Leveraging Machine Learning in the Age of Digital Transformation: Strategies for Employee Retention in IT-Tech - Case Study

Abstract

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.