A Predictive and Scalable Framework for B2b Commerce Platforms Using Micro services, Micro frontends, And Machine Learning Models

The rapid evolution of digital technologies has transformed the landscape of business-to-business (B2B) commerce, creating an urgent need for scalable, intelligent, and adaptive platforms. Traditional B2B commerce systems are often limited by rigid architectures, high operational costs, and inefficiencies in managing diverse data sources. This study explores the modernization of B2B commerce platforms through the integration of predictive analytics and advanced machine learning models, specifically linear regression (LR), random forest (RAF), and support vector regression (SVR). The research emphasizes the role of scalable micro services and micro services architectures in enabling modularity, resiliency, and a seamless user experience. By leveraging LR, RAF, and SVR, the platform improves demand forecasting, dynamic pricing, and customer behavior analysis, allowing organizations to improve supply chain processes and decision-making. Comparative performance analysis demonstrates that SVR achieves higher accuracy in predicting nonlinear patterns, while LR serves as a lightweight and interpretable underlying model. The combined use of these tech