We found that these classifiers performed well. To evaluate performance, we use accuracy, precision, recall, and f score metrics and discussed the results. In our research work, Machine learning algorithms Naive Bayes and random forest are utilized to achieve the objective of spam detection. Email filtering is a key tool for detecting and combating spam.
Spam detection and filtration are major issues for email providers and users. These emails are also involved in many cyber crimes like phishing, vishing, identity stolen, data stolen and more. Email spam, sometimes termed junk emails or undesired emails that consumes computing resources, users time and information. In this paper, we are discussing Email spam. Spam may be in the form of text messages,web messages, images and others also. So spams are also a sub-category under all the categories. Email is categorized into many categories based on its content like primary, social, promotional, and spam. Email is used in business and education and almost everywhere for communications.
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