🏏 Cricket Match Score Prediction System 🚀

Table of Contents

• Overview
• Features
• Installation
• Usage
• Preview
• Model
• URLs
• Contributing
• License
• Contact

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📌 Overview

A machine learning-powered web application to predict final cricket scores based on:

• Current runs, overs played, wickets lost
• Batting team, bowling team, venue
• Smart features: run rate, projected score, pressure factor, death overs flag

✨ Built using Python, scikit-learn, and Streamlit.
🌐 Live Demo: cricketscorepredictionsystem.streamlit.app

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✨ Features

✅ Predicts realistic final cricket scores (average, min & max)
✅ Shows upcoming overs prediction as bar graph
✅ Clean, modern, mobile-friendly UI
✅ View sample dataset
✅ Lightweight, fast, easy to deploy anywhere

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⚙ Installation (Step-by-Step)

✅ Step 1: Clone the repository

git clone https://github.com/dhruvpatel16120/Cricket-Score-Prediction-System.git
cd Cricket-Score-Prediction-System

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✅ Step 2: Create & activate virtual environment

🪟 Windows

python -m venv env
env\Scripts\activate

🐧 Linux / macOS

python3 -m venv env
source env/bin/activate

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✅ Step 3: Install dependencies

pip install -r requirements.txt

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✅ Step 4: Train the ML model

python train_model.py

⚠️Saves model & preprocessor to data/ folder

✅ Step 5: Run the Streamlit app

streamlit run Cricket-Score-Prediction.py

✅ Then open your browser

go to http://localhost:8501

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📂 Project Structure

📂 data/
   ├── ipl.csv               # Cleaned IPL dataset
   ├── best_model.pkl        # Trained ML model
   └── preprocessor.pkl      # Saved preprocessor
📂 preview/                   # Screenshots for README
train_model.py               # Model training script
Cricket-Score-Prediction.py  # Streamlit app
requirements.txt
README.md
LICENSE
.gitignore

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🚀 Usage

1. Train the model:

python train_model.py

2. Run the Streamlit app:

streamlit run Cricket-Score-Prediction.py

3. Open your browser:
   Go to http://localhost:8501

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📸 Preview of Web APP

🏏 Main UI	📊 Prediction Output

📈 Upcoming Overs Graph	📂 View Dataset

⚠️ Screenshots stored in preview/ folder)

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🤖 Model

• Gradient Boosting Regressor (lightweight, fast, robust)
• One-Hot Encoding for teams & venue
• Standard scaling for numerical features
• Smart derived features:
  • current_run_rate
  • projected_score
  • pressure_factor
  • is_death_overs

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🌐 URLs

• 🚀 Live App: cricketscorepredictionsystem.streamlit.app
• 📦 GitHub Repo: Cricket-Score-Prediction-System
• 🧑‍💻 Portfolio: dhruvpatelofficial.vercel.app

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🤝 Contributing

Contributions, ideas, bug reports and improvements are always welcome! 🚀

To contribute to this project:

1. Fork the repository
   Click the Fork button at the top right of this page.

2. Clone your fork locally:

   git clone https://github.com/dhruvpatel16120/Cricket-Score-Prediction-System.git
   cd Cricket-Score-Prediction-System

3. Create a new branch:

   git checkout -b feature-branch

4. Make your changes

5. Commit your changes:

   git commit -m "Add: meaningful description of your change"

6. Push to your fork:

   git push origin feature-branch

7. Open a Pull Request
   Go to your fork on GitHub → click "Compare & pull request" → describe your changes → Create pull request.

✅ Tips for great contributions:

• Follow the existing code style & formatting
• Add helpful comments where needed
• Update README.md if you add or change features
• Test everything locally before submitting

⭐ Thank you for helping improve this project! Your contribution makes it better for everyone. 🌱✨

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📄 License

• This project is licensed under the MIT License – see LICENSE.

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✏ Contact

Created by: Dhruv Patel

⭐ Star the repo if you like it!
💡 Feedback & suggestions are welcome!

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