Spam Detection Using Machine Learning

Overview

This project implements a Spam Detection system using the Naive Bayes classification algorithm. It is integrated with a Tkinter GUI to allow users to classify emails or text messages as either spam or not spam. The project leverages regular expressions, file input/output operations, and machine learning model handling.

Features

• Spam Detection: Classifies text data as spam or not using a trained Naive Bayes classifier.
• GUI Interface: User-friendly interface built with Tkinter.
• Regular Expressions: Preprocessing step to clean and format data before classification.
• Model Persistence: The trained model is saved and loaded using serialization (e.g., pickle).

Requirements

To run the project, ensure the following dependencies are installed:

pip install -r requirements.txt

requirements.txt should include the following:

numpy
pandas
scikit-learn
tkinter

Usage

1. Train the model by providing a dataset of spam and non-spam messages. The model will be serialized and saved for future use.
2. Start the application:
3. Use the GUI to input a message and get the spam classification result.

Project Structure

├── app.py              # Main application script with Tkinter GUI
├── model.py            # Naive Bayes classifier and text preprocessing
├── data/               # Directory for storing datasets (CSV files)
├── saved_model.pkl     # Serialized Naive Bayes model
├── README.md           # Project documentation
└── requirements.txt    # Python package dependencies