Performance Evaluation of Machine Learning Algorithms for Email Spam Detection

Sending huge number of unwanted mails causes security threat to users. In spite of various security approaches, spammers cause much vulnerability in the internet. This paper discusses the efficient methods of using some of the popular algorithms for building a machine learning model which can classify whether a mail is a spam or ham. UCI Machine Learning Repository Spambase Data Set is used for the experiment. The performance of five important machine learning classification algorithms viz. Logistic Regression, Decision Tree, Naive Bayes, KNN and SVM are evaluated in order to train and build an effective machine learning model for email spam detection. Weka tool is used for training and testing the data set.