Data Smart: Using Data Science to Transform Information into Insight

At a simplistic level, you feed a supervised AI algorithm some historical data, purchases at Target for example, and you tell the algorithm, “Hey, these purchases were from pregnant people, and these other purchases were from not-so-pregnant people.”

Luckily, there's an excellent free tool called OpenSolver that's available for the Windows versions of Excel that addresses this deficiency. With OpenSolver, you can still build your model in the regular Solver interface, but OpenSolver provides a button that you press to use its Simplex LP algorithm implementation, which is blazingly fast. To set

One way to strike this balance is to use clustering to create a market segmentation of your customers so that you can market to segments of your base with targeted content, deals, etc.

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

You name it; if there's good historical data, a trained AI model can help.

But where forecasting and supervised machine learning differ greatly is in their canonical problem spaces.

It is easy, especially with sparse datasets (only a few observations), to get a model that fits quite well but whose fit is statistically insignificant, meaning that the relationship between the features and the independent variable may not actually be real.

Six percent of RetailMart's customer households are pregnant at any given time according to surveys the company has conducted.

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.

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.