Revolutionizing Human Resources for Safer Automotive Work Environments

The automotive sector must be guaranteed to reduce injuries and enhance worker wellbeing. Based on historical incident data and real-time sensor data, this research suggests a Random Forest classifier with Principal Component Analysis for feature extraction to forecast workplace safety hazards. By reducing dimensionality, PCA increases computational efficiency while maintaining important safety-related characteristics. Because of its resilience in managing a variety of safety characteristics, such as worker tiredness levels, machine performance, and environmental factors, the Random Forest classifier was selected. To anticipate high-risk areas and identify possible hazards, the model is trained using historical workplace accident statistics. According to the results, this AI-powered strategy improves predictive accuracy and helps HR put proactive safety measures like early hazard detection and efficient shift scheduling into place. This research shows how machine learning has the ability to completely transform human resource safety management in the automotive industry.

Ҳали таржима қилинмаган