Spam E-mail Classification using ML, Angular, and Flask

Technical advancements are resulting in the development of tactics, that when used by the wrong people, can make a huge loss to society. Spammers nowadays use emails to trick common people, because of the increase in the use of emails to communicate important information. The Unwanted mail that spammers send for their benefit is termed spam mail. The increase in the cases of spam and the loss associated with it threatens people. To counter these attacks some filtering mechanism should be introduced so that the emails can be filtered into two parts that are normal mail and spam mail. This paper tries to solve this problem by using several Machine Learning algorithms, like Random Forest, AdaBoost, Baggage boosting XGBoosting, Decision Tree, Extra Tree Classification algorithm, and Support Vector Machine. The final result depends on the result of each algorithm, and hence greater accuracy to the solution of the problem, the concept of majority voting is used for evaluating the final result. The project deploys the ensembled model in the Flask server to provide the services through APIs to other developers. The user interface is created to provide the user with an interface where they can test their emails. This UI uses these APIs for spam prediction services.