E-mail Spam Detection Using Machine Learning – KNN

Email is a primary mode of communication for software developers due of its convenience. Employing a spam filter is required in order to maintain efficient communication. This examination will primarily concentrate on a spam prevention software programme. This article discusses how the Machine Learning model that Google updated on Collab can recognize and prevent almost all spam and phishing emails. This indicates that their email spam filter is so efficient that only one message out of one thousand is allowed to pass through. There are various different approaches to machine learning that can be used to identify spam; but, in recent years, the “KNN” method has become increasingly prominent. In order to accomplish the goals of this post, we will do research into the operation of spam classification algorithms and attempt to determine how these systems arrive at their findings. The challenge of deciding whether an email should be classified as spam or not is referred to as “spam detection.”