Spam Detection in Emails using Machine Learning

Abstract

In this era of digital world, a lot of emails are received every day, and most of them are not of any relevance to us, some contain suspicious links which can cause harm to our system in some way or the other. These emails may be employed for phishing, the spread of malware, and other illegal actions. Most email service providers have added some kind of spam detection to address this. These techniques are not flawless, thus there is still a need for more precise and powerful spam detection technologies. Through the use of spam detection, this can be avoided. It is the process of determining whether an email is legitimate or whether it is spam of some form. Delivering pertinent emails to the recipient while separating junk emails is the goal of spam detection. Every email service provider already includes spam detection, but it is not always accurate; occasionally, it labels useful emails as spam. The project focuses on the comparative analysis approach on various datasets, three datasets were taken, two of which are made by us.