https://github.com/md-emon-hasan/ml-project-t20-cricket-match-score-prediction
🏏 Data Science projects that predicts cricket scores based on input parameters as batting team, bowling team, and current match conditions.
https://github.com/md-emon-hasan/ml-project-t20-cricket-match-score-prediction
artificial-intelligence cricket cricket-prediction cricket-scorecard data-science predictive-modeling sports
Last synced: 27 days ago
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🏏 Data Science projects that predicts cricket scores based on input parameters as batting team, bowling team, and current match conditions.
- Host: GitHub
- URL: https://github.com/md-emon-hasan/ml-project-t20-cricket-match-score-prediction
- Owner: Md-Emon-Hasan
- License: mit
- Created: 2024-08-12T17:42:01.000Z (about 1 year ago)
- Default Branch: master
- Last Pushed: 2024-08-14T19:22:28.000Z (about 1 year ago)
- Last Synced: 2024-11-13T20:42:11.566Z (11 months ago)
- Topics: artificial-intelligence, cricket, cricket-prediction, cricket-scorecard, data-science, predictive-modeling, sports
- Language: Jupyter Notebook
- Homepage: https://t20-cricket-match-score-prediction.onrender.com/
- Size: 15.3 MB
- Stars: 1
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# T20 Cricket Match Score Prediction
Welcome to the **T20-Cricket-Match-Score-Prediction** repository! This project focuses on predicting scores for T20 cricket matches using machine learning techniques. The application leverages machine learning models to forecast match outcomes based on various features, providing valuable insights for cricket enthusiasts and analysts.
## 📋 Contents
- [Introduction](#introduction)
- [Topics Covered](#topics-covered)
- [Getting Started](#getting-started)
- [Live Demo](#live-demo)
- [Best Practices](#best-practices)
- [FAQ](#faq)
- [Troubleshooting](#troubleshooting)
- [Contributing](#contributing)
- [Additional Resources](#additional-resources)
- [Challenges Faced](#challenges-faced)
- [Lessons Learned](#lessons-learned)
- [Why I Created This Repository](#why-i-created-this-repository)
- [License](#license)
- [Contact](#contact)
---
## 📖 Introduction
This repository features a project aimed at predicting scores for T20 cricket matches using a machine learning model. The project includes data preprocessing, model training, and deployment aspects, demonstrating the use of machine learning for sports analytics and prediction.
---
## 🔍 Topics Covered
- **Machine Learning Models:** Implementing models for match score prediction.
- **Data Preprocessing:** Techniques for preparing cricket match data for modeling.
- **Feature Engineering:** Creating and selecting features for better model performance.
- **Model Evaluation:** Assessing the performance of the prediction model.
- **Deployment:** Deploying the model using Flask for web-based interaction.
---
## 🚀 Getting Started
To get started with this project, follow these steps:
1. **Clone the repository:**
```bash
git clone https://github.com/Md-Emon-Hasan/T20-Cricket-Match-Score-Prediction.git
```
2. **Navigate to the project directory:**
```bash
cd T20-Cricket-Match-Score-Prediction
```
3. **Create a virtual environment and activate it:**
```bash
python -m venv venv
source venv/bin/activate # On Windows use `venv\Scripts\activate`
```
4. **Install the dependencies:**
```bash
pip install -r requirements.txt
```
5. **Run the application:**
```bash
python app.py
```
6. **Open your browser and visit:**
```
http://127.0.0.1:5000/
```
---
## 🎉 Live Demo
Check out the live version of the T20 Cricket Match Score Prediction app [here](https://t20-cricket-match-score-prediction.onrender.com/).
---
## 🌟 Best Practices
Recommendations for maintaining and improving this project:
- **Model Updating:** Regularly update the model with new match data to keep predictions accurate.
- **Error Handling:** Implement robust error handling for both user input and system errors.
- **Security:** Secure the Flask application by implementing proper validation and HTTPS in production.
- **Documentation:** Keep the documentation up-to-date for better usability and future enhancements.
---
## ❓ FAQ
**Q: What is the purpose of this project?**
A: This project aims to predict scores for T20 cricket matches using machine learning, providing insights for cricket enthusiasts and analysts.
**Q: How can I contribute to this repository?**
A: Please refer to the [Contributing](#contributing) section for guidelines on contributing.
**Q: Where can I learn more about machine learning?**
A: Explore resources like [Scikit-learn Documentation](https://scikit-learn.org/stable/user_guide.html) and [Kaggle](https://www.kaggle.com/learn/overview) to expand your knowledge.
**Q: Can I deploy this app on cloud platforms?**
A: Yes, you can deploy the Flask app on platforms such as Heroku, Render, or AWS.
---
## 🛠️ Troubleshooting
Common issues and their solutions:
- **Issue: Flask App Not Starting**
*Solution:* Ensure that all dependencies are installed and the virtual environment is activated properly.
- **Issue: Model Not Loading**
*Solution:* Verify the path to the model file and ensure it is accessible and not corrupted.
- **Issue: Inaccurate Predictions**
*Solution:* Check if the input features are correctly formatted and the model is well-trained.
---
## 🤝 Contributing
Contributions are welcome! Here's how you can contribute:
1. **Fork the repository.**
2. **Create a new branch:**
```bash
git checkout -b feature/new-feature
```
3. **Make your changes:**
- Add new features, fix bugs, or enhance documentation.
4. **Commit your changes:**
```bash
git commit -am 'Add a new feature or update'
```
5. **Push to the branch:**
```bash
git push origin feature/new-feature
```
6. **Submit a pull request.**
---
## 📚 Additional Resources
Explore these resources for more insights into machine learning and Flask development:
- **Flask Official Documentation:** [flask.palletsprojects.com](https://flask.palletsprojects.com/)
- **Machine Learning Tutorials:** [Kaggle](https://www.kaggle.com/learn/overview)
- **Data Science Resources:** [Towards Data Science](https://towardsdatascience.com/)
---
## 💪 Challenges Faced
Some challenges during development:
- Handling large datasets and feature engineering.
- Ensuring accurate model predictions and proper evaluation.
- Deploying the application and managing dependencies.
---
## 📚 Lessons Learned
Key takeaways from this project:
- Effective use of machine learning for sports score prediction.
- Importance of thorough data preprocessing and feature engineering.
- Deployment considerations and challenges for web applications.
---
## 🌟 Why I Created This Repository
This repository was created to showcase a practical application of machine learning for predicting cricket match scores. It demonstrates how to build, train, and deploy a predictive model using Flask.
---
## 📝 License
This repository is licensed under the [MIT License](https://opensource.org/licenses/MIT). See the [LICENSE](LICENSE) file for more details.
---
## 📬 Contact
- **Email:** [iconicemon01@gmail.com](mailto:iconicemon01@gmail.com)
- **WhatsApp:** [+8801834363533](https://wa.me/8801834363533)
- **GitHub:** [Md-Emon-Hasan](https://github.com/Md-Emon-Hasan)
- **LinkedIn:** [Md Emon Hasan](https://www.linkedin.com/in/md-emon-hasan)
- **Facebook:** [Md Emon Hasan](https://www.facebook.com/mdemon.hasan2001/)
---
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