Comparison of Supervised Machine Learning Algorithms for Spam E-Mail Filtering

Spam is an Unsolicited Commercial Email(UCE).It involves sending messages by email to numerous recipients at the same time (Mass Emailing).Spam mails are used for spreading virus or malicious code, for fraud in banking, for phishing, and for advertising. So it can cause serious problem for internet users such as loading traffic on the network, wasting searching time of user and energy of the user, and wastage of network bandwidth etc.To avoid spam/irrelevant mails we need effective spam filtering methods. This Paper compares and discusses performance measures of certain categories of supervised machine learning techniques such as Naive Bayesian and k-NN algorithms and also proposes a better Na�¯ve Bayesian approach.