Predicting Tech Employee Job Satisfaction Using Machine Learning Techniques Sumali J. Conlon1 Lakisha L. Simmons2 Feng Liu3

Abstract

High-tech industry employees are among the most talented groups of people in the workforce, and are therefore difficult to recruit and retain. We analyze employee reviews submitted by employees from five technology companies. Following the Cross-Industry Standard Process for Data Mining (CRISP-DM) and the data science life cycle process, we use machine learning techniques to analyze employees’ reviews. Our goal is to predict an overall measure of whether employees are satisfied or not, using other information from the reviews, such as employer attitudes towards upper management. We also use predictive analysis to determine which features are more helpful in determining an employee’s overall job satisfaction. Finally, we analyze which prediction algorithm provides the most accurate predictions. We find the percentage of true positives we correctly identify in the holdout sample is 97.4%, while the percentage of true negatives correctly identified is 72.5%.

Prediction of US airline passenger satisfaction using machine learning algorithms

“…In the research of Conlon et al [10], they show that it is possible to analyse and accurately forecast employee job satisfaction using supervised machine learning approaches. According to the AUC measure, the Nystroem Kernel SVM Classifier algorithm performs the best, with an accuracy rate of more than 96%.…”