This paper explores the extent to which stylistic features contribute to the task of authorship attribution in forensic contexts. Drawing on a filtered subset of the Enron email corpus, the study operationalizes stylistic indicators across four groups: lexical, syntactic, orthographic, and discoursal. Using R Programming Language for feature engineering and logistic regression modelling, we systematically assessed both the individual and interactive effects of these features on attribution accuracy. Results show that n-gram similarity consistently outperformed all other features, with the combined model of n-gram similarity and its interaction with other features achieving accuracy, precision and F1 scores of 91.6%, 93.3% and 91.7% respectively. The model was subsequently evaluated on a subset of the TEL corpus to assess its applicability in a forensic setting. The findings highlight the dominant role of lexical similarity and suggest that integrating interaction effects can yield further performance gains in forensic authorship analysis.