Spam Detection Using Machine Learning

Emails are essential in present century communication however spam emails have contributed negatively to the success of such communication. Studies have been conducted to classify messages in an effort to distinguish between ham and spam email by building an efficient and sensitive classification model with high accuracy and low false positive rate. Regular rule-based classifiers have been overwhelmed and less effective by the geometric growth in spam messages, hence the need to develop a more reliable and robust model. Classification methods employed includes SVM (support vector machine), Bayesian, Naïve Bayes, Bayesian with Adaboost, Naïve Bayes with Adaboost. However, for this project, the Bayesian was employed using Python programming language to develop a classification model. Keywords: machine learning (ML), machine learning classifier, Naïve Bayes, SVM, Adaboost, spam classification, ham. DOI: 10.7176/CEIS/11-3-04 Publication date:May 31st 202