Spam E-mail Detection Using Machine Learning Techniques

In recent years, email spams have emerged as a significant concern, coinciding with the rapid expansion of Internet usage. Certain individuals exploit this medium for unlawful activities, including phishing and fraudulent schemes. These malicious actors often distribute harmful links via spam emails, potentially compromising computer systems and infiltrating personal data. The implementation of email spam detection mechanisms is crucial for preventing unwanted messages from cluttering users’ inboxes, thereby enhancing their overall experience. This project aims to identify spam emails using machine-learning techniques. Machine learning, which is a subset of Artificial Intelligence, enables systems to learn and improve based on experience without explicit programming. This study focuses on the logistic regression algorithm, which is a probabilistic classifier that predicts outcomes based on the likelihood of an object. This method was chosen for email spam detection because of its superior precision and accuracy.