Comparative Analysis of Soft Computing Algorithms for Predicting Employee Turnover in IT Industry

The COVID-19 pandemic, information technology (IT) enterprises in India have been dealing with a systemic problem of high attrition for the last several years. This has led to employee preferences for remote work and knowledge-based losses for the companies. To improve their knowledge and expertise in Industry 5.0's disruptive technologies and use generative artificial intelligence (AI) in future projects, mid-career professionals depart from their existing companies. A number of training and development initiatives were introduced by Indian IT companies in an effort to motivate staff members to advance their skills. Indian IT organizations are thus developing policies and procedures that emphasize employee growth and career planning to retain staff members; this has turned into a unique retention strategy. The greatest loss to the corporation is personnel turnover since it results in significant indirect costs. In addition to giving employers the chance to resolve any regulatory concerns and enhance employee work-life balance, the research project is to create a model that can anticipate employee departures. The Quantitative Research approach (Data-Driven Decision Making) is used in this research study and data is analysed using statistical analysis and predictive analysis. The proposed predictive model is developed based on soft computing algorithms. Utilizing the IBM HR dataset, the suggested model is verified. As expected, the algorithm does a better task of forecasting who will leave the organization than it does at predicting who will not according to the projected results.