Kuppusami, Sanchitha2023-08-312023-08-3120232023-08-31http://hdl.handle.net/1828/15327Machine learning is widely used in various applications because of its advantages, but not everyone can apply it effectively as it requires the use of textual programming. To overcome this, there are several approaches that help users perform machine learning classification without writing code. However, for most machine learning models, it is difficult to understand how they arrive at a particular result. This challenge has triggered a lot of research on interpretable ML methods. However, these methods also require the user to learn how to code and implement them. In this work we introduce iVLAIR, a web application tool that eases machine learning classification for a wider audience and makes data more understandable to the users by transforming the data into visualizations, thereby improving the model interpretability. We also conduct an evaluation with machine learning experts and non-experts to compare iVLAIR with the python approach for performing machine learning classification.enAvailable to the World Wide Webvisualizationinterfaceml for novicesno-codeThesis