COMPARISON OF METHODOLOGICAL APPROACHES: CRISP-DM VERSUS OSEMN METHODOLOGY USING LINEAR REGRESSION AND STATISTICAL ANALYSIS

Abstract

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 combinative technique in order to be updated with the latest advances in the sector. There are two methodologies, the CRISP-DM and OSEMN,

that is 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 methodology. In our study, we examine both CRISP-DM framework and OSEMN framework and we perform a comparative analysis. We have conducted an empirical study where the experiment was organized into three study cases, each provided insightful results whether which methodology has better model fit and which has a more accurate prediction rate. The study cases suggested that CRISP-DM offers a better performance and accurate approach. All things considered, this research advances our knowledge of best methods, providing practitioners and researchers with direction on which strategy is best suited for their data analysis assignments.