Enhancing Employee Retention with AI: Predictive Analytics and Decision Support Systems

Maintaining a competitive advantage over their respective rivals depends on companies giving PADS (Predictive Analytics and Decision Support Systems) top priority. Conversely, experts in human resources often find it difficult to predict, from the current data, the expectations of employees. This work targets an original PADSS idea, Deep Decision Tree (DDT), with an aim of extracting already existing data. The authors of this work used the DDT method since it is efficient in handling big datasets. By means of an analysis of a range of data including publicly employed individuals, companies, and products that make use of technology, this paper aims to show how DDT could be able to better fulfill the employment requirements of the future. When compared to other approaches regarded to be quite conventional, the DDT method provides better accuracy and scalability. Professionals in the field of human resources can then use this data to make well-informed decisions about the configuration of digital workforce. This work significantly advances the field by laying a fresh basis for PADSS by means of advanced machine learning techniques.