https://github.com/omkar4965/ipl-win-predictor
IPL-Win-Predictor
https://github.com/omkar4965/ipl-win-predictor
machine-learning pyhton3 sklearn streamlit
Last synced: about 2 months ago
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IPL-Win-Predictor
- Host: GitHub
- URL: https://github.com/omkar4965/ipl-win-predictor
- Owner: Omkar4965
- License: mit
- Created: 2025-04-05T12:53:35.000Z (about 1 year ago)
- Default Branch: master
- Last Pushed: 2025-04-05T13:39:23.000Z (about 1 year ago)
- Last Synced: 2025-04-09T19:54:48.834Z (about 1 year ago)
- Topics: machine-learning, pyhton3, sklearn, streamlit
- Language: Python
- Homepage: https://ipl-win-predictor-omkxr.onrender.com/
- Size: 13.7 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 🏏 IPL Win Predictor
A machine learning-based Streamlit web app that predicts the probability of a team winning an IPL match based on live match conditions.
## 🔥 Why This Project?
This 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**.
## 🌐 Live Demo
👉 [Try it here](https://ipl-win-predictor-omkxr.onrender.com/)
---
## 💽 Tech Stack
- **Frontend**: Streamlit
- **Backend**: Python
- **Model**: Machine Learning with Scikit-learn
- **Deployment**: Render
---
## ✨ Getting Started
### 📁 Clone the repo
```bash
git clone https://github.com/yourusername/ipl-win-predictor.git
cd ipl-win-predictor
```
### 📦 Install dependencies
```bash
pip install -r requirements.txt
```
### ▶️ Run the application
```bash
streamlit run app.py
```
---
## 🏏 How It Works
1. **Select the teams and city**.
2. **Enter the current match situation** – target, current score, overs completed, and wickets fallen.
3. **Click “Predict Probability”**.
4. The model will output the chances of each team winning in percentage.
---
## 🧠 Model Inputs
- Batting team
- Bowling team
- Host city
- Target runs
- Current score
- Overs completed
- Wickets fallen
From these inputs, the model calculates:
- Runs left
- Balls left
- Wickets remaining
- Current Run Rate (CRR)
- Required Run Rate (RRR)
These features are then passed to a pre-trained model to compute win probabilities.
---
## 📈 Future Improvements
- Live match data integration
- Enhanced UI with match graphics
- Support for more cricket leagues
- Add confidence intervals in prediction
---
## 💚 License
This project is licensed under the **MIT License**.
---
## ✉️ Contact
For any queries or suggestions, feel free to reach out to [Omkar Chavan](https://www.linkedin.com/in/omkar-chavan-476a63249/).