NLP-Driven Strategies for Effective Email Spam Detection: A Performance Evaluation

In the digital age, spam emails have emerged as a persistent and pervasive nuisance, inundating our inboxes and bringing with them an array of potential threats. These threats encompass phishing attacks, the distribution of malicious software, and breaches of privacy. With the rapid expansion of internet users, this problem has grown exponentially and has been exploited for illicit and unethical purposes. The act of sending unauthorized and potentially harmful links through email has seen a significant uptick in recent years, posing a direct threat to our systems and personal security. Recognizing the urgent need to identify and mitigate fraudulent emails, this project is dedicated to the development of robust techniques for spam email detection. Leveraging the power of machine learning and deep learning algorithms, including Multinomial Naive Bayes, Recurrent Neural Networks, and Support Vector Machines, we aim to tackle this issue head-on. Our approach involves applying these advanced algorithms to large datasets, enabling us to select the most effective solution for spam email detection. The criteria for our selection process are centered around precision and accuracy, ensuring that we employ the algorithm that offers the highest level of reliability and performance in safeguarding our email inboxes.