Data Intelligence for Workforce Management: Machine Learning Models for Payroll and Resource Optimization

This study provides a comprehensive analysis of team pay analytics and resource optimization platforms designed for large-scale operational organizations in the aviation, utilities, and field services industries. The research examines the implementation of data-driven workforce management systems that integrate scheduling algorithms, compensation modeling, and performance analytics to optimize labor allocation while ensuring fair compensation practices. Using machine learning approaches, specifically gradient boosting regression and XGBoost regression, the study analyzes employment data from 22 employees across multiple dimensions, including hours worked, skill levels, shift types, overtime hours, and weekly compensation. The method uses team learning techniques to predict weekly pay based on employee characteristics, with both algorithms demonstrating exceptional training performance, achieving R² values of 0.9997. However, the pilot phase results revealed significant performance degradation, with R² values dropping to approximately 0.90, indicating overfitting challenges common in team learning applications. XGBoost showed slightly superior generalization capabilities with better error control and built-in regularization algorithms compared to standard gradient boosting. The platform addresses important challenges in workforce management, including complex scheduling requirements, regulatory compliance, variable pay structures, and team-based productivity optimization. Key findings reveal strong positive correlations (0.91-0.98) between working hours, skill levels, overtime hours, and weekly pay, suggesting merit-based pay systems. While both machine learning models show promise for salary prediction with 90% accuracy on unobserved data, the research demonstrates that proper validation and regularization strategies are essential for practical use in human resource analytics and compensation planning systems.