There is limited work aimed at solving the core task of noun classification for Nguni languages. The task focuses on identifying the semantic categorisation of each noun and plays a crucial role in the ability to form semantically and morphologically valid sentences. The work by Byamugisha (2022) was the first to tackle the problem for a related, but non-Nguni, language. While there have been efforts to replicate it for a Nguni language, there has been no effort focused on comparing the technique used in the original work vs. contemporary neural methods or a number of traditional machine learning classification techniques that do not rely on human-guided knowledge to the same extent. We reproduce Byamugisha (2022)’s work with different configurations to account for differences in access to datasets and resources, compare the approach with a pre-trained transformer-based model, and traditional machine learning models that relyon less human-guided knowledge. The newly created data-driven models outperform the knowledge-infused models, with the best performing models achieving an F1 score of 0.97.