{"id":18767145,"url":"https://github.com/abhi-lab2/ipl-data-analysis","last_synced_at":"2026-04-14T06:33:13.344Z","repository":{"id":166487087,"uuid":"290757393","full_name":"Abhi-lab2/IPL-data-Analysis","owner":"Abhi-lab2","description":"IPL data analysis for future predictions","archived":false,"fork":false,"pushed_at":"2022-05-04T15:40:55.000Z","size":138,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-10-24T21:32:00.135Z","etag":null,"topics":["data-analysis","data-science","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":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Abhi-lab2.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":"2020-08-27T11:28:24.000Z","updated_at":"2022-05-04T15:37:13.000Z","dependencies_parsed_at":"2023-06-04T22:45:14.108Z","dependency_job_id":null,"html_url":"https://github.com/Abhi-lab2/IPL-data-Analysis","commit_stats":null,"previous_names":["Abhi-lab2/IPL-data-Analysis"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Abhi-lab2/IPL-data-Analysis","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Abhi-lab2%2FIPL-data-Analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Abhi-lab2%2FIPL-data-Analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Abhi-lab2%2FIPL-data-Analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Abhi-lab2%2FIPL-data-Analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Abhi-lab2","download_url":"https://codeload.github.com/Abhi-lab2/IPL-data-Analysis/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Abhi-lab2%2FIPL-data-Analysis/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31785630,"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":["data-analysis","data-science","python"],"created_at":"2024-11-07T19:06:35.076Z","updated_at":"2026-04-14T06:33:13.339Z","avatar_url":"https://github.com/Abhi-lab2.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"## IPL-data-Analysis\n\n Intermediate Machine Learning Python Resource Sports Structured Data Technique\n\nThis article was published as a part of the Data Science Blogathon.\nIntroduction\n\nIn cricket and IPL, Data Science is used in a somewhat unique and interesting manner. In 2008, IPL came, which completely revolutionized the cricket world, because before IPL never had such an immense amount of money invested into cricket.\n\n  ![Landing Page](https://miro.medium.com/max/1400/1*nUNPCaiC2nHTk5Dk77rexg.png)\n\n\nData is basically information about anything. For example the no. of fruits on a tree or the flavor of your ice cream or even the no. of stars in the universe or how much % of peoples like the government. All of this is nothing but data. \nThere is an immense amount of data all around us in our lives, but simply having data around us is of no use to us. It is important for us to know what data is useful and what data should analyze and how be recognizing patterns, we can make use of that data. Let’s think, What are we going to do by counting the no. of leaves on the tree? What use would that be😅?  \nIt is useless data and is of no use to us. But the % of peoples that favor the government is useful data. It would be useful in politics. It can help the government understand what they should change and how they can transform themselves. \nThis data would come in handy during the elections but it isn’t sufficient to simply record that data if you don’t analyze it, compare it, and improve it. The recording, studying, and observing data and then using it to arrive at the decision, is called Data Science.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fabhi-lab2%2Fipl-data-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fabhi-lab2%2Fipl-data-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fabhi-lab2%2Fipl-data-analysis/lists"}