{"id":27067996,"url":"https://github.com/omkar4965/ipl-win-predictor","last_synced_at":"2026-05-10T02:37:04.200Z","repository":{"id":286286203,"uuid":"960959174","full_name":"Omkar4965/IPL-Win-Predictor","owner":"Omkar4965","description":"IPL-Win-Predictor","archived":false,"fork":false,"pushed_at":"2025-04-05T13:39:23.000Z","size":14,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-04-09T19:54:48.834Z","etag":null,"topics":["machine-learning","pyhton3","sklearn","streamlit"],"latest_commit_sha":null,"homepage":"https://ipl-win-predictor-omkxr.onrender.com/","language":"Python","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/Omkar4965.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":"2025-04-05T12:53:35.000Z","updated_at":"2025-04-05T13:39:27.000Z","dependencies_parsed_at":"2025-04-05T14:29:09.249Z","dependency_job_id":null,"html_url":"https://github.com/Omkar4965/IPL-Win-Predictor","commit_stats":null,"previous_names":["omkar4965/ipl-win-predictor"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Omkar4965%2FIPL-Win-Predictor","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Omkar4965%2FIPL-Win-Predictor/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Omkar4965%2FIPL-Win-Predictor/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Omkar4965%2FIPL-Win-Predictor/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Omkar4965","download_url":"https://codeload.github.com/Omkar4965/IPL-Win-Predictor/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248103911,"owners_count":21048245,"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","pyhton3","sklearn","streamlit"],"created_at":"2025-04-05T20:17:21.901Z","updated_at":"2026-05-10T02:37:04.139Z","avatar_url":"https://github.com/Omkar4965.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 🏏 IPL Win Predictor\n\nA machine learning-based Streamlit web app that predicts the probability of a team winning an IPL match based on live match conditions.\n\n## 🔥 Why This Project?\n\nThis project helps cricket fans and analysts get a real-time prediction of a team’s winning chances during an IPL match using **Supervised Learning**. It also showcases **ML deployment using Streamlit and Render**.\n\n## 🌐 Live Demo\n\n👉 [Try it here](https://ipl-win-predictor-omkxr.onrender.com/)\n\n![image](https://github.com/user-attachments/assets/d9a88e16-070b-4da5-ba71-a8d1c77551ed)\n\n\n---\n\n## 💽 Tech Stack\n\n- **Frontend**: Streamlit  \n- **Backend**: Python  \n- **Model**: Machine Learning with Scikit-learn  \n- **Deployment**: Render\n\n---\n\n## ✨ Getting Started\n\n### 📁 Clone the repo\n\n```bash\ngit clone https://github.com/yourusername/ipl-win-predictor.git\ncd ipl-win-predictor\n```\n\n### 📦 Install dependencies\n\n```bash\npip install -r requirements.txt\n```\n\n### ▶️ Run the application\n\n```bash\nstreamlit run app.py\n```\n\n---\n\n## 🏏 How It Works\n\n1. **Select the teams and city**.\n2. **Enter the current match situation** – target, current score, overs completed, and wickets fallen.\n3. **Click “Predict Probability”**.\n4. The model will output the chances of each team winning in percentage.\n\n---\n\n## 🧠 Model Inputs\n\n- Batting team  \n- Bowling team  \n- Host city  \n- Target runs  \n- Current score  \n- Overs completed  \n- Wickets fallen  \n\nFrom these inputs, the model calculates:\n\n- Runs left  \n- Balls left  \n- Wickets remaining  \n- Current Run Rate (CRR)  \n- Required Run Rate (RRR)\n\nThese features are then passed to a pre-trained model to compute win probabilities.\n\n---\n\n## 📈 Future Improvements\n\n- Live match data integration  \n- Enhanced UI with match graphics  \n- Support for more cricket leagues  \n- Add confidence intervals in prediction\n\n---\n\n## 💚 License\n\nThis project is licensed under the **MIT License**.\n\n---\n\n## ✉️ Contact\n\nFor any queries or suggestions, feel free to reach out to [Omkar Chavan](https://www.linkedin.com/in/omkar-chavan-476a63249/).\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fomkar4965%2Fipl-win-predictor","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fomkar4965%2Fipl-win-predictor","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fomkar4965%2Fipl-win-predictor/lists"}