abdullahashfaqvirk / SMS-Spam-Detection

A machine learning application designed to classify SMS messages as spam or non-spam, offering real-time analysis to identify potentially harmful content.

SMS Spam Detection

The SMS Spam Detection application is a machine learning tool designed to classify SMS messages as either spam or not spam. It utilizes a pre-trained model to analyze messages and identify potentially harmful content. Developed using the Flask web framework, the application features an interactive interface for real-time message classification.

Dataset: SMS Spam Collection Dataset

Technologies Used

• Backend: Flask (Python)
• Frontend: HTML5, CSS3, TailwindCSS, JavaScript
• Containerization: Docker, Docker Compose
• Machine Learning: Scikit-learn, XGBoost, NLTK, Seaborn, Matplotlib, Pandas, NumPy

Demo

Installation

Prerequisites

Ensure the following are installed on your local machine:

• Python 3.8+
• Docker and Docker Compose (optional for containerized deployment)

Clone the Repository

git clone [e-mail]:abdullahashfaq-ds/SMS-Spam-Detection.git
cd SMS-Spam-Detection

Method 01: Virtual Environment Setup

python -m venv venv
# On Windows, use:
venv\Scripts\activate
# On Linux/MacOS, use:
source venv/bin/activate
# To set up the production environment:
pip install -r requirements.txt
# To set up the development environment:
pip install -r requirements.dev.txt
# To run the project:
python app.py

Method 02: Docker Setup

For a containerized environment, build and run the container:

docker build -t spam-detection .  
docker run -p 5000:5000 spam-detection 

Access the application at http://localhost:5000

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Contact

For inquiries or support, please open an issue on GitHub or contact [e-mail].