because we were speaking to h2o we'll be using the h2o deep learning estimator library we read in our normal packages we then begin by initiating your spa context and your h2o context followed by now this is where the fun begins right we read in our data set using spark we transform that spark data set into h2o because remember we're working with h2o models right now you can't pass in a spark data frame into an h2o library so hence you need to convert it then over here we defined our features list now because this is an online data set it's