In this second of our 3-part series, we will transition to Binary Logistic Regression to predict outcomes with a categorical response variable or target. We will also venture further into modern-day predictive analytics by discussing various feature and model selection techniques. We will look beyond p-values for model reduction and discuss the benefits and disadvantages of several strategies for selecting high-performing predictive models.