Strategic Employee Performance Analysis in the USA: Deploying Machine Learning Algorithms Intelligently

Abstract

Strategic employee performance assessment assists organizations in steering productivity, affirming employee satisfaction, and accomplishing strategic organizational goals. Machine learning algorithms provide several benefits over mainstream techniques in assessing employee performance. This research paper aimed to explore the deployment of machine learning algorithms in assessing employee performance. The prime objective of employee performance analysis is to assess an employee's achievement during a specific time frame. The dataset for this research revolved around the leadership team of a global retailer's specific store level in the USA, extending over 18 months. The dataset for this study was subjected to Python programming software for intensive and comprehensive data analysis as well as for visualization purposes. From the experiment design, it was evident that XG-Boost seems to be the best-performing model overall. In particular, it had the greatest AUC for both holdout and training data (0.86 and 0.88, respectively), and it has a relatively low runtime (16 minutes) and maximum memory utilization (12%). By contrast, Random Forest displayed an average AUC for training data (0.79) but a lesser AUC for holdout data (0.51), which indicates that it may be overfitting the training data; besides, it had a longer runtime than XG-Boost.