SpamShield is an AI-powered email and SMS spam classifier. It helps you filter unwanted messages using advanced machine learning. With SpamShield, you can enjoy a cleaner inbox and safer replies.
- Email Classification: Automatically identifies spam emails.
- SMS Spam Detection: Filters unwanted SMS texts.
- Safe Reply Generation: Creates safe responses to messages.
- User-Friendly Interface: Built with Streamlit for easy use.
- Data Visualization: Provides insights with graphs using Matplotlib.
- Operating System: Windows, MacOS, or Linux
- Python Version: Python 3.7 or later
- Memory: At least 4 GB RAM
- Storage: 200 MB of free space
To get SpamShield, visit this page to download: Releases Page.
- Visit the Releases Page: Click the link above.
- Select the Latest Version: Look for the most recent release.
- Download the Installer: Click the file suitable for your operating system.
- Run the Installer: Follow the prompts to install SpamShield.
Once you have installed SpamShield, follow these steps to use it:
- Open the Application: Find SpamShield in your applications and launch it.
- Upload Your Dataset: Click on the upload button to select your email or SMS messages.
- Run the Classifier: Click the "Classify" button to start filtering.
- Review the Results: See the classification output on your screen. Move unwanted messages to your spam folder easily.
- Time-Saving: Quickly identify spam messages.
- Increased Privacy: Reduce the risk of phishing scams.
- User Empowerment: Control what messages you receive.
- Machine Learning Power: Enjoy cutting-edge technology without needing technical skills.
SpamShield uses the following technologies:
- Python: For building the application logic.
- Streamlit: For creating an interactive user interface.
- ML Algorithms: Specifically, TF-IDF and Naive Bayes for classification tasks.
- Pandas & Numpy: For data handling and analysis.
- Matplotlib: For data visualization.
If you encounter any issues, feel free to reach out for support. You can also contribute to the project by reporting bugs or suggesting features.
This project is open-source and available under the MIT License. You can use it freely, but please credit the original authors when sharing.
For updates and discussions, follow us on our community channels:
- GitHub Issues: Report here
- Community Forum: Join discussions about spam detection.
- Documentation: Access detailed guides and tutorials.
- FAQ: Find answers to common questions about using SpamShield.
Thank you for using SpamShield. Together, we can create a spam-free communication environment.