AnacondaCon 2018. Gus Cavanaugh. It’s April 15, 1912. Reports are streaming into New York that the HMS Titanic just sank. You are in the mortuary business in New York City. You know the White Star Line is paying for all funeral services for the deceased, but you only have a partial list of passengers with identified outcomes (perished/survived). Your competitors all have access to the same initial reports. Can you predict which of the remaining passengers on the manifest are most likely to have perished? If so, you can then contact their families first (before the other funeral homes) and sell them your services. This presentation will cover how to use the passenger data with identified outcomes (perished/survived) to predict the outcome of the remaining passengers using Anaconda Enterprise. You’ll learn how to approach this problem with simple pivot tables and then build a predictive model using machine learning—all readily available and easy to use inside of Anaconda Enterprise.