Predicting potential resignations in the companies using machine learning techniques

The ongoing digitalization process has ushered in a transformative era for businesses, offering numerous advantages. It has streamlined operations, enhanced connectivity, and facilitated unprecedented data-driven decision-making. However, it also brings challenges, with a central concern being the shifting dynamics of the labor market and evolving employee demands. Organizational priorities now revolve around identifying and retaining skilled employees. In an era where a company's value is closely tied to its workforce's skills, knowledge, and innovation, attracting and retaining talent is not just a preference but a strategic imperative. Success hinges on acquiring and retaining top talent. To tackle this challenge, organizations are adopting sophisticated tools and metrics, with Key Performance Indicators (KPIs) becoming essential for gauging employee engagement. These metrics serve as barometers for an organization's workforce health. The true test lies in proactively using these metrics to identify potential resignations and develop effective retention strategies. This abstract explores strategies for recognizing early signs of employee attrition and crafting retention tactics. Leveraging advanced technologies such as Support Vector Machines, Artificial Neural Networks, and other machine learning algorithms, the study aims to provide deeper insights into factors driving resignations. By harnessing data analytics and machine learning, organizations can tailor retention efforts accordingly. In summary, this research comprehensively explores the challenges of digitalization on the workforce and how organizations can respond. Emphasizing proactive, data-driven approaches, the study offers valuable insights for businesses navigating talent retention in the digital age.