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AI has contributed in changing many industries, providing new and inventive solutions to complicated challenges. Nevertheless, efficient application of AI projects needs a structured and combined technique to be updated with the latest advances in the sector. There are two methodologies, CRISP-DM and OSEMN, which are used to explain the data science project life cycle on a high level. The six-phase method framework known as the Cross Industry Standard Process for Data Mining (CRISP-DM) accurately depicts the data science life cycle. On the other hand, the overall workflow performed by data scientists is categorized under the OSEMN (Obtain, Scrub, Explore, Model, iNterpret) methodology. In our study, we examine both CRISP-DM and OSEMN frameworks, and we perform a comparative analysis. We have conducted an empirical study where the experiment was organized into three case studies, each provided insightful results whether which methodology has better model fit and which has a more accurate prediction rate. The case studies suggested that CRISP-DM offers a better performance and accurate approach. All things considered, this research advances our knowledge of best methods for the selection and use of data mining methodologies, providing practitioners and researchers with direction on which strategy is best suited for their data analysis assignments.

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Comparison of Methodological Approaches: CRISP-DM vs OSEMN Methodology Using Linear Regression and Statistical Analysis

  • 会议论文

  • 发表日期：

    2025年

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  • Ketjona Shameti,
  • Betim Cico

摘要

AI has contributed in changing many industries, providing new and inventive solutions to complicated challenges. Nevertheless, efficient application of AI projects needs a structured and combined technique to be updated with the latest advances in the sector. There are two methodologies, CRISP-DM and OSEMN, which are used to explain the data science project life cycle on a high level. The six-phase method framework known as the Cross Industry Standard Process for Data Mining (CRISP-DM) accurately depicts the data science life cycle. On the other hand, the overall workflow performed by data scientists is categorized under the OSEMN (Obtain, Scrub, Explore, Model, iNterpret) methodology. In our study, we examine both CRISP-DM and OSEMN frameworks, and we perform a comparative analysis. We have conducted an empirical study where the experiment was organized into three case studies, each provided insightful results whether which methodology has better model fit and which has a more accurate prediction rate. The case studies suggested that CRISP-DM offers a better performance and accurate approach. All things considered, this research advances our knowledge of best methods for the selection and use of data mining methodologies, providing practitioners and researchers with direction on which strategy is best suited for their data analysis assignments.