The integration of machine learning (ML) models in enterprise systems has revolutionized business forecasting and strategic decision-making processes. This paper presents a comprehensive analysis of advanced predictive analytics frameworks applied to enterprise environments, focusing on the implementation of various ML algorithms for business forecasting and strategic decision support. Through empirical evaluation of multiple predictive models including Random Forest, Support Vector Machines, and Neural Networks, we demonstrate significant improvements in forecasting accuracy and decision-making efficiency. Our results indicate that ensemble methods achieve up to 85% accuracy in sales forecasting, while deep learning models excel in complex pattern recognition tasks with 92% precision. The findings suggest that organizations implementing advanced predictive analytics experience enhanced operational efficiency, reduced costs, and improved strategic planning capabilities.
Avula et al. (Fri,) studied this question.