• Dr. P. Selvarani
• A. Kanagalashmi ,
• M. Keerthika
• M. Kathambari
• 4, Mancy Arokiya Mary

Email spam has grown significantly in recent years along with the rapid growth of internet users. People are utilizing them in unlawful and unethical ways. Fraud, phishing, and conducts. Spam is waste of time to the user since they have to sort the unwanted junk mail and it consumes storage space and communication bandwidth. They can easily send their shady message to so many email addresses in a single stroke. In our Proposed work using machine learning techniques. This paper will discuss machine learning algorithms and apply all of them to our data. The task of classifying an incoming email as either spam or not spam can be described as an AI-based binary classification issue. Accuracy, precision, recall, TPR – True Positive Rate, TNR – True Negative Rate, FPR – False Positive rate, FNR - False Negative Rate are a few metrics that can be used to assess the effectiveness of a spam detection system

Email Spam Detection, Machine Learning Algorithms, Security, TPR, FPR, TNR, FNR.

"A SURVEY OF EMAIL SPAM DETECTION USING ARTIFICIAL INTELLIGENCE", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 6, page no.j125-j134, June-2023, Available :http://www.jetir.org/papers/JETIR2306918.pdf

"A SURVEY OF EMAIL SPAM DETECTION USING ARTIFICIAL INTELLIGENCE", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 6, page no. ppj125-j134, June-2023, Available at : http://www.jetir.org/papers/JETIR2306918.pdf