Cricket Match Score Prediction System

Table of Contents

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

Overview

The Cricket Match Score Prediction System is a machine learning application designed to predict the score of a cricket match based on various inputs such as overs played, current runs, wickets lost, and more. This application uses a Random Forest Regressor model for prediction and provides a user-friendly interface using Streamlit.

Features

• Predicts the final score of a cricket match based on user inputs.
• Displays model performance metrics including R-squared, Mean Squared Error, Mean Absolute Error, and Root Mean Squared Error.
• Visualizes the actual vs. predicted scores.
• Interactive and user-friendly web interface.
• Footer with a stylish, animated underline effect for the creator's portfolio link.

Installation

1. Clone the repository:

   git clone https://github.com/dhruvpatel16120/cricket-score-prediction.git
   cd cricket-score-prediction
   

2. Create a virtual environment:

   python -m venv env
   source env/bin/activate  # On Windows use `env\Scripts\activate`
   

3. Install dependencies:

   pip install -r requirements.txt
   

4. Ensure the dataset file is available: Place your ipl.csv file in the appropriate directory.

Usage

1. Run the Streamlit app:

   streamlit run Cricket-Score-Prediction.py
   

2. Open your browser: Navigate to http://localhost:8501 to interact with the application.

Model

The model used for prediction is a Random Forest Regressor. The following steps are performed during the model training and prediction:

1. Data Preprocessing:

   • One-Hot Encoding for categorical features.
   • Standard Scaling for numerical features.
   • Polynomial Features to capture interactions.

2. Model Training:

   • Hyperparameter tuning using Grid Search CV.
   • Model evaluation using metrics such as R-squared, Mean Squared Error, and more.

URLs

The application is deployed and can be accessed at the following URLs:

• Website

Contributing

Contributions are welcome! Please follow these steps to contribute:

1. Fork the repository.
2. Create a new branch:

   git checkout -b feature-branch
   

3. Make your changes and commit them:

   git commit -m 'Add new feature'
   

4. Push to the branch:

   git push origin feature-branch
   

5. Create a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

Created by Dhruv Patel - Portfolio

If you have any questions or suggestions, feel free to reach out!