Keywords: exploratory, analytics, visualization, decision tree, regression, neural network, model comparison
A typical real-world predictive modeling workflow includes (often iteratively) data cleaning and exploration, model fitting, model validation, model comparison, final model selection, and deployment of the final predictive model. This real-world process should be the backbone of any course in analytics, teaching students the framework and the tools for analytics problem-solving in real life. Data and model visualizations are the key to helping students connect the abstract concepts to the model methods and options. We use a case study to illustrate a visual approach to teaching the analytics workflow.