Graduation Term

Summer 2025

Degree Name

Master of Science (MS)

Department

Department of Mathematics

Committee Chair

Olcay Akman

Committee Member

Xiaotian Dai

Abstract

Employee turnover poses substantial challenges for technology firms, and understanding its key drivers through predictive modeling is essential for developing effective retention strategies. This study investigates factors influencing employee turnover in technology companies by implementing a predictive modeling approach on the IBM HR Analytics Employee Attrition dataset. The research aims were identifying key factors contributing to employee attrition, developing predictive models to forecast turnover risk, and analyzing interactions among significant predictors. By examining a range of features, the results highlight significant variables (Over Time, Monthly Income, Marital Status, etc.) of attrition and offer actionable insights for developing targeted employee retention strategies. Moreover, the model diagnostics suggest that the proposed GLMs fitted well and adequate according to the dataset. This project provides the value of predictive modeling in driving employee turnover decisions and optimizing attrition of tech companies.

Access Type

Thesis-Open Access

DOI

https://doi.org/10.30707/ETD.1763755358.956555

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