An optimized model for assessing the influence of employee factors on organizational revenue

In today's competitive company climate, understanding how personnel variables influence organizational income creation is crucial for long-term success. The aim of the research is to use machine learning (ML) to create reliable models for analyzing employee characteristics like engagement and happiness and how they affect the organization's ability to generate income. Businesses can have a deeper understanding of workforce dynamics and implement data-driven talent management strategies by utilizing advanced predictive analytics. This strategy promotes long-term financial development and stability by optimizing organizational performance in addition to increasing staff productivity. A novel model, the Scalable Snow Ablation-Optimized Adaptive Random Forest Tree (SSA-ARFT) algorithm, is introduced to predict organizational outcomes with high precision. The data used is derived to examine various factors influencing employee performance and happiness in a typical business setting. Z-score normalization is used as a preprocessing step to standardize variables and mitigate the impact of outliers, ensuring uniformity across the dataset. Principal Component Analysis (PCA) is used to extract features to reduce dimensionality while maintaining important information for accurate predictions. This method improves computational efficiency and guarantees that the model focuses on the most important factors affecting employee performance and organizational success. The experimental findings show that SSA-ARFT performs noticeably better than conventional forecasting techniques. The performance of the model has been assessed by accuracy (94.66%), precision (90%), recall (78.2%), F1-score (88.1%), RMSE (3.031), MAPE (4.5610) and MAE (3.032). Compared to traditional models, the hybrid approach predicts the influence of employee factors on organizational revenue with greater accuracy and durability. The SSA-ARFT method helps analysts and investors improve the forecast accuracy of employee revenue by removing the drawbacks of conventional methods.