Book Image

Data Smart

By : John W. Foreman
Book Image

Data Smart

By: John W. Foreman

Overview of this book

Data Science gets thrown around in the press like it's magic. Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. It's a brave new world where seemingly meaningless data can be transformed into valuable insight to drive smart business decisions. But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the "data scientist," to extract this gold from your data? Nope. Data science is little more than using straight-forward steps to process raw data into actionable insight. And in Data Smart, author and data scientist John Foreman will show you how that's done within the familiar environment of a spreadsheet. Why a spreadsheet? It's comfortable! You get to look at the data every step of the way, building confidence as you learn the tricks of the trade. Plus, spreadsheets are a vendor-neutral place to learn data science without the hype. But don't let the Excel sheets fool you. This is a book for those serious about learning the analytic techniques, math and the magic, behind big data.
Table of Contents (18 chapters)
Free Chapter
1
Cover
2
Credits
3
About the Author
4
About the Technical Editors
5
Acknowledgments
18
End User License Agreement

Chapter 3
Naïve Bayes and the Incredible Lightness of Being an Idiot

In the previous chapter, you hit the ground running with a bit of unsupervised learning. You looked at k-means clustering, which is like the chicken nugget of the data mining world: simple, intuitive, and useful. Delicious too.

In this chapter you're going to move from unsupervised into supervised artificial intelligence models by training up a naïve Bayes model, which is, for lack of a better metaphor, also a chicken nugget, albeit a supervised one.

As mentioned in Chapter 2, in supervised artificial intelligence, you “train” a model to make predictions using data that's already been classified. The most common use of naïve Bayes is for document classification. Is this e-mail spam or ham? Is this tweet happy or angry? Should this intercepted satellite phone call be classified for further investigation by the spooks? You provide “training data,” i.e. classified examples...