Spam Email Detection Using Machine Learning

The rapid development of digital communication has resulted in a huge volume of email includingunsolicited spam, which can cause serious problems such as criminal fraud, time wastage and difficulty inidentifying useful emails The aim of this study is to develop pattern-based machine learning that accuratelydetects and filters spam emails It can do that. By leveraging algorithms to analyze email content, senderinformation, and metadata attributes, we address the growing need for an efficient, scalable solution to thisproblem. Our approach involves pre-processing email data through tokenization, stopword extraction,stemming, and vectorization, followed by feature extraction focusing on content-based, metadata, behavioralattributes. We look at how different machine learning models some including Naive Bayes, Random Forest,Gradient Boosting are performed Model performance is evaluated using , and F1-scores The study concludesthat clustering methods, especially random forests, provide solutions that are difficult for, balances accuracyand computational efficiency. Although deep learning models such as CNN and NLP-based transformersprovide good detection capabilities, their inherent robustness limits their practical application in small-scaleapplications Future work should focus on nature further integration of advanced language processingtechniques to improve the effectiveness and efficiency of spam email detection