Spam Email Filtering

Nowadays, the technical world faces many common issues that usually worry the normal technology user. One of the most prominent of these issues is the problem of spam, which means the use of digital platform sending like gamil or X to send random messages on a large and frequent basis to many users who usually do not want to receive them, either because of the promotion of advertisements or for other reasons.


Introduction

The approach of the 'Spam Email Filtering' project is to find a technical solution using artificial intelligence that contributes to putting an end to the problem of email spam so that the normal user can distinguish between messages that contain spam and messages that do not contain it using an artificial intelligence model dedicated to solving this issue.

Implementation

We can divide implementation into some main steps :

1. Dataset: A collection of emails labeled as ‘spam’ or ‘not spam’and other features (words or tokens).

2. Libraries: • Pandas for data handling • Scikit-learn for model training and evaluation • Tkinter for the graphical user interface (GUI).

3. Model Training: Implements the training of a Naïve Bayes classifier (`MultinomialNB`) using Scikit-learn.

4. GUI Implementation: Provides a Tkinter GUI for classifying emails as spam or not spam.

5. Documentation: write comments for every side from the code showing how the code works and how the algorithms are implemented.

6. Classify Email Function : A function to classify a user-input email using the trained Naïve Bayes model.

Testing

The method we use to verify the results is by placing any email message in our model, where the model automatically filters this email and checks whether it contains spam or not.

Conclusion

Finally, artificial intelligence in our contemporary time is one of the most powerful tools in the technical field, and everyone must benefit from this tool either in developing current technical systems or solving technical problems that were previously intractable, which contributes to raising the quality of life in societies. In this project, we were able to benefit from many factors that will contribute to developing our thinking about artificial intelligence, which may give us greater opportunities in the future to develop and build projects of better quality and fewer errors.