Predictive analytics and machine learning algorithms: Enhancing decision-making accuracy in dynamic market environments

In contemporary business environments characterized by volatility, uncertainty, complexity, and ambiguity (VUCA), organizations increasingly rely on predictive analytics and machine learning (ML) algorithms to enhance decision-making accuracy. This research examines the implementation and effectiveness of various ML algorithms—including Random Forest, Gradient Boosting Machines, Neural Networks, and Support Vector Machines—in dynamic market contexts. Through comprehensive analysis of algorithm performance metrics, feature importance mechanisms, and real-world application scenarios, this study demonstrates that ensemble methods achieve superior predictive accuracy (R² > 0.85) compared to traditional statistical approaches. The research reveals that Random Forest and Gradient Boosting algorithms exhibit exceptional robustness in handling non-linear market dynamics, while deep learning approaches show promise for complex temporal pattern recognition. Key findings indicate that algorithm selection must align with specific market characteristics, data availability, and computational constraints. This study contributes to the growing body of knowledge on data-driven decision support systems and provides practical frameworks for implementing ML-based predictive analytics in organizational contexts.