Employee Attrition Prediction Using Machine Learning with Interactive Analytical Insights

Abstract

The text discusses the development of a wireless women safety device designed to improve women’s security during emergencies. Due to the increasing number of crimes and unsafe situations faced by women, there is a growing need for a portable and reliable safety system that can provide immediate assistance and communication. The proposed system uses an ESP32 microcontroller, GPS module, and GSM module to create a smart emergency alert device. When the user presses a panic button, the system detects the user’s current location using GPS and sends an emergency SMS with location details to predefined contacts through the GSM module. A buzzer is also activated to attract nearby attention during emergencies. The device is portable, user-friendly, and intended to ensure quick response and assistance. The literature review highlights the evolution of women safety technologies, including GPS-based tracking systems, GSM-based emergency communication devices, IoT-enabled smart safety systems, wearable technologies, and systems using sensors and machine learning for threat detection. Although many safety devices exist, challenges such as high cost, smartphone dependency, and lack of reliability still remain. The proposed methodology explains the working process of the device. A rechargeable 7.4V lithium-ion battery powers the system, while a buck converter reduces the voltage to 5V for stable operation of all components. When the device is switched on, the ESP32 initializes communication with the GPS and GSM modules to enable location tracking and emergency communication.

Introduction

The text explains the development of a machine learning-based employee attrition prediction system designed to help organizations reduce employee turnover and improve workforce planning. Employee attrition is a major issue because it increases hiring costs, reduces productivity, and disrupts organizational stability. Traditional statistical methods are limited in capturing complex patterns in HR data, making machine learning a more effective solution.

The literature review shows that early methods like Decision Trees and Logistic Regression provided moderate accuracy, while ensemble models such as Random Forest and Gradient Boosting improved performance but often lacked interpretability. Some studies introduced dashboards and explainable models, but there is still a gap in combining accurate prediction, visualization, and interpretability in a single system.

To address this, the proposed system integrates Random Forest-based prediction with an interactive Streamlit application that provides analytics and explainable insights. The methodology includes several steps: collecting HR data, preprocessing and cleaning it, performing exploratory data analysis, selecting and transforming features, and training a Random Forest model to predict attrition risk.

The system is further enhanced with visualization tools that show attrition trends across departments, roles, and demographics, as well as an explainability module that highlights key factors influencing predictions. Finally, it provides HR decision support recommendations such as workload adjustment, training, and career development strategies.

Conclusion

This paper presence an end-to-end employee attrition prediction system that integrates machine learning with an interactive analytics platform. By leveraging structured HR data and a Random Forest classifier, the proposed approach effectively identifies employees at risk of attrition while maintaining balanced performance across key evaluation metrics. The inclusion of analytics dashboards, bulk prediction, and model insights enhances interpretability and supports informed HR decision- making. Experimental results demonstrate that the model achieves reliable prediction accuracy and minimizes misclassification of high-risk employees. The developed Streamlit application ensures practical usability by enabling real-time predictions and visual analysis. Overall, the proposed system offers a scalable and explainable solution for proactive employee retention management in organizational environments.

References

[1] Saradhi, V. V., and Palshikar, G. K., “Employee churn prediction,” Expert Systems with Applications, vol. 38, no. 3, pp. 1999–2006, 2011. [2] Zhao, Y., Hryniewicki, M. K., Cheng, F., Fu, B., and Zhu, X., “Employee turnover prediction with machine learning: A reliable approach,” Proceedings of the International Conference on Data Mining Workshops, pp. 737–745, 2018. [3] Kaur, H., Singh, P., and Kaur, M., “A machine learning approach for employee attrition prediction,” International Journal of Computer Applications, vol. 176, no. 22, pp. 20–25, 2020. [4] Mishra, A., and Mishra, D., “Human resource analytics: A data-driven approach to employee attrition,” Journal of Organizational Computing and Electronic Commerce, vol. 29, no. 4, pp. 281–299, 2019. [5] Breiman, L., “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001. [6] IBM Analytics, “IBM HR analytics employee attrition dataset,” IBM Data Science Case Studies, 2016.

Copyright

Copyright © 2026 Jeeva K, Gorantla Gopichand, Gorrepati Balaji , Grandhi Himavanth Kumar. 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.