Comparison of machine learning methods in email spam detection

Unsolicited bulk emails, also known as Spam, make up for approximately 60% of the global email traffic. Despite the fact that technology has advanced in the field of Spam detection since the first unsolicited bulk email was sent in 1978 spamming remains a time consuming and expensive problem. This report compares the performance of three machine learning techniques for spam detection including Random Forest (RF), k-Nearest Neighbours (kNN) and Support Vector Machines (SVM). Despite the rising popularity of instant messaging technologies in recent years, email continues to be the dominant medium for digital communications for both consumer and business use. Following industry estimations (Symantec Corporation, 2016, pp 31 1), approximately 200 billion emails were sent each day in 2015.