Email Spam Detection

About the Project

I developed an email spam detection system using logistic regression, achieving an impressive accuracy of 98%. The model was trained on a comprehensive dataset of labeled emails, allowing it to effectively distinguish between spam and non-spam messages. The project is version-controlled using GitHub, facilitating collaboration and continuous integration. For deployment, I containerized the application using Docker, ensuring consistent performance across different environments. This streamlined approach not only enhances the model's reliability but also makes it scalable and easy to maintain.

Technologies Used

Getting Started

To get a local copy up and running follow these simple steps.

Installation

1. Clone the repo

   git clone https://github.com/tkarim45/Email-Spam-Detection-End-to-End-Deployment.git

2. Install Python packages

   pip install requirements.txt

3. Run the app

   python app.py

4. Access the app in your browser

   http://localhost:8080

Deployment on Docker

To deploy the app using Docker, follow these steps:

1. Build the Docker image

   docker build -t email-spam-detection .

2. Run the Docker container

   docker run -p 8080:8080 email-spam-detection

Usage

The app provides a simple interface for users to input an email and receive a prediction on whether it is spam or not. The model is highly accurate and can be used to filter out unwanted emails effectively.

Roadmap

The project is currently in the final stages of development. Future updates will focus on improving the model's performance and adding more features to the app. I plan to integrate additional machine learning algorithms to enhance the accuracy of the spam detection system further. I also aim to deploy the app on a cloud platform to make it accessible to a wider audience.

Contributing

Contributions are what make the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project

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

Distributed under the MIT License. See LICENSE for more information.