Machine Learning-Driven Human Resource Decision Strategies for Enhancing Employee Retention and Organizational Growth

The study is devoted to Machine Learning (ML) technologies and its usage in the Human Resource (HR) decision-making process to enhance employee retention to grow an organization in a sustainable way. Despite the fact that there is growing data-driven practices among management, the practices of HR are intuitively-informed. Because of this, the HR does not realize how complex the workforce is. This study considers predictive analytics to evaluate employee characteristics including job satisfaction, tenure, income, and work-life balance using the Random Forest Classification Algorithm. The predictive model was found to be highly accurate with an 88 percent accuracy on the testing data, a great level of precision (0.82), and recall (0.79), which denotes a very high level of accuracy in predicting the turnover in employees. Job satisfaction, tenure and income are the most significant predictors of employee retention. Being in Sales and IT had a disproportional and negative influence on retention. It is an ML model that improves reaction to HR decision-making to proactive and specific retention policy can be created. Intuition combined with the ML techniques brings about objectivity, predictability and strategic planning of the HR policies. This study explains why ML technologies are crucial in making HR a major force in managing innovations inside the organization, as well as, employee engagement and sustainability.