Feature Engineering for Improved Digital Business Model Accuracy

Abstract

The rapid growth of digital businesses has intensified the demand for accurate and robust business models capable of predicting market behavior and optimizing operational efficiency. The Background of this study highlights the challenges faced by digital enterprises in handling high-dimensional, heterogeneous data, which often compromise model accuracy and predictive reliability. The Object of this research is to examine how feature engineering techniques can enhance the performance and precision of digital business models, providing more reliable insights for decision-making. Employing a mixed-methods approach as the Method, the study integrates quantitative evaluation using machine learning algorithms on diverse digital business datasets alongside qualitative analysis of feature selection, transformation, and extraction processes. The Result demonstrates that strategic feature engineering significantly improves model accuracy, reduces overfitting, and enhances interpretability, with models incorporating engineered features outperforming baseline models by measurable margins in predictive metrics. Furthermore, specific feature selection and transformation methods show a notable impact on optimizing model performance across various digital business scenarios. The Conclusion emphasizes that feature engineering is a critical component in developing high-accuracy digital business models, serving as a bridge between raw data and actionable insights. By systematically applying feature engineering techniques, digital enterprises can enhance forecasting reliability, operational strategy design, and overall business agility, contributing to sustainable growth in competitive digital markets. This study underscores the importance of integrating advanced data preprocessing strategies as part of the digital business modeling pipeline to maximize predictive efficiency and strategic decision support.