Detection of Human Resource outcomes and Organizational performance in commercial banks using Machine learning techniques

Technological advancements and sustainable competitive advantage pose a challenge to the majority of service businesses worldwide. It becomes essential to comprehend human resource outcomes in order to overcome these obstacles. This study used an efficient sampling technique to distribute a structured questionnaire to 315 respondents as part of a quantitative research design. Due to the potential for challenging situations like debt and unfavourable economic effects that affect both the bank and society at large, poor performance is essential to the operation of commercial banks. In this review, five unique techniques for Machine Learning (ML) were utilized to fabricate models of performance to predict the future performance of business banks as per HR results. The reproductions were assembled utilizing the Python programming tool with ML libraries. The consequences of the examination obviously show how significant HR results are to accomplishing organisational achievement, and the simulations worked with the five ML classifier from the exploration have an accuracy of 89.9% to 77.5% in forecasting higher bank performance.