## Chapter 3. The Data Mining Process - CRISP-DM Methodology

At this point, our backpack is quite full of exciting tools; we have the R language and an R development platform. Moreover, we know how to use them to summarize data in the most effective ways. We have finally gained knowledge on how to effectively represent our data, and we know these tools are powerful. Nevertheless, what if a real data mining problem suddenly shows up? What if we return to the office tomorrow and our boss finally gives the OK: *Yeah, you can try using your magic R on our data, let's start with some data mining on our customers database; show me what you can do*. OK, this is getting a bit too fictional, but you get the point—we need one more tool, something like a structured process to face data mining problems when we encounter them.

When dealing with time and resource constraints, having a well-designed sequence of steps to accomplish our objectives becomes a crucial element to ensure the data mining activities...