Email-Spam-Classifier

Overview

This project provides code for classifying emails as spam or not spam using machine learning techniques. With the increase in online consumption of products and services, consumers face a huge problem with the abundance of spam messages in their inboxes. These messages are often promotional or fraudulent, causing important emails to get lost in the clutter.

Project

• Email Classification: Uses a trained model to classify emails as spam or non-spam.
• GUI Application: Includes a graphical user interface for easy interaction and testing.

Files

• AI_ClassAssignmentNLP.ipynb: Jupyter notebook containing the code and explanation for the classifier.

• GUI_SPAM.py: Python script for the graphical user interface.

• README.md: Project documentation.

• Sample Prediction.ipynb: Jupyter notebook demonstrating sample predictions using the trained model.

• Screenshot 1 - S.png: Screenshot of the GUI with a spam classification example.

• Screenshot 2 - NS.png: Screenshot of the GUI with a non-spam classification example.

• classifier.pkl: Pre-trained classifier model file.

• count_vectorizer.pkl: Pre-trained count vectorizer model file.

Install the required libraries:

pip install -r requirements.txt

Running the GUI:

python GUI_SPAM.py

Sample Predictions:

Open Sample Prediction.ipynb in Jupyter Notebook to see examples of how to use the model for classifying emails.