The Spam Filter Engine is a machine learning-powered solution designed to filter out unwanted emails that are sent by bots or in languages other than the specified language (considered as spam). This engine can be deployed on a server, exposing an endpoint that accepts message bodies in JSON format. The engine then uses a machine learning model to classify the message as either "Ham" (legitimate) or "Spam."
- Real-time spam email classification.
- Endpoint for API integration.
- Scalable and deployable on a server.
- Easy-to-use JSON input format.
Before using the Spam Filter Engine, make sure you have the following prerequisites installed:
- Python 3.x
- Scikit-learn (for machine learning)
- Any necessary model files and data
The project directory structure may include the following files and directories:
spam_filter_engine/ ├── mail_data.csv # Dataset for training ├── model.pkl # Pre-trained machine learning model ├── mail_spam_checker.py # Main file ( For Model Training ) ├── spma_checker.py # Run this file for model output ├── tutorial.js # Tutorial for implementing in Node.js ├── requirements.txt # List of project dependencies └── README.md # Project documentation
-
Clone the repository to your server or development environment:
git clone https://github.com/yourusername/spam_filter_engine.git
-
Navigate to the project directory:
cd spam_filter_engine -
Run the Main file:
python3 email_spam_checker.py
-
Then run the spam_checker.py
python3 spam_checker.py
-
In input field give our email context, then it process and show your index numbers ( 0 - Ham / 1 - Spam )
- You can replace the pre-trained machine learning model (
model.pkl) with your own trained model. - Modify the API endpoints or input format to suit your project's specific requirements.
- Add additional features such as logging, monitoring, or user authentication as needed.
This project is licensed under the MIT License. See the LICENSE file for details.