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

Predicting Pregnant Customers at RetailMart Using Logistic Regression

If you look at the predicted values coming out your linear regression, it's clear that while the model is useful for classification, the prediction values themselves are certainly in no way class probabilities. You can't be pregnant with 125 percent probability or -35 percent probability.

So is there a model whose predictions are actually class probabilities? Once such model that we can build is called a logistic regression.

First You Need a Link Function

Think about the predictions currently coming out of your linear model. Is there a formula you can shove these numbers through that will make them stay between 0 and 1? It turns out, this kind of function is called a link function, and there's a great one for doing just that:

exp(x)/(1 + exp(x))

In this formula, x is our linear combination from column W on the Linear Model tab, and exp is the exponential function. The exponential function exp(x) is just...