Classifying Email Spam Using K-Nearest Neighbors, Logistic Regression, and AdaBoost

The volume of undesirable spam email messages progressively surges daily. Analyzing these spam emails manually is impractical and presents challenges in terms of categorization. This is necessary to make effective classification techniques. Machine learning (ML) approaches are the most effective means to achieve accurate spam classification among the available options. This study, therefore, thoroughly examines diverse machine learning algorithms, including K-Nearest Neighbors (KNN), Logistic Regression (LR), and AdaBoost, to tackle the predicament of email spam classification. The research endeavor follows a sequential process encompassing multiple stages. It commences with data collection, followed by applying preprocessing methodologies and identifying pertinent features, and culminates in deploying a machine learning algorithm. The effectiveness of the developed algorithms is also comprehensively assessed, taking into account metrics such as accuracy, precision, recall, F1 score, and AUC.