Email Spam Detection Using Machine Learning and Natural Language Processing

Abstract

Email is one of the most common forms of digital communication used by individuals, businesses, educational institutions, and organizations. At the same time, the growing number of unwanted and potentially harmful emails has become a major problem for both users and email service providers. Spam messages can include advertisements, fake offers, misleading information, phishing attempts, or dangerous links and attachments. Automatically identifying these messages is therefore important for keeping email communication safe and manageable. Machine learning and Natural Language Processing (NLP) provide useful ways to detect spam by learning patterns from email content. This research compares four machine learning algorithms for email spam detection: Naive Bayes, Logistic Regression, Support Vector Machine (SVM), and Random Forest. The proposed process includes collecting the data, preprocessing the email text, extracting features using Term Frequency-Inverse Document Frequency (TF-IDF), dividing the dataset, training the models, and evaluating their performance. Accuracy, precision, recall, and F1-score are used to compare the models. The analysis shows that the quality of text preprocessing and feature representation has a strong effect on classification results. Naive Bayes offers a fast and simple starting point, while Logistic Regression and SVM are well suited to high-dimensional text data. Random Forest provides an ensemble-based alternative that can learn nonlinear patterns. Overall, machine learning can be used effectively for automated spam filtering, although changing spam techniques, misleading messages, and increasingly sophisticated phishing emails continue to create challenges.