π¨ email-spam-detector - Detect Spam Easily and Swiftly

π Overview
The Email Spam Detector helps you identify spam in emails and SMS messages. It uses machine learning to classify messages as spam or not spam. With a user-friendly web form and a JSON endpoint, it provides an easy way to make predictions. Simply enter your message, and the tool will analyze it for you.
π Getting Started
To get started with the Email Spam Detector, follow these steps:
- Visit the Releases Page: Go to the Releases page.
- Download the Application: Choose the latest version available and download it by clicking the download link for your operating system.
- Follow Installation Instructions: After downloading, follow the instructions provided on the releases page to install the application.
π» System Requirements
- Operating System: Windows 10 or later, macOS, or a compatible Linux distribution.
- Memory: At least 4 GB of RAM.
- Storage: Minimum of 100 MB of free disk space.
- Python: Version 3.6 or higher (comes with integrated installation).
π₯ Download & Install
- Download the Application:
- Visit the Releases page.
- Locate the latest release and download the file suitable for your operating system.
- Install the Application:
- Once downloaded, open the file you downloaded.
- Follow the on-screen instructions. This will involve clicking βNextβ a few times and accepting the terms.
- Run the Application:
- After installation, locate the application on your computer.
- Double-click the icon to launch the Email Spam Detector.
π How to Use
- Open the Application: Start the Email Spam Detector.
- Input your Message: In the web form, type or paste the email or SMS content you want to analyze.
- Submit for Analysis: Click the βAnalyzeβ button.
- View Results: The application will show you if the message is spam or not along with a confidence score.
βοΈ Features
- Web Form: Easily input messages for quick analysis.
- JSON Endpoint: Get predictions programmatically if you use other applications that support JSON.
- Machine Learning Models: Utilizes TF-IDF with SVM, Logistic Regression, and Naive Bayes for accurate spam detection.
π€ How the Spam Detector Works
The Email Spam Detector analyzes your text using the following steps:
- Text Processing: Text is cleaned and formatted for analysis.
- Feature Extraction: The application extracts important features from the text using TF-IDF.
- Prediction: The processed text is fed into trained machine learning models to predict if the message is spam.
π Example Use Cases
- Personal Use: Identify spam messages in your emails.
- Business Use: Help customer service representatives filter out spam SMS and emails.
- Educational Purposes: Learn about machine learning and text classification.
π Support and Feedback
If you encounter any issues or have feedback, please open an issue on our GitHub Issues page. We welcome your input to improve the application.
π Community and Contributions
Join our community to share your experiences and learn from others. Check our Discussion page for tips, tricks, and guidance.
π Additional Resources
- Machine Learning Basics: Check out resources on machine learning and text classification.
- Python Programming: If youβre new to Python, consider learning the basics to further customize your experience.
π License
This project is licensed under the MIT License. See the LICENSE file for details.
π Topics
- data
- machine-learning
- numpy
- pandas
- python
- randomforest
- scikit-learn
- spam-classifier
- spam-detection
- svm
