E-mail Spam Detection Using Machine Learning

Abstract

Spam e-mail detection involves applying algorithms and rules to locate and delete unwanted or unwelcome e-mails. These algorithms and guidelines frequently look at an e-mail's content, the sender's reputation, and other factors to determine if it is likely to be spam. For users' inboxes to be free of unwanted or potentially hazardous information, the ability to recognize spam e-mails is essential. Spam e-mail detection is critical for protecting customers from unsolicited and potentially harmful information that can clog their inboxes and compromise their security. Mails are Categorized as 'Spam' or 'Ham' in the dataset. By performing analysis and applying machine learning algorithms for building the predictive system, the model will predict the upcoming mail as spam or ham according to the research on the data. Various machine learning classification algorithms are used, out of which the Multi-Layer Perceptron (MLP) gives the most accurate results and prediction over the data with about 98% accuracy.