Sarah, the regional sales manager from the Chapter 4 example, is back for more help. Business is booming, her sales team is signing up thousands of new clients, and she wants to be sure the company will be able to meet this new level of demand. She was so pleased with our assistance in finding correlations in her data that she is now hoping we can help her do some prediction as well. She knows that there is some correlation between the attributes in her data set (things like temperature, insulation, and occupant ages), and she's now wondering if she can use the data set from Chapter 4 to predict heating oil usage for new customers. You see, these new customers haven't begun consuming heating oil yet, there are a lot of them (9,442 to be exact), and she wants to know how much oil she needs to keep in stock in order to meet these new customers' demand. Can she use data mining to examine household attributes and known past consumption quantities to anticipate and meet her new customers' needs?