Added pipeline for phishing website detection

Prediction Models/phishing_detection/README.md

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+# Phishing URL Detector
+
+A machine learning-based system that detects potential phishing URLs by analyzing various URL features. The system uses a Voting Classifier model trained on multiple URL characteristics to determine if a URL is legitimate or potentially malicious.
+
+## Features
+
+- Real-time URL analysis
+- 30 different feature extractions including:
+  - Domain-based features
+  - URL-based features
+  - HTML and JavaScript features
+  - Domain age and registration features
+- User-friendly command-line interface
+- Clear warning system for potentially malicious URLs
+- Continuous checking capability
+
+## Prerequisites
+
+```bash
+pip install joblib numpy requests beautifulsoup4 whois python-whois dnspython tldextract
+```
+
+## Installation
+
+1. Clone the repository:
+
+```bash
+git clone https://github.com/yourusername/phishing-detector.git
+cd phishing-detector
+```
+
+2. Install required packages:
+
+```bash
+pip install -r requirements.txt
+```
+
+3. Ensure all files are in the same directory:
+   - `voting_classifier_model.pkl` (trained model)
+   - `features_extraction.py` (feature extraction code)
+   - `predict.py` (prediction script)
+
+## Usage
+
+1. Run the prediction script:
+
+```bash
+python predict.py
+```
+
+2. Enter the URL you want to check when prompted:
+
+```
+Enter URL to check (or 'quit' to exit): https://example.com
+```
+
+3. The system will analyze the URL and provide one of two responses:
+
+   - ✅ This URL appears to be LEGITIMATE
+   - ⚠️ Warning: This URL is potentially PHISHING
+
+4. You can choose to check another URL or exit the program.
+
+## How It Works
+
+1. **Feature Extraction**: The system extracts 30 different features from the provided URL using the `features_extraction.py` module, including:
+
+   - IP address presence
+   - URL length
+   - Shortening service usage
+   - '@' symbol presence
+   - SSL certificate status
+   - Domain age
+   - HTML/JavaScript features
+   - And many more...
+
+2. **Prediction**: The extracted features are processed by a trained Voting Classifier model that combines multiple machine learning algorithms to make a final prediction.
+
+## Error Handling
+
+The system includes robust error handling for:
+
+- Invalid URLs
+- Network connection issues
+- Feature extraction failures
+- Model loading problems
+
+## Contributing
+
+1. Fork the repository
+2. Create your feature branch (`git checkout -b feature/AmazingFeature`)
+3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)
+4. Push to the branch (`git push origin feature/AmazingFeature`)
+5. Open a Pull Request
+
+## License
+
+This project is licensed under the MIT License - see the LICENSE file for details.
+
+## Disclaimer
+
+This tool is for educational and research purposes only. While it can help identify potential phishing URLs, it should not be the sole factor in determining a URL's legitimacy. Always exercise caution when visiting unknown websites.
+
+## Acknowledgments
+
+- Feature extraction methods based on common phishing detection techniques
+- Model trained on [dataset reference]
+- Thanks to all contributors who participated in this project