Leveraging Machine Learning in the Age of Digital Transformation: Strategies for Employee Retention in IT-Tech - Case Study - Narayanamurthy, Rajadurai (Digital Repository of Theses, 2024)
In today's competitive job market, employee retention remains a critical concern for Information Technology (IT) companies. With turnover rates escalating, the urgency to understand, predict, and improve employee retention has become paramount. This thesis aims to develop predictive models for employee retention in the IT sector using machine learning algorithms to forecast an employee’s likelihood of leaving the company, thereby aiding in timely intervention strategies. Data from several IT companies, encompassing a wide array of variables such as age, tenure, job satisfaction, and performance metrics, were collected and rigorously analyzed. Different machine learning models, including Logistic Regression, Random Forest, and Support Vector Machines, were trained and evaluated based on their prediction accuracy, precision, recall, and F1 Score. The results were further subjected to cross-validation to ensure robustness and reliability. Our study found that Random Forest outperformed other models, with an accuracy rate of 92%. Important features like job satisfaction levels, age, and recent promotions were identified as significant predictors of employee retention. These insights not only provide a scientific basis for human resources decisions but also pave the way for more personalized, data-driven employee retention strategies. By leveraging machine learning algorithms to predict employee attrition, this research offers a novel approach to an age-old problem, reflecting the potential for technology to revolutionize human resources management in the IT industry. The models and insights derived from this study aim to serve as a catalyst for future research and practical applications focused on optimizing workforce stability and job satisfaction.