Browsing by Author "Sifani, Edwin Sifani"

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    Predictive Modelling of Taxpayer Compliance Behaviour Using Machine Learning At NamRA
    (Namibia University of Science and Technology, 2025-07-31) Sifani, Edwin Sifani
    This study explored the application of machine learning techniques for predictive modelling of taxpayer compliance behaviour at the Namibia Revenue Agency (NamRA). Multiple classification algorithms were systematically optimised using 5-fold cross-validated Grid Search and Randomised Search, as implemented in the scikit-learn library (v1.2), to enhance predictive accuracy. The hyperparameter search spaces were tailored to each model’s architecture; for instance, Random Forest optimisation included the number of estimators and maximum depth, while Gradient Boosting models emphasised learning rate and structural parameters. The optimisation process yielded notable improvements, with cross-validated accuracy scores ranging from 64% to 68%. The best-performing model, an optimised Random Forest classifier, achieved an accuracy of 68%. These findings demonstrated the efficacy of hyperparameter tuning in improving model performance and underscore the potential of machine learning to support data-driven compliance management at NamRA. The use of SHAP and LIME in this study provided valuable interpretability of taxpayer compliance predictions, highlighting key factors such as income group, the COVID-19 period, taxpayer registration office, and marital status. These insights align with existing research and reveal how financial capacity, macroeconomic disruptions, and administrative or demographic variables influence compliance. SHAP offers a global view of feature importance, while LIME provides personalised explanations, enhancing trust and communication. Despite modest predictive accuracy, the interpretability benefits support targeted policy interventions and suggest future improvements through richer data and fairness assessments.