In this paper, we build a spam detector using 4 Machine Learning models and evaluate them with test data using different performance metrics used. The dataset we used was from a shuffled sample of email subjects and bodies containing both spam and ham emails in different proportions, which we converted into lemmas. As per our analysis, Naive Bayes