Employee Performance Prediction: An Integrated Approach of Business Analytics and Machine Learning

Abstract

Workforce performance prediction plays an instrumental role in human resource management since it facilitates pinpointing and nurturing high-performing staff, fortifying employee planning, and boosting overall productivity. This study presents a consolidated approach that integrates business analytics and machine learning methodology to forecast personnel performance. The proposed model leverages data-driven info from distinct sources, entailing performance metrics, staff data, and contextual factors, to tailor accurate predictive models. The study examined different aspects of data analytics such as feature engineering, data preprocessing, model selection, and evaluation metrics. The findings of this report demonstrate the efficiency of the consolidated approach in forecasting workforce performance, therefore presenting valuable insights for companies to make informed decisions associated with talent management and resource allocation.