Email\ Sms Spam Classification Model

Abstract

Many people nowadays rely significantly on emails and texts from unknown senders. Regrettably, this opens the door for spammers to overwhelm inboxes with spam messages linked to a variety of topics. These spam communications not only clog our inboxes, but they also slow down our internet connections and potentially steal crucial information from our contact lists. Detecting spammers and spam content is a difficult and continuous research problem. Email spam is the practice of sending mass communications by email, and because the recipient bears the majority of the expense, it is effectively postage-due advertising. Spam emails are a type of commercial advertising that is economically feasible due to the low cost of using email as a message delivery medium. This suggested model employs Bayes' theorem and the Naive Bayes' Classifier to determine whether or not a given message is spam. Also, the IP addresses of the senders are frequently reviewed to aid with spam identification.

Keywords: Machine Learning, Spam classification, spam messages