Comparison of <scp>CRISP</scp> ‐ <scp>DM</scp> Versus <scp>OSEMN</scp> Methodologies Using Linear Regression and Statistical Analysis

ABSTRACT Artificial intelligence (AI) has transformed numerous sectors by offering innovative solutions to complex problems. However, effective implementation of AI projects requires a systematic and integrative approach to stay current with the latest developments in the field. Despite these improvements, realising successful AI and data science initiatives involves employing systematic approaches that can flexibly respond to changing technology and data environments. Two techniques are used to elucidate the life cycle of a high‐level data science project. The six‐phase methodology termed CRISP‐DM (Cross Industry Standard Process for Data Mining) precisely illustrates the data science life cycle. However, the entire workflow conducted by data scientists is performed using the OSEMN methodology (Obtain‐Scrub‐Explore‐Model‐iNterpret). Here, the CRISP‐DM and OSEMN frameworks have been evaluated and compared. An empirical study has been conducted experimenting with four study scenarios, each producing enlightening results with respect to the model fit and the prediction rate. According to four case studies, CRISP‐DM provides a more accurate and efficient method. All issues taken into account, this study contributes to better understanding the most effective approaches for choosing and applying data mining procedures, giving researchers and practitioners guidance on the most appropriate approach for their data analytic tasks. By comparing and contrasting these approaches, this study adds to the discussion of best practices within the data science community for researchers and practitioners when it comes to selecting the most suitable framework for data analysis.