Abstract: The Cross Industry Standard Process for Data Mining (CRISP-DM) framework was developed in the 1990s and has been widely used as the most relevant and comprehensive leading principle for conducting analytics projects. Despite the wide acceptance and adoption of the CRISP-DM framework, the current business analytics discipline often focuses on the modeling phase and overlooks the interplays between the phases. Consequently, students often lack a comprehensive understanding of the entire analytics process. This teaching case is created to demonstrate the importance of the data analytics life cycle and how six phases collectively contribute to the success of analytics projects using R. This case collects real-life data and follows the six CRISP-DM phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. At the end of the project, students will learn the importance of the data analytics life cycle, especially the data understanding and preparation phases, which often receive minimal attention in business analytics projects. This project will also demonstrate the importance of storytelling, ensuring that critical insights are conveyed to the audience.
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Recommended Citation: Lee, F., Baxter, C., (2023). Using Supervised Machine Learning and CRISP-DM to Predict an Acquittal Verdict Information Systems Education Journal21(4) pp 23-36. http://ISEDJ.org/2023-4/ ISSN : ISSN: 1545-679X. A preliminary version appears in The Proceedings of EDSIGCON 2022