Adaptive Prediction of Spam Emails : Using Bayesian Inference

Abstract

As the recent advancement in communication technologies revolutionizes the world, Email is emerging as a wide-known communication paradigm in various business processes. Email is an effective, brisk and minimal effort correspondence approach. Email Spam is non-asked for information sent to the E-letter drops. Spam could be an enormous downside each for clients and for ISPs. As per examination these days client gets a considerable measure of spam messages then non-spam messages. In some cases spam messages may damage the reputation of a particular business process. It can be observed that in most of the popular mailing services the spam filters are being biased for the profit from the ads, i.e. they are allowing some exception for some companies that pay for advertising. This is not an ethical practice, but it is very profitable for their future business processes. Our aim is to build a spam detector using machine learning in python with the packages NLTK, Matplotlib, Word cloud, Math, pandas, NumPy. With this proposed model, we can state a specified message as spam or non-spam. It can be implemented by using Bayes’ Theorem, a simple yet powerful theorem.