Email Guard: Enhancing Security Through Spam Detection

The technique of identifying and distinguishing unwanted or unsolicited emails, generally known as “spam,” from valid emails in a user’s inbox is email spam detection. Primary purpose of email spam detection is to lessen the disruption and aggravation caused by spam emails, which usually contain advertisements, phishing scams, malware, or irrelevant content. Incoming emails are categorized as spam or non-spam (sometimes known as “ham”) using a variety of approaches and algorithms. In this study, five machine learning methods are examined for recognizing email spam: Decision Trees, K-Nearest Neighbors (K-NN), Support Vector Machines (SVM), Logistic Regression, and Naive Bayes. Evaluate these algorithms based on their efficiency and ability to respond to changing spam patterns. The results provide useful information that may be used to determine the best algorithm for email spam detection.