The need for automated methods for identifying refactoring items is prelevent in many software projects today. Symptoms of refactoring needs is the concept of code smells within a software system. Recent studies have used single model machine learning to combat this issue. This study aims to test the possibility of improving machine learning code smell detection using ensemble methods. Therefore identifying the strongest ensemble model in the context of code smells and the relative sensitivity of the strongest perfoming ensemble identified. The ensemble models performance was studied by performing experiments using WekaNose to create datasets of code smells and Weka to train and test the models on the dataset. The datasets created was based...