Classifying Email as High and Low Risk: An Effective Approach to Spam Email Classification

Email is one of the most widely used and popular forms of communication due to its accessibility on a worldwide scale, the relative speed at which messages can be transferred, and the low sending costs. Today, a large portion of the population depends on the messages or emails sent by spammers, and it gives them a great opportunity to send spam messages to people about their interests. The rise in email-based threats is directly attributed to the flaws in e-mail protocols and the rise in electronic commerce and financial activities. Spam emails are sneaking into users' mailboxes without their consent. They utilize more network resources and require more time to check and delete spam emails. Spam overflows inboxes with absurd emails. greatly reduces the speed of our internet. stealing vital information, such as contact information, from the user In the digital age, spam email has grown to be a serious issue, and it is crucial to identify and filter spam emails in order to preserve the integrity of communication channels. Traditional techniques of classifying email simply consist of identifying email as spam or not spam, We introduced the concept of further classifying spam email as high risk or low risk. Ensemble learning is used for binary classification as spam or ham, it includes three models, namely Multinomial Naive Bayes, SVM, and Decision Tree, which gave an accuracy of 98.95%. For further classification of the spam mail into two subcategories as high or low risk, SVM produced an outcome of 84.37% accuracy.