Author : S. Sumahasan 1
Date of Publication :31st July 2021
Abstract: Spam is any unwanted digital communication that is sent in bulk from compromised machines. In this work, the suggested strategy is a model that uses the Bag of Words technique to calculate the frequency of words and Supervised Machine Learning techniques such as Naïve Bayes and Support Vector Machine to categorise the message. The suggested system shows the message's categorization as well as the most prevalent spam terms discovered in the message. We compare the performance of the Naïve Bayes and Support Vector Machine algorithms in this study. The feature of adding additional spam messages to the collection improves accuracy as well.
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