The CRISP-DM process was designed specifically for data mining. However, it is flexible and thorough enough to be applied to any analytical project, whether it is predictive analytics, data science, or machine learning. Don't be intimidated by the numerous lists 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 that make it so flexible:
The process has the following six phases:
- Business understanding
- Data understanding
- Data preparation
- Modeling
- Evaluation
- Deployment
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 as detailed as 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 these process steps will be used in later chapters as a framework in the actual application of the machine-learning methods in general and the R code, in particular.