Study of Spam Email Filtering Methods using Supervised Machine Learning Techniques

The development of the Internet paved the way for the commercial exchange of data-extensive messages in the form of E-Mails. But, a big issue is spam E-Mails which are unwanted and incessant emails that the user may or may not have signed up for. Hence, users need to have a filter that distinguishes spam emails from ham to avoid unnecessary and potentially harmful messages. This study proposes a framework for automatically detecting spam emails using supervised machine learning techniques. The models are trained on an openly available dataset with additional methods that help in gaining insight into the data. The performance and the accuracy of different models for segregating incoming emails are then tested and compared.