Statistics for Data Science

Data prep and preprocessing

Let's start with the obvious—our data!

The more experience a data scientist gains with working with ANN models, the more he or she will come to understand the importance of formally reviewing and preparing the data ahead of he or she can begin attempting to train or fit the model.

We discussed data cleaning in Chapter 3,  A Developer Approach to Data Cleaning, so you should have a good understanding of the importance of and the process of data cleaning and cleansing at this point, so we'll focus here on some data preparation efforts more specific to our topic of artificial neural network models.

It is a popular opinion among data scientists that it may be good practice to normalize your data before training an ...