Document Abstract
Spam is also known junk mail. E mail is undoubtedly a very effective, cheap and easy method of communication these days. But, due to social networks and advertisers, most of the emails contain unwanted information called spam. Generally, a spam is e-mail advertising for some product sent to a mailing list or newsgroup. In order to filter the messages and separate the genuine messages from the junk mail, the spam
filters are preferred. Even though more number of classification techniques has been developed for spam classification, still none of the algorithms produces 100% accuracy, In this paper, spam dataset is analyzed using CLEMENTINE data mining tool for email spam classification. Initially, various classification algorithms are applied over this dataset and cross validation is done for each of these classifiers. Finally, best classifier for email spam is identified based on the Training and testing accuracy of various models and Performance measures.