Abstract: Employee attrition poses a critical challenge to organizations, leading to increased costs, reduced productivity, and disruptions in workforce stability. This project aims to address this challenge by leveraging data analytics and machine learning to analyse employee behaviour and predict attrition trends. By employing a robust dataset and sophisticated algorithms, the study identifies key factors such as job satisfaction, work-life balance, compensation, and career advancement opportunities that contribute to employee turnover.

The project utilizes advanced machine learning techniques, including classification algorithms, to predict the likelihood of employee attrition with high accuracy. The analysis reveals actionable insights into attrition patterns, helping organizations proactively mitigate turnover risks. The machine learning model developed in this study integrates data preprocessing, feature selection, and hyperparameter optimization to enhance predictive performance, ensuring practical utility in real-world scenarios.

This research highlights the significance of data-oriented decision-making in human resource management. By understanding the drivers of attrition, organizations can implement targeted interventions to enhance employee satisfaction and retention. The results of this study demonstrate the potential of machine learning oriented solutions to support strategic workforce planning, thereby fostering a more engaged and sustainable workforce.

Keywords: Employee attrition, Machine learning, Predictive analytics, Workforce management, Employee retention strategies.

| DOI: 10.17148/IARJSET.2025.12642