Shifting gears away from the Space Shuttle, let's work through a practical example of deep learning, using the h2o package. We will do this on data I've modified from the UCI Machine Learning Repository. The original data and its description is available at https://archive.ics.uci.edu/ml/datasets/Bank+Marketing/.  What I've done is, take the smaller dataset bank.csv, scale the numeric variables to mean 0 and variance of 1, create dummies for the character variables/sparse numerics, and eliminate near zero variance varaibles.  The data is available on github https://github.com/datameister66/data/ named also bank_DL.csv. In this section, we will focus on how to load the data in the H20 platform and run the deep learning code to build a classifier to predict whether a customer will respond to a marketing campaign.