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.