Summary

In this chapter, we looked at how to use sklearn and Keras, how to import data from a UCI repository using the pandas read_csv function, and how to preprocess that data. One of the ways to handle missing data, whether in healthcare applications or not, is to remove the rows or instances that have missing attributes. We then learned how to describe the data and print out histograms so we know what we're working with, followed by doing a train/test split with model_selection from sklearn. Furthermore, we also learned how to convert one-hot encoded vectors for a categorical classification, by defining simple neural networks using Keras. We then looked at types of activation function, such as softmax, for categorical classifications with categorical_crossentropy. In contrast, when we got to our binary classification, we used a sigmoid activation function and a binary_crossentropy...