{"id":20259533,"url":"https://github.com/bdurga26/fake-news-detection","last_synced_at":"2026-04-14T10:33:11.343Z","repository":{"id":228049063,"uuid":"772964738","full_name":"BDurga26/Fake-News-Detection","owner":"BDurga26","description":"This project aims to find whether the given news is real or fake by using Machine learning Algorithms.","archived":false,"fork":false,"pushed_at":"2024-04-04T04:55:57.000Z","size":24471,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-08-27T05:26:08.998Z","etag":null,"topics":["ds","ml","numpy","pandas","python","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/BDurga26.png","metadata":{"files":{"readme":"README.md","changelog":"news.csv","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":"2024-03-16T11:17:17.000Z","updated_at":"2024-03-16T14:43:04.000Z","dependencies_parsed_at":"2025-03-03T18:50:43.499Z","dependency_job_id":null,"html_url":"https://github.com/BDurga26/Fake-News-Detection","commit_stats":null,"previous_names":["bdurga26/fake-news-detection"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/BDurga26/Fake-News-Detection","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BDurga26%2FFake-News-Detection","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BDurga26%2FFake-News-Detection/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BDurga26%2FFake-News-Detection/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BDurga26%2FFake-News-Detection/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/BDurga26","download_url":"https://codeload.github.com/BDurga26/Fake-News-Detection/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BDurga26%2FFake-News-Detection/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31793215,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-14T02:24:21.117Z","status":"ssl_error","status_checked_at":"2026-04-14T02:24:20.627Z","response_time":153,"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":["ds","ml","numpy","pandas","python","sklearn"],"created_at":"2024-11-14T11:15:14.128Z","updated_at":"2026-04-14T10:33:11.329Z","avatar_url":"https://github.com/BDurga26.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"This project aims to predict whether the given news is real or fake.\u003cbr /\u003e\nIt takes the news(String) as input and gives a string REAL or FAKE.\u003cbr /\u003e\n\n\n![p1](https://github.com/BDurga26/Fake-News-Detection/assets/103586967/86e4d0d6-2f88-48d7-99c7-6f9e4b763ea8)\n\n\nThis is the application developed using Streamlit. We can add user input to it.\n\n\n\n![p2](https://github.com/BDurga26/Fake-News-Detection/assets/103586967/806c659b-a0d2-4795-ac20-d00acf4016b3)\n\n\n\nThe algorithms used are Logistic Regression with an accuracy of 92%.\u003cbr /\u003e\nDecision Tree with an accuracy of 79%.\u003cbr /\u003e\nRandom forest with an accuracy of 89%\u003cbr /\u003e\nNaive bayes with an accuracy of 82%.\u003cbr /\u003e\nPassive Agressive with an accuracy of 93%\u003cbr /\u003e\nSVM with an accuracy of 94%.\u003cbr /\u003e\nThe SVM model is saved using joblib as it gets highest accuracy and vector file which is used to convert text to numerics.\u003cbr /\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbdurga26%2Ffake-news-detection","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbdurga26%2Ffake-news-detection","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbdurga26%2Ffake-news-detection/lists"}