Using Machine Learning Techniques for Predicting Email Spam

Abstract

The email has become one of the most efficient and cost-effective methods of communication in recent years. However, as the number of email users grows, so does the number of spam emails. Email management has become a big and rising concern for both people and companies as a consequence of its sensitivity to abuse. Spam, or the unsolicited sending of unwanted email messages, is one example of misuse. Spam is defined as unsolicited bulk email, or email sent to a large number of people without their consent. Half of the users receive 10 or more spam emails each day, while some users receive hundreds of unwanted emails per day. Online spiders are used by many spammers to discover email addresses on web pages. Because spam emails can fill up the storage space of a file server quickly, they could cause a very severe problem for many websites with thousands of users for this in this study; we present a method for spam filtering using some machine learning techniques to predict whether an email is spam or no.