A MACHINE LEARNING APPROACH TO SPAM EMAIL DETECTION WITH SVM AND RANDOM FOREST

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