(untitled) (Phoenix: International Multidisciplinary Research Journal ( Peer reviewed High Impact Journal ), 2023)
In today's technologically advanced society, emails have become the norm for both professional and personal communication. However, advertising agencies and social networking websites are to blame for the fact that the vast majority of emails sent out include irrelevant or unwanted content. In the context of email, "spam" refers to unsolicited communications sent to a user. There is a need to filter out spam emails and distinguish them from legitimate ones since they cause recipients time and money. While several algorithms and filters have been developed to identify spam emails, spammers continually improve and hone their spamming methods, reducing the efficacy of the current filters. In this research, we offer a method for dealing with spam that makes use of binary and continuous chance distributions to eliminate unwanted correspondence. Naive Bayes and Decision Trees methods were used to construct the classifier model. Over fitting is examined as it pertains to the efficiency and precision of selecting bushes. Finally, the superior classifier model is identified mostly on the basis of its accuracy in distinguishing spam from other types of emails.