Abstract

A Many businesses and people now have easier ways to communicate thanks to electronic mail. Spammers who send unwanted emails use this technique to their advantage in order to make false gains. With machine learning algorithms that are enhanced using bio-inspired techniques, the goal of this paper is to show a method for spam email detection. A literature review is conducted to investigate the effective techniques used on various datasets to produce excellent results. On seven distinct email datasets, extensive research was conducted to apply machine learning models using Naive Bayes, Logistic Regression, Support Vector Machine, Random Forest, Decision Tree, and Multi-Layer Perceptron, along with feature extraction and pre-processing. To enhance the effectiveness of classifications, algorithms were put into place. Overall, the greatest performance was shown by logistic regression.  Our findings are compared to those of other machine learning models in order to determine which model is the most appropriate.