  In recent years, product categorisation has been a common issue for
E-commerce companies who have utilised machine learning to categorise their
products automatically. In this study, we propose an ensemble approach, using a
combination of different models to separately predict each product's category,
subcategory, and colour before ultimately combining the resultant predictions
for each product. With the aforementioned approach, we show that an average
F1-score of 0.82 can be achieved using a combination of XGBoost and k-nearest
neighbours to predict said features.
