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.
