{"id":15132664,"url":"https://github.com/dipeshgoyal013/ipl_win_probability","last_synced_at":"2026-02-05T05:35:09.069Z","repository":{"id":257403902,"uuid":"857721014","full_name":"dipeshgoyal013/IPL_Win_probability","owner":"dipeshgoyal013","description":"A project which help you to check win probability of batting team in inning 2nd","archived":false,"fork":false,"pushed_at":"2024-09-25T11:02:05.000Z","size":1771,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-05T21:44:49.947Z","etag":null,"topics":["machine-learning","matplotlib","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/dipeshgoyal013.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":"2024-09-15T12:38:57.000Z","updated_at":"2024-09-25T11:02:08.000Z","dependencies_parsed_at":null,"dependency_job_id":"655d9b03-f06a-4095-9ff5-a9c54aa60ce5","html_url":"https://github.com/dipeshgoyal013/IPL_Win_probability","commit_stats":{"total_commits":4,"total_committers":1,"mean_commits":4.0,"dds":0.0,"last_synced_commit":"dffba1d3e1bebab32a0281bdccab92f65deb3e17"},"previous_names":["dipeshgoyal013/ipl_win_probability"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dipeshgoyal013%2FIPL_Win_probability","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dipeshgoyal013%2FIPL_Win_probability/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dipeshgoyal013%2FIPL_Win_probability/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dipeshgoyal013%2FIPL_Win_probability/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/dipeshgoyal013","download_url":"https://codeload.github.com/dipeshgoyal013/IPL_Win_probability/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247406068,"owners_count":20933802,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","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":["machine-learning","matplotlib","numpy","pandas","python","sklearn"],"created_at":"2024-09-26T04:22:11.642Z","updated_at":"2026-02-05T05:35:08.964Z","avatar_url":"https://github.com/dipeshgoyal013.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# IPL_Win_probability\nWelcome to the \"IPL Win Predictor\" project! This machine learning model, built using logistic regression, predicts the probability of a team winning an IPL match based on the current match situation. Get ready to make data-driven predictions!\n\n## About This Project\nThe \"IPL Win Predictor\" leverages logistic regression to provide insights into the probability of a team winning an IPL match. This model analyzes various match features, team performance, and player statistics to offer real-time predictions.\n\n## Explore the Project\n\n### Features \n* Real-Time Predictions: Get live predictions for IPL match outcomes based on the current match situation.\n\n* Interactive Interface: The predictor is deployed on Streamlit, offering a user-friendly interface for exploring match scenarios.\n\n* Customizable Inputs: Adjust the match parameters and teams to simulate different match scenarios.\n\n### Usage\nTo make predictions, provide the following parameters when prompted:\n\n* Batting Team: The team currently at bat.\n* Bowling Team: The team currently bowling.\n* City: The location of the match.\n* Current runs: The current score of batting team.\n* Overs Completed: The number of overs completed.\n* Wickets: The number of wickets lost.\n* Target Runs: The total runs scored by a bowling team.\nThe predictor will calculate the probability of the batting team winning based on these parameters and the current match situation.\n\n## Technologies Used\nThis project leverages the following technologies:\n\n* Python\n* Logistic Regression\n* NumPy\n* pandas\n* sklearn\n\n## Predict with Confidence\nExplore the \"IPL Win Predictor\" and make data-driven predictions about IPL match outcomes. Get real-time insights and enhance your understanding of match dynamics.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdipeshgoyal013%2Fipl_win_probability","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdipeshgoyal013%2Fipl_win_probability","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdipeshgoyal013%2Fipl_win_probability/lists"}