• Utsab Ray
• Ipsita Sengupta
• Dr. Karabi Ganguly

Email is growing as a well-known communication paradigm in many corporate operations as recent advancements in communication technologies transform the world. Email is an efficient, quick, and low-effort method of correspondence. Email spam is unsolicited information sent to E-letter drops. Spam might be a major issue for both customers and ISPs. According to research, clients now receive far more spam messages than non-spam emails. In some circumstances, spam messages can harm the credibility of a company process. It can be seen that the spam filters in most popular email systems are skewed for ad profit, i.e. they provide an exception for some organizations who pay for advertising. This is not the case. Many organizations and people have found that electronic mail has simplified communication procedures. Spammers use this strategy for dishonest gain by sending unsolicited emails. The goal of this work is to offer a method for detecting spam emails using machine learning algorithms optimized with bio-inspired methodologies. On seven separate email datasets, considerable research was conducted to apply machine learning models such as Nave Bayes, Vector Machine, Stochastic Forest, Decision Tree, and Multi-Layer Perceptron, as well as extraction and classification, and pre-processing. Thus, in the end, a predictive model is developed which will predict the status of any email easily. .

Email, spam, machine learning, email datasets, predictive model.

"Machine Learning predictive model to detect spam email", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 2, page no.f243-f249, February-2023, Available :http://www.jetir.org/papers/JETIR2302527.pdf

"Machine Learning predictive model to detect spam email", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 2, page no. ppf243-f249, February-2023, Available at : http://www.jetir.org/papers/JETIR2302527.pdf