Sure! Here's a casual yet informative README for your Cricket Score Prediction project. Just replace [image] with your actual image.

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🏏 Cricket Score Prediction

This is a machine learning-powered Cricket Score Prediction system that analyzes past match data and predicts the final score based on various match conditions like batting team, bowling team, overs completed, wickets fallen, and recent performance.

It uses Random Forest & XGBoost models trained on 3000+ T20 matches to make accurate predictions.

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🚀 Features

✅ Predicts final score based on current match conditions
✅ Uses Random Forest & XGBoost models
✅ Streamlit-based web app for easy predictions
✅ Data extracted from YAML match files (ball-by-ball data)
✅ Feature Engineering: Considers factors like last 5 overs' performance, current run rate, and wickets left

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📂 Project Structure

📦 Score-Card-Prediction
 ┣ 📂 data/                 # Contains match data (Not included in repo)
 ┣ 📜 dataset_level1.pkl    # Preprocessed dataset (ignored in GitHub)
 ┣ 📜 dataset_level2.pkl    # Final cleaned dataset (ignored in GitHub)
 ┣ 📜 rfc.pkl               # Random Forest Model (ignored in GitHub)
 ┣ 📜 xgboost.pkl           # XGBoost Model (ignored in GitHub)
 ┣ 📜 data_extraction.ipynb # Code for data extraction from YAML files
 ┣ 📜 feature_extraction.ipynb # Code for feature engineering
 ┣ 📜 app.py                # Streamlit web app
 ┣ 📜 requirements.txt      # Python dependencies
 ┗ 📜 README.md             # You’re reading this 😉

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🛠 Setup & Run Locally

1️⃣ Clone the Repo

git clone https://github.com/your-username/Score-Card-Prediction.git
cd Score-Card-Prediction

2️⃣ Install Dependencies

pip install -r requirements.txt

3️⃣ Run the Ipynb files So it create 3 pkl files for pipeline the data and model

streamlit run app.py

4️ Run the Streamlit App

streamlit run app.py

Now, open localhost:8501 in your browser and start predicting cricket scores! 🎉

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📊 Model Performance

Model	R² Score	MAE	RMSE
Random Forest	93.87%	5.56	8.28
XGBoost	98.0%	4.2	6.5

✅ XGBoost gives better accuracy, but RFC was used for comparison.

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📌 Future Improvements

🔹 Add pitch & weather conditions for better predictions
🔹 Use LGBM or Deep Learning (LSTM) for more accurate results
🔹 Integrate live match data API for real-time score updates

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🙌 Contributions & Feedback

If you have suggestions, feel free to open an issue or drop a PR!
Let’s make this model even better. 🚀

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