Data Smart: Using Data Science to Transform Information into Insight

Using k-means clustering, you can find the best segments and then try to understand why they're the best segments.

this way, clustering is called exploratory data mining, because these clustering techniques help tease out relationships in large datasets that are too hard to identify with an eyeball.

minimize your company's costs, find the class schedule with the fewest morning classes,

This story about Target is just one of many that have peppered the press recently. Watson won Jeopardy!. Netflix offered a million dollar prize to improve its recommendation system. The Obama re-election campaign used artificial intelligence to help direct ground, online, and on the air media and fundraising operations. And then there's Kaggle.com,

it's relatively easy to compute prediction intervals around exponential smoothing forecasts, so you're going to do a bit of that too. Getting Acquainted with Time Series Data NOTE The Excel workbook used in this chapter, “SwordForecasting.xlsm,” is available for download at the book's website at www.wiley.com/go/datasmart.

just cleverly turns past data into a formula or set of rules that it uses to predict a future case. As we saw in the case of naïve Bayes in Chapter 3, it's the AI model's ability to recall this data and associated decision rules, probabilities, or coefficients that make it so effective.

So whether we're talking sending coupons in the mail, approving loans, or dropping bombs, the balance you strike between these performance metrics is a strategic decision. Comparing One Model

It's spent moving data to send through the AI model. It's not spent running the AI model, and it's certainly not spent training the AI model.

Just when you think you have a good demand forecast, a flood disrupts your supply chain, limiting your supply, forcing you to raise prices, and throwing your sales completely out of whack.