Paper Key : IRJ************467
Author: Priyank Santosh Iyer
Date Published: 19 Oct 2023
Abstract
Email communication, an indispensable aspect of modern life, is frequently compromised by the relentless deluge of spam. These unsolicited and often malicious emails not only inundate inboxes but also pose significant security threats. In response to this challenge, machine learning algorithms have emerged as a promising solution for automating the detection of email spam. This research paper presents a comprehensive investigation into the application of machine learning techniques for email spam detection. In this paper, we evaluated the performance of four distinct models namely, K- Nearest Neighbor(KNN), Logistic Regression(LR), Nave Bayes(NB), Random Forest(RF), and Decision Tree(DT), all trained on the same dataset. The study encompasses an extensive evaluation of these algorithmsperformance, including metrics like accuracy, precision, recall, and F1 score. Additionally, the research considers the impact of feature selection and extraction methods on detection accuracy. The findings provide valuable insights for improving email spam detection systems, enhancing the precision of classification, and ultimately fortifying the security of electronic communication.