Kaur, Simran2019-12-172019-12-1720192019-12-17http://hdl.handle.net/1828/11375There are many types of fraudulent activities happening today that open the loopholes of security, but email is a cheaper and widely known method for delivering false messages to potential victims. Spam is a form of email messages that is not only annoying for users but can provide a conduit for fraudulent or deceptive content delivery. In this project, a spam detector to identify an email as either spam or ham is built using n-gram analysis and supervised machine learning models. Three different algorithms are implemented and compared, namely naïve-Bayes, logistic regression and support vector machines (SVM). Experimental evaluation of the detector using a public dataset shows that the SVM and logistic regression attain the highest accuracy.enAvailable to the World Wide WebN-gramsSpam detectionproject