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Running head: THE DATA MINING PROCESS IT 1Module 02 Written Assignment - The Data Mining Process ITStudent’s NameInstitutionCourse InstructorDate
THE DATA MINING PROCESS IT 2The Cross-Industry Standard Process For Data MiningThe Cross-Industry Standard Process for Data Mining, also known as CRISP-DM offers an organized technique to planning data mining project. It’s a well-proven and robust approach. It is an open standard procedure that defines common techniques utilized by data mining professionals. It’s the most extensively utilized analytics framework (Huber, Wiemer, Schneider & Ihlenfeldt, 2019). CRIS-DM breaks data mining process into 6 major phases as shown in the process matrix below. The order of the stages isn’t stringent and moving backward and forth amongst various stages as it’s always needed. The arrows within the process diagram show the most frequent and vital dependencies amongst stages. The outside circle in the diagram indicates the recurring nature of the data mining. The process of data mining carries on following the deployment of a solution. The learned lessons all through the process could provoke new, generally more focused corporate questions, and future processes of data mining will profit from the previous process experiences. Diagram 1: CRISP-DM phases
THE DATA MINING PROCESS IT 3Phase 1: Business UnderstandingThe 1stphase of CRISP-DM process entails understanding what you require accomplishing from business viewpoint. Your company could have a competing objective and constraint that requires being appropriately balanced. The objective of this phase entails uncovering crucial factors that may impact the result of a project. Overlooking this phase may imply that much effort is placed in generating the correct answers to the inappropriate questions (Hou, 2018). An example of the processes involved in this step includes:Firstly, we require comprehending the requirements. After that, we’ve to establish the