Predicting Employee Attrition Using Machine Learning in Tech Industry: A Methodological Approach

Nistala, Venkata Kameswara Sai Maneesh (2024) Predicting Employee Attrition Using Machine Learning in Tech Industry: A Methodological Approach. Masters thesis, Dublin, National College of Ireland.

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Abstract

Employee attrition in tech industry is a critical issue which leads to increased recruitment costs and operational disruptions as well as a loss of valuable institutional knowledge. Traditional methods such as exit interviews and employee surveys which provide limited insights since they focus on post-departure feedback which is often too late for intervention. This research explores data-driven approaches for predicting employee attrition, allowing organizations to identify employees at risk before they decide to leave. Some significant ethical considerations regarding the use of employee data for this purpose are also discussed, keeping in view the main ideas of privacy and bias along with transparency.

Predictive models have still been relatively underutilized, while there is a huge gap in research around the ethical consequences of the usage of such predictive models. The present study tries to fill the current gaps by presenting a comprehensive framework in the studies for employee turnover prediction with an emphasis on embedding ethical safeguards relating to privacy, bias detection, and transparency. This will make sure that predictive tools prove to be effective without compromising standards for ethics. Logistic regression, decision trees, and random forests were applied to the IBM HR Analytics Attrition Dataset in order to find the most influential factors on employee turnover. A high degree of data pre-processing was performed to ensure accuracy and robustness of the results. Ethical considerations-such as anonymizing data and mitigating model bias-were integrated within the research process in an effort to protect privacy and fairness for employees.

The random forest model proved to be the most accurate in identifying key predictors of attrition, such as career development opportunities and work-life balance. Also, the study ensured that demographic factors such as age and gender did not have an undue impact on the model's predictions. These findings will therefore give HR professionals actionable insights by which to design effective retention strategies while maintaining ethical integrity.

This report uses the statistical models to predict the turnover among tech employees, thereby contributing to growing field of HR analytics. It also emphasizes need to incorporate ethical considerations like the protection of privacy and the minimization of bias, in data-driven methods so that predictive models are both fair and interpretable.

This research provides significant insights that HR professionals can use to prevent employee turnover by implementing targeted intervention programs. One also needs to ensure that the model adheres to ethical guidelines that prevent using the model for malicious intents and thereby becomes a part of the ethical technology movement. This enables organizations to strike a balance between business requirements and ethical responsibility.

Although the models described demonstrate impressive predictive power, additional studies are in order, examining the influence of predictive tools on workplace culture, including employee trust. There also remain questions about how much of a say employees should have in the use of their data, and whether they should be notified when predictive models are applied to their personally identifiable information. Further research is needed to find a balance between predictive performance vs. employee consent vs. long-term ethics

Item Type:	Thesis (Masters)
Supervisors:	Name Email Rifai, Hicham UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science H Social Sciences > HD Industries. Land use. Labor > Specific Industries > I.T. Industry Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning H Social Sciences > HD Industries. Land use. Labor > Issues of Labour and Work > Staff Turnover
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	03 Sep 2025 15:21
Last Modified:	03 Sep 2025 15:21
URI:	https://norma.ncirl.ie/id/eprint/8761

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