The CRISP-DM process was designed specifically for the data mining. However, it is flexible and thorough enough that it can be applied to any analytical project, whether it is predictive analytics, data science, or machine learning. Don't be intimidated by the numerous list of tasks as you can apply your judgment to the process and adapt it for any real-world situation. The following figure provides a visual representation of the process and shows the feedback loops, which facilitate its flexibility:

Figure from CRISP-DM 1.0, Step-by-step data mining guide

The process has the following six phases:

For an in-depth review of the entire process with all of its tasks and subtasks, you can examine the paper by SPSS, CRISP-DM 1.0, step-by-step data mining guide, available at https://the-modeling-agency.com/crisp-dm.pdf.

I will discuss each of the steps in the process, covering the important tasks. However, it will not be in the detailed level of the guide, but more high level. We will not skip any of the critical details but focus more on the techniques that one can apply to the tasks. Keep in mind that the process steps will be used in the later chapters as a framework in the actual application of the machine learning methods in general and the R code specifically.