HR Analytics to Track Employee Performance using Python

Abstract

HR analytics has become one of the powerful tools through which organizations can monitor and optimize the performance of their employees. The purpose of this research was to study the role of HR analytics in performance management. This will involve discussing its benefits, challenges, and ethical issues. Through an in-depth literature review and analysis of case studies, we investigated key performance indicators relevant to the employee performance tracking and their alignment with organizational goals. Against the background of such approaches, this paper assesses the different available HR analytics solutions and expands upon howML can strengthen the analytical power of data. It discusses possible approaches for the deployment of analytics within current performance management systems, recommendations to address concerns regarding privacy and potential biases, and summarizes our findings on the basis of the research-noting significant overall impact on workforce management, but also significant current shortcomings and areas for further research work. This study complements the growing body of knowledge on HR analytics and offers practical insights for organizations interested in using data-based approaches to performance management.

Introduction

The paper discusses the growing importance of data-driven HR analytics in improving employee performance management and organizational success. HR analytics integrates multiple data sources to provide insights on employee behavior, performance trends, and alignment with business goals. Key Performance Indicators (KPIs) measure effectiveness, while machine learning (ML) enhances predictive capabilities. Benefits include improved employee engagement, resource utilization, and decision-making, but challenges like data privacy, bias, and system integration remain. The study evaluates leading HR analytics platforms and offers implementation strategies.

A literature survey reviews recent research on ML applications for predicting employee turnover and evaluating performance across various industries. The core focus is on predicting HR attrition, a costly issue for organizations. Using the IBM HR dataset, the study applies ML models such as logistic regression, random forest, SVM, and decision trees to identify factors influencing employee attrition and predict who is likely to leave.

Key findings include:

• Younger employees (ages 28-39) show higher attrition rates.

• Attrition varies by job role, with laboratory technicians and sales executives having the highest turnover.

• Most employees work in R&D (65%), followed by sales (30%) and HR (4%).

• The dataset is analyzed using Python tools and interactive dashboards to support HR decision-making.

The paper highlights the potential of HR analytics to forecast attrition, enabling proactive policy adjustments to reduce turnover and sustain workforce management.

Conclusion

The employee attrition, the natural departure of employees from the workforce due to various factors, has been analyzed using machine learning models—Logistic Regression, Random Forest, and Support Vector Machines (SVM). The organization’s overall attrition rate is 16.12%, with age-related trends and gender disparities being particularly noticeable. Male employees have higher attrition than female employees, even though they make up a larger portion of the workforce. Employees between the ages of 30 and 40 show a trend toward lower attrition as they get older. Additionally, frequent travelers show higher attrition rates, while non-travelers exhibit the lowest rates. It is surprising to learn that, out of all the departments, the Research and Development department has the lowest attrition rate even though it employs the most people. The predictive performance of the machine learning models varied. Logistic regression achieved an 86.73% accuracy but showed moderate precision (43.37%) and recall (50%), failing to predict positive instances. Random Forest performed slightly better with 87.75% accuracy, demonstrating higher precision (83.94%) and recall (54.93%). However, it misclassified one instance and missed 35 positive instances in its confusion matrix. SVM mirrored Logistic Regression\'s metrics, also achieving 86.73% accuracy with identical precision, recall, and F1 score. Monthly income, age, years since last promotion, and factors pertaining to work satisfaction and involvement were among the top contributing features to attrition, according to the ML models. One crucial element that showed a major influence on attrition was time. Therefore, resolving issues with overtime may lower the company’s rate of attrition.

References

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Copyright

Copyright © 2025 Sakshi Sonavane, Sakshi Pawar, Tejas Gupta, Swati Bhoir. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.