SPAM AND HAM DETECTION PREDICTION USING MACHINE LEARNING (2023)
Ayub Baig, A. Kalyani , G. Keerthi, G. Tejaswi, Ch. Naga Shilpa JCR. 2023: 67-77
Email has become one of the most important forms of communication. In 2014, there are estimated to be 4.1 billion email accounts worldwide, and about 196 billion emails are sent each day worldwide. Spam is one of the major threats posed to email users. In 2013, 69.6% of all email flows were spam. Links in spam emails may lead to users to websites with malware or phishing schemes, which can access and disrupt the receiver’s computer system. These sites can also gather sensitive information from. Additionally, spam costs businesses around $2000 per employee per year due to decreased productivity. Therefore, an effective spam filtering technology is a significant contribution to the sustainability of the cyberspace and to our society. Current spam techniques could be paired with content-based spam filtering methods to increase effectiveness. Content-based methods analyze the content of the email to determine if the email is spam. The goal of our project was to analyze machine learning algorithms such as logistic regression, and naive bayes classifier algorithm and determine their effectiveness as content-based spam filters.
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