/quickheaven/scs-3251-statistics-for-data-science/blob/5721d97fecd5b56f98a38e33a36ee0b459aff6af/spambase_dataset_loader_nb.ipynb)\n* [Spambase Model Helper](https://nbviewer.org/github/quickheaven/scs-3251-statistics-for-data-science/blob/5721d97fecd5b56f98a38e33a36ee0b459aff6af/spambase_model_helper_nb.ipynb)\n\nTeam members:\n\n| Name | Github Repo |\n| --- | --- |\n| Arjie Cristobal  | https://github.com/quickheaven |\n\n\n## Introduction\n\n### Spambase Dataset\nThe \"spam\" concept is diverse: advertisements for products/web sites, make money fast schemes, chain letters, pornography...\n\t\nOur collection of spam e-mails came from our postmaster and individuals who had filed spam.  Our collection of non-spam e-mails came from filed work and personal e-mails, and hence the word 'george' and the area code '650' are indicators of non-spam.  These are useful when constructing a personalized spam filter.  One would either have to blind such non-spam indicators or get a very wide collection of non-spam to generate a general purpose spam filter.\n\n\n### Dataset Source:\n\n* Hopkins,Mark, Reeber,Erik, Forman,George, and Suermondt,Jaap. (1999). Spambase. UCI Machine Learning Repository. https://doi.org/10.24432/C53G6X.\n\nLink: [Spambase](https://archive.ics.uci.edu/dataset/94/spambase)\n\n## Presentation\n\u003ca href=\"https://github.com/quickheaven/scs-3251-statistics-for-data-science/blob/ae9b29e4606362d7cd49f7f42b29bd5c4e4b60c6/documents/Spam%20Email%20Classification%20-%20Arjie%20Cristobal.pptx\"\u003e\n\t\u003cimg src=\"./images/Presentation_Front.PNG\" width=\"800\" height=\"600\" /\u003e\n\u003c/a\u003e\n\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fquickheaven%2Fscs-3251-statistics-for-data-science","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fquickheaven%2Fscs-3251-statistics-for-data-science","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fquickheaven%2Fscs-3251-statistics-for-data-science/lists"}