Improving Spam Detection with Feature Engineering and Adaptive Learning Approaches

Spam email detection is a critical component of securing and maintaining reliable digital communication systems. This study explores the effectiveness of various machine learning algorithms in classifying spam, with an emphasis on enhancing accuracy and precision through systematic preprocessing, advanced feature engineering, and text preprocessing. Six models were evaluated: Logistic Regression, Support Vector Classifier, Multinomial Naïve Bayes, K-Nearest Neighbors, AdaBoost, and Bagging Classifier using a comprehensive preprocessing pipeline that included Term Frequency–Inverse Document Frequency vectorization, feature scaling, and the incorporation of engineered features such as character counts. Experimental results reveal that Multinomial Naïve Bayes consistently achieved the highest precision 1.00 and strong accuracy 0.979 when paired with feature scaling, while Logistic Regression delivered robust and stable performance across multiple configurations with precision exceeding 0.96, making it a reliable choice for real-world deployment. Although Support Vector Classifier and AdaBoost exhibited competitive baseline performance, Support Vector Classifier showed limitations when handling numeric features, whereas AdaBoost maintained consistent results across scenarios. These findings underscore the critical role of tailored preprocessing and ensemble learning in improving classification outcomes and highlight the comparative strengths of different algorithms in real-world spam detection. In particular, Multinomial Naïve Bayes proved highly effective for precision-critical tasks, while Logistic Regression emerged as a dependable solution for environments requiring consistent reliability. Overall, this work advances machine learning-based spam filtering by identifying models that successfully balance precision, adaptability, and computational efficiency.