Assessing Machine Learning Algorithms for Detecting Email Spam: A Performance Analysis

Abstract

One of the alarming tactics employed by spammers is sending malicious links through spam emails. Clicking on such links can not only harm your system but also allow unauthorized access to sensitive information. Moreover, these spammers go to great lengths to create fake profiles and email accounts, deceiving recipients into thinking they are legitimate individuals. These scammers primarily target those who may not be well-versed in identifying these fraudulent schemes, making the situation even more concerning. To combat this growing menace, we must find effective ways to distinguish legitimate emails from spam. As a potential solution, we are currently working on a project that leverages the power of machine learning techniques. Our aim is to build a robust email spam detection system, capable of differentiating fraudulent messages from genuine ones. In this regard, our research will delve into various machine learning algorithms, carefully analyzing their strengths and weaknesses. By applying these algorithms to our vast datasets, we will assess their performance, with a particular focus on precision and accuracy. Ultimately, our goal is to identify the most effective algorithm that can significantly enhance the email spam detection process.