Abstract

<p>Email spam comes in various forms, the most popular being to promote outright scams or marginally legitimate business <br>schemes. Spam typically is used to promote access to inexpensive pharmaceutical drugs, weight loss programs, online degrees, <br>job opportunities and online gambling. Spam is commonly used to conduct email fraud. This paper presents a model for detecting <br>spam email using Support Vector Classifier and Random Forest Classifier. In this paper a ucl spambase dataset was trained using <br>Support Vector Classifier and Random Forest Classifier. Random Forest Classifier had about 91.36% which is the highest <br>accuracy while Support Vector Classifier had about 89.21% accuracy. This paper uses Random Forest Classifier in detecting <br>spam emails, which is then saved and loaded.</p>