Review on Efficient Spam Detection Technique using Machine Learning

Abstract

People's communication methods are being transformed by electronic mail because of its affordability, speed, and simplicity. Due to their widespread exposure, spam emails have become a serious roadblock in electronic communication. The amount of time users sifting through incoming mail and eliminating spam necessitates the implementation of spam detection software. The main objective is to create suitable filters that can correctly recognise these emails and deliver outstanding performance in the majority of cases. This project makes use of Spam Detection to tell spam from valid email. SVM, a machine learning method, is employed in this case to assess it. SVMs and other approaches of machine learning (AI) Spam detection can benefit greatly from machine (SVM) detection. This project's classification is based on its features. In the email world, spam is a term that refers to unsolicited commercial communications or emails that deceive the recipient. With the use of artificial intelligence and machine learning, spam messages can be identified. Spam filtering is a popular application of machine learning techniques. Machine learning classifiers are used to identify emails as either ham (legitimate messages) or spam (unwanted messages) using these techniques.