Authors - Roseline Oluwaseun Ogundokun, Rotimi-Williams Bello, Pius Adewale Owolawi, Chunling Tu, Etienne A. van Wyk Abstract - Extramarital affairs can undermine trust and lead to relational break-down, yet the ability to anticipate risk factors remains limited. Recent studies have used deep learning approaches to predict infidelity, achieving high classification accuracy but at the cost of interpretability and resource requirements. This article proposes a novel research to examine whether simple, interpretable mod-els, k-nearest neighbours (KNN), linear regression (LR) and support vector regression (SVR), can predict the amount of time individuals spend in extramarital affairs using readily available socio-demographic and relational features. Using the well-known affairs dataset comprising 6,366 observations and nine variables, we apply feature engineering, cross-validation training and regression-based evaluation to compare model performance. Our findings indicate that although KNN outperforms LR and SVR in terms of accuracy, all models struggle to capture variance; mean squared error (MSE) values remain high, and the coefficient of determination (𝑅2) values close to zero. We discuss the implications for predictive counselling and outline future research directions.
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