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| title: Spam Email Detection | |
| emoji: 💌 | |
| colorFrom: pink | |
| colorTo: blue | |
| sdk: gradio | |
| sdk_version: 3.17.0 | |
| app_file: app.py | |
| # Email Spam and Phishing URL Detection | |
| This project utilizes Naive Bayes classification to detect whether an email is spam or not, and XGBoost classification to determine if a URL within an email is phishing or legitimate. | |
| # Getting Started | |
| ## Project Overview | |
| The project consists of two main components: | |
| 1. **Email Spam Detection**: This component employs Naive Bayes classification to classify emails as either spam or not spam based on their content features. | |
| 2. **Phishing URL Detection**: This component uses XGBoost classification to identify whether URLs within emails are associated with phishing attempts or legitimate websites. | |
| ## Prerequisites | |
| Make sure you have Python 3.10 installed on your system. You can download it from [](python.org) | |
| ## Requirements | |
| Ensure you have the following dependencies installed. You can install them using `pip install -r requirements.txt`. | |
| - gunicorn==22.0.0 | |
| - python-dateutil==2.8.2 | |
| - gradio==4.32.1 | |
| - gradio_client==0.17.0 | |
| - requests==2.31.0 | |
| - beautifulsoup4==4.12.3 | |
| - googlesearch_python==1.2.4 | |
| - urlextract==1.9.0 | |
| - numpy==1.26.3 | |
| - pandas==2.2.0 | |
| - scikit-learn==1.5.0 | |
| - urllib3==2.1.0 | |
| - python-whois==0.9.4 | |
| - xgboost==2.0.3 | |
| - lxml==5.2.2 | |
| ## Setup and Installation | |
| 1. Clone the repository: | |
| ```bash | |
| git clone https://github.com/your-username/email-spam-phishing-detection.git | |
| cd email-spam-phishing-detection | |
| 2. Install dependencies: | |
| ```bash | |
| pip install -r requirements.txt``` | |
| ## Usage | |
| 1. **Data Preparation:** | |
| - Ensure the datasets `spam.csv` and `urldata.csv` are available in the `data/` directory. | |
| 2. **Model Training:** | |
| - If necessary, modify and run the `notebook.ipynb` Jupyter notebook to train or fine-tune the machine learning models. | |
| - Trained models will be saved in the `models/` directory. | |
| 3. **Run the Application:** | |
| - Execute `app.py` to start the application. | |
| - Access the application at [Hugging Face Space](https://huggingface.co/spaces/akshatsanghvi/spam-email-detection) | |
| ## Acknowledgements | |
| - The email spam classification model is trained using the `spam.csv` dataset, sourced from [Dataset: Spam/ham mail](https://www.kaggle.com/datasets/mfaisalqureshi/spam-email?resource=download)). | |
| - The URL phishing detection model is trained using the `urldata.csv` dataset, sourced from [Phishing Websites Dataset](https://www.kaggle.com/datasets). | |
| ## License | |
| This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. | |